微泡强化冷等离子体同时降解水中多种微污染物的研究

IF 12.8 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-07-15 Epub Date: 2025-03-11 DOI:10.1016/j.watres.2025.123435
Qiuyun Lu , Deepak Panchal , Lingling Yang , Ziya Saedi , Mohamed Gamal El-Din , Xuehua Zhang
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引用次数: 0

摘要

消除水中顽固的有机微污染物对于防止生物积累和保护生态系统至关重要。冷等离子体活化技术是一种清洁、可持续、高效的降解水中微污染物和病原体的方法。在这项研究中,我们重点了解了利用最近开发的微泡增强冷等离子体活化(MB-CPA)技术同时降解流动水中多种微污染物(最多8种)的过程。采用超高效液相色谱-三重四极杆质谱联用(UHPLC-QQQ-MS)分析了处理时间对微污染物降解的影响。我们发现,在强活化条件下,所有化合物的降解效率都迅速提高,可以导致模型化合物的去除率超过98%。在较长的处理时间或较快的流速下,对所有易降解或难降解的化合物都有足够高的去除率。在温和的活化条件下,降解效率变化较大。电子自旋共振测量显示,处理后的合成河水和纯水中羟基自由基的丰度更高,突出了水基质对降解效率的影响。这项工作的理解可能有助于设计激活过程,并最大限度地减少能量消耗,从而通过冷等离子体技术同时消除多种复杂水体中的污染物。
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Simultaneous degradation of multiple micropollutants in flowing water by mild and strong microbubble-enhanced cold plasma activation
Elimination of stubborn organic micropollutants from water is crucial for bioaccumulation prevention and ecosystem protection. Cold plasma activation technology is a clean, sustainable, and highly effective approach to the degradation of micropollutants and pathogens in contaminated water. In this study, we focus on understanding the processes of simultaneous degradation of multiple micropollutants (8 types at maximum) in flowing water by the recently developed microbubble-enhanced cold plasma activation (MB-CPA) technology. The degradation of micropollutants with the treatment time was analyzed by using ultrahigh performance liquid chromatography coupled to a triple quadrupole mass spectrometer (UHPLC-QQQ-MS). We found that the degradation efficiencies of all compounds increase rapidly under strong activation conditions that can lead to above 98% removal of a model compound. After long treatment duration or at a fast flow rate, the removal efficiency was sufficiently high for all compounds that were either easy or hard to degrade. The large variation in degradation efficiencies was present under mild activation conditions. The electron spinning resonance measurements reveal a greater abundance of hydroxyl radicals in treated synthetic river water than pure water, highlighting the effects of water matrix on the degradation efficiency. The understanding from this work may help to design the activation process and minimize the energy consumption for the simultaneous elimination of pollutants in diverse and complex water bodies by cold plasma technology.
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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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