Assessing the Impact of Urban Green Spaces on Air Quality Improvements

Thesis Abstract

Urban air pollution poses significant health and environmental challenges in rapidly expanding cities, necessitating sustainable interventions to improve air quality. Amid these challenges, urban green spaces have been increasingly recognized for their potential to mitigate air pollution through natural filtration, temperature regulation, and increased vegetation cover. Nonetheless, empirical evidence quantifying the extent of their impact remains limited, particularly in densely populated urban environments where green spaces are unevenly distributed. This study aims to assess the effectiveness of urban green spaces in enhancing air quality, specifically focusing on particulate matter (PM2.5 and PM10) and gaseous pollutants (NO2, SO2, and O3). The research seeks to establish a quantitative understanding of the relationship between green space characteristics and air pollutant levels, informing urban planning policies and environmental management practices. The study adopts a mixed-methods approach, integrating quantitative field measurements with spatial analysis. The research design is cross-sectional, involving systematic sampling across ten strategically selected urban green spaces within the metropolitan area. The population includes all accessible green spaces within the city boundaries, with a sample size of 150 data collection points evenly distributed across these sites. Air quality data are collected using calibrated portable air quality monitors over a six-month period, capturing hourly concentrations of PM2.5, PM10, NO2, SO2, and O3. Geographic Information System (GIS) tools facilitate spatial analysis of green space properties, such as vegetation density, canopy cover, and proximity to pollution sources. Data collected from satellite imagery and ground surveys are integrated to characterize vegetation metrics. Additionally, climatic variables such as temperature, humidity, and wind speed are recorded to control for environmental influences. Data analysis incorporates multiple techniques. Descriptive statistics summarize pollutant concentrations and green space attributes. Inferential analysis employs multiple linear regression models to assess the impact of green space variables on air pollutant levels, controlling for meteorological factors. Factor analysis reduces dimensionality of vegetation attributes, while spatial autocorrelation is examined using Moran’s I statistic. The study is grounded in two theoretical frameworks the Ecosystem Services Theory, which posits that green spaces provide tangible benefits, including air purification, and the Urban Heat Island Theory, which underscores the role of vegetation in temperature regulation, indirectly affecting air quality. It is anticipated that the findings will reveal a statistically significant inverse relationship between green space characteristics—particularly vegetation density and canopy cover—and concentrations of PM2.5, PM10, and gaseous pollutants. The research expects to demonstrate that green spaces with higher vegetation density are more effective in reducing air pollutants, with spatial variations influenced by proximity to major roads and industrial zones. These insights aim to quantify the mitigation capacity of urban green spaces in polluted environments, thereby filling a critical gap in empirical knowledge. This study contributes to the existing literature by providing robust, location-specific evidence on the pollution-mitigating effects of urban greenery, employing rigorous analytical models. The findings will inform urban planners and policymakers on how to optimize green space distribution and design interventions for maximal air quality improvement. The main conclusion underscores the importance of strategic green space planning as a cost-effective, sustainable measure to combat urban air pollution. Recommendations include increasing green space connectivity, promoting indigenous vegetation, and integrating green infrastructure into urban development frameworks. Future research avenues suggested involve longitudinal studies to assess seasonal variations and the long-term sustainability of green space interventions in air quality management.