Assessment of heavy metal contamination status in sediments and identification of pollution source in Daye Lake, Central China

Zhang, Jin; Li, Zhao Hua; Chen, Jin; Wang, Min; Tao, Ran; Liu, Dan

doi:10.1007/s12665-014-3047-6

Cited by 48 publications

(22 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fact that Cu and Pb have a similar distribution indicates that they might have a similar source in the sediment of this river basin, as shown in a previous study [28]. The spatial distribution characteristics of Cu could be explained by industry waste residue resulting from Cu-Sulfur separation processes, which will stay in the waste residue if not used efficiently.…”

Section: Spatial Variation Of the Cu And Pb Contaminationssupporting

confidence: 69%

“…The Tieshan tailing area and its adjacent smelters are a major source of Cu and Pb pollution, which may deteriorate the water quality of Tianzi Lake and bring great harm to the water ecological environment of Daye. In addition, the Daye Non-Ferrous Metal Company located in the upper reach of the rivers that flow into Tianzi Lake, could be the major source of Cu and Pb emission [28].…”

Section: Spatial Variation Of the Cu And Pb Contaminationsmentioning

confidence: 99%

“…In the laboratory, all of the sediment samples were air dried, ground, screened through a sieve of 2 mm mesh size, and then digested by HCL-HNO 3 -HF-HCLO 4 , using the Chinese national standard method (GB/T17140) [28]. The concentrations of copper (Cu) and lead (Pb) were analyzed by atomic absorption spectrophotometer (SPSIC4510, Shanghai, China).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Measuring Spatial Distribution Characteristics of Heavy Metal Contaminations in a Network-Constrained Environment: A Case Study in River Network of Daye, China

Wang

Nie

2017

Sustainability

View full text Add to dashboard Cite

Abstract:Measuring the spatial distribution of heavy metal contaminants is the basis of pollution evaluation and risk control. Considering the cost of soil sampling and analysis, spatial interpolation methods have been widely applied to estimate the heavy metal concentrations at unsampled locations. However, traditional spatial interpolation methods assume the sample sites can be located stochastically on a plane and the spatial association between sample locations is analyzed using Euclidean distances, which may lead to biased conclusions in some circumstances. This study aims to analyze the spatial distribution characteristics of copper and lead contamination in river sediments of Daye using network spatial analysis methods. The results demonstrate that network inverse distance weighted interpolation methods are more accurate than planar interpolation methods. Furthermore, the method named local indicators of network-constrained clusters based on local Moran' I statistic (ILINCS) is applied to explore the local spatial patterns of copper and lead pollution in river sediments, which is helpful for identifying the contaminated areas and assessing heavy metal pollution of Daye.

show abstract

Section: Spatial Variation Of the Cu And Pb Contaminationssupporting

confidence: 69%

Section: Spatial Variation Of the Cu And Pb Contaminationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measuring Spatial Distribution Characteristics of Heavy Metal Contaminations in a Network-Constrained Environment: A Case Study in River Network of Daye, China

Wang

Nie

2017

Sustainability

View full text Add to dashboard Cite

show abstract

“…1). Previous studies reveal that the mining wastes are rich in Pb, Cd, Cr, Zn and Cu (Wang and Wang 2007;Zhang et al 2014). Once entering the lake, the mining wastes likely carry a large influx of metals which become sediment.…”

Section: Evidence For Metal Contaminationmentioning

confidence: 99%

“…In addition, the use of ZnSO 4 as an inhibitor in the Cu-S separation processes has Fig. 4 Results of cluster analysis of multiple proxies using the method of Ward's minimum variance: a variables and b samples Bull Environ Contam Toxicol decreased Cu concentrations, but increased Zn concentrations in waste residues (Zhang et al 2014). Meanwhile, reduced atmospheric emissions from industrial activities probably caused declines in Cu, Pb and Sr, especially after the closure of the thermal power plant in 2000.…”

Section: Evidence For Metal Contaminationmentioning

confidence: 99%

Sedimentary Evidence of Environmental Degradation in Sanliqi Lake, Daye City (A Typical Mining City, Central China)

Zeng

Ning

et al. 2015

Bull Environ Contam Toxicol

View full text Add to dashboard Cite

To reconstruct the history of environmental degradation in Sanliqi Lake (Daye City, central China), multiple proxies were analyzed in a sedimentary core which was dated using (137)Cs and spheroidal carbonaceous particles (SCPs). The results show that Sanliqi Lake has experienced serious degradation during the past 60 years, resulting from a large influx of metals and nutrients. Expansion of agricultural and industrial activities between 1945 and 1993 enhanced nutrient and metal enrichment, indicated by increases in metals, SCPs, magnetic susceptibility, total phosphorus, total nitrogen and total organic carbon. Further enrichment of Zn, Cd, Ni and Cr after 1993 was linked to a recent intensification of mining activities. Decreases in Cu and Pb after 2006 probably resulted from recent environmental remediation. This study verified the coupling between lake sediment pollution and human activities in Daye City during the past 60 years. The reconstructed history of lake pollution can provide reference information for continued restoration of Sanliqi Lake and other similar heavily polluted lakes in the developing regions.

show abstract

A PETAR method for risk assessment of human health and environment on the regional scale

Guo

Liu

Lan

et al. 2022

Integr Envir Assess & Manag

View full text Add to dashboard Cite

Risk assessments are necessary to effectively reveal the state of the degradation of living environments on a regional scale. However, risk assessments are often limited by time, cost, and technology, which make conducting effective evaluations difficult. Thus, in this study, the procedure for ecological tiered assessment of risk (PETAR) method was used to analyze the human health and environmental risks in Daye, China. This method first used the United States Environmental Protection Agency's risk assessment approach to qualitatively determine the risk sources, pressures, receptors, and effect endpoints and constructed a conceptual model of threats to the human living environment. Each risk-prone subregion was then evaluated using the fuzzy logic method. Next, a quantitative assessment was conducted for the subregions with the most serious environmental degradation. Finally, quantitative analyses were performed to verify the original hypotheses. The results showed that the high-risk areas were distributed in the industrial regions of Daye, wherein mining and processing clusters and mining settlements are widespread and confirmed the locations of the particular subregions with the most serious human health and environmental risks. This study also validated the practicality of the PETAR method for human health risk assessments in mining areas with large-scale, multifactor, and multihazard paths.

show abstract

Assessment of heavy metal contamination status in sediments and identification of pollution source in Daye Lake, Central China

Cited by 48 publications

References 25 publications

Measuring Spatial Distribution Characteristics of Heavy Metal Contaminations in a Network-Constrained Environment: A Case Study in River Network of Daye, China

Measuring Spatial Distribution Characteristics of Heavy Metal Contaminations in a Network-Constrained Environment: A Case Study in River Network of Daye, China

Sedimentary Evidence of Environmental Degradation in Sanliqi Lake, Daye City (A Typical Mining City, Central China)

A PETAR method for risk assessment of human health and environment on the regional scale

Contact Info

Product

Resources

About