Heavy Metals and Their Ecological Risk Assessment in Surface Sediments of the Changjiang River Estuary and Contiguous East China Sea

Abstract

Marine heavy metal pollution has been an important global environmental issue in recent years. Concentrations of nine heavy metals (Pb, Cr, Ni, Cu, Zn, Co, Cd, As, and Hg) from marine sediments (2015, n = 38) in the Changjiang River Estuary and contiguous East China Sea were determined. The total contents of nine heavy metals ranged from 134.0 μg/g to 357.8 μg/g, with Cr as the most abundant component. Pearson correlation coefficient matrix of the heavy metals, granularity, and total organic carbon (TOC) in the samples were determined, and a preliminary ecological risk assessment was conducted in three aspects. According to the investigation, heavy metals were commonly found in samples and affected by terrestrial inputs to a large extent. Their concentration distributions were significantly affected by granularity as well as TOC. Preliminary ecological risk assessment showed that Cd and Hg were identified as the dominant heavy metals in the sediment samples from the study areas and showed a strong ecological risk. Overall, the situation of heavy metal pollution in the sediments of the Changjiang River Estuary and contiguous East China Sea was not optimistic in 2015, and it is worthy of further investigation into whether the environmental condition has improved with the strengthening of governmental environmental protection.

1. Introduction

With rapid economic development, ever-increasing port throughput, and the strengthening exploitation of ocean resources, marine pollution has considerably increased and is a cause for serious concern worldwide [1,2]. As an increasingly severe threat to the environment, heavy metals have been widely recognized for their diverse origins [3,4,5,6,7], persistence [8,9], toxicity [10,11], and bioaccumulation performance [12,13]. Worse, biomagnification of many heavy metals across the food chain has resulted in high concentrations and potential effects in the topmost food chain, particularly in humans [14,15]. Given that, heavy metal pollution has been increasingly studied worldwide [16,17,18].

Once heavy metals are discharged into nature, they can enter coasts and oceans by surface runoff [19,20]. Surface sediment, as an important material cycling node, is of vital importance for sorption and transport of heavy metals in water [21,22,23]. China has a long coastline, in which a great number of economically developed cities are located. Anthropogenic activities greatly impact these estuarine and coastal areas, which are a typical region of active land–sea interactions [24]. According to a survey, heavy metals that drain from rivers into the ocean reached about 24,000 tons, which included 16,243 tons of Zn (zinc), 3318 tons of Cu (copper), 3188 tons of As (arsenic), 858 tons of Pb (lead), 83 tons of Cd (cadmium), and 49 tons of Hg (mercury) in the year of 2015 [25]. Wang et al. analyzed the ecological risk assessment of heavy metals in sediments of China’s coastal waters and figured out that certain hotspots existed had relatively high ecological risks [26].

Heavy metals in sediments of the Changjiang River estuary area and contiguous East China Sea were selected in 2015 as research objects in the current study. Mainland area contiguous with the present study area ranks among best developed regions in China, into which rivers such as the Changjiang River, Qiantang, Oujiang, and Minjiang enter. Although the origins of these heavy metals vary, the release of the upper and local streams of these water systems serves as a major mass transport [27,28]. For instance, up to ~12,000 tons of heavy metals directly flowed from the Changjiang River to the East China Sea in 2015 alone [25]. Many related studies have been reported. The levels of heavy metals in sediments of the Changjiang River estuary area were analyzed by Han et al. [29] and He et al. [30]. Li et al. [31] researched heavy metals’ inter-annual variability and distribution in the Changjiang River estuary sediments. In addition, Chen et al. [32] investigated the pollution characteristics of heavy metals in sediments of the intertidal zone of typical bays in Zhejiang Province. All the investigates in this region substantiated that the concentrations in the inner estuary were overall higher than those in coastal and offshore areas. Thus, a comprehensive study of heavy metals in these areas is of great significance in environmental assessment.

The current study investigated the spatial distributions of nine heavy metal concentrations in sediments of the Changjiang River Estuary and contiguous East China Sea and focused on the relationships between heavy metal distributions and dynamic influence factors, including sediment physicochemical properties and environmental factors. Meanwhile, the primary environmental risk evaluation of typical heavy metals in the investigated regions was conducted. This study aims at a more comprehensive understanding of the overall heavy metal situation in sediments of the study region.

