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Usingtotalsuspendedsolids(TSS)andturbidityasproxiesforevaluationofmetaltransportinriverwater

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Applied Geochemistry 68 (2016) 1e9

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Applied Geochemistry

journal homepage: www.elsevier .com/locate/apgeochem

Using total suspended solids (TSS) and turbidity as proxies forevaluation of metal transport in river water

T. Nasrabadi a, *, H. Ruegner b, c, Z.Z. Sirdari d, M. Schwientek b, c, P. Grathwohl c

a University of Tehran, Graduate Faculty of Environment, Tehran, Iranb Water & Earth System Science (WESS) Competence Cluster, c/o Eberhard Karls University of Tübingen, H€olderlinstr. 12, 72074 Tübingen, Germanyc Eberhard Karls University of Tübingen, Center of Applied Geoscience, H€olderlinstr. 12, 72074 Tübingen, Germanyd Islamic Azad University, Islamshahr Branch, Department of Civil Engineering, Tehran, Iran

a r t i c l e i n f o

Article history:Received 23 January 2016Received in revised form8 March 2016Accepted 10 March 2016Available online 11 March 2016

Keywords:Heavy metalDissolved concentrationParticle-boundRegressionProxy

* Corresponding author. Graduate faculty of enviro#25, Azin Avenue, Ghods Street, Enghelab Square, 141

E-mail addresses: tnasrabadi@ut.ac.ir, tnasrabadi@

http://dx.doi.org/10.1016/j.apgeochem.2016.03.0030883-2927/© 2016 Elsevier Ltd. All rights reserved.

a b s t r a c t

The present study was carried out in Haraz basin (Iran) that is located in south of the Caspian Sea. Thegoal of this study was to establish correlations amongst total suspended solids concentration (TSS) andturbidity with total pollutant concentrations to evaluate the dissolved and particle-bound concentrationsof major toxic metals. It also aimed to validate TSS and/or turbidity measurements as proxies to monitorpollutant fluxes. Eight metals, namely nickel, lead, cadmium, copper, zinc, cobalt, arsenic and strontiumwere analyzed for dissolved and total concentrations in water at ten locations within the catchment. TSSand turbidity were also measured. Sampling campaigns were designed to cover both the rainy(December) and the dry (May) season within the basin. The robust relationship between TSS (202e1212 mg/l) and turbidity (63e501 NTUs) in both seasons warranted their interchangeable potential asproxies within the observed ranges. Total element concentrations were plotted in separate attemptsversus TSS and turbidity for all locations and both events. Very good linear correlations were attainedwhere the slopes represent the metals concentration on suspended solids and the intercept the dissolvedconcentration in water. The results achieved by these linear regressions were in very good agreementwith independently measured values for dissolved concentration and concentrations on river bed sed-iments taken at the same locations. This demonstrates that turbidity and/or TSS measurements may beused for monitoring of metal loads if once calibrated against total concentration of metals. The resultsalso revealed that in the lower Haraz catchment metal concentrations on suspended and river bedsediment were homogeneously distributed along the investigated river stretch. This is assumed to be dueto intensive gravel and sand mining activities in the upper and middle part of the catchment.

© 2016 Elsevier Ltd. All rights reserved.

1. Introduction

Heavy metals/metalloids are among the most notorious threatsfor water and soil ecosystems. Transfer of such pollutants from solidto aqueous phase at the interface of sediments/particles facilitatestheir entry into the food chain and further bioaccumulation inneighboring fauna and flora (Fabure et al., 2015; Nasrabadi et al.,2015; Kargar et al., 2012; Biati et al., 2010; Ogundiran et al., 2012;Conceicao et al., 2013; Bu-Olayan and Thomas, 2013). A vast vari-ety of geogenic (rocks weathering and soil erosion) and anthro-pogenic sources (mining activities, urban and agricultural run-offs,

nment, University of Tehran,7853111 Tehran, Irangmail.com (T. Nasrabadi).

