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The Scientific World Journal
Volume 2012 (2012), Article ID 865150, 8 pages
http://dx.doi.org/10.1100/2012/865150
Research Article

Evaluation of Land Use Regression Models for Nitrogen Dioxide and Benzene in Four US Cities

1National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Mail Code E205-03, Research Triangle Park, NC 27711, USA
2Alion Science and Technology Inc., Durham, NC 27713, USA
3National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Mail Code 58-A, Research Triangle Park, NC 27711, USA

Received 17 September 2012; Accepted 4 October 2012

Academic Editors: F. Amato, G.-C. Fang, and A. W. Gertler

Copyright © 2012 Shaibal Mukerjee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO2) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. LURs were successfully developed to estimate pollutant concentrations throughout the study areas. Comparisons of development and predictive capabilities of LURs from these four cities are presented to address this issue of uniform application of LURs across study areas. Traffic and other urban variables were important predictors in the LURs although city-specific influences (such as border crossings) were also important. In addition, transferability of variables or LURs from one city to another may be problematic due to intercity differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult.