Research On Joint Toxicity Of Multiple Heavy Metal Compounds To Photobaeterium Phosphoreum

With the rapid development of society and economy, heavy metal pollution in Chinabecomes more and more serious. The high toxicity, wide sources, unnoticeable, long-term, difficult to eliminate of the Heavy metal pollution has made its prevention and control a huge problems. Heavy metal pollution also poses a great threat to biology and human beings through the food chain. The heavy metal lead, cadmium, chromium, copper and so on are all typical heavy metal pollutants,this year occurred in China’s heavy metal pollution incidents are associated with them. In the actual environment, there usually have the coexistence of various heavy metal pollutants. When there are two or more chemicals working on biology at the same time, there may be caused different toxic reaction with the substances acting alone. They influence each other, bring about produce antagonistic, additive or synergistic effect. So it has more practical significance to study the combined toxicity between two or more chemical pollutants. It can also provide more reliable basis for the establishment of environmental standards, ecological risk assessment and environmental pollution control.In this thesis, we take photobacterium phosphoreum (T3) as indication organism, test the joint toxicity of8heavy metal compounds (Cu(NO3)2·3H2O, Cd(NO3)2·4H2O, Zn(NO3)2·6H2O, Pb(NO3)2, Cr(NO3)399H2O, Ni(NO3)2·6H2O, Co(N03)2-6H20, and Sr(NO3)2)with fixed ratio design method and orthogonal experiment design method. We get the joint toxicity of8kinds of heavy metals in qualitative analysis.Then analyze the significant of8heavy metals to select4heavy metals. Use factorial experimental design method testing the joint toxicity of4heavy metals. And establish multiple linear regression model and BP neural network model to analyze mixture toxicity action, analysis and compare these two models’prediction results.The concrete research contents and results are as follows:Use fixed ratio design method to test the joint toxicity of8heavy metals, and use joint toxicity evaluation method (Toxic Unit (TU), Additive Index(AI), Mixture Toxicity Index(MTI), Similarity parameter method(λ)) to analyze the joint toxicity of8heavy metals. The results are as follows:The joint toxicity in equimolar ratio of mixed heavy metals is synergy, the joint toxicity in equal toxicity ratio are all the same and different from the equimolar ratio mixtures. The results show that the combined effects of four different evaluation method to determine the mixture is sometimes inconsistent, the concentration of heavy metals combined toxicity of mixture also have certain influenceUse the orthogonal experimental design method to test the8heavy metals’joint toxicity. Analyze the8heavy metals’significant (the critical quantityof F are0.05and0.01) by SPSS. The results are as follows: The joint toxicity between eight heavy metals Ni2+、Co2+、Cr3+and Pb2+have a significant effect, while Cu2+、Cd2+、Zn2+、and Sr2+do not. The results are not consistent with the single toxicity. It means that the heavy metal mixture system of combined toxicity luminescent bacteria and pollutants not only single strong toxicity of pollutants, but also is affected by single low toxicity, and the solubility of metals in the environment, physicochemical properties.Use the factorial experimental design method to test the4heavy metals’joint toxicity. Construct the prediction model of joint toxicity of heavy metals in multiple linear regression and artificial neural network, and analyze its joint toxicity and toxic effect rules in quantitatively. The results are as follows:The joint toxicity between Ni2+、Co2+、Cr3+and Pb2+mainly performance multiple interactions. The interactions between Ni2+、Co2+and Cr3+weak the single toxicity and binary interaction of Pb+. The binary or quaternary interaction between heavy metals mainly perform antagonism, while ternary interaction mainly synergy. What’s more, the single toxicity function coefficient is small, all shows that heavy metals exist in water mainly perform joint toxicity, and the single toxicity influence is relatively weak.The test data proved that both the multivariate regression model and BP-artificial neural network model on the four heavy metals have good prediction efficiency. And BP-artificial neural network model has a better prediction. To increase the concentration of Co2+, the toxicity of the mixture is becoming weak, while the other three heavy metals have an opposite effect.