Chemosphere 75 (2009) 858–865

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Chemosphere

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S.TR.E.A.M., system for trace element assessment with mosses. An equationto estimate mercury concentration in freshwaters

Mattia Cesa a,*, Alesssandro Bizzotto b, Claudio Ferraro b, Flaminio Fumagalli a, Pier Luigi Nimis a

a Department of Life Sciences, University of Trieste, via Giorgieri 10, I-34127 Trieste, Italyb A.R.P.A.V., via Cereria 15, I-36061 Bassano del Grappa (VI), Italy

a r t i c l e i n f o

Article history:Received 21 July 2008Received in revised form 3 January 2009Accepted 23 January 2009Available online 27 February 2009

Keywords:BiomonitorBryophytesHeavy metalsPollutionStream

0045-6535/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.chemosphere.2009.01.074

* Corresponding author. Tel.: +39 040 558 3884; faE-mail address: mattiacesa@libero.it (M. Cesa).

a b s t r a c t

Hundred experiments of Hg bioaccumulation with the aquatic moss Rhynchostegium riparioides (Hedw.)C.E.O. Jensen transplanted under laboratory conditions were carried out with the aim of (1) measuringthe metal uptake at increasing water concentrations (0.25–128 lg Hg2+ L�1) and increasing exposuretime (24–189 h), (2) studying the influence of pH (6.3–8.5) and water concentration of Na (3–114 mg L�1), Ca (62–125 mg L�1) and Mg (13–54 mg L�1) on the metal uptake, (3) achieving a databasefor mathematical and statistical elaborations, and, (4) producing an equation modelling the uptake. A lin-ear uptake was observed for water concentrations 64 lg Hg2+ L�1, while a saturation curve was observedat higher concentrations. Uptake followed a 3-stage trend for increasing exposure times: a phase of rapidaccumulation (4–5 d), followed by an equilibrium plateau (2–3 d) and then by a second accumulationphase. The factor influence study revealed that variations in pH or water concentration of alkaline metals,within the range of typical values in freshwaters of NE Italy, did not produce significant differences(p > 0.05) in the Hg uptake ratio (0.496 � 105

6 Bio Accumulation Factor 6 1.73 � 105). From a databaseof 28 Hg concentrations in mosses exposed to 0.25–4 lg Hg2+ L�1 for 24–114 h, a mathematical equationwas produced, to assess Hg micro-contamination in water. The difference between predicted and realconcentration was generally included in the range ± 50%.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

In the last 35 years, aquatic mosses and liverworts have beenwidely employed as biomonitors of trace element pollution infreshwaters (Whitton, 2003), due to their ability of accumulatinglarge amounts of trace elements like heavy metals (Empain, 1973;Wehr and Whitton, 1983; Mouvet, 1986; Lopez and Carballeira,1993; Carballeira and Lopez, 1997; García et al., 2000; Yurukovaand Gecheva, 2004) and radionuclides (Vray et al., 1992; Merschand Kass, 1994; Beaugelin-Seiller et al., 1995). These plants are sup-plied with anionic chemical groups on the cell wall, which act asnon-selective binding (or exchange) sites for micronutrients andelements in the cationic form (Clymo, 1963; Pickering and Puia,1969; Haseloff and Winkler, 1980; Breuer and Melzer, 1990; Brownand Bates, 1990). In the short term (a few hours or days after thewater contamination has started), uptake occurs with passive, rapidand reversible mechanisms of adsorption, which involve the extra-cellular compartment and are mainly dependent on the exposuretime and the concentration in water (Mouvet, 1985; Kelly et al.,1987; Brown and Wells, 1990; Mouvet and Claveri, 1999). Theseproperties are exploited in the transfer technique called ‘‘moss

ll rights reserved.

x: +39 040 568855.

bags”, which consists of transplanting a moss from a relativelyunpolluted spring to the survey area by using a net or other perme-able supports. Transplanted mosses are considered among the besttools for trace element assessment in freshwaters (Mouvet, 1986;Kelly et al., 1987; Mersch and Claveri, 1998) and were successfullyused to monitor both chronic and intermittent pollution (Siebertet al., 1996; Mersch and Reichard, 1998; Rasmussen and Andersen,1999; Lee et al., 2002; Yurukova and Gecheva, 2003).

