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Meta analysis of heavy metal pollution and sources in surface sediments of Lake Taihu, China
Journal Pre-proofs Meta analysis of heavy metal pollution and sources in surface sediments of Lake Taihu, China Yong Niu, Xia Jiang, Kun Wang, Jiandong Xia, Wei Jiao, Yuan Niu, Hui Yu PII: DOI: Reference:
S0048-9697(19)34500-0 https://doi.org/10.1016/j.scitotenv.2019.134509 STOTEN 134509
To appear in:
Science of the Total Environment
Received Date: Revised Date: Accepted Date:
19 June 2019 2 August 2019 16 September 2019
Please cite this article as: Y. Niu, X. Jiang, K. Wang, J. Xia, W. Jiao, Y. Niu, H. Yu, Meta analysis of heavy metal pollution and sources in surface sediments of Lake Taihu, China, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134509
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Meta analysis of heavy metal pollution and sources in surface sediments of Lake Taihu, China Yong Niua, Xia Jiang a, Kun Wang a, Jiandong Xia a, Wei Jiaoa, b, Yuan Niu a*, Hui Yu a* a
National Engineering Laboratory for Lake Pollution Control and Ecological Restoration,
Institute of lake environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China b
Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental
Protection, College of Resources and Environment, Linyi University, Linyi 276000, China; *Corresponding author at: Institute of lake environment, Chinese Research Academy of Environmental Sciences, Beijing, China E-mail address: [email protected]; [email protected]
Highlights：
Systematically analyze the heavy metal pollution in the sediments of Taihu Lake in the past 20 years.
The Cd accumulation index indicated moderate–heavy pollution and Cd was the main contributor of potential ecological risks
Pb is the main contributor to the total toxicity of the sediments in Taihu Lake.
Industrial source accounts for 64.9% of the heavy metals in the sediments of Taihu Lake.
Abstract：Heavy metal concentrations in Taihu Lake sediment from studies performed between 2000 and 2018 were analyzed and Monte Carlo uncertainty analysis of heavy metal geo-accumulation, potential ecological risk and toxicity data for Taihu Lake sediment was 1 / 31
performed to allow heavy metal pollution of Taihu Lake sediment to be described clearly, objectively, and comprehensively. Five main conclusions were drawn. (1) Most attention should be paid to As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn pollution. (2) The geo-accumulation indices showed that Cd is the most important pollutant and that the probabilities of Taihu Lake sediment being moderately polluted, moderately–heavily polluted, and heavily polluted were found to be 53.6%, 34.9%, and 18.7%, respectively. (3) Cd is the main contributor to potential ecological risks and had cumulative low risk, moderate risk, and considerable risk probabilities of 63.0%, 27.0%, and 10.0%, respectively. (4) Toxicity unit evaluation results indicated that Pb is the main contributor of toxicity in Taihu Lake sediment and had cumulative low toxicity, moderate toxicity, and high toxicity probabilities of 53.0%, 36.8%, and 5.6%, respectively. (5) Positive matrix factorization model results indicated that industrial sources are the main suppliers of heavy metals to Taihu Lake sediment, contributing 64.9% of the heavy metals. The summarized results and conclusions will improve local government awareness of heavy metal pollution in Taihu Lake and will aid in the development of appropriate pollution control measures. The results will also provide reference data for future studies of heavy metal pollution in sediment from Taihu Lake and other lakes. Key words：Lake Taihu, Sediment, Heavy Metal, Geoaccumulation index, Potential ecological risk, Toxic units
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1. Introduction The accumulation of heavy metals in the aquatic environment poses serious risks to aquatic ecosystems (Green and Planchart 2018). The enrichment of heavy metals through food chains also poses serious risks to human health (Gall et al., 2015). Numerical sediment quality guidelines, such as threshold effect levels, probable effect levels (PELs), effect range low values (ERLs), and effect range median values (ERMs), have been used in many studies to screen heavy metal contamination of sediment (MacDonald et al., 2000; Kwok et al., 2014). The eutrophication of Taihu Lake has attracted international attention. The risks posed by heavy metals in Taihu Lake have increased since 1980 (Shuchun and Bin 2010). Heavy metals are becoming enriched in fish in Taihu Lake because of biomagnification, and pose serious risks to humans consuming fish from Taihu Lake (Fu et al., 2013). Heavy metal concentrations in sediment in Taihu Lake have been investigated in many studies. Different heavy metal concentrations have been found in different studies. For example, the mean Cd concentration in Taihu Lake sediment was 22 times higher in a study published in 2016 (Yang et al., 2016) than in a study published in 2013 (Yang et al., 2013). Yang et al. (2013) found that Hg was the main indicator of potential ecological hazards in Taihu Lake, but Qin et al. (2012) found that Cd was the main indicator of ecological hazards in Taihu Lake. The concentrations of heavy metals in Taihu Lake sediment have marked spatial variations because of variability in heavy metal inputs, seasonal variations, and spatial variations in the pH, dissolved oxygen concentration, and other conditions (Chen et al., 2017; Huang et al., 2017; Li et al., 2018). Such variations make it difficult for those responsible for managing Taihu Lake to understand heavy metal pollution in Taihu Lake sediment and develop pollution prevention strategies. A better method is therefore required for 3 / 31
describing heavy metal pollution in Taihu Lake sediment. Meta-analysis is a statistical method for comprehensively analyzing large amounts of analytical data from numerous studies to integrate the results (Moher et al 2015). Generally, meta-analysis extracts one or more outcomes as “effect sizes” from each study. The effect sizes are designed to bring the outcomes of the different studies to the same scale. This is achieved using metrics including odds and risk ratios, standardized mean differences, z-transformed correlation coefficients, and logarithmic response ratios (Gurevitch et al., 2018). Meta-analysis was first used in the medical field, and meta-analysis results are often used as reference data when making clinical treatment decisions (Simpson and Pearson, 1904). Meta-analysis has recently started to be used to analyze environmental pollution data to allow comprehensive analyses of pollution to be achieved. For example, Shao et al. (2016) assessed temporal trends in heavy metal pollution of farmland topsoil in the Yangtze River Delta and used meta-analysis to make up for a lack of long-term monitoring data. Duan et al. (2016) integrated data from 2450 publications to map the spatial distributions of heavy metals in farmland soil in China. Zhang et al. (2016) used meta-analysis to analyze Cd pollution of cultivated land in China. Meta-analyses of published data are valuable for studying pollution of various environmental media when monitoring data are unavailable. Meta-analysis has not yet been developed into a unified analysis process for performing environmental pollution assessments, and currently available methods usually involve collecting and re-analyzing pollutant data from relevant publications to characterize the pollution state of the area of interest. Here, we use data from previous studies of heavy metals in Taihu Lake sediment, and systematically analyze heavy metal pollution in the sediment using the basic principles of 4 / 31
meta-analysis. Monte Carlo methods, a broad class of computational algorithms that provide approximate solutions to various mathematical problems by performing statistical sampling experiments on a computer (Fishman, 2013), were used to conduct uncertainty analyses of the heavy metal accumulation features, potential ecological risks posed by heavy metals, and the toxicity levels of heavy metals to clearly and comprehensively investigate heavy metal pollution of Taihu Lake sediment. The positive matrix factorization (PMF) model was used to determine the sources of heavy metals to Taihu Lake sediment from the watershed pollution prevention and control perspective. The results provide reference data for preventing and controlling heavy metal pollution in Taihu Lake. The methods used are expected to be able to be used to determine the pollution statuses of other lakes. 2. Materials and Methods 2.1 Data Collection Several electronic databases were used to collect the monitoring data of metals in the sediment of Taihu Lake, i.e. ISI Web of Science, China National Knowledge Infrastructure (CNKI) and Wan Fang Data. The search terms in these databases were “metals” and “Taihu”, covering the studies from 2000 to 2018. Finally, a total of 851 publications were collected and 24 of them were chosen, with over 340 data records acquired. Firstly, the publications that were selected for this research should involve the investigation of the surface sediments (≤top 5 cm) in the entire Taihu Lake. Secondly, they should include the following information, including the clear number of survey spots, reports on heavy metal concentration, and survey time. In all the reviewed studies, the total concentrations of heavy metals in sediment were basically analyzed by digesting with single or mixed acids, and strict quality control and assurance were applied. The bibliography search is 5 / 31
detailed in the Fig. 1.
Fig.1 Literature selection process and results 2.2 Data Processing In the actual investigation, the more the number of survey spots, the greater the representativeness of the obtained concentration level. Therefore, the sample number weighted mean (SNWM for short) was conducted using number of survey spots in this research.
Ni is the sampling number in the data record i, Ci is heavy metals concentration in the data record i, and n is the number of the data records. Ni and Ci are obtained from the original studies. The classical Monte Carlo simulation method was adopted to deal with the uncertainty of the evaluation results when analyzing the accumulation characteristics of heavy metals and the toxicity risk of sediments in Taihu Lake. This research comprehensively evaluated the accumulation situation and toxicity level of heavy metal in the sediments of Taihu Lake. The Monte Carlo simulation in this was performed using the CrystalBall tool software. The evaluation methods of the geoaccumulation index (Igeo for short), potential ecological risk index (RI for short)
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and the toxic units (TU for short) are as follows： The Igeo of heavy metals was calculated according to the follows: Igeo=log2[Cn/(1.5 Bn)] Where Cn is the concentration of heavy metals measured in sediment and Bn is their baseline shown in Table 1. 1.5 is the background matrix correction factor due to lithogenic effects. The geoaccumulation index consists of 7 grades or classes as follows (Müller 1979): Igeo≤0, Uncontaminated; 0 ＜ Igeo ＜ 1, Uncontaminated to moderately contaminated; 1 ＜ Igeo ＜ 2, Moderately contaminated；2＜Igeo＜3，Moderately to heavily contaminated；3＜Igeo＜4，Heavily contaminated；4＜Igeo＜5，Heavily to extremely contaminated；5＜Igeo，Extremely contaminated. The methodology developed by Hakanson was used to calculate the potential ecological risk (RI) caused by the overall contamination in rivers of the Lake Taihu Basin (Häkanson 1980). Eir = Tir × Cis /Cin n
RI =
E n 1
i r
RI, the potential ecological risk；Eir, ecological risk coefficient of an individual element; Tir, the toxic response factor for a given heavy metal, Cu, Zn, Pb, Ni, Cr, Cd, As and Hg are 2, 5, 5, 1, 10, 30, 5 and 40, respectively; Cis, the concentrations of heavy metals in surface sediment, mg/kg; Cin, reference values of heavy metal, mg/kg. Table 1 Terminology used to describe the potential ecological risk Grades of potential Grades of ecological risk of a Eir value
RI value
ecological risk to the
single metal environment Eir＜40
RI＜150
Low risk 7 / 31
Low risk
40≤Eir＜80
Moderate risk
150≤RI＜300
Moderate risk
80≤Eir＜160
Considerable risk
300≤RI＜600
High risk
160≤Eir＜320
High risk
RI≥600
Higher risk
Eir≥320
Very high risk
Toxic units are used to evaluate the impact of heavy metals in sediments on the water environment (Ginebreda et al. 2014). Toxic units (TU) were also calculated to normalize the toxicities caused by various heavy metals, which allow the comparison of their relative effects and can be defined as the ratio of the determined concentration (Ci) to PEL value (Pi) (Pedersen, 1998; MacDonald et al. 2000 ). The sum toxic units (ΣTU) are sum of TUi (Niu et al. 2015a): TUi=Ci/Pi According to Pedersen (1998), the Classification of different levels of toxicity as follows: STU＜4, low toxicity level; 4≤ STU≤6, moderately toxicity level; STU＞6, Heavily toxicity level. In the absence of heavy metal fraction and biological exposure dose data, the TU values were based on total concentrations and were able to give good preliminary indications of the effects. 2.3 Receptor model PMF 5.0 Paatero first proposed the PMF model in 1994, and the method was approved for identifying sources of atmospheric pollution by the US Environmental Protection Agency (Paatero 1997). The greatest advantage of the PMF model is that no source profiles are required and uncertainty is used to weight all the data (Guan et al., 2017). The potential sources of heavy metals to Taihu Lake sediment were identified using the PMF 5.0 model to analyze the pollution sources using the distributions of eight heavy metals in Taihu Lake. The goal of the PMF model was to solve the mass balances of the species of interest using the concentrations and source profiles of the species
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of interest, as shown in Equation 1 (Norris et al., 2014). +ɛik （i=1,2,3…m; k=1,2,3…n）
（1）
Where Eik is the concentration of heavy metal; Ai is factor to sample contribution; Bjk is profile species of each source; i, k are the number of samples and chemical species, respectively, and ɛik represents the sample/species. Factor contributions and profiles are derived by the PMF model minimizing the objective function Q (Equation 2): (2) Sample concentration and uncertainty are two necessary data sets for PMF model. In this study, the concentration of each sample was above the detection limit, and the uncertainty value was calculated according to the following formula.
