Session 4: Policy & AQ Management, Performance Targets, Communication & Interpretation of AQ Data

Virtual Fall Series

Event Date

Location
Virtually through Zoom

Session moderated by R. Subramanian, OSU-Efluve/CNRSMelissa Lunden, Aclima

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Measuring public opinion and knowledge of air pollution to develop community specific communication strategies

Presented by: Brianne Suldovsky, Portland State University

Summary: The availability and demand for localized air quality information from communities is on the rise. However, not all information and not all communities are the same. City of Portland Bureau of Planning and Sustainability teamed up with Portland State University Communications researchers to take a step back from deploying sensors to first better understand what different communities want out of air quality information. Effective engagement and communication strategies will depend on a community’s existing knowledge, opinion about air quality, experiences with inequities, and more. This study measures public opinion surrounding air pollution in Portland, Oregon. Specifically, we examine the extent to which the Portland public prioritizes air pollution as an environmental issue, whether they believe that science and technology will solve the air pollution problem, their level of air pollution knowledge, and their air pollution risk perceptions. We also assess their relative support for air quality sensors (including traditional monitoring methods versus emerging methods) and examine the best mechanisms for communicating air quality information. The goal of this work is to better support air quality communicators and municipalities making decisions surrounding air quality measurements. Study results will be used to inform engagement strategies, existing and future sensor deployments and sensor data sharing strategies.


Overview of the U.S. EPA Work on Developing Sensor Performance Testing Protocols and Targets for Ozone and PM2.5 Air Sensors

Presented by: Rachelle Duvall, US EPA

Summary: The increased availability and use of air sensors have encouraged innovation in air quality monitoring approaches. The development of air sensors continues to expand and evolve at a rapid pace. However, it is common knowledge that the data quality from air sensor devices is highly variable, making it difficult for consumers to choose a sensor appropriate for an application of interest because of inconsistent performance characterization and reporting. In the U.S., performance standards and certification processes exist for instruments used for regulatory air monitoring under the Federal Reference and Equivalent Method (FRM/FEM) Program, but it is recognized that air sensors will not meet those stringent requirements. To help support consumers and developers, the U.S. EPA has developed two reports outlining testing protocols, metrics, and target values to evaluate the performance of ozone and fine particulate matter (PM2.5) air sensors for use in non-regulatory supplemental and informational monitoring applications. The goal of this work is to provide a consistent approach for evaluating sensor performance while also helping provide confidence in the data quality, encouraging improvements and development in the marketplace, and helping users select appropriate sensors for the desired application. This presentation will provide an overview on the U.S. EPA’s efforts in developing guidance for evaluating performance of ozone and PM2.5 air sensors. 


A Brief Overview of EPA Communication & Interpretation Sensor Activities

Presented by: Kristen Benedict, US EPA

Summary: This presentation will provide a brief overview of recent EPA activities to address frequently asked questions on the communication and interpretation of sensor data. Topics covered will include the motivation behind the release of new educational videos on air sensors, preliminary results of a screen shot project comparing the results of various public facing websites during smoke and non-smoke events, and a summary of an Air Quality Exchange workgroup discussion on the increasing amount of air quality information being shared by various public and private entities.


Air quality data analysis: life after r2

Presented by: John Saffell, Alphasesne Ltd.

Summary: Air quality (AQ) is increasingly being measured using high density, low cost sensor networks, rather than low density, equivalent or transfer standard monitoring stations.  The next step for these AQ networks is improving sensor performance so that data analysis is more robust.   

Companies that assemble, calibrate and maintain AQ networks can improve AQ sensor data quality with proprietary algorithms, but recent field tests of the same AQ networks by different universities and research organisations in different locations have resulted in conflicting conclusions. Why?

