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Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research
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    Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research
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    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
    *Phone: +61 2 6201 5298; fax: +61 2 6201 5999; e-mail: [email protected]
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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2017, 51, 21, 12473–12480
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    https://doi.org/10.1021/acs.est.7b03035
    Published September 26, 2017
    Copyright © 2017 American Chemical Society

    Abstract

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    Exposure to traffic related nitrogen dioxide (NO2) 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 NO2 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 NO2 data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO2. 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.

    Copyright © 2017 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.7b03035.

    • BME_NO2_Sydney_SI.docx describes the full details of our data preparation, parameter selection procedure and some technical detail about our data sets (PDF)

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    Cited By

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    This article is cited by 8 publications.

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    2. Georgia Miskell, Jennifer A. Salmond, David E. Williams. Solution to the Problem of Calibration of Low-Cost Air Quality Measurement Sensors in Networks. ACS Sensors 2018, 3 (4) , 832-843. https://doi.org/10.1021/acssensors.8b00074
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    5. Minsu Kim, Dominik Brunner, Gerrit Kuhlmann. Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning. Remote Sensing of Environment 2021, 264 , 112573. https://doi.org/10.1016/j.rse.2021.112573
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    8. Christine T. Cowie, Frances Garden, Edward Jegasothy, Luke D. Knibbs, Ivan Hanigan, David Morley, Anna Hansell, Gerard Hoek, Guy B. Marks. Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia. Environmental Research 2019, 174 , 24-34. https://doi.org/10.1016/j.envres.2019.03.068

    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2017, 51, 21, 12473–12480
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.est.7b03035
    Published September 26, 2017
    Copyright © 2017 American Chemical Society

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