Reliable assessment and prediction of moderate preoxidation of sodium hypochlorite for algae-laden water treatment

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2024-09-05 DOI:10.1016/j.watres.2024.122398
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Abstract

Chemical moderate preoxidation for algae-laden water is an economical and prospective strategy for controlling algae and exogenous pollutants, whereas it is constrained by a lack of effective on-line evaluation and quick-response feedback method. Herein, excitation-emission matrix parallel factor analysis (EEM-PARAFAC) was used to identify cyanobacteria fluorophores after preoxidation of sodium hypochlorite (NaClO) at Excitation/Emission wavelength of 260(360)/450 nm, based on which the algal cell integrity and intracellular organic matter (IOM) release were quantitatively assessed. Machine learning modeling of fluorescence spectral data for prediction of moderate preoxidation using NaClO was established. The optimal NaClO dosage for moderate preoxidation depended on algal density, growth phases, and organic matter concentrations in source water matrices. Low doses of NaClO (<0.5 mg/L) led to short-term desorption of surface-adsorbed organic matter (S-AOM) without compromising algal cell integrity, whereas high doses of NaClO (≥0.5 mg/L) quickly caused cell damage. The optimal NaClO dosage increased from 0.2–0.3 mg/L to 0.9–1.2 mg/L, corresponding to the source water with algal densities from 0.1 × 10⁶ to 2.0 × 10⁶ cells/mL. Different growth stages required varying NaClO doses: stationary phase cells needed 0.3–0.5 mg/L, log phase cells 0.6–0.8 mg/L, and decaying cells 2.0–2.5 mg/L. The presence of natural organic matter and S-AOM increased the NaClO dosage limit with higher dissolved organic carbon (DOC) concentrations (1.00 mg/L DOC required 0.8–1.0 mg/L NaClO, while 2.20 mg/L DOC required 1.5–2.0 mg/L). Compared to other predictive models, the machine learning model (Gaussian process regression-Matern (0.5)) performed best, achieving R2 values of 1.000 and 0.976 in training and testing sets. Optimal preoxidation followed by coagulation effectively removed algal contaminants, achieving 91%, 92%, and 92% removal for algal cells, turbidity, and chlorophyll-a, respectively, thereby demonstrating the effectiveness of moderate preoxidation. This study introduces a novel approach to dynamically adjust NaClO dosage by monitoring source water qualities and tracking post-preoxidation fluorophores, enhancing moderate preoxidation technology application in algae-laden water treatment.

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对次氯酸钠在含藻水处理中的中度预氧化进行可靠评估和预测
对富含藻类的水体进行化学适度预氧化是一种既经济又有前景的控制藻类和外源污染物的策略,但由于缺乏有效的在线评估和快速反应反馈方法而受到限制。本文采用激发-发射矩阵并行因子分析(EEM-PARAFAC)来识别次氯酸钠(NaClO)在260(360)/450 nm激发/发射波长下预氧化后的蓝藻荧光团,并在此基础上定量评估藻细胞的完整性和胞内有机物(IOM)的释放。建立了荧光光谱数据的机器学习模型,用于预测使用 NaClO 进行中度预氧化的情况。中度预氧化的最佳 NaClO 剂量取决于原水基质中的藻类密度、生长阶段和有机物浓度。低剂量的 NaClO(<0.5 毫克/升)可在短期内解吸表面吸附的有机物(S-AOM),而不会损害藻类细胞的完整性,而高剂量的 NaClO(≥0.5 毫克/升)则会迅速造成细胞损伤。NaClO 的最佳用量从 0.2-0.3 mg/L 增加到 0.9-1.2 mg/L,对应于藻类密度从 0.1 × 10⁶ 到 2.0 × 10⁶ cells/mL 的原水。不同的生长阶段需要不同剂量的 NaClO:静止期细胞需要 0.3-0.5 mg/L,对数期细胞需要 0.6-0.8 mg/L,衰退期细胞需要 2.0-2.5 mg/L。天然有机物和 S-AOM 的存在增加了 NaClO 的用量限制,溶解有机碳(DOC)浓度越高(1.00 mg/L DOC 需要 0.8-1.0 mg/L NaClO,而 2.20 mg/L DOC 需要 1.5-2.0 mg/L)。与其他预测模型相比,机器学习模型(高斯过程回归-Matern (0.5))表现最佳,在训练集和测试集中的值分别达到了 1.000 和 0.976。最佳预氧化后混凝法可有效去除藻类污染物,藻细胞、浊度和叶绿素-a 的去除率分别达到 91%、92% 和 92%,从而证明了适度预氧化的有效性。本研究介绍了一种通过监测原水水质和跟踪预氧化后荧光团来动态调整 NaClO 投加量的新方法,从而提高了适度预氧化技术在含藻水处理中的应用。
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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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