This paper proposes a quantitative method to optimize the existing river monitoring network based on a modified approaching degree model, T test, and Euclidean distance. In this study, the Liaohe River located in Liaoning province, China, was taken as a research object. Samples were collected from 8 sampling sites throughout the monitoring network, and water quality parameters were analyzed every 2 months from January 2009 to December 2010. The results show that the average concentrations of the ammonia nitrogen (NH4+-N) and chemical oxygen demand (COD) were beyond grade III of the Environmental Quality Standards for Surface Water of China (GB3838-2002), and they were the main water quality parameters. After optimization, the number of monitoring sections along the Liaohe River was reduced to five from the original eight, thus saving 37.5% of the monitoring cost; meanwhile, there is no significant difference between the un-optimized and optimized monitoring networks, and the optimized monitoring network remains to be able to perform as good as the original one. In addition, the total data attainment rate was improved greatly, and the duplicate setting degree of monitoring points decreased significantly compared with other optimal methods. The optimized monitoring network proves to be more efficient, reasonable, and economically feasible, so this quantitative method can help optimize the changing orderly river monitoring networks.
Keywords: Approaching degree model; Liaohe River; Optimal design; River monitoring network.