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太湖流域典型城镇地表水中AOX的污染特征
引用本文:王佳琦,隋倩,黄韵竹,黄绍新,吕树光,徐东炯,孔明,张毅敏,王斌.太湖流域典型城镇地表水中AOX的污染特征[J].中国环境科学,2020,40(5):2190-2195.
作者姓名:王佳琦  隋倩  黄韵竹  黄绍新  吕树光  徐东炯  孔明  张毅敏  王斌
作者单位:1. 华东理工大学资源与环境工程学院, 国家环境保护化工过程环境风险评价与控制重点实验室, 上海 200237;2. 常州市环境监测中心, 江苏省环境保护水环境生物监测重点实验室, 江苏 常州 213001;3. 生态环境部南京环境科学研究所, 江苏 南京 210042;4. 清华大学环境学院, 北京 100084
基金项目:水体污染控制与治理科技重大专项(2017ZX07202006);国家自然科学基金面上项目(21577033,21777042)
摘    要:采用微库仑法,分析常州市武南区域地表水和污水处理厂出水中可吸附有机卤素(AOX)的浓度,揭示了该区域AOX的污染特征.结果表明,区域内地表水中AOX的年均浓度为41.9μg/L,与其他研究结果相当或偏低,夏季浓度高于冬季.从空间分布上看,永安河中段、滆湖北部和京杭运河以北区域污染相对严重,其中断头浜截流是造成AOX污染突出的一个重要原因.市政污水处理厂和纺织工业污水处理厂出水的年污染负荷较高,是两类重要的排放源,对地表水中AOX污染有较大的贡献.

关 键 词:可吸附有机卤素(AOX)  地表水  污水处理厂  空间分布  季节变化  
收稿时间:2019-09-25

Characterization of AOX in the surface water of a typical city in Taihu Lake watershed
WANG Jia-qi,SUI Qian,HUANG Yun-zhu,HUANG Shao-xin,Lü Shu-guang,XU Dong-jiong,KONG Ming,ZHANG Yi-min,WANG Bin.Characterization of AOX in the surface water of a typical city in Taihu Lake watershed[J].China Environmental Science,2020,40(5):2190-2195.
Authors:WANG Jia-qi  SUI Qian  HUANG Yun-zhu  HUANG Shao-xin  Lü Shu-guang  XU Dong-jiong  KONG Ming  ZHANG Yi-min  WANG Bin
Abstract:Concentrations of adsorbable organic halogens (AOX) in surface water and effluents of wastewater treatment plants (WWTPs) in Wunan area of Changzhou were analyzed by microcoulometry, and the AOX contamination characteristics in this area were revealed. Results showed the mean concentration of AOX in surface water was 41.9μg/L, similar to or lower than those in other researches. More severe contamination of AOX was observed in summer, and in the middle section of Yongan River, the north part of Gehu Lake and the northern part of Jinghang Canal. The blocking of river was regarded as an important cause for the severe AOX contamination. Annual pollution loads of AOX from municipal and textile WWTPs were higher, reflecting the large emission contributions by these two sources to the surface water.
Keywords:adsorbable organic halogens  surface water  wastewater treatment plants (WWTPs)  spatial distribution  seasonal variation  
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