Abstract
In previous studies, inhalation cancer risk was estimated using conventional risk assessment method, which was normally based on compound-specific analysis, and cannot provide substantial data for source-specific particulate matter concentrations and pollution control. In the present study, we applied an integrated risk analysis method, which was a synthetic combination of source apportionment receptor model and risk assessment method, to estimate cancer risks associated to individual PAHs coming from specific sources. Personal exposure particulate matter samples referring to an elderly panel were collected in a community of Tianjin, Northern China, in 2009, and 12 PAH compounds were measured using GC-MS. Positive matrix factorization (PMF) was used to extract the potential sources and quantify the source contributions to the PAH mixture. Then, the lung cancer risk of each modeled source was estimated by summing up the cancer risks of all measured PAH species according to the extracted source profile. The final results indicated that the overall cancer risk was 1.12 × 10−5, with the largest contribution from gasoline vehicle emission (44.1%). Unlike other risk estimation studies, this study was successful in combining risk analysis and source apportionment approaches, which allow estimating the potential risk of all source types and provided suitable information to select prior control strategies and mitigate the main air pollution sources that contributing to health risks.
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Acknowledgements
We appreciate Prof. Sverre Vedal from the University of Washington for his suggestions and comments on this article.
Funding
This study was funded by the “National Basic Research Program of China” (Grant No. 2011CB503801).
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Main finding of the work
This study found gasoline and diesel exhausts were the largest contributors to the inhalation cancer risk of personal PAHs exposure in Chinese megacities.
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Han, B., You, Y., Liu, Y. et al. Inhalation cancer risk estimation of source-specific personal exposure for particulate matter–bound polycyclic aromatic hydrocarbons based on positive matrix factorization. Environ Sci Pollut Res 26, 10230–10239 (2019). https://doi.org/10.1007/s11356-019-04198-y
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DOI: https://doi.org/10.1007/s11356-019-04198-y