Gao Yidan, Zhong Shifa, Torralba-Sanchez Tifany L, Tratnyek Paul G, Weber Eric J, Chen Yiling, Zhang Huichun
Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, OH 44106, United States.
OHSU-PSU School of Public Health, Oregon Health & Science University, 181 SW Sam Jackson Park Road, Portland, OR 97239, United States.
Water Res. 2021 Mar 15;192:116843. doi: 10.1016/j.watres.2021.116843. Epub 2021 Jan 15.
Due to the increasing diversity of organic contaminants discharged into anoxic water environments, reactivity prediction is necessary for chemical persistence evaluation for water treatment and risk assessment purposes. Almost all quantitative structure activity relationships (QSARs) that describe rates of contaminant transformation apply only to narrowly-defined, relatively homogenous families of reactants (e.g., dechlorination of alkyl halides). In this work, we develop predictive models for abiotic reduction of 60 organic compounds with diverse reducible functional groups, including nitroaromatic compounds (NACs), aliphatic nitro-compounds (ANCs), aromatic N-oxides (ANOs), isoxazoles (ISXs), polyhalogenated alkanes (PHAs), sulfoxides and sulfones (SOs), and others. Rate constants for their reduction were measured using a model reductant system, Fe(II)-tiron. Qualitatively, the rates followed the order NACs > ANOs ≈ ISXs ≈ PHAs > ANCs > SOs. To develop QSARs, both conventional chemical descriptor-based and machine learning (ML)-based approaches were investigated. Conventional univariate QSARs based on a molecular descriptor E (energy of the lowest-unoccupied molecular orbital) gave good correlations within classes. Multivariate QSARs combining E with Abraham descriptors for physico-chemical properties gave slightly improved correlations within classes for NCs and NACs, but little improvement in correlation within other classes or among classes. The ML model obtained covers reduction rates for all classes of compounds and all of the conditions studied with the prediction accuracy similar to those of the conventional QSARs for individual classes (r = 0.41-0.98 for univariate QSARs, 0.71-0.94 for multivariate QSARs, and 0.83 for the ML model). Both approaches required a scheme for a priori classification of the compounds for model training. This work offers two alternative modeling approaches to comprehensive abiotic reactivity prediction for persistence evaluation of organic compounds in anoxic water environments.
由于排放到缺氧水环境中的有机污染物种类日益增多，为进行水处理的化学持久性评估和风险评估，有必要对反应活性进行预测。几乎所有描述污染物转化速率的定量构效关系（QSARs）仅适用于定义狭窄、相对同质的反应物家族（例如卤代烷的脱氯反应）。在本研究中，我们针对60种具有不同可还原官能团的有机化合物开发了非生物还原预测模型，这些化合物包括硝基芳香族化合物（NACs）、脂肪族硝基化合物（ANCs）、芳香族N-氧化物（ANOs）、异恶唑（ISXs）、多卤代烷烃（PHAs）、亚砜和砜（SOs）等。使用模型还原剂体系Fe(II)-钛铁试剂测定了它们的还原速率常数。定性地说，反应速率遵循以下顺序：NACs > ANOs ≈ ISXs ≈ PHAs > ANCs > SOs。为了开发QSARs，我们研究了基于传统化学描述符和基于机器学习（ML）的方法。基于分子描述符E（最低未占分子轨道能量）的传统单变量QSARs在各类别内具有良好的相关性。将E与用于物理化学性质的亚伯拉罕描述符相结合的多变量QSARs在NCs和NACs类别内的相关性略有改善，但在其他类别内或类别之间的相关性改善不大。所获得的ML模型涵盖了所有化合物类别的还原速率以及所研究的所有条件，预测准确性与各单个类别的传统QSARs相似（单变量QSARs的r = 0.41 - 0.98，多变量QSARs的r = 0.71 - 0.94，ML模型的r = 0.83）。两种方法都需要一种对化合物进行先验分类的方案用于模型训练。这项工作为缺氧水环境中有机化合物持久性评估的综合非生物反应活性预测提供了两种替代建模方法。
