Spatial distribution and risk assessment of heavy metals in seawater and sediments in Jieshi Bay, Shanwei, China

The contamination of heavy metals due to human activities has attracted great attention and may lead to serious environmental problems. This research was performed on seawater, sediments, and organisms in Jieshi Bay, China. The level of Cu, Pb, Zn, Cd, Hg, and As, respectively, was measured in each environment to acquire a comprehensive understanding of their sources and distribution and to accomplish a risk assessment. The results showed that the concentration of heavy metals in autumn was higher than those in spring, and surface water has a higher heavy metal content than bottom water. The main sources of these heavy metals could be surface runoff and industrial wastewater discharge in the said bay. Hg was the main pollutant in the seawater. Hg, Cu, Zn, and Cd could easily accumulate in organisms than other heavy metal contents. In addition, even though the concentration of Pb did not exceed the Seawater Quality Standard, decision-makers should still be attentive to the Pb content in fish because of bioaccumulation from seafood product consumption.

1. Introduction

Heavy metal is one of the many global concerns, and the increasing quantity of heavy metals in seafood has threatened human health. Heavy metals (HMs) are characterized by high toxicity, difficult degradation, and easy biological enrichment. Some heavy metals are also important components of environmental endocrine disruptors, which are considered as one of the main characteristic pollutants harmful to water organisms and are typical marine priority control pollutants (Jiang et al., 2012; Wang et al., 2020). Once a heavy metal enters the ocean, it bioaccumulates and bio-amplifies in food webs, thereby harming ecosystems and human health (Jiang et al., 2012; Wang et al., 2018). Heavy metals may come from the natural weathering and erosion of rocks, many dispersed pollution sources, polluted rivers, domestic wastewater, industrial and agricultural sewage discharge, etc. (Birch et al., 1996; Matthiessen et al., 1999). The discharge of heavy metals into seawater environments may lead to bioaccumulation and bio-amplification in predators and, eventually, harm human health through the food chain (Bosch et al., 2016; Baltas et al., 2017; Liu et al., 2021; Liu et al., 2022).

Since the industrial revolution, the industrial construction and population boom in a bay’s watershed has caused a sharp rise in heavy metal pollution. This dramatic increase in heavy metals flowing into the bays has overburdened the waterbody (Zhen et al., 2016; Zhixin et al., 2016). Some bays in China were severely polluted by heavy metals—for example, Cd and Hg pollution in Bohai Bay (Wei et al., 2008; Hu et al., 2013a), Liaodong Bay (Hu et al., 2013b; Zhang A. et al., 2017), and Hangzhou Bay (Pang et al., 2015; Fang et al., 2016) and Cu and Zn pollution in Xiangshan Bay and Quanzhou Bay (Sun et al., 2014; Liu et al., 2019; Yan et al., 2020; Zhang M. et al., 2020). The Jieshi Fishing Port is one of the top 10fishing ports in Guangdong, with vast fishing grounds and rich resources, and the protection of its ecological environment is particularly important. Although in the recent years there has been abundant research on the geochemical characteristics of heavy metals in the sediments of the South China Sea, investigations on heavy metals in Jieshi Bay are quite limited; most scholars have focused on the distribution characteristics of heavy metals and have only considered the water quality or sediment without studying the ecological risks of heavy metals from the perspective of the ecosystem. This paper investigated the spatial distribution of heavy metals in Jieshi Bay in spring and autumn, identified the degree of pollution of heavy metals, analyzed the possible sources and correlations of various heavy metals, and evaluated the ecological risks and potential threats to human health from heavy metals in this area. We hope that these studies will contribute to a comprehensive understanding of the distribution, source, accumulation, transport, and ecological risks of heavy metals in the various media of marine ecosystems and help the government to scientifically supervise the protection of coastal and marine ecosystems and public health.

2. Materials and methods

2.1. Study area

Jieshi Bay, located in the south of Shanwei City, Guangdong Province (22°39′–22°52′ N, 115°31′–115°49′ E), is a bay that connects to the South China Sea. The bay covers an area of 890 km², with an average depth of 5–18 m and a maximum depth of 20.5 m. The mouth of the bay faces south, having a width of 27 km and a depth of 18 km. It gradually becomes shallow from the mouth to the inside of the bay. The climate in Jieshi Bay is a typical South Asian tropical oceanic monsoon climate, with an annual average temperature of 22.8°C and an annual average precipitation of 2,019.8 mm. The irregular diurnal tide has affected the hydrodynamic condition of Jieshi Bay, making it special to study. The rising tide in the bay flows to the north-northwest, while the falling tide flows to the southeast. The large waves generated by northeast gales and tropical cyclones can reach 3 m in height. There are many harbors along the coast of Jieshi Bay, which has an average coastline of length 23.3 km. There are many excellent breeding harbors along the coast, with vast breeding areas and rich marine fishery resources, and it has a long history of seawater aquaculture.

The region is rich in seawater, port, and fishery resources and has significant ecological and economic value. In recent years, industry, mariculture, and tourism in this region have developed rapidly, and human activities have had a certain impact on the marine environment. To comprehend the spatial distribution and evaluate the ecological risks of HMs in the bay’s semi-closed estuary and marine ecosystem, the presence of Cu, Pb, Cd, Hg, As, and Zn in seawater, sediments, and organisms was investigated. The results of the present research may contribute to scientifically regulating enterprises and managing government policies.

2.2. Sample collection and analysis

In April 2021 (spring) and August 2021 (summer), 20 seawater sampling sites, 10 sediment sites, and 12 ecological sites were selected in Jieshi Bay, China (Figure 1). All the samples were collected and preserved according to the Specification for Marine Monitoring (GB17378.3-2007) (Ma et al., 2008). Since the near-shore seawater depth ranged from 0 to 20.5 m, the bottom seawater sample was collected from both 0.5 m below the water surface and 2 m above the bottom for a water depth range of 10 to 20.5 m.

FIGURE 1

Figure 1 Location of sampling sites in Jieshi Bay, China.

Seawater sample (500 ml) was collected and stored at 4°C. The sediment sample was taken using a grab sampler (China Qingdao Juchuang Environmental Protection Group Co., Ltd., JC8000S) from 0 to 5 cm above the bottom and stored in a polyethylene bag at 0–4°C. A 1.5-kg biological sample was grabbed from the selected site and then washed with seawater and cryopreserved. A 5-L acrylic sampler was used for surface water sampling. The surface seawater was filtered immediately once received and stored at 4°C. All sampling methods were performed in accordance with GB17378.3-2007. The seawater sample was filtered with a 0.45-µm filter and then acidified to pH<2 with sulfuric acid. The sediment sample was stored in a dry and ventilated environment, ground with an agate mortar, and then sifted through 160-mesh sieves. The biological samples were dissected and freeze-dried for 24 h. A 200-mg muscle tissue sample was extracted and then acidified with HNO₃–H₂O₂ (4:2) solution and heated to 120–140°C. Hg and As were tested with atomic fluorescence spectroscopy (Beijing Titan Instruments Co., Ltd, AFS-930). Cu, Pb, Zn, and Cd were measured with atomic absorption spectroscopy 10 times and measured with UV–visible spectrophotometry (The PinAAcle series of atomic absorption spectrometers, 900Z). The samples were analyzed using the national standard (GBW07309). The quality control of the standards was performed by using the national standard (GBW07309), the error of the parallel samples was less than 5%, and the recoveries of the standards were between 95% and 110%.

