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.

中文翻译：

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可靠评估和预测次氯酸钠中度预氧化用于含藻水处理


对富含藻类的水进行化学适度预氧化是控制藻类和外源性污染物的一种经济且有前景的策略，但它受到缺乏有效的在线评估和快速响应反馈方法的限制。在此，激发-发射矩阵平行因子分析 （EEM-PARAFAC） 用于鉴定次氯酸钠 （NaClO） 在 260（360）/450 nm 激发/发射波长下预氧化后的蓝藻荧光团，在此基础上定量评估藻类细胞完整性和细胞内有机质 （IOM） 释放。建立了使用 NaClO 预测中度预氧化的荧光光谱数据的机器学习模型。中度预氧化的最佳 NaClO 剂量取决于水源基质中的藻类密度、生长阶段和有机物浓度。低剂量的 NaClO （<0.5 mg/L） 导致表面吸附的有机物 （S-AOM） 的短期解吸，而高剂量的 NaClO （≥0.5 mg/L） 迅速导致细胞损伤。最佳 NaClO 剂量从 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 个细胞/mL 的源水。不同的生长阶段需要不同的 NaClO 剂量：固定期细胞需要 0.3-0.5 mg/L，对数期细胞需要 0.6-0.8 mg/L，衰变细胞需要 2.0-2.5 mg/L。天然有机物和 S-AOM 的存在增加了 NaClO 剂量限制，溶解有机碳 （DOC） 浓度较高（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 （Gaussian process regression-Matern （0.5）） 表现最好，R2 值为 1.000 和 0。训练集和测试集中有 976 个。最佳预氧化后混凝有效去除藻类污染物，分别去除了 91%、92% 和 92% 的藻类细胞、浊度和叶绿素-a，从而证明了适度预氧化的有效性。本研究引入了一种通过监测水源水质和跟踪后预氧化荧光团来动态调节 NaClO 剂量的新方法，增强了适度预氧化技术在含藻水处理中的应用。