modelling assessment of regional groundwater contamination due to historic smelter emissions of...

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ology 96 (2008) 48–68www.elsevier.com/locate/jconhyd

Journal of Contