废水处理过程中与抗生素耐药基因正相关的药物发生及去除预测

Bingwen Liu, Yang Xu, Fan Liu, Bing Li, Xiaonan Li, Runjie Zha, Shanquan Wang, Yong Qiu
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引用次数: 0

摘要

污水处理厂(WWTPs)是环境中药物的重要来源,确定其与抗生素耐药基因(ARGs)的关系及其去除行为对风险控制具有重要意义。本研究研究了废水和污泥中常见药物(包括抗生素和非抗生素)与ARGs的相关性,并应用机器学习预测了与ARGs正相关的典型药物的去除效率。23种靶药中,抗生素和非抗生素均与典型ARG在废水中呈阳性(如咖啡因与blaTEM、aadD、qnrS的相关系数分别为0.738、0.609、0.936,p<0.01),而在污泥中呈阴性(如氟沙星与tetO、tetW、qnrS的相关系数分别为-0.922、-0.933、-0.902,p<0.01),说明废水中药物促进ARG传播的风险较高。此外,基于本研究和前人研究的不同废水处理工艺对药物的去除效率,通过工艺操作参数和废水特性,利用随机森林模型很好地预测了与ARGs高度相关的典型药物的去除效率。HRT和温度被认为是最重要的解释变量。本研究可为污水处理厂的药物控制提供综合参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Occurrence and removal prediction of pharmaceuticals positively correlated with antibiotic resistance genes in wastewater treatment processes

Occurrence and removal prediction of pharmaceuticals positively correlated with antibiotic resistance genes in wastewater treatment processes
Wastewater treatment plants (WWTPs) are important source of pharmaceuticals in the environment, identifying their relationship with antibiotic resistance genes (ARGs) and removal behavior is important for risk control. In this study, the correlations of common pharmaceuticals (including antibiotics and non-antibiotics) and ARGs both in wastewater and sludge were investigated, and machine learning was applied to predict the removal efficiencies of typical pharmaceuticals positively correlated with ARGs. Among the 23 kinds of target pharmaceuticals, both antibiotics and non-antibiotics were found to be positive with typical ARGs in wastewater (e.g. correlation coefficients between caffeine and blaTEM, aadD and qnrS were 0.738, 0.609, 0.936, p < 0.01) but negative in sludge (e.g. correlation coefficients between ofloxacin and tetO, tetW and qnrS were -0.922, -0.933 and -0.902, p<0.01), indicating the high risk of pharmaceuticals in wastewater promoting ARG spread. Furthermore, based on pharmaceutical removal efficiencies in different wastewater treatment processes in this and previous studies, the removal efficiencies of typical pharmaceuticals highly correlated with ARGs were well predicted through process operation parameters and wastewater characteristics by Random Forest model. HRT and temperature were identified as the most important explanatory variables. This study could provide comprehensive references for controlling pharmaceuticals in WWTPs.
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