Restricted to the complex nature of dissolved organic matter (DOM) in various aquatic environments, the mechanisms of enhanced iodinated disinfection byproducts (I-DBPs) formation in water containing both I⁻ and IO₃⁻ (designated as I⁻/IO₃⁻ in this study) during the ultraviolet (UV)-chloramine sequential disinfection process remains unclear. In this study, four machine learning (ML) models were established to predict I-DBP formation by using DOM and disinfection features as input variables. Extreme gradient boosting (XGB) algorithm outperformed the others in model development using synthetic waters and in cross-dataset generalization of surface waters. Shapley additive explanation (SHAP) analysis, partial dependence plots (PDPs), and individual conditional expectation (ICE) analysis were then employed to explain the models' workings and feature interactions, aiding in identification and quantification of underlying mechanisms. A type of DOM component (namely DC_b) was found as the greatest contributor and identified as reduced quinones associated with broken-down lignin within higher plant-derived fulvic substance, serving as precursors and electron shuttles for I-DBP formation. Based on the interactional effects acquired from explanation results, the ejection of e⁻_aq from excited DOM and pre-existing I⁻ in the I⁻/IO₃⁻ system were identified responsible for the enhanced generation of I-DBPs compared to that in the I⁻ or IO₃⁻ alone systems; extra DOM scavenged reactive iodine species (RIS), contributing to a limited enhancement. These findings and the methodology developed here together enhance our understanding of the mechanisms how DOM limitedly promotes I-DBP formation during UV-chloramine sequential disinfection of I⁻/IO₃⁻-containing water and facilitate effective online monitoring in the future.

中文翻译：

---

在接受 UV-氯胺顺序消毒的碘化物/含碘酸盐的水中增强碘化消毒副产物的形成：机器学习辅助识别反应机制


受限于各种水生环境中溶解有机物 （DOM） 的复杂性，在紫外线 （UV）-氯胺顺序消毒过程中，在含有 I ⁻ 和 IO ₃ ⁻ （ ⁻ 本研究中指定为 I ⁻ /IO ₃ ）的水中增强碘消毒副产物 （I-DBP） 的形成机制尚不清楚。在这项研究中，建立了四个机器学习 （ML） 模型，以 DOM 和消毒特征作为输入变量来预测 I-DBP 的形成。极端梯度提升 （XGB） 算法在使用合成水的模型开发和地表水的跨数据集泛化方面优于其他算法。然后采用 Shapley 加法解释 （SHAP） 分析、部分依赖图 （PDP） 和个体条件期望 （ICE） 分析来解释模型的工作原理和特征交互，有助于识别和量化潜在机制。发现一种 DOM 成分（即 DC_b）是最大的贡献者，并被鉴定为与高级植物来源的黄腐物质中分解的木质素相关的还原醌，作为 I-DBP 形成的前体和电子穿梭剂。根据从解释结果中获得的交互效应，确定了与单独的 I ⁻ 或 IO ₃ 系统相比，从激发的 DOM 中射出的 e ⁻ _aq 和 I ⁻ / IO ₃ ⁻ 系统中预先存在的 I ⁻ 是导致 I-DBP 产生增强的 ⁻ 原因;额外的 DOM 清除了反应性碘物质 （RIS），有助于有限的增强。 这些发现和这里开发的方法共同增强了我们对 DOM 如何在含 I ⁻ /IO ₃ ⁻ 的水的紫外线氯胺顺序消毒过程中有限地促进 I-DBP 形成的机制的理解，并促进未来的有效在线监测。