Hao-Ting ChangYinq-Rong ChernAji Kusumaning AsriWan-Yu LiuChin-Yu HsuTA-CHIH HSIAOKai Hsien ChiShih-Chun Candice LungChih-Da Wu2025-05-062025-05-062025-04https://www.scopus.com/record/display.uri?eid=2-s2.0-105000657169&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/728915This study addresses a gap in atmospheric greenhouse gas research, focusing on methane (CH4), a gas with a global warming potential 80 times greater than carbon dioxide (CO2). Unlike prior studies that focus on emission sources and reduction strategies, this research emphasizes the spatiotemporal variations in atmospheric CH4 concentrations, providing new perspectives on global climate mitigation efforts. A novel GeoAI-based ensemble mixed spatial prediction model was developed, integrating multiple machine learning algorithms and considering various factors to accurately estimate CH4 concentrations across Taiwan. In the context of global net-zero emissions, this study offers a robust approach to assess spatial variations in CH4 concentrations, providing valuable insights into the effectiveness of greenhouse gas reduction policies and climate strategies. Key factors influencing CH4 levels include aquaculture, livestock, transportation land use, wind speed, national CH4 emissions, net greenhouse gas emissions, population density, quarry sites, solar radiation, seasonal variations, residential areas, temples, CO2 removal levels, and primary pollutants (e.g., NO2, NOx, PM2.5, PM10, CO, CO2, SO2, and O3). Seasonal analysis revealed higher CH4 concentrations in spring and winter, and lower levels in summer and autumn. The model demonstrated high explanatory power with R2 values of 0.99, 0.82, 0.98, and 0.67 across training, testing, cross-validation, and external validation datasets. This study presents a model that enhances the understanding of the dynamic factors influencing methane concentration variations. The methodology developed in this research can serve as a reference for other regions and timeframes, potentially offering key insights for the formulation of effective global climate mitigation strategies.GeoAI-based ensemble mixed spatial prediction modelGlobal warmingGreenhouse gasMachine learningMethane[SDGs]SDG3[SDGs]SDG6[SDGs]SDG13[SDGs]SDG14journal article10.1016/j.jenvman.2025.125110