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Forecasting PM2.5 from Wildfires: Suggestions for Models Used for Air Quality Management
Citation:
Pierce, T., G. Hagler, J. Iiames, AND S. Michelsen-Correa. Forecasting PM2.5 from Wildfires: Suggestions for Models Used for Air Quality Management. International Technical Meeting on Air Pollution Modeling and its Application, Chapel Hill, NC, May 22 - 26, 2023.
Impact/Purpose:
This presentation describes out Air Resource Advisors (ARAs) in the Intergency Wildland Fire Air Quality Response Program use a variety of data and models to conduct wildfire smoke forecasting on a day to day basis during a deployment. This real-world application of air quality models in an emergency setting has revealed insights into model and model input limitations that will support future model development to simulate wildfire smoke scenarios. The four presenters all served as ARAs at various wildfires in 2022.
Description:
Wildfires annually burn several million ha across the U.S. and cause widespread episodes of poor air quality, associated with elevated concentrations of fine particulate matter (PM2.5) and volatile organic compounds (VOCs). In the U.S., Air Resource Advisors (ARAs) are assigned by the Interagency Wildland Fire Air Quality Response Program to assist with understanding and predicting smoke impacts on the public and fire personnel. To develop daily smoke forecasts, ARAs use a variety of tools, most notably a suite of air quality models supported by the U.S. Forest Service in Seattle, Washington. This presentation conveys insights from EPA-affiliated ARAs deployed to wildfires in 2022, which may yield improved models broadly used for air quality management. A few examples based on experience include: (1) detailed infrared imagery from aircraft is collected and archived daily – when coupled with satellite imagery this could improve the temporal and spatial mapping of emissions; (2) on days when little or no growth in the fire perimeter is reported, emissions may be underestimated due to difficulties in collecting daily IR imagery (from both aircraft and satellites) and/or active burning of biomass within the fire perimeter; (3) modeling the planetary boundary layer (PBL) near large wildfires is especially challenging, especially in mountainous terrain. Lingering smoke from nighttime burning can result in reduced growth of the PBL in the daytime, causing both elevated concentrations of PM2.5 near the surface and reduced fire activity. On the other hand, heat from the active portion of a fire could penetrate an elevated inversion, resulting in more emissions and vertical transport than would otherwise be estimated. By participating directly in an operational setting for making real-time forecasts of PM2.5, valuable lessons can be learned and as will be summarized in this presentation, could yield improvements in models like those used by the USEPA for air quality management.
