www.elsevier.com/locate/marpolbul

Marine Pollution Bulletin 54 (2007) 441–451

Fate of persistent organic pollutants in the water column:Does turbulent mixing matter?

Elena Jurado a, Jose-Manuel Zaldıvar b, Dimitar Marinov b, Jordi Dachs a,*

a Department of Environmental Chemistry, IIQAB-CSIC, Jordi Girona 18-26, Barcelona 08034, Catalunya, Spainb European Commission, DG Joint Research Centre, Institute for Environment and Sustainability, Ispra (VA), Italy

Abstract

The effect of vertical turbulent mixing on the dynamics of persistent organic pollutants has long been overlooked and its role is stillhardly understood. Here we present the first comprehensive analysis of the role of turbulent diffusion on the distribution of those con-taminants and its interplay with sinking fluxes. To this end, a 1D dynamic coupled hydrodynamic-contaminant model has been devel-oped and applied to a Mediterranean continental shelf environment. The hydrodynamic sub-model is adapted from COHERENS, thecontaminant sub-model is an improvement from the BIODEP model and considers the contaminant in 3 phases: dissolved-colloidal-par-ticulate. The simulation highlights the role of turbulence in determining the POP distribution and variability in the water column. Inshort, turbulent flux of contaminants strengthens the upward diffusion of sediment entrained contaminants and determines the extentto which inputs from the atmosphere mix into the water column. It acts in parallel with degradation and sinking fluxes, the combinedeffect yielding a surface enriched – depth depleted – benthic layer enriched region distribution, which presents similarities to reported exper-imental measures.� 2006 Elsevier Ltd. All rights reserved.

Keywords: POPs; PCBs; Turbulence; Hydrodynamic model; Contaminant model; Sinking

1. Introduction

The oceans play an important role in controlling theenvironmental transport, fate and sinks of persistentorganic pollutants (POPs) at regional and global scales(Wania and Mackay, 1996; Dachs et al., 2002). The occur-rence of these hydrophobic and semivolatile compounds isof special concern due to their ubiquity, toxicity and accu-mulation in food webs (Jones and de Voogt, 1999). It hasbeen demonstrated that water-column processes have astrong impact on the air–sea exchange of POPs, since theirefficient removal from the mixed surface water layerreduces the volatilization rates and captures atmosphericpollutants (Dachs et al., 1999a,b; Scheringer et al.,

0025-326X/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.marpolbul.2006.11.028

* Corresponding author. Tel.: +34 9340 06118; fax: +34 93 204 59 04.E-mail address: jdmqam@cid.csic.es (J. Dachs).

2004). Nevertheless, there is a complex interplay of pro-cesses controlling the vertical transport of contaminantsin the water column the knowledge of which is limiteddue to the scarcity of measurements of vertical fluxes ofPOPs associated to settling particles (Schulz-Bull et al.,1988; Dachs et al., 1997a,b; Gustafsson et al., 1997; Dachset al., 1999a,b). On the other hand, marine sediments havebeen hypothesized to become a pool of contaminantsavailable for mixing throughout the water column, espe-cially during poorly stratified periods (Baker et al., 1991;Berglund et al., 2001; Bogdan et al., 2002; Ko et al.,2003). From the above, the POPs transformations and fatein water column need to be more adequately parameter-ized in particular for shallow water bodies and the oceanshelf zone.

Namely, POP water column processes are: partitioningonto colloidal and suspended/settling particulate matter,degradation of dissolved compounds, incorporation to

442 E. Jurado et al. / Marine Pollution Bulletin 54 (2007) 441–451

trophic chains and transport of contaminants due to mix-ing of water masses by turbulent diffusion and verticaladvection. Among them, the influence of mixing of watermasses by turbulent diffusion on POPs distribution hasusually not been considered in environmental models(Mackay, 2001; Dachs et al., 2002; Palm et al., 2004).The reason deals, apart from the intrinsic complexity tomodel highly variable turbulent fluxes, that most modelsassume that vertical mixing within the surface layer is fast,and therefore, it is not the kinetic limiting process. In dee-per layers and in the open ocean the vertical movement ofwater due to turbulent diffusion is often assumed negligibledue to the disparity between horizontal and vertical scales,which generates much lower vertical diffusivities comparedto horizontal diffusivities. Furthermore, it is assumed thatvertical flux of POPs bound to settling particles dominatesover other removal processes (Dachs et al., 2002; Waniaand Daly, 2002).

The traditional approach to model POPs in the watercolumn is to consider two well-mixed boxes during stratifi-cation periods and one well-mixed the rest of the time (Sch-warzenbach et al., 2003; Meijer et al., 2006). The extensenumber of 0D models for these hydrophobic organic com-pounds (Wania and Mackay, 1996; Scheringer et al., 2000;Dalla Valle et al., 2003; Dueri et al., 2005) contrasts withthe lack of spatially and temporally resolved models, withthe exception of the recently developed coastal lagoonmodel for herbicides (Carafa et al., 2006), but its chemicalbehavior differs from the POPs, and the recent onereported for HCH by Ilyna et al. (2006), but not appliedto chemicals with higher hydrophobicities and thus stron-ger interactions with particles and sediments.

Here we develop a 1D dynamic coupled hydrodynamic-contaminant model to analyze the influence of vertical mix-ing on the distribution of POPs in the water column. Theeffect of seasonal dynamics of phytoplankton on POPs fatehas been also considered. Using a 1D instead of a 3D ver-sion is adequate for open-sea areas, where the atmospherictransport is the prevalent route for the introduction of con-taminants (Tolosa et al., 1997). Furthermore, it avoids thedifficulties associated with providing time-series of bound-ary conditions, reduces computation time and prepares asimpler model variant which after verification gives theoption to lately easily extend it to 3D model version usingthe COHERENS framework. The model has been appliedto a buoy station located in the Mediterranean continentalshelf, where strong seasonal stratification occurs and itseffects can be investigated.

Particular attention is given to answer the followingquestions: (i) the role played by vertical turbulent mixingon the distribution and variability of POPs in the water col-umn; (ii) the extent of atmospheric and sediment inputs inthe water body; (iii) the importance of turbulent diffusionversus other processes that also take place in the water col-umn, such as sinking of particle-bound contaminants anddegradation of dissolved contaminants and (iv) the validityof estimated vertical profiles.

