Ecological Modelling 184 (2005) 83–101

Modelling partitioning and distribution of micropollutantsin the lagoon of Venice: a first step towards a

comprehensive ecotoxicological model

S. Carrera, G. Coffarob, M. Boccic,∗, A. Barbantic

a Santa Croce 1670, Venezia, Italyb Via Partecipazio 22, Padova, Italy

c Thetis S.p.A. Castello 2737/f, Venezia, Italy

Abstract

The model presented in this paper integrates a large amount of recent and ad hoc collected data concerning environmentalcontamination from micropollutants in the lagoon of Venice. This model represents the first step in setting up of an ecotoxi-cological model for the Venice lagoon, to simulate fate of contaminants from abiotic matrices to organisms. Distribution andpartitioning of organic and inorganic contaminants are modelled by a two-dimensional model, based both on deterministic andempirical submodels and adapted to a large spectrum of different substances (polychlorinated dibenzo-dioxins/polychlorinateddibenzo-furans (PCDD/F), polychlorinated biphenyls (PCBs), heavy metals). The model was successfully calibrated on a wideset of experimental data. Sensitivity analysis showed that the model is generally not very sensible to parameters values butit is sensible to external conditions (e.g., pollutants loads). Distribution of dissolved and total concentrations of contaminantsw t integratedi ith variousw model form©

K

1

i

con-ntlysed-mi-the

stor-ities

0

as obtained for a series of PCDD/F and PCBs congeners and for eight heavy metals. These distributions represennformation on ecosystem health, complementary to monitoring data and they are useful to be used for comparisons water quality criteria. Simulation scenarios under different external conditions are proposed as examples of use of theanagement purposes.2004 Elsevier B.V. All rights reserved.

eywords: Partitioning of contaminants; Dioxins; PCBs; Heavy metals; Modelling contamination; Lagoon of Venice; Ecotoxicology

. Introduction

The lagoon of Venice is one of the most stud-ed ecosystems in the world, nevertheless studies and

∗ Corresponding author.E-mail address:bocci.m@thetis.it (M. Bocci).

researches on environmental aspects regardingtamination from micropollutants started quite rece(end of 1990s). The results of local surveys oniment quality showed evidence of diffuse contanation of organic and inorganic compounds inchannels of the industrial area and in the Venice hiical centre and this suggested to the public Author

304-3800/$ – see front matter © 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.ecolmodel.2004.11.019

84 S. Carrer et al. / Ecological Modelling 184 (2005) 83–101

to finance monitoring programs and specific stud-ies on aspects concerning the presence of persistentchemicals in the environment. These activities focusedon:

- measurements of contamination levels in the variousenvironmental matrixes (sediments, waters, organ-isms);

- evaluation of pollutants loadings into the lagoonecosystem (atmospheric depositions; loads from thewatershed; direct discharges, particularly from theindustrial area of Porto Marghera).

The availability of a large but heterogeneous col-lection of data and the need for an analysis at ecosys-tem level of the processes related with the mobilityof contaminants and their transfer along the trophicweb, claim for the use of models as a tool to synthesizeknowledge and to produce scenarios that can be ap-plied for ecosystem understanding and to address themanagement.

The Venice Water Authority (MAV) recently foun-ded, through its Concessionaire Consorzio Venezia

Nuova, monitoring programs of the environmentalquality of the lagoon. Within those programs, the wa-ter quality of the lagoon is monitored monthly for thetrophic aspects and for the presence of dissolved met-als and POPs. In the framework of these monitoringprograms, the studyARTISTA(trophic network analy-sis, sediment contamination and environmental toxic-ity) was set up to investigate:

- the contamination and partitioning of organic andinorganic compounds in the different environmentalmatrixes (sediment, water, particulate matter, organ-isms);

- the flows of contaminants at sediment–water inter-face and the estimation of internal loadings;

- the structure of the trophic webs in the shallow waterareas of the lagoon;

- the estimations of contaminants’ bioavailability andtransfer of micropollutants from the environmentto the organisms, finalized to the evaluation ofrisk for the ecosystem and for the humans (bythe consumption of fish food) due to chemicalcontamination.

F eloped int ensive

ig. 1. Conceptual diagram of the ecotoxicological model devhis paper constitutes the “partitioning” module of this compreh

for the lagoon of Venice withinARTISTAstudy. The model presentedmodel.

S. Carrer et al. / Ecological Modelling 184 (2005) 83–101 85

The final objective is to set up an ecotoxicologicalmodel for the simulation, at lagoon scale, of contami-nants’ partitioning and their transfer along the trophicnetwork.

