Multiscale characterization of urban air quality from observations and integrated datasets

Abstract

Urban air quality is shaped by processes operating across multiple spatial scales, from regional interactions that govern population exposure to local emission sources within cities. This thesis investigates these multiscale dynamics through two complementary analyses that together highlight the need for integrated, high-resolution approaches. The first study examines compound heat wave and PM2.5 pollution events across urban and surrounding rural areas in the contiguous United States over a 23-year period (2000–2022) using reconstructed high-resolution datasets. Results show that urban areas systematically experience more frequent, prolonged, and intense events than their rural surroundings, with approximately 98% of cities exhibiting stronger co-occurrence. Spatial patterns of compound events closely resembled those of PM2.5 pollution episodes, suggesting that air pollution plays a dominant role in driving compound extremes, while recent increases in the western U.S. are linked to wildfire emissions. The second study focuses on within-city methane (CH4) emissions, using a major landfill site in Oklahoma City as a case study to integrate mobile measurements, satellite observations, and WRF-Chem simulations. Near-surface CH4 concentrations showed a strong morning-to-afternoon variability driven by boundary layer evolution, with early-morning peaks exceeding 30,000 ppb that declined substantially by the afternoon hours. WRF-Chem demonstrated the spatial structure of the CH4 plume well, and the ratio of observed to simulated enhancements was used to inversely estimate the landfill emission rate, yielding a mobile-derived estimate consistent with the satellite observation to within roughly 7% (996.92 and 928.07 kg CH4 hr–1 respectively). These results demonstrate that urban air quality cannot be fully understood without resolving both regional-scale environmental stressors and fine-scale emission and transport processes. This work underscores the importance of integrating ground-based measurements, satellite observations, and process-based modeling to achieve a consistent, multiscale characterization of urban air quality, with implications for improving emission quantification and informing mitigation strategies in cities.