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Application of a mass-balance model to predict PCDD/F turnover in a Balticcoastal estuary

O. Magnus Karlsson a,b,*, J. Mikael Malmaeus a, Sarah Josefsson c, Karin Wiberg c, Lars Håkanson b

a IVL Swedish Environmental Research Institute, P.O. Box 210 60, SE-100 31 Stockholm, SwedenbDepartment of Earth Sciences, Uppsala University, Villavägen 16, SE-752 36 Uppsala, SwedencDepartment of Chemistry, Umeå University, SE-901 87 Umeå, Sweden

a r t i c l e i n f o

Article history:Received 3 December 2009Accepted 1 April 2010Available online 13 April 2010

Keywords:PCDD/Fdioxinmass-balancemodelresuspensionsedimentationBaltic Sea

a b s t r a c t

A dynamical, process-based mass-balance model was applied to quantify the transports of poly-chlorinated-p-dibenzodioxins and dibenzofurans (PCDD/Fs) to, within and from the Kallrigafjärden Bay,a coastal estuary in the Baltic Sea, and to predict the PCDD/F levels in the water and sediments of the bay.Before the modelling, a one-year sampling programme was implemented in 2007e2008 to measure theflows of PCDD/Fs in tributaries entering the system, the fluxes from the adjacent sea and the levels inwater, sediment and fish within the estuary. The collected data set was used as input data to the modeland for validation purposes.

The model was originally developed and tested for suspended particulate matter and phosphorus inBaltic coastal areas. In this work, it was run monthly without any tuning of the original model variables.The simulation results compared favourably with the field measurements of levels of 15 PCDD/Fcongeners in the water and sediments of the bay. The dominating fluxes of PCDD/Fs were the exchangewith the adjacent sea, followed by riverine input, atmospheric deposition and sediment burial.

Although the sediment-water exchange was of minor importance for the overall mass-balance due tothe rapid water turnover and limited areas favourable for the long-term deposition of fine sedimentarymatter, sensitivity analyses indicated that the model’s predictive capability was improved with about 5%by taking the sediment burial and release into account.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Polychlorinated dibenzo-p-dioxin and dibenzofuran (PCDD/F)contamination of aquatic ecosystems is an environmental problemof global concern (WHO,1989). For example, high levels of PCDD/Fshave been found in shellfish and fish from areas in South Viet Nam(Baughman and Meselson, 1973), in fish and bird eggs from theGreat Lakes, North America (Norstrom et al., 1982; Stalling et al.,1983) in shellfish, crabs and fish from the Tokyo Bay, Japan(Sakurai et al., 2000), in mussels, crabs and fish from the Grenlandfjords, Norway (Knutzen et al., 2003) and in crabs, fish and sealsfrom the Baltic Sea (Rappe et al., 1981, 1987; Kiviranta et al., 2003).

The environmental status of the Baltic Sea, regarding organicpollutants, has in general improved significantly compared withthe situation three or four decades ago (Olsson et al., 2004).However, unlike other classical organic pollutants, the decline in

the levels of PCDD/Fs in biota from the Baltic Sea seems to haveabated (Kiviranta et al., 2003). This is an unsatisfactory situationsince PCDD/Fs are harmful to many aquatic organisms and due totheir bioaccumulative and biomagnifying characteristic, they alsoconstitute a threat to predatory animals and also to humans (WHO,1989). Long-term human exposure to PCDD/Fs may have immu-nological effects and likely carcinogenic or reproductive effects(WHO, 1989, 1997). Levels of PCDD/Fs in fatty fish in the Baltic Seafrequently exceed established European Union guideline levels forhuman consumption, especially in one of the sub-basins, theBothnian Sea (Kiviranta et al., 2003; Vuorinen et al., 2004; Bignertet al., 2007). The reasons why fish from this particular basin havethe highest levels are unclear and have initiated intensive research(Olsson et al., 2005; Parmanne et al., 2006; Cornelissen et al., 2008;Armitage et al., 2009; Sundqvist et al., 2009a,b).

PCDD/Fs in the Baltic Sea originate from riverine inputs, directemissions, sediment-water interactions and atmospheric de-position. Attempts to create a PCDD/F budget for the Baltic Sea(Assmuth and Jalonen, 2005; Armitage et al., 2009) have shownthat on a large scale the atmospheric deposition is the mostimportant external source of PCDD/Fs to the Baltic Sea. The

* Corresponding author. IVL Swedish Environmental Research Institute, P.O. Box210 60, SE-100 31 Stockholm, Sweden.

E-mail address: magnus.karlsson@ivl.se (O.M. Karlsson).

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Estuarine, Coastal and Shelf Science

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Estuarine, Coastal and Shelf Science 88 (2010) 209e218

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quantification of the net atmospheric deposition, i.e. the net flux ofthe wet and dry deposition and the gaseous exchange, is usuallybased on modelling efforts rather than direct measurements(Assmuth and Jalonen, 2005; Gusev, 2008; Armitage et al., 2009).