Several works focused on the processes of trace element up-take/release by the cells of mosses and liverworts, both under lab-oratory conditions (Mouvet et al., 1985; Mersch et al., 1993b;Claveri and Mouvet, 1995; Claveri et al., 1995; Carballeira et al.,1998; Gagnon et al., 1998; Cenci, 2000; Martinez et al., 2003;Nuñez et al., 2004; Bleuel et al., 2005; Davies, 2007) and in the field(Mouvet, 1984; Wehr et al., 1987; Martinez et al., 1993; Merschet al., 1993a; Bruns et al.,1995, 1997; Vàzquez et al., 2000; Carbal-leira et al., 2001; Figueira and Ribeiro, 2005). These studies shedlight on the role of several factors:

� Chemical (water hardness, salinity, pH, concentration of cations,anions or chelating agents); the increase in water concentrationof cationic species generally induces competition among tracemetals and the other cations for the binding sites during theadsorption phase.

M. Cesa et al. / Chemosphere 75 (2009) 858–865 859

� Physical (time, temperature, UVB radiation); these variablesmight influence both the adsorption kinetics and the active pro-cesses of bioaccumulation (absorption).

� Physiological, involved in trans-membrane transport, intracellu-lar storage and detoxification processes.

Some authors tried to model the 2-compartment exchangekinetics in Scapania undulata, Fontinalis antipyretica and Fissidenspolyphyllus exposed to 1–200 lg L�1 of bivalent Cd, Co, Cu, Ni, Pbor Zn for 60 min (Vàzquez et al., 1999) and in Rhynchostegium ripa-rioides exposed to 1–5 mg L�1 of Zn2+ for 144 h (Martins andBoaventura, 2002) or to 42-342 lg L�1 of Cu2+ for 1–168 h (Claveriet al., 1994) under laboratory conditions. The former authors evendeveloped a methodology for the assessment of accidental Cu pol-lution using R. riparioides: thanks to specific sampling strategiesand calculations, the mathematical model would be able to predictthe most probable scenarios which have caused the pollutionevent. To our knowledge, this work is unique of its kind, since mossbags provide more than a simple indication, as a biomonitor shoulddo, but also data comparable to instrumental measures in water(retrospective assessment).

Within the three-years Project ‘‘Vicenza Moss Bags” (Cesa,2008) we wanted to test this particular ability of aquatic mossesin NE Italy, a region characterised by an intense development ofindustrial and urban areas. Serious environmental problems derivefrom dispersed and not-censed wastes of trace elements, whichhave often caused freshwater, soil, and water-table pollution. Thespatial and temporal pattern of such events, otherwise not detect-able by sporadic analyses on water samples, was described by theuse of moss bags (Cesa et al., 2006).

Among the 11 trace elements we studied under laboratory con-ditions during a recent experiment (Al, As, Cd, Cr, Cu, Fe, Hg, Mn,Ni, Pb and Zn; Cesa et al., 2008), mercury presented the best qual-ifications for providing an uptake model similar to that of Claveriet al. (1994):

� It shows one of the highest uptake ratios: the Bio ConcentrationFactor – BCF – of Hg in R. riparioides exposed to 0.04 lM for 7 dreached 105 (together with Cr), unlike those of other elements,which ranged from 102 and 104.

� The final Hg concentration in R. riparioides exposed to 0.04–4 lM for 7 d was only slightly influenced by trace element inter-actions: mosses were exposed to different concentrations of 11trace elements, according to a saturated fractional factorialdesign, with the aim of measuring the main effects and first-order interactions of influent factors. The multiple regressionmodel showed that interactions among elements exist andinduce both antagonism and synergism, but the effects on Cdand Hg accumulation are relatively weak.