2. Materials and Methods

2.1. Study Area and Sampling

In this study, samples were taken from the Changjiang River Estuary (March 2015, n = 27) and contiguous zone of East China Sea (June 2015, n = 11), the locations of which were ascertained based on the Science Fund’s Shared Voyage (Figure 1). The top 2 cm of sediment samples were collected using a box-grab, stored in polyethylene sealed packets, and frozen at −20 °C before experimentation.

2.2. Chemical Analysis

For the analyses of heavy metal concentrations, sediment samples were air-dried in the laboratory, put through the homogenization process with an agate mortar, and sifted through an 80-mesh (180 µm) sieve to remove non-sediment impurities such as crustacean tissues. For the measurement of elements Pb, Cr (chromium), Ni (nickel), Cu, Zn, Co (cobalt), Cd, and Al (aluminum), about 0.3 g of powdered dry samples were thoroughly dissolved in sealed Teflon containers using concentrated HF–HNO₃–HClO₄ and analyzed by inductively coupled plasma optical emission spectrometer (ICP-OES, Perkin Elmer, Optma 8000DV). In addition, atomic fluorescence spectrometer (AFS, Beijing Jitian Instruments Co., Ltd., AFS-933, Beijing, China) was used to measure contents of As and Hg after the sediment samples were digested by an aqua regia–H₂O solution system [33].

The crystallite dimension of sediment samples was measured by a laser particle analyzer (Bettersize Instruments Ltd., BT-9300ST). Organics and carbonates in fresh subsamples should be eliminated by immersion in H₂O₂ of 10% and HCl of 1 mol/L before particle size analysis. A total organic carbon (TOC) analyzer (Elementar, Liquid TOC II)) was utilized to determine the TOC of the surface sediment.

2.3. Quality Assurance and Quality Control

All laboratory vessels were pre-cleaned with nitric acid and ultra-pure water to avoid unintended contamination. Parallel specimens were collected about every 10 stations. Procedural blanks were run through the whole testing process. The limit of quantitation (LOQ) was determined using a signal-to-noise ratio (S/N) of 10:1 for the analytes. Method accuracy and precision were tested by the recovery (RE) of standard substances. Precision was likewise determined by standard deviation (SD) of ten replicate analyses of a single sample. The results were expressed by using 95% confidence interval determined using a two-tailed Student’s t distribution. The statistical software was Origin, and the graphs of heavy metals distributions were obtained by Surfer. Information on method performance is detailed in

Table 1. Information on method performance in the investigated matrices (n = 10).

Element	Linear Range (μg/g)	Linearity (R2)	LOQ (μg/g)	Recovery ± SD (%)	RSD (%)	Analytic Method
Pb	0–50	0.9985	0.013	95.1 ± 3.1	3.26	ICP-OES
Cr	0–250	0.9997	0.004	99.2 ± 5.1	5.14	ICP-OES
Ni	0–100	0.9998	0.007	103.4 ± 1.4	1.35	ICP-OES
Cu	0–100	0.9985	0.003	97.5 ± 3.6	3.69	ICP-OES
Zn	0–500	0.9992	0.013	102.1 ± 2.5	2.45	ICP-OES
Co	0–100	0.9994	0.009	98.6 ± 2.9	2.94	ICP-OES
Cd	0–2.5	0.9999	0.001	101.4 ± 1.7	1.68	ICP-OES
As	0–25	0.9994	0.020	97.3 ± 3.9	4.01	AFS
Hg	0–1	0.9999	0.010	98.7 ± 2.7	2.74	AFS

. The units (µg/g) for linear range and LOQ referred to the weight of sediment digested and dissolved, which can be obtained by entering the parameters in the instrument workstation, such as quantitative volume and sample quality.

2.4. Data Analysis

In this study, enrichment factor (EF), geoaccumulation index (I_geo), and pollution load index (PLI) were investigated to conduct the preliminary ecological risk assessment.

