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Remote Sensing-Based Air Quality and Atmospheric Pollution Modeling Using AI

Deepak GuptaInstitute of Technology and Management, Gwalior, IndiaIsmoilov BoburTermez University of Economics and Service, Termez, UzbekistanSokhibjan MuminovOybek DjabbarovKarshi State University, UzbekistanJamshid KhasanovUrgench State Pedagogical Institute, UzbekistanGulkhan DadenovaKimyo International University in Tashkent, UzbekistanSaodatjon MadirimovnaTashkent University of Information Technologies Named After Muhammad al-Khwarizmi, Uzbekistan

Advances in computational intelligence and robotics book seriesbook series2026

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ABI

Annotatsiya

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.

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Mavzular

Air Quality Monitoring and Forecasting Air Quality and Health Impacts COVID-19 impact on air quality

Identifikatorlar

DOI: 10.4018/979-8-3373-8745-1.ch005

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