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Evaluation of land use regression models (LURs) for nitrogen dioxide and benzene in four U.S. Cities.
Citation:
Mukerjee, S., L. Smith, L. Neas, AND G. Norris. Evaluation of land use regression models (LURs) for nitrogen dioxide and benzene in four U.S. Cities. The Scientific World Journal . Hindawi Publishing Corporation, New York, NY, 2012(865150):1-8, (2012).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
Spatial analysis studies have included 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 inter-city differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult.
URLs/Downloads:
REVISED FINAL FINAL 4 CITIES LUR MANUSCRIPT TSWJ.PDF (PDF, NA pp, 565.008 KB, about PDF)The Scientific World Journal