EF represents the enrichment level of heavy metal concentrations in sediments investigated in comparison to levels of background free of pollution; it can be calculated with equation:

$$\mathrm{EF}=\frac{{(C/X)}_{sample}}{{(C/X)}_{baseline}}$$

(1)

where C represents the target heavy metal and X is the reference element mainly combined with silicate minerals [34]. Al was selected as the reference element to calculate EF in this paper. Based on the synthesized previous research on soil geochemical baselines of eastern coastal China [35], the background concentrations of target elements are 30.0, 24.3, 68.0, 0.090, 13.4, 72.4, 22.5, 9.29, and 0.040 μg/g for Ni, Pb, Zn, Cd, Co, Cr, Cu, As, and Hg, respectively [36]. When the EF value is less than 2, it indicates that given elements might entirely derive from the earth crust under nature weathering, suggesting no or minimal pollution. When the EF value is between 2 and 5, 5 and 20, and 20 and 40, it suggests moderate enrichment, significant enrichment, and very highly enriched, respectively. Meanwhile, when the EF value is over 40, it indicates extremely enriched and that a part or a majority of elements might originate from non-crustal materials, including pollution from human behaviors [37].

I_geo quantitatively evaluates the degree of heavy metal contamination while synthetically considering human activities and natural geological processes. The specific calculation formula is shown as follows:

$$I_{\mathrm{geo}}={\log }_2(\frac{C_i}{k{B}_i})$$

(2)

where C_i is the measured concentration of the examined metal i, B_i is the background concentration of metal i, and factor k (k = 1.5) is introduced as the possible variation in background values due to anthropogenic influences or lithologic variations [38]. On the basis of increasing values, I_geo assesses the degree of metal pollution in terms of seven classes to express different contamination levels ([39];

Table 2. Dividing standards of enrichment factor, pollution load index, and potential ecological risk index [37,39,40].

Classification	Enrichment Factor (EF)		Geoaccumulation Index (Igeo)		Potential Ecological Risk Index (Eri and RI)
	EF	Concentration Level	Igeo	Contamination Level	PLI	Contamination Level
class 1	<2	Depletion to minimal enrichment suggestive of no or minimal pollution	<0	Practically unpolluted	≤1	No pollution
class 2	2–5	Moderate enrichment, suggestive of moderate pollution	0–1	Unpolluted to moderately polluted	1–2	Slight pollution
class 3	5–20	Significant enrichment, suggestive of a significant pollution signal	1–2	Moderate pollution	2–3	Moderate pollution
class 4	20–40	Very highly enriched, indicating a very strong pollution signal	2–3	Moderately to heavily polluted	>3	High pollution
class 5	>40	Extremely enriched, indicating an extreme pollution signal	3–4	Heavily polluted
class 6			4–5	Heavily to extremely polluted
class7			≥5	Extremely polluted

).

PLI quantitatively evaluates the degree of heavy metal contamination while synthetically considering pollution contributions of multiple elements. The specific calculation formula is showed as follows:

$$C{F}_i=\frac{c_i}{c_{oi}}, \mathrm{PLI}=\sqrt[n]{C{F}_1\times C{F}_2\times C{F}_3\times \cdot \cdot \cdot \times C{F}_n}$$

(3)

where c_i and c_oi represent the measured concentration and background concentration of element i, CF_i and PLI represent single contamination factor of element i and total pollution load of all elements. Based on increasing values, PLI assesses pollution level in terms of four classes to express different contamination levels [40].

3. Results and Discussion

3.1. Spatial Distribution of Heavy Metals in Surface Sediments of the Changjiang River Estuary and Contiguous East China Sea

The concentrations of nine heavy metals are depicted in Figure 2 and summarized in

Table 3. Summary statistics of heavy metals, reference element, crystallite dimension, and TOC in 38 investigated sediment samples.

Statistics	Ni (μg/g)	Pb (μg/g)	Zn (μg/g)	Cd (μg/g)	Co (μg/g)	Cr (μg/g)	Cu (μg/g)	As (μg/g)	Hg (μg/g)	Al (μg/g)	Grain Size (μm)	TOC (%)
Minimum value	16.45	7.04	31.93	0.21	12.32	43.77	8.34	2.39	0.05	6468	0.66	0.36
Maximum value	45.27	25.33	124.11	1.29	26.03	103.90	53.67	8.77	0.41	111,086	189.20	0.88
Average value	29.32	16.09	68.67	0.48	18.77	69.22	24.47	5.59	0.19	83,990	31.63	0.73
Standard deviation	7.29	4.94	21.45	0.26	3.63	13.95	11.13	2.07	0.09	12,480	42.40	0.11
Variable coefficient (%)	24.9	30.7	31.2	53.4	19.3	20.2	45.5	37.0	45.8	14.9	134.1	15.3
Average value of the Changjiang River Estuary	29.40	16.52	67.32	0.52	18.84	70.29	25.92	4.90	0.12	83,964	28.69	0.76
Average value of the East China Sea	29.14	15.03	71.97	0.38	18.60	66.60	20.89	5.86	0.23	84,052	38.83	0.67

, indicating that the average values (μg/g) follow the order of Cr (69.2) > Zn (68.7) > Ni (29.3) > Cu (24.5) > Co (18.8) > Pb (16.1) > As (5.6) > Cd (0.48) > Hg (0.19).

