A machine-learning approach clarifies interactions between contaminants of emerging concern
(2022) In One Earth 5(11). p.1239-1249- Abstract
Humans and biotas are exposed to a cocktail of contaminants of emerging concern (CECs), but mixture regulation is lagging behind. This is largely attributed to inadequate experimental data of mixture risk; revealing intricate interactions among CECs in mixtures with random combinations remains a formidable challenge. Here, we propose a new framework comprised of 5,720 lab tests of mixture risk for 100 CECs with random combinations, extended prediction of mixture risk in any CEC combination via a new machine learning model, and validation in field sites. We identify a general concave-down relationship between CEC number and ecological risk of algae, invertebrates, and fish under different lab conditions and in more than 900 field sites... (More)
Humans and biotas are exposed to a cocktail of contaminants of emerging concern (CECs), but mixture regulation is lagging behind. This is largely attributed to inadequate experimental data of mixture risk; revealing intricate interactions among CECs in mixtures with random combinations remains a formidable challenge. Here, we propose a new framework comprised of 5,720 lab tests of mixture risk for 100 CECs with random combinations, extended prediction of mixture risk in any CEC combination via a new machine learning model, and validation in field sites. We identify a general concave-down relationship between CEC number and ecological risk of algae, invertebrates, and fish under different lab conditions and in more than 900 field sites worldwide. We propose a new “redundancy mechanism” to clarify interactions among CECs, suggesting implications in grouping CECs by action mode for developing mixture regulatory frameworks. Our framework provides a blueprint for addressing cocktail effects of multi-factors with random combinations in different disciplines.
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- author
- Chen, Jian ; Wang, Bin ; Huang, Jun ; Deng, Shubo ; Wang, Yujue ; Blaney, Lee ; Brennan, Georgina L. LU ; Cagnetta, Giovanni ; Jia, Qimeng and Yu, Gang
- publishing date
- 2022-11-18
- type
- Contribution to journal
- publication status
- published
- keywords
- biodiversity, carbon/nitrogen fixation, chemical cocktails, field validation, global mixture risk, neural network model, primary production, random selection test
- in
- One Earth
- volume
- 5
- issue
- 11
- pages
- 11 pages
- publisher
- Cell Press
- external identifiers
-
- scopus:85141948692
- ISSN
- 2590-3330
- DOI
- 10.1016/j.oneear.2022.10.006
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2022 Elsevier Inc.
- id
- b2f8df1e-922d-468e-a933-b5c0eab9e512
- date added to LUP
- 2024-10-15 14:54:13
- date last changed
- 2024-12-10 20:47:56
@article{b2f8df1e-922d-468e-a933-b5c0eab9e512,
abstract = {{<p>Humans and biotas are exposed to a cocktail of contaminants of emerging concern (CECs), but mixture regulation is lagging behind. This is largely attributed to inadequate experimental data of mixture risk; revealing intricate interactions among CECs in mixtures with random combinations remains a formidable challenge. Here, we propose a new framework comprised of 5,720 lab tests of mixture risk for 100 CECs with random combinations, extended prediction of mixture risk in any CEC combination via a new machine learning model, and validation in field sites. We identify a general concave-down relationship between CEC number and ecological risk of algae, invertebrates, and fish under different lab conditions and in more than 900 field sites worldwide. We propose a new “redundancy mechanism” to clarify interactions among CECs, suggesting implications in grouping CECs by action mode for developing mixture regulatory frameworks. Our framework provides a blueprint for addressing cocktail effects of multi-factors with random combinations in different disciplines.</p>}},
author = {{Chen, Jian and Wang, Bin and Huang, Jun and Deng, Shubo and Wang, Yujue and Blaney, Lee and Brennan, Georgina L. and Cagnetta, Giovanni and Jia, Qimeng and Yu, Gang}},
issn = {{2590-3330}},
keywords = {{biodiversity; carbon/nitrogen fixation; chemical cocktails; field validation; global mixture risk; neural network model; primary production; random selection test}},
language = {{eng}},
month = {{11}},
number = {{11}},
pages = {{1239--1249}},
publisher = {{Cell Press}},
series = {{One Earth}},
title = {{A machine-learning approach clarifies interactions between contaminants of emerging concern}},
url = {{http://dx.doi.org/10.1016/j.oneear.2022.10.006}},
doi = {{10.1016/j.oneear.2022.10.006}},
volume = {{5}},
year = {{2022}},
}