Apart from these technical aspects, Hg plays a particularly rele-vant role as a pollutant: it may cause serious and non-reversiblediseases to animal and human beings, especially through the bio-magnification of the methylated form (Brambati, 1997; Clarckson,1997; Morel et al., 1998). Furthermore, Hg concentrations in fresh-waters are often below the instrumental detection limits, leadingto latent micro-contamination of rivers and soils.

To our knowledge, no laboratory studies on Hg uptake in aqua-tic bryophytes appear in the European literature, excepting a bioas-say carried out in Poland (Samecka-Cymerman and Kempers,1995), which aimed at evaluating the lethal effects of mercury inthe liverwort Scapania undulata. Authors also measured the finalconcentration in bryophytes after a 14 d exposure to solutions con-taining 64, 128, 256, 512, 1024 lg Hg2+ L�1, and observed thataccumulation rate (1) could considerably change, depending onthe population the liverworts were chosen from, and (2) could in-

crease in dead plants, probably resulting from the degeneration ofthe plasmalemma exposing new exchange sites. Unfortunately, re-sults cannot be compared to those of our studies, since S. undulatashowed a BCF (�103) much lower than R. riparioides (�105) did, theexposure time was considerably longer and bryophytes were notcollected from a single place.

The present study aimed at modelling the uptake of Hg in mossbags of R. riparioides at the typical water conditions of rivers in NEItaly. In particular, we aimed at:

(1) Measuring the uptake at increasing concentrations in water(with special regard for those below the commonest instru-mental detection limits) and increasing exposure time.

(2) Studying the influence of pH and Na, Ca and Mg concentra-tions on uptake.

(3) Achieving a database for mathematical and statisticalelaborations.

(4) Producing an equation able to estimate the mean concentra-tion of Hg in the water during biomonitoring surveys

2. Material and methods

Submerged tufts of Rhynchostegium riparioides (Hedw.) C.E.O.Jensen (= Platyhypnidium riparioides (Hedw.) Dixon) were collectedfrom the uncontaminated springs of River Meschio (Province ofTreviso), from a site where water is always running and deep en-ough to ensure the total immersion throughout the year (Wehret al., 1983; Kelly et al., 1987; Mersch and Claveri, 1998). The mossbranches were rinsed in the water of the spring, to remove organicparticles, mud and sand, and then homogenised. Moss bags con-taining 20–30 g of moist material and made of a plastic net(4 mm holes) were transported to the laboratory for experimenta-tion within a few hours. Some chemical–physical parameters (notthe water temperature) have been monthly measured by the localEnvironmental Agency in 2006: pH 7.7 ± 0.2, conductivity245 ± 9 lS cm�1, dissolved Na 0.89 ± 0.37 mg L�1, K 0.42 ± 0.13mg L�1, Ca 50.6 ± 15.9 mg L�1, Mg 5.27 ± 2.50 mg L�1 (mean ± st.dev.).

2.1. Experimental design

4 work sessions were carried out at the A.R.P.A.V. laboratory ofBassano del Grappa (Province of Vicenza):

1. Forty preliminary observations (from November 2006 to Janu-ary 2007) on Hg uptake at increasing water concentrations(0.25–128 lg Hg2+ L�1) and increasing exposure time (26–168 h).

2. A factor influence study (55 experiments, from March 2007 toSeptember 2007) for the main chemical variables supposed toaffect uptake within the first 5 d: pH (6.3–8.5) and water con-centration of Na (5, 50, 100 mg L�1), Ca (65, 130 mg L�1) andMg (15, 45 mg L�1).

3. Five final experiments (December 2007) under the best condi-tions tested (0.25–4 lg Hg2+ L�1 in water, 24–120 h of expo-sure) to achieve a database.

