Using Advanced GeoAI-Based Ensemble Mixed Spatial Prediction Model to Estimate Ambient Ammonia
Year: 2026
Type: article
Abstract: Ambient ammonia (NH 3 ) 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 3 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 3 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 3 may be influenced by NH 3 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
Source: Aerosol and Air Quality Research
Institutions: Ming Chi University of Technology, National Cheng Kung University, Chia Nan University of Pharmacy and Science, National Pingtung University of Science and Technology, Chang Gung University of Science and Technology, Kaohsiung Medical University, National Chung Hsing University, National Health Research Institutes
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $2,090
OpenAlex ID: W7120950903