Fig. 1describes the conceptual structure of the studyARTISTA.

This article presents the results of the first concep-tual step of the ecotoxicological model concerning thesimulation of contaminants’ transfer and partitioning,considering the contamination of sediments and exter-nal sources of pollution.

The estimation of pollutants’ concentrations in thewater represents the immediate result of the model;this information is an essential prerequisite to simulatethe propagation of contamination to the organisms (bythe way of bioconcentration) but, at the same time, itrepresents an important outcome to evaluate the waterquality conditions of the lagoon, independently from,or complementary to, experimental measurements.The local interest in Venice for the possibility ofpredicting concentrations of contaminants in thelagoon waters is motivated also from the existenceof water quality objectives for the lagoon specificallydefined by law (decree 23.IV.98); these water qualityobjectives regard the concentrations of various heavymetals and the 2,3,7,8-TCDD in the dissolved phaseand anticipated the criteria estabilished by nationaldecree 367/03 in accordance with EU directive2000/60.

The contaminants of interest for this work are heavym s),p oly-c tedb theirb theirp

2

temw thot thee fromt hreeo lledc any

minor water bodies, providing a mean dischargeof 35 m3/s.

This study integrates a large amount of experimentalinformation on aspects concerning the contamination:the model, is therefore, strictly based on real dataand the modelling simulations are strengthenedby the most updated knowledge on the lagoonecosystem.

Contamination levels of sediments are the startingpoint to simulate partitioning and represent a forcingfunction to the model. Concentrations of heavy metalsand organic compounds in the sediments are known forthe whole lagoon from 1997, when an extensive surveyon sediment quality was carried out (MAV-CVN, 1999;Critto and Marcomini, 2001; Barbanti et al., 2002).This data set includes data from about 100 stationslocated in shallow water areas and other data for saltmarshes, canals, river mouths; the data refer to the first15 cm of surface sediments and regard thetotal con-centrationof contaminants and other complementaryinformation (granulometry, density, organic carboncontent, etc.). This study focuses on shallow water ar-eas; these data were integrated with other informationon sediment contamination collected from literatureand from the recent results of the other researches (e.g.,Bellucci et al., 1999; MAV-CVN, 2000a; Bellucciet al., 2000). These data were interpolated byInverse Distance Weighted method (number ofneighbourhoods 4–6, power 2) to obtain distri-butions of contaminants in the sediments to beu olel

e oft reg-u thew ber2 utri-e s aree apera m.A m-e ededa byi ect(

t 001a lity

etals, polychlorinated dibenzo-dioxins (PCDDolychlorinated dibenzo-furans (PCDFs), and phlorinated biphenyls (PCBs); they were selececause of their persistence in the ecosystem,ioaccumulation characteristics, the evidences ofresence in the lagoon environment.

. Data used for the study

The lagoon of Venice is an estuarine ecosysith a surface of 550 km2, with an average depf about 1.3 m. The shallow water areas (from−40

o −150 cm m.s.l.) represent about the 45% ofntire lagoon area. The lagoon is separated

he sea by long sandy islands, interrupted by tutlets. Nine major tributaries (rivers and controanals) drain into the lagoon together with m

sed to run simulations extended to the whagoon (Fig. 2a).

Regarding the water quality, an updated picturhe lagoon state is available through the results oflar monitoring projects; 28 stations located overhole lagoon are monitored monthly, since Septem000, for physical–chemical parameter and for nnts, at 12 stations dissolved metals concentrationvaluated as well. These data are named in this psMELa1 from the name of the monitoring prograverage annual distributions of water quality paraters (e.g., TSS concentration) in the lagoon nes forcing functions for the model were obtained

nterpolation from the data of this monitoring projFig. 2b).

In addition to these data, withinARTISTAstudy,wo sampling campaigns were carried out during 2t 10 stations of the 28 included in the water qua

86 S. Carrer et al. / Ecological Modelling 184 (2005) 83–101

Fig. 2. Example of interpolation of experimental data in the case of (a) sediments and (b) water quality.

Fig. 3. Location and names of experimental stations for water quality monitoring and for the studyARTISTAon sediment contamination andmobility.

S. Carrer et al. / Ecological Modelling 184 (2005) 83–101 87

monitoring. These campaigns were addressed to thecollection of data on contamination by organic andinorganic micropollutants of sediments, waters andparticulated matter and to the experimental estima-tion of the diffusive fluxes of these substances atsediment–water interface. Experimental stations are in-dicated inFig. 3; note that stations labels are usedthroughout this paper.