Unc
c 2 (0.5 MDL) 2
(3)
Where Unc is uncertainty of the concentration; is error fraction; c is the concentration of heavy metal; MDL is the method detection limit (Norris et al., 2014). 3. Result and Discussion 3.1 Selected studies Table 2 shows the summary of heavy metal concentration in sediment according to the chosen 24 papers. Among the heavy metals in the sediments of Taihu Lake, Cu, Zn, Pb, Ni, Cr, Cd, As and Hg deserve significant attention. As for the mean value of each element, the Cd SNWM was relatively high, which was 3.4 times that of the environmental background value. The Hg SNWM was relatively low, which was 0.2 times the environmental background value, and the concentrations of other elements were slightly higher than the environmental background value. 9 / 31
From the perspective of coefficient of variation (CV), Cd and Hg reached the CV level of 85% and 83%, respectively, Cu and Pb reached the CV level of 51% and 61%, respectively, and the CV of other elements were 30%-37%. This result indicated that the concentrations of Cd and Hg had large variation in the space. In addition to indicating the relatively easily-enriched area of Taihu Lake, the result also showed that there was a large uncertainty in the concentration levels of Cd and Hg in Taihu Lake sediments. Table 2 Statistical description of the publications information and sediment guideline values Heavy metal (mg/kg) Category Cu
Zn
Pb
Ni
Cr
Cd
As
Hg
Maximum
97.5
223.1
133.2
79.5
147.5
1.97
21.4
0.34
Minimum
20.8
57.4
10.1
28.3
28.3
0.09
5.9
0.05
SNWM
29.5
64.9
35.4
27.9
65.4
0.43
7.8
0.06
S.D.
17.96
34.40
23.47
12.37
20.62
0.55
4.47
0.09
CV(%)
51%
36%
61%
30%
30%
85%
37%
83%
TELa
35.7
123
18
35
37.3
0.596
5.9
0.174
PELa
197
315
36
91.3
90
3.53
17
0.486
ERLa
70
120
35
30
80
5
33
0.15
ERMa
390
270
110
50
145
9
85
1.3
Background valueb
22.3
62.6
26.2
26.7
45.9
0.126
10
0.289
S.D.: standard deviation; CV: coefficients of variation; a TEL: threshold effect level, PEL: probable effect level (MacDonald et al. 2000; MacDonald and Ingersoll, 2002), ERL: effects range low, ERM: effects range median (MacDonald et al. 2000; MacDonald and Ingersoll, 2002); b Soil 10 / 31
background concentrations of heavy metals in Taihu basin (Wenchuan et al. 2001). Fig. 2 shows the collection year and the mean value of the concentration of each element. Since the State Council approved the implementation of the “Twelfth Five-Year Plan for Comprehensive Prevention and Control of Heavy Metal Pollution” in 2011, intensive investigation of the sediments in Taihu Lake was carried out in 2010. According to the sediment analysis report collected between 2010 and 2011 (Fig. 2), the reports by different researchers showed some differences in the average concentration of heavy metals in the sediments of Taihu Lake. Among all the mean values of concentrations in these reports, the maximum of Cu, Zn, Pb, Ni, Cr, Cd, As, Hg was 4.7 times, 3.9 times, 13.1 times, 2.8 times, 5.2 times, 21.9 times, 3.6 times and 6.8 times the minimum of these elements, respectively. Cd showed the largest difference in the concentration obtained by different researchers. The maximum mean Cd concentration was 1.97 mg/kg, and was found by Yang et al. (2016). The minimum mean Cd concentration was 0.089 mg/kg, and was found by Yang et al. (2013). Full lake sediment surveys were performed in both studies, but different results were found because different numbers of sampling sites were used in the different studies.
100
Cu
240
Zn
80
Pb
200
80
100
Ni
80
60 160
60
60
40 120 40 80
40
20
20 40 2000
2004
2008
2012
2016
Cr
100
20 2000
2004
2008
2012
2016
2000
Cd
2.0
2004
2008
2012
As
20
80
1.5
15
60
1.0
10
40
0.5
5
2016
2000
2004
2008
2012
2016
Hg
0.15
0.10
0.05
20
0.0 2000
2004
2008
2012
2016
0 2000
2004
2008
2012
2016
0.00 2000
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2004
2008
2012
2016
2000
2004
2008
2012
2016
Fig.2 Average concentration of heavy metal in sediments of lake taihu
Note: the x-axis
represents the sampling date (2000–2017) not the year the paper was published (2000–2018)
With regard to screen environmental risk resulted from heavy metals in sediment, two sets of SQGs were adopted, namely the threshold effect level (TEL)/probable effect level (PEL) and the effect range low (ERL)/effect range median (ERM) values (MacDonald et al. 2000). ERLs and TELs are categorized into the low range values referring to the concentrations below which adverse effects upon sediment dwelling fauna will be infrequently. PELs and ERMs were intended to allow contaminant concentrations above which harmful effects on sediment-dwelling organisms are expected to occur frequently to be identified (MacDonald et al., 2000). Comparing the concentrations of heavy metals in the sediments of Taihu Lake with ERL and ERM, it was found that the maximum concentrations of Pb, Ni and Cr were higher than those of ERM, the maximum concentrations of Cd and As were lower than those of ERL, and the maximum concentrations of other elements were distributed between ERL and ERM. Compared with TEL and PEL, the maximum concentrations of Pb, Cr and As were higher than those of PEL point, and the maximum concentrations of the remaining elements were between TEL and PEL. Cr is an essential trace element, but excess exposure to Cr can cause hepatic metabolism disorders, skin cancer, and other diseases (Tian et al., 2018; Al et al., 2019). As and Pb are non-essential elements that have strong toxic effects on humans, particularly children and teenagers (Whitehead and Buchanan, 2019). These effects include immune diseases, internal organ failure, neurological damage, and respiratory diseases (Chikkanna et al., 2019; Taylor et al., 2019). 3.3 Igeo, RI and TU evaluation based on Monte Carlo In order to better reflect the heavy metal pollution in the sediments of Taihu Lake, Monte Carlo simulation sampling was performed based on the reported distribution characteristics of element concentrations. The simulation calculation of geoaccumulation index was conducted for 1000 times, and 8 heavy metal elements in Taihu Lake sediments were counted to obtain the Igeo, as
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shown in Fig. 3. In the sediments of Taihu Lake, the Igeo of Cd was the highest, with 24.1% below the moderate pollution level, 22.3% at the moderate pollution level, 34.9% at the moderate-heavy pollution level, and 18.7% at the heavy pollution level. In addition, Pb and Hg reached a moderate pollution level of 1.9% and 11.7%, respectively. High Cd concentrations in Taihu Lake sediment have been found to be caused by emissions from electroplating and nickel–cadmium battery manufacturing plants (Martinková et al., 2016; Xiao et al., 2019), which have played important roles in the economic development of the Taihu Lake Basin (Chao et al., 2010). In a study of heavy metal pollution in the Taihu Lake Basin, Li et al. (2017) found that chemical fertilizers used in agriculture are key sources of Cd to soil in the basin. Decreasing Cd pollution will require the sources of Cd at the watershed scale (particularly electroplating and battery manufacturing plants) to be controlled.
Fig. 3 Geoaccumulation rose chart of respective heavy metal in sediment of lake taihu The Monte Carlo method was used to complete 1000 sampling calculations using the RIs for heavy metals in Taihu Lake sediment, and the results are shown in Figs. 4 and 5. As shown in Fig.
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4, the RIs for heavy metals in Taihu Lake sediment had cumulative probabilities of 63.0% for low risk, 27.0% for moderate risk, and 10.0% for considerable risk. The high Cd Igeo values also caused high RIs. Cd contributed 71% of the potential ecological risk (Fig. 5). Ran et al. (2015) found that the algal biomass decreased as the Cd concentration in an aquatic environment increased above the threshold of 1.0 mg/L. It has been found in many studies that heavy metals negatively affect microbial community diversity (Huang et al., 2016). Indirect ecological risks posed by changes in microbial communities are difficult to predict (Zhu et al., 2017).
Low risk Moderate risk
High risk
Higher risk
100
300
200 Count
60 40
100 20 0
0 0
100
200
300
400
500
600
700
RI
Fig.4 Cumulative percentage of the sum of RI in the lake Taihu
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Cumulative percentage
80
120
Eir / RI (%)
100 80 60 40 20 0 Cu
Zn
Pb
Ni
Cr
Cd
As
Hg
Element Fig.5 Contributions of respective heavy metal to RI For the toxicity characteristics of heavy metals in the sediments of Taihu Lake, Monte Carlo was also used to complete 1000 sampling calculations, and the statistical results were drawn in Fig. 6 and Fig. 7. Fig. 6 shows the risk distribution characteristics of the total toxicity of sediments in Taihu Lake. According to the figure, in terms of the heavy metal toxicity in Taihu Lake sediment, the cumulative probability of 53.0% was at low toxicity level, 36.8% at moderate toxicity level, and 5.6% at high toxicity level. According to Fig. 6 which shows the toxicity and total toxicity contribution of different metal elements, the total toxicity of heavy metals in the sediments of Taihu Lake was Pb, Cr, As, Ni, Zn, Cu, Cd and Hg in descending order. A great deal of research shows that lead has obvious toxicity affection organisms, including nerve, blood, growth, gastrointestinal tract and immune disease though inducing oxidative stress and apoptosis (Babayigit et al. 2016). More effective pollution source analyses need to be performed to allow strategies to be developed to prevent heavy metals being emitted into Taihu Lake.
