Abstract

Air pollution is a major environmental health concern and has been associated with respiratory disease, cardiovascular morbidity, metabolic disorders, premature mortality, and additional adverse health outcomes. Air pollution often exhibits substantial variability at fine spatial scales, yet conventional regulatory monitoring networks are too sparse to resolve neighborhood-level exposure gradients that may exist in many urban and rural communities. Hyperlocal differences in pollutant concentrations—driven by local emission sources, land use characteristics, atmospheric chemistry, and meteorological conditions—can lead to systematic differences in long-term exposure among populations living only short distances apart. Improving exposure characterization therefore requires dense monitoring strategies and spatial modeling approaches capable of resolving fine-scale spatial variability, particularly in under-monitored regions. This dissertation integrates high-resolution air pollutant measurements and spatial modeling to characterize hyperlocal variability across diverse environmental contexts. The first study developed and evaluated intra-urban land-use regression models to estimate nitrogen oxides (NOx and NO2) within a compact 12 km2 urban domain in Louisville, Kentucky. Using a 60-site passive sampling network, annual and warm seasonal models (2019, 2021-2023) explained a large portion of spatial variability (Adjusted R2 = 0.56 – 0.76 for annual NOx, 0.56 – 0.63 for annual NO2, 0.57 – 0.71 for warm-season NOx, 0.49 – 0.59 for warm-season NO2). Ordinary kriging was applied when residual spatial autocorrelation was detected, improving predictive performance. The second study characterized hyperlocal ozone (O3) variability across ~12 km² urban domains in Louisville, Kentucky and St. Louis, Missouri using repeated two-week integrated passive sampling. Two-week average ozone mixing ratios varied across sites by approximately 4 to 59 ppb depending on sampling month, and annual or seasonal averages differed by roughly 6 to 15 ppb within the same urban domain. Site spatial rankings shifted across seasons and years, but a persistent neighborhood-scale ozone hotspot was identified in St. Louis during the 2023 campaign. In Louisville, a statistically significant increase in ozone mixing ratios was observed from 2019 to 2024. The third study conducted air quality monitoring near a beef slaughterhouse and a hog concentrated animal feeding operation (CAFO) in rural Missouri using two-week integrated passive samplers for ammonia (NH3) and hydrogen sulfide (H2S), along with calibrated low-cost sensors for PM2.5 and PM10. Across both facilities, observed NH3 mixing ratios ranged from approximately 1 to 53 ppb and H2S mixing ratios ranged from 0 to 1 ppb. NH3 and H2S mixing ratios near the CAFO were significantly associated with the frequency of winds from the facility, whereas such wind associations were not observed near the slaughterhouse. PM2.5 and PM10 concentrations were generally within applicable air quality standards and were not associated with facility-specific wind directions. Collectively, this dissertation contributes to improving air quality characterization across urban and rural settings and informing environmental health research and decision-making.

Committee Chair

Jay Turner

Committee Members

Jenna Ditto; Karen DeMatteo; Randall Martin; Ray Yeager

Degree

Doctor of Philosophy (PhD)

Author's Department

Energy, Environmental & Chemical Engineering

Author's School

McKelvey School of Engineering

Document Type

Dissertation

Date of Award

4-29-2026

Language

English (en)

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