As a sudden heavy metal pollution accident occurs in a drainage basin, decision makers need to quickly select the optimal emergency treatment technology and formulate emergency schemes according to the actual accident characteristics. Therefore, a two-step identification method of emergency treatment technology for sudden heavy metal pollution accidents based on Dempster-Shafer (D-S) evidence theory is proposed, in order to screen the optimal emergency treatment technology effectively and solve the conflict among fusion data in the process of index quantification. Firstly, the available technologies were screened preliminarily by the primary identification indexes (technical characteristic indexes). Secondly, the weight synthesis method based on the D-S evidence theory and attribute assignment was utilized to score the secondary identification indexes (technical evaluation indexes) of each available technology comprehensively. Finally, the optimal emergency treatment technology for this sudden pollution accident was obtained. Taking the sudden arsenic pollution accident of the Picang flood diversion channel in Linyi, Shandong Province as an example, the activated alumina adsorption dam technology was extracted successfully, which is in accordance with the actual treatment situation. The result shows that the method has strong feasibility and practicability, which can provide strong decision support for dealing with sudden pollution accidents in drainage basins efficiently.

No abstract

The sudden pollution accident in a river basin is combined with accident cause, cause factor (pollutants) and the receptive environment all together, this study brings up the method to establish the information chain of sudden pollution accidents in a river basin which can describe the causality of all information. Take the sudden oil pollution accident in a river basin as an example, the information system of sudden pollution accidents in a river basin is established from five aspects: pollution source characteristics, pollution characteristics, regional environment characteristics, lash-up treatment technology status and pollution impact characteristics. Through index abstraction and structure representation for the above information, and connect the interactive relationship of the node variables with directed edge line segments, based on which the expert knowledge analysis synthesis method is applied to achieve the optimal causal direction relationship, and the information chain of sudden oil pollution accident in the river basin is ultimately established. This information chain can sufficiently show the causal relationship of each information elements in the sudden pollution accident and can supply decision making basis for examining and distinguishing the status of a sudden water pollution accident in a river basin and determining the precise lash-up treatment technology.

Eleven parameters such as pH, dissolved oxygen (DO), permanganate index (COD Mn ), ammonia nitrogen (NH 3 -N), total phosphorus (TP), total nitrogen (TN), fluoride(F -), cadmium (Cd), lead (Pb), petroleum and fecal coliform were selected to analyze the main pollution factors and the temporal and spatial distribution characteristics of Hongya Section of the Qingyi River by principal component analysis (PCA) and SPSS software, which based on water monitoring data in the year of 2011, 2013 and 2015. Four principal components were verified through PCA, and the result showed that the change of water quality is the trend of year-on-year deterioration in 2011, 2013 and 2015.The water quality in other seasons was better than that in autumn the water quality in Guidufu Section was better than it was in Muchengzhen Section. 156

According to the current situation of water quality in drainage basin, the key to improve the prediction accuracy is to select the appropriate prediction model of water quality. The time series method excellently reflected the continuity of the future data in the case of emphasizing historical data. What’s more, the time series method has the higher short-term prediction accuracy and simple modeling process. So, the time series method was used to establish the Auto-Regressive and Moving Average (ARMA) model for the time series of the concentration of dissolved oxygen (DO), biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), ammonia nitrogen (NH3-N) and total nitrogen (TN) at the Guidu fu section of Qingyi River from January 2011 to December 2015. Then, the concentrations of the five water quality indicators from January to June 2016 were predicted, which were verified and analyzed with the measured values. The results show that the model has fine fitting effect and higher prediction accuracy, which can accurately reflect the current and future change trends of the water quality.