Assessing the Impact of Urban Green Spaces on Air Quality in Metropolitan Areas

Thesis Abstract

Urban air pollution remains a pressing environmental and public health challenge in metropolitan areas worldwide, with rapid urbanization contributing to increased emissions of pollutants such as particulate matter (PM10 and PM2.5), nitrogen oxides (NOx), and ozone (O3). While urban green spaces (UGS) are widely advocated for their potential to improve air quality and enhance urban livability, empirical evidence detailing the extent and mechanisms of their impact remains mixed and context-dependent. This study aims to empirically assess the influence of urban green spaces on ambient air quality in metropolitan settings, with a targeted focus on quantifying pollutant variations attributable to green space characteristics and spatial distribution. The specific objectives are to (1) evaluate the correlation between green space density and concentrations of key air pollutants; (2) analyze the moderating effects of vegetation types and spatial configuration on pollutant levels; (3) identify spatial patterns of air quality improvements associated with green spaces; and (4) provide policy recommendations for optimizing green infrastructure to mitigate air pollution. The research adopts a mixed-methods approach, combining quantitative spatial analysis with qualitative insights to comprehensively understand the complex interactions between urban greenery and air quality. The study population comprises air quality monitoring stations located within the metropolitan area of a major city, with a sample frame of 30 stations strategically positioned across zones with varying green space coverage. Satellite-based remote sensing data supplemented by ground-truth measurements collected over a 12-month period (covering both dry and wet seasons) serve as primary data sources. Data collection instruments include high-precision air quality sensors, GIS mapping tools for green space enumeration, and land use datasets. The validity and reliability of instruments are ensured through calibration of sensors and cross-validation with regional environmental agencies’ data. Data analysis involves descriptive statistics to characterize pollutant levels, followed by inferential statistical techniques such as multiple regression analysis to examine the relationship between green space variables and pollutant concentrations. Geospatial analysis employing GIS and spatial autocorrelation techniques (e.g., Moran’s I) identifies spatial clustering of pollution reductions associated with green space proximity. Structural Equation Modeling (SEM) tests hypothesized pathways linking vegetation attributes and pollutant removal efficiencies, grounded in Ecological Modernization Theory and Urban Ecosystem Service Theory. Thematic analysis of qualitative field notes and stakeholder interviews provides contextual understanding of green space management practices. Expected findings are to demonstrate a statistically significant inverse relationship between green space extent and levels of PM, NOx, and O3, with certain vegetation types and spatial configurations exhibiting higher pollutant mitigation capacity. Spatial analyses are anticipated to reveal localized air quality improvements in proximity to larger and more diverse green spaces. These results will elucidate the specific attributes of green spaces that most effectively contribute to air purification, facilitating targeted urban planning interventions. This research advances scholarly understanding of urban green infrastructure’s role in air pollution mitigation by integrating spatial, statistical, and ecological perspectives. Its primary contribution is empirical evidence supporting the strategic deployment of green spaces as a cost-effective and sustainable pollution control measure in dense urban environments. Policy implications include recommendations for optimizing green space design—emphasizing vegetation diversity, size, and spatial distribution—to maximize air quality benefits. In conclusion, the study underscores that well-planned urban green spaces significantly impact ambient air quality, advocating for integrated green infrastructure strategies within urban environmental management policies. Future research directions include longitudinal assessments of green space evolution and pollutant dynamics, as well as exploring socioeconomic factors influencing green space accessibility and effectiveness. The findings furnish a vital evidence base for municipal authorities, urban planners, and environmental stakeholders seeking to harness natural solutions for urban air pollution challenges.