Journal of Hazardous Materials 416 (2021) 125818

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Journal of Hazardous Materials

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Effect of river- connectivity on heavy metal diffusion and source identification of heavy metals in the middle and lower reaches of the River

Mingke Luo a,b, Hui Yu a,b, Qian Liu a, Wei Lan a, Qiaoru Ye a, Yuan Niu a,b,*, Yong Niu a,b,* a National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Institute of Lake Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, b State Environmental Protection Key Laboratory for Lake Pollution Control, Chinese Research Academy of Environmental Sciences, Beijing 100012, China

ARTICLE INFO ABSTRACT

Editor: Dr. C. LingXin Metal pollution poses a significant threat to ecological security and human health. Current research on the causes, sources and distribution of metal pollution in the Yangtze River plain is lacking. This study investigated Keywords: the accumulation, risk, distribution, and sources of heavy metals in 62 along the Yangtze River, and Sediment analyzed the relationship between river-lake connectivity, economic structure, population and metal diffusion. Cd The mean concentrations of Cr, Cu, Hg, Zn, Cd, Pb and As in the surface sediments of these lakes were 90.8, 60.1, Metal diffusion 0.06, 102, 0.89, 42.7, and 6.01 mg/kg, respectively. Most (99%) of the lake sediments were contaminated with Risk assessment Soil erosion Cd, and the lakes in the middle reach and southern bank of the Yangtze River had a higher ecological risk. Cr originated from the natural environment, whereas Zn, Cu, Pb, Cd and As were affected by human activities. The lakes disconnected from the Yangtze River had higher concentrations of Cu, Zn, Pb and As, while the lakes connected to the river had higher concentrations of Cd and Cr. This comprehensive analysis determined the pollution characteristics of heavy metals, illustrated the causes of non-point pollution in the Yangtze River plain, and showed that soil-water erosion is important in metal diffusion.

1. Introduction body, causing secondary contamination (Jiang et al., 2018). Therefore, heavy metal analyzes of lake sediments are often used to monitor With industrialization and urbanization, metal pollution in aquatic environmental pollution, and this can also be used to study the environments has become a major social problem because metals can anthropogenic impacts on ecosystems and assess the health risks(Han have high toxicity, can bioaccumulate, and are not degradable (Luo et al., 2020). Various methods (Ke et al., 2017; Niu et al., 2020) have et al., 2019; Lingamdinne et al., 2019). Heavy metals are imported into been proposed to evaluate the contamination level and environmental the environment through geochemical reactions and human activities, risk in sediments, such as the pollution load index (PLI), contamination such as weathering of rocks, industrial discharge, agricultural activities factor (CF), enrichment factor (EF), geo-accumulation index (Igeo), and and municipal sewage discharge (Koduru et al., 2019; Khan et al., 2020); potential ecological risk index (EI). The results of these analyzes are anthropogenic impacts are the main factor for the enrichment of metals more reasonable and scientifically sound when multiple methods are in lake sediments. Some studies have shown that the sediment in aquatic combined (Jia et al., 2020). ecosystems is a sink and carrier for heavy metals (Deng et al., 2020). The Yangtze River has the highest density and number of lakes along Metals can accumulate in sediment by physical adsorption, chemical its middle to lower reaches (Li et al., 2019). The middle and lower precipitation, and aquatic degradation, and this accumulation can pose reaches of the Yangtze River Basin, namely the Yangtze Plain, also is the a risk to human health through the food chain (Ding et al., 2020; Fan region to a third of the Chinese population at nearly 400 million (Zhang et al., 2017; Lingamdinne et al., 2018). When environmental conditions et al., 2020). Cities in the Yangtze River Basin have well developed change, the heavy metals in sediment can be released into the water economies, industries, and societies. The discharge of wastewater into