The species of biological samples from which muscle tissues were extracted were fish, crab, and other invertebrates. The study involving animals was reviewed and approved by the Ethics Committee of South China Institute of Environment Sciences.

2.3. Analytical assessment method

2.3.1. Pollution assessment

Three methods to evaluate the degree of pollution in the study area were applied, and they were water quality index assessment, coefficient of variation method, and principal component analysis.

As an indicator of water quality, the Water Quality Index (WQI) is widely applied in water quality assessments (Tyagi et al., 2013; Kükrer and Mutlu, 2019), turning complex water quality data into information that is understandable and useable to the public. It provides a single number that expresses the overall water quality at a certain location and time based on several water quality parameters. The calculation formula is as follows:

$\begin{array}{ll}\text{WQI}=\frac{1}{n}\sum _{i=1}^n\frac{C_i}{C_{i0}}& (1)\end{array}$

where C_i is the measured concentration of HM, C_i0 is the first-class standard concentration of HM in the Seawater Quality Standard (GB3097-1997), and n is the number of water quality elements that were considered. In this assessment, Cu, Pb, Zn, Cd, Hg, and As were the HMs involved. WQI< 1 means that the pollution in seawater was slight and could be ignored, 1 ≤ WQI< 2 means that the seawater pollution is small, 2 ≤ WQI< 3 means that the seawater pollution is medium, 3 ≤ WQI< 5 means that the seawater pollution is strong, and WQI ≥ 5 means that the seawater is seriously polluted.

To reflect the volatility of the analytical chemistry data, coefficient variation (CV) (Brown, 1998; Bedeian and Mossholder, 2000) was used, which is a standardized measurement of the dispersion of a probability distribution or frequency distribution. The calculation formula is as follows:

$\begin{array}{ll}CV=\frac{\sigma }{\mu }& (2)\end{array}$

where σ is the standard deviation of the measured concentration of the elements, and µ is the mean of the measured concentration of the elements.

Principal component analysis (PCA) (Shin and Lam, 2001; Li et al., 2006) is one of the most popular multivariate statistical techniques, describing chemical compound properties in order to extract and express information from the data.

2.3.2. Risk assessment

In this paper, the potential ecological hazard index method established by Swedish scholar Hankanson was used to evaluate the pollution level of heavy metals in sediments (Hakanson, 1980). Compared with the single-factor pollution index method, the potential ecological hazard index method more comprehensively considers the synergism between different heavy metals and the difference in pollution level and toxicity (Rezaee Ebrahim Saraee et al., 2011; Liu et al., 2021) as calculated in Equation (3):

$\begin{array}{ll}{\text{E}}_f^i=T_f^i\times C_f^i=T_f^i\times \frac{C^i}{C_n^i}& (3)\end{array}$

where ${\text{E}}_f^i$ is the potential ecological hazard coefficient of the metal, $T_f^i$ is heavy metal toxicity response coefficient, $C_f^i$ is the pollution coefficient of the metal, Cⁱ is the measured concentration of the heavy metal, and $C_n^i$ is the evaluation standard for the metal (this paper adopts the national standard for class I sediments).

The comprehensive potential ecological hazard index of heavy metals in a single location is the sum of the potential ecological hazard indexes:

$\begin{array}{ll}{E}_{RI}=\sum _1^n{E}_r^i& (4)\end{array}$

The potential ecological hazard index of a single metal can be divided into five levels from low to high, and the comprehensive potential ecological hazard index can be divided into four levels. The pollution degree of single or multiple pollutants at a certain point can be evaluated through the evaluation standard of the potential ecological hazard index. The specific evaluation standard is shown in Table 1 (Hakanson, 1980; Yi et al., 2016).

TABLE 1

Table 1 Potential ecological harm index evaluation criteria.

Target hazard quotients (THQs) are a method used to evaluate the risk of pollutants to human health (Gu et al., 2018), calculated as Equation (5):

$\begin{array}{ll}\text{THQ}=\frac{\text{EF}\times \text{ED}\times \text{FIR}\times \text{c}\times {10}^{-3}}{\text{RFD}\times \text{WAB}\times \text{TA}}& (5)\end{array}$

where EF is contaminant exposure frequency (365 days/year), ED is the exposure years of the pollutants (the average lifespan of man is 70 years), and FIR is the food intake rate of the human body. The statistical data of the Food and Agriculture Organization of the United Nations (FAO) were used, in which the intake of fish is 36 g/day, and the intake of crustaceans is 5.42 g/day. C is heavy metal content in seafood (mg/kg), RFD is the daily reference dose of pollutants [Hg = 0.0005 mg/(kg·day), Cd = 0.001 mg/(kg·day), Pb = 0.004 mg/(kg·day), As = 0.0003 mg/(kg·day)],WAB is the average human body weight (60 kg), TA is the mean exposure time (365 days/year × ED) to non-carcinogenic sources (Storelli, 2008), and TTHQ is the sum of the hazard quotients of various heavy metals in seafood. If the hazard quotient is less than 1, it means that the seafood does not pose a health risk to human beings. On the contrary, when the hazard quotient is greater than 1, it indicates that there is a potential risk in consumption.

To evaluate the risk of heavy metal intake to human health, it is necessary not only to evaluate the hazard quotient of seafood but also to determine the dietary intake. The World Health Organization (WHO) formulated the provisional allowable weekly intake (PTWI) scale for heavy metals. The allowable weekly intakes of Hg, Cd, Pb, and As are 5, 7, 25, and 15 μg/(kg·body weight), respectively (Agusa et al., 2007). The formula for the weekly assessed intake of heavy metals (EWI) is as follows:

$\begin{array}{ll}\text{EWI }=\frac{\text{c}\times \text{FIR}\times 7}{\text{WAB}}& (6)\end{array}$

2.3.3 Partitioning and bioaccumulation

For the evaluation of sediments, biota-sediment and bio-accumulation factors are commonly applied to evaluate the ecological risk of sediments (Hao et al., 2013; Hao et al., 2019). The bio-accumulation factor (BAF) is used to describe the ability of marine organisms to accumulate trace metals from the surrounding medium and is calculated via Equation (7):

$\begin{array}{ll}\text{BAF}=\frac{C_{\text{organisms}}}{C_{\text{sediments}} \text{or} C_{\text{seawater}}}& (7)\end{array}$

where C_sediments is the measured concentration in sediments, C_seawater is the measured concentration in seawater, and C_organisms is the measured concentration in organisms.

Since a marine organism and its food are exposed to similar environments, the ratio of a substance’s lipid-normalized concentration in the tissue of an aquatic organism to its organic carbon-normalized concentration in surface sediment does not change substantially over time. Thus, a BAF< 100 or a biota-sediment accumulation factor< 1 indicated no significant accumulating effect between organisms and the surrounding environment.

