RESEARCH ARTICLE

Ecological risk assessment and sources of heavy metalsin sediment from Daling River basin

Lei Zhao &DongMi &Yifu Chen &LuoWang &Yeqing Sun

Received: 19 August 2014 /Accepted: 23 October 2014# Springer-Verlag Berlin Heidelberg 2014

Abstract To investigate the distribution, source, and ecolog-ical risk of heavy metals in Daling River basin, 28 surfacesediments collected in this region were analyzed by experi-mental and theoretical methods. Seven heavy metals, includ-ing Pb, Cr, Hg, Cu, As, Cd, and Zn, were detected in allsamples. Monte Carlo simulation was used to assess theecological risks of these heavy metals. It was found that thepollution of Cd was the most serious; the ecological risks inDaling River and Bohai Bay were significantly higher thanthose in estuary, Bohai Sea, and wetland, but overall, theecological risks of these heavy metals were low to aquaticorganisms in Daling River basin at present. Correlation anal-ysis, principal component analysis, and cluster analysisshowed that these heavy metals might originate from the samepollution sources located near Daling River and Bohai Bay.

Keywords Heavymetal . Sediment . Ecological risk .MonteCarlo . Source . Daling River basin

Introduction

With the rapid development of industry and agriculture, heavymetal pollution is becoming increasingly serious in the wholeword (Heikens et al. 2001; Uluturhan and Kucuksezgin 2007).Large amounts of heavy metal are emitted into the water andaccumulated in aquatic food chain easily, resulting in sublethaleffects or death on aquatic organism (Almeida et al. 2002;Jones et al. 2001; McGeer et al. 2000). Sediments have thecapacity for accumulating heavy metals from overlying wa-ters; therefore, the enrichment of heavy metals in sediments isoften a preferred indicator of the contamination status (Soareset al. 1999; Xiao et al. 2013).

Sediments also provide habitat and a food source for ben-thic fauna (Yi et al. 2011). They have been used to assess thepollution of water bodies and reflect the source of pollutionextensively, which can provide the information of historicaldeposition of pollutants (Fox et al. 2001). Furthermore, sedi-ments could also be a secondary contamination source be-cause pollutants may be directly and indirectly toxic to theaquatic biota and even other organisms throughout the marinefood web (Karageorgis et al. 2001; Pekey et al. 2004). Thedistribution of heavy metals in sediments could be used tostudy anthropogenic impacts on ecosystems as well as evalu-ate the ecological risk posed by human waste discharges(Kwon and Lee 2001; Zheng et al. 2008).

Ecological risk assessment is to evaluate the likelihoodthat adverse ecological effects may occur or are occurringas a result of exposure to one or more stressors (Posthumaet al. 2010). The most traditional methods used to evaluatethe ecological risk of heavy metals in sediments include thepotential ecological risk index (Håkanson 1980) and theindex of geo-accumulation (Chen et al. 2007). In recentyears, a new method called probabilistic risk assessment isdescribed for ecological risk assessment (Hope 2006; Sol-omon et al. 1996). In contrast with conventional

Responsible editor: Philippe Garrigues

Electronic supplementary material The online version of this article(doi:10.1007/s11356-014-3770-2) contains supplementary material,which is available to authorized users.

L. Zhao :Y. Chen : L. Wang :Y. SunCollege of Environmental Science and Engineering, DalianMaritimeUniversity, Dalian 116026, Liaoning, People’s Republic of China

L. Zhao :Y. Chen :Y. Sun (*)Institute of Environmental Systems Biology, Dalian MaritimeUniversity, Dalian 116026, Liaoning, People’s Republic of Chinae-mail: yqsun@dlmu.edu.cn

D. Mi (*)Department of Physics, Dalian Maritime University,Dalian 116026, Liaoning, People’s Republic of Chinae-mail: mid@dlmu.edu.cn

Environ Sci Pollut ResDOI 10.1007/s11356-014-3770-2

deterministic methods, probabilistic risk assessment is gen-erated by multiple iterations using Monte Carlo simulationbased on the distribution of exposure concentrations andtoxicity data, which takes the uncertainty of data intoconsideration (Burmaster and Anderson 1994; Chowet al. 2005; Guo et al. 2012; Kooistra et al. 2005). Thismethod has been employed in different ecological riskassessments, such as to assess the ecological risk of phe-nols and polycyclic aromatic hydrocarbons to river andlake (Guo et al. 2012; Yang et al. 2006; Zhong et al.2010) and to evaluate the risk of mercury and copper toaquatic ecosystems (Duvall and Barron 2000; Schuler et al.2008).

