Comprehensive risk assessment of heavy metals in lake sediment from public parks in Shanghai

Yang, Jing; Chen, Ling; Liu, Li-Zao; Shi, Wei-Ling; Meng, Xiang−Zhou

doi:10.1016/j.ecoenv.2014.01.010

Cited by 136 publications

(48 citation statements)

References 34 publications

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“…These results indicated that PC1 had different geochemical behaviors and was mainly sourced from mixed anthropogenic inputs, including industrial wastes (such as manufacturing, metallurgy, chemical industry) and sewages. Previous studies have reported that input of Cd and Cu to the oceans is dominated by riverine sources [19]. Therefore, these metals should be due to anthropogenic inputs from riverine sources containing both naturally occurring components in the water and sediment and a large proportion of anthropogenic wastes ended up into estuaries and oceans, which is in accord with the above-mentioned geochemical fractions analysis.…”

Section: Potential Factors Influencing Chemical Fractionsupporting

confidence: 69%

“…The pH value was significantly positively correlated with the percentages of residual fraction of the selected metals, indicating that decreased pH could promote the transformation ability of metal speciation into mobile phases. Yang et al [19] found that this correlation may associate with the effect of urbanization and industrialization on local scale. Especially in a highly developed Pearl River Delta region, the low pH in surface runoff enhances dissolution of non-residual fractions of metals in catchment sediments with the increasing of acid rain, subsequently increases the transportation of metals into receiving rivers and induces the effect of neutralization with alkalinity.…”

Section: Potential Factors Influencing Chemical Fractionmentioning

confidence: 99%

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Multivariate Analyses and Evaluation of Heavy Metals by Chemometric BCR Sequential Extraction Method in Surface Sediments from Lingdingyang Bay, South China

Cao¹,

Tian²,

Yang³

et al. 2015

Sustainability

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Sediments in estuary areas are recognized as the ultimate reservoirs for numerous contaminants, e.g., toxic metals. Multivariate analyses by chemometric evaluation were performed to classify metal ions (Cu, Zn, As, Cr, Pb, Ni and Cd) in superficial sediments from Lingdingyang Bay and to determine whether or not there were potential contamination risks based on the BCR sequential extraction scheme. The results revealed that Cd was mainly in acid-soluble form with an average of 75.99% of its total contents and thus of high potential availability, indicating significant anthropogenic sources, while Cr, As, Ni were enriched in the residual fraction which could be considered as the safest ingredients to the environment. According to the proportion of secondary to primary phases (KRSP), Cd had the highest bioavailable fraction and represented high or very high risk, followed by Pb and Cu with medium risks in most of samples. The combined evaluation of the Pollution Load Index (PLI) and the mean Effect Range Median Quotient (mERM-Q) highlighted that the greatest OPEN ACCESSSustainability 2015, 7 4939 potential environmental risk area was in the northwest of Lingdingyang Bay. Almost all of the sediments had a 21% probability of toxicity. Additionally, Principal Component Analysis (PCA) revealed that the survey region was significantly affected by two main sources of anthropogenic contributions: PC1 showed increased loadings of variables in acid-soluble and reducible fractions that were consistent with the input from industrial wastes (such as manufacturing, metallurgy, chemical industry) and domestic sewages; PC2 was characterized by increased loadings of variables in residual fraction that could be attributed to leaching and weathering of parent rocks. The results obtained demonstrated the need for appropriate remediation measures to alleviate soil pollution problem due to the more aggregation of potentially risky metals. Therefore, it is of crucial significance to implement the targeted strategies to tackle the contaminated sediments in Lingdingyang Bay.