2.3.4 Data analysis tools

Software and statistical tools were utilized to accomplish the spatial distribution, data analysis, and correlation exploration procedures. The Kriging interpolation was completed by ArcGIS (ESRI, v10.2) to draw the spatial distribution of the study area. SSD curves were generated by Buzzlioz 2.0 in the R software. The correlation analysis figures were drawn with Origin and Excel.

3. Results and discussion

3.1. Distribution and pollution assessment of HMs in seawater

The concentration variation ranges of Cu, Pb, Cd, Hg, As, and Zn are shown in Table 2. Zn was the element with the highest concentration, while the concentration of Cd was relatively low. From highest to lowest, the mean concentrations of all the heavy metal elements in the seawater were Zn (3.84µg/L) > As (2.29µg/L) > Cu (2.04µg/L) > Pb (0.93µg/L) > Hg (0.06µg/L)> Cd (0.02µg/L), and the same rule applies to spring and autumn and the bottom layer. The dissolution of trace metals in the surface water and bottom water followed the same time change law for Cu, Pb, Cd, and As; the mean was greater than in spring. The average concentrations of Hg and Zn in autumn were greater than those in spring; this is due to differences in water temperature in different seasons. Temperature is a factor that influences the existing speciation of Hg and Zn, which, in very warm autumn weather, are released more frequently. More Cu, Pb, Cd, and As were released in spring when the water temperature was low (Gerringa et al., 2001; Gundersen and Steinnes, 2003; Nystroem et al., 2005).

TABLE 2

Table 2 Heavy metal concentrations in seawater.

According to the horizontal distribution of heavy metal content in the surface and bottom seawater of each site (Figure 2), the concentration of Hg in seawater in spring was higher than in autumn, and the concentration of pollutants in the coastal area was higher than in the offshore area, mainly because the coastal area is more affected by the discharge of industrial and domestic sewage. The water quality of the bottom seawater of Jieshi Bay is significantly better than that of the upper seawater, mainly because the upper seawater is greatly affected by aquaculture, industrial pollutant discharge, and other activities. In Jieshi Bay, heavy metal concentrations in seawater have an obvious seasonal trend, with autumn surface waters containing higher concentrations of Cu, Pb, Cd, Hg, and As than those of spring. These results could be associated with seasonal river flow changes in Guangdong. The distribution of Guangdong’s rainy seasons is due to high-pressure recessions; studies show that the monsoon rainfall increases in summer but decreases in spring. The rainy season in Shanwei starts in late March, and the flood season spans from April to September (Zhang G. et al., 2017; Liu et al., 2018). There is more rain in spring, and the concentration of heavy metals in seawater decreases due to rainfall dilution.

FIGURE 2

Figure 2 (A) The concentrations of HMs in seawater for Cu, Pb, Cd. (B) The concentrations of HMs in seawater for Hg, As, Zn.

To compare the variability between disparate groups and characteristics, the coefficient of variation was calculated (Table 2) to learn how much the data varied relatively. The result of the coefficient variation showed a relatively high variation in HMs, except As, indicating a diverse distribution of HMs in the study area, which might be from other different sources.

The comprehensive pollution index of HMs in the seawater of Jieshi Bay is shown in Figure 3. It shows that, regarding the bottom and surface seawaters of Jieshi Bay, the comprehensive pollution index of the heavy metals Cu, Pb, Hg, Cd, As, and Zn was less than an average of 1; thus, the pollution in the bottom seawater of Jieshi Bay is slight and could be ignored. The bottom seawater is clean. The mean of the comprehensive pollution index measured at two meteorological stations was greater than 1 only for the surface seawater in autumn, while the pollution comprehensive index for all other timepoints was less than 1. It can be seen that, in autumn, the respective stations on the west side of the bay and the east side of the bay’s mouth were slightly affected by pollution. According to the annual wind speed variation statistics from Lufeng Meteorological Station, warm and cold air masses are active and cyclones are frequent. According to a study on the hydrodynamic characteristics of the sea area, the waves of Jieshi Bay are strong: their current velocity is large, their reflow was dominant in the past, their flow direction is perpendicular to the shoreline, strong waves are mainly in the E direction, and their occurrence frequency is about 27.3%. It can be speculated that the high comprehensive index of seawater pollution recorded in autumn at the stations on the west side of the Bay and the northeast side of the bay’s mouth was mainly due to a seasonal phenomenon caused by the diffusion of high concentrations of heavy metals in the near-shore surface caused by high wind speeds and tidal current. The west and east sides of the bay’s mouth appear to have larger pollution indices. In the new Shanwei port area and the west side of the Wukan waterway, particularly the mouth on the east side of the channel, the combined Pb and Hg ion concentration was higher, and it may be that the meteorological station was affected by the port wastewater, such as emissions from ships (Zhan et al., 2010).

FIGURE 3

Figure 3 WQI of HMs in seawater.

Based on the water quality monitoring results taken over the years, it can be seen that Pb in Jieshi Bay has experienced an obvious upward trend, while other elements have experienced a steady downward trend (Gao and Chen, 2012; Pan et al., 2014; Gu, 2018; Zhao et al., 2018). Compared with other studies on heavy metals in seawater, the content of toxic metals, such as Cu, Pb, Cd, As, and Zn, in the seawater of Jieshi Bay was low, but Hg was slightly higher than in other research areas (Gu et al., 2012; Xiao et al., 2013; Li et al., 2017; Tang et al., 2018; Zhao et al., 2018; Lao et al., 2019; Nour, 2019; Zhang C. et al., 2020). On the whole, the seawater of Jieshi Bay is not polluted by heavy metals at present, but it should be noted that, with the expansion of the port, potential heavy metal pollution may be brought on by improvements in ship throughput and the rapid development of industry.

3.2 Distribution and risk assessment of HMs in sediment

The content of heavy metals in sediments can reflect the degree of heavy metal pollution in an area, which can provide a relevant basis to further understand the migration and transformation of various heavy metals in sediments as well as their possible sources. It can be seen from Table 3 that the sediment quality in the study area was good. From highest to lowest, the mean concentrations of all the heavy metal elements in the sediment were Zn (96.10mg/kg) > Pb (18.55mg/kg) > Cu (12.45mg/k.mg/kg) > As (6.13mg/k.mg/kg) > Hg (0.06mg/k.mg/kg) > Cd (0.02mg/kg). The coefficient of variation for Hg and As in spring, as well as for Cd in autumn, was greater than 50%, which is highly variable (CV > 36%). It can be inferred that Hg, As, and Cd in the sediments of Jieshi Bay may have been influenced by man-made sources, resulting in a high coefficient of variation (Wilding, 1985; Ma and Zhao, 2010; Liu et al., 2016). The anthropogenic source of Cd in sediments is mainly from industrial and agricultural activities, the anthropogenic source of Hg is mainly atmospheric deposition from human activities (Li et al., 2006), and the anthropogenic source of As in sediments is mainly from industrial activities (Mohammadi et al., 2015; Huang et al., 2018). This conclusion is also consistent with the large spatial dispersion of heavy metals in seawater found in this study.