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Low toxicity
Moderate toxicity
High toxicity
100 80
Count
150 60 100
40
50
0
20
0
2
4
6
8
10
Cumulative percentage
200
0
ΣTU
Fig.6 Cumulative percentage of the sum of toxic units in the lake Taihu 60
TU/ΣTU (%)
50 40 30 20 10 0
Cu
Zn
Pb
Ni
Cr
Cd
As
Hg
Element
Fig.7 Contributions of respective heavy metal to the sum of toxic units 3.4Pollution Source Analysis By running the model for multiple times and adjusting the number of factors, we could find the minimum Q and achieved residual error control. It was found that the best simulation effect was achieved when the 4-factor Seed Number was set to 86 and the rotation coefficient Fpeak was set to -0.5 with 20 times of iterations. The residual error showed a normal distribution between -3 and
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3, and the fitting coefficient (R2) between the measured and simulated concentration of elements is above 0.8. The difference between the Q value and the theoretical Q value was less than 10%, showing that the model analysis effect met the requirements. Factor 1 explained only 6.6% of the contributions of different sources of heavy metals to the heavy metal concentrations in Taihu Lake sediment and had high factor loadings for Cd and Ni. Factor 1 was associated with Ni and Zn emissions from automotive lubricants and the decomposition of metal components and Cd and Cu emissions from car tires during use (Tian et al., 2015; Singh et al., 2018). Factor 1was therefore associated with transportation sources. Factor 2 explained 22% of the contributions of different sources of heavy metals to the heavy metal concentrations in Taihu Lake sediment and had high factor loadings for As, Cd, Cu, Ni, Pb, and Zn (each element contributed 20%–33% of the factor 2 loading). As, Cd, and Zn are indicators of agricultural fertilizer and pesticide use in the Taihu Lake Basin (Skordas K et al., 2005；Li et al., 2017). Sewage application to land would have increased the Ni concentrations in farmland soil (Wu et al., 2008). Factor 2 was therefore associated with agricultural sources. Factor 3 explained a very large proportion (64.9%) of the contributions of different sources of heavy metals to the heavy metal concentrations in Taihu Lake sediment and had high factor loadings for Cr and Hg. These metals are typically emitted by the dyeing, paper, printing, and textile industries, which have played important roles in the economic development of the Taihu Lake Basin (Chao et al., 2010). As, Cd, Cu, Ni, Pb, and Zn made similar contributions to factor 3, and this was similar to the features of complex industrial sources (Chao et al., 2010; Niu et al., 2015b). Factor 3 was therefore associated with industrial sources. Factor 4 explained only 6.6% of the contributions of different sources of heavy metals to the 17 / 31
heavy metal concentrations in Taihu Lake sediment and had low factor loadings (<10%) for all of the heavy metals. In geochemical baseline studies, natural sources of heavy metals produce the baseline concentrations in soil and sediment. Much larger amounts of heavy metals have been found to have been contributed to Taihu Lake sediment by anthropogenic sources than natural sources (Wang et al., 2019). Similar accumulation characteristics have been found in studies of the vertical distributions of heavy metals in Taihu Lake sediment (Yang et al., 2016), indicating that non-anthropogenic sources of heavy metals make small contributions to heavy metal concentrations in Taihu Lake sediment. Factor 4 was therefore associated with natural sources. Table 2 Factor analysis results of PMF model Source contribution rate / % Element Factor.1
Factor.2
Factor.3
Factor.4
Cu
6.8
26.4
59.7
7.0
Zn
9.0
27.9
55.6
7.4
Pb
5.4
26.0
61.9
6.8
Ni
11.5
29.7
51.0
7.8
Cd
13.6
29.6
48.7
8.0
Cr
0
10.0
85.5
4.6
As
6.3
26.3
60.5
6.9
Hg
0
0
96.5
3.5
Total Contribution Rate
6.6
22.0
64.9
6.5
Based on the above pollution settings, the industrial source was the main source of heavy metals in the sediments of Taihu Lake (Table 2), accounting for 64.9%. Since the 1990s, the cities around 18 / 31
Taihu Lake have prioritized the development of heavy chemical industries. The industries such as machinery and electronics, petrochemicals, automobile manufacturing, and leather textiles have developed rapidly since that time. The consequences are that the wastewater discharge during industrial development has brought continuous impact to heavy metal pollution in Taihu Lake. Secondly, agricultural source, transportation source and natural source account for 35.1% of the total impact. Related studies have shown that heavy metals and other pollutants are easily adsorbed on soil colloids and organic matter after entering the water body, and are rapidly deposited and fixed in the sedimentary facies in the areas with slow water flow. Taihu Lake Basin is located in the plain area. As a result, the slow river network current is more likely to deposit heavy metals and other pollutants. The level of heavy metal pollution in river sediments is much higher than that of the lakes (Niu et al., 2015b). According to the connection between rivers and lakes, it is necessary to prevent the heavy metals in the sedimentary facies of the river network from moving to the lake as a result from human activities such as water transport and dredging, in addition to strengthening the monitoring of the wastewater discharge from industrial source. 4. Conclusions Heavy metal concentrations found in Taihu Lake sediment in studies performed between 2000 and 2018 were used to systematically analyze heavy metal pollution in Taihu Lake sediment. Several conclusions were drawn. As, Cd, Cr, Cu, Hg, Pb, Ni, and Zn were found to be the heavy metals of most concern in Taihu Lake sediment between 2000 and 2018. From the cumulative pollution and potential ecological risk perspectives, Taihu Lake sediment was found to be most seriously polluted with Cd, which had an Igeo moderate–heavy pollution probability of 53.6% and contributed 63% of the potential ecological risks (assessed using the RI). From the toxicity risk 19 / 31
control perspective, Pb pollution needs to be controlled better than currently, to decrease the probability of Pb in Taihu Lake sediment being at the moderate toxicity level (36.8% between 2000 and 2018) and high toxicity level (5.6% between 2000 and 2018). The PMF 5.0 model indicated that heavy metals in Taihu Lake sediment have mainly been supplied by industrial sources, which have contributed 64.9% of the total heavy metal concentrations. Agricultural sources, transportation sources, and natural sources have contributed 22%, 6.6%, and 6.5%, respectively, of the total heavy metal concentrations. From the heavy metal pollution prevention and control perspective, it is necessary to prevent heavy metals in river sediment being transported into Taihu Lake because of human activities such as water transportation and dredging. Wastewater discharges from industrial sources also need to be monitored more effectively than is currently the case. The results provide comprehensive and quantitative reference data for heavy metal pollution in Taihu Lake. The methods used can be used to assess the pollution statuses of other lakes for which large differences in environmental pollution statuses have been found in previous studies. Acknowledgments: This work was financially supported by the National Natural Science Foundation (41807494), Major Science and Technology Program for Water Pollution Control and Treatment (No. 2018ZX07208-005). We also thank the editors and anonymous reviewers for comments regarding the manuscript. We also thank Gareth Thomas, PhD, from Liwen Bianji, Edanz Group China, for editing the English text of a draft of this manuscript. Reference Al Hossain, M. A., Yajima, I., Tazaki, A., Xu, H., Saheduzzaman, M., Ohgami, N., Kato, M. 2019. Chromium-mediated hyperpigmentation of skin in male tannery workers in Bangladesh. 20 / 31
Chemosphere, 229, 611-617. Whitehead, L. S., & Buchanan, S. D. 2019. Childhood Lead Poisoning: A Perpetual Environmental Justice Issue?. Journal of Public Health Management and Practice, 25, S115-S120. Babayigit, A., Thanh, D. D., Ethirajan, A., Manca, J., Muller, M., Boyen, H. G., Conings, B. 2016. Assessing the toxicity of Pb-and Sn-based perovskite solar cells in model organism Danio rerio. Scientific reports, 6, 18721. Chao J Y, Zhang Y M, Liu Z, Zhuang W, Wang W M. 2010. Discharge abating effect of Industry Restructuring - A Case Study of Jiangsu and zhejiang Sections of the Taihu Lake Basin. Journal of Ecology and Rural Environment, 26( 1), 73-76. (in Chinese) Chen, C., Jiang, X., Zhan, Y., Jin, X., Zhao, Z. 2011. Speciation distribution and potential ecological risk assessment of heavy metals in sediments of Taihu Lake. China Environmental Science, 31(11), 1842-1848. Chen, C., Zheng, B., Jiang, X., Zhao, Z., Zhan, Y., Yi, F., Ren, J. 2013. Spatial distribution and pollution assessment of mercury in sediments of Lake Taihu, China. Journal of Environmental Sciences, 25(2), 316-325. Chen, M., Ding, S., Zhang, L., Li, Y., Sun, Q., Zhang, C. 2017. An investigation of the effects of elevated phosphorus in water on the release of heavy metals in sediments at a high resolution. Science of the Total Environment, 575, 330-337. Chen, S. L., Wang, P. Z., Qin, Z. M. 2007. Distribution characterization of heavy metal pollution in typical sediment from Lake Taihu region. Jiangsu Journal of Agriculture Science, 23(2), 124-130. Chikkanna, A., Mehan, L., Sarath, P. K., & Ghosh, D. 2019. Arsenic Exposures, Poisoning, and 21 / 31
Threat to Human Health: Arsenic Affecting Human Health. In Environmental Exposures and Human Health Challenges (pp. 86-105). IGI Global. Deng B, Zhu L, Liu M, Liu N N, Yang L P, Du Y. 2011. Sediment quality criteria and ecological risk assessment for heavy metals in Taihu Lake and Liao River. Research of Environmental Sciences, 24(1): 33-42. (in Chinese) Duan, Q., Lee, J., Liu, Y., Chen, H., Hu, H. 2016. Distribution of heavy metal pollution in surface soil samples in China: a graphical review. Bulletin of environmental contamination and toxicology, 97(3), 303-309. Fishman, G. 2013. Monte Carlo: concepts, algorithms, and applications. Springer Science & Business Media. Fu, J., Hu, X., Tao, X., Yu, H., Zhang, X. 2013. Risk and toxicity assessments of heavy metals in sediments and fishes from the Yangtze River and Taihu Lake, China. Chemosphere, 93(9), 1887-1895. Gall, J. E., Boyd, R. S., Rajakaruna, N. 2015. Transfer of heavy metals through terrestrial food webs: a review. Environmental monitoring and assessment, 187(4), 201. Ginebreda, A., Kuzmanovic, M., Guasch, H., de Alda, M. L., López-Doval, J. C., Muñoz, I., Barceló, D. 2014. Assessment of multi-chemical pollution in aquatic ecosystems using toxic units: compound prioritization, mixture characterization and relationships with biological descriptors. Science of the total environment, 468, 715-723. Green, A. J., Planchart, A. 2018. The neurological toxicity of heavy metals: A fish perspective. Comparative Biochemistry and Physiology Part C: Toxicology Pharmacology, 208, 12-19. Gu Z F, Wu W. 2005. Investigation and evaluation of heavy metal pollution in the sediment of 22 / 31