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Section: Potential Factors Influencing Chemical Fractionsupporting

confidence: 69%

Section: Potential Factors Influencing Chemical Fractionmentioning

confidence: 99%

Multivariate Analyses and Evaluation of Heavy Metals by Chemometric BCR Sequential Extraction Method in Surface Sediments from Lingdingyang Bay, South China

Cao¹,

Tian²,

Yang³

et al. 2015

Sustainability

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“…Water 2017, 9, 524 7 of 13 The sites in Cluster III were mainly distributed in DML, DL, XL, and YQR, with TLI values of 53.73, 61.43, 50.11, and 46.62 respectively. The lakes DML, DL, and XL were lightly eutrophic, while YQR was considered mesotrophic.…”

Section: The Clustering Results Of Sampling Sitesmentioning

confidence: 99%

Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks

Sha

Wang

2017

Water

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Abstract:One of the most important water quality problems affecting lakes and reservoirs is eutrophication, which is caused by multiple physical and chemical factors. As a representative index of eutrophication, the concentration of chlorophyll-a has always been a key indicator monitored by environmental managers. The most influential factors on chlorophyll-a may be dependent on the different water quality patterns in lakes. In this study, data collected from 27 lakes in different provinces of China during 2009-2011 were analyzed. The self-organizing map (SOM) was first applied on the datasets and the lakes were classified into four clusters according to 24 water quality parameters. Comparison amongst the clusters revealed that Cluster I was the least polluted and at the lowest trophic level, while Cluster IV was the most polluted and at the highest trophic level. The genetic algorithm optimized back-propagation neural network (GA-BPNN) was applied to each lake cluster to select the most influential input variables for chlorophyll-a. The results of the four clusters showed that the performance of GA-BPNN was satisfied with nearly half of the input variables selected from the predictor pool. The selected factors varied for the lakes in different clusters, which indicates that the control for eutrophication should be separate for lakes in different provinces of one country.

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“…The potential ecological risk index (RI) is the most popular method used in order to evaluate the hazards of the trace elements for both human and environmental ecosystems, which indicate the concentration of trace elements in biological toxicology, environmental chemistry and ecology (Table 1; Guo et al, 2010;Jiang et al, 2014;Yang et al, 2014) On the one hand, the results of RI values indicate some sites in the Gunnamatta Bay have considerable to very high risk (exceeding and/or slightly less than 120 RI), This is because these sites are close to discharge points; d e f g h watercraft and boatyards as well as sediment types that are dominated by muddy particles. Moreover, the highest RI value at some sites (north-east) in Gunnamatta Bay exceeded 120 (RI about 288), which was caused from boatyards compared with other sites (Fig.7).…”

Section: Potential Ecological Risk (Ri)mentioning

confidence: 99%

Spatial distribution of sediment particles and trace element pollution within Gunnamatta Bay, Port Hacking, NSW, Australia

Alyazichi

Jones

McLean

2015

Regional Studies in Marine Science

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A combination of geochemical analysis and hydrodynamic measuring has been established in order to provide an explanation for the spatial distribution of both sediment particles and trace element pollution Gunnamatta Bay, NSW, Australia. Fifty nine samples of surface sediment were collected to determine the spatial concentrations of trace elements in the bay. Moreover, current track pathways and velocities have been measured in the bay. The distribution of trace elements such as chromium, nickel, copper, zinc, arsenic and lead had similar patterns in surface sediments. Trace element pollution is concentrated along the current trajectory in the inner part of the bay, which has deeper sites and higher percentages of mud particles. The highest concentrations of these metals were found to be in sample GU55 from the northeast of the bay, which is close to a surface discharge point and moored boats. concentrations of these metals were found to be in sample GU55 from the northeast of the bay, which is close to a surface discharge point and moored boats.

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Comprehensive risk assessment of heavy metals in lake sediment from public parks in Shanghai

Cited by 136 publications

References 34 publications

Multivariate Analyses and Evaluation of Heavy Metals by Chemometric BCR Sequential Extraction Method in Surface Sediments from Lingdingyang Bay, South China

Multivariate Analyses and Evaluation of Heavy Metals by Chemometric BCR Sequential Extraction Method in Surface Sediments from Lingdingyang Bay, South China

Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks

Spatial distribution of sediment particles and trace element pollution within Gunnamatta Bay, Port Hacking, NSW, Australia

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