Evidence map›Paper›PMID 34882502›Full record

ArticleEnvironmental health perspectives2021

Development and Evaluation of a Holistic and Mechanistic Modeling Framework for Chemical Emissions, Fate, Exposure, and Risk.

Li Li, Alessandro Sangion, Frank Wania, James M Armitage, Liisa Toose, Lauren Hughes, Jon A Arnot

Abstract read
In one paragraph

Article in Environmental health perspectives, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Measuring historical pollution: Natural history collections as tools for public health and environmental justice research.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. HExpPredict:Environmental health perspectives · 2023
    Article
  8. Developing an internal threshold of toxicological concern (iTTC).Journal of exposure science & environmental epidemiology · 2022
    Article
  9. Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment.Journal of exposure science & environmental epidemiology · 2022
    Review
  10. The chemical landscape of high-throughput new approach methodologies for exposure.Journal of exposure science & environmental epidemiology · 2022
    Review
  11. Review
  12. Article
  13. Review
  14. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Li LiSchool of Public Health, University of Nevada, Reno, Reno, Nevada, USA.ORCID 0000-0002-5157-7366
Alessandro SangionDepartment of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada.
Frank WaniaDepartment of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada.ORCID 0000-0003-3836-0901
James M ArmitageAES Armitage Environmental Sciences, Inc., Ottawa, Ontario, Canada.ORCID 0000-0002-8526-0324
Liisa TooseARC Arnot Research and Consulting, Toronto, Ontario, Canada.ORCID 0000-0002-6184-4691
Lauren HughesARC Arnot Research and Consulting, Toronto, Ontario, Canada.ORCID 0000-0001-6197-5328
Jon A ArnotDepartment of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada.ORCID 0000-0002-4295-4270

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLarge numbers of chemicals require evaluation to determine if their production and use pose potential risks to ecological and human health. For most chemicals, the inadequacy and uncertainty of chemical-specific data severely limit the application of exposure- and risk-based methods for screening-level assessments, priority setting, and effective management.

objectiveWe developed and evaluated a holistic, mechanistic modeling framework for ecological and human health assessments to support the safe and sustainable production, use, and disposal of organic chemicals.

methodsWe consolidated various models for simulating the PROduction-To-EXposure (PROTEX) continuum with empirical data sets and models for predicting chemical property and use function information to enable high-throughput (HT) exposure and risk estimation. The new PROTEX-HT framework calculates exposure and risk by integrating mechanistic computational modules describing chemical behavior and fate in the socioeconomic system (i.e., life cycle emissions), natural and indoor environments, various ecological receptors, and humans. PROTEX-HT requires only molecular structure and chemical tonnage (i.e., annual production or consumption volume) as input information. We evaluated the PROTEX-HT framework using 95 organic chemicals commercialized in the United States and demonstrated its application in various exposure and risk assessment contexts.

resultsSeventy-nine percent and 97% of the PROTEX-HT human exposure predictions were within one and two orders of magnitude, respectively, of independent human exposure estimates inferred from biomonitoring data. PROTEX-HT supported screening and ranking chemicals based on various exposure and risk metrics, setting chemical-specific maximum allowable tonnage based on user-defined toxicological thresholds, and identifying the most relevant emission sources, environmental media, and exposure routes of concern in the PROTEX continuum. The case study shows that high chemical tonnage did not necessarily result in high exposure or health risks.

conclusionRequiring only two chemical-specific pieces of information, PROTEX-HT enables efficient screening-level evaluations of existing and premanufacture chemicals in various exposure- and risk-based contexts. https://doi.org/10.1289/EHP9372.

Indexed as

Environmental ExposureOrganic ChemicalsHumansRisk AssessmentUncertaintyUnited StatesOrganic Chemicals

Identifiers

PMID34882502
PMCPMC8658982

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.