2022 Program Topics

Session Topics:

Our Technical Program Committee has accepted over 150 abstracts to give oral presentations at the 2022 conference with an additional 80 poster displays. There were so many submissions, we opened up three new sessions to accommodate the demand. The presenters listed below are excited to share their knowledge and experience with conference attendees in May. After reviewing the list, register yourself online here and consider becoming an exhibitor or sponsor of the conference.

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Air Sensor Use in India

Learn about air quality sensor use within India. These presentations will highlight the successes and challenges of deploying networks within India to help attendees understand the process . These presentations will give an understanding of what is needed to improve future deployments with the goal of improving air quality throughout the nation.

Session Moderators:

Josh Apte, UC Berkeley, Suresh Dhaniyala, Clarkson University

Presentations:

Application of Machine Learning Regression Algorithms for Calibration of Low-Cost PM2.5 Sensor
Presented by: Manoranjan Sahu, Indian Institute of Technology

Low-cost sensors (LCS) can construct a high spatial and temporal resolution PM2.5 network but are affected by environmental parameters such as relative humidity and temperature. The data generated by LCS are inaccurate and require calibration against a reference instrument. This study has applied nine machine learning (ML) regression algorithms for Plantower PMS 5003 LCS calibration and compared their performance with Thermal Fisher Scientific SHARP model 5030 as a reference instrument.The nine ML algorithms applied in this study are: (a) Multiple Linear Regression (MLR); (b) Lasso regression (L1); (c) Ridge regression (L2); (d) Support Vector Regression (SVR); (e) k- Nearest Neighbor (kNN); (f) Multilayer Perceptron (MLP); (g) Regression Tree (RT); (h) Random Forest (RF); (i) Gradient Boosting (GB). The comparison exhibits that kNN, RF and GB have the best performance out of all the algorithms with trainscores of 0.99 and test scores of 0.97, 0.96 and 0.95 respectively. This study validates the capability of ML algorithms for the calibration of LCS.

*Author did not provide PPT for public distribution, please contact Manoranjan Sahu at mrsahu@iitb.ac.in with questions
Quantifying long-term exposures to fine particulate matter (PM2.5) using real-time low-cost sensors in the Tamil Nadu Air Pollution and Health Effects (TAPHE-II) cohort, India
Presented by: Naveen Puttaswamy, Sri Ramachandra Institute of Higher Education and Research

Background: Exposures to PM2.5is typically measured for 24 or 48 hours in cohort studies involving pregnant women. Low-cost sensors (LCS) offer spatially and temporally resolved real-time PM monitoring capabilities that can capture long-term exposures to household air pollution and provide a better estimate of pregnancy period exposures to PM2.5. Methods: We used atoms™ and aerogram™ real-time PM sensors developed in India to monitor indoor PM2.5in a sub-set (n=50) of the TAPHE-II cohort households (n=300) from rural (n=23) and urban (n=27) locations of the Tamil Nadu state in southern India. These LCS record PM, T, and Rh at 1-minute time intervals and transmit data in real-time to the cloud. Pump and filter (37mm PTFE, 0.2µm) set-up was collocated for 24-h alongside real-time PM sensors in 16 households to develop indoor-specific calibration equations. In addition, all sensors were collocated with a reference-grade monitor (BAM1020) and linear models were fit to derive calibration co-efficient. Results: Continuous PM data was monitored on average (s.d.) for 26 (11) and 74 (43) days in rural and urban households, respectively. The average data availability was 94.3% (10.4) across all households monitored. The NRMSE for different sensors ranged from 9.5% to 47.7%. Correlation between 24-h gravimetric and uncalibrated indoor real-time PM2.5 were 0.62 (p=0.000) and 0.36 (p=0.014) among rural and urban households, respectively. Typical daily average PM2.5 levels were high during evening cooking especially in exclusive biomass fuel using household followed by mixed-fuel and liquefied petroleum gas (LPG) users. Future work: work is underway to complete indoor PM monitoring in a total of 60 TAPHE-II households. LCS will be collocated with pump and filter set-up (i.e. gravimetric) with 24-h sampling at three time periods in a sub-set of 15 households.
(View Presentation PDF)

*Author did not provide PPT for public distribution, please contact Naveen Puttaswamy at naveen@ehe.org.in with questions
From lab-scale research to multi city-scale implementation of low-cost sensors: A comprehensive overview of past five years works
Presented by: Sachchida Nand Tripathi, Department of Civil Engineering, Indian Institute of Technology, Kanpur

As of early December 2021, India has 327 continuous air quality monitoring stations (CAAQMS) in 169 cities. There is an evident need for air quality monitoring (AQM) in India's 7000+ census cities but, investments in extending CAAQMS densities in India would be both impractical and costly. Researchers at IIT Kanpur, in collaboration with national and international institutes have conducted research works, which aimed towards establishing India's first scientifically validated and calibrated low-cost AQM network for PM and gases by comprehensively analyzing them under diverse environments. Sensor calibration approach based on k-nearest neighbor distance metric learning provided R2 of up to 0.92 and 0.82 for O3 and NO2 calibration, respectively. Sensors can measure PM2.5 ~10% errors when RH corrections are done using empirical nonlinear equations. Gaussian process regression based on the fly sensor calibration method was also developed for PM2.5 sensors in Delhi. Over 22 reference nodes, the method used a leave one-out cross-validation procedure, which resulted in an overall prediction error of 30% (RMSE 33 μg.m-3) on a 24-h scale. In another work for PM2.5 sensors calibration, a domain adaptation based method that can reduce the need for co-located data is proposed and the R2 is observed as high as 0.88. A calibration approach based on model-agnostic-meta-learning showed the RMSE as low as 10.3 μg.m-3 in PM2.5 measurements. To provide real-time data across Indian cities with different characteristics, the project is now being implemented in 4 more cities namely Jaipur (arid), Guwahati (hills), Chennai (metropolitan), and Kanyakumari (sea-shore). In another undergoing work for Lucknow, 20 PM2.5 sensors co-located with the reference showed a marked improvement from pre-calibration (MAPE 37-82%) to post-calibration correlation (MAPE 12-16%). The sensor network AQM will be enhanced with microsatellite imagery to estimate PM2.5concentrations at a finer resolution.

*Author did not provide PPT for public distribution, please contact Sachchida Nand Tripathi at snt@iitk.ac.in with questions
A sensor network to map air quality across the rural-to-urban spectrum in North India
Presented by: Saumya Singh, University of California, Berkeley

The Indo-Gangetic Plain in north India (IGP) experiences severe air pollution, with typical annual-average fine particulate matter (PM2.5) concentrations in the range 75-150 µg m-3, posing the largest risk for ill health. The heterogeneity of air pollution sources and adverse meteorology in this region challenges the regulatory and policy attempts made towards improving air quality. Existing policies emphasize a city-centric approach, despite air pollution being a regional challenge in the IGP. A key factor impeding effective air pollution management is the absence of monitored air quality data from smaller cities and rural areas which are home to more than 60 % of the population. Here, we present results from our ongoing project SAMOSA (Sensor-based AirMeasurement Observatory for South Asia)to characterize rural vs. urban air pollution gradients in India’s most populous/largest state Uttar Pradesh (UP) using Purple Air (PA) low-cost monitors in stratified sampling scheme across different settlement types (villages, towns, cities, etc) to cover a broad range of conditions. Our experimental approach employs a carefully maintained network of lower-cost PM2.5 sensors (Purple Air [PA] II) calibrated on the basis of hourly-average measurements made against multiple BAMs located at three sites representing in situ urban and rural regions. We deployed more than 50 PA sensors in a clustered pattern to characterize within-settlement variability along a gradient from remote to rural to highly urban conditions. PM2.5 concentrations exceeded the Indian daily standard more than 70% of the time in both rural and urban sites. Overall, we found that PM2.5 concentrations in villages and small towns were often similar to or higher than the nearest large city. These initial results suggest that additional observations in rural settings and smaller cities and towns will provide valuable new information about the sources, dynamics, and consequences of PM2.5 across this region.
(View Presentation PDF)
Minimizing the effect of humidity on particulate matter PM2.5 by using a heated inlet with an ambient particulate monitor
Presented by: Ayyan Karmakar, Oizom Instruments Pvt. Ltd.

The study analyses the hourly measurements of ambient PM2.5taken by low-cost sensors, with the main objective of finding out the effect of the heated inlet on the sensors working on laser scattering principle. Most of the low-cost sensors do not have any heated inlet for reducing the effect of humidity on data quality, however we used two of our Dustroid ambient air quality monitors, one with a heated inlet and one without a heated inlet. This collocation project involves the collection of data for approximately 2 months starting from the 25th July to 25th Sep 2021. Hourly PM2.5measurements were analyzed with the reference instrument Beta Attenuation Monitor (BAM)1020 using different statistical methods such as Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Regression analysis.Results derived from the various statistical tests show that heated inlets enhance the accuracy of particulate matter data and reduce the effect of humidity on the performance of the low cost laser-scattering sensor.

*Author did not provide PPT for public distribution, please contact Ayyan Karmakar at ayyan@oizom.com with questions
Considerations when deploying a sensor-based air quality network
Presented by: Edurne Ibarrola, Kunak Technologies

Governments are betting on complementary networks to the official stations, to monitor the air quality at a hyperlocal level. However, when deploying an air quality network, it is important to take into account some considerations. The utility of sensor networks continues growing. Proper QA&QC procedures allow users to better understand the quantitative capabilities of their air quality monitoring solution and be resource-efficient to keep the overall cost of the network operation low. Thus, developing, optimizing, and refining advanced techniques for sensor calibration and validation is an important area of ongoing research and is essential to obtain reliable data from sensor-based networks. The first point to consider when deploying a network is the correct installation and maintenance to ensure the proper performance of the devices and the quality of the data. The final user must consider that not only one device will need maintenance, but dozens or hundreds of them, which increases the cost of the project and the difficulties to carry it out. According to this, the air quality sensor systems must have a well-known QA&QC procedure, allowing the proper maintenance and calibration when deploying a network. Besides, it is necessary a platform that allows and efficiently facilitates this. Some real cases are shown. In Albacete (Spain) 10 devices were deployed; in Antwerp (Belgium) 20 mobile devices were installed on top of post vans; a smaller network in Bombay (India); and a green port application, with 25 devices in 5 ports in Balearic Islands (Spain).
(View Presentation PDF)
Supplementing air pollution data using low-cost sensor network – CSTEP studies
Presented by: Dr. Pratima Singh, Center for Study of Science, Technology and Policy

Lack of understanding on air pollution levels, sources, and the polluting activities at the city level has hindered the development of effective policies. With insufficient scientific evidence, clean air action plans have been inefficient for improving the air quality levels. To aid in generating scientific evidence, filling data gaps, and developing policy strategies at the city level, CSTEP is carrying out monitoring exercises in various cities of India. CSTEP is conducting low-cost sensor (LCS) monitoring in the Punjab state to understand the influence of stubble burning on the ambient air quality level in the state. The measurements are carried out in 5 districts in various geographical landscapes (covering urban, rural, and industrial areas) for measuring PM2.5 levels. These generated data sets will help 1) understand the air quality trend (hourly, daily, and monthly historical data), 2) prepare state-level strategies for improving the air quality, and 3) understand the indoor and outdoor air pollution levels. CSTEP is also using different kinds of LCS’s in the city of Bengaluru to generate evidence. Data from such a dense network of LCS will help run models and predict the pollution levels at high resolution. Such a data set with an understanding of the pollution levels becomes crucial in the absence of reference-grade monitors or sparsely placed monitors. High-resolution modeled data will help understand the 1) hotspots and 2) propose interventions for air quality management. CSTEP is using LCS network for the following 1) conducting inter-comparison of various LCS monitors and understanding their functioning and performance, 2) using LCS in mobile monitoring study to better understand the hyper-local air quality level, and 3) generating data and carrying out modeling for creating awareness and building capacity of citizen groups (alerting the citizens on the neighborhood air quality level, decision making at city-level and communication and outreach).
(View Presentation PDF)
Improving Air Quality in 133 'Non Attainment' cities of India with Low-Cost Sensors & National Clean Air Policies
Presented by: Ronak Sutaria, Respirer Living Sciences Pvt. Ltd.

As of November 2021, India has around 300 Continuous Ambient Air Quality Monitoring Stations (CAAQMS - reporting air quality data every 15 minutes) and 800 "manual" monitoring stations (which report 2 air quality data points every week). India's Rs. 4000 Crore (USD $650 Million) flagship national policy for clean air called "National Clean Air Programme (NCAP)" is based on Particulate Matter (PM10) data collected from the 'manual' monitors. Advances in Low-Cost Air Quality Sensors in India have been supported by several Indian government agencies (such as the Department of Science and Technology, Ministry of Environment, Forests and Climate Change and few state Pollution Control Boards). Based on field evaluation and validation results, the central government in India has now started exploring applications which use low-cost air quality sensor data. The "Atmos - Realtime Air Quality" monitoring network has been part of several state and central government funded initiatives around low-cost air quality sensors. In this paper, we will present the performance metrics (including calibration factors, correlation R-square, precision, bias and error percentages) of the low-cost sensors from 20 colocated sites (with FEM/FRM grade BAM monitors) in India. The paper will also discuss government directives related to low-cost air quality sensors and the 'citizen and urban planning' applications that are expected to be built using the LCS data. Brief overview of the air quality monitoring initiatives across India and the role of LCS in the 133 'non attainment' cities of India will also be discussed.
(View Presentation PDF)

View Session Recording on Youtube

Advanced measurement approaches for fenceline and fugitive monitoring applications

This session will explore the development and application of emerging air measurement approaches to characterize fugitive emissions and to evaluate their air quality impacts on fenceline communities.

Lead Session Chairs:

Ingrid George, US EPA & Arsineh Hecobian, Chevron

Presentations:

Combining low cost PID sensors and triggered canisters to document acute air toxics exposure episodes near oil and gas development
Presented by: Jeffrey Collett, Colorado State University

Ambient concentrations of air toxics and other VOCs were measured near large, multi-well oil and gas pads in suburban Broomfield, Colorado. Monitoring began prior to drilling and continues into the production phase. Spatial networks of time-integrated VOC canisters provide clear indication of impacts of oil and gas (O&G) operations on local air quality, but cannot document acute exposure events that result from short periods of elevated emissions. A PTR-MS provides 90s time resolution measurements of BTEX, helping to identify acute exposure episodes, but lacks measurement of O&G marker species (e.g., light alkanes) needed to document the responsible source. Amobile laboratory hunts and characterizes plumes in nearby neighborhoods, but practical deployment schedules limit opportunities to capture transient events. VOC plumes from O&G operations often impact a location for just minutes. Benzene in these plumes needs to be quantified at ppbv levels to compare against acute exposure health guideline levels. To meet these needs, we have utilized APIS PID sensors with triggered canisters to continuously monitor ambient VOC levels and collect high quality, speciated VOC concentration data when VOC-rich plumes are observed. Multiple sensors are placed around an O&G operation to improve spatial coverage. Our high time resolution measurements identified a variety of short-term pollution episodes, especially during transition and maintenance activities, including pipe pulling, coiled tubing operations, and separator maintenance.. The composition of detected plumes was tied to O&G operations by elevated light alkane concentrations, a low i/n-pentane ratio and local transport conditions. Concentrations of benzene in captured plumes reached as high as 52 ppbv.
(View Presentation PDF)
Pairing high- and low-cost sensing technologies to understand cumulative health impacts for fenceline communities
Presented by: Kirsten Koehler, Johns Hopkins University

While it is known that there are broad impacts of the petrochemical industry on the environment and public health, the current regulatory monitoring network is too sparse to fully quantify the exposures to fenceline communities and understand the cumulative impacts from multi-chemical exposures. We conducted a detailed monitoring campaign in a highly industrialized corridor between Wilmington, DE and Philadelphia, PA in September, 2021. The study included a fixed site with chemically resolved gas and particle phase measurements, a lower-cost monitoring network in fenceline communities for particulate matter (PM1,2.5,10), and a mobile monitoring laboratory for fenceline monitoring of agents known to be associated with the petrochemical industry. The approach enabled continuous monitoring at some locations with detailed characterization on a mobile platform that included repeated measures in key communities of interest to establish robust concentration surfaces. The highly spatially and temporally resolved measurements were compared with locations of known sources. Preliminary results show good agreement between the lower-cost sensors and regulatory monitors at co-located sites for PM2.5. Bipolar wind rose plots suggest that peaks in PM2.5 are associated with winds from regions with higher density of industrial sites. Mobile measurements suggest that exposures to some chemicals are higher for fenceline communities and that while exposures were highly temporally variable, high concentration plumes extended into residential areas. For other chemicals with strong contributions from traffic or gasoline filling stations, differences between fenceline communities and the urban background were smaller. This study represents a novel pairing of low-cost sensor technology, high sensitivity instruments commonly used for atmospheric science applications, and cumulative risk assessment approaches to address environmental injustice for fenceline communities.
(View Presentation PDF)
Development and Evaluation of a Novel Continuous and Concurrent Sampling System for Sub-ppb Level Detection of Volatile Organic Compounds in an Industrialized Area in Los Angeles
Presented by: Pami Mukherjee, South Coast Air Quality Management District

In April 2020 South Coast Air Quality Management District begun operation of 10 community air monitoring stations in communities near 7 major petroleum refineries in Los Angeles, California. In addition to refineries, these areas also contain multiple oil-wells and numerous industrial facilities that are potential sources of a wide range of Volatile Organic Compounds (VOC) and air toxics. The stations are equipped with Automated Gas Chromatography (Auto-GC) developed by Tricorntech Corp., Taiwan. Auto-GC instrument, followed by EPA PAMS VOC sampling guideline, is designed to collect ambient samples for the first 40 minutes of the hour followed by analysis of the sample in next 20 minutes. Based on our experience operating community air monitoring sites, air pollution plumes with elevated VOC are often short-lived, lasting for ~10 minutes, therefore resulting in some plumes not being captured. Moreover, the 40-minute sample concentration will report lower (averaged) value than the actual short peak concentration of VOC present in the plume. To overcome these shortcomings, we developed a sampling system (CSS) to sample the ambient air and analyze the previous sample simultaneously. In this design we will split the hour into three sampling segments, 20 minute each with >15-minute of sample collection. Therefore, we can achieve continuous and more frequent ambient samples without altering its sub ppb range detection capability. In this presentation we will present the details of CSS approach and discuss the results of testing of CSS deployment and testing the Rule 1180 sites. The CSS module will be tested for real time ambient sampling in colocation with the established Auto-GC and Optical multi-pollutant analyzers to evaluate its accuracy and efficiency.
(View Presentation PDF)
On line monitoring of odor unit (OU) emissions and odor sources identification, by using a new generation of agas and odors analyzers
Presented by: Jean-Christophe Mifsud, Ellona

Dynamic olfactometry (ES 137225) is the standard technique for odor intensity measurements, but not adapted for industrial sites which need continuous monitoring and fast results. Additionally, these sites need odor sources identification solutions to ensure efficient remediation. The harbour of Ventspils, (Latvia), can host up to 20 petrol tankers, and the Baltic wind can push the gas and odors towards the city. The municipality does not have the tools neither to quantify nor to identify the sources of pollution and nuisances. The port authorities deployed an array of WT1 devices on different sites close to the sources and at the fence line. During the initial training period, the devices were trained with four different types of samples at different dilution levels (black fuel oil, solvent naphta, petrol, kerosene), and a successful correlation model was established following the 13725 standard between sensory measurements with dynamic olfactometry and the WT1 outputs, allowing the quantification of odors as well as the identification with an accuracy superior to a 0,85 R2. Additionally, a PCA (Principal Component Analysis) and a LDA (Linear Discriminant Analysis) were built and the WT1 modules proved to differentiate accurately the different sources of odor and pollution. The RUBIX WT1 gas and odors sensing modules allow not only on-line monitoring of Odor Unit and various gas emissions, but also allow odor fingerprint identification. The paper will describe the methodology combining a range of smart sensors with AI statistical data processing techniques, and the experimental plan, including the training with dynamic olfactometry.
(View Presentation PDF)
Monitoring volatiles using a mobile real-time mass spectrometer
Presented by: Leslie Silva, Syft Technologies

Rapid air-quality assessment is essential to managing and reducing the impact of toxic or unpleasant volatile compounds on communities. Often deployment of instrumentation at or near the source using vehicle mobilized instrumentation is essential to an effective response. The use of a mobile, real-time volatiles analyzer will be discussed in this context, exploring how it is used to conduct continuous volatiles measurements whilst moving around the area of assessment. This could be crucial for rapid response to an alert from a sensor, where higher-order speciation or characterization of the emission is necessary. The analyzer deployed is a Voice200ultra, selected-ion flow-tube mass-spectrometer (SIFT-MS), contained within a mobile laboratory. SIFT-MS is a soft chemical ionization technique that can be used to directly quantify volatile analytes and achieves selectivity of analysis by exploiting several different chemical ionization mechanisms within a single analysis. The mobile laboratory is also equipped with GPS tracking and plotting software and a weather station mounted to the roof. Concentration data can be plotted in real time for all VOCs being monitored. The benefits of mobile SIFT-MS are highlighted in this presentation through the speciated monitoring of malodor compounds such as reduced-sulfurs and amines. This provides major advantages over conventional approaches because these species are often not amenable to chromatography and can be difficult to sample due to thermal instability, reactivity, or loss to the sample container walls. Furthermore, the processes or emissions responsible for malodors can be dynamic and short-lived necessitating continuous monitoring at the site to correctly identify the source.
(View Presentation PDF)
Complementary and Emerging Techniques for Fenceline Monitoring
Presented by: Steven Schill, Sonoma Technology

Many industrial fenceline monitoring programs are motivated by a combination of industry proactivity, advances in monitoring technology, community concerns, and regulatory actions. There is a growing need across various commercial industries and geographic regions for ambient monitoring to determine which pollutants are crossing facility boundaries, at what time, and in what quantities. Effective fenceline monitoring approaches include point monitors and passive samplers, open-path absorption spectroscopy, and more. These approaches encompass a wide scope of technologies with varying costs, benefits, and operational considerations. In this presentation, Sonoma Technology scientists will draw on extensive experience with fenceline monitoring to discuss complementary and emerging monitoring solutions, key factors for technology decision making, and other important considerations, such as design, implementation, and operation of effective monitor networks. Techniques to evaluate source-receptor relationships in real time will also be discussed.
(View Presentation PDF)
Benefits of Using Sensor Technology in Conjunction with Traditional Sampling
Presented by: Austin Heitmann, Montrose Air Quality Services

As of May 1, 2021, all new multiwell facilities drilled in Colorado are required to monitor for their choice of BTEX, benzene, methane, or total VOCs, with the objective of protecting public health. While the default choice for many operators is total VOCs due to, among others, its low cost, scalability, and multiple technology options there is no health guidance values that the results from these monitors can be compared to. These monitors have demonstrated that they are capable of detecting unplanned emission events and provide an understanding of the duration and relative size of permitted emissions. Over 3,500 speciated gas samples have been collected in conjunction with these realtime monitoring programs. An advantage of including speciated sampling in these programs is that these results can then be compared to federal, state, or locally established health guidance values to help support that public health is being protected or whether a further investigation is necessary. The sensor data fills a data gap identifying when unplanned emissions occur, providing real-time actionable data that can be used to mitigate elevated speciated VOC levels by enhanced understanding of where and when these emissions are occurring. This presentation will share examples of events that were mitigated using low cost total VOC sensors, discuss results of collocated total VOC sensors, passive sorbent tubes, and summa canister samples, and how to properly compare these results to public health guidance values.

*Author did not provide PPT for public distribution, please contact Austin Heitmann at aheitmann@montrose-env.com with questions

View Session Recording on Youtube

Best practices from Breathe London

Lessons learned from integrating hundreds of air quality sensors with the largest and most advanced regulatory monitoring network in Europe

The Breathe London Network launched in January 2021. Following a successful 2-year pilot, the Mayor of London took over the funding of the network, appointing Imperial College London to deliver the programme. The network integrates air quality measurements from hundreds of sensors with reference-grade measurements provided by London's regulatory air monitoring network — the largest and most advanced such network in Europe.

Over 350 air quality sensors will be installed in boroughs across London by the summer of 2022. Every Breathe London Node is co-located at reference sites and checked by researchers at Imperial College before deployment. Once deployed, the data is continually cross-checked against reference sites in real-time.

Aside from technological innovation, one of the most remarkable components of Breathe London is that it puts air quality monitoring into the hands of London’s communities. Thanks to support from Bloomberg Philanthropies, community groups can apply for fully-funded sensors in their neighborhoods — offering a new way to engage citizens with air quality.

The operators of this network — Imperial College London, the Greater London Authority, and Clarity — will share valuable information and real-world learnings about the use of air sensors at the city scale. Topics will include:

Distribution - who should own the sensors, and how should they be distributed to ensure equitable air monitoring coverage?
Siting and installation - how to select and prioritize monitoring sites? What indemnity provisions should be made, who is responsible for maintenance, and who is liable for making sure devices are properly installed?
QA/QC and calibration - how to ensure data accuracy?
Community engagement - how to use a sensor network to build relationships between community groups and local government?
Research applications - how has data from the network been used for case studies and research, and what other potential applications exist?

Presentation:

Lessons learned from integrating hundreds of air quality sensors with the largest and most advanced regulatory monitoring network in Europe
Presented by: Benjamin Barratt, Imperial College London

The Breathe London Network launched in January 2021. Following a successful 2-year pilot, the Mayor of London took over the funding of the network, appointing Imperial College London to deliver the programme. The network integrates air quality measurements from hundreds of sensors with reference-grade measurements provided by London's regulatory air monitoring network — the largest and most advanced such network in Europe. Over 350 air quality sensors will be installed in boroughs across London by the summer of 2022. Every Breathe London Node is co-located at reference sites and checked by researchers at Imperial College before deployment. Once deployed, the data is continually cross-checked against reference sites in real-time. Aside from technological innovation, one of the most remarkable components of Breathe London is that it puts air quality monitoring into the hands of London’s communities. Thanks to support from Bloomberg Philanthropies, community groups can apply for fully-funded sensors in their neighborhoods — offering a new way to engage citizens with air quality. The operators of this network — Imperial College London, the Greater London Authority, and Clarity — will share valuable information and real-world learnings about the use of air sensors at the city scale. Topics will include: Distribution - who should own the sensors, and how should they be distributed to ensure equitable air monitoring coverage? Siting and installation - how to select and prioritize monitoring sites? What indemnity provisions should be made, who is responsible for maintenance, and who is liable for making sure devices are properly installed? QA/QC and calibration - how to ensure data accuracy? Community engagement - how to use a sensor network to build relationships between community groups and local government? Research applications - how has data from the network been used for case studies and research, and what other potential applications exist?
(View Presentation PDF)

View Session Recording on Youtube

Clean Air Monitoring and Solutions Network: getting useful, actionable data out of low cost sensors for air quality action (New Parallel Symposium)

Lead Symposium Chairs:

Albert Presto & Dan Westervelt of CAMS-Net, R. Subramanian, QEERI & OSU-Efluve

The Clean Air Monitoring and Solutions Network (CAMS-Net) establishes an international network of networks that unites scientists, decision-makers, city administrators, citizen groups, the private sector, and other local stakeholders in co-developing new methods and best practices for real-time air quality data collection, data sharing, and solutions for air quality improvements. CAMS-Net brings together a vast network of multidisciplinary member networks from all around the globe. The project establishes a mechanism for international collaboration, builds technical capacity, shares knowledge, and trains the next generation of air quality practitioners and advocates, including domestic and international graduate students and postdoctoral researchers. We welcome presentations on topics including but not limited to:

calibration/correction factor development
data sharing, management, and standardization
application of sensor data to modeling and satellite data
role of sensors in source attribution
public engagement and capacity building
decolonization of science and responsible science
public policy and health

Presentations:

Maximizing the information content from hyperlocal air quality networks - low cost and reference
Presented by: Olalekan Popoola, University of Cambridge

Traditionally, ambient air quality is monitored using reference/equivalent methods often at hourly resolutions. This approach is sufficient for assessing trends and compliance; however, a significant opportunity can be missed particularly in performing more robust data analysis and interpretation by adopting this limited temporal sampling approach. Low-cost sensor (LCS) networks have often been operated at higher time resolution for wide range of pollutants, and we will show in this presentation how this also offers a new data analysis approach for reference instruments and networks. In this talk, we will present data from a hyperlocal network of LCS (AQMesh) in Glasgow, Scotland and for the first time, reference air quality network also operated at high time resolution for periods in 2021. We will present results from the analysis of the high time resolution air quality network data for NOX, PM and size speciated PM measurements.
(View Presentation PDF)
Estimation of hourly BC from BAM tapes using image reflectance-based method
Presented by: Abhishek Anand, Carnegie Mellon University

Exposure to ambient air pollution accounts for 4.2 million deaths annually. Yet, air quality information is not adequate in many densely populated lands to make informed policy decisions, especially in the Global South. A critical need is better quantification of fine particulate matter (PM2.5) composition, which can in turn be used to identify crucial pollutant sources. A major barrier is the high capital and operational costs of research-grade monitors. This study investigates a cost-effective way to leverage existing monitors to expand the limited air quality dataset in these areas. The US Department of State collects air pollutant data at US Embassies around the world to inform US personnel and citizens of air quality overseas. These measurements use Beta Attenuation Monitors (BAMs) to measure hourly ambient PM2.5concentrations. BAMs collect PM2.5 onto a filter tape and estimate particle concentrations by measuring attenuation of beta rays using the Beer-Lambert law. We utilize these BAM tapes to determine hourly ambient black carbon (BC) concentration. Each filter tape spot is individually photographed with a cell phone camera on a reference card containing a set of predefined grayscales corresponding to different BC concentrations. The image is then processed through a custom computer-vision Python script, which performs spatial correction, adjusts for lighting variations, locates the filter spot and then compares the reflectance of red light from the spot with that of the grayscales on the reference card to estimate BC concentration. The method was calibrated using filters with known BC concentrations determined from co-located aethalometer measurements and offline thermal-optical EC analysis. The limit of detection is approximately 1.1 µg.m-3for 1-hr samples. This presentation will show results from analysis of BAM tapes collected at three locations: a near-roadway site in Pittsburgh, PA; the US Embassies in Addis Ababa, Ethiopia and Abidjan, Côte d’Ivoire.
(View Presentation PDF)
Evaluation of Correction Models for a Low-Cost Fine Particulate Matter Sensor Using the Canadian AQHI+ System
Presented by: Brayden Nilson, University of Northern British Columbia

We evaluated four colocation-based correction models for low-cost fine particulate matter (PM2.5) sensors from PurpleAir (PA) along with four other models from the literature. The models we developed were trained using 32 colocations sites across Canada and the United States, selected based on the proximity of PA sensors to regulatory Federal Equivalent Method (FEM) monitors. An additional 15 colocation sites were used for evaluating the performance of the models. An automated quality control process was implemented to ensure the best available data were used. Comparative evaluations were made between these correction models using the Air Quality Health Index+ (AQHI+) system, a modification of the Canadian AQHI that only considers PM2.5 concentrations. Using the AQHI+ system allowed for a direct comparison of performance across the range of observed concentrations. Emphasis was placed on improved performance in the moderate and high concentration ranges, which are the most important for health-related management decisions in North America. We provide a robust framework for the evaluation of low-cost PM2.5 sensor correction models and present an optimized correction model for North American PA sensors. We found that one of the models we developed, utilizing a hygroscopicity-based correction, performed consistently better than the other models we tested.

*Author did not provide PPT for public distribution, please contact Brayden Nilson at nilson@unbc.ca with questions
Partnerships in low-cost air quality monitoring and outreach in North Carolina
Presented by: Brian Magi, UNC Charlotte

Low-cost air pollution sensors have emerged as a viable way to explore air quality at much finer spatial and temporal scales than data that is readily available from the existing regulatory air pollution monitoring network. Low-cost sensor data collection offers pathways to citizen science and community-based participatory research because the cost is low and the data is open-access. There are several layers of analysis that necessitate a co-researcher design to community-led inquiries, and in this presentation, I will discuss some of the successes and the roadblocks and challenges associated with low-cost air monitoring data that have happened and continue to happen in North Carolina. Successes range from peer-reviewed publications to story maps, public comment, and even motivating direct changes in regulatory monitoring. Importantly, the successes are entirely connected to engaged multi-layered partnerships among academic and government air quality and health researchers, stakeholder and community groups, and regulatory agencies. I will talk about this massive intersection, and how I use my expertise in air quality science to help facilitate long-term relationships alongside my contributions to technical data analysis. I will argue that the framework of analysis and relationship-building is broadly applicable beyond North Carolina, and that fundamentally, community climate resilience benefits from multi-layered partnerships.

*Author did not provide PPT for public distribution, please contact Brian Magi at bmagi@uncc.edu with questions
Maximizing insights from air quality sensor networks through continuous performance evaluation
Presented by: Daniel (Dan) Peters, Environmental Defense Fund

As the global use of lower-cost air quality sensors (LCS) continues to grow, data quality issues including poor accuracy, drift, and environmental interference remain a critical obstacle to a user’s ability to obtain meaningful information from sensor measurements. Even if a sensor has already been tested and calibrated in a lab or field environment, a sensor’s performance and calibration parameters may vary by region (or season) depending on numerous factors including the local characteristics of air pollution and meteorology. Due to the uncertainty and variability in LCS data quality, with no universal performance standards or QA/QC protocol, the end user is responsible for ensuring the data they obtain is robust and of appropriate quality for the intended application. Here, we present results from a network of 100 electrochemical NO2 sensors as part of the Breathe London pilot project (BL) and describe the steps that were taken to develop a detailed understanding of sensor performance over the entire duration of the 26-month measurement campaign. We discuss how this project-long understanding of sensor performance informed our interpretation of the data produced by the BL network. We validate our findings by comparing BL network results to data from an extensive network of London reference monitors, including comparisons of temporal patterns (e.g., diurnal, day-of-week, and monthly averages) as well as differences in pollution profiles between near-road and urban background monitoring sites. Through these comparisons, we show how long-term collocation intercomparisons of BL sensors at two reference sites could be used as a proxy to understand overall network performance and develop corrections. We discuss how the findings could be applied to other LCS projects, including those with less reference monitoring infrastructure, provided that some source of traceable, reliable measurements is available to perform ongoing representative evaluations of sensors.
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Closing the air pollution data gap in sub-Saharan Africa through low cost sensors, capacity building, international networking, and data science methods
Presented by: Daniel Westervelt, Lamont-Doherty Earth Observatory of Columbia University

In sub-Saharan Africa, sparse air pollution monitoring imparts high uncertainty to estimates of exposure and impact.To address this problem, we have launched several internationally-supported communities of practice that spans multiple languages and cultures to unites scientists, decision-makers, city administrators, citizen groups, the private sector, and other air quality advocates and practitioners in developing new methods and best practices to hasten synthesis and implementation of clean air solutions. A cornerstone of our method includes novel collaboration accelerators such as networks of networks and communities of practice. To this end, we have launched the Clean Air Monitoring and Solutions Network (CAMS-Net), which aims to establish an international network of networks around the idea of getting useful, actionable data out of low-cost sensors to help spur air pollution mitigation strategies. We have also established two large communities of practice for air quality research and management in East and West Africa. A certificate program was held for around 100 attendees from East Africa in July 2021. Lessons learned from this and similar efforts will be presented in this talk. I will present our team's efforts at closing the air pollution data gap in sub-Saharan Africa through reference monitoring, low-cost sensors, and data science techniques. Results will include some of the first field calibrations of low-cost sensors (LCS) for PM2.5 in sub-Saharan Africa. In each city, we show that raw LCS are highly correlated with reference monitors (r2>0.8) but can be significantly biased (MAE ~ 14 µg m-3). We show that colocation with reference monitors and simple data science methods (such as random forest, linear regression, or Gaussian Mixture Regression) can reduce MAE to around 3 µg m-3 for a year of collocated data. We will also discuss emerging new high-density networks for PM2.5 in several other cities across sub-Saharan Africa.
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Air quality monitoring with low-cost sensors in Pioneer Valley of Western Massachusetts: strategies for sensor deployment and calibration
Presented by: Dong Gao, Yale University

Air pollution continues to be a global public health threat. With the development of sensor and internet technologies, low-cost air sensors (LCS) have emerged as an effective tool for air quality monitoring at a high time and spatial resolution. The use of LCS is of main interest for citizen science initiatives and can help answer community-driven and locally motivated questions. A collaborative air quality monitoring project has recently been conducted in Pioneer Valley of Western Massachusetts, focusing on the Springfield, Chicopee and Holyoke areas. An LCS network with 60 sensors was built up, with the goal of measuring air pollution levels and providing data to inform public health responses in the city known as having the highest rates of asthma in the country. To determine the suitable locations for sensor deployment, a weighted site selection analysis was performed by assigning varying importance levels to different factors such as traffic density, proximity to emission hotspots, and distribution of at-risk vulnerable populations. Pre- and post-deployment calibration were conducted for the deployed sensors. For pre-deployment calibration purpose, all sensors were collocated at a regulatory station for weeks. The sensor data was compared to reference methods, and calibration factors were derived for each sensor. Inter-comparison of sensor data showed good correlation between sensors (R2=0.98~0.99). Different field calibration methods (e.g., multiple linear regression, land use regression, and machine learning methods) were examined for post-deployment calibration. This study developed a formal approach for optimally locating a dense LCS network of air pollution monitoring and derived an area-specific calibration model for Pioneer Valley.
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Validation of LCS for air quality index in Jakarta, Indonesia
Presented by: Driejana Driejana, Institut Teknologi Bandung

Low-cost sensor (LCS) PurpleAir PA IIfield validation was done in Jakarta, Indonesia. The study was part of Community Air Quality Monitoring (CAQM) [https://kopernik.info/id/berita-acara/news/kopernik-partners-with-islamic-development-bank-to-deliver-community-air-quality-monitoring-project],a collaboration of Kopernik, PulseLab Jakarta [https://medium.com/pulse-lab-jakarta/designing-a-community-based-air-quality-monitoring-system-to-mitigate-air-pollution-health-risks-3e527e607427] and Institut Teknologi Bandung-UDARA project. CAQM was a pilot project designed for mitigation of air pollution health risks at community level. PM10 and PM2.5concentrations were converted into Indonesian Air Quality Index (ISPU) and published in pantauudara.org [pantauudara.org]. LCSs were validated by co-location with PM10 and PM2.5BAM F-701-20FEM analyzers of DKI Jakarta Environment Agency (DLH) monitoring network. Three PA II units were deployed for 52 days from July to August 2020 at Kelapa Gading, North Jakarta. Each PA II unit has a pair of PMS5003 sensors, so that a total of six sensors were used for precision and accuracy measurements. After validation, two units were moved to Galur, (Jakarta) and Rasau Jaya (West Kalimantan) and one unit was left at the reference site until November 2020.The accuracy of the measurement was measured by comparison of 24-hr average PM10 and PM2.5with the data from BAM analyzers.The inter-model comparison showed good overall precision with average of 92 – 93% for PM1.0, PM2.5and PM10. LCS showed relatively good performance for PM10(r2=60%), butlow accuracy(r2<15%) for PM2.5. The performance was relatively similar after 127 days. The average proportion of PM2.5 in PM10 was 86.3% and PM1.0in PM2.5 was 63.3% suggesting that the majority of PM10 mass concentration was fine particles. Due to LCS PM2.5 large discrepancy to the reference method, and the fact that the large proportion of PM10 was PM2.5; ISPU was published only based on PM10.
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Field-calibrated PM2.5 Measurements, Regional Trend Assessments, and Sensor Intercomparison Results from Low-Cost Monitoring Networks in Accra, Ghana and Lomé, Togo
Presented by: Garima Raheja, Columbia University

Metropolises in sub-Saharan Africa experience high levels of ambient air pollution, yet remain scarcely measured by reference-grade monitors. Combining insights from three years of data collected by newly established and field-calibrated Purple Air low-cost sensor networks, we present the first measurements of PM2.5concentrations in Lomé, Togo. For Accra, Ghana, we utilize an 18-node network of low-cost Clarity low-cost sensors to analyze regional trends, and assess the reductions in PM2.5caused by intentional interventions as well as the COVID-19 pandemic. For both low-cost sensor networks, we apply simple methods such as multiple linear regression, as well as novel more complex methods such as Gaussian mixture regression to develop correction models. We find that the annual average PM2.5in Lomé is 21.1 µg m-3and is heavily influenced by the Harmattan. The network-wide annual mean PM2.5in Accra is 26.3 µg m-3, 5.3 times higher than WHO daily guidelines and 19% higher than neighboring Lomé. Both the PurpleAir network in Lomé and the Clarity network in Accra are validated using results of co-located sensors: 18 Clarity Nodes, 2 PurpleAir, 2 Modulair, 1 Teledyne T640, and 1 Met-One Beta Attenuation Monitor at FEM instrumentation sites in Accra, Ghana. We find that Clarity devices have a mean absolute error (MAE) and R2of 3.36 µg m-3and 0.76 when compared with Teledyne (FEM) PM2.5; Purple Air devices have a MAE and R2of 2.6 µg m-3and 0.88, and Modulair devices have a MAE and R2of 1.66 µg m-3and 0.89. This sensor intercomparison study is contributing to the development of a universal low-cost sensor data correction algorithm. Using these results, we show that low-cost sensors, when combined with data science-based correction techniques, have the potential to close the air pollution data gap in resource-limited areas such as West Africa.
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Public Engagement in Air Quality Management in Kenya
Presented by: Godwin Opinde, Kenyatta University

Air quality management is a collective responsibility and public engagement is integral to effective management of air. The nature of public engagement influences compliance and enforcement of this aspect of the environment. Policies, laws, plans and established institutions play a significant role in ensuring its effective governance. In addition, the involvement of the public in monitoring air quality entrenches awareness creation that significantly influences the attitude and behaviour of the public on the management of air quality. This paper examines public engagement in processes and efforts geared towards ensuring clean air for improved health and mitigation of climate change in Nairobi, Kenya. It employs reviews and targeted interviews to assess public engagement in the formulation of air quality laws, policies and plans and examine the nature of public involvement in air quality monitoring. The paper identifies challenges and opportunities in the engagement process and recommends strategies for meaningful public engagement that enhances compliance and enforcement in air quality governance.
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Observation of aerosol spatio-temporal variations over Ghana using MODIS-derived Aerosol Optical Depth
Presented by: James Nimo, University of Ghana

Ambient particulate matter pollution in sub-Saharan Africa cities has become a major public health concern. There is limited data to assess the increasing trends in the sub-region due to the lack of monitoring stations. Remote sensing data from satellites includes aerosol optical depth (AOD) is accessible for assessment of air pollution at increasingly fine resolutions. In this study AOD data derived from the MODIS Aqua and Terra satellites were used to assess the levels of aerosols over Ghana.The results were validated with a NASA AERONET station data in the eastern region of Ghana. AOD at 550 nm was retrieved from MODIS 10 km Collection 6.1 level 2 dataset using the combined dark target and deep blue algorithm from 2013 to 2018. HYSPLIT model was used to analyse the origins of air masses and their trajectories over the 10 regions in Ghana during the peak of the dry season. The study observed an increasing trend of AOD over the northern and southern parts with highest mean 0.56 ± 0.02and lowest mean 0.15 ± 0.2over Ghana during the dry and wet seasons. About 49.05% of AODs retrieved on 1x1 kmof MODIS fall within the sensor error envelope of paired validation data against the AERONET. The Aqua and Terra sensors had slopes of 0.45 (with R = 0.78, RMSE = 0.34 and MAE = 0.29) and 0.21 (with R = 0.33, RMSE = 0.56 and MAE = 0.42). Strong outbreak of dust experienced over Ghana from 500 to 1500 m originated from continental sources in the Saharan desert, Mediterranean Sea and other biomass burning from within Ghana and neighbouring countries. Consistent cloud cover using the deep blue and dark target algorithm was observed over the southern part of Ghana.The resulted slopes indicate that MODIS algorithm underestimated the AODs.
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Assessment of diurnal and seasonal variation of ambient particulate matter (PM2.5) in Juja, Kenya
Presented by: Josephine Kanyeria, Jomo Kenyatta University of Agriculture and Technology

Air pollution is a major environmental concern that affects human health worldwide. Despite recent studies indicating ambient air pollution is a growing global concern strongly linked to rapid global urbanization, little has been done to monitor the air quality levels in towns outside Nairobi, Kenya. Juja is one of the largest growing towns subjected to increased population, intense human activities and located along the busy Thika Super Highway. Thus, there is a strong need to monitor ambient Particulate Matter within the town. The purpose of this study was to assess the diurnal and seasonal variations of Ambient Particulate Matter (PM2.5) in Juja, Kenya. The data was collected November 2019 to April 2021 at JKUAT Institute of Energy and Environmental Technology (IEET) department, a residential house within Kalimoni and an additional site along the busy Thika Superhighway. The PM level was measured using the Purple Air Monitoring Sensor – PA-II-SD in μg/m3on a 24hour cycle. Data analysis was done quantitatively using Excel, SPSS and R programming. The PM2.5 level from the low-cost Purple Air Sensors were later calibrated against a reference BAM-1022 to yield corrected PM values. The results revealed that the overall PM2.5 concentration was higher during the dry season (July - August 2020) compared to March - May 2020 (wet season) where it dropped by 5-10μg/m3 on average. The highest daily PM2.5 concentration was recorded at 44μg/m3(JKUAT) and 43μg/m3 (Residential) both exceeding the WHO guidelines and the USEPA National Air Quality Standards. JKUAT had an annual mean concentration of 15μg/m3, also exceeding the WHO guidelines of 10μg/m3. In addition, comparing the month of April 2021 from the previous year, the daily mean dropped by 5-10μg/m3– the period of the new Covid -19 lockdown. Data analysis is currently ongoing and will examine the data from other additional sites.
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Air quality in Togo: Monitoring status and CAMS-Net opportunities
Presented by: Kokou SABI, Université de Lomé

In 2016, ambient air pollution caused more than 4.2 million premature deaths, including 300,000 deaths of children under five. The largest share of deaths among children is due to fine particulate matter (PM2.5) pollution, especially in low- and middle-income countries (WHO, 2016). As a result, air pollution has become a health emergency. PM2.5 has a greater impact on human health compared to larger particles due to its smaller size which facilitates their penetration into the human body. The World Health Organization recognizes several sources of PM2.5 in the air: combustion of biomass, road traffic, agricultural sources, and industrial and waste management sources. Togo likely experiences high levels of air pollution but the exact quantities of atmospheric pollution are not well known. More specifically, the largest Togolese city of Lomé (population 1.5 million) experienced up to 7.24 tonnes of PM emission in 2019 with an estimated rate of increase of about 0.82 tones per year, according to studies carried out at the Atmospheric Chemistry Laboratory (LCA) of the University of Lomé (UL). To deal with this challenge, LCA has joined forces with the CAMS-Net to find solutions. In this initiation phase, two students are enrolled in a thesis on the themes including (i) Atmospheric measurements of PM2.5 using low-cost sensors calibrated in the ambient air of the city of Lomé in Togo; and (ii) air quality modeling, characterization, and distribution of pollutant emission sources in West Africa. LCA has already deployed five (5) low-cost sensors (PurpleAir) fixed in the city of Lomé for two (2) years. LCA will continue with CAMS-Net's assistance to obtain equipment and technical capacity building to develop new methods and best practices for the collection and analysis of real data on air quality in the city of Lomé and eventually other parts of Togo. The CAMS-Net project meeting is an opportunity for knowledge exchange, during which this initial knowledge of the state of pollution in Togo will be shared and ideas will be presented to boost research on air quality in the near future.
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Evaluation of a reduced-complexity model against low-cost sensors in India and the United States
Presented by: Medinat Akindele, Carnegie Mellon

It is no secret that ambient air pollution has more adverse effects in developing countries with high ambient PM2.5 concentrations. Models have an essential role in managing air pollution by estimating PM2.5 concentrations. However, the state-of-the-art air quality models, chemical transport models (CTMs), are not readily available in those regions. They are computationally expensive and restricted to regulatory agencies and specific organizations. These limitations prompted the development of reduced-complexity models (RCMs) to increase the accessibility of air quality models. RCMs have a simplified representation of atmospheric processes, but they are faster, easier to use, and have broader policy applications. Most RCMs are built for the US and commonly evaluated against CTMs and measurements from regulatory grade monitors. That presents a critical need for a more general and globally applicable RCM. Low-cost sensors have been deployed in several countries and have higher spatial coverage than traditional monitoring networks. In this work, we present an RCM that is location agnostic by applying region-specific emissions and meteorology. This RCM borrows a framework from the APEEP Gaussian plume model with chemistry. The model predicts annual-average PM2.5 concentrations from primary PM2.5, SO2, NOx, NH3, and VOC emissions. The PM2.5 predictions are evaluated against low-cost sensor measurements in India and the United States. Future work will extend this model to developing countries in Sub-Saharan Africa and others in South Asia. RCMs and low-cost sensors can democratize air quality resources and reduce barriers in regulating air pollution.