4. Data elaboration to produce the uptake equation.

2.2. Experimental apparatus

The experimental apparatus is a water flow system, a crystalaquarium with five independent basins (25 � 50 � 40 cm), whichreceives a continuous input of non chlorinated tap water

Table 1Preliminary observations. Nominal Hg concentration in the five basins and exposuretime for each block of experiments (I–VI), Hg concentration in mosses before (blanks)and after exposure, main chemical–physical water parameters.

Block Hg concentration Exposure time (h)

Water (lg L�1) Moss (lg g�1)

I 0 1.55 1612 917 1614 774 1618 891 16116 4420 161

II 0 1.48 1614 962 1618 2090 16116 1820 16132 5064 161

III 0 0.96 1610.25 37.9 1610.5 133 1611 240 1612 439 161

IV 0.25 84 1631 426 1634 1208 16316 4518 16364 13419 163

V 0.5 180 1622 666 1628 3226 16232 8160 162128 18559 162

VI 0.25 4.66 2612.4 6532.6 11421.2 14539.9 168

1 16.3 2649.3 65126 114134 145196 168

4 57.3 26184 65535 114564 145934 168

Mean ± SD

pH (units): 7.42 ± 0.05Conductivity (lS cm�1; 20 �C): 351 ± 19Temperature (�C): 13.9 ± 0.8Hg conc. in blanks (lg g�1): 0.69 ± 0.54

860 M. Cesa et al. / Chemosphere 75 (2009) 858–865

(�450 mL min�1) and a continuous input of chemical solutionsthrough a 20-channels peristaltic pump (�1.2 mL min�1) preparedas follows:

� Mercury – plastic tanks (15 L) or bins (70 L) filled up by a tapwater solution of Hg2+ (from HgSO4 previously dissolved inHNO3 65%) further acidified to reach pH < 2.

� Sodium – plastic tanks filled up by a tap water solution (10 L) of0 (control), 500 or 1000 g NaCl.

� Calcium – plastic bins filled up by a tap water solution (40 L) of 0(control) or 560 g CaCO3 and 820 ml HNO3 65%.

� Magnesium – plastic bins filled up by a tap water solution (40 L)of 0 (control) or 640 g (MgCO3)4

*Mg(OH)2*5H2O and 1000 mL

HNO3 65%.� Acidic/basic inputs – plastic tanks filled up by a tap water solu-

tion (10 L) of 400 or 800 mL HNO3 65% (acidic input), a tap watersolution (10 L) without reagents (control, neutral input), or abidistilled water solution (15 L) of 40–80 g NaOH (basic inputs).

Inputs are mixed by centrifuge pumps submerged into each ba-sin, to maintain physical and chemical conditions homogeneous.Funnels generate an air gurgle that maintains a range of 9.1–9.6 mg L�1 of dissolved oxygen and 13–19 �C of temperature(depending on the season). Exceeding water overflows to thewaste. The structure is covered by a transparent top and a nyloncloth, to avoid external contamination, and receives natural sun-light from a window. A single moss bag was placed inside eachbasin.

2.3. Samples and analyses

Water conductivity, temperature and pH were measured once aday. Na, Ca and Mg water concentrations were measured in sam-ples collected at the start and the end of each experiment.

After the exposure period, the apical shoots of the moss (2–4 cm) were cut by hand and rinsed for a few seconds in bi-dis-tilled water to remove contaminants non fixed to the cell wall.Samples were dried for 3 d at 45 �C and weighed (50–150 mg).An aliquot was led to constant mass at 105 �C (residual watercontent 3–8%). Samples were totally mineralised under high tem-perature (200 �C) and pressure (75 bar) in quartz containers witha solution of HNO3 65%, HCl 37% and H2O2 30%. An aliquot of HF50% was added after digestion to complete the dissolution ofsilicates.

Analyses were performed by a Perkin–Elmer atomic absorptionspectrophotometer. Emission spectroscopy was used for Na, atom-ic absorption for Ca, Mg and Hg. The air/acetylene flame was usedNa, Ca and Mg, the cold vapours for Hg. Six samples of a certifiedreference material (the aquatic plant Lagarosiphon maior – BCR60) were analyzed to test the accuracy of measures: 100% < recov-ery (Hg) < 112%.

