黑河流域水环境因子分析及水环境质量综合评价

发布时间：2018-04-28 00:33

本文选题：黑河流域 + 水环境因子分析　；参考：《宁夏大学》2017年硕士论文

【摘要】：近年来,随着西部大开发战略的实施和丝绸之路经济带的建设,我国西北地区发展速度不断加快。由于生产规模的不断扩大和人口的快速增长,大量农药化肥残留和一部分未经处理的工业生活污水排入河流,导致西北地区的河流水体受到不同程度的污染,水环境污染问题已经成为限制西北地区经济社会发展的一大障碍。本文以黑河流域中的10个小流域(八宝河、野牛沟、北大河、梨园河、红水河、山丹河、张掖湿地、中游干流、额济纳河、东居延海)为研究对象,通过对黑河水体进行采样测定,分析研究影响黑河水环境的主要污染因子,探讨黑河流域的水质状况和富营养状况,以期为黑河流域水环境综合治理、水资源保护与可持续利用提供基础数据和理论依据。本研究取得的主要结果如下:(1)运用主成分分析法和因子分析法对黑河流域水环境因子进行分析,得出2013年4月份黑河流域水质影响因子为pH、氨氮(NH3-N)、总氮(TN)、总磷(TP)、高锰酸盐指数(CODMn),7 月份为 NH3-N、TN、叶绿素 a(chl.a)、TP、CODMn,12 月份为 TN、CODMn、NH3-N、TP、chl.a;2014年4月份黑河流域水质影响因子为chl.a、TN、CODMn、TP、NH3-N、透明度(SD),7月份黑河流域水质影响因子为TP、chl.a、CODMn、TN、电导率(EC)、NH3-N,12月份为TN、TP、EC、CODMn、NH3-N、pH;2015年4月份黑河流域水质影响因子为pH、NH3-N、TN、TP、EC,7 月份为 TN、NH3-N、CODMn、chl.a、TP,12 月份为 chl.a、CODMn、TN、SD、TP。各种水环境因子对黑河水环境的影响在各个时期有一些差别,对黑河流域水质影响最大的因子主要为氮、磷营养盐,氮、磷营养盐含量的变化导致浮游藻类和其它有机物含量的变化,从而对水体水质产生相应的影响。(2)运用灰色关联法和BP神经网络法对黑河流域水质进行评价,八宝河、野牛沟、北大河、梨园河、红水河、山丹河、中游干流、额济纳河流域的水质为Ⅱ～Ⅲ类,水质状况良好;张掖湿地水质为Ⅱ类,主要是由于张掖湿地水生生物量较高,水体净化能力强;东居延海流域水质常年为V类,主要是由于东居延海是黑河流域末端,营养盐和污染物富集,造成水质状况较差。运用灰色关联法和BP神经网络法进行水质评价,评价结果会有所差别,本研究认为BP神经网络评价的结果更为客观,而且利用BP神经网络的输出结果进行分析,可以避免灰色关联法那样“一刀切”的方式,便于更加准确的分析比较水质状况及其变化。(3)使用综合营养状态指数法和BP神经网络法对黑河流域富营养状态进行评价,得出八宝河流域平均营养指数为42.96,野牛沟流域为47.81,北大河流域为43.55,梨园河流域为43.19,红水河流域为42.99,山丹河流域为43.41,张掖湿地为48.54,中游干流为43.56,额济纳河流域为43.60,东居延海流域为82.18,东居延海流域的营养状况为重度富营养,营养状况不容乐观,它的总氮指数很高,其余流域富营养化状况主要以中营养级为主,营养状况整体良好。运用综合营养状态指数法和BP神经网络法分别进行富营养评价,评价结果差别较大,因为对于黑河这样的西北内陆河流,透明度不能完全反映富营养化的状态,使得综合营养状态指数法的评价结果偏于保守。使用BP神经网络法可以去除透明度的影响,相对于综合营养指数法更加贴近实际情况。
[Abstract]:In recent years, along with the implementation of the western development strategy and the construction of the Silk Road Economic Belt, the development speed of the Northwest China is accelerating. Due to the continuous expansion of the production scale and the rapid growth of the population, a large number of pesticide and chemical fertilizer residues and a part of the untreated industrial sewage water are discharged into the river, leading to the river body in the northwest region. To a different degree of pollution, water environmental pollution has become a major obstacle to the economic and social development in Northwest China. This paper takes the 10 small basins in the Heihe River Basin (the eight treasure River, the bison gully, the north big river, the Liyuan River, the red water river, the Shandan River, the Zhangye wetland, the middle reaches of the river, the Ejina River and the Dong Yan Hai sea) as the research object. The main pollution factors affecting the black river environment are analyzed and studied. The water quality and eutrophication status of the Heihe River Basin are discussed in order to provide the basic data and theoretical basis for the comprehensive treatment of water environment in the Heihe basin and the protection and sustainable utilization of water resources. The main results are as follows: (1) the use of principal components is used. The water environmental factors of Heihe basin were analyzed by analysis and factor analysis. The factors of water quality in the Heihe basin in Heihe in April 2013 were found to be pH, ammonia nitrogen (NH3-N), total nitrogen (TN), total phosphorus (TP), permanganate index (CODMn), NH3-N, TN, chlorophyll a (chl.a), TP, CODMn, December. 