Speaker: Xiaomeng Jin, Rutgers University.

Title: Observing the air quality impacts of wildfires from space

Abstract: Wildfire occurrences have experienced a notable surge over the past few decades in the western United States. Climate change is expected to increase the frequency of fire-prone weather conditions. While the impacts of wildfires on particulate matter air quality are well known, the extent to which biomass burning emissions affect gaseous air pollutants, such as ozone and its precursors, is less clear. Wildfires emit large amounts of ozone precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), which react in the presence of sunlight to produce tropospheric ozone. The episodic and transient nature of wildfires makes it challenging to observe the air quality impacts of wildfires. Here, we present how we use new-generation satellite observations to study wildfire emissions, and how they affect the formation of secondary air pollutants when mixed with anthropogenic emissions. Satellite observations show that fires cause widespread increase of ozone precursors emissions, which partially offset the reduction of precursor emissions from decades of anthropogenic emission control programs. The impacts of wildfire NOx emissions are concentrated near-field over NOx-limited region, and impacts of VOC emissions are more widespread, which extend to the VOC-limited urban areas, both contributing to more efficient ozone production.

Bio: Xiaomeng Jin is an assistant professor at the Department of Environmental Sciences at Rutgers, The State University of New Jersey. She received her PhD in Earth and Environmental Sciences in 2020 from Columbia University and then worked as an NOAA Climate and Global Change Postdoc Fellow at the Department of Chemistry, University of California, Berkeley. She studies atmospheric chemistry and her research aims to advance the understanding of the source, chemical formation, and impacts of air pollution, especially surface ozone and fine particulate matter pollution, using remote sensing observations along with computer models and in-situ measurements.