*Author did not provide PPT for public distribution, please contact Medinat Akindele at makindel@andrew.cmu.edu with questions
An evaluation of particulate matter (PM2.5) in the City of Nairobi, Kenya, using nephelometers
Presented by: Otienoh Oguge, Center for Advanced Studies in Environmental Law & Policy (CASELAP), University of Nairobi

Particulate matter (PM) is a pollutant of high public interest globally but relatively little data exist for a time series comparison with reference data for the City of Nairobi. Given that PM is present in the atmosphere in concentrations that can vary greatly according to location, we set 10 nephelometers (E-sampler 9800) in eight planning zones of Nairobi and collocated one with a reference monitor (BAM-1022). Here we compare the data-collection capabilities of the low-cost sensor to that of collocated BAM. We obtained real-time hourly data entailing PM2.5 concentrations averages from the BAM and the collocated E-sampler for 138 days providing 3,312 valid data pairs. The E-sampler correlated well with the BAM with coefficient of determination (R2) of 0.64 over the four months. However, this was variable with the E-sampler indicating a higher PM load than the BAM during the first four weeks of September 2019 (R2=0.32, n=888) and in late November through early January 2020 (R2=0.34, n=1128), despite similarity in the vertical flux. Variation in PM2.5 between end of September and late November 2019 showed a stronger association (R2=0.76, n=1296) between the two sensors. PM2.5 pollution was found to vary in different parts of the city being highest 38.01 ug/m3 around the central business district, and lowest 12.72 ug/m3 in the low-density southern suburbs of the city. RH was the most significant atmospheric factor to affect PM2.5 values (R2=-0.32, df=137, p<0.001). An order of magnitude rises in the relative humidity (RH) led to a decrease in the level of PM2.5concentration by a factor of 0.14 ug/m3. Our findings show that low-cost sensors can improve the spatial and temporal acquisition of air quality data following calibration and standardization. It also supports existing knowledge that has demonstrated the need for observing accurate values for atmospheric factors, particularly RH in obtaining valid and reliable data from low-cost air quality monitors.
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Monitoring tropospheric airborne particles along a section of the busiest road in East and Central Africa (Thika road, Kenya) using low-cost monitors
Presented by: Paul Njogu, Jomo kenyatta university of agriculture and technology

Air pollution monitoring in Kenya is poorly developed due to high cost of monitoring equipment and low emphasis by the Government. Vehicular emissions are one of the greatest contributors to gaseous and particles to the troposphere. Kenyan roads are filled with vehicles mainly more than eight years after manufacture, such vehicles do not meet emission standards. Public health impacts of air pollutants are well known, this paper presents findings of air quality monitoring along one of Africa’s busiest roads, Thika Superhighway in Kenya. The road passes through residential areas with various institutions of learning. Its thus important to monitor the air quality to cushion the health of the public. Hourly data was collected from two locations along the busy road connecting Nairobi city and Thika town using low-cost Purple Air Sensors during the month of October 2021. The road has a daily traffic load of close to 150,000 vehicles. The data was corrected using colocation data from a BAM reference monitor mounted in a similar location. The levels of PM10 ranged from 5.2 – 72.5 µg/m3 whereas the mean hourly concentration was 22.4 ± 10.2 µg/m3. All recorded 24 hourly averages were within the 50 µg/m3 limit set by the WHO and the Government of Kenya. Wind speeds ranged from 3 - 12 m/s, it was observed that as winds speed increased, the PM10 levels decreased. Daily PM10 variations show two peaks, one at 0800 – 0400 hrs and 1500 – 1900 hours with a minima occurring at 0900 – 1200 hours, there were however wide temporal variations. The trends follow the traffic volumes and weather patterns. The use of LCM presents a cheap method that can be used by African government and local authorities to monitor air quality trends to enable informed appropriate actions, pollution mitigation and policy development.
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Evaluation of lower-cost air quality monitors for monitoring ambient air pollution and around athletic stadiums in Qatar
Presented by: R Subramanian, Qatar Environment & Energy Research Institute

Air pollution is one of the largest environmental health risks, with an estimated toll of almost 7 million deaths in 2019 (HEI State of Global Air). Fine particulate matter mass (PM2.5) is a key contributing pollutant; in dusty and rapidly growing middle-eastern cities, nitrogen dioxide (NO2) and coarse PM (PM10) are also important. Traditional "reference-grade" monitors are expensive and resource-intensive, so monitoring networks are usually sparse; QEERI operates a network of six such stations across Greater Doha. Micro-environments such as high traffic, industrial activity, and construction can result in hyperlocal pollution not captured by these reference stations. Lower-cost air quality monitors using sensors can fill this data gap, enabling high spatial and temporal resolution measurements of air pollution that can also be used to develop and improve air quality models. However, low-cost optical PM2.5/PM10 sensors, whether nephelometers or optical particle counters, need to be evaluated for conditions unique to Qatar and the middle-east generally. We are conducting extensive field testing of a variety of lower-cost devices for PM and NO2 measurements across Doha. We find that sensor design can limit product performance especially for super-micron aerosol. Changes in aerosol size distribution (e.g. during dust storms) result in significant underestimation of PM2.5 mass by nephelometric PM sensors. OPC-based sensors perform much better but they also need to be properly characterized. As seen elsewhere, aerosol hygroscopic growth can also impact sensor performance. For NO2, localized calibration models using machine learning significantly improve over as-reported sensor performance even if that uses ML models but developed elsewhere/in factory lab settings. We shall discuss these results and optimal sensing strategies for Qatar especially targeting large athletic activities.

*Author did not provide PPT for public distribution, please contact R Subramanian at suramachandran@hbku.edu.qa with questions
Contrasting Pattern of PM2.5 Concentrations in Urban-Rural Pair Sensors from Nepal
Presented by: Rejina Maskey Byanju, Central Department of Environmental Science, Tribhuvan University

Nearly half of Nepal’s population lives in a lowland region known as the Terai, immediately north of the border with India. Air quality management in the Terai is encumbered by a lack of ambient measurements and significant uncertainty in the local vs. transboundary pollutant contributions. The Government of Nepal is expanding its air pollution monitoring network to the Terai but faces technological limitations to keep their expensive equipment calibrated and fully operational. Low-cost sensors can provide great assistance to the management of Terai air quality and quantification of transboundary pollution. This research aims to assess PM2.5 concentrations in the Terai and transboundary contributions using TSI BlueSky sensors. Our network of sampling locations across the Terai includes urban centers that are each paired with a nearby rural site. After a lengthy intercomparison, the first pair of sensors was deployed to urban and rural Birgunj in October 2021 and will run for the next 2 years. Preliminary results show daily average concentrations in urban Birgunj are only slightly higher (87.9 μg/m3) than the rural site 15 km away (84.6 μg/m3) implying a very large transboundary contribution. However, the rural measurements contain sporadic peaks due to frequent agricultural residue burning and other biomass burning that are not found in the urban time series. Additional pairs of BlueSky sensors will be deployed to the Terai over the coming months and analytical techniques employed to characterize the PM2.5 concentrations and source contributions in this populous region. Keywords: Agricultural residue, Biomass burning, Urban-rural pair, TSI Blue Sky Sensor
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Insights into Urban CO2 Emissions from BEACO2N
Presented by: Ronald C. Cohen, UC Berkeley

There is significant political and scientific interest in the changing landscape of urban carbon dioxide (CO2) emissions. The Bay area Environmental Air-quality & CO2Network (BEACO2N) uses low-cost sensors to continuously measure CO2and key air pollutants at high spatial resolution throughout an urban area. About 50 nodes are located at 2km spacing in the San Francisco Bay Area. We are partnering with USC in Los Angeles, and the University of Strathclyde in Glasgow, Scotland and the University of Leicester, UK. Integration of the BEACO2N dataset with other data sources (transport models, satellite data, and traffic datasets) provides insights into spatial, temporal, and sectoral changes in urban CO2emissions in these cities. Here we discuss several recent developments out of the BEACO2N project. Bayesian inverse modeling of the BEACO2N CO2measurements using a particle dispersion model and satellite-derived photosynthesis estimates provides an updated 1x1km map of emissions. Coupling the BEACO2N derived emission inventory with traffic datasets gives insight into fuel efficiency trends and the accuracy of models of the speed-dependence of fleetwide vehicle emission factors. These methods show the diversity of applications of dense ground-based measurements for improved understanding of urban emissions.
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Air quality monitoring with TSI BlueSky sensors in the megacity Dhaka, Bangladesh
Presented by: Shahid Uz Zaman, University of Dhaka

The capital city of Dhaka, Bangladesh has a population of ~21 million and is growing at an astounding rate of 4.2% annually. Accompanying the recent growth is increasingly poor air quality, although the region lacks adequate monitoring necessary to assess health impacts, and the potential success of future mitigation strategies. With this in mind, we will discuss a US Department of State (DOS) funded project that involves developing a PM2.5 sensor network in collaboration with local stakeholders aimed at improving the air quality in the region. The project involves careful placement of ~ 70 sensors across Dhaka at key locations that will be used to determine the impact of sources and source regions on PM2.5 concentrations. We will discuss site selection, sensor calibration and maintenance, as well as PM2.5 concentrations measured at locations across Dhaka and discuss the contributions of local versus regional sources on PM2.5 levels across the city.

*Author did not provide PPT for public distribution, please contact Shahid Uz Zaman at shahiduzzamanadil@gmail.com with questions
Field calibration and performance evaluation of low-cost sensors
Presented by: Sinan Yatkin, Joint Research Centre

The calibration of eighty-five AirSensEUR sensor systems including CO, CO2, NO, NO2 and O3 gas sensors, and two models of PM sensors were performed by 2-weeks co-location at Air Quality Monitoring Stations (AQMS) in four European cities, namely Ispra (IT), Antwerp (BE), Oslo (NO) and Zagreb (HR). Calibration was followed by the evaluation of sensor performance for several months of co-location at the same AQMS as calibration, at different AQMS and seasons. Sensor calibration models were established by an automatic process using statistical tools and laboratory experiments to determine the variables needed in the models. Measurement uncertainty, linear regression of sensor predicted data versus reference data and root-mean-squared error were utilized to evaluate the sensor performance. The calibration models for CO, NO, O3 and PM sensors used few and repeatable variables. The coefficients of calibration models were found site-dependent showing that calibration should be carried out locally rather than globally for the best performance. Nevertheless, some sensors calibrated at one AQMS predicted reasonably well at other AQMS and different seasons, meaning that calibration models derived at one AQMS/season can be applied to multiple sites/seasons. Overall, CO, O3, PM2.5 and PM1 calibration models were successful in prediction of pollutants while the performances of NO and NO2 sensors were highly dependent on similarities of pollutants, temperature and humidity conditions between prediction and calibration periods.
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Determination of local traffic emission and non-local background source contribution to on-road air pollution using fixed-route mobile air sensor network
Presented by: Zhi Ning, Hong Kong University of Science and Technology

Traffic-related air pollutants (TRAP) are major contributors to deteriorating urban air quality and pose a serious threat to pedestrians. From both a scientific and a regulatory standpoint, it is important and challenging to understand the contributions of local and non-local sources to accurately apportion specific sources such as traffic emissions contribution to on-road and near-road microenvironment air quality. In this study, we deployed mobile sensors on-board buses to monitor NO, NO2, CO and PM2.5 along ten important routes in Hong Kong. The measurements include two seasons: April 2017 and July 2017. Two types of baseline extraction methods were evaluated and applied to separate local and background concentrations. The results show NO and NO2are locally dominated air pollutants in spring, constituting 72%-84% and 58%-71%, respectively, with large inter-road variation. PM2.5 and CO largely arise from background sources, which contribute 55%-65% and 73%-79% respectively. PM2.5 displays a homogeneous spatial pattern, and the contributions show seasonal change, decreasing during summer. Regional transport pollution is the primary contributor during high pollution episodes. Isolated vehicle plumes show highly skewed concentration distributions. There are characteristic polluted segments on routes and they are most evident at rush hours. The most polluted road segments (top 10%) cluster at tunnel entrances and congested points. Some of these polluted locations were observed in Hong Kong’s Low Emission Zones and suggest limitations to the existing control strategies, which only address larger buses. Our work gives new insights in the importance of regional cooperation to improve background air pollution combined with local control strategies to improve roadside air quality in Hong Kong.

*Author did not provide PPT for public distribution, please contact Zhi Ning at zhining@ust.hk with questions
Six years of the Pittsburgh RAMP network: Lessons learned and where we go from here
Presented by: Albert Presto, Carnegie Mellon University

Since 2016, the Carnegie Mellon University (CMU) Center for Atmospheric Particle Studies (CAPS) has maintained a network of RAMP low-cost sensors across the Pittsburgh region. Over that time, we have used the network for a number of purposes, including: (1) quantifying long-term spatial and temporal trends in multiple air pollutants (PM2.5, CO, NO2, and O3), (2) enabling and empowering communities to collect air quality data in locations that are meaningful to them and to subsequently push for change, (3) informing the public with data products aimed at multiple audiences ranging from casual to “power users,” (4) quantifying the impacts of point source emissions on both acute and chronic PM2.5 concentrations, and (5) performing baseline sampling prior to the introduction of new emissions sources including a soon-to-open petrochemical facility. This talk will highlight key results and discuss lessons learned. These lessons and insights include multiple aspects of operating the sensor network, ranging from purely operational (e.g., sensor maintenance, calibration, and upkeep) to insights gained from working directly with community groups. We will also discuss potential future directions for the RAMP network.
(View Presentation PDF)
Developing a Novel Sensor Technology for Measuring Particulate Matter on Unmanned Aircrafts
Presented by: Andres Munevar, Embry-Riddle Aeronautical University (Poster Presentation)

Atmospheric Particulate Matter (PM) has been identified as a detrimental criteria pollutant to human health and the environment. Current PM monitoring strategies are limited in capturing variations in atmospheric PM concentrations at fine temporal and spatial scales due to their high cost and size limitations. Recently, low-cost PM sensors (LCPMS) have been used to address these shortcomings enabling expanded coverage and higher spatiotemporal resolution. However, the lack of three-dimensional monitoring hinders our ability to fully characterize variations of PM concentrations with altitude. The aim of this study is to develop a robust method to integrate lightweight and compactLCPMS to detect PM concentrations using unmanned aircraft (UA). Three replicates of two types of optical particle counters were used namely Alphasense OPC N3 and PurpleAir PA-II-SD. To evaluate the performance of LCPMS at different meteorological conditions, calibration of the sensors was conducted in Orlando, FL for two months in the summer and winter seasons in 2021. The LCPMS were collocated with a Vaisala AQT400 PM monitor and a PM reference monitor with simultaneous measurements of temperature and relative humidity used for data correction. Consequent to the field testing of LCPMS at the stationary level, the sensors were integrated into an existing UA-hosted meteorological instrumentation suite. Air quality sensor performance was evaluated at Embry-Riddle Aeronautical University campus at Daytona Beach, FL by comparing data from the UA integrated LCPMS with the Vaisala AQT420 at the same altitude. Comparisons were made at different temperatures and relative humidity levels up to 400 feet above ground level at 50 ft increments. This novel method is tested by conducting flights in a sequence of increasingly complex scenarios designed to test interference of rotor-induced flow with the PM data. This work has implications for devising measures in urban, remote, and impractical locations.
Measuring the Spatial and Temporal Variations of Air Pollution in Complex Urban Environments: Results from the Richmond Air Monitoring Network
Presented by: Boris Lukanov, Physicians, Scientists and Engineers for Healthy Energy (PSE) (Poster Presentation)

Low-cost sensor networks are an attractive option for characterizing spatially heterogeneous pollutant concentrations in urban environments and detecting air pollution episodes at locations not covered by government-operated air monitoring sites. Recent developments in instrumentation, communication, and data analysis have enabled the deployment of such distributed sensor networks in support of both regulatory activities and scientific research/citizen science. The Richmond Air Monitoring Network is a dense network of 50 Aeroqual AQY air monitors collecting PM2.5, NO2and O3measurements every minute of the day across various locations in Richmond, California. Sites were selected through a community outreach process and span a wide range of urban land-use characteristics, including industrial, commercial, residential, near-highway, near local and arterial roads, near traffic intersections, and others.

We present initial analyses of data collected by the network over a two-year study period. Sensor measurements were used to determine the spatial variability of air pollution over various timescales, including hourly, weekly, and seasonally. A breakdown of air pollutant concentrations by time of day reveals general daily trends for the three different air pollutants by neighborhood and land use area. Hotspots and neighborhood-level air pollution episodes were identified and compared with data from the small number of regulatory sites in the region. Strong fluctuations in air pollution were periodically observed over hourly, diurnal, and weekly cycles, reflecting the effects of localized traffic and industrial facilities in the area.

The results demonstrate how a dense network of community air monitors can be used to reveal the complex spatiotemporal dynamics of air pollution within urban neighborhoods. The findings also illustrate how a distributed sensor network can be useful for addressing current limitations in the spatial coverage of government air monitoring.
Air Quality Sensors for Smart City Applications in the Netherlands
Presented by: Burcu Zijlstra, OnePlanet Research Center (Poster Presentation)

Despite significant progress in the past decade, ambient air pollution remains a concern in the European Union, where elevated levels of airborne particulate matter and gases lead to both adverse health effects to the public and their deposition results in long-term harm to sensitive ecosystems. Highly accurate but sparsely distributed air quality measurement stations deployed by public health institutes are essential to measurement of average air quality over the country. However, a significant data gap remains: local pollution sources in cities related to human activity.

To address this gap, we present a study that aims to quantify the impact of (1) two types of construction (highway and housing) in the cities of Dordrecht and Utrecht, (2) different traffic scenarios in the city of Apeldoorn in the Netherlands on air quality. IoT sensor units supplied by Connected Worlds that measure particulate matter (PM), NO2 and microclimate are spatially distributed around the pollution sources. Potential pollution-emitting activities are documented to facilitate the data analysis. Sensor data is combined with weather data to quantify the local pollution and measure the spread. We use advanced calibration algorithms to minimize the effects of local microenvironment on the low-cost sensor response and accuracy, and use sensors co-located with reference stations to evaluate raw and calibrated sensor performance over the deployment period. First results show that there is short-lasting but significant increase in particulate matter concentration during a housing demolition in Utrecht despite the undertaken pollution-reducing measures.

Insights from this study can be used to both assess the suitability of IoT sensor use in measuring local pollution, and evaluate the effectiveness of pollution-reducing measures such as using water sprays to limit PM release in construction, or using a car-priority traffic scenario to reduce the number of car stops in the city.
Community Awareness and Risk Perception of Industrial Air Pollution in Rural Kenya
Presented by: Eunice Omanga, URADCA (Poster Presentation)

Background: Developing countries have limited air quality management systems due to inadequate legislation and lack of political will among others. Maintaining a balance between economic development and sustainable environment is a challenge hence investments in pollution prevention technologies get sidelined in favor of short-term benefits from increased production and job creation. This lack of air quality management capability translates into lack of air pollution data, hence the false belief that there is no problem.

Objectives: Assess the population’s environmental awareness, explore their perception of air pollution threat to their health; and identify the most important factors influencing their perception.

Methods: A quantitative questionnaire gathered information on demographic, health status, environmental perception and environmental knowledge of residents to understand their view of pollution in their neighborhood. Focus group discussions (FGDs) allowed for corroboration of the quantitative data.

Results: Four out of five respondents perceived industrial air pollution as posing a considerable risk to them despite the fact that the industry was the largest employer in the area. Respondents also argued that they had not being actively involved in identifying solutions to the environmental challenges. The study revealed the most important factors influencing the residents’ air pollution risk perception were environmental awareness and family health status.

Conclusion: This study availed information to policy makers and researchers concerning public awareness and attitudes towards environmental air pollution pertinent to development and implementation of environmental policies for public health.

Key words: Environmental, perception, air, pollution, risk, rural.
Assessment of NO2 and PM2.5 Variabilities in Nairobi and Evaluation of Low-Cost Sensor Performance in Long-Term Deployments
Presented by: Ezekiel W. Nyaga, Universitè de Paris (Poster Presentation)

The rapid increase in population, economic activities, and motorization have led to the deterioration of air quality in Nairobi. Exposure to particulate matter (PM2.5) is a leading risk factor responsible for 4 to 9 million deaths in the whole world each year, the majority occurring in low and middle-income countries where air quality measurements remain scarce (WHO). The existing knowledge of air quality in Nairobi is based on short-term campaigns lasting from weeks to a few months. Here, we present preliminary findings of sensor deployments and collocations with reference monitors spanning more than twenty-three months in selected sites across Nairobi. The NO2 sensor performance and interannual variabilities of PM2.5 and NO2 as reported by the Clarity nodes were investigated, though an important caveat is that the actual values and variabilities may change once corrections are applied. Preliminary analysis of uncorrected sensor data suggests that the NO2 concentrations are 43.21 µg/𝑚3 and 41.57 µg/𝑚3 at Kenyatta University (KU) and the University of Nairobi (UoN) urban sites and 30.14 µg/𝑚3 at the Innovations for Poverty Action (IPA) suburban site. The corrected PM2.5 concentrations at KU, Buru Buru (suburban), Marurui (suburban), UoN, and IPA sites are 35.87 µg/𝑚3, 35.28 µg/𝑚3, 33.15 µg/𝑚3, 28.00 µg/𝑚3 and 26.81 µg/𝑚3 respectively. The diurnal pattern of PM2.5 and NO2 follows that of the traffic cycle, therefore suggesting a strong influence of vehicle emissions on the concentrations of pollutants across the sites. The inter-comparison of one-year measurements made by all sensors at the IPA site show different rates of decline in NO2 sensors performance during the collocation period. The next steps (which will be presented at ASIC) involve development of PM2.5 and NO2 calibration models and the evaluation of performance and reliability of low-cost sensors for long-term measurements in places with minimal to no coverage by regular air monitoring networks.
Reducing car passengers exposure to pollution by control of air recirculation using high-resolution air quality maps
Presented by: Herve Borrel, Airlib Inc. (Poster Presentation)

It has been established that automobile in-cabin air quality can be improved by controlling the air recirculation flap. This is achieved in at least two ways: keeping high pollution out of the cabin by closing the flap at the right times, and opening the flap enough to prevent excessive concentration of CO2 from passenger breathing. This study focuses on evaluating in-cabin pollution reduction using flap open/close strategies based on real-time air quality map information received by the vehicle. Traffic pollution data was collected from Air Quality Sensors on-board vehicles driven for months within a city. This data was used to create high-resolution pollution maps. Using these maps, a flap open/close algorithm was designed and applied to a set of real trips. The passengers exposures to traffic pollution inside the vehicle were then calculated and compared, with and without flap control. Results show that the in-cabin pollution reduction achieved with flap control is significant, even with a limited amount of data collected to create the maps. It is expected that the maps will gain in predictive value, as the amount of data collected to calculate them increases. This should in turn increase the pollution reduction efficiency. The method could be deployed worldwide without the need for new hardware. It could improve in-cabin air quality for 100s of millions of car users.
Bi-weekly low-cost NO2 sensor collocation for improved calibration performance
Presented by: Jason A. Miech, Arizona State University (Poster Presentation)

The calibration of low-cost air quality sensors (LCSs) has been of great interest lately as their capabilities increase and the need to supplement aging air monitoring networks increases. For LCSs measuring nitrogen dioxide (NO2) the calibration models need to account for several environmental factors, such as relative humidity, temperature, and interfering species (O3). In this study, we examined the impact of extreme, fluctuating environmental conditions on the performance of twelve LCS NO2 sensors in Maricopa County, AZ from April 2021 to October 2021 at twelve different O3 monitoring sites, three of which also had a NO2 Federal Reference Method (FRM) instrument. We found that changing temperature and RH directly impacted calibration performance. When comparing the initial 2-week calibration period in April (T= 25°C, RH= 20%) to a collocation period in July (T= 34°C, RH= 44%), the root-mean-square error (RMSE) increased by an average of 74% (∆2.4 ppb) and the R2 decreased by 97% (∆0.8). To address this issue, we regularly rotated the sensors between the nine O3 sites and the three NO2 sites and used data from these collocations to develop several calibration models for each sensor unique to that period and its environmental conditions. Additionally, we randomized the dates to be used in the training and evaluation datasets to ensure an even distribution of the environmental factors. Using this period-specific calibration method, the RMSE of the April period compared to the July period increased by an average of 7% (∆0.4 ppb) and the R2decreased by 44% (∆0.4), a substantial improvement over a calibration solely based on the initial collocation. The primary conclusion is that NO2 LCS calibration performance sharply drops off when they encounter environmental conditions not included in their calibration training. We will show how focusing the calibration training dataset on more period-specific conditions can greatly improve the LCS performance.
Distant calibration for improved data quality from low-cost air quality sensors: a multi-testbed validation
Presented by: Jelle Hofman, Flemish Institute for Technological Research (VITO) (Poster Presentation)

There is a demand for higher spatiotemporal and personalized air quality monitoring strategies to gain a better understanding on pollutant dynamics and improved representativity of people’s exposure to air pollution. IoT sensor technologies can meet those requirements but need proper calibration and interpretation in order to obtain reliable and actionable data. Our recent publication (Hofman et al., 2022) reports on a distant calibration procedure that is able to improve low-cost PM and NO2 sensor data using remote reference station data, i.e. without the need for recurrent co-location calibration campaigns. The algorithm accounts for sensor gain and offset, while compensating for observed sensitivities of low-cost optical (PM) and electrochemical (NO2) sensors. We report on different validation exercises using five different sensor testbeds (SDS011, OPC-N3, SPS30, NO2-A43F) at various urban and background locations in Belgium and the Netherlands. The results demonstrate that the distant calibration approach improves the sensor data quality (accuracy, linearity and correlation) up to sensitizing and supplementary (EU Class 1) data quality levels and can be scaled to different sensor networks. Thanks to its cloud implementation and openly available input data, this scalable calibration can be provided “as a service” on top of existing sensor networks. Although this approach has shown to improve sensor data, the ultimate performance will still depend on the applied sensor type, unit (design of sensor box) and granularity of the available reference monitoring network.

Reference:

Hofman, J., Nikolaou, M., Shantharam, S.P., Stroobants, C., Weijs, S., La Manna, V.P., 2022. Distant calibration of low-cost PM and NO2 sensors; evidence from multiple sensor testbeds. Atmos Pollut Res 13, 101246.
Air quality use cases: assessing the impact of different events using air quality data from model and sensors networks
Presented by: Jill Chevalier, eLichens (Poster Presentation)

Road traffic is a main target source of action to improve air quality (AQ). The monitoring and evaluation of the impact of emission abatement actions on AQ is essential to better inform decision-makers. However, short-term, or localised actions remain very complex to evaluate without the implementation of significant means of modelling and measurements allowing to consider, the impact of meteorological variability on the concentration levels.

High-resolution, continuous data on pollutants concentrations are needed to observe possible impacts of changes in mobility policies. However, model-only cannot reflect the reality accurately enough, as the local and short-term changes due to peculiar events might not be caught. A dense network of station is of paramount importance to catch such events.

eLichens developed a mapping method to provide hourly, street-level data on urban areas. The model uses a neural network pollution model improved by fusing measurements from a network of eLos (eLichens outdoor station). eLos is a real-time recalibrated low-cost AQ station, providing hourly measurements of NO2, O3, PMs and CO2, temperature and humidity.

Using this data, we assessed the impact of different events on AQ: Covid-19 lockdowns, summer fires or changes in local mobility policies. For long-lasting events, we compare the present data to the data from the previous year to get rid of the meteorological variability. We detected a drop in nitrogen dioxide concentrations in several cities during lockdowns. In Grenoble, France, the creation of a bike lane changed the traffic, creating jams. However, we evidenced a decrease in pollution levels.
Real-time combustion emissions monitoring in Mexicali using networked sensors
Presented by: Julien Caubel, Distributed Sensing Technologies (Poster Presentation)

Mexicali is a community on the California-Mexico border that hosts a large border crossing, several highways, and many industrial/shipping facilities. Furthermore, local residential heating and cooking needs are still often met using wood and other biomass fuels. As a result, residents are disproportionately affected by air pollution from transportation, residential heaters, and other combustion sources harmful to human health. Despite these impacts, air quality (AQ) monitoring in Mexicali remains limited (~5 stations cover the 44 sq. mile municipality), as existing AQ instruments are typically expensive and difficult to deploy.

We operated ObservAir sensors at two regulatory sites in Mexicali to monitor concentrations of black carbon (BC) aerosols, carbon monoxide, and nitrogen oxide. These pollutants are products of incomplete combustion, and are therefore strongly correlated to the combustion activities that drive harmful air quality. The sensors are also outfitted with wireless communications, patented environmental compensation features, and a weatherproof enclosure that enable long-term, outdoor deployments nearly anywhere. Throughout the study, sensors were collocated with regulatory PM2.5and BC monitors, and data was sent in real-time to a dedicated web dashboard that provides pollution alerts and maintenance notifications.

Using the data collected, we investigate AQ trends and validate the ObservAir’s BC data against the regulatory standard. The dataset demonstrates that the ObservAir accurately monitors combustion-specific pollutants, providing a more comprehensive picture of AQ impacts than PM2.5measurements alone. With sufficient context, the multi-pollutant data may be used to attribute pollution contributions from different combustion sources. When analyzed alongside relevant health metrics, these near real-time AQ assessments will ultimately inform and validate public policies to better protect vulnerable urban populations in Mexicali and elsewhere.
Impacts of air pollution on the Heath of inhabitants in the city of Douala: CAMEROON
Presented by: Robert Mbiake, University (Poster Presentation)

IMPACT OF AIR POLLUTION ON THE HEALTH OF INHABITANTS IN THE CITY OF DOUALA: CAMEROON

The poor quality of the air in must Sub-Saharan cities becomes over time, a normal human’s life despite the worst impact on their health. They are two main causes that pollute the air. In one hand, the increasing of these cities at their breakneck speed and their rapid growth with an anarchic urbanization which is obviously accompanied with a number of second-hand cars (70%), motorbikes (300 000 at Douala city) with the dirty fuel constituted with crude oil where there is naturally lead, high sulphur. In the other hand, the air is also polluted there by the use of wood or charcoal for cooking in households and for the commercial food along the roadside. The main exposure persons are those permanently in contact with this air.

For this work which will be the subject of our presentation, it consists in establishing the link between poor air quality and some diseases. First of all, we leaded previously some research to ensure that the city of Douala was really polluted. This research showed that unfortunately the concentration of PM10 could reach in some places 8 times higher than the WHO threshold while the PM2,5 concentration is 7 times (183,43 µg/m3) higher than the WHO threshold (25 µg/m3).

Following this confirmation, we built two groups, one for outdoor workers constituted with motorbike riders, small traders congregated at the crossroad for their activities, fuel station service sellers and the second group was made up with indoor workers constituted by shopkeepers and storekeepers and women householders.

We submitted a questionnaire formulated in accordance with British Medical Research Council (BMRC) and the American Thoracic Society Disease Lung Division ATS-DLD 78-C. Actually, 1,500 participants have responded to our questionnaire. Over the 16 clinical symptoms identified, 07 were regularly cited with the following frequencies:Colds (84.38 ± 1.6%); dry cough (75.86 ± 1.6%), headache (74.24 ± 1.7%), stinging eyes (66.29 ± 1.5%), general tiredness (63.07 ± 1.7%), runny nostrils (53.31 ± 1.1%) and watery eyes (52.38 ± 1.2%).

The analysis of theses collected clinical manifestations was made from multivariable logistic model, Fischer and Pearson’s test, standard deviation and biostatistics analysis.

The obtained results are suitable. It establishes the existence of a correlation between age and symptoms felt between smokers, alcoholics and clinical manifestations leading us to consider them as a cofounding factor.

We have identified among these diseases those which could either be caused or accelerated following the breathing of polluted air.

Keywords: Air Quality – Pollution – Clinical Manifestation – exposed persons – Biostatistics.
Determination of Particulate Matter in the City of Nairobi, Kenya Using Satellite Remote Sensing
Presented by: Moses Njeru, University of Nairobi (Poster Presentation)

Particulate matter (PM2.5) has been known to have adverse health effects and to impact climate and our environment. In Nairobi, the capital city of Kenya, PM2.5 pollution has become a serious environmental problem due to the rapid industrialization, urbanization and expansion. However, because of scarcity in air monitoring stations, there is limited data and this inhibits policy making in the city. Satellite data might offer a viable solution to this problem by filling gaps between monitoring stations data. This research aims at using satellite data and data from low cost sensors to derive the surface concentrations of PM2.5 in Nairobi County. Precisely, satellite AOD obtained from MODIS sensor will be retrieved and associated to ground measurements of five optical particle counters in five areas in Nairobi County: Ruai area (mining and quarrying), Viwandani area (industrial site), University of Nairobi (urban area), Dandora area (waste disposal) and Kiamumbi (Background site). Land Use Regression (LUR) statistical model will then be used to analyze variability of the data correlated with land use data obtained from the Geographical Information System (GIS) data and meteorological data local weather station at the University of Nairobi.
How accurate are out-of-the-box low-cost particulate matter sensor systems?
Presented by: Nuria Castell, NILU-Norwegian Institute for Air Research (Poster Presentation)

Increased availability of commercially-available low-cost air quality sensors combined with increased interest in their use by citizen scientists, community groups, and professionals is resulting in a rapid adoption despite data quality concerns. We have characterized three out-the-box PM sensor systems under different environmental conditions using field colocation against reference equipment. The sensor systems integrate Plantower 5003, Sensirion SPS30 and

Alphasense OCP-N3 PM sensors. The first two use photometry as a measuring technique, while the third one is an optical particle counter. For the performance evaluation we co-located 3 units of each manufacturer and compared the results against optical (FIDAS) and gravimetric (KFG) methods for a period of 7 weeks (28 August to 19 October 2020). During the period from 2nd and 5th October, unusually high PM concentrations were observed due to a long-range transport episode. The results show that the highest correlations between the sensor systems and the optical reference are observed for PM1, with coefficients of determination above 0.9, followed by PM2.5. All the sensor units struggle to correctly measure PM10, and the coefficients of determination vary between 0.45 and 0.64. This behaviour is also corroborated when using the gravimetric method, where correlations are significantly higher for PM2.5 than for PM10. During the long range transport event the performance of the photometric sensors was heavily affected, and PM10 was largely underestimated. The sensors also showed a decrease in accuracy when the ambient size distribution was different from the one the manufacturer had calibrated the sensor, and during weather conditions with high relative humidity. When interpreting and communicating air quality data measured using low-cost sensor systems it is important to consider such limitations in order not to risk misinterpretation of the resulting data.
Micromitigation: a Citizen Science Project for Volatile Organic Compound Adsorption in Ambient Air using Activated Carbon
Presented by: Rebecca E. Skinner, Counter Culture Labs (Poster Presentation)

The session describes Micromitigation, which uses granulated activated carbon (GAC) to mitigate VOCs. The threat to human health posed by VOC toxic air contaminants is under-appreciated. The Micromitigation Working Group, hosted by Counter Culture Labs in Oakland, California, seeks to establish an open-source protocol to abate VOC air pollution. We place small screened panels of GAC in hotspots, then have the panels desorbed, contaminants incinerated, and the material reactivated.

Conditions which facilitate adsorption in ambient air, rather than in closed canisters, have not been studied, and the mixing ratio of ambient air is idiosyncratic. The group is testing three strategies to adsorb effectively despite the low partial pressure of pollutants in ambient air. These are: repeated flows to adsorbent material, achieving saturation over a longer duration; deployment locations which maximize acute air pollutant concentrations; and increasing adsorption surface area with panels open to the air.

In our initial experiment, screened panels of 4x8 mesh coconut-shell GAC placed in emissions hotspots in San Francisco and Oakland were tested at an analytical laboratory by TD-GC-MS analysis using a modified EPA Method TO-17 protocol. The results indicated significant hydrocarbons, especially aliphatic HCs as well as toluene, and long-chain HCs. Even such preliminary results indicate that passive ambient adsorption over a period of time is effective. The GAC was not blinded by PM 2.5, nor did competition from H20 preclude adsorption of VOCs. This process can be improved, and we seek new participants to work with us.

This method could empower communities to mitigate a long-ignored carcinogen threat. Micromitigation could could be carried out by community groups with consultation from university or high-school chemistry teachers; and could be a useful demonstration project for environmental sciences, chemistry, and sensors and computing education
Developing regional low-cost sensor (LCS) calibration models during wildfire episodes to improve sensor performance over broad concentration ranges
Presented by: Sakshi Jain, The University of British Columbia (Poster Presentation)

It has been established previously that low-cost sensors (LCS) have environmental and cross-sensitivities that require robust calibration at regular intervals across the range of expected concentrations. However, this presents a challenge to calibrate LCS for extreme concentrations during wildfire episodes which has higher health and environmental consequences, despite being infrequent and short-term.

