Air pollution remains a significant environmental and public health issue globally, exacerbated by rapid urbanization and industrialization, leading to fluctuating air quality levels. This study utilizes a network of air quality sensors in Chennai, a rapidly expanding metropolis on India’s southeast coast, to analyze pollutant concentrations and their meteorological correlations, assessing temporal trends and spatial variability across different localities. Using data from 40 low-cost sensors collected over two years, the study identifies spatio-temporal air quality hotspots at a granular, locality-specific level. Time series clustering techniques are applied to examine similarities and differences between locations. The findings highlight substantial spatial disparities and temporal patterns in PM2.5 concentrations. To enhance interpretability and inform public health action, a dynamic PM2.5 Risk Index is developed, translating pollution data into a locally contextualized health risk scale. While focused on Chennai, this study aims to establish a scalable framework for hyperlocal spatio-temporal hotspot assessment, with potential applications at state and national levels.