Ni, Co, Cr, Pb, and Cu exhibited a similar spatial distribution pattern throughout the studied areas, that is, high concentrations were generally observed in the outer Hangzhou Bay and Zhejiang coast with mud areas. This finding was aligned with the Pearson correlation coefficient matrix for the heavy metals (

Table 4. Pearson correlation coefficient matrix of heavy metals, major elements, crystallite dimension and TOC in the surface sediments.

	Ni	Pb	Zn	Cd	Co	Cr	Cu	As	Hg	Al	TOC	Crystallite Dimension
Ni	1	0.884 **	0.745 **	0.491 **	0.942 **	0.934 **	0.861 **	0.739 **	0.653 **	0.402 *	0.209	−0.467 **
Pb		1	0.546 **	0.593 **	0.825 **	0.832 **	0.762 **	0.643 **	0.699 **	0.329 *	0.172	−0.361 *
Zn			1	0.292	0.729 **	0.675 **	0.656 **	0.628 **	0.395 *	0.263	0.121	−0.295
Cd				1	0.490 **	0.557 **	0.578 **	0.498 **	0.520 **	0.328 *	0.169	−0.201
Co					1	0.925 **	0.725 **	0.587 **	0.586 **	0.360 *	0.173	−0.393 *
Cr						1	0.778 **	0.657 **	0.705 **	0.400 *	0.209	−0.384 *
Cu							1	0.866 **	0.708 **	0.477 **	0.288	−0.434 **
As								1	0.668 **	0.258	0.295	−0.472 **
Hg									1	0.226	0.405 *	−0.358 *
Al										1	0.077	−0.098
TOC											1	−0.639 **
Crystallite dimension												1

** Correlation is significant at p < 0.01 level (two-tailed); * Correlation is significant at p < 0.05 level (two-tailed).

), that is, Ni, Co, Cr, Pb, and Cu yielded highly positive correlations among one another. This observation suggested that these heavy metals might come from identical sources or have comparable geochemical behavior during deposition, such as their enrichments in close-grained deposits. Several other heavy metals were also partly correlated, but each one had a different characteristic. Zn contents had relatively high values in the outer Hangzhou Bay, whereas much higher concentrations were mainly distributed in the coast adjacent to the borderline between Zhejiang and Fujian. The localities with high Hg contents mostly occurred in the Zhejiang coast. The high contents of As were mostly located in the coastal areas close to the mainland, especially in the estuary areas of the Changjiang and Qiantang rivers. Overall, of all the nine analyzed heavy metals, the concentration of Cd had the worst correlation with the other ones. This condition might be related to its strong migration ability and active chemical property or its different anthropogenic sources [41]. The high Cd concentrations were mainly observed in the Changjiang River estuary area, Hangzhou Bay, and the coastal waters of Zhejiang.

Generally, in our research, nine heavy metals in sediments of the Changjiang River Estuary showed higher level distribution compared with those in the sediments of the East China Sea, except for zinc. This is possible because of the high zinc concentrations distributed throughout the coast adjacent to the borderline between Zhejiang and Fujian, rather than the Changjiang River Estuary. Meanwhile, compared with other research on Changjiang River Estuary [29,30,31], the whole concentration of heavy metals was similar to the research in this study. We also obtained a slightly higher level of Hg, probably due to an optimized test condition [33]. The tendency of heavy metals’ spatial distribution was obviously decreased with increasing distance from the mainland, which also demonstrated the influence of human activities on the distribution of heavy metals in this area. The anthropogenic factor brought by the highly developed economy of the mainland area contiguous with the present study area might be an underlying cause of the high levels of heavy metals.

Table 5. Comparisons of heavy metal mean levels (μg/g) in the sediments of the Changjiang River Estuary and the East China Sea and other coastal zones in the world.

