Abstract
Wildfires are common dry-season occurrence in forested areas and cause various air quality and public health issues. To supplement the risk management of such wildfires, we ran a case study of the 2023 Canadian wildfires. The 2023 Canadian wildfire was a product of a dry late spring, ,which eleas to record fires. In this study, we run the HYSLPIT models with different fire emissions (GBBEPx, FEER, CFAS, etc) and different plume rise schemes (Briggs and Sofiev) to study the impact of emission and plume rise estimation on wildfire air quality forecast. To determine the accuracy of each sensitivity experiment, we compared the PM2.5 concent6rations from the HYSPLIT runs with ground measurements from AirNow stations across the study areas of New York, Philadelphia, and Washington DC. We found that when using the GFAS emission we would get the highest PM2.5 concentration readings, up to 108 μg/m2 in New York. However, when compared to the AirNOW, PM2.5 station readings we found that the GFAS readings were consistently several hours late. Out of all emissions, we found that FEER reported the smallest PM2.5 concentrations as they never reached above 25 μg/m2.
How to Cite:
, I., , A. & , Y.,
(2023) “Comparison modelling of PM2.5 concentrations in the 2023 Canadian Wildfires between various emissions data products (GBBEPx, GFAS, FEER) in HYSPLIT ”,
Journal of Student-Scientists' Research 5.
doi: https://doi.org/10.13021/jssr2023.3915
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