Abstract:

Comprehensive assessment of water quality is an important basis for the calculation of water environmental capacity and the implementation of water pollution control in water environment monitoring. Many different methods employ multiple factors to assess the comprehensive conditions of water quality, but no common consensus has been reached. Therefore, the study of water quality assessment using multiple factors could facilitate the comprehensive assessment of water quality. The linear transformation based on Euclidean distance was adopted to categorize the multiple water quality variables in this study. Firstly, both the raw monitor data and five grades surface water environment quality standards (GB3838-2002) were standardized, then the standardized variables were transformed to the same linear space using Matlab 2010b. The minimum Euclidean distance between vectors of water quality and the five standards in the linear space were used to identify the different categories of water quality variables. Using the published water quality monitoring data in 19 sections, including water quality monitoring data of Qiantang River tributaries, East Village section of Yangjiang, Xu, and Panlong River, and 4 Wells monitoring data of XianYang City, and JingYang County. Linear space transformation method was applied to comprehensive evaluation of water quality on each section. We used Matlab 2012b to perform calculations. Our results indicated that the linear space transformation based on minimum Euclidean method was suitable for the comprehensive assessment of water quality, and compatible with other analytical methods, such as varying weights continental distance model, grey clustering, fuzzy comprehensive index, and BP neural network. In the case of no pollution factor weighted, the evaluation result of water quality comprehensive evaluation based on linear transformation method is lighter than result base on factor weighted methods. However, even under the condition that water quality is very good, we can also use “Euclidean distance matrix” to recognize the difference between water quality monitoring data. In addition, under the condition of keeping evaluation methods unchanged, the linear space transformation method was suitable for the improved water quality standard of surface water, in the future. Without weighted pollution factors, the linear space transformation method can also compatible with current approaches in a simplified manner. At the meantime, the pollution and other monitor factors in standardization all can be treated as one direction positive/negative factors, not two directions needed. This can also facilitate data processing in water quality assessment