Study On Correlations Between Typical Quality Indicators And Evaluation Of Quality Model For Monitoring Scenic Surface Raw Waters In Countryside

In recent years, environmental pollution problems become more and more serious especially in underdeveloped rural areas due to lack of residents’environmental awareness and inadequate environmental supervision of government, and caused the water pollution situation which exerts bad impacts on people’s lives. Thus it is necessary that the utilization of modern means to achieve better water management.In this thesis, scenic surface water in Qiaokou Wangcheng is main target and is selected to carry out our study on relationships among water quality monitoring indicators and application of water quality predictive models.Several indicators that can be representative, with simple means of detection, and can well reflect the town’s overall water quality are selected according to the "surface water quality standard GB3838-2002". These indicators which establishing village environmental monitoring indicator system are:temperature, pH, DO, CODcr, ammonia nitrogen,TP,TN,SS,nitrate, conductivity and UV254. Based on the data from preliminary detection work, the overall analysis on scenic surface water was carried out according to on-site monitoring data. It is confirmed that the TP conforms to level IV water standard,the COD conforms to level Ⅲ water standard, the TN ranges from level IV to V and other indicators conform to level Ⅰ water standard. According to Matter element analysis method,9landscape water monitoring sites were optimized, and it is determine that three sites B1, B5, B9were the final optimized monitoring sites to provide data to correlation analysis.This thesis carries out correlation analysis among indicators according to on-site monitoring data in different seasons and temperatures, and investigate the correlationship between DO and COD, COD and UV254, DO and TN, DO and TP, and among TN, ammonia nitrogen and nitrate nitrogen. It is observed that these groups have good linear relationship, except group of DO and COD and group of Do and TP. For further verification, this thesis uses the relative error method to verify with the monitoring data of January and March.It is observed that the correlationships of DO and COD, DO and TP are greatly effected by temperature, therefore the model can not be applied when water temperature changes.As for the relationship of TN and ammonia nitrogen, nitrate nitrogen, COD and UV254,the linear relationships change a little with temperature changing Thus they can be applied at different temperatures. According to relationships between different indicators as illustrated before, we can choose some indicators which can be simply detected to monitor whole scenic water on-line in order to archive surface water quality management.Finally, this article uses both Grey model and BP neural network to forecast the water quality indicators of scenic water in Qiaokou and indicates modeling approach and procedures of both model. This article also verifies the prediction accuracy with on-site data.It is concluded that accuracy of two models is good. Two models have its own characteristics. The Grey model requires less data and its accuracy of long time span is relative worse than BP neural network. It is to say the BP neural network requires continuous long time data, and it is immune to changing temperature in the water discussed in this article. Thus we can choose models according to different situations.