Spatial analysis and land use regression of VOCs and NO2 in Dallas, Texas during two seasons

Abstract

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.

Spatial Distribution and Temporal Variation in Ambient Ozone and Its Associated NOx in the Atmosphere of Jeddah City, Saudi Arabia

“…Such variations could be attributed to the poor dependence of NO x on meteorological conditions. Similar trends observed for NO and NO 2 consisted of a winter peak and summer minima (Khodeir, 2009;Bigi and Harrison, 2010;Smith et al, 2011). This enhanced seasonality of NO x levels in winter may be partly attributed to increased fossil fuels for domestic heating and driving.…”

“…The seasonal variation in NO x with an increasing trend towards the winter has been reported by other authors in Cairo (Khodeir, 2009); Dallas (Smith et al, 2011); Delhi (Guttikunda and Gurjar, 2011) and New Jersey (RobertsSemple et al, 2012). Such variations could be attributed to the poor dependence of NO x on meteorological conditions.…”

Volatile organic compounds at two oil and natural gas production well pads in Colorado and Texas using passive samplers

“…Passive samplers were reuseable PerkinElmer style tubes containing Carbopack X sorbent (Supelco FLM Carbopack X Deactivated Stainless Steel TD Tubes, 89 mm, part number 28686-U, Sigma-Aldrich Co. LLC, St. Louis, MO) deployed for 14-day sampling periods. The passive sampler has been evaluated in lab and field studies and is comparable to standard methods for VOCs (McClenny et al, 2005;2006;Mukerjee et al, 2009;Smith et al, 2011;Thoma et al, 2011Thoma et al, , 2015. Duplicate sampling was conducted for all sampling periods to evaluate precision, and quality control (QC) samples consisting of field blanks and field spikes were also prepared for all sampling periods to allow evaluation of tube background contributions and recoveries of target compounds from the Carbopack X sorbent tubes.…”

Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

“…Hourly traffic count Different forms of traffic volume/intensity data have been used in many previous investigations of the LUR modeling technique. These included annual average daily traffic count (Briggs et al, 2000;Mölter et al, 2010) and simulated traffic data (Jason et al, 2008;Smith et al, 2011;Dons et al, 2013). In the present study, annual average daily traffic data have also been used to derive the VKT variable for models Dublin 1-6.…”

“…Predictor variables have also included meteorological conditions such as wind speed, wind direction, and temperature (Arian et al, 2007;Chen et al, 2010;Sahsuvaroglu et al, 2012). Predictor variables have included temporal factors to account for annual, seasonal, monthly, daily, and hourly variations (Chen et al, 2010;Mölter et al, 2010;MacIntyre et al, 2011;Smith et al, 2011;Dons et al, 2013Dons et al, , 2014. Data on specific known sources of air pollution emissions, in addition to general land use factors, have also been included in various published LUR models.…”