Downwind Ozone Changes of the 2019 Williams Flats Wildfire: Insights From WRF-Chem/DART Assimilation of OMI NO2, HCHO, and MODIS AOD Retrievals

Abstract

This study investigates the impacts of the Williams Flats wildfire (August 3-9, 2019) on ozone conccentrations and chemistry in the downwind region using an assimilation framework with satellite retrievals and meterological measurements. By assimilating satellite-retrieved nitrogen dioxide (NO2) and formaldehyde (HCHO) columns, we improve surface ozone simulations (index of agreement of 0.94 compared to station measurements) and utilize aerosol optical depth (AOD) retrievals to simulate spatial dynamics of wildfire plumes better. Comparing simulated ozone with the Fire Influence on Regional to Global Environments and Air Quality aircraft measurements reveals a fair simulation of ozone in smoke and non-smoke plumes within the lower troposphere (7.00 ppbv mean absolute error). Comparing near-distance and far-distance locations, the study observed higher-than-average concentrations of nitrogen oxides, peroxyacetyl nitrate, nitric acid, and oxygenated volatile organic compounds due to wildfire emissions, which increased ozone concentrations by 3–5 ppbv during the wildfire in near-distance locations and 2–3 ppbv in far-distance locations. During more intense wildfire days, above 120 ppbv carbon monoxide plumes transported over the Rocky Mountains to the east. Ozone regime indicators showed a clear transition area downwind of the wildfire region that exacerbated the formation of ozone downwind. Our findings suggest that a joint assimilation of NO2 column, HCHO column, and AOD can enhance our understanding of wildfire-associated ozone chemistry and dynamics.

Type
Publication
JGR Atmosphere, Volume 128, Issue 11
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Supplementary notes can be accessed here.

Prof. Yunsoo Choi
Prof. Yunsoo Choi
Professor of Atmospheric Chemistry, Air-Quality Modeling, AI (Deep Learning/Machine Learning), Satellite Remote Sensing

My research interests include Atmospheric Chemistry, Air-Quality Modeling and AI (Deep Learning/ Machine Learning).

Mahsa Payami
Mahsa Payami
Graduate student of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Air-Quality and Artifical Intelligence.

Ali Mousavinezhad
Ali Mousavinezhad
Ph.D. candidate of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Air-Quality, Numerical Modeling.

Nima Khorshidian
Nima Khorshidian
Graduate Student of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Air-Quality, and Numerical Modeling.