Location	Ni	Pb	Zn	Cd	Co	Cr	Cu	As	Hg	Sampling Time	Reference
The Changjiang River Estuary, China	33.5	21.0	89.5	0.25	-	84.7	24.3	10.3	0.04	2007	[30]
The Changjiang River Estuary, China	-	21.70	63.92	0.58	-		26.57	10.41	0.110	2012–2016	[31]
The Changjiang River Estuary, China	-	30.47	66.91	0.15	-	34.64	17.46	7.86	0.05	2014	[29]
The Changjiang River Estuary and contiguous East China Sea	29.3	16.1	68.7	0.48	18.8	69.2	24.5	5.59	0.19	2015	In this study
The west Guangdong coastal region, China	-	44.3	140	0.38	-	87.0	43.8	20.8	0.13	2008	[43]
The Liaodong Bay, China	22.5	31.8	71.7	-	-	46.4	19.4	8.30	0.04	2009	[42]
The South Yellow Sea and northern part of the East Sea, China	30.5	20.7	70.3	-	-	69.5	17.4	-	-	2009	[44]
The Tupilipalem Coast, India	6.43	5.67	14.2	0.71	-	9.25	3.55	-	-	2015	[47]
The Red Sea, Saudi Arabia	13.7	3.54	16.8	-	5.34	20.2	18.7	-	1.83	2018	[20]
The Apulia region, Italy	14.1	52.2	82.3	0.25	-	19.1	41.0	9.10	0.24	2010	[46]
The Marmara Sea, Turkey	49.1	32.9	85.5	-	-	115	39.3	-	-	2010	[45]

–: no data or unavailable.

shows the comparisons of heavy metal levels of samples in our study and other coastline regions in the world. Most heavy metal concentrations in surface sediments of the Changjiang River Estuary and contiguous zone of East China Sea were lower than those in other Chinese coastal areas [42,43,44]. The study of Wang et al. also proved that the ecological risks of China’s coastal waters decreased in the following order: Bohai Sea > Yellow Sea > South China Sea > East China Sea [26]. However, this finding could not prove that the contamination of heavy metals in this region was optimistic, which was illustrated by the comparison of heavy metals in the sediments of China’s coastal areas with other coastal zones in the world. Most of these other countries’ studied zones are not adjacent to the open ocean, but are situated on the shores of inland seas or gulfs. Even so, China’s coastal areas as a whole retained a relatively high pollution level of heavy metals [20,45,46,47]. Different hydrological environments and sampling times, of course, may also contribute to different pollution levels in the studies. Meanwhile, particle size must also be taken into account in the objective comparisons of heavy metal levels in the sediments of the study area and other coastal zones in the world. Moreover, the literature results have validated the influence of human activities or anthropogenic sources on heavy metal pollution in offshore sediments.

3.2. Dynamic Influence Factors of Heavy Metal Distribution

Our data showed that with the increase of distance from mainland, the concentrations of heavy metals in samples were gradually reduced, and the concentration in the coastal sites was relatively high. This pattern suggested that heavy metals in this area were partly affected by terrigenous input. In addition, the coefficient of variation (CVs) representing the discreteness of heavy metal distribution can also reflect the non-natural influence, which is manifested as the higher the CV value, the stronger the interference. According to Wilding’s standard, CVs are classified into three grades as follows: CV < 15% is slight variation; 15–36% is moderate variation; CV > 36% is high variation [48]. Investigation showed that none of the nine target heavy metals’ CV values were classified to slight variation, but the numbers of CV values classified to moderate variation and high variation were five and four, respectively. Thus, the influence of unnatural factors such as land-based pollutants caused by highly industrialized coastal regions and the inputs of great rivers should not be neglected.

The factors that influence the dispersion patterns of heavy metals are complex [6,49,50]; they can be heavy metal terrigenous sources, sediment characteristics, hydrologic factors, and geochemical processes. Herein, we analyzed a number of these influence factors by applying statistical analyses. The compositions of granularity and TOC fraction are listed in Table 3. Pearson correlation coefficient matrix for heavy metal concentrations, granularity, and TOC in the samples is depicted in Table 4.