While the datasets for PCDD/F levels in Baltic Sea sediments(Verta et al., 2007; Sundqvist et al., 2009a) and fish (Kiviranta et al.,2003; Bignert et al., 2007; Karl and Ruoff, 2007) are fairly extensive,reported Baltic water concentration data are scarce (Cornelissenet al., 2008). One reason for this is the low concentrations atwhich PCDD/Fs normally occur in natural waters, which makes ittechnically difficult and expensive to analyse them. One approachto circumvent this is to pump large volumes of water through filtersand polymers with capacity to retain PCCD/Fs. This method waschosen by Broman et al. (1991) who studied the PCDD/F concen-trations in seawater from different parts of the open Baltic Sea.

To improve the knowledge and understanding of backgroundlevels, fluxes and dynamics of PCDD/Fs in coastal areas, a one yearsampling programme on PCDD/F levels in water, sediment andstationary fish was implemented in the Kallrigafjärden Bay, a semi-

enclosed coastal estuary along the south-western coast of theBothnian Sea (Karlsson et al., 2009). Moreover, budgets for 17 toxiccongeners (common chemical structure but with individualnumbers and positions of chlorine atoms) of dioxins and furanswere established (Malmaeus et al., 2009). In this paper, we utiliseddata from the sampling programme in the Kallrigafjärden Bay withthe specific aim to develop and test a mass-balance model for thePCDD/F turnover in the study area. A more general aim was toimprove the understanding of processes important for PCDD/Fturnover in aquatic systems on an ecosystem level.

2. Materials and methods

2.1. Study area

The study area, Kallrigafjärden Bay is situated along the Swedishcoast of southern Bothnian Sea, a sub-basin of the Baltic Sea (Fig. 1)and it is one of relatively few enclosed bays in the region. Physicaland chemical characteristics of the bay are summarised in Table 1.

Fig. 1. Map of the study area including sampling stations. Red line demarks operational border between the Kallrigafjärden Bay and the adjacent sea. (For interpretation of thereferences to colour in this figure legend, the reader is referred to the web version of this article.)

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The salinity in the Kallrigafjärden Bay during the one-year samplingperiod 2007e2008 averaged 3.7with lower values (2.7) during highflow events in the tributaries (see further Karlsson et al., 2009). Inthe adjacent brackish sea, the salinity averaged 4.4 at the samplingstation and further out a typical value is 5e5.5 (Kullenberg, 1981).The tidal amplitude is low in the Baltic Sea, e1 cm (Keruss andSennikovs, 1999) due to its size and enclosedness. The watertemperature in coastal areas of the Bothnian Sea usually reaches upto ca. 20� C during the summer whereas ice normally covers thecoastal waters between January and March. The ice season during2008 was, however, unusually short with ice covering the Kallri-gafjärden Bay only during a few weeks in February.

Due to the land subduction during the last glaciation, thebottom areas of the Bothnian Sea still rise with 5e9 mm yr�1.The rise is highest in the northern part of the basin. The value of theland rise in the Kallrigafjärden Bay is 6 mm yr�1 (Håkanson andBryhn, 2008). As the coast is flat, shoreline changes related toland uplift are marked. The large-scale circulation pattern in theBaltic Sea creates a southward-flowing net current along theSwedish coast (Kullenberg, 1981), and hence there is a transport ofwater, suspended and dissolved matter with origin in the BothnianSea into the study area. The population density around the Kallri-gafjärden Bay is low. There are no known point sources of PCDD/Fsin the vicinity of the bay. Two streams discharge in the bay: theForsmarksån River and the Olandsån River. The Forsmarksån Riverdrains a 375 km2 large catchment area dominated by forested land.The average discharge is 3 m3 s�1, and the runoff is rich in humicmatter, strongly coloured and has a pH-value close to neutral andrelatively low nutrient concentrations. The Olandsån River, on theother hand, transports nutrient- and particle-rich water with anaverage discharge of 6 m3 s�1. The catchment area, 886 km2, isdominated by forested land, but the proportion of farmland isrelatively high (27%). The municipality Gimo, with 3000 inhabi-tants, discharges its effluent water into the river approximately30 km upstream of the river mouth.

