Abstract
Large wildfires are an increasing threat to the western U.S. In the 2017 fire season, extensive wildfires occurred across the Pacific Northwest (PNW). To evaluate public health impacts of wildfire smoke, we integrated numerical simulations and observations for regional fire events during August-September of 2017. A one-way coupled Weather Research and Forecasting and Community Multiscale Air Quality modeling system was used to simulate fire smoke transport and dispersion. To reduce modeling bias in fine particulate matter (PM2.5) and to optimize smoke exposure estimates, we integrated modeling results with the high-resolution Multi-Angle Implementation of Atmospheric Correction satellite aerosol optical depth and the U.S. Environmental Protection Agency AirNow ground-level monitoring PM2.5 concentrations. Three machine learning-based data fusion algorithms were applied: An ordinary multi-linear regression method, a generalized boosting method, and a random forest (RF) method. 10-Fold cross-validation found improved surface PM2.5 estimation after data integration and bias correction, especially with the RF method. Lastly, to assess transient health effects of fire smoke, we applied the optimized high-resolution PM2.5 exposure estimate in a short-term exposure-response function. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 183 (95% confidence interval: 0, 432), with 85% of the PM2.5 pollution and 95% of the consequent multiple-cause mortality contributed by fire emissions. This application demonstrates both the profound health impacts of fire smoke over the PNW and the need for a high-performance fire smoke forecasting and reanalysis system to reduce public health risks of smoke hazards in fire-prone regions.
Highlights
Toxic smoke from fire hotspots during the fire season poses a serious health threat in many fire-prone regions around the world
We used this integrated assessment of smoke concentrations to conduct a case study for a series of large fire events over the Pacific Northwest (PNW) region during summer 2017, evaluating PM2.5 modeling performance as well as regional health effects of the wildfire smoke by separating fire emission contributions from other sources
The final model evaluation results were obtained by averaging all the results from the 10 validation processes. We evaluated their performance in terms of multiple statistical metrics such as mean absolute error (MAE), fractional bias (FB), R-squared (R2 ), and root mean squared error (RMSE)
Summary
Toxic smoke from fire hotspots during the fire season poses a serious health threat in many fire-prone regions around the world. In contrast to most other areas of the USA, that have continuously improved air quality during the last three decades, the fire-prone Northwestern region shows increasing trends in both ground-based PM2.5 pollution extremes and space-based aerosol optical depth (AOD) [10] These increasing pollution trends have been attributed to a prevalence of wildfires across the Northwest [10], as supported by many other regional smoke monitoring and modeling studies. We applied a machine learning (ML)-based data integration approach based on the three major assessment elements—ground monitoring PM2.5 concentrations, satellite AOD retrievals, and source-oriented CTM simulations—to identify fire source contributions to regional PM2.5 pollution and population health exposure We used this integrated assessment of smoke concentrations to conduct a case study for a series of large fire events over the PNW region during summer 2017, evaluating PM2.5 modeling performance as well as regional health effects of the wildfire smoke by separating fire emission contributions from other sources
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