3. Results

3.1. Preliminary observations

The 40 experiments were carried out in six blocks. Every blockincluded at most five experiments (one for each basin). Experi-ments of blocks I–V aimed at observing the way uptake varies withthe concentration in water after a 161–163 h exposure; experi-ments of block VI aimed at observing the way uptake varies withexposure time at three selected concentrations in water. Table 1shows experimental conditions and the concentration of Hg inmosses before and after exposure.

The moss response in blocks I and II was unclear since uptakewas not proportional to concentrations in water (oscillatingtrend, Fig. 1A), probably due to a malfunctioning of the system.A linear uptake was observed in block III (r2 = 0.99, p < 0.01,n = 5; Fig. 1B) until concentrations in water were lower than 2–4 lg L�1; over this threshold (blocks IV and V, Fig. 1C) the mossresponse turned to a saturation kinetic. The results of blocks I–Vindicated that the range of concentrations in water 0.25–4 lg L�1

is the best experimental condition to observe a linear uptake,therefore the experiments of block VI were planned on thisassumption.

The results of block VI showed that the relationship between fi-nal concentration in mosses and exposure time is not constant(Fig. 1D). Three stages were observed: (1) a linear uptake until114 h of exposure, (2) an equilibrium phase from 114 to 145 h,and (3) a second uptake phase. These results will be discussed inthe next session.

0

1000

2000

3000

4000

5000

6000

0 5 10 15 20 25 30 35

Nominal concentration in the water (µg Hg L-1)

Con

c. in

mos

s (µ

g H

g g-

1 )Block I Block II

050

100150200250300350400450500

0.0 0.5 1.0 1.5 2.0 2.5

Nominal concentration in the water (µg Hg L-1)

Con

c. in

mos

s (µ

g H

g g-

1 )

Block III

02000400060008000

100001200014000160001800020000

0 20 40 60 80 100 120 140

Nominal concentration in the water (µg Hg L-1)

Con

c. in

mos

s (µ

g H

g g-

1 )

Blocks IV and V

1

10

100

1000

0 50 100 150 200Exposure time (h)

Con

c. in

mos

s (µ

g H

g g-

1 )

0.25 µg L-1 in water (Block VI) 1 µg L-1 4 µg L-1

A B

C D

Fig. 1. Preliminary observations. Hg concentration in mosses exposed to different Hg concentrations in the water for 161–163 h (A, B, C) and to 0.25, 1, 4 lg L�1 for increasingtime (D).

M. Cesa et al. / Chemosphere 75 (2009) 858–865 861

3.2. Factor influence study

The 55 experiments were carried out in 11 blocks, three ofwhich were replicated twice. Every block included five experi-ments (one for each basin). The exposure time was set in 111–113 h, to prevent the equilibrium phase observed in preliminaryexperiments. Table 2 shows the experimental conditions and themoss response after exposure. Uptake ratio was calculated accord-ing to the definition of Bio Concentration Factor (BCF, Eq. (1)):

BCF ¼ MCðaÞ �MCðbÞWC

� d ð1Þ

where MC(a) and MC(b) are the moss concentration in bag and themean concentration in blanks (lg g�1), respectively, WC is the con-centration in water (lg L�1), and d is the water density(�1000 g L�1).

BCF ranged between 0.496 � 105 and 1.73 � 105, and Shapiro-Wilks and Lillierfors’ tests found no evidence (p > 0.05) againstthe hypothesis of normal distribution. Multiple regression withANOVA found no statistical correlation (R2 = 0.21, p > 0.05, n = 55)among BCF and pH, concentration of Hg, Na, Ca and Mg in water,conductivity.

