The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Creating National Air Pollution Models for Population Exposure Assessment in Canada
Open accessAbstract
BACKGROUND: Population exposure assessment methods that capture local-scale pollutant variability are needed for large-scale epidemiological studies and surveillance, policy, and regulatory purposes. Currently, such exposure methods are limited. METHODS: We created 2006 national pollutant models for fine particulate matter [PM with aerodynamic diameter ≤ 2.5 μm (PM2.5)], nitrogen dioxide (NO2), benzene, ethylbenzene, and 1,3-butadiene from routinely collected fixed-site monitoring data in Canada. In multiple regression models, we incorporated satellite estimates and geographic predictor variables to capture background and regional pollutant variation and used deterministic gradients to capture local-scale variation. The national NO2 and benzene models are evaluated with independent measurements from previous land use regression models that were conducted in seven Canadian cities. National models are applied to census block-face points, each of which represents the location of approximately 89 individuals, to produce estimates of population exposure. RESULTS: The national NO2 model explained 73% of the variability in fixed-site monitor concentrations, PM2.5 46%, benzene 62%, ethylbenzene 67%, and 1,3-butadiene 68%. The NO2 model predicted, on average, 43% of the within-city variability in the independent NO2 data compared with 18% when using inverse distance weighting of fixed-site monitoring data. Benzene models performed poorly in predicting within-city benzene variability. Based on our national models, we estimated Canadian ambient annual average population-weighted exposures (in micrograms per cubic meter) of 8.39 for PM2.5, 23.37 for NO2, 1.04 for benzene, 0.63 for ethylbenzene, and 0.09 for 1,3-butadiene. CONCLUSIONS: The national pollutant models created here improve exposure assessment compared with traditional monitor-based approaches by capturing both regional and local-scale pollution variation. Applying national models to routinely collected population location data can extend land use modeling techniques to population exposure assessment and to informing surveillance, policy, and regulation.
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van Donkelaar, A., Martin, R. V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., & Villeneuve, P. J. (2010) · Environmental Health Perspectives · doi:10.1289/ehp.0901623 · Cited by 1,623
Self-citationA review of land-use regression models to assess spatial variation of outdoor air pollution
Hoek, G., Beelen, R., de Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P., & Briggs, D. (2008) · Atmospheric Environment · doi:10.1016/j.atmosenv.2008.05.057 · Cited by 1,395
A review and evaluation of intraurban air pollution exposure models
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Self-citationSatellite remote sensing of surface air quality
Martin, R. V. (2008) · Atmospheric Environment · doi:10.1016/j.atmosenv.2008.07.018 · Cited by 551
Self-citationHenderson, S. B., Beckerman, B., Jerrett, M., & Brauer, M. (2007) · Environmental Science & Technology · doi:10.1021/es0606780 · Cited by 503
Self-citationLiu, Y., Paciorek, C. J., & Koutrakis, P. (2009) · Environmental Health Perspectives · doi:10.1289/ehp.0800123 · Cited by 453
Concentrations of submicrometre particles from vehicle emissions near a major road
Hitchins, J., Morawska, L., Wolff, R., & Gilbert, D. (2000) · Atmospheric Environment · doi:10.1016/s1352-2310(99)00304-0 · Cited by 399
Lamsal, L. N., Martin, R. V., van Donkelaar, A., Steinbacher, M., Celarier, E. A., Bucsela, E., Dunlea, E. J., & Pinto, J. P. (2008) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2007jd009235 · Cited by 396
Self-citationLiu, L., Poon, R., Chen, L., Frescura, A.-M., Montuschi, P., Ciabattoni, G., Wheeler, A., & Dales, R. (2009) · Environmental Health Perspectives · doi:10.1289/ehp.11813 · Cited by 354
Correlation of nitrogen dioxide with other traffic pollutants near a major expressway
Beckerman, B., Jerrett, M., Brook, J. R., Verma, D. K., Arain, M. A., & Finkelstein, M. M. (2008) · Atmospheric Environment · doi:10.1016/j.atmosenv.2007.09.042 · Cited by 328
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Cited by
Chen, H., Kwong, J. C., Copes, R., Tu, K., Villeneuve, P. J., van Donkelaar, A., Hystad, P., Martin, R. V., Murray, B. J., Jessiman, B., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(16)32399-6 · Cited by 776
Self-citationThe Ozone Monitoring Instrument: overview of 14 years in space
Levelt, P. F., Joiner, J., Tamminen, J., Veefkind, J. P., Bhartia, P. K., Stein Zweers, D. C., Duncan, B. N., Streets, D. G., Eskes, H., van der A, R., et al. (2018) · Atmospheric Chemistry and Physics · doi:10.5194/acp-18-5699-2018 · Cited by 481
Self-citationLi, T., Shen, H., Yuan, Q., Zhang, X., & Zhang, L. (2017) · Geophysical Research Letters · doi:10.1002/2017gl075710 · Cited by 414
Exposure to ambient air pollution and the incidence of dementia: a population-based cohort study
Chen, H., Kwong, J. C., Copes, R., Hystad, P., van Donkelaar, A., Tu, K., Brook, J. R., Goldberg, M. S., Martin, R. V., Murray, B. J., et al. (2017) · Environment International · doi:10.1016/j.envint.2017.08.020 · Cited by 394
Self-citationvan Donkelaar, A., Martin, R. V., Spurr, R. J. D., & Burnett, R. T. (2015) · Environmental Science & Technology · doi:10.1021/acs.est.5b02076 · Cited by 266
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Metadata
- Authors
- Hystad, P., Setton, E., Cervantes, A., Poplawski, K., Deschenes, S., Brauer, M., van Donkelaar, A., Lamsal, L., Martin, R., Jerrett, M., & Demers, P.
- Venue
- Environmental Health Perspectives
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-08
- Volume
- 119
- Issue
- 8
- Pages
- 1123-1129
- ISSN
- 0091-6765 (ISSN-L)
- 1552-9924
- OpenAlex
- W1963738994
- Cited by
- 283
- References
- 29
- FWCI
- 5.36
- Type
- journal article
- Language
- en
- Open access
- gold
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