Priority control sequence of 34 typical pollutants in effluents of Chinese wastewater treatment plants

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Ruonan He, Xingyue Wu, Hongxin Mu, Ling Chen, Haidong Hu, Jinfeng Wang, Hongqiang Ren, Bing Wu
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引用次数: 1

Abstract

The identification of the priority control sequence of pollutants in effluents of wastewater treatment plants (WWTPs) has important implications for the management of water quality. This study chose 34 typical pollutants based on their representativeness and detection rates in municipal wastewater. The occurrence frequency and concentration of these pollutants in 168 Chinese WWTP effluents were measured at the national level. The data on in vitro toxicity (67 assays) and in vivo toxicity (216 species) for target pollutants were obtained from the public toxicity database and our experimental data. An environmental health prioritization index (EHPi) method was proposed to integrate the occurrence frequency, concentration, removal rate, and in vitro and in vivo toxicity to determine the priority control sequence of target pollutants. Ethynyl estradiol, 17β-estradiol, estrone, diclofenac, and atrazine were the top 5 pollutants identified by the EHPi score. Several pollutants with high EHPi scores showed spatial differences. Besides the EHPi method which was from the single pollutant perspective, the combined toxicity of pollutants (300 pairs of binary combinations) was also measured based on in vitro toxicity assays to evaluate the key pollutants from the pollutant-pollutant interacting perspective. The pollutants (such as ofloxacin and acetaminophen) that could have significant synergetic effects with many other pollutants are worthy of prior attention. This study shed new light on the identification of the priority control sequence of pollutants in WWTP effluents. The results provide meaningful data for the effective management and control of wastewater water quality.

中国污水处理厂出水34种典型污染物的优先控制序列
污水处理厂出水污染物优先控制序列的确定对水质管理具有重要意义。根据其在城市污水中的代表性和检出率,选择了34种典型污染物。在全国范围内测定了168个污水处理厂污水中这些污染物的出现频率和浓度。目标污染物的体外毒性(67项测定)和体内毒性(216种)数据来自公共毒性数据库和我们的实验数据。提出环境健康优先指数法(EHPi),综合考虑目标污染物的发生频率、浓度、去除率、体内外毒性等因素,确定目标污染物的优先控制顺序。乙炔雌二醇、17β-雌二醇、雌酮、双氯芬酸和阿特拉津是EHPi评分前5位的污染物。EHPi得分较高的几种污染物存在空间差异。除了从单一污染物角度出发的EHPi方法外,还采用体外毒性试验的方法测定了污染物(300对二元组合)的联合毒性,从污染物-污染物相互作用的角度对关键污染物进行评价。可能与许多其他污染物具有显著协同效应的污染物(如氧氟沙星和对乙酰氨基酚)值得优先关注。该研究为污水处理厂污水中污染物优先控制顺序的确定提供了新的思路。研究结果为污水水质的有效管理和控制提供了有意义的数据。
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来源期刊
Water Research
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.
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