* Corresponding authors at: National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Institute of Lake Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China. E-mail addresses: [email protected] (Y. Niu), [email protected] (Y. Niu). https://doi.org/10.1016/j.jhazmat.2021.125818 Received 22 December 2020; Received in revised form 1 April 2021; Accepted 1 April 2021 Available online 20 April 2021 0304-3894/© 2021 Elsevier B.V. All rights reserved. M. Luo et al. Journal of Hazardous Materials 416 (2021) 125818 the Yangtze River Basin accounts for 40% of all discharges in China. firstto fully investigate the accumulation of heavy metals and determine Metal pollution in wastewater enters the water environment and is the reason for Cd pollution throughout the whole basin. The results will deposited in aquatic sediments, and toxic metals in the Yangtze River provide a reference value for future environmental research in the Basin have gained widespread concern (Li et al., 2020; Yang et al., middle and lower reaches of the Yangtze River Basin, and will also 2020). In 2016, the “Outline of the Yangtze River Economic Belt support effective watershed management. Development Plan” was put forward to protect and restore the ecological environment of the Yangtze River (Zhang et al., 2021). In 2019, the 2. Materials and methods Ministry of Ecology and Environment and the National Development and Reform Commission unveiled the “Action Plan for the Uphill Battle 2.1. Sample collection for the Conservation and Restoration of the Yangtze River” as the country pushes forward environmental protection. Long-term develop­ Superficialsediment samples (0–10 cm, reflectingthe pollution level ment and social stability require the investigation of the accumulation of in the last 10 years) from 62 lakes were collected along the middle and and risk posed by heavy metals in lake sediments in the Yangtze Plain, lower reaches of the Yangtze River in 2014–2017 from Hunan Province, and the research described in this study will provide valuable informa­ Hubei Province, Jiangxi Province, Anhui Province, and Jiangsu Prov­ tion for the “Great Protection of the Yangtze River.” ince. The lakes sampled were all larger than 10 km2, reflecting the During the last decades, researchers have studied sediment heavy historical chemical deposition of sediment from the entire river basin. A metals in different lakes in the Yangtze Plain, including global positioning system was used to record accurate locations of all (Feng et al., 2019), Taihu lake (Yao et al., 2019), (Dai et al., lake samples. Fig. 1 shows the lake sites, and Table S1 presents the 2018), and Wanghu lake (Shen et al., 2019). Most studies have only characteristics of the 62 lakes and the number of samples collected. The considered the source and environmental risk of metal contamination in number of samples collected was 5, 10, or 20, corresponding to lake one or a few lakes. Although Xu et al., (2020) analyzed the potential areas of 10–100 km2, 100–500 km2, or > 500 km2, respectively. Sam­ environmental risks of metals in lake sediments in East China, but the pling locations were selected based on sediment thickness to represent correlation and regularity of heavy metals among lakes on the two sides, the entire area of the lake. For each lake, sediments were collected with the middle and lower reaches of the Yangtze River were not clear. Cheng a stainless-steel grab sampler and sealed in polyethylene bags, and ◦ et al., (2021) evaluated the concentration and distribution of heavy stored at 4 C. The samples were freeze-dried, homogenized, and sieved metals in the sediments of 42 urban lakes in China, but the source of before laboratory analysis. heavy metals has not been identified. The influence of industry, agri­ The data for GDP, primary, secondary, and tertiary industry, popu­ culture, and population density on heavy metal pollution is also un­ lation, and agricultural planting area in 2017 and 2007 were collected known. There has not been a systematic or comprehensive study of the from the official website of the local Bureau of Statistics. potential environmental risks of heavy metal pollution in lakes, espe­ 2 cially for lakes greater than 10 km . Importantly, the reason for heavy 2.2. Sample pretreatment and analysis metal diffusion from non-point source pollution is unclear. Hydrological connectivity (river-lake connectivity) originates from Sediment samples (approximately 0.1 g) were digested with a HCl- ecological concepts and represents the exchange of substances, energy, HNO3-HF-H2O2 mixture in a microwave digester using an appropriate and organisms through the hydrologic cycle (Rinderer et al., 2018). It is digestion program (Wang et al., 2019). After cooling, the digested so­ used to study the dynamic variability in water flow,sediment transport ´ lutions were filtered and diluted to 50 mL with deionized water. The and materials (Xie et al., 2020). Napiorkowski et al., (2019) studied the concentrations of Zn, Cu, and Cr were measured with inductively influence of hydrological connectivity on the zooplankton community coupled plasma–optical emission spectrometry (ICP-OES); Cd and Pb structure and diversity in floodplain lakes of the Vistula River, and concentrations were determining with inductively coupled plasma–mass confirmed zooplankton in floodplainlakes connected with the river (or spectrometry (ICP-MS); and Hg and As concentrations were determined transitional) than in isolated ones. Jiang et al., (2021) and Liu and Wang with atomic fluorescence spectrometry (AFS). (2018) examined the effects of river-lake disconnection on the alpha and All analyzes were conducted in duplicate to ensure accuracy, and the beta diversity of fish in Yangtze River floodplain lakes, and found that results were expressed as mean concentrations. Reference standards, loss of hydrological connectivity is a major cause of biodiversity and blank samples and analysis methods were applied to improve the ac­ aquatic functional diversity decline in river floodplains. Teng et al., curacy of related experiments. All of the reagents in this study were (2020) assessed the impacts of hydrological connectivity on inundation guaranteed analytical purity or higher. Deionized water (DW, area and habitat quality, and found higher landscape diversity and 18.2 MΩ cm 1, Milli-Q) was used for all experiments. higher productivity in connected lakes compared with those of isolated ones, both of which are responsible for maintaining higher biodiversity. Rusnak´ et al. presented a field-based channel-bluff connectivity study 2.3. Pollution identification tools based on a sediment cascade approach (Rusnak´ et al., 2020). However, there is less research on the effect of river-lake connectivity on the To assess the current degree and risk of heavy metal pollution in the diffusion of metal pollution in the Yangtze Plain. Yangtze River basin, the contamination factor and geo-accumulation Our work investigated the accumulation, distribution, environ­ index were used. mental risk and source of heavy metals in the surface sediments of 62 lakes (≥10 km2) along the middle and lower reaches of the Yangtze 2.3.1. Contamination factor (CF) River. This study analyzed the relationships between GDP, economic The CF is a relatively simple calculation method that reflects metal structure, population, agricultural planting area, and metal pollution in pollutant by human activities. It is calculated with Eq.(1) (Kumar et al., lakes with varying connectivity from 2007 to 2017. The objectives of 2020) this research were to (1) evaluate the contamination level and ecological CF = Cs/Bs (1) risks using geo-accumulation index, contamination factor, and potential ecological risk index methods; (2) analyze the spatial distribution and where Cs and Bs are the content of metal S in the measured sample and correlation of heavy metals in the Yangtze Plain; (3) determine the key the background concentration, respectively (mg/kg). The CF can be pollution areas, lakes, and industries along the Yangtze River; (4) study classified into four grades to assess the contamination level: CF < 1 the effect of river-lake connectivity on the diffusion of heavy metals; and represents low pollution, 1≤ CF <3 represents moderate pollution, 3≤ (5) propose management strategies for heavy metals. This study is the CF <6 represents considerable pollution, and 6≤ CF represents very high

