An Investigation into Which Methods Best Explain Children's Exposure to Traffic-Related Air Pollution.

There have been several methods employed to quantify individual-level exposure to ambient traffic-related air pollutants (TRAP). These include an individual's residential proximity to roads, measurement of individual pollutants as surrogates or markers, as well as dispersion and land use regression (LUR) models. Hopanes are organic compounds still commonly found on ambient particulate matter and are specific markers of combustion engine primary emissions, but they have not been previously used in personal exposure studies. In this paper, children's personal exposures to TRAP were evaluated using hopanes determined from weekly integrated filters collected as part of a personal exposure study in Windsor, Canada. These hopane measurements were used to evaluate how well other commonly used proxies of exposure to TRAP performed. Several of the LUR exposure estimates for a range of air pollutants were associated with the children's summer personal hopane exposures (r = 0.41-0.74). However, all personal hopane exposures in summer were more strongly associated with the length of major roadways within 500 m of their homes. In contrast, metrics of major roadways and LUR estimates were poorly correlated with any winter personal hopanes. Our findings suggest that available TRAP exposure indicators have the potential for exposure misclassification in winter vs. summer and more so for LUR than for metrics of major road density. As such, limitations are evident when using traditional proxy methods for assigning traffic exposures and these may be especially important when attempting to assign exposures for children's key growth and developmental windows. If long-term chronic exposures are being estimated, our data suggest that measures of major road lengths in proximity to homes are a more-specific approach for assigning personal TRAP exposures.

n-alkane profiles of engine lubricating oil and particulate matter by molecular sieve extraction.

As part of the Canadian Atmospheric Fine Particle Research Program to obtain reliable primary source emission profiles, a molecular sieve method was developed to reliably determine n-alkanes in lubricating oils, vehicle emissions, and mobile source dominated ambient particulate matter (PM). This work was also initiated to better calculate carbon preference index values (CPI: the ratio of the sums of odd over even n-alkanes), a parameter for estimating anthropogenic versus biogenic contributions in PM. n-Alkanes in lubricating oil and mobile source dominated PM are difficult to identify and quantify by gas chromatography due to the presence of similar components that cannot be fully resolved. This results in a hump, the unresolved complex mixture (UCM) that leads to incorrect n-alkane concentrations and CPI values. The sieve method yielded better chromatography, unambiguous identification of n-alkanes and allowed examination of differences between n-alkane profiles in light (LDV) and heavy duty vehicle (HDV) lubricating oils that would have been otherwise difficult. These profile differences made it possible to relate the LDV profile to that of the PM samples collected during a tunnel study in August 2001 near Vancouver (British Columbia, Canada). The n-alkane PM data revealed that longer sampling times result in a negative artifact, i.e., the desorption of the more volatile n-alkanes from the filters. Furthermore, the sieve procedure yielded n-alkane data that allowed calculation of accurate CPI values for lubricating oils and PM samples. Finally, this method may prove helpful in estimating the respective diesel and gasoline contributions to ambient PM.