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    • 长三角生态绿色一体化示范区优先污染物筛选及协同监测探讨

    CHEN Ying,YANG Kai,YANG Meng-jie,LU Shi-qiang,ZHU Yong-qing,XU Lu,QIAN Mei-yin, SHI Ling-xi

    Resources and Environment in the Yangtze Basin（2022）

    Cited 2|Views9
    Abstract
    有效识别区域内水环境优先污染物,是长三角生态绿色一体化示范区实现水环境协同监测体系建设的重要内容.在对一体化示范区内典型跨界河流太浦河的多个断面进行2017～2019年每季度一次的109项全指标监测的基础上,结合区域环境及产业特征,选取污染物的检出频率、空间分布、最大占标率和致癌性作为评估指标,采用熵权法和TOPSIS法对《地表水环境质量标准》基本项目以外的特定80项污染物进行优先污染物筛选.结果表明:(1)锑的潜在环境风险高;镍、二氯甲烷、丙烯酰胺的风险比较高;钴、钛、硼、硫化物、阿特拉津、微囊藻毒素-LR、1,4-二氯苯、氯苯的风险一般.(2)目前江苏吴江、浙江嘉善、上海青浦三个行政区存在水环境监测管理协同性不足及数据共享程度较低的现状问题,与统一标准、统一监测、统一执法的水环境共治目标存在一定差距.(3)基于区域监测现状与优先污染物筛选结果,从完善监测指标与信息公开、加强协同监测和数据共享、健全一体化水环境预警体系等方面提出了完善长三角一体化示范区水环境协同监测体系的建议.
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
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    【要点】：论文针对长三角生态绿色一体化示范区水环境优先污染物进行识别和监测，提出完善水环境协同监测体系的建议，创新点在于结合区域特征进行污染物筛选并提出针对性的协同监测策略。
    

    【方法】：通过熵权法和TOPSIS法，结合区域环境及产业特征，对一体化示范区内特定80项污染物进行优先污染物筛选。
    

    【实验】：对太浦河多个断面进行2017～2019年每季度一次的109项全指标监测，使用的数据集为监测数据，结果表明锑、镍、二氯甲烷等污染物存在潜在风险，并提出相应的水环境协同监测体系完善建议。