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Fig. 1. Study area and lake sites in the Yangtze River. pollution (Jiang et al., 2018). Table 1 i 2.3.2. Geo-accumulation index The classification of Es and RI. The geo-accumulation index (Igeo) is often used to analyze the Assessment Low Moderate Considerable High Very geochemical characteristics of contaminants in lake sediments, and is criterion high i ＜ – – – ≥ calculated with Eq. (2): (Li et al., 2020; Sun et al., 2019) Es 40 40 80 80 160 160 320 320 RI ＜ 150–300 300–600 ≥600 Igeo = log2(Cs/1.5Bs) (2) 150 where Cs is the detected concentration of a metal S in surface sediment (mg/kg) and Bs is the background level of metal S in the soil (mg/kg). 2.5. Statistical analysis The Igeo values are interpreted as follows: Igeo ≤ represents no pollution, 0 < Igeo ≤ 1 represents unpolluted to moderate pollution, 1 < Igeo ≤ 2 Descriptive statistics were conducted using IBM SPSS Statistics 25.0 represents moderate pollution, 2 < Igeo ≤ 3 represents moderate to and Origin 2018. IBM SPSS Statistics 25.0 was used to calculate the heavy pollution, 3 < Igeo ≤ 4 represents heavy pollution, 4 < Igeo ≤ 5 minimum, maximum, coefficientof variation (CV) and average value of represents heavy to extreme pollution, and 5 < Igeo represents extreme heavy metal concentration in all lakes. Pearson’s correlation analysis pollution (Dai et al., 2018). (CA) was conducted to analyze the correlations between metal con­ centrations. The source of the metal contamination was estimated by 2.4. Environmental risk assessment principal component analysis (PCA). The lake sites and the distribution characteristics of heavy metals in the Yangtze River Basin were mapped Hakanson introduced the potential ecological risk index (RI) to using GIS (ArcGIS 10.5 software). assess the environmental ecological risk in a variety of research di­ rections. This method comprehensively considers the concentration, 3. Results type, sensitivity, and toxicity of pollutants being studied, and is expressed in Eq. (3) (Wang et al., 2015): 3.1. Heavy metal concentrations in surface sediments ∑ ∑ i n i n i Cs RI = Es = Ts (3) The sediment metal content in 62 lakes along the middle and lower i=1 i=1 Bi s reaches of the Yangtze River are presented in Table 2 and Fig. 2. Seven

i i heavy metals were measured, and the average concentration and ranges where Cs is the detected value of metal S in surface sediment (mg/kg); Bs – i in the sediments are as follows: 90.8 mg/kg (44.8 155 mg/kg) for Cr, is the background concentration of metal S in the soil (mg/kg); Ts is the 60.1 mg/kg (17.5–667 mg/kg) for Cu, 0.06 mg/kg (0.00–0.82 mg/kg) ======toxicity coefficient(Cu Pb 5, Cr 2, As 10, Cd 30, Zn 1, Hg for Hg, 0.89 mg/kg (0.10–15.6 mg/kg) for Cd, 6.01 mg/kg i 40) (Zhu et al., 1987); Es is the potential ecological risk factor for metal (0.00–53.5 mg/kg) for As, 102 mg/kg (42.5–693 mg/kg) for Zn, and S; and RI is the total potential ecological risk for all metals under 42.7 mg/kg (19.5–208 mg/kg) for Pb. The average concentrations of the i consideration. Table 1 presents the values for Es and RI classification. seven metals follow the trend Zn > Cr > Cu >Pb > As > Cd > Hg. The Cr concentrations in Poyang, Wushan, Datong, and Cehu lake

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Table 2 The concentration of heavy metals in lakes (mg/kg).

Areas Cr Cu Zn Cd Pb Hg As

Lake sediment Mean 90.8 60.1 102 0.89 42.7 0.06 6.01 SD 23.4 90.2 83.2 2.04 24.6 0.13 10.6 CV 0.26 1.50 0.82 2.28 0.58 2.15 1.76 Minimum 44.8 17.5 42.5 0.10 19.5 0.00 0.00 Maximum 155 667 693 15.6 208 0.82 53.5 Lake watera Maximum ND 0.08 0.255 0.002 0.382 0.00 0.046 Hunan Background values in soils 70.0 25.0 90.0 0.08 27.0 0.12 18.6 Hubei 86.0 30.7 83.6 0.17 26.7 0.08 12.3 Jiangxi 46.2 20.8 69.0 0.10 32.1 0.08 8.32 Anhui 82.7 27.8 63.2 0.06 25.0 0.01 11.4 Jiangsu 77.8 22.3 62.6 0.13 26.2 0.29 10.0 Zhejiang 52.9 17.6 70.1 0.07 23.7 0.09 9.20 China 61.0 22.6 74.2 0.10 26.0 0.07 11.2

ND: No detection. a National water quality monitoring section data in lakes of the Yangtze River Basin;

contaminated levels; Daye (5.92), Cihu (4.4), Saicheng (4.08), Taihu (3.41), and Tiangang lakes (4.38) were considered heavily to extremely polluted with Cd. Approximately 42% of lakes were uncontaminated to moderately contaminated with Cu, and Daye (2.45), Cihu (2.29), and Tiangang (4.00) lakes were moderately to heavily contaminated. Around 40% of lakes were uncontaminated to moderately contaminated lake with Pb, and Daye (1.1), Cihu (2.38) and Tiangang (1.17) lakes were moderately polluted. Most of the Igeo values for Cr, Zn, Hg, and As were less than 0, which means these lakes were not polluted by these metals. However, Poyang lake was moderately polluted with Cr. Daye lake and Cihu lake were considered moderately polluted with Zn. Cihu lake had the highest Hg and As contamination. The Igeo values indicated that metal pollution in the Yangtze River Basin followed the trend Cd > Cu > Hg > Pb > Zn > Cr > As. The degree of contamination of the lakes followed the trend Cihu lake > Daye lake > Tiangang lake > Saicheng lake > Zhupo lake > Baoan lake > Taihu lake.

3.3. Environmental risk assessment

Fig. 2. Box plot presenting the concentration of 7 metals in lake sediments. The RI and EI values for the heavy metals in lake sediments are shown in Fig. 4 and Fig. 5. The calculated EI values for Cr, Zn, As, and Pb were all <40, meaning that the risk posed by these four metals was low. sediments were relatively high (>140 mg/kg). The Cu concentrations However, the average EI value for Cd in all lakes was 201 (22.4–2720), (>200 mg/kg) in Tiangang, Daye, Cihu, and Bali lake sediments were indicating that Cd was the main contaminant and requires careful con­ also higher than in other lakes. The lakes with the highest sediment trol. The EI indices of Hg in 18 lakes (72% in Hubei Province) were also concentration of Zn were Cihu, Daye, and Dianshan lakes (>180 mg/ relatively high, indicating that Hg poses a serious potential ecological kg). Daye, Cihu, Saicheng, and Taihu lakes had relatively high amounts risk. The EI for Cu in most lakes was <40, except for in Tiangang lake of Cd in surface sediments (>2.0 mg/kg). Cihu, Daye, Tiangang, and (120), Bali lake (50.6), and Daye lake (40.9), which means Cu poses a Chenjia Lakes had high Pb in sediments (>64.0 mg/kg). Cihu, Zhupo, relatively high ecological risk in these lakes. Overall, the total potential and Niulang lakes had higher Hg in sediments (>0.2 mg/kg), and the As risk in lake sediments followed a trend of Cd > Hg > Cu > Pb > As > Cr concentrations in sediments of Cihu, Liangzi, Baoan, Taibo, Sanshan, > Zn. The Hg and Cd contamination should be considered in the envi­ and Tangshun lakes were above 20.0 mg/kg. These high metal con­ ronmental protection policy for the Yangtze River Basin. Liu et al. centrations in surface sediments could become a source for metal (2020) found a similar phenomenon in soils along the coastal areas of contamination in the future. The CVs of lake sediments in the Yangtze the Bohai Sea and the Yellow Sea. River Basin ranged from 0.26 to 2.28, with moderate to high variation. The RI index indicates the biological sensitivity to toxicants and The lowest CV was for Cr and the highest CV was for Cd. The high CVs presents the potential ecological risk posed by the toxic contaminants. indicate that the concentration of metals in the Yangtze River is quite Approximately 53.2% of the lake sediments posed a low ecological risk variable, suggesting that they are influenced by anthropogenic (RI < 150), and 32.3% of the lake sediments were classifiedas moderate activities. risk (150 ≤ RI < 300), indicating that the studied areas may be polluted by some heavy metal elements. The RI indicated a highly polluted or 3.2. Ecological risk assessment considerably polluted state in Daye lake (2782), Cihu lake (1491), Tiangang lake (1075), Saicheng lake (794), Taihu lake (524), and The Igeo values for all lake sediments are shown in Fig. 3. According Dongting lake (516). These lake sediments also had high Cd and Hg, also to this classification, the calculated Igeo of metals (except Cd) ranged indicating a relatively high ecological risk. Generally, the RI values of from 0 to 1, indicating that the studied metals were at uncontaminated metal pollution indicates an impact by human activities. Because Cd and to moderately contaminated levels. The Cd indices in 50% of the 62 Hg are not biodegradable, the relevant government departments should lakes were at uncontaminated to moderately contaminated levels, and develop more stringent emission standards to limit discharge of these 50% of the lakes were at moderately contaminated to heavily toxic metals into the Yangtze River Basin.

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Fig. 3. The Radar of Igeo for heavy metals in lake sediments.

