Metal Mines Heavy Metal Water Pollution Assessment And Prediction

Heavy metal pollution caused by development of mineral resources is one of the primary environmental problems in the process of resource utilization. With the speed and scale of mineral resources exploitation increasing, environmental pollution caused by mining is worsening, especially the heavy metal pollution and acid water pollution due to metal mining, pays an inestimable role in influencing the surrounding environment. In order to find suitable method and tools to scientifically assess and reasonable predict on heavy metal water pollution, the paper took a copper mine for instance carried out systematic scientific research on this subject by blind number theory and time series analysis. The study was based on collection of mine environmental monitoring data and field sample analysis, and combined with knowledge utilization of mining engineering, environmental engineering, systems engineering and statistics.The article discussed the serious perniciousness to the environment and the human body that caused by heavy metal pollution, and summarized water pollution evaluation and prediction methods and research progress at home and abroad. Appropriate evaluation and prediction method were selected subsequently. Analytical studied unascertained characteristic of surface water and river water environment and judged the water environment issues were unascertained problems. The unique advantages of unascertained number in resolving the unascertained problem field were approached, and blind number theory was ultimately confirmed as the best mathematical tools for water pollution assessment.The blind number model of synthetic indexing to assess heavy metal pollution was build integrate with blind number theory. Suitable classification standards of single pollution indexing and synthetic pollution indexing were chose and blind number expression of heavy metal pollutants concentration were structured according to the copper mine surrounding environment and the function of the pollutants carrying river. Then blind number operations were carried out, thus, single pollution indexes of heavy metal and synthetic indexes in every sampling point were obtained, pollution classification was done. The results show that: among the pollutants in river water in mine area, the class of pollution of Copper, Lead, Zinc, Cadmium, Iron, Nickel was worst, inferior, inferior, worst, worst, worst. So, it's polluted seriously. Contamination degree order was: Iron (Fe)> Cadmium (Cd)> Copper (Cu)> Nickel (Ni)> Zinc (Zn)> Lead (Pb). Referred to the contamination degree of all sampling sites, in addition to "drinking-water 1" and "contrast 1" were good, the "backwater" points was poor, attributed to lower pollution lever, the water quality of the rest monitoring points were all attribute to worst. In general, river water in the copper mine was seriously polluted by heavy metal must be strengthening prevention and cure. Auto regressive integrated moving average model (ARIMA) was established based on the history heavy metal pollution concentration of this copper mine River. According to Akaike information criterion, combined with autocorrelation function graph and the partial autocorrelation function graph of smoothed sequence, model ARIMA (2, 1, 1) was obtained. Then carried on predictions of copper's pollution concentration of the monitoring point "export 2" in January to June in year 2008, and tested the predicted results. Compared with the actual measured values, we found that the residual values of prediction were all within 5%. Therefore, the application of this model to study prediction of the subject was reasonable and feasible.