The Fall Air Sensors International Series aims to build upon the knowledge that was shared at the recent Air Sensors International Conference held in Bangalore, India by reviewing work presented at ASIC, Pasadena in May, 2022. Continuing to foster the connections made in Bangalore and increase the knowledge of low-cost sensor users will help the country further its goals toward cleaner air. You can review the content from the Bangalore, India conference here or from the Pasadena, California, US conference here.

ASIC, Bangalore brought together researchers, academics and industry members that have conducted a significant amount of work with low-cost sensors in India. Networks are still in the process of development and regulators are slowly beginning to understand the value of LCS use alongside regulatory-grade monitors, but there is still significant hesitation and concern with their use. As users and developers, participants showed interest in learning from the experiences presented at ASIC, Pasadena, so the UC Davis Air Quality Research Center is crafting webinars and AMAs to offer participants the opportunity to reconnect with those they met at the Bangalore conference and continue working toward solutions for local air quality monitoring issues. 

Below is the series and topic schedule based on a variety of presentations offered at the ASIC, Pasadena, California event in May 2022.

As a registrant you are responsible to:

1. Pre-watch the selected presentations from the recorded Pasadena presentation (listed below the description as you scroll down this page) Presenters will give the presentation listed live for you to listen in on, and then ask questions about!
2. Join the discussion board on MS Teams
3. Share questions before the webinar that can be asked of the presenter related to the topic they are covering. 

Live Author Q&A Discussion Schedule

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.

PDF of Presentation

Watch Recording Here

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.

PDF of Presentation

Watch Recording Here

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. Email Dan

PDF of Presentation

Watch Recording Here

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.

Watch Recording Here

NEW: How LCS can be combined in useful ways with satellite and satellite-derived data

PLUS: A short review of "Air quality forecasting at sub-city-scale by combining models, satellites, and surface measures"

Presented by: Carl Malings, Postdoctoral Program Fellow, NASA GSFC

In some ways, low-cost air quality sensors and Earth-orbiting satellites are on opposite sides of the cost spectrum when it comes to instruments for assessing air pollution. However, they often provide complementary details about air quality, and there exist many opportunities to integrate their data together to develop deeper insights. Dr. Carl Malings (Morgan State University & NASA) will provide a brief introductions to the capabilities and limitations of both low-cost sensors and satellites in terms of air quality measurement, as well as discuss how these different data sources might work together to improve our understanding of air quality, citing relevant case study examples.

PDF of Presentation

How LCS can be combined in useful ways with satellite and satellite-derived data with extended Q&A

Watch Recording of "Air quality forecasting at sub-city-scale by combining models, satellites, and surface measures" Here

Communicating Air Sensor Data on the AirNow Fire and Smoke map

Presented by: Karoline Barkjohn & Ron Evans, U.S. Environmental Protection Agency Office of Research and Development

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.

PDF of Presentation

Watch Recording Here

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
• Andrea Clements, US EPA Office of Research and Development
• Ryan Brown, US EPA Region 4
• Ron Evans, US EPA Office of Air Quality Planning and Standards

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 Recording