3.4. Comparisons of regional characteristics polluted by mines and modern industries located along the middle reach of the Yangtze River and its southern bank. Therefore, we suggest that To better analyze the distribution of heavy metals in the Yangtze these key areas and lakes should be subject to a comprehensive and River Basin, these lakes were classified according to northern (NB) and accurate survey to improve the ecological environment. southern banks (SB), middle (MR) and lower reaches (LR), and by the five provinces. The average CF values for the surface sediments are 4. Discussion presented in Fig. 6. The results showed that the average sediment con­ centrations of Cd, Cu, and Pb were significantly higher than the back­ 4.1. Source identification for heavy metals in surface sediments ground value; the actual CF values for Cd, Cu, and Pb were 6.78, 2.24, and 1.58, respectively. The CF values for the heavy metals indicated that The relationships between the metal concentrations could reflectthe Cd was the main pollutant with a high enrichment level, and approxi­ influences of anthropogenic activities and natural factors (He et al., mately 60 lakes were polluted by Cd. The trend for the degree of Cd 1987). We used Pearson’s correlation analysis to study the correlations contamination in the lakes was Hunan Province (8.84) > Anhui Province between the heavy metals, and the results of the correlation coefficient (8.66) > Hubei Province (6.93) > Jiangxi Province (6.17) > Jiangsu matrix are presented in Table 3. No significant relationship was Province (3.66). The CF values for Cu in Anhui (2.76) and Jiangxi (2.72) observed between Cr and other metals. Positive correlations (P < 0.01) Provinces indicated a moderate level of contamination. Similarly, the CF were observed between Cu, Zn, Cd, and Pb: Cu-Zn (0.373), Cu-Cd for Cr in Jiangxi Province (2.02) indicated moderate contamination. In (0.417), Cu-Pb (0.523), Cd-Zn (0.545), Pb-Zn (0.940), and Pb-Cd contrast, the CF values for As and Hg in lakes indicated that these metals (0.523), indicating that these heavy metals could have a similar were not significantlyenriched. The CF values for of As and Hg in 73% of source. Very significantassociations (P < 0.01) were found between Hg, the lakes were <1, indicating that there was little accumulation of these As, Zn and Pb: Hg-As (0.918), Hg-Zn (0.681), Hg-Pb (0.663), Zn-As two metals in most lakes. Although the CF values for As and Hg in these (0.528), and Pb-As (0.532), reflecting that these metals have a similar lakes were relatively low, some lake sediments did have moderate to degree of pollution and/or the same pollution source (Yi et al., 2011). considerable enrichment of As and Hg: Hg in Cihu lake (10.2), Zhupo However, there were no significant associations between Hg-As and lake (5.21), and Niulang lake (3.00), and As in Cihu lake (4.35), Taibo Cd-Cu, indicating that these two groups of heavy metals are affected by lake (2.82), and Baoan lake (2.00). multiple factors (Kükrer et al., 2014). The CF values for Zn, Cr, Hg, Cd, Pb, and As in the lakes along the To determine and analyze the source of metal contamination in the middle reach of the Yangtze River were higher than those in the lower Yangtze River Basin, PCA was performed. Table 4 lists the results of the reach. The CF values for the seven metals in the lakes along the southern PCA for heavy metals. Three principal components (PCs) were obtained bank of the Yangtze River were higher than those along the northern through eigenvalues > 1, explaining 84.3% of the total variation. PC1 bank. These results indicate that the lakes along the middle reach and explained 50.6% of the total variance and correlated with Hg, As, Cu, southern bank of the Yangtze River have a higher accumulation of Zn, Pb, and Cd. PC2 was correlated to Cu, Cd, Hg, and As, accounting for metals. The reason may be that these lakes (like Daye lake, Cihu lake, 19.4% of the total variance. This suggests that the enrichment of Cu, Cd, Zhupo lake, Dongting lake, Bali lake, and Baoan lake) are heavily Hg, and As is not affected by a single factor, but PC1 was the main

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Fig. 4. Hotmaps of RI for heavy metals in lake sediments.

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Fig. 5. The EI of heavy metals in lake sediments.

effecting factor for Hg and As. PC3 explained 14.4% of the total variance and Cd leakage and pollution around industrial areas. Therefore, PC1 and only loaded with Cr. Pb and Zn were significantly positively was assigned to an industrial factor. The loading values of Cu and Cd in correlated with high values in PC1. Some researchers (Gao et al., 2019; PC1 and PC2 are very close, indicating that these two metals are influ­ Cave et al., 2020) have shown that Pb and Zn are important contami­ enced by various factors. Previous studies (Aendo et al., 2020) have nants from smelting and mining activities and accumulate in areas found that Cu and Cd contamination might result from the use of ag­ where Pb-Zn is present. The average concentrations of Pb and Zn were rochemicals, including fertilizer and pesticides, so PC2 could represent higher than the background values, and had similar spatial distribution an agricultural factor. The metal concentrations, Igeo, RI, and EI all trends in the 20 most polluted lakes, including Cihu lake, Daye lake, indicate that the concentration of Cr is close to background levels and Baoan lake, Tiangang lake, and Wuhan Donghu lake. Cihu lake, Daye poses low potential ecological risk. Therefore, Cr could originate from lake, and Baoan lake were located in Daye city, Hubei Province, which is the natural environment and represents a natural factor. an important mining city in central China. Some industrial wastewater In the whole Yangtze River Basin, Cr mainly originated from natural violations or non-compliance emissions could lead to Zn, Pb, Hg, As, Cu soil. Zn, Pb, Hg, and As are primarily derived from the industrial

