Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale
A spatially varying calibration and a down-weighting strategy have been proposed to incorporate volunteer-generated low-cost air quality data from PurpleAir sensors into large-scale PM2.5 exposure assessment while minimizing the negative impacts of their significant uncertainty. The inclusion of PurpleAir measurements allowed the exposure estimates to better reflect PM2.5 spatial details and hotspots such as wildfire smokes. The proposed calibration and modeling strategies can be used in regions with insufficient reference-grade measurements to improve air pollution exposure assessment.
Bi, J., Wildani, A., Chang, H. H., & Liu, Y. (2020). Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at A Large Spatial Scale. Environmental Science & Technology. 10.1021/acs.est.9b06046
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