To address this challenge, we deployed 16 LCS units to measure PM2.5 and NOx across Metro Vancouver (MV) during, before and after the 2020 wildfire episode and collected pollutant data from 6 regulatory monitoring stations. Collected RAMP data were then down-averaged to 5-min resolution and missing data points were imputed using Kalman filter. Since forest fire concentrations are regional, we used a baseline detection algorithm (rolling ball) to separate the regional component and constructed separate models for the regional (baseline) and local signals. Fitting calibrations to the regional signal removed the need for side-by-side colocation.

We built a general calibration model for regional signal using median baseline concentrations across all outdoor RAMPs at each time stamp and training against median baseline signal across all MV stations using either regression or hybrid regression-random forests. A generalized calibration model was preferred over individual RAMP calibrations to make it transferable to units that may not have been deployed outdoors during wildfires.

For the local signal calibration, we use collocation data before and after forest fire and calibrated via standard published approaches. Outputs from regional and local signal calibration were combined to establish the final calibrated PM2.5 or NOx during the wildfire episode. Performance will be assessed on a withheld testing data set in Vancouver.

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Community Air Sensor Use

Community leadership in air quality research has increased rapidly in the last decade, in part facilitated by the emergence of low-cost sensor technologies. In this session, we will hear from community leaders and their partners about the interests, needs, and experiences of those participating in community-based air quality monitoring projects. The discussion will include building strong collaborations that maximize benefits to communities while minimizing extraction and improving the usefulness of sensors and related resources. Speakers may also address how to implement projects that produce data, which can be leveraged for positive policy outcomes and local action.

Discussion topics may include, but are not limited to the following:

Examples of specific potential benefits to communities participating in these types of projects (e.g., capacity building, training, or access to equipment)
Examples of how groups have successfully leveraged data to inform positive policy outcomes and local action
Input from community members on what types of resources or support are not typically included in these projects but, if added, could enhance participation or project success
Examples of projects or collaborations discussing how the active participation of community members in the research (i.e., data collection, data analysis, and data interpretation) has increased the success of the project

Lead Session Chairs:

Ashley Collier-Oxandale, South Coast AQMD, Jan-Michael Archer, University of Maryland School of Public Health, & Gwendylon Smith, Community Health Aligning Revitalization Resilience & Sustainability (CHARRS), Jill Johnston, University of Southern California, Aubrey Burgess, City and County of Denver, Colorado

Presentations:

Community-driven open-data on Pakistan’s air pollution problem
Presented by: Abid Omar, Pakistan Air Quality Initiative

Pakistan has an air quality data problem with little to no monitoring across the country. Community-driven air quality monitoring has been instrumental in filling the air quality data gap in Pakistan, and in creating a grassroots citizen’s movement advocating for clean air. Real-time data from this community network has put a magnifying glass on air pollution, where citizens can react to their local air pollution, evaluate impact of emission-reduction policies, and ultimately providing the impetus for a cleaner environment. This paper presents Pakistan as a case study of a typical low-income country, similar to other developing countries across South Asia and Africa, lacking reference-standard monitoring equipment, or the technical capacity to manage them. Pakistan’s community-based nationwide network of low-cost real-time air quality monitors has helped fill the data gap in Pakistan and has been instrumental in many ways, by engaging the community and corporate citizens in participatory monitoring, by creating a network of ambassadors for air quality awareness, and finally by providing the baseline data for the government to initiate reference-standard monitoring. The impact from this community monitoring network has been tremendous in kick-starting awareness and furthering monitoring in one of the most air-polluted regions of the world. Learnings from this initiative are shared to enable other communities, cities and countries to harness a similar network of low-cost monitors to kick-start positive environmental change in their region, and how to engage and develop a community of citizen scientists to deploy, maintain and manage air quality sensors in a decentralized network.
(View Presentation PDF)
Environmental Justice for fence-line communities
Presented by: Gertrude “Naeema” Gilyard, C.A.U.S.E (Community Action Unified By Strength & Engagement)

Partnerships and coalitions, including with Municipalities and Federal Agencies, are needed to help achieve Environmental Justice for fence-line communities whose health is compromised by exposure to air pollution and harmful toxins. Residents in fence-line communities face financial barriers to obtain air monitors with filters for small particles that create data, the potential to analyze the data, and translate the chemical compounds in a report that details the health impacts of environmental stressors. Partnership and collaboration with and assistance from all government agencies could help reduce or remove these barriers. It is the responsibility of States, Counties, Municipalities and Federal Agencies, who regulate and have oversight of companies and industries that release toxins into the atmosphere, to hold them accountable for their violations. States, Counties, Municipalities and Federal Agencies have access to sensitive air monitors and the capabilities to quantify the amount of toxins released by a company that exceeds the allowable threshold. With this in mind government agencies are in a position to support the health of fence-line communities by being more collaborative and transparent, collecting air monitoring data at community fence-line and providing access to that data and its analysis. Their first priority should be to protect the health of fence-line communities by holding violators of the U.S. EPA Clean Air Act accountable for violations. Collaboration with fence-line communities to address toxic air releases and enforcing regulations are good first steps.
(View Presentation PDF)
From the Ground Up- An Environmental Justice Approach to Community Science and Air Monitoring
Presented by: Gustavo Aguirre Jr, CCEJN

California's Central Valley is known and associated with the beautiful mountain ranges of the Sierra Nevadas and endless rolling valley lands filled with agriculture such as almonds, grapes, citrus and limitless crops covering the vast majority of our lands. Between all that, here in the South Valley we host some of the largest concentrations of CAFOs, Daries and produce & extract nearly 75% of all of California oil and gas. This exhausting mixture of sources of emissions makes for one interesting fog of sacrifice zones to propel this industry to operate. See how a grassroot organization is turning the tide with community empowerment and community science research using air sensors and community air networks.
(View Presentation PDF)
Citizen Science and Community Monitoring: Tools for Community Revitalization
Presented by: Omar Muhammad, LAMC

Omar Muhammad has worked as a community advocate and activist since 2007 as a volunteer for the Lowcountry Alliance for Model Communities (LAMC). He has served as LAMC’s web-site content coordinator and community engagement liaison. Currently, he serves as LAMC’s Executive Director. Omar sits on the Mitigation Workgroup which advises LAMC on implementation of the Mitigation Agreement between the South Carolina State Ports Authority, Palmetto Railways and LAMC. Omar is an advisor to the executive boards for the Union Heights Community Council, Serve With Joy, Distinguish Gentlemen Mentoring Organization and Communities For Justice; He is a research consultant for the Charleston Community Research to Action Board (CCRAB), the Clean Power Plan Environmental Justice Analysis Workgroup for the State of South Carolina and the Clean Power Plan Advisory Workgroup for the State of South Carolina. He is also an Environmental Justice Hub member for SC DHEC; a founding member for the South Carolina Environmental Justice Network and a founding member for the Moving Forward Network’s Southeast Regional Network. Omar completed a 9-month training with the United States Environmental Protection (USEPA) Region IV’s Environmental Justice Academy and was selected Valedictorian for the inaugural class. He is also a past participant in a joint EPA Region IV and South Carolina Department of Health and Environmental Control Leaders in Environmental Action Pilot (LEAP) inaugural class. He has successfully led efforts to engage the LAMC communities through various outreach strategies and is responsible for the EJRADAR.
(View Presentation PDF)
Community, Health, and Science: Establishing the Pioneer Valley Air Quality Network
Presented by: Anna Woodroof, Earthwatch Institute

As the popularity of low-cost air quality sensor grow so does the appeal and the demand for data and an understanding of the measurements that these sensors provide. The Pioneer Valley Healthy Air Network is one example of how three communities in Western Massachusetts with historically high rate of asthma and poor air pollution are working in conjunction with nonprofits, scientists and health experts to address the challenge of air pollution in these communities. Originally commissioned by the Massachusetts Attorney General’s Office’s Environmental Protection Division and the Massachusetts Municipal Vulnerability Program, the Pioneer Valley Air Quality Network deployslow-cost air quality sensors with city and school partners to provide real world datasets to help assess hyper-local air quality. By engaging with community advisory groups to co-builda school-based dashboard, the Network works to educate and inform residence of local air quality and actions to mitigate exposure. This partnership has combined both a bottom-up and top-down direction and buy-in. The presenters from Yale School of Public Health, Earthwatch Institute, and the Public Health Institute of Western Massachusetts will share challenges and successes from the experience of this program. Earthwatch Institute and Yale School of Public Health have partnered on additional projects to provide education and tools to monitor and understand data coming from low-cost air quality sensors.

*Author did not provide PPT for public distribution, please contact Anna Woodroof at awoodroof@earthwatch.org with questions
Air Quality Investigation and Research for Equity (AIRE) in Commerce City, CO
Presented by: Aracely Navarro, Cultivando

Commerce City, CO is one of the most polluted cities in the entire United States. It is also a community that it predominantly Latinx and low-income, making it a community suffering from environmental racism and injustice. They are surrounded by oil and gas refineries which contribute to some of the worst air quality in the nation and contribute to poor health outcomes for the community. Cultivando is a community-based non-profit organization that utilizes the Promotora model to promote health and racial justice in Commerce City. Due to the great need to address environmental racism, Cultivando, in partnership with Boulder A.I.R., implemented the community-based Air Quality Investigation and Research for Equity (AIRE) program. This is a year-long research study that utilizes a stationary air monitor which measures 50 different air toxics in real-time, a mobile air lab and simple home-based air monitors, which will measure various pollutants including VOC's, particulates and radioactivity. This is a unique project because no similar project has ever been done in this community before. This research is also unique because it is community led project that uses state of the art air monitoring technology. The aim of this project is to quantify the amounts of air pollution in Commerce City, CO and also investigate the health impact on surrounding communities to address environmental racism and injustice.
(View Presentation PDF)
Community led air monitoring informs land use policies in Kansas City
Presented by: Beto Lugo, CleanAirNowKC

Regulatory ambient air monitoring under the Clean Air Act is concerned with compliance with federal and state standards for criteria pollutants, not what community members are breathing at the fenceline to industrial facilities. The EPA cannot detect major contaminants to which residents are exposed daily. Even when regulatory monitors are available, regulators prioritize industry over public health. The placement of regulatory monitors lack inclusion of community representatives in decisions, these networks fail to capture air pollution hot spots. As a result, CleanAirNowKC (CANKC) and community members in Kansas City living in areas with high concentrations of diesel emissions and industrial polluters are forced to live with elevated levels of pollution and the associated health effects without the resources necessary to advocate for improved air pollution standards. (CANKC) created a community-driven open source air monitoring network to measure PM2.5, NO2, and O3 and black carbon concentrations in different locations about which the community has expressed concern. These monitors, which measure hyperlocal pollution at intervals shorterthan federal monitors, have measured levels that exceed EPA standards. These resources can serve an important role in civic engagement, advocating for community-driven and just policies to improve public health; this project has enabled participation in decision making policies and land use plans in Kansas City. This data will also be integrated into an interactive map with an overlay that includes toxic release facilities in close proximity to residential homes, this will help the community members identify major air pollution contributors in their neighborhoods.
(View Presentation PDF)
Aires Nuevos: Driving Meaningful Air Quality Action in Latin America
Presented by: Christi Chester Schroeder, IQAir North America

Aires Nuevos is a Citizen Air Quality Network created in 2020 to promote community-based air quality data generation and communication to reduce early childhood air pollution exposure in Latin America. Based on the IQAir air quality monitoring and communication platform, Aires Nuevos has installed and published 95 low-cost air quality monitors in 28 cities in Mexico, Uruguay, Peru, Brazil, Argentina, Ecuador, Chile, and Colombia building a sensor network capable of monitoring air quality for nearly 1.5 million children under the age of 4. Aires Nuevos uses a grass-roots approach to combatting air pollution by engaging the local community and offering air pollution education using real-time data accessible for free on IQAir’s website and mobile app. In each city, working groups consisting of local public officials, university researchers, and local community members collaborate using sensor data to identify local policies that will result in limiting air pollution exposure to young children. In this presentation, the authors will discuss some of the lessons learned in building up an air quality monitoring network across multiple countries and how the air quality data generated is starting to affect local policies to better protect citizens and improve air quality in the region.
(View Presentation PDF)
Community-engaged air sensor analysis: Visualizing PM2.5 data from PurpleAir sensors in Southeast Los Angeles
Presented by: Claire Bai, University of Southern California

The advent of using low cost sensors to monitor air quality in environmental justice (EJ) communities has prompted a need to understand and interpret data among members and activists. PurpleAir PM2.5 sensors have gained popularity due to their accessibility and increased work of communities to characterize neighborhood air quality. However, less attention is paid to community-oriented data analyses. Our initial work focused on outdoor PurpleAir sensors placed along a major freeway in an international trade corridor in southeast Los Angeles County connecting the Ports complex to a major railyard. Sensors were included if they had coverage for at least 30% of the year and at least 80% reliability between channels. Data was identified for 26 sensors in 2019 and 41 in 2020, and combined to average daily PM2.5 levels for all sensors, and by 3 regions: south of the railyard, the freeway corridor and the Port area. Using R, we created time variation plots compared to the closest NAAQS reference monitor, calendar plots, bar plots for percentage of time above NAAQS thresholds by region, and seasonal daily variation plots. The visualizations were shared with multiple EJ organizations for feedback, who emphasized the importance of clear messaging and health-related information associated with PM2.5 levels. We later developed a Shiny app, hosted on the University of Southern California’s Environmental Health Centerswebsite [https://envhealthcenters.usc.edu/pm-2-5-shiny], to provide an interactive platform that compares visualizations by region and calendar year. We hope to expand this initial work to develop tools for PM2.5 data visualization across Los Angeles.
(View Presentation PDF)
Revolutionising air quality monitoring using DIY and IoT approaches to beat air pollution in Africa
Presented by: Collins Gameli Hodoli, Clean Air One Atmosphere

The emergence and use of low-cost environmental sensing tools have shifted the paradigm on how air quality monitoring is carried out. This includes community engagement integrated with the democratisation of air quality monitoring. This consequently gives power back to the people to understand baseline air quality levels to influence behavioural changes and appreciate air pollution and climate change mitigation policies. Specifically, for communities in the global south, utility of low-cost sensors (LCS) has provided citizens with relatively cheaper and simpler mechanisms for understanding the impacts of our activities on local air quality including vehicular emissions as well as other anthropogenic activities and natural sources. Of a particular importance is how the utility of LCS can help expand community’s understanding of local air quality levels and corresponding health effects tied to health advice using “dot it yourself” (DIY) and “internet of things” (IoT) approaches. Clean Air One Atmosphere (CAOA) has collaborated with local and international organisations and research institutions including regulatory and local governmental agencies to deploy LCS in West Africa as well as establish links between air quality experts from the global north and academic institutions in West Africa. CAOA also initiated the first ever mobile application that meaningfully communicate air quality data with corresponding health effects and provide health tips for subscribers as well as a general advice on how individuals can reduce exposure and contribute to reducing atmospheric emissions in the global south. The application "Yakokoe" populates open-source data from all over Africa to sensitise citizens.

*Author did not provide PPT for public distribution, please contact Collins Gameli Hodoli at cghodoli@gmail.com with questions
Low-Cost Air Pollution Sensor Characterizes Excessive Smoke from a Neighborhood Restaurant and Highlights Gaps in Environmental Health Laws: An Observational, Citizen Science Study
Presented by: Nicholas Newman, University of Cincinnati, College of Medicine, Dept of Pediatrics

Air pollution monitoring is done using a sparce monitoring network and is designed to provide background concentrations of pollutants. Due to this design, small area variations due to local emission sources are missed. The advent of low-cost air pollution sensors provides an opportunity to fill this gap, but air quality & public health agencies are not staffed or funded for this type of monitoring. Using trained citizen scientists to measure local level air pollution is one method to address this gap. We describe the development and implementation of an air pollution monitoring and community engagement plan in response to resident concerns regarding excessive smoke production from a neighborhood restaurant. Neighborhood-level particulate matter <2.5 micron in diameter (PM2.5) was measured using a portable sensor. In 82% (14/17) of the air monitoring sessions, the peak PM2.5levels were at or above 35.5µg/m3, considered “Unhealthy for Sensitive Groups” by the US EPA. The highest PM2.5 readings were in the immediate vicinity of the source. Results were shared with the community at a health fair and with local government officials. Neither of these resulted in any action to address the source of the smoke emissions. Agencies cited a lack of jurisdiction as the primary reason. Current environmental public health laws may not adequately address non-industrial or non-vehicular sources of PM2.5. Community members using modern lower cost air pollution sensors may demonstrate potentially important local sources of air pollution, but environmental public health laws will need updating to address these.

*Author did not provide PPT for public distribution, please contact Nicholas Newman at nicholas.newman@cchmc.org with questions
Improving Tribal and Citizen Science with Low-Cost Air Sensor Collocation Shelters
Presented by: Ryan Brown, US EPA Region 4

The collocation of air quality sensors with regulatory monitors at existing air monitoring sites, where sensor data can be compared against quality-assured reference data, is a cost-effective method for understanding the performance of lower-cost air sensors. In some cases, collocations can allow for location-specific correction factors to be developed to improve sensor accuracy. This presentation discusses a project during which 20 collocation shelters, which provide weather shielding and security for sensors, were built and recently deployed at state/local/tribal air monitoring locations across the U.S. to provide the infrastructure for monitor and sensor collocation opportunities. This work is a collaboration between the U.S. Environmental Protection Agency (EPA) and multiple state, local, and tribal air monitoring agencies. Participating agencies are working to make these shelters accessible to the public and to encourage communities to collocate sensors with regulatory monitors and incorporate sensor data quality assurance and correction into their sensor projects. These efforts lead to increased community engagement, leveraged expertise of the regulatory air monitoring staff, lowered barriers for collocation, and more quality-assured sensor data. As part of the project, EPA Office of Research and Development commissioned and published a design document which will allow shelters to be built and installed at regulatory air monitoring sites across the U.S. The presentation will also focus on specific challenges related to site access, security, and infrastructure that state, local, and tribal air monitoring agencies addressed in implementing this project.
(View Presentation PDF)
Air Quality Chicago: Mobile Monitoring and Capacity-building with Chicago's Environmental Justice Communities.
Presented by: Tiffany Werner, Environmental Law & Policy Center

As technology advances and low-cost sensors are becoming more accessible, understanding one’s exposure to air quality pollution is becoming easier to do each year. While arming communities with handheld sensors to collect real time air quality data is useful for public education and awareness can it also be used as an advocacy tool to push for clean air policy? Understanding the quantity of data needed to properly advocate for policy change is difficult, especially when using low-cost, mobile sensors, but not impossible. In Chicago, the Environmental Law & Policy Center (ELPC) has spent the last five years exploring how armingcommunities with handheld air quality monitors to assess street by street PM2.5 levels can empower community members to become vocal advocates for clean air policy and enforcement. Using AirBeams, ELPC has been partnering with residentsto collect data around their neighborhoods and near pollution sources. With targeteddata collection andempowered community scientistswe have been able to gain the attention of policy makers on air quality injustice and are now exploring how solutions such as vegetative buffers and fleet electrification can mitigate neighborhood PM2.5 levels.

*Author did not provide PPT for public distribution, please contact Tiffany Werner at twerner@elpc.org with questions
Using low-cost sensors for hyperlocal PM2.5 assessment: lessons learnt from two experiments.
Presented by: Jalal Awan, Pardee RAND Graduate School (Poster Presentation)

According to the WHO, air pollution is the largest single environmental health risk, estimated to kill 1 in 8 people globally, due to heart disease, stroke, respiratory disease and cancer. Moreover, blacks and Hispanics are often more exposed to air pollution than whites, despite contributing less towards producing it. Community-based air quality monitoring provides an interesting avenue for research. The widespread diffusion of Internet-of-Things (IoT) connected devices, high-speed telecommunication networks and open data are reconfiguring the way data underpinning policy and science are being produced and consumed. One such recent paradigm in air quality monitoring is the use of low-cost sensor networks for more granular, neighborhood-level air quality assessments. In my poster presentation, a summary of current paradigms in air pollution monitoring are presented along with a discussion on key findings from two experiments deploying 30 low-cost air quality sensors in the City of Santa Monica and Pittsburgh. RAND researchers based in the two cities were chosen as a convenience sample and correlations of PM2.5 across various dimensions (environmental variables, time of the year / day / month, wildfire events etc) over a 2 year deployment period were compared. Based on findings from our experiment, I provide policy level guidance for federal and local agencies aimed at expanding research and use of community-science based air quality monitoring. Further discussion (and my dissertation topic) based on our findings on performance evaluation of low cost sensors in field and calibration methods against EPA’s FRM / FEM is also presented.
A Low-Cost Industrial Particulate Profiler Simultaneously Reporting PM10 and PM2.5
Presented by: Jennifer Brown, MetOne Instruments (Poster Presentation)

Met One Instruments, Inc. has developed an industrial-grade optical particulate profiler that simultaneously measures and reports ambient particulate matter at PM10, and PM2.5cut points. This device, known as the “ES-412 Simultaneous Particulate Profiler,” can operate for up to two months without user intervention and will report these parameters with hourly sensitivity of less than 0.1 mg/m3. The ES-412 offers wireless, remote PM monitoring in a low-profile, field-deployable, weatherproof unit. It is lightweight and self-contained. The ES-412 provides several features and diagnostics, such as active flow control, generally not found in low-cost PM sensors. The ES-412 is a complete system with integrated cellular communications, a customized webpage dashboard, a 3-year data plan, an AC power supply, and a transport case. Users have instant access to data on their smartphone, tablet, or computer. Data from the ES-412 is backed up internally and then transmitted to a nearby cell tower every 15 minutes. In the event of cell tower communication loss, the ES-412 will attempt communication continuously until it is regained. When this occurs, the ES-412 will transmit all missing data when communication is re-established, providing data security.

This presentation will report field test results from several field sites operating collocated, US-EPA-designated PM10 and PM2.5monitors (Met One Instruments BAM-1020). We will provide accuracy and relative standard deviation (CV) measurements that demonstrate near equivalency for factory-calibrated ES-412 monitors. Local span calibrations are not allowed on an EPA-designated PM monitor. However, if the ES-412 is span calibrated by collocating it with an EPA-designated monitor or sampler, it can be demonstrated that accuracy can generally be maintained for significant periods of time.

We will also report how the ES-412 has benefitted local school districts in the area during wildfire season.
Evaluating personal exposure in a Vancouver neighbourhood through community collaboration and the implementation of a low-cost sensor network
Presented by: Rivkah Gardner-Frolick, University of British Columbia (Poster Presentation)

Exposure to air pollution is dependent on a person’s daily movement through microenvironments. These microenvironments reflect the strong contribution of local sources to community air quality patterns. To quantify small-scale spatial gradients in communities concerned about air quality, monitoring campaigns often use low-cost sensors (LCS). One such community, the Strathcona neighborhood in Vancouver (Canada), contains a variety of industrial and transportation emissions sources within its boundaries. In addition, Strathcona is home to many sociodemographic groups that are especially vulnerable to air pollution, such as Indigenous residents, unhoused residents, and the elderly. Implementing a network of LCS in this area to map fine-scale air quality in combination with surveys of time-activity patterns will help to assess air quality patterns, personal exposure, and potential exposure mitigation strategies.

This study is a collaboration between academic researchers and the Strathcona community. The Strathcona Residents Association is the main community representative and has contributed to sensor network design, spatial mapping, data interpretation, and data visualization. We are assessing air quality by deploying 16 LCS units (SENSIT RAMP) across Strathcona for a period of 6 months to measure PM2.5, CO, and NO2. Each monitor is placed where there is community concern about high-emitting sources or vulnerable populations. Spatial mapping for each season will use land use regression and incorporate community knowledge of sources and receptors, enabling better estimation of small-scale sources and quantification of the value added by community input. Visualization of the spatial map will be done collaboratively and will include representation of sources of concern and vulnerable receptors from Strathcona residents in addition to air pollution concentrations. The collaborative map will produce easily accessible information for residents and practitioners.

View Session 1D Recording on Youtube

View Session 2D Recording on Youtube

Communication Strategies for Understanding, Insight, and Action

The proliferation of individual sensors and sensor networks has led to an exciting, data-rich environment; however, communicating information supported by the data remains challenging. Effective communication often needs contributions from diverse talents in addition to air quality professionals (i.e. graphic designers, data and interface engineers, communication specialists) as well as the inclusion of contextual information (sources, meaningful locations, interpretive materials) to allow users to learn from the data without expert guidance. This session will feature discussions of any and all aspects that help communicate air quality information in a clear and comprehensible manner.

Lead Chairs:

Michael Ogletree, City & County of Denver, Dept. of Public Health & Environment, & Melissa Lunden, Aclima

Presentations:

Love My Air Network- A national collaboration on messaging and education
Presented by: Aubrey Burgess, City and County of Denver

Love My Air Network is an idea-sharing network with the goal of replicating Denver’s Love My Air program across the United States. The project was founded in 2018 by the City and County of Denver and started in Denver Public Schools with the goal of reducing air pollution and limiting exposure through behavior change, advocacy, and community involvement. This presentation will demonstrate how this program has grown into a flexible initiative where participating entities aim to build/ grow their air quality sensor networks and pair data with community action. Local governments and universities across the nation have begun work to adopt aspects of Denver’s framework to develop a culturally- appropriate program in their own neighborhoods. Participating entities can contribute to a repository of living documents, open-source data, and collaborative conversations. We hope to increase air quality literacy and involve the community in air quality monitoring, education, and decision-making.
(View Presentation PDF)
Experiences and Lessons Learned with Community Monitoring Near a Refinery
Presented by: Patrick Clark, Montrose Air Quality Services, LLC

In 2021, Suncor Energy (U.S.A.) Inc. (Suncor) developed CCND Air Monitoring, a community air monitoring program in the neighborhoods surrounding its refinery in Commerce City, Colorado. The purpose of the voluntary program is to provide the community with easy-to-access air monitoring information from sensors reporting in near real-time, as well as through laboratory analysis of air samples initiated by the sensors and a mobile monitoring van. Suncor designed the program with input from stakeholders including regulators, local governments, public health professionals, and most importantly, the people who live in the community. Montrose Air Quality Services, a third-party team of engineers, scientists, analysts and technicians, runs the program. Currently eight sites are in operation utilizing a combination of low cost and mid-tier sensors. Data is fed to a public-facing website, as well a free mobile app in English and Spanish. This presentation will share the experiences and discuss lessons learned from both a technology and program management standpoint. It will discuss feedback from the community and as insights from the data. The presentation will also outline next steps to improve the program.
(View Presentation PDF)
Testing Visual Communication Strategies of Air Quality in Pittsburgh: A behavioral science approach
Presented by: Ashley Angulo, University of Oregon

Our research team developed and tested a website which displayed air quality in the greater Pittsburgh area. The research project began with the wide intention of isolating key display dimensions that affect communication accuracy and usability. We identified and tested three dimensions of our pollution map display: key guide, color overlay, and route option. The key-guide is the primary sense-making tool that allows for interpretation of the current concentration of the pollutant. Color-overlay refers to the visual use of color that is added on top of the geographic pane of the map, akin to a weather map. Lastly route option refers to the choice-set of paths testing trade-offs between distance and pollutant exposure. We tested two display patterns for the guide: a table and dial display. Both displayed three ranges of exposure that related to unhealthy, concerning, and acceptable levels of pollutants, but varied in the manner in which this information was displayed. Participants in our experiments and community surveys found the dial guide more user-friendly than the table guide. Specific details of each guide to be discussed in detail. We tested five different color overlays ranging in complexity, hue, and implicit meaning. Amongst our findings, we identified a disconnect between accuracy in responses and subjective ratings of liking. Additionally, certain colors exacerbated pollution concern more so than others. We also tested traffic-light color overlays commonly used in health domains.

*Author did not provide PPT for public distribution, please contact Ashley Angulo at aangulo@uoregon.edu with questions
Community-focused Monitoring in California: Building Bridges between Community Members and Industrial Facilities
Presented by: Josette Marrero, Sonoma Technology

There has been a growing national awareness about the effects of exposure to air toxics and how they may disproportionately impact certain communities. In response to these growing concerns, regulations have been passed to help provide information to those who live, attend school, and work near refineries. For example, the U.S. Environmental Protection Agency and California state regulators have passed rules requiring the continuous monitoring of various air toxics at refinery fencelines and have mandated that this information be made readily available to the public. This trend is likely to continue throughout the U.S., as both the federal government and state legislatures are seeking to support air quality monitoring in areas where environmental justice is of concern. With a thoughtful approach, these monitoring efforts can be mutually beneficial for both industries and their neighboring communities as they build trust, provide transparency, and make knowledge available to all stakeholders. In this presentation, Sonoma Technology scientists will discuss approaches to community-focused monitoring programs and highlight relevant cases in California. We will discuss key elements of these monitoring programs and how the data are presented on public websites or summarized in reports to ensure common understanding among end users, including industrial facility employees, regulators, and community members. We will discuss the importance of how a combination of high-precision instrumentation and low-cost sensors can be used to achieve monitoring goals. We will also focus on how establishing regular avenues of communication between industries and the public can lead to positive interactions when facility changes are needed or when unexpected events occur.
(View Presentation PDF)
Communicating Air Sensor Data on the AirNow Fire and Smoke map
Presented by: Karoline Barkjohn, U.S. Environmental Protection Agency Office of Research and Developement

Residents and local agencies in smoke impacted areas often use air sensors to provide more localized air quality data. In 2020, air sensor measurements were added to the AirNow Fire and Smoke map. Alongside permanent and temporary smoke monitor measurements, PurpleAir sensor measurements now appear as “Low Cost Sensor” icons. Monitor and sensor icons appear on the map as distinct shapes colored by NowCast AQI category. Clicking on an icon provides information on particulate matter concentrations at various averaging intervals, trends based on the past 30 minutes of air sensor data, and recommendations for health protective actions. Before sensor data is displayed on the map, PurpleAir sensor measurements are corrected and quality assured (i.e. points removed where A and B channels disagree), resulting in higher accuracy measurements and improved category estimation for the NowCast AQI. However, the uncertainty in the sensor, temporary monitor, and stationary monitor measurements is variable and dependent on time-averaging intervals. In this talk we will discuss how insights into local air quality can be gleaned from data on the map at various averaging intervals and how these insights can be transformed into actions to reduce exposure to smoke. Based on our work with PurpleAir sensors, we will highlight how to quantify and improve the accuracy of data from other sensor networks so that it can be also be utilized to provide insights on local air quality.
(View Presentation PDF)
Community engagement through text-based communication with air quality sensors
Presented by: Surya Venkatesh Dhulipala, University of British Columbia

We present a novel mode of communication for disseminating air quality data in a community setting. We fitted our low-cost air quality sensors (LCAQS) with placards containing QR codes, that when scanned, begin a friendly text message conversation with users. For this study, we partnered with Hello Lamp Post, a startup based in UK. Overall, we present 6 months’ worth of user engagement data (mostly students). To quantify the air quality on University of British Columbia (UBC) campus, we installed a network of 8 LCAQS across UBC campus in June 2021 to measure air pollutant concentrations at different traffic intersections. At the same locations, we installed placards with QR codes for community engagement. Within a two-month period (August – September 2021), a total of 624 interactions and 190 conversations were recorded. 93% of users said they would like to see air quality sensors around their home and near major traffic intersections. A further 50% of users complained about current modes of communication around air pollution exposure on UBC campus. Other feedback included – “I have no idea where to look”, “make more accessible” and “I have not heard much about air pollution exposure on campus”. Often, local communities are unable to interpret publicly available data from city-wide or province-wide air quality monitoring stations and make informed decisions. We directly asked users (mostly students) about their preferred formats for reading air quality data, perceptions about air quality on campus and their satisfaction levels about current modes of communication. Our user engagement data can also be used to make localized decisions – one community may prefer dense sensor networks for monitoring air quality whereas another may be hesitant. Other possible applications of user-engagement data will also be discussed in this presentation.

*Author did not provide PPT for public distribution, please contact Surya Venkatesh Dhulipala at surya.dhulipala@ubc.ca with questions
Empowering communities through data dashboards
Presented by: Tara Webster, Colorado Department of Public Health and Environment

Pulling data together is just the first step in creating meaning for different audiences. At the Colorado Department of Public Health and Environment, improving data access and transparency are key parts of our efforts to work towards environmental justice. This talk will present lessons learned through our work compiling disparate datasets to inform questions from policy makers and community groups from areas that have been disproportionately impacted by pollution. Our goal is to use data dashboards to move towards frameworks of engagement that support community empowerment. We will walk through several case studies to explain the context, the questions, and the data dashboard we created. We will highlight efforts to improve on the historically fragmented nature of past data collection efforts and the challenges of bringing these datasets together to meet people where they are. We will also discuss approaches to communicating human health risks, especially in areas with great uncertainty. Throughout these case studies, we will discuss how we identify opportunities for improvement and engagement to better serve the needs of our stakeholders.
(View Presentation PDF)
The Enhanced U.S. EPA Air Sensor Guidebook
Presented by: Andrea Clements, U.S.EPA (Poster Presentation)

The knowledge base surrounding air sensor applications and use has rapidly developed over the past decade. Around 2012, the availability and use of air sensors began to expand rapidly. At this time, air sensor users had a wealth of questions including how to use sensors, select sensors for specific applications, understand sensor performance, collect data, interpret the data, and more. In response, the U.S. EPA developed the Air Sensor Guidebook in 2014. The Guidebook was designed to provide basic information on air quality, guidance on selecting appropriate sensors for a given application, considerations for data quality, and sensor performance needs for different applications. The Guidebook has been one of the most popular resources available on the U.S. EPA’s Air Sensor Toolbox website. Since the Guidebook was released 7 years ago, the use and understanding of sensors has continued to grow and more information regarding best practices, sensor use, sensor performance, and other key topics is now available. The U.S. EPA is updating the original Air Sensor Guidebook to reflect this new information and to continue to support users and manufacturers of sensors. This presentation will provide a highlight of the updates to the Guidebook including the addition of new topics such as sensor performance guidance, determining the purpose for monitoring, and planning and conducting an air quality monitoring study.

Disclaimer: Although this abstract was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.

View Session Recording on Youtube

Filling in the air quality data gap and enabling air quality management in LMICs using low-cost sensors

Many low- and middle-income countries have little to no air quality monitoring due to the high cost of traditional monitoring equipment, which also impedes progress on air quality management and pollution exposure estimation in these countries. Low-cost sensors can help fill in the air quality data gap and empower local researchers and government officials to estimate population exposure and develop air quality management plans. We invite presentations on case studies and innovative solutions to combat air pollution using low-cost sensors.

Lead Session Chairs:

Amanda Kaufman, US EPA, R. Subramanian, QEERI & OSU-Efluve, & Rob Pinder, US EPA

Current Presenters:

Redspira: Sharing information to transform communities.
Presented by: Alberto Mexia, Redspira

The collaborative environmental monitoring network REDSPIRA emerged in 2018 as a social responsibility initiative, with the purpose of offering a solution to the deficit of information on air quality. This technological platform is integrated by hardware and software. The hardware is made up of low-cost sensors installed at different points in a given geographic area, designed to measure air pollution; while the software includes both mobile applications and a web portal that displays in a georeferenced way the location of the sensors and their measurements. One advantage that REDSPIRA offers, thanks to its collaborative nature, is its ability to integrate monitoring infrastructure and air quality data collected by both environmental authorities and citizens into a common platform. In 2020, the NGO "Foundation for Air Quality Research" was created. with the aim of operating and ensuring the growth and consolidation of the network, adding socially responsible companies to donate sensors, and dozens of volunteers who host them, providing them with electricity, internet and adequate protection. In addition, funds have been obtained for the expansion, research and development of the technologies, obtaining in 2019 a grant of $ 50,000 dollars (United States Environmental Protection Agency, 2020) in alliance with the Imperial County Air Pollution District, to expand the binational air monitoring network using 30 particle sensors and low-cost sensors in the Mexicali-Imperial atmospheric basin. The project also considered the incorporation of 30 schools in the Mexicali valley to the Air Quality Flags Program, through the Border 2020 program.
(View Presentation PDF)
Overview of the LCS-SA Campaign: Opportunities for the application of low-cost air quality sensors in South Africa
Presented by: Brigitte Language, North-West University

Air pollution is a leading environmental health risk in low- and middle-income countries. Ambient and household air pollution have an impact on the health of vulnerable population groups. Internationally, there is appropriate evidence showing links between impacts and patterns of exposure to air pollution. However, there is less evidence in African countries. Policy- and decision-makers, and people advocating for clean air, including communities themselves, need evidence to understand the severity of Africa's air pollution problem and the associated burden. This highlights the importance of hard evidence and data to understand, explain and call for action, and interventions to prevent air pollution and its harmful health impacts. South Africa has a network of 183 ground-based air quality monitoring stations (AQMS). AQMSs are sparse and do not capture HAP as the AQMS network is biased in location towards industrial areas. The AQMSs typically monitor criteria pollutants such as PM10, PM2.5, SO2, NO2, O3, and CO. The AQMS network is plagued with high levels of required maintenance and lower levels of data recovery, which is linked to the cost of maintaining such a network. A solution to the lack of data in ambient and household settings is the concept of low-cost air quality sensors (LCS), which are relatively inexpensive and easy to deploy in an area of interest. Most of the LCSs used in South Africa are developed and manufactured outside the country. We questioned the precision, accuracy, reliability, rigidity and usability within a South African setting. The Low-Cost Sensor South Africa (LCS-SA) campaign was launched to understand the advantages and limitations of using LCS. The LCS and reference instrument co-location campaign was conducted during 2021 at the SAWS AQMS, situated on the Vanderbijlpark Campus of the NWU. This site is ideal as it is centrally located in a region experiencing a wide range of pollutant concentrations resulting from multiple sources.

*Author did not provide PPT for public distribution, please contact Brigitte Language at brigittelanguage@gmail.com with questions
Intercomparison of Low-Cost PM2.5 Sensors with Federal Regulatory Monitor in Sub-Saharan Africa
Presented by: Emmanuel Appoh

Urbanization, migration and urban population growth has increased in Sub-Saharan African (SSA) cities over the last decades, affecting air quality in cities. Low-cost sensors are becoming popular in use especially in cities where regulatory grade monitors are lacking. Air quality monitoring in SSA is limited as a result of the high cost associated with Federal Reference Monitors (FRM). Clarity Node and PurpleAair sensors are currently being used in Ghana on a lower scale for measuring PM mass concentration as an indicator of air quality. Evaluation of sensors with FRM in SSA is still a work-in-progress. Additional efforts documenting data quality are necessary. In this study, 2 PurpleAir, 2 Modulair and 18 Clarity node sensors were evaluated by collocating them at University of Ghana air quality monitoring station equipped with Teledyne T640. PM2.5 measurements were compared with data generated from the FRM from April-July 2021. The study showed that (i) the R2 values between the T640, Clarity Node, PurpleAir and ModulAir were 0.77, 0.88 and 0.89 respectively. (ii) With respect to the FRM the 3 sensors have mean absolute error of 3.36, 2.60 and 1.66 (µg m-3) respectively. The research period covers relatively short time and it is necessary to carry out longer-term studies covering both dry and wet seasons, including meteorological conditions. From the findings, it is clear that these sensors have the potential to supplement, but not replace high-quality and reliable air quality monitoring systems. The implications of the results for future urban air quality assessment using low-cost sensors in SSA are discussed.

*Author did not provide PPT for public distribution, please contact Emmanuel Appoh at emmanuel.appoh@epa.gov.gh with questions
Air sensing to action in the African context: design and deployment of a community-driven digital air quality sensing network for African cities.
Presented by: Engineer Bainommugisha, AirQo/Makerere University

African cities are characterised by unique contexts for air quality sensing including deployment environments, the technology infrastructure, varying degrees of regulatory frameworks, capacity, public education and awareness. This talk will cover the lessons learned and success stories from the AirQo initiative based in Kampala, Uganda. Founded at Makerere University, AirQo designs and develops of a community-driven low-cost air sensing network to fill the air quality data gaps in African cities. The AirQo system includes: (i) a network of over 100 custom air sensing devices hosted by communities (ii) digital air quality platforms to facilitate timely access to air quality information by decision makers and the public (iii) machine learning-based calibration models for data quality (iv) Translation of air quality data into actions such as citizen and community engagement to raise awareness, and providing evidence to inform regulations and air quality management plans in African cities.

*Author did not provide PPT for public distribution, please contact Engineer Bainomugisha at baino@airqo.net with questions
Improving low-cost PM2.5 sensor networks through retrospective analyses and satellite observations
Presented by: Michael R Giordano, AfriqAir

Over much of the Global South, air quality monitoring, especially for PM2.5, is lacking. Low-cost PM sensors (LCS) offer one way to help close that data gap. Unfortunately, the various sensitivities of LCS require that local calibrations (usually done with reference or research-grade sensors) are applied before low-cost data quality can be assured. This requirement can be a problem in many parts of the world where expensive reference or research-grade equipment is simply unavailable. Two ways to potentially alleviate this requirement are to use either satellite observations or data assimilation tools such as retrospective analyses to calibrate LCS. The two approaches differ in both spatial and temporal resolution but both can provide useful information. Here we will discuss both these approaches using various satellites and the Modern Era Retrospective analysis for Research and Applications (MERRA) tool from NASA. Satellite AOD and MERRA aerosol speciation are used to calibrate LCS. This work will demonstrate how this process works and how it performs for 3 areas in sub-Saharan Africa: Ghana, Kenya, and South Africa. Results suggest that simple hygroscopicity corrections from the MERRA reanalysis datasets and AOD measurements from satellites do not generally improve the correlation between LCS and reference monitors but can both reduce the mean-normalized bias by up to 20% and can increase the accuracy by up to 25% (reductions in MAE, CvMAE) for otherwise uncalibrated LCS, depending on area.

