Evaluating the Performance of Low-Cost PM2.5 Sensors in Mobile Settings

Priyanka deSouza; An Wang; Yuki Machida; Tiffany Duhl; Simone Mora; Prashant Kumar; Ralph Kahn; Carlo Ratti; John L Durant; Neelakshi Hudda

doi:10.1021/acs.est.3c04843

Evaluating the Performance of Low-Cost PM_2.5 Sensors in Mobile Settings

Environ Sci Technol. 2023 Oct 17;57(41):15401-15411. doi: 10.1021/acs.est.3c04843. Epub 2023 Oct 3.

Authors

Priyanka deSouza^{1

2}, An Wang³, Yuki Machida³, Tiffany Duhl⁴, Simone Mora³, Prashant Kumar^{5

6}, Ralph Kahn⁷, Carlo Ratti³, John L Durant⁴, Neelakshi Hudda⁴

Affiliations

¹ Department of Urban and Regional Planning, University of Colorado Denver, Denver, Colorado 80217, United States.
² CU Population Center, University of Colorado Boulder, Boulder, Colorado 80309, United States.
³ MIT Senseable City Lab, Cambridge, Massachusetts 02139, United States.
⁴ Department of Civil and Environmental Engineering, Tufts University, Medford, Massachusetts 02155, United States.
⁵ Global Centre for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH Surrey, U.K.
⁶ Institute for Sustainability, University of Surrey, Guildford, GU2 7XH Surrey, U.K.
⁷ NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States.

PMID: 37789620
DOI: 10.1021/acs.est.3c04843

Abstract

Low-cost sensors (LCSs) for measuring air pollution are increasingly being deployed in mobile applications, but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and those of higher-quality instruments? Can data from LCSs be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions, we deployed low-cost PM_2.5 sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM_2.5 reference monitors (Teledyne T640) at nearby regulatory sites. Next, using the reference measurements, we developed different models to correct the OPC-N3 and DustTrak measurements and then transferred the corrections to the mobile setting. We observed that more complex correction models appeared to perform better than simpler models in the stationary setting; however, when transferred to the mobile setting, corrected OPC-N3 measurements agreed less well with the corrected DustTrak data. In general, corrections developed by using minute-level collocation measurements transferred better to the mobile setting than corrections developed using hourly-averaged data. Mobile laboratory speed, OPC-N3 orientation relative to the direction of travel, date, hour-of-the-day, and road class together explain a small but significant amount of variation between corrected OPC-N3 and DustTrak measurements during the mobile deployment. Persistent hotspots identified by the OPC-N3s agreed with those identified by the DustTrak. Similarly, maps of PM_2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak measurements agreed well. These results suggest that identifying hotspots and developing generalizable maps of PM_2.5 are appropriate use-cases for mobile LCS data.

Keywords: PM2.5; air quality; hotspots; low-cost sensors; mobile monitoring.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Environmental Monitoring / methods
Environmental Pollutants*
Particulate Matter / analysis

Substances

Air Pollutants
Environmental Pollutants
Particulate Matter