TABLE 3

Table 3 Heavy metal concentrations in sediments.

It can be seen from the survey data on sediment heavy metals taken in this area for the last 15 years that heavy metal pollution in Jieshi Bay generally presents a downward trend (Qiu et al., 2011; Ye et al., 2018; Zhang L. et al., 2020), with Pb and As significantly declining, Cu and Zn remaining basically stable, and Hg and Cd slightly increasing. Combined with the coefficient of variation, it can be inferred that human activities have a great influence on the presence of Pb, As, Hg, and Cd in the sediments of this sea area.

The potential ecological hazard index method was used to evaluate the content of heavy metals in the sediments in the study area. The results in Table 4 showed that the potential ecological hazard index of six heavy metals, in descending order, was Hg > As > Pb > Cu > Cd > Zn, all of which are less than 40 and belong to the low hazard degree, among which, Hg, As, and Pb have a relatively high hazard degree. Hg and As were affected by man-made sources in the coefficient of variation. Therefore, the potential ecological hazard index of sediments in the study area was mainly affected by coastal industrial and agricultural activities and anthropogenic emissions. The comprehensive potential ecological hazard index was higher in autumn, but the overall hazard degree was low.

TABLE 4

Table 4 Potential hazard index of heavy metals in sediments.

Compared with the coastal waters of the surrounding South China Sea, the overall quality of Jieshi Bay sea sediment is good (Liu et al., 2012; Wang et al., 2013; Zhang et al., 2015; Zhang et al., 2016; Wang et al., 2022; Zhang et al., 2022). We found that most of the heavy metal contents in the surface sediment was at the medium level, though the Hg and Zn contents were comparatively high in some sea areas. Although there was no obvious heavy metal pollution, we should still pay attention to human activities, which can, in part, lead to potential heavy metal pollution.

3.3 Concentrations and human health risk of HMs in organisms

The concentrations of Cu, Zn, As, Pb, Cd, and Hg in fish and crustaceans in summer and autumn are shown in Table 5. No shellfish were collected at the survey station; only fish and crustaceans were collected. The average contents of various metals in crustaceans captured in spring and autumn were Cu—13.8 and 12.6 mg/kg, Pb—0.20 and 0.59 mg/kg, Cd—0.140 and 0.212 mg/kg, Hg—0.017 and 0.009 mg/kg, As—1.50 and 1.20 mg/kg, and Zn—8.50 and 18.50 mg/kg, respectively. The average heavy metal contents in spring and autumn were Cu—1.3 and 0.8mg/kg, Pb—1.12 and 0.05mg/kg, Cd—0.044 and 0.005 mg/kg, Hg—0.022 and 0.007 mg/kg, As—0.40 and 0.40 mg/kg, and Zn—3.55 and 12.6 mg/kg, respectively. The differences between the same elements in different species may be related to the living habits, physiological characteristics, and specific physiological methods of metal accumulation in various marine organisms (Wenxiong and Jinfen, 2004). Crustaceans generally live in an environment close to the bottom of the sea, with polychaetes and other zooplanktons in the sediment serving as their main feeding source, causing them to absorb more heavy metals. The HM content in fish was low, which is closely related to their water layer habitat and feeding habits (Yi et al., 2014).

TABLE 5

Table 5 Biological quality monitoring results for Jieshi Bay.

In spring, the average content of each pollutant in organisms was (in specific order) Cu > Zn > As > Pb > Cd > Hg, and in autumn, the average content of each pollutant in organisms was Zn > Cu > As > Pb > Cd > Hg. Cu and Zn are essential trace elements in living organisms, so their content in living organisms was higher than other metals. Pb and Hg are inimical to living organisms, so their contents were relatively low. Only in spring did the presence of Pb in fish exceed the relevant standards. As Pb ions in organisms may cause protein inactivation (Pourrut et al., 2008), higher Pb content in environmental media may affect the biological production of testosterone and estrogen; therefore, it can cause reproductive toxicity in human beings (Yang et al., 2019; Liu et al., 2022). Given the above-mentioned phenomenon of excessive Pb in seawater in autumn, in the future, the control of Pb emissions should be prioritized with respect to water pollution control in this region.

As can be seen from Table 6, the weekly assessed intake (EWI) of heavy metals in seafood in this region was lower than the PTWI for human beings as established by WHO. The harm quotient values of Pb, Cd, Hg, and As in crustaceans were all less than 1, indicating that there was no potential risk from Pb, Cd, Hg, and As to human health when eating this kind of seafood. The harm quotient values of the heavy metals Cd and Hg in fish were less than 1, indicating that there is no potential risk from Cd and Hg to human health when eating this kind of seafood. However, the hazard quotients of Pb and As in fish were greater than 1, indicating that there is a certain health risk in eating fish. The anthropogenic sources of Pb are mainly lead mining, metallurgy, and waste gas. Lead is a toxic heavy metal that can cause great harm to the human body. Lead and its compounds can cause harm to the nervous system, hematopoietic system, digestive system, kidney system, cardiovascular system, and endocrine system after entering the body. If the content is too high, lead poisoning will occur. As exists widely in the natural environment, and its anthropogenic sources are mainly smelting activities and chemical synthesis agents. As is divided into two forms: an organic form without toxicity and an inorganic form with high toxicity. The total amount of As was measured in laboratory tests and analyses, and the organic form of As was about 90%. As poisoning can cause abdominal pain, vomiting, and muscle weakness and can persist in the body for a long time.

TABLE 6

Table 6 Weekly assessed intake and hazard quotients of heavy metals in marine organisms.

3.4 Interrelationship and source analysis of HMs

In this study, the coefficient of variation was selected to quantitatively reflect the difference in heavy metal fluctuation degrees on a spatial scale among the monitoring stations. Excel statistical software was used to conduct a statistical analysis of the survey data, and the results are shown in Table 2. Except for As, the coefficient of variation for the elements was relatively large. This indicates that the spatial dispersion of heavy metal content in the water between the survey stations was large; thus, the heavy metal pollutants in the study sea area may have come from different pollution sources.

Since the HMs were hardly absorbed in the seawater, they were not stable. They were easily affected and transferred by hydrodynamic and environmental conditions. Thus, the sources of HMs in seawater were difficult to distinguish. The coefficient of variation for the HMs presented strong correlations, which means that they may originate from the same source; thus, the homology of the source must be determined by correlating the HMs (Li et al., 2009). Pearson’s correlation coefficient was used to measure the statistical association between the continuous variables of interest. The results of Pearson’s correlation coefficient matrix for the HMs in Jieshi Bay (Figure 4) showed a moderate degree of correlation between Cd and As in the surface water. For the bottom water, the HMs had weak correlations. In conclusion, Cd and As in surface water may share common sources, and the other HMs might not have a strong relationship.

FIGURE 4

Figure 4 The correlation analysis of HMs in the seawater of Jieshi bay, respectively (P<0.05, P<0.1).

PCA is a mathematical tool that represents variations present in a dataset using a small number of factors (Granato et al., 2018). For visual analysis, a two- or three-dimensional projection of samples is usually constructed with axes (principal components, PCs) as factors. PCA was applied to explore similarities and hidden patterns among the HMs to find potential sources for the principal components (Table 7). PC1, accounting for 30.36% of the total data variance, had high positive loading for Cd and As. PC2, accounting for 19.97% of the total data variance, had negative loading for Pb and Zn. PC3, accounting for 17.22% of the total data variance, had negative loading for Cu and Hg. The accumulated percentage of variance in the three PCs was 67.54%, which means that these three PCs could represent the principal component of the samples. The characteristic performance factor variable of principal component 1 had a high load of Cd and As elements, indicating that Cd and As may have the same source (Figure 5). The results of the principal component analysis are similar to the Pearson correlation analysis. The waters around Jieshi Bay are surrounded by several Shanwei towns (Lufeng City, Jieshi Town, Taihu Town, Jinxiang Town, and Jiesheng Town). The industrial park is distributed around the continental area of Jieshi Bay, and it is reasonable to assume that a large amount of Cd and As in this area comes from surface runoff and industrial wastewater discharge. The characteristic performance factor variable of principal component 2 had a high load of Pb and Zn elements, but the Zn load in principal component 2 was negative, so it is difficult to judge whether the source is the same. The results of the principal component analysis are similar to the Pearson correlation analysis. The characteristic performance factor variables of principal component 3 had a high load of Cu and Hg elements, but the Hg load on principal component 3 was negative, so it is difficult to judge whether the source is the same. The results of the principal component analysis are similar to the Pearson correlation analysis. Based on the analysis of the correlation coefficient relationship matrix of heavy metals in seawater, except for Cd and As, elements such as Cu, Pb, Zn, and Hg had little correlation with each other, and the probability of a common source is low, which is consistent with the conclusion of the Pearson correlation analysis.

TABLE 7

Table 7 Principal component analysis results for heavy metals in the seawater of Jieshi Bay.

FIGURE 5

Figure 5 PCA analysis of HMs in seawater.

The ability of marine organisms to accumulate heavy metals was quantified with BAF: BAF_sediment > 1 indicates that the organism can accumulate heavy metals. BAF_seawater > 1 indicates that the organism has a strong enrichment capacity for heavy metals (enrichment capacity is proportional to the value), and BAF_seawater > 100 indicates that the organism has a strong enrichment capacity for heavy metals. The data in Table 8 show that, in addition to the Pb data from different stations, the BAF_seawater of Cu, Cd, Hg, As, and Zn was greater than 100, indicating that the organisms in this sea area have an enrichment capacity for these six heavy metals, especially Cu, Cd, and Zn, which had the highest BAF_seawater values. However, as essential elements for biological growth, Cu and Zn levels are relatively high in fish and crustaceans, indicating that the organisms in this sea area have a stronger enrichment capacity for Cd. There were excessive Zn and Hg levels in the seawater of the study area, and the organisms also showed a strong enrichment ability, especially for Zn. In the spring sample data, it was found that the BAF_sediment value of Cd for organisms was greater than 1, and the BAF_sediment values of Cu and Hg in some sites were within the critical value. In the autumn sample data, it was found that the BAF_sediment value of Cd for organisms in most sites was greater than 1. The BAF_sediment value of Cu at some sites was within the critical value, and the BAF_sediment value of other heavy metals was low.

TABLE 8

Table 8 Biological enrichment index of heavy metals in each phase of Jieshi Bay.

BAF_seawater and BAF_sediment were used to evaluate the accumulation of heavy metals in the medium. Cd was the most accumulated element. According to relevant studies, the metabolic period of Cd in organisms is longer than that of other metals, which is also the reason why the Cd content in organisms exceeds that of other metals (Lin et al., 2017). Moreover, Cd and Ca have similar geochemical properties (Zhang, 1991), which also makes it easier for Cd to accumulate in organisms. The chemical form of heavy metals is also an important factor affecting the content of heavy metals in marine organisms. Cd in the sediments of East Guangdong Sea, where Jieshi Bay is located, mainly exists in an acid-soluble state, which is easier to absorb by organisms. On the whole, organisms in this sea area have a stronger ability to enrich heavy metals in seawater, and the quality of seawater is more important for people who mainly eat seafood.

4. Conclusion

In summary, this study analyzed the distribution characteristics of six heavy metals in the seawater, sediments, and organisms in Jieshi Bay. Generally, the level of heavy metal content was relatively low, and the main sources for these heavy metals could be surface runoff and industrial wastewater discharge in the bay. For seawater, the concentration of the heavy metals in autumn was higher than those in spring, and the surface water has higher heavy metal content than the bottom water. Hg is the main limiting factor according to the statistics. For sediments, the potential ecological risks of Hg, As, and Pb in sediments were relatively high compared with the rest of the contents. For the organisms, the content of heavy metals in fish was much lower than that in crustaceans, and lead in fish had exceeded the standard in spring. The seawater contributes more to bioaccumulation in fish and crustaceans than sediments, indicating that it is important to protect the seawater quality because of the bioaccumulation from seafood product consumption. Even if the heavy metals have not threatened the environment in the bay, it remains necessary to enhance pollutant management while especially developing the discharge of wastewater.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Ethics statement

The study involving animals was reviewed and approved by the Ethics Committee of South China Institute of Environment Sciences.

Author contributions

WZ: main contributor to conception, design of the work, and result analysis. QC: drafting of the article, revising it critically for important intellectual content, and ensuring the discussions related to the work. MH: data analysis and interpretation. YL: data collection and contributes to conception design. DW: data collection. DZ: using PCA analysis to determine the correlationship of heavy metals. All authors contributed to the article and approved the submitted version.

Funding

This study is supported by the Major Science and Technology Program for Water Pollution Control and Treatment, Ministry of Ecology and Environment(2018ZX07601-002-05).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Agusa T., Kunito T., Sudaryanto A., Monirith I., Kan-Atireklap S., Iwata H., et al. (2007). Exposure assessment for trace elements from consumption of marine fish in southeast Asia. Environ. pollut. 145, 766–777. doi: 10.1016/j.envpol.2006.04.034

PubMed Abstract | CrossRef Full Text | Google Scholar

Baltas H., Sirin M., Dalgic G., Bayrak E. Y., Akdeniz A. (2017). Assessment of metal concentrations (Cu, zn, and Pb) in seawater, sediment and biota samples in the coastal area of Eastern black Sea, Turkey. Mar. pollut. Bull. 122, 475–482. doi: 10.1016/j.marpolbul.2017.06.059

PubMed Abstract | CrossRef Full Text | Google Scholar

Bedeian A. G., Mossholder K. W. (2000). On the use of the coefficient of variation as a measure of diversity. Organizational Res. Methods 3, 285–297. doi: 10.1177/109442810033005

CrossRef Full Text | Google Scholar

Birch G., Evenden D., Teutsch M. (1996). Dominance of point source in heavy metal distributions in sediments of a major Sydney estuary (Australia). Environ. Geology 28, 169–174. doi: 10.1007/s002540050090