*Author did not provide PPT for public distribution, please contact Michael R Giordano at mike@afriqair.org with questions
Prospects of emerging low-cost air quality sensors for bridging air pollution epidemiologic evidence gaps in Africa
Presented by: A. Kofi Amegah, University of Cape Coast

The burden of disease (BOD) attributable to ambient air pollution exposure in Africa is growing and yet estimates of its magnitude and impact are underestimated due to lack of air quality monitoring capacity as well as limited air pollution epidemiological studies. Integrated exposure response (IER) functions used to estimate the BOD attributable to air pollution are based on data from North America and Western Europe, locations with lower ambient PM2.5 exposures, and hence, have limited application to Africa. There is therefore the urgent need to expand air quality monitoring networks in Africa to enable the conduct of high quality air pollution epidemiologic studies to better quantify the BOD attributable to air pollution exposure in Africa and provide the evidence base to trigger investments in air pollution control for public health protection. The growing influx of low-cost sensors represents an excellent opportunity for bridging these data gaps and in this presentation, I would outline how the Ghana Urban Air Quality which seeks to deploy a dense network of low cost sensors interspersed with reference grade monitors in urban settlements of Ghana has been leveraged for investigating adverse respiratory and cardiovascular outcomes among street traders, a major occupational group in cities of Africa and other developing regions. The presentation would also highlight the use of low-cost sensor networks in other LMIC countries in bridging the air pollution epidemiologic evidence gaps in these countries.
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First measurements of PM2.5 and NO2 in Mombasa, Kenya
Presented by: Daniel Westervelt, Lamont-Doherty Earth Observatory of Columbia University

Air pollution caused an estimated 1.2 million premature deaths in Africa in 2019. Many cities have limited or non-existent air quality monitoring networks which makes air quality management difficult or even impossible, including Mombasa, a major port city in Kenya with a population of at least 1.2 million people and growing. To address this lack of air pollution data, we deploy a reference Beta Attenuation Monitor 1022 (BAM-1022) and five Clarity Node-S low cost sensors in diverse environments within Mombasa. Locations include 2 urban sites on the main Mombasa island, one residential site, one site near the largest port in East Africa and the international airport, and one site in proximity to Bamburi Cement Factory, a major point source slightly north of the city. Low-cost PM2.5 from the Clarity Node-S are calibrated against the reference BAM-1022 and yield high correlation on daily and hourly timescales but moderate bias. Initial results show relatively good air quality for the latter half of 2021 (~10 µg m-3 average, 58 µg m-3 maximum) at the urban and residential sites, perhaps modulated by pristine air mixing in from the Indian Ocean. These levels are lower than similar time frame mean levels in Nairobi which are about 23 µg m-3 in urban environments. Concentrations at the industrial cement site in Mombasa are elevated, with a 2021 average of ~13 µg m-3 and peak hourly concentrations reaching 70 µg m-3. Concentrations at the port site are significantly higher than any other site in Mombasa, with mean concentrations of about 23 µg m-3and maximum hourly values up to 75 µg m-3. NO2 concentrations in Mombasa are relatively low, at only about 13 ppbv compared to 16 ppbv in Nairobi, though better understanding and characterization of low-cost NO2 sensors are needed before placing high confidence in this data. Ongoing work will examine additional data as it comes in and attempt to understand sources and explanations for the air quality in Mombasa.
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Assessment of Traffic-derived Air Pollutants by Smart Sensors: Comparison of Pollutants at Street Levels
Presented by: Mahesh Senarathna, Postgraduate Institute of Science, University of Peradeniya

Traffic-Related Air Pollution (TRAP) is a primary source of urban atmospheric pollution worldwide. Changes in city traffic flow is sensitive to air pollution levels. When city’s streets are congested with traffic due to improper planning, the levels of air pollution at street levels may vary and this variation sometimes could not be captured by stationary air pollution monitors. This study aimed to evaluate air pollution before and during a new traffic plan established in March 2019 in the city of Kandy, Sri Lanka, using smart sensor technology. Street-level air pollution data (Particulate Matter < 2.5 µm in diameter (PM2.5) and Nitrogen dioxide (NO2)) was obtained using a mobile air quality sensor unit before and during the new traffic plan. This sensor unit was mounted to a Police traffic motorcycle which travelled in selected streets around the city four times per day. Air pollution in road segments was compared before and during the new traffic plan, and air pollution trends at different times of the day were compared using data from a stationary smart sensor. During the monitoring period, both PM2.5and NO2 levels were well above the World Health Organization (WHO) 24-hour standards, regardless of the traffic plan period. Most of the road segments had comparatively higher air pollution levels during compared to before the new traffic plan. For any given time (morning, midday, afternoon, evening), day of the week, and period (before or during the new traffic plan), the highest PM2.5and NO2 concentrations were observed at the road segment from Girls High School to Kandy Railway Station. The mobile air pollution monitoring data provided evidence that the mean concentration of PM2.5during the new traffic plan (116.71µgm-3) was significantly higher than before the new traffic plan (92.32µgm-3) (p <0.007). Careful planning and improving the road infrastructure before implementing a new traffic plan could reduce air pollution in urban areas.
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Low-cost PM2.5 measurements in a binational metropolitan area along the U.S.-Mexico border
Presented by: Mayra Chavez, University of Texas at El Paso

The Paso del Norte (PdN) encompasses a binational metropolitan area along the U.S./Mexico border that includes El Paso, Texas in the U.S and Ciudad Juárez, Chihuahua in Mexico. Air quality in the area is a major concern for the 2.5 million residents due to social, economic, geological, and political factors. Resource disparity between the two international jurisdictions has hindered the progress of developing a systemic air quality management plan for the airshed. A binational air quality study using low-cost sensors was therefore conducted to generate PM2.5 data to supplement the limited PM monitoring network in the airshed. The study attempts to 1) evaluate the efficacy of low-cost sensors when compared to reference monitors, and 2) advance scientific measurement and analysis of air quality for exposure assessment in the PdN using low-cost air sensors. We conducted a 2-month air monitoring campaign at 17 elementary schools and 14 industrial sites throughout the PdN with a control site collocated at a state-operated air monitoring station equipped with a FRM instrument. Quality control for the PM2.5 data included removal of anomalies based on internal inconsistencies between different channels; removal of outliers; and application of a calibration equation considering concurrent humidity, temperature, and FRM readings. Confidence in the sensors’ performance is evaluated through the agreement between internal channels and inter-sensor consistency. Multivariate linear correlation with FRM-quality data provided a unique calibration equation and a performance evaluation for each sensor. The adjusted PM2.5 data were used to generate a concentration map for identifying potential hotspots near industrial facilities and schools affected by traffic. The monitoring campaign is extended for a 12-month period in the PdN. Collocated monitoring at two reference stations will be continued for quality control and performance evaluation of the low-cost sensors.
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Using low-cost PM2.5 and GPS sensors with surveys to understand exposure in informal settlements in Nairobi, Kenya
Presented by: Michael Johnson, Berkeley Air Monitoring Group

We monitored personal PM2.5(Purple Air PA-II-SD) and GPS location (Columbus P-1) for 71 mothers in two informal settlement communities outside Nairobi, Kenya (Dagoretti and Starehe). Participants were outfitted with backpacks to carry the instruments for 24-hour periods, and ambient PM2.5monitors were installed in each community. Time-activity surveys were administered to contextualize the PM2.5and location data with the sources and activities contributing to exposure. Mean daily exposures were relatively high (43.9 and 44.5 µg/m3, in Dagoretti and Starehe, respectively), exceeding the WHO annual interim 1 target (35µg/m3), and all participants had exposures above the WHO annual guideline (10 µg/m3). Diurnal ambient PM2.5 patterns typically tracked well with the personal exposures, although the daily median personal exposures were higher than ambient, which was generally quite high as well (27.2 and 35.8 µg/m3, respectively). Exposures during cooking with wood or charcoal were higher than during other activities, and participants who used these solid fuels for cooking had a daily mean PM2.5 exposure of 59.7 µg/m3 (n=16), compared to 40.3 µg/m3 for those who did not (n=57; t-test p = 0.003). The results suggest the most promising and practical intervention to reduce exposures in the target population would be to transition households using wood and/or charcoal for cooking to clean fuels such as LPG, ethanol, or electricity.
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Spatial variation of fine particulate matter levels in Nairobi before and during the COVID-19 curfew: implications for environmental justice
Presented by: Priyanka deSouza, University of Colorado Denver

The temporary decrease of PM2.5concentrations in many parts of the world due to the COVID-19 lockdown spurred discussions on urban air pollution and health. However there has been little focus on sub-Saharan Africa, as few African cities have air quality monitors and if they do, these data are often not publicly available. Spatial differentials of changes in PM2.5concentrations as a result of COVID also remain largely unstudied. To address this gap, we use a serendipitous mobile air quality monitoring deployment of eight Sensirion SPS 30 sensors on motorbikes in the city of Nairobi starting on 16 March 2020, before a COVID-19 curfew was imposed on 25 March and continuing until 5 May 2020. We developed a random-forest model to estimate PM2.5surfaces for the entire city of Nairobi before and during the COVID-19 curfew. The highest PM2.5concentrations during both periods were observed in the poor neighborhoods located to the east of the city center. Changes in PM2.5were heterogeneous over space. PM2.5concentrationsincreasedduring the curfew in rapidly urbanizing, lower-middle-class neighborhoods likely because residents switched from LPG to biomass fuels due to loss of income. Our results indicate that COVID-19 and policies to address it may have exacerbated existing air pollution inequalities in the city of Nairobi. The quantitative results are preliminary, due to sampling limitations and measurement uncertainties, as the available data came exclusively from low-cost sensors. This research serves to highlight that spatial data that is essential for understanding structural inequalities reflected in uneven air pollution burdens and differential impacts of events like the COVID pandemic. With the help of carefully deployed low-cost sensors with improved spatial sampling and at least one reference-quality monitor for calibration, we can collect data that is critical for developing targeted interventions that address environmental injustice in the African context.
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Annual observations of Air Quality using Cost-efficient Sensors in Cabo Verde
Presented by: Sandra M. S. Freire, University of Cabo Verde

In Cabo Verde, Saharan dust is the primary source of air pollution in the particulate matter form. Several studies carried out particularly in Praia and on the São Vicente Island have shown the impact of dust from the Sahara Desert on the local air quality. In this study, Size-resolved (PM10, and PM2.5) aerosol concentrations were measured in Praia city and Sal Island between January 2020 and January 2021 using two low-cost sensors in Sal and Praia, AERONET and satellite observation. Results show a seasonal trend of PM concentration on both sites with the largest concentrations in Praia. The presented PM data for a year-long record are addressed for the potential impact of local air pollution on public health during the COVID-19 pandemic and provide input for the long-term air quality management policies in Cabo Verde.
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Practical challenges of using PurpleAir-II-SD Low-cost sensors for Air Quality Monitoring in sub-Saharan Africa: The Measuring Air Quality in Africa for Advocacy (MA3) Experience
Presented by: Babatunde Awokola, Medical Research Council Gambia at LSHTM

Background: The future of citizen science and widespread Air Quality measurement in sub-Saharan Africa and similar settings lie in the successful deployment of low-cost sensor networks. As attractive as this prospect is, the deployment of the latter is froth with challenges that affect the overall aim of continuous air pollutant measurement. We report the practical challenges we learnt in the process of executing the Measuring Air Quality in Africa for Advocacy (MA3) project in seven African countries. Methodology: The devices were given to sixteen participants at an AQM workshop. Thirteen exposure scientists from seven countries (Gambia, Kenya, Uganda, Benin Republic, Burkina Faso, Cameroon and Nigeria) installed the instruments and participated in data collection throughout July 2019. The data was downloaded from the SD memory cards, sent via email, cleaned and analysed, with the percentage of time data was logged (or data recovery rate) computed from the data. A log of all challenges encountered was kept by all scientists, zipped and sent to the Principal Investigators weekly. Results: Practical challenges experienced in the process of use of the Purple Air-II-SD sensors were power and power pack outages, SD memory card issues, internet connectivity problems and sensor hardware maintenance concerns. The details of these were found in table 1. There was only one site with a 100% data recovery rate (Nairobi, Kenya), with Bariga-Lagos-Nigeria site having the least data logging -72.1%. Fajara-Gambia and Ouagadougou-Burkina Faso sites had daily averages below the WHO thresholds. Most sites recorded daily averages higher than the WHO recommended threshold of 25 μg/m3. Details in table 2 Conclusion: Even though its use is froth with some operational challenges, PM2.5longitudinal measurement can be reasonably satisfactorily executed in sub-Saharan African countries using the Purple Air-II-SD device as these challenges were surmountable through creative solutions.
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Experience of Mobile Air Quality Monitoring with IoT technology in the Historic Center of Lima.
Presented by: George Castelar Ulfe, Municipalidad de Lima (Poster Presentation)

One of the biggest issues facing the city of Lima is the poor air quality. According to WHO (2005), the air exceeds by 2.4 times the recommended values for PM2.5/PM10. In this sense, Lima's historic centre is no exception to this problem. Therefore, it is necessary to develop more research studies in order to determine the main sources of pollution.

The objectives of this study are to evaluate the temporal and spatial pollutant concentration, identify the areas with the highest concentrations of pollutants, and determine the amount of exposed vulnerable population. The air quality measurement used IOT technology installed in an electric vehicle, and was performed around the historical area called “Damero de Pizarro”. The study involved measurements of meteorological parameters (relative humidity and temperature), and the concentrations of 7 atmospheric pollutants (PM2.5, PM10, CO, O3, NO2, SO2and H2S) during the months of June, July and August 2021. The results were considered in 4 shifts: 8:00 to 10:00; 10:00 to 12:00; 14:00 to 16:00 and 16:00 to 18:00 hours.

The obtained baseline values did not show an excess of the limits established in the national environmental air quality standards. However, the average concentrations of PM2.5and PM10were relatively high during the first 2 shifts (8:00 am to 12:00 pm). In addition, 18% of the exposed population was found to be the most vulnerable to air pollution. Moreover, it was also found that the highest concentrations of pollutants were found on the roads with the most traffic.

Finally, this study generated empirical evidence that allows the local government to promote actions in favour of improving air quality of its citizens. Projects like the pedestrianisation of streets in the study area and the implementation of a future Low Emission Zone (LEZ) are promoted within the framework of the "Action Plan for the Improvement of Air Quality in Lima and Callao 2021-2025".
Evaluation of On-Campus Tree Planting Impact on Community Air Quality Using Low-Cost Sensor Measurements
Presented by: Ningxin Wang, Sonoma Technology (Poster Presentation)

Legislative rules over the last five years (e.g., AB 617 by CARB and Rule 1180 by South Coast AQMD) have led to an increase in community-level air quality monitoring. Although agency-managed sites with regulatory air monitors are designed to protect public health by measuring ambient air within communities and ensuring that federal and state air-quality standards are met, they can be limited spatially when monitoring a specific community facility such as a school. Technological advancements in “low-cost” air quality sensors provide a more affordable and portable option for community air monitoring.

In this study, we explore the impact of tree planting on community air pollution levels at two near-road schools in Fresno using low-cost sensor measurements. Fresno is among one of the most polluted areas in the U.S., frequently exceeding both the California and U.S. EPA ambient air quality standards for concentrations of fine particulate matter (PM2.5). Tehipite Middle School and Leavenworth Elementary School are both located in areas that are in the highest 10% of CalEviroScreen percentiles for census blocks in California and in close vicinity to major highways - California State Routes 99, 180, and 41. Several species of trees are planted along the fence between the school and adjacent major roads, and along the playground boundary. Previous studies have shown that vegetation can decrease ambient black carbon (BC) and/or PM2.5 downwind of the vegetation. We deploy low-cost sensors (i.e., Clarity node S), micro-aethalometers (MA350, Aethlabs), together with meteorological equipment at multiple sites on campus, and collect data on PM2.5, nitrogen dioxide (NO2) and BC concentrations. Approximately one month of data are collected before and after tree planting. Pollutant levels are evaluated together with meteorological conditions before and after tree planting. In addition, sensor performance is explored by comparing data among sites and between sensors.
Performance evaluation of low-cost electrochemical sensors for ozone in two polluted urban areas.
Presented by: Pamela Ayala, Airflux SPA (Poster Presentation)

Low-cost electrochemical sensors (LCS) were used for ozone monitoring in two highly polluted urban areas, one corresponding to the capital and main city of Chile, and the other to an industrial coastal zone. This secondary pollutant is formed from photochemical reactions of primary pollutants present in the atmosphere, through non-linear processes. Due to their complex formation dynamics, it is important to evaluate the performance of Alphasense LCS, which combine a nitrogen dioxide (NO2-A43F) and oxidant gas (OX-A431) sensor as a pair, in two zones that present weather conditions. , topographic and different emission sources. The voltage outputs registered by the sensors were transformed to concentration units, through a multiple linear regression model, where the predictors considered as temperature, reluctant humidity and interfering pollutants, depended on the area where they were installed. Through the data in concentration units, the performance of the sensors is evaluated according to objective values of statistical metrics such as mean square error, standard deviation, among others; which are stipulated by the US Environmental Protection Agency; it is also expected to characterize the dynamics of ozone in different areas and its precursors.
Breathe2Change initiative: Connecting Science and Society for a Smoke-Free Air
Presented by: Rodrigo Gibilisco, LEA - Laboratorio de Estudios Atmosféricos- INQUINOA (CONICET-UNT), Facultad de Bioquímica, Química y Farmacia, Universidad Nacional de Tucumán

Ambient air pollution is driven by many factors. While the increase in the industrial activity is one of the main concerns in developed economies, the open burning of biomass does so in countries with developing economies. In Latin America for example, the extended use of fire as a cheap and accessible tool for land clearing for agricultural use and elimination of waste represents the main threat to the environment and people's health. In Argentina, more than 1 million hectares were burned down during 2020, of these fires, a total of 95% were due to human intervention.[1] The impact that these fires have over the air quality at local and regional levels are until today unknown since more than 90% of the Argentinian territory lacks of reference stations for air quality monitoring. To fill-in this gap and to build institutional systems to address this issue, this initiative aims to create the first Citizen-with-Scientists powered air quality monitoring network in Tucumán, Argentina. During the last few years, multiple projects have explored the potential of environmental sensors for air pollution monitoring. [2] The use has been generally approached in two different ways: (A) citizen science and educational activities; and (B) a more sophisticated scientific approach. This initiative will focus on achieving both, from scientific experimentation to citizen participation, involving academic institutions from Germany and Argentina, non-governmental organizations, policy makers and citizens through the implementation of a network of 40 low-cost air quality sensor modules to be used for the first air quality assessment in the province of Tucumán, Argentina. [3] [1] https://www.argentina.gob.ar/ambiente/fuego/reporte-diario-manejo-del-fuego [https://www.argentina.gob.ar/ambiente/fuego/reporte-diario-manejo-del-fuego] [2]Karagulian, et al.(2019). Review of the performance of low-cost sensors for air quality monitoring. Atmosphere, 10(9), 506. [3] www.Breathe2change.org

*Author did not provide PPT for public distribution, please contact Rodrigo Gibilisco at rodrigogibilisco@gmail.com with questions
What is the Impact of Common Sources of Error on Air Quality LCS Measurements Performance? A Practical Guide
Presented by: Sebastian Diez, University of York

Accurately measuring atmospheric pollutants is critical for decision-making and designing policies aiming to improve air quality and reduce human health exposure. The advent of inexpensive sensor-based technologies means that there are now a growing number of measuring devices available that could be useful for this purpose. However, not all this range available today will necessarily be adequate to the problem that the user wants to tackle. It is therefore key to ask whether the information provided by instrument "X" is appropriate for the intended purpose. Clearly focusing on the question to be answered, and defining the required data quality accordingly, is key to identifying those instruments/techniques capable of meeting this requirement. Since the measurement uncertainty ultimately determines the information content, it is therefore critical to estimate this parameter in a robust and transparent way, thus allowing the potential application of the instrument in question to be defined. In this work, we explore the nature of common air pollution measurement sources of errors in the real world and the implications they have for traditional uncertainty metrics and other potentially more insightful approaches to assessing measurement uncertainty. For this, we employed first simulated datasets combining different sources of error/interferences from (i) a non-target chemical, (ii) physical parameters and (iii) electromagnetic fields. Then we study real-world data from the QUANT project which involves a range of LCS technologies and multiple reference instruments in 3 urban sites in the UK. We then use this information to explore the performance of these technologies and develop methods that will enable their integration into the air quality monitoring infrastructure and use in atmospheric chemistry research. This work will ultimately make it possible to optimize the applications of these technologies based on the quality of the LCS data.
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View Session 6A Recording on Youtube

Indoor Sensing for Air Quality Control and Ventilation Applications

Sensors are increasingly being used indoors to monitor Indoor Air Quality (IAQ), track infiltration of outdoor air pollution or wildfire smoke, maintain adequate ventilation for occupant comfort and well-being, control indoor sources and environmental factors, and operate air cleaning devices to improve IAQ. This session will showcase indoor air sensor deployments for these applications as stand-alone or embedded in smart building systems across several settings (residential, educational, occupational, etc.).

Lead Session Chairs:

Ajith Kaduwela, CARB, Rima Habre, University of Southern California, Heidi Vreeland, US EPA

Presentations:

Wildfire smoke and ash: particle size, chemistry, and measurement needs
Presented by: Jeff Wagner, California Dept. of Public Health

Wildfire smoke is a mixture of particle- and gas-phase chemicals produced by incomplete combustion of various fuels, including biomass, building materials, and vehicles. Indoor exposures to wildfire smoke particulate matter (PM) in populated areas depend upon on smoke and ash PM size, chemistry, transport distance, duration, and meteorology, as well as indoor penetration and filtration. This presentation discusses how these variables are addressed with different study designs and method choices. Wildfire smoke exposures have been associated with increased hospital admissions and symptoms for respiratory, cardiovascular, and cerebrovascular events in the general population and wildland firefighters. However, the specific impacts of excursions on the order of hours to weeks of wildfire smoke (~200-500 nm tar balls) and ash (coarse, inorganic, metal-rich PM) possess several unknowns. Current guidelines for wildfire smoke exposure are based on guidelines established for non-wildfire PM2.5 and PM10 over specific durations. However, several studies suggest that PM enriched in organic carbon or metals can have differential health effects, as can size distribution subcomponents within PM2.5 (PM1) or PM10 (PMcoarse). Similarly, varying PM exposure and measurement durations may correspond to different chronic or acute health endpoints. Improved characterization of PM size, composition, and exposure durations can help elucidate their relevant inhalation and filtration parameters, as well as their potential for health effects. The identification of wildfire-specific tracers may enable better classification of smoke episodes. For these reasons, the use of multiple, complementary measurement methods is sometimes desirable. Recent and current wildfire PM projects are discussed that combine different continuous/low-cost sensors and integrated samples, including measurements of UVPM, PM1, PM2.5, PMcoarse, and individual particle chemistry and morphology.
(View Presentation PDF)
Testing of a Low-Cost Sensor and Sampling Platform Alongside Reference Instruments in a Home Kitchen
Presented by: Jessica Tryner, Colorado State University

People in the United States spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small “Home Health Boxes” (HHBs) to measure indoor PM2.5, PM10, CO2, CO, NO2, and O3 concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers’ calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to published air quality guidelines) and (b) both indicated that gas cooking burners were the dominant source of CO and NO2 pollution (with 1-h average NO2 concentrations exceeding 100 ppb during normal cooking); however, HHB and reference O3 data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO2 (from 408 to 58 ppm), CO (from 645 to 572 ppb), NO2 (from 22 to 14 ppb), and O3 (from 21 to 7 ppb); additionally, correlation between HHB and reference O3 data improved (Pearson’s r increased from 0.02 to 0.75). Mean 168-h PM2.5 and PM10 concentrations derived from nine filter samples were 19.4 µg m-3 (6.1% relative standard deviation) and 40.1 µg m-3 (7.6% relative standard deviation). The 168-h PM2.5 concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91).
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Low-cost high-performance VOC sensor systems: comparison with analytical measurements and long-term stability
Presented by: Johannes Amann, Saarland University, Lab for Measurement Technology

With air quality being one target in the sustainable development goals set by the United Nations for 2030, the accurate monitoring of Indoor Air Quality (IAQ) is more important than ever. Volatile organic compounds (VOCs) from a wide range of sources (building materials, furniture, household items, cooking, cleaning, human metabolism) are the main pollutant of concern indoors. One inexpensive and promising solution for VOC monitoring for IAQ are metal oxide semiconductor (MOS) gas sensors. To fully exploit the potential of MOS-based sensor systems for accurate quantification of the total VOC concentration as well as specific VOCs of interest, advanced operating modes, calibration, and data evaluation methods are required. This presentation provides an overview of the potential and limits of MOS gas sensor systems for IAQ monitoring using temperature cycled operation (TCO), calibration with complex randomized gas mixtures and data-based models trained with advanced machine learning. We studied the performance of several sensors in a typical office environment over a period of more than one year. In addition to monitoring normal ambient air, release tests were performed with compounds that were included in the lab calibration, but also with additional VOCs. The tests were accompanied by different analytical systems (GC-MS with Tenax sampling, mobile GC-PID, and GC-RCP). The results show excellent quantitative agreement between analytical systems and MOS gas sensor systems under real-world conditions. The study shows that MOS sensors are highly suitable for determining the overall VOC concentrations with high temporal resolution and, with some restrictions, also for selective measurements of individual components. Based on these results, MOS sensor systems are well suited for demand-controlled, and thus energy efficient ventilation in typical indoor environments.
(View Presentation PDF)
Investigating Indoor Air Quality in On-Campus Residences Using Low Cost Air Quality Sensors
Presented by: Ran Zhao, University of Alberta

University and college students spend the majority of their life studying and living on campus. Indoor air quality (IAQ) significantly affects their health, wellbeing, and productivity. Investigations of IAQ in student residences are sparse, with a few previous studies measuring IAQ with research-grade instruments. A major challenge lies in the fact that IAQ is dependent on multiple factors and can vary drastically between buildings and even between individual rooms. The use of a low-cost air sensor network is expected to provide IAQ data not accessible with a traditional measurement approach. My research group has initiated a pilot project to deploy a network of air sensors in the University of Alberta (U of A) campus residences to monitor key IAQ parameters, including particular matter, temperature, RH, and CO2 concentration. The overall objective of this project is to provide insights into IAQs in student residences, which serve as a guide for building ventilation, energy management, and student health. In particular, we aim to compare residences with and without in-suite kitchens, a new air handling system (an HRV), and the presence of potential external sources (e.g., food court). Community engagement and education are also essential missions of this project. Participants were recruited from the campus community, and a home-built sensor is being deployed in their individual residences. The sensor records and send in-situ IAQ data to ThingSpeak, an online data repository. The monitoring is currently underway, and preliminary results and conclusions will be presented.
(View Presentation PDF)
Development of ASTM Standard Test Methods for PM2.5 and CO2 Sensors Used for Indoor Air Quality Measurements
Presented by: Wilton Mui, South Coast Air Quality Management District

Real-time indoor air quality sensing devices, which are used to provide occupants with information on indoor air pollutant concentrations and concurrently provide data to ventilation systems for improved system operation, have become increasingly popular with consumers and manufacturers. However, the effectiveness of these devices in accurately assessing pollutant concentrations is limited. A variety of pollutants can be measured using sensors, with several being the subjects of future standardization activity. The focus of the two ASTM International standards covered in this presentation is to provide a mechanism for evaluating the performance of PM2.5/CO2 sensors or sensor systems for indoor applications through laboratory-based testing. Under a pass-through grant from the DOE to South Coast AQMD, the Air Quality Sensor Performance Evaluation Center (AQ-SPEC) drafted the test standards, conducted the engineering and scientific work, including laboratory setup and testing, data processing and analysis. Using the established AQ-SPEC laboratory protocol as a starting framework, new comprehensive protocols were developed to evaluate commercially-available indoor air quality sensors for their ability to measure a wide range of pollutant concentrations, recover from loss of power, and perform under various climate conditions and in the presence of interferents. The performance of indoor air quality sensors was evaluated based on data recovery, intra-sensor variability, accuracy, precision, and correlation to Federal Reference/Equivalent Method instruments. We summarize the requirements and procedures of these test methods.
(View Presentation PDF)
Development of a bespoke sensing unit for deploying in smart homes
Presented by: Hamid Omidvarborna, University of Surrey (Poster Presentation)

The substantial developments of air quality sensors for indoor monitoring offer a great potential to ensure healthy living for residents. The use of air quality sensors is also included in the concept of smart homes and further connected homes to develop smart neighbourhoods or cities, where innovative technologies are implemented to proactively address a number of factors including the health and wellness of inhabitants. Building trust in the quality of data for making informed decisions at different scales requires proper data collection. Therefore, elements of air quality sensors should be properly selected and their performance should be rigorously evaluated. Here, we selected, designed and manufactured a bespoke unit to measure various indoor elements, including but not limited to, temperature, relative humidity (RH), noise, particulate matter (PM) in different mass fractions (PM1, PM2.5, and PM10) and size distributions, and carbon dioxide (CO2) for the aim of deployment in MyGlobalHome prototype building at the Innovation Centre and later at the pilot demonstration building, University of Surrey, Guildford, the UK during pre- and post-occupancy. The sensors are evaluated inside the state-of-the-art Environmental-Pollution (Envilution®) Chamber under the conditions that each sensor might experience inside homes. The methods for performance evaluation are elaborated in Omidvarborna et al. (2020). The correlation coefficients during performance evaluation are promising, where strong correlations for temperature (R2=0.99), RH (R2=0.99), PM1(R2=0.93), PM2.5(R2=0.92), PM10(R2=0.91), and CO2(R2=0.99) were obtained under-represented indoor environments. For further evaluation, the sensors are currently being integrated into a single unit with other communication features to become a part of the prototype building during the trial test. After successful evaluation during a long term occupancy trial, the sensors will be integrated into the design of smart homes.
Understanding heterogeneity and sources of black carbon in residential neighborhoods in the Capital Region of New York State
Presented by: Md Aynul Bari, University at Albany (Poster Presentation)

With recent findings of improving outdoor air quality due to current COVID-19 pandemic, there is an interest in understanding the potential impact on indoor air quality. Black carbon (BC) is a potent short-lived climate pollutant and an important component of particulate matter emitted from fossil fuel combustion and biomass burning (e.g., wood stoves) and has linked to adverse health outcomes. In the United States, current observation networks for BC are limited in characterizing exposure across neighborhood scales. Little information is available about BC concentrations at indoor environments. To address this gap, an exploratory study has been conducted to determine spatiotemporal variation of indoor and outdoor concentrations of BC, identify indoor-generated and potential local source impacts in New York State Capital Region covering urban and rural residential neighborhoods including Environmental Justice (EJ) communities.

Indoor and outdoor sampling has been performed in 4 homes of each selected neighborhoods to collect data from at least 20 homes. We leveraged both low-cost sensors and microAeth instruments to measure BC. Indoor measurements are taken at a breathing height (~1.5 m) within the living room, while outdoor sampling are deployed in backyards. Data on meteorological parameters e.g., outdoor wind speed, wind direction and relative humidity, temperature, as well as questionnaire-based housing characteristics, and occupants’ activities were also collected for each home. Our preliminary data suggests that outdoor concentrations (at backyards) were significantly higher than indoors suggesting an influence of potential local sources. Significant variations in indoor concentrations were observed among neighborhoods depending on the location of homes. The findings can benefit the general people to improve their knowledge, raise awareness and empower communities to take actions and inform policy makers to improve indoor air quality and public health.
Assessment of PM2.5 concentration and transport in indoor environments using low-cost air quality monitors
Presented by: Sumit Sankhyan, University of Colorado Boulder (Poster Presentation)

Fine particulate matter (PM2.5) is an important constituent of air pollution and has been linked to a variety of health effects.Consumer-grade, low-cost PM sensors are gaining popularity as a convenient tool for consumers to monitor indoor air quality inside their homes. We investigated five commercially available air quality monitors (IQAir AirVisual Pro, Foobot Home, PurpleAir PA-II-SD, and PurpleAir PA-I-Indoor) and compared them to a research-grade optical particle monitor (OPS 3330, TSI Inc.) by deploying them in four homes of different sizes over a period of 9-12 weeks each. Two identical units of each monitor were deployed in the kitchen and bedroom of each home to evaluate PM2.5 transport between those spaces. Indoor monitors were collocated for 3 days at the beginning and end of each deployment period to assess their accuracy over time.A second component of the project included an investigation on the effects of deploying a consumer-grade portable air cleaner in the kitchen and in the bedroom on PM2.5levels.Preliminary results show a range of correlation levels between low-cost monitors and the OPS, with R2 values ranging from 0.75 to 0.94, and among pairs of the same low-cost monitor models, with R2 values between 0.60 and 0.99.
Cloud Connected Sensor Networks And Building Reopening In 2022
Presented by: Timothy Quinn, SGS Galson (Poster Presentation)
Covid-19 has disrupted indoor work environments and occupancy levels worldwide. We will examine the science behind airborne transmissions of virus’ new mitigating strategies. We shall examine 4 things common in indoor office spaces, by monitoring their levels with new cloud-based sensor technology we can help predict the likelihood of their presence in these spaces and possible transmission to workers. What responsibility does industry have to provide safe work environments, clean and disinfect buildings and use real time monitoring equipment?

When a mesh sensor network is deployed building managers and system operators can gain real-time values and alerts that can keep them informed about the operation of interventions implemented to mitigate the transmission of COVID-19 throughout the workspace and keep the most vulnerable spaces in the building operating safely. Real-time monitoring creates a framework to provide the assurance of a healthy indoor environment and the confidence people need to resume their daily lives.
Air pollution exposures in rural and urban solid fuel-using households in sub-Saharan Africa
Presented by: Stephanie Parsons, North Carolina State University

Use of solid fuels, including fuel wood and charcoal, for cooking is ubiquitous in both rural and urban settings in sub-Saharan Africa and is associated with extreme air pollutant exposures. Introduction of alternative fuels or cooking devices have often targeted these populations, but few studies have quantified individual cook exposures in urban settings, where other sources of air pollution exposure are more prevalent. Here, we present a synthesis of multiple field studies of personal and household CO and PM2.5exposures collected in urban and rural settings in Malawi and Rwanda and in urban Lusaka, Zambia. Measurements collected include both baseline exposures (in households using traditional fuels/combustion devices) and in households who have adopted alternative stoves (pellet + gasifier stove, improved charcoal stoves). Observations were collected with personal and kitchen monitoring and via surveys describing fuels and stoves in use and household (e.g. cooking location and building materials) and personal characteristics. Fuel selection and seasonality are dominant drivers of exposure variability. For example, in the Malawi study, 99% of rural cooks primarily used biomass and had 2.5 times higher 24-hour average PM2.5exposure (433 ± 484 μg m-3) than urban cooks (172 ± 164 μg m-3), while 82% of urban cooks used charcoal primarily and had CO exposures (7.1 ± 7.0 ppm) almost three times greater than rural cooks (2.7 ± 2.0 ppm). In Lusaka, CO exposures were consistent across both traditional and ‘improved’ charcoal stove users, but substantially reduced for users of pellet+gasifier stoves. CO exposures were driven by evening peaks, presumably from charcoal use for indoor space heating. Lusaka PM exposures showed no consistent variation across stove/fuel types, and were mainly driven by the regional/urban characteristics of PM2.5 concentration. We conclude by summarizing lessons learned from field studies in diverse settings.
(View Presentation PDF)

View Session Recording on Youtube

Innovative Sensor Technologies

The session will focus on innovative sensor technologies whether in development or just coming to market and may discuss sensor design, performance evaluations, and/or novel applications. This session will highlight sensors designed to measure hard to detect pollutants (speciated volatile organic compounds (VOCs), hazardous air pollutants (HAPS), biological analytes, etc) and novel sensor designs (micro-electromechanical systems (MEMS) based sensors, sensor arrays, wearable sensors that monitor the body's response to air pollution). Sensors designed or used for novel applications that might not solely focus on air pollutants, e.g. emergency response, would be welcome.

Lead Session Chairs:

Melissa Lunden, Aclima & Andrea Clements, US EPA

Presentations:

Expanding stationary and mobile PM2.5 measurement capabilities near fires
Presented by: Ashley Bittner, North Carolina State University

This study used two easily deployable lower-cost air sensor systems to collect high-frequency measurements of fine particulate matter (PM2.5) near fires. The data collection systems include (1) portable, solar-powered backpack monitors equipped with a PurpleAir (PA-II-SD) sensor and (2) a mobile monitoring system equipped with a pDR-1500 (Thermo Scientific). During a fire event, the backpack monitors are deployed as temporary fixed-site monitors to capture longer-term temporal variation, while the mobile monitoring system is driven along routes to characterize spatial variation upwind and downwind, including higher-concentration smoke pulses near the burn. This monitoring approach was tested during prescribed burns in Tall Timbers, FL and Konza Prairie, KS and during the Monument wildfire in Humboldt County, CA. We present these examples to demonstrate the ability of this monitoring approach to improve the spatiotemporal resolution of PM2.5measurements compared to the nearest regulatory air quality monitoring network site.The results show that both prescribed fire and wildfire plumes can have localized impacts while also contributing to widespread elevated PM2.5 concentrations. Additionally, we explore alternative applications of this technology, including evaluating predictions from commonly used smoke dispersion models, assessing real-time local air quality impacts from the burning of spilled oil, and mobile deployments on all-terrain vehicles and helicopters. The views expressed in this abstract are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency. The mention of trade names does not constitute endorsement.
(View Presentation PDF)
Unmanned Aerial Air Quality measurements: the potential for industrial fire plumes characterization with onboard low-cost sensor measurements.
Presented by: Brice Berthelot, Ineris

During an industrial fire, potentially hazardous substances may be released outside the site by atmospheric transfer. In order to characterize their potential impact, it is essential to gather data as quickly and reliably as possible on the consequences of the event on the environment and on the population. However, recent events related to the Lubrizol 2019 factory fire in Rouen have highlighted the difficulty in certain circumstances of listing the composition of fumes and knowing the mechanisms that contributed to their emission. These shortcomings taint the numerical models used to prioritize the fallout zones and thus distort the assessment of the impact of the fire on the environment and the health of the populations. The DESIHR project (Drones en Essaims pour la Surveillance de Sites Industriels à Hauts Risques), winner of an French National Research Agency grant, aims to develop innovative tools and methods based on the use of a fleet of autonomous drones to characterize in real-life situations the substances present in a fire plume as well as their emission and propagation conditions. The definition of the control laws governing the behaviour of each drone must in this context be based on real-time air quality information from sensors onboard each mobile vector. In order to limit the mass and the energy cost, low-cost sensors are among others considered in the DESIHR project. The objective of the work presented here is to address the potential for industrial fire plumes characterization with onboard low-cost sensor measurements in complex environments. On the basis of experiments carried out before the start of the project, it will address issues ranging from the selection of pollutants monitored and subsequent sensors to the use of data by drones, through the definition of innovative protocols for qualifying measurement performance based on real air matrices.

*Author did not provide PPT for public distribution, please contact Brice Berthelot at brice.berthelot@ineris.fr with questions
A Low-Cost Industrial-Grade Carbon Sensor
Presented by: David A. Gobeli, Ph.D., Met One Instruments, Inc.

Met One Instruments, Inc. has developed an industrial-grade black carbon “BC” sensor-based upon near-infrared (880 nm) attenuation across filter media upon which ambient PM is being deposited in real-time. This device, known as the “C-12 Carbon Monitor”, can operate for up to two months without user intervention and report “BC” with a sensitivity of less than 10 ng/m3. The base version of the C-12 Carbon Monitor, costing less than $3,000 and using a roll of filter tape allowing more than 1,000 spots, can be augmented with an additional illumination wavelength at 370 nm, and a PM2.5fractionator. The C-12 Carbon Monitor comes standard with a Cloud modem and GPS, and can be set up and report data within 10 minutes. The instrument is lightweight and self-contained in a weatherproof enclosure, meaning that no environmental shelter is required for its operation. In this presentation, we will report on field tests conducted at a near-roadside location with the base version of the C-12 monitor. In it, we demonstrate near-equivalence to our BC-1054 MultiSpectrum carbon monitor since there is no reference standard for BC. We also provide data on a version of the C-12 monitor with an optional UV (370 nm) illumination channel, demonstrating its ability to perform source apportionment computations.
(View Presentation PDF)
A Compact High-Precision Microfluidic Platform for Wearable Sensing of Particulate Matter
Presented by: Ehsan Ashoori, Michigan State University

Particulate matter (PM) is one the main air pollutants which has been linked with severe health conditions. In a recent study, 10.2 million premature deaths have been attributed to only fossil-fuel component of PM2.5 (PM with diameter smaller than 2.5 µm). Many existing commercial PM monitoring devices are bulky and expensive. They are typically used for stationary or portable weather monitoring stations and hence, they are not appropriate for monitoring PM with high spatiotemporal resolution for everyday use of individuals. In this work, toward implementing a wearable PM monitoring system, we present a microfluidic platform for separating PM into different size bins as well detecting particles with high precision. For separation, we implemented deterministic lateral displacement (DLD) technique where I-shape pillars were built into the microfluidic channel to achieve high separation efficiency. This is a necessary step for the sensing stage where the accuracy of the particle detection is sensitive to the size of particles. The implemented DLD separator showed nearly 100% separation efficiency for a critical separation size of 2.5 µm. For the sensing stage, we examined two different methods to achieve high sensitivity. We first utilized differential capacitive measurement technique to detect the presence of particles in the sample. We designed and fabricated the sensor as well as the readout circuit and were able to detect particle concentration levels that are equivalent to air quality index of 151 which is considered to be “unhealthy” according to the US environmental protection agency. To better detect the PM particles with dielectric constants similar to the microfluidic medium in use, we also developed electrochemical sensor for PM detection. We successfully implemented cyclic voltammetry as well as differential pulse voltammetry techniques. The promising results that these methods produced enable detection of a vast majority of particle pollutants.

*Author did not provide PPT for public distribution, please contact Ehsan Ashoori at ashoorie@msu.edu with questions
Detecting toxic metals in ambient particulate matter using a low-cost and near real-time analyzer
Presented by: Hanyang Li, Air Quality Research Center, University of California Davis

Urban populations are exposed to hazardous air pollutants (HAPs), including the gaseous and particulate matter that are known or suspected to cause cancer or other serious health effects. Airborne trace metals are part of the HAPs, which originate from both natural processes and anthropogenic emissions. XRF and ICP/MS have been traditionally used to measure ambient metal concentrations, but their high cost and lab-intensive analysis make them unsuitable to deploy in vulnerable neighborhoods. To fill this gap, we have developed an inexpensive instrument for measuring the presence of toxic metals in ambient PM (named as Toxic-metal Aerosol Real Time Analyzer, TARTA). TARTA was developed based on the theory of spark-induced breakdown spectroscopy. It consists of an aerosol sampling system, electrical components for spark generation, a delay generator to suppress the initial continuum radiation, and an optical spectrometer for spectra collection. Air pollution is first drawn through an inlet nozzle that deposits particles onto the flat surface of a tungsten electrode. After sampling for a period of time, a spark discharge is generated between the electrodes to form a plasma which atomizes the deposited particles. The emitted light from the plasma is then collected by a spectrometer for the determination of aerosol composition and concentration via machine learning methods. Our laboratory and field testing of TARTA show that the instrument is capable of detecting multiple metal particles in near real-time. Specifically, TARTA has LODs ranging from 0.04 µg m-3 at a sampling duration of 30 minutes and a flow rate of 15 l min-1. The measurement uncertainty is estimated to be 19% based on a multivariate calibration model. During the field experiments at Caldecott Tunnel and a rural environment, the relatively high temporal resolution of TARTA enables us to examine the dynamics of metal particle emissions and compare the results against real-time total PM concentration.