Taihu Lake. Gansu Science and Technology, 21(12): 21-22. (in Chinese) Guan, Q., Wang, F., Xu, C., Pan, N., Lin, J., Zhao, R., Luo, H. 2018. Source apportionment of heavy metals in agricultural soil based on PMF: A case study in Hexi Corridor, northwest China. Chemosphere, 193, 189-197. Gurevitch, J., Koricheva, J., Nakagawa, S., Stewart, G. 2018. Meta-analysis and the science of research synthesis. Nature, 555(7695), 175. Häkanson, L. 1980. An ecological risk index for aquatic pollution control. A sedimentological approach. Water research,14(8), 975-1001. Hou J, Wang C, Wang P F, Qian J. 2012. Sediment quality guidelines and potential ecological risk assessment for heavy metals based on equilibrium partitioning approach in Taihu Lake . Acta Scientiae Circumstantiae,32(12): 2951-2959. (in Chinese) Huang, D., Xue, W., Zeng, G., Wan, J., Chen, G., Huang, C., Xu, P. 2016. Immobilization of Cd in river sediments by sodium alginate modified nanoscale zero-valent iron: impact on enzyme activities and microbial community diversity. Water research, 106, 15-25. Huang, Y., Zhang, D., Xu, Z., Yuan, S., Li, Y., Wang, L. 2017. Effect of overlying water pH, dissolved oxygen and temperature on heavy metal release from river sediments under laboratory conditions. Archives of Environmental Protection, 43(2), 28-36. 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. Journal of Environmental Sciences, 24(9), 1539-1548. Kwok, K. W., Batley, G. E., Wenning, R. J., Zhu, L., Vangheluwe, M., Lee, S. 2014. Sediment quality guidelines: challenges and opportunities for improving sediment management. 23 / 31
Environmental Science and Pollution Research, 21(1), 17-27. Li X, Jiang X J, Gao H J. 2017. Pollution Assessment and Source Analysis of Soil Heavy Metals in Taihu Lake Basin. Transactions of the Chinese Society for Agricultural Machinery, 48(S1): 247-253. Li, Y., Lin, Y., Wang, L. 2018. Distribution of heavy metals in seafloor sediments on the East China Sea inner shelf: Seasonal variations and typhoon impact. Marine pollution bulletin, 129(2), 534-544. Liu, E., Birch, G. F., Shen, J., Yuan, H., Zhang, E., Cao, Y. 2012. Comprehensive evaluation of heavy metal contamination in surface and core sediments of Taihu Lake, the third largest freshwater lake in China. Environmental Earth Sciences, 67(1), 39-51. Liu, Y., Xiao, L. 2014. Comprehensive evaluation of ecological risk of heavy metal pollution in the surface sediments of Taihu Lake. Environ. Prot. Sci, 40, 46-50. (in Chinese) Liu, Y., Peng, Y., Yue, D., Yin, Q., Xiao, L. 2015. Assessment of heavy metal enrichment, bioavailability, and controlling factors in sediments of Taihu Lake, China. Soil and Sediment Contamination: An International Journal, 24(3), 262-275. MacDonald, D. D., Ingersoll, C. G. 2002. A guidance manual to support the assessment of contaminated sediments in freshwater ecosystems. US Environmental Protection Agency, Great Lakes National Program Office. MacDonald, D. D., Ingersoll, C. G., Berger, T. A. 2000. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Archives of environmental contamination and toxicology, 39(1), 20-31. Martinková, E., Chrastný, V., Francová, M., Šípková, A., Čuřík, J., Myška, O., & Mižič, L. 2016. 24 / 31
Cadmium isotope fractionation of materials derived from various industrial processes. Journal of hazardous materials, 302, 114-119. Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Stewart, L. A. 2015. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic reviews, 4(1), 1. Müller, G. 1979. Schwermettalle in den sedimenten des Rheins-Veranderungen seit 1971. Umschau Wissensch Tech., 79, 778-783. Niu, Y., Jiao, W., Yu, H., Niu, Y., Pang, Y., Xu, X., Guo, X. 2015a. Spatial evaluation of heavy metals concentrations in the surface sediment of Taihu Lake. International journal of environmental research and public health,12(12), 15028-15039. Niu, Y., Niu, Y., Pang, Y., Yu, H. 2015b. Assessment of heavy metal pollution in sediments of inflow rivers to Lake Taihu, China. Bulletin of environmental contamination and toxicology, 95(5), 618-623. Norris, G., Duvall, R., Brown, S., Bai, S. 2014. Epa positive matrix factorization (pmf) 5.0 fundamentals and user guide prepared for the us environmental protection agency office of research and development, washington, dc. Washington, DC. Paatero, P. 1997. Least squares formulation of robust non-negative factor analysis. Chemometrics and intelligent laboratory systems, 37(1), 23-35. Pedersen, F., Bjørnestad, E., Andersen, H. V., Kjølholt, J., Poll, C. 1998. Characterization of sediments from Copenhagen Harbour by use of biotests. Water Science and Technology, 37(6-7), 233-240. Qin Y W, Zhang L, Zheng B H, Cao W. 2012. Speciation and pollution characteristics of heavy 25 / 31
metals in the sediment of Taihu Lake. Environmental Science, 33(12): 4291-4299.（in Chinese） Ran, X., Yue, H., Fu, X., Kang, Y., Xu, S., Yang, Y., Wu, Z. 2015. The response and detoxification strategies of three freshwater phytoplankton species, Aphanizomenon flos-aquae, Pediastrum simplex, and Synedra acus, to cadmium. Environmental Science and Pollution Research, 22(24), 19596-19606. Shao, D., Zhan, Y., Zhou, W., Zhu, L. 2016. Current status and temporal trend of heavy metals in farmland soil of the Yangtze River Delta Region: Field survey and meta-analysis. Environmental pollution, 219, 329-336. Shi Z F. 2010. The characteristics of pollution and ecological risk assessment of heavy metals in sediments of Taihu Lake. Northwest A F University. (in Chinese) Shuchun, Y., Bin, X. 2010. Nutrients and heavy metals in multi-cores from Zhushan Bay at Taihu Lake, the largest shallow lake in the Yangtze Delta, China. Quaternary International, 226(1-2), 23-28. Simpson, R. J. S., Pearson, K. 1904. Report on certain enteric fever inoculation statistics. The British Medical Journal, 1243-1246. Singh, D. V., Bhat, J. I. A., Bhat, R. A., Dervash, M. A., Ganei, S. A. (2018). Vehicular stress a cause for heavy metal accumulation and change in physico-chemical characteristics of road side soils in Pahalgam. Environmental monitoring and assessment, 190(6), 353. Skordas, K., Kelepertsis, A. 2005. Soil contamination by toxic metals in the cultivated region of Agia, Thessaly, Greece. Identification of sources of contamination. Environmental Geology, 48(4-5), 615-624. 26 / 31
Tao, Y., Yuan, Z., Xiaona, H., Wei, M. 2012. Distribution and bioaccumulation of heavy metals in aquatic organisms of different trophic levels and potential health risk assessment from Taihu lake, China. Ecotoxicology and Environmental Safety, 81, 55-64. Taylor, M. P., Isley, C. F., Glover, J. 2019. Prevalence of childhood lead poisoning and respiratory disease associated with lead smelter emissions. Environment international, 127, 340-352. Tian, H. Z., Zhu, C. Y., Gao, J. J., Cheng, K., Hao, J. M., Wang, K., Zhou, J. R. 2015. Quantitative assessment of atmospheric emissions of toxic heavy metals from anthropogenic sources in China: historical trend, spatial distribution, uncertainties, and control policies. Atmospheric Chemistry and Physics, 15(17), 10127-10147. Tian, X., Zhang, H., Zhao, Y., Mehmood, K., Wu, X., Chang, Z., Zhou, D. 2018. Transcriptome analysis reveals the molecular mechanism of hepatic metabolism disorder caused by chromium poisoning in chickens. Environmental Science and Pollution Research, 25(16), 15411-15421. Wang, S., Wang, W., Chen, J., Zhao, L., Zhang, B., Jiang, X. 2019. Geochemical baseline establishment and pollution source determination of heavy metals in lake sediments: A case study in Lihu Lake, China. Science of The Total Environment, 657, 978-986. Wenchuan, Q., Dickman, M., Sumin, W. 2001. Multivariate analysis of heavy metal and nutrient concentrations in sediments of Taihu Lake, China. Hydrobiologia, 450(1-3), 83-89. Whitehead, L. S., & Buchanan, S. D. 2019. Childhood Lead Poisoning: A Perpetual Environmental Justice Issue?. Journal of Public Health Management and Practice, 25, S115-S120. Wu, G. H., Su, R. X., Li, W. Q., Zheng, H. Q. 2008. Source and Enrichment of 27 / 31
Heavy Metals in
Sewage-irrigated Area Soil of Dagu Discharge Channel. Environmental science, 2008, 29(6):1693-1698. (in Chinese) Xiang, Y., Miao, Q. L., Feng, J. F. 2006. Pollution of heavy metals in the bottom mud of Lake Taihu and its assessment of potential ecological risk. Journal of nanjing Institute of meteorology, 29(5), 700-705. Xiao, L., Guan, D., Chen, Y., Dai, J., Ding, W., Peart, M. R., Zhang, C. 2019. Distribution and availability of heavy metals in soils near electroplating factories. Environmental Science and Pollution Research, 1-15. Yang C, Wang P F, Liu J J, Wang C, Hou J, Qian J. 2016. Vertical distribution and migration of heavy metals in sediment cores of Taihu Lake. J. Agro Environ. Sci, 35: 548-557. (in Chinese) Yang H, Chen G G, Liu H Y, Liang X H, Zhang M. 2013. Pollution of heavy metals in the sediments from the lower reaches of the Yangtze River and its potential ecological risk assessment. Earth and Environment, 41: 160-165. (in Chinese) Yin, H., Gao, Y., Fan, C. 2011. Distribution, sources and ecological risk assessment of heavy metals in surface sediments from Lake Taihu, China. Environmental Research Letters, 6(4), 044012. Yuan X Y, Wang A H, Xu N Z. 2004. Chemical partitioning of heavy metals and their characteristics for sediments from Lake Taihu. Geochimica, (6): 611-618. (in Chinese) Yuan, H. Z., Shen, J., Liu, E. F., Wang, J. J., Meng, X. H. 2011. Assessment of nutrients and heavy metals enrichment in surface sediments from Taihu Lake, a eutrophic shallow lake in China. Environmental geochemistry and health, 33(1), 67-81. 28 / 31
Yuan, X., Chen, J., Ji, J., Tao, Y., Wang, R. 2002. Characteristics and environmental changes of pollution elements in Taihu sediments and soils near the lake. Acta sedimentologica sinica, 20(3), 427-434. Zhang, X., Zhong, T., Chen, D., Cheng, M., Liu, L., Zhang, X., Li, X. 2016. Assessment of arsenic (As) occurrence in arable soil and its related health risk in China. Environmental geochemistry and health, 38(3), 691-702. Zhang, Y. P., Qu, W. C. 2001. Determination of heavy metal contents in the sediments from Taihu Lake and its environmental significance. Rock and Mineral Analysis, 20(1), 34-37. Zhang, Y., Han, Y., Yang, J., Zhu, L., Zhong, W. 2017. Toxicities and risk assessment of heavy metals in sediments of Taihu Lake, China, based on sediment quality guidelines. Journal of Environmental Sciences, 62, 31-38. Zhang, Y., Hu, X., Yu, T. 2012. Distribution and risk assessment of metals in sediments from Taihu Lake, China using multivariate statistics and multiple tools. Bulletin of environmental contamination and toxicology, 89(5), 1009-1015. Zhu, Y. G., Gillings, M., Simonet, P., Stekel, D., Banwart, S., Penuelas, J. 2017. Microbial mass movements. Science, 357(6356), 1099-1100.
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Highlights Systematically analyze the heavy metal pollution in the sediments of Taihu Lake in the past 20 years. The Cd accumulation index indicated moderate–heavy pollution and Cd was the main contributor of potential ecological risks Pb is the main contributor to the total toxicity of the sediments in Taihu Lake. Industrial source accounts for 64.9% of the heavy metals in the sediments of Taihu Lake.