2. Model description

2.1. Model design

The model consists in a 1D dynamic coupled hydrody-namic-contaminant model applied to the water columnand forced with seasonal phytoplankton abundance andmeteorological data. The water column is subdivided intoa number of discrete layers and the concentrations do varyin time and space. The structure of the model and its phys-ical part is based on COHERENS (http://www.mum-m.ac.be/coherens), a complex 3D/1D hydrodynamic finitedifference model for coastal seas which is freely available forscientific purposes (Luyten et al., 1999, 2003). Its hydrody-namic performance has been verified in various environ-ments like the North Sea and Sacca di Goro (Umgiesseret al., 2002; Marinov et al., 2006). Changes introduced heremainly concern the introduction and coupling of a contam-inants module into the program structure of COHERENS.In addition, the model includes the suspended particulatematter (TSM) as an individual compartment, because ofits key role in the re-distribution of POPs in marine envi-ronments (Baker et al., 1991; Swackhamer and Skoglund,1991).

Due to its 1D nature, the model presented is adequatefor areas in which horizontal concentration variationscan be disregarded and where bottom deep currents canbe assumed negligible. According to the 1D parameteriza-tion, horizontal advection and diffusion transports areneglected and just the transport of substances by verticalturbulent diffusion and advection by sinking are consid-ered. For that reason the model is not suitable for regionsof deep water formation and down/up-welling advectionzones where the vertical fluxes should be considered (Loh-mann et al., 2006). However, the 1D approach is adequateto assess the objectives listed above.

The present model has been applied to a buoy station inthe Mediterranean continental platform (44N, 14E – openNorth Adriatic), in a location without strong anthropo-genic influences and with an average depth of 50 m. Atthese depths internal water mixing is mainly generated byatmospheric forcing at the air–sea interface, being tidalfriction unimportant. Since the northern Adriatic basin issubject to strong forcing functions, it produces a clear sea-sonality in the ecosystem (Zavatarelli et al., 1998), conse-quently results are adequate to evaluate the temporalvariation of contaminants dynamics. The model is writtenand implemented in FORTRAN and it has been discretizedin 50 vertical layers (NZ = 50) and time step of 60 s. A lin-ear interpolation has been applied to recalculate the forcingfunctions with hourly frequency.

2.2. Hydrodynamic modeling

The hydrodynamic part of the model solves the 1Dequation of momentum (adopting the Boussinesq approxi-mation and vertical hydrostatic equilibrium), continuity,

E. Jurado et al. / Marine Pollution Bulletin 54 (2007) 441–451 443

temperature and salinity. Further, all vertical eddy viscos-ity and diffusivity coefficients in the momentum and scalarequations are evaluated by classical two-equation turbu-lence closure scheme. For a detailed description of thehydrodynamic momentum, scalar and turbulent modelequations, the reader should refer to COHERENS (Luytenet al., 1999).

The state variables of the hydrodynamic sub-model aretemperature, salinity and eddy diffusion coefficients. Theforcing consists in meteorological conditions obtained from6 h measures provided by reanalysis from the EuropeanCenter for Medium Weather Forecast (ECMWF, http://www.ecmwf.int/): air temperature at 2 m, wind speed anddirection at 10 m, precipitation rate, cloud cover, relativehumidity. They are referred to the whole year 2003 and tothe buoy location (44N, 14E). Besides, this hydrodynamicpart was initialized using a constant profile for temperature(T = 10 �C) and a constant rise of salinity with depth(S = 38 + 0.5*(NZ-k + 1)/50, where k = [1, 2, . . .,NZ]).

2.3. Contaminant modeling

The contaminants sub-model, coupled to the hydrody-namic sub-model, is based on the BIODEP model describedelsewhere (Meijer et al., 2006) and contains all the equationsthat describe the chemical partitioning, boundary and inter-nal related fluxes. The contaminant part has been com-pletely incorporated in the original COHERENS modelframework. Considered state variables are the total concen-tration in the open water ðCT

Wðz; tÞ ½ng m�3�Þ and the totalconcentration in the surface mixed sediment layer – SMSL– ðCT

sedðtÞ ½ng m�3�Þ, the latter expressed relative to bulkvolume. Thus the model not only considers contaminantprocesses in the water column but as well in the sedimentssurface mixed layer. In particular, 7 representative poly-chlorinated biphenyls (PCBs) (PCB 28, PCB 52, PCB 101,PCB 118, PCB 138, PCB 153, PCB 180) have been chosenas the contaminants of study.

Atmospheric and deep sediments concentrations areconsidered as the drivers of the contaminants sub-model,hence the contaminant forcing data consists of PCB mea-sured concentrations in rain (Mandalakis and Stephanou,2004), atmospheric (gas + particulate) (Mandalakis andStephanou, 2002) and deep sediments (Moret et al., 2001)(see Table A.1 in Appendix), representative of pristineopen-sea Mediterranean. Their choice is hampered by thescarcity of data for atmospheric PCB concentrations inthe offshore Mediterranean. Forcing concentrations havebeen assumed yearly constant, not influenced by processesin the water column. Finally, initial concentrations in thesimulations are homogeneous – 10 ng m�3 in the water col-umn and 105 ng m�3 in the sediments, but this does notimpact the final results because the model was conse-quently rerun for few years under same forcing conditionsuntil two consecutive years yielded equal results.

In the water column persistent organic pollutants aremodeled either as truly-dissolved form ðCdis

W ½ng m�3�Þ,

sorbed to colloids ðCDOCW ; ½ngm�3�Þ or to suspended solids

ðCpartW ; ½ng m�3�Þ. A similar 3-phase approach has also been

considered in the sediments:

CTW;i ¼ C dis

W;i þ CDOCW;i þ Cpart

W;i ð1ÞCT

sed ¼ Cdissed þ CDOC

sed þ Cpartsed ð2Þ

where i is evaluated water layer (1: bottom water layer, NZ:surface water layer).