*Author did not provide PPT for public distribution, please contact Hanyang Li at hynli@ucdavis.edu with questions
RADICAL: Developing an electronic sensor for detecting short-lived atmospheric radicals and other gases
Presented by: Justin Holmes, University College Cork

Atmospheric radicals, particularly hydroxyl and nitrate, are the drivers of chemical processes that determine atmospheric composition and thus influence local and global air quality and climate. However, the detection of these short-lived atmospheric radicals is far from routine, and only a few labs worldwide can accurately measure their concentrations in air. Current techniques for measuring radicals are based on spectroscopic and mass spectrometric methods, which although sensitive and robust, are technically complex, cumbersome and expensive. This presentation will provide an overview, and a discussion of the latest results, from the EU-funded project ‘RADICAL’ which is developing a small, low-cost sensor to electrically detect short-lived atmospheric radicals in real-time. This will be the first gas sensor built from an array of junctionless nanowire transistors, which has proven popular for liquid-based sensors. Although challenging, RADICAL sensors not only have the potential to be rolled out on a global scale but can also be adapted to detect other important atmospheric gases, particularly on short-timescales. The project team welcome ideas and future collaborations for how these sensors might be best applied in real-life environmental monitoring situations. Website: https://radical-air.eu/

*Author did not provide PPT for public distribution, please contact Justin Homes at coughlin.justin@epa.gov with questions
IoT VOC Monitoring with a Fully Autonomous MEMS-based Analyzer
Presented by: Nabil Saad, OMNISCENT

IoT chemical sensing for environmental health & safety is definitely the future. In today’s refineries, petrochemical plants and cities operations, conventional methodologies are costly and do not provide real-time actionable data with compounds specificity for critical intervention. The Omniscent platform offers autonomous real-time molecular level data insight that enables EH&S officers to swiftly intervene and prevent costly disasters. Using advanced MEMS & Cloud technologies, we have developed an autonomous and portable VOC analyzer capable of detecting and speciating more than 20 VOCs in near real-time. The technology is using ambient air as a carrier gas, therefore reducing the consumables requirements to an absolute minimum. Users can view and compare the analysis results with historical data anytime through an internet or cellular connection from anywhere in the world. The MEMS-based micro Gas Chromatograph technology has a small footprint design that lends itself to portability and can be powered from solar panels or other 24V outlets. The data is exhibited in a tabulated format or a chromatogram format and can be correlated in the portal's user interface with Wind Speed and directions measurements for leak source attribution purposes. The complete solution has either LTE and WiFi communication module embedded in the microGC analyzer, which is integrated with an anemometer and the solar panels on a mobile platform for ease of deployment and relocation at the fenceline or for area VOC monitoring.
(View Presentation PDF)
USEPA Alternative Method 082, Next Generation Air Quality Monitoring, Forget the school and use the tool
Presented by: Shawn Dolan, Virtual TEchnology LLC

Opacity of Visible emissions has been used to tune and automate combustion sources for hundreds of years, in the late 1800's Ringleman developed the Ringleman scale which has been used to determine the efficiency of burn ever since. Enter the world of Particulate sensors alerting the public of the dangers of particulate, and the concentration they breath at a specific point on the globe. Both technologies very fitting for the task but neither good for communicating health risks.Ringleman can tell us the fuel is being burned efficiently, but not the particulate exposure to the population. Particle monitors are only good for that specific place on earth and 10 feet away is an entirely different exposure profile. The Digital Opacity Compliance System Third Generation (DOCS III), is the bridge to this dilemma, DOCS III uses digital camera technology to record visible emission events from various sources, Deisel generators, Trucks, Ships, Stationary Sources, and fugitive Emissions from farms to process vents. The DOCS III uses patent pending algorithms to determine opacity of the scene, viewed through the digital camera. With a valid opacity DOCS III converts the Opacity value to concentrations of Particulate sizes, e.g. PM 0-3m, PM 3-10 M and PM>10M, additionally the scene is evaluated for flow and flux to speciate the Volatile concentrations in the Opacity. Re applying the original Air Pollution monitoring to todays high technology and we now have the ability to measure Particulate for Wild fire from 5 miles away, the PM from a HD Deisel truck running 65 MPH down the hwy. The Particulate from a dust storm in the open desert, or a mining blast in a quarry. The volatiles from a quenched flare, or the exhaust stacks of a CruiseLine.
(View Presentation PDF)
Performance Evaluation of the Auxiliary Electrode in Improving Data Quality from 4-pin Electrochemical Gas Sensors
Presented by: Anna Farquhar, Aeroqual Ltd (Poster Presentation)

Electrochemical gas sensors (GSEs) are a low-cost option for the measurement of pollutant gases in outdoor environments. They are commonly deployed in low-cost sensor networks. The reliability of GSEs is impacted by changing environmental conditions, including temperature and humidity. Various strategies have been deployed to mitigate the impact of temperature or humidity changes, including the development of the auxiliary electrode (AE) in 4-pin sensors. The AE sits below the working electrode (WE) and responds to changes in temperature and humidity only.

In this work we evaluate the performance of the AE in improving the output of carbon monoxide and nitrogen dioxide sensors from two manufacturers. An empirical algorithm that subtracts the output of the AE from the WE was used to correct for the steady-state temperature offset of the baseline concentration. However, the WE of a sensor shows large amplitude fluctuations (± 20 ppb) in response to rapid changes in humidity. In this work we show that the AE also responds to rapid changes in humidity, however the direction and magnitude of the AE fluctuations are different from the direction and magnitude of the WE fluctuations. By subtracting the AE output from the WE output the already large baseline fluctuations are magnified rather than mitigated in many cases. This is especially concerning for the NO2 sensor. NO2 concentrations in ambient environments are of a similar magnitude to the baseline humidity fluctuations, so cannot be discriminated from baseline fluctuations. In this presentation we demonstrate the limited usefulness of the AE in real-world applications and how if improperly implemented the AE can negatively impact data quality. We also describe alternative options for mitigating the effects of humidity that could be employed in low-cost sensor networks.
AROMA-ETO: Part-Per-Trillion Sensitive, Realtime Ethylene Oxide Measurements in Ambient Air
Presented by: Anthony Miller, Entanglement Technologies (Poster Presentation)

Ethylene oxide (EtO) is a common chemical used in chemical manufacturing and commercial sterilization processes. It has recently received a large degree of focus for federal, state, and municipal governments as well as industry and community groups due to its increased potential cancer risk from long-term inhalation exposure. Lab quality, part-per-trillion measurements of EtO in air is needed to accurately identify areas of concern, understand background concentrations, and keep industrial workers safe.

In this work, the development and performance validation of Entanglement Technologies’ AROMA-ETO will be described. AROMA-ETO is a thermal desorption cavity ringdown spectroscopy (TD-CRDS) analyzer, which delivers real-time, in-field measurements of EtO with detection limits in the low part-per-trillion range. This capability enables rapid field surveys and assessments that are required by policymakers and industrial operators to make time-sensitive decisions to reduce EtO-related health risks, including stopping leaks and releases before they can cause harm or non-compliance. The system is also ideal for measuring EtO in ambient air as it is able to accurately measure background concentrations and identify hot spots. Ambient air data of EtO concentrations collected using the AROMA-ETO will be presented alongside other use cases. Entanglement Technologies’ AROMA platforms are used for mobile monitoring, industrial EHS, fenceline monitoring, emergency response, tracking fugitive emissions, among others.
An array of low-cost PM sensors to characterize the structure of roadside microscale atmospheric flows
Presented by: Aron Jazcilevich, Universidad Nacional Autónoma de México (Poster Presentation)

Street concentration fields change in time scales measured in seconds. This is because they are subject to immediate emissions and turbulence produced by vehicular wake. PM concentrations may vary from 20 micrograms/m3 to a spike of more than 1000 micrograms/m3in about 3 seconds. Furthermore, the distribution of pollutants on the roadside is uneven, generating local maxima concentrations spots depending on traffic, street design and existing meteorological conditions. These factors complicate the analysis of acute exposure events.

An array consisting of 224 low-cost PM2.5sensors placed on the roadside obtains instantaneous concentration maps on a plane perpendicular to the road. This allows us to obtain the position and preferred height and distance to the road of maximum concentrations. The knowledge gained by using this array benefits the study of acute roadside exposure phenomena, location of specific risk zones, urban design, and in the evaluation of ecological barriers.
Source apportionment of speciated VOCs with low-cost metal oxide sensors
Presented by: Caroline Frischmon, University of Colorado Boulder (Poster Presentation)

Source apportionment of speciated VOCs allows communities to address pollution concerns in the order of highest impact to human and environmental health. Although regulatory-grade instruments are capable of VOC source attribution, their high cost prevents measurements on the spatial variability of these VOC sources. Performing source attribution with low-cost (LC) sensors instead opens the door to explore the spatial variability of local VOC hotspots because we can deploy the sensors as a network.

This study deployed an array of LC metal oxide sensors into four Colorado communities that range from rural to urban, and which are all situated near oil and gas development. LC sensors were deployed for about one month, and these data were fit to regulatory-grade measurements of speciated VOCs using artificial neural networks (ANNs). Positive Matrix Factorization (PMF) grouped VOCs by their likely sources, such as wet and dry components of oil and gas operations, to begin discerning VOC source contributions and compositions at higher spatial resolution.

Fits using ANNs had R2values of 0.6 for calibration despite concentration ranges that were orders of magnitude below the LC sensors’ prescribed detection limits. The fitted data captured baseline trends well but failed to estimate peaks. Grouping species by source using PMF generally improved fits, which we hypothesize is because the grouped concentrations were higher than that of individual species. PMF analysis revealed an oil and gas factor in each community; however, other source factors, such as biogenic and combustion, varied based on which reference species data were available in each site and what other sources are likely present in a rural, urban, or suburban environment.

These results highlight the potential for VOC source attribution with LC metal-oxide sensors. Future works should seek to improve spike-finding and address challenges with species that are measured well below sensor detection limits.
A Compact High-Precision Microfluidic Platform for Wearable Sensing of Particulate Matter
Presented by: Ehsan Ashoori, Michigan State University

Particulate matter (PM) is one the main air pollutants which has been linked with severe health conditions. In a recent study, 10.2 million premature deaths have been attributed to only fossil-fuel component of PM2.5 (PM with diameter smaller than 2.5 µm). Many existing commercial PM monitoring devices are bulky and expensive. They are typically used for stationary or portable weather monitoring stations and hence, they are not appropriate for monitoring PM with high spatiotemporal resolution for everyday use of individuals. In this work, toward implementing a wearable PM monitoring system, we present a microfluidic platform for separating PM into different size bins as well detecting particles with high precision. For separation, we implemented deterministic lateral displacement (DLD) technique where I-shape pillars were built into the microfluidic channel to achieve high separation efficiency. This is a necessary step for the sensing stage where the accuracy of the particle detection is sensitive to the size of particles. The implemented DLD separator showed nearly 100% separation efficiency for a critical separation size of 2.5 µm. For the sensing stage, we examined two different methods to achieve high sensitivity. We first utilized differential capacitive measurement technique to detect the presence of particles in the sample. We designed and fabricated the sensor as well as the readout circuit and were able to detect particle concentration levels that are equivalent to air quality index of 151 which is considered to be “unhealthy” according to the US environmental protection agency. To better detect the PM particles with dielectric constants similar to the microfluidic medium in use, we also developed electrochemical sensor for PM detection. We successfully implemented cyclic voltammetry as well as differential pulse voltammetry techniques. The promising results that these methods produced enable detection of a vast majority of particle pollutants.

*Author did not provide PPT for public distribution, please contact Ehsan Ashoori at ashoorie@msu.edu with questions
Using the PurpleAir monitor as a Sensitive Nephelometer
Presented by: James Ouimette, Sonoma Ecology Center (Poster Presentation)

The first objective of this presentation is to show that the Plantower PMS5003 sensors in the PurpleAir monitors can be used to measure and predict the 1-h average submicron aerosol light scattering coefficient nearly as well as expensive integrating nephelometers. Second, to show that the effectiveness of the PurpleAir in estimating PM2.5is due both to the PMS5003 behaving like an imperfect integrating nephelometer and to the mass scattering efficiency of ambient PM2.5aerosols being roughly constant. Recent research (https://doi.org/10.5194/amt-2021-170 [https://doi.org/10.5194/amt-2021-170]) demonstrated that the PMS5003 is not an optical particle counter and that the particle number concentrations it reports in six size bins are not a meaningful representation of particle size distribution. The PMS5003 first size channel, “ >0.3 um”, is a measure of the scattering coefficient, not number concentration. The PMS5003 is equivalent to a cell-reciprocal nephelometer that uses a 657 nm perpendicularly-polarized light source and integrates light scattering from 18 to 166 degrees. Yearlong field data at NOAA’s Mauna Loa Observatory and Boulder Table Mountain sites indicated a strong linear relationship between the 1-h average of the “>0.3 μm” size channel and the submicrometer aerosol scattering coefficient bsp1 measured by TSI 3563 integrating nephelometers. The relationship was bsp1 = 0.015 × “>0.3 μm” (R2 = 0.97) from 0.4 Mm-1to 500 Mm-1. This corresponds to 1-h average submicrometer aerosol mass concentrations ranging from approximately 0.2 to 200 µg m-3. A physical-optical model of the PMS5003 was developed to estimate scattered light intensity on the photodiode. Field data showed that 1-h average “>0.3 μm” was linearly proportional to the model-predicted scattered light intensity over 4 orders of magnitude. The presentation will show that model predictions were consistent with experimental data on the PMS5003 response to a variety of aerosols and relative humidity.
Testing of a Low-Cost Sensor and Sampling Platform Alongside Reference Instruments in a Home Kitchen
Presented by: Jessica Tryner, Colorado State University

People in the United States spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small “Home Health Boxes” (HHBs) to measure indoor PM2.5, PM10, CO2, CO, NO2, and O3 concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers’ calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to published air quality guidelines) and (b) both indicated that gas cooking burners were the dominant source of CO and NO2 pollution (with 1-h average NO2 concentrations exceeding 100 ppb during normal cooking); however, HHB and reference O3 data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO2 (from 408 to 58 ppm), CO (from 645 to 572 ppb), NO2 (from 22 to 14 ppb), and O3 (from 21 to 7 ppb); additionally, correlation between HHB and reference O3 data improved (Pearson’s r increased from 0.02 to 0.75). Mean 168-h PM2.5 and PM10 concentrations derived from nine filter samples were 19.4 µg m-3 (6.1% relative standard deviation) and 40.1 µg m-3 (7.6% relative standard deviation). The 168-h PM2.5 concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91).
(View Presentation PDF)
Autonomous Low-Cost Ozone Sensors: Development, Calibration, and Application to Study Urban-Rural Gradients
Presented by: Shantanu Jathar, Colorado State University (Poster Presentation)

Ozone (O3), a criteria pollutant and atmospheric oxidant, is not routinely measured in rural and remote environments and hence exposure to ozone pollution in these regions remains poorly understood. In this work, we built, calibrated, and deployed five low-cost, autonomous ozone samplers (called MOOS) in northern Colorado, a region that is non-compliant for ozone during the summertime. The autonomous sampler included the following components: (i) an Aeroqual SM50, a heated metal oxide ozone sensor, mounted inside a custom radiation shield, (ii) a power system that consisted of a 30 W solar panel, 108 Wh lithium-ion battery, and charge controller, (iii) a Particle Boron to acquire, process, and transmit data to the Cloud, and (iv) an environmental sensor to measure temperature and relative humidity. In a three-week long colocated study, we found that all MOOS, calibrated to a single day of reference data, compared well against reference monitors with a measurement error between 4-6 ppbv. Manufacturer and laboratory-based calibrations performed much poorly and over- and under-estimated ozone levels at higher and lower ozone mixing ratios, respectively. When deployed in northern Colorado for an additional three weeks to measure and study the east-west gradient, we found that MOOS calibrations based on the colocated study did as well or better than a calibration based on a single day of reference data in the field. Compared to the colocated study, the field study resulted in larger measurement errors for all five MOOS (<9 ppbv). Furthermore, there was modest variability in the field performance across the different MOOS (4 to 9 ppbv) that could not be explained by environmental differences between the different sites (e.g., proximity of the MOOS to the reference monitor, land use type, temperature). Overall, our study indicates that the MOOS shows promise in being used to supplement routine ambient monitoring and characterizing regional ozone pollution.
Sensor Control - a versatile platform forlow-cost AQ multisensor systems
Presented by: Tobias Baur, Saarland University, Lab for Measurement Technology (Poster Presentation)

The gas sensor market is constantly evolving, partly due to the still increasing interest in environmental measurements to fulfill the legislative guidelines or the United Nations sustainable development goals, limiting the exposure of people to certain gases, but also in the context of IoT devices for comfort. New, often digital sensors are rapidly introduced in the market, so that sensors are often replaced by newer versions within a short timeframe. Designing electronics to put these rapidly developing sensors into operation is time consuming and an obstacle to fast evaluation of new sensor systems that make full use of the sensors´ capabilities. Therefore, a hard- and software platform for multisensor systems, especially metal oxide semiconductor (MOS) gas sensors, but also miniaturized photoacoustic CO2 sensors or digital environmental sensors (p, T, RH) is presented.

The goal was to create a hardware system offering a suitable compromise between easy installation and commissioning of various sensors and useful functionalities for sensor signal capturing also allowing simple optimization of their operating mode for different applications. Different versions of the system allow the evaluation of a variety of analog and digital MOS sensors and to control temperature cycled operation (TCO) to increase selectivity, sensitivity, and stability. The presentation of the platform will discuss the three categories electronic hardware, firmware, and control software. The system can be combined with modules for wireless data transfer or local data storage on SD cards as well as different power supplies to also allow mobile applications. The platform is therefore suitable for research as well as outreach activities but can also be directly implemented as a component in application specific sensing solutions.

View Session 6B (Part 1) Recording on Youtube

View Session 6B (Part 2) Recording on Youtube

Merging sensor data with other air pollution data sources: methods and benefits

Air pollution data can be generated using ground-based reference monitors and low-cost sensors, satellite retrievals, and air quality models (chemical transport models, reduced complexity models, land-use regression models, etc.) Each of these has advantages and drawbacks, but the combination of different data streams can overcome individual challenges and provide unique insights into urban air pollution across the globe. We invite presentations on existing and new methods of merging these data streams and case studies that illustrate the benefits of such combined approaches.

Lead Session Chairs:

R. Subramanian, QEERI & OSU-Efluve, & Ethan McMahon, US EPA

Current Presenters:

Data fusion for air quality mapping using low-cost sensor observations
Presented by: Alicia Gressent, INERIS

The recent technological developments and the strong increased interest for public information lead to a fast-growing use of low-cost sensors (LCS) for air quality monitoring. In Nantes (a French city), LCS have been installed in the city center and deployed on driving school cars, ambulances and service vehicles to measure PM concentrations. We used the large amount of PM10 observations provided by the sensors for air quality mapping to show the potential added-value with respect to the dispersion model (ADMS-Urban) calculations (Gressent et al., 2020). Data fusion was performed by combining the preprocessed fixed and mobile LCS observations in November 2018 and the 2016 annual average of the ADMS-Urban outputs. The measurement uncertainty related to the LCS and the dispersion of the data are considered in data fusion that is achieved at hourly resolution. Results show that considering the model alone implies 8% bias whereas including the LCS observations reduces the bias to 2.5%. However, the fusion tends to smooth the PM10 peaks. In addition, the effect of the measurement uncertainty has been investigated by doubling it or reducing it to the reference station measurement uncertainty. The sensitivity study demonstrates that the performance is increasing by reducing the uncertainty. This highlights the importance to estimate accurately the measurement uncertainty of the devices to ensure relevant air quality mapping. The method efficiency is also quite limited by the low correlation between the sensor observations and the model used as external drift in the kriging that may be explained by the remaining bias on LCS data. This has been addressed in Rollin et al., in prep, for PM2.5 by developing a new methodology to identify “rendez-vous” between sensors that relies on a graph approach linking measurements of sensors close to each other’s that allows characterizing and correcting the measurement bias.
(View Presentation PDF)
Closing the gap between air pollution data sources, tools and end users in LMIC
Presented by: Beatriz Cardenas, WRI Mexico

Air pollution in cities at LMIC is one of the health and environmental most urgent problems to attend. Citizens and government officers are increasingly demanding access to a wide variety of air pollution data sources to attend data and knowledge needs. These range from identification of sources of pollution, ambient concentrations, microenvironment, and personal exposure to information ahead of time of high pollution events to protect the most vulnerable ones and less reactive actions. Using a few case studies that are part of the World Resources Institute (WRI)'s air quality data strategy, we will discuss how local insights can be generated by aggregating global data sources and integrating them with local data. This strategy focuses on translating existing science into decision-relevant, action-provoking, and locally relevant tools. Examples of how different air pollution data sources may be used in combination for specific demands in Latin American cities will include: 1) Development and piloting data and modeling tools that aggregate diverse datasets (satellites, ground monitoring data (from both reference and LCS networks) to provide actionable information in cities. 2) Participatory science processes using portable and small sensors to evaluate the effectiveness of intervening at neighborhood or city scale. 3) Development of bottom-up participatory emissions inventories in which local authorities are key data providers and users. 4) Visualization and auralization tools to see and listen to air pollutants dynamics. Based on these experiences, a reflection on the benefits and barriers will inspire and promote collaborative efforts to close the gap between existing and available air pollution sources, scientific and technical tools and end users (citizens and officers) in cities from LMIC to motivate actions.

*Author did not provide PPT for public distribution, please contact Beatriz Cardenas at beatriz.cardenas@wri.org with questions
Air quality forecasting at sub-city-scale by combining models, satellites, and surface measures
Presented by: Carl Malings, Postdoctoral Program Fellow, NASA GSFC

While there are a variety of sources for air quality information, no one source simultaneously allows for high accuracy, low bias, fine spatial resolution, wide spatial coverage, high temporal frequency, and the capability for near-term forecasting of air quality. Global models, like the NASA’s Goddard Earth Observing System - Composition Forecasting (GEOS-CF) model, provide global coverage and forecasting capabilities, but operate at relatively coarse spatial resolution and require ground-truthing with in-situ data. Polar-orbiting satellite data products, like those of the ESA TROPOspheric Monitoring Instrument (TROPOMI), provide higher-spatial-resolution remote sensing of atmospheric composition, but are limited by cloud cover and overpass times and report column-integrated quantities. Surface measurements, both from regulatory-grade monitors and low-cost networks, measure “nose-level” air quality, but may not represent concentration variability across large spatial domains, and (in the case of low-cost sensors) are subject to interference and biases. There exists a great potential to combine these diverse data sources together, using the strengths of some to offset the weaknesses of others to build a more comprehensive picture of air quality. This presentation will summarize results from ongoing efforts to produce such a combined forecast, with application case studies for surface-level Nitrogen Dioxide forecasting in several major US cities. Furthermore, we will examine the relative impacts and benefits of different data sources on the forecasting accuracy at different spatial and temporal scales. Finally, we will examine the potential for integrating low-cost sensors into such a system, both in terms of using these integrated air quality estimates as a baseline from which to calibrate networks of low-cost sensors in the field, and in terms of using dense networks of low-cost sensors to refine the spatial resolution of integrated air quality forecasts.
(View Presentation PDF)
Integrating multi-modal transportation data with low-cost air quality sensor data to improve understanding of traffic-related air pollution
Presented by: James Hindson, University of British Columbia

In order to quantify air quality on the University of British Columbia (UBC) campus, a network of 8 low-cost air quality sensors were installed in June 2021 at several traffic intersections. The “Remote Air Quality Monitoring Platform” (RAMP) sensors measure PM2.5, Ozone (O3), Carbon Monoxide (CO), Carbon Dioxide (CO2) and Nitrogen Oxides (NOX) every 15 seconds. The RAMP sensors are solar-powered, battery-operated and can measure wind speed and direction (with attachments). Each sensor is connected to the Rogers Communications 5G network testbed at UBC, allowing for real-time calibration and reporting of air quality data. Multi-modal transportation sensors have also been installed at some of these intersections that collect anonymized data through the use of video technology, tracking and quantifying cars, trucks, buses, pedestrians and bicyclists (partnership with Numina and Blue City). Large scale, anonymized mobile data has also been analyzed to understand and map local population movements including pedestrians, cars and public transport (partnership with downtown.ai). Here we present our findings on linking transportation sensor traffic as well as mobile location data to pollution concentrations measured by the RAMP sensors. Merging these data stream enabled a greater understanding of the relationship between roadside air pollution levels and traffic count by mode. Exposure levels experienced by pedestrians and cyclists around the chosen intersections can also be explored. Data will be presented for the period June 2021 to October 2021, covering a number of interesting pollution events such as wildfires. At most sensor locations, preliminary results show a clear correlation between vehicular count and CO and NOX levels, with significant contribution from buses and trucks. Results indicate that pedestrian and bicyclist peak counts coincide with peak pollutant levels, suggesting increased exposure levels.
(View Presentation PDF)
Supporting timely, high-resolution air quality data availability in Africa by fusing satellite observations of aerosol optical depths, PM2.5 model data, and PM2.5 surface-based measurements
Presented by: Nathan Pavlovic, Sonoma Technology, Inc

Globally, over 4 million premature deaths per year can be attributed to fine particulate matter (PM2.5) pollution, the majority of which occur in lower- and middle-income countries. The availability of timely, accurate, and high-spatial resolution PM2.5 data can increase awareness of global air quality conditions and help protect public health. However, in many parts of the world, there are a limited number of ground-based air quality monitors. Satellite observations offer global coverage and have the potential to provide a comprehensive view of PM2.5 in near-real time when combined with data from ground-level monitors and air quality models. In this work, we assess the viability of an uncertainty-weighted data fusion approach to provide high-resolution PM2.5 concentration data in Africa. We estimate ground-level PM2.5 concentrations using aerosol optical depth (AOD) retrievals from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm applied to NASA Moderate Resolution Imaging Spectroradiometer (MODIS) observations in combination with Copernicus Atmosphere Monitoring Service (CAMS) near-real time air quality model data. Using ground-level PM2.5 data, we fused satellite-based estimates with observations and assessed the performance of the application. Fusion of surface data with satellite-derived PM2.5 shows improvements in agreement with independent observations compared with satellite-derived PM2.5 alone, where tested. Based on the results presented here, we plan to use Google Earth Engine to implement the data fusion approach and develop daily real-time PM2.5 concentrations over Africa. These results can support further efforts to evaluate and enhance the use of satellite data to estimate ground-level PM2.5. Using the system we have developed as a foundation, data fusion harnessing growing monitoring networks across Africa, including low-cost sensors, presents the opportunity to fill the air quality monitoring gap.
(View Presentation PDF)
Integration of Air Quality Sensor Data into the South Coast AQMD Real-Time Air Quality Index Map
Presented by: Nico Schulte, South Coast Air Quality Management District

Despite significant progress, air quality in the South Coast Air Quality Management District (South Coast AQMD) remains among the worst in the nation. South Coast AQMD provides a real-time 5 km resolution air quality index (AQI) map for 17 million residents in a region encompassing major portions of Los Angeles, Orange, Riverside, and San Bernardino counties in Southern California. This AQI map, deployed in late 2020, combines regulatory-grade monitor data, consumer-grade sensor data (i.e., PurpleAir PA-II), and data from the NOAA National Air Quality Forecast Capability, a chemical transport model that uses a bias correction algorithm to improve accuracy. The model and sensor data supplement the regulatory data by filling in the gaps between regulatory monitors in the region, where South Coast AQMD operates a network of 42 air monitoring stations. However, data gaps still exist in areas where ozone and PM2.5concentrations routinely reach unhealthy levels. To improve the accuracy of the AQI map in between the monitors and existing sensors, we worked with volunteers (members of the public) to host additional PurpleAir PA-II and also Aeroqual AQY sensors at strategic locations throughout the South Coast AQMD jurisdiction and integrated data from these additional sensor networks into a revised version of the AQI map. Sensor data is corrected using remote calibration techniques (i.e., MOment MAtching (MOMA) calibration and network management framework) which are used in real-time to correct the sensor measurement data before integration into the AQI map. Collocated sensor measurements at regulatory monitoring stations are used to estimate measurement uncertainty, which is used to weigh sensor data through the residual Kriging method. We will demonstrate the improved spatial coverage in the AQI map as we aim to provide accurate AQI data at a neighborhood scale.
(View Presentation PDF)
The AirHeritage Hierarchical Network: Sensing, Calibration, Deployment strategies for fixed, mobile air quality monitoring and modeling in urban scapes.
Presented by: Saverio De Vito, ENEA

The AirHeritage project aims to improve citizens and administrators AQ knowledge in small and medium cities in Italy, empowering awareness, behavioral change and adherence to participated remediaton policies. Funded by EU Urban Innovative Action framework, the project involved the city of Portici, located 7km south of Naples and partners from academia, research agencies, environmental protection, associations and SMEs. It deployed a hierarchical network relying of fixed and mobile stations, involving regulatory grade analyzers and low cost sensors to map citizens exposure through crowdsensing and long term fixed monitoring. Concentration of NO2, O3, CO, PM1 – 2.5 – 10. Monitored data are shared and integrated in ultra high resolution (20m) exposure maps. Data from fixed stations, both low cost and regulatory grade, are assimilated in 3D chemical transport model using weather data and 3D urban scape data for now- and forecasting purposes. 7 Fixed and 30 Mobile MONICA™ platforms, relying on electrochemical sensors, are calibrated twice a year and used in 4 opportunistic measurement campaigns. Platforms are co-located with local regulatory grade analyzer for at least 3 weeks each. Colocation data driven calibration algorithms are derived, validated and executed at the edge on citizens smartphone through an Android App resulting in real time exposure estimations. In each session, routes are color coded using European Air Quality Index allowing to assess exposure on the move and selecting low exposure paths. The same result is available, using modeled air quality maps, to citizens which do not received MONICA analyzers. Shared data are fused through IDW and by computing measurements median on a predefined grid discarding low populated cells. This contribution will show the results of 6 colocation driven calibrations and 4 mobile crowdsensing campaigns in the project, exploring architectural (Sensors and IoT management), accuracy, engagement and communication issues.
(View Presentation PDF)
Using Crowd-Sourced Low-Cost Sensors in a Land Use Regression of PM2.5 in 6 US Cities
Presented by: Tianjun Lu, California State University, Dominguez Hills

Assessing exposure to ambient Fine Particulate Matter (PM2.5) is important for improving human health. Low-cost sensor networks, located by a variety of users (i.e., crowd sourcing), are expanding rapidly on a global scale thereby increasing measurement density and coverage for land use regression (LUR) models. Few studies have developed methods (based on hygroscopic growth factors) to integrate low-cost sensors into LUR models across multiple cities, limiting the ability of modelers to fully utilize growing low-cost sensor networks worldwide. We developed five LUR models to predict annual average PM2.5concentrations using combinations of regulatory (six-city: n = 68; national: n = 757) and low-cost monitors (n = 149) from six US cities. We found that developing hybrid LURs that include the low-cost (i.e., PurpleAir) network may better capture within-city variation. LURs with the 6-city PurpleAir data only (10-fold CV R2 = 0.66, MAE = 2.01 µg/m3) performed slightly worse than a conventional LUR based on the 6-city regulatory data (10-fold CV R2 = 0.67, MAE = 0.99 µg/m3). Hybrid models that included both 6-city low-cost and national regulatory data performed similarly to existing national models that rely on regulatory data (Hybrid models: 10-fold CV R2 = 0.85, MAE = 1.02 µg/m3; regulatory monitor models: R2 = 0.83, MAE = 0.72 µg/m3). Integrating crowd-sourced low-cost sensor networks in LUR models has promising applications to help identify intra-city exposure patterns especially for rural areas and low and middle income countries where high-grade regulatory monitor networks are sparse or do not exist.
(View Presentation PDF)
Detecting toxic metals in ambient particulate matter using a low-cost and near real-time analyzer
Presented by: Hanyang Li, Air Quality Research Center, University of California Davis

Urban populations are exposed to hazardous air pollutants (HAPs), including the gaseous and particulate matter that are known or suspected to cause cancer or other serious health effects. Airborne trace metals are part of the HAPs, which originate from both natural processes and anthropogenic emissions. XRF and ICP/MS have been traditionally used to measure ambient metal concentrations, but their high cost and lab-intensive analysis make them unsuitable to deploy in vulnerable neighborhoods. To fill this gap, we have developed an inexpensive instrument for measuring the presence of toxic metals in ambient PM (named as Toxic-metal Aerosol Real Time Analyzer, TARTA). TARTA was developed based on the theory of spark-induced breakdown spectroscopy. It consists of an aerosol sampling system, electrical components for spark generation, a delay generator to suppress the initial continuum radiation, and an optical spectrometer for spectra collection. Air pollution is first drawn through an inlet nozzle that deposits particles onto the flat surface of a tungsten electrode. After sampling for a period of time, a spark discharge is generated between the electrodes to form a plasma which atomizes the deposited particles. The emitted light from the plasma is then collected by a spectrometer for the determination of aerosol composition and concentration via machine learning methods. Our laboratory and field testing of TARTA show that the instrument is capable of detecting multiple metal particles in near real-time. Specifically, TARTA has LODs ranging from 0.04 µg m-3 at a sampling duration of 30 minutes and a flow rate of 15 l min-1. The measurement uncertainty is estimated to be 19% based on a multivariate calibration model. During the field experiments at Caldecott Tunnel and a rural environment, the relatively high temporal resolution of TARTA enables us to examine the dynamics of metal particle emissions and compare the results against real-time total PM concentration.

*Author did not provide PPT for public distribution, please contact Hanyang Li at hynli@ucdavis.edu with questions
Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection
Presented by: Jianzhao Bi, University of Washington (Poster Presentation)

High-resolution exposure modeling is critical for assessing the health effects of ambient PM2.5 in epidemiological studies. Using sparse regulatory PM2.5 measurements as principal model inputs may affect the models’ accuracy in predicting PM2.5 spatial distribution. In this study, we used the PM2.5 measurements from a publicly available commercial low-cost PM2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the ACT-AP study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 “gold-standard” monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation in the Puget Sound region of Washington. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R2 and RMSE for two-week concentration averages improved from 0.84 and 2.22 μg/m3 to 0.92 and 1.63 μg/m3, respectively. The external spatial validation R2 and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 μg/m3 to 0.79 and 0.88 μg/m3, respectively. The PurpleAir monitors with shorter PCA distances improved the model’s prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.
Citizen-Driven Air Sensing Network to Study Intra-Urban Heat (UHI) and Pollution Island (UPI)
Presented by: Lu Liang, University of North Texas (Poster Presentation)

Defined by higher ambient air pollution in urban areas as compared to the non-urban counterparts, the urban pollution island (UPI) effect presents a critical socio-environmental and health issue to urban dwellings. Dallas-Fort Worth has been witnessed as one of the fastest-growing metroplexes and its air quality has been consistently ranked among the worst in the U.S. However, whether the accompanied significant urbanization has resulted in an overall increase in the UPI remains unknown. This project will utilize low-cost sensors to build an urban observation network in a growing county of DFW, for enhanced capacity of real-time, near-surface, and representative PM2.5 monitoring. We have adopted a stratified sampling design modified from the New York City Community Air Survey to place the 100 PurpleAir sensors in six sampling strata. A variety of advanced machine learning algorithms are tested to calibrate the sensor by collocating the PA sensors with a reference instrument. Using the calibrated PM2.5 data, we will model daily PM2.5 concentration at the 30-m resolution through the integration of ground sensor measurements with satellite observations. The high spatial-temporal resolution urban pollution mapping results will shed light on revealing places and times when people will be more exposed to air pollution, with further implications for health and other socio-economic outcomes.
The power of joint noise and traffic related air pollution measurements: resolve meteorological variability in air pollution data and provide short-term policy support
Presented by: Luc Dekoninck, ghent University (Poster Presentation)

Traffic is the main source of noise exposure in urban and suburban environments and is a significant source of various air pollutants. The correlation of the exposure in the two disciplines isn’t very strong when evaluated in total noise levels (dBA). When including the spectral content of the noise exposure, the engine related contribution correlates very strongly with the combustion related air pollutants. The method is established in small projects but is not included in large scale air pollution health impact assessments so far. This presentation illustrates the added value of joint noise and air pollution assessments and the potential applications for short-term policy support.

The main issue is the complex relation between traffic counts, traffic dynamics and emissions. The traffic dynamics is the key to understand the local emission variation. It results in non-linear relations between traffic and exposure in both disciplines. The spectral content of the noise identifies and quantifies the non-linear relations between traffic and exposure in both disciplines. The technique doesn’t require traffic data and works standalone in the absence of traffic data and on network segments with little combustion based traffic (bicycle highway). The spectral noise based variables acts as a proxy for the traffic and traffic dynamics.

The noise measurements are less affected by meteorology compared to air pollution, enabling the disentanglement of local traffic dynamics and meteorological variability in the air pollution data. Joint measurements increase the efficiency of air pollution only measurements with at least a factor ten. The methodology works in both fixed or mobile applications and provides an analytical advantage in the air pollution modeling. The’ noise only’ application extend the health impact assessment. The enhanced efficiency of the multidisciplinary approach provides short-term feedback to the local governments resulting in unprecedented policy support.
Supporting timely, high-resolution air quality data availability in Africa by fusing satellite observations of aerosol optical depths, PM2.5 model data, and PM2.5 surface-based measurements
Presented by: Nathan Pavlovic, Sonoma Technology, Inc

Globally, over 4 million premature deaths per year can be attributed to fine particulate matter (PM2.5) pollution, the majority of which occur in lower- and middle-income countries. The availability of timely, accurate, and high-spatial resolution PM2.5 data can increase awareness of global air quality conditions and help protect public health. However, in many parts of the world, there are a limited number of ground-based air quality monitors. Satellite observations offer global coverage and have the potential to provide a comprehensive view of PM2.5 in near-real time when combined with data from ground-level monitors and air quality models. In this work, we assess the viability of an uncertainty-weighted data fusion approach to provide high-resolution PM2.5 concentration data in Africa. We estimate ground-level PM2.5 concentrations using aerosol optical depth (AOD) retrievals from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm applied to NASA Moderate Resolution Imaging Spectroradiometer (MODIS) observations in combination with Copernicus Atmosphere Monitoring Service (CAMS) near-real time air quality model data. Using ground-level PM2.5 data, we fused satellite-based estimates with observations and assessed the performance of the application. Fusion of surface data with satellite-derived PM2.5 shows improvements in agreement with independent observations compared with satellite-derived PM2.5 alone, where tested. Based on the results presented here, we plan to use Google Earth Engine to implement the data fusion approach and develop daily real-time PM2.5 concentrations over Africa. These results can support further efforts to evaluate and enhance the use of satellite data to estimate ground-level PM2.5. Using the system we have developed as a foundation, data fusion harnessing growing monitoring networks across Africa, including low-cost sensors, presents the opportunity to fill the air quality monitoring gap.
(View Presentation PDF)

View Session Recording on Youtube

Mobile Monitoring & Monitoring Mobile Sources

Lead Session Chairs:

Jessa Ellenburg, 2B Technologies & Melissa Lunden, Aclima

Presentations:

Opportunistic mobile air quality mapping using service fleet vehicles: from point clouds to actionable insights
Presented by: Jelle Hofman, Flemish Institute for Technological Research (VITO)

Within the framework of the VLAIO City of Things program, IMEC The Netherlands deployed mobile Kunak® sensor systems on a postal service fleet, collecting high spatiotemporal air quality data (NO2, O3 and PM1, PM2.5and PM10) “at every doorstep” in the city of Antwerp, Belgium. Before deployment, all 20 sensor systems were co-located next to a regulatory monitoring station for validation and calibration purposes. After deployment, they collected opportunistic data, at 10 second resolution during daytime and 5 minute resolution (when parked/static) during nighttime, from March till September, 2021. Data validation and monitoring was foreseen via the Kunak Cloud. Three devices remained next to the air quality monitoring station to evaluate the sensor performance (precision, accuracy, correlation) over time. Calibration resulted in good sensor performance (R²=0.85-0.93, MAE = 3.35-4.35) up to supplementary (<25% expanded uncertainty) data quality levels. When mounted in their mobile housing but not moving, their correlation slightly lowered (R²=0.77-0.88, MAE = 3.29-5.21) due to the lower ventilation rate. Mobile measurements (e.g. n=2080067 for June 2021) were aggregated at street segment level and evaluated in terms of spatial coverage, range, temporal dynamics and representativity with regard to reference measurements. After ~6 months of operation, the sensor performance had lowered but remained acceptable for 2 out of 3 devices (R²=0.5-0.71, MAE=7.78-13.64), nevertheless indicating the need for recalibration. This mobile sensor testbed enabled the collection of real-time fine-grained air quality data to gain a better understanding of spatiotemporal pollutant dynamics and exhibited range in urban air pollution exposure. Proper data aggregation methods, performance validation and coverage thresholds are needed to draw representative pollutant maps from the collected mobile point measurements.
(View Presentation PDF)
Mobile air sensing to detect PM2.5 hot spots in Houston, Texas
Presented by: Tim Dye, TD Environmental Services

Mobile air monitoring is a new and innovative way to collect air quality data over a large area. Like stationary air monitoring, the size, sophistication, and cost of mobile monitoring technology has been decreasing over the last five years. The City of Houston and the Environmental Defense Fund (EDF) have been at the forefront of this data collection method. In 2017, EDF used two Google Street View cars with reference instruments to map hyperlocal air quality [https://www.edf.org/airqualitymaps/houston] across 35 neighborhoods. Then in 2018, EDF partnered with the City of Houston to deploy roof-mounted, mid-cost instruments on two city vehicles that measure PM2.5and Black Carbon, testing operational viability of this type of system. In 2021, building on the successful 2018 pilot, the City of Houston deployed ten low-cost mobile sensor units on city vehicles that measure PM2.5, NO2, CO, and CO2. To maximize spatial coverage with minimal use of additional staff time, 10 mobile units were deployed on municipal vehicles to collect PM2.5data continuously as staff drive predominantly “business as usual” routes to collect baseline concentrations with broad geographic coverage, supplemented by occasional targeted driving around sources of interest to improve accuracy of baseline calculations in those locations. This project is currently collecting data to determine PM2.5 hot spots within the city and improve spatial coverage for air quality data. Data will be used with a control chart to determine where additional effort (monitoring, investigation, enforcement) should take place. This talk will present the technology used, present lessons learned, and discuss several case studies from this project.