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Fig. 5. (continued). production process, and Cu and Cd were affected by many factors, such human activities. In earlier studies, Yi et al., (2011) found that metals in as industry, or agriculture. The source of metal pollutions varies in the middle and lower reaches of the Yangtze River may originate from different watersheds based on industrial structures, hydrogeological metal processing, electroplating industries, industrial wastewater, and characteristics, and economic development (Xu et al., 2021). Dai et al., domestic sewage. Xu et al. (2020) found a similar phenomenon for lake (2018) indicated that Cr, Pb, and Zn in Poyang lake are mainly derived sediments of the middle and lower reaches of the Yangtze River. from lithogenic and human activities, and Cu and Cd are mainly from anthropogenic sources such as mining wastewater and fertilizer. Deng 4.2. Effect of river-lake connectivity on the diffusion of heavy metals et al. (2020) found that Pb in Taihu lake mainly originated from do­ mestic sewage, agricultural wastewater discharge, and petroleum com­ To investigate the influence of river-lake connectivity on the diffu­ bustion; Cr, Ni, and Zn were influenced by the electroplating and alloy sion of metal contaminants, we divided the lakes into two categories: the manufacturing industries; Cu and As mainly derived from pesticide and Yangtze River connected lakes (CL) and disconnected lakes (DL). The industrial activities; Cd and Hg primarily originated from industrial metal concentrations, sediment grain size, GDP, primary industry, sec­ wastewater. The top three lakes in Yangtze River Basin show that the ondary industry, tertiary industry, population, and agricultural planting concentrations of Zn, Cu, Pb, Cd and As were greatly influenced by area for each of the two lake groups were analyzed, and the results are

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Fig. 5. (continued). shown in Fig. 7 and Table S2. With rapid economic development and The metals attached to sediment particles could be detected by the urbanization, the Yangtze River Economic Belt has caused great damage particle distribution (Liu et al., 2017). The median size of the sediment to the environment, and the contaminants in developed areas were particles can reflect the particle distribution, hydrodynamic intensity higher than those in underdeveloped areas (Zhang et al., 2021). Table S2 (motive energy) and depositional environment of lake sediments in the presented that the GDP, primary industry, secondary industry, tertiary studied area (Wang et al., 2020). Generally, when the runoff enters the industry, population and agricultural planting area of the DL were lake, large particles sink before smaller particles. Owing to their large higher than these of the CL, indicating that the DL are more developed. surface area, smaller particles have a high adsorption capacity for metals Fig. 7 shows that the concentrations of Cu, Zn, Pb, and As in DL sedi­ (Luo et al., 2018). Table 3 shows that there is a significant negative ments were higher than in CL sediments, the Cd concentration in the CL relationship between the Cr concentration and sediment grain size. Most was two times higher than that of the DL, and the Cr concentration in the of the CL are closer to the Yangtze River than the DL, so the Cr con­ CL was higher than that of the DL. The distribution of Cd and Cr were not centration is affected by hydrodynamics. The data presented in Sections consistent with the extent of economic development, indicating that Cd 3.2 and 3.3 indicate that these lakes are not polluted by Cr. Therefore, and Cr are not affected by social development but moved by soil erosion. when Cr is not affected by human activities, the river-lake connectivity

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Fig. 5. (continued). promotes downstream enrichment of Cr. particles. The agricultural planting area of the studied basin in 2017 In Section 4.1, we indicated that Cd is affected by industry and (2.8 × 104 km2) was higher than in 2007 (2.3 × 104 km2), indicating agriculture, and Section 3.4 indicated that most lakes are polluted by Cd. that the agricultural activities could increase the amount of soil-water Cd was a priority pollutant in the coastal development areas (Xu et al., erosion. Hou et al., (2020) found soil erosion was worse in the lower 2021), (Nan, 2016), Baiyangdian Lake (Wang et al., 2020), regions of the Yangtze River basin. Previous studies (Li et al., 2018) have Yangtze River Estuary (Wang et al., 2020) and Pearl River Delta (Zhao shown that suspended matter can carry more than 90% of the particles et al., 2018), indicating that Cd pollution may now be a common envi­ that enter the water, and that polluted particles are the main source of ronmental problem in China. However, the distribution of Cd in the DL heavy metal polluted sediments. Therefore, soil-water erosion is an of the Yangtze River Basin was not correlated with economic develop­ important factor for the Cd pollution. ment, indicating that Cd may be migrating throughout the basin. In the Cr mainly originated from natural soil, and Cd was derived from non- river/lake system, suspended matter from soil erosion can easily adsorb point pollution. Widely distributed Cd and Cr are more likely to move heavy metals from the water, then settle in the sediment (Zeng et al., through surface runoff. The connected, downstream and multi-branched 2020). Because the concentration product (Ksp) of Cd is lower than the lakes more easily accumulate small particles and heavy metal pollutants. Ksp of other elements, Cd is easily precipitated and carried by the Ciazela et al., (2018) found that the distance, type of connection, and

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Fig. 6. The average values of CFs in lake sediments.