There usually exist complex relationships among differentheavy metals in sediments (Sun et al. 2010). In fact, there are alarge variety of reasons affecting the relative abundance ofheavy metals, such as accumulation effects in the lower reach(Bai et al. 2009). So far, the main reasons include the originalheavy metal contents of rock and parent materials, variousprocesses of soil formation, salinity, landfill, land use, andorganic matter (Bai et al. 2012; Lado et al. 2008; Li et al. 2009;Xiao et al. 2011; Zhao et al. 2013). However, these concen-trations are not always uniform throughout the drainage basinand may vary from site to site due to different sources ofanthropogenic inputs (Lai et al. 2010; Liu et al. 2009). Thismakes it necessary to assess the ecological risk of heavymetals and to identify and control pollution sources inenvironments.

Daling River is located in northeastern China and has alength of 397 km and a drainage area of 2.35×103 km2, whichis an important water supply and irrigation resource in LiaoheRiver delta (Wang et al. 2013). As a consequence of anthro-pogenic activities, enormous quantities of pollutants havebeen discharged into the Daling River basin. Heavy metalsin wastewater and sewage have been discharged into theDaling River basin and accumulated in sediments, where theaquatic ecosystem may be threatened. There has been littlemonitoring for heavy metals in this important area, and theecological risk assessment and pollution sources of heavymetals in this area have not been reported previously. There-fore, to protect the water resources of this area, it is of greatimportance to evaluate the heavy metals pollution status inDaling River basin.

In the present work, we investigate the influence ofheavy metal pollution on Daling River basin by evaluatingthe ecological risk of these heavy metals in sediment,ranking the risk of these compounds by Monte Carlosimulation, and identifying further the possible pollutionsources by Pearson correlation analysis, principal compo-nent analysis (PCA), and cluster analysis. To our knowl-edge, this is the first report evaluating the ecological risk ofheavy metals in Daling River basin by statistical analysisand Monte Carlo simulation.

Materials and methods

Data sources

The exposure concentrations of heavy metals came from 28sediment samples, which were collected from five regions inDaling River basin (see Fig. 1). Sample sites can be classifiedinto different types by geography in this study, includingDaling River (R01-06), wetland (W01-05), estuary (E01-04), Bohai Bay (B01-04), and Bohai Sea (S01-09). The wet-lands of Daling River basin are dominated by extensive reed(Phragmites australis). Sediment samples were collectedusing a bucket grab and then packed in solvent-rinsed glassbottles with Teflon-lined caps. After collection, they werestored at −20 °C until extraction.

Sediment samples were freeze-dried and passed through a1-mm clean plastic sieve to remove shell fragments. Sievedsediments were ground in an agate mortar. The powderedsediments were then transferred to a clean nylon membranesieve (0.071mm) and shaken to obtain a perfect homogeneouspowder. Samples were microwave-digested for determiningcontents of heavy metals. Further details of sample digestioncan be found in the article of Yi et al. (2011).

The concentrations of Pb, Cr, As, Cd, Cu, and Zn weredetermined by an inductively coupled plasma mass spectrom-eter (ICP-MS 7700X), using the US EPA Method 6020 (USEPA 2007). Hg was measured using a Hydra-C DMA(Teledyne Leeman Labs). Each treatment included at leastone reagent blank and a representative reference standardand, typically, a sample replicate to avoid any possible con-tamination. Satisfactory recoveries were obtained for Pb (94–106 %), Cr (98–103 %), As (97–102 %), Cd (93–98 %), Cu(97–106 %), Zn (96–103 %), and Hg (97–105 %).