2.2. Utilised data

Field measurements were carried out from September 2007 toAugust 2008. Water concentrations of PCDD/Fs were measured atfour stations, including the two tributaries, the ÖregrundsgrepenBight and the Kallrigafjärden Bay (Fig. 1). A specially designedhigh-volume filtration system described in Broman et al. (1991)was used, allowing separation of the particulate and dissolved

fractions of each PCDD/F congener. The sampling methodology isfurther described in Karlsson et al. (2009). Surface sediment(upper 0e5 cm layer) samples were collected in September 2007at four locations within the Kallrigafjärden Bay (Fig. 1). Three ofthe samples (AeC) were collected from areas that previously hadbeen classified as accumulation areas (A-areas, Håkanson et al.,1984), i.e. areas with long-term deposition of cohesive finematter. The water content of the surface sediment wase85% andorganic content measured as loss on ignition (LOI) was e15% oftotal dry weight (dry wt). These values are typical for A-areas andalso implicate a mud content ofe85% of total dry wt (Håkansonand Jansson, 1983). At the shallow sampling site D (waterdepth 2 m) we found a mixture of coarse detritus, silt and claytypical for erosion and transport areas (ET-areas). We alsomeasured the PCDD/F contents in pooled muscle homogenatesfrom 10 individuals (15e20 cm length) of perch (Perca fluviatilis),a non-migratory and lean fish species. Fish were caught in theautumn of 2007 and the spring of 2008 in the Kallrigafjärden Bayand in a reference coastal area locatede30 km north of the Kall-rigafjärden Bay and without riverine inflow. All samples wereprepared and analyzed according to a tested method based onSwedish standard SS-EN 1948:1e3 (Karlsson et al., 2009;Sundqvist et al., 2009a). Levels of the 17 PCDD/F congenerswith 2378-subsitution were determined, but since the concen-trations of 234678-HxCDF and 123789-HxCDF in several caseswere below the detection limit, these congeners were omittedfrom the modelling efforts. In the following, the total level of theremaining 15 PCDD/Fs is referred to as

PPCDD/F15. The input

data and results (e.g. predicted concentrations, mass flows) weresummed after adjusting the values by the appropriate toxicequivalency factors (WHO-TEFs, Van den Berg et al., 2006) to give2,3,7,8-TCDD toxicity equivalents (TEQ).

Flowmeasurement data for the tributaries were supplied by theSwedish Meteorological and Hydrological Institute (SMHI) froma national river-monitoring programme. Detailed informationabout the water exchange between the Kallrigafjärden Bay and theÖregrundsgrepen Bight during our sampling period was not avail-able. However, based on salinity measurements, we could estimatethe water inflow from the sea to be 20 m3 s�1. The water retentiontime has previously been measured to average 8 days during baseflow conditions in the tributaries (Håkanson et al., 1984), which isequivalent toe19 m3 s�1 in water exchange.

Literature values of atmospheric deposition fluxes were used.One study of atmospheric deposition of PCDD/Fs sampled at theSwedish west coast (Wiberg et al., 2009) reports values between0.5 and 1.3 pg TEQ m�2 d�1. Another study estimated the PCDD/Fdeposition over the Baltic Sea to 1.1 pg TEQ m�2 d�1 during thewintermonths (Wiberg et al., 2009). The latter study also presentedmodel estimates of annual deposition in the Bothnian Sea corre-sponding to 0.7 pg TEQ m�2 d�1, including wet and dry depositionand gaseous exchange. We hence assume that the net atmosphericdeposition of PCDD/F in our study area was 0.7 pg TEQ m�2 d�1.Based on a general ambient air congener pattern from a compila-tion of 26worldwide air measurements (Lohmann and Jones,1998),individual contributions of 17 congeners to the atmosphericdeposition were estimated.

2.3. The model

We used a general process-based dynamic modelling approachoriginally derived and tested in Baltic coastal areas for suspendedparticulate matter (SPM) (Håkanson et al., 2004). It has also beenapplied to total phosphorus (TP) (Håkanson and Eklund, 2007). Themodel structure, equations and numerical methods for solvingthe first-order ordinary differential equations (ODE) that arise from

Table 1Physical and chemical characteristics of the Kallrigafjärden Bay. Data from Karlssonet al. (2009)

Average depth (m) 2.1Maximum depth (m) 10Surface area (km2) 6.4Section area opening (m2) 3200Drainage area (km2) 1276Average tributary inflow (m3 s�1) 9Average water retention time (days) 8Average proportion of accumulation areas (%) 6Salinity 3.7Secchi depth (m) 2.5Colour (mg Pt l�1) 18SPM concentration (mg l�1) 5TOC concentration (mg l�1) 10Tot-P concentration (mg l�1) 16Tot-N concentration (mg l�1) 400Chl-a concentration (mg l�1) 3.4Average proportion of accumulation (A) areas (%) 6Water content surface sediments A-areas (%) 84Loss of ignition surface sediments A-areas (% dw) 14

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the mass-balance are described in detail in Håkanson and Bryhn(2008). An outline of the model applied for the KallrigafjärdenBay is given in Fig. 2. The water volume is separated at the depth ofthe theoretical average wave base into two compartments: surfacewater and deep water. In the shallow Kallrigafjärden Bay, the deep-water volume is very small. The model was run on a monthly basisassuming that turbulent diffusion obliterates concentration gradi-ents within each compartment during each time step and hencethe compartments are treated as completely mixed. The transferbetween the particulate and the dissolved phase of PCDD/Fs isdone by means of a partitioning coefficient (the particulate frac-tion, PF). Ideally, the PF-value should be determined from a generalvalidated sub-model taking into account several processes, e.g.resuspension and biouptake. Such a model exists for e.g. phos-phorus (Håkanson and Bryhn, 2008), but not for PCDD/Fs. Onereason for that is that dioxin analyses are extremely expensive andto develop a sub-model for PF would require a comprehensive dataset that was not available at this stage. Therefore, the PF-value foreach PCDD/F congener was determined from empirical data fromthe sampling programme in the Kallrigafjärden Bay (Karlsson et al.,2009). This can be seen as a first step in developing a more generalsub-model for the PF-value for PCDD/Fs.

