Distribution, Assessment and Coupling Relationship of Heavy Metals And

Sustainable Cities and Society 50 (2019) 101665

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Distribution, assessment and coupling relationship of heavy metals and macroinvertebrates in sediments of the Weihe River Basin T ⁎ Xinxin Wanga, Ping Sua, Qidong Lina, Jinxi Songa,b, , Haotian Suna, Dandong Chengb,c, Shaoqing Wanga, Jianglin Penga, Jiaxu Fua a Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China b State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, 712100, China c University of Chinese Academy of Sciences, Beijing, 100049, China

ARTICLE INFO ABSTRACT

Keywords: In order to evaluate the distribution of heavy metals and macroinvertebrates and to clarify the coupling re- Sediments lationship between them, a total of 54 samples of the 0–10 cm layer and 10–20 cm layer sediments were col- Heavy metal lected from 27 sites in the Weihe River Basin. Geo-accumulation index, Ecological potential risk index, Pollution Macroinvertebrates load index, and Consensus-based sediment quality guidelines were employed to evaluate the ecological risk Redundancy analysis associated with heavy metals in sediments. Macroinvertebrate community structure was analyzed, following Bioindicators which the quality surrounding habitat was evaluated basing on the indexes. Our results confirmed that the orders of pollution level were 10–20 cm > 0–10 cm at different layers and Cd > Ni > Pb > Cu > Cr > Zn for different metals. Through correlation analysis and redundancy analysis, macroinvertebrate metrics exhibited significant correlations with heavy metals. Gastropods and R (Ratio of Ephemeroptera and Trichoptera index to Chironomidae) were selected as bioindicators in overall qualitative analysis. In the 0–10 cm layer, Gastropods and Family biotic index could be selected as bioindicators. Whereas, in the 10–20 cm layer, suitable bioindi-

cators were Oligochaetes and DM (Maralef richness index). Heavy metals, especially Cd, in sediments may pose adverse impact on macroinvertebrate community.

1. Introduction reactions, such as adsorption and desorption, oxidation and reduction, and precipitation and dissolution (Şengör, Spycher, Ginn, Sani, & Widespread heavy metals are highly toxic, non-degradable and bio- Peyton, 2007). Under certain external conditions, sediments con- accumulative in the aquatic environment, whose contaminations have taminated with heavy metals may be released into the water body as been recognized as priority environment issue worldwide (Demirbas, “secondary release”, which increases the bioavailability of heavy metals 2008; Fu et al., 2014; Zheng et al., 2015). After being discharged into resulting in a potential source of pollution (Cantwell, Burgess, & King, rivers, heavy metals indwell in the water body in signiﬁcant amounts, 2008; Hiller, Jurkovic, & Sutriepka, 2010; Yang et al., 2014). Conse- which are adsorbed by the suspending solids in the water and accu- quently, properly monitoring heavy metal pollution in sediments is of mulated in river sediments after sedimentation (Almeida et al., 2002). urgent need. Since long-term accumulation could lead to much higher heavy metal Currently, both chemical analysis and biomonitoring are used to content in sediments than in water body, sediments are the main sto- monitor heavy metals in aquatic ecosystems. The former is the most rage for heavy metal pollutants in river (Costas, Pardo, Méndez- direct and accurate method of monitoring, but not feasible in large- Fernández, Martínez-Madrid, & Rodríguez, 2018). Heavy metals in se- scale sampling to provide powerful information on possible toxicity to diments can undergo a host of highly reversible physicochemical organisms and ecosystems (Zhou, Zhang, Fu, Shi, & Jiang, 2008).

⁎ Corresponding author at: Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China. E-mail addresses: [email protected] (X. Wang), [email protected] (P. Su), [email protected] (Q. Lin), [email protected] (J. Song), [email protected] (H. Sun), [email protected] (D. Cheng), [email protected] (S. Wang), [email protected] (J. Peng), [email protected] (J. Fu). https://doi.org/10.1016/j.scs.2019.101665 Received 2 February 2019; Received in revised form 7 June 2019; Accepted 16 June 2019 Available online 19 June 2019 2210-6707/ © 2019 Elsevier Ltd. All rights reserved. X. Wang, et al. Sustainable Cities and Society 50 (2019) 101665

