دورية أكاديمية
أعرض في EDS

Explainable geospatial-artificial intelligence models for the estimation of PM 2.5 concentration variation during commuting rush hours in Taiwan.

التفاصيل البيبلوغرافية
العنوان: Explainable geospatial-artificial intelligence models for the estimation of PM 2.5 concentration variation during commuting rush hours in Taiwan.
المؤلفون: Wong PY; Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan., Su HJ; Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan., Candice Lung SC; Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan; Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan., Liu WY; Department of Forestry, National Chung Hsing University, Taichung, Taiwan; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung, Taiwan., Tseng HT; Department of Information Management, National Central University, Taoyuan, Taiwan., Adamkiewicz G; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA., Wu CD; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung, Taiwan; Department of Geomatics, National Cheng Kung University, Tainan, Taiwan; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan. Electronic address: chidawu@mail.ncku.edu.tw.
المصدر: Environmental pollution (Barking, Essex : 1987) [Environ Pollut] 2024 May 15; Vol. 349, pp. 123974. Date of Electronic Publication: 2024 Apr 12.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier Applied Science Publishers Country of Publication: England NLM ID: 8804476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-6424 (Electronic) Linking ISSN: 02697491 NLM ISO Abbreviation: Environ Pollut Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Barking, Essex, England : Elsevier Applied Science Publishers, c1987-
مواضيع طبية MeSH: Particulate Matter*/analysis , Air Pollutants*/analysis , Environmental Monitoring*/methods , Artificial Intelligence* , Air Pollution*/statistics & numerical data, Taiwan ; Transportation
مستخلص: PM 2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM 2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM 2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM 2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM 2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM 2.5 values, SO 2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM 2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
فهرسة مساهمة: Keywords: Commuting periods; Dusk; Geo-AI model; Morning; PM(2.5)
المشرفين على المادة: 0 (Particulate Matter)
0 (Air Pollutants)
تواريخ الأحداث: Date Created: 20240414 Date Completed: 20240504 Latest Revision: 20240504
رمز التحديث: 20240505
DOI: 10.1016/j.envpol.2024.123974
PMID: 38615837
قاعدة البيانات: MEDLINE
الوصف
تدمد:1873-6424
DOI:10.1016/j.envpol.2024.123974