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Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research

机译:混合多个二氧化氮数据源以进行长期的健康研究邻域估计

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摘要

Exposure to traffic related nitrogen dioxide (NO_2) air pollution is associated with adverse health outcomes. Average pollutant concentrations for fixed monitoring sites are often used to estimate exposures for health studies, however these can be imprecise due to difficulty and cost of spatial modeling at the resolution of neighborhoods (e.g., a scale of tens of meters) rather than at a coarse scale (around several kilometers). The objective of this study was to derive improved estimates of neighborhood NO_2 concentrations by blending measurements with modeled predictions in Sydney, Australia (a low pollution environment). We implemented the Bayesian maximum entropy approach to blend data with uncertainty defined using informative priors. We compiled NO_2 data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO_2. The spatial model produced a posterior probability density function of estimated annual average concentrations that spanned an order of magnitude from 3 to 35 ppb. Validation using independent data showed improvement, with root mean squared error improvement of 6% compared with the land use regression model and 16% over the chemical transport model. These estimates will be used in studies of health effects and should minimize misclassification bias.
机译:暴露于交通相关的二氧化氮(NO_2)空气污染与不良健康后果相关。固定监测点的平均污染物浓度通常用于估算健康研究的暴露程度,但是由于在邻域分辨率(例如数十米的尺度)上而不是粗略的空间建模的难度和成本,这些可能不准确规模(约几公里)。这项研究的目的是通过将测量值与模拟的预测值混合在一起,在澳大利亚悉尼(低污染环境)中得出邻里NO_2浓度的改进估计值。我们实施了贝叶斯最大熵方法,以混合使用信息先验定义的不确定性的数据。我们从固定地点的监测器,化学物质运输模型和基于卫星的土地利用回归模型中收集了NO_2数据,以估算社区的年平均NO_2。空间模型产生了估计的年平均浓度的后验概率密度函数,其范围从3 ppb到35 ppb。使用独立数据进行的验证显示有所改善,与土地利用回归模型相比,均方根误差改善了6%,在化学运输模型中,改善了16%。这些估计值将用于健康影响研究,并应最大程度地减少分类错误。

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  • 来源
    《Environmental Science & Technology》 |2017年第21期|12473-12480|共8页
  • 作者单位

    Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research, University of Sydney, Sydney, Australia University of Canberra, Canberra, Australia;

    Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research Sydney, Australia & School of Biological Sciences, University of Tasmania, Hobart, Australia;

    Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research Sydney, Australia & School of Public Health, The University of Queensland, Herston, Australia;

    School of Public Health, University of Sydney, Sydney, Australia;

    School of Public Health, University of Sydney, Sydney, Australia;

    Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research Sydney, Australia & CSIRO, Melbourne, Australia;

    Institute of Health and Biomedical Innovation & School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia;

    Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research, University of Sydney, South West Sydney Clinical School, University of NSW & Ingham Institute for Applied Medical Research, Sydney, Australia;

    Centre for Air Quality and Health Research and Evaluation, NESP Clean Air and Urban Landscapes, School of Population and Global Health, The University of Western Australia, Perth, Australia;

    University of North Carolina, Chapel Hill, United States;

    Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research, University of Sydney, South West Sydney Clinical School, University of NSW & Ingham Institute for Applied Medical Research, Sydney, Australia;

    Centre for Air Quality and Health Research and Evaluation, Woolcock Institute of Medical Research & University Centre for Rural Health, North Coast, School of Public Health, University of Sydney, Sydney, Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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  • 入库时间 2022-08-17 13:58:00

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