Calibration of PurpleAir low-cost particulate matter sensors: model development for air quality under high relative humidity conditions
| Autor: | M. E. Mathieu-Campbell, C. Guo, A. P. Grieshop, J. Richmond-Bryant |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Atmospheric Measurement Techniques, Vol 17, Pp 6735-6749 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1867-1381 1867-8548 |
| DOI: | 10.5194/amt-17-6735-2024 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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