Remote Sensing-Based Air Quality and Atmospheric Pollution Modeling Using AI
Abstract
Remote sensing technology combined with artificial intelligence has revolutionized atmospheric pollution monitoring and air quality assessment. This chapter explores the integration of satellite-based remote sensing with machine learning and deep learning techniques for estimating ground-level pollutant concentrations including PM2.5, PM10, NO2, O3, and other atmospheric pollutants. The chapter examines various AI methodologies including Random Forest, XGBoost, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Graph Neural Networks applied to data from multiple satellite platforms such as MODIS, Sentinel-5P TROPOMI, Himawari-8, and VIIRS. Through comprehensive analysis of spatiotemporal modeling approaches, ensemble methods, and data fusion techniques, this chapter demonstrates how AI-enhanced remote sensing overcomes traditional monitoring limitations, provides full spatial coverage, and enables real-time air quality predictions.