Wong, Pei-Yi翁佩詒

Postdoctoral Research Fellow

Research Interests

My research interest focuses on exploring large-scale variations in air pollution concentrations. By collecting air quality monitoring data, satellite imagery, and land use/land cover information (LULC), I integrated geographic information systems (GIS), big data analysis, and machine learning algorithms to develop Geospatial-Artificial Intelligence (Geo-AI) models for estimating the spatiotemporal changes in air pollution concentrations. My current research targets key pollutants, including PM2.5, NO2, and CO. Additionally, I conducted field sampling using volumetric air sampler to collect outdoor fungal spore samples in Tainan City. These samples were analyzed through microscopic counting to estimate the total fungal spore concentrations in the ambient air. I also developed Geo-AI models to predict the spatial distribution of outdoor fungal spore concentrations in Tainan. I further used the estimated air pollution concentration to investigate the environmental health effects. Furthermore, I analyzed genetic polymorphisms in asthmatic children to understand the combined effects of PM2.5, fungal spores, and single nucleotide polymorphisms (SNPs) on childhood asthma.

Representative Publications

Wong, P. Y., C. Y. Hsu, J. Y. Wu, T. A. Teo, J. W. Huang, H. R. Guo, H. J. Su, C. D. Wu, John D. Spengler. 2021. Incorporating Land-Use Regression into Machine Learning Algorithms in Estimating the Spatial-Temporal Variation of Carbon Monoxide in Taiwan. Environmental Modelling & Software (SCI, IF=5.471, Water Resources, Rank 16/100, Q1, JIF Percentile 84.50%)

Wong, P. Y., H. Y. Lee, Y. T. Zeng, Y. R. Chern, N. T. Chen, S. C. C. Lung, H. J. Su, C. D. Wu. 2021. Using a Land Use Regression Model with Machine Learning to Estimate Ground Level PM2.5. Environmental Pollution (SCI, IF=9.988, Environmental Sciences, Rank 28/279, Q1, JIF Percentile 90.14%)

Wong, P. Y., H. J. Su, H. Y. Lee, Y. C. Chen., Y. P. Hsiao, J. W. Huang, T. A. Teo, C. D. Wu, John D. Spengler. 2021. Using land-use machine learning models to estimate daily NO2 concentration variations in Taiwan. Journal of Cleaner Production (SCI, IF=11.072, Environmental Sciences, Rank 24/279, Q1, JIF Percentile 91.58%)

Wong, P. Y., H. Y. Lee, L. J. Chen, Y. C. Chen, N. T. Chen, S. C. C. Lung, H. J. Su, C. D. Wu, Jose Guillermo Cedeno Laurent, Gary Adamkiewicz, John D.Spengler. 2022. An alternative approach for estimating large-area indoor PM2.5 concentration – A case study of schools. Building and Environment (SCI, IF=7.093, Engineering, Civil, Rank 10/138, Q1, JIF Percentile 93.12%)

Wong, P. Y., H. J. Su, S. C. C. Lung, C. D. Wu. 2023. An ensemble mixed spatial model in estimating long-term and diurnal variations of PM2.5 in Taiwan. Science of The Total Environment (SCI, IF=10.754, Environmental Sciences, Rank 26/279, Q1, JIF Percentile 90.86)

Wong, P. Y., H. J. Su, S.C.C. Lung, W.Y. Liu, H.T. Tseng, G. Adamkiewicz, C. D. Wu. 2024. Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan. Environmental Pollution (SCI, IF=7.6, Environmental Sciences, Rank 37/358, Q1, JIF Percentile 89.8%)

Wong, P.Y., H.J. Su, H.J. Chao, W.C. Pan, H.J. Tsai, T.C. Yao, W.Y. Liu, S.C.C. Lung, G. Adamkiewicz, C. D. Wu. 2024. An Innovative Geo-AI Approach in Estimating High-Resolution Urban Ambient Fungal Spore Variations. Earth Systems and Environment (SCI, IF=5.3, Geosciences, Multidisciplinary, Rank 26/254, Q1, JIF Percentile 90.0%)

Wong, P. Y., Zeng, Y. T., Su, H. J., Lung, S. C. C., Chen, Y. C., Chen, P. C., Hsiao, T. C, G. Adamkiewicz, C. D. Wu. 2024. Effects of feature selection methods in estimating SO2 concentration variations using machine learning and stacking ensemble approach. Environmental Technology & Innovation, 103996 (SCI, IF=6.7, Environmental Sciences, Rank 44/385, Q1, JIF Percentile 87.8%)

Babaan, J., F. T. Hsu, P. Y. Wong, P. C. Chen, Y. L. Guo, S. C. C. Lung, Y. C. Chen, C. D. Wu*. 2023. A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan. Journal of Hazardous Materials (SCI, IF=13.6, Environmental Sciences, Rank 10/274, Q1, JIF Percentile 96.5%)

Hsu, C. Y., T. W. Lin, J. Babaan, A. K. Asri, P. Y. Wong, K. H. Chi, T. H. Ngo, Y. H. Yang, W. C. Pan, C. D. Wu*. 2023. Estimating the daily average concentration variations of PCDD/Fs in Taiwan using a novel Geo-AI based ensemble mixed spatial model. Journal of Hazardous Materials (SCI, IF=13.6, Environmental Sciences, Rank 10/274, Q1, JIF Percentile 96.5%)

Research

Developing Geo-AI models for PM2.5, NO2, CO, and fungal spores to improve prediction accuracy and variable interpretability  By integrating GIS, remote sensing, LULC, and machine learning algorithms, I developed Geo-AI models that achieved approximately a 20% increase in prediction accuracy compared to conventional modelling approaches, such as land-use regression. For example, the Geo-AI models explained 94% of the variation in PM2.5, 91% for NO2, 85% for CO, and 96% for fungal spores. Furthermore, by utilizing Shapley Additive Explanations (SHAP) value, the key influential factors were clarified for each target air pollutant. By addressing machine learning interpretability while improving prediction ability, the estimated air pollution concentration can be further applied to investigate their health impacts in environmental epidemiological studies.

Reference: Wong et al. 2021a,b,c; Wong et al. 2023; Wong et al. 2024a,b.

  • Ph.D.
    Department of Environmental and Occupational Health,
    National Cheng Kung University, Taiwan (2025)
  • M.S.
    Department of Environmental and Occupational Health,
    National Cheng Kung University, Taiwan (2020)
  • B.S.
    Department of Public Health,
    Kaohsiung Medical University, Taiwan (2018)
  • (02) 2787-5994

  • peiyiwong

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