Suppr超能文献

用于预防化学性水污染的集成数据驱动跨学科框架。

Integrated data-driven cross-disciplinary framework to prevent chemical water pollution.

作者信息

Ateia Mohamed, Sigmund Gabriel, Bentel Michael J, Washington John W, Lai Adelene, Merrill Nathaniel H, Wang Zhanyun

机构信息

United States Environmental Protection Agency, Center for Environmental Solutions & Emergency Response, Cincinnati, OH 45220, USA.

Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, USA.

出版信息

One Earth. 2023 Aug;6(8). doi: 10.1016/j.oneear.2023.07.001.

Abstract

Access to a clean and healthy environment is a human right and a prerequisite for maintaining a sustainable ecosystem. Experts across domains along the chemical life cycle have traditionally operated in isolation, leading to limited connectivity between upstream chemical innovation to downstream development of water-treatment technologies. This fragmented and historically reactive approach to managing emerging contaminants has resulted in significant externalized societal costs. Herein, we propose an integrated data-driven framework to foster proactive action across domains to effectively address chemical water pollution. By implementing this integrated framework, it will not only enhance the capabilities of experts in their respective fields but also create opportunities for novel approaches that yield co-benefits across multiple domains. To successfully operationalize the integrated framework, several concerted efforts are warranted, including adopting open and FAIR (findable, accessible, interoperable, and reusable) data practices, developing common knowledge bases/platforms, and staying vigilant against new substance "properties" of concern.

摘要

获得清洁和健康的环境是一项人权,也是维持可持续生态系统的先决条件。在化学生命周期各个领域的专家传统上都是孤立工作的,这导致上游化学创新与下游水处理技术开发之间的联系有限。这种零散且历来被动应对的新兴污染物管理方法已造成了巨大的外部社会成本。在此,我们提出一个综合的数据驱动框架,以促进各领域的积极行动,有效解决化学水污染问题。通过实施这一综合框架,不仅将提高各领域专家的能力,还将创造采用新方法的机会,从而在多个领域产生协同效益。为使综合框架成功运作,需要做出多项协同努力,包括采用开放和符合FAIR(可查找、可访问、可互操作和可重用)的数据实践、开发通用知识库/平台,以及对新出现的相关物质“特性”保持警惕。

相似文献

1
Integrated data-driven cross-disciplinary framework to prevent chemical water pollution.
One Earth. 2023 Aug;6(8). doi: 10.1016/j.oneear.2023.07.001.
2
The Minderoo-Monaco Commission on Plastics and Human Health.
Ann Glob Health. 2023 Mar 21;89(1):23. doi: 10.5334/aogh.4056. eCollection 2023.
3
INSIGHT: An integrated framework for safe and sustainable chemical and material assessment.
Comput Struct Biotechnol J. 2025 Mar 29;29:125-137. doi: 10.1016/j.csbj.2025.03.042. eCollection 2025.
8
The 2023 Latin America report of the Countdown on health and climate change: the imperative for health-centred climate-resilient development.
Lancet Reg Health Am. 2024 Apr 23;33:100746. doi: 10.1016/j.lana.2024.100746. eCollection 2024 May.
10
Opportunities for improving data sharing and FAIR data practices to advance global mental health.
Glob Ment Health (Camb). 2023 Mar 3;10:e14. doi: 10.1017/gmh.2023.7. eCollection 2023.

引用本文的文献

1
A Hazard-Based Approach Enables the Efficient Identification of Chemicals of Concern in Plastics.
Environ Sci Technol. 2025 Aug 12;59(31):16144-16155. doi: 10.1021/acs.est.5c02912. Epub 2025 Jul 29.
2
Mapping the chemical complexity of plastics.
Nature. 2025 Jul;643(8071):349-355. doi: 10.1038/s41586-025-09184-8. Epub 2025 Jul 9.
3
Elevated PFAS Precursors in Septage and Residential Pump Stations.
Environ Sci Technol Lett. 2025 Mar 25;12(4):454-460. doi: 10.1021/acs.estlett.5c00246.
4
From "forever chemicals" to fluorine-free alternatives.
Science. 2024 Jul 19;385(6706):256-258. doi: 10.1126/science.ado5019. Epub 2024 Jul 18.
5
Should Transformation Products Change the Way We Manage Chemicals?
Environ Sci Technol. 2024 May 7;58(18):7710-7718. doi: 10.1021/acs.est.4c00125. Epub 2024 Apr 24.

本文引用的文献

1
Sunrise of PFAS Replacements: A Perspective on Fluorine-Free Foams.
ACS Sustain Chem Eng. 2023 May 15;11:7986-7996. doi: 10.1021/acssuschemeng.3c01124.
3
The future of chemistry is language.
Nat Rev Chem. 2023 Jul;7(7):457-458. doi: 10.1038/s41570-023-00502-0.
4
Addressing chemical pollution in biodiversity research.
Glob Chang Biol. 2023 Jun;29(12):3240-3255. doi: 10.1111/gcb.16689. Epub 2023 Apr 8.
5
Machine learning enables interpretable discovery of innovative polymers for gas separation membranes.
Sci Adv. 2022 Jul 22;8(29):eabn9545. doi: 10.1126/sciadv.abn9545. Epub 2022 Jul 20.
6
Greening the pharmacy.
Science. 2022 Jul 15;377(6603):259-260. doi: 10.1126/science.abp9554. Epub 2022 Jul 14.
7
Environmental Fate of Cl-PFPECAs: Predicting the Formation of PFAS Transformation Products in New Jersey Soils.
Environ Sci Technol. 2022 Jun 21;56(12):7779-7788. doi: 10.1021/acs.est.1c06126. Epub 2022 May 26.
8
Pollution and health: a progress update.
Lancet Planet Health. 2022 Jun;6(6):e535-e547. doi: 10.1016/S2542-5196(22)00090-0. Epub 2022 May 18.
9
Stop squandering data: make units of measurement machine-readable.
Nature. 2022 May;605(7909):222-224. doi: 10.1038/d41586-022-01233-w.
10
Global Historical Production, Use, In-Use Stocks, and Emissions of Short-, Medium-, and Long-Chain Chlorinated Paraffins.
Environ Sci Technol. 2022 Jun 21;56(12):7895-7904. doi: 10.1021/acs.est.2c00264. Epub 2022 May 10.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验