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

Thesis Abstract

Urban air quality degradation poses a significant environmental and public health challenge in rapidly expanding cities, necessitating a comprehensive understanding of the mitigating role of urban green spaces across diverse districts. This study aims to conduct a comparative analysis of the relationship between urban green space (UGS) distribution and air quality indices (AQIs) in different city districts, with the overarching goal of identifying spatial and functional disparities influencing air pollution levels. The specific objectives include assessing the spatial distribution, functional attributes, and vegetation cover of green spaces; measuring air pollutant concentrations (PM2.5, PM10, NO2, and O3); examining the correlations between green space variables and air quality; and evaluating the influence of socio-economic and urban planning factors on these relationships. Employing a cross-sectional observational research design, the study integrates quantitative data collection and spatial analysis techniques. The population encompasses all urban districts within the city’s administrative boundary, with a stratified random sampling method selecting a total of 15 districts, representing high, medium, and low green space coverage. Data collection methods involve the use of remote sensing technologies (Landsat 8 satellite imagery) to quantify green space extent and vegetation indices, complemented by ground-based air quality monitoring using portable sensors over a six-month period, capturing seasonal variability. Additionally, structured questionnaires assess community engagement and perceptions related to green spaces. Instrument validity and reliability are established through pilot testing and calibration of sensors, ensuring data accuracy. Data analysis employs descriptive statistics to characterize the distribution of green spaces and pollutant levels, Geographic Information Systems (GIS) spatial analysis to map green space patterns, and inferential statistics including Pearson correlation, multiple regression analysis, and ANOVA to determine relationships among variables. The study further employs Structural Equation Modeling (SEM) to test the theoretical framework based on the Eco-Design Theory and the Biophilia Hypothesis, which posit that proximity to natural environments enhances air quality and promotes sustainable urban planning. Expected findings indicate a significant inverse relationship between green space availability and levels of airborne particulate matter and nitrogen dioxide, particularly in districts with higher vegetation density and well-maintained parks. The results are anticipated to demonstrate spatial heterogeneity in green space effectiveness, influenced by socio-economic factors and urban layout. The findings will contribute new empirical evidence to the limited literature on the comparative effectiveness of green spaces in diverse urban contexts, facilitating spatial planning strategies grounded in evidence-based insights. This research advances knowledge by integrating environmental monitoring with spatial analytics and socio-economic assessment, emphasizing the importance of strategic urban green space planning to mitigate air pollution. The study concludes that well-distributed, vegetatively rich green spaces significantly improve air quality in urban districts, thereby promoting ecological sustainability and public health. Recommendations include adopting integrated urban greening policies, enhancing community participation in green space development, and prioritizing environmental equity across districts. The study also suggests avenues for future research focusing on longitudinal impacts of green space interventions and the role of specific plant species in air purification, fostering deeper understanding of nature-based solutions in urban health management.