This phase speciation determines the dynamics of theorganic compound in the system: dissolved POPs arebioavailable since they can be incorporated by passivediffusion to the biota; POPs sorbed to particles can bedeposited by gravity; POPs sorbed to colloidal materialcan not sink and diminish the accumulation of contami-nants to trophic chains. In either phase they are subjectto diffusion because of turbulence of water masses. Fur-thermore, the phase speciation depends on the physico-chemical properties of the contaminant, abundance andtypology of organic matter in the media, and other proper-ties of the surrounding media, such as temperature amongothers. Assuming equilibrium at all times between thephases, previous equations can then be rewritten as

CTW;i ¼ Cdis

W;ið1þ KDOCDOCþ KpTSMphysi þ BCF TSMbiol

i Þð3Þ

CTsed ¼ Cdis

sedð1þ KDOC;sedDOCsed þ Kd;sedTSMsed=/sedÞ ð4Þ

where DOC (ng m�3) is the dissolved organic carbon con-centration and TSMphys and TSMbiol (ng m�3) the concen-tration of total suspended matter in the water column in theso-called ‘‘physical’’ and ‘‘biological’’ particles, respec-tively, /sed [0–1] the porosity of the SMSL. DOC has beenassumed constant, but the TSM has been determined ateach time and depth point in the contaminants sub-model.In particular, TSMphys accounts the atmospheric aerosol in-puts and the resuspension of particles from the sedimentswith a fraction of organic matter ðf phys

OM Þ of 0.4 (or 0.2 asfraction of OC), while TSMbiol represents the total particu-late organic matter in the water column from biologicalorigin. TSMbiol has been considered constant all the watercolumn and forced to follow the temporal variation of chlo-rophyll-a amount in the surface. The porosity is assumed0.85. KDOC and KDOC,sed (m3 ng�1) are the partition coeffi-cients of the chemical between the colloidal and the trulydissolved phases, Kp and Kd,sed (m3 ng�1) are the partitioncoefficient of the chemical between the suspended (otherthan phytoplankton) and sediment particles and the trulydissolved phase and BCF (m3 ng�1) is the bioconcentrationfactor, i.e., the ratio between dissolved and phytoplanktonPOP concentration at equilibrium. Moreover, BCF hasbeen assumed to be correlated to the octanol–water parti-tion coefficient and modeled using a two compartmentsystem (Del Vento and Dachs, 2002). Several recent studies(Sobek et al., 2004; Garcıa-Flor et al., 2005) have shownthat experimental water-particle partitioning coefficient(Kp) values are significantly higher than the traditionalparameterization of Kd, even in oligotrophic conditions.

444 E. Jurado et al. / Marine Pollution Bulletin 54 (2007) 441–451

These observed Kp are in fact similar to the ones predictedfor phytoplankton, and thus, these have been used in thepresent work ðKp ¼ f phys

OM � BCFÞ. Furthermore, these arealso internally consistent with the partitioning of PCBsbetween gas and aerosol phase as given by the octanol–airpartition constant. On the other hand, all previously namedpartition coefficients are function of the water temperature,taken from the outputs of the hydrodynamical part of themodel. Derivations and values of the previous parametersare shown in detail in Table A.2.1 and A.2.2 in Appendix.The TSM modeling is explained in Appendix C.

Apart from the fluxes of contaminants that take part inthe water column, we have considered as well the processesthat occur in the SMSL, and boundary fluxes withsurrounding media such as the atmosphere and deep sedi-ments. In particular, the following processes are considered(see Fig. 1): atmospheric exchange, i.e. diffusive gaseousabsorption and volatilization, gaseous and particle-boundwet deposition (rain), dry particle deposition; internalwater column processes, i.e. sinking of particle-bound con-taminants, turbulent diffusion, degradation and sorption toDOC and suspended particles; fluxes between the SMSLand the water column, i.e., resuspension and diffusion fromthe pore water; and finally fluxes between the SMSL andthe deep sediments such as burial, pore water diffusionand biodiffusion (generated by biological reworking ofsediments).

The fluxes between atmosphere and water are based inparameterizations described elsewhere (Dachs et al.,1999a,b; Jurado et al., 2004, 2005). Sinking fluxes in eachlayer of the water body are computed as the sum of thesinking of contaminants bounded to ‘‘physical’’ particlesand to ‘‘biological’’ particles. The former is characterizedby a constant settling velocity, the latter is assumed to be

lapa

di

pa

WETDEPOSITION

DRYDEPOSITION

AIR -WAEXCHAN

RESUSP

SINKING

BURIALDD

DEGRADATION

DEGRADATION

water

surface mixed sediment layer

(SMSL)

deepsediment

ABSORPTION

largerparticles

disso

particles

WETDEPOSITION

DRYDEPOSITION

AIR -WAEXCHAN

RESUSP

SINKING

BURIALDD

DEGRADATION

DEGRADATION

column

ABSORPTION

Fig. 1. Fluxes of POPs accounted in the 1D co

proportional to the flux of organic matter collected in thebottom but with a seasonal variability that follows chloro-phyll a concentrations (Baines et al., 1994). The sediment–water exchanges follow largely the parameterizationreported elsewhere (Schwarzenbach et al., 2003; Dueriet al., 2005). Tables with the equations and parameter val-ues for each of the processes are available in the Appendixsection (see Table B.1 and Table B.2).

The time-dependent concentrations in each water layer i

and as well the sediment concentrations are estimatedbased on a mass balance approach, i.e., change = inputs–outputs ± transformations:

dCTW;i=dt ¼ ð1=hiÞðF AW abs � F AW vol þ F DD þ F WD

þ F SINK;iþ1 � F SINK;i � F DEGR;i

þ F WS sed=/sed � F WS wat=/sed

þ F RES sed=/sed � F RES wat=/sedÞ ð5Þ

where hi (m) is the depth of the evaluated water layer,FAW_abs (ng m�2 s�1) the gaseous diffusive absorption flux,FAW_vol (ng m�2 s�1) the volatilization flux, FDD

(ng m�2 s�1) the dry deposition flux, FWD (ng m�2 s�1)the wet deposition flux (gaseous and particulate), FSINK,i

(ng m�2 s�1) the sinking flux (physical and biological) asso-ciated to water layer i, FDEGR,i (ng m�2 s�1) the chemicaland biological degradation flux in the water layer i, FWS_sed

(ng m�2 s�1) the diffusive flux from SMSL to the water col-umn, FWS_wat (ng m�2 s�1) diffusive flux from the watercolumn to the SMSL, FRES_sed (ng m�2 s�1) the sedimentresuspension flux, FRES_wat (ng m�2 s�1) accounts thedesorption of contaminants from the resuspended sedi-ments. The units of sediments fluxes refer to the bulkvolume of the SMSL (interstitial water volume + solids

co

CC

CC

CC

CC

C

po

CC

C

CC

TER GE

VOLATILIZATION

ENSION DIFFUSION SMSL-WATER

IFFUSION SMSL-EEP SEDIMENTS BIODIFFUSION

TURBULENT DIFFUSION

lved colloidal

CC

CC

CC

CC

C

pore water: dissolved,colloidal

CC

C

CC

TER GE

VOLATILIZATION

ENSION DIFFUSION SMSL-WATER

IFFUSION SMSL-EEP SEDIMENTS BIODIFFUSION

TURBULENT DIFFUSION

upled hydrodynamic-contaminant model.

