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Nitrogen dioxide prediction in Southern California using land use regression modeling: potential for environmental health analyses


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.

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This publication was supported by grant number 5-R21-CA094723-03 from the National Cancer Institute. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute. Dr. Jerrett's time was partly funded by grants number, 5P30 ES07048, 5P30 ES05605-14, 5P01 ES09581, and IP01 ES11627 from the National Institute of Environmental Health Sciences.

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Correspondence to Paul B English.

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Ross, Z., English, P., Scalf, R. et al. Nitrogen dioxide prediction in Southern California using land use regression modeling: potential for environmental health analyses. J Expo Sci Environ Epidemiol 16, 106–114 (2006).

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