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S0048-9697(19)34500-0 https://doi.org/10.1016/j.scitotenv.2019.134509 STOTEN 134509
To appear in:
Science of the Total Environment
Received Date: Revised Date: Accepted Date:
19 June 2019 2 August 2019 16 September 2019
Please cite this article as: Y. Niu, X. Jiang, K. Wang, J. Xia, W. Jiao, Y. Niu, H. Yu, Meta analysis of heavy metal pollution and sources in surface sediments of Lake Taihu, China, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134509
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Meta analysis of heavy metal pollution and sources in surface sediments of Lake Taihu, China Yong Niua, Xia Jiang a, Kun Wang a, Jiandong Xia a, Wei Jiaoa, b, Yuan Niu a*, Hui Yu a* a
National Engineering Laboratory for Lake Pollution Control and Ecological Restoration,
Institute of lake environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China b
Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental
Protection, College of Resources and Environment, Linyi University, Linyi 276000, China; *Corresponding author at: Institute of lake environment, Chinese Research Academy of Environmental Sciences, Beijing, China E-mail address: [email protected]; [email protected]
Highlights：
Systematically analyze the heavy metal pollution in the sediments of Taihu Lake in the past 20 years.
The Cd accumulation index indicated moderate–heavy pollution and Cd was the main contributor of potential ecological risks
Pb is the main contributor to the total toxicity of the sediments in Taihu Lake.
Industrial source accounts for 64.9% of the heavy metals in the sediments of Taihu Lake.
Abstract：Heavy metal concentrations in Taihu Lake sediment from studies performed between 2000 and 2018 were analyzed and Monte Carlo uncertainty analysis of heavy metal geo-accumulation, potential ecological risk and toxicity data for Taihu Lake sediment was 1 / 31
performed to allow heavy metal pollution of Taihu Lake sediment to be described clearly, objectively, and comprehensively. Five main conclusions were drawn. (1) Most attention should be paid to As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn pollution. (2) The geo-accumulation indices showed that Cd is the most important pollutant and that the probabilities of Taihu Lake sediment being moderately polluted, moderately–heavily polluted, and heavily polluted were found to be 53.6%, 34.9%, and 18.7%, respectively. (3) Cd is the main contributor to potential ecological risks and had cumulative low risk, moderate risk, and considerable risk probabilities of 63.0%, 27.0%, and 10.0%, respectively. (4) Toxicity unit evaluation results indicated that Pb is the main contributor of toxicity in Taihu Lake sediment and had cumulative low toxicity, moderate toxicity, and high toxicity probabilities of 53.0%, 36.8%, and 5.6%, respectively. (5) Positive matrix factorization model results indicated that industrial sources are the main suppliers of heavy metals to Taihu Lake sediment, contributing 64.9% of the heavy metals. The summarized results and conclusions will improve local government awareness of heavy metal pollution in Taihu Lake and will aid in the development of appropriate pollution control measures. The results will also provide reference data for future studies of heavy metal pollution in sediment from Taihu Lake and other lakes. Key words：Lake Taihu, Sediment, Heavy Metal, Geoaccumulation index, Potential ecological risk, Toxic units
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1. Introduction The accumulation of heavy metals in the aquatic environment poses serious risks to aquatic ecosystems (Green and Planchart 2018). The enrichment of heavy metals through food chains also poses serious risks to human health (Gall et al., 2015). Numerical sediment quality guidelines, such as threshold effect levels, probable effect levels (PELs), effect range low values (ERLs), and effect range median values (ERMs), have been used in many studies to screen heavy metal contamination of sediment (MacDonald et al., 2000; Kwok et al., 2014). The eutrophication of Taihu Lake has attracted international attention. The risks posed by heavy metals in Taihu Lake have increased since 1980 (Shuchun and Bin 2010). Heavy metals are becoming enriched in fish in Taihu Lake because of biomagnification, and pose serious risks to humans consuming fish from Taihu Lake (Fu et al., 2013). Heavy metal concentrations in sediment in Taihu Lake have been investigated in many studies. Different heavy metal concentrations have been found in different studies. For example, the mean Cd concentration in Taihu Lake sediment was 22 times higher in a study published in 2016 (Yang et al., 2016) than in a study published in 2013 (Yang et al., 2013). Yang et al. (2013) found that Hg was the main indicator of potential ecological hazards in Taihu Lake, but Qin et al. (2012) found that Cd was the main indicator of ecological hazards in Taihu Lake. The concentrations of heavy metals in Taihu Lake sediment have marked spatial variations because of variability in heavy metal inputs, seasonal variations, and spatial variations in the pH, dissolved oxygen concentration, and other conditions (Chen et al., 2017; Huang et al., 2017; Li et al., 2018). Such variations make it difficult for those responsible for managing Taihu Lake to understand heavy metal pollution in Taihu Lake sediment and develop pollution prevention strategies. A better method is therefore required for 3 / 31
describing heavy metal pollution in Taihu Lake sediment. Meta-analysis is a statistical method for comprehensively analyzing large amounts of analytical data from numerous studies to integrate the results (Moher et al 2015). Generally, meta-analysis extracts one or more outcomes as “effect sizes” from each study. The effect sizes are designed to bring the outcomes of the different studies to the same scale. This is achieved using metrics including odds and risk ratios, standardized mean differences, z-transformed correlation coefficients, and logarithmic response ratios (Gurevitch et al., 2018). Meta-analysis was first used in the medical field, and meta-analysis results are often used as reference data when making clinical treatment decisions (Simpson and Pearson, 1904). Meta-analysis has recently started to be used to analyze environmental pollution data to allow comprehensive analyses of pollution to be achieved. For example, Shao et al. (2016) assessed temporal trends in heavy metal pollution of farmland topsoil in the Yangtze River Delta and used meta-analysis to make up for a lack of long-term monitoring data. Duan et al. (2016) integrated data from 2450 publications to map the spatial distributions of heavy metals in farmland soil in China. Zhang et al. (2016) used meta-analysis to analyze Cd pollution of cultivated land in China. Meta-analyses of published data are valuable for studying pollution of various environmental media when monitoring data are unavailable. Meta-analysis has not yet been developed into a unified analysis process for performing environmental pollution assessments, and currently available methods usually involve collecting and re-analyzing pollutant data from relevant publications to characterize the pollution state of the area of interest. Here, we use data from previous studies of heavy metals in Taihu Lake sediment, and systematically analyze heavy metal pollution in the sediment using the basic principles of 4 / 31
meta-analysis. Monte Carlo methods, a broad class of computational algorithms that provide approximate solutions to various mathematical problems by performing statistical sampling experiments on a computer (Fishman, 2013), were used to conduct uncertainty analyses of the heavy metal accumulation features, potential ecological risks posed by heavy metals, and the toxicity levels of heavy metals to clearly and comprehensively investigate heavy metal pollution of Taihu Lake sediment. The positive matrix factorization (PMF) model was used to determine the sources of heavy metals to Taihu Lake sediment from the watershed pollution prevention and control perspective. The results provide reference data for preventing and controlling heavy metal pollution in Taihu Lake. The methods used are expected to be able to be used to determine the pollution statuses of other lakes. 2. Materials and Methods 2.1 Data Collection Several electronic databases were used to collect the monitoring data of metals in the sediment of Taihu Lake, i.e. ISI Web of Science, China National Knowledge Infrastructure (CNKI) and Wan Fang Data. The search terms in these databases were “metals” and “Taihu”, covering the studies from 2000 to 2018. Finally, a total of 851 publications were collected and 24 of them were chosen, with over 340 data records acquired. Firstly, the publications that were selected for this research should involve the investigation of the surface sediments (≤top 5 cm) in the entire Taihu Lake. Secondly, they should include the following information, including the clear number of survey spots, reports on heavy metal concentration, and survey time. In all the reviewed studies, the total concentrations of heavy metals in sediment were basically analyzed by digesting with single or mixed acids, and strict quality control and assurance were applied. The bibliography search is 5 / 31
detailed in the Fig. 1.
Fig.1 Literature selection process and results 2.2 Data Processing In the actual investigation, the more the number of survey spots, the greater the representativeness of the obtained concentration level. Therefore, the sample number weighted mean (SNWM for short) was conducted using number of survey spots in this research.
Ni is the sampling number in the data record i, Ci is heavy metals concentration in the data record i, and n is the number of the data records. Ni and Ci are obtained from the original studies. The classical Monte Carlo simulation method was adopted to deal with the uncertainty of the evaluation results when analyzing the accumulation characteristics of heavy metals and the toxicity risk of sediments in Taihu Lake. This research comprehensively evaluated the accumulation situation and toxicity level of heavy metal in the sediments of Taihu Lake. The Monte Carlo simulation in this was performed using the CrystalBall tool software. The evaluation methods of the geoaccumulation index (Igeo for short), potential ecological risk index (RI for short)
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and the toxic units (TU for short) are as follows： The Igeo of heavy metals was calculated according to the follows: Igeo=log2[Cn/(1.5 Bn)] Where Cn is the concentration of heavy metals measured in sediment and Bn is their baseline shown in Table 1. 1.5 is the background matrix correction factor due to lithogenic effects. The geoaccumulation index consists of 7 grades or classes as follows (Müller 1979): Igeo≤0, Uncontaminated; 0 ＜ Igeo ＜ 1, Uncontaminated to moderately contaminated; 1 ＜ Igeo ＜ 2, Moderately contaminated；2＜Igeo＜3，Moderately to heavily contaminated；3＜Igeo＜4，Heavily contaminated；4＜Igeo＜5，Heavily to extremely contaminated；5＜Igeo，Extremely contaminated. The methodology developed by Hakanson was used to calculate the potential ecological risk (RI) caused by the overall contamination in rivers of the Lake Taihu Basin (Häkanson 1980). Eir = Tir × Cis /Cin n
RI =
E n 1
i r
RI, the potential ecological risk；Eir, ecological risk coefficient of an individual element; Tir, the toxic response factor for a given heavy metal, Cu, Zn, Pb, Ni, Cr, Cd, As and Hg are 2, 5, 5, 1, 10, 30, 5 and 40, respectively; Cis, the concentrations of heavy metals in surface sediment, mg/kg; Cin, reference values of heavy metal, mg/kg. Table 1 Terminology used to describe the potential ecological risk Grades of potential Grades of ecological risk of a Eir value
RI value
ecological risk to the
single metal environment Eir＜40
RI＜150
Low risk 7 / 31
Low risk
40≤Eir＜80
Moderate risk
150≤RI＜300
Moderate risk
80≤Eir＜160
Considerable risk
300≤RI＜600
High risk
160≤Eir＜320
High risk
RI≥600
Higher risk
Eir≥320
Very high risk
Toxic units are used to evaluate the impact of heavy metals in sediments on the water environment (Ginebreda et al. 2014). Toxic units (TU) were also calculated to normalize the toxicities caused by various heavy metals, which allow the comparison of their relative effects and can be defined as the ratio of the determined concentration (Ci) to PEL value (Pi) (Pedersen, 1998; MacDonald et al. 2000 ). The sum toxic units (ΣTU) are sum of TUi (Niu et al. 2015a): TUi=Ci/Pi According to Pedersen (1998), the Classification of different levels of toxicity as follows: STU＜4, low toxicity level; 4≤ STU≤6, moderately toxicity level; STU＞6, Heavily toxicity level. In the absence of heavy metal fraction and biological exposure dose data, the TU values were based on total concentrations and were able to give good preliminary indications of the effects. 2.3 Receptor model PMF 5.0 Paatero first proposed the PMF model in 1994, and the method was approved for identifying sources of atmospheric pollution by the US Environmental Protection Agency (Paatero 1997). The greatest advantage of the PMF model is that no source profiles are required and uncertainty is used to weight all the data (Guan et al., 2017). The potential sources of heavy metals to Taihu Lake sediment were identified using the PMF 5.0 model to analyze the pollution sources using the distributions of eight heavy metals in Taihu Lake. The goal of the PMF model was to solve the mass balances of the species of interest using the concentrations and source profiles of the species
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of interest, as shown in Equation 1 (Norris et al., 2014). +ɛik （i=1,2,3…m; k=1,2,3…n）
（1）
Where Eik is the concentration of heavy metal; Ai is factor to sample contribution; Bjk is profile species of each source; i, k are the number of samples and chemical species, respectively, and ɛik represents the sample/species. Factor contributions and profiles are derived by the PMF model minimizing the objective function Q (Equation 2): (2) Sample concentration and uncertainty are two necessary data sets for PMF model. In this study, the concentration of each sample was above the detection limit, and the uncertainty value was calculated according to the following formula.
