Evaluating estimation methods for wildfire smoke and their implications for assessing health effects

Growing wildfire smoke represents a substantial threat to air quality and human health in the US and across much of the globe. However, the impact of wildfire smoke on human health remains imprecisely understood, due to uncertainties in both the measurement of population wildfire smoke exposure and dose-response functions linking exposure to health. Here, we compare daily wildfire smoke-related surface fine particulate matter (PM2.5) concentrations estimated using three approaches, including two chemical transport models (CTMs): GEOS-Chem and Community Multiscale Air Quality (CMAQ), and one machine learning (ML) model over the contiguous US in 2020, a historically active fire year. We study the consequences of these different approaches for estimating smoke PM2.5 concentrations and the effects of smoke PM2.5 on mortality. In the western US, compared against surface PM2.5 measurements from US Environmental Protection Agency (EPA) and PurpleAir sensors, we find that CTMs overestimate PM2.5 concentrations during extreme smoke episodes by up to 3-5 fold, while ML estimates are largely consistent with surface measurements. However, in the eastern US, where smoke levels were much lower in 2020, CTMs show modestly better agreement with surface measurements. We develop a calibration framework that integrates CTM- and ML-based approaches and yields estimates of smoke PM2.5 concentrations that outperform each individual approach. When combining the estimated smoke PM2.5 concentrations with county-level mortality rates, we find consistent effects of low-level smoke on mortality but large discrepancies on the effects of high-level smoke exposure across different methods. Our research highlights the benefits and costs of different estimation methods for understanding the health impacts of wildfire smoke, and demonstrates the importance of bench-marking estimates with available surface measurements.