An Analysis of Urban Air Quality and Health Impacts Using High Resolution Chemical Transport Model Simulations

Montgomery, Anastasia

doi:https://doi.org/10.21985/n2-00hc-xk14

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An Analysis of Urban Air Quality and Health Impacts Using High Resolution Chemical Transport Model Simulations

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Air pollution is a pervasive environmental issue that has significant impacts on human health. Urban areas are particularly susceptible to high levels of air pollution due to concentrated emissions and populations. Urban air pollution has been linked to a range of adverse health effects, including respiratory diseases, cardiovascular diseases, and increased mortality rates. Pollution arises from the release of chemicals into the atmosphere, largely stemming from industrial activities, transportation, and energy generation. Understanding the sources, transport, and effects of air pollutants is essential, though limitations in computational capability and data availability has long hindered the identification of high-impact hotspots at health relevant scales. Recent developments into chemical transport models and non-regulatory monitoring techniques now play a crucial role, as these methods contribute to the identification of pollutant levels, the identification of vulnerable populations, and the support of evidence-based decision-making for effective pollution control strategies. This dissertation investigates air quality over the Midwestern United states, centered over Lake Michigan-Chicago, using the latest developments in air quality modeling and observational tools. This research employs novel geospatial statistics and analytical methods to investigate concentrations of health-hazardous pollutants, namely nitrogen dioxide (NO2), ozone (O3), and fine particulate matter (PM2.5). Chapter 2 presents a high-resolution simulation of air pollution over the Southern Lake Michigan-Chicago region. Chemical transport models (CTMs) allow researchers to simulate how pollutants disperse and transform in the atmosphere. High-resolution simulations may enhance the accuracy of the model and increase its usefulness for public health assessments and policymaking. In this chapter, I examine the performance of a 1.3 km and 4 km simulation of pollutant concentrations over a four-month period (August 2018, October 2018, January 2019, and April 2019) in the Southern Lake Michigan-Chicago region. The 1.3 km simulation exhibits slightly better performance compared to the 4 km simulation. The study reveals distinct urban and rural patterns of pollution, with urban areas experiencing significantly higher concentrations of NO2 and PM2.5 (20% – 60% higher than rural areas), while O3 is simulated to be lower in urban areas (-6% compared to rural areas). Furthermore, the simulation highlights significant disparities in pollutant concentrations across neighborhoods in Chicago, with features such as highways contributing to substantial variations in pollution levels. This simulation provides valuable insights into the O3 chemistry regime in Chicago, finding that the O3 regime is transitional and VOC-limited, depending on the month of study. Overall, this research contributes to a better understanding of air pollution dynamics in the Southern Lake Michigan-Chicago region, shedding light on the spatial distribution of pollutants and their underlying chemistry. Chapter 3 presents a hotspot analysis of Chicago air quality by using three novel air quality products: a low-cost sensor network, observations from a satellite instrument, and a chemical transport model. The study addresses the lack of intraurban data validation by assessing the spatial agreement of air pollution patterns across multiple high-resolution datasets and applies the hotspot analysis to make recommendations for researchers and policymakers. I apply a hotspot clustering algorithm, Getis Ord Gi*, to identify areas of agreement and disagreement among the data products. The analysis reveals a Consensus hotspot on the West side of Chicago, indicating elevated pollution levels across different data products, wind directions, and seasons. This hotspot, predominantly inhabited by Hispanic and Latino people, requires urgent intervention as an environmental justice priority. Additionally, a medium-agreement hotspot identified by the low-cost sensors and satellite (i.e., Observational hotspot) highlights the need for additional regulatory monitoring in the affected community. Furthermore, a highway hotspot shows variations in NO2 concentrations near recessed and elevated highways, a feature not captured in model simulations. These findings provide insights into areas of high pollution exposure, underscores the importance of targeted interventions, and recommends additional development of monitoring tools for improved air quality management in Chicago. By integrating this hotspot approach into air quality management frameworks, policymakers can develop targeted interventions and implement sustainable practices to mitigate the effects of air pollution on vulnerable populations. In Chapter 4, the WRF-CMAQ simulation is used to investigate air pollution from an environmental justice perspective in Chicago. By integrating socioeconomic characteristics with air quality data, the study aims to uncover disparities in exposure and the contributing factors to environmental injustice. This research is the first to examine intraurban air pollution using a high-resolution CTM, addressing the limitations of coarser models that overlook significant pollution sources like highways within cities. This study is also the first comprehensive analysis to consider multiple pollutants, health outcomes, and their connection to environmental justice issues in Chicago. Findings reveal that pollutants and demographics exhibit high spatial variability, with no strong linear relationship between pollutants and racial, ethnic, or economic demographics. Although average exposure disparities in race and ethnicity are relatively small compared to baseline health rates, the Black population consistently experiences significantly higher rates of mortality, asthma, and pediatric asthma hospitalizations related to pollution. Racial and ethnic exposure disparities persist across income levels, indicating that income does not alter the relationship between pollution exposure or health outcomes. To address these inequalities, policies should consider both impact and exposure, as areas of health impact may not align with areas of high exposure. The study highlights the importance of incorporating health and exposure information in addressing air pollution injustices and advocates for equitable solutions. In summary, this PhD research is the first to apply a 1.3 km high-resolution CTM to study Chicago air pollution. By applying this high-resolution data, this research also presents frameworks to isolate areas of outsized exposure and analyze dataset disagreement through hotspot analysis. Further, the data generated in this research is applied to analyze the link of exposure and public health at urban scales. By advancing CTM research, developing new methods of evaluation, and pioneering methods to investigate pollution inequalities at intraurban scales, this research ultimately supports the development of evidence-based pollution analysis and effective controls.

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