Strategies for reducing exposure to ambient air pollution in urban areas may be less effective as pollutants and their sources have shifted from being dominated by large point sources to more complex mixtures that include a sizeable fraction of traffic-related air pollutants (TRAP). In past decades, urban air pollution management strategies were designed to control pollutant emissions from point sources, while traffic-related emissions primarily were controlled by federal regulations. This approach now may not address the exposures experienced by vulnerable individuals that can result in adverse health impacts and inequities in the distribution of health impacts. New tools and methods are needed to characterize exposures from emission sources including traffic. This dissertation aims to address this need by applying and evaluating several methods to estimate exposures, focusing on TRAP.
The first aim examines trends in emissions, concentrations and source apportionments of fine particulate matter (PM2.5, particles with a diameter less than 2.5 µm) in two large Midwest U.S. cities, Detroit, Michigan, and Chicago, Illinois. The analysis reveals that the fraction of PM2.5 due to mobile sources and other local emissions have increased (Detroit) or stayed constant (Chicago), even as total PM2.5 concentrations have decreased in both cities. The methodology demonstrated in this aim could be used to compare trends in the share of PM2.5 contributed by vehicles across major cities; many cities have different local regulations and fleet mixes that may affect trends in vehicle-related PM2.5, and the methods in this aim could be used to identify potentially preferred pollution reduction strategies.
The second aim provides an operational evaluation of RLINE, a research-level line-source dispersion model developed by the United States Environmental Protection Agency (EPA) for the near-road environment. Model performance was best at sites close to major roads, during downwind conditions, during weekdays, and during certain seasons. Implications for regulatory, health impact and epidemiologic applications include the importance of selecting appropriate pollutants, using appropriate monitoring approaches, considering prevailing wind directions during study design, and accounting for uncertainty.
The third aim examines the sensitivity of exposure estimates produced by the RLINE model to the model’s meteorological, emission and traffic allocation data. The application focuses on health studies examining near-road exposures to TRAP. Overall, results highlight the need for appropriate model inputs, especially meteorological inputs, in dispersion model applications designed to estimate near-road concentrations and exposures to TRAPs.
The fourth aim quantifies source contributions to individual exposures and provides an apportionment of exposures. Results show that most of the exposure was derived from background levels, although contributions from non-commercial traffic sources provided important contributions during the evening and early morning periods in the “indoor-at-home” micro-environment. Using the presented methodology for exposure apportionment, interventions incorporating the temporal and spatial nature of exposure could be applied to potentially lower the exposure of individuals in vulnerable groups.
This dissertation identified results that emphasize the need to target mobile sources of air pollutants in policies and regulations intended to decrease pollutant concentrations in urban areas, and it provides methods to estimate exposures. Predicting exposures of vulnerable and susceptible populations, including low-income and minority individuals living near major roads, may be particularly challenging, but these populations also are likely to suffer a disproportionate share of vehicle-related health impacts. The modeling approaches examined in this dissertation can help characterize exposures and evaluate strategies that reduce adverse impacts.PHDEnvironmental Health SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145852/1/cmilando_1.pd
