Correction and Accuracy of PurpleAir PM 2.5 Measurements for Extreme Wildfire Smoke.

Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE

Original Publication: Basel, Switzerland : MDPI, c2000-

Air Pollutants*/analysis ; Wildfires* ; Air Pollution*/analysis; United States ; Particulate Matter/analysis ; Smoke/analysis ; Environmental Monitoring/methods

PurpleAir particulate matter (PM) sensors are increasingly used in the United States and other countries for real-time air quality information, particularly during wildfire smoke episodes. Uncorrected PurpleAir data can be biased and may exhibit a nonlinear response at extreme smoke concentrations (>300 µg/m3). This bias and nonlinearity result in a disagreement with the traditional ambient monitoring network, leading to the public’s confusion during smoke episodes. These sensors must be evaluated during smoke-impacted times and then corrected for bias, to ensure that accurate data are reported. The nearby public PurpleAir sensor and monitor pairs were identified during the summer of 2020 and were used to supplement the data from collocated pairs to develop an extended U.S.-wide correction for high concentrations. We evaluated several correction schemes to identify an optimal correction, using the previously developed U.S.-wide correction, up to 300 µg/m3, transitioning to a quadradic fit above 400 µg/m3. The correction reduces the bias at each air quality index (AQI) breakpoint; most ambient collocations that were studied met the Environmental Protection Agency’s (EPA) performance targets (twelve of the thirteen ambient sensors met the EPA’s targets) and some smoke-impacted sites (5 out of 15 met the EPA’s performance targets in terms of the 1-h averages). This correction can also be used to improve the comparability of PurpleAir sensor data with regulatory-grade monitors when they are collectively analyzed or shown together on public information websites; the methods developed in this paper can also be used to correct future air-sensor types. The PurpleAir network is already filling in spatial and temporal gaps in the regulatory monitoring network and providing valuable air-quality information during smoke episodes.

West J Med. 2002 May;176(3):157-62. (PMID: 12016236)
Indoor Air. 2020 Mar;30(2):213-234. (PMID: 31709614)
Sci Total Environ. 2020 Dec 15;748:141396. (PMID: 32798875)
J Air Waste Manag Assoc. 2020 Jun;70(6):583-615. (PMID: 32240055)
J Air Waste Manag Assoc. 2001 Jan;51(1):109-20. (PMID: 11218418)
Environ Res. 2016 Oct;150:227-235. (PMID: 27318255)
Atmos Environ (1994). 2021;247:. (PMID: 33889052)
Sensors (Basel). 2020 Jun 30;20(13):. (PMID: 32630124)
Atmos Meas Tech. 2016 Nov 1;9(11):5281-5292. (PMID: 32802212)
Sci Total Environ. 2018 May 15;624:586-595. (PMID: 29272827)
Environ Pollut. 2019 Feb;245:932-940. (PMID: 30682749)
Environ Res. 2019 Jun;173:462-468. (PMID: 30981117)
Atmos Pollut Res. 2022 Apr;13(4):1-9. (PMID: 36777262)
Atmos Meas Tech. 2020;13(11):6343-6355. (PMID: 33777248)
Atmos Meas Tech. 2021 Jun 22;4(6):. (PMID: 34504625)
Geohealth. 2021 May 01;5(5):e2020GH000359. (PMID: 33977180)
Sensors (Basel). 2022 Jun 23;22(13):. (PMID: 35808235)
J Air Waste Manag Assoc. 2020 Feb;70(2):158-179. (PMID: 31403397)
Atmos Environ (1994). 2021;246:. (PMID: 33746555)
J Air Waste Manag Assoc. 2020 Nov;70(11):1165-1185. (PMID: 32915705)
Proc Natl Acad Sci U S A. 2018 Jul 31;115(31):7901-7906. (PMID: 30012611)
Sensors (Basel). 2020 Aug 25;20(17):. (PMID: 32854443)

internal United States EPA EPA

Keywords: PM2.5; PurpleAir; air quality index (AQI); air sensor; correction; evaluation; wildfire smoke

0 (Particulate Matter)
0 (Smoke)
0 (Air Pollutants)

Date Created: 20221223 Date Completed: 20221226 Latest Revision: 20240909

20240909

PMC9784900

10.3390/s22249669

36560038