industrial and municipal sewer overflows, etc.) may cause toxicmetals discharge into water bodies. The metals can be dissolved inthe water column, absorbed/adsorbed to the sediments/particu-lates or accumulated in biota. Accordingly, during recent years agreat attention has been paid to studies where the behavior ofmetals in different media is discussed (La Colla et al., 2015; Hejabiet al., 2011; Adekola and Eletta, 2007; Li et al., 2006; Jain et al.,2005; Chow et al., 2005; Olivares-Rieumont et al., 2005; Hope,2006). In comparison with other media, river bed and suspendedsediments play a more significant role in overall pollution andenvironmental risks (Foerstner and Muller, 1973; Foster and Hunt,1975; Throne and Nickless, 1981; Sakai et al., 1986; Karbassi et al.,2007; Nasrabadi and Shirani Bidabadi, 2013). A remarkable num-ber of relevant studies focus on concentrations associated withsuspended or bed sediments, but only few try to assess the

T. Nasrabadi et al. / Applied Geochemistry 68 (2016) 1e92

speciation of different metals in these matrices. In the last fewdecades, researchers have followed a variety of sequential extrac-tion techniques to estimate the fractionation of metals in sediments(Chester and Hughes, 1967; Tessier et al., 1979; Forstner andWittmann, 1981; Horowitz et al., 1999; Stamatis et al., 2006;Tessier et al., 1980; Elsokkary and Muller, 1990; Korfali andDavies, 2000; Rauret, 1998; Pardo et al., 1990). Dozens ofmethods for determining several forms of metals in sediments aredescribed in literature (Kersten and Foerstner 1991; Lopez-Sanchezet al., 1993; Das et al., 1995; Li-Jyur et al., 2003). The most wide-spread methods are based on sequential extraction schemes wheredifferent reagents are utilized consecutively to extract defined solidphases (Nasrabadi et al., 2010a). Such methods are all time-consuming, labor-intensive and expensive. Thus generatingcontinuous or at least semi continuous temporal/spatial data sets ishardly possible. Focusing on dissolved and adsorbed phases andconsideration of proxies might be a feasible substitution for theabove mentioned complex speciation schemes. Proxies that areeasy and cheap to measure would be very valuable to monitorpollutant concentrations and loads. A vast range of organic pol-lutants as well as nitrogen and phosphorus has been proved to betransported by suspended solids in river systems (Meyer andWania, 2008; Meyer et al., 2011; Schwarz et al., ; 2011; Rügneret al., 2014; Quesada et al., 2014; Spackman Jones et al., 2011;Hornsburgh et al., 2010; Slaets et al., 2014). Ruegner et al. (2013,2014) and Schwientek et al. (2013) showed that concentrations ofpolycyclic aromatic hydrocarbons (PAHs) in rivers correlate very

Fig. 1. Major land use within the study area (adapted from Nasrabadi et al., 2011): B baredmixed forest/orchard, FP fish pond, I1 agricultural area with low limitation, I2 agriculturalorchard/agriculture, R1 grazing land high density, R2 grazing land medium density, RD gra

well with turbidity and TSS in a number of small catchments insouthern Germany (Neckar at Tuebingen: 2.300 km2; tributaries:39e139 km2). Not many studies considered toxic metals. Kirchneret al., 2011 for example, studied the dependence of total Hg con-centrations on total suspended solids in a river downstream ofGambonini mine, San Francisco in order to quantify the remedia-tion effectiveness by comparing contaminant rating curves.Chebboa and Gromaire (2004) determined the fractions of Pb, Znand Cu associated to suspended solids in surface runoff in Paris andattributed high concentrations to local roofing materials.

