Comparative Analysis of Urban Green Space and Air Quality Impacts

Thesis Abstract

Urbanization has intensified exposure to ambient air pollutants while diminishing green space, potentially altering ecosystem services and public health outcomes. This study examines how variations in urban green space influence air quality across five mid-sized cities with similar demographic profiles but differing urban morphologies, addressing the gap in cross-city comparative evidence on green infrastructure benefits under divergent policy regimes. The aim is to quantify the relationship between green space extent and air pollutant concentrations, and to identify contextual modifiers of this relationship. Specific objectives are (i) to assess spatial patterns of green space cover using high-resolution land-use data; (ii) to quantify concentrations of PM2.5, NO2, and O3 from 2018 to 2023 using fixed-site network data and supplemental passive samplers; (iii) to evaluate short- and long-range air quality impacts of green space using regression and spatial econometrics; (iv) to determine mediating factors such as traffic density, meteorology, and urban canyons; and (v) to formulate policy-relevant recommendations for urban planning and green infrastructure investment. A mixed-methods approach guides the investigation. The study adopts a cross-sectional design with a repeated-measures component across five cities, sampling 40–60 urban blocks per city to capture heterogeneity in green space distribution. Data collection comprises (a) quantitative air quality data from municipal monitoring networks complemented by 40 calibrated passive samplers deployed for two-week periods in each season over three years, (b) high-resolution land-cover data derived from satellite imagery and municipal GIS layers to compute vegetation indices (NDVI) and green space per capita, and (c) socio-demographic and traffic-related covariates from census and transportation datasets. Instruments include standardized air quality calibration protocols, GIS-based mapping tools, and a semi-structured interview guide for urban planners to capture policy context. Analytical strategies include descriptive statistics, spatial autocorrelation tests, and panel regression models to estimate the impact of green space metrics on pollutant concentrations while controlling for meteorology, traffic, and socio-economic factors. Spatial econometric models (Spatial Lag and Spatial Error) address potential spillovers between neighborhoods. Mediation analysis tests whether traffic density and meteorological conditions mediate the green space–air quality relationship. Anonymized stakeholder insights are analyzed thematically to triangulate quantitative findings and illuminate policy applicability. Robustness checks employ alternative green space definitions (percentage cover, tree canopy, and per capita green space) and subgroup analyses by city typology (compact vs. dispersed urban forms). Expected findings anticipate a negative association between green space extent and concentrations of PM2.5 and NO2, with diminishing returns at higher green space thresholds. Ozone (O3) responses may exhibit nuanced patterns due to regional photochemical interactions; urban canyons and proximity to major roads are expected to moderate these effects. The analysis should reveal that the strength of the green space–air quality relationship is contingent on city-scale factors such as traffic intensity, climate, and governance structures, with stronger effects in cities implementing dense, well-maintained green corridors and reduced vehicle emissions. The study contributes to knowledge by providing a rigorous cross-city empirical assessment of how urban green spaces influence air quality, clarifying the conditions under which green infrastructure yields measurable air quality benefits and informing scalable urban planning strategies. It advances methodological integration of high-resolution remote sensing, ground-based monitoring, and spatial econometrics within environmental science. Policy implications include targeted design of green networks to maximize pollutant uptake, prioritization of green corridors in high-traffic districts, and integration of air quality objectives into urban development plans. The main conclusion is that while urban green spaces can improve air quality, their effectiveness is highly context-dependent, necessitating complementary measures such as traffic management and emission controls to achieve meaningful air quality gains across diverse urban forms. The recommendations advocate for strategic allocation of green space investments, enhanced data sharing between environmental and planning agencies, and longitudinal monitoring to evaluate long-term impacts.