CrossRef Full Text | Google Scholar

Bosch A. C., O'neill B., Sigge G. O., Kerwath S. E., Hoffman L. C. (2016). Heavy metals in marine fish meat and consumer health: A review. J. Sci. Food Agric. 96, 32–48. doi: 10.1002/jsfa.7360

PubMed Abstract | CrossRef Full Text | Google Scholar

Brown C. E. (1998). Coefficient of Variation. In: Applied Multivariate Statistics in Geohydrology and Related Sciences. (Berlin, Heidelberg: Springer). doi: 10.1007/978-3-642-80328-4_13

CrossRef Full Text | Google Scholar

Fang H., Huang L., Wang J., He G., Reible D. (2016). Environmental assessment of heavy metal transport and transformation in the hangzhou bay, China. J. hazardous materials 302, 447–457. doi: 10.1016/j.jhazmat.2015.09.060

CrossRef Full Text | Google Scholar

Gao X., Chen C.-T. A. (2012). Heavy metal pollution status in surface sediments of the coastal bohai bay. Water Res. 46, 1901–1911. doi: 10.1016/j.watres.2012.01.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Gerringa L. J., De Baar H. J., Nolting R., Paucot H. (2001). The influence of salinity on the solubility of zn and cd sulphides in the scheldt estuary. J. Sea Res. 46, 201–211. doi: 10.1016/S1385-1101(01)00081-8

CrossRef Full Text | Google Scholar

Granato D., Santos J. S., Escher G. B., Ferreira B. L., Maggio R. M. (2018). Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective. Trends Food Sci. Technol. 72, 83–90. doi: 10.1016/j.tifs.2017.12.006

CrossRef Full Text | Google Scholar

Gu Y.-G. (2018). Heavy metal fractionation and ecological risk implications in the intertidal surface sediments of zhelin bay, south China. Mar. pollut. Bull. 129, 905–912. doi: 10.1016/j.marpolbul.2017.10.047

PubMed Abstract | CrossRef Full Text | Google Scholar

Gundersen P., Steinnes E. (2003). Influence of pH and TOC concentration on Cu, zn, cd, and Al speciation in rivers. Water Res. 37, 307–318. doi: 10.1016/S0043-1354(02)00284-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Gu Y.-G., Ning J.-J., Ke C.-L., Huang H.-H. (2018). Bioaccessibility and human health implications of heavy metals in different trophic level marine organisms: a case study of the south China Sea. Ecotoxicology Environ. Saf. 163, 551–557. doi: 10.1016/j.ecoenv.2018.07.114

CrossRef Full Text | Google Scholar

Gu L., Song B., Tong Z. Q., Ma J. H. (2012). Spatial distribution and potential ecological risk assessment of heavy metals in roadside soils on different operated times along the lianyungang-horgas highway. Advanced Materials Res., 356–360, 630–635. Trans Tech Publ. doi: 10.4028/www.scientific.net/AMR.356-360.630

CrossRef Full Text | Google Scholar

Hakanson L. (1980). An ecological risk index for aquatic pollution control. A sedimentological approach. Water Res. 14, 975–1001. doi: 10.1016/0043-1354(80)90143-8

CrossRef Full Text | Google Scholar

Hao Z., Chen L., Wang C., Zou X., Zheng F., Feng W., et al. (2019). Heavy metal distribution and bioaccumulation ability in marine organisms from coastal regions of hainan and zhoushan, China. Chemosphere 226, 340–350. doi: 10.1016/j.chemosphere.2019.03.132

PubMed Abstract | CrossRef Full Text | Google Scholar

Hao Y., Chen L., Zhang X., Zhang D., Zhang X., Yu Y., et al. (2013). Trace elements in fish from taihu lake, China: Levels, associated risks, and trophic transfer. Ecotoxicology Environ. Saf. 90, 89–97. doi: 10.1016/j.ecoenv.2012.12.012

CrossRef Full Text | Google Scholar

Huang Y., Deng M., Wu S., Japenga J., Li T., Yang X., et al. (2018). A modified receptor model for source apportionment of heavy metal pollution in soil. J. hazardous materials 354, 161–169. doi: 10.1016/j.jhazmat.2018.05.006

CrossRef Full Text | Google Scholar

Hu B., Li G., Li J., Bi J., Zhao J., Bu R. (2013a). Spatial distribution and ecotoxicological risk assessment of heavy metals in surface sediments of the southern bohai bay, China. Environ. Sci. pollut. Res. 20, 4099–4110. doi: 10.1007/s11356-012-1332-z

CrossRef Full Text | Google Scholar

Hu B., Li J., Zhao J., Yang J., Bai F., Dou Y. (2013b). Heavy metal in surface sediments of the liaodong bay, bohai Sea: Distribution, contamination, and sources. Environ. Monit. Assess. 185, 5071–5083. doi: 10.1007/s10661-012-2926-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Jiang X., Wang W., Wang S., Zhang B., Hu J. (2012). Initial identification of heavy metals contamination in taihu lake, a eutrophic lake in China. J. Environ. Sci. 24, 1539–1548. doi: 10.1016/S1001-0742(11)60986-8

CrossRef Full Text | Google Scholar

Kükrer S., Mutlu E. (2019). Assessment of surface water quality using water quality index and multivariate statistical analyses in saraydüzü dam lake, Turkey. Environ. Monit. Assess. 191, 1–16. doi: 10.1007/s10661-019-7197-6

CrossRef Full Text | Google Scholar

Lao Q., Su Q., Liu G., Shen Y., Chen F., Lei X., et al. (2019). Spatial distribution of and historical changes in heavy metals in the surface seawater and sediments of the beibu gulf, China. Mar. pollut. Bull. 146, 427–434. doi: 10.1016/j.marpolbul.2019.06.080

PubMed Abstract | CrossRef Full Text | Google Scholar

Li F., Fan Z., Xiao P., Oh K., Ma X., Hou W. (2009). Contamination, chemical speciation and vertical distribution of heavy metals in soils of an old and large industrial zone in northeast China. Environ. geology 57, 1815–1823. doi: 10.1007/s00254-008-1469-8

CrossRef Full Text | Google Scholar

Li H., Kang X., Li X., Li Q., Song J., Jiao N., et al. (2017). Heavy metals in surface sediments along the weihai coast, China: Distribution, sources and contamination assessment. Mar. pollut. Bull. 115, 551–558. doi: 10.1016/j.marpolbul.2016.12.039

PubMed Abstract | CrossRef Full Text | Google Scholar

Lin L., Wei H., Huang H. (2017). Contamination status and bioaccumulation of the heavy metals in the surface sediments and benthos in daya bay. Ecol. Sci. 36, 173–181. doi: 10.14108/j.cnki.1008-8873.2017.06.024

CrossRef Full Text | Google Scholar

Liu R., Jiang W., Li F., Pan Y., Wang C., Tian H. (2021). Occurrence, partition, and risk of seven heavy metals in sediments, seawater, and organisms from the eastern sea area of Shandong peninsula, yellow Sea, China. J. Environ. Manage. 279, 111771. doi: 10.1016/j.jenvman.2020.111771