3.3. Final experiments

The five final experiments were carried out with the only twofactors affecting Hg uptake, under the best experimental condi-tions: concentration in water (0.25–2 lg Hg2+ L�1) and exposuretime (24–189 h). Na, Ca and Mg concentrations and pH were thoseof the tap water (background). Table 3 shows experimental condi-tions and the moss response.

The mean concentration in mosses of the control basin, 1.71 lgg�1 (n = 8), was 1–2 orders of magnitude higher than the usual val-ues in blanks: this probably depended on the persistence of Hgresiduals in basins after the cleaning procedure, then accumulatedby mosses. A 3-stage uptake was still observed, but the equilibriumphase occurred after 4 d only (Table 3), while during preliminaryobservations (block VI) it occurred after 5 d of exposure (Table 1,Fig. 1D).

3.4. Uptake equation

The non parametric Kruskal-Wallis ANOVA found no statisticaldifference (p > 0.05) in BCFs among the final experiments (expo-sure time 6 95 h), the block VI of preliminary observations (expo-sure time 6 114 h) and the factor influence study (111 h) under thesame conditions (background for pH and Na, Ca, Mg in water).Therefore, the three datasets (n = 15, 9 and 4, respectively) wereunified.

According to the linearity of Hg uptake for concentrations64 lg L�1, the fundamental equation is:

y ¼ mxþ q ð2Þ

where y is the concentration in moss (lg g�1), x the concentrationin water (lg L�1), m the line slope, and q the mean concentrationin the control or blanks (lg g�1).

A statistical relationship (r2 = 0.98; p < 0.01; n = 8) was foundbetween m and t, the exposure time 6114 h:

m ¼ at2 þ bt ð3Þ

where a (0.0071) and b (0.332) are two constants.The final equation, resulted by replacing m in Eq. (2) with the

second term of Eq. (3), presents x as the dependent variable:

Table 2Factor influence study. Nominal Hg concentration and Na, Ca, Mg concentrationsmeasured in the five basins, main chemical–physical water parameters and Hgconcentration in mosses after exposure.

Water Moss

Hglg L�1

Namg L�1

Camg L�1

Mgmg L�1

pHunits

Cond. lS cm�1 at20 �C

Hglg g�1

0.25 3 62 12 7.7 294 170.25 56 63 12 7.8 531 300.25 95 64 13 7.8 751 240.25 3 125 14 7.5 594 250.25 4 62 52 7.5 630 31

0.50 3 60 12 7.7 330 340.50 55 64 13 7.7 574 500.50 113 63 12 7.7 793 690.50 3 121 13 7.5 630 710.50 3 63 52 7.5 682 59

1.00 3 64 12 7.8 307 1221.00 63 65 12 7.8 547 1511.00 114 67 13 7.8 775 1311.00 4 120 13 7.6 589 1591.00 4 62 53 7.5 639 111

2.00 4 62 13 7.6 320 1802.00 65 67 12 7.6 559 2272.00 131 66 12 7.6 810 2252.00 4 127 13 7.5 600 2592.00 4 60 53 7.5 657 191

0.25 3 63 13 7.7 356 250.25 56 65 13 7.8 587 340.25 110 67 13 7.8 798 270.25 3 120 14 7.6 637 380.25 2 64 51 7.5 680 27

0.50 3 65 13 7.6 351 460.50 50 66 14 7.6 589 730.50 90 68 14 7.7 812 560.50 3 64 55 7.5 684 860.50 3 125 14 7.4 646 74

2.00 3 63 13 7.6 350 1732.00 45 67 13 7.7 583 2262.00 80 68 13 7.7 810 2082.00 3 122 14 7.4 636 2272.00 3 65 54 7.6 687 226

0.25 4 69 15 6.3 454 300.25 4 68 15 6.7 424 130.25 4 66 15 7.5 391 190.25 8 66 15 7.7 401 290.25 11 62 15 8.3 409 24

0.50 4 71 14 6.4 478 660.50 4 70 16 6.8 446 250.50 4 70 16 7.5 413 380.50 9 70 16 7.9 425 480.50 12 62 15 8.3 430 31

1.00 8 64 14 8.1 367 1461.00 3 63 14 6.5 420 671.00 3 64 14 6.8 393 1491.00 11 61 13 8.5 376 1161.00 3 65 14 7.7 364 106

2.00 3 62 14 6.1 378 1872.00 3 61 14 6.7 349 1452.00 12 58 12 8.4 333 2542.00 8 62 14 8.1 334 3192.00 3 62 14 7.7 331 290

Water temperature (�C; Mean ± SD): 17.0 ± 1.7.Exposure time (h; min–max): 111–113.