Median granularity ranged between 0.7 and 189 μm, with a mean of 31.6 μm. In general, fine-grained samples were dispersed in the interior estuary and nearshore area. Sediments gradually became coarse in the nearshore to the seaward direction, and the mean sediment granularity increased. Table 4 indicates that there is an inverse correlation between heavy metal concentrations and grain sizes generally. The data are in line with previous studies showing that the smaller the particle size of sediments is, the greater the adsorption capacity of the heavy metals will be [51]. In view of the strong influence of grain sizes on the distribution of heavy metals, sediment sieving sizes were investigated in the comparisons of heavy metal levels in this study and previous research (Table 5). The levels of most heavy metals in sediments of study area increased with decreasing particle sizes at the similar sampling time. This condition may be explained by the fact that the smaller the particle size of sediments is, the larger the specific surface area will be, and the greater the amount of equilibrium adsorption of heavy metals will be. Furthermore, a previous study confirmed that many heavy metals in in fine sediments had the potential to be mobilized and metabolized in addition to their potential to be enriched [52]. Overall, the sorption of the heavy metals by the sediments greatly depends on the particle size distribution.

The TOC contents of sediments were 0.36–0.88%, with a mean value of 0.73%. The high contents of TOC were generally located in the estuary areas close to the mainland. Studies have found that the distribution of heavy metals has a correlation with TOC contents to some extent [53,54]. A phenomenon in which heavy metal concentration level had a positive correlation with TOC contents (Table 4) was revealed through correlation analyses. This observation might be related to the great affinity and numerous polar functional groups of organic matter, which can be more combined with heavy metals. In addition, the organic fraction of the sediments with high levels of nutrients can be metabolized, which supports microbial activity as well as bottom feeding organisms [52]. However, none of the correlations between metals and TOC, with the exception of Hg, were significant. Thus, compared with sediment grain sizes, TOC contents exerted a weaker influence on heavy metal distribution patterns.

Studies also have focused on the influence of the hydrological environment on heavy metals’ distribution [27,30]. The shelf circulation in the East China Sea is constituted by the Zhejiang Fujian Coastal Current (ZFCC), Taiwan Warm Current (TWC), and Kuroshio Current (Figure 3, [55]). Among them, the ZFCC is a part of the Chinese longshore current system, which originates from the Changjiang River estuary and Hangzhou Bay area, with many other river systems flowing into it along the way. It generally flows from north to south along the coast, though its path varies with the seasons of the year [56]. According to the existing hydrologic analysis and short-term measurement, the TWC persistently flows to the north for the whole year. Meanwhile, a warm and salty Kuroshio Current flows northward along the East China Sea shelf break. This formed the mud areas in the Yangtze Estuary and Fujian and Zhejiang coasts. High TOC values in sediments were observed mainly at the center of the mud areas, which were negatively correlated with particle sizes. This is contradictory to the distribution regularities in the field of soil science, that show that high TOC contents may result in larger particle size. This unique hydrological phenomenon affected the distribution of heavy metals in this area. The findings visually suggested a high consistency between argillaceous zone and high heavy metal concentration zone (Figure 3). All the five sampling positions (A14, D7, D10, A8, and A26) with a total concentration more than 300 μg/g are located in the mud areas. Furthermore, the heavy metal contents in sediments of four sampling cross-sections across the muddy areas also confirm this phenomenon, that the pollution level of heavy metals in muddy areas is higher than that in non-muddy areas.

Furthermore, the spread of terrigenous pollutants into the ocean is slowed down by the TWC, which flows from south to north throughout the year. This may be a cause of large deposits of heavy metals in the coastal areas of Zhejiang and Fujian (Figure 2 and Figure 3). This resistance gradually increases from north to south, resulting in a relatively stable sedimentary environment in the south East China Sea. Heavy metal contents at two sets of sites located on either side of the TWC (D7, DH8, D9 and D10, D11) support preliminary yet convincing evidence of this phenomenon. To illustrate this point, the heavy metal at site D7 amounted to 330.5 μg/g and dropped to 280.4 μg/g at site D8, which is a reasonable result based on the influence of mud area distribution. Yet, the heavy metal content dropped by a factor of x2 at site D9 (177.8 μg/g), which was much greater compared with another site pair with similar geographical distance. Hence, the heavy metal transport and occurrence in sediments could be partly controlled by the circulation of oceanic currents in this area.

