Sustainable Modeling of the Urban Air Quality in Abu Dhabi Using Machine Learning and Open-Source Satellite Data

This study develops a predictive AI model to monitor and forecast air quality in Abu Dhabi using publicly available satellite-based environmental datasets. The study uses datasets from NASA’s MODIS, Copernicus Atmosphere Monitoring Service (CAMS), and OpenAQ, alongside meteorological data from the UAE’s National Center of Meteorology. Supervised learning techniques, including Random Forest and LSTM neural networks, are applied to analyze trends in PM2.5, PM10, NO2, and CO concentrations in relation to temperature, humidity, wind patterns, and urban development indices. This study also discusses the impact of seasonal events, such as sandstorms and traffic emissions, on air quality. The novelty of this work lies in addressing the limitations of sparse sensor networks by employing a real-time hybrid machine learning model that accurately forecasts air quality in Abu Dhabi using only satellite and key meteorological data.