研究报告
杨浩,李一平,蒲亚帅,姚向阳,张剑.张家港市河道水质时空分布特征研究分析[J].环境科学学报,2021,41(10):4064-4073
张家港市河道水质时空分布特征研究分析
- Spatio-temporal distribution characteristics and the river water quality of Zhangjiagang City
- 基金项目：国家重点研发计划（No.2017YFC0405203）；国家自然科学基金重点项目（No.52039003）；中央高校基本科研业务费专项资金资助（No.B200204014）
- 作者
- 单位
- 杨浩
- 河海大学环境学院, 南京 210098
- 李一平
- 河海大学环境学院, 南京 210098
- 蒲亚帅
- 河海大学环境学院, 南京 210098
- 姚向阳
- 张家港市水资源管理处, 苏州 215600
- 张剑
- 张家港市水资源管理处, 苏州 215600
- 摘要：为探索张家港市河道水体水质现状及时空分布特征，本研究应用主成分分析对2018年张家港市13条重要河道中的水温、pH值、DO、电导率、浊度、高锰酸盐指数、总磷、氨氮等水质指标进行分析，识别主导水体变化的环境因子.研究得出：①2018年张家港市河道水质整体较好，大部分河道水体处于III类水；②主成分分析表明，氨氮、总磷（TP）、高锰酸盐指数（CODMn）和电导率（EC）的变化主导着研究区域水质变化，4个环境因子之间呈显著正相关；③空间分析表明张家港河是监测河流中污染最为严重的河流，港口桥监测断面为水质污染最严重区域，张家港市市区及东南区域河道污染劣于其他地区；④季节上水质污染程度为冬季 > 春季 > 秋季 > 夏季.通过多元统计方法对河道水质时空变化进行分析，为制定可持续的城市河流水污染控制策略提供了新思路.
- Abstract：To explore the present situation of river water quality in Zhangjiagang City and its spatial-temporal distribution characteristics, water quality parameters including water temperature, pH, dissolved oxygen (DO), electrical conductivity(EC), turbidity(Turb), permanganate index(CODMn), total phosphorus(TP) and ammonia nitrogen from January to December 2018 were analyzed. The principal component analysis was used to identify dominant environmental factors of water quality variety. It is concluded that:①The overall river water quality in Zhangjiagang is good, and most river water qualities are in class III; ② Principal component analysis results showed that the dominated environmental factors of regional water quality change were ammonia nitrogen, total phosphorus(TP), permanganate index(CODMn) and electrical conductivity(EC), which showed a significant positive correlation between each other (p<0.05).③ Spatial analysis results showed that Zhangjiagang river was the most polluted and the monitoring section of the port bridge was the most seriously polluted area. ④ The seasonal variation for water pollution in urban river was in the following order:winter>spring>autumn> summer. The spatial and temporal distribution of river water quality was analyzed by multivariate statistical analysis, which provides an insight into enacting a sustainable river water pollution control.
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