*Author did not provide PPT for public distribution, please contact Tim Dye at tim@tdenviro.com with questions
Development of ASTM Standard Test Methods for PM2.5 and CO2 Sensors Used for Indoor Air Quality Measurements
Presented by: Wilton Mui, South Coast Air Quality Management District

Real-time indoor air quality sensing devices, which are used to provide occupants with information on indoor air pollutant concentrations and concurrently provide data to ventilation systems for improved system operation, have become increasingly popular with consumers and manufacturers. However, the effectiveness of these devices in accurately assessing pollutant concentrations is limited. A variety of pollutants can be measured using sensors, with several being the subjects of future standardization activity. The focus of the two ASTM International standards covered in this presentation is to provide a mechanism for evaluating the performance of PM2.5/CO2 sensors or sensor systems for indoor applications through laboratory-based testing. Under a pass-through grant from the DOE to South Coast AQMD, the Air Quality Sensor Performance Evaluation Center (AQ-SPEC) drafted the test standards, conducted the engineering and scientific work, including laboratory setup and testing, data processing and analysis. Using the established AQ-SPEC laboratory protocol as a starting framework, new comprehensive protocols were developed to evaluate commercially-available indoor air quality sensors for their ability to measure a wide range of pollutant concentrations, recover from loss of power, and perform under various climate conditions and in the presence of interferents. The performance of indoor air quality sensors was evaluated based on data recovery, intra-sensor variability, accuracy, precision, and correlation to Federal Reference/Equivalent Method instruments. We summarize the requirements and procedures of these test methods.
(View Presentation PDF)
High resolution mapping of on-road air pollution using a large taxi-based mobile sensor network in Shanghai
Presented by: Yuxi Sun, Hong Kong University of Science and Technology

Severe urban air pollution caused by rapid urbanization has become a major environmental and social problem around the world. Traffic emissions are major sources of urban air pollution in most of megacities. To explore heterogeneity in concentrations of traffic-related air pollution (TRAP) in city area, we established a mobile sensor network on 150 taxis in Shanghai, China. Each taxi was equipped with an air monitoring device to capture spatial and temporal patterns of CO, NO2, and PM2.5concentrations from November 2019 to December 2020. Through our regular quality assurance and quality control campaign of devices, 15,045,137 intermittent data points during 3647.7 hours were collected and used in this study period. Road coverage and pollutant concentration spatial distribution were better identified and quantified. Since the sampling stage spanned the Coronavirus Disease of 2019 (COVID-19) outbreak, the average concentrations of CO, NO2, and PM2.5in Shanghai decreased by 33.59%, 33.83% and 45.55% during the lockdown period compared to the before period, respectively. Meanwhile, the local contribution related to traffic emissions changed slightly before and after the epidemic period, and the background contribution changes may relate to the seasonal variation. Such real-time air monitoring systems can be a valuable tool for policymakers and environmental protection agencies to implement effective policies for the future development of cities. Besides, our analysis provides new insights into changes in urban air quality, particularly during the COVID-19 epidemic period. In the following research, we will combine real-time pollutant concentration data measured by mobile devices with traffic emission inventories, urban geographical features, meteorological information, and other factors to study the horizontal relationship between them and establish relevant models, which can be applied to air pollution prediction on other fields.
(View Presentation PDF)
Next-Generation Heavy-Duty Vehicle Enforcement with Roadside Emissions Monitoring Devices
Presented by: Hang Liu, California Air Resources Board (Poster Presentation)

Heavy-duty (HD) vehicles are a significant source of emissions that contribute to adverse health outcomes. The California Air Resources Board (CARB) has adopted and implemented multiple regulations over the years to reduce emissions from HD vehicles. Previous studies have shown that a small percentage of HD vehicles are responsible for a disproportionate portion of total HD emissions. With more than one million trucks operating annually in California, it is important to be able to efficiently identify high-emitting vehicles to target enforcement efforts, especially in communities heavily impacted by truck traffic.

CARB’s Enforcement Division has been developing and deploying the Portable Emissions AcQuisition System (PEAQS) roadside emissions monitoring devices as a support tool for current and future heavy duty diesel enforcement programs. PEAQS devices estimate vehicle emissions by capturing truck exhaust plumes and utilizing sensors to measure black carbon, oxides of nitrogen (NOx), and carbon dioxide for identifying high emitting vehicles. Two devices have been deployed since August 2019, with each generating millions of second-by-second emission records and tens of thousands of vehicle license plate records from the integrated automatic license plate reader (ALPR) systems each month. CARB’s Enforcement Division plans to deploy more devices to establish a monitoring network within the State. This presentation will show how this large dataset from a network of PEAQS units can be used to draw insights on HD traffic patterns, vehicle composition, and vehicle emission characteristics. It will also demonstrate how CARB has been developing decision support systems to process, analyze, and visualize PEAQS and other data to support CARB’s next-generation data-driven enforcement program.
Mobile Monitoring on Trash Trucks Using Sensors and Drive-By Calibrations with Reference Grade Monitors
Presented by: Jessa Ellenburg, 2B Technologies (Poster Presentation)

In collaboration with the City and County of Denver, and as part of a grant from the National Institutes of Health, the concept of Park-By and Drive-By calibrations of mobile sensors was evaluated. Five sensor suites (PAMs) measuring PM2.5, CO, NO2 and CO2 were installed on five City of Denver trash trucks in August of 2021. Additionally, two reference grade monitoring packages (AQSyncs) were installed where the trash trucks park and one was installed at a transfer station the trucks visit. The goals of this are study are to determine:

-Feasibility of a co-located calibration to keep sensor data quality high

-Recommended duration of the co-location

-Recommended frequency of the co-location

-Calibration algorithms for data correction in the cloud

-Recommended frequency of data calibration adjustments

This study will help determine the feasibility of using city fleet vehicles such as trash trucks to map air pollutants through the city. Potential applications and benefits of such programs from a city perspective will also be presented.

View Session Recording on Youtube

Performance targets for air quality sensors

Currently, there is not a uniform certification or standard that sets performance targets for air quality sensors. Moreover, performance needs will depend on the application of a sensor and evaluation approaches may vary for different applications. This session will highlight efforts to develop, compare and/or apply performance targets, performance parameters, and testing protocols or programs to understand sensor performance. It will also address scientific insights on performance parameters and how to deal with sensor performance over time. New insights in performance targets for air quality sensors are welcome!

Lead Session Chairs:

Marine Van Poppel, VITO, Flemish Institute for Technological Research NV, & Rachelle Duvall, US EPA

Presentations:

ASTM Standards for the Performance Evaluation of Outdoor Air Quality Sensors
Presented by: Geoff Henshaw, Aeroqual Ltd

Over the past ten years, the availability of lower priced air quality sensors has encouraged innovative measurement of air quality by a wide range of users. There has also been a significant effort to understand how to use the technology and this has identified that data quality from these devices can be variable. Over the past few years several entities have undertaken the development of methods to objectively evaluate air sensors and create criteria for performance-based grades. This presentation describes the work undertaken by ASTM International, a consensus based standards organisation, to develop standards that will support users of these devices. ASTM established a working group in 2018 to develop a standard for the evaluation of outdoor air sensors focused on the criteria pollutants: CO, SO2, NO2, O3, PM2.5 and PM10. The standard has evolved to include both laboratory and field tests that provide information on instrument repeatability, sensitivity, linearity, cross- interferences, drift, and comparability with Federal Equivalent Methods. The metrics selected, issues resolved to achieve consensus and the compatibility with the recent indoor PM2.5 standard ASTM D8405 will be discussed. Test examples will be presented to illustrate how different sensors perform. ASTM also began work in 2020 on a specification for outdoor air sensors that would create grades of performance based on the standard test practice. The key criteria and their rationale will be presented and compared and contrasted with approaches by other organisations.
(View Presentation PDF)
Is PM sensor testing really testing the sensors? Experiences from 400 days of field tests in the Life VAQUUMS project.
Presented by: Jordy Vercauteren, Flemish Environment Agencey

We often get the question what the best low-cost PM-sensor is, or whether a certain PM-sensor is 'good enough' to use for certain applications. But what does 'the best' or 'good enough' really mean?During the Life VAQUUMS project we tried to answers these questions. Six different sensor types (5 units per type) were compared with the EU gravimetric reference and an automated equivalent method (Palas Fidas 200). The sensors were: + Honeywell HPMA 115S0 + Dylos DC1700 + Nova Fitness SDS011 + Plantower PMS7003 + Winsen ZH03B + Shinyei PPD60PV In general sensors showed acceptable to good correlation for PM2.5(R² between 0.62 and 0.84). Due to the high proportion of PM2.5 in PM10 some sensors did show some correlation for PM10, but this could be considered artificial since there was poor to non-existing correlation for the PMcoarse fraction (=PM10- PM2.5). Apart from correlation we also looked at: + the importance of (human expert) data validation; + the effect of additional calibration; + the between sensor-uncertainty; + the effect of relative humidity on the sensor/reference ratio + the uncertainty at the limit value when compared to the reference methods Additional analysis of SDS011 sensors co-located with Fidas Palas 200 monitors at 8 different sites in Flanders showed that other locations can give less favourable results than the urban test site in Borgerhout, Antwerp. These differences could be linked to more frequent episodes of high relative humidity at other locations. The presence of vegetation close to the monitoring sites appears to play a role which indicates that test results will often bedependent on time and location of the test and that a thorough understanding of aerosols in the atmosphere and the limitations of the sensors is vital for the applications of PM-sensors. Life VAQUUMS website:https://vaquums.eu/ [https://vaquums.eu/]
(View Presentation PDF)
A French certification scheme for the evaluation of sensor systems dedicated to the ambient air quality monitoring.
Presented by: Laurent Spinelle, Ineris

The continuous interest for sensor systems dedicated to the air quality monitoring led the French national agencies (Ministry in charge of the environment, the French National Reference Laboratory for monitoring air quality (LCSQA) members and the regional monitoring networks (AASQA)) to study the reliability of these new devices. However, there is currently no national or European normative framework regulating their uses or giving the guidelines to evaluate their performances against reference measurement systems. Two members of the LCSQA (Ineris & LNE) joined forces to establish a voluntary certification scheme called Air Quality Sensor that focused on the evaluation of the metrological performances of sensor systems for both gaseous and particulate matter. This evaluation, based on the European standard drafts of the working group on sensor systems for air quality monitoring (CEN TC264/WG42), is divided in two steps: This evaluation focuses on fixed measurement performed with a stationary sensor system. In a first stage, this voluntary evaluation is aimed at measuring PM2.5 and NO2 and will be further extended to other pollutants such as O3 and PM10. At the end of the two parts of the metrological assessment and after an audit of the devices production, a performance division is assigned to each pollutant measured by the sensor system, based on specific metrological criteria described in the protocol for evaluating sensor systems for ambient air quality monitoring at fixed point (MO-1347, Evaluation Protocol for Sensor Systems for Ambient Air Quality Monitoring at Fixed Site, https://prestations.ineris.fr/en/certification/certification-sensors-system-air-quality-monitoring).
(View Presentation PDF)
Performance evaluation of sensors for gaseous pollutants and particulate matter in ambient air: status of European standardization
Presented by: Martine Van Poppel, VITO

European standardization of test protocols to evaluate performance characteristics of sensor systems is needed and is an important step to include sensor system measurements into the monitoring of air quality for regulatory and non-regulatory purposes. TC264 WG42 (Working Group 42 of Technical Committee 264 on Air Quality of the European Committee for Standardization) has been working on a protocol for the evaluation of sensor systems for air quality monitoring. The protocol defines procedures and requirements for the evaluation of sensor systems. It applies to sensor systems as individual measurement devices for outdoor measurements at fixed sites. The procedure evaluates if the measurement uncertainty defined in Directive 2008/50/EC as Data Quality Objective (DQO) for indicative measurements and for objective estimation is met. The protocol includes also a less demanding performance for non-regulatory sensor measurements. This results in three sensor performance classes. The protocol includes pollutants regulated under Directive 2008/50/EC (PM10, PM2.5, O3, NO, NO2, CO, SO2 and benzene), and also guidance is given for CO2. The protocol for gases includes lab and field tests. Two routes are possible: extended lab tests and short field test, or only extended field tests. The protocol defines concentration levels and interferences to be tested. It defines also the number and types of field sites, seasons and concentration levels. Classification of sensor systems is based on performance requirements and DQO. DQO is calculated as expanded uncertainty at Limit Value. Performance requirements include response time, lack of fit, repeatability, Limit of Detection, between sensor uncertainty and data capture. The protocol for PM sensors is currently being developed. A lab test has been introduced to evaluate if sensor systems can measure also coarse PM. This presentation will discuss the current procedure and open issues and compare to other standardization initiatives.
(View Presentation PDF)
Highlights on U.S. EPA Efforts on Developing Performance Testing Protocols and Targets for Air Sensors
Presented by: Rachelle Duvall, U.S. EPA

As the development and use of air sensors continues to expand rapidly, understanding the performance of sensors remains a critical need given the variability in sensor data quality. To help support consumers and developers of air sensor technologies, the U.S. Environmental Protection Agency (U.S. EPA) published reports in 2021 outlining recommended testing protocols, metrics, and target values to evaluate the performance of ozone (O3) and fine particulate matter (PM2.5) air sensors used in non-regulatory supplemental and informational monitoring applications. The U.S. EPA is expanding these efforts to cover additional pollutants including particles with diameters of 10 microns or less (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). The goal of this work is to provide a consistent approach for evaluating sensor performance while also helping provide confidence in sensor data quality, encouraging technology improvements and development in the marketplace, and assisting users select appropriate sensors for their application of interest. This presentation will provide highlights on the U.S. EPA’s completed and future work in developing guidance for evaluating the performance of air sensors. Disclaimer: Although this abstract was reviewed by EPA and approved for presentation, it may not necessarily reflect official Agency policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
(View Presentation PDF)
Lessons Learned in designing, developing, and implementing the South Coast AQMD AQPortal environmental data management solution
Presented by: Vasileios Papapostolou, South Coast Air Quality Management District

Technological advances along with regulatory advances mandating increased air quality monitoring have provided the opportunity for air districts to expand their monitoring capabilities and build hierarchical networks that include regulatory-grade instruments, research-grade equipment, and consumer-grade air quality sensors. These expanding monitoring networks collect data at increased spatial and temporal resolution to support different purposes: community, fence-line, supplemental, and incident response monitoring. These expanding networks create data management challenges to ingest, store, process, analyze and visualize the collected data. The South Coast Air Quality Management District has developed the AQPortal, a single-point of access cloud-based data management platform to meet these new data management challenges. AQPortal supports over 10 District-level and several external data streams, removes data and analytical silos, and establishes new data science workflows for data analysts and scientists to develop comprehensive data visualizations for public consumption. This presentation will discuss lessons learned during the design, development, and implementation of the AQPortal for environmental data management. We will review a use case scenario in which AQPortal was utilized to develop a publicly available data dashboard for air quality sensors deployed in support of community air monitoring networks under the California Assembly Bill 617 Community Air Monitoring and Emission Reduction plans efforts.
(View Presentation PDF)
Using International Standards to prove the performance of low-cost sensors - the regulatory perspective
Presented by: Richard Gould, Environment Agency

Reliable data is critical for compliance assessment and regulation. Under European legislation, data quality is specified through Data Quality Objectives (DQOs) and measurement uncertainty at the limit value, expressed as a 95% confidence interval. European legislation for air quality specifies maximum, allowable uncertainties for both reference monitoring instruments and for indicative instruments (i.e. low-cost sensors). Reference instruments have to meet the requirements for performance specified in European Standards, which specify performance criteria and test procedures. These Standards ensure that reference instruments meet the required uncertainty of measurement. Additionally, legislation states that such reference instruments must be type-approved. Until now, there has been an absence of equivalent performance standards and test procedures for indicative instruments. However, the European Standards Body, CEN, has been developing European Standards for indicative instruments measuring gases and particulate matter. These two developing standards have been produced to fill the gaps to test, validate and certify indicative sensors. This presentation describes how regulatory bodies will be able to use the two, new European Standards for low-cost sensors to assure the performance of such indicative-monitors, and how this will benefit manufacturers of low-cost sensors, the organisations which use them, and importantly, how certified low-cost sensors will be vital in both measuring and improving ambient air-quality, and how these sensors can be used in synergy with reference instruments and modelling.

*Author did not provide PPT for public distribution, please contact Richard Gould at richard.gould@environment-agency.gov.uk with questions
What is the Impact of Common Sources of Error on Air Quality LCS Measurements Performance? A Practical Guide
Presented by: Sebastian Diez, University of York

Accurately measuring atmospheric pollutants is critical for decision-making and designing policies aiming to improve air quality and reduce human health exposure. The advent of inexpensive sensor-based technologies means that there are now a growing number of measuring devices available that could be useful for this purpose. However, not all this range available today will necessarily be adequate to the problem that the user wants to tackle. It is therefore key to ask whether the information provided by instrument "X" is appropriate for the intended purpose. Clearly focusing on the question to be answered, and defining the required data quality accordingly, is key to identifying those instruments/techniques capable of meeting this requirement. Since the measurement uncertainty ultimately determines the information content, it is therefore critical to estimate this parameter in a robust and transparent way, thus allowing the potential application of the instrument in question to be defined. In this work, we explore the nature of common air pollution measurement sources of errors in the real world and the implications they have for traditional uncertainty metrics and other potentially more insightful approaches to assessing measurement uncertainty. For this, we employed first simulated datasets combining different sources of error/interferences from (i) a non-target chemical, (ii) physical parameters and (iii) electromagnetic fields. Then we study real-world data from the QUANT project which involves a range of LCS technologies and multiple reference instruments in 3 urban sites in the UK. We then use this information to explore the performance of these technologies and develop methods that will enable their integration into the air quality monitoring infrastructure and use in atmospheric chemistry research. This work will ultimately make it possible to optimize the applications of these technologies based on the quality of the LCS data.
(View Presentation PDF)
Using a Remote Calibration Technique to Improve Data Quality for Large Networks of Particulate Matter Sensors
Presented by: Ashley Collier-Oxandale, South Coast Air Quality Management District

A continual challenge in the long-term deployment and management of large sensor networks lie with data quality and calibration. For example, calibration procedures may require physical co-locations – limiting the scale of deployments. Alternatively, global correction equations may not account for changes driven by PM source type, seasonal influences, or drift. Using remote calibration techniques may offer an alternative approach that supports the deployment and maintenance of large sensor networks. Here we applied a technique, entitled MOMA (MOment MAtching), to PM2.5data from PurpleAir PA-II sensors. MOMA was developed by Aeroqual Ltd and is currently being explored under a collaboration between the South Coast Air Quality Management District's Air Quality Sensor Performance Evaluation Center (AQ-SPEC) Program and Aeroqual Ltd. Under this collaboration, the MOMA approach is being piloted with different sensor networks to determine its suitability as a sensor agnostic tool for improving data quality and enhancing the benefits of large-scale networks. This approach involves identifying suitable proxy sites from regulatory monitoring networks. Then, at regular intervals, or when drift is detected via a drift detection algorithm, appropriate calibration periods are determined and used to calculate gains and offsets for each sensor, allowing sensors to remain in the field through calibration. In this presentation, we will examine the efficacy of this approach for sensors co-located at regulatory air monitoring stations and deployed over multiple years (2017-2020), in addition to sharing interesting observations from corrected data. Different failure modes and how well this approach addresses these cases will also be discussed. In terms of implementation, we will illustrate how an open-source R package, AirSensor, can be leveraged to streamline data access and Quality Control (QC) processing, thus enabling the use of this calibration technique with sensor networks.
(View Presentation PDF)
Particle Sizing Performance Evaluation of Low-Cost Particulate Matter Sensors
Presented by: Emilio Molina Rueda, Colorado State University (Poster Presentation)

Outputs from low-cost particulate matter sensors typically include mass and number concentrations reported across multiple size bins. However, particle sizing is challenging even for laboratory-grade instruments, especially those that rely on light scattering. Under ideal conditions, discrete pulses would be obtained for individual particles. Mapping each pulse to a size bin, based on amplitude and width, would be a relatively simple classification problem, although other factors (e.g., scattering coefficients) would introduce uncertainty. Real conditions, in contrast, lead to ambiguous, noisy signals that can’t be interpreted easily. Pulse overlaps due to multiple particles in the detector and sampling errors affect the signal in different ways. If coincidence errors (i.e., multiple particles seen as a single “larger” particle) are sporadic, size and count errors are introduced. If pulse overlap is widespread, particle sizing becomes more complex because identifying different particle sizes in a combined signal is not trivial. Although data reported by some low-cost sensors are well-correlated with mass concentration changes, size distributions range from having no basis in measurement data to vaguely representative. We evaluated the particle sizing performance of multiple sensors including the Sensirion SPS30 and the Piera IPS-7100. We conducted laboratory experiments with monodisperse, polydisperse, and bi-modal aerosols, in addition to an ambient monitoring test to explore whether sensor outputs are estimated from actual particle sizes and counts. Preliminary results from ambient monitoring show that counts of particles <1 µm reported by the IPS-7100 and SPS30 are highly correlated with data reported by a collocated GRIMM EDM 180 monitor (r = .92, p < .001 and r = .94, p < .001 respectively). Both sensors had significantly lower correlation coefficients for particles in the 1 to 2.5 µm range (r = .54, p < .001 and r = .27, p < .001), and for larger particles.
Evaluation of AQY1 Sensors and Implications for Community-led Projects
Presented by: Joshua Stratton, Rider University (Poster Presentation)

While access to lower-cost sensors has greatly increased over the last decade, questions remain about their reliability for community-led projects. This study aimed to compareAeroqual’s AQY1 sensors (n=6) with state operated real time ozone (O3) (ultraviolet absorption), nitrogen dioxide (NO2) (chemiluminescence) and fine particulate matter (PM2.5) monitors (beta-attenuation) under real-world conditions in New Jersey (USA). Aeroqual AQY1 sensors were evaluated to understand their potential for community-led hotspot monitoring projects utilizing high-time-resolution data (<60 min averages). Undergraduate students operated, maintained, and analyzed the data from these sensors as part of a larger project to understand potential pollutant hotspots around campus. During collocation, AQY1 O3 concentrations were correlated (R2= 0.62-0.82) with the reference monitor concentrations (monitor mean =33 ppb and maximum = 80 ppb) but were largely underestimated (slope = 0.22-0.29). NO2 concentrations were low (monitor mean = 8 ppb and maximum = 25 ppb) with little correlation between sensors and the reference monitor. NO2 measurements were found to exhibit a dependence on relative humidity and a shifting response over the study period. During collocation, AQY1 PM2.5 concentrations agreed with the reference monitor when including impacts of fireworks during the 4th of July celebrations (R2= 0.83-0.85 and slope = 0.99-1.09), however, it decreased when excluding this episode (slope = 0.42-0.50, R2 = 0.40-0.51). A calibration from this study period corrected for large underestimations of O3, but NO2 remained inaccurate.O3 and PM2.5measurements show promise for community-led projects based on the accuracy following correction at elevated concentrations. However, data correction, harmonization, and interpretation will likely require some significant amount of effort from community groups likely necessitating partnerships with local air monitoring agencies and EPA regional offices.
How to get accurate measurements from micro-sensors?
Presented by: Julie Pelletier, ECOMESURE (Poster Presentation)

The covid-19 crisis has accelerated public awareness about air pollution. The deployment of massive quantities of low-cost sensors is already ongoing in schools (CO2 sensors) and cities (PM2.5 sensors) even though most sensors are lacking accreditation/certification. Meanwhile new monitoring stations manufacturers are also appearing on the market, some with little to no expertise in air quality monitoring. How accurate are these modern technologies? How to ensure the sensors provide precise and meaningful data?

The accuracy of the sensors depends on two key factors: the technology used (electrochemical, optical, PID, infrared, MOx...) and the calibration method and verification process applied to guarantee the quality of the values measured.

Thanks to its 30 years’ experience in metrology and maintenance of air quality sensing material, Ecomesure has developed a disruptive technology to improve the calibration process of its devices using Artificial Intelligence.

The process involves an automated multi-point calibration followed by a cross-comparison in an ISO-certified laboratory between calibrated sensors and reference devices data. Side-by-side comparison tests between calibrated sensors are performed to ensure true repeatability.

The combination of these two methods ensures the quality of measurement at the factory and certifies the quality of the data measured by the sensors.

In the field intercomparison with Air quality monitoring reference stations can also be carried depending on the project and expected accuracy of measurements in order to adjust the sensor offset (e.g. Canton of Geneva, Port Autonome de Strasbourg, ENGIE etc.).

Technological breakthroughs make it possible to maximize these operations and further improve the reliability of measurements by integrating artificial intelligence models at the time of calibration. This is the object of a new patent that Ecomesure has filed and will present in the event.
A French certification scheme for the evaluation of sensor systems dedicated to the ambient air quality monitoring.
Presented by: Laurent Spinelle, Ineris

The continuous interest for sensor systems dedicated to the air quality monitoring led the French national agencies (Ministry in charge of the environment, the French National Reference Laboratory for monitoring air quality (LCSQA) members and the regional monitoring networks (AASQA)) to study the reliability of these new devices. However, there is currently no national or European normative framework regulating their uses or giving the guidelines to evaluate their performances against reference measurement systems. Two members of the LCSQA (Ineris & LNE) joined forces to establish a voluntary certification scheme called Air Quality Sensor that focused on the evaluation of the metrological performances of sensor systems for both gaseous and particulate matter. This evaluation, based on the European standard drafts of the working group on sensor systems for air quality monitoring (CEN TC264/WG42), is divided in two steps: This evaluation focuses on fixed measurement performed with a stationary sensor system. In a first stage, this voluntary evaluation is aimed at measuring PM2.5 and NO2 and will be further extended to other pollutants such as O3 and PM10. At the end of the two parts of the metrological assessment and after an audit of the devices production, a performance division is assigned to each pollutant measured by the sensor system, based on specific metrological criteria described in the protocol for evaluating sensor systems for ambient air quality monitoring at fixed point (MO-1347, Evaluation Protocol for Sensor Systems for Ambient Air Quality Monitoring at Fixed Site, https://prestations.ineris.fr/en/certification/certification-sensors-system-air-quality-monitoring).
(View Presentation PDF)
Calibrating low-cost sensors for wildfire smoke: how algorithmic corrections to low-cost sensor data can help meet USEPA and other performance targets
Presented by: Levi Stanton, Clarity Movement Co. (Poster Presentation)

With the proliferation of low-cost sensor technology and the consequent availability of expanded air quality datasets, calibrated low-cost sensor performance is improving every year. Clarity regularly generates seasonally and regionally-specific calibration models to bring sensor performance in line with data quality guidelines — such as the recently released United States Environmental Protection Agency (USEPA) PM2.5 Performance Targets.

Anticipating a long and challenging wildfire season in 2021, we developed a new and improved PM2.5 calibration model to account for elevated particulate matter air pollution in Western North America. This presentation will describe the approach we took in developing this new model and provide performance metrics for the model, which represent a substantial improvement in sensor performance during periods of elevated ambient particulate matter.

Using collocated Clarity Node-S devices at 13 reference sites across five states (California, Idaho, Montana, Oregon, and Washington) we generated a dataset of collocated particulate matter measurements for the period from July 2019 to July 2021. We separated the dataset into model training and model testing datasets. One location (in Montana) was kept out of the training dataset to serve as a completely independent site and to test how well the model performs in different geographies and climates.

We evaluate the performance of the model using relevant USEPA PM2.5 Performance Targets. While the uncalibrated PM2.5 measurements do not meet all USEPA performance targets, the calibrated PM2.5 data (both hourly and 24-hour) met all of the recommended targets for data quality.
Sensortoolkit: A Python Library for Standardizing the Ingestion, Analysis, and Reporting of Air Sensor Data for Performance Evaluations
Presented by: Samuel Frederick, Oak Ridge Associated Universities (Poster Presentation)

Past efforts to establish open-source software tools for reporting sensor performance have been limited. Sensor and reference data formats vary widely, and in turn, many existing software packages evaluate only one type of sensor model. Other packages allow broader utilization of air quality data yet may not be specifically tailored for evaluating sensor performance against reference data. Additionally, these packages do not provide means for summarizing sensor performance in a reporting template using common statistical metrics and figures. To encourage broader utilization of the U.S. Environmental Protection Agency’s (EPA) recommended performance metrics and target values for sensors measuring fine particulate matter (PM2.5) and ozone (O3), EPA developed a new, open-source Python library named “sensortoolkit”. The library compares collocated sensor data against reference monitor data and includes methodology to re-format both datasets into a standardized format using an interactive setup module. Library modules are included for calculating EPA’s recommended sensor performance metrics and for making relevant plots. These metrics and plots can be used to better understand sensor accuracy, precision between sensors of the same make and model, and the influence of meteorological parameters at 1-hr and 24-hr averages. Results can be compiled into the reporting template included alongside EPA’s performance targets documents. The sensortoolkit library is designed for any user, from novices to researchers.
Field calibration and performance evaluation of low-cost sensors
Presented by: Sinan Yatkin, Joint Research Centre

The calibration of eighty-five AirSensEUR sensor systems including CO, CO2, NO, NO2 and O3 gas sensors, and two models of PM sensors were performed by 2-weeks co-location at Air Quality Monitoring Stations (AQMS) in four European cities, namely Ispra (IT), Antwerp (BE), Oslo (NO) and Zagreb (HR). Calibration was followed by the evaluation of sensor performance for several months of co-location at the same AQMS as calibration, at different AQMS and seasons. Sensor calibration models were established by an automatic process using statistical tools and laboratory experiments to determine the variables needed in the models. Measurement uncertainty, linear regression of sensor predicted data versus reference data and root-mean-squared error were utilized to evaluate the sensor performance. The calibration models for CO, NO, O3 and PM sensors used few and repeatable variables. The coefficients of calibration models were found site-dependent showing that calibration should be carried out locally rather than globally for the best performance. Nevertheless, some sensors calibrated at one AQMS predicted reasonably well at other AQMS and different seasons, meaning that calibration models derived at one AQMS/season can be applied to multiple sites/seasons. Overall, CO, O3, PM2.5 and PM1 calibration models were successful in prediction of pollutants while the performances of NO and NO2 sensors were highly dependent on similarities of pollutants, temperature and humidity conditions between prediction and calibration periods.
(View Presentation PDF)
Air Quality Sensors Deployed on Mobile Platforms: A Performance Evaluation Protocol and Recent Advances
Presented by: Wilton Mui, South Coast Air Quality Management District

Performance evaluation studies for air quality sensors collecting stationary measurements have been conducted by various academic and government bodies, and current efforts by international standards organizations may lead to convergence of these methods. In contrast, such performance studies and methods are nonexistent for sensors used in mobile deployments, even though sensors are being used in this manner by academics, government agencies, non-governmental organizations, and citizen scientists. Nonstationary measurements with air quality sensors are a relatively nascent but growing use-case, and questions of appropriateness and data quality will become increasingly important. The South Coast Air Quality Management District’s Air Quality Sensor Performance Evaluation Center (AQ-SPEC) has developed a novel evaluation protocol in which air sensors are compared to reference- or research-grade instruments while deployed on a mobile platform. Air sensors are assessed in testing phases of various degrees of environmental control, ranging from placement in a controlled-flow sampling duct to unsheltered mounting on a vehicle rooftop. These evaluations probe the performance of air sensors in mobile monitoring setups that may be appropriate for community members to carry out at the neighborhood level. The testing procedures aim to quantify the performance of air sensors and the effects of sensor siting, orientation, and vehicle velocity, which can provide guidance to users on appropriate sensors and configurations for their use-case. Recent advances in the design of a new mobile platform are also discussed.
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The Potential of Air Sensors for Personalizing and Advancing Human Health Research

This session will showcase human exposure and health studies that have used or integrated air sensors to assess personal or population level exposure to air pollutants and investigate their associations with a range of health outcomes. Given advances in wearables and sensing technologies, air sensors are providing increasingly fast, personalized, and spatiotemporally resolved exposure data to inform health risk assessments and just in time interventions.

Lead Session Chairs:

Susan Stone, US EPA & Rima Habre, University of Southern California

Presentations:

Personalized environmental sensing for health research and disease management - lessons learnt and future challenges
Presented by: Benjamin Barratt, Imperial College London

The overarching aim of most air quality measurement campaigns is to better understand where air pollution comes from, how it behaves and how it impacts human health. While air sensor campaigns now regularly accomplish the first two aims, relatively little attention is given to the third. Over the past decade, the Environmental Research Group has conducted a series of large personal monitoring campaigns directly linking acute and chronic health responses to exposure to environmental stressors. These range from tracking the cardiovascular health of elderly residents in Beijing, to the lung function of asthmatic children in Sub Saharan Africa, to the respiratory health of COPD patients in London. Each campaign has provided insight into the strengths and limitations of this research method, along with valuable practical lessons in how to, and how not to, carry out personal air sensor campaings for human health research. While campaign results have provided valuable evidence for disease prevention and management, there are many outstanding challenges, including extrapolation of results over longer time periods, participant burden, data interpretation and transferrability of results between cohorts. These challenges must be overcome if personalised sensing is to realise its full potential.

*Author did not provide PPT for public distribution, please contact Benjamin Barratt at b.barratt@imperial.ac.uk with questions
Ecologically-Valid, Multimodal Data Collection Platforms to Measure the Effects of Indoor Air Quality on Sleep Quality
Presented by: Zoltan Nagy, The University of Texas at Austion

An adequate amount of good-quality sleep is essential for a person's physiological and psychological well-being, but can be affected by a wide variety of factors. The Indoor Air Quality (IAQ) of one's bedroom environment has recently garnered attention since many of the health effects caused by indoor air also directly affect sleep. A few studies have assessed the relationship between IAQ and sleep quality in controlled environments which can affect both self-report and measured sleep quality outcomes. Examining the relationship within a participant's home environment is provides a more ecologically valid measurement which helps to reduce participant bias. Provided the wide variety and availability of consumer-grade IAQ sensors, a study of this type is now more feasible. Here, we present results from a field study conducted with 20 participants over 2.5 months in Austin, TX. We monitored five components of IAQ using a purpose-built environmental monitor, the BEVO Beacon, and measured sleep quality with wearable activity trackers and short sleep surveys sent four times a week to participants. When compared to prior related studies, our study was conducted over the longest period and includes measurements of the most IAQ parameters. We found significant degradation in self-reported and device-measured sleep quality during nights with elevated CO, CO2, and temperature. Evenings with elevated PM2.5 and TVOC concentrations were found to be associated with general improvements in sleep quality which indicates a need to study relationships between these aggregate IAQ measures and sleep quality more closely. Our study highlights the application of consumer-grade, affordable IAQ and sleep quality monitoring devices and the complicated relationship that exist between these two domains.
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Indoor Air Quality Data Captured from Consumer-Grade Devices and Its Effect on Occupant Mood
Presented by: Hagen Fritz, The University of Texas

Poor mental health can hinder a person's productivity, ability to learn, or participate fully in society. Researchers have found particular effects of outdoor air pollution on mood, primarily focusing on depression, but less is known about the effect of Indoor Air Quality (IAQ) on mood. To address this question, we use information fusion across disparate datasets collected during a 2.5 month study including 20 participants to understand how parameters of a participant's IAQ affect four components of mood: discontent, sadness, loneliness, and stress. We collect data on four components of IAQ -- carbon dioxide, particulate matter (PM), total volatile organic compounds, and temperature -- through the use of our own consumer-grade device and mood data through Ecological Momentary Assessments (EMAs) completed four times a week by participants on their smartphones. Using GPS data provided by participants' smartphones, we determine instances when EMAs were submitted while at their residence and the amount of time the participant was home prior to submitting. We summarize IAQ parameters during these periods to understand (1) how IAQ conditions at home influence EMA outcomes, (2) if the length of time participants are at home before submitting influences their mood, and (3) if mood responses are more pronounced following more extreme measurements of IAQ. Initial results indicate associations between increased CO2 and elevated responses on all four components of mood. Elevated temperatures were associated with increased discontentment and stress while elevated PM concentrations seemed to produce positive responses, showing associations with decreases in sadness and loneliness. Our study is one a few that directly compares IAQ measurements and components of mood and helps to emphasize the importance of proper IAQ control for the benefit of the psychological health of building occupants.
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Daily Associations of Air Pollution and Pediatric Asthma Risk using the Biomedical REAI-Time Health Evaluation (BREATHE) Kit
Presented by: Hua Hao, Department of Population and Public Health Sciences, University of Southern California

Background:Exposure to air pollution is associated with pediatric asthma exacerbation, yet acute effects are poorly understood. Methods:We conducted a panel study of 40 children aged 8-16 years with moderate to severe asthma. We used the BREATHE kit to monitor personal exposure to particulate matter of aerodynamic diameter <2.5 µm (PM2.5), lung function, medication use, and asthma symptoms for 14 days. Hourly ambient (PM2.5, nitrogen dioxide (NO2), ozone (O3)) and traffic-related (NOx and PM2.5) air pollution exposures were modeled based on location. We used mixed effects models to examine the association of same day and lagged (up to 2 days) exposures with daily changes in % predicted forced expiratory volume in 1 sec (FEV1) and % predicted peak expiratory flow (PEF), rescue inhaler puffs, and symptoms. Results:Participants were on average 12.0 years old with mean (SD) morning %predicted FEV167.9% (17.3%), PEF 69.1% (18.4%), and rescue inhaler use of 1.4 (3.5) puffs/day. Participants reported chest tightness, wheeze, trouble breathing, and cough symptoms on 36.4%, 17.5%, 32.3% and 42.9%, respectively (N=217 person-days). One SD increase in previous day O3 was associated with reduced morning (beta [95% CI]: -4.11 [-6.86, -1.36]), evening (-2.65 [-5.19, -0.10]) and daily %predicted FEV1(-3.45 [-6.42, -0.47]). Daily (lag 0) traffic-related PM2.5exposure was associated with reduced morning %predicted PEF (-3.97 [-7.69, -0.26]). Exposure (lag 0) to ambient O3, NOx, and NO was associated with rescue inhaler use (rate ratio [95% CI]: O3 1.52 [1.02, 2.27], NOx1.61 [1.23, 2.11], NO 1.80 [1.37, 2.35]). Traffic-related PM2.5(lag 0) was associated with “feeling scared of trouble breathing” symptom (odds ratio [95% CI]: 1.83 [1.03, 3.24]). Conclusions:Our study demonstrates the potential of informatics, air sensors, and wearables at enabling the collection of highly space and time resolved, contextual and personal data for understanding acute pediatric asthma triggers.