Unc
c 2 (0.5 MDL) 2
(3)
Where Unc is uncertainty of the concentration; is error fraction; c is the concentration of heavy metal; MDL is the method detection limit (Norris et al., 2014). 3. Result and Discussion 3.1 Selected studies Table 2 shows the summary of heavy metal concentration in sediment according to the chosen 24 papers. Among the heavy metals in the sediments of Taihu Lake, Cu, Zn, Pb, Ni, Cr, Cd, As and Hg deserve significant attention. As for the mean value of each element, the Cd SNWM was relatively high, which was 3.4 times that of the environmental background value. The Hg SNWM was relatively low, which was 0.2 times the environmental background value, and the concentrations of other elements were slightly higher than the environmental background value. 9 / 31
From the perspective of coefficient of variation (CV), Cd and Hg reached the CV level of 85% and 83%, respectively, Cu and Pb reached the CV level of 51% and 61%, respectively, and the CV of other elements were 30%-37%. This result indicated that the concentrations of Cd and Hg had large variation in the space. In addition to indicating the relatively easily-enriched area of Taihu Lake, the result also showed that there was a large uncertainty in the concentration levels of Cd and Hg in Taihu Lake sediments. Table 2 Statistical description of the publications information and sediment guideline values Heavy metal (mg/kg) Category Cu
Zn
Pb
Ni
Cr
Cd
As
Hg
Maximum
97.5
223.1
133.2
79.5
147.5
1.97
21.4
0.34
Minimum
20.8
57.4
10.1
28.3
28.3
0.09
5.9
0.05
SNWM
29.5
64.9
35.4
27.9
65.4
0.43
7.8
0.06
S.D.
17.96
34.40
23.47
12.37
20.62
0.55
4.47
0.09
CV(%)
51%
36%
61%
30%
30%
85%
37%
83%
TELa
35.7
123
18
35
37.3
0.596
5.9
0.174
PELa
197
315
36
91.3
90
3.53
17
0.486
ERLa
70
120
35
30
80
5
33
0.15
ERMa
390
270
110
50
145
9
85
1.3
Background valueb
22.3
62.6
26.2
26.7
45.9
0.126
10
0.289
S.D.: standard deviation; CV: coefficients of variation; a TEL: threshold effect level, PEL: probable effect level (MacDonald et al. 2000; MacDonald and Ingersoll, 2002), ERL: effects range low, ERM: effects range median (MacDonald et al. 2000; MacDonald and Ingersoll, 2002); b Soil 10 / 31
background concentrations of heavy metals in Taihu basin (Wenchuan et al. 2001). Fig. 2 shows the collection year and the mean value of the concentration of each element. Since the State Council approved the implementation of the “Twelfth Five-Year Plan for Comprehensive Prevention and Control of Heavy Metal Pollution” in 2011, intensive investigation of the sediments in Taihu Lake was carried out in 2010. According to the sediment analysis report collected between 2010 and 2011 (Fig. 2), the reports by different researchers showed some differences in the average concentration of heavy metals in the sediments of Taihu Lake. Among all the mean values of concentrations in these reports, the maximum of Cu, Zn, Pb, Ni, Cr, Cd, As, Hg was 4.7 times, 3.9 times, 13.1 times, 2.8 times, 5.2 times, 21.9 times, 3.6 times and 6.8 times the minimum of these elements, respectively. Cd showed the largest difference in the concentration obtained by different researchers. The maximum mean Cd concentration was 1.97 mg/kg, and was found by Yang et al. (2016). The minimum mean Cd concentration was 0.089 mg/kg, and was found by Yang et al. (2013). Full lake sediment surveys were performed in both studies, but different results were found because different numbers of sampling sites were used in the different studies.
100
Cu
240
Zn
80
Pb
200
80
100
Ni
80
60 160
60
60
40 120 40 80
40
20
20 40 2000
2004
2008
2012
2016
Cr
100
20 2000
2004
2008
2012
2016
2000
Cd
2.0
2004
2008
2012
As
20
80
1.5
15
60
1.0
10
40
0.5
5
2016
2000
2004
2008
2012
2016
Hg
0.15
0.10
0.05
20
0.0 2000
2004
2008
2012
2016
0 2000
2004
2008
2012
2016
0.00 2000
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2004
2008
2012
2016
2000
2004
2008
2012
2016
Fig.2 Average concentration of heavy metal in sediments of lake taihu
Note: the x-axis
represents the sampling date (2000–2017) not the year the paper was published (2000–2018)
With regard to screen environmental risk resulted from heavy metals in sediment, two sets of SQGs were adopted, namely the threshold effect level (TEL)/probable effect level (PEL) and the effect range low (ERL)/effect range median (ERM) values (MacDonald et al. 2000). ERLs and TELs are categorized into the low range values referring to the concentrations below which adverse effects upon sediment dwelling fauna will be infrequently. PELs and ERMs were intended to allow contaminant concentrations above which harmful effects on sediment-dwelling organisms are expected to occur frequently to be identified (MacDonald et al., 2000). Comparing the concentrations of heavy metals in the sediments of Taihu Lake with ERL and ERM, it was found that the maximum concentrations of Pb, Ni and Cr were higher than those of ERM, the maximum concentrations of Cd and As were lower than those of ERL, and the maximum concentrations of other elements were distributed between ERL and ERM. Compared with TEL and PEL, the maximum concentrations of Pb, Cr and As were higher than those of PEL point, and the maximum concentrations of the remaining elements were between TEL and PEL. Cr is an essential trace element, but excess exposure to Cr can cause hepatic metabolism disorders, skin cancer, and other diseases (Tian et al., 2018; Al et al., 2019). As and Pb are non-essential elements that have strong toxic effects on humans, particularly children and teenagers (Whitehead and Buchanan, 2019). These effects include immune diseases, internal organ failure, neurological damage, and respiratory diseases (Chikkanna et al., 2019; Taylor et al., 2019). 3.3 Igeo, RI and TU evaluation based on Monte Carlo In order to better reflect the heavy metal pollution in the sediments of Taihu Lake, Monte Carlo simulation sampling was performed based on the reported distribution characteristics of element concentrations. The simulation calculation of geoaccumulation index was conducted for 1000 times, and 8 heavy metal elements in Taihu Lake sediments were counted to obtain the Igeo, as
12 / 31
shown in Fig. 3. In the sediments of Taihu Lake, the Igeo of Cd was the highest, with 24.1% below the moderate pollution level, 22.3% at the moderate pollution level, 34.9% at the moderate-heavy pollution level, and 18.7% at the heavy pollution level. In addition, Pb and Hg reached a moderate pollution level of 1.9% and 11.7%, respectively. High Cd concentrations in Taihu Lake sediment have been found to be caused by emissions from electroplating and nickel–cadmium battery manufacturing plants (Martinková et al., 2016; Xiao et al., 2019), which have played important roles in the economic development of the Taihu Lake Basin (Chao et al., 2010). In a study of heavy metal pollution in the Taihu Lake Basin, Li et al. (2017) found that chemical fertilizers used in agriculture are key sources of Cd to soil in the basin. Decreasing Cd pollution will require the sources of Cd at the watershed scale (particularly electroplating and battery manufacturing plants) to be controlled.
Fig. 3 Geoaccumulation rose chart of respective heavy metal in sediment of lake taihu The Monte Carlo method was used to complete 1000 sampling calculations using the RIs for heavy metals in Taihu Lake sediment, and the results are shown in Figs. 4 and 5. As shown in Fig.
13 / 31
4, the RIs for heavy metals in Taihu Lake sediment had cumulative probabilities of 63.0% for low risk, 27.0% for moderate risk, and 10.0% for considerable risk. The high Cd Igeo values also caused high RIs. Cd contributed 71% of the potential ecological risk (Fig. 5). Ran et al. (2015) found that the algal biomass decreased as the Cd concentration in an aquatic environment increased above the threshold of 1.0 mg/L. It has been found in many studies that heavy metals negatively affect microbial community diversity (Huang et al., 2016). Indirect ecological risks posed by changes in microbial communities are difficult to predict (Zhu et al., 2017).
Low risk Moderate risk
High risk
Higher risk
100
300
200 Count
60 40
100 20 0
0 0
100
200
300
400
500
600
700
RI
Fig.4 Cumulative percentage of the sum of RI in the lake Taihu
14 / 31
Cumulative percentage
80
120
Eir / RI (%)
100 80 60 40 20 0 Cu
Zn
Pb
Ni
Cr
Cd
As
Hg
Element Fig.5 Contributions of respective heavy metal to RI For the toxicity characteristics of heavy metals in the sediments of Taihu Lake, Monte Carlo was also used to complete 1000 sampling calculations, and the statistical results were drawn in Fig. 6 and Fig. 7. Fig. 6 shows the risk distribution characteristics of the total toxicity of sediments in Taihu Lake. According to the figure, in terms of the heavy metal toxicity in Taihu Lake sediment, the cumulative probability of 53.0% was at low toxicity level, 36.8% at moderate toxicity level, and 5.6% at high toxicity level. According to Fig. 6 which shows the toxicity and total toxicity contribution of different metal elements, the total toxicity of heavy metals in the sediments of Taihu Lake was Pb, Cr, As, Ni, Zn, Cu, Cd and Hg in descending order. A great deal of research shows that lead has obvious toxicity affection organisms, including nerve, blood, growth, gastrointestinal tract and immune disease though inducing oxidative stress and apoptosis (Babayigit et al. 2016). More effective pollution source analyses need to be performed to allow strategies to be developed to prevent heavy metals being emitted into Taihu Lake.
