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© 2024. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The primary source of measurement error from widely used particulate matter (PM) PurpleAir sensors is ambient relative humidity (RH). Recently, the US EPA developed a national correction model for PM2.5 concentrations measured by PurpleAir sensors (Barkjohn model). However, their study included few sites in the southeastern US, the most humid region of the country. To provide high-quality spatial and temporal data and inform community exposure risks in this area, our study developed and evaluated PurpleAir correction models for use in the warm–humid climate zones of the US. We used hourly PurpleAir data and hourly reference-grade PM2.5 data from the EPA Air Quality System database from January 2021 to August 2023. Compared with the Barkjohn model, we found improved performance metrics, with error metrics decreasing by 16 %–23 % when applying a multilinear regression model with RH and temperature as predictive variables. We also tested a novel semi-supervised clustering method and found that a nonlinear effect between PM2.5 and RH emerges around RH of 50 %, with slightly greater accuracy. Therefore, our results suggested that a clustering approach might be more accurate in high humidity conditions to capture the nonlinearity associated with PM particle hygroscopic growth.

Details

Title
Calibration of PurpleAir low-cost particulate matter sensors: model development for air quality under high relative humidity conditions
Author
Mathieu-Campbell, Martine E 1   VIAFID ORCID Logo  ; Guo, Chuqi 2 ; Grieshop, Andrew P 3   VIAFID ORCID Logo  ; Richmond-Bryant, Jennifer 4   VIAFID ORCID Logo 

 Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA 
 Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA 
 Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA 
 Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA; Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA 
Pages
6735-6749
Publication year
2024
Publication date
2024
Publisher
Copernicus GmbH
ISSN
18671381
e-ISSN
18678548
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3132757173
Copyright
© 2024. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.