volume 352 pages 124109

Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: a systematic scoping review

Maria Kalyva 1
Gunn Elisabeth Vist 2
Michael Guy Diemar 3
Graciela López-Soop 1
T.J. Bozada 4
Thomas Luechtefeld 4, 5
E.L. Roggen 3
Hubert Dirven 1
Mathieu Vinken 6
Trine Husøy 1
Publication typeJournal Article
Publication date2024-07-01
scimago Q1
wos Q1
SJR2.205
CiteScore16.0
Impact factor7.3
ISSN02697491, 18736424
Abstract
Exposure assessment is a crucial component of environmental health research, providing essential information on the potential risks associated with various chemicals. A systematic scoping review was conducted to acquire an overview of accessible human exposure assessment methods and computational tools to support and ultimately improve risk assessment. The systematic scoping review was performed in Sysrev, a web platform that introduces machine learning techniques into the review process aiming for increased accuracy and efficiency. Included publications were restricted to a publication date after the year 2000, where exposure methods were properly described. Exposure assessments methods were found to be used for a broad range of environmental chemicals including pesticides, metals, persistent chemicals, volatile organic compounds, and other chemical classes. Our results show that after the year 2000, for all the types of exposure routes, probabilistic analysis, and computational methods to calculate human exposure have increased. Sixty-three mathematical models and toolboxes were identified that have been developed in Europe, North America, and globally. However, only twelve occur frequently and their usefulness were associated with exposure route, chemical classes and input parameters used to estimate exposure. The outcome of the combined associations can function as a basis and/or guide for decision making for the selection of most appropriate method and tool to be used for environmental chemical human exposure assessments in Ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment (ONTOX) project and elsewhere. Finally, the choice of input parameters used in each mathematical model and toolbox shown by our analysis can contribute to the harmonization process of the exposure models and tools increasing the prospect for comparison between studies and consistency in the regulatory process in the future.
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Kalyva M. et al. Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: a systematic scoping review // Environmental Pollution. 2024. Vol. 352. p. 124109.
GOST all authors (up to 50) Copy
Kalyva M., Vist G. E., Diemar M. G., López-Soop G., Bozada T., Luechtefeld T., Roggen E., Dirven H., Vinken M., Husøy T. Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: a systematic scoping review // Environmental Pollution. 2024. Vol. 352. p. 124109.
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RIS Copy
TY - JOUR
DO - 10.1016/j.envpol.2024.124109
UR - https://linkinghub.elsevier.com/retrieve/pii/S0269749124008236
TI - Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: a systematic scoping review
T2 - Environmental Pollution
AU - Kalyva, Maria
AU - Vist, Gunn Elisabeth
AU - Diemar, Michael Guy
AU - López-Soop, Graciela
AU - Bozada, T.J.
AU - Luechtefeld, Thomas
AU - Roggen, E.L.
AU - Dirven, Hubert
AU - Vinken, Mathieu
AU - Husøy, Trine
PY - 2024
DA - 2024/07/01
PB - Elsevier
SP - 124109
VL - 352
PMID - 38718961
SN - 0269-7491
SN - 1873-6424
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2024_Kalyva,
author = {Maria Kalyva and Gunn Elisabeth Vist and Michael Guy Diemar and Graciela López-Soop and T.J. Bozada and Thomas Luechtefeld and E.L. Roggen and Hubert Dirven and Mathieu Vinken and Trine Husøy},
title = {Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: a systematic scoping review},
journal = {Environmental Pollution},
year = {2024},
volume = {352},
publisher = {Elsevier},
month = {jul},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0269749124008236},
pages = {124109},
doi = {10.1016/j.envpol.2024.124109}
}