Assessment of Urbanization Impacts on Ambient Air Quality Using High-Resolution Sensor Networks and Advanced Data Analytics

Abstract

The Rapid urbanization has significantly altered land-use patterns, transportation systems, and energy consumption, leading to substantial degradation of ambient air quality in urban regions. Conventional air quality monitoring stations, while accurate, provide limited spatial resolution and often fail to capture localized pollution variability. This study presents a comprehensive assessment of urbanization- induced air quality changes using high-resolution sensor networks combined with advanced data analytics techniques. A dense network of low-cost air quality sensors was deployed across heterogeneous urban zones to monitor particulate matter, nitrogen oxides, carbon monoxide, and ozone concentrations in real time. Spatio-temporal data analysis, statistical modeling, and machine learning techniques were applied to quantify pollution patterns and identify key urbanization drivers. The results reveal strong correlations between urban land-use intensity, traffic density, and pollutant concentration gradients. The findings demonstrate the effectiveness of high-resolution sensor networks in capturing urban air quality dynamics and provide actionable insights for sustainable urban planning and pollution mitigation strategies.