School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

Combining Air Quality Modeling and Observations for Policy-Relevant Applications

By: Nash Skipper

Advisor: Dr. Armistead Russell (CEE)

Committee Members:  Dr. Jennifer Kaiser (CEE), Dr. Yongtao Hu (CEE), Dr. Nga L. Ng (CHBE), Dr. Rodney J. Weber (EAS)

Date and Time:  Wednesday April 5, 2023 12:30pm

Location:

ES&T L1175 / Zoom: https://gatech.zoom.us/j/91305546967; Passcode: 854224

Chemical transport models (CTMs) are numerical models which simulate atmospheric chemistry and physics based on first principles. CTMs are used to estimate the impacts of air quality policies and regulations as well as for more fundamental research. CTMs can be biased with respect to observed air pollutant concentrations; however, observational data can be combined with CTMs to improve model performance. Here, several applications of CTMs for policy-relevant analyses are studied and opportunities for incorporating observations to complement CTM results are explored. Specific applications are the air quality impacts of electric vehicle (EV) adoption in California, air quality impacts near ports in the Los Angeles area resulting from supply chain disruption caused by the Covid-19 pandemic, and US background ozone.
A CTM was applied to quantify the air quality co-benefits of EV in adoption in California. Air quality benefits scaled approximately linearly with increasing EV adoption. The simulated population-weighted statewide annual average PM2.5 concentration was reduced by about 0.5 µg/m3 in a scenario with 100% adoption of EVs.
Impacts of container ship backlogs at the Ports of Los Angeles and Long Beach were analyzed using a combination of satellite-based observations and CTM simulations to separate meteorological and emission impacts. After adjusting for year-to-year fluctuations in meteorology, a 28% emission-related increase in tropospheric NO2 column totals was found in areas immediately downwind of the ports.
An observation-model data fusion approach was developed to apportion CTM biases to US background and US anthropogenic sources and to reduce ensemble variation of US background ozone estimates. This approach was extended to estimate CTM biases in specific sources of US background ozone including naturally occurring sources, anthropogenic pollution from outside the US, and stratospheric ozone.
Overall, results show that CTMs can provide useful policy-relevant information as can observations but that sometimes more information can be gained by combining observations and CTM results than either can provide in isolation.

Complete announcement, with abstract, is attached.