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

被引：7

作者：

deSouza, Priyanka ^{[6
,7
]}

Wang, An ^{[1
]}

Machida, Yuki ^{[1
]}

Duhl, Tiffany ^{[2
]}

Mora, Simone ^{[1
]}

Kumar, Prashant ^{[3
,4
]}

Kahn, Ralph ^{[5
]}

Ratti, Carlo ^{[1
]}

Durant, John L. ^{[2
]}

Hudda, Neelakshi ^{[2
]}

机构：

[1] MIT Senseable City Lab, Cambridge, MA 02139 USA

[2] Tufts Univ, Dept Civil & Environm Engn, Medford, MA 02155 USA

[3] Univ Surrey, Global Ctr Clean Air Res GCARE, Sch Sustainabil Civil & Environm Engn, Fac Engn & Phys Sci, Guildford GU2 7XH, Surrey, England

[4] Univ Surrey, Inst Sustainabil, Guildford GU2 7XH, Surrey, England

[5] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA

[6] Univ Colorado Denver, Dept Urban & Reg Planning, Denver, CO 80204 USA

[7] Univ Colorado Boulder, CU Populat Ctr, Boulder, CO 80309 USA

来源：

ENVIRONMENTAL SCIENCE & TECHNOLOGY | 2023年 / 57卷 / 41期

基金：

英国工程与自然科学研究理事会;

关键词：

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

D O I：

10.1021/acs.est.3c04843

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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 PM2.5 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 PM2.5 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 PM2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak measurements agreed well. These results suggest that identifying hotspots and developing generalizable maps of PM2.5 are appropriate use-cases for mobile LCS data.

引用

页码：15401 / 15411

页数：11

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