Water quality data from the regular monitoring pro-gram and fromARTISTAexperimental campaigns con-stituted the data set used for model’s set up and cali-bration.

The model takes into account the loads of contam-inants from the watershed and from direct industrialdischarges, considering recent data available throughthe results of monitoring programs carried out onmain tributaries (MAV-CVN, 1999; Collavini et al.,2001; Bettiol et al., 2001) and on the industrial wastes(MAV, 2002). Particularly, the information regardingthe direct discharges related to industrial activitiescomes from the database developed and updated bythe Venice Water Authority.

3. Methodology

The developed model integrates different modules,namely:

- a deterministic partition model to simulate the dis-tribution of organic contaminants between solid andliquid phase;

- a statistic approach to determine the partitioning ofheavy metals;

- a finite-elements hydrodynamic-dispersion model tosimulate the conservative dispersion of the dissolvedform of contaminants.

The CRUP system (MAV-CVN, 1996) was used; itsimulates the dispersion of a conservative substanceon a typical tidal cycle±35 cm. Model results aregiven as quasi-stationary distributions of contaminantsconcentrations. According to the approach used bythe CRUP system, the lagoon was divided with agrid consisting of 516 square cells (1 km width) andthe finite-elements results were averaged at a largerscale on the grid. The typical output of the model

F RUP m del.

ig. 4. Lagoon representation: 516 cells (1 km size) from the C odel (MAV-CVN, 1996) and contaminants flows considered in the mo

88 S. Carrer et al. / Ecological Modelling 184 (2005) 83–101

is therefore a set of 516 concentrations, as depictedin Fig. 4.

The integration of the three modules allows thenon-conservative simulation of contaminants transportand partitioning. The main processes described by themodel are: inputs of substances from the drainage basinthrough rivers, loads of pollutants contained in dis-charges of industrial activities, advective transport inthe lagoon and exchange with the sea at the three out-lets, settling, diffusion and resuspension from contam-inated sediments (see againFig. 4).

3.1. Modelling total water concentration

The basic idea of the model consists in the esti-mation of contaminant’s concentration in the watercolumn by superimposing the effects of advective

transport of contaminant’s dissolved forms and solidtransport of contaminant’s particulate phase, deter-mined only by deposition and resuspension processesconsidered at the local (i.e., zero-dimensional) scale.

The total contaminant concentration in water is thusdetermined summing up two terms: the first is the out-put of the dispersive model integrated in the CRUPsystem (CDCRUP), whose forcing functions are “ex-ternal” loads from rivers and industries and “internal”loads represented by the transfer of substances fromcontaminated sediments through the diffusion process.

The second addendum of total concentration takesinto account the local contribution of resuspendedmatter. Namely, the contribution of particle-associatedpollutants to total concentration is estimated assumingthat the average concentration of suspended solids(m) in the cell is a good indicator of its propensity

F –15 cm tter collectedic

ig. 5. Comparison of sediment concentrations measured in 0

n traps located in 10 stations in the lagoon (data fromARTISTAstudy), dotteoncentration.

core samples with concentrations measured in suspended ma

d lines represent the hypothesissediment concentration= particulate

S. Carrer et al. / Ecological Modelling 184 (2005) 83–101 89

to resuspend matter. To the suspended matter isassigned a concentration equal to that measured in thesurface sediment (based on experimental evidences(MAV-CVN, 2003)), CSED, so we can estimate:

CTOT = CDCRUP+ mCSED

This approach assumes that the distribution of contam-ination in the sediment is constant over a long time pe-riod (years) so that the solid advective transport is notaffecting such a contamination in the short mediumtime-span (less than a year). The comparison ofmicropollutants’ sediment concentrations measured insame sites of the lagoon after a period of about 3 years(1998–2001) (MAV-CVN, 1999; ARTISTAstudy) con-firms this hypothesis with regard to general trends.

The second assumption concerns the fact that sus-pended solids are considered to be originated mostly by“local” resuspension. This assumption is confirmed bythe comparison of sediment concentrations measuredin 0–15 cm core samples with concentrations measuredin suspended matter collected in sediment traps locatedin 10 stations included in the studyARTISTA. In Fig. 5some examples, corresponding to a congener of PCDD,one of PCDF, one of PCB and a heavy metal, are re-ported.

To summarise, the steps for the estimation of to-tal contaminant concentration in the water column arelisted below and represented in the conceptual diagramof the model reported inFig. 6a :

1 rectent

2 tra-

3 16

4 m-

5 olids

6

l thec . Inf ces,t onw ro-

dimensional model was applied, considering only site-specific processes (Fig. 6b).

