Openly accessible low-cost measurements in PM2.5 exposure modeling: guidance for monitor deployment

Abstract

BACKGROUND AND AIM: High-resolution, high-quality exposure modeling is critical for assessing the health effects of PM2.5 in epidemiological cohorts. Sparse ground-level PM2.5 measurements, as key model input, may result in two critical issues in high-resolution exposure prediction: (1) they may affect the models’ accuracy in predicting the spatial distribution of PM2.5; (2) internal evaluation based on these measurements may not reliably reflect the model performance at the locations of interest (e.g., cohort residential locations). This study aimed to take advantage of PM2.5 measurements from an openly accessible low-cost PM2.5 network, PurpleAir, with an external validation dataset at residential locations of an epidemiological cohort to improve the accuracy of exposure prediction at the cohort locations, and propose metrics assessing the similarity between the monitor and cohort locations to guide future monitor deployment. METHODS: We utilized a spatiotemporal modeling framework to incorporate PM2.5 measurements from 51 agency/non-agency stations and 58 PurpleAir monitors in the Puget Sound region of Washington into high-resolution exposure assessment. A similarity metric based on principal component analysis (PCA) was developed to assess the PurpleAir monitors’ representativeness of the cohort locations. RESULTS:After including calibrated PurpleAir measurements as part of the dependent variable, the spatiotemporal validation (at the two-week level) R2 (root-mean-square error, RMSE) improved from 0.84 (2.22 μg/m3) to 0.92 (1.63 μg/m3). The spatial validation (in the longer term) R2 (RMSE) improved from 0.72 (1.01 μg/m3) to 0.79 (0.88 μg/m3). The exposure predictions showed a more realistic spatial pattern as well. We found that the PurpleAir monitors with shorter PCA distances could improve the model’s prediction accuracy more substantially than monitors with longer PCA distances. CONCLUSIONS:To our knowledge, this was the first attempt to evaluate the benefits of low-cost PM2.5 measurements for long- and short-term exposure prediction at cohort residential locations and to provide practical guidance for future monitor deployment with similarity metrics. KEYWORDS: PurpleAir, High-resolution, Exposure assessment, Fine particulate matter, Long-term, Short-term