Although the latter cannot detect the specific concentration of heavy 33°50′N-37°18′N) is a typical watershed in Guantian Economic Zone metals, it is able to capture chemicals remaining in the organism as a with drainage area of approximately 13.48 × 104 km2. As the largest response metric, thereby assessing the integrated ecotoxicity of pollu- tributary of the Yellow River, the WR originates from the north side of tants, reflecting the corresponding deleterious degree in river pollution Niaoshu mountain in Gansu Province, and mainly flows across Tianshui (Ancion, Lear, & Lewis, 2010; Zhou et al., 2008). Through the feeding in Gansu Province, the Guanzhong Plain of Shaanxi Province, and behavior of aquatic organisms, heavy metals can accumulate in the merges into the Yellow River in Tongguan County, Weinan City, with a viscera and tissues of aquatic organisms (Solà & Prat, 2006), hinder length of about 818 km. On account of different geological structures, growth and reproduction, alter biological density and diversity, and the north and south banks of the WR are asymmetric water systems. The may cause population decline (Gray & Delaney, 2008), and affect the south tributaries consist of massive small-scale watersheds with low composition of other biomes through food webs. Therefore, some sediment yield, steep river channel slope and rapid flow of water. aquatic organisms such as fishes, periphytic algae, plankton and mac- However, large tributaries are mostly concentrated on the north bank, roinvertebrates were used as bioindicators for heavy metal pollution such as the Jinghe River (JR), the BeiLuohe River (LR), the Shichuan (Burger, 2006). Using water as direct habitat, fishes, periphytic algae River, the Qishui River, etc (Zhang et al., 2018). Most of them flow from and plankton can quickly avoid pollution and are therefore not sensitive the northwest to the southeast through the Loess Plateau with a high to sediment pollution. Comparing with other bioindicators, macro- content of sediment invertebrates have merits in comprehensively evaluating river health and low river channel ratio, which are distributed in a fan shape and (Ding et al., 2017; Xu, Wu, & Yin, 2016). Having relatively long-life bring massive reworked loess (Chen et al., 2014; Song, Xu, Hui, Li, & Li, cycles with poor mobility, macroinvertebrates inhabit sediments and 2010). The JR is the largest tributary of the WR, followed by the LR, possess a limited capacity to evade adverse conditions such as heavy which accounts for 33.7% and 20% of the total area of the WR Basin, metal pollution (Kröncke & Reiss, 2010; Zhang & Liu, 2014). It is well respectively. The annual sediment transport of the JR is as high as 296 documented that sediments contaminated with heavy metals pose a million tons, followed by the LR with 106 million tons. The study area threat to macroinvertebrate community (Davis, Volesky, & Mucci, encompassed the WR Basin, which mainly consists of the WR and its 2003; Song et al., 2015). Conversely, macroinvertebrates exposed to two main tributaries—the JR and LR (Fig. 1). There are few human heavy metal pollutants also reflect the presence of hazardous heavy activities in the upper reaches of the WR, and more and more middle- metals owing to their coupling relationship (De Castro-Català et al., and large-sized cities are concentrated downstream with a total agri- 2016; Ryu et al., 2011). Therefore, adopting macroinvertebrates as a cultural area of about 10,000 km2 (Wang et al., 2018; Xue et al., 2016). monitoring tool is an effective approach to assess heavy metal pollution More than 80% of the industrial wastewater and domestic sewage from in sediments from the perspective of long-term effects of pollution nearby cities have introduced a mass of heavy metals into the WR Basin mitigation. causing the deterioration of riverine systems. In the Weihe River (WR) Basin, abundant human activities and various industries have existed along the rivers in recent decades, which has gradually intensified pollution, especially heavy metal con- 2.2. Field sampling taminations. The heavy metal pollution of sediments in the Baoji (Wang, Lu, Lei, Zhai, & Huang, 2011), Xi'an (Lei et al., 2008), and 2.2.1. Sediments collection and analysis Shaanxi (Han et al., 2012; Luo, Song, & Wang, 2013; Yang et al., 2014) Sediment samples were collected from 27 sites: 11 in the WR (W1- sections of the WR Basin were analyzed to conduct river health risk W11), 8 in the JR (J1-J8) and 8 in the LR (L1-L8) during September assessments. The previous researches in the WR Basin mainly per- 2017 (Fig. 1). Each sample consisted of the mixture of 3 subsamples formed macroinvertebrate community structure and its relationship collected within an area of approximately 1.0 m2. A thin-walled trans- with physicochemical variables (Yin et al., 2013; Zhang et al., 2015)or parent polycarbonate tube with up and down openings was erected into land use (Li et al., 2018). However, there is a lack of systematic research the riverbed sediment to a depth of more than 20 cm. Sediment samples on the coupling relationship between macroinvertebrate community were collected from 0 to 10 cm and 10–20 cm layers, where are ap- structure and heavy metal pollution based on stratifi cation in sedi- plicable using the ET0204 piston column sampler. Samples were then ments. The depth of sediment collected by many researchers was only dispensed into air-tight polyethylene bags, cryopreserved, and shipped directly at 0–20 cm or 0–10 cm (Hiller et al., 2010; Ke et al., 2017; back to the laboratory within 12 h. After naturally air-dried and dis- Siddiqui & Pandey, 2019; Wang, Liu, Lu, Zhang, & Liu, 2015). Because carding any foreign objects, sediment samples were grounded with an the abundance, biomass and species of macroinvertebrates vary with agate mortar, passed through a 200-mesh nylon sieve. The concentra- sediment depth (Xu, Wang, Pan, & Zhao, 2012), in addition to the tions of Cr, Cu, Ni, Pb, Zn and Cd were then assayed via inductively overall qualitative analysis, the two-layer quantitative analysis was coupled plasma mass spectrometry (ICP-MS, X SeriesⅡ), where the se- carried out to express in detail the relationship between the toxic effects diment samples were digested with HCL-HNO3-HF-H2O2 mixture. The of heavy metals and the structural characteristics of macroinvertebrate reference material (GBW07363 and GBW07429; the Center of National community. The objectives of this research were: 1) to assess the pre- Standard Reference Material of China) and blanks in each set of tests sent statues of heavy metal pollution in sediments and ecological risks; were measured in duplicate using the same procedure. The relative 2) to determine the distribution characteristics of macroinvertebrate deviations between the measured values and the reference values in the community structure and sediment quality around macroinvertebrates; reference material were less than 10%. Two samples were randomly 3) to clarify the coupling relationship between macroinvertebrate selected for repeated test. Each sample was measured twice for mean community and heavy metal pollution. With China’s sustainable de- values. velopment strategy with the rubric of “Eco-urbanism” (Sharifi, 2016), the results of this research can provide referential data and corresponding theoretical basis for biomonitoring of heavy metal pollution 2.2.2. Physicochemical factors testing in sediments and river health assessment of the WR Basin. Indicators such as water temperature, dissolved oxygen, pH, elec- trical conductivity and total dissolved solids were recorded using por- 2. Materials and methods table water quality meter (HACH HQ40d) in situ. Flow rate was ob- tained using a portable flow meter (MGG/KL-DCB). Other indices, 2.1. Description of the study area including total nitrogen, total phosphorus, nitrite ion and nitrate ion were determined following the standard analytical protocols (Clesceri, Formerly known as Wei Water, the WR Basin (104°00′E-110°20′E, Greenberg, Eaton, Rice, & Franson, 2005).