Table 3 Pearson correlation analysis of heavy metals. Cr Cu Zn Cd Pb Hg

Cu -0.062 Zn 0.069 0.373** Cd -0.001 0.417** 0.545** Pb 0.014 0.523** 0.940** 0.523** Hg 0.004 0.163 0.681** 0.188 0.663** As 0.023 0.089 0.528** 0.139 0.532** 0.918** Median size -0.324* -0.013 -0.050 -0.077 0.006 -0.030

* Correlation is significant at P < 0.5, ** Correlation is significant at P < 0.01.

Table 4 The PCA result for heavy metal in lake sediments.

PC1 PC2 PC3

Eigenvalue (> 1) 3.54 1.36 1.01 Total variance % 50.5 19.4 14.4 Cumulative % 50.5 69.9 84.3 Fig. 7. The concentration of metals in connected lakes (CL) and disconnected Cr 0.026 -0.134 0.983 lakes (DL). Cu 0.501 0.622 -0.063 Zn 0.920 0.076 0.089 Cd 0.574 0.575 0.098 (soil particles) are easily transported and diffused through surface Pb 0.934 0.149 0.022 runoff. Therefore, source control and erosion reduction are two main Hg 0.832 -0.500 -0.098 strategies to prevent the diffusion of metal contaminants. As 0.737 -0.585 -0.096 5. Conclusions flooding frequency affected the metal transport in a river-oxbow lake system. Jeong et al. (1987) found that rainfall increased the concen­ In this work, the concentrations, risks, distribution and sources of tration of heavy metals in the total suspended matter. Because of the seven heavy metals in 62 lake sediments along the middle and lower river-lake connectivity, the Cd and Cr adsorbed on the suspended matter reaches of the Yangtze River were comprehensively investigated. The

