数字农科院2.0

Fe3+-enhanced UV254/chlorine process for meta-nitrophenol degradation: Experimental insights, computational modelling, and halonitromethane toxicity assessment

文献类型： 外文期刊

作者： Muhammad Asif;Lin Deng;Hidayat Ullah Khan;Changbo Zhang;Rajendra Prasad Singh;Gongde Wu

作者机构：

关键词： Ecotoxicity;Fe3+ ion;HNMs;Meta-nitrophenol;Simulation;UV254/chlorine

期刊名称： Separation and Purification Technology

ISSN： 1873-3794

年卷期： 2025 年 378 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： The rising prevalence of recalcitrant contaminants in water due to industrial discharge, chemical runoff, and environmental pollution necessitates advanced treatment technologies. For the first time, this study systematically investigates the effect of Fe3+ on the degradation of recalcitrant contaminant meta-nitrophenol (MNP) during the UV254/chlorine process through experimental and theoretical approaches. The degradation efficiency of MNP during the UV254/chlorine process enhanced from 43 % to 87 % in the presence of Fe3+, demonstrating Fe3+’s significant catalytic role. Probe experiments identified hydroxyl radicals (•OH, 44 %) and reactive chlorine species (RCSs, 33 %) as the primary contributors to MNP degradation. Increasing chlorine and Fe3+ concentrations within an optimal range enhanced MNP degradation, whereas higher MNP concentrations and alkaline pH inhibited the degradation. At optimum conditions, the lower electrical energy per order (EE/O) was 2.28 kWh/m3/order. During the simulation of experimental data, the eXtreme Gradient Boosting model exhibited superior predictive performance, presenting higher R2 values. Three plausible degradation pathways for MNP were elucidated based on intermediate products (IPs) detected experimentally and theoretically (DFT calculations). Furthermore, the ecotoxicity of MNP and IPs was assessed computationally. Finally, the formation and toxicity of halonitromethanes were systematically evaluated. These findings demonstrate that the Fe3+-enhanced UV254/chlorine process is highly effective and sustainable for degrading persistent organic pollutants. Integrating experimental validation with machine learning models provides a robust framework for optimizing advanced oxidation processes under complex environmental conditions, contributing to developing next-generation water treatment technologies.

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