國家衛生研究院 NHRI:Item 3990099045/6407
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 12145/12927 (94%)
造访人次 : 912749      在线人数 : 1177
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻
    主页登入上传说明关于NHRI管理 到手机版


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


    题名: 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
    贡献者: 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
    显示于类别:[陳主智] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    SCP84860623725.pdf556KbAdobe PDF627检视/开启


    在NHRI中所有的数据项都受到原著作权保护.

    TAIR相关文章

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