E. Jurado et al. / Marine Pollution Bulletin 54 (2007) 441–451 445

volume), this is why /sed is used as a conversion factor. Inthe previous formula FAW_abs, FAW_vol, FDD, FWD are onlyaccounted if i = NZ (surface), FWS_sed, FWS_wat, FRES_sed,FRES_wat are only accounted if i = 1 (bottom watercolumn).

For the SMSL the following differential equation isused:

dCTsed=dt ¼ ð1=zmixÞðF sink;1/sed � F WS sed þ F WS wat

� F RES sed þ F RES wat � F BU þ F SS deep

� F SS SMSL þ F BIO deep � F BIO SMSL

� F DEGR;sedÞ ð6Þ

where zmix is the thickness of the SMSL (assumed 0.01 m),FBU (ng m�2 s�1) the burial flux, FSS_deep (ng m�2 s�1) fluxof pore water diffusion from deep sediments to SMSL,FSS_SMSL (ng m�2 s�1) flux of pore water diffusion fromSMSL to deep sediments, FBIO_deep (ng m�2 s�1) biodiffu-sion flux from deep sediments to SMSL, FBIO_SMSL

(ng m�2 s�1) biodiffusion flux from SMSL to deep sedi-ments, FDEGR,sed (ng m�2 s�1) the degradation flux in thesediments.

Turbulent diffusive transport have been additionallyapplied into contaminant equations through parametersdriven by the hydrodynamical sub-model. To note, by tur-bulent flux is understood as the flux of contaminants gen-erated by turbulent diffusion in the body of water, notthe molecular diffusivity. In short, turbulent flux is evalu-ated by the model through the Fick 1st and 2nd laws:Fturb = kdC/dx, dC/dtjx=cte = dFturb/dxjt=cte where x (m)accounts for the vertical dimension and k (m2 s�1) the tur-bulent diffusivity.

3. Results and discussion

Using the same climate conditions repeatedly (January2003–December 2003), the model was run for several‘‘years’’ (i.e., repetitions of the standard year) until theresults of two consecutive years were equal. This representsa ‘‘long-term steady state’’ situation where concentrationsand fluxes vary throughout the year, but not between years.Hence it ensures that model outcomes are independent of

Fig. 2. Depth-time distribution of modeled water tempera

initial values. A typical 1-year simulation takes about 2 hin a Pentium IV 500 MHz PC. Results presented here areextracted from the last year of model integration.

3.1. Hydrodynamic model results

Vertical-temporal distributions of water temperatureand eddy diffusivity, obtained from the hydrodynamicmodule, are shown in Fig. 2. They are useful to betterunderstand the fate of the POPs. Fig. 2a shows thespring-summer thermocline, typical of temperate climate,promoted by the large gradients of temperature betweensurface and deep waters. Vertical modeled diffusivities(Fig. 2b) are in the range of 0.01–0.1 m2 s�1. Higher valuesare located close to the surface and they are largely sheardriven, since the wind stress at the surface is a primary mix-ing agent, although significant convective mixing is alsodriven by the heat loss to the atmosphere during winterand at nights. Another factor that plays an important rolein mixing is the precipitation events. Diffusivities at deeperwaters are lower by one order of magnitude. The model isable to reproduce a seasonal trend: strong mixing, i.e., highvalues of vertical diffusivity, during winter and minimumvalues during the formation of the seasonal thermocline,where the vertical stratification inhibits turbulence.

3.2. Contaminants distribution: water body regions

Fig. 3 shows the temporal variability in the vertical pro-files for a low and a highly hydrophobic PCB. To note, thehydrophobicity of POPs, linked to the tendency to sorb toparticles, is one of the most determinant physicochemicalproperties determining their fate. Three regions can be dis-tinguished. Deep water does show a huge influence fromthe contaminated sediments, mainly due to sediment diffu-sion of chemicals from the interstitial waters to deepwaters. Conversely, the surface waters are extremely sensi-ble to atmospheric inputs with pulses of concentrations dueto atmospheric wet deposition. The extent to which atmo-spheric entrained contaminants mix in the water is highlyseasonal and it follows the shape of the turbulent layer,which encloses the part of the water column with higher

ture (a) and eddy diffusivity (b) in the water column.

Fig. 3. Depth-time distribution of the total concentration in the water column for PCB 28 (a) and PCB 180 (b).

446 E. Jurado et al. / Marine Pollution Bulletin 54 (2007) 441–451

eddy diffusivity coefficients (see Fig. 2). The layer in mid-depth waters has low concentrations and they are reason-ably constant all year round. The occurrence of threeregions, differs from traditional model approaches of tworegions during stratification periods in warm months andone the rest of the year, each one being well mixed inter-nally (Schwarzenbach et al., 2003; Meijer et al., 2006).Besides, the more volatile and less hydrophobic congeners(Fig. 3a) present stronger vertical and temporal concentra-tion gradients than the more hydrophobic one (Fig. 3b).

The seasonal-averaged vertical variability of all PCBcongeners in the water column is depicted in Fig. 4, whichclearly shows that variability can be 10-fold in surfacewaters while in sediments and mid-water column remainclose to a constant value. More volatile and less hydropho-bic congeners are subject to stronger seasonal trends tem-poral gradients. The high surface variability (Fig. 4a) islinked to atmospheric forcings, i.e., input of contaminantsdue to precipitation pulses, influence of higher tempera-tures during the summer, and influence of high windspeeds. During summer, volatilization losses depletes com-pounds, but higher temperatures in the water column favor

Fig. 4. Time series of the total concentration and for all PCB congeners atdifferent depths of the water column: surface (a), mid-water column (b),bottom water column (c).

an enrichment of compounds in the dissolved phase incomparison to the particulate phase. In addition, the springand autumn phytoplankton blooms, increase the particu-late-bound PCBs during those periods, noted especially inthe bottom water layer (Fig. 4c), where atmospheric influ-ence has been diluted.