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Fig. 1. Research sites map of the WR Basin: WR, JR and LR.

Table 1 samples were identified to the species level where possible with twee- Abbreviation table for assessment index. zers under dissecting microscope, sorted out, counted, weighed using electronic balance (accurate to 0.1 mg) after blotting dry and finally Assessment index Abbreviations fixed with formaldehyde solution. Geo-accumulation index Igeo Potential ecological risk index PRI Pollution load index PLI 2.3. Metal risk assessment the probable effect concentrations PEC The mean probable effect concentration quotient MPECQ In order to comprehensively assess the ecological risk of heavy Shannon-Wiener index HS metals in sediments, different methods were applied, including Geo- Maralef richness index D M accumulation index (Ma et al., 2016; Song et al., 2015), Potential Ephemeroptera and Trichoptera index ET Ratio of ET to Chironomidae R ecological risk index (Hakanson, 1980; Pejman, Bidhendi, Ardestani, Family biotic index FBI Saeedi, & Baghvand, 2015), Pollution load index (Islam, Ahmed, Raknuzzaman, Habibullah-Al-Mamun, & Islam, 2015) and Consensus- based sediment quality guidelines (MacDonald, Ingersoll, & Berger, 2.2.3. Macroinvertebrates collection and analysis 2000; Siddiqui & Pandey, 2019). The abbreviations of the assessment Macroinvertebrates were sampled within 5 m of sediment collec- index are listed in Table 1. The supplementary materials provided the tion. Three parallel samples were collected at random using Surber details of the evaluation method (SI 1, Risk assessment methods). Both network (30 cm × 30 cm, 60-mesh screen) from sampling depth of the overall and the different-depth evaluations of pollutants were per- – – fi 0 10 cm and 10 20 cm layers, which were rinsed and ltered on site. formed. Then the remaining impurities together with macroinvertebrates were all packed in pre-numbered wide-mouthed sample bottles, fixed with 2.4. Biological assessment 75% alcohol, sealed, cryopreserved, and shipped back to the laboratory within 12 h. Macroinvertebrates were manually picked in white pans, The diversity indices of macroinvertebrate community structure preserved in 75% ethanol for further identification of species. Then, were calculated to assess the ability of macroinvertebrates to respond to

3 X. Wang, et al. Sustainable Cities and Society 50 (2019) 101665 contaminations, which include Shannon-Wiener index and Maralef value. No significant differences were detected in pH (ANOVA: richness index (Edegbene, Elakhame, Arimoro, Osimen, & Odume, F = 2.930, p = 0.073), EC (ANOVA: F = 2.275, p = 0.125), V 2019; Johnston & Roberts, 2009). The community structure char- (ANOVA: F = 1.038, p = 0.370), TN (ANOVA: F = 0.282, p = 0.757) acteristics were also analyzed from two aspects of richness and pro- and TP (ANOVA: F = 2.079, p = 0.147) among different river systems. portion of species including: the abundance, the biomass, the relative abundance and the relative biomass. The following indexes were then 3.2. Heavy metal levels and risks assessment computed: 1) Ephemeroptera and Trichoptera index, 2) Ratio of ET to Chironomidae, 3) Family biotic index (SI 2, Tolerance Values) 3.2.1. Heavy metal levels (Mandaville, 2002; Young et al., 2014). When compared with other rivers in the world, the metal levels in the WR Basin are lower (Table 3). Especially, the level of Cd in the 2.5. Statistical analysis world is at least 6 times higher than the WR Basin (Attrill & Thomes, 1995; Grabowski, Houpis, Woods, & Johnson, 2001; Lasheen & Ammar, After Levene's test for homogeneity of variances (p < 0.05) among 2009; Vital & Stattegger, 2000). However, the heavy metal contents in the JR, LR and WR, one-way ANOVA was applied to compare means of the WR Basin sediments were at the middle level among the surveyed scores. Pearson correlation analysis (CA) was used to extract informa- Chinese rivers (Ke et al., 2017; Wang, Wang, Liu, Yu, & Shen, 2015; tion on correlations between macroinvertebrates and heavy metals Wang, Liu et al., 2015; Yan et al., 2016; Zhang et al., 2017). The con- (p < 0.05 or p < 0.01). The statistical analyses were carried out using centration of Cd were 2 times higher than the Shaanxi background SPSS19.0. In order to detect the interactions between macro- values, while the values of Cr, Cu, and Zn were slightly higher. Ac- invertebrate community and heavy metal pollution, significantly re- cording to the heavy metal concentration standards from the guidelines levant variable factors were retained from logarithmically transforming for the protection and management of aquatic sediment quality, con- the variable by prior selection and Monte-Carlo test (p < 0.05). centrations of most of heavy metals were between lowest effect level Finally, Redundant analysis (RDA) was performed using CANOCO and severe effect level (Islam et al., 2015). version 5.0. The vertical distributions of heavy metal in sediments were analyzed to determine the transfer or accumulation of heavy metal pollu- 3. Results tion (Fig. 2), including the 0–10 cm and 10–20 cm layers. The mean concentrations of heavy metals in the 10–20 cm layer were greater than 3.1. Physicochemical variables the 0–10 cm layer, except for Cr. The mean concentrations of Ni and Cu in the two layers were lower than or near the background values. While The values of the physicochemical-biological properties are sum- the mean concentrations of heavy metals in the 0–10 cm and 10–20 cm marized in Table 2. Significant differences in T (ANOVA: F = 5.835, layers sediments of the WR were higher than the background values, p = 0.009), DO (ANOVA: F = 11.127, p = 0.000) and NO3 (ANOVA: except for Ni. Especially for Cd, the concentrations of Cd in two layers F = 4.971, p = 0.016) were detected across the three river locations, were all higher than the background value. with high T, low DO and high NO3 levels observed in the WR. TDS (ANOVA: F = 7.916, p = 0.002) and NO2 (ANOVA: F = 6.656, 3.2.2. Risks assessment p = 0.005) differed significantly among different river systems, with Igeo based on the background value in sediments of the WR Basin the LR being the highest mean value and the WR being the lowest mean were calculated to estimate the pollution level of metals. Apart from