In terms of sediment dynamics, the study area is subdivided intoET- and A-areas. This can be modelled by calculating the depth ofthe theoretical wave base (the maximum depth at which wind/wave-driven turbulence can cause resuspension) and the arealextent of each compartment from hypsographic data. However, inthis actual case, we had empirical data showing that A-areas, wherefine sediment accumulate in the long term, constitute only about6% of the total area (Håkanson et al., 1984). To calculate the influx ofPCDD/Fs to the Kallrigafjärden Bay from the streams and the sea,the water flows were multiplied with the concentrations of eachcongener in the different water bodies. For missing data points,concentrations were interpolated linearly. The calculated values ofinflows for respective PCDD/F congener from rivers, waterexchange with surrounding sea and atmospheric deposition arepresented in Malmaeus et al. (2009).

The following section briefly describes the equations related tosedimentological processes. A more comprehensive exposition ofthese equations is given in Håkanson and Bryhn (2008).

2.3.1. SedimentationSedimentation of particulate PCDD/Fs from the surface water to

the deeper layer, and from the deep-water layer to the sediments, isrelated to a sedimentation rate, which is defined from the settling

velocity of the suspended particles (v in m month�1) and the meandepth (Dm in m). Basically, sedimentation (FSED in mg month�1) isgiven by:

FSED ¼ MWðv=DmÞPF (1)

whereMW (mg) is the total amount in thewater compartments andPF is the particulate fraction of PCDD/Fs in the water. The PF-valuesfor respective PCDD/F congener based on our sampling programmeare presented in Karlsson et al. (2009). The settling velocity (v) isdetermined from following equation:

v ¼ vdefYSPMYsalYTcrit (2)

where vdef ¼ a default-settling velocity (6 m month�1), YSPM ¼ adimensionless moderator expressing how SPM concentrations inwater influence aggregation and sedimentation of particles, Ysal ¼ adimensionless moderator expressing how the salinity influencesaggregation and sedimentation of particles and YTcrit ¼ a dimen-sionless moderator expressing temperature criteria for how the icewould influence the turbulence of the system and hence alsosedimentation of particles. The dimensionlessmoderator technique(Håkanson and Peters, 1995) is a simple approach that, instead ofbuilding a large sub-model, uses empirical data to quantify howimportant rates and processes depend on environmental factors. Adimensionless moderator (Yx) has the following general definition:

Yx ¼ ð1þ ampðXact=Xnorm � 1ÞÞ (3)

where the amplitude value (amp) quantifies how changes in actualvalues (Xact) relative to a normal reference value (Xnorm) willinfluence the variable that YX is operating on over a given range. Ifthe actual value in a given system at a given time is equal to thenorm-value, i.e. Xact ¼ Xnorm, then Yx ¼ 1. The dimensionlessmoderators applied in this work are developed from empirical datacovering a large number of areas in the Baltic Sea (Håkanson andEklund, 2007; Håkanson and Bryhn, 2008) and hence they arenot site-specific.

2.3.2. ResuspensionBy definition, the materials settling on ET-areas will not stay

permanently where they were deposited but will be resuspended,mainly by wind/wave activity. If the age of the material (TET) is setto a very long period, e.g. 10 years, these areas will function asaccumulation areas; if, on the other hand, the age is set to 1 week orless, they will act as erosion areas. Here the default value for TET isset to 1 month.

Fig. 2. Outline of the model, ET-sediments represent erosion and transport areas where settling fine matter, by time will be resuspended. A-sediments represent accumulation areaswith a net deposition of fine matter.

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Resuspension back to surface water from ET-areas, FETSW(mg month�1), i.e., mostly wind/wave-driven advective fluxes, isgiven by:

FETSW ¼ METRresð1� Dm=DmaxÞ (4)

whereMET (mg) is the amount of PCDD/Fs in the ET-sediments, Rresis the resuspension rate (month�1), Dm is the average water depthand Dmax the maximum water depth). Rres is calculated from thefollowing equation:

Rres ¼ 1=AgeETYTET (5)

where AgeET, as mentioned, has a default value of 1 month and YTETis a dimensionless moderator expressing how salinity differencesbetween the sea and the coastal area influences the age of ET-sediments. If the salinity within the coastal area has the same valueas in the adjacent sea, YTET has the value 1. However, in estuarieswhere the salinity is lowered due to the fresh water input, thesedimentological processes are affected and a so-called zone ofmaximum turbidity (Bowden, 1984) is developed where both thesedimentation and the average age of ET-sediments increase.

