Spatiotemporal Patterns of SO₂, NO₂, and CO Using Satellite Data Integrated Machine Learning in EEC Thailand
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Abstract
Air pollution causes a major global environmental challenge especially in industrialized regions such as the Eastern Economic Corridor (EEC) of Thailand. This study assesses the spatiotemporal trends of SO₂, NO₂ and CO concentrations in the EEC (2019–2022) using Sentinel-5P satellite data, ERA5 meteorological variables and machine learning models (Random Forest, XGBoost, LightGBM). Results revealed that pollutant fluctuations were significantly influenced by wind, aerosol index, surface pressure and dew point. XGBoost provided superior accuracy (R²: SO₂=0.95, NO₂=0.90, CO=0.96). The study demonstrates satellite and machine learning efficacy in air quality monitoring to identify pollution hotspots and critical environmental drivers. This is to support air quality management in Thailand.
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