Assessment of pollutions and identification of sources of heavy metals in sediments from west coast of Shenzhen, China

Huang, Fenglan; Xu, Yang; Tan, Zhenhao; Wu, Zhibing; Xu, Hong; Shen, Liangliang; Xu, Xu; Han, Qingguo; Guo, Hai; Hu, Zhangli

doi:10.1007/s11356-017-0362-y

Cited by 45 publications

(16 citation statements)

References 48 publications

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“…Heavy metal contamination in coastal areas has attracted attention due to metal toxicity, bioaccumulation, and persistence [1,2]. Heavy metals include mercury (Hg), cadmium (Cd), arsenic (As), lead (Pb), and chromium (Cr), which are not required for normal human metabolic activity.…”

Section: Introductionmentioning

confidence: 99%

Concentrations, Distribution, and Ecological Risk Assessment of Heavy Metals in Daya Bay, China

Tang

Yan

et al. 2018

Water

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Surface sediment samples were collected from 19 sites throughout Daya Bay, China to study the concentrations, spatial distributions, potential ecological risk, and possible sources of heavy, including metals copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), nickel (Ni), lead (Pb), mercury (Hg), and chromium (Cr). The mean concentrations of the eight heavy metals were 24, 109, 6.5, 0.09, 35.3, 26.8, 0.07, and 109 µg g −1 , respectively. The concentrations of most heavy metals were within range of those recorded in previous years. The spatial distribution pattern of most heavy metals were similar, with lowest values recorded along the southeast coast and the open sea area; the highest values were recorded in the northern Daya Bay, especially the northwest. Cu, Zn, As, Cd, Pb, and Hg were classified as Class I, and Ni and Cr were classified as Class II according to the Sediment Quality Guidelines (SQGs) of China. The potential ecological risk (E i f) indices of Cu, Zn, As, Pb, Ni, and Cr specify that these metals pose low risk to the ecosystem of the Bay, whereas Cd and Hg pose a very high risk in some sites. The geoaccumulation indices (I geo) of Cu, Zn, As, Ni, and Cr specify weak or no pollution in Daya Bay, whereas those of Pb, Cd, and Hg in some sites indicate moderate or even high pollution. Spatial distribution, carbon/nitrogen analysis, Pearson correlation, and principal components analysis indicated that Cu, Zn, As, Pb, Ni, Cr, total organic carbon (TOC), and total nitrogen (TN) originated from the same sources. Ballast water or sewage from the cargo ships that park at the harbors or anchor in the Bay were the important sources for Cu, Zn, As, Pb, Ni, Cr, TOC, and TN. Other anthropogenic sources, such as agricultural runoff and aquaculture, might also be responsible, whereas Hg and Cd originated from other point sources.

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Section: Introductionmentioning

confidence: 99%

Concentrations, Distribution, and Ecological Risk Assessment of Heavy Metals in Daya Bay, China

Tang

Yan

et al. 2018

Water

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“…The prominent manifestations of this deterioration include the replacement of ecological areas by construction land and frequent land reclamation near the sea. Many studies have noted that Shenzhen has suffered from a series of increasingly serious environmental problems, including mangrove degradation, heavy metal pollution in the soil, and a marine environment destroyed by rapid urbanization [52][53][54]. In response, the government adopted a series of policies and measures from 2000 to 2015.…”

Section: Study Areamentioning

confidence: 99%

A Spatiotemporal Analysis of the Effects of Urbanization’s Socio-Economic Factors on Landscape Patterns Considering Operational Scales

Liu

Chen

et al. 2020

Sustainability

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Landscape patterns are significantly affected during the urbanization process. Identifying the spatiotemporal impacts of urbanization’s socio-economic factors on landscape patterns is very important and can provide scientific evidence to support urban ecological management and guide managers to establish appropriate sustainability policies. This article applies multiscale geographically weighted regression (MGWR) to reveal the relationships between landscape patterns and the socio-economic factors of urbanization in Shenzhen, China, from 2000 to 2015, in five-year intervals. MGWR is a powerful extension of geographically weighted regression (GWR) that can not only reveal spatial heterogeneity patterns but also measure the operational scale of covariates. The empirical results indicate that MGWR is superior to GWR. Furthermore, the changes in operational scale represented by the spatial bandwidth of MGWR in different years reflect temporal changes in the spatial relationships of given factors, which is significant information for urban studies. These multiscale relationships between landscape patterns and the socio-economic factors of urbanization, revealed via MGWR, are useful for strategic planning around urban dynamic development and land resource and ecological landscape management. The results can provide additional insight into landscape and urbanization studies from a multiscale perspective, which is important for local, regional, and global urban planning.

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“…ere are multiple methods used to investigate the potential sources of HMs in soil and water environment, which include correlation analysis, principal component analysis (PCA), positive matrix factorization, geographic information systems, enrichment factor, etc. [23,[25][26][27][28][29]. Although these methods can identify the common characteristics of selected HMs quickly via classification or dimension reduction, they cannot calculate the contribution rates of each heavy metal [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Contamination Features and Source Apportionment of Heavy Metals in the River Sediments around a Lead-Zinc Mine: A Case Study in Danzhai, Guizhou, China

Sun

Gao

Jing

et al. 2021

Journal of Chemistry

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The spatial patterns, ecological risks, and sources of heavy metals (HMs), including Pb, Zn, Mn, Cu, Cd, Hg, and As in river sediments, were identified around a lead-zinc mine of Danzhai, Guizhou, China. The concentrations of selected HMs and their coefficient variations indicated that the river sediments around this typical lead-zinc mine were obviously contaminated with HMs. Anthropogenic activities had further enhanced the accumulation of HMs. The higher contents of the most common selected HMs were mainly distributed in the area close to the lead-zinc mine. Based on the combined evaluations of the single factor pollution index, geo-accumulation index, and potential ecological risk index, it indicated that the ecological risks of Hg, Cd, Zn, and Pb were high or extremely high, and of Mn, Cu, and As were slight or none in the sediments around this lead-zinc mine. It was found that lead-zinc mining and smelting activities, coal mining activities, and agricultural activities (livestock and poultry breeding) are the primary sources of selected HMs, based on the results of correlation analysis together with principal component analysis (PCA) and positive matrix factorization (PMF) model. The pollution of HMs in the river sediments around a lead-zinc mine was predominantly caused by lead-zinc mining and smelting activities. Therefore, for environmental persistence, lead-zinc mining and smelting activities should be given careful consideration and under close surveillance.

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Assessment of pollutions and identification of sources of heavy metals in sediments from west coast of Shenzhen, China

Cited by 45 publications

References 48 publications

Concentrations, Distribution, and Ecological Risk Assessment of Heavy Metals in Daya Bay, China

Concentrations, Distribution, and Ecological Risk Assessment of Heavy Metals in Daya Bay, China

A Spatiotemporal Analysis of the Effects of Urbanization’s Socio-Economic Factors on Landscape Patterns Considering Operational Scales

Contamination Features and Source Apportionment of Heavy Metals in the River Sediments around a Lead-Zinc Mine: A Case Study in Danzhai, Guizhou, China

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