The contamination and environmental risk assessment of the toxic elements in sediments from the middle-downstream (Zhuzhou-Changsha section) of the Xiangjiang River in Hunan Province of China were studied. The results...The contamination and environmental risk assessment of the toxic elements in sediments from the middle-downstream (Zhuzhou-Changsha section) of the Xiangjiang River in Hunan Province of China were studied. The results show that As, Cd, Pb and Zn are major contaminants in sediments, and average concentrations of these elements significantly exceed both the Control Standards for Pollutants in Sludge of China (GB4284-84) for agricultural use in acidic soils and the effect range median (ERM) values. The average concentrations of As, Cd and Pb in the river water slightly exceed the limit of Surface Water Environment Quality Standard (GB3838-2002). The concentrations of As and Cr in depth profiles extensively change, but slight changes are observed in Pb and Zn. Cd and Zn in most sediment samples can easily enter the food-chain and bring possible ecotoxicological risk to organisms living in sediments according to the risk assessment code.展开更多
Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality dat...Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.展开更多
基金Project (20507022) supported by the National Natural Science Foundation of ChinaProject (EREH050303) supported by the Foundation of Ministry of Education Key Laboratory of Environmental Remediation and Ecosystem Health
文摘The contamination and environmental risk assessment of the toxic elements in sediments from the middle-downstream (Zhuzhou-Changsha section) of the Xiangjiang River in Hunan Province of China were studied. The results show that As, Cd, Pb and Zn are major contaminants in sediments, and average concentrations of these elements significantly exceed both the Control Standards for Pollutants in Sludge of China (GB4284-84) for agricultural use in acidic soils and the effect range median (ERM) values. The average concentrations of As, Cd and Pb in the river water slightly exceed the limit of Surface Water Environment Quality Standard (GB3838-2002). The concentrations of As and Cr in depth profiles extensively change, but slight changes are observed in Pb and Zn. Cd and Zn in most sediment samples can easily enter the food-chain and bring possible ecotoxicological risk to organisms living in sediments according to the risk assessment code.
基金Project (2012ZX07501002-001) supported by the Ministry of Science and Technology of China
文摘Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.
