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Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden
Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry. Environment and Health Administration, Sweden.
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Number of Authors: 8
2017 (English)In: Journal of Exposure Science and Environmental Epidemiology, ISSN 1559-0631, E-ISSN 1559-064X, Vol. 27, no 6, 575-581 p.Article in journal (Refereed) Published
Abstract [en]

Both dispersion modeling (DM) and land-use regression modeling (LUR) are often used for assessment of long-term air pollution exposure in epidemiological studies, but seldom in combination. We developed a hybrid DM-LUR model using 93 biweekly observations of NOx at 31 sites in greater Stockholm (Sweden). The DM was based on spatially resolved topographic, physiographic and emission data, and hourly meteorological data from a diagnostic wind model. Other data were from land use, meteorology and routine monitoring of NOx. We built a linear regression model for NOx, using a stepwise forward selection of covariates. The resulting model predicted observed NOx (R-2 = 0.89) better than the DM without covariates (R-2 = 0.68, P-interaction < 0.001) and with minimal apparent bias. The model included (in descending order of importance) DM, traffic intensity on the nearest street, population (number of inhabitants) within 100 m radius, global radiation (direct sunlight plus diffuse or scattered light) and urban contribution to NOx levels (routine urban NOx, less routine rural NOx). Our results indicate that there is a potential for improving estimates of air pollutant concentrations based on DM, by incorporating further spatial characteristics of the immediate surroundings, possibly accounting for imperfections in the emission data.

Place, publisher, year, edition, pages
2017. Vol. 27, no 6, 575-581 p.
Keyword [en]
criteria pollutants, empirical/statistical models, exposure modeling
National Category
Earth and Related Environmental Sciences Occupational Health and Environmental Health
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URN: urn:nbn:se:su:diva-148843DOI: 10.1038/jes.2016.40ISI: 000413539800006PubMedID: 27485990OAI: oai:DiVA.org:su-148843DiVA: diva2:1156494
Available from: 2017-11-13 Created: 2017-11-13 Last updated: 2018-01-13Bibliographically approved

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