15 / 31
Low toxicity
Moderate toxicity
High toxicity
100 80
Count
150 60 100
40
50
0
20
0
2
4
6
8
10
Cumulative percentage
200
0
ΣTU
Fig.6 Cumulative percentage of the sum of toxic units in the lake Taihu 60
TU/ΣTU (%)
50 40 30 20 10 0
Cu
Zn
Pb
Ni
Cr
Cd
As
Hg
Element
Fig.7 Contributions of respective heavy metal to the sum of toxic units 3.4Pollution Source Analysis By running the model for multiple times and adjusting the number of factors, we could find the minimum Q and achieved residual error control. It was found that the best simulation effect was achieved when the 4-factor Seed Number was set to 86 and the rotation coefficient Fpeak was set to -0.5 with 20 times of iterations. The residual error showed a normal distribution between -3 and
16 / 31
3, and the fitting coefficient (R2) between the measured and simulated concentration of elements is above 0.8. The difference between the Q value and the theoretical Q value was less than 10%, showing that the model analysis effect met the requirements. Factor 1 explained only 6.6% of the contributions of different sources of heavy metals to the heavy metal concentrations in Taihu Lake sediment and had high factor loadings for Cd and Ni. Factor 1 was associated with Ni and Zn emissions from automotive lubricants and the decomposition of metal components and Cd and Cu emissions from car tires during use (Tian et al., 2015; Singh et al., 2018). Factor 1was therefore associated with transportation sources. Factor 2 explained 22% of the contributions of different sources of heavy metals to the heavy metal concentrations in Taihu Lake sediment and had high factor loadings for As, Cd, Cu, Ni, Pb, and Zn (each element contributed 20%–33% of the factor 2 loading). As, Cd, and Zn are indicators of agricultural fertilizer and pesticide use in the Taihu Lake Basin (Skordas K et al., 2005；Li et al., 2017). Sewage application to land would have increased the Ni concentrations in farmland soil (Wu et al., 2008). Factor 2 was therefore associated with agricultural sources. Factor 3 explained a very large proportion (64.9%) of the contributions of different sources of heavy metals to the heavy metal concentrations in Taihu Lake sediment and had high factor loadings for Cr and Hg. These metals are typically emitted by the dyeing, paper, printing, and textile industries, which have played important roles in the economic development of the Taihu Lake Basin (Chao et al., 2010). As, Cd, Cu, Ni, Pb, and Zn made similar contributions to factor 3, and this was similar to the features of complex industrial sources (Chao et al., 2010; Niu et al., 2015b). Factor 3 was therefore associated with industrial sources. Factor 4 explained only 6.6% of the contributions of different sources of heavy metals to the 17 / 31
heavy metal concentrations in Taihu Lake sediment and had low factor loadings (<10%) for all of the heavy metals. In geochemical baseline studies, natural sources of heavy metals produce the baseline concentrations in soil and sediment. Much larger amounts of heavy metals have been found to have been contributed to Taihu Lake sediment by anthropogenic sources than natural sources (Wang et al., 2019). Similar accumulation characteristics have been found in studies of the vertical distributions of heavy metals in Taihu Lake sediment (Yang et al., 2016), indicating that non-anthropogenic sources of heavy metals make small contributions to heavy metal concentrations in Taihu Lake sediment. Factor 4 was therefore associated with natural sources. Table 2 Factor analysis results of PMF model Source contribution rate / % Element Factor.1
Factor.2
Factor.3
Factor.4
Cu
6.8
26.4
59.7
7.0
Zn
9.0
27.9
55.6
7.4
Pb
5.4
26.0
61.9
6.8
Ni
11.5
29.7
51.0
7.8
Cd
13.6
29.6
48.7
8.0
Cr
0
10.0
85.5
4.6
As
6.3
26.3
60.5
6.9
Hg
0
0
96.5
3.5
Total Contribution Rate
6.6
22.0
64.9
6.5
Based on the above pollution settings, the industrial source was the main source of heavy metals in the sediments of Taihu Lake (Table 2), accounting for 64.9%. Since the 1990s, the cities around 18 / 31
Taihu Lake have prioritized the development of heavy chemical industries. The industries such as machinery and electronics, petrochemicals, automobile manufacturing, and leather textiles have developed rapidly since that time. The consequences are that the wastewater discharge during industrial development has brought continuous impact to heavy metal pollution in Taihu Lake. Secondly, agricultural source, transportation source and natural source account for 35.1% of the total impact. Related studies have shown that heavy metals and other pollutants are easily adsorbed on soil colloids and organic matter after entering the water body, and are rapidly deposited and fixed in the sedimentary facies in the areas with slow water flow. Taihu Lake Basin is located in the plain area. As a result, the slow river network current is more likely to deposit heavy metals and other pollutants. The level of heavy metal pollution in river sediments is much higher than that of the lakes (Niu et al., 2015b). According to the connection between rivers and lakes, it is necessary to prevent the heavy metals in the sedimentary facies of the river network from moving to the lake as a result from human activities such as water transport and dredging, in addition to strengthening the monitoring of the wastewater discharge from industrial source. 4. Conclusions Heavy metal concentrations found in Taihu Lake sediment in studies performed between 2000 and 2018 were used to systematically analyze heavy metal pollution in Taihu Lake sediment. Several conclusions were drawn. As, Cd, Cr, Cu, Hg, Pb, Ni, and Zn were found to be the heavy metals of most concern in Taihu Lake sediment between 2000 and 2018. From the cumulative pollution and potential ecological risk perspectives, Taihu Lake sediment was found to be most seriously polluted with Cd, which had an Igeo moderate–heavy pollution probability of 53.6% and contributed 63% of the potential ecological risks (assessed using the RI). From the toxicity risk 19 / 31
control perspective, Pb pollution needs to be controlled better than currently, to decrease the probability of Pb in Taihu Lake sediment being at the moderate toxicity level (36.8% between 2000 and 2018) and high toxicity level (5.6% between 2000 and 2018). The PMF 5.0 model indicated that heavy metals in Taihu Lake sediment have mainly been supplied by industrial sources, which have contributed 64.9% of the total heavy metal concentrations. Agricultural sources, transportation sources, and natural sources have contributed 22%, 6.6%, and 6.5%, respectively, of the total heavy metal concentrations. From the heavy metal pollution prevention and control perspective, it is necessary to prevent heavy metals in river sediment being transported into Taihu Lake because of human activities such as water transportation and dredging. Wastewater discharges from industrial sources also need to be monitored more effectively than is currently the case. The results provide comprehensive and quantitative reference data for heavy metal pollution in Taihu Lake. The methods used can be used to assess the pollution statuses of other lakes for which large differences in environmental pollution statuses have been found in previous studies. Acknowledgments: This work was financially supported by the National Natural Science Foundation (41807494), Major Science and Technology Program for Water Pollution Control and Treatment (No. 2018ZX07208-005). We also thank the editors and anonymous reviewers for comments regarding the manuscript. We also thank Gareth Thomas, PhD, from Liwen Bianji, Edanz Group China, for editing the English text of a draft of this manuscript. Reference Al Hossain, M. A., Yajima, I., Tazaki, A., Xu, H., Saheduzzaman, M., Ohgami, N., Kato, M. 2019. Chromium-mediated hyperpigmentation of skin in male tannery workers in Bangladesh. 20 / 31
Chemosphere, 229, 611-617. Whitehead, L. S., & Buchanan, S. D. 2019. Childhood Lead Poisoning: A Perpetual Environmental Justice Issue?. Journal of Public Health Management and Practice, 25, S115-S120. Babayigit, A., Thanh, D. D., Ethirajan, A., Manca, J., Muller, M., Boyen, H. G., Conings, B. 2016. Assessing the toxicity of Pb-and Sn-based perovskite solar cells in model organism Danio rerio. Scientific reports, 6, 18721. Chao J Y, Zhang Y M, Liu Z, Zhuang W, Wang W M. 2010. Discharge abating effect of Industry Restructuring - A Case Study of Jiangsu and zhejiang Sections of the Taihu Lake Basin. Journal of Ecology and Rural Environment, 26( 1), 73-76. (in Chinese) Chen, C., Jiang, X., Zhan, Y., Jin, X., Zhao, Z. 2011. Speciation distribution and potential ecological risk assessment of heavy metals in sediments of Taihu Lake. China Environmental Science, 31(11), 1842-1848. Chen, C., Zheng, B., Jiang, X., Zhao, Z., Zhan, Y., Yi, F., Ren, J. 2013. Spatial distribution and pollution assessment of mercury in sediments of Lake Taihu, China. Journal of Environmental Sciences, 25(2), 316-325. Chen, M., Ding, S., Zhang, L., Li, Y., Sun, Q., Zhang, C. 2017. An investigation of the effects of elevated phosphorus in water on the release of heavy metals in sediments at a high resolution. Science of the Total Environment, 575, 330-337. Chen, S. L., Wang, P. Z., Qin, Z. M. 2007. Distribution characterization of heavy metal pollution in typical sediment from Lake Taihu region. Jiangsu Journal of Agriculture Science, 23(2), 124-130. Chikkanna, A., Mehan, L., Sarath, P. K., & Ghosh, D. 2019. Arsenic Exposures, Poisoning, and 21 / 31
Threat to Human Health: Arsenic Affecting Human Health. In Environmental Exposures and Human Health Challenges (pp. 86-105). IGI Global. Deng B, Zhu L, Liu M, Liu N N, Yang L P, Du Y. 2011. Sediment quality criteria and ecological risk assessment for heavy metals in Taihu Lake and Liao River. Research of Environmental Sciences, 24(1): 33-42. (in Chinese) Duan, Q., Lee, J., Liu, Y., Chen, H., Hu, H. 2016. Distribution of heavy metal pollution in surface soil samples in China: a graphical review. Bulletin of environmental contamination and toxicology, 97(3), 303-309. Fishman, G. 2013. Monte Carlo: concepts, algorithms, and applications. Springer Science & Business Media. Fu, J., Hu, X., Tao, X., Yu, H., Zhang, X. 2013. Risk and toxicity assessments of heavy metals in sediments and fishes from the Yangtze River and Taihu Lake, China. Chemosphere, 93(9), 1887-1895. Gall, J. E., Boyd, R. S., Rajakaruna, N. 2015. Transfer of heavy metals through terrestrial food webs: a review. Environmental monitoring and assessment, 187(4), 201. Ginebreda, A., Kuzmanovic, M., Guasch, H., de Alda, M. L., López-Doval, J. C., Muñoz, I., Barceló, D. 2014. Assessment of multi-chemical pollution in aquatic ecosystems using toxic units: compound prioritization, mixture characterization and relationships with biological descriptors. Science of the total environment, 468, 715-723. Green, A. J., Planchart, A. 2018. The neurological toxicity of heavy metals: A fish perspective. Comparative Biochemistry and Physiology Part C: Toxicology Pharmacology, 208, 12-19. Gu Z F, Wu W. 2005. Investigation and evaluation of heavy metal pollution in the sediment of 22 / 31
