湟水河流域西宁段水质综合评价的可视化研究

发布时间：2018-07-23 10:44

【摘要】：湟水河流域水质监测数据的信息挖掘对水质状况的了解、水污染的综合治理及水的分类应用都很有意义。数据可视化通过图形形式,将数据信息表达出来,形象直观生动地反映出湟水河流域水质监测数据表达出的信息。本文对国内外水质监测数据的分析模型及可视化研究现状作了综述。根据湟水河流域的水文资料及环境特点,选取西宁湟水河流域枯水期52个监测点,18个水质监测指标。测得湟水河西宁流域52个样本,18个指标的数据阵([B]52×18)。主要研究内容:1.优化布点,集中采样,分析测定。按照西宁湟水河流域的水功能划分情况、周围环境和流域特点。布设了52个监测点,包含了西宁市湟水河干流、甘河工业园区、海湖新区、北川河、南川河和沙塘川河。于2015年11月份,采集水样。参考国家标准和环境标准分析检测了52个样品的pH、电导率、浊度、氟化物、全盐量、COD、BOD、氨氮、亚硝酸盐氮、六价铬、Ca、Fe、Cu、Zn、Mg、K、Na等18个监测指标,获得了52个监测点,18个指标的监测数据。2.水质综合评价的模型研究。应用主成分分析、因子分析、聚类分析等化学计量学方法对监测数据进行建模分析。得到了西宁湟水河流域的9个综合污染因子并用层次分析法对监测点水质做了分类。因子模型适用性检验结果表明监测数据不适于因子分析,反映出各指标间相关性比较差。选取4个指标,建立了的加权叠加回归综合评价模型,与单指标评价法结合对西宁湟水河流域水质做了综合评价。3.西宁湟水河流域水质综合评价的可视化研究。应用ArcGIS软件的ArcMap模块,借助全球河网图层,对监测点位和加权叠加回归综合评价模型所得的结果作了可视化。对监测点位应用湟水河河网图,标明了采样点在湟水河的相对位置。对加权叠加回归综合评价结果在谷歌地形图下,用安全预警五级分类颜色和不同的形状,形象直观地展示出监测点的水质分类情况。本文以西宁湟水河流域水质综合评价为研究背景,通过布点、采样、测试、数据处理,建立了加权叠加回归综合评价模型,对湟水河的综合评价提供新的思路。利用全球河网图层,对监测点位和加权叠加回归模型所得的结果作了可视化分析。用预警五级分类颜色,形象直观地展示出监测点的水质分类情况,为湟水河污染综合治理提供了水质监测的可视化信息技术。
[Abstract]:The information mining of water quality monitoring data in Huangshui River basin is of great significance to the understanding of water quality, the comprehensive treatment of water pollution and the application of water classification. Data visualization is used to express the data information in graphic form, which can directly and vividly reflect the information expressed by the water quality monitoring data in the Huangshui River Basin. In this paper, the analysis model and visualization research status of water quality monitoring data at home and abroad are summarized. According to the hydrological data and environmental characteristics of the Huangshui River Basin, 52 monitoring points and 18 water quality monitoring indexes were selected in the dry season of Xining Huangshui River Basin. The data matrix of 52 samples and 18 indexes in Xining watershed of Huangshui River was obtained ([B] 52 脳 18). The main research content is: 1. Optimization of distribution, centralized sampling, analysis and measurement. According to the water function of Xining Huangshui River basin, the surrounding environment and watershed characteristics. There are 52 monitoring sites, including the main stream of Huangshui River in Xining City, Ganhe Industrial Park, Haihu New area, Beichuan River, Nanchuan River and Shatangchuan River. Water samples were collected in November 2015. The pH, conductivity, turbidity, fluoride, total salt content of COD _ DBOD, ammonia nitrogen, nitrite nitrogen, hexavalent chromium (Cr _ (2) _ (2), CaFeCuFE _ (Zn) mg ~ (2 +) K _ (+) Na were determined in 52 samples with reference to the national and environmental standards. 52 monitoring points and 18 monitoring data were obtained for the determination of pH, conductivity, turbidity, fluoride, total salt content, ammonia nitrogen, nitrite nitrogen and hexavalent chromium. Study on the Model of Comprehensive Evaluation of Water quality. The principal component analysis, factor analysis, cluster analysis and other chemometrics methods are used to model and analyze the monitoring data. Nine comprehensive pollution factors in Xining Huangshui River basin were obtained and the water quality of monitoring points was classified by analytic hierarchy process (AHP). The test results of the applicability of the factor model show that the monitoring data is not suitable for factor analysis, which reflects the poor correlation among the indicators. The comprehensive evaluation model of weighted superposition regression was established by selecting four indexes, and the water quality of Xining Huangshui River basin was evaluated by combining with single index evaluation method. Visualization study on Water quality Comprehensive Evaluation in Xining Huangshui River Basin. By using ArcMap module of ArcGIS software and with the help of global river network layer, the results of monitoring points and weighted superposition regression comprehensive evaluation model are visualized. The relative position of the sampling points in the Huangshui River is indicated by the application of the Huangshui River network map to the monitoring points. The results of comprehensive evaluation of weighted superposition regression under the Google topographic map, the color and different shapes of the five levels of safety warning are used to visualize the water quality classification of the monitoring points. Based on the comprehensive evaluation of water quality in Xining Huangshui River basin, a comprehensive evaluation model of weighted superposition regression is established through placement, sampling, testing and data processing, which provides a new idea for comprehensive evaluation of Huangshui River. Based on the global river network layer, the results of monitoring points and weighted superposition regression model are analyzed visually. By using the color classification of the five levels of early warning, the water quality classification of the monitoring points is displayed visually and visually, which provides visual information technology for the comprehensive control of the pollution of the Huangshui River.
【学位授予单位】：青海师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X832

【参考文献】