Comparative Analysis of Urban Green Spaces and Air Quality in Two City Districts

Thesis Abstract

Urban environments are characterized by complex interactions between built infrastructure, natural ecosystems, and human activities, which collectively influence air quality and public health outcomes. Despite the recognized importance of urban green spaces (UGS) in mitigating air pollution and enhancing urban livability, disparities in green space distribution and quality remain across different city districts, often correlating with socio-economic and urban planning considerations. This study aims to conduct a comparative analysis of the relationship between urban green spaces and air quality in two distinct districts of a metropolitan city, designated as District A and District B, to identify spatial variations and inform sustainable urban planning strategies. The specific objectives are to quantify the extent and distribution of green spaces in both districts, assess and compare levels of key air pollutants—namely particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2)—in these districts, and evaluate the association between green space metrics and air quality indicators. The study adopts a cross-sectional research design, integrating spatial analysis with empirical air quality measurements, and is grounded on the ecological modernization theory and the urban ecosystem theory. The ecological modernization theory underscores the potential for technological and structural adaptations, such as green infrastructure, to improve urban environmental quality, while the urban ecosystem theory provides a framework for understanding the interactions between natural and built components within city landscapes. The population comprises urban areas within the city’s boundary, with a focus on two representative districts selected based on socio-economic and land-use diversity. A stratified random sampling method is employed to select 200 sampling units—100 in each district—ensuring representation across residential, commercial, and mixed land-use zones. Primary data collection involves high-resolution satellite imagery analyzed through Geographic Information Systems (GIS) for green space assessment, employing metrics such as green cover percentage, patch size, and connectivity indices. Air quality data are collected via fixed-site sensors deployed in each district over a three-month period, capturing 24-hour averages for PM2.5, PM10, NO2, and SO2, with supplementary data sourced from existing environmental monitoring stations to enhance temporal coverage. Data collection instruments include digital sensors validated through calibration with reference-grade monitors, and GIS software for spatial analysis. Data analysis combines descriptive statistics to quantify green space extent and air pollutant levels with inferential statistical techniques, including ANOVA to compare means between districts, and multiple regression analysis to examine the influence of green space variables on air quality indicators. Spatial analysis through GIS mapping visualizes the distribution of green spaces and pollutant hotspots, facilitating a visual comparison and spatial correlation assessment. The study hypothesizes that districts with higher green cover will exhibit significantly lower levels of airborne pollutants, with the regression models expected to explain a substantial proportion of variance in air quality metrics attributable to green space variables. The anticipated findings are that District A, characterized by more extensive and connected green spaces, will have lower average concentrations of PM2.5, PM10, NO2, and SO2 compared to District B, which has limited green infrastructure. A statistically significant inverse relationship between green space metrics and pollutant levels is expected, supporting the hypothesis that urban green spaces contribute to improved air quality. This research contributes to the body of knowledge by providing empirical evidence on the spatial variability of green spaces and their environmental benefits within a single urban context, addressing current gaps in localized urban environmental assessment and planning. The study concludes that strategic enhancement and connectivity of green spaces possess the potential to yield meaningful improvements in air quality, with policy implications for urban planning, environmental management, and public health. Recommendations include integrating green infrastructure into urban development plans, prioritizing green space connectivity, and implementing targeted interventions in districts with limited greenery. The research also suggests avenues for further study, such as longitudinal assessments of green space development and air quality trends, and exploring socio-economic factors influencing green infrastructure proliferation.