Spatial modeling of ambient concentrations of volatile organic compounds in Montreal, Canada

Abstract

Background: Volatile organic compounds (VOCs) are components of the complex mixture of air pollutants within cities and can cause various adverse health effects. Therefore, it is necessary to understand their spatial distribution for exposure assessment in epidemiological studies. Objectives: The objective was to model measured concentrations of five VOCs within the city of Montreal, Canada, developing spatial prediction models that can be used in health studies. Methods: We measured concentrations using 3M 3500 Organic Vapor Monitors, over 2-week periods, for three monitoring campaigns between 2005 and 2006 in over 130 locations in the city. Using GC/MSD (Gas Chromatography/Mass Selective Detector), we measured concentrations of benzene, n-decane, ethylbenzene, hexane, and trimethylbenzene. We fitted four different models that combine land-use regression and geostatistical methods to account for the potential spatial structure that remains after accounting for the land-use variables. The fitted models also accounted for possible variations in the concentration of air pollutants across campaigns. Results: The highest concentrations for all VOCs were found in December with hexane being the most abundant followed by ethylbenzene. We obtained predicted surfaces for the VOCs for the three campaigns and mean surfaces across campaigns. We found higher concentrations of some VOCs along highways and in the Eastern part of Montreal, which is a highly industrialized area. Conclusions: Each of the fitted models captured the spatial and across-campaigns variability for each VOC, and we found that different VOCs required different model structures.

Ambient exposures to selected volatile organic compounds and the risk of prostate cancer in Montreal

“…We fitted Bayesian spatial regression models using data from three dense sampling campaigns in Montreal 28 in which over 130 sites were used. A strength of our spatial model was that both the spatial- and campaign-specific variability was accounted for, instead of averaging the data across campaigns, and this provided comparisons of concentrations across campaigns and VOCs.…”

“…The multipoint calibration curve had an R 2 > 0.999 and we have shown that co-located samplers were consistent with each other. 28…”

“…The multipoint calibration curve had an R 2 > 0.999 and we have shown that co-located samplers were consistent with each other. 28 We used a combination of land-use regression and geostatistical methods 28 to estimate the spatial distribution for each of the five VOCs across the monitoring campaigns. We used common land-use predictors (e.g., buildings, open areas) 26,29,30 and included average and total NOx and total daily traffic volume 31 estimated from emission modeling system.…”