The primary objective of this Walter A. Rosenblith New Investigator Award study is to create fine-scale daily PM2.5 source impacts from major source and fuel categories from 2011-2020. This objective builds on recent work developing fine-scale daily air pollution exposure products in the United States, which have driven recent epidemiological studies on air pollution health effects.
Poster abstract for HEI Annual Conference 2022
Air pollution source impacts at fine scales for long-term regulatory accountability and environmental justice
Lucas RF Henneman
George Mason University, Fairfax, VA
The primary objective of this study is to create fine-scale daily PM2.5 source impacts from major source and fuel categories from 2011-2020. This objective builds on recent work developing fine-scale daily air pollution exposure products in the United States, which have driven recent epidemiological studies on air pollution health effects. These exposure products are produced from an ensemble of chemical transport models, ground-based air quality measurements, satellite observations, meteorological data, and land use metrics combined using statistical techniques such as land use regression and/or machine learning. Much of the same input data and statistical techniques used in ambient concentration ensemble modeling can be leveraged to create similarly detailed PM2.5 source impacts fields.
A second objective of this study is to use the source impacts fields to quantify exposure by population groups, with particular focus on historically disadvantaged populations. This portion of the study will follow on recent work establishing a framework for environmental justice studies of air pollution sources. As in these studies, I will leverage population-weighted exposure contributed from various source categories to identify exposure inequities.
The workflow will proceed as follows. First, I will use source apportionment to quantify PM2.5 source impacts at over 150 Chemical Speciation Network (CSN) monitoring sites across the United States from 2011-2020. Next, I will develop PM2.5 source impacts fields using the Community Multiscale Air Quality (CMAQ). For each source category, I will build statistical machine learning models to predict monitor-based source impacts using relevant predictors, including the CMAQ source impacts, physical proxies such as land use, and meteorology. With output from multiple trained statistical models for each source category, I will create an ensemble average with uncertainty for each day and location in the contiguous United States from 2011-2020. Using these source impacts field and methods that assess both average and extreme exposures, I will quantify exposure disparities across population groups.