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首页> 外文期刊>Atmospheric environment >Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NO_X) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)
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Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NO_X) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

机译：比较通用克里金法和土地利用回归法来预测动脉粥样硬化和空气污染（MESA空气）的多民族研究中的气态氮氧化物（NO_X）浓度

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摘要

Background: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land-use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. Methods: The measurements of gaseous oxides of nitrogen (NO_X) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R2 and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and CIS software. Results: UK models consistently performed as well as or better than the analogous LUR models. The best CV R2 values for season-specific UK models predicting log(NO_x) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R2 values for season-specific LUR models predicting log(NO_x) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The twostage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R~2 values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. Conclusion: High quality LUR and UK prediction models for NOX in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.
机译：背景：用于评估长期暴露于环境空气污染对健康的影响的流行病学研究可为公共政策提供参考。这些研究依赖于暴露模型，该模型使用从污染监测站点收集的数据来预测对象位置的暴露。土地利用回归（LUR）和通用克里金法（英国）已被建议作为潜在的预测方法。我们在数据集中评估了这些方法，其中包括来自加利福尼亚州洛杉矶的三个季节的测量数据。方法：本研究中使用的氮氧化物气体（NO_X）的测量来自“快照”采样活动，该活动是动脉粥样硬化和空气污染（MESA空气）多民族研究的一部分。洛杉矶的测量值是在夏季，秋季和冬季的三个为期两周的时段内收集的，每个时段约有150个站点。该设计包括在繁忙道路两侧的监视器群集，以捕获与交通相关的污染的近场梯度。 LUR和UK预测模型是使用基于地理信息系统（GIS）的协变量创建的。协变量的选择基于10倍交叉验证（CV）R2和均方根误差（RMSE）。由于UK需要专门的软件，因此还使用了计算上更简单的两步过程，使用易于获得的回归和CIS软件对UK模型进行近似拟合。结果：UK模型的性能始终优于或优于LUR模型。预测log（NO_x）的特定季节英国模型的最佳CV R2值分别在夏季，秋季和冬季分别为0.75、0.72和0.74（CV RMSE 0.20、0.17和0.15）。预测log（NO_x）的特定季节LUR模型的最佳CV R2值是0.74、0.60和0.67（CV RMSE 0.20、0.20和0.17）。与UK相比，两阶段逼近的性能也优于LUR，与夏季，秋季和冬季的CV R〜2值分别为0.75、0.70和0.70（CV RMSE 0.20、0.17和0.17）的完整UK模型差不多。 。结论：根据对MESA Air收集的数据，针对三个季节开发了洛杉矶NOX的高质量LUR和UK预测模型。在我们的研究中，英国的表现始终优于LUR。同样，两步方法比LUR模型更有效，其性能等于或稍逊于UK。

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  • 来源
    《Atmospheric environment》 |2011年第26期|p.4412-4420|共9页
  • 作者

    Laina D. Mercer; Adam A. Szpiro; Lianne Sheppard; Johan Lindstrom; Sara D. Adar; Ryan W. Allen; Edward L Avol; Assaf P. Oron; Timothy Larson; L.-J. Sally Liu; Joel D. Kaufman;

  • 作者单位

    Department of Biostatistics, University of Washington, WA, USA;

    Department of Biostatistics, University of Washington, WA, USA;

    Department of Biostatistics, University of Washington, WA, USA,Department of Environmental and Occupational Health Sciences, University of Washington, WA, USA;

    Centre for Mathematical Sciences, Lund University, Sweden,Department of Statistics, University of Washington, WA, USA;

    Department of Epidemiology, University of Michigan, MI, USA;

    Simon Fraser University, Faculty of Health Sciences, Canada;

    Department of Preventive Medicine, University of Southern California, CA, USA;

    Department of Environmental and Occupational Health Sciences, University of Washington, WA, USA;

    Department of Civil and Environmental Engineering, University of Washington, WA, USA;

    Department of Environmental and Occupational Health Sciences, University of Washington, WA, USA;

    Department of Environmental and Occupational Health Sciences, University of Washington, WA, USA;

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

    universal kriging; land-use regression; spatial modeling; air pollution; exposure assessment; los angeles;

    机译：通用克里格法;土地利用回归;空间模型;空气污染;暴露评估;洛杉矶;

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