1. ^aZevRoss Spatial Analysis, 303 Fairmount Ave., Ithaca, New York 14850, USA
2. ^bDivision of Environmental and Occupational Disease Control, California Department of Health Services, 1515 Clay St., #1700, Oakland, California 94612, USA
3. ^cImpact Assessment Inc., 1515 Clay St., #1700, Oakland, California 94612, USA
4. ^dDivision of Biostatistics, Department of Preventive Medicine and Department of Geography, University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, California 90089-9011, USA

Correspondence: Dr. P.B. English, Environmental Health Investigations Branch, Division of Environmental and Occupational Disease Control, California Department of Health Services, 1515 Clay St., #1700, Oakland, CA 94612, USA. Tel.: +1 510 622 4508; Fax: +1 510 622 4505; E-mail: PEnglish@dhs.ca.gov

Received 11 October 2004; Accepted 8 June 2005; Published online 27 July 2005.

Abstract

We modeled the intraurban distribution of nitrogen dioxide (NO₂), a marker for traffic pollution, with land use regression, a promising new exposure classification technique. We deployed diffusion tubes to measure NO₂ 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 NO₂ 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 NO₂ 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.