Spatial analysis and land use regression of VOCs and NO 2 in Dallas, Texas during two seasons† Luther A. Smith, a Shaibal Mukerjee, * b Kuenja C. Chung c and Jim Afghani c Received 29th November 2010, Accepted 19th January 2011 DOI: 10.1039/c0em00724b Passive air sampling for nitrogen dioxide (NO 2 ) and select volatile organic compounds (VOCs) was conducted at 24 fire stations and a compliance monitoring site in Dallas, Texas, USA during summer 2006 and winter 2008. This ambient air monitoring network was established to assess intra-urban gradients of air pollutants to evaluate the impact of traffic and urban emissions on air quality. Ambient air monitoring and GIS data from spatially representative fire station sites were collected to assess spatial variability. Pairwise comparisons were conducted on the ambient data from the selected sites based on city section. These weeklong samples yielded NO 2 and benzene levels that were generally higher during the winter than the summer. With respect to the location within the city, the central section of Dallas was generally higher for NO 2 and benzene than north and south. Land use regression (LUR) results revealed spatial gradients in NO 2 and selected VOCs in the central and some northern areas. The process used to select spatially representative sites for air sampling and the results of analyses of coarse- and fine-scale spatial variability of air pollutants on a seasonal basis provide insights to guide future ambient air exposure studies in assessing intra-urban gradients and traffic impacts. Introduction The Dallas, Texas metropolitan area, with a population of over two million, had a growing concern about air quality due to elevated levels of nitrogen oxides and hazardous air pollutants potentially influencing ozone nonattainment. To gain a more complete overview of volatile organic compounds (VOCs) and nitrogen dioxide (NO 2 ) levels in the City of Dallas, the US Environmental Protection Agency’s (EPA) Region 6 and Office of Research and Development conducted monitoring of air toxics. These ambient monitoring data were analyzed to examine differences between sections of the city and combined with variables calculated in a geographic information system (GIS) to develop predicted pollutant levels across the city. A large number of studies assessing spatial differences of urban air pollutants have employed the exposure prediction technique known as land use regression (LUR) modeling. In these studies, monitoring networks are typically established at a number of sites in an urban area using passive or other field- portable air monitoring devices. Monitored data combined with geographic information system (GIS)-derived variables such as proximity to roadways are used to develop LURs. The LURs can be used to predict ambient levels at residential locations to aid spatially based epidemiologic health studies 1–5 as well as inform decisions regarding placement of monitoring sites. Prior to the current study, EPA conducted air exposure monitoring studies at elementary schools in El Paso, Texas 6 and a Alion Science and Technology, Inc., Durham, NC, 27713, USA b National Exposure Research Laboratory, US Environmental Protection Agency (E205-03), Research Triangle Park, NC, 27711, USA. E-mail: mukerjee.shaibal@epa.gov; Fax: +1 919 541 4787; Tel: +1 919 541 1865 c US Environmental Protection Agency, Region 6, 1445 Ross Avenue, Dallas, TX, 75202, USA † Electronic supplementary information (ESI) available. Tables of ancillary variables considered for use in LUR models, passive method evaluation, mean concentrations at each site, and maps of measured NO 2 and benzene concentrations. See DOI: 10.1039/c0em00724b Environmental impact Passive air sampling for NO 2 and select VOCs was conducted in Dallas, Texas during summer and winter seasons. Monitoring sites were statistically chosen based on GIS data to be spatially representative of air pollution explanatory variables throughout the city and, thus, able to assess intra-urban gradients of air pollutants. City section comparisons and land use regression (LUR) modeling results revealed spatial gradients of the pollutants with results differing by season. The combination of passive monitoring and GIS and statistical approaches employed here may be useful in identifying local influences on pollutant levels thus helping in evaluating monitoring locations and better inferring pollutant concentrations across city-wide geographic areas. This journal is ª The Royal Society of Chemistry 2011 J. Environ. Monit., 2011, 13, 999–1007 | 999 Dynamic Article Links C < Journal of Environmental Monitoring Cite this: J. Environ. Monit., 2011, 13, 999 www.rsc.org/jem PAPER Downloaded by US EPA RTP and AWBERC on 05 April 2011 Published on 15 February 2011 on http://pubs.rsc.org | doi:10.1039/C0EM00724B View Online