Smart City Sustainable Development Modelling Through Quantitative Analysis of Air Pollutants: A study on Chennai, India

This research paper focuses on conducting a predictive analysis of PM2.5 and PM10 levels in Chennai, India, to contribute to understanding air pollution management and sustainable development. Historical data on air pollutant levels will be analyzed to identify trends, followed by the development of a predictive model using Statistical Package for the Social Sciences (SPSS) software. The study aims to assess existing pollution control measures, propose interventions based on the predictive analysis, and forecast future concentrations of PM2.5 and PM10. The outcomes are expected to enhance understanding of air pollutant patterns, improve forecasting accuracy, and support sustainable urban development and air quality management in Chennai. This abstract outlines the research objectives, methodology involving data collection, analysis, and predictive modeling using SPSS software, as well as the expected outcomes and implications for sustainable development initiatives in Chennai.