Resuspension from ET-areas to deepwater below the theoreticalwave base areas, FETDW (mg month�1), is given by:

FETDW ¼ METRresðDm=DmaxÞ (6)

Note thatDm/Dmax is used as a distribution coefficient to regulatehowmuch of the resuspendedmaterial fromET-areas that will go tothe surface water (Eq. (5)) or to the deep water compartment (Eq.(6)). If thebasin isU-shaped,Dm/Dmax is about 1 (i.e.,DmaxzDm) andall resuspended matter from ET-areas will flow to the deeper areas.If, on the other hand, the basin is shallow and Dm/Dmax small, mostresuspended matter will go to the surface water compartment.

2.3.3. BurialBurial (Fbur) is the transport of PCDD/Fs from surficial A-sedi-

ments to sediment layers deeper than 10 cm. It is given by:

Fbur ¼ MA=AgeA (7)

where MA is the mass (mg) of PCDD/Fs in the given sedimentcompartment. The dwell time of PCDD/Fs in the A-compartment(AgeA in months) is calculated from the following algorithm:

AgeA ¼Bioturbation factor$Thickness of active layer$

ð1�Water content=100Þ$Bulk density$10�6=

ðGross sedimentation$30Þ ð8ÞThe bioturbation factor has the numerical value 1 in areas where

oxygen levels are sufficient for the development of a macro-zoobenthos fauna (>20% oxygen saturation) and elsewhere hasa value ranging from 1 to 2 depending on the depth of the bioactivesediment layer (Håkanson and Eklund, 2007). The thickness of theactive layer has a default value of 10 cm and water content hasa standard value of 75%. The bulk density is calculated from watercontent, organic content and typical mineral bulk density(2.6 g cm�3, Håkanson and Jansson, 1983). Gross sedimentation(mg m�2 day�1) is the Fsed of SPM in mg dry wt per A-area unit.

2.3.4. Land upliftDue to land uplift, the depth of the wave base is successively

moved upwards. This leads to a new supply of eroded matter fromformer A-areas now lifted above the wave base. Moreover, theerosion also increases on previously raised ET-areas. The upwardtransport of PCDD/Fs due to land uplift is estimated using a methodbased on the hypsographic curve of the basins (Håkanson andBryhn, 2008).

2.3.5. DiffusionMolecular diffusion is the flux of molecules as a result of random

movements, acting to level out concentration gradients withina compartment or fugacity gradients between compartments (e.g.between sediment and water). Although a slow process, moleculardiffusion may be an important transport process for manysubstances in calm waters. PCCD/F release from accumulationsediments to the overlying water column passes the sediment porewater. The results from the sampling programme (Karlsson et al.,2009) suggest however, that the concentration gradient betweenthe sediment pore water and the overlying water column wasinsignificant in the Kallrigafjärden Bay. Therefore, the algorithm fordiffusive flow, which is included in the model (Håkanson andEklund, 2007), was set to zero in these calculations.

First, the model was run with SPM and TP as target variables tocheck that the model setup for the Kallrigafjärden Bay was correct.

TEQ water yearly basis

month

m

Q

E

T

g

p

-3

Modelled values

Modelled smoothed values

Empirical yearly mean

Fig. 3. Modelled and empirical (yearly mean) concentrations ofP

PCDD/F15(pg TEQ m�3) in surface waters of Kallrigafjärden Bay. Dotted horizontal lines showempirical yearly mean � 2 standard deviations.

TEQ water monthly basis

month

m

Q

E

T

g

p

3

-

Modelled values

Empirical

Fig. 4. Modelled and empirical (monthly mean) concentrations ofP

PCDD/F15(pg TEQ m�3) in surface waters of Kallrigafjärden Bay. The error bars show empiricalyearly mean � 2 estimated standard deviations.

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Empirical data on SPM- and TP-concentrations were yielded inparallel with the sampling of PCDD/Fs. It was thus possible to testthe model performance for these parameters. These simulationsshowed that the model was capable of reproducing both SPM- andTP-concentrations in the Kallrigafjärden Bay with a high accuracy.Secondly, the model was runwith PCDD/F (TEQ) levels inwater andsediments as target variables. Finally, the model was run for eachindividual PCDD/F congener. To ensure that the initial conditionsfor the model did not affect the simulation results and to avoidnumerical instability the model was run for five years. The outputdata from the last year was used for further evaluation. It is alsoworth mentioning that there was no tuning of the model in anystep of the modelling scheme. This means that the same modelvariables and model setup were used for each substance. We didnot tune the model (e.g. by adjusting the default values for modelvariables) because that would have obstructed the test of themodelperformance since we did not have any data from other coastalareas to validate such actions against.