2. Theory

By causing light to be scattered, suspended particles concen-tration may have a meaningful correlation to turbidity. Using lab-oratory nephelometers and optical backscatter sensors offers thecapability to easily detect the turbidity in the lab and in-situ,respectively. Although a variety of parameters like density, sizeand shape of particles as well as water color may affect the rela-tionship between the values of TSS and turbidity (Downing, 2006),turbidity may be considered as a reliable and feasible proxy forsuspended sediment and thus pollutant concentrations within agiven basin if once a precise and meaningful correlation betweenTSS and turbidity was established. Such correlations have beendiscussed in detail over awide range of case studies. A common androutine linear relationship may be defined as:

land, DF dry farming, OC outcrop, F1 high density forest, F2 Medium density forest, FOarea with limitation, IO mixed agriculture/orchard, L2 reservoir, O orchard, OI mixedzing land/dry farming, U Urban area, U1 installations.

Fig. 2. The study area in lower Haraz basin in Mazandaran Province in Northern Iran as well as the sampling stations.

Table 1TSS vs. turbidity relationships from this and similar studies (No.: number of samples;m ± standard error and R2 from linear regressions;mMean ± standard deviation representsthe average of all TSS/turbidity ratios).

Case study Turbidity range No. am (mg l�1 NTU�1) R2 mMean (mg l�1 NTU�1) Reference

Lower Haraz basin 63e501 20 2.28 ± 0.06 0.96 2.36 ± 0.45 This studySteinlach 14e888 26 1.67 ± 0.04 0.97 1.60 ± 0.40 Ruegner et al., 2014Ammer 13e560 31 2.79 ± 0.10 0.94 2.35 ± 0.85

a Intercept is set to zero in all cases.

y = 2,28xR² = 0,96

0

200

400

600

800

1000

1200

1400

0 100 200 300 400 500 600

Tota

l sus

pend

ed so

lids (

mg/

L)

Turbidity (NTU)

Fig. 3. TSS-turbidity correlation for water samples collected in lower Haraz basin.

T. Nasrabadi et al. / Applied Geochemistry 68 (2016) 1e9 3

TSS ¼ m Turbidity ½NTU� (1)

Ruegner et al. (2013) found linear relationships between TSS andturbidity with m values of 1e2.8 mg l�1 NTU�1 (average1.9 mg l�1 NTU�1) for naturally suspended sediments in rivers in S-Germany. Other studies report slightly lower or higher m values(e.g. 1.1 mg l�1 NTU�1 for particles from karstic springs or up to3 mg l�1 NTU�1 for suspended sediments in the Lake Tahoe basin,respectively; Schwarz et al., 2011; Stubblefield et al., 2007).

Total concentration in river water includes the dissolved andparticulate-bound fraction of a contaminant:

Cw;tot ¼ Cwþ Csus TSS (2)

Cw,tot, Cw and Csus are the total and dissolved concentrations of apollutant in river water and its concentration on suspended parti-cles, respectively. The slope of a linear regression in a TSS (inmg l�1)versus Cw,tot (in mg l�1) plot thus corresponds to Csus (in mg mg�1)and its intercept to the dissolved concentration in river water Cw (inmg l�1). In a turbidity (in NTU) versus Cw,tot (in mg l�1) plot, the slope

R² = 0.930

0.005

0.01

0.015

0.02

0.025

0.03

0 500 1000 1500

Tota

l con

cent

raon

(mg/

L)

Total suspended solids (mg/L)

Co

R² = 0.870

0.010.020.030.040.050.06

0 500 1000 1500

Tota

l con

cent

raon

(mg/

L)

Total suspended solids (mg/L)

Cu

R² = 0.890

0.020.040.060.08

0.10.120.14

0 500 1000 1500

Tota

l con

cent

raon

(mg/

L)

Total suspended solids (mg/L)

Zn

R² = 0.880

0.0050.01

0.0150.02

0.0250.03

0.035

0 500 1000 1500

Tota

l con

cent

raon

(mg/

L)

Total suspended solids (mg/L)

Pb

R² = 0.950

0.002

0.004

0.006

0.008

0.01

0.012

0 500 1000 1500

Tota

l con

cent

raon

(mg/

L)

Total suspended solids (mg/L)

Cd

R² = 0,910

0.020.040.060.08

0.10.120.14

0 500 1000 1500

Tota

l con

cent

raon

(mg/

L)

Total suspended solids (mg/L)

As

R² = 0.900

0.2

0.4

0.6

0.8

1

0 500 1000 1500

Tota

l con

cent

raon

(mg/

L)

Total suspended solids (mg/L)

Sr

R² = 0.940

0.02

0.04

0.06

0.08

0 500 1000 1500

Tota

l con

cent

raon

(mg/

L)

Total suspended solids (mg/L)

Ni

Fig. 4. Linear regressions of total concentration of elements in water and total suspended solids (TSS) during two December (dry, open rectangles) and May (wet, open triangles)sampling campaigns.

