Evaluation of land use regression models for NO2 in El Paso, Texas, USA. Academic Article uri icon

start page

  • 135

end page

  • 142

abstract

  • Developing suitable exposure estimates for air pollution health studies is problematic due to spatial and temporal variation in concentrations and often limited monitoring data. Though land use regression models (LURs) are often used for this purpose, their applicability to later periods of time, larger geographic areas, and seasonal variation is largely untested. We evaluate a series of mixed model LURs to describe the spatial-temporal gradients of NO(2) across El Paso County, Texas based on measurements collected during cool and warm seasons in 2006-2007 (2006-7). We also evaluated performance of a general additive model (GAM) developed for central El Paso in 1999 to assess spatial gradients across the County in 2006-7. Five LURs were developed iteratively from the study data and their predictions were averaged to provide robust nitrogen dioxide (NO(2)) concentration gradients across the county. Despite differences in sampling time frame, model covariates and model estimation methods, predicted NO(2) concentration gradients were similar in the current study as compared to the 1999 study. Through a comprehensive LUR modeling campaign, it was shown that the nature of the most influential predictive variables remained the same for El Paso between 1999 and 2006-7. The similar LUR results obtained here demonstrate that, at least for El Paso, LURs developed from prior years may still be applicable to assess exposure conditions in subsequent years and in different seasons when seasonal variation is taken into consideration.Copyright © 2012 Elsevier B.V. All rights reserved.

date/time value

  • 2012

Digital Object Identifier (DOI)

  • 10.1016/j.scitotenv.2012.05.062

PubMed Identifier

  • 22728301

volume

  • 432

number

keywords

  • Air Pollutants
  • Environmental Monitoring
  • Human Activities
  • Models, Theoretical
  • Nitrogen Dioxide
  • Regression Analysis
  • Seasons
  • Texas