In addition to seasonal changes in ocean currents, climatic factors such as the amount of rainfall and seasonal changes in industrial activity should also be responsible for the differences in the diffusion trends of heavy metal concentrations. In southern China, the wet season is usually from April to September, which may lead to a higher amount of terrigenous pollutants from land. This tendency was roughly consistent with the data from the East China Sea Branch of the State Oceanic Administration [25]. The sampling time for the East Sea was June, during the wet season. Oppositely, the sampling time in the Changjiang Estuary was March, during the dry season. That may be the reason that the heavy metal concentrations in some sites in the East China Sea are higher than those in the Changjiang River estuary. Furthermore, considering the complexity of sources of heavy metals in sediments, heavy metals that were transported via the particulate phase into the sea and deposited with the particles also should be further investigated.

3.3. Preliminary Ecological Risk Assessment

Heavy metals in sediments that change affected by the external environment may cause secondary pollution by being released back into the water. An appropriate evaluation method must thus be selected to accurately reflect the pollutant situation via the ecological risk assessment of heavy metals. This work discusses preliminary ecological risk evaluation of the heavy metals in the exterior sediments of the Changjiang River estuary area and contiguous East China Sea in three aspects (EF, PLI, and RI), as shown in

Table 6. Enrichment factor, geoaccumulation index, and pollution load index of heavy metals in the surface sediments of the Yangtze Estuary and contiguous East China Sea.

PLI				Element	EF				Igeo				CF
Maximum		Minimum			Maximum		Minimum		Maximum		Minimum		Maximum		Minimum
Value	Site	Value	Site		Value	Site	Value	Site	Value	Site	Value	Site	Value	Site	Value	Site
2.26	A14	0.67	D3	Ni	1.5	A24	0.1	A14	0.01	A14	−1.45	D3	1.5	A14	0.6	D3
				Pb	1.3	A24	0.1	A14	−0.53	A14	−2.37	A16	1.0	A24	0.3	A16
				Zn	2.1	D10	0.1	A14	0.28	D10	−1.68	A27	1.8	D10	0.5	A27
				Cd	15	A15	0.9	A14	3.10	A15	0.49	D3	12.9	A15	2.1	D3
				Co	2.1	A8	0.2	A14	0.37	A14	−0.71	D3	1.9	A14	0.9	D3
				Cr	1.4	A8	0.1	A14	−0.06	A26	−1.31	D3	1.4	A26	0.6	D3
				Cu	2.1	A24	0.2	A14	0.67	A14	−2.02	D3	2.4	A14	0.4	D3
				As	2.0	A18	0.2	A14	0.33	A18	−1.54	D4	1.9	A18	0.5	D4
				Hg	11	A24	0.6	A14	2.54	A24	−0.56	D3	8.7	A24	1.0	D3

and Figure 4, Figure 5 and Figure 6.

EF is a broadly applied proxy to estimate anomalies in geochemistry and impact of human behaviors on sediment chemistry. The EF values in the investigated samples ranged between 0.10 and 14.98. The descending order of average EF values was Cd (5.23) > Hg (4.46) > Co (1.54) > As (1.29) > Cu (1.17) > Zn (1.10) > Ni (1.07) > Cr (1.05) > Pb (0.73). All the EF values of Pb, Cr, and Ni belonged to class 1, demonstrating no enrichment of these elements in the investigated samples. Analogously, an overwhelming majority of As, Cu, and Zn EF values also belonged to class 1, and barely a few values belonged to class 2. Statistical analysis showed that average EF values of As, Cu, Zn, Ni, Cr, and Pb were between 0.73 and 1.29, which again suggested the primary natural sources of these metals. Co had a slightly higher average EF value, close to 1.5, and 13% of the values belonged to class 2. However, the worst negative conclusion was on Cd and Hg. None of the Cd EF values and only 8% of Hg EF values belonged to class 1. Meanwhile, average EF values of Cd (5.23) and Hg (4.46) were significantly above the line of minimal enrichment level, showing obvious contaminant enrichments. Cd and Hg in the investigated regions might be mostly derived from non-crustal sources and are indicative of contamination in the Changjiang River catchment and study area.

