UC Davis

Air pollution exposure is associated with increased risk for multiple adverse health outcomes including neurological disorders, asthma, diabetes, ischemic heart disease, and death. Race/ethnicity and income are predictors for air pollution exposure across most cities in the United States due to past discriminatory housing practices such as redlining. Future public policies must address this Environmental Justice issue so that all socio-economic groups have access to clean air.Predicting air pollution exposure disparities under different future scenarios is a difficult task because non-linear chemistry governs the formation of many pollutants. Historical relationships between pollutant concentrations and land-use or even pollutant concentrations and emissions may therefore not be useful when predicting future conditions. Chemical Transport Models (CTMs) predict air pollution concentrations based on fundamental chemical and physical equations that can accurately transition to future conditions. The research in this thesis explores how to refine CTM inputs and configuring CTM spatial resolution to accurately quantify air pollution exposure disparities in the present day and in the future. In Chapter 2, ten major spatial surrogates describing the detailed locations of air pollution emissions in California are created/updated for the base year 2010 and future years from 2015 to 2040. The updated spatial surrogates generally improve CTM predictions for PM mass and EC concentrations in the Sacramento area (~10% for PM, ~3% for EC), the Bay Area (~3% for PM, ~1.5% for EC), and the region surrounding Los Angeles (~5% for PM, ~4% for EC). The updated spatial surrogates also improve predicted NOx concentrations in the core region of Los Angeles (~6%). Chapter 3 explores the relationship between domain size and spatial resolution that affects predicted air pollution disparities in present day and future simulations when data support from measurements is not available. Overall WRF/Chem CTM accuracy improves approximately 9% as spatial resolution increases from 4 km to 250 m in present-day simulations. Exposure disparity results are consistent with previous findings: minorities experience higher exposure than White residents. Predicted exposure disparities are found to be a function of the model configuration. CTM configurations that use spatial resolution/domain size of 1 km / 103 km2 and 4 km / 104 km2 over Los Angeles can detect a 0.5 µg m-3 exposure difference with statistical power greater than 90%. Chapter 4 conducts a comprehensive analysis of health co-benefits, racial disparities, and source / composition in air pollution exposure under six future energy scenarios and four future meteorology scenarios in California for future year 2050. Deeper reductions in the carbon intensity of energy sources progressively are found to reduce exposure to PM2.5 mass and PM0.1 mass for all California residents. The three energy scenarios that achieve an ~80% reduction in GHG emissions relative to 1990 levels simultaneously produce the greatest reduction in PM exposure for all California residents and the greatest reduction in the racial disparity of that exposure. The EJ assessment shows that adoption of low-carbon energy sources in the year 2050 reduces the race/ethnicity disparity in air pollution exposure in California by as much as 20% for PM2.5 mass and by as much as 40% for PM0.1 mass. Future studies should apply the methods developed in this thesis to other locations across the United States in order to better understand how future policies such as a transition to low carbon energy can help to reduce air pollution exposure disparities by race/ethnicity.