Data quality is usually judged by r2, the coefficient of determination. This is not surprising since it is an easy statistical tool which has been used successfully for many physical sensors. Using r2 should be reviewed when analysing chemical sensors which have more degrees of freedom.  r2 assumes a simple linear regression, which means that both the reference and low cost sensors have either no other or the same regressors. Also, spikes in the data due to local pollution events distort r2 calculations and good quality data during a stable period can yield a poor r2 due to the limited range of pollutant concentration.

Conflicting studies using r2 as the measure of data quality point to the ignored problem that measurements are affected from the specific environment: is it roadside, background urban, suburban or a rural location? Is the climate equatorial, desert, arctic, coastal, temperate? 

We propose a different approach to validating AQ data quality. Each AQ network remembers its environment. We must deconvolve the specific environment to obtain better results. This can be achieved mathematically but needs good quality data to operate correctly, more so than with the simpler r2 calculation. We discuss this approach and ask whether results should be expressed with their 95% confidence intervals, not r2.


Can we set performance targets for low cost sensors?

Presented by: John Saffell, Alphasesne Ltd.

Summary: Air quality (AQ) networks and personal monitors using low cost sensors are generating results at a rapidly expanding rate. But can these results be accepted as valid measurements? We can rephrase this question: do air quality sensors and sensor systems meet their performance targets?

We first review performance targets that have been set for gases, particles and VOCs. Targets can be based on national or international concentration limits, sensor technology capabilities, typical concentrations, limits of detection, or laboratory and field validation capabilities. Performance targets should also depend on the application: reference and equivalent measurements, fixed site urban networks, mobile monitoring, IAQ, personal exposure or citizen science. We consider how the total measurement error should reflect the application.

Users of low cost AQ sensors often request a performance certificate to a national or international standard; there are test standards for reference and equivalent analytical systems, but to date there are no test standards for low cost AQ sensors. CEN 264, Working Group 42 has been working since 2015 on a classification and validation performance standard for low cost gas and particle sensors. The first draft is available March 2020 and allows classification of low cost sensors from near-equivalence capability and simpler classification of citizen science AQ boxes.  EDF and ASTM in North America are also working towards standards for AQ networks. We discuss progress.

Work with UNEP over the last years in Nairobi and other LMIC locations has reminded us of other issues that must be included when considering performance targets. The sensor system alone can be tested and validated in the lab, but field validation of performance must also include siting and deployment, sensor drift and regular field validation. A final consideration is the geographical location and diurnal and seasonal patterns which strongly affect field measurement quality. 


Data is policy: presentation as aesthetics and public infrastructure  

Presented by: Rebecca E. Skinner, San Francisco AQ/ Manylabs

Summary: This presentation discusses the way in which NAAQS criteria pollutant data is presented to non-technical end users. Beyond aesthetic issues, the author suggests that such environmental data is essential urban infrastructure. Data presentation is policy.

Readings of NAAQS pollutants in an air district, AirNow, or AQ data company presentation are available through a web browser, on your phone or your computer, or occasionally on the monitoring device itself. The information is shown as a number, with the canonical AQI colors from green, yellow, and orange, through red and purple. 
 There are many possibilities beyond this default display convention. Graphic depiction of the data may be more easily intuitively grasped, for instance, with a so-called "fuel gauge". AQI level depiction using colored lights, for instance LED and LIFX bulbs, is seen in projects found on Facebook's Purple Air User groups, and in Louisville's Smart City project.

Beyond the issues of graphic depiction, public data presentation can be considerably increased. One option would be to have AQ accompany the time and temperature on the LED tickertape readout on public buildings, schools and banks. AQI data would be a worthy public service announcement in in numerous venues: displays on top of cabs and bus stations, on the live screens of office building lobbies, near escalators, and in elevators.

Yet environmental data display is more than an aesthetic question. When framed as public infrastructure, possibly combined with connectivity, electrical, or other municipal service, it is a harbinger of future social needs and technical prospects. The increasing exigency of environmental data in an era of climate emergency makes the communication of AQ data more important and more intriguing than it may initially appear.