To evaluate the model performance, we compared the modelledvalues with empirical data from the sampling programme in theKallrigafjärden Bay. It is important to keep in mind that the modelwas run for the entire system with a monthly time step but the

empirical data for the model comparison represent discretesamples from specific sampling sites and occasions from onespecific year. Thus, the empirical data are not fully time- and areacompatible with the modelled values. It would have been prefer-able to have access tomore empirical data, but due to the high costsof PCDD/F analyses this was not feasible. Therefore, the interpre-tation of the inter-annual variability must be done with precaution.It is possible that there is a seasonal variation in PCDD/F concen-trations in the water that may be reflected in both the predictedand empirical concentrations but the water concentrations are alsoaffected by short time events, e.g. the weather before the samplingoccasions. For example, the wind situation affects the resuspensioncycles. It was beyond the scope of this work to consider such factorssince it would have required a much more comprehensive andcostly sampling programme. Nevertheless, from an ecological pointof view, one may advocate that it is the long-term annual averagethat is of primary importance since, e.g. the bioaccumulation in fishis determined from the long-term exposure to PCDD/Fs.

3. Results

3.1. Model output

We present modelled values ofP

PCDD/F15 TEQ levels in waterand sediment compared with observations from the sampling pro-gramme aswell as confidence limits for the observations in Figs. 3e5.Fig. 3 shows modelled values plotted against the empirical yearlyaverage

PPCDD/F15 concentration (g TEQ m�3) in the surface water

of the Kallrigafjärden Bay. Fig. 4 shows the equivalent concentrationsbut on amonthly basiswhile Fig. 5 shows themodelled and empiricalconcentrations in the sediments assuming that the averagemeasured concentrations fromstationsAeC during the one samplingoccasion were representative for the whole studied period.

The model seems capable to predict yearly average values ofPCDD/F concentrations in the water column quite well (Fig. 3). InFig. 3, we plotted both modelled monthly values and smoothedmodelled values, versus the empirical yearly average. The modelledconcentrations lie well within the confidence limits of the empir-ical yearly average (estimated as two standard deviations of thesample). Fig. 4 shows predicted monthly water concentrationsversus empirical concentrations and confidence limits for the foursampling occasions. The number of observations was too few tocalculate the monthly uncertainty bands for the empiricalconcentrations. Instead we used a literature value for CV of SPM in

Water

y = 0.9887x - 0.0163

R2 = 0.9846

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5

log Mod

pm

E

go

l

Sediment

y = 0.9761x + 0.1766

R2 = 0.9956

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0

log Mod

pm

E

go

l

Fig. 6. Modelled (Mod) versus empirical (Emp) yearly mean total levels of 15 PCDD/F congeners in the Kallrigafjärden Bay surface water and sediments.

TEQ sediment monthly basis

month

t

w

y

r

d

g

Q

E

T

g

p

1

-

Modelled values

Empirical mean values

Fig. 5. Modelled and empirical mean contents ofP

PCDD/F15 (pg TEQ g dry wt�1) inaccumulation sediments of Kallrigafjärden Bay. The error bars show empirical yearlymean � 2 standard deviations.

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Baltic coastal areas (0.52) from Håkanson and Duarte (2008) tocreate confidence limits. Our interpretation of Fig. 4 is that to beable to make meaningful comparisons of modelled and empiricalmonthly values, the empirical data set must be more extensive.From Fig. 5, we can conclude that the predicted values of PCDD/Fcontent in the sediments (pg TEQ g dry wt�1) lie within theconfidence limits for the empirical sediment contents. We couldfurther conclude that there was a strong positive correlationbetween the empirical and modelled logarithmic yearly meanvalues of individual PCDD/F congeners (r2 ¼ 0.98 for water andr2 ¼ 0.99 for sediment data; Fig. 6).

In Table 2 and Fig. 7, the fluxes of PCDD/Fs to, from and withinthe Kallrigafjärden Bay are presented on a yearly basis. The fluxfrom the adjacent sea is the dominating inflow, followed by riverinetransport and atmospheric deposition, while the contribution fromland uplift was insignificant. The outflow is also dominated by thewater exchange with the Öregrundsgrepen Bight (e15 mg yr�1), butburial in accumulation sediments was not negligible (e1 mg yr�1).

3.2. Sensitivity analysis

From Fig. 6, it might appear as we have built the ultimate modelfor PCDD/F turnover in coastal areas since the r2-values of theregressions are close to 1, the slope of the regression lines are alsoclose 1 and the intercepts are close to 0. However, as seen fromFig. 8 such an argument needs to be further elaborated. In Fig. 8, itcan be noted that there is almost just as good correlation betweenthe logarithmic concentrations of individual congeners whencomparing concentrations in the sea with concentrations withinthe Kallrigafjärden Bay. This means that since inflow from the sea isthe dominating flux of PCDD/Fs to the Kallrigafjärden Bay, and sincewe use the concentrations in the sea as a driving variable, a modelperformance with less accuracy than given in Fig. 8 (r2 ¼ 0.94)should be regarded as a failure.