3.3. Influence of forcing POP fluxes

The vertical water column boundaries are the lower partof the atmosphere and the surface mixed sediment layer(SMSL). The magnitude and variability of exchange withthose external compartments have a direct impact on theentire water column. Atmospheric-based fluxes are farmore variable than the sediments ones. Still, the magnitudeof the sediment-based fluxes is greater, influenced by thehigh POP concentrations in the sediments compared tothe ones in the water column and supported by sinkingfluxes.

Concerning POPs transfer through water surface, thewet deposition and diffusive fluxes are dominant (seeFig. 5a). The figure displays only PCB 28 but the impor-tance of wet deposition fluxes was found for all the studiedPCBs in the present exercise. The volatilization flux showsto be major for the lower chlorinated compounds. How-ever, the representation of those accumulated fluxes hidesthe intrinsic seasonality and time variability. For example,volatilization is stronger during warm periods, and wetdeposition acts like pulses, generally major from Septemberto January but still highly random. Finally, degradationand dry deposition fluxes can be assumed negligible inthe surface boundary layer.

At the other extreme of the water column, the diffusionof contaminants from the sediment interstitial waters todeep waters plays a decisive role (Fig. 5b), especially forthe low chlorinated PCBs. They exceed atmospheric inputfluxes by more than one order of magnitude and areresponsible of introducing amounts of contaminants tothe water column, compensated mainly by the sinkingfluxes. This is in part due to historical loadings, but alsodue to remineralization of organic matter in the sedimentthat induce a fugacity gradient from sediment to adjacent

Fig. 5. Accumulated yearly fluxes for PCB28 at the surface (a) and at the bottom water layer (b). In positive value are represented the input fluxes, innegative value the output fluxes.

Fig. 6. Depth-time distribution of the total net turbulent flux for PCB 28.

E. Jurado et al. / Marine Pollution Bulletin 54 (2007) 441–451 447

waters, a process that has been described elsewhere (Gobasand Maclean, 2003). Indeed, organic carbon content in sed-iments are considered to be 5 and 10-fold lower than bio-logical and physical sinking particles. Conversely, otherfluxes such as resuspension of particle-sorbed contaminantsappear not to be as important as diffusion from sediments.Nevertheless, in shallow water columns, resuspension maybe important due to wind driven turbulence. Finally, themass balance in the SMSL, shows that contaminants areintroduced from deep sediments mainly through bio-diffusion.

3.4. Internal fluxes: turbulent, sinking and degradation fluxes

The discussion of the internal contaminant fluxes in thewater column covers turbulent, sinking and degradationfluxes. They determine the extent of the forcing fluxesdescribed before. The turbulent flux between two layersacts tending to eliminate the concentration gradients andis composed in fact by two fluxes acting in parallel and withopposite signs. The sense of the net flux is determined bythe concentration gradient: it goes from the higher to thelower concentration layer. This net flux may be zero ifthe concentration distribution is completely homogeneous(dC/dx = 0). In the water column, this picture gets morecomplicated if we think that each water layer is affectedby the turbulent fluxes not from one but from two adjacentlayers. The total net turbulent flux, resulting from addingthe fluxes from the ‘‘inferior’’ and ‘‘superior’’ layers, maythen result positive when it creates an increase of theamount of chemical, and vice versa. It is represented inFig. 6 for PCB28 but the discussion covers all PCBs in con-cern. The turbulent fluxes show a high variability, espe-cially close to the surface. In deep waters the total netturbulent fluxes are almost always positive and are thehighest due to the important gradients of contaminantbetween sediments and water column. In the mid-depth,fluxes of total net turbulence are virtually negligible.Despite their complexity and extreme variability, net turbu-lent fluxes can be visualized as acting upward in the benthic

region and acting downward close to the surface, except forperiods of output of contaminants (favored by highlywindy periods, no rain, and hot periods). On the otherhand, turbulent fluxes are greater for the more volatilecompounds, since they are associated to major concentra-tion gradients.

The sinking fluxes (physical and biological), depicted inFig. 7 for a highly hydrophobic PCB, result from the sink-ing of particle-bound contaminants. They are favored bylower temperatures, by higher concentrations of particlesand in some cases by turbulent mixing. Superimposed tothe figure is shown the precipitation pulses for year 2003.Rain entrains particles and pollutants that fall in the watercolumn and leave a significant ‘‘path’’ in the observeddepth-temporal plot. Also resuspension favors the increaseof particles at high depths. For a lower chlorinated PCB asignificant decrease of those fluxes would be noted.

The individual effect of sinking and turbulent flux is dif-ficult to discern since they both depend on a same variable,the POP concentration. However, the former acts only toparticle-sorbed contaminants, in contrast to turbulent mix-ing which affects also dissolved and colloidal sorbed con-taminants. What should be kept in mind is that thedistribution of contaminants in the three water columnregions is result of the interplay of sinking and turbulentfluxes. If sinking did not exist, the contaminated

Fig. 9. Yearly accumulated fluxes for PCB28 at different depths of thewater column.

Fig. 7. Depth-time distribution of the sinking flux (physical + biological)for PCB 180. The profile of precipitation pulses is superimposed.

448 E. Jurado et al. / Marine Pollution Bulletin 54 (2007) 441–451

sediment-influenced deep waters would not find a barrier todiffuse upward and also atmospheric inputs would pene-trate less in the water column; if turbulent fluxes did notexist, contaminants introduced from the sediments wouldnot move upward through the water column. Furthermore,the influence of a major sinking in the contaminants distri-bution is noted in the decrease of absolute vertical concen-tration gradients. Among other reasons, the downwardforce of turbulent fluxes is diminished. This explains amore homogeneous mixing and as well major values inthe surface region for PCB 180 (Fig. 2b), and it is respon-sible of higher concentration values in the mid-depthwaters.

Fig. 8 shows whether sinking or turbulent flux domi-nates the vertical transport on a temporal and spatial scale.As can be seen the sinking fluxes tend to overcome turbu-lent fluxes in the poorly stratified periods and in the so-called turbulent layer. On the other hand, sinking is moreimportant for the more chlorinated PCBs. However, thisgraph masks the fact that in the benthic region the magni-tude of both fluxes is similar, their different contributionbecomes important close to the surface, where turbulentfluxes can be more than ten times the sinking ones. In themid-depths their magnitude is minimum, since the amountof contaminants is nearly zero. Due to such low values ofsinking and turbulent fluxes in this region, contaminantscoming from the atmosphere may not diffuse downwardwhen reaching the boundary.