Theinternal loadswere determined considering theprocess of diffusion, driven by the gradient betweencontaminant’s pore water concentration and bottomwater concentration at the sediment–water interface. Apositive gradient, which means that concentrations ininterstitial water are higher than concentrations in watercolumn, involves a positive flux of contaminant fromsediment to the water, due to the first Fick’s law. Molec-ular weight is the most important substance-specificcharacteristic driving the process, whereas the porosityof the sediment is a site-specific parameter of primaryimportance. The diffusion velocity,vd, was quantifiedwith the empirical formula (Di Toro et al., 1981):

vd = 69.35φM2/3(Cdint − Cbw) (mg m−2 year−1)

whereφ is the porosity,M the molecular weight,Cdintthe interstitial water concentration andCbw is the bot-tom water concentration.

The gradient of concentrations was computed us-ing a constant unique value for bottom water concen-tration for the whole lagoon, obtained by averagingin time and space the concentrations measured in thestudyARTISTA. In cases where the gradient resultednegative, the diffusion flux was set to zero. In fact,notwithstanding the fact that sediments can sometimesact as a sink, it was decided to set the diffusion flux tozero mainly owing to two facts: first, measured inter-sw bot-t uredi hes -i aterc avea ouldh iffu-s uldh r ofo se ofz tedfl

ingt of5

d

. Determination of external loads—rivers and didischarges into the lagoon from watershed treatmplants and industries.

. Interpolation of sediment contaminant concentions,CSED (516 cells grid basis).

. Determination of internal loads – diffusion – (5cells grid basis).

. 2D simulation of distribution of dissolved containants – CDCRUP – (516 cells grid basis).

. Interpolation of annual average suspended sconcentrations,m (516 cells grid basis).

. Calculation of CTOT = m× CSED+ CDCRUP (516cells grid basis).

The approach just described was applied to alonsidered substances with exception of PCDD/Fact, in the case of these very lipofilic substanhe dissolved fraction is negligible in comparisith the particulate one, therefore a simplified ze

titial water concentrations in the 10ARTISTAstationsere almost always higher than the corresponding

om water concentrations; second, the fluxes measn the ARTISTAstudy were always directed from tediment to the water column (seeTable 1). Choosng a lower value for the estimate of the bottom woncentration (equal for the whole lagoon) could hvoided the phenomena of negative fluxes but wave resulted in an overestimation of the global dion flux from the sediments, since the gradient woave been higher in all sites. The highest numbeccurrences of negative fluxes was found in the cainc and this number is around 15% of total calculauxes.

(The diffusion flux was then calculated considerhe area,A, of the sediment–water interface for each16 cells, so:

iffusion flux = Avd (mg year−1)

90 S. Carrer et al. / Ecological Modelling 184 (2005) 83–101

This number (converted to a daily flux) was passedas input to the dispersive model and treated inthe same way as point sources are treated. Thesevalues were compared for the order of magnitudewith the fluxes experimentally estimated at two

stations in the lagoon withinARTISTAstudy as shownin Table 1.

The different environmental variables used in themodel and the main information source used to quantifythem is summarized inTable 2.

Fig. 6. (a) Conceptual diagram of the mode

l for metals and PCBs and (b) for PCDD/F.

S. Carrer et al. / Ecological Modelling 184 (2005) 83–101 91

Table 1Comparisons between measured and simulated diffusion fluxes atthe sediment–water interface

Simulations Experimental data ∆ (%)mg day−1 m−2

As 0.122 0.439 260Cd 0.009 0.004 56Cr 0.045 0.140 211Cu 0.181 0.120 34Hg 0.001 NdNi 0.112 0.045 60Pb 0.006 0.140 2233Zn 1.527 1.950 28∑

PCBa 0.05 25 49900

a ng day−1 m−2.

3.2. Partitioning of substances in water

The procedure described in the previous para-graph leads to the estimation of total concentration of

contaminants in the water column. In each cell, con-taminants are expected to reach a partitioning equilib-rium between solid and liquid phases, determined bythe type and quantity of possible substrates that arepresent in the cell’s area. Adsorbing and de-adsorbingkinetics, i.e., accumulation (and release) of a substanceon a solid particle’s surface, as well as absorption pro-cess, i.e., penetration of the substance in the structureof the solid phase, are the main processes involved inpartitioning.

