Wildfire Emission Modeling: Integrating BlueSky and SMOKE

Atmospheric chemical transport models are used to simulate historic meteorological episodes for developing air quality management strategies. Wildland fire emissions need to be characterized accurately to achieve these air quality management goals. The temporal and spatial estimates of emissions from fires, both wild and prescribed, have been problematic primarily because of uncertainty in the size and location of sources, and their temporal and spatial variability. Therefore, methods to estimate wildfire emissions that characterize their temporal and spatial variability are needed. The US Forest Service (USFS) and the US Environmental Protection Agency (EPA) have signed an interagency agreement to improve the episodic modeling of fires with improved fuel loading data, fire location information, and fire behavior modeling (including plume behavior), using meteorological inputs. The USFS has developed a tool known as BlueSky to predict cumulative impacts of smoke from forest, agricultural, and range fires. The BlueSky modeling framework combines state of the art emissions, meteorology, and dispersion models to generate predictions of smoke impacts across the landscape. The Sparse Matrix Operator Kernel Emission (SMOKE) processing system is a tool that creates gridded, speciated, and temporally allocated emission estimates for use in atmospheric chemical models. Portions of these tools have been combined to allow for an accurate characterization of fuel loading, temporal and spatial distribution of fire emissions, and a more accurate representation of fire plumes. By combining these two tools, the ability to simulate the impact of wildfires on air quality and develop air quality management strategies will be enhanced. This paper shows results from combining these two tools and an example from an air quality modeling simulation.