Air quality management benefits from an in-depth understanding of the emissions associated with, and composition of, local PM2.5 concentrations. Here, we investigate the changing role of biomass burning emissions to North American PM2.5 exposure by combining multiple satellite-, ground-, and simulation-based data sets biweekly at a 0.01° × 0.01° resolution from 2000 to 2022. We also developed a Buffered Leave Cluster Out (BLeCO) method to address autocorrelation and computational cost in cross-validation. Biomass burning emissions contribute an increasingly large fraction to PM2.5 exposure in the United States and Canada, with national annual population-weighted mean contributions increasing from 0.4 μg/m3 (3-5%) in 2000-2004 to 0.8-0.9 μg/m3 (9-14%) by 2019-2022, led by western North American 2019-2022 annual contributions of 1.4-1.9 μg/m3 (15-27%) and maximum seasonal contributions of 3.3-5.5 μg/m3 (29-49%). Other components such as nonbiomass burning Organic Matter (OM) and nitrate can be regionally as (or more) important, albeit with distinct seasonal variability. The contribution of total OM to PM2.5 exposure in the United States in 2016-2022 is 42.2%, comparable to all other anthropogenically sourced components combined. Comparison of BLeCO and random 10-fold cross-validation suggests that random 10-fold cross-validation may significantly underrepresent true uncertainty for total PM2.5 concentrations due to the clustered nature of PM2.5 ground-based monitoring.
© 2024 The Authors. Published by American Chemical Society.