Fig. 8. Comparison between turbulent and sinking fluxes for PCB 28 (b) andturbulent flux dominates.

Chemical and biological degradation of contaminants isgenerally not significant when compared to sinking andturbulent fluxes. Yet, for lower chlorinated compoundsand between 30 and 40 m it is dominant (see Fig. 9).

3.5. Comparison of model results to experimental trends

To assess the adequacy of the model in predictingtrends, estimated concentrations and sinking fluxes havebeen compared preferentially with measurements per-formed in the central Mediterranean. A detailed validationof the results obtained presents the difficulty that profilesand time series of PCB concentrations in the Mediterra-nean are scarce, if not nonexistent (Tolosa et al., 1997).

PCB 180 (a). In black absolute sinking flux dominates, in white absolute

E. Jurado et al. / Marine Pollution Bulletin 54 (2007) 441–451 449

Concentrations found here are among the rangesdefined as background concentration defined by OSPARcommission (OSPAR, 2004) and are of the same order ofmagnitude to levels detected in open Mediterranean waters(Tolosa et al., 1997) and in different stations of the westernMediterranean continental shelf (Dachs et al., 1997a,b).However, they are about one order of magnitude lowerthan the measured in the north-western coastal Mediterra-nean (Garcıa-Flor et al., 2005) and in Adriatic Sea samplescollected between 1976 and 1987 in the Rijeka Bay from(Picer and Picer, 1992; Picer, 2000) or in Venice (Moretet al., 2005), which are recognized as quite polluted zones.This is due to the fact that these measures are found closeto coastal areas, under the influence of human activities,instead atmospheric and sediments inputs used here aremore representative of pristine open-sea regions.

Measured concentrations at different depths show low-est values in deepest samples, highest values close to thesurface (Dachs et al., 1997a; Schulz-Bull et al., 1998; Gar-cıa-Flor et al., 2005). Such vertical profiles agree with ourresults, in which surface waters (in this case 0–20 m) areenriched in PCBs by a factor of five in comparison todeep waters. Furthermore, temporal variability detectedhere, function of the depth, is consistent to the measuredtrends: raised surface short-term variability (Garcıa-Floret al., 2005) and low variability close to the sediments,due to the fact that the capacity of sediments to accumu-late POPs is so large (Dalla Valle et al., 2005). However,the bottom water column concentrations have been foundto vary in shallower aquatic bodies than the studied inthis modeling exercise and mainly related to storms thatoriginate high resuspension events (Hornbuckle et al.,2004).

Mass settling fluxes determined by sediment traps at dif-ferent depths in the Western Mediterranean such as theAlboran Sea (Dachs et al., 1997a), the Lacaze-DuthiersCanyon (Fowler et al., 1990) and Lake Superior (Bakeret al., 1991) demonstrate that particulate PCB fluxes varyboth temporally and vertically. Seasonally, they fluctuateby one order of magnitude, co-varying with the generalcycles of primary productivity in surface waters and withthe maximum flux occurring in non-stratified periods.Besides, the depth profile presents maximums close to thesurface layers, a posterior decrease with depth and finallyan increase close to the bottom. Finally, it has been high-lighted in various articles (Baker et al., 1991; Morrisonet al., 2000; Bogdan et al., 2002; Hornbuckle et al., 2004)the importance of physical turbulence generating resus-pended particles which cause mass settling fluxes toincrease. Those observations agree with temporal and ver-tical trends found in sinking fluxes in our modeling exer-cise. However, the amount of benthic fluxes seems to beunderestimated in the model. This could be attributed tothe intrinsic limitations of using a 1D model, when extra-polating to 3D lateral advective transport would originatea major amount of resuspended particles and highersinking fluxes would be expected close to the bottom.

4. Conclusions

In order to evaluate the role of turbulent mixing a novel1D coupled hydrodynamic-contaminant model has beendeveloped and applied to a 50 m water column. It is thefirst time that turbulent fluxes and its interplay with otherprocesses is explicitly assessed. Results are consistent withmeasured vertical and temporal PCB profiles. Further-more, on a short-term basis, it approximates better thePOPs associated variability in surface and enrichment indeep waters than the classical approach of two well-mixedbox model approach for stratified periods and one for nonstratified periods.

Turbulent vertical diffusion is essential to explain thetemporal variability of POP concentrations and the extentto which atmospheric and sediment inputs mix in the watercolumn. It acts in parallel with other internal water fluxes,i.e. degradation and sinking fluxes, the combined effectyielding a surface enriched – mid-depth depleted – benthic

layer enriched distribution. Deep water show a huge influ-ence from the contaminated sediments, mainly due tochemical diffusion from the sediment interstitial waters todeep waters. Conversely, the surface waters are extremelysensible to atmospheric inputs with pulses of concentra-tions due to atmospheric wet deposition. The extent towhich atmospheric entrained contaminants mix in thewater is highly seasonal and it follows the shape of theturbulent layer, deeper in autumn–winter, shallower insummer. The layer in the mid-depth water has lowconcentrations and they are reasonably constant all yearround.

Turbulent fluxes and sinking of particle-bound contam-inants overcome the degradation fluxes. As a general trend,sinking has been proven the dominant flux for the morehydrophobic compounds and especially in the non-strati-fied periods. Sinking favors the homogeneous contaminantdistribution in the surface region and it is responsible ofhigher concentration values in the water column notaffected by atmospheric or contaminant inputs (the bottomwaters).

Finally, model results show the necessity to reconsiderthe existing sampling strategies, accounting for the impor-tant vertical gradients close to the sediments, and the sea-sonal variations and short-term variability in the surface(atmospheric) influenced region.

Acknowledgements

This work was supported by the sixth framework pro-gramme through the project Thresholds of EnvironmentalSustainability (European Commission FP6, SUST-DEV,IP Project 003933-2). Meteorological data used in thisstudy were acquired from the ECMWF 40 Years Re-Anal-ysis (http://www.ecmwf.int/), the remote sensing data wereacquired as part of the SeaWIFS Project (http://sea-wifs.gsfc.nasa.gov/SEAWIFS.html).

