Probabilistic Assessment of PM2.5 and Ozone Co-Occurring Extremes under Wildfire Smoke in the Contiguous United States

Abstract Wildfire smoke has become a significant cause of extreme air quality events across the United States. However, its role in the joint occurrence of fine particulate matter (PM2.5) and ozone extremes remains poorly understood. Most prior studies examined the two pollutants separately, even though there is growing evidence that co-occurrence may increase health risks. In this study, we developed a probabilistic spatiotemporal framework to quantify the risk of PM2.5-ozone co-occurring extremes under the influence of wildfire smoke across the contiguous United States from 2004 to 2023. The spatiotemporal Bayesian neural field (ST-BayesNF) model integrates satellite products on smoke coverage, ground measurements of PM2.5 and ozone, and meteorological reanalysis to estimate daily probabilities of co-occurring extremes at a spatial resolution of 5 km. Using a neural field, the model can explicitly capture nonlinear covariate effects, complex spatial dependence, and temporal evolution, as well as providing uncertainty quantification. Model assessments demonstrate that the ST-BayesNF model is able to differentiate coextreme from noncoextreme days and that the model has strong sensitivity to smoke density and its interaction with boundary layer height and near-surface temperature. Predictive results show a significant increase in co-occurrence risk during smoke events, and such amplified risks emerge not only in the western US but increasingly in the Midwest and Northeast due to long-range smoke transport. The ST-BayesNF model provides a probabilistic, spatiotemporally explicit approach for characterizing increased compound extremes under a changing fire regime.