Risk analysis

Håkanson’s method could be used to evaluate the potentialecological risk of metal contaminants in sediments (Håkanson1980). The potential ecological risk index (RI) may reflect thesensitivity of various biological communities to toxic sub-stances and show the potential ecological risk caused byvarious pollutants in the environment (Yi et al. 2011). Accord-ing to Håkanson’s method, the RI of metal contaminants insediments can be calculated using the following equation:

RI ¼X

i¼1

m

E ir ð1Þ

where,

Eri ¼ Tr

iC fi ð2Þ

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and

Cif ¼ Ci=Ci

n ð3Þ

RI is calculated as the sum of all risk factors for heavymetals in sediments, Er

i is the potential ecological risk forsingle factor, Tr

i is the toxic response factor for a given metal,Cfi is the contamination factor, Ci is the measure concentration

of metals in sediment, and Cni is the reference value for metals.

See Hilton et al. (1985) for more details.In the present study, instead of Håkanson’s RI, the

probabilistic distribution of RI was calculated usingMonte Carlo simulation to randomly sample values fromthe distribution of exposure concentrations. A certainamount of iterations in a particular Monte Carlo simu-lation was needed to calculate the ecological risk, wherethe exposure concentrations were derived stochasticallyfrom the corresponding log-normal distributions to gen-erate the distributions of risk index for the 5,000 itera-tions, which incorporated both uncertainty andvariability.

Statistical analysis

SPSS 17.0 was used for the statistical analysis to obtain thefeatures of the datasets. Changes were considered as statisti-cally significant (*) if the p value <0.05. Raw data were ln-transformed to achieve normal distribution allowing for theuse of the Shapiro-Wilk tests (under R version 2.15.2). Inaddition, Monte Carlo simulation, PCA, Pearson correlation,and cluster analysis were performed under R software. Thebest known hierarchical clustering algorithm was selected toanalyze the pollution sources by the average linkage method,where the similarity was assessed by Minkowski distance. Aseries of R program, included statistical calculations and evenplot artworks in this study, will be freely available uponrequest to corresponding authors.

Results and discussion

Distribution of heavy metals in sediments

The concentrations of heavy metals in sediments fromDaling River basin are shown in Fig. 2. It can be seen that

Fig. 1 Location of sample sitesin study area

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the highest mean concentrations of Pb, Cr, Hg, Cu, and Znwere all in Daling River. However, the mean concentra-tions of Pb, Cr, Cu, Zn, and As in Daling River were stillmuch lower than those in many other rivers, such as Yang-tze River (Bai et al. 2012; Yi et al. 2011), Pearl River (Liet al. 2007), Scheldt River (Vandecasteele et al. 2003),Yenisey River (Guay et al. 2010), etc., while the meanconcentrations of Pb, Cr, Cu, Zn, and As were obvioushigher than those in Mekong River (Cenci and Martin2004), Amazon River (Vital and Stattegger 2000), Missis-sippi River (Grabowski et al. 2001), Danube River (Woitkeet al. 2003), etc.

The results also showed that the mean concentrations ofthese heavy metals gradually decrease from Daling River toestuary and then Bohai Sea. Contrary to this study, somerecent results have indicated that heavy metal concentrationsin the upper reaches of rivers were usually lower than those inthe downstream because of accumulation effects in the lowerreach (Bai et al. 2009; Roline 1988). In the present work,reducing concentrations along Daling River basin may beexplained by the deposition of suspended sediments contain-ing heavy metals.

The highest mean concentration of As (13.60 mg/kg dryweight (dw)) was observed in wetland, similar with that inDanube River (Woitke et al. 2003) but lower than that inYangtze River basin (33.92 mg/kg dw) (Yi et al. 2011) andYilong Lake (15.46 mg/kg dw) (Bai et al. 2011). Thisfinding is consistent with the conclusion that As concen-tration was significantly affected by bulk density, silt, andtotal P in the marsh soil of Yellow River Delta, China (Baiet al. 2012).

The highest mean concentrations of Cd (0.82 mg/kgdw) were found in Bohai Bay, which were lower than thatin Yilong Lake (0.76 mg/kg dw) (Bai et al. 2011) buthigher than that in Yangtze River basin (0.48 mg/kg dw)(Yi et al. 2011) and Yellow River Delta (0.56 mg/kg dw)(Bai et al. 2012). Previous studies showed that Cd con-centration was closely related to soil organic matter andtotal P in tidal freshwater marshes and significantly cor-related with soil moisture, total S, and total P in tidal saltmarshes (Bai et al. 2012; Xie et al. 2014). It indicates thatincreasing Cd concentrations in Bohai Bay is likely to bethe consequence of anthropogenic contaminations by hu-man activities.

