System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Ahead of print

PubMed: 42314637 DOI: 10.1016/j.jenvman.2026.130232

Mapping climate-conditioned PM 2.5 response under climate change in Taiwan using a high-resolution Geo-AI model.

J Environ Manage 412:130232

Authors: Pei-Yi Wong, Wei-Lun Hsu, Chieh-Ying Chen, Chien-Fei Chen, Chia-Wei Hsu, Shih-Chun Candice Lung, Ta-Chih Hsiao, Chia-Pin Yu, Wan-Yu Liu, Pau-Chung Chen, Wei Jie Seow, Chien-Hao Sung, Chih-Da Wu

Affiliations

  • Department of Environmental Science and Engineering, National Pingtung University of Science and Technology, Pingtung, Taiwan; Center for Sustainable Energy and Climate Change Research and Demonstration, National Pingtung University of Science and Technology, Pingtung, Taiwan.
  • Department of Geomatics, National Cheng Kung University, Tainan, Taiwan.
  • Department of Sociology, Anthropology and Criminal Justice, Clemson University, South Carolina, United States.
  • Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan; Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan.
  • Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan; Graduate Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan.
  • School of Forestry and Resource Conservation, National Taiwan University, Taipei, Taiwan.
  • Department of Forestry, National Chung Hsing University, Taichung, Taiwan; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung, Taiwan.
  • National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Public Health, National Taiwan University College of Public Health, Taipei, Taiwan; Department of Environmental and Occupational Medicine, National Taiwan. University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
  • Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore.
  • Department of Geomatics, National Cheng Kung University, Tainan, Taiwan; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung, Taiwan; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan; Chronic Diseases and Health Promotion Research Center, Chang Gung University of Science and Technology, Puzi City, Taiwan.

Abstract

This study develops a high-resolution Geo-AI framework to quantify the impact of future climate change on PM 2.5 concentrations using Taiwan as a subtropical, monsoon-influenced island case. The model integrates long-term ground-based monitoring data (1994-2019), multi-scale geo-environmental predictors, and statistically downscaled CMIP6 meteorology, implemented using a Gradient Boosting Machine. The resulting model demonstrates strong predictive performance (R 2 = 0.81 and RMSE = 8.69 μg/m 3) and effectively captures PM 2.5 dynamics within complex islands and coastal environments. By explicitly coupling a Geo-AI model with Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) climate scenarios, this study extends data-driven PM 2.5 modeling from historical estimation to climate-conditioned future projection, addressing a key methodological gap in existing air-quality research. SHAP-based interpretability analysis identifies temperature and precipitation as dominant predictors, underscoring their central role in shaping future aerosol variability. The SHAP results further indicate that both temperature and precipitation exhibit nonlinear relationships across different temporal and regional scales and overall inverse associations with PM 2.5 concentrations, clarifying the climate-driven effects of warming and hydrological change on PM 2.5 dynamics under humid subtropical conditions. Across four Shared Socioeconomic Pathway scenarios, projected PM 2.5 concentrations consistently decline in the near and midterm (between -1.25 and -1.5 μg/m 3), followed by increasing spatial heterogeneity in the long term, with localized PM 2.5 hotspots emerging under severe warming conditions. These findings suggest that climate change may generate uneven air-quality responses across space, highlighting the limitations of regional mean assessments and the need for high-resolution, climate-informed mitigation and adaptation strategies. The proposed framework provides a transferable tool for climate-responsive air-quality planning in humid subtropical, monsoon-influenced, and densely populated regions worldwide.

MeSH

Ecology, Climate, Air, Environment, Temperature