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An advanced spatio-temporal model for particulate matter and gaseous pollutants in Beijing, China

机译：北京北京颗粒物质和气态污染物的先进时空模型

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摘要

Modeling fine-scale spatial and temporal patterns of air pollutants can be challenging. Advanced spatio-temporal modeling methods were used to predict both long-term and short-term concentrations of six criteria air pollutants (particulate matter with aerodynamic diameter less than or equal to 10 and 2.5 mu m [PM10 and PM2.5], SO2, NO2, ozone and carbon monoxide [CO]) in Beijing, China. Monitoring data for the six criteria pollutants from April 2014 through December 2017 were obtained from 23 administrative monitoring sites in Beijing. The dimensions of a large array of geographic covariates were reduced using partial least squares (PLS) regression. A land use regression (LUR) model in a universal kriging framework was used to estimate pollutant concentrations over space and time. Prediction ability of the models was determined using leave-one-out cross-validation (LOOCV). Prediction accuracy of the spatio-temporal two-week averages was excellent for all of the pollutants, with LOOCV mean squared error-based R-2 (R-mse(2)) of 0.86, 0.95, 0.90, 0.82, 0.94 and 0.95 for PM10, PM2.5, SO2, NO2, ozone and CO, respectively. These models find ready application in making fine-scale exposure predictions for members of cohort health studies and may reduce exposure measurement error relative to other modeling approaches.
机译：建模细尺空气污染物的空间和时间模式可能是挑战性的。先进的时空建模方法用于预测六个标准空气污染物的长期和短期浓度（空气动力学直径小于或等于10和2.5μm[PM10和PM2.5]，SO2， No2，臭氧和一氧化碳[CO]）在北京，中国。 2014年4月至2017年4月至2017年4月的六个标准污染物的监测数据是从北京的23个行政监测网站获得的。使用部分最小二乘（PLS）回归减少了大量地理协调因子的尺寸。通用Kriging框架中的土地利用回归（LUR）模型用于估算空间和时间的污染物浓度。使用休假交叉验证（LOOCV）确定模型的预测能力。时空两周平均值的预测准确性对于所有污染物都是优异的，LOOCV平均基于误差的R-2（R-MSE（2））为0.86,0.95,0.90,0.82,0.94和0.95 PM10，PM2.5，SO2，NO2，臭氧和CO。这些模型在为队列健康研究成员进行微量曝光预测时，这些模型可以应用于对其他建模方法进行曝光测量误差。

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  • 来源
    《Atmospheric environment》 |2019年第8期|120-127|共8页
  • 作者

    Xu Jia; Yang Wen; Han Bin; Wang Meng; Wang Zhanshan; Zhao Zhiping; Bai Zhipeng; Vedal Sverre;

  • 作者单位

    Univ Washington Dept Environm & Occupat Hlth Sci Seattle WA 98195 USA|Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing Peoples R China;

    Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing Peoples R China;

    Univ Washington Dept Environm & Occupat Hlth Sci Seattle WA 98195 USA|Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing Peoples R China;

    Univ Washington Dept Environm & Occupat Hlth Sci Seattle WA 98195 USA|Univ Buffalo Dept Epidemiol & Environm Hlth Buffalo NY USA|Univ Buffalo RENEW Inst Buffalo NY USA;

    Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing Peoples R China;

    Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing Peoples R China;

    Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing Peoples R China;

    Univ Washington Dept Environm & Occupat Hlth Sci Seattle WA 98195 USA|Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Particulate matter; Air pollution; Spatio-temporal model; Geo-statistical model; Beijing;

    机译：颗粒物质;空气污染;时空模型;地理统计模型;北京;

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