Johnathan Rush - ESS Open Archive

Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM2.5) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a daily high-resolution PM2.5 machine-learning model covering the contiguous US from 2003 through 2023. XIS uses aerosol optical depth from satellites and a parsimonious set of additional predictors to make predictions at arbitrary points, capturing near-roadway gradients and allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. Averaging across all years in site-level cross-validation, the weighted mean absolute error of predictions (MAE) was 2.09 μg/m3, a substantial improvement over the mean absolute deviation from the median, which was 4.15 μg/m3. Comparing XIS to a leading product from the US Environmental Protection Agency, the Fused Air Quality Surface Using Downscaling (FAQSD), we obtained a 17% reduction in MAE. We also found a stronger relationship between PM2.5 and social vulnerability with XIS than with the FAQSD. Thus, XIS has potential for reconstructing environmental exposures, and its predictions have applications in environmental justice and human health.