Fig. 2 Concentrations of heavymetals measured in sedimentsfromDaling River basin. 1DalingRiver. 2 Estuary. 3 Bohai Sea. 4Wetland. 5 Bohai Bay

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Ecological risk assessment

Probabilistic ecological risk assessment for a single factor

Statistical distributions of heavy metal concentrations weretested prior to probabilistic ecological risk assessment.Table S1 depicted the results of Shapiro-Wilk tests, wherethe concentrations of heavy metals in Daling River basin wereln-transformed. The results indicated that the exposure con-centrations of Pb, Cr, Hg, As, Cd, Cu, and Zn were individ-ually fitted to a log-normal distribution (p>0.05), respectively.

According to the degree standards used to describe thesingle risk factor (Er

i) as suggested by Håkanson (1980), therisk probabilities of single heavy metal from Daling Riverbasin in different risk degrees were shown in Table 1. Theresults showed that Cd and Hg posed relatively high ecolog-ical risk in these areas. Monte Carlo simulations indicated thatthe moderate risk probability of Cd (14.81 %) was 6.09 timeshigher than that of Hg (2.43 %). And what is more, theconsiderable risk probability of Cd (1.76 %) was 4.51 timeshigher than that of Hg (0.39 %). Furthermore, the high riskprobability of Cd was (0.07±0.04) %. The results also indi-cated that the ecological risk of Pb, Cr, As, Cu, and Zn inDaling River basin was low. Consequently, it was possible torank the risk of these seven heavy metals to the ecosystem asCd > Hg > (Pb ≈ Cr ≈As ≈Cu ≈ Zn), suggesting that Cd is themost important risk factor to Daling River basin. This result isalso consistent with many previous studies, where Cd isusually a serious pollution factor while the ecological risksof other metals are low (Fu et al. 2009; Huang et al. 2009;Kooistra et al. 2001; Lado et al. 2008; Liu et al. 2009; Sunet al. 2010; Sundaray et al. 2011).

Probabilistic ecological risk assessment for all factors

The Shapiro-Wilk normality test was used to determine thenormal distribution of RI in Daling River basin, which indi-cated that RI was fitted to a log-normal distribution (W=0.94,p=0.10>0.05). Normal distribution and Q−Q plot of ln-transformed RI were shown in Figs. S1(a) and S1(b),respectively.

According to degree standards used to describe all riskfactors (RI) as suggested by Håkanson (1980), the risk prob-abilities of RI in Daling River basin for different risk degrees

were shown in Fig. 3. The results showed that the low,moderate, considerable, and very high risk probabilities ofRI were 75.57±0.67, 16.17±0.60, 6.41±0.39, and 1.88±0.22 %, respectively. It is suggested that the potential ecolog-ical risk of Pb, Cr, Hg, Cu, As, Cd, and Zn in sediments wasrelatively high in Daling River basin.

And what is more, the low ecological risk of heavy metalsin these areas was obviously threefold higher than the others.Therefore, it indicated that Daling River basin is classified asthe low contamination, but still, it was a fact in the moderate,considerable, and very high ecological risk of heavy metals insediments. From the view of this point, much more attentionshould be paid in analyzing, evaluating the ecological risk,and controlling the heavy metals discharged in Daling Riverbasin.

Table S2 showed the results of Shapiro-Wilk tests of ln-transformed RI, which indicated that RI in different sampletypes were all fitted to a log-normal distribution (p>0.5).According to the degree standards by Håkanson (1980), onecan obtain the risk probabilities of all heavy metals in DalingRiver, estuary, Bohai Sea, wetland, and Bohai Bay shown inTable 2. The risk probabilities in estuary, Bohai Sea, andwetland were very similar for all risk degrees. Nevertheless,the probabilities of low ecological risk in Daling River andBohai Bay were significantly lower than those in estuary,Bohai Sea, and wetland while higher in moderate, consider-able, and high ecological risk. Based on the results above,Daling River basin belongs to the low ecological risk area,while the ecological risks of Daling River and Bohai Bay aresignificantly higher than that of other areas, which may berelated to different sources of anthropogenic inputs in theseareas.

