Decoding impact of antibiotics stress to nitrogen removal in constructed wetlands: Overestimated?

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-07-04 DOI:10.1016/j.watres.2025.124165

Jianghui Feng , Zhikun Zou , Zhiyong Zhang , Haiyan Li , Baoling Yuan , Ming-Lai Fu

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Abstract

Antibiotics and nitrogen presented in aquatic environments posing significant ecological risks, which could be addressed through constructed wetlands (CWs). However, the complex removal mechanisms of antibiotics and nitrogen influenced by various factors, had remained difficult to elucidate with traditional univariate experiments. To overcome these limitations, machine learning models were employed to decoding a database comprising 4218 data points covering diverse input features such as wetland characteristics, influent water quality, antibiotics categories, and microbial community composition. The code of core structure (CCS), hydrophobicity reference values (XLogP3), and Wiener Index (WI) were used to represent different antibiotics categories. The results demonstrated that antibiotics removal efficiency was primarily governed by the molecular structure and concentration of antibiotics, with WI and antibiotics concentration accounting for over 65 % of the removal variance. Influent water quality and constructed wetlands volume significantly influenced nitrogen removal (52.4 % and 10.6 %, respectively), with system size and dissolved oxygen dynamics offering potential areas for optimization. Additionally, Actinobacteria played a crucial role in both nitrogen and antibiotics removal, underscoring microbial community composition as a key mechanism. Interestingly, antibiotics had little effect on TN removal efficiency (5.1 %). These insights would provide a foundation for optimizing the design and operation of constructed wetlands under antibiotics stress, offering a novel framework for improving wastewater treatment performance.

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抗生素应激对人工湿地氮去除的解码影响：高估了吗？

水生环境中存在的抗生素和氮具有重大的生态风险，可以通过人工湿地（CWs）来解决。然而，抗生素和氮的复杂去除机制受多种因素的影响，很难用传统的单变量实验来阐明。为了克服这些限制，机器学习模型被用来解码一个包含4218个数据点的数据库，这些数据点涵盖了不同的输入特征，如湿地特征、进水水质、抗生素类别和微生物群落组成。采用核心结构编码（CCS）、疏水性参考值（XLogP3）和Wiener指数（WI）代表不同抗生素类别。结果表明，抗生素的去除效率主要受抗生素的分子结构和浓度的影响，WI和抗生素浓度占去除方差的65%以上。进水水质和人工湿地体积显著影响氮去除（分别为52.4%和10.6%），系统规模和溶解氧动力学为优化提供了潜在的领域。此外，放线菌在氮和抗生素的去除中都起着至关重要的作用，强调微生物群落组成是一个关键机制。有趣的是，抗生素对TN去除效率的影响很小（5.1%）。这些见解将为抗生素胁迫下人工湿地的优化设计和运行提供基础，为提高废水处理性能提供新的框架。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.