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

Ivan C. Hanigan, Grant J. Williamson, Luke D. Knibbs, Joshua Horsley, Margaret I. Rolfe, Martin Cope, Adrian G. Barnett, Christine T. Cowie, Jane S. Heyworth, Marc L. Serre, Bin Jalaludin, Geoffrey G. Morgan

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)12473-12480
Number of pages8
JournalEnvironmental Science and Technology
Volume51
Issue number21
DOIs
Publication statusPublished - 7 Nov 2017

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Nitrogen Dioxide
nitrogen dioxide
Health
Land use
land use
probability density function
Air pollution
Probability density function
entropy
Pollution
Entropy
atmospheric pollution
health
exposure
Satellites
pollution
Monitoring
monitoring
prediction
cost

Cite this

Hanigan, I. C., Williamson, G. J., Knibbs, L. D., Horsley, J., Rolfe, M. I., Cope, M., ... Morgan, G. G. (2017). Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research. Environmental Science and Technology, 51(21), 12473-12480. https://doi.org/10.1021/acs.est.7b03035
Hanigan, Ivan C. ; Williamson, Grant J. ; Knibbs, Luke D. ; Horsley, Joshua ; Rolfe, Margaret I. ; Cope, Martin ; Barnett, Adrian G. ; Cowie, Christine T. ; Heyworth, Jane S. ; Serre, Marc L. ; Jalaludin, Bin ; Morgan, Geoffrey G. / Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research. In: Environmental Science and Technology. 2017 ; Vol. 51, No. 21. pp. 12473-12480.
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abstract = "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.",
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Hanigan, IC, Williamson, GJ, Knibbs, LD, Horsley, J, Rolfe, MI, Cope, M, Barnett, AG, Cowie, CT, Heyworth, JS, Serre, ML, Jalaludin, B & Morgan, GG 2017, 'Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research', Environmental Science and Technology, vol. 51, no. 21, pp. 12473-12480. https://doi.org/10.1021/acs.est.7b03035

Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research. / Hanigan, Ivan C.; Williamson, Grant J.; Knibbs, Luke D.; Horsley, Joshua; Rolfe, Margaret I.; Cope, Martin; Barnett, Adrian G.; Cowie, Christine T.; Heyworth, Jane S.; Serre, Marc L.; Jalaludin, Bin; Morgan, Geoffrey G.

In: Environmental Science and Technology, Vol. 51, No. 21, 07.11.2017, p. 12473-12480.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research

AU - Hanigan, Ivan C.

AU - Williamson, Grant J.

AU - Knibbs, Luke D.

AU - Horsley, Joshua

AU - Rolfe, Margaret I.

AU - Cope, Martin

AU - Barnett, Adrian G.

AU - Cowie, Christine T.

AU - Heyworth, Jane S.

AU - Serre, Marc L.

AU - Jalaludin, Bin

AU - Morgan, Geoffrey G.

PY - 2017/11/7

Y1 - 2017/11/7

N2 - 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.

AB - 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.

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KW - Australia

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KW - Environmental Exposure

KW - Environmental Monitoring

KW - Information Storage and Retrieval

KW - Nitrogen Dioxide

KW - Particulate Matter

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U2 - 10.1021/acs.est.7b03035

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