Comparison with the Håkanson method

The potential risk of heavy metals in Daling River basin canalso be evaluated by the conventional Håkanson’s method(1980). Table S3 showed the calculated results, which indi-cated that the ecological risks of all heavy metals were low,and relatively speaking, Cd was the highest risk factor toDaling River basin. This conclusion is consistent with thepresent Monte Carlo simulation. In addition, the results basedon Håkanson’s method also indicated that the total ecologicalrisk of these metals in most of the sampling sites were low,

Table 1 Risk probabilities ofheavy metals from Daling Riverbasin in different risk degrees(%). Results were expressed asarithmetic mean±1×standard de-viation (SD), which are generatedby Monte Carlo simulation after3,000 to 5,000 iterations

Risk degree Pb Cr Hg As Cd Cu Zn

Low 100 100 97.12±0.27 100 83.36±0.60 100 100

Moderate 0 0 2.43±0.24 0 14.81±0.57 0 0

Considerable 0 0 0.39±0.10 0 1.76±0.21 0 0

High 0 0 0 0 0.07±0.04 0 0

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except for some sites in Daling River (B04 and B06) andBohai Bay (B01). The trends of RI based on Håkanson’smethod were also consistent with those by the Monte Carlosimulation. However, the conventional Håkanson’s methodcould not estimate the probabilities of different ecological riskdegrees, due to no consideration of the uncertainty and incom-pleteness of concentration measurements, while the presentmethod based on Monte Carlo simulation was an experimen-tal probabilistic method to solve this problem since computerscould easily simulate a large number of experimental trialsthat have random outcomes (Papadopoulos and Yeung 2001).Such an analysis is closer with the underlying physics ofactual measurement processes that are probabilistic in nature(Harwood 2000), and it facilitates the environmental protec-tion departments to make the corresponding decisionseffective.

Heavy metal pollution source analysis

Correlation between heavy metals

Correlations between different heavy metals were shown inFig. 4, which the upper triangle described the correlation ofraw data in different heavy metals and the lower triangledescribed the correlation coefficients. Significant correlationswere found between Pb and Cr (r=0.58), Pb and Hg (r=0.57),Hg and Cr (r=0.71), Pb and As (r=0.75), As and Cr (r=0.76),As and Hg (r=0.60), Pb and Cd (r=0.48), Hg and Cd (r=0.73), As and Cd (r=0.50), Pb and Cu (r=0.68), Cr and Cu (r=0.79), Hg and Cu (r=0.72), As and Cu (r=0.73), Cu and Cd(r=0.49), Zn and Cr (r=0.53), Hg and Zn (r=0.60), Zn andCd (r=0.63), and Zn and Cu (r=0.79) at p<0.01 level. Previ-ous study has suggested that the strong correlation between

Fig. 3 Risk probabilities of RI inDaling River basin for differentrisk degrees. a The low risk. bThe moderate risk. c Theconsiderable risk. d The very highrisk

Table 2 Risk probabilities of heavy metals from different sample types of Daling River basin in different risk degrees (%). The results were expressed asarithmetic mean±1×standard deviation (SD), which are generated by Monte Carlo simulation after 3,000 to 5,000 iterations

Risk degree Daling River Estuary Bohai Sea Wetland Bohai Bay

Low 0.58±0.01 0.78±0.01 0.85±0.01 0.84±0.01 0.57±0.01

Moderate 0.23±0.01 0.16±0.01 0.11±0.01 0.11±0.01 0.24±0.01

Considerable 0.13±0.01 0.05±0.01 0.03±0.01 0.04±0.00 0.13±0.01

High 0.06±0.00 0.01±0.00 0.01±0.00 0.01±0.01 0.06±0.00

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heavy metals in sediments may reflect that these metals hadsimilar pollution level and similar pollution sources (BaptistaNeto et al. 2000; Yi et al. 2011), which holds true in thepresent study.