11 M. Luo et al. Journal of Hazardous Materials 416 (2021) 125818 influence of river-lake connectivity on the migration of metals was Ciazela, J., Siepak, M., Wojtowicz, P., 2018. Tracking heavy metal contamination in a analyzed. The average concentrations of Cr, Cu, Hg, Zn, Cd, Pb and As in complex river-oxbow lake system: middle Odra Valley, Germany/Poland. Sci. Total Environ. 616–617, 996–1006. lake surface sediments were 90.8, 60.1, 0.06, 102, 0.89, 42.7, and Dai, L., Wang, L., Li, L., Liang, T., Zhang, Y., Ma, C., Xing, B., 2018. Multivariate 6.01 mg/kg, respectively. The lakes along the middle reach and south­ geostatistical analysis and source identification of heavy metals in the sediment of – ern bank of the Yangtze River had a higher metal accumulation. The Poyang Lake in China. Sci. Total Environ. 621, 1433 1444. Deng, J., Zhang, J., Yin, H., Hu, W., Zhu, J., Wang, X., 2020. Ecological risk assessment potential risks posed by the metals in the study area followed the trend and source apportionment of metals in the surface sediments of river systems in Lake Cd > Hg > Cu >Pb > As> Cr > Zn, where Cd was the most widely Taihu Basin, China. Environ. Sci. Pollut. Res. 27 (21), 25943–25955. distributed and most concentrated element. According to the total risk Deng, M., Yang, X., Dai, X., Zhang, Q., Malik, A., Sadeghpour, A., 2020. Heavy metal pollution risk assessments and their transportation in sediment and overlay water for values for the heavy metals, Daye lake, Cihu lake, Zhupo lake, Tiangang the typical Chinese reservoirs. Ecol. Indic. 112, 106166. lake, Dongting lake, Taihu lake, Bali lake and Baoan lake posed a rela­ Ding, T., Du, S., Zhang, Y., Wang, H., Zhang, Y., Cao, Y., Zhang, J., He, L., 2020. tively high ecological risk. PCA and Pearson’s correlation analysis Hardness-dependent water quality criteria for cadmium and an ecological risk assessment of the Shaying River Basin, China. Ecotoxicol. Environ. Saf. 198, 110666. showed that Cr mainly originated from the natural environment; Cd and Fan, Y., Li, H., Xue, Z., Zhang, Q., Cheng, F., 2017. Accumulation characteristics and Cu were affected by industry and agriculture throughout the whole potential risk of heavy metals in soil-vegetable system under greenhouse cultivation basin; and Zn, Pb, Hg, and As mainly originated from the mining in­ condition in Northern China. Ecol. Eng. 102, 367–373. dustry. The DL had higher concentrations of Cu, Zn, Pb, and As Feng, Y., Bao, Q., Xiao, X., Lin, M., 2019. Geo-accumulation vector model for evaluating the heavy metal pollution in the sediments of Western Dongting Lake. J. Hydrol. compared with CL, whereas the concentrations of Cd and Cr in the DL 573, 40–48. were lower than these in the CL. Through a comprehensive analysis of Gao, R., Xue, C., Zhao, X., et al., 2019. Source and possible leaching process of ore metals the metal concentrations, sediment grain size, and agricultural planting in the Uragen sandstone-hosted Zn-Pb deposit, Xinjiang, China: Constraints from lead isotopes and rare earth elements geochemistry. Ore Geol. Rev. area in 2007 and 2017, the soil particles in runoff are an important Han, R., Zhou, B., Huang, Y., Lu, X., Li, S., Li, N., 2020. Bibliometric overview of research factor for Cd and Cr diffusion throughout the whole basin. Heavy metal trends on heavy metal health risks and impacts in 1989–2018. J. Clean. Prod. 276, diffusion will be monitored during rainfall events and the relationship 123249. He, M., Shen, H., Li, Z., 1987. Ten-year regional monitoring of soil-rice grain between soil properties and utilization degree will be investigated in contamination by heavy metals with implications for target remediation and food future experiments. To improve water quality, pollution source control safety. Environ. Pollut. 2019 (244), 431–439. should be strengthened and erosion should be prevented to reduce the Hou, X., Shao, J., Chen, X., Li, J., Lu, J., 2020. Changes in the soil erosion status in the middle and lower reaches of the Yangtze River basin from 2001 to 2014 and the diffusion of contaminants in the Yangtze River Basin. impacts of erosion on the water quality of lakes and reservoirs. Int. J. Remote Sens. 41 (8), 3175–3196. CRediT authorship contribution statement Jeong, H., Choi, J.Y., Lee, J., 1987. Heavy metal pollution by road-deposited sediments and its contribution to total suspended solids in rainfall runoff from intensive industrial areas. Environ. Pollut. 2020 (265), 115028. Mingke Luo: Conceptualization, Methodology, Writing - original Jia, X., Fu, T., Hu, B., Shi, Z., Zhou, L., Zhu, Y., 2020. Identification of the potential risk draft, Investigation. Hui Yu: Supervision, Funding acquisition. Qian Liu: areas for soil heavy metal pollution based on the source-sink theory. J. Hazard. Mater. 393, 122424. Investigation. Wei Lan: Investigation. Qiaoru Ye: Visualization. Yuan Jiang, Q., He, J., Ye, G., Christakos, G., 2018. Heavy metal contamination assessment of Niu: Project administration, Resources, Writing - review & editing. surface sediments of the East Zhejiang coastal area during 2012–2015. Ecotoxicol. Yong Niu: Conceptualization, Methodology, Funding acquisition, Environ. Saf. 163, 444–455. Investigation. Jiang, X., Zheng, P., Cao, L., Pan, B., 2021. Effects of long-term floodplaindisconnection on multiple facets of lake fish biodiversity: decline of alpha diversity leads to a regional differentiation through time. Sci. Total Environ. 763, 144177. Declaration of Competing Interest Ke, X., Gui, S., Huang, H., Zhang, H., Wang, C., Guo, W., 2017. Ecological risk assessment and source identificationfor heavy metals in surface sediment from the Liaohe River protected area, China. Chemosphere 175, 473–481. The authors declare that they have no known competing financial Khan, F.S.A., Mubarak, N.M., Tan, Y.H., Karri, R.R., Khalid, M., Walvekar, R., interests or personal relationships that could have appeared to influence Abdullah, E.C., Mazari, S.A., Nizamuddin, S., 2020. Magnetic nanoparticles — the work reported in this paper. incorporation into different substrates for dyes and heavy metals removal a review. Environ. Sci. Pollut. Res. 27 (35), 43526–43541. Koduru, J.R., Karri, R.R., Mubarak, N.M., 2019. Smart Materials, Magnetic Graphene Acknowledgments Oxide-based Nanocomposites for SUSTAINABLE WATER Purification. Springer International Publishing,, Cham, pp. 759–781. Kükrer, S., S¸ eker, S., Abacı, Z.T., Kutlu, B., 2014. Ecological risk assessment of heavy This study was financiallysupported by the National Natural Science metals in surface sediments of northern littoral zone of Lake Çıldır, Ardahan, Turkey. Foundation of China, China (41807494, 31500423), Major Science and Environ. Monit. Assess. 186 (6), 3847–3857. Technology Program for Water Pollution Control and Treatment of Kumar, V., Sharma, A., Pandita, S., Bhardwaj, R., Thukral, A.K., Cerda, A., 2020. A review of ecological risk assessment and associated health risks with heavy metals China, China (No. 2018ZX07208-005), and National Fundamental in sediment from India. Int. J. Sediment Res. 35 (5), 516–526. Research Project for Science and Technology of China, China (No. Li, L., Geng, S., Wu, C., Song, K., Sun, F., Visvanathan, C., Xie, F., Wang, Q., 2019. 2014FY110400-01). We thank Tara Penner, MSc, from Liwen Bianji, Microplastics contamination in different trophic state lakes along the middle and lower reaches of Yangtze River Basin. Environ. Pollut. 254, 112951. Edanz Editing China (www.liwenbianji.cn/ac), for editing the English Li, R., Tang, C., Cao, Y., Jiang, T., Chen, J., 2018. The distribution and partitioning of text of a draft of this manuscript. trace metals (Pb, Cd, Cu, and Zn) and metalloid (As) in the Beijiang River. Environ. Monit. Assess. 190 (7), 399. Li, R., Tang, X., Guo, W., Lin, L., Zhao, L., Hu, Y., Liu, M., 2020. Spatiotemporal Appendix A. Supporting information distribution dynamics of heavy metals in water, sediment, and zoobenthos in mainstream sections of the middle and lower Changjiang River. Sci. Total Environ. Supplementary data associated with this article can be found in the 714, 136779. Li, Y., Chen, H., Teng, Y., 2020. Source apportionment and source-oriented risk online version at doi:10.1016/j.jhazmat.2021.125818. assessment of heavy metals in the sediments of an urban river-lake system. Sci. Total Environ. 737, 140310. 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