title: Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden.
abstract: 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 NOxat 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(R2=0.89) better than the DM without covariates (R2=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 NOxlevels (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.
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Michal
authors.initials: M
authors.lastname: Korek
authors.affiliation: Environment and Health Administration, Stockholm, Sweden.|Department of Environmental Science and Analytical Chemistry, Stockholm University, Stockholm, Sweden.
authors.forename: Christer
authors.initials: C
authors.lastname: Johansson
authors.affiliation: Environment and Health Administration, Stockholm, Sweden.
authors.forename: Nina
authors.initials: N
authors.lastname: Svensson
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.|Centre for Occupational and Environmental Medicine, Stockholm County Council, Stockholm, Sweden.
authors.forename: Tomas
authors.initials: T
authors.lastname: Lind
authors.affiliation: National Institute for Public Health and The Environment (RIVM), Utrecht, The Netherlands.|Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
authors.forename: Rob
authors.initials: R
authors.lastname: Beelen
authors.affiliation: Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
authors.forename: Gerard
authors.initials: G
authors.lastname: Hoek
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.|Centre for Occupational and Environmental Medicine, Stockholm County Council, Stockholm, Sweden.
authors.forename: Göran
authors.initials: G
authors.lastname: Pershagen
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.|Centre for Occupational and Environmental Medicine, Stockholm County Council, Stockholm, Sweden.
authors.forename: Tom
authors.initials: T
authors.lastname: Bellander
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis* / Q000706:statistics & numerical data*; D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D009585:Nitrogen Dioxide / Q000032:analysis*; D052638:Particulate Matter / Q000032:analysis*; D013548:Sweden; D001335:Vehicle Emissions / Q000032:analysis*
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter; D001335:Vehicle Emissions; D009585:Nitrogen Dioxide