Inhalation cancer risk estimation of source-specific personal exposure for particulate matter–bound polycyclic aromatic hydrocarbons based on positive matrix factorization
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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.
KeywordsPolycyclic aromatic hydrocarbons Lung cancer risk assessment Source apportionment Positive matrix factorization
We appreciate Prof. Sverre Vedal from the University of Washington for his suggestions and comments on this article.
This study was funded by the “National Basic Research Program of China” (Grant No. 2011CB503801).
- Amador-Munoz O et al (2010) Solvent extracted organic matter and polycyclic aromatic hydrocarbons distributed in size-segregated airborne particles in a zone of Mexico City: seasonal behavior and human exposure. Atmos Environ 44:122–130. https://doi.org/10.1016/j.atmosenv.2009.07.012 CrossRefGoogle Scholar
- Aydin YM, Kara M, Dumanoglu Y, Odabasi M, Elbir T (2014) Source apportionment of polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) in ambient air of an industrial region in Turkey. Atmos Environ 97:271–285. https://doi.org/10.1016/j.atmosenv.2014.08.032 CrossRefGoogle Scholar
- Boström C-E et al (2002) Cancer risk assessment, indicators, and guidelines for polycyclic aromatic hydrocarbons in the ambient air. Environ Health Perspect 110:451Google Scholar
- Du X, Dong S (2013) Tianjin Statistical Yearbook. China Statistics Press, BeijingGoogle Scholar
- Duval M, Friedlander S (1981) Source resolution of polycyclic aromatic hydrocarbons in the LosAngeles atmosphere application of a CMB with first-order decay US EPA Report EPA-600/2-81-161. US Government Printing Office, Washington, DCGoogle Scholar
- Han B et al. (2014) Characterizations, relationship, and potential sources of outdoor and indoor particulate matter bound polycyclic aromatic hydrocarbons (PAHs) in a community of Tianjin, Northern China Indoor Air Accepted https://doi.org/10.1111/ina.12145
- Hopke PK, Ramadan Z, Paatero P, Norris GA, Landis MS, Williams RW, Lewis CW (2003) Receptor modeling of ambient and personal exposure samples: 1998 Baltimore Particulate Matter Epidemiology-Exposure Study. Atmos Environ 37:3289–3302. https://doi.org/10.1016/S1352-2310(03)00331-5 CrossRefGoogle Scholar
- Liu M, Feng J, Hu P, Tan L, Zhang X, Sun J (2016) Spatial-temporal distributions, sources of polycyclic aromatic hydrocarbons (PAHs) in surface water and suspended particular matter from the upper reach of Huaihe River, China. Ecol Eng 95:143–151. https://doi.org/10.1016/j.ecoleng.2016.06.045 CrossRefGoogle Scholar
- Norris G, Duvall R, Brown S, Bai S (2014) EPA positive matrix factorization (pmf) 5.0 fundamentals and user guide prepared for the US Environmental Protection Agency Office of Research and Development. Washington, DC Inc, PetalumaGoogle Scholar
- Norris G, Vedantham R, Duvall R, Brown S, Prouty J, Prouty J (2011) EPA positive matrix factorization 4.2 fundamentals and user guide. USEPA Office of Research and Development, Washington, DCGoogle Scholar
- Norris G et al (2009) Guidance document for PMF applications with the Multilinear Engine. U.S. Environmental Protection Agency, Washington, DCGoogle Scholar
- Rajput N, Lakhani A (2010) Measurements of polycyclic aromatic hydrocarbons in an urban atmosphere of Agra, India. Atmosfera 23:165–183Google Scholar
- Tian F, Chen J, Qiao X, Wang Z, Yang P, Wang D, Ge L (2009) Sources and seasonal variation of atmospheric polycyclic aromatic hydrocarbons in Dalian, China: factor analysis with non-negative constraints combined with local source fingerprints. Atmos Environ 43:2747–2753. https://doi.org/10.1016/j.atmosenv.2009.02.037 CrossRefGoogle Scholar
- Wang Z, Duan X, Ping L, Jing N, Huang N, Zhang J (2009) Human exposure factors of Chinese people in environmental health risk assessment. Res Environ Sci 22:1164–1170Google Scholar
- World Health Organization (2010) WHO Guidelines for indoor air quality: selected pollutants. WHO Regional Office for Europe, KøbenhavnGoogle Scholar
- Williams RW et al (2001) Preliminary particulate matter mass concentrations associated with longitudinalpanel studies: assessing human exposures of high risk subpopulations to particulate matter. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-01/086 (NTIS PB2002-100444)Google Scholar
- Zheng X et al (2017) Characterizing particulate polycyclic aromatic hydrocarbon emissions from diesel vehicles using a portable emissions measurement system. Sci Rep 7. https://doi.org/10.1038/s41598-017-09822-w