Comparison of Fire Emission Inventories and Fire Emission Inventory Processor Updates

Comparison of Fire Emission Inventories and Fire Emission Inventory Processor Updates

Johnson, J., ¹ Vennam, P., ¹ Li, F., ¹ Tuite, K., ¹ Emery, C., ¹ and Yarwood, G.¹

¹ Ramboll Americas Engineering Solutions

7250 Redwood Blvd., Suite 105

Novato, CA 94945

Corresponding Author Contact Information: jjohnson@ramboll.com (415) 899-0752

Fires are large emission sources affecting ozone and PM_2.5 concentrations over regional scales. Therefore, accurate Fire Emission Inventories (FEIs) are needed for exceptional event analyses and State Implementation Plan (SIP) modeling. Emission estimates from currently available FEIs can differ by an order of magnitude so the decision of which FEI to include in modeling may have a significant impact on modeled air quality. In 2022, we developed a Python-based tool to process different FEIs into model-ready fire emission inputs and included options for temporal emission allocation and vertical plume rise scheme. Using this new tool, we developed fire emissions and evaluated photochemical model performance using four different FEIs throughout April and May of 2019 when transport of smoke from biomass burning in Mexico and Central America to Texas was frequent. We used Comprehensive Air quality Model with extensions (CAMx) for that analysis. We found that the three Fire Radiative Power (FRP)-based FEIs showed substantially smaller ozone biases and overall better statistical agreement with observations as compared to FINN, which uses a burned area methodology. EPA’s fire emission inventory uses FINN for Mexico and Central America fires. Recently, we updated the tool to implement a fifth FEI, the newly released Regional ABI and VIIRS fire Emissions (RAVE) product. RAVE possesses a unique combination of high temporal and spatial resolution that appears well-suited for high-resolution photochemical modeling. We developed fire emissions for the five available FEIs for a Summer 2021 Western U.S. CAMx modeling application, a period of active wildfires and poor air quality. We then evaluated ozone and PM_2.5 model performance against surface measurements and selected the two best performers for further evaluation. Finally, we evaluated the ozone and PM_2.5 impacts from alternate treatments of NOx speciation, temporal allocation, plume rise, and semi-volatility of primary organic carbon emitted by the fires. We plan to augment air quality model testing for additional years and seasons using all available FEIs and to evaluate ozone and PM_2.5 model performance against observations.