Precise regulation of UV/H2O2 processes: •OH generation/reaction and DOM transformation as the main free radical scavenger

IF 12.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-02-14 DOI:10.1016/j.watres.2025.123282
Yuanna Zhang , Wenjun Sun , Wenxuan Yin
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Abstract

The widespread application of UV/H2O2 is limited by the empirical operational practices, which can lead to excessive energy, chemical input and the generation of uncontrollable by-products. This study presents a precise regulation approach based on the characteristics of free radical generation/reaction and the chemical transformation of organics, assessed through a pilot experiment. The findings indicated that increasing H2O2 dosage was more effective than increasing UV dose in enhancing •OH generation and pollutant removal. As the H2O2 dosage and UV dose increased, the relative influence of water quality on pollutant removal gradually diminished. Dissolved organic matter (DOM) in the water quality background accounted for the largest proportion of •OH scavenging (76.4 %) and the most complex component. Moreover, changes in operating conditions were accompanied by the uncontrollable production of low-molecular-weight (LMW) DOM. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) revealed the chemical transformation of LMW DOM at the molecular level. Higher H₂O₂ dosages triggered more extensive oxidative degradation, resulting in more complex effluent compositions. Considering treatment efficiency, energy consumption, and effluent product composition, H₂O₂ dosages of 5–10 mg/L and UV doses of 350–450 mJ/cm² were identified as optimal. This research contributes to the efficient purification of organic micropollutants in water using UV/H2O2 technology with low energy consumption, minimal chemical input, and relatively controllable by-products.

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UV/H2O2过程的精确调控:•OH生成/反应和DOM转化为主要的自由基清除剂
UV/H2O2的广泛应用受到经验操作实践的限制,这可能导致过多的能量,化学投入和产生不可控的副产物。本研究提出了一种基于自由基生成/反应和有机物化学转化特征的精确调控方法,并通过中试实验进行了评估。结果表明,增加H2O2剂量比增加UV剂量更有效地促进•OH的生成和污染物的去除。随着H2O2投加量和UV投加量的增加,水质对污染物去除率的相对影响逐渐减弱。水体背景中溶解有机物(DOM)对•OH的清除作用占比最大(76.4%),也是最复杂的组分。此外,操作条件的变化伴随着低分子量(LMW) DOM的不可控生成。傅里叶变换离子回旋共振质谱(FT-ICR MS)揭示了LMW DOM在分子水平上的化学转化。较高的H₂O₂剂量引发更广泛的氧化降解,导致更复杂的流出物组成。综合考虑处理效率、能耗和出水产物组成,确定5 ~ 10 mg/L的H₂O₂投加量和350 ~ 450 mJ/cm²的UV投加量为最佳。本研究为利用UV/H2O2技术高效净化水中有机微污染物提供了一种低能耗、化学投入少、副产物相对可控的方法。
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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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