Distribution Of Heavy-metal In Sediment And Water Quality Prediction Model In Xiangjiang River From Zhuzhou To Changsha

With the rapid development of the human society and the improvement of the degree of industrialization, pollution of water bodies become more and more serious, especially heavy metals pollution has been a topic of concern. In the paper, heavy metal in the Xiangjiang from Zhuzhou to Changsha was selected for the study, based on the survey sampling and analysis, GIS and geostatistics software was used to carry out a series of studies, known sampling points of the semi-variogram was discussed and Kriging was used to analysis content of the heavy metal sediment, grey theory GM (1,1) prediction model was used in Pb of Valley which was the more serious pollution, and research results indicate that:(1) The average contents of heavy metal sediment, with the exception of Cr, the average contents was lower than the natural background criteria of national soil quality standards in the natural background values, the other elements are different degrees higher than the natural background criteria of national soil quality standards in the natural background values, the total variability trend of six heavy metals contents was: Pb>Cr> Cd> Zn> Cu>As.(2) The semi-variograms of sediment heavy metal Cd, Pb, Cu, Zn, As, Cr were fitted by spherical model. The spatial correlation distance of heavy metals in the 10 km distance above, the sampling point distance of 4 km, it is indicate that the content of heavy metals in sediment on the spatial variability of the evaluation is available.(3) Using Kriging interpolation method, a study area six heavy metals contour map was produced. In the study area, high content areas of six heavy metals are concentrated in two regions, Zhuzhou, a curved section of Xiangtan and Changsha.(4) Using grey prediction model, relatively serious pollution of the heavy metals Pb was predicted the spatial distribution of the forecast. The measured values and predictive value were similar, the model accuracy was eligibility. Using the prediction model, Pb in water spatial variation trends was well predicted.