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Environmental Science and Pollution Research

, Volume 26, Issue 10, pp 10230–10239 | Cite as

Inhalation cancer risk estimation of source-specific personal exposure for particulate matter–bound polycyclic aromatic hydrocarbons based on positive matrix factorization

  • Bin HanEmail author
  • Yan You
  • Yating Liu
  • Jia Xu
  • Jian Zhou
  • Jiefeng Zhang
  • Can Niu
  • Nan Zhang
  • Fei He
  • Xiao Ding
  • Zhipeng Bai
Research Article
First Online:

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.

Keywords

Polycyclic aromatic hydrocarbons Lung cancer risk assessment Source apportionment Positive matrix factorization 
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Notes

Acknowledgements

We appreciate Prof. Sverre Vedal from the University of Washington for his suggestions and comments on this article.

Funding information

This study was funded by the “National Basic Research Program of China” (Grant No. 2011CB503801).

Supplementary material

11356_2019_4198_MOESM1_ESM.docx (40 kb)
ESM 1 (DOCX 39 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijingChina
  2. 2.Department of Environmental and Occupational Health Sciences, School of Public HealthUniversity of WashingtonSeattleUSA
  3. 3.Research Center for Eco-Environmental ScienceChinese Academy of ScienceBeijingChina
  4. 4.College of Environmental Science and EngineeringNankai UniversityTianjinChina
  5. 5.Energy Research InstituteNanyang Technological UniversitySingaporeSingapore
  6. 6.Division of Environmental and Water Resources, School of Civil and Environmental EngineeringNanyang Technological UniversitySingaporeSingapore
  7. 7.School of Public HealthHebei UniversityBaodingChina
  8. 8.Hubei Provincial Meteorological Service CenterWuhanChina
  9. 9.Department of Building, School of Design and EnvironmentNational University of SingaporeSingaporeSingapore

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