Table 2 Physicochemical-biological environmental properties for habitat types from diﬀerent rivers, in the WR Basin.

Parameters JR LR WR

Mean ± SD Min Max Mean ± SD Min Max Mean ± SD Min Max

Hydro-environmental conditions Temperature (T-℃) 18.6 ± 4.8a 7.3 22.1 20.6 ± 1.3b 18.9 22.7 23.2 ± 1.8b 19.8 26.5 Dissolved oxygen (DO-mg/L) 8.8 ± 0.6a 7.8 9.7 9.3 ± 0.8a 7.9 10.2 6.9 ± 0.2b 8.7 9.4 pH 9.26 ± 0.07 9.16 9.34 9.33 ± 0.32 9.09 10.08 9.07 ± 0.24 8.72 9.43 Conductivity (EC-mS/cm) 823.9 ± 365.7 291.0 1398.0 908.3 ± 378.8 239.0 1367.0 624.5 ± 150.7 350.0 811.0 Total dissolved solids (TDS- mg/L) 452.5 ± 189.9a 207.0 788.0 577.4 ± 146.8a 362.0 774.0 320.2 ± 81.3b 233.0 449.0 Velocity (V-m/s) 0.63 ± 0.43 0.08 1.44 0.38 ± 0.26 0.08 0.93 0.64 ± 0.50 0.33 2.10 Total nitrogen (TN-mg/L) 4.82 ± 1.50 2.88 7.40 5.14 ± 1.16 3.26 6.66 5.28 ± 1.25 3.73 7.00 Total phosphorus (TP-mg/L) 0.21 ± 0.08 0.14 037 0.16 ± 0.04 0.12 0.22 0.21 ± 0.05 0.15 0.29 a a b Nitrite ion (NO2-mg/L) 1.72 ± 0.31 1.30 2.14 2.24 ± 0.56 1.08 2.83 1.27 ± 0.70 0.38 2.88 a b b Nitrate ion (NO3 -mg/L) 3.51 ± 1.40 1.94 6.35 4.98 ± 1.86 2.17 7.09 5.59 ± 1.09 3.87 7.25 Heavy metal: Sediment (mg/kg) Chromium (Cr) 79.87 ± 18.6 62.58 124.98 72.46 ± 12.8 54.04 94.91 75.04 ± 12.6 53.78 106.68 Nickel (Ni) 28.61 ± 9.1 19.34 56.43 25.29 ± 2.5 21.29 31.92 26.92 ± 4.1 19.3 39.60 Copper (Cu) 21.56 ± 6.1 15.63 37.60 18.56 ± 2.4 14.67 25.28 24.33 ± 7.9 14.97 48.59 Zinc (Zn) 71.13 ± 51.37 133.23 66.39 ± 7.6 54.13 82.60 73.59 ± 12.9 55.77 100.57 Cadmium (Cd) 0.179 ± 0.05 0.138 0.310 0.183 ± 0.03 0.137 0.227 0.195 ± 0.05 0.145 0.369 Lead (Pb) 20.63 ± 3.3 16.98 29.87 19.49 ± 2.5 15.30 24.60 21.90 ± 2.8 17.09 27.17

Biological descriptive parameters Mean abundance (ind/m2) 124.8 22.0 1222.2 345.7 33.0 400.0 70.4 11.0 144.4 Number of taxa (N) 65 18 164 60 8 162 18 4 67 ET 11.4 12.5 18.1 R 1.7 0.5 0.23

Notes: SD represents standard deviation. Different superscript letters indicate that the values in the row are significantly different. Black letters in brackets indicate abbreviations.

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Table 3 Comparison of heavy metal mean concentrations in sediments with values taken from the open publications (mg/kg).

Locations Cr Ni Cu Zn Cd Pb References

Weihe River Basin 75.70 26.94 21.80 70.79 0.19 20.81 This observation Yellow River 62.4 23.60 40.70 68.40 0.085 15.20 Yan et al. (2016) Yangzi River estuary 79.1 31.9 24.7 82.9 0.19 23.8 Wang, Wang et al. (2015) Liaohe River 35.06 17.73 17.82 50.24 1.20 10.57 Ke et al. (2017) Haihe River Basin 112.41 40.77 96.56 219.33 NA 56.90 Zhang et al. (2017) Huaihe River NA 32.79 31.30 183.57 NA 53.43 Wang, Liu et al. (2015) Thames estuary 59 34 61 219 1.3 179 Attrill and Thomes (1995) Mississippi River 2.08 17.1 31.8 45.8 1.6 18.7 Grabowski et al. (2001) Amazon River NA NA 11.4 78.0 NA 17.0 Vital and Stattegger (2000) Nile River 41.32 60.6 45.6 73.6 2.4 4.6 Lasheen and Ammar (2009) BK in Shaanxi 62.50 28.80 21.40 69.40 0.094 21.40 BK of Soil Elements in China, 1990 BK in Gansu 70.20 35.20 24.10 68.50 0.116 18.80 LEL 26 16 16 NA 0.6 31 Islam et al. (2015) SEL 110 75 110 NA 10 250

