Global effect factors for exposure to fine particulate matter

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

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  • Author: Fantke, Peter

    Quantitative Sustainability Assessment, Sustainability, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet, 2800, Kgs. Lyngby, Denmark

  • Author: McKone, Thomas E.

    University of California at Berkeley

  • Author: Tainio, Marko

    Polish Academy of Sciences, Poland

  • Author: Jolliet, Olivier

    University of Michigan, Ann Arbor

  • Author: Apte, Joshua Schulz

    University of Texas at Austin, United States

  • Author: Stylianou, Katerina S.

    University of Michigan, United States

  • Author: Illner, Nicole

    Technical University of Denmark, Denmark

  • Author: Marshall, Julian D.

    University of Minnesota, United States

  • Author: Choma, Ernani F.

    University of Washington, United States

  • Author: Evans, John S.

    Harvard T.H. Chan School of Public Health, United States

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We evaluate fine particulate matter (PM2.5) exposure–response models to propose a consistent set of global effect factors for product and policy assessments across spatial scales and across urban and rural environments. Relationships among exposure concentrations and PM2.5-attributable health effects largely depend on location, population density, and mortality rates. Existing effect factors build mostly on an essentially linear exposure–response function with coefficients from the American Cancer Society study. In contrast, the Global Burden of Disease analysis offers a nonlinear integrated exposure–response (IER) model with coefficients derived from numerous epidemiological studies covering a wide range of exposure concentrations. We explore the IER, additionally provide a simplified regression as a function of PM2.5 level, mortality rates, and severity, and compare results with effect factors derived from the recently published global exposure mortality model (GEMM). Uncertainty in effect factors is dominated by the exposure–response shape, background mortality, and geographic variability. Our central IER-based effect factor estimates for different regions do not differ substantially from previous estimates. However, IER estimates exhibit significant variability between locations as well as between urban and rural environments, driven primarily by variability in PM2.5 concentrations and mortality rates. Using the IER as the basis for effect factors presents a consistent picture of global PM2.5-related effects for use in product and policy assessment frameworks.
Original languageEnglish
Book seriesEnvironmental Science and Technology
Volume53
Issue number12
Pages (from-to)6855-6868
ISSN1382-3124
DOIs
Publication statusPublished - 2019
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