Partitioning process is usually described byisotherms, i.e., plot of contaminant’s concentration insuspended solids versus contaminant’s concentrationdissolved in the liquid phase. Such isoterms are mathe-matically described by many formulations (Langmuir,Freundlich, BET) but, dealing with low concentrationsfrequently found in environment, the linear approxi-mation of Langmuir’s isotherm is widely used. Thisimplies that the ratio between solid and liquid phases

Table 2Model characteristics and information sources

Forcing functions Symbol Source

Sediment concentrations CSED Interpolated from data about 100 sites (MAV-CVN, 1999)Suspended solids m Interpolated from 2 years monthly data in 28 sites (MELa1)External Loads Lext Rivers (MAV-CVN, 2000b) and industries (MAV, 2001)Sediment porosity φ Interpolated from data about 100 sites (MAV-CVN, 1999)Sediment density ρ Interpolated from data about 100 sites (MAV-CVN, 1999)Sediment pH pH Interpolated from data about 100 sites (MAV-CVN, 1999)Organic carbon in sediment CORG Interpolated from data about 100 sites (MAV-CVN, 1999)Fraction of organic carbon in sediment focSilt sediment content SILTSand sediment content SANDRedox potential in sediment Eh

Total organic carbon in water TOC roject)Particulate organic carbon in water POC )Alkalinity in water ALKCation exchange capac-

ity = (200× (%CORG)/100) + (50× (%SILT)/100)CEC

Sediment–water interface area for each cell A

State variablesTotal water concentration CTOTw

Dissolved water concentration Cdw

Particulate water concentration Cpw

ParametersPartition coefficient in sediments Kd sed

Partition coefficient in water column K

d w

Bottom water concentration Cbw

Diffusion rate vd

Octanol–water partition coefficient Kow

Interpolated from data about 100 sites (MAV-CVN, 1999)Interpolated from data about 100 sites (MAV-CVN, 1999)Interpolated from data about 100 sites (MAV-CVN, 1999)

Interpolated from data about 100 sites (MAV-CVN, 1999)Interpolated from 2 years monthly data in 28 sites (MELa1 pInterpolated from 2 years monthly data in 28 sites (MELa1

Interpolated from 2 years monthly data in 28 sites (MELa1)Interpolated from 2 years monthly data in 28 sites (MELa1)

Bathymetric map of the lagoon (1992)

92 S. Carrer et al. / Ecological Modelling 184 (2005) 83–101

is constant (when concentrations are low), i.e.:

Kd = v

Cdw

wherev is the contaminant concentration in solid phase(mg/g), Cdw the contaminant concentration in liquidphase (mg) andKd is the partition coefficient (l/g).

If the particulated concentration is expressed bywater volume we get:

Cpw = m × v (mg/l)

Thus, total water concentration can be expressed by:

Cw = Cdw + Cpw

= Cdw + mv = Cdw + mKdCdw (mg/l)

and the dissolved fraction, i.e., the ratio betweendissolved and total concentrations, is:

Fd = 1

1 + Kdm

The estimate of the correct partition coefficientKdfor each substance, subject to different environmentalconditions, is the next problem to solve, and will bediscussed in the following paragraphs.

3.2.1. Determination of Kd for organiccontaminants

With regard to organic micropollutants such as diox-ins and PCBs, the association with the organic fractiono micc ndsr inantpt co-e eo , foc.I s:

F

T eos ribet ro-p ,1

K

Table 3Logarithmic values of octanol–water partitioning coefficients usedin the model (Mackay et al., 1992; Eisler, 2000)

Substance LogKow Substance LogKow

2378-TCDD 5.38–8.48 81-CB 5.6–6.512378-PeCDD 6.20–9.69 77-CB 5.6–6.5123478-HxCDD 6.85–9.53 123-CB 6.35–6.75123678-HxCDD 6.85–9.53 118-CB 6.35–7.12123789-HxCDD 6.85–9.53 114-CB 6.2–6.661234678-HpCDD 7.92–10.32 105-CB 6.35–6.612346789-OCDD 7.33–10.56 126-CB 6.2–6.92378-TCDF 5.6–6.73 167-CB 6.7–7.312378-PeCDF 6.19–6.92 156-CB 7.0–7.223478-PeCDF 6.5–7.60 157-CB 7.0–7.2123478-HxCDF 7.2 169-CB 6.7–7.43123678-HxCDF 7.2 189-CB 6.7–7.7123789-HxCDF 7.2 180-CB 6.7–7.4234678-HxCDF 7.2–7.7 170-CB 6.7–7.31234678-HeptaCDF 6.90–8.001234789-HeptaCDF 7.92–8.1012346789-OctaCDF 6.94–9.99

Table 3shows the range ofKow values utilised for eachsubstance.