T. Nasrabadi et al. / Applied Geochemistry 68 (2016) 1e94

corresponds to Csus m (in mg l�1 NTU�1).The aim of this study is to establish correlations of TSS with

turbidity and with total pollutant concentrations to (1) estimatedissolved and particle-bound concentrations and (2) tomake use ofTSS and/or turbidity measurements as proxies to estimate majortoxic metals transport in lower Haraz basin south of the CaspianSea.

3. Materials and methods

The lower part of the Haraz River basin located south of theCaspian Sea, Iran was considered as the study area. The drainagearea and the length of main stem are around 4060 km2 and 185 km,respectively. The mean discharge of the river in the lower Harazbasin was approximately 30 m3 s-1 during the last fifty years whilethe annual means varied between 15 and 55 m3 s-1 (Mohseni-Bandpei and Yousefi, 2013). Generally, the study area is mainly

covered by dense forests, rice paddies and urban space. A majoranthropogenic activity is given by sand mining installations (seeFig. 1). These intensive sand and gravel mining activities usuallytake place close to or directly within the river channels. Once alocation is exploited activities move to the next suitable locationsuch that numerous locations within the river stretch are affectedover time. Jurassic passive-margin deposits are represented by theclastic (sandstone-shale) Shemshak Formation, and the Lar andDelichi carbonates. Comprising largely submarine tuffs, the eoceneKaradj formation is suggestive of the onset of active continental-margin magmatism (Davidson et al., 2004). A detailed descriptionof the geology may be found in Nasrabadi et al. (2010a).

Considering the information on pollution by metals within thebasin gained during recent years (Karbassi et al., 2008; Biati andKarbassi, 2010; Nasrabadi, 2015; Nasrabadi et al., 2010a), eight el-ements namely nickel, lead, cadmium, copper, zinc, cobalt, arsenicand strontiumwere selected for further analysis. Sampling of water

Table 2Total/dissolved concentrations of elements in 10 sampling locations (mean concentrations are inserted in mg/L).

Sampling location Ni Pb Cd Cu Zn Co As Sr

Rainy season (December 2012)1 37/19 11/4 4/4 24/16 77/44 14/9 85/57 531/3322 39/21 12/7 5/3 26/18 80/40 15/10 90/65 546/3643 44/31 13/3 6/3 33/17 85/51 17/12 94/70 563/4204 67/39 29/6 11/3 56/21 125/79 24/13 121/98 780/6325 47/40 19/5 7/4 32/15 97/89 17/11 99/76 601/5136 57/48 25/8 9/4 38/22 104/81 20/15 105/91 631/4727 46/38 20/8 7/3 33/19 97/75 18/17 97/88 609/5218 43/37 16/7 6/3 31/15 91/68 16/12 95/81 561/4939 42/32 16/7 5/3 31/13 89/62 15/11 93/73 555/50110 39/31 13/7 5/3 25/18 89/69 15/10 89/64 541/362Max 67/48 29/8 11/4 56/22 125/89 24/17 121/68 780/632Min 37/19 11/3 4/3 24/13 77/40 14/9 85/57 531/332Mean 46/34 17/6 6.5/3.3 33/17 94/66 17/12 97/76 592/461SD 9.3/8.8 5.9/1.7 2.1/0.48 9.2/2.8 13/16 3.0/2.5 10/13 73/92Dry season (May 2012)1 31/18 6/4 4/4 18/13 75/42 13/9 81/55 498/2982 34/18 8/5 5/2 21/11 78/40 13/9 82/59 521/3853 38/27 10/3 5/3 26/12 75/45 14/11 85/67 531/4214 62/26 28/4 10/3 49/17 113/87 21/16 110/87 659/5415 41/32 13/5 5/3 36/14 83/72 16/11 95/68 581/5026 53/39 21/6 8/4 39/20 99/75 19/11 101/91 619/4837 40/31 14/5 6/3 34/16 88/71 17/14 93/82 587/5078 37/29 10/6 5/3 26/15 80/64 14/12 88/75 538/4529 33/22 11/7 5/3 22/11 81/60 14/12 85/63 538/47410 30/27 9/7 4/3 17/13 75/63 13/10 81/59 520/403Max 62/39 28/7 10/4 49/20 113/87 21/16 110/91 659/541Min 30/18 6/3 4/2 17/11 75/40 13/9 81/55 498/298Mean 40/27 13/5.2 5.7/3.1 29/14 85/62 15/12 90/71 559/446SD 10/6.5 6.7/1.3 1.9/0.57 10/2.9 12/15 2.8/2.2 9.6/12 51/72

