Ambient polycyclic aromatic hydrocarbon exposure and breast cancer risk in a population-based Canadian case-control study.

PURPOSE: Polycyclic aromatic hydrocarbons (PAHs) represent a class of ubiquitous pollutants recognized as established human carcinogens and endocrine-disrupting chemicals. PAHs have seldom been modeled at the population-level in epidemiological studies. Fluoranthene is a prevalent PAH in urban settings and correlates with the occurrence of other PAHs. The purpose of this study was to evaluate associations between long-term residential exposure to ambient PAHs and breast cancer risk, both pre- and post-menopausal, in Canada. METHODS: Using the National Enhanced Cancer Surveillance System (NECSS), a national-scale Canadian population-based case-control study, annual fluoranthene exposures were estimated using the GEM-MACH-PAH chemical transport model on the basis of geocoded residential histories throughout a 20-year exposure window. Odds ratios (ORs) and 95% confidence intervals (CIs) controlling for potential confounders were estimated using logistic regression. Separate analyses were conducted for Ontario and national samples given a finer-resolution exposure surface and additional risk factor information available for Ontario. RESULTS: Positive associations were observed between fluoranthene exposure and premenopausal breast cancer, with inconsistent findings for postmenopausal breast cancer. For premenopausal breast cancer, adjusted ORs of 2.48 (95% CI: 1.29, 4.77) and 1.59 (95% CI: 1.11, 2.29) were observed when comparing the second highest category of exposure to the lowest, among the Ontario and national samples, respectively. For postmenopausal breast cancer, adjusted ORs were 1.10 (95% CI: 0.67, 1.80) and 1.33 (95% CI: 1.02, 1.73). Associations for the highest level of exposure, across both samples and menopausal strata, were non-significant. CONCLUSION: This study provides support for the hypothesis that ambient PAH exposures increase the risk of premenopausal breast cancer.

Improving Insights on Air Pollutant Mixtures and Their Origins by Enhancing Local Monitoring in an Area of Intensive Resource Development.

An "event-based" approach to characterize complex air pollutant mixtures was applied in the Oil Sands region of northern Alberta, Canada. This approach was developed to better-inform source characterization and attribution of the air pollution in the Indigenous community of Fort McKay, within the context of the lived experience of residents. Principal component analysis was used to identify the characteristics of primary pollutant mixtures, which were related to hydrocarbon emissions, fossil fuel combustion, dust, and oxidized and reduced sulfur compounds. Concentration distributions of indicator compounds were used to isolate sustained air pollution "events". Diesel-powered vehicles operating in the mines were found to be an important source during NOx events. Industry-specific volatile organic compound (VOC) profiles were used in a chemical mass balance model for source apportionment, which revealed that nearby oil sands operations contribute to 86% of the total mass of nine VOC species (2-methylpentane, hexane, heptane, octane, benzene, toluene, m,p-xylene, o-xylene, and ethylbenzene) during VOC events. Analyses of the frequency distribution of air pollution events indicate that Fort McKay is regularly impacted by multiple mixtures simultaneously, underscoring the limitations of an exceedance-based approach relying on a small number of air quality standards as the only tool to assess risk.

Deposition Mapping of Polycyclic Aromatic Compounds in the Oil Sands Region of Alberta, Canada and Linkages to Ecosystem Impacts.

This study produced gridded deposition estimates of polycyclic aromatic compounds (PACs), including 17 polycyclic aromatic hydrocarbons (PAHs), 21 alkylated PAHs (alk-PAHs), and 5 dibenzothiophenes (DBTs), over the oil sands region of Alberta, Canada and surrounding communities. Gridded annual total deposition of PACs in 2011 ranged from 55 to 175 000 µg m-2 yr-1 and the mean and median fluxes were 1700 and 760 µg m-2 yr-1, respectively. The domain-wide mean dry and wet deposition were 600 and 1100 µg m-2 yr-1. PAHs, alk-PAHs and DBTs contributed 19%, 74%, and 7% to the total dry deposition, and 42%, 49%, and 9% to the total wet deposition. Dominant chemical species contributing to total deposition were naphthalene, retene and phenanthrene for PAHs and C2-benz[a]anthracene/triphenylene/chrysene, C2-fluoranthene/pyrene and C2-fluorene for alk-PAHs. The highest PAC deposition was found over the surface mineable area, which received 9 times the deposition flux of outlying areas. Additional deposition hotspots were also observed south of the surface mineable area notably over in situ bitumen production sites. The deposition of alk-PAHs impacted a more extensive area than that of PAHs or DBTs. This result suggests that atmospheric deposition is a key process in wildlife exposure to PACs across the region.

A method to quantitatively apportion pollutants at high spatial and temporal resolution: the Stochastic Lagrangian Apportionment Method (SLAM).

We introduce a method to quantify upwind contributions to concentrations of atmospheric pollutants. The Stochastic Lagrangian Apportionment Method (SLAM) carries out the following: (1) account for chemical transformations and depositional losses; (2) incorporate the effects of turbulent dispersion; (3) simulate the locations of the sources with high spatial and temporal resolution; and (4) minimize the impact from numerical diffusion. SLAM accomplishes these four features by using a time-reversed Lagrangian particle dispersion model and then simulating chemical changes forward in time, while tagging and keeping track of different sources. As an example of SLAM's application, we show its use in apportioning sources contributing to ammonia (NH3) and ammonium particulates (p-NH4(+)) at a site in southern Ontario, Canada. Agricultural emissions are seen to dominate contributions to NH3 and p-NH4(+) at the site. The source region of NH3 was significantly smaller than that of p-NH4(+), which covered numerous states of the American Midwest. The source apportionment results from SLAM were compared against those from zeroing-out individual sources ("brute force method"; BFM). The comparisons show SLAM to produce almost identical results as BFM for NH3, but higher concentrations of p-NH4(+), likely due to indirect effects that affect BFM. Finally, uncertainties in the SLAM approach and ways to address such shortcomings by combining SLAM with inverse methods are discussed.

Constraints on carbon monoxide emissions based on tall tower measurements in the US Upper Midwest.

We interpret a full year of high-frequency CO measurements from a tall tower in the U.S. Upper Midwest with a time-reversed Lagrangian Particle Dispersion Model (STILT LPDM) and an Eulerian chemical transport model (GEOS-Chem CTM) to develop top-down constraints on U.S. CO sources in 2009. Our best estimate is that anthropogenic CO emissions in the U.S. Upper Midwest in 2009 were 2.9 Tg, 61% lower (a posteriori scale factor of 0.39) than our a priori prediction based on the U.S. EPA's National Emission Inventory for 2005 (NEI 2005). If the same bias applies across the contiguous U.S., the inferred CO emissions are 26 Tg/y, compared to the a priori estimate of 66 Tg/y. This discrepancy is significantly greater than would be expected based solely on emission decreases between 2005 and 2009 (EPA estimate: 23% decrease). Model transport error is an important source of uncertainty in the analysis, and we employ an ensemble of sensitivity runs using multiple meteorological data sets and model configurations to assess its impact on our results. A posteriori scale factors for the U.S. anthropogenic CO source from these sensitivity runs range from 0.22 to 0.64, corresponding to emissions of 1.6-4.8 Tg/y for the U.S. Upper Midwest and 15-42 Tg/y for the contiguous U.S. The data have limited sensitivity for constraining biomass + biofuel burning emissions and photochemical CO production from precursor organic compounds. Our finding of a NEI 2005 overestimate of CO emissions is consistent with recent assessments for individual cities and with earlier analyses based on the NEI 1999, implying the need for a better mechanism for refining such bottom-up emission estimates in response to top-down constraints.