3.2.2. Determination of Kd for inorganiccontaminants (heavy metals)

Inorganic micropollutants, including heavy metals,follow very complex dynamics, which are influencedby a huge number of variables. Heavy metals can un-dergo different kinds of speciation and can be adsorbedin different amounts owing to different geo-chemicalconditions of the environment. Metals may associateand adsorb to soil and organic carbon depending onthe presence of competing ions, on water chemistryand on metal’s speciation itself, in turn depending onpH and redox potential.

The Kd coefficient represents a number ableto synthesise the various processes influenced bymany unmeasured variables. It can be measured orestimated using deterministic or empirical models.The application of a deterministic model wouldrequest to describe the chemical–physical dynamicsof all possible substrates and substances involved inthe reactions, as well as to describe the processesof competition of different chemicals in differentenvironmental conditions. In this study, due to theenormous amount of information that would be re-quested for the use of a deterministic model, empirical

f solid phase represents a favourite thermodynaondition. Weak intermolecular attractions and bouepresent other possible interactions, but the domrocess is the equilibrium with organic carbon, thusKd

akes into account the substance-specific partitionfficient with organic carbon,Koc, and the percentagf organic carbon in suspended solids as substrate

n this case, the dissolved fraction can be written a

d−org = 1

1 + Kocfocm

he coefficientKoc is strictly connected with thctanol–water partition coefficient,Kow, typical of eachubstance. Many different formulations may deschis relationship; the following relationship was posed byKarichoff et al. (1979), as quoted inChapra997and has been adopted in this work:

oc = 6.17× 10−7 Kow

S. Carrer et al. / Ecological Modelling 184 (2005) 83–101 93

models were used to estimate a value ofKd for eachsubstance.

The methodology involves the use of multipleregression analysis, which helps to get to an estimateof dependent variable (Kd) as a function of otherindependent variables with a relation such as:

Kd = a + b1X1 + · · · + bnXn

where the coefficients of the regression are minimisingthe sum of quadratic errors between observed andestimated values.

The following steps were performed to obtain acorrect multiple regressions:

1. analysis of correlation matrix to select only signifi-cant variables to be included in the regression;

2. analysis of dispersion plot to discover non linearrelationship among variables;

3. determination of coefficients of regressions;4. computation and comparison between different

multiple regressions;5. evaluation of the significance of the regression (F

test);6. evaluation of significance for single variables (Stu-

dent’st-test);7. evaluation of the quality of the regression (correla-

tion factorR2);8. computation of the standard error.

This procedure leaded to the identification of theregression equations reported inTable 4, together withthe corresponding correlation factors.

4. Results

4.1. Sensitivity analysis

Sensitivity analysis was carried out to optimize cal-ibration process and to evaluate model’s simulationcapability and results robustness.

Sensitivity was computed as:

S = �X/X

�p/p

whereS is the sensibility,X the state variable,p is theparameter or forcing function.

Both dissolved water concentration and total waterconcentration were used for sensitivity analysis, withreference to model parameters and to the most im-portant forcing function (e.g., external loadings). Statevariables variations were computed for each of the 516cells and then averaged. Parameters and forcing func-tion values were varied by 10% their reference value.

Results of sensitivity analysis are reported inFig. 7a and b, respectively for one metal (arsenic)and for one PCB (PCB-105). The values reportedin the graphs for each variable are an average of

TR sor heav

5.57 1

42 41

02 714575 6701856 8

× As +ρ + 0.

.0286× 0

.0499× 815× ρ − 0.04× Ni 3%SAND 2017× E

able 4egression equations for the estimation of partition coefficientKd f

Regression equations

Water columnAs Log(Kd) =−0.75× TOC +Cd Kd = 12907870× m−1.3

Cr Kd = 2682× ALK − 75562Cu Kd =−6266× m+ 170105Ni Kd = 1106× ALK − 31134Pb Kd = 12757648× POC− 4Zn Kd = 40600617× POC− 1

SedimentAs Log(Kd) = 5.96× φ − 0.04Cd Log(Kd) = 8.77× φ + 1.97×Cr Log(Kd) = 0.0066× Eh + 0Cu Log(Kd) = 0.0021× Eh + 2Hg Kd = 3944093× φ + 10597Ni Log(Kd) = 0.18× CEC + 0Pb Kd =−280× Eh − 1473×Zn Log(Kd) = 2.9552× φ + 0.0

y metals in waters and sediment (symbols are explained inTable 2)