T. Nasrabadi et al. / Applied Geochemistry 68 (2016) 1e9 5

and suspended sediments were executed within two distinctcampaigns e one in May and the other in December 2012. May andDecember have the lowest and highest amount of average monthlyprecipitation by 34.5 and 121.2 mm, respectively (Nasrabadi et al.,2011). So the campaigns were designed to cover both rainy anddry seasons within the basin. Ten sampling locations were selected(Fig. 2) in order to cover variability in land use (agricultural, in-dustrial and urban activities), geology and hydrology within thecatchment area. Locations largely coincide with locations selectedin former studies to allow for comparison of results.

Samples were collected in 500 ml high density polyethylene(HDPE) bottles, which had been carefully washed and rinsed inadvance. Water was collected at a certain distance from theriverbed and sufficiently far from the banks, natural or artificialobstacles and turbid water zones. The bottles were first rinsed withriver water before being filled. The samples designed for determi-nation of dissolved metal concentration (Cw) were filtered through0.45 mm filters, while the ones for determination of total concen-trations were not filtered. Filtered samples were then acidified topH 2with 1%Merck nitric acid and transported to the laboratory foranalysis.

To determine total concentration of metals in water samples(Ctot) a digestion process according to US EPA, Method 200.2 (1999)was considered. Each sample was first well shaken for homogeni-zation before a 50 mL sub-sample was taken for digestion andadded to a mixture of concentrated (70%) nitric acid and concen-trated (40%) hydrochloric acid (1.0 ± 0.1 ml conc. HNO3 and0.50 ± 0.05 ml conc. HCl). The samples were then digested for2e2.5 h at 95 ± 5 �C. Details are well described in US EPA Method200.2. Method blank, laboratory control sample and lab duplicateswere considered according to the mentioned reference. For eachmetal, average accuracy values laid within 90e110%. The analysis ofmetals in final solutions was carried out by an inductively coupledplasma atomic emission spectrometer (ICP-AES) according to EPAe

3005 method. Accuracy was determined by measurement of stan-dards and of duplicates and deviation from standards was less than±4% for each metal.

The concentration of total suspended solids (TSS) was deter-mined according to Standard methods 2540 D by filtering a definedwater volume through a membrane filter (cellulose nitrate0.45 mm) and weighing the dried residues. Turbidity was analyzedby a nephelometer (Hach 2100N Turbiditimeter) and reported inNephelometric Turbidity Units (NTU). Calibration was based onformazin, which is an aqueous suspension of hydrazine sulfate andhexamethylenetetramine.

For linear regressions in TSS vs. turbidity plots catchment spe-cific slopes (m values) were calculated. For Cw,tot vs. TSS or turbidityplots element specific intercepts and slopes (Cw and Csus values,respectively) were calculated. For all regression coefficients (slopesand intercepts) standard errors were calculated according to algo-rithms used in Excel 2013 software. Details of calculations can befound e.g. in Sachs and Hederich (2006).

