PREDICTIVE MODELING OF URBAN AIR QUALITY IN KARACHI USING MACHINE LEARNING AND OPEN-SOURCE SATELLITE DATA
Keywords:
Air Quality Forecasting, Urban Air Pollution, Satellite Remote Sensing, Aerosol Optical Depth (AOD), PM₂.₅ Prediction, Machine Learning, LSTM Neural Networks, Random Forest Regression, XGBoost, Smart Cities, Environmental Monitoring, Climate Resilience, Public Health and Air QualityAbstract
This research aims to develop a predictive AI model to forecast and monitor air quality in Karachi by utilizing publicly available environmental and satellite datasets, significantly eliminating the dependency on non-reliable extensive physical sensor infrastructure. The study leverages data from Copernicus Atmosphere Monitoring Service (CAMS), OpenAQ and NASA's MODIS, analyzed with meteorological inputs from the Pakistan Meteorological Department (PMD). Supervised learning techniques, including LSTM neural networks and Random Forest, are used to analyze concentrations of PM2.5, PM10, and NO₂ in relation to humidity, PH, temperature, urban activity proxies and wind patterns. The impact of seasonal events like monsoon winds, traffic surges and smog are also examined. The final goal is to deliver forecasts and real-time air quality alerts through digital platforms, significantly contributing to public health resilience and smart city development in Karachi.
