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    Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale
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    • Jianzhao Bi
      Jianzhao Bi
      Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
      More by Jianzhao Bi
    • Avani Wildani
      Avani Wildani
      Department of Computer Science, Emory University, Atlanta, Georgia 30307, United States
      More by Avani Wildani
    • Howard H. Chang
      Howard H. Chang
      Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
      More by Howard H. Chang
    • Yang Liu*
      Yang Liu
      Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
      *E-mail: [email protected]
      More by Yang Liu
      Orcidhttp://orcid.org/0000-0001-5477-2186
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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2020, 54, 4, 2152–2162
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    https://doi.org/10.1021/acs.est.9b06046
    Published January 12, 2020
    Copyright © 2020 American Chemical Society
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    Abstract

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    Low-cost air quality sensors are promising supplements to regulatory monitors for PM2.5 exposure assessment. However, little has been done to incorporate the low-cost sensor measurements in large-scale PM2.5 exposure modeling. We conducted spatially varying calibration and developed a downweighting strategy to optimize the use of low-cost sensor data in PM2.5 estimation. In California, PurpleAir low-cost sensors were paired with air quality system (AQS) regulatory stations, and calibration of the sensors was performed by geographically weighted regression. The calibrated PurpleAir measurements were then given lower weights according to their residual errors and fused with AQS measurements into a random forest model to generate 1 km daily PM2.5 estimates. The calibration reduced PurpleAir’s systematic bias to ∼0 μg/m3 and residual errors by 36%. Increased sensor bias was found to be associated with higher temperature and humidity, as well as longer operating time. The weighted prediction model outperformed the AQS-based prediction model with an improved random cross-validation (CV) R2 of 0.86, an improved spatial CV R2 of 0.81, and a lower prediction error. The temporal CV R2 did not improve due to the temporal discontinuity of PurpleAir. The inclusion of PurpleAir data allowed the predictions to better reflect PM2.5 spatial details and hotspots.

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    Supporting Information

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    • Quality control for PurpleAir PM2.5 measurements (Section 1); evaluation of PurpleAir PM2.5 measurements (Section 2); nonlinearity of PurpleAir systematic bias (Section 3); validation of scale factor ρ (Section 4); PurpleAir calibration based on subsets of AQS stations (Table S1); HAC clustering feature space (Table S2); numbers and densities of continuous AQS stations (Table S3); spatial distribution of GWR slopes (Figure S1); clustered subdomains (Figure S2); ten-fold CV scatter plots (Figure S3); PM2.5 predictions at Ferguson Fire (Figure S4); PM2.5 predictions of the nonweighted model (Figure S5); PurpleAir dual-channel hourly measurements (Figure S6); AQS and PurpleAir hourly measurements (Figure S7); determination of scale factor ρ (Figure S8) (PDF)

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    Cite this: Environ. Sci. Technol. 2020, 54, 4, 2152–2162
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