PCA and cluster analysis

The results of PCA for heavy metal concentrations are shownin Table S4. According to the available results, all these heavymetals could be grouped into a three-component model, whichaccounted for 87.87 % of total variance of seven variablesused in this analysis. All seven kinds of heavy metals showeda strong association with the first component (F1) (67.29 %).The results imply that Pb, Cr, Hg, Cu, As, Cd, and Zn can bedefined as anthropogenic components and may originate fromsimilar pollution sources. This is consistent with the conclu-sion from previous Pearson correlation analysis. The distribu-tion of sample sites predicted by the first to third componentsis shown in Fig. S2. F1 values of Daling River and estuarywere associated, which are lower than those in Bohai Sea andwetland, indicating that these sites had high concentrations of

Fig. 4 Correlations betweendifferent heavy metals insediment (n=28)

Fig. 5 Cluster analysis dendrogram indicating the relatedness of siteswith heavy metal contamination in different types of sediment samples,including Daling River (R01–06), wetland (W01–05), estuary (E01–04),Bohai Bay (B01–04), and Bohai Sea (S01–09) in Daling River basin

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all seven heavy metals. This may be related to an obvioussewage draining exit located near Daling River.

For the second component (F2), Cd and Zn also showedrelatively high values (a contribution rate of 12.14%). And Cdshowed relatively high values in the third component (F3) (acontribution rate of 8.28 %) (see Table S4). Those results mayindicate that Cd and Zn could be defined as anthropogeniccomponents. F2 and F3 values of Bohai Bay were lower thanthose in other regions in Daling River basin (see Fig. S2).Previous study has reported that Cd and Zn may mainly comefrom the precipitation of aerosol particles released by traffic,industrial activities (Bai et al. 2009; Garcia and Millán 1998;Sun et al. 2010), freshwater, salinity (Bai et al. 2012), landfill,land use (Sridhara Chary et al. 2008), etc. in different sam-pling sites (river, wetland, estuary, and sea) (Bai et al. 2012;Gao et al. 2013; Xiao et al. 2012; Xiao et al. 2013; Xie et al.2014), suggesting that Bohai Bay may be influenced not onlybyDaling River but also by different sources of anthropogenicinputs.

Cluster analysis was also used to identify the heavy metalpollution sources. Figure 5 showed the dendrogram of clusteranalysis from different types of sediment samples. It can beseen that the group, including R04, R03, B01, R06, and R05,corresponds to highly contaminated sites, where ecotoxico-logical damage might be occurring. The results are consistentwith the analysis by PCA, indicating that there may be otherpotential pollution sources located near Bohai Bay. Furtherstudies should be performed to determine the pollutionsources of Cd and Zn in Bohai Bay and Daling River basin.Accordingly, Daling River and Bohai Bay should be givenfirst priority in initial remediation efforts.

Conclusions

The present work showed that the average concentrations ofseven heavy metals Pb, Cr, Hg, Cu, As, Cd, and Zn insediments of Daling River basin were 44.17, 84.17, 0.22,50.43, 13.60, 0.82, and 218.33 mg/kg dw, respectively. Basedon Monte Carlo simulation, it was found that the moderate,considerable, high risk probabilities of Cd were 14.81, 1.76,and 0.39 %, which indicated that Cd was the most importantheavy metal risk factor in Daling River basin. The probabilityof ecological risk for all factors which exceeded the minimalthreshold (95) was 24.43 %, indicated that the potential eco-logical risk for heavy metals in Daling River basin was low atpresent. While the ecological risks of Daling River and BohaiBay were still significantly higher than those in estuary, BohaiSea, and wetland, Pearson correlation analysis, PCA, andcluster analysis indicated that heavy metal pollution in DalingRiver basin might originate from the same sources locatednear Daling River and Bohai Bay. Therefore, the sources of

heavy metal pollution should be paid more attention andshould be strictly monitored by relevant Chinese governmentdepartments.

Acknowledgments This research was supported by grants from theInstitute of Environmental Systems Biology of Dalian Maritime Univer-sity, National Science & Technology Pillar Program of China in 2010(2010BAC68B02), and Liaoning Science & Technology Program(2007405010) to Yeqing Sun. Chao Liu from Zhejiang University,Huiling Zhu from Sun Yat-Sen University, and Jie Chen from East ChinaNormal University are thanked for providing the related references.

Conflict of interest The authors declare that they have no conflict ofinterest.

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