دورية أكاديمية
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Reducing sample size by clustering: A way to make risk assessment feasible for large groups of organic compounds?

التفاصيل البيبلوغرافية
العنوان: Reducing sample size by clustering: A way to make risk assessment feasible for large groups of organic compounds?
المؤلفون: Hoondert RPJ; KWR Water Research, Groningenhaven 7, Nieuwegein 3433 PE, The Netherlands., Wols BA; KWR Water Research, Groningenhaven 7, Nieuwegein 3433 PE, The Netherlands; Wetsus, Oostergoweg 9, Leeuwarden 8911 MA, The Netherlands., Bäuerlein PS; KWR Water Research, Groningenhaven 7, Nieuwegein 3433 PE, The Netherlands E-mail: patrick.bauerlein@kwrwater.nl.
المصدر: Journal of water and health [J Water Health] 2024 Aug; Vol. 22 (8), pp. 1527-1540. Date of Electronic Publication: 2024 Jul 04.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: IWA Pub Country of Publication: England NLM ID: 101185420 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1477-8920 (Print) Linking ISSN: 14778920 NLM ISO Abbreviation: J Water Health Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London ; Avenel, NJ : IWA Pub., c2003-
مواضيع طبية MeSH: Water Pollutants, Chemical*/analysis , Water Pollutants, Chemical*/chemistry , Drinking Water*/chemistry , Drinking Water*/analysis, Risk Assessment/methods ; Cluster Analysis ; Humans ; Organic Chemicals/chemistry ; Sample Size
مستخلص: This research addresses the presence of substances of very high concern (SVHCs) confronting the drinking water sector. Responding adequately to the potential hazards by SVHCs, knowledge of emission pathways, toxicity, presence in drinking water sources, and removability during water production is crucial. As this information cannot be received for each compound individually, we employed a detailed clustering approach based on chemical properties and structures of SVHCs from lists with over 1,000 compounds. Through this process, 915 substances were divided into 51 clusters. We tested this clustering in risk assessment. To assess the risks, we developed toxicity prediction models utilizing random forests and multiple linear regression. These models were applied to make toxicity predictions for the list of compounds. This study shows that clustering is a viable approach to reducing sample size. In addition, the toxicity models provide insights into the potential human health risks. This research contributes to more informed decision-making and improved risk assessment in the drinking water sector, aiding in the protection of human health and the environment. This principle is generally applicable. If in a group a suitable representative is found, data from experiments with this compound can be used to gauge the behaviour of chemicals in this group.
Competing Interests: The authors declare there is no conflict.
(© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/).)
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معلومات مُعتمدة: Joint Research Programme of KWR, the water utilities and Vewin
فهرسة مساهمة: Keywords: SVHC; clustering; drinking water; prediction; toxicity
المشرفين على المادة: 0 (Water Pollutants, Chemical)
0 (Drinking Water)
0 (Organic Chemicals)
تواريخ الأحداث: Date Created: 20240830 Date Completed: 20240830 Latest Revision: 20240830
رمز التحديث: 20240902
DOI: 10.2166/wh.2024.127
PMID: 39212285
قاعدة البيانات: MEDLINE
الوصف
تدمد:1477-8920
DOI:10.2166/wh.2024.127