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu Y., Kuang W., Xu J., Chen J., Sun X., Lin C., et al. (2022). Distribution, source and risk assessment of heavy metals in the seawater, sediments, and organisms of the daya bay, China. Mar. pollut. Bull. 174, 113297. doi: 10.1016/j.marpolbul.2021.113297

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu Y., Ma Z., Lv J., Bi J. (2016). Identifying sources and hazardous risks of heavy metals in topsoils of rapidly urbanizing East China. J. Geographical Sci. 26, 735–749. doi: 10.1007/s11442-016-1296-x

CrossRef Full Text | Google Scholar

Liu Q., Wang F., Meng F., Jiang L., Li G., Zhou R. (2018). Assessment of metal contamination in estuarine surface sediments from dongying city, China: use of a modified ecological risk index. Mar. pollut. Bull. 126, 293–303. doi: 10.1016/j.marpolbul.2017.11.017

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu Q., Xu X., Zeng J., Shi X., Liao Y., Du P., et al. (2019). Heavy metal concentrations in commercial marine organisms from xiangshan bay, China, and the potential health risks. Mar. pollut. Bull. 141, 215–226. doi: 10.1016/j.marpolbul.2019.02.058

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu J., Yan W., Chen Z., Lu J. (2012). Sediment sources and their contribution along northern coast of the south China Sea: Evidence from clay minerals of surface sediments. Continental Shelf Res. 47, 156–164. doi: 10.1016/j.csr.2012.07.013

CrossRef Full Text | Google Scholar

Li Y., Yu Z.-M., Song X.-X. (2006). Application of principal component analysis (PCA) for the estimation of source of heavy metal contamination in marine sediments. Huan Jing ke Xue= Huanjing Kexue 27, 137–141. doi: 10.13227/j.hjkx.2006.01.026

PubMed Abstract | CrossRef Full Text | Google Scholar

Matthiessen P., Reed J., Johnson M. (1999). Sources and potential effects of copper and zinc concentrations in the estuarine waters of Essex and Suffolk, united kingdom. Mar. pollut. Bull. 38, 908–920. doi: 10.1016/S0025-326X(99)00090-9

CrossRef Full Text | Google Scholar

Ma Y., Xu H., Yu T., He G., Zhao Y., Fu Y. (2008). The specification for marine monitoring-part 5: Sediment analysis (Beijing: China Standardization Press).

Google Scholar

Ma Q., Zhao G. (2010). Effects of different land use types on soil nutrients in intensive agricultural region. J. Natural Resour. 25, 1834–1844.

Google Scholar

Mohammadi R. M., Sobhan A. S., Karimi H., Sorooshnia R. (2015). Natural and anthropogenic source of heavy metals pollution in the soil samples of an industrial complex; a case study. Iran. J. Toxicol. 9, 29.

Google Scholar

Nour H. E. S. (2019). Distribution, ecological risk, and source analysis of heavy metals in recent beach sediments of sharm El-sheikh, Egypt. Environ. Monit. Assess. 191, 1–12. doi: 10.1007/s10661-019-7728-1

CrossRef Full Text | Google Scholar

Nystroem G. M., Ottosen L. M., Villumsen A. (2005). Electrodialytic removal of Cu, zn, Pb, and cd from harbor sediment: Influence of changing experimental conditions. Environ. Sci. Technol. 39, 2906–2911. doi: 10.1021/es048930w

PubMed Abstract | CrossRef Full Text | Google Scholar

Pang H.-J., Lou Z.-H., Jin A.-M., Yan K.-K., Jiang Y., Yang X.-H., et al. (2015). Contamination, distribution, and sources of heavy metals in the sediments of andong tidal flat, hangzhou bay, China. Continental Shelf Res. 110, 72–84. doi: 10.1016/j.csr.2015.10.002

CrossRef Full Text | Google Scholar

Pan J., Pan J.-F., Wang M. (2014). Trace elements distribution and ecological risk assessment of seawater and sediments from dingzi bay, Shandong peninsula, north China. Mar. pollut. Bull. 89, 427–434. doi: 10.1016/j.marpolbul.2014.10.022

PubMed Abstract | CrossRef Full Text | Google Scholar

Pourrut B., Perchet G., Silvestre J., Cecchi M., Guiresse M., Pinelli E. (2008). Potential role of NADPH-oxidase in early steps of lead-induced oxidative burst in vicia faba roots. J. Plant Physiol. 165, 571–579. doi: 10.1016/j.jplph.2007.07.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Qiu Y.-W., Lin D., Liu J.-Q., Zeng E. Y. (2011). Bioaccumulation of trace metals in farmed fish from south China and potential risk assessment. Ecotoxicology Environ. Saf. 74, 284–293. doi: 10.1016/j.ecoenv.2010.10.008

CrossRef Full Text | Google Scholar

Rezaee Ebrahim Saraee K., Abdi M. R., Naghavi K., Saion E., Shafaei M. A., Soltani N. (2011). Distribution of heavy metals in surface sediments from the south China Sea ecosystem, Malaysia. Environ. Monit. Assess. 183, 545–554. doi: 10.1007/s10661-011-1939-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Shin P., Lam W. (2001). Development of a marine sediment pollution index. Environ. pollut. 113, 281–291. doi: 10.1016/S0269-7491(00)00192-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Storelli M. (2008). Potential human health risks from metals (Hg, cd, and Pb) and polychlorinated biphenyls (PCBs) via seafood consumption: estimation of target hazard quotients (THQs) and toxic equivalents (TEQs). Food Chem. Toxicol. 46, 2782–2788. doi: 10.1016/j.fct.2008.05.011

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun W., Yu J., Xu X., Zhang W., Liu R., Pan J. (2014). Distribution and sources of heavy metals in the sediment of xiangshan bay. Acta Oceanologica Sin. 33, 101–107. doi: 10.1007/s13131-014-0456-z

CrossRef Full Text | Google Scholar

Tang H., Ke Z., Yan M., Wang W., Nie H., Li B., et al. (2018). Concentrations, distribution, and ecological risk assessment of heavy metals in daya bay, China. Water 10, 780. doi: 10.3390/w10060780

CrossRef Full Text | Google Scholar

Tyagi S., Sharma B., Singh P., Dobhal R. (2013). Water quality assessment in terms of water quality index. Am. J. Water Resour. 1, 34–38. doi: 10.12691/ajwr-1-3-3

CrossRef Full Text | Google Scholar

Wang X., Fu R., Li H., Zhang Y., Lu M., Xiao K., et al. (2020). Heavy metal contamination in surface sediments: A comprehensive, large-scale evaluation for the bohai Sea, China. Environ. pollut. 260, 113986. doi: 10.1016/j.envpol.2020.113986

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang Y., Wang Y., Chen X., Zhang L. (2022). Source-to-sink process and risk assessment of heavy metals for the surface sediment in the northern South China Sea. Environmental Science and Pollution Research doi: 10.21203/rs.3.rs-1593522/v1

CrossRef Full Text | Google Scholar

Wang S.-L., Xu X.-R., Sun Y.-X., Liu J.-L., Li H.-B. (2013). Heavy metal pollution in coastal areas of south China: a review. Mar. pollut. Bull. 76, 7–15. doi: 10.1016/j.marpolbul.2013.08.025

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang X., Zhao L., Xu H., Zhang X. (2018). Spatial and seasonal characteristics of dissolved heavy metals in the surface seawater of the yellow river estuary, China. Mar. pollut. Bull. 137, 465–473. doi: 10.1016/j.marpolbul.2018.10.052

PubMed Abstract | CrossRef Full Text | Google Scholar

Wei M., Yanwen Q., Zheng B., Zhang L. (2008). Heavy metal pollution in tianjin bohai bay, China. J. Environ. Sci. 20, 814–819. doi: 10.1016/S1001-0742(08)62131-2

CrossRef Full Text | Google Scholar

Wenxiong W., Jinfen P. (2004). The transfer of metals in marine food chains: A review. Acta Ecologica Sin. 24, 599–604.