2014 April Heihe Basin The influence factors of water quality are chl.a, TN, CODMn, TP, NH3-N, and transparency (SD). In July, the factors of water quality in Heihe basin are TP, chl.a, CODMn, TN, conductivity (EC), NH3-N, December, April. The effects of various water environmental factors on the environment of N, SD and TP. have some differences during each period. The main factors affecting the water quality in Heihe basin are the changes of nitrogen, phosphorus nutrients, nitrogen and phosphorus content, which lead to the changes in the content of planktonic algae and other organic matter, and have a corresponding effect on the water quality. (2) use grey customs. The water quality of Heihe river basin is evaluated by combined method and BP neural network method. The water quality of the eight treasure River, wild cattle gully, North Dahe River, Liyuan River, red water river, Shandan River, middle reaches stream, Ejina river basin water quality are class II to III, and the water quality of Zhangye wetland is class II, mainly because of the high aquatic biomass of the wetland in Zhangye and the strong purification ability of the water body; East The water quality of the river basin is V, which is mainly due to the accumulation of nutrients and pollutants in the end of the Heihe River Basin, and the water quality is poor. The evaluation results of the water quality are different by using the grey correlation method and the BP neural network method. The result of this study is that the result of the evaluation of the BP God via the network is more objective, and the BP God is used. Through the analysis of the output results of the network, it can avoid the "one size fits all" method like the grey correlation method, which facilitates more accurate analysis and comparison of the water quality and its changes. (3) the comprehensive nutrition state index method and BP neural network method are used to evaluate the eutrophic state of the Heihe basin, and the average nutrition index of the eight treasure river basin is 42.96. The bison Valley is 47.81, the north big river basin is 43.55, the pear garden river basin is 43.19, the red water river basin is 42.99, the Shandan river basin is 43.41, the Zhangye wetland is 48.54, the middle reaches of the river is 43.56, the Ejina river basin is 43.60, the East reside Sea Basin is 82.18, the nutrition status of the Dong Ju Yan Hai River Basin is severe eutrophication, nutrition status is not optimistic, it is not optimistic, it is not optimistic, it is not optimistic nutrition status, it is not optimistic, it is not optimistic, it is not optimistic nutrition status. The total nitrogen index is very high. The eutrophication status of the other basins is mainly based on the middle nutrition grade, and the nutritional status is good as a whole. The eutrophication evaluation is carried out by the comprehensive nutrition state index method and the BP neural network method respectively. The result is different because the transparency of the northwest inland river flow such as Heihe can not completely reflect the eutrophication. The evaluation results of the comprehensive nutrition state index method are conservative. The use of BP neural network method can remove the influence of transparency, which is more close to the actual situation compared with the comprehensive nutrition index method.

【学位授予单位】：宁夏大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TV213.4

【参考文献】