I_geo is another frequently used proxy to evaluate heavy metal enrichment in sediments. The I_geo values of these heavy metals in the investigated samples ranged from −2.37 to 3.10. The mean values of I_geo mostly varied less than 0 and showed the decreasing order of Cd (1.51) > Hg (1.28) > Co (−0.13) > As (−0.43) > Cu (−0.62) > Zn (−0.64) > Ni (−0.66) > Cr (−0.68) > Pb (−1.25). According to the classification of I_geo (Müller, 1979), Pb and Cr in all the investigated samples were unpolluted in terms of the relatively low I_geo values (<0). The I_geo values of Ni, Zn, and Cu in a vast majority of the samples were also less than 0. On the contrary, approximately one third of the samples with higher I_geo values (>0) for As and Co suggested weak enrichments in some stations of the study area. The worst case of heavy metal pollution still included Cd and Hg with great I_geo values; the I_geo values of Cd were greater than 0 in all the studied samples. From Müller’s classification, Cd and Hg in the sediments of the study area indicated slight to moderate pollution degrees.

PLI is another frequently used proxy to assess heavy metal enrichment in sediments, which ranged from 0.67 to 2.26 in the investigated samples. PLI is calculated from the CFof every single element. The results of the CF were close to EF on account of the similar definition. The mean values of CF showed the decreasing order of Cd (4.79) > Hg (4.12) > Co (1.40) > As (1.20) > Cu (1.09) > Zn (1.01) > Ni (0.98) > Cr (0.96) > Pb (0.66). The data again revealed that the enrichment of Cd and Hg in the study areas was significant. Based on the contribution of these two elements, the analytical results of PLI showed a certain degree of heavy metal pollution. According to Tomlinson’s classification (Tomlinson et al., 1980), PLIs of class 1, class 2, and class 3 were 18.4%, 76.3%, and 5.3%, respectively. Overall, the vast majority of sites indicated a slight to moderate pollution level.

Overall, similar results of the EF and I_geo indices suggested that Pb, Cr, Ni, Zn, and Cu in the studied samples did not exhibit obvious enrichments. Although As and Co showed slight enrichments in some cases, E_rⁱ values investigations indicated that they and the above five types of heavy metals all showed slight potential ecological risk. However, all the three evaluation methods indicated that Hg and Cd strongly polluted the sediments of the Changjiang River Estuary and contiguous East China Sea. The dominant position of heavy metals in samples with the source of weathering detritus in the downstream and estuary of the Changjiang River can also be revealed from previous investigations [57], but anthropogenic activities have caused enrichments of Cd, Hg, and other heavy metals in recent years [29,30].

In addition, the Chinese government has made notable efforts on environmental protection [58,59]. The Chinese Government Information Publicity showed that pollutant emissions are effectively controlled with the active economy. By taking the mass control of contaminants carried by the Changjiang River as an example, the pollutions of heavy metals have been significantly reduced in recent years. Additionally, the amount of the most threatened Cd and Hg released directly into the East China Sea showed a wavelike decrease change during 2016–2020 according to the filing (Figure 7) [60,61,62,63,64]. Li et al. also confirmed that the quality of the ecological environment of this sea area demonstrated a trend of fluctuating improvement over time, especially after 2015, after a long period of macro-control against environmental protection [31]. The sampling time of this study was in 2015; at this time node, the heavy metal pollution situation in the sediments of the Changjiang River Estuary and contiguous East China Sea is alarming.

4. Conclusions

Nine target heavy metals (Pb, Cr, Ni, Cu, Zn, Co, Cd, As, and Hg) in surface sediment samples obtained in 2015 from the Changjiang River Estuary and contiguous zone of the East China Sea were quantitatively analyzed. The average contents (μg/g) of nine heavy metals ranked with the descending order of Cr (69.2) > Zn (68.7) > Ni (29.3) > Cu (24.5) > Co (18.8) > Pb (16.1) > As (5.6) > Cd (0.48) > Hg (0.19). Most of the heavy metals exhibited a similar spatial distribution pattern throughout the studied area and were strongly influenced by terrigenous input. Meanwhile, both granularity and TOC had a certain influence on heavy metal concentrations; particularly, the former had a stronger correlation than the latter. Cd and Hg were considered to be main heavy metals in samples collected in the study area and showed a strong ecological risk. Cd was the most substantial element enriched in the sediments, with an average EF value of 5.04. Hg showed the greatest ecological risk and contribution to RI value due to its high toxic response factor, with Eri values ranging from 40.82 to 349.15, with a mean of 164.89. Whether the macro-control policy has a positive effect on the distribution of heavy metals and their mixture toxicity assessment in the sediments of this area should be further investigated.