高价金属控制的微污染物和溶解有机物反应模式的分子水平制图

IF 12.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Shuiqin Shi , Linke Jiang , Kaiting Zhang , Tongzhen Li , Junmei Yan , Lianbao Chi , Mingbao Feng
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引用次数: 0

摘要

高价金属基氧化体系因其对难降解有机污染物的高反应性和选择性而受到广泛关注。然而,通过这些氧化系统消除有机微污染物(特别是其高度关注的转化产物(TPs))和溶解有机物(DOM)的分子见解仍不清楚。本研究评价了高铁酸盐(VI)/亚硫酸盐(Fe(VI)/S(IV))、高锰酸盐(VII)/亚硫酸盐(Mn(VII)/S(IV))、钴(II)/过氧单硫酸盐(Co(II)/PMS)、铜(II)/过氧单硫酸盐(Cu(II)/PMS)等不同前体(磺胺类、卡马西平和阿特拉津)对9种TPs的降解,并阐明了DOM的分子水平变化。结果表明,在优化条件下,Mn(VII)/S(IV)和Co(II)/PMS对TP的去除率在20 min内可达99%。分析氧化产物(OPs)表明了它们不同的转化途径,多种OPs表现出增强的持久性、移动性或慢性毒性。傅里叶变换离子回旋共振质谱分析表明DOM分子中存在成分重组。DOM的连续转化,包括氧加成、脱羧和含氮/含硫部分的选择性攻击,产生富氧的低分子量组分。机器学习和SHAP分析确定分子量是DOM氧化反应性的最主要预测因子。这些发现增强了对高价金属介导的氧化系统减少TP的有效性的理解,并为氧化水处理过程中DOM的转化提供了进一步的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Molecular-level mapping of high-valent metal-governed reaction patterns of micropollutants and dissolved organic matter

Molecular-level mapping of high-valent metal-governed reaction patterns of micropollutants and dissolved organic matter
High-valent metal-based oxidation systems have been receiving extensive attention for their high reactivity and selectivity toward recalcitrant organic contaminants. However, the molecular insights into the elimination of organic micropollutants (especially their highly concerned transformation products (TPs)) and dissolved organic matter (DOM) by these oxidation systems remain unclear. This study evaluated the degradation of nine TPs of different precursors (sulfonamides, carbamazepine, and atrazine) under ferrate(VI)/sulfite (Fe(VI)/S(IV)), permanganate(VII)/sulfite (Mn(VII)/S(IV)), cobalt(II)/peroxymonosulfate (Co(II)/PMS), and copper(II)/peroxymonosulfate (Cu(II)/PMS)-based oxidation together with the elucidation of the molecular-level alterations of DOM. Findings revealed both Mn(VII)/S(IV) and Co(II)/PMS achieved superior TP removal (up to 99 % in 20 min) under optimized conditions. Analyzing oxidized products (OPs) indicated their distinct transformation pathways, with multiple OPs exhibiting enhanced persistence, mobility, or chronic toxicity. Fourier-transform ion cyclotron resonance mass spectrometry analysis suggested the compositional restructuration in DOM molecules. Successive transformation of DOM, including oxygen addition, decarboxylation, and selective attack of nitrogen/sulfur-containing moieties, occurred to yield oxygen-enriched, low-molecular-weight fractions. Machine learning and SHAP analysis identified molecular weight as the most dominant predictor of DOM reactivity toward oxidation. These findings enhance the understanding of the availability of TP abatement by high-valent metal-mediated oxidation systems and provide further insights into the DOM transformation during oxidative water treatment.
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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