4. Results and discussion

TSS-turbidity relationships: Besides the slope value of linearregression (m) and standard errors of the regressions, mean valuesand standard deviation of TSS/turbidity ratios (m Mean) were alsocalculated. The overall results (Table 1) are in good agreement withliterature data on turbidity-TSS correlations for suspended sedi-ments (e.g. Ruegner et al., 2014), in particular with samplingcampaigns which cover a comparable turbidity range and whichwere performed in catchments with comparable land-use (Ammerand Steinlach in S-Germany as well as the Haraz catchment in Iranwere all characterized by a mix of agricultural/urban land-use andforest; Grathwohl et al., 2013; Nasrabadi et al., 2011, respectively).

Table 1 e TSS vs. turbidity relationships from this and similarstudies (No.: number of samples; m ± standard error and R2 from

R² = 0,9400.005

0.010.015

0.020.025

0.03

0 200 400 600

Tota

l con

cent

raon

(mg/

L)

Turbidity (NTU)

Co

R² = 0,910

0.010.020.030.040.050.06

0 200 400

Tota

l con

cent

raon

(mg/

L)

Turbidity (NTU)

Cu

R² = 0,930

0.01

0.02

0.03

0.04

0 200 400 600

Tota

l con

cent

raon

(mg/

L)

Turbidity (NTU)

Pb

R² = 0,950

0.0020.0040.0060.008

0.010.012

0 200 400 600

Tota

l co

ncen

tra

on (m

g/L)

Turbidity (NTU)

Cd

R² = 0.940

0.05

0.1

0.15

0 200 400 600

Tota

l con

cent

raon

(mg/

L)

Turbidity (NTU)

As

R² = 0,920

0.2

0.4

0.6

0.8

1

0 200 400 600

Tota

l con

cent

raon

(mg/

L)

Turbidity (NTU)

Sr

R² = 0,930

0.02

0.04

0.06

0.08

0 200 400 600Tota

l con

cent

raon

(mg/

L)

Turbidity (NTU)

Ni

R² = 0,940

0.05

0.1

0.15

0 200 400 600

Tota

l con

cent

raon

(mg/

L)

Turbidity (NTU)

Zn

Fig. 5. Linear regressions of total concentration of elements in water and turbidity during two December (dry, open rectangles) and May (wet, open triangles) sampling campaigns.

Ni

PbCd

Cu

Zn

Co

As

Sr*

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100

Regr

essio

n ba

sed

con

cent

raon

of

diss

olve

d m

etal

s (μg

/l)

Measured concentra on of dissolved metals (μg/l)

TSS based Conc.

Turbidity based Conc.

1:1-line

Fig. 6. Comparison of measured (from laboratory analysis) and regression based(intercept from plots) dissolved metal concentrations; *Sr values divided by 10 to fitinto scales shown.

Table 3Results from linear regressions shown in Figs. 4 and 5; Cw: concentrations of ele-ments in water (intercept ± standard error); Csus: concentration in suspended solids(slope ± standard error); Kd is the distribution coefficient ¼ Csus/Cw.

Analyte Data analyzed Cw (mg/L) Csus (mg/kg) R2 Kd (L/kg)

Ni TSS based 28 ± 1.1 34 ± 2.1 0.94 1200turbidity based 28 ± 1.2 34 ± 2.3 0.93 1200

Pb TSS based 5.7 ± 1.0 21 ± 1.9 0.88 3700turbidity based 5.3 ± 0.8 22 ± 1.5 0.93 4300

Cd TSS based 3.1 ± 0.2 7.0 ± 0.4 0.95 2200turbidity based 3.0 ± 0.2 7.1 ± 0.4 0.95 2300

Cu TSS based 17 ± 1.5 32 ± 2.9 0.87 1900turbidity based 16 ± 1.3 33 ± 2.5 0.91 2100

