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<p>Ambient ammonia (NH<sub>3</sub>) contributes to odor and particulate matter (PM), making it essential to understand its characteristics for effective reduction of both. This study developed a Geo-AI-based ensemble mixed spatial model (EMSM) to predict NH<sub>3</sub> concentrations in Tainan, Taiwan. Morning average NH₃ data from 45 sites (2021–2022) were used to construct and validate the model. We employed Geo-AI techniques, integrating kriging, five machine learning algorithms, and ensemble methods to develop an EMSM. The EMSM estimated NH<sub>3</sub> variations over a two-year period by applying in-situ, geospatial, meteorological, and social factors. The results indicated the EMSM achieved a model performance of up to 94%, surpassing all other compared models. The results of the spatiotemporal resolution analysis suggest that fluctuations in NH<sub>3</sub> may be influenced by NH<sub>3</sub> kriging based, paddy, petrochemical raw material industry, all road, and public restroom quantity. These findings offer precise estimates that can inform pollution control strategies and support epidemiological study.</p> Graphical abstract <p></p>

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Using Advanced GeoAI-Based Ensemble Mixed Spatial Prediction Model to Estimate Ambient Ammonia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月10日

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  • Chin-Yu Hsu,
  • Yu-Ting Zeng,
  • Chia-Wei Hsu,
  • Yong-Wen Chen,
  • Aji Kusumaning Asri,
  • Pei-Yi Wong,
  • Ying I. Tsai,
  • Chih-Da Wu

摘要

Ambient ammonia (NH3) contributes to odor and particulate matter (PM), making it essential to understand its characteristics for effective reduction of both. This study developed a Geo-AI-based ensemble mixed spatial model (EMSM) to predict NH3 concentrations in Tainan, Taiwan. Morning average NH₃ data from 45 sites (2021–2022) were used to construct and validate the model. We employed Geo-AI techniques, integrating kriging, five machine learning algorithms, and ensemble methods to develop an EMSM. The EMSM estimated NH3 variations over a two-year period by applying in-situ, geospatial, meteorological, and social factors. The results indicated the EMSM achieved a model performance of up to 94%, surpassing all other compared models. The results of the spatiotemporal resolution analysis suggest that fluctuations in NH3 may be influenced by NH3 kriging based, paddy, petrochemical raw material industry, all road, and public restroom quantity. These findings offer precise estimates that can inform pollution control strategies and support epidemiological study.

Graphical abstract