Taihu Lake. Gansu Science and Technology, 21(12): 21-22. (in Chinese) Guan, Q., Wang, F., Xu, C., Pan, N., Lin, J., Zhao, R., Luo, H. 2018. Source apportionment of heavy metals in agricultural soil based on PMF: A case study in Hexi Corridor, northwest China. Chemosphere, 193, 189-197. Gurevitch, J., Koricheva, J., Nakagawa, S., Stewart, G. 2018. Meta-analysis and the science of research synthesis. Nature, 555(7695), 175. Häkanson, L. 1980. An ecological risk index for aquatic pollution control. A sedimentological approach. Water research,14(8), 975-1001. Hou J, Wang C, Wang P F, Qian J. 2012. Sediment quality guidelines and potential ecological risk assessment for heavy metals based on equilibrium partitioning approach in Taihu Lake . Acta Scientiae Circumstantiae,32(12): 2951-2959. (in Chinese) Huang, D., Xue, W., Zeng, G., Wan, J., Chen, G., Huang, C., Xu, P. 2016. Immobilization of Cd in river sediments by sodium alginate modified nanoscale zero-valent iron: impact on enzyme activities and microbial community diversity. Water research, 106, 15-25. Huang, Y., Zhang, D., Xu, Z., Yuan, S., Li, Y., Wang, L. 2017. Effect of overlying water pH, dissolved oxygen and temperature on heavy metal release from river sediments under laboratory conditions. Archives of Environmental Protection, 43(2), 28-36. 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. Journal of Environmental Sciences, 24(9), 1539-1548. Kwok, K. W., Batley, G. E., Wenning, R. J., Zhu, L., Vangheluwe, M., Lee, S. 2014. Sediment quality guidelines: challenges and opportunities for improving sediment management. 23 / 31
Environmental Science and Pollution Research, 21(1), 17-27. Li X, Jiang X J, Gao H J. 2017. Pollution Assessment and Source Analysis of Soil Heavy Metals in Taihu Lake Basin. Transactions of the Chinese Society for Agricultural Machinery, 48(S1): 247-253. Li, Y., Lin, Y., Wang, L. 2018. Distribution of heavy metals in seafloor sediments on the East China Sea inner shelf: Seasonal variations and typhoon impact. Marine pollution bulletin, 129(2), 534-544. Liu, E., Birch, G. F., Shen, J., Yuan, H., Zhang, E., Cao, Y. 2012. Comprehensive evaluation of heavy metal contamination in surface and core sediments of Taihu Lake, the third largest freshwater lake in China. Environmental Earth Sciences, 67(1), 39-51. Liu, Y., Xiao, L. 2014. Comprehensive evaluation of ecological risk of heavy metal pollution in the surface sediments of Taihu Lake. Environ. Prot. Sci, 40, 46-50. (in Chinese) Liu, Y., Peng, Y., Yue, D., Yin, Q., Xiao, L. 2015. Assessment of heavy metal enrichment, bioavailability, and controlling factors in sediments of Taihu Lake, China. Soil and Sediment Contamination: An International Journal, 24(3), 262-275. MacDonald, D. D., Ingersoll, C. G. 2002. A guidance manual to support the assessment of contaminated sediments in freshwater ecosystems. US Environmental Protection Agency, Great Lakes National Program Office. MacDonald, D. D., Ingersoll, C. G., Berger, T. A. 2000. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Archives of environmental contamination and toxicology, 39(1), 20-31. Martinková, E., Chrastný, V., Francová, M., Šípková, A., Čuřík, J., Myška, O., & Mižič, L. 2016. 24 / 31
Cadmium isotope fractionation of materials derived from various industrial processes. Journal of hazardous materials, 302, 114-119. Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Stewart, L. A. 2015. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic reviews, 4(1), 1. Müller, G. 1979. Schwermettalle in den sedimenten des Rheins-Veranderungen seit 1971. Umschau Wissensch Tech., 79, 778-783. Niu, Y., Jiao, W., Yu, H., Niu, Y., Pang, Y., Xu, X., Guo, X. 2015a. Spatial evaluation of heavy metals concentrations in the surface sediment of Taihu Lake. International journal of environmental research and public health,12(12), 15028-15039. Niu, Y., Niu, Y., Pang, Y., Yu, H. 2015b. Assessment of heavy metal pollution in sediments of inflow rivers to Lake Taihu, China. Bulletin of environmental contamination and toxicology, 95(5), 618-623. Norris, G., Duvall, R., Brown, S., Bai, S. 2014. Epa positive matrix factorization (pmf) 5.0 fundamentals and user guide prepared for the us environmental protection agency office of research and development, washington, dc. Washington, DC. Paatero, P. 1997. Least squares formulation of robust non-negative factor analysis. Chemometrics and intelligent laboratory systems, 37(1), 23-35. Pedersen, F., Bjørnestad, E., Andersen, H. V., Kjølholt, J., Poll, C. 1998. Characterization of sediments from Copenhagen Harbour by use of biotests. Water Science and Technology, 37(6-7), 233-240. Qin Y W, Zhang L, Zheng B H, Cao W. 2012. Speciation and pollution characteristics of heavy 25 / 31
metals in the sediment of Taihu Lake. Environmental Science, 33(12): 4291-4299.（in Chinese） Ran, X., Yue, H., Fu, X., Kang, Y., Xu, S., Yang, Y., Wu, Z. 2015. The response and detoxification strategies of three freshwater phytoplankton species, Aphanizomenon flos-aquae, Pediastrum simplex, and Synedra acus, to cadmium. Environmental Science and Pollution Research, 22(24), 19596-19606. Shao, D., Zhan, Y., Zhou, W., Zhu, L. 2016. Current status and temporal trend of heavy metals in farmland soil of the Yangtze River Delta Region: Field survey and meta-analysis. Environmental pollution, 219, 329-336. Shi Z F. 2010. The characteristics of pollution and ecological risk assessment of heavy metals in sediments of Taihu Lake. Northwest A F University. (in Chinese) Shuchun, Y., Bin, X. 2010. Nutrients and heavy metals in multi-cores from Zhushan Bay at Taihu Lake, the largest shallow lake in the Yangtze Delta, China. Quaternary International, 226(1-2), 23-28. Simpson, R. J. S., Pearson, K. 1904. Report on certain enteric fever inoculation statistics. The British Medical Journal, 1243-1246. Singh, D. V., Bhat, J. I. A., Bhat, R. A., Dervash, M. A., Ganei, S. A. (2018). Vehicular stress a cause for heavy metal accumulation and change in physico-chemical characteristics of road side soils in Pahalgam. Environmental monitoring and assessment, 190(6), 353. Skordas, K., Kelepertsis, A. 2005. Soil contamination by toxic metals in the cultivated region of Agia, Thessaly, Greece. Identification of sources of contamination. Environmental Geology, 48(4-5), 615-624. 26 / 31
Tao, Y., Yuan, Z., Xiaona, H., Wei, M. 2012. Distribution and bioaccumulation of heavy metals in aquatic organisms of different trophic levels and potential health risk assessment from Taihu lake, China. Ecotoxicology and Environmental Safety, 81, 55-64. Taylor, M. P., Isley, C. F., Glover, J. 2019. Prevalence of childhood lead poisoning and respiratory disease associated with lead smelter emissions. Environment international, 127, 340-352. Tian, H. Z., Zhu, C. Y., Gao, J. J., Cheng, K., Hao, J. M., Wang, K., Zhou, J. R. 2015. Quantitative assessment of atmospheric emissions of toxic heavy metals from anthropogenic sources in China: historical trend, spatial distribution, uncertainties, and control policies. Atmospheric Chemistry and Physics, 15(17), 10127-10147. Tian, X., Zhang, H., Zhao, Y., Mehmood, K., Wu, X., Chang, Z., Zhou, D. 2018. Transcriptome analysis reveals the molecular mechanism of hepatic metabolism disorder caused by chromium poisoning in chickens. Environmental Science and Pollution Research, 25(16), 15411-15421. Wang, S., Wang, W., Chen, J., Zhao, L., Zhang, B., Jiang, X. 2019. Geochemical baseline establishment and pollution source determination of heavy metals in lake sediments: A case study in Lihu Lake, China. Science of The Total Environment, 657, 978-986. Wenchuan, Q., Dickman, M., Sumin, W. 2001. Multivariate analysis of heavy metal and nutrient concentrations in sediments of Taihu Lake, China. Hydrobiologia, 450(1-3), 83-89. Whitehead, L. S., & Buchanan, S. D. 2019. Childhood Lead Poisoning: A Perpetual Environmental Justice Issue?. Journal of Public Health Management and Practice, 25, S115-S120. Wu, G. H., Su, R. X., Li, W. Q., Zheng, H. Q. 2008. Source and Enrichment of 27 / 31
Heavy Metals in
Sewage-irrigated Area Soil of Dagu Discharge Channel. Environmental science, 2008, 29(6):1693-1698. (in Chinese) Xiang, Y., Miao, Q. L., Feng, J. F. 2006. Pollution of heavy metals in the bottom mud of Lake Taihu and its assessment of potential ecological risk. Journal of nanjing Institute of meteorology, 29(5), 700-705. Xiao, L., Guan, D., Chen, Y., Dai, J., Ding, W., Peart, M. R., Zhang, C. 2019. Distribution and availability of heavy metals in soils near electroplating factories. Environmental Science and Pollution Research, 1-15. Yang C, Wang P F, Liu J J, Wang C, Hou J, Qian J. 2016. Vertical distribution and migration of heavy metals in sediment cores of Taihu Lake. J. Agro Environ. Sci, 35: 548-557. (in Chinese) Yang H, Chen G G, Liu H Y, Liang X H, Zhang M. 2013. Pollution of heavy metals in the sediments from the lower reaches of the Yangtze River and its potential ecological risk assessment. Earth and Environment, 41: 160-165. (in Chinese) Yin, H., Gao, Y., Fan, C. 2011. Distribution, sources and ecological risk assessment of heavy metals in surface sediments from Lake Taihu, China. Environmental Research Letters, 6(4), 044012. Yuan X Y, Wang A H, Xu N Z. 2004. Chemical partitioning of heavy metals and their characteristics for sediments from Lake Taihu. Geochimica, (6): 611-618. (in Chinese) Yuan, H. Z., Shen, J., Liu, E. F., Wang, J. J., Meng, X. H. 2011. Assessment of nutrients and heavy metals enrichment in surface sediments from Taihu Lake, a eutrophic shallow lake in China. Environmental geochemistry and health, 33(1), 67-81. 28 / 31
Yuan, X., Chen, J., Ji, J., Tao, Y., Wang, R. 2002. Characteristics and environmental changes of pollution elements in Taihu sediments and soils near the lake. Acta sedimentologica sinica, 20(3), 427-434. Zhang, X., Zhong, T., Chen, D., Cheng, M., Liu, L., Zhang, X., Li, X. 2016. Assessment of arsenic (As) occurrence in arable soil and its related health risk in China. Environmental geochemistry and health, 38(3), 691-702. Zhang, Y. P., Qu, W. C. 2001. Determination of heavy metal contents in the sediments from Taihu Lake and its environmental significance. Rock and Mineral Analysis, 20(1), 34-37. Zhang, Y., Han, Y., Yang, J., Zhu, L., Zhong, W. 2017. Toxicities and risk assessment of heavy metals in sediments of Taihu Lake, China, based on sediment quality guidelines. Journal of Environmental Sciences, 62, 31-38. Zhang, Y., Hu, X., Yu, T. 2012. Distribution and risk assessment of metals in sediments from Taihu Lake, China using multivariate statistics and multiple tools. Bulletin of environmental contamination and toxicology, 89(5), 1009-1015. Zhu, Y. G., Gillings, M., Simonet, P., Stekel, D., Banwart, S., Penuelas, J. 2017. Microbial mass movements. Science, 357(6356), 1099-1100.
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Highlights Systematically analyze the heavy metal pollution in the sediments of Taihu Lake in the past 20 years. The Cd accumulation index indicated moderate–heavy pollution and Cd was the main contributor of potential ecological risks Pb is the main contributor to the total toxicity of the sediments in Taihu Lake. Industrial source accounts for 64.9% of the heavy metals in the sediments of Taihu Lake.
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