862 M. Cesa et al. / Chemosphere 75 (2009) 858–865

x ¼ y� qtð0:0071t þ 0:332Þ ð4Þ

Two different values of q may be used: (1) the mean concentrationin the control (1.71 lg g�1), which provides the best interpolation ofexperimental data but reflects a system contamination, or (2) the

mean concentration in blanks (0.23 lg g�1), which is closer to theexpected value for mosses in clean water but provides a worseinterpolation. The first one was adopted.

4. Discussion

4.1. Hg concentration in water

The uptake well fitted to a linear model for concentrations inwater <2–4 lg Hg2+ L�1. As concentrations increased, a saturationtrend occurred, similar to that observed by Claveri et al. (1994),Samecka-Cymerman and Kempers (1995), Vàzquez et al. (1999),Martins and Boaventura (2002) and Fernandez et al. (2006) forAl, Cd, Co, Cu, Hg, Ni and Zn in Fontinalis and Rhynchostegium spp.

4.2. Moss physiology (?)

The final concentrations in mosses during January (blocks IVand V) were twice as high as those in mosses exposed to identicalwater concentrations one month before (block III, Table 1). Thisvariation in uptake efficiency could depend on physiological fac-tors, perhaps influent during the last days of exposure: mosses col-lected during December 2006 presented some physical damageand a dark colour as a consequence of a flood, while mosses col-lected one month later did not appear stressed.

4.3. Exposure time

The uptake varied with the exposure time following a 3-stagetrend (first accumulation phase, plateau, second accumulationphase), which may be explained as follows, according to the sug-gestions of Pickering and Puia (1969) and Foulquier and Hébrard(1976): (1) Uptake starts with a rapid and passive adsorption oftrace elements on the cell surface (extracellular), (2) Exchange sitesreach saturation (equilibrium between uptake and release), and (3)Active transport mechanisms start the element absorption (intra-cellular). More investigations on the relationship between Hg con-centration in the moss and exposure time are required: actually, itis unknown whether the equilibrium phase could arise before orlater the fifth day and whether it could be longer or shorter than24 h (as we observed).

4.4. Water chemistry

The non significant influence of pH, conductivity and Na, Ca, Mgconcentrations in water upon Hg uptake probably reflects the highaffinity of this metal for the exchange sites. This supports the re-sults of our preceding study (Cesa et al., 2008): Hg and Cd uptakeis influenced by trace element interactions only slightly. While allthe observations on Hg are consistent, Gagnon et al. (1998) found asignificant (p < 0.05) decrease in Cd BCF as water hardness in-creases, but results refer to a longer exposure time (28 d).

4.5. Modelling

Results suggested to base our methodology of retrospectiveassessment on low concentrations (<4 lg Hg2+ L�1) and shortexposure times (<5 d), to maximise the linearity of the moss re-sponse and to obtain a simple mathematical equation. On thesebases, our method should be sensitive enough to characterise themicro-contamination events not detectable by traditional chemicalanalyses. To test the fit of Eq. (4) to experimental data, the differ-ence between predicted and real concentration in water was calcu-lated and then related to the concentration in mosses and theexposure time. The difference ranged between ±50%, with the

Table 3Final experiments. Nominal Hg concentration and Na, Ca, Mg concentrations measured in the five basins, main chemical–physical water parameters, exposure time and Hgconcentration in mosses after exposure (nd = lost sample).