Notes: NA, not available; BK, background values; LEL, lowest eﬀect level; SEL, severe eﬀect level. site J1, the mean Geo-accumulation index (MIgeo) for all sites were Cd was moderately negative (Igeo ≤ 0), indicating that most of the sediments were practi- contaminated (12≤≤Igeo ). According to the values of the in- cally uncontaminated (Fig. 3A). Individually, at 82.5% of the sites, the dividual potential ecological risk factors (Eri) and the potential ecolo- pollution level of Cd was uncontaminated to moderately contaminated gical risk index (PRI) in Fig. 3B, the contributions for individual heavy

(01≤≤Igeo ), while the pollution levels of other metals were practically metal were ranked in the following order: Cd (74.93%) > Ni uncontaminated (Igeo ≤ 0). Especially at W9 site, the pollution level of (7.29%) > Pb (6.75%) > Cu (6.55%) > Cr (3.12%) > Zn (1.36%). The

Fig. 2. Spatial distribution of heavy metal in sediments of the WR Basin (mg/kg), including the 0–10 cm and 10–20 cm layers from the WR, JR and LR. The red short dashed lines represent the background values of heavy metals in sediments.

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Fig. 3. Heavy metal risk assessment at each research site of the WR Basin: A. Igeo of sediments; B. PRI of sediments; C. PLI and MPECQ of heavy metal in sediments of the 0–10 cm and 10–20 cm layers. The dash line represents the critical level value.

Eri for Ni, Pb, Cu, Cr and Zn were below 40, indicating low potential layer ranged from 0.9 to 1.6 and 0.8 to 1.9, respectively. The PLI values ecological hazard for these metals. Whereas, Cd posed moderate risks of 81.5% sites exceed 1, which indicates the possibility of environ- with Eri value greater than 40. Approximately 80% sites showed mental deterioration. According to the consensus-based SQG, the moderate risks (PRI > 65), while the remaining sites showed low probable effect concentrations (PEC) were used to evaluate the possible ecological risks (PRI < 65). In particular, the PRI of J1 and W9 sites adverse effects of heavy metals in sediments. The mean probable effect reached 108 and 130 respectively. concentration quotient (MPECQ) of sediments varied from 0.27 to 0.60 In order to assess the heavy metal pollution of the different layers, (Fig. 3C). In the 0–10 cm and 10–20 cm layers at J1, the MPECQ value the pollution load index (PLI) of the 0–10 cm and 10–20 cm layers were reached 0.48 and 0.60, respectively, indicating that sediment at J1 site shown in Fig. 3C. The PLI values in the 0–10 cm layer and 10–20 cm was heavily contaminated by heavy metals.

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Fig. 4. Distribution of benthic macroinvertebrates in the WR Basin: A. the mean abundance (A1 represents the 0–10 cm layer and A2 represents the 10–20 cm layer); B. the mean biomass (B1 represents the 0–10 cm layer and B2 represents the 10–20 cm layer); C. the relative abundances of common macro-

invertebrates; D. HS index; E. DM index.

3.3. Macroinvertebrate community structure and metrics

The total abundance and biomass of macroinvertebrates were 11–1222 in../m2 (mean: 168 in../m2) and 3–2891.11 mg/m2 (mean: 393.967 mg/m2), respectively. Spatial differences existed in the abundance distribution of macroinvertebrates in the WR Basin (Fig. 4A). In the 0–10 cm and 10–20 cm layers, the mean abundances were 86.7 and 81.3 in../m2, respectively. The spatial distribution of biomass among all sites in the WR and JR was significantly different, while the difference among all sites in the LR was insignificant (Fig. 4B). In the 0–10 cm and 10–20 cm of layers, the mean biomasses were 257.7 mg/m2 and 118.3 mg/m2, respectively. The survey covered a total of 74 species, 1189 individuals, be- longing to 3 phyla (Arthropoda, Annelida and Mollusca), 5 classes (Insecta, Gastropoda, Oligochaeta, Crustacea and Hirudinea) in the WR Basin. Crustacea and Hirudinea were found in low numbers and rare frequency, which were classified as Others. Gastropods, Insects, Fig. 5. The FBI of benthic macroinvertebrates in the WR Basin. Oligochaetes are common in the JR, LR and WR, and their relative abundances are shown in Fig. 4C. The ET and R values of different other factors. For example, heavy metals in sediments is affected by the rivers in the WR Basin are shown in Table 1. For the spatial distribution, physicochemical factors caused by chemical exchanges between sedi- HS index ranged from 0.42 to 3.43, which reached the lowest at L6 ments and water, and is also closely related to the macroinvertebrate (Fig. 4D), while DM index ranged from 0.48 to 4.28, which reached the community structure. According to the CA (SI 3, Relationship), physi- lowest at W3 (Fig. 4E). The results demonstrated that HS and DM index cochemical indices (T, TDS, TN, TP and NO2) showed significant cor- with larger variations in the WR Basin had similar trends in the three relations with heavy metal pollution. Whereas no obvious correlations rivers. FBI value of the both layers varied widely (Fig. 5), reaching a existed between heavy metal pollution and other indices (DO, PH and maximum of 9 at W9. Overall, 70% of FBI values were greater than 6. V). RDA was subsequently used to further investigate the effects of Since the 0–10 cm layer at L5 and the 10–20 cm layer at J6 and W8 did physicochemical indices on heavy metal pollution in sediments, which not collect macroinvertebrates, the FBI of these sites was not calculated. extracted axes with eigenvalues of 0.410 and 0.083 that explained 53.4% of the variance in heavy metal pollution (SI 4, Relationship). EC, TDS and NO showed similar directions, which were positively corre- 3.4. Relationship between macroinvertebrate metrics and heavy metal 2 lated with Cr and PRI and negatively correlated with Cu (Fig. 6A). TN metrics exhibited significant correlation with Ni, Cd, Zn and MPECQ; T with Pb; TP with MI and mean PLI (MPLI). As crucial factor existed in sediments, heavy metals interacts with geo

