• 关键词：多元统计法  污染物通量  空间分布特征  影响因素

• 基金项目：国家水体污染控制与治理科技重大专项（No.2014ZX07206005，2017ZX07301006004）

• 黄志伟
• 环境保护部华南环境科学研究所国家水环境模拟与污染控制重点实验室, 广东省水与大气污染防治重点实验室, 广州 510535

• 曾凡棠
• 环境保护部华南环境科学研究所国家水环境模拟与污染控制重点实验室, 广东省水与大气污染防治重点实验室, 广州 510535

• 范中亚
• 环境保护部华南环境科学研究所国家水环境模拟与污染控制重点实验室, 广东省水与大气污染防治重点实验室, 广州 510535

• 杨汉杰
• 环境保护部华南环境科学研究所国家水环境模拟与污染控制重点实验室, 广东省水与大气污染防治重点实验室, 广州 510535

• 李伟杰
• 环境保护部华南环境科学研究所国家水环境模拟与污染控制重点实验室, 广东省水与大气污染防治重点实验室, 广州 510535

• 曾海龙
• 环境保护部华南环境科学研究所国家水环境模拟与污染控制重点实验室, 广东省水与大气污染防治重点实验室, 广州 510535

• 林澍
• 环境保护部华南环境科学研究所国家水环境模拟与污染控制重点实验室, 广东省水与大气污染防治重点实验室, 广州 510535

• 摘要：通过对北港河进行水文水质同步监测，采用多元统计法对26个采样点位10个监测指标进行分析，识别出流域水环境污染特征及主要影响因素，并进一步估算了主要污染物的通量，以期为有限资料条件下的河流污染治理提供依据.结果表明：北港河水系污染严重，主要污染物为氮、磷及有机污染物；表征生活污水、面源污染与畜禽养殖废水对水质影响的主成分的贡献率为47.95%，表征工业污染的主成分的贡献率为12.50%；重污染企业数、人口密度、建设用地占比与流域氮、耗氧有机物具有显著正相关性，表明重污染企业数、人口密度与建成区分布是影响水质的最重要指标；按污染轻重，可将流域划分为3类分区：上游轻污染区、谷饶溪及东寮坑重污染区、其他区域则为中度污染区.污染物通量变化趋势分析显示，COD_Cr、NH₄⁺-N通量变化趋势基本与流量变化趋势一致，而TP通量则受上游点源排放影响出现一定差异性.通量变化趋势主要受水闸调度影响呈现开闸大幅度上升、闭闸大幅度下降的规律，在闭闸初期则受练江干流高水位顶托作用影响，流量及污染物通量为负值，练江干流水倒灌入北港河；此外，监测期间COD_Cr、NH₄⁺-N最高日污染通量出现在第2次开闸期间，分别为39.23、4.98 t·d^-1，TP最高日污染通量则出现在第1次开闸期间，为547.36 kg·d^-1.污染通量贡献率分析表明，干流水质主要受谷饶溪及东寮坑影响，其中，谷饶溪COD_Cr、NH₄⁺-N、TP污染贡献率分别达到了64%、47%、22%，东寮坑COD_Cr、NH4⁺-N、TP的贡献率则分别为26%、28%、25%，建议加强对该汇水区内生活污水、工业废水等点源污染控制.

• Abstract：In this paper, multivariate statistical analysis were employed to investigate 10 water quality of samples collected from 26 monitoring sites in Beigang River at flood season. The characteristics of water environment pollution, main influencing factors and fluxes of the main contaminants were studied to provide theoretical basis for river pollution control. The results show that the type of pollutants mainly include the nitrogen, phosphorus andorganic contaminant.The contribution rate of domestic sewage, non-point sources pollutions and livestock and poultry farming wastewater to water quality was 47.95%, and the industrial pollution was 12.50%. The numbers of heavy pollution factories, population density and the distribution of the built-up area are the most important indexes influencing water quality due to their high positive correlation. The watershed can be divided to 3 categories according to the degree of pollution:lowly polluted(LP) sites in upstream area, highly polluted(HP) sites in the Gurao River and Dongliao River and moderately polluted(MP)in other areas. The pollutant variation reveals that the variations of COD_Cr and NH₄⁺-N are consistent with the trend of flux, while TP was influenced by the upstream point-source emissions. The trend of flux variation was mainly affected by the floodgate dispatching, which showed that the opening gate was greatly increased but reduced when closing. In the initial stage of the close gate, the flow and pollutant flux affected by the high water level are negative. The maximum daily pollution flux of COD_Cr (39.23 t·d^-1) and NH₄⁺-N (4.98 t·d^-1) occurred during the second opening period and TP (547.36 kg·d^-1) occurred during the first opening gate. The trend of pollutant variation revealed that the variations of COD_Cr and NH₄⁺-N are consistent with the trend of flux, while TP was influenced by the upstream point-source emissions. The trend of flux variation was mainly affected by the floodgate dispatching, which showed that the opening gate was greatly increased but reduced when closing. In the initial stage of the close gate, the flow and pollutant flux affected by the high water level are negative. The maximum daily pollution flux of COD_Cr (39.23 t·d^-1) and NH₄⁺-N (4.98 t·d^-1) occurred during the second opening period and TP (547.36 kg·d^-1) occurred during the first opening gate. The contribution of pollution flux showed that that pollution loads of COD_Cr, NH₄⁺-N and TP were mainly come from the tributaries of Gurao and Dongliao. The contribution of COD_Cr, NH₄⁺-N and TP reached 64%, 47% and 22% respectively in the tributary of Gurao and 26%, 28% and 25% respectively in the tributary of Gurao Dongliao. In conclusion, it is suggested to strengthen the control of point source pollution such as domestic sewage and industrial wastewater in the key sub-watershed.

• Key words：multivariate statistical analysis  pollutant flux  spatial heterogeneity  influencing factors

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