450 E. Jurado et al. / Marine Pollution Bulletin 54 (2007) 441–451

Supplementary data

Further details on methodology to estimate contami-nants speciation and related processes are found in Appen-dix A and B, respectively. The modeling of the TotalSuspended Matter (TSM) is given in Appendix C. Supple-mentary data associated with this article can be found, inthe online version, at doi:10.1016/j.marpolbul.2006.11.028.

References

Baines, S.B., Pace, M.L., Karl, D.M., 1994. Why does the relationshipbetween sinking flux and planktonic primary production differ betweenlakes and oceans? Limnology Oceanography 39 (2), 213–226.

Baker, J.E., Eisenreich, S.J., Eadie, B.J., 1991. Sediment trap fluxes andbenthic recycling of organic carbon, polycyclic aromatic hydrocar-bons, and polychlorobiphenyl congeners in Lake Superior. Environ-mental Science and Technology 25, 500–509.

Berglund, O., Larsson, P., Ewald, G., Okla, L., 2001. Influence of trophicstatus on PCB distribution in lake sediments and biota. EnvironmentalPollution 113 (2), 199–210.

Bogdan, J.J., Budd, J.W., Eadie, B.J., Hornbuckle, K.C., 2002. The effectof a large resuspension event in southern Lake Michigan on the short-term cycling of organic contaminants. Journal of Great LakesResearch 28 (3), 338–351.

Carafa, R., Marinov, D., Dueri, S., Wollgast, J., Ligthart, J., Canuti, E.,Viaroli, P., Zaldıvar, J.M., 2006. A 3D hydrodynamic fate andtransport model for herbicides in Sacca di Goro coastal lagoon(Northern Adriatic). Marine Pollution Bulletin 52 (10), 1231–1248.

Dachs, J., Bayona, J.M., Albaiges, J., 1997a. Spatial distribution, verticalprofiles and budget of organochlorine compounds in Western Med-iterranean seawater. Marine Chemistry 57 (3–4), 313–324.

Dachs, J., Bayona, J.M., Raoux, C., Albaiges, J., 1997b. Spatial, verticaldistribution and budget of polycyclic aromatic hydrocarbons inWestern Mediterranean seawater. Environmental Science and Tech-nology 31 (3), 682–688.

Dachs, J., Bayona, J.M., Venugopalan, I., Albaiges, J., 1999a. Monsoon-driven vertical fluxes of organic pollutants in the western Arabian sea.Environmental Science and Technology 33 (22), 3949–3956.

Dachs, J., Eisenreich, S.J., Baker, J.E., Ko, F.-C., Jeremiason, J.D.,1999b. Coupling of phytoplankton uptake and air–water exchange ofpersistent organic pollutants. Environmental Science and Technology33 (20), 3653–3660.

Dachs, J., Lohmann, R., Ockenden, W.A., Mejanelle, L., Eisenreich, S.J.,Jones, K.C., 2002. Oceanic biogeochemical controls on globaldynamics of persistent organic pollutants. Environmental Scienceand Technology 36 (20), 4229–4237.

Dalla Valle, M., Marcomini, A., Sfriso, A., Sweetman, A.J., Jones, K.C.,2003. Estimation of PCDD/F distribution and fluxes in the VeniceLagoon, Italy: combining measurement and modelling approaches.Chemosphere 51, 603–616.

Dalla Valle, M., Jurado, E., Dachs, J., Sweetman, A.J., Jones, K.C., 2005.The maximum reservoir capacity of soils for persistent organicpollutants: implications for global cycling. Environmental Pollution134 (1), 153–164.

Del Vento, S., Dachs, J., 2002. Prediction of uptake dynamics of persistentorganic pollutants by bacteria and phytoplankton. EnvironmentalToxicology and Chemistry 21 (10), 2017–2099.

Dueri, S., Zaldıvar, J.-M., Olivella, A., 2005. Dynamic modelling of thefate of DDT in Lake Maggiore: Preliminary results. Ispra, EuropeanCommission, Joint Research Centre: EUR 21663 EN: 33.

Fowler, S.W., Ballestra, S., Villeneuve, J.P., 1990. Flux of transuraniumnuclides and chlorinated hydrocarbons in the northwestern Mediter-ranean. Continental Shelf Research 10, 1005–1023.

Garcıa-Flor, N., Guitart, C., Balos, M., Dachs, J., Bayona, J.M.,Albaiges, J., 2005. Enrichment of organochlorine contaminants inthe sea surface microlayer: an organic carbon-driven process. MarineChemistry 96, 331–345.

Gobas, F.A.P.C., Maclean, L.G., 2003. Sediment–water distribution oforganic contaminants in aquatic ecosystems: the role of organic carbonmineralization. Environmental Science and Technology 37, 735–741.

Gustafsson, O., Gschwend, P.M., Buesseler, K.O., 1997. Settling removalrates of PCBs into the northwestern Atlantic derived from 238U–234Th desquilibria. Environmental Science and Technology 31 (12),3544–3550.

Hornbuckle, K.C., Smith, G.L., Miller, S.M., 2004. Magnitude and originof polychlorinated biphenyl (PCB) and dichlorodiphenyltrichloroeth-ane (DDT) compounds resuspended in southern Lake Michigan.Journal of Geophysical Research 109, C05017. doi:10.1029/2003JC00191.

Ilyna, T., Pohlmann, T., Lammel, G., Sundermann, J., 2006. A fate oftransport ocean model for persistent organic pollutants and itsapplication to the North Sea. Journal of Marine Systems 63 (1–2),1–19.

Jones, K.C., de Voogt, P., 1999. Persistent organic pollutants (POPs): stateof the science. Environmental Pollution 100 (1–3), 209–221.

Jurado, E., Jaward, F.M., Lohmann, R., Jones, K.C., Simo, R., Dachs, J.,2004. Amospheric dry deposition of persistent organic pollutants tothe Atlantic and inferences for the global oceans. EnvironmentalScience and Technology 38 (21), 5505–5513.

Jurado, E., Jaward, F.M., Lohmann, R., Jones, K.C., Simo, R., Dachs, J.,2005. Wet deposition of persistent organic pollutants to the globaloceans. Environmental Science and Technology 39 (8), 2426–2435.

