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A modular mechanistic framework for estimating exposure to SVOCs: Next steps for modeling emission and partitioning of plasticizers and PFAS


Estimates of human exposure to semi-volatile organic compounds (SVOCs) such as phthalates, phthalate alternatives, and some per- and polyfluoroalkyl substances (PFAS) are required for the risk-based evaluation of chemicals. Recently, a modular mechanistic modeling framework to rapidly predict SVOC emission and partitioning in indoor environments has been presented, in which several mechanistically consistent source emission categories (SECs) were identified. However, not all SECs have well-developed emission models. In addition, data on model parameters are missing even for frequently studied SVOCs. These knowledge gaps impede the comprehensive prediction of the fate of SVOCs indoors. In this paper, sets of high-priority phthalates, phthalate alternatives, and PFAS were identified based on chemical occurrence indoors and additional selection criteria. These high-priority chemicals served as the basis for exploring model parameter availability for existing indoor SVOC emission and partitioning models. The results reveal that additional experimental and modeling work is needed to fully understand the behavior of SVOCs indoors and to predict exposures with greater confidence and lower uncertainty. Modeling approaches to fill some of the identified gaps are proposed. The prioritized sets of chemicals and proposed new modeling approaches will help guide future research. The inclusion of polar phases in the framework will further expand its applicability and scope.

Impact statement

This paper compiles data on high-priority chemicals commonly found indoors and information on the availability of applicable models and model parameters to predict emission, partitioning, and subsequent exposure to these chemicals. Modeling approaches for a selection of the missing SECs (source emission categories) are proposed, to illustrate the path forward. The comprehensive data set helps inform researchers, exposure assessors, and policy makers to better understand the state of the science regarding modeling of indoor exposure to semi-volatile organic compounds (SVOCs) and per- and polyfluoroalkyl substances (PFAS).

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Fig. 1: Relative distribution of selection criteria points by chemical group.
Fig. 2

Data availability

Search terms used for Google Scholar search engines are documented in Table S1 in the SI. Further details of the literature review of existing SVOC emission and partitioning models, as well as the equations and a full list of references, can be found in the SI. The complete versions of the lists of high-priority compounds can be found in a separate Excel spreadsheet in the SI.


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The authors would like to thank John Bucher at the National Toxicology Program (NTP) for his support and valuable insights.


Funding for this work was provided by the National Toxicology Program (NTP). CMAE also acknowledges support from the National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP).

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CMAE was responsible for designing and managing the project, conducting the literature reviews concerning PFAS, writing the manuscript, and creating the tables, spreadsheets, and Fig. 1. CB was responsible for conducting the literature reviews concerning phthalates and phthalate alternatives, contributing to the manuscript, and developing the models and Fig. 2. CW provided feedback to the literature reviews. JCL was responsible for acquisition of funding, provided feedback during all stages of the project and contributed to the manuscript.

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Correspondence to Clara M. A. Eichler.

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Ethical approval was not required for this study because the compiled and analyzed data originated only from published scientific research articles or from freely available sources.

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Eichler, C.M.A., Bi, C., Wang, C. et al. A modular mechanistic framework for estimating exposure to SVOCs: Next steps for modeling emission and partitioning of plasticizers and PFAS. J Expo Sci Environ Epidemiol 32, 356–365 (2022).

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  • Exposure Modeling
  • Phthalates
  • PFAS
  • Chemicals in products


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