With the endorsement of US EPA, low-cost sensors are becoming an increasingly popular option for collecting real-time air quality data that can help fill gaps in locations where data from more costly sensors are scarce.
For decades, the United States Environmental Protection Agency (US EPA) and state, local, and tribal air agencies have been monitoring ambient air quality as a key component of air pollution management programs. A network of central-site air monitoring stations throughout the US, currently totaling more than 4,000 stations, has historically provided high-quality ambient concentration data for the US EPA’s criteria air pollutants, including particulate matter (PM2.5 and PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and lead. A smaller network of less than 300 central-site monitors has been used to measure so-called “air toxics,” which include volatile organic compounds (VOCs), like benzene and ethylene oxide, and metals, such as arsenic and chromium. While central-site monitors have contributed to significant improvements in air quality nationwide, they are costly to build and maintain, such that a small number of monitors is often used to represent large areas and there is a scarcity of air monitoring data for many areas of the country.
Low-cost sensors, which range in cost from less than $100 to about $2,500, are portable, lightweight, require little power to run, and have wireless communication capabilities.”
To help communities fill gaps in air quality data, the development and use of low-cost sensors for collecting real-time air quality data (i.e., nearly instantaneous data availability) is gaining increasing attention. Low-cost sensors, which range in cost from less than $100 to about $2,500, are portable, lightweight, require little power to run, and have wireless communication capabilities. Portable low-cost sensors are also particularly suited for community-scale exposure monitoring, as they can be installed near specific sources of pollution to better characterize local levels of air contaminants (see Figure), or can be used to support aerial monitoring (e.g., unmanned aerial vehicles [UAVs]/drones) of a wider geographic area for improved characterization of spatiotemporal trends.
Community-based air quality monitoring using low-cost sensors has many potential benefits:
To support this new paradigm, US EPA has endorsed the use of low-cost sensors as a viable supplement to traditional air monitoring by regulatory agencies (see US EPA’s Air Sensor Toolbox here). In November 2022, as a part of the American Rescue Plan (ARP), the agency allocated $53.4 million to fund 132 air monitoring projects in 37 states, including a number of projects proposing the installation of networks of low-cost sensors (US EPA, 2022). This funding included $20 million in grants intended to improve ambient air quality monitoring in and near underserved communities across the US. US EPA has also been working to create guides and protocols for the proper use of low-cost sensors for detection of trace gases, PM2.5 and PM10, and other pollutants. The agency’s Center for Environmental Measurement and Modeling recently released its “Enhanced Air Sensor Guidebook,” proposing best practices for using air sensors for community users and providing recommendations for planning and implementing monitoring studies at the community level (Clements et al., 2022).
As with any emerging technology, networks of inexpensive sensors have some notable limitations when it comes to reliable detection of [criteria] air pollutants.”
This growing interest in localized air quality monitoring has led to the establishment of low-cost sensor networks in conjunction with regulatory air quality networks to enhance and improve spatiotemporal air quality forecast models. The most common low-cost sensor networks have been developed to measure concentrations of airborne PM2.5 and PM10, NO2, CO, and O3. As with any emerging technology, networks of inexpensive sensors have some notable limitations when it comes to reliable detection of these air pollutants. For example, low-cost sensors for PM2.5 and PM10 typically rely on light scattering, which is an optical method that does not directly measure mass concentrations. Low-cost PM2.5 and PM10 sensors are often highly sensitive to environmental conditions, including relative humidity and temperature, and their performance has been shown to be dependent on particle size, chemical composition, and study site (DeSouza et al., 2022). Wallace et al. (2022) analyzed 83 million hourly average PM2.5 concentration estimates obtained from the PurpleAir network, one of the largest low-cost sensor networks for monitoring PM2.5 in the world. This study found that PurpleAir PM2.5 measurements were highly correlated with existing regulatory air monitoring data, as long as the sensors were optimally calibrated. The vital importance of proper calibration, as well as the need for frequent recalibration, can stretch the capabilities of community users to reliably use low-cost sensors.
Although networks of low-cost air quality sensors are being installed rapidly in communities across the country, the notable limitations of the current generation of low-cost sensors make it unlikely that they will replace US EPA’s conventional central-site monitors anytime soon for determining regulatory compliance with air quality standards and guidelines. Some key remaining challenges include improving the robustness and longevity of low-cost sensors, reducing their sensitivity to environmental conditions, and developing standardized protocols for their calibration and performance evaluation. Nevertheless, low-cost sensors are driving a revolutionary shift in air pollution monitoring and analysis and have empowered local citizens with the tools to better understand the air quality in their communities. As the technology continues to improve, low-cost sensors are expected to close the gaps in air quality monitoring and provide greater awareness of short- and long-term air quality trends across the US.
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Clements, A; Duvall, R; Greene, D; Dye, T. 2022. “The Enhanced Air Sensor Guidebook.” Report to US EPA, Office of Research and Development, Center for Environmental Measurement and Modeling. EPA/600/R-22/213. 195p., September. Accessed on April 19, 2023, at https://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryId=356426&Lab=CEMM.
Desouza, P; Kahn, R; Stockman, T; Obermann, W; Crawford, B; Wang, A; Crooks, J; Li, J; Kinney, P. 2022. “Calibrating networks of low-cost air quality sensors.” Atmos. Meas. Tech. 15(21):6309-6328. doi: 10.5194/amt-15-6309-2022.
US EPA. 2022. “Selections for the ARP Enhanced Air Quality Monitoring Competitive Grant.” December 14. Accessed on April 19, 2023, at https://www.epa.gov/arp/selections-arp-enhanced-air-quality-monitoring-competitive-grant.
Wallace, L; Zhao, T; Klepeis, NE. 2022. “Calibration of PurpleAir PA-I and PA-II monitors using daily mean PM2.5 concentrations measured in California, Washington, and Oregon from 2017 to 2021.” Sensors 22(13):4741. doi: 10.3390/s22134741.