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

Abstract

Urbanization continues to alter environmental systems, producing significant deterioration in urban air quality across developing and developed regions. Conventional monitoring stations, although technically sophisticated, are too sparse to reflect the micro-level variations produced by traffic flows, construction intensity, land-use transitions, and demographic pressures. This study assesses the impacts of urbanization on atmospheric conditions by deploying a high resolution network of low-cost sensors that measure PM2.5, PM10, NO2, CO, and O3. These measurements were analysed alongside urban growth indicators such as road density, population concentration, and expansion of built-up surfaces. Machine learning and spatial analytics were employed to understand pollution dynamics and predict future trends. Results reveal pronounced pollution hotspots, strong correlations between urban activity and pollutant increments, and substantial improvements in predictive performance when sensor data are integrated with land-use attributes. The study provides a detailed evaluation of how contemporary urban growth reshapes air quality and highlights the transformative value of sensor-driven environmental intelligence for sustainable planning.