Water Moss Exposure time (h)Hg concentration (lg L�1) Hg concentration (lg g�1)

Basin 1 0.50 14.3 24Basin 2 control 0.91 24Basin 3 0.25 6.68 24Basin 4 2.00 35.5 24Basin 5 1.00 34.7 24

Basin 1 0.50 14.9 47Basin 2 control 2.35 47Basin 3 0.25 10.9 47Basin 4 2.00 47.7 47Basin 5 1.00 nd 47

Basin 1 0.50 47.9 70Basin 2 control 1.72 70Basin 3 0.25 15.8 70Basin 4 2.00 117 70Basin 5 1.00 78.78 70

Basin 1 0.50 50.0 95Basin 2 control 1.74 95Basin 3 0.25 26.8 95Basin 4 2.00 201 95Basin 5 1.00 115 95

Basin 1 0.50 39.2 117Basin 2 control 1.67 117Basin 3 0.25 18.6 117Basin 4 2.00 215 117Basin 5 1.00 106 117

Basin 1 0.50 73.7 141Basin 2 control 1.68 141Basin 3 0.25 33.5 141Basin 4 2.00 299 141Basin 5 1.00 123 141

Basin 1 0.50 64.8 165Basin 2 control 1.52 165Basin 3 0.25 nd 165Basin 4 2.00 244 165Basin 5 1.00 140 165

Basin 1 0.50 76.5 189Basin 2 control 0.65 189Basin 3 0.25 38.2 189Basin 4 2.00 448 189Basin 5 1.00 247 189

MIN–MAXNa (mg L�1): 3–4.Ca (mg L�1): 58–74.Mg (mg L�1): 14–14.pH (units): 7.6–7.8.Conductivity (lS cm�1; 20 �C): 339–347.Temperature (�C): 13.4–13.6.Dissolved oxygen (mg L�1): 9.1�9.6.

M. Cesa et al. / Chemosphere 75 (2009) 858–865 863

exception of 3–5 results. The uptake equation accurately predictedthe order of magnitude of concentrations in water, and its accuracyincreased together with concentrations, showing a maximum for2–4 d of exposure.

5. Conclusions

Mercury bioaccumulation in moss bags of Rhynchostegium ripa-rioides under laboratory conditions occurred with a great uptakeratio (BCF � 105) but a wide variability range (a factor of 3 betweenmin and max BCF). Nevertheless, the uptake ratio was not statisti-cally correlated (p > 0.05) to:

� Hg concentration in water between 0.25 and 4 lg L�1,� Na, Ca and Mg concentrations within the range of typical values

for polluted and unpolluted rivers of NE Italy,

� pH within the range of circumneutral freshwaters.

A linear Hg uptake was observed for concentrations in water<4 lg L�1 and exposure times 6114 h. Under these experimentalconditions a dataset was achieved and a mathematical equationproduced, to estimate the mean concentration of Hg in water fromthat in mosses and the exposure time. The relative difference be-tween predicted and real concentration was generally included inthe range ±50% and the equation provided the best performancesfor 4 lg L�1 and 2–4 d of exposure. The equation is a prototypeof what we called S.TR.E.A.M. (System for Trace Element Assess-ment with Mosses) and should be improved by more observationsand a calibration in the field. It may develop into a powerful tool tomonitor mercury micro-contamination in freshwaters, especiallywhen concentrations are lower than the detection limit of analyt-ical instruments and/or rapidly change in space and time.

864 M. Cesa et al. / Chemosphere 75 (2009) 858–865

Acknowledgements

We are grateful to the Province of Vicenza, ATO Bacchiglioneand ATO Brenta for funding. This work has been possible thanksto the availability, efficiency and specialization of the ARPAV labo-ratory of Bassano del Grappa and its personnel. A particular thankto Dr. Marcello Volpe who provided us the peristaltic pump and toDr. Stefano Covelli for suggestions. The statistical calculations wereperformed using OpenStat (an open source software created byWilliam G. Miller, 2007).

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