In this context, it is relevant to ask whether we could get just asgood predictive power by applying a simple mass-balance calcu-lation and only take the water dynamics and inflows from the seaand rivers into account, thus omitting the sedimentologicalprocesses. To test this, the model was runwith alternative settings:a) with the algorithms for resuspension “turned off” (Fig. 9) and b)with uniform values for the particulate fraction (PF ¼ 0.56) insteadof the individual empirically determined values (Fig. 10). In bothcases, the model still predicts individual congeners in water andsediments with quite high accuracy, but not as good as in Fig. 6,where the original model setup was used. Hence, we get a clearindication on that overall model performance is improved byincluding the sedimentological processes.

4. Discussion

Water turnover in coastal areas of the Baltic Sea is generally fast.A typical water retention time is 5 days (Persson et al., 1994). Thismeans that the inflow from the adjacent sea generally has a majorimpact on the water quality in Baltic coastal areas. When choosingthe Kallrigafjärden Bay as study area, one important prerequisitewas to find an area where the inflow from the sea was not totallydominating over all other flows. Our modelling results show thatwe succeeded with this, but the inflow from sea is still veryimportant. An indirect validation of our modelling results can bedone by comparing the PCDD/F levels in fish muscle originatingfrom the Kallrigafjärden Bay with corresponding contents (fish ofsame species, age and size caught at the same period of the year)from reference areas. The PCDD/F contents in perch from theKallrigafjärden Bay were of the same magnitude as found in ourreference area (Karlsson et al., 2009) and other rural backgroundlocations in southern Bothnian Sea (Olsson et al., 2005). This wasexpected since neither the riverine inflow of PCDD/Fs nor theupward transport from sediments was pronounced.

Land uplift is a very important process for the particle substanceflow in the Baltic Sea on a basin scale (Jonsson et al., 1990;Håkanson and Bryhn, 2008). Our modelling results show that

Csea vs. CKallriga

y = 0.8286x + 0.2282

R2 = 0.9421

0.0

0.5

1.0

1.5

2.0

2.5

-0.5 0.0 0.5 1.0 1.5 2.0 2.5

log Csea

C

go

la

gi

rl

la

K

Fig. 8. A regression between empirical yearly mean total concentrations for 15congeners of PCDD/Fs (pg TEQ m�3) in surface water of the Öregrundsgrepen Bight(adjacent sea) and the Kallrigafjärden Bay.

Fig. 7. Process-related fluxes ofP

PCDD/F15 (mg TEQ yr�1) to, from and within theKallrigafjärden Bay.

Table 2Ranking ofmodelled and empirically determined fluxes (mg TEQ yr�1) of PCCD/Fs to,from and within Kallrigafjärden Bay

Flux Value Type of variable

(mg yr�1)

InflowsInflow from adjacent sea 10.9 DrivingRiverine transport 2.9 DrivingAtmospheric deposition 1.6 DrivingLand uplift <0.01 Modelled

OutflowsOutflow to adjacent sea 14.5 ModelledSediment burial 0.9 Modelled

Internal flowsSedimentation 2.4 ModelledResuspension 1.5 Modelled

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within a single coastal area the effects of land uplift are insignifi-cant. This is in line with other results frommass-balance modellingin coastal areas (Håkanson et al., 2004; Håkanson and Eklund,2007). However, this does not exclude land uplift from being animportant source of pollutants to the water column in areas witha high degree of sediment contamination as hypothesised byJonsson et al. (1986).

To our best knowledge there have not been any other attemptsto model PCDD/F turnover in individual coastal areas. Assmuth andJalonen (2005) calculated a budget for PCDD/Fs for the whole BalticSea and Armitage et al. (2009) modelled the fate of PCDD/Fs in thetwo largest basins of the Baltic Sea, the Baltic proper and theBothnian Sea. The basins were divided into two zones: the coastalzone and the open water. No direct comparisons with our resultsare possible since the geographical scales differ but the differentresults resemble one other when comparing the relative values ofdifferent PCDD/F flows. Wulff et al. (1993) modelled the large-scaledistribution of chlorinated organic matter in the Baltic Sea. One ofthe major findings of their work was the low trapping efficiency ofcoastal areas, in this case pulpmill recipients, many of them locatedalong the coast of the Bothnian Sea and having similar morpho-metrical characteristics as the Kallrigafjärden Bay. Our results alsoshow that the capability of the coast to act as a filter for

allochthonous matter is relatively small. Kobayashi et al. (2006)applied a 3-D hydrodynamical model to predict dioxin concentra-tions in the seawater of Tokyo Bay, Japan. The simulation resultscompared favourably with the field measurements for both theparticulate and dissolved phases. However, the particulate-phasedioxin concentrations in the bottom layers were underestimated,which might be explained by the fact that the resuspension processwas not taken into account in the model.

