Predicting behaviors and effects of biomass burning
Davis, Aika Yano
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Wildfires and prescribed burns are important sources of air pollutants and can significantly affect air quality at urban locations across large regions. Air quality forecasts generated with Eulerian numerical models can provide valuable information to environmental regulators and land managers about the potential impacts of fires. However, the ability of these models to simulate concentrated fire-related smoke plumes is limited since they lack fire specific physics and chemistry. A sub-grid plume model was coupled with a chemical transport model to address this issue. The modeling framework centered on a fire plume transport model, Daysmoke, and the Community Multiscale Air Quality modeling system (CMAQ) is used to simulate several fire episodes. The studied episodes were used to understand uncertainty in fire emissions and its effect on plume transport modeling and to verify the coupled system’s performance. The system was also used to simulate prescribed burning scenarios with five varying parameters: age of fuel bed, season, acreage, ignition type, and time of the day. Key findings relating to burn efficiency and emission reduction on future prescribed burnings will be discussed.