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Using Generative Art to Convey Past and Future Climate Transitions
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Mapping climate-conditioned PM2.5 response under climate change in Taiwan using a high-resolution Geo-AI model.

Pei-Yi Wong1, Wei-Lun Hsu2, Chieh-Ying Chen2

  • 1Department 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.

Journal of Environmental Management
|June 18, 2026
PubMed
Summary
This summary is machine-generated.

This study uses a Geo-AI framework to predict future air quality, finding that climate change will generally decrease PM2.5 levels but create localized hotspots under severe warming. This highlights the need for high-resolution, climate-informed air quality planning.

Keywords:
CMIP6 downscaled climate dataClimate-conditioned PM(2.5) projectionGeospatial artificial intelligence (geo-AI)High-resolution air quality modelingHumid subtropical and monsoon-influenced regionsIPCC AR6 climate scenarios

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Fa&#231;ade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
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Published on: December 12, 2025

Area of Science:

  • Environmental Science
  • Artificial Intelligence
  • Climate Science

Background:

  • Air quality, specifically PM2.5 concentrations, is influenced by climate change.
  • Existing models often lack the resolution to capture complex island and coastal dynamics.
  • Bridging the gap between historical air quality estimation and future climate-conditioned projection is crucial.

Purpose of the Study:

  • To develop and validate a high-resolution Geo-AI framework for quantifying climate change impacts on PM2.5 concentrations.
  • To project future PM2.5 concentrations under various climate scenarios using Taiwan as a case study.
  • To identify key meteorological drivers influencing PM2.5 variability and their relationships.

Main Methods:

  • Integration of long-term ground-based monitoring data (1994-2019) and geo-environmental predictors.
  • Utilization of statistically downscaled CMIP6 meteorology within a Gradient Boosting Machine framework.
  • Application of SHAP (SHapley Additive exPlanations) for model interpretability and driver analysis.

Main Results:

  • The Geo-AI model achieved strong predictive performance (R² = 0.81, RMSE = 8.69 μg/m³).
  • Temperature and precipitation were identified as dominant predictors, exhibiting nonlinear, inverse relationships with PM2.5.
  • Projected PM2.5 concentrations show a near-to-midterm decline but increasing spatial heterogeneity and localized hotspots in the long term under severe warming.

Conclusions:

  • Climate change impacts on PM2.5 are complex, with potential for uneven spatial responses.
  • High-resolution, climate-informed assessments are necessary, moving beyond regional mean evaluations.
  • The developed Geo-AI framework offers a transferable tool for climate-responsive air quality planning in vulnerable regions worldwide.