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Fig. 6. Biplot of the RDA result correlating heavy metal and: A. Physicochemical indices; B. Benthic metrics in the overall sample; C. Benthic metrics in the 0–10 cm sample; D. Benthic metrics in the 10–20 cm sample of the WR Basin. Permutation was evaluated with Monte-Carlo test and both signiﬁcance of all canonical axes were achieved (p < 0.05), suggesting the signiﬁcant correlation heavy metals and physicochemical indices/ benthic metrics.

Table 4 positive correlations with Cu, Zn, Ni and Cd. Moreover, A14 showed The macroinvertebrate metrics and their code. signiﬁcantly negative correlations with all metal metrics. A11 was only

Macroinvertebrate metrics Code Macroinvertebrate metrics Code related to Pb, and its relationship with other metals was not obvious. From the quantitative analysis of the 0–10 cm layer sediments, the abundance of Gastropods A1 the individual of Gastropods A9 correlations with the heavy metal risk indexes (SI 7, Relationship) ex- the relative abundance of A2 the relative individual of A10 hibited in A1, A2, A3, A4, A5 and A6. Among these metrics, A6 ex- Gastropods Gastropods fi the biomass of Gastropods A3 the relative individual of A11 hibited signi cant correlations with majority of risk indexes, including Insects Eri of Zn (r = 0.57, p < 0.01), Eri of Ni (r = 0.51, p < 0.01), PECQ of the relative biomass of A4 the relative individual of Others A12 Zn (r = 0.56, p < 0.01), PECQ of Ni (r = 0.50, p < 0.01) and Igeo of Gastropods Zn (r = 0.48, p < 0.05). In order to further investigate the relationship the abundance of Insects A5 DM A13 between heavy metals and macroinvertebrate metrics in the 0–10 cm the biomass of Insects A6 R A14 the biomass of Oligochaetes A7 FBI A15 layer, RDA extracted two axes with eigenvalues of 0.168 and 0.082 that the relative biomass of A8 explained 27.2% of the variance in macroinvertebrate community (SI 8, Oligochaetes Relationship). Axis 1 was positively correlated with Zn (r = 0.502) while Cr (r = 0.394) was positively correlated with Axis 2 (Fig. 6C). The similar directions of Zn, Ni, Cd and PLI, showed significantly po- The overall qualitative analysis of the WR Basin revealed clear sitive correlations with A1, A2, A3 and A4. Furthermore, A5 and A6 correlation between heavy metal risk indexes and macroinvertebrate showed negative correlations with Cr. A15 is positively correlated with metrics (Table 4). Among the metrics, A9, A10, A11, A12 and A14 Cr, yet negatively correlated with Cd. fi showed signi cant correlations with the risk indexes, except for Eri of From the quantitative analysis of the 10–20 cm layer sediment, A7, fi Ni (SI 5, Relationship). For example, A9 exhibited signi cant correla- A8 and A13 exhibited correlation with the heavy metal risk indexes (SI tions with majority of risk indexes, including Eri of Cr (r = 0.52, 9, Relationship). Among these metrics, A8 exhibited positive correla- p < 0.01), Eri of Zn (r = 0.40, p < 0.05), MIgeo (r = 0.39, p < 0.05), tions with majority of risk indexes, including Igeo of Cu (r = 0.46, MPLI (r = 0.41, p < 0.05) and MPECQ (r = 0.58, p < 0.05). The A10 p < 0.05), Eri of Cu (r = 0.45, p < 0.05), PECQ of Cu (r = 0.40, fi also showed signi cant correlations with Eri of Cr (r = 0.45, p < 0.05) p < 0.05) and MPECQ (r = 0.42, p < 0.05). RDA extracted two axes and MPECQ (r = 0.40, p < 0.05). To further determine the coupling with eigenvalues of 0.088 and 0.078 that explained 19.5% of the var- relationship, the heavy metal and macroinvertebrate metrics were se- iance in the macroinvertebrate community (SI 10, Relationship). Axis 1 lected to conduct the RDA, which extracted two axes with eigenvalues was positively correlated with Cu (r = 0.460) and Axis 2 with Pb of 0.162 and 0.101 that explained 33.6% of the variance in macro- (r = 0.266) (Fig. 6D). Cu, Ni and MPECQ showed significantly positive invertebrate community (SI 6, Relationship). Axis 1 was positively correlation with A15, A7 and A8, respectively, but negative correlation correlated with MPECQ (r = 0.634) and Cr (r = 0.576), while Zn (r = with A3. In addition, Pb also showed positive correlation with A13 and -0.231) was negatively correlated with Axis 2. Cr, Ni, Cu, Zn and A5. MPECQ showed similar directions, indicating their relatively similar effects on macroinvertebrates (Fig. 6B). A10, A9 and A11 among these metrics, showed positive correlations with Cr and Pb. A12 also showed

8 X. Wang, et al. Sustainable Cities and Society 50 (2019) 101665

4. Discussion Macdonald, 2006). Meanwhile, a high potential toxicity hazard existed in the 0–10 cm and 10–20 cm layers of J1 and W9 sites, which was

