title: Nitrogen dioxide prediction in Southern California using land use regression modeling: potential for environmental health analyses.
abstract: We modeled the intraurban distribution of nitrogen dioxide (NO(2)), a marker for traffic pollution, with land use regression, a promising new exposure classification technique. We deployed diffusion tubes to measure NO(2) levels at 39 locations in the fall of 2003 in San Diego County, CA, USA. At each sample location, we constructed circular buffers in a geographic information system and captured information on roads, traffic flow, land use, population and housing. Using multiple linear regression, we were able to predict 79% of the variation in NO(2) levels with four variables: traffic density within 40-300 m of the sampling location, traffic density within 300-1000 m, length of road within 40 m and distance to the Pacific coast. Applying this model to validation samples showed that the model predicted NO(2) levels within, on average, 2.1 p.p.b for 12 training sites initially excluded from the model. Our evaluation of this land use regression model showed that this method had excellent prediction and robustness in a North American context. These models may be useful tools in evaluating health effects of long-term exposure to traffic-related pollution.
authors.affiliation: ZevRoss Spatial Analysis, 303 Fairmount Ave., Ithaca, New York 14850, USA.
authors.forename: Zev
authors.initials: Z
authors.lastname: Ross
authors.forename: Paul B
authors.initials: PB
authors.lastname: English
authors.forename: Rusty
authors.initials: R
authors.lastname: Scalf
authors.forename: Robert
authors.initials: R
authors.lastname: Gunier
authors.forename: Svetlana
authors.initials: S
authors.lastname: Smorodinsky
authors.forename: Steve
authors.initials: S
authors.lastname: Wall
authors.forename: Michael
authors.initials: M
authors.lastname: Jerrett
mesh_terms: D002140:California; D004779:Environment Design; D004784:Environmental Monitoring / Q000379:methods / Q000706:statistics & numerical data*; D040362:Geographic Information Systems; D006801:Humans; D016014:Linear Models; D015999:Multivariate Analysis; D009585:Nitrogen Dioxide / Q000032:analysis*; D012680:Sensitivity and Specificity; D001335:Vehicle Emissions / Q000032:analysis*
chemical_list: D001335:Vehicle Emissions; D009585:Nitrogen Dioxide