Zn TSS based 69 ± 1.9 45 ± 3.6 0.89 650turbidity based 68 ± 1.4 47 ± 2.8 0.94 680

Co TSS based 12 ± 0.3 10 ± 0.7 0.93 830turbidity based 12 ± 0.3 10 ± 0.6 0.94 860

As TSS based 78 ± 1.4 34 ± 2.6 0.91 430turbidity based 78 ± 1.1 36 ± 2.1 0.94 450

Sr TSS based 482 ± 8.5 210 ± 16.3 0.90 440turbidity based 479 ± 7.8 219 ± 15.0 0.92 460

T. Nasrabadi et al. / Applied Geochemistry 68 (2016) 1e96

Ni

Pb

Cd

Cu

Zn

Co

As

Sr*

0

10

20

30

40

50

60

0 10 20 30 40 50 60 70 80Esm

ated

con

cent

raon

of m

etal

s in

susp

ende

d se

dim

ents

(mg/

kg)

Previously measured concentra on of metals in bed sediments (mg/kg)

TSS based Conc.

Turbidity based Conc.

1:1-line

Fig. 7. Comparison of metal concentrations in suspended solids (Csus) and bed sedi-ments from Nasrabadi et al. (2010a); *Sr values divided by 10 to fit into scales shown.

T. Nasrabadi et al. / Applied Geochemistry 68 (2016) 1e9 7

linear regressions; mMean ± standard deviation represents theaverage of all TSS/turbidity ratios).

Fig. 3 indicates the good linear correlation between TSS andturbidity in water samples taken during the two events (May andDecember 2012). A TSS and turbidity range of 202e1212 mg/l and63e501 NTUs was covered, respectively. Maximum turbidity andTSS values during the rainy season were only slightly increasedcompared to the dry season although higher rate of precipitationand consequently increased generation of urban and agriculturalrun-offs in December would suggest increased concentrations.However, it is noteworthy to mention that in both periods the sandmining activities in riverbanks are in progress which may insert arelatively constant TSS concentration in the base flow of the river.

Metal concentrations: Total and dissolved concentrations of eightmetals are shown in Table 2. As it is seen element concentrations insamples collected during rainy season show again only slightlyhigher values in comparison with the samples from the dry seasoncampaign, as also TSS/turbidity values are only slightly increased.Stations 4, 5, 6 and 7 located in central parts of the study area showfor most metals higher total concentrations (and partly also dis-solved concentrations) of metals than the other locations. This isattributed to more intense riverbank mining activities in this sec-tion of the study area and possibly also to other impacts which arisefrom urban and industrial land use (Figs. 1 and 2).

TSS and/or turbidity - Ctot relationships: In a parallel approach TSSand/or turbidity vs. Ctot relationships were evaluated in order tocalculate dissolved and particle-bound concentrations ofmentioned elements through linear regressions as given by Equa-tion (2). Total element concentration versus TSS plots for eachelement are shown in Fig. 5. Very good linear correlations of TSSand Ctotwere observed for all elements (Cd, Co, As, Pb, Cu, Ni, Sr andZn) across all selected locations (R2 values vary from 0.87 for copperto 0.95 for cadmium). The same holds for linear correlations ofturbidity and Ctot.These results are plotted in Fig. 6. High values forR2 (from 0.91 for copper to 0.95 for cadmium) were achieved here.Detailed results for TSS and turbidity based data are shown inTable 3.

The observed correlations not only hold for the two investigatedevents but also for all locations where samples were taken. Thisstrongly suggest that at least the smaller grain size fraction of theriver sediment e which makes up the suspended particle load e

shows only little heterogeneity across the middle/lower part of thecatchment which was investigated here. As already mentioned, theall season sand/gravel mining activities in the river may havecaused a continuous suspended sediment transport over years.Subsequent sedimentation and re-suspension of these fine sedi-ment particles during distinct events might have caused theobserved homogenous pattern in pollutant loadings of suspended

particles along the lower course of the river. This finding is incontrast to the strong heterogeneity of metal concentrations typi-cally observed in river sediments. Thus, often applied finger-printing strategies, which use the heterogenous patterns of riversediments for the identification of the particles’ sources (e.g. Franzet al., 2013; Walling, 2013) would not be suited in this or compa-rable cases or would at least require a thorough proof of feasibility.