4.1. Sediment pollution assessment consistent with the results of Igeo and PRI. The abundance and biomass distribution of macroinvertebrates in For the overall view of the WR Basin, the water quality varied from the 0–10 cm and 10–20 cm layers was rather different, because a good relatively poor in the WR to the best situation in the LR. Due to the high habitat is indispensable for the survival of macroinvertebrates (Nedeau degree of urbanization along the WR, sewage discharge and human et al., 2003). The highest FBI values were detected at W9 for both disturbance may cause the increase of T and NO3, and the low DO layers, indicating that sediments cannot provide suitable habitat at W9. during eutrophication. Deterioration of river water quality can be at- This is consistent with the results of the above risk evaluation, revealing tributed to domestic and industrial sewage outflows and abundant ri- more anthropogenic pollutions at W9. Overall, 70% of FBI values were parian activities (Naser, 2013). By accelerating the adsorption of metal greater than 6 (Fig. 5), indicating that the locations in the WR Basin ions, high values of TN, TP and TDS would drive more heavy metals may be in poor quality. into sediments, especially the most sensitive Cu. Interactions could exist between heavy metals in sediments and water quality indices. Previous 4.2. Heavy metal levels in relation to macroinvertebrate metrics studies had shown that high values of TN and TP would transfer heavy metals into sediments (Fu et al., 2014). The river, after receiving the Sediments contaminated with heavy metals may inhibit or promote sewage, is likely to further cause sediments pollution, which subse- the growth of macroinvertebrate, in turn, macroinvertebrate commu- quently affects the quality of its surrounding environment (Singh, nity also reflects the qualities of sediment (Yi, Yang, & Zhang, 2011). Mohan, Sinha, & Dalwani, 2004). Changes in the structural composition of macroinvertebrate community From an overall perspective, the heavy metal contents in sediments can respond to pollutants, which have been used to monitor and assess was relatively moderate compared to the background values, major the heavy metal pollution (De Castro-Català et al., 2016; Zhou et al., water systems in China and guidelines (Table 3), while Cr and Cd were 2008). Biomonitoring can help predicting and assessing the pollution most likely to cause pollutions. According to the result of Igeo and PRI, load of heavy metals in river sediments, thus providing a sounder the risk of Cd pollution was higher than other metals. Previous studies foundation for sustainable river management in the urban environ- in the Shaanxi section also found that Cd was the most polluted heavy ments (Costa, Burlando, & Priadi, 2016). metal in the WR (Song et al., 2015; Yang, Yao, Wang, Zhang, & Guan, For the overall qualitative analysis of the WR Basin, Cr and Pb 2017). The high concentration of heavy metal elements was closely showed significantly positive correlations with the individual of related to complex anthropogenic activities such as crustal elements, Gastropods, the relative individual of Gastropods and the relative in- chemical or metals/metalloids used in industries, and other technical dividual of Insects, indicating that tolerant species―the Gastropods and activities (Hazarika, Srivastava, & Das, 2017). Higher risks were found Insects (mostly Chironomus spp.) were significantly correlated with the at J1, which could be attributed to pollution caused by factories such as concentrations of Cr and Pb. Sensitive and moderately contaminated machinery manufacturing and agriculture near the upstream tributary species disappeared and only highly tolerant species survived in dete- of J1. W9 also had a higher risk owing on the inflow of industrial and riorative environment (Arimoro, 2009; Bian, Zhou, & Fang, 2016). In- domestic wastewater and traffic activities. Heavy metals in river sedi- creased abundance of tolerant species with proximity to heavy metal ments had a certain relationship with their nearby traffic activities, pollution was responsible for the dominance of Gastropods and Chir- such as Cr in stainless steels and alloy steels for auto parts, Cu in car onomus. Thus, it was appropriate to choose Gastropods as bioindicator. lubricants (Fu et al., 2014), Cd and Pb emitted from gasoline vehicle Previous studies found that Gastropods that naturally accumulate me- (Hussain, Rahman, Prakash, & Hoque, 2015). tals to high concentrations could be used as potential bioindicator for After investigation of macroinvertebrate community, the structure biomonitoring of metal pollution (Zhou et al., 2008). The relative in- was contributed by Insects, following Gastropod, Oligochaete and dividual of Others (Crustacea and Erpobdellidae) also showed sig- Others in the three rivers (Fig. 4C). Often due to the different habitat nificantly positive correlations with Cu, Zn, Ni and Cd, since Erpob- environments, it has a certain impact on the diversity of surrounding dellidae has high tolerant values (Mandaville, 2002), while rare organisms (Vollmer, Pribadi, Remondi, Rustiadi, & Grêt-Regamey, Crustaceans are not representative as bioindicators. Laboratory simu- 2016). Significant changes in abundance, biomass and diversity of lations showed that the proportion of surviving macroinvertebrates was macroinvertebrate community have been widely applied in assessing controlled by the concentration of Cu (Clements, Pete, & Brinkman, the ambient conditions of sediments, especially heavy metal pollution 2013) and Zn (Wesner, Kraus, Schmidt, Walters, & Clements, 2014).

