人民长江

• 论文 • 上一篇    下一篇

哈尔滨浅层地下水污染现状及影响因子分析

田旭鹏 卞建民 方展   

  • 出版日期:2016-08-14 发布日期:2016-08-14

Analysis of situation of shallow groundwater pollution in Harbin and influential factors

  • Online:2016-08-14 Published:2016-08-14

摘要: 为查明人类活动驱动下的哈尔滨市浅层地下水污染现状及主要影响因子,利用松嫩平原地下水污染调查评价项目的数据,对浅层地下水主要化学特征进行了简单的描述。在此基础上,采用单因子污染指数法与内梅罗综合指数法对地下水污染现状进行评价,最后利用统计分析软件SPSS对采样点进行聚类分析。结果表明:研究区浅层地下水水化学类型以Ca-HCO3、Ca-Cl-HCO3、Ca-SO4-HCO3型水为主,水化学作用以溶滤和人类活动混合作用为主;地下水污染属于区域性污染,以轻度污染为主;除Fe、Mn以外,主要污染指标还有总硬度、TDS、pH、NO-、NH+4、Cl-、SO2-4、As。Fe、Mn含量超标主要是受原生地质环境影响;研究区地下水污染的主要污染源为工业污染、农业污染、生活污染及地表水补给污染,人类活动主要影响水体的总硬度、TDS、pH、NO-3、NH+4、Cl-、SO2-4、As、总磷。

关键词: 浅层地下水, 水化学特征, 污染评价, 聚类分析, 哈尔滨

Abstract:

With the monitoring data, the chemical characteristics of the groundwater quality of Songnen Plain in Heilongjiang Province are described to ascertain the pollution status quo and the main influential factors driven by human activities in the research region. On this basis, the single factor pollution index method and Nemero comprehensive index method are used to assess the status quo of groundwater pollution. The statistical analysis software SPSS is used to conduct cluster analysis on the sampling sites to find out the primary pollutants and impact factors. The result show that the hydrochemistry of the shallow groundwater is dominated by Ca-HCO3,Ca-Cl-HCO3, Ca-SO4- HCO3 type; the water chemical action mainly includes leaching and human activities; the groundwater pollution is in light degree; the major pollution parameters are total hardness, TDS, pH, NO-3, NH+4, Cl-, SO2-4, As, Fe and Mn, and especially, over-standard of the Fe and Mn are caused by original geological environment; the industrial pollution, agricultural pollution, domestic sewage pollution and pollution of surface water supplement are the main pollution sources in the research area, however the total hardness, TDS, pH, NO-3, NH+4, Cl-, SO2-4, As and TP are mainly influenced by human activities.

Key words: shallow groundwater;hydrochemical characteristics;pollution assessment, clustering analysis;Harbin