Application of Data Mining and AI&ML in Aerosol Pollution and Aerosol Atmospheric Rivers

In recent years, the application of data mining, artificial intelligence, and machine learning (AI&ML) techniques has revolutionized the field of earth and environmental science, particularly in the study of atmospheric pollutants such as aerosol pollution and aerosol atmospheric rivers (AARs). This chapter provides a comprehensive overview of the diverse applications of data mining and AI&ML techniques in aerosol science, highlighting recent advancements, methodological approaches, and a case study to predict the spatio-temporal patterns of AARs using convolutional autoencoders. Data mining techniques enable the extraction of valuable insights and patterns from large environmental datasets, while AI&ML techniques facilitate the development of predictive models for aerosol concentrations, dispersion patterns, and atmospheric interactions. Clustering and classification algorithms identify aerosol pollution hotspots and predict pollution events, while association rule mining techniques reveal correlations between aerosol pollution and meteorological variables. AI&ML models, including neural networks and support vector machines, forecast aerosol concentration levels and classify pollution severity, aiding in air quality forecasting and early warning systems. The case study demonstrates the efficacy of these techniques in identifying aerosol pollution patterns, predicting AARs, and integrating satellite data for global aerosol monitoring. Despite challenges such as data quality and model interpretability, future research directions aim to enhance data accessibility, improve integration techniques, and explore emerging AI&ML methodologies for more accurate predictions and informed decision-making in aerosol pollution management.