R2

0.80.790.90.90.80.90.8

0.92× Ph 0.55024× %SILT− 3.81 0.70Cr + 3.0427 0.7φ + 2.3137 0.522825059 0.8

− 0.38× CORG +1.59 0.6+ 16405× CORG + 67866 0.5

h + 0.6102× pH 0.62

94 S. Carrer et al. / Ecological Modelling 184 (2005) 83–101

Fig. 7. Results of sensitivity analysis.

the absolute values resulted from the +10% and the−10% sensitivity Results highlight that the modelis little sensible to variation of values assigned tomain parameters, the larger effect is shown on organicmicropollutants particularly on the value of partitioncoefficient in water. In simulating metal partitioning,sediment characteristics show to be the most importantfactor. The low sensibility of the parameter bottomwater concentration,Cbw, is considered a goodresult, because it compensates the roughness of itsestimate.

It is interesting to note that external loadings areimportant to determine the values of predicted concen-trations. On this basis, the model is feasible to be usedto simulate efficiently scenarios under different loadingconditions.

4.2. Model calibration

The model was run for 17 congeners of PCDD/F,for 10 PCBs “dioxin like” and for eight heavy metals:in the following some examples of results are given forthe different classes of substances.

In the case oforganic substances, due to thecharacteristics of the data set (very low concentrationsof organic compounds in the waters, wide variabilityof the concentration itself in the same place, magnitudeof error of experimental determination), model resultswere compared with the range of observed data andcalibration was performed manually. Calibrationresults for some of the congeners are depicted asexample in Fig. 8; analytical detection limits arehighlighted in the graphs with the arrow on they-axis.

S.C

arre

re

tal./E

colog

icalM

od

ellin

g1

84

(20

05

)8

3–

10

1

Fig. 8. Model calibration, some examples of results for organic substances (PCDD, PCDF, and PCBs). SeeFig. 3for stations labels.

95

96 S. Carrer et al. / Ecological Modelling 184 (2005) 83–101

Results show that, in the case of dioxins, the tetra-,penta-, and exa- substitutes have concentrations belowdetection limits and the model correctly reproduces thisbehaviour giving concentrations near to zero. In the

case of epta- and octa-dioxins, the model reproducesvery well the spatial variability of contamination, giv-ing higher concentrations in the central lagoon. As ex-perimental data do, model’s results give values below

Fig. 9. Model calibration, results for heav

y metals. SeeFig. 3for stations labels.

S. Carrer et al. / Ecological Modelling 184 (2005) 83–101 97

the analytical detection limits for all the dissolved con-centrations.

In the case of furans too (total concentrations), themodel reproduces very well the experimental values,giving values below detection limits when expected andreproducing correctly spatial variability of concentra-tions when these are higher than detection limit.

Results for PCBs show the capability of the modelto reproduce spatial variability of concentrations, evenbetter that for dioxins and furans, also in dissolvedphase, when values are higher than detection limits.

For heavy metals, calibration was performed byminimizing the average square difference betweenmodel results and experimental values.

Fig. 9compares simulated values for heavy metalswith the average values recorded during 2 years of mon-itoring. Calibration was performed with data recordedat shallow water stations.

Field data do not evidence marked spatial variabilityand also the model gives very similar values in differentareas.

To globally quantify the performances of the modelan average error (SC) was computed for each class ofsubstances as:SC=

100(∑ |yi−y∗

i |)n

ym

The average errors calculated for each class of sub-s

eringo ana-l ft thec n re-s ctedv

Ps’m an-a sulto allert ford .

The performance of the model should be evalu-ated considering the objective of the study, the uncer-tainty inherent in the analytical determination of or-ganic micro-pollutants and the high variability in theenvironment of dissolved metals concentrations and ofthe variables used as forcing functions. It must be re-membered that this model constitutes a module of acomprehensive bioaccumulation model developed inthe perspective of ecological risk evaluations. For suchreasons, our target with the model was the capturing theorder of magnitude of most relevant micro-pollutantsdistributed in the water of the lagoon, the achieved aver-age error of 70–80% is therefore considered a satisfyingresult.

4.3. Simulations

The calibrated model produces a series of maps oftotal and dissolved contaminants concentrations. Theygive a picture of the distribution of micropollutants inthe lagoon and can be used to point out main pointsources, most polluted areas and to do comparisons be-tween different contaminants. These distribution mapswill be used by the bioaccumulation module of theframework of the complete ecotoxicological model tosimulate fate in the organisms.

The distribution of total and dissolved concentra-tions was obtained for each organic and inorganicchemical simulated.