(Ryu et al., 2011). In the WR Basin, HS and DM indexes of macro- Regarding R value, the results showed significantly negative correla- invertebrates at most sites were moderately high, indicating that the tions with all metal metrics, which is consistent with the previous environmental conditions of sediments were moderately polluted (Cai, findings that R is sensitive to changes in sediments and water with Ma, Gao, Zhang, & Lin, 2002). Low species richness occurred at L6 and greater value in better habitat environment (Mandaville, 2002). W3, which may be attributed to external-source disturbances, leading Therefore, the R value, as more suitable biological indicator compared to a fragile state of the macroinvertebrate community (Nedeau, Merritt, to other metrics, can be used to indicate the pollution status of heavy & Kaufman, 2003; Parr & Mason, 2003). The ET values were ranked as metals in the WR Basin. follows: WR > LR > JR, while rank of the R value was: JR > LR > The distribution differences of macroinvertebrates in various depths WR (Table 1). Since ET is sensitive to environmental changes, Chir- could be related to differences in sediment characteristics that influ- onomids being the opposite, R can therefore more accurately reflect enced the bioturbation ability of the macroinvertebrates to burrow and environmental stress (Mandaville, 2002). The R value showed dete- oxidize in sediments (Mucha, Vasconcelos, & Bordalo, 2004). Mainly riorate tendency for the WR, compared to the JR and LR, which was happening in the depth between 0 and 20 cm (Song et al., 2015), in- consistent with the above results of water quality analyses. vertebrate bioturbation can alter sediment permeability (Song, Chen, From a vertical layer perspective, the PLI and MPECQ values were Cheng, Wang, & Wang, 2010), which may accelerate the top-down evaluated for overall toxicity and bio-toxic effects of heavy metals in transfer of heavy metals (Benoit, Shull, Robinson, & Ucran, 2006). sediments. 70% of the PLI values at 10–20 cm were greater than those Therefore, to further clarify the differences between the two layers, the at 0–10 cm, indicating that the possibility of adverse effects on aquatic relationships between macroinvertebrates metrics and heavy metal organisms in the 10–20 cm layer was higher than that in the 0–10 cm characteristics in sediments are discussed separately as follows. layer. The MPECQ varied from 0.27 to 0.60, revealing that sediments In terms of the 0–10 cm layer, Zn, Ni, Cd and PLI showed sig- have 15–29% probability of toxicity at all sites (Long, Ingersoll, & nificantly positive correlations with the abundance of Gastropods, the

9 X. Wang, et al. Sustainable Cities and Society 50 (2019) 101665 relative abundance of Gastropods, the biomass of Gastropods and the Macroinvertebrates are the main organisms in sediments, and their relative biomass of Gastropods. This indicated that Gastropods are community structure have a coupling relationship with the heavy highly tolerant of the heavy metal exposure, therefore, were selected as metal pollutants in their surrounding environment. The overall bioindicators for the 0–10 cm layer. Cr exhibited negative correlations qualitative analysis showed that Gastropods and R were selected as with the abundance of Insects and the biomass of Insects. bioindicators. Gastropods were positively sensitive to Cr and Pb, Macroinvertebrate community affected by metals usually exhibited while the R values were negatively sensitive to six metals. In reduction in abundance or biomass (Costas et al., 2018; Ryu et al., quantitative analysis of the 0–10 cm layer, Gastropods responding 2011), especially for sensitive taxa in Insects. Furthermore, FBI de- to Zn, Ni, Cd and PLI were also selected as biological indicator. The termined by tolerant value was positively correlated with Cr, yet ne- FBI could be selected as biological indicator owing to its obvious gatively correlated with Cd. The toxicity coefficient of Cd (30) is higher response to the ecological risk of Cd. Moreover, in quantitative than that of Cr (2) (Hakanson, 1980; Yan et al., 2016), which may be analysis of the 10–20 cm layer, suitable bioindicators were responsible for the decrease in tolerant species. The FBI based tolerance Oligochaetes and DM, which were sensitive to Ni and Pb, respec- values can be selected as a biological indicator of heavy metal con- tively. whereas Gastropods and FBI were not as bioindicators like in taminations in the 0–10 cm layer. the 0–10 cm layer. From the 10–20 cm layer, Cu, Ni and MPECQ showed significantly positive correlation with the biomass of Oligochaetes and the relative Declaration of Competing Interest biomass of Oligochaetes. Oligochaetes with highly tolerant value be- longed to the preferred bioindicators to assess the adverse effects, This is to acknowledge that there is no financial interest or benefit which is consistent with the researches of Yangtze River and Taihu Lake that has arisen from the direct applications of this research. (Bian et al., 2016; Xu, Ma, Tian, Lv, & Zhao, 2011). Different from the 0–10 cm layer, the 10–20 cm layer didn’t exhibit significant relationship Acknowledgements between metals and Gastropods or FBI owing to the difference of the abundance distributions of macroinvertebrates between the two layers. This study was supported by the National Natural Science The number of Gastropods in the 10–20 cm layer was too small to Foundation of China (Grant Nos. 51679200, 51379175), the Hundred qualify as a representative indicator species. Pb showed positive cor- Talents Project of the Chinese Academy of Sciences (Grant No. relation with DM and the abundance of Insects, indicating that the A315021406) and Science and Technology Project of Shaanxi abundance of tolerant taxa in Insects increased with increasing Pb Provincial Water Resources Department (Grant No. 2018slkj-12). we concentration. Therefore, DM is a potential bioindicator to assess the are especially grateful to the Editor, Associate Editor, anonymous re- effects of Pb on macroinvertebrate community. Differences among in- viewers for their helpful comments and suggestions, which have im- dicators in 0–10 cm and 10–20 cm layers could be resulted from the proved the quality of the manuscript. presence of different macroinvertebrates with distinctive preferences for different depths. The selection of bioindicators should be based on References actual distribution of macroinvertebrates. The different responses of macroinvertebrates to heavy metal con- Almeida, J. A., Diniz, Y. S., Marques, S. F., Faine, L. A., Ribas, B. O., Burneiko, R. C., ... taminations is due to multiple factors such as macroinvertebrates Novelli, E. L. (2002). 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