Ko, F.-C., Sanford, L.P., Baker, J.E., 2003. Internal recycling of particlereactive organic chemicals in the Chesapeake Bay water column.Marine Chemistry 81, 163–176.

Lohmann, R., Jurado, E., Pilson, M.E.Q., Dachs, J., 2006. Oceanic deepwater formation as a sink of persistent organic pollutants. GeophysicalResearch Letters 33, L12607. doi:10.1029/2006GL02595.

Luyten, P.J., Jones, J.E., Proctor, R., Tabor, A., Tett, P., Wild-Allen, K.,1999. COHERENS-a coupled hydrodynamical–ecological model forregional and shelf seas: user documentation. MUMM Report, Man-agement Unit of the Mathematicals Models of the North Sea: 911 pp.

Luyten, P.J., Jones, J.E., Proctor, R., 2003. A numerical study of the longand short term temperature variability and thermal circulation in thenorth sea. Journal of Physical Oceanography 33, 37–56.

Mackay, D., 2001. Multimedia Environmental Models. In: The FugacityApproach. Lewis Publishers, Florida.

Mandalakis, M., Stephanou, E.G., 2002. Study of atmospheric PCBconcentrations over the eastern Mediterranean Sea. Journal ofGeophysical Research 107 (D23), 4716–4726.

Mandalakis, M., Stephanou, E.G., 2004. Wet deposition of polychlori-nated biphenyls in the eastern Mediterranean. Environmental Scienceand Technology 38 (11), 3011–3018.

Marinov, D., Norro, A., Zaldıvar, J.-M., 2006. Application of COHE-RENS model for hydrodynamic investigation of Sacca di Gorocoastal lagoon (Italian Adriatic Sea shore). Ecological Modelling 193,52–68.

Meijer, S.N., Dachs, J., Fernandez, P., Camarero, L., Catalan, J., DelVento, S., Van Drooge, B.L., Jurado, E., Grimalt, J.O., 2006.Modelling the dynamic air–water–sediment coupled fluxes and occur-rence of polychlorinated biphenyls in a high altitude lake. Environ-mental Pollution 140 (3), 546–560.

Moret, I., Piazza, R., Benedetti, M., Gambaro, A., Barbante, C., Cescon,P., 2001. Determination of polychlorobiphenyls in Venice Lagoonsediments. Chemosphere 43 (4–7), 559–565.

Moret, I., Gambaro, A., Piazza, R., Ferrari, S., Manodori, L., 2005.Determination of polychlorobiphenyl congeners (PCBs) in the sur-face water of the Venice lagoon. Marine Pollution Bulletin 50 (2), 167–174.

Morrison, H.A., Whittle, D.M., Haffner, G.D., 2000. The relativeimportance of species invasions and sediment disturbance in regulating

E. Jurado et al. / Marine Pollution Bulletin 54 (2007) 441–451 451

chemical dynamics in western Lake Erie. Ecological Modelling 125,279–294.

OSPAR, C., 2004. OSPAR/ICES Workshop on the evaluation and updateof B/RCs and EACs, The Hague.

Palm, A., Cousins, I., Gustafsson, O., Axelman, J., Grunder, K., Broman,D., Brorstrom-Lunden, E., 2004. Evaluation of sequentially-coupledPOP fluxes estimated from simultaneous measurements in multiplecompartments of an air–water–sediment system. Environmental Pol-lution 128 (1–2), 85–97.

Picer, M., 2000. DDTs and PCBs in the Adriatic Sea. Croatica ChemicalActa 73 (1), 123–186.

Picer, N., Picer, M., 1992. Inflow, levels and the fate of some persistentchlorinated hydrocarbons in the Rijeka Bay area of the Adriatic Sea.Water Research 26 (7), 899–909.

Scheringer, M., Wegmann, F., Fenner, K., Hungerbuhler, K., 2000.Investigation of the cold condensation of persistent organic pollutantswith a global multimedia fate model. Environmental Science andTechnology 34, 1842–1850.

Scheringer, M., Stroebe, M., Wania, F., Wegmann, F., Hungerbuhler, K.,2004. The effect of export to the deep sea on the long-range transportpotential of persistent organic pollutants. Environmental Science andPollution Research 11 (1), 41–48.

Schulz-Bull, D.E., Petrick, G., Duinker, J.C., 1988. Chlorinated biphenylsin North Atlantic surface and deep water. Marine Pollution Bulletin 19(10), 526–531.

Schulz-Bull, D.E., Petrick, G., Bruhn, R., Duinker, J.C., 1998. Chloro-biphenyls (PCB) and PAHs in water masses of the northern NorthAtlantic. Marine Chemistry 61 (1–2), 101–114.

Schwarzenbach, R.P., Gschwend, P.M., Imboden, D.M., 2003. Environ-mental Organic Chemistry. Wiley-Interscience, New Jersey.

Sobek, A., Gustafsson, O., Hajdu, S., Larsson, U., 2004. Particle-waterpartitioning of PCBs in the photic zone: a 25-month study in theopen Baltic sea. Environmental Science and Technology 38 (5), 1375–1382.

Swackhamer, D.L., Skoglund, R.S., 1991. The role of phytoplankton inthe partitioning of hydrophobic organic contaminants in water. In:Baker, R. (Ed.), . In: Organic Substances And Sediments In Water,vol. II. Lewis publishers, pp. 91–105.

Tolosa, I., Readman, J.W., Fowler, S.W., Villeneuve, J.P., Dachs, J.,Bayona, J.M., Albaiges, J., 1997. PCBs in the western Mediterranean.Temporal trends and mass balance assessment. Deep-Sea Research II44 (3–4), 907–928.

Umgiesser, G., Luyten, P.J., Carniel, S., 2002. Exploring the thermal cycleof the Northern North Sea area using a 3-D circulation model: theexample of PROVESS NNS station. Journal of Sea Research 48, 271–286.

Wania, F., Daly, G.L., 2002. Estimating the contribution of degradationin air and deposition to the deep sea to the global loss of PCBs.Atmospheric Environment 36 (36–37), 5581–5593.

Wania, F., Mackay, D., 1996. Tracking the distribution of persistentorganic pollutants. Environmental Science and Technology 30 (9),390A–396A.

Zavatarelli, M., Raicich, F., Bregant, D., Russo, A., Artegiani, A., 1998.Climatological biogeochemical characteristics of the Adriatic Sea.Journal of Marine Systems 18 (1–3), 227–263.