國家衛生研究院 NHRI:Item 3990099045/6407
English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 12145/12927 (94%)
造訪人次 : 924937      線上人數 : 964
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋
    主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於NHRI ‧ 管理 到手機版


    請使用永久網址來引用或連結此文件: http://ir.nhri.org.tw/handle/3990099045/6407


    題名: Spatiotemporal modeling with temporal-invariant variogram subgroups to estimate fine particulate matter PM2.5 concentrations
    作者: Chen, CC;Wu, CF;Yu, HL;Chan, CC;Cheng, TJ
    貢獻者: Division of Biostatistics and Bioinformatics
    摘要: Short-term exposure estimation of daily air pollution levels incorporating geographic information system (GIS) into spatiotemporal modeling remains a great challenge for assessing corresponding acute adverse health effects. Due to daily meteorological effects on the dispersion of pollutants, explanatory spatial covariables and their coefficients may not be the same as in classical land-use regression (LUR) modeling for long-term exposure. In this paper, we propose a two-stage spatiotemporal model for daily fine particulate matter (PM 2.5) concentration prediction: first, daily nonlinear temporal trends are estimated through a generalized additive model, and second, GIS covariates are used to predict spatial variation in the temporal trend-removed residuals. To account for spatial dependence on meteorological conditions, the dates of the study period are divided by the sill of the daily empirical variogram into approximately temporal-invariant subgroups. Within each subgroup, daily PM 2.5 estimations are obtained by combining the temporal and spatial parts of the estimations from the two stages. The proposed method is applied to the modeling of spatiotemporal PM 2.5 concentrations observed at 18 ambient air monitoring stations in Taipei metropolitan area during 2006-2008. The results showed that the PM 2.5 concentrations decreased whereas the relative humidity and wind speed increased with the sill subgroups, which may be due to the effects of daily meteorological conditions on the dispersions of the particles. Also, the covariates and their coefficients of the LUR models varied with subgroups and had in general higher adjusted R-squares and smaller root mean square errors in prediction than those of a single overall LUR model.
    日期: 2012-07
    關聯: Atmospheric Environment. 2012 Jul;54:1-8.
    Link to: http://dx.doi.org/10.1016/j.atmosenv.2012.02.015
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1352-2310&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000306200600001
    Cited Times(Scopus): http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84860623725
    顯示於類別:[陳主智] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    SCP84860623725.pdf556KbAdobe PDF627檢視/開啟


    在NHRI中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