In the case of PCDD/F, due to the zero-dimensionaln sultsa cen-t andm

( a-t

ver-a ps;i riv-i allyi hernl re-t tora rset ncef nalc

tances are:

PCDD/F = 82%

PCB= 70%

Metals= 75%

These average numbers were calculated considnly the substances and the set of results above the

ytical detection limit, but thequalitativeevaluation ohe performance of the model should consider alsoases, reported in the graphs showing calibratioults, when both the analytical result and the predialue are below the analytical detection limit.

In many cases, especially for smallest POolecules, the concentration in water is below thelytical detection limit, so that we can accept the ref the model whenever the predicted value is sm

hat the analytical detection limit. This is the caseioxins congeners from tetra- to hexa-substitution

ature of the model for these substances, the rere given only for those cells where sediment con

rations of contaminants are available. For PCBsetals results are given for all the 516 cells.Fig. 10reports, as example, the maps of

∑PCDD/F

total concentration), of∑

PCB (dissolved concentrion), have dissolved arsenic and dissolved zinc.

Generally, the effect of external loads on the oll distribution of contamination is evident in all ma

n some of them the contamination of sediments, dng the effect of internal loads is evident, especin the central lagoon and in some areas of soutagoon. The washout effect, linked to the typicalention time of the cells, is the third important facffecting the distribution of contamination; of cou

he effect of washout is related to the cells’ distarom the sea and to the direction of main interhannels.

98 S. Carrer et al. / Ecological Modelling 184 (2005) 83–101

Fig. 10. Examples of maps of contaminants’ distributions produced by the model.

S. Carrer et al. / Ecological Modelling 184 (2005) 83–101 99

Fororganic contaminants, higher concentrations ofPCDD/F are evident in the central lagoon, in the areanear the lagoon border, north and south of Venice, inrelation with higher contaminants concentrations in thesediments. PCBs distributions show the influence bothof sediment contamination and of discharges from thewatershed, exhibiting higher concentrations in the ar-eas in front of the Industrial zone but also in front ofmajor rivers inflowing the lagoon, in the northern andsouthern basin.

Among themetals, arsenic, but also cadmium andlead, shows decreasing gradient from internal areas tooutlets, driven by the influence of rivers inputs and oftidal exchange. In the case of zinc, the high concentra-tions of central lagoon are evident.

The model can be used to produce scenarios. To givean impression of model’s capability,Fig. 11shows themaps of dissolved copper concentrations computed inthe reference conditions (present loadings and forcingfunction as described in this paper) and in three differ-ent simulation scenarios. As examples, the followingcases were considered:- 30% reduction of average annual total suspended

solids concentration;- 50% reduction of contaminant concentration in the

sediments of the central lagoon between Venice andPorto Marghera industrial area;

- 50% reduction of dissolved pollutants loading fromthe watershed and from direct discharges forms theindustrial area of Porto Marghera.

Fig. 11. Examples of simulation scenarios for dissolve

d copper concentrations (�g/l) produced by the model.

100 S. Carrer et al. / Ecological Modelling 184 (2005) 83–101

In the tested cases, the difference between the sce-narios and the reference situation are little, the dis-solved concentrations are similar in the four consideredcases.

5. Conclusions

The growing interest in the toxicity problem for thelagoon of Venice stimulated the production of manystudies on the subject, involving the investigation ofpollution’s sources and accumulation sites. These stud-ies generated a large amount of data, which claimedfor an integrated and olistic overview and, at the sametime, evidenced the need for a management tool, like amodel, able to interpret them and forecast possible sce-narios of interest for the ecotoxicological risk assess-ment. The model represents a tool capable to elaboratethe fundamental inputs of contaminants in the lagoon,to model their prevailing distribution processes, andto provide estimates of partitioning in dissolved andparticulated phase of a wide spectrum of substances,including heavy metals and most important POPs.

The results of the model are useful to perform “ex-posure analysis” in an ecological risk assessment per-spective. These results can be used for comparison withwater quality objectives defined for the lagoon by re-cent legislation, as well as with other reference valuesproposed by scientific communities and by the stake-holders.

on-t entst ula-t as as odelf o bea

iatet .

A

hor-i aireC .G.B tedt and

knowledge on the Venice lagoon ecosystem. This pa-per is dedicated to the memory of Prof. Giuseppe Ben-doricchio who gave fundamental inputs in designingthe wholeARTISTAstudy and supervised in progressthe activities of the project.

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The authors are grateful to the Venice Water Autty for financing the study through its concessiononsorzio Venezia Nuova. A particular thank to Aernstein and L. Montobbio who strongly suppor

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