*Author did not provide PPT for public distribution, please contact Hua Hao at hhao@usc.edu with questions
Integration of Tools for Real-time Assessment of Residential Air Quality and Asthma Symptoms: Challenges and Lessons Learned
Presented by: Luz Huntington-Moskos, University of Louisville School of Nursing

Introduction: The COVID-19 crisis has altered cleaning practices and time spent at home.Research assessingresidential exposure to cleaning/disinfecting products among adults with asthma and the impact on asthma symptoms are lacking.This presentation willdiscuss the challenges and lessons learned duringintegration of an air quality monitor, spirometer, and ecological momentary assessment (EMA) to assess residential air quality and asthma symptoms among adults with asthma. Methods: This study is being conducted with 50 adults with uncontrolled asthma. We are assessing the feasibility and usability of: (1) providing participants with a commercially available indoor air quality monitor (Awair Omni) to continuously capture total volatile organic compound (VOCs) and particulates (PM2.5); (2) EMA notifications via a smartphone app to capture context of real-time indoor air quality, alert participants to high residential VOCs and PM2.5, and assess real-time asthma symptoms. We measure lung function with a home spirometer (ZEPHYRx). Results: Data collection using 3 software platforms (Awair Omni, ZEPHYRx, PiLR EMA) with diverse dashboards and technological requirements required creativity and diligence. Additional time for novel integration between the Awair Omni and the PiLR EMA platforms and testing of the integration was required. Developing study instructional materials for each platform in an iterative process with the study team was critical to minimizing burden on study participants. Feedback from an asthma Community Advisory Board led to modifications in instructional materials, reduced EMA notifications, limited data collection to 14 days, and added a 3-day run-in period. Conclusions: This presentation will provide insight into the integration of diverse technologies, including indoor air quality monitors while assessing real-time events along with real-time symptoms. The lessons presented will guide development of future studies using personal air monitors.
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Reducing personal exposure of recreational runners to airborne particles in urban environments
Presented by: Mar Viana, IDAEA-CSIC

The aim of this work was to estimate the quantitative exposure reductions achievable by monitoring personal exposure to fine particles during recreational runs. We used portable monitors to characterise personal exposure of runners across the city, and quantified the reductions achievable by modifying the routes and times of day. The exposures were then correlated with data on running habits as a function of age and gender, to identify the most exposed population groups. Portable, personal particle monitors (AirBeam2, HabitatMap.org) were provided to volunteer runners to map personal exposures to particles (PM10, PM2.5, PM1) across a 2x2 km area in central Barcelona (Spain). The time resolution of the monitors was 5 seconds, and the position and route of the runners were traced by GPS from their mobile phones, to which the sensors were connected via Bluetooth. Meteorological variability was accounted for by comparison with reference PM2.5 data from a local air quality monitoring station. When using the portable monitors to select the cleanest routes, runners were able to reduce their average exposures by 44%. When comparing exposures along the major roads (most polluted environments) and the cleanest routes (e.g., crossing park areas), average reductions of up to 71% in personal exposure to PM2.5 were achieved. We conclude that it is possible to achieve significant reductions in personal exposure (up to 71%) without significantly modifying running habits. Due to running habits, female runners tend to suffer higher exposers, mostly during morning runs.
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Feasibility study on the application of low-cost sensors for epidemiological investigations
Presented by: Miriam Chacón-Mateos, University of Stuttgart

The World Health Organization has updated on its global air quality guidelines on September 2021. The new standards resulted from the findings based on the latest epidemiological studies. This update was only possible thanks to the improvement on the technologies for air monitoring and personal exposure. In this context, low-cost sensors appear as a useful tool to increase the number of participants, ensuring adequate statistical power in epidemiological studies. In order to prove the feasibility of low-cost sensors for health studies, a pilot study with patients suffering from COPD (chronic obstructive pulmonary disease) or Asthma was carried out in Stuttgart (Germany) in cooperation with University Hospital Charité in Berlin and an outpatient pulmonary practice in Stuttgart. For this purpose, two different low-cost sensor boxes were designed to measure particulate matter, nitrogen dioxide, temperature, and relative humidity inside the houses as well as outside in the vicinity of the buildings where the patients live and spend most of the time during a 30 days time period. To avoid the effect of the hygroscopic growth, a low-cost dryer was included in the outdoor boxes. Participants completed spirometry, a questionnaire assessing respiratory symptoms and a protocol of activities at a daily basis. In this talk we will focus on technical details of the methodology used in the pilot project considering the calibration of the sensors, the validation of the measurement results and the data post-processing needed to get a useful data set for carrying out an epidemiological study. Furthermore, some results will be presented and an evaluation approach for quality assurance and the possibilities and limits of low-cost sensors for epidemiological studies will be discussed.
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Advancing personal air pollution exposure for pregnancy studies using air sensors
Presented by: Yisi Liu, University of Southern California

Background:Epidemiological studies typically assign ambient exposures to air pollution at participants’ residential location as the surrogate of personal exposures. Ignoring time-activity and mobility patterns introduces exposure measurement error, especially for pregnant woman, whose mobility may change during pregnancy and early postpartum. Methods:We conducted 4-day continuous,personal PM2.5 and geolocation monitoring in 62 pregnant, Hispanic women enrolled in the MADRES cohort during the 1st and 3rd trimester and 4-6 months postpartum using the RTI MicroPEM v3.2A and our madresGPS app, respectively. Ambient PM2.5 exposure was estimated at each participant’s residential location using inverse distance squared weighted interpolation from regulatory air monitors. We compared sub-daily level of personal and ambient PM2.5 exposure and examined variation of exposure measurement error (i.e., the absolute difference between personal and ambient PM2.5 exposure) over time using mixed effect models. Results:A total of 478 person-days were measured with a mean (SD) daily personal PM2.5 exposure of 15.5 (18.3) µg/m3. The exposure measurement error decreased 0.53 (95% CI: 0.02, 1.04) µg/m3in the 3rd trimester, but increased 1.94 (95% CI: 1.27, 2.61) µg/m34-6 months postpartum as compared to the 1st trimester. Exposure measurement error was larger in winter (2.69 µg/m3, 95% CI: 2.03, 3.35) and on weekends (2.06 µg/m3, 95% CI: 1.68, 2.44). In addition, exposure error was larger when women visited commercial and services locations (2.13 µg/m3, 95% CI: 1.35, 2.91), but lower when they took pedestrian trips as compared to home residence (-3.61 µg/m3, 95% CI: -5.20, -2.01). Conclusion:Our findings highlight the discrepancy in personal exposure measurement as compared to the typically used ambient exposure at residential location. The results suggest the importance of considering time-activity and mobility in exposure assessment in pregnancy studies.
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Development and evaluation of a Low cost TVOC sensor system for indoor and workplace exposure assessment.
Presented by: Alan Rossner, Clarkson University, The Institute for a Sustainable Environment (Poster Presentation)

While VOCs are a broad array of compounds, health concerns with chronic exposure to compounds such as aromatics (BTEX) and chlorinated hydrocarbons are an ongoing concern in many indoor air environments. Indoor air concentrations of pollutants are often higher for TVOCs, CO and CO2 than outdoor concentrations. While established methods exist to collect time weighted average samples, future exposure assessment strategies should employ monitoring of VOCs with higher spatial and temporal resolution, even though low cost sensors will likely have a reduced data quality. The aim of this study is to evaluate three low cost TVOC sensors (Adafruit SGP 30, AMS 811 and AMS-MLV –P2) along with CO and CO2 sensors in chamber studies and field studies under a series of environmental conditions and concentrations. The chamber study design included exposing the sensors to 5 different VOCs at three different humidity levels (30, 50, and 80% RH), then three different temperatures (15, 22 and 30 oC). The performance of the low cost sensors was compared to both more expensive sensors and a GC/MS. To date, the results vary depending upon the type of sensor and type of VOC and to a lesser extent the environmental conditions of the chamber. In low relative humidity environments (<20%) there was little to no sensor response at known concentrations of each VOCs. In addition, responsiveness of the sensors has been observed to be chemical specific, with higher output signal in some VOCs (e.g. Toluene vs Trichloroethylene), when tested in known concentrations. Inter sensor variability was extensive, typically varying 6-7 times for each VOC. The chamber studies allow for a validation of performance, but also as a calibration of the different sensors. While laboratory testing is providing the performance in a controlled environment, field testing (in progress) will provide an assessment of the performance of each sampler under conditions similar to actual use.
Mobile Sensing: A Quick-Start Guide to Equipping Vehicles with Air Quality Sensors
Presented by: Berj Der Boghossian, South Coast Air Quality Management District (Poster Presentation)

Air quality sensors are becoming pivotal for developing new stationary networks while also expanding existing networks and advancing regional spatiotemporal mapping of air quality. In addition to fixed governmental air monitoring station networks, these stationary networks allow for large areas of unmonitored air quality to exist. Mobile monitoring is becoming an increasingly useful approach to collect useful air quality information in these unmonitored areas. California State legislation (i.e., Assembly Bill 617) requires air quality monitoring in communities heavily impacted by nearby air pollution sources. The Air Quality Sensor Performance Evaluation Center (AQ-SPEC) at the South Coast Air Quality Management District has been developing mobile platform vehicles equipped with regulatory-/research-grade analyzers and consumer-grade sensors. The AQ-SPEC scope of the mobile platform development was centralized on creating a Mobile Sensor Performance Evaluation Protocol from which new and innovative methods of equipping vehicles with sensors emerged. A standardized “quick-start guide” to equipping a vehicle with sensors to properly sample and measure air pollutants while on a mobile platform was created to aid community scientists to conduct air monitoring studies at the neighborhood level to better understand daily personal exposure to air pollution on their own.
Using air quality sensors to better quantify — and fight back against — the negative health impacts of exposure to wildfire smoke
Presented by: Ryan Higgins, Clarity (Poster Presentation)

A growing body of scientific research is finding that the air pollution resulting from wildfire smoke carries deadly health impacts far beyond the heat and flames of the fires themselves. These public health impacts represent one of the greatest societal costs of destructive, uncontrolled wildfire — but to date, these costs have not been effectively measured or communicated to decision-makers or communities, largely due to a lack of sufficiently granular air quality data.

Blue Forest and partners are working to better quantify the air quality-related public health costs of wildfire smoke and more explicitly connect forest restoration work that reduces the risk of severe wildfire with these costs. To support this effort, Blue Forest and the California Council on Science and Technology (CCST) were recently awarded an Innovative Finance for National Forests Grant (IFNF) to develop a cost-benefit model of reduced wildfire smoke impacts with forest management.

As part of the IFNF award and improved air quality monitoring, Blue Forest has installed air quality sensors at sites in fire-prone areas across Northern California. The sensors are part of a broader network being deployed by the Feather River Air Quality Management District (FRAMQD), which will allow the District to better document how smoke is impacting communities in the area — as well as to be more proactive in providing alerts and advisories to the public when needed.

Presenters will discuss how air quality sensors are being used to study the health impacts of wildfire smoke at a more granular level than previously possible — and to develop a cost-benefit model of reduced wildfire smoke impacts with forest management.
Comparison of Low-Cost Pollution Sensors Against Industrial Mining Dusts in a Calm-Air Aerosol Chamber
Presented by: Justin Patts, CDC / NIOSH (Poster Presentation)

Low-cost dust monitors (LCDM), developed and marketed for air pollution monitoring are being implemented in communities to help understand the local and even hyper-local air quality. While prices vary, most of these integrated sensing packages cost less than $300 USD. In mining environments, area and wearable dust monitors have been around for decades but still average around $5000 per unit and are cost-prohibitive to deploy in sufficient quantity to enable a rich, real time understanding of mine dust levels. The objective of this study is to determine the potential suitability of low-cost dust sensors for applications within the mining industry. To accomplish this, calm-air aerosol chamber testing was conducted comparing the time-integrated average output of low-cost dust sensors to reference measurements while exposed to dust types commonly found in the metal/non-metal mining sector. Eight low-cost dust monitors and one industrial wearable dust monitor were simultaneously exposed to five different types of dust each on a different day. To quantify the performance of each sensor versus the reference we performed a first order least squares regression and report both the R2 as well as the correction factor (slope). The mean R2 of the LCDM responses relative to the reference standard was 87.6 and 74.0 for the AM520 (statistically significant at p <0.005). The mean correction factor was 4.04 for all the LCDM and 1.9 for the AM520 across all dust types (p < 0.001). The data collected from the low-cost dust sensors show that they are capable under these conditions of predicting the concentrations of dust in a dynamic range, and future studies should further explore their capability to operate in real-world mining scenarios.
High-resolution air quality data for exposure management
Presented by: Karim Tarraf, Hawa Dawa GmbH (Poster Presentation)

Given the increasing urgency of air pollution-related diseases and, on the other hand, the advances made in air quality measurements and data science, this paper introduces a study on the use of air quality management for an exposure reduction of risk groups.

The study was done for the Belfast Department of Health as part of a SBRI project aimed at conceptualising and developing digital solutions that utilise high-resolution air pollution data to guide urban planning, health service developments, and self-management of citizens and patients with risk factors. On the city level, the focus was on prioritising measures to minimise risk, separating unavoidable pollution and people at risk, and on mobility management. On the individual level, the focus was on avoiding routes with high pollution and planning activities at low-exposure times and locations.

The core of the study was the correlation of high-resolution air quality data with demographic data, health (prescription) data and various points of interest categories. Based on these analytics, dashboards for urban planning and city-level health management were set up as well as a mobile application for risk group self-management.

The study showed that high-resolution air pollution data combined with other data sources such as population and health data allow us to understand where people with different risk profiles are exposed to air pollution.

This information can provide a basis for quantifying the benefits of different interventions regarding the health risks of air pollution. Various tools utilising high-resolution air pollution data can support exposure management on an individual or city level.

An air quality management approach focused on exposure reduction of those at risk can provide a basis for effectively prioritising different, potentially dynamic intervention choices.
Zooming in to zoom out: What we can learn about health risks from hyper local measurements
Presented by: Kristy Richardson, Colorado Department of Public Health and Environment (Poster Presentation)

Understanding the impacts of air pollutants on human health remains a challenge of both spatial and temporal scales. While air regulations have historically focused on regional scales, we increasingly recognize the importance of zooming in to finer scales to meet people where they live and breathe. At the Colorado Department of Public Health and Environment, we use multiple technologies to get data representative of community exposures. This includes a range of spatial scales, from low cost photoionization detectors deployed in community members’ yards to high-tech mobile vans with remote-sensing equipment. Each type of data brings unique opportunities and challenges. We will summarize our approach to assessing health risks based on these different data sources. Similar challenges exist in traditional approaches to understanding health risks, which often had a zoomed out view, considering more serious outcomes and long-term exposures. Based on what we hear from people living with concerns about air pollutants, we know we need to zoom in to better understand short-term health impacts from very short exposures. These health impacts, like headaches and nosebleeds, are not often well represented in toxicity studies. We will discuss our approach to collecting meaningful data given these constraints and identify opportunities for future improvements. Finally we’ll reflect on how to zoom out, while incorporating the lessons learned at the more local scale to create better policy and health outcomes.
Winter and Spring Air Quality in Amager, DK with focus on spatial and timely distribution of pollutants.
Presented by: Maria del Pilar Contreras, Denmark Technical University (Poster Presentation)

Air pollution is a serious problem affecting people’s health and polluting the environment. Establishing Mobile measurements can get a representative sample of the air pollutants present in the area since they are covering spatial mapping distribution of Particulate Matter and Ultrafine Particles (PM and UFP’s).

The aim of this study was to collect and analyze outdoor Particle Matter and Ultrafine Particle concentration as a result of the use of wood stoves in a residential area in Amager, Denmark by using a P-Trak (Ultrafine Particle Counter) and PAM (Personal Air Monitor).

I measured PM and UFP’S by riding a bike and following the same route and at the same time twice a day, and three days a week over the course of 4 months. In addition, some measurements were made in the backyard of a residential house located in Amager, where the backyard is directly exposed to PM and UFP’s as a consequence of the use of wood stoves by a nearby source.

During the study hotspots were established, and measurements at these hotspots showed high values for PM2.5126 μg/m3, and PM10187 μg/m3(Particulate Matter) that are higher than the limit values established by the WHO (World Health Organization).

The data analyzed displayed that residential wood stoves generated high amounts of ultrafine particles and Particulate Matter, and inhabitants used wood stoves frequently in cold weather. Besides, the need for air measurement stations close to sources of ultrafine particles is necessary in order to show a more representative sample of the air quality. Traffic pollution influenced the number of ultrafine particles collected during the bike trip, but not in the same degree as wood stoves. Other factors such as weather conditions, windspeed and temperature influence the number of Particulate Matters perceived in the air as well.

This project was supervisor by Dr. Teis Nørgaard Mikkelsen and Dr. Kåre Press-Kristensen.

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Sensor Networks: From nuts and bolts to real-world impacts

Large sensor networks can offer powerful new insights about air quality for environmental justice, air quality management, atmospheric science, and community engagement. This session aims to cover the intersection between operational considerations (how to collect and calibrate robust, high quality and reliable data) and real-world impacts (what powerful things can you accomplish with networks of sensors).

Lead Session Chairs:

Karoline Barkjohn, US EPA, Josh Apte, UC Berkeley, Jessa Ellenburg, 2B Technologies

Presentations:

Hyper-Local Air Quality Sensor Network in the Town of Cheverly, MD
Presented by: Karen Moe, Green Infrastructure Committee

Concerned about air quality in its immediate vicinity, the Town of Cheverly partnered with the Community Engagement, Environmental Justice, and Health Laboratory, Maryland Institute for Applied Environmental Health at the University of Maryland to create a hyper-local air monitoring network of sensors. Results are needed to provide a baseline for gauging the immediate and cumulative effects of additional industrial development and road traffic.Equally important, is the need to address community concerns regarding pubic health impacts of local air pollution in industrial areas with heavy truck traffic, and provide guidance regarding exposure risks. By April 2021, a network of 21 commercial PurpleAir PA-II-SD air quality sensors were installed in the town (an area of 1.32 sq. miles). In July, the Maryland Dept. of Environment joined the partnership to pilot a local air monitoring project to conduct the Cheverly Targeted Inspection Initiative. The steps taken to implement the network working with host volunteers, and engaging the community to address growing concerns about cumulative impacts of air pollution on public health are discussed. How to present meaningful data is a key goal of the partnership moving forward from data collection to providing actionable air quality information for Town managers and for residents.
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Community Monitoring: Using Citizen Science, Technical Expertise, & Lived Experiences for Real World Impacts
Presented by: Luis Olmedo, Comite Civico del Valle

Good air quality is a fundamental right that everyone should have. But that is not the case, especially throughout communities of color & low-income neighborhoods where industry pollutes because it’s cheaper for them to do so, the environment is not well taken care of, and regulators have struggled to fulfill their role to improve air quality. Because of this inaction, many community groups such as ours, Comite Civico del Valle, have initiated local community air-quality monitoring networks (CAMNs) to fill data gaps the government has & provide a public service to disadvantaged communities. Our IVAN Air CAMN was established using the principles of citizen science, participatory community input, technical expertise, and lived experiences from community advisors. In the last half-decade, the network has collected data and pushed for action, either through legislation (California’s AB617) or community programs (Air Notification Program). As the principles are what make the IVAN network a valuable community resource, our technological approach is open to innovation, with methane monitors now in the network and more innovation on the horizon. The network will soon deploy mobile monitoring & maintenance operations with a Zero Emissions Vehicle, pushing towards reducing emissions as we manage our more than 60 stationary particulate matter monitoring sites throughout the Imperial and Eastern Coachella Valleys. The approach has led us to create one of the largest and sustainable community monitoring networks in California.
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Using a Remote Calibration Technique to Improve Data Quality for Large Networks of Particulate Matter Sensors
Presented by: Ashley Collier-Oxandale, South Coast Air Quality Management District

A continual challenge in the long-term deployment and management of large sensor networks lie with data quality and calibration. For example, calibration procedures may require physical co-locations – limiting the scale of deployments. Alternatively, global correction equations may not account for changes driven by PM source type, seasonal influences, or drift. Using remote calibration techniques may offer an alternative approach that supports the deployment and maintenance of large sensor networks. Here we applied a technique, entitled MOMA (MOment MAtching), to PM2.5data from PurpleAir PA-II sensors. MOMA was developed by Aeroqual Ltd and is currently being explored under a collaboration between the South Coast Air Quality Management District's Air Quality Sensor Performance Evaluation Center (AQ-SPEC) Program and Aeroqual Ltd. Under this collaboration, the MOMA approach is being piloted with different sensor networks to determine its suitability as a sensor agnostic tool for improving data quality and enhancing the benefits of large-scale networks. This approach involves identifying suitable proxy sites from regulatory monitoring networks. Then, at regular intervals, or when drift is detected via a drift detection algorithm, appropriate calibration periods are determined and used to calculate gains and offsets for each sensor, allowing sensors to remain in the field through calibration. In this presentation, we will examine the efficacy of this approach for sensors co-located at regulatory air monitoring stations and deployed over multiple years (2017-2020), in addition to sharing interesting observations from corrected data. Different failure modes and how well this approach addresses these cases will also be discussed. In terms of implementation, we will illustrate how an open-source R package, AirSensor, can be leveraged to streamline data access and Quality Control (QC) processing, thus enabling the use of this calibration technique with sensor networks.
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Calibration of citizen sensor networks using a mobile air monitoring platform
Presented by: Brian LaFranchi, Aclima Inc.

Citizen air sensors are typically low cost, easy to install, and can deliver valuable data at high temporal resolution from many locations at once. However, generating high quality data from these air sensors can be challenging. Sensor accuracy and precision is often unclear out-of-the-box and can also degrade over time. Even with collocation alongside reference instrumentation prior to deployment, sensor performance is strongly dependent on the conditions during collocation and after deployment. This presentation will focus on results from a field study conducted by scientists and operational staff at Aclima and the California Air Resources Board in Sacramento, CA between October 2020 and February 2021 involving a heterogeneous network of regulatory stationary sites, mobile monitoring, and a dense network of various types of air sensors. The study demonstrates the feasibility of using a mobile air monitoring platform as a calibration transfer method between a single stationary reference site and a network of air sensors measuring particulate matter (PM2.5), nitrogen dioxide (NO2), and/or ozone (O3). The presentation will incorporate the results and lessons learned from the study towards development of a methodology that is broadly applicable to a wide variety of stationary sensor networks.
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Operationalizing air sensor data for EH&S at the nation’s second-largest school district
Presented by: Carlos Torres, Los Angeles Unified School District

The increased frequency and intensity of wildfires pose risks for sensitive groups such as children at schools. Exposure to outdoor air pollution can inhibit cognitive development and negatively impact academic achievement — studies have found air pollution levels to be a statistically significant predictor of student performance. Access to real-time air quality data is critical in order for schools and school district staff to make informed decisions that protect the health and safety of our students and school communities. In 2020, the Los Angeles Unified School District (LAUSD) completed a pilot study with 22 air quality sensors placed at schools most impacted by episodes of poor air quality. As of December 2021, LAUSD has procured and installed more than 200 air quality sensors to provide a uniform network across their entire school district. The district was divided into grid squares of approximately 1.6 miles, with a sensor placed in each grid — resulting in one of the largest and densest air sensor networks in the world. This first-of-its-kind network provides real-time monitoring across more than 200 school campuses, yielding reliable, local air quality data for over 1,000 schools and offices and 700,000 students. The sensors will be used during emergency events such as wildfires and poor air quality episodes — which occur frequently in Los Angeles — to determine what actions need to be taken at the school level. Potential actions include keeping students indoors, restricting outdoor activities, attending to students with asthma or other respiratory/cardiovascular illness — and even school closures in the most extreme conditions. Presenters from LAUSD, the Coalition for Clean Air, and Clarity Movement will discuss how this air sensor network has allowed LAUSD to operationalize air quality in their school operations, as well as the intriguing possibilities this network holds for research related to air quality and health in Los Angeles.
(View Presentation PDF)
Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information
Presented by: Ellen Considine, Department of Biostatistics, Harvard T.H. Chan School of Public Health

Reference monitoring networks are sparse. Low cost sensors (LCS) are increasingly being used by the public to fill the gaps in air quality monitoring. In this project, we investigate different factors that influence the accuracy of information when individuals interpret measurements from the nearest LCS or monitor as their air pollution exposure. This has implications for the integration of LCS data into real-time air quality reporting platforms. Three main components of error (between what is reported and experienced) in real-time air quality reporting are distance to the nearest monitor or sensor, local variability in air quality, and sensor (device) measurement error. We examine these components under various hypothetical LCS placement strategies using simulations based on data from California. For these simulations, we assume that the “true” PM2.5 in each 1x1km grid is obtained from an existing PM2.5 prediction model and that PM2.5 is measured without error at the fixed locations of EPA reference monitors. Then, we select locations to have LCS and induce sensor measurement error at those locations. By varying the amount and type of sensor measurement error, we can observe changes in accuracy of exposure information when LCS are integrated. We evaluate the accuracy of daily air quality information available from individuals’ nearest monitor or sensor with respect to absolute errors and misclassifications of the Air Quality Index, stratified by Census tract-level socioeconomic and demographic characteristics. Comparing air quality information accuracy under different hypothetical LCS placement strategies (at schools, near major roads, and in environmentally and socioeconomically marginalized census tracts) and different numbers of LCS also illustrates how real-time air quality reporting could be improved (or, in some cases, worsened) by using LCS, both for the population overall and for marginalized communities specifically.
(View Presentation PDF)
High density sensor network for air quality monitoring and source identification in Shanghai Ports
Presented by: Mei Han, The Hong Kong University of Science and Technology

The sensor network is increasingly known as an effective way to explore the characteristics of local emissions with greater spatiotemporal details. Here we present how high-density air and meteorological sensor network provides unprecedented insights into the patterns of pollutant emissions at two ports of Shanghai during the China International Import Expo (CIIE). The diurnal variations of pollutants reasonably indicate the non-local emissions have little impact on the air quality in the port area. The nitrogen oxides (NOx) is identified as the major local pollutant of the ports, while there is no obvious sources of CO and particulate pollution have been found in this area. The ports area is divided into three types of areas for NOx pollution, including the source area, susceptible area and less affected operations and berth area. The roads in the source area are identified as the main local source because the trucks in this area emit a large amount of NOx, which impact on the surrounding susceptible area and the operation and berth area under different wind conditions. In addition, the emission controls during the CIIE have sufficiently improved the air quality of ports area and reduced its negative impact on the surrounding environment. This study reveals the sensor network can accurately locate the key pollution sources and their variations in a complex emission area by analyzing the heterogeneity of the monitoring data, with strong implications for designing effective control measures.

*Author did not provide PPT for public distribution, please contact Han Mei at hmei@connect.ust.hk with questions
Smart and Trustworthy AIR quality network (STAIR): practical considerations in network design and community outreach
Presented by: Haofei Yu, UC Fresno

The Smart and Trustworthy AIR quality network (STAIR) is a low-cost sensor network designed to measure ambient concentrations of particulate matter (PM) and carbon dioxide (CO2) at up to 100 locations across the Orlando, FL area. Some of primary purposes of this highly interdisciplinary project are to design a cyber-secured sensor network that can provide reliable and trustworthy data, and to utilize this network as a tool to facilitate the communications between citizens and the government and to enhance trust in governance. Monitor deployment is still underway and we anticipate the network to be operational in early 2022. Here, we will briefly discuss some of the valuable lessons we learned in community outreach, and in designing, deploying, and maintaining the STAIR network. Our low-cost sensor network has been shown to be a powerful tool in engaging citizens in ambient air quality concerns.
(View Presentation PDF)
Air quality use cases: assessing the impact of different events using air quality data from model and sensor network
Presented by: Jill Chevalier, eLichens

Road traffic is a main target source of action to improve air quality (AQ). The monitoring and evaluation of the impact of emission abatement actions on AQ is essential to better inform decision-makers. However, short-term, or localised actions remain very complex to evaluate without the implementation of significant means of modelling and measurements allowing to consider, the impact of meteorological variability on the concentration levels. High-resolution, continuous data on pollutants concentrations are needed to observe possible impacts of changes in mobility policies. However, model-only cannot reflect the reality accurately enough, as the local and short-term changes due to peculiar events might not be caught. A dense network of station is of paramount importance to catch such events. eLichens developed a mapping method to provide hourly, street-level data on urban areas. The model uses a neural network pollution model improved by fusing measurements from a network of eLos (eLichens outdoor station). eLos is a real-time recalibrated low-cost AQ station, providing hourly measurements of NO2, O3, PMs and CO2, temperature and humidity. Using this data, we assessed the impact of different events on AQ: Covid-19 lockdowns, summer fires or changes in local mobility policies. For long-lasting events, we compare the present data to the data from the previous year to get rid of the meteorological variability. We detected a drop in nitrogen dioxide concentrations in several cities during lockdowns. In Grenoble, France, the creation of a bike lane changed the traffic, creating jams. However, we evidenced a decrease in pollution levels.

*Author did not provide PPT for public distribution, please contact Jill Chevalier at jill.chevalier@elichens.com with questions
Using sensors to measure the impact of air pollution on early childhood. Lima Air Quality Network for Children Project.
Presented by: Kyara Diaz Carrasco, Municipalidad de Lima

Until 2019, the province of Lima only had 19 reference stations of air quality, this number of stations is insufficient to cover the extension of the city (the biggest city in Perú with a third of the entire country population). This lack of monitoring systems, open and systematized information conceals the problem and makes it difficult for the authorities and citizenship to take action to protect the environmental health of the most vulnerable groups. In response to this, the Metropolitan Municipality of Lima is implementing an Air Quality Monitoring Network focused on children to design and implement actions that reduce air pollution and the exposure of infants. To this end, the project will be implemented in 8 phases; the first, involves the installation of the Air Quality Network for children; the second, consists of capacity building for teachers and caregivers in the use of the air quality data; the third, seeks to bring air quality knowledge to children, while the fifth and sixth phases focus on generating research on children's exposure to air pollution with information from the network. An air quality normative focused on children will be drawn up in the seventh stage. Finally, based on the previous results, in the eighth phase, the aim is to coordinate with the institutions involved to design and implement actions for the benefit of children. Currently, the first and second phases of the project have been completed and the third phase is being implemented for affiliated institutions, training more than 200 caregivers and teachers, and around 1,400 children on air quality. These three phases are dynamic, restarting each time new institutions join the Network. This project seeks to generate sufficient data for studies of children's exposure to pollution to contribute to the improvement of air quality management, encourage greater commitment on behalf of stakeholders and guide actions to reduce exposure to adequate environmental levels.
(View Presentation PDF)
A tale of our hometown: how the low-cost sensor network helped in changing the air pollution legislation and started the fight with smog in Poland
Presented by: Marcin Szwagrzyk, Airly

Poland has one of the worst air pollutions in Europe, mostly due to individual, coal-based combustion. Cracow - a former capital city - has been known to be the most polluted city in Poland due to its unfavorable topographic location and high density of pollution sources. However, due to a lack of public awareness, this issue has not been addressed for decades. In the winter of 2017/18 there happened to be one of the biggest pollution episodes in Europe in the XXI century. In Cracow, day after day the enormous exceedances of PM’s daily norms occurred, reaching 1000% of daily WHO PM10 norms. Citizens became vividly interested in tracking the pollution levels, with whom help Airly started its mission and set up a pro bono low-cost sensor network of almost 100 sensors. The screenshots from the Airly mobile application, showing terrible air quality norms exceedances, flooded social media all over the country. Therefore, the Cracow authorities were forced to act and the ban on combustion with solid fuel was introduced within the city borders. In the following years, the Airly network in Cracow and its vicinity started to show an interesting pattern of pollution. The air quality in the city itself rapidly improved, yet in the neighbouring towns and villages, pollutants concentrations remained high. The following led to the emergence of the so-called ‘Cracow bagel’ effect. Moreover, the Airly sensor network began to expand and many other Polish cities, which were lacking proper air quality monitoring before, began to realize the scale of the problem. Some of them were found to have pollution levels much higher than Cracow itself which wasn’t known before. Data from thousands of Airly sensors, working from 2017 are now forming a fascinating image of the air pollution in recent years, which has been shaped by many factors such as topography, climate change, urbanization and legal actions. This was not possible with only a few costly official stations.

*Author did not provide PPT for public distribution, please contact Marcin Szwagrzyk at m.szwagrzyk@airly.org with questions
From CO and CO2 Measurements to Emissions Maps
Presented by: Naomi Asimow, UC Berkeley

High-density, low-cost air quality sensor networks promise insight into urban emissions and neighborhood-scale concentration gradients of pollutants. Such concentration maps can show intra-community heterogeneity of air pollutants and often reveal environmental injustice (i.e. disproportionately high concentrations of harmful pollutants in low-income communities of color). However, specific quantitative information on the emission sources of the air pollutants that drive these concentration gradients are essential to understanding and managing them. Here we use hourly carbon monoxide (CO) and carbon dioxide (CO2) measurements from The Bay area Environmental Air-quality & CO2Network (BEACO2N) to develop updated maps of urban CO and CO2 emissions in the San Francisco Bay Area and CO2emissions in Glasgow, Scotland. By coupling dense measurements with a prior emissions inventory, meteorological data, and a particle dispersion model we develop improved maps of urban emissions and an understanding of underlying processes.
(View Presentation PDF)
Evaluating the Spatial and Temporal Sensitivity of Sensor Networks to the Calibration Algorithm Applied
Presented by: Priyanka deSouza, University of Colorado Denver

Low-cost monitoring networks are increasingly being used to supplement regulatory networks and to aid in a neighborhood-scale understanding of air pollution levels. However, the variability and uncertainty inherent in the measurements from such devices presents many challenges. In order to correct the data from low-cost sensor networks, one or multiple sensor devices are usually collocated with a reference-grade monitor in an environment that is representative of the sampling conditions. This co-location time frame serves as the training period for which a data-adjustment method is developed that incorporates the sensor raw data and corrects the data to most closely match the reference-grade data. The calibration algorithm developed is then applied to measurements from the entire network of low-cost sensors. Although there has been a lot of work evaluating the efficacy of different correction algorithms, less work has been done on evaluating the sensitivity of spatial and temporal trends in the low-cost air quality monitoring network to the calibration algorithm applied. Such an evaluation is especially important as several groups are now routinely using non-linear algorithms and machine learning techniques to calibrate their low-cost sensor networks. Accordingly, this research evaluates the spatial and temporal trends of the Love My Air network in Denver comprising of ~ 40 low-cot sensors in public schools to the correction method applied. Our work allows for the development of a robust air quality management plan using the data from the Love My Air network
(View Presentation PDF)
Increasing Community Participation in Air Pollution Mitigation in Indore City, India
Presented by: Tim Dye, TD Environmental Services

India’s National Clean Air Plan has recently called for the expansion of air quality monitoring using both reference instruments and air sensors, marking a change in the country’s monitoring trajectory. As a pilot project, the U.S. Agency for International Development-funded Building Healthy Cities (BHC) project has deployed a network of 20 low-cost air quality sensors in slum and non-slum areas in Indore, India. A city with a population of 2 million, Indore has only one continuous PM2.5 reference station. This mixed-method, longitudinal study is led by BHC and was co-created with the City of Indore and Indore School of Social Work to fill in air quality monitoring gaps across the city and mobilize communities on the issue of air quality. The sensor air quality data is shared to the city’s Integrated Command and Control Center, allowing the public and decision-makers to also access the data. A unique element of this study is the Clean Air Guides – local community members that were trained to install and operate the air sensors, collect qualitative data on sources of air pollution in their neighborhoods, and lead community advocacy efforts to improve air quality. These Clean Air Guides learned about the effects of air quality on health and began interacting with residents in their communities to explain air quality concepts, identify neighborhood-specific sources of air pollution, and help advocate for addressing local air quality. This presentation will briefly review the technical logistics of setting up the network and will focus on the successes and challenges of the Clean Air Guides model. We will discuss how we are working to continuously engage the Guides, share examples of how the Guides used local air quality data to advocate for change, and show journey maps created by the Guides to document changes over time.
(View Presentation PDF)
Aires Nuevos: Driving Meaningful Air Quality Action in Latin America
Presented by: Christi Chester Schroeder, IQAir North America

Aires Nuevos is a Citizen Air Quality Network created in 2020 to promote community-based air quality data generation and communication to reduce early childhood air pollution exposure in Latin America. Based on the IQAir air quality monitoring and communication platform, Aires Nuevos has installed and published 95 low-cost air quality monitors in 28 cities in Mexico, Uruguay, Peru, Brazil, Argentina, Ecuador, Chile, and Colombia building a sensor network capable of monitoring air quality for nearly 1.5 million children under the age of 4. Aires Nuevos uses a grass-roots approach to combatting air pollution by engaging the local community and offering air pollution education using real-time data accessible for free on IQAir’s website and mobile app. In each city, working groups consisting of local public officials, university researchers, and local community members collaborate using sensor data to identify local policies that will result in limiting air pollution exposure to young children. In this presentation, the authors will discuss some of the lessons learned in building up an air quality monitoring network across multiple countries and how the air quality data generated is starting to affect local policies to better protect citizens and improve air quality in the region.
(View Presentation PDF)
Evaluation and modeling of data from low-cost air quality sensors for accurate PM2.5 estimation
Presented by: Dinushani Senarathna, Department of Mathematics, Clarkson University (Poster Presentation)

PM2.5 is a critical air quality parameter associated with many air pollution-mediated adverse health effects. In the United States, the Environmental Protection Agency (EPA) provides precise measurements of PM2.5 but a sparse distribution of EPA monitoring sites limits the availability of such data at high spatial resolution. Recent development in low-cost sensors such as PurpleAir (PA) sensors, which can be deployed at high density, show promise to overcome this challenge. However, data generated by these sensors are noisier and tend to overestimate PM2.5 relative to EPA measurements Although models can be used to improve the accuracy of PM2.5 estimates from PA sensor measurements, the validity of these models for sensors in different locations is not fully understood. In this work, we used the PA sensor network and EPA data from Cook County in the Chicago area to evaluate the robustness of correction models and their applicability for sensors in other locations. Our objectives were, (a) to investigate the impact of distance of PA sensor from EPA on model accuracy, and (b) to determine if data from multiple low-cost sensors can be used to generate more precise estimates of PM2.5. Our results demonstrated a dependence of model prediction accuracy on distance, with the accuracy reduced significantly for distances > 30 km from the PA sensor site. Moreover, a higher prediction accuracy was observed with models built using multiple PA sensors (R2 = 0.50 ~ 0.70, RMSE = 2.5 ~ 3.0) than models built with single PA sensor (R2 = 0.30 ~ 0.60, RMSE = 1.9 ~ 3.5), although the improvements were minimal when data from more than 3 sensors were used. Our results indicate that the accuracy of PM2.5 estimation from low-cost sensor data can be improved by considering the distance and incorporating multiple sensors in the model.
Sensor networks to evaluate local air quality impacts from changing traffic scenarios near an elementary school
Presented by: Jelle Hofman, Flemish Institute for Technological Research (VITO) (Poster Presentation)

To improve local air quality, safety and wellbeing near an elementary school, the municipality of Kampenhout (Belgium) implemented traffic restriction measures during school hours. In an attempt to evaluate the resulting air quality impact, two commercially available sensor boxes (Airly and Kunak) were applied for a 3-month period (12/2/2021-14/5/2021) at three locations near the school: two traffic locations (one at school entrance and one outside traffic restriction area) and one background location. Before and after deployment, the sensor performance was evaluated next to an AQMS (Air Quality Monitoring Station). From the co-location measurements, sensors showed low inter-sensor variability and good correlation with reference measurements, but sometimes low accuracy. After a linear slope correction, good sensor performance was achieved for PM1 (R² = 0.82-0.87, MAE = 2.16-2.33 µg m-3), NO (R² = 0.88-0.98, MAE = 1.20-2.77 µg m-3)and NO2(R² = 0.92-0.95, MAE = 3.38-4.67 µg m-3). Different traffic scenarios were tested during deployment, all lasting for about 3 weeks; business as usual (baseline), full road closure (schoolstraat) and single lane closure preventing cut-through traffic (knip), while a holiday period was included as well. Largest absolute concentration differences between the considered locations was observed for NO, followed by NO2 and PM1. From the comparison with simultaneous background measurements, we quantified the local road traffic contribution at the school location in the order NO>>NO2>PM1. After normalizing for the fluctuating background concentration, the implemented traffic scenarios resulted in significant concentration reductions up to 60% for NO and 89% for NO2, while no observable effect was obtained for PM1. This case study, therefore, demonstrates that air quality sensors can quantify local impacts from (even short-termed) traffic measures and can empower local governments to implement new air quality management plans.
Building an aerosol sensing sensor network and inspiring citizen scientists
Presented by: Kerry Kelly, University of Utah

Since 2018, the AQ&U network has been collecting aerosol measurements from a growing number of low-cost sensors (more than 200) in the Salt Lake Valley. AQ&U integrates low-cost, research-grade, and reference measurements with robust data screening, event-specific calibrations, and a Gaussian Process model to understand neighborhood-scale PM2.5 concentrations as well as uncertainty estimates. It provides near-real time visualizations of PM2.5 concentration along with uncertainty estimates through a public-facing website. AQ&U also provides a rich framework for citizen science. Here, we discuss AQ&U’s community engagement strategies, particularly those to encourage participation of under-served communities, and highlight two of our most successful efforts to engage students as citizen scientists. The first effort centers around a hands-on activity to build and test an aerosol sensor from LegosTM and simple microelectronics. It emphasizes the underlying principles of aerosol light scattering. This has been our college’s most requested outreach activity and has been successfully demonstrated at hundreds of high-school classrooms, numerous STEM tabling events, and the National Science and Engineering Fair. The second effort focuses on an interactive, team-based teaching module using local real-world measurements. This activity's goal is to engage students in generating and testing hypotheses while also encouraging citizen scientists to use real-world air quality data for their own interests, such as exploration, science fair projects, or environmental oversight. This second activity has been a successful strategy for virtual learning. Finally, we discuss lessons learned. COI Declaration: Drs. Kelly, Gaillardon, and Whitaker have a financial interest in Tetrad Sensor Network Solutions, LLC, which commercializes environmental measurement technologies.
(View Presentation PDF)
Citizen Science Monitoring Of Air Pollution From Residential Wood Burning Using Low-Cost Sensors
Presented by: Nuria Castell, NILU – Norwegian Institute for Air Research

Conventional monitoring systems such as reference stations can provide accurate and reliable pollution data in the urban environment, but only in single points. In this study, we investigated how low-cost sensor technologies mounted at citizens’ houses can contribute to fill existing gaps in pollution monitoring. During winter 2021 we engaged with 20 residents in Kristiansand, Norway to monitor air pollution using low-cost sensors. For this study we employed Airly sensor units monitoring PM10, PM2.5, and PM1. The selection of the sensor systems was based on their usability and reliability. The Airly sensor systems integrate a Plantower PMS5003 and showed a correlation of 0.6 against FIDAS optical reference-equivalent for PM2.5 and of 0.8-0.9 for PM1 hourly observations. Additionally, a Kleinfiltergerät providing measurement of fine particle mass concentration (PM2.5) was installed in the garden of one of the houses showing R2=0.8 for PM2.5 daily averages. The diurnal pattern of the data collected with the sensors clearly showed two peaks, one in the morning, around 7-8 and one in the evening around 17-20, both likely associated with residential wood burning. Those peaks were not picked up by the reference station in Kristiansand, that is located close to a road. However, they were very clear, particularly in the afternoon and in the cooler months in the residential areas where wood burning is used for residential heating. Pollution levels from PM2.5 where especially high in one of the neighbourhoods that is located is a small valley in the northern part of Kristiansand. The study shows that citizen science data, when data quality routines are in place, can contribute to in-situ environmental monitoring in urban environments, providing measurements than can help science and authorities in locations where we do not have reference stations. This work was partly supported by NordForsk (http://nordicpath.nilu.no).
(View Presentation PDF)
Atlanta Rail and Port Sensor Project: An Air Quality Pilot Study
Presented by: Ryan Brown, US EPA Region 4 (Poster Presentation)

The US Environmental Protection Agency (EPA) Region 4 and Georgia Environmental Protection Division (EPD) conducted a pilot air quality study around the largest railyard in Atlanta, Georgia using lower-cost air sensor technology. The objectives of the Atlanta Rail and Port Sensor (RAPS) project were to evaluate air quality near the railyard and to evaluate the utility of air sensor technologies in understanding near-source air quality patterns. Ten PurpleAir particulate matter sensors and an Aethlabs microaethalometer black carbon sensor were used to collect over a year of air quality data at various distances and directions around the railyard. Sensor PM2.5 measurement precision and accuracy were evaluated at an Atlanta area regulatory air monitoring site by collocating the sensors with regulatory PM2.5 measurements before and after deployment near the railyard. After data adjustment using EPA’s national correction equation and removal of data that did not meet project quality assurance indicators, sensor PM2.5 hourly measurement data varied approximately 10 to 20 percent compared sensor to sensor (coefficient of variation) and approximately 25 to 50 percent compared to higher quality, regulatory monitoring measurements (normalized root mean squared error). The collocated sensor metrics showed more variance during the collocation period after deployment primarily due to humidity sensor drift on a few sensors. EPA’s national correction equation includes a humidity factor. Overall, in combination with local meteorological data, sensor measurements were of sufficient quality to detect and quantify some patterns in hourly PM2.5 concentrations near the railyard, due to good sensor to sensor precision. This presentation will also share lessons learned and suggestions for study design, field data collection, sensor data processing, quality assurance screening, and analysis for similar air sensor studies.
The AirHeritage Hierarchical Network: Sensing, Calibration, Deployment strategies for fixed, mobile air quality monitoring and modeling in urban scapes.
Presented by: Saverio De Vito, ENEA

The AirHeritage project aims to improve citizens and administrators AQ knowledge in small and medium cities in Italy, empowering awareness, behavioral change and adherence to participated remediaton policies. Funded by EU Urban Innovative Action framework, the project involved the city of Portici, located 7km south of Naples and partners from academia, research agencies, environmental protection, associations and SMEs. It deployed a hierarchical network relying of fixed and mobile stations, involving regulatory grade analyzers and low cost sensors to map citizens exposure through crowdsensing and long term fixed monitoring. Concentration of NO2, O3, CO, PM1 – 2.5 – 10. Monitored data are shared and integrated in ultra high resolution (20m) exposure maps. Data from fixed stations, both low cost and regulatory grade, are assimilated in 3D chemical transport model using weather data and 3D urban scape data for now- and forecasting purposes. 7 Fixed and 30 Mobile MONICA™ platforms, relying on electrochemical sensors, are calibrated twice a year and used in 4 opportunistic measurement campaigns. Platforms are co-located with local regulatory grade analyzer for at least 3 weeks each. Colocation data driven calibration algorithms are derived, validated and executed at the edge on citizens smartphone through an Android App resulting in real time exposure estimations. In each session, routes are color coded using European Air Quality Index allowing to assess exposure on the move and selecting low exposure paths. The same result is available, using modeled air quality maps, to citizens which do not received MONICA analyzers. Shared data are fused through IDW and by computing measurements median on a predefined grid discarding low populated cells. This contribution will show the results of 6 colocation driven calibrations and 4 mobile crowdsensing campaigns in the project, exploring architectural (Sensors and IoT management), accuracy, engagement and communication issues.
(View Presentation PDF)
Community engagement through text-based communication with air quality sensors
Presented by: Surya Venkatesh Dhulipala, University of British Columbia

We present a novel mode of communication for disseminating air quality data in a community setting. We fitted our low-cost air quality sensors (LCAQS) with placards containing QR codes, that when scanned, begin a friendly text message conversation with users. For this study, we partnered with Hello Lamp Post, a startup based in UK. Overall, we present 6 months’ worth of user engagement data (mostly students). To quantify the air quality on University of British Columbia (UBC) campus, we installed a network of 8 LCAQS across UBC campus in June 2021 to measure air pollutant concentrations at different traffic intersections. At the same locations, we installed placards with QR codes for community engagement. Within a two-month period (August – September 2021), a total of 624 interactions and 190 conversations were recorded. 93% of users said they would like to see air quality sensors around their home and near major traffic intersections. A further 50% of users complained about current modes of communication around air pollution exposure on UBC campus. Other feedback included – “I have no idea where to look”, “make more accessible” and “I have not heard much about air pollution exposure on campus”. Often, local communities are unable to interpret publicly available data from city-wide or province-wide air quality monitoring stations and make informed decisions. We directly asked users (mostly students) about their preferred formats for reading air quality data, perceptions about air quality on campus and their satisfaction levels about current modes of communication. Our user engagement data can also be used to make localized decisions – one community may prefer dense sensor networks for monitoring air quality whereas another may be hesitant. Other possible applications of user-engagement data will also be discussed in this presentation.

