Research On Ash River Basin Water Quality Model And Evaluation Techniques

Since the21st century, environmental issues are being paid more and moreattention to, and the river basin water pollution problem has become one of the mostserious environmental problems in the world. The water quality of Ash River, aclass-one tributary of China’s seven major river systems, the Songhua River, isround the inferior class V all over the year. The water pollution problem hasseriously affected the coastal industrial and agricultural development. In order tosolve the Ash river pollution problems better and faster, this topic build a set of AshRiver basin water quality models and evaluation system.This topic first studied the motion law of pollutants in the river, namely thepollutants enter the watershed, and they will be subject to the migration of the flow,dispersion and degradation of the three movements. Three factors together, theconcentration of pollutants changes over time and based on which the river basinwater quality basic model can be established. And then targeted the Ash Riverhydrological characteristics, with some reasonable assumptions of the model,established the two-dimensional orthogonal surface coordinate system on the streamfunction and potential function. Consider the wet season and dry season and theinstantaneous source and stable source of processes, combined with the movementcharacteristics of NH₃-N, TN, TP, COD_crand other pollutants in the water, thepollutant dispersion model of these four groups of contaminants in the Ash RiverBasin is obtained. Enter the actual concentration of water pollutants and the waterenvironment evaluation criteria into the water quality assessment model based onBP neural network, and then the level of contamination of the Ash River Basin willbe given.Analyzing the cross-section measurement results with Ash River Basin waterquality model, it can be seen that the model consistent with experimental results.With the evaluation of known cross-section, comparing with Nemerow index, it canbe determined that the BP neural network for water quality assessment is moreobjective to meet the requirements of the Ash River Basin Water QualityAssessment, with a high degree of practical value.