Assessing the Factors that Influence the Spatial Relationship between Nitrogen Dioxide and other Ambient Air Pollutants
Amanda J. Wheeler (1), Alice Grgicak-Mannion (2), JEFFREY R. BROOK (3), Neil Bellack (1), Ryan Kulka (1), Keith van Ryswyk (1), David van Rijswijk (1), Pat Rasmussen (1), Hongyu You (1) and Xiaohong Xu (4)
(1) Healthy Environments and Consumer Safety Branch, Health Canada, (2) Great Lakes Institute for Environmental Research, University of Windsor, (3) Processes Research, Environment Canada, (4) Department of Civil and Environmental Engineering, University of Windsor
Abstract Number: 341
Preference: Poster Presentation
Last modified: November 9, 2009
Working Group: sq3
Background: There have been several land use regression (LUR) models developed around the world, particularly for nitrogen dioxide (NO$_2). Many of the model predictors include indicators of traffic emissions such as proximity to roadways. The objective of this research is to identify factors that influence the strength of the spatial relationship between nitrogen dioxide (NO$_2) and other ambient air pollutants measured at several locations across Windsor, Ontario. This will be valuable for interpreting whether NO$_2 might be representing other ambient air pollutants in these models. Ambient levels of NO$_2 have been associated with adverse outcomes such as hospital admissions and non-accidental mortality despite concerns regarding the biological plausibility of NO$_2.
Methods: Spatial sampling to investigate intra-urban variability occurred once per season over a two-week period in 2005 and 2006, for a total of eight sessions. Ninety-nine locations were used over the two year period. At each site the mean values for all seasons were calculated for NO$_2, fine (PM$_(2.5)) and coarse particulate matter (PM$_(10-2.5)), black carbon content of the PM$_(2.5) filters using reflectance, acid vapour (nitric, formic and acetic), volatile organic compounds (VOCs) (number of individual VOCs analysed = 26), and polycyclic aromatic hydrocarbons (PAHs) (number of individual PAHs analysed = 24). A number of geographical variables including road network density, number of industrial point sources, and vehicle fleet data were calculated for each location within a 300m buffer of the site. To interpret the spatial relationship between NO$_2 and the other measured ambient air pollutants the Spearman Rank Correlation Coefficients were calculated between NO$_2 and each pollutant. These correlations were repeated using data from only those sites that were within 300 meters of a known source such as a busy roadway, truck route or industrial emitter.
Results: Mean NO$_2 concentrations for all sites in both years were 14.5 µg/m$^3 (Std Dev = 3.1). Preliminary results indicate that sites with higher density traffic located within a 300m buffer, determined as average daily traffic count >48,000 vehicles, had elevated mean NO$_2 concentrations of 16.5 µg/m$^3 (Std Dev = 2.6) compared to sites with low traffic density, determined as average daily traffic count <1,200, within 300m buffers where the mean was 13.0 µg/m$^3 (Std Dev = 2.8). For those sites with higher traffic density it was found that significant associations existed between NO$_2 and black carbon (r = 0.65, p = 0.006), PM$_(10-2.5) (r = 0.51, p = 0.02) and toluene (r = 0.50, p = 0.02) compared with the lower density traffic sites where these associations were not significant for PM$_(10-2.5) or toluene and only marginally significant for black carbon (r = 0.43, p = 0.05).
Discussion: Preliminary results indicate that the associations between NO$_2 and a variety of pollutants vary according to proximity to sources. It is important to understand what NO$_2 could be representing when assessing intra-urban variability and any potential impact upon health.