As highlighted by Flemming and Delafontaine (2000), the use ofthe sediment content (mass of a component per unit mass ofsediment) may lead to data misinterpretation in terms of identi-fying pollutant hot-spots for sediment dwelling organisms. Arecalculation of the sediment PCDD/F contents to true concentra-tions using bulk density, in the Kallrigafjärden Bay as well as inother estuaries, could possibly lead to different interpretations andultimately increase our understanding of the bio-availability ofPCDD/Fs. To increase the general applicability of our model, theparameterisation of the diffusion algorithm needs to be revised sothat characteristics known to influence the PCDD/Fs diffusion fromsediments such as temperature and sediment carbon content(Armitage et al., 2009) are included. Furthermore it is well knownthat salinity affects the aggregation and sedimentation of particlesin estuaries e.g. in relation to the concept of the zone of maximum

Water when PF- value uniform

y = 0.8897x + 0.1988

R2 = 0.8513

0.0

0.5

1.0

1.5

2.0

2.5

-1.0 0.0 1.0 2.0 3.0

log Mod

pm

E

go

l

Sediment when PF-value uniform

y = 1.0056x + 0.2969

R2 = 0.8867

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0

log Mod

pm

E g

ol

Fig. 10. Modelled (Mod) versus empirical (Emp) yearly mean values for 15 congeners of PCDD/Fs in the Kallrigafjärden Bay surface water and sediments when the particulatefraction-values (PF) uniformly have been set to 0.56.

Water when resuspension

turned off

y = 0.9022x + 0.2162

R2 = 0.8556

0.0

0.5

1.0

1.5

2.0

2.5

-1.0 0.0 1.0 2.0 3.0

log Mod

pm

E

go

lSediment when resuspension

turned off

y = 1.0056x + 0.2969

R2 = 0.8867

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0

log Mod

pm

E

go

l

Fig. 9. Modelled (Mod) versus empirical (Emp) yearly mean values for 15 congeners of PCDD/Fs in the Kallrigafjärden Bay surface water and sediments when the model algorithmsfor resuspension have been turned off.

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turbidity (Bowden, 1984). In the model, this is handled bya dimensionless moderator (Ysal) that acts on the sedimentationrate (Eq. (2)). It also possible that salinity influences the partition-ing between dissolved and particulate phases of PCDD/Fs as foundfor e.g. tracemetals (Turner,1996) and hence needs to be accountedfor if expanding the domain of the model. However, the PF-valuefor PCDD/Fs in the freshwater of the tributaries was slightly higher(0.84, Karlsson, unpublished data) compared to the average PF-value within the estuary of 0.82, indicating that salinity may not beimportant to the PCDD/Fs partitioning between dissolved andparticulate phases, and keeping in mind that the salinity is lowwithin the studied estuary. At the present stage, the domain for theapplied model is restricted to the Baltic Sea coastal areas.

5. Conclusions

A model has been developed that on a yearly basis was capableto reproduce the turnover of PCDD/Fs in water and sediments ofa Baltic coastal area. The dominating flow of PCDD/Fs was the waterexchange with adjacent sea. This is characteristic for Baltic Seacoastal areas. Nevertheless, sedimentological processes were ofimportance for the PCDD/Fs turnover. Sensitivity analysis clearlyshows that the model performance was improved by incorporatingthese processes in the model. In the future, it would be veryinteresting to test this model also in other coastal areas especially atmore contaminated sites. The fact that the equations defining thefluxes are general for any aquatic ecosystem and have been vali-dated for suspended particulate matter and phosphorus raiseexpectations. It would also be very interesting to set up a model forthe Kallrigafjärden Bay based on the fugacity approach (Mackay,2001) and compare the results from such a model with ours. Inthe end, it might turn out that a combination of the more chemicalapproach taken in fugacity modelling, that should be good for, e.g.determining fluxes between air and water, and the approach takenhere, based on knowledge of general transport processes in aquaticsystems, would be optimal to model PCDD/F turnover in coastalareas. It is important to gain further insights in which processesthat are important for the fate of PCDD/Fs in coastal estuaries sincethere are many PCDD/F polluted coastal regions worldwide that areessential as nurseries and pantries for aquatic life and as a resourcefor fishery.

Acknowledgements

This study was financed by The Foundation for Research andDevelopment of Ångpanneföreningen (Sweden), The SwedishAssociation of Graduate Engineers Environmental Fund and TheSwedish Research Council Formas via grants from The Foundationfor the Swedish Environmental Research Institute. Karlsson wasalso funded by the Faculty of Science and Technology, UppsalaUniversity, and Josefsson was funded by the Centre for Environ-mental Research in Umeå. Dan Lindgren is thanked for preparingthe figures and Gunnar Andersson for a critical review of theapplied statistical methods.

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