*Author did not provide PPT for public distribution, please contact Surya Venkatesh Dhulipala at surya.dhulipala@ubc.ca with questions
Idling vehicle detection using a low-cost air quality sensor package
Presented by: Tristalee Mangin, University of Utah (Poster Presentation)

Concentrated vehicle engine idling can cause microenvironments of poor air quality, and areas with high idling, such as schools or hospitals, are frequented by individuals at increased risk for negative impacts from poor air pollution. Posted signage is an example of a mitigation strategy used to reduce engine idling. Is it possible to improve adherence to messaging using dynamic feedback about air quality metrics and the percent of idling vehicles? The project aims to develop a SmartAir system that integrates air quality sensors and idling vehicle detection to provide dynamic feedback to drivers. Low-cost sensors are an attractive option for detecting idling vehicles because a small sensor network is affordable and would have a small physical footprint.

This study develops, tests, and calibrates a low-cost air quality sensor package, hereafter named the SmartAir sensor node, to identify idling vehicles in real-time. Each SmartAir sensor node measures five gaseous combustion products: carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), and volatile organic compounds (VOCs). Individual low-cost sensors were laboratory tested to evaluate sensor response to concentration, temperature, and relative humidity and compare intra-sensor variability among 12 sensors of the same make and model for each gaseous species. The temperature and relative humidity ranged from 0°C–40°C and 0%–80% respectively, and concentration ranged from 0–5000 ppm CO2, 0–10 ppm CO, 0–5 ppm NO, 0–5 ppm NO2, and 0–1000 ppb VOC. Finally, five SmartAir sensor nodes were used to measure vehicle emissions from controlled field tests that included a variety of driver behaviors, such as stopping time, engine idling decisions, and distance from the sensor. This presentation will discuss whether calibration strategies can sufficiently address intra-sensor variability and present the computational methods used to detect idling vehicles in the field.
Spatiotemporal analysis of PM2.5 using data from Environmental Protection Agency (EPA) and low-cost sensor networks
Presented by: Vijay Kumar, Department of Mathematics, Clarkson University (Poster Presentation)

PM2.5has been linked to numerous pollution-mediated adverse health effects and their monitoring is key for taking preventative and mitigative measures. Accurate measurements of PM2.5concentrations are available at EPA sites, but such data lacks spatial resolution due to a limited number of monitoring locations. In recent years the deployment of low-cost sensor networks has opened up the possibility of acquiring air quality data at a high spatiotemporal resolution. However, the sensitivity, noise, and accuracy of data acquired by low-cost sensors remain a concern. Here, we studied PM2.5measurements made from EPA and Purple Air (PA) sensor networks in the Chicago area to understand the parameters influencing the performance characteristics of the low-cost sensor network. We decomposed the PM2.5time series data into short-term, seasonal, and long-term components using the Kolmogorov–Zurbenko filter. We then extracted different frequency signals of PM2.5data for each of the filtered components. Our analysis shows that the meteorological variables such as temperature, humidity, wind speed, and wind direction impact the long-term trends of PM2.5 but do not have any influence on the short-term component. Furthermore, the PA sensor data capture long-term variations in the PM2.5data with reasonable accuracy, while the short-term variations (<12 hours) are less clear in comparison to the EPA data. We hypothesize that the low-cost sensor networks may have different detection sensitivity to aerosol from different sources and hence care must be taken in their use for evaluating the impact of air quality mitigation policies.

View Session 1C Recording on Youtube

View Session 2C Recording on Youtube

Standard, Supplemental and Informational Monitoring

As regions move to more hybrid air monitoring networks which mix sensors, reference monitors, and other instrumentation, a better understanding of how to manage such complex networks is a high priority for many agencies. This session will cover topics pertaining to data quality, disparate data aggregation and harmonization, and methods/projects which showcase different approaches to the management of hybrid air quality networks.

Lead Session Chairs:

Colin Barrette, US EPA, Michael Ogletree, City & County of Denver, Dept. of Public Health & Environment, Vasileios Papapostolou, South Coast AQMD

Presentations:

Aggregating and Harmonizing Air Quality Data on a Global Scale
Presented by: Chris Hagerbaumer, OpenAQ

Despite the urgency of confronting air pollution, only 50% of governments worldwide produce air quality data, leaving 1.4 billion citizens without access to fundamental information that could protect them from the harmful effects of air pollution. Where air quality data does exist, data are often in inconsistent and temporary data sharing formats or buried in hard-to-find websites, making it difficult for the public to readily access and make use of the data. The OpenAQ platform aggregates and harmonizes real-time and historical air quality data from 155 countries from a variety of sources including reference-grade government monitors to community-led low-cost sensors to research-grade data. The open source platform hosts over 18 billion data points and the open API serves an average of 18 million requests per month. The data has been used for a wide variety of applications, from air quality forecasting systems produced by NASA scientists, to public awareness raising apps, to data-driven media communications. By filling a basic data-access gap and building foundational open source tools, OpenAQ has empowered diverse stakeholders to use data toward the end goal of clean air. What methods have been used to aggregate and harmonize air quality data on a global scale? What kinds of partnerships have helped reshape the air quality data ecosystem? Looking into the future, how do we make data more accessible to catalyze more effective and efficient action? Drawing from data sharing partnerships that the organization has built over the past few years, OpenAQ will share key lessons learned on how to work on a global scale to make air quality data accessible and usable to all.
(View Presentation PDF)
A real-time calibration and device management system for air quality sensors deployed in hierarchical networks
Presented by: Lena Weissert, Aeroqual Ltd

Regular calibration and maintenance are critical to provide reliable real-time data from sensor-based air monitoring networks. Given the costs related to individual site visits and calibrations, new approaches are required for large scale sensor networks to be viable. We report on the performance of the MOment MAtching (MOMA) network management framework that allows sensors to be managed remotely ensuring reliable data can be accessed in real-time without having to physically move any sensors. The framework has been operating for 13 months and has been applied to a mixed asset network of 50+ air quality sensors measuring O3, NO2, PM2.5and PM10and 13 regulatory air monitoring stations operated by South Coast AQMD in Southern California. The sensors were calibrated at monthly intervals using previously developed algorithms that match the sensor distribution to the distribution of a regulatory measurement which is referred to as a ‘proxy’. In addition, sensor diagnostics were developed to monitor sensor health and trigger maintenance requirements which minimises data loss and maximises sensor life. The performance of the network management framework was assessed by sensor co-location and proxy comparisons using RMSE and other statistical metrics. Regular calibration resulted in a significant reduction in the RMSE compared to uncalibrated data across all pollutants. We observed higher month to month variability in the performance of the calibration framework for PM2.5 which was associated with changes in wind direction affecting pollution sources and proxy suitability. The combination of regular calibration and maintenance, facilitated by the MOMA framework, enables the collection of reliable real-time data at neighbourhood scales. This offers an opportunity to identify local effects on air pollution and determine the probability of exceedances at different times and locations providing valuable information to local communities.

*Author did not provide PPT for public distribution, please contact Lena Weissert at lena.weissert@aeroqual.com with questions
Using low-cost PM2.5 and GPS sensors with surveys to understand exposure in informal settlements in Nairobi, Kenya
Presented by: Michael Johnson, Berkeley Air Monitoring Group

We monitored personal PM2.5(Purple Air PA-II-SD) and GPS location (Columbus P-1) for 71 mothers in two informal settlement communities outside Nairobi, Kenya (Dagoretti and Starehe). Participants were outfitted with backpacks to carry the instruments for 24-hour periods, and ambient PM2.5monitors were installed in each community. Time-activity surveys were administered to contextualize the PM2.5and location data with the sources and activities contributing to exposure. Mean daily exposures were relatively high (43.9 and 44.5 µg/m3, in Dagoretti and Starehe, respectively), exceeding the WHO annual interim 1 target (35µg/m3), and all participants had exposures above the WHO annual guideline (10 µg/m3). Diurnal ambient PM2.5 patterns typically tracked well with the personal exposures, although the daily median personal exposures were higher than ambient, which was generally quite high as well (27.2 and 35.8 µg/m3, respectively). Exposures during cooking with wood or charcoal were higher than during other activities, and participants who used these solid fuels for cooking had a daily mean PM2.5 exposure of 59.7 µg/m3 (n=16), compared to 40.3 µg/m3 for those who did not (n=57; t-test p = 0.003). The results suggest the most promising and practical intervention to reduce exposures in the target population would be to transition households using wood and/or charcoal for cooking to clean fuels such as LPG, ethanol, or electricity.
(View Presentation PDF)
AirNow Fire and Smoke Map
Presented by: Ron Evans, USEPA

In recent years, data from particulate matter (PM) sensors have become a valuable tool for broadening our understanding of air quality impacts from wildfire smoke and demonstrating the localized nature of smoke plumes. In 2020, EPA, in conjunction with state, tribal and local air monitoring agencies and the US Forest Service, initiated the Sensor Data Pilot on the AirNow Fire and Smoke map. This pilot displayed publicly available PurpleAir sensor data side-by-side with data from the permanent and temporary ambient air quality monitors. Because PM sensor data are often biased compared to federal reference method monitors, a correction equation was developed from a long-term sensor collocation across geographically diverse areas, spanning a wide range of PM concentrations. This correction was improved increase accuracy at extremely high concentrations and validated with smoke-impacted collocations during the 2020 fire season. Data cleaning and quality control steps were developed to quality assure the sensor data that is displayed on the map. This presentation will provide an overview of the AirNow Fire and Smoke map and describe the research behind the sensor data processing that has enabled the addition of thousands of new air quality observations helping communities monitor smoke impacting their area. The presentation will focus on updates Version 2 released in July 2021and planned updates for Version 3 to be released in Summer 2022. This abstract was reviewed by EPA and approved for publication; it may not necessarily reflect official Agency policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
(View Presentation PDF)
Rapid Assessment of Community Air Quality Using Real-time Mobile Air Monitors
Presented by: Evan Williams, The University of Texas at El Paso (Poster Presentation)

This study evaluates the suitability of assessing air quality in a residential community using air pollution monitoring devices installed in a moving vehicle. Ambient outdoor levels of three criteria pollutants (O3, NO2 and PM) were continuously recorded by three U.S. EPA-certified FEM air pollution monitoring devices installed inside a vehicle traveling repeatedly on the same route in a moderate to low traffic community. Spatio-temporal mobile air quality data were aggregated and compared to data collected at two fixed stations, one permanently operated by a state regulatory agency near a university campus, and another temporarily installed by the research team near a major interstate highway.

Hourly mobile and stationary pollution data appeared to agree very well with each other for all three pollutants. The magnitudes and the trends of the mobile PM data (both PM10 and PM2.5) resembled those recorded at the state-operated station. O3 was found to be ubiquitously distributed in the region and presented the best agreement between mobile and fixed-station data. The immediate, complicated photochemical reactions of NOx at tailpipe might have contributed to the incongruity between the mobile and fixed-station data, yet NO2 data appeared to follow a similar trend and peaks.

It appears promising that community exposures to transportation related air pollutants can be represented by short-term spatio-temporal measurements using mobile air pollution monitors. Mobile air pollution measurements provide a rapid assessment of the air quality in a community without installing multiple stationary sites. Further research on the adequacy of the mobile air pollution data for exposure and health assessment will greatly enhance the applicability of mobile monitoring in community air quality studies.
Temporal variations of ambient air pollutants and meteorological infuences on their concentrations in Tehran during 2012–2017
Presented by: Fatemeh Yousefian, Kashan university of medical sciences (Poster Presentation)

We investigated temporal variations of ambient air pollutants and the infuences of meteorological parameters on their concentrations using a robust method; convergent cross mapping; in Tehran (2012– 2017). Tehran citizens were consistently exposed to annual PM2.5, PM10 and NO2 approximately 3.0–4.5, 3.5–4.5 and 1.5–2.5 times higher than the World Health Organization air quality guideline levels during the period. Except for O3, all air pollutants demonstrated the lowest and highest concentrations in summertime and wintertime, respectively. The highest O3 concentrations were found on weekend (weekend efect), whereas other ambient air pollutants had statistically signifcant (P<0.05) daily variations in which higher concentrations were observed on weekdays compared to weekend (holiday efect). Hourly O3 concentration reached its peak at 3.00 p.m., though other air pollutants displayed two peaks; morning and late night. Approximately 45% to 65% of AQI values were in the subcategory of unhealthy for sensitive groups and PM2.5 was the responsible air pollutant in Tehran. Amongst meteorological factors, temperature was the key infuencing factor for PM2.5 and PM10 concentrations, while nebulosity and solar radiation exerted major infuences on ambient SO2 and O3 concentrations. Additionally, there is a moderate coupling between wind speed and NO2 and CO concentrations.
The concentration of BTEX compounds and health risk assessment in municipal solid waste facilities and urban areas
Presented by: Fatemeh Yousefian, Tehran university of medical sciences (Poster Presentation)

In this study, human exposure to benzene, toluene, ethylbenzene, xylenes (BTEX), along with their respective risk assessment is studied in four major units (n = 14-point sources) of the largest municipal solid waste management facilities (MSWF) in Iran. The results were compared with four urban sites in Tehran, the capital of Iran. Workers at the pre-processing unit are exposed to the highest total BTEX (151 μg m− 3 ). In specific, they were exposed to benzene concentrations of 11 μg m− 3. Moreover, the total BTEX (t-BTEX) concentrations measured over the conveyor belt were 198 μg m− 3 at most, followed by trommel (104), and active landfills (43). The mean concentration of ambient t-BTEX in Tehran is 100 μg m− 3. On average, xylenes and toluene have the highest concentrations in both on-site and urban environments, with mean values of 24 and 21, and 41 and 37 μg m− 3, respectively. Even though the non-carcinogenic risk of occupational exposure is negligible, BTEX is likely to increase the chance of carcinogenic risks (1.7E-05) for workers at the pre-processing unit. A definite carcinogenic risk of 1.3E-04, and non-carcinogenic effect, of HI = 1.6 were observed in one urban site. With the exception of the pre-processing unit, the citizens of Tehran had higher exposure to BTEX. Overall, BTEX concentrations in the largest MSWF of Iran remain an issue of public health concern.
Reducing personal exposure of recreational runners to airborne particles in urban environments
Presented by: Mar Viana, IDAEA-CSIC

The aim of this work was to estimate the quantitative exposure reductions achievable by monitoring personal exposure to fine particles during recreational runs. We used portable monitors to characterise personal exposure of runners across the city, and quantified the reductions achievable by modifying the routes and times of day. The exposures were then correlated with data on running habits as a function of age and gender, to identify the most exposed population groups. Portable, personal particle monitors (AirBeam2, HabitatMap.org) were provided to volunteer runners to map personal exposures to particles (PM10, PM2.5, PM1) across a 2x2 km area in central Barcelona (Spain). The time resolution of the monitors was 5 seconds, and the position and route of the runners were traced by GPS from their mobile phones, to which the sensors were connected via Bluetooth. Meteorological variability was accounted for by comparison with reference PM2.5 data from a local air quality monitoring station. When using the portable monitors to select the cleanest routes, runners were able to reduce their average exposures by 44%. When comparing exposures along the major roads (most polluted environments) and the cleanest routes (e.g., crossing park areas), average reductions of up to 71% in personal exposure to PM2.5 were achieved. We conclude that it is possible to achieve significant reductions in personal exposure (up to 71%) without significantly modifying running habits. Due to running habits, female runners tend to suffer higher exposers, mostly during morning runs.
(View Presentation PDF)
An Exploration of Gas Chromatograph and tVOC Sensor Data Collected During Two Different Releases from Oil and Natural Gas Well Pads in Colorado
Presented by: Alicia Frazier, Colorado Department of Public Health and Environment, Denver, CO, USA

Colorado currently uses both tVOC sensors and gas chromatographs for monitoring around oil and gas development sites. The sensors have primarily been used as air quality indicators in health and odor-based air quality complaints. With the passing of recent oil and gas monitoring regulations, however, further study was needed to see how well the sensors performed in real-world settings, and how effective they could be in a regulatory capacity. To date, the state has recorded two instances of releases at well pads near semi-rural communities which resulted in hourly spikes of benzene concentrations to levels greater than 9 ppb. At these times, both a tVOC sensor and a GC were on site and operational. This case study will show how the sensor compared to the GC in a practical field setting, as well as it's capability to help identify these types of incidents, and the potential impact in the regulatory world.

*Author did not provide PPT for public distribution, please contact Alicia Frazier at alicia.frazier@state.co.us with questions

View Session 5D (Part 1) Recording on Youtube

View Session 5D (Part 2) Recording on Youtube

Swimming in Data: The Current and future state of data management platforms

The volume and variety of air quality data is increasing exponentially. Communities, agencies and researchers need ways to manage this data so they can use it to make decisions. A data management platform ingests, stores, curates, processes, and distributes data for use in applications and helps ensure that data are timely, reliable, accurate, and of high quality. This session will focus on: Useful, open community system and tools Lessons learned from past efforts Best practices for data management Current gaps and barriers with existing platforms Future needs for data management platforms

Lead Session Chairs:

Tim Dye, TD Environmental & Ethan McMahon, US EPA

Current Presenters:

Lessons Learned in designing, developing, and implementing the South Coast AQMD AQPortal environmental data management solution
Presented by: Vasileios Papapostolou, South Coast Air Quality Management District

Technological advances along with regulatory advances mandating increased air quality monitoring have provided the opportunity for air districts to expand their monitoring capabilities and build hierarchical networks that include regulatory-grade instruments, research-grade equipment, and consumer-grade air quality sensors. These expanding monitoring networks collect data at increased spatial and temporal resolution to support different purposes: community, fence-line, supplemental, and incident response monitoring. These expanding networks create data management challenges to ingest, store, process, analyze and visualize the collected data. The South Coast Air Quality Management District has developed the AQPortal, a single-point of access cloud-based data management platform to meet these new data management challenges. AQPortal supports over 10 District-level and several external data streams, removes data and analytical silos, and establishes new data science workflows for data analysts and scientists to develop comprehensive data visualizations for public consumption. This presentation will discuss lessons learned during the design, development, and implementation of the AQPortal for environmental data management. We will review a use case scenario in which AQPortal was utilized to develop a publicly available data dashboard for air quality sensors deployed in support of community air monitoring networks under the California Assembly Bill 617 Community Air Monitoring and Emission Reduction plans efforts.
(View Presentation PDF)
Air sensor data management, visualization, and analysis: understanding and meeting the needs of government air quality organizations in the United States
Presented by: Gayle Hagler, US EPA Office of Research and Development

Air sensor use has grown in multiple sectors in the United States, including use by air agencies (federal, state, local, tribal) for a variety of non-regulatory supplemental and informational monitoring purposes. Realizing the full benefit of this new technology, however, is limited by the extent to which the data can be attained, processed, and analyzed by the user. To understand this particular end user sector, the United States Environmental Protection Agency (EPA) Office of Research and Development led unstructured and open-ended interviews in late 2019 with 19 government air organizations to understand their current practices, unmet needs, and future outlook for using air sensor data. Based on the dialogues, the organizations were grouped by type (e.g., state vs. local air agency) and three identified levels of use: Level 1) limited use (e.g., educational demonstrations); Level 2) Growing use in temporary monitoring, data quality evaluation; and Level 3) Sensors are routinely integrated into meeting the organization’s goal. We found that organizational type was not a good predictor of their level. After this dialogue stage, a cross-EPA team evaluated the landscape of existing and in-development solutions to the identified unmet needs, focusing on Level 2 users who faced the greatest barriers to progression in their use of air sensor data. The unmet needs we targeted for this study include data hosting, data quality, code sharing, and data analytics tools. This presentation will cover the interview findings, assessment of solutions, and gaps that remain.
(View Presentation PDF)
Unlocking the Value in Sensor Data
Presented by: Graeme Carvlin, Puget Sound Clean Air Agency

How do we take the mountains of publicly available sensor data and create meaningful data displays and reports that answer people’s air quality questions? Graeme will discuss the Sensor Dashboard app suite he has developed (http://apps.pscleanair.gov [http://apps.pscleanair.gov]). This toolkit includes ways to view, download, and analyze QC’d and calibrated sensor data. Two new modules, Surface and Compare, allow the creation of 2D air pollution surfaces and comparison between Purple Air sensors respectively. Graeme will show application of these tools to examples of air pollution concerns in the Puget Sound region.
(View Presentation PDF)
Universal Data Structures for Air Quality Data
Presented by: Jonathan Callahan, Desert Research Institute

Air quality data comes in a bewildering array of formats from an ever increasing list of regulatory monitors and low-cost sensors. Data providers make data available in csv, json and binary formats at everything from raw, “engineering level” to highly processed final versions. Ingesting, harmonizing, aggregating and storing this data for use by downstream applications is a formidable challenge that requires an understanding of the fundamental nature of environmental monitoring time series data. This presentation describes a “universal” data structure that supports both raw engineering data and national collections of aggregated data associated with stationary air quality sensors. A suite of R packages has been created that helps with data ingest, harmonization, restructuring and spatial assignment of data into this format. Additional R packages provide analysis and visualization of air quality data that has been converted into this format. In an era of hybrid monitoring networks, increasing data volumes and intense public interest in air quality data, it is incumbent upon the air quality community to work toward compact, uniform data standards that allow the ready exchange of air quality data. The data format described in this presentation is maximally compact, allowing data from thousands of devices to be stored in easily downloadable archive files. Archive files can be read in by software written in all common languages. Example scripts written in R, python and javascript will demonstrate the interoperability of this format.
(View Presentation PDF)
Integrating an in-house developed sensor platform with the existing AQM network and its off-the-shelf DAS solution
Presented by: Matt Shrensel, Oregon Department of Environmental Quality

Oregon Department of Environmental Quality (ODEQ) Air Quality Monitoring recently doubled its particulate monitoring network using its own in-house sensor platform (SensOR™). The sensor platform leverages ODEQ’s existing systems, low-cost Plantower particulate sensors, open source computer code, and existing staff time. We adapted our sensor platform and data acquisition system (EnviDAS) to provide data seamlessly into our existing informational-level particulate monitoring network. This includes support for ODEQ’s near-realtime Air Quality Index, USEPA’s AirNow, and wildfire smoke monitoring. This approach enabled ODEQ to get granular control over data, customize data collection, and use existing review and validation procedures. This approach further allowed ODEQ to make measurements that were consistent with other continuous informational-level monitors maintained by ODEQ, providing consistency across ODEQ’s network. We will discuss advantages and disadvantages of this approach.
(View Presentation PDF)
Using spatiotemporal infrastructure to manage and process air quality data for a rapid response to COVID-19 impact on air quality
Presented by: Phil Yang, George Mason University

Air quality is such a critical public health factor, that worsened air takes the lives of approximately 7M annually. It also intertwines with other disasters and provides an indicator for both environmental and human response to these disasters. We’ve utilized the spatiotemporal infrastructure built by the NSF Spatiotemporal I/UCRC, including cloud computing, big data analytics, and GPU computing to process various sources of air quality observations to investigate how the global pandemic has impacted the air quality in China, California, the U.S. and globally: a) a spatiotemporal data discovery portal was utilized to identify the data of interest in a smart fashion; b) spatiotemporal analytical tools are built to conduct statistical analyses. Machine learning tools are used to discover correlations among air quality and socioeconomic factors; c) the results are visualized with the use of various tools, d) cloud computing (AWS and OpenStack) was utilized to provide a rapid response and GPU computing was used to speed up the learning & mining process. The spatiotemporal infrastructure was able to provide efficient support to the on-demand and complex requirements of analyzing the pandemic and air quality.
(View Presentation PDF)

View Session Recording on Youtube

Youth-Focused Education and Youth-Lead Initiatives

As our future air monitoring professionals, leaders, and policy makers, youth represent some of the most important users of air sensors. This session will focus on youth-lead initiatives and efforts to educate and include youth in air monitoring.

Lead Session Chairs:

Ajith Kaduwela, CARB, Jessa Ellenburg, 2B Technologies, Aubrey Burgess, City and County of Denver, Colorado

Presentations:

Using Air Monitoring Projects to Plant Social, Academic, and Economic Seeds in African American Youth
Presented by: Gwen Smith, Community Health Aligning Revitalization Resilience & Sustainability (CHARRS)

To maximize the future impact of the air monitoring field, projects must be more intentional about depositing resources and skills in youth and focus on greater integration of challenges impacted communities directly face. This presentation will discuss specific challenges African American youth face in gaining access to the field and how citizen science and air monitoring projects are used as tools to improve math and science performance and exposure to long-term career opportunities.
(View Presentation PDF)
Tribal Air Quality Education and Outreach
Presented by: Mansel Nelson, Northern Arizona University

In partnership with the USEPA, the Institute for Tribal Environmental Professionals (ITEP) established Air Sensor Loan and Education Programs to enable tribal communities to learn about air quality. ITEP staff will share about ITEP’s work providing air quality sensors to tribal communities and tribal schools. Tribal staff and school staff can use the sensors to monitor air quality both inside and outside, for a variety of situations, including during wildfire seasons. The program also includes Air Sensor Toolbox Educational Resources, such as lesson plans and resource guides. ITEP interns have helped expand sensor networks in Alaska, as well as in Arizona, Oklahoma and other states. The ITEP staff are also encouraging the use of sensors to help monitor air quality for school indoor environments. Concerns about COVID exposure has highlighted the importance of adequate ventilation and filtration.

*Author did not provide PPT for public distribution, please contact Mansel Nelson at mansel.nelson@nau.edu with questions
Utilizing Indoor Air Quality Measurements as a Youth-Action Project During COVID-19
Presented by: Sarah Peterson, Denver Public Schools

The COVID-19 pandemic has underscored the importance of understanding indoor air quality and the role that ventilation plays in protection from airborne illness. This session will focus on the role that students can play in both leading initiatives around air quality measurements in school sites as well as student advocacy at the school and district level for leaders to make science-based decisions to best support the safety of students and staff in the building. The session will feature sample unit and lesson plans, artifacts from student learning, and a discussion on how partnerships with local public health entities can best support in class learning.

*Author did not provide PPT for public distribution, please contact Sarah Peterson at sarah_peterson@dpsk12.org with questions
Engaging Youth and the Community in Citizen Science with Air Sensors
Presented by: Christina Yoka, Cleveland Department of Public Health - Division of Air Quality

Cleveland Division of Air Quality (CDAQ) is the Ohio EPA designated local air agency serving Cuyahoga County. CDAQ frequently receives inquiries from residents and community organizations about utilizing low cost air quality sensors to expand on the EPA ambient air monitoring network. In many instances, CDAQ collaborates with these local organizations on air sensor deployment. These partnerships are a valuable tool used to inform the community about air quality issues and an opportunity for CDAQ to learn more about specific community concerns. In addition to working with community organizations, CDAQ uses low cost air quality sensors to deliver project based learning experiences for youth. CDAQ has a limited number of particulate matter sensors that are available for schools, summer camps and youth groups. CDAQ developed a curriculum that is designed to engage students in the full citizen science experience. Youth are empowered to develop project ideas that address local issues that interest the student. The EPA Handbook for Citizen Science Quality Assurance has been a tool used by CDAQ to develop this curriculum. Major topics of the program include developing citizen science projects, quality assurance, air sensor siting, and data analysis. CDAQ has also incorporated the University of Utah AirU Teaching Module on Building Lego Air Quality Sensors into overall programming. This presentation will provide an overview of the key areas of CDAQ’s air sensor programs with youth and the community; strategies used for program implementation; evaluation results from youth and educators; and lessons learned from initial implementation.
(View Presentation PDF)
Air Quality InQuiry: Adapting air quality sensors for use in high school settings in the United States and universities in Mongolia
Presented by: Helena Pliszka, University of Colorado Boulder

Air quality sensors have been a growing tool employed in the field of citizen science to enhance public understanding and engage local stakeholders in issues of air pollution. We adapted low cost air quality sensor packages, known as pods, to educational settings in suburban and rural high schools in Colorado and universities in Mongolia to increase students’ exposure to environmental and STEM fields. Our science engineering outreach program, known as Air Quality InQuiry (AQIQ), trains university students to mentor high school students to build and execute their own projects using pods. The program started in 2014 in the US and 2020 in Mongolia, and currently reaches 12 schools and 686 students in Colorado and 6 schools and 130 students in 5 rural provinces and the capital in Mongolia. The program follows a project-based curriculum housed on TeachEngineering.org that covers five major educational modules: air quality concepts, pod use, project brainstorming, data analysis and scientific poster presentation. We conducted a comparative analysis of high school students’ pre-and-post program surveys and end-project posters among 2018-19, 2019-20, and 2020-21 academic years. Key results suggest that despite the fluctuating learning modes as a result of the COVID-19 pandemic, science, engineering and air quality-related learning outcomes remained relatively stable. 2019-20 survey results from a high school classroom suggest an increase in student agreement that “science is personally relevant,” and that “engineering is helpful to [students’] future careers.” Further analysis will investigate results from the current academic year and will strive to evaluate the long-term impact that this program may have on participating students’ college and career decision-making. Our vision for the AQIQ program and expectations for future results center on empowering high school students to investigate and solve air quality issues within their own communities.
(View Presentation PDF)
Building an aerosol sensing sensor network and inspiring citizen scientists
Presented by: Kerry Kelly, University of Utah

Since 2018, the AQ&U network has been collecting aerosol measurements from a growing number of low-cost sensors (more than 200) in the Salt Lake Valley. AQ&U integrates low-cost, research-grade, and reference measurements with robust data screening, event-specific calibrations, and a Gaussian Process model to understand neighborhood-scale PM2.5 concentrations as well as uncertainty estimates. It provides near-real time visualizations of PM2.5 concentration along with uncertainty estimates through a public-facing website. AQ&U also provides a rich framework for citizen science. Here, we discuss AQ&U’s community engagement strategies, particularly those to encourage participation of under-served communities, and highlight two of our most successful efforts to engage students as citizen scientists. The first effort centers around a hands-on activity to build and test an aerosol sensor from LegosTM and simple microelectronics. It emphasizes the underlying principles of aerosol light scattering. This has been our college’s most requested outreach activity and has been successfully demonstrated at hundreds of high-school classrooms, numerous STEM tabling events, and the National Science and Engineering Fair. The second effort focuses on an interactive, team-based teaching module using local real-world measurements. This activity's goal is to engage students in generating and testing hypotheses while also encouraging citizen scientists to use real-world air quality data for their own interests, such as exploration, science fair projects, or environmental oversight. This second activity has been a successful strategy for virtual learning. Finally, we discuss lessons learned. COI Declaration: Drs. Kelly, Gaillardon, and Whitaker have a financial interest in Tetrad Sensor Network Solutions, LLC, which commercializes environmental measurement technologies.
(View Presentation PDF)
Championing Environmental Awareness in Communities Through Air Sensor Loan Programs
Presented by: Ryder Freed, U.S. EPA R9

Air sensors are exploratory and educational tools for communities. In recent years, the public has increasingly turned to these technologies to measure air quality. In response, U.S. EPA established pilot air sensor loan programs to better facilitate access to sensors and provide resources necessary for operating them. The loan programs include a living museum [The Morton Arboretum (IL)], Tribal Partners [Heritage University on the Yakama Reservation (WA), the Nez Perce Tribe (ID), and the Institute for Tribal Environmental Professionals (ITEP)], and national, urban, and rural libraries [Los Angeles Public Library System (CA), Evansville-Vanderburgh Library (IN), L’Anse Public Library (MI), Bayliss Library/Superior District (MI), and the Nez Perce Tribal Community libraries (ID)]. These programs offer community members an opportunity to borrow particulate matter (PM) sensors (AirBeam2 and/or PurpleAir) to collect PM measurements to learn about local air quality and pollution sources or to collect data to support community science projects. As part of the loan programs, five lesson plans were developed to guide participants through investigations of (1) outdoor air, (2) indoor air, (3) personal exposure, (4) vegetative barriers, and (5) smoke. These interactive lesson plans were shared with librarians and tribal air quality and educational professionals who plan to use the materials in educational programming with communities. The materials will also be posted to EPA’s Air Sensor Toolbox webpage [https://www.epa.gov/air-sensor-toolbox/educational-resources-related-air-sensor-technology] for anyone to use. An overview of these innovative, EPA-supported loan programs will be discussed, along with lessons learned. Disclaimer: Although this abstract was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

*Author did not provide PPT for public distribution, please contact Ryder Freed at freed.ryder@epa.gov with questions
Algorithmic Correction of MOS Gas Sensor for Ambient Temperature and Relative Humidity Fluctuations
Presented by: Akarsh Aurora, Ashland High School (Poster Presentation)

Algorithmic Correction of MOS Gas Sensor for Ambient Temperature and Relative Humidity Fluctuations
Making Air Pollution Visual – Educational Resources using Air Sensors to Explore Air Quality
Presented by: Andrea Clements, U.S.EPA (Poster Presentation)

Poor air quality is not always visible to the human eye. Since it is hard to see, many people are unaware of the risks associated with their daily exposures. Air sensors can provide visual information about air quality by displaying colors and/or numbers that correspond to pollutant concentrations, making them powerful tools for educational efforts. To support educators and air sensor loan pilot programs, U.S. EPA created five lesson plans designed to introduce air quality concepts including (1) outdoor air quality, (2) indoor air quality, (3) personal exposure, (4) vegetative barriers, and (5) wildfire smoke. The first 4 lessons can be conducted with any handheld sensor that measures and/or interactively maps fine particulate matter (PM2.5), allowing participants to explore how pollution levels change in their community. The last lesson uses crowdsourced PM2.5 sensor data displayed on a map alongside satellite fire detections and smoke observations to allow participants to investigate the impacts of wildfire smoke in their community. Each lesson plan includes background information, hands-on activities to explore air quality conditions and pollution sources and provides questions that help participants consider how air quality can be improved. Each lesson plan includes an instructor guide, participant guide, introductory slides, worksheet, resource list, frequently asked question document, and an alignment to the Next Generation Science Standards (NGSS).

Disclaimer: Although this abstract was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
Lessons Learned From a Clean Air Equity Pilot for Students in Low Income Communities
Presented by: Andrew Clark, Sustainable Silicon Valley (Poster Presentation)

Recent events surrounding the COVD pandemic have underscored the fact that low-income communities are disproportionately exposed to poor air quality. The Environmental Protection Agency and CARB have identified in Disadvantaged (SB535) and Low Income Communities (AB1550), (https://ww3.arb.ca.gov/cc/capandtrade/auctionproceeds/communityinvestments.htm [https://ww3.arb.ca.gov/cc/capandtrade/auctionproceeds/communityinvestments.htm] ). In response, Sustainable Silicon Valley has created the Clean Air Equality Project (CAEP) to conduct a community air monitoring program focused on middle and high school students in disadvantaged and low-income communities. Using low-cost sensors to gather hyper local data, we engaged students from diverse communities across the SF Bay Area who live with divergent air quality. CAEP participants use lightweightportable, smartphone-linked air quality sensor technologies to perform measurements, as local federal reference measurements are sparse. (https://www.backpaqlabs.com/docs/BackpAQ Personal Air Quality Monitor User Guide V0.98.1.pdf [https://www.backpaqlabs.com/docs/BackpAQ%20Personal%20Air%20Quality%20Monitor%20User%20Guide%20V0.98.1.pdf]) Low-cost sensors have been making great advances and offer the potential to obtain hyper-local information on air quality. Incorporating them into a user-friendly, easy-to-carry design is challenging, as is ensuring adequate data and spatial privacy. While these low-cost sensors do not have the accuracy and precision of federal reference methods, with proper inter-calibration and interpretation, they can shed light on the different air quality being experienced by diverse socio-economic communities.
RELAQS: Research and Education with Low-cost Air Quality Sensors
Presented by: Ben Crawford, University of Colorado Denver (Poster Presentation)

Low-cost air quality sensors have tremendous potential as tools to support learning, in addition to their use for research and monitoring applications. Although low-cost sensors are increasingly used in a variety of education-focused activities, there are few studies designed to specifically assess the efficacy of low-cost sensors to help students meet course learning outcomes. In this project, custom built low-cost sensor kits are evaluated in a high school Advanced Placement (AP) environmental science course during the spring 2022 semester. The sensor kits measure particulate matter, VOCs, and carbon dioxide and each unit is powered by solar panels and rechargeable batteries. Sensor measurements are transmitted to an online database where data are visualized and downloadable in near real-time using a custom interactive dashboard interface. For this research, new classroom activities incorporating the sensors and their data are designed to meet specific US NGSS (Next Generation Science Standards) and AP standards. The evaluation study (two classes, ~25 students each) is designed so that one group of students participates in the sensor-based activities while a control group engages in traditional non-sensor activities. Progress towards learning outcomes in both groups is assessed by administering concept inventory quizzes before and after the activities, through responses to a survey instrument, and through student interviews. Here we present preliminary results from this study and ideas for future research directions, towards the overall goal to improve K-12 and undergraduate STEM education.
Incorporating Personal Monitoring Utilizing Low-cost Sensors into the Undergraduate Curriculum
Presented by: Joshua Stratton, Rider University (Poster Presentation)

Environmentally aware and motivated students are abundant in today’s undergraduate classrooms. While some of these talented young scientists may become professionals in environmental related fields, many of them could serve as leaders for community-led projects in the future. This study aimed to challenge undergraduate students to design, execute, and interpret a personal monitoring project using mobile particulate matter (PM) sensors. This project included experimental design, data collection, data harmonization, manipulation, analysis via a programming language, data visualization, and a final in-class dissemination to their peers. Students were provided with a battery powered Arduino microcontroller, Plantower PMS5003 PM sensor, relative humidity (RH) and temperature (T) sensor (DHT-22), and local data storage to conduct this work. Students used their mobile phone GPS to track their location for comparison with PM concentrations. Students operated these sensors and extracted data from local storage. Although several students reported no previous experience with programing languages, all students were able to organize, merge, analyze, and visualize data using simple R templates provided within the course and tools such as EPA’s Real Time Geospatial Data Viewer (RETIGO). Based on student surveys, students reported a positive experience and were likely to engage in future community-led projects. However, data management and manipulation were the most difficult task based on student feedback. While student feedback recommended help from a technical expert, this study suggests motivated individuals may be capable of minor data management.
Making the Invisible Visible: Blending STEM and Air Quality for Student Learning
Presented by: Olivia Ryder, Sonoma Technology (Poster Presentation)

Improved air quality sensor technology is creating new educational opportunities.

The Kids Making Sense® (KMS) air quality curriculum unites STEM education with hands-on projects and mobile air sensors to teach students about air pollution and how to reduce exposure. The KMS curriculum meets Next Generation Science Standards and Common Core Standards for grades 6-12 and has been successfully implemented in over 300 classrooms worldwide. Students learn about air pollution, particulate matter (PM), sources of PM, the health effects of PM, and regulatory monitoring. Working in teams, students design a study to monitor PM around their communities, use air sensors to collect credible air quality data, and analyze their findings using interactive online mapping tools. Our new AQ-go PM sensors allow students to measure air pollution, and are transparent to allow them to examine the sensor components. Finally, students develop an air quality awareness campaign to share what they’ve learned with members of their school or community.

In parallel with the KMS air quality measurement program, and in partnership with the Blue Lake Rancheria Tribe, we developed a Build A Sensor Kit and lesson module that allows students to dive deeper into learning how particle sensors work. Participants learn about the components that comprise an air quality sensor, the electronics and data transfer process, and how to program their own sensors. Students learn valuable skills in engineering, electronics, and coding in this hands-on module. Providing students with the components and guidance to build their own sensor empowers them to be curious and spurs student-initiated inquiry.

In this talk, we will describe the programs above, discuss key outcomes from teachers who have implemented the programs, and highlight the strengths of using hand-held sensor technology in classrooms or as outreach to engage students and empower them to create change.

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Event Activity	Date
Abstract Submissions Open	September 28, 2021
Exhibitor / Sponsorship Applications Open	November 8, 2021
Abstract Submission Deadline	November 29, 2021
Early Bird Registration Opens	December 1, 2021
Early Bird Registration Ends	January 31, 2022
General Registration Available	February 1 - April 15, 2022
Late Registration Available	April 16 - May 4, 2022
Pre-Conference Training	May 10, 2022
General Sessions Day 1	May 11, 2022
Evening Welcome Reception	May 11, 2022
General Sessions Day 2	May 12, 2022
General Sessions Day 3	May 13, 2022

2022 Program Topics

Session Topics:

Air Sensor Use in India

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Advanced measurement approaches for fenceline and fugitive monitoring applications

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Best practices from Breathe London

Lessons learned from integrating hundreds of air quality sensors with the largest and most advanced regulatory monitoring network in Europe

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Clean Air Monitoring and Solutions Network: getting useful, actionable data out of low cost sensors for air quality action (New Parallel Symposium)

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Community Air Sensor Use

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Communication Strategies for Understanding, Insight, and Action

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Filling in the air quality data gap and enabling air quality management in LMICs using low-cost sensors

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Indoor Sensing for Air Quality Control and Ventilation Applications

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Innovative Sensor Technologies

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Merging sensor data with other air pollution data sources: methods and benefits

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Mobile Monitoring & Monitoring Mobile Sources

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Performance targets for air quality sensors

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The Potential of Air Sensors for Personalizing and Advancing Human Health Research

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Sensor Networks: From nuts and bolts to real-world impacts

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Standard, Supplemental and Informational Monitoring

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Swimming in Data: The Current and future state of data management platforms

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Youth-Focused Education and Youth-Lead Initiatives

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Event Activity

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