Coupling of the Weather Research and Forecasting Model with AERMOD for Wildfire-induced Pollution Analysis

YUCHENG HE, Sanika Nishandar, Jiachen Zhang, Marko Princevac, University of California, Riverside

     Abstract Number: 650
     Working Group: Aerosol Exposure

Abstract
Wildfire-induced emissions can cause detrimental impacts on public health. While some regions lack air quality monitoring stations, even for those equipped with monitors, point measurements can be biased representing the air quality in the mesoscale neighborhood due to topographic and meteorological differences. The prediction of air quality through models is a very effective tool for the local authorities to make objective policies for the health benefit of residents. AERMOD is the current EPA-preferred regulatory guideline model for refined permit modeling, which can plot spatial pollution distribution. AERMOD can achieve high prediction accuracy with proper emission inventory and meteorological inputs. To overcome the limitation of lacking hourly meteorological observation data near the source, this study deployed the Weather Research and Forecasting (WRF) Model. WRF is capable of generating onsite past, as well as 384-hour forecasts meteorological inputs for AERMOD. The emission inventory relevant to the fire is estimated by coupling the FINN model, VIIRS satellite detection, and the LANDFIRE database. The WRF-predicted temperature is compared with observation data, while the AERMOD-predicted PM2.5 concentration is compared with measurement data. Both predictions demonstrated satisfactory agreement with the measurement. Such WRF-AERMOD system can predict the pollution distribution near the wildfire incident and assist the local agencies forecast the urban pollution distribution induced by wildfires.