國家衛生研究院 NHRI:Item 3990099045/15531
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    題名: Estimating morning and evening commute period O(3) concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology
    作者: Hsu, CY;Lee, RQ;Wong, PY;Candice Lung, SC;Chen, YC;Chen, PC;Adamkiewicz, G;Wu, CD
    貢獻者: National Institute of Environmental Health Sciences
    摘要: Elevated levels of ground-level ozone (O(3)) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O(3) concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O(3) levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R(2) of 0.91. Internal and external validation procedures and verification of O(3) concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O(3) levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O(3) levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies.
    日期: 2024-02
    關聯: Journal of Environmental Management. 2024 Feb;351:Article number 119725.
    Link to: http://dx.doi.org/10.1016/j.jenvman.2023.119725
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0301-4797&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:001135090500001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85182501828
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