To validate the approach, the dissolved concentrations in waterCw derived from regression data (Table 3) were compared with themean concentrations of dissolved metals from direct measure-ments, both from rainy and dry seasons (Table 2). These data are invery close agreement which is shown in Fig. 6. The error betweenindependently measured and regression based dissolved concen-trations for metals are between 0.1 e 8.5% and 0.2e8% for TSS andturbidity-based test attempts, respectively.

In addition, the particulate-bound concentrations Csus derivedfrom regression data (Table 3) were compared with bed sedimentsconcentrations measured at the same locations in August 2007(Nasrabadi et al., 2010a; for a grain size fraction < 63 mm). For mostmetals these data sets are in good agreement (Fig. 7). For As, Cu andCo the difference in reported values is below 10%, while for Pb, Niand Cd the estimated difference is around 30%. For Zn and Srconcentrations on suspended solids are remarkably lower thanconcentrations in river bed sediments, 46e82 mg kg�1 and220e550 mg kg�1, respectively (Nasrabadi et al., 2010b). Whetherthis is due to heterogeneities in sediment sampling or due todifferent grain size distributions of suspended and river bed sedi-ments has to be further investigated.

Distribution coefficients (Kd) were calculated by dividing theCsus -values from regressions by the intercept (Cw). These metalspecific Kd values reflect geochemical distributions between solidphases and dissolved concentrations and may be considered as analarming parameter showing the risk level of a toxic element'sbioavailability. In particular when shifted to lower values metalsmay then show an affinity to be released more easily into waterphase. Whether the already relatively low Kd values for elements asAs, Co, Zn and Sr observed in this study may be related to increasedrisks has to be further investigated by geochemical modeling.Remarkably high values of As in river water up to 100 mg l�1 andthus ten times greater than published WHO guidelines of 10 ppbhave already been reported within the area (Nasrabadi et al., 2015).

5. Conclusions

Very robust linear regressions were obtained between totalelement concentrations in water and total suspended solids andturbidity, respectively. Slope and intercept of these regressionscorrespond to the element concentrations associated with thesuspended solids and the dissolved concentration in water. Both,Csus and Cw determined form the regressions agree very well withindependently measured concentrations in sediment and watersamples. The correlations between total concentrations in waterand TSS or turbidity hold for all sampling locations over a largespatial scale as well as for wet and dry conditions indicating thatsuspended solids in the river represent an integral measure ofsediment quality in the total catchment. In the Haraz catchmentpollutants are mostly of geogenic origin but fluxes are likelyenhanced bymining activities which release particles into the river.Further anthropogenic pressures such as agriculture or urbaniza-tion were less important for the pollutant fluxes observed. Finally,this new data set confirms earlier correlations of suspended solidsand turbidity over large ranges of TSS (202e1212 mg/l) andturbidity (63e501 NTUs). Thus turbidity measurements may serveas easy and low cost proxies not only to monitor suspended solidsin rivers, but also to determine fluxes of particle associated

T. Nasrabadi et al. / Applied Geochemistry 68 (2016) 1e98

pollutants such as heavy metals and metalloids in catchments. Toour knowledge this is the first study focusing on particle facilitatedmetal transport at that scale and it would be interesting to comparethis results with data form other catchments with different geol-ogy, hydrological dynamics and land use.

Acknowledgments

This study was possible through the fruitful collaboration be-tween the corresponding author from University of Tehran, Iranand the research team of WESS, Eberhardt Karls Universitaet Tue-bingen. The connection was established by a DAAD (DeutscheAkademische Austausch Dienst) scholarship which is highlyappreciated by the board of authors. The study was also supportedby the EU FP7 Collaborative Project GLOBAQUA (Grant Agreementno 603629).

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