Google Scholar

Wilding L. (1985). “Spatial variability: its documentation, accomodation and implication to soil surveys,” in Soil spatial variability(Netherlands, Pudoc, Wageningen), 166–194.

Google Scholar

Xiao R., Bai J., Huang L., Zhang H., Cui B., Liu X. (2013). Distribution and pollution, toxicity and risk assessment of heavy metals in sediments from urban and rural rivers of the pearl river delta in southern China. Ecotoxicology 22, 1564–1575. doi: 10.1007/s10646-013-1142-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang W., Huang D., Chen J., Chen X., Liu W., Wang Y. (2019). Research on ecological environment quality in the sea area near the second petrochemical sewage pipeline discharge outlet in daya bay. J. Mar. Sci. 37, 85–91.

Google Scholar

Yan Y., Han L., Yu R.-L., Hu G.-R., Zhang W.-F., Cui J.-Y., et al. (2020). Background determination, pollution assessment and source analysis of heavy metals in estuarine sediments from quanzhou bay, southeast China. Catena 187, 104322. doi: 10.1016/j.catena.2019.104322

CrossRef Full Text | Google Scholar

Ye L., Huang M., Zhong B., Wang X., Tu Q., Sun H., et al. (2018). Wet and dry deposition fluxes of heavy metals in pearl river delta region (China): Characteristics, ecological risk assessment, and source apportionment. J. Environ. Sci. 70, 106–123. doi: 10.1016/j.jes.2017.11.019

CrossRef Full Text | Google Scholar

Yi Y.-J., Sun J., Tang C.-H., Zhang S.-H. (2016). Ecological risk assessment of heavy metals in sediment in the upper reach of the Yangtze river. Environ. Sci. pollut. Res. 23, 11002–11013. doi: 10.1007/s11356-016-6296-y

CrossRef Full Text | Google Scholar

Yi B., Zhou P., Zhou J., Li H., Zhang H., Wu L., et al. (2014). Distribution and quality assessment of Hg, Pb, cd and as in marine organisms from daya bay during 2007–2010. Mar. Environ. Sci. 33, 226–231.

Google Scholar

Zhang Y. (1991). A background value study on heavy metal elements in the sediments of daya bay. Tropic oceanology/Redai Haiyang. Guangzhou 10, 76–80.

Google Scholar

Zhang G., Bai J., Xiao R., Zhao Q., Jia J., Cui B., et al. (2017). Heavy metal fractions and ecological risk assessment in sediments from urban, rural and reclamation-affected rivers of the pearl river estuary, China. Chemosphere 184, 278–288. doi: 10.1016/j.chemosphere.2017.05.155

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang Z., Jin J., Zhang J., Zhao D., Li H., Yang C., et al. (2022). Contamination of heavy metals in sediments from an estuarine bay, south China: Comparison with previous data and ecological risk assessment. Processes 10, 837. doi: 10.3390/pr10050837

CrossRef Full Text | Google Scholar

Zhang L., Shi Z., Zhang J., Jiang Z., Wang F., Huang X. (2015). Spatial and seasonal characteristics of dissolved heavy metals in the east and west guangdong coastal waters, south China. Mar. pollut. Bull. 95, 419–426. doi: 10.1016/j.marpolbul.2015.03.035

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang L., Shi Z., Zhang J., Jiang Z., Wang F., Huang X. (2016). Toxic heavy metals in sediments, seawater, and molluscs in the eastern and western coastal waters of guangdong province, south China. Environ. Monit. Assess. 188, 1–14. doi: 10.1007/s10661-015-4999-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang M., Sun X., Xu J. (2020). Heavy metal pollution in the East China Sea: A review. Mar. pollut. Bull. 159, 111473. doi: 10.1016/j.marpolbul.2020.111473

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang A., Wang L., Zhao S., Yang X., Zhao Q., Zhang X., et al. (2017). Heavy metals in seawater and sediments from the northern liaodong bay of China: Levels, distribution and potential risks. Regional Stud. Mar. Sci. 11, 32–42. doi: 10.1016/j.chemosphere.2017.05.155

CrossRef Full Text | Google Scholar

Zhang L., Yan W., Xie Z., Cai G., Mi W., Xu W. (2020). Bioaccumulation and changes of trace metals over the last two decades in marine organisms from guangdong coastal regions, south China. J. Environ. Sci. 98, 103–108. doi: 10.1016/j.jes.2020.05.007

CrossRef Full Text | Google Scholar

Zhang C., Zheng Z., Yao S., Jia H., Xian X., Wang L. (2020). Ecological risk of heavy metals in sediment around techeng island special marine reserves in zhanjiang bay. J. Ocean Univ. China 19, 561–568. doi: 10.1007/s11802-020-4042-z

CrossRef Full Text | Google Scholar

Zhan S., Peng S., Liu C., Chang Q., Xu J. (2010). Spatial and temporal variations of heavy metals in surface sediments in bohai bay, north China. Bull. Environ. contamination Toxicol. 84, 482–487. doi: 10.1007/s00128-010-9971-6

CrossRef Full Text | Google Scholar

Zhao B., Wang X., Jin H., Feng H., Shen G., Cao Y., et al. (2018). Spatiotemporal variation and potential risks of seven heavy metals in seawater, sediment, and seafood in xiangshan bay, China, (2011–2016). Chemosphere 212, 1163–1171. doi: 10.1016/j.chemosphere.2018.09.020

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhen G., Li Y., Tong Y., Yang L., Zhu Y., Zhang W. (2016). Temporal variation and regional transfer of heavy metals in the pearl (Zhujiang) river, China. Environ. Sci. pollut. Res. 23, 8410–8420. doi: 10.1007/s11356-016-6077-7

CrossRef Full Text | Google Scholar

Zhixin N., Xia Z., Weixu C., Jingqin L., Xiaoping H. (2016). Distribution, speciation and risk assessment of trace metals in surface sediment of the zhujiang estuary. Mar. Environ. Sci. 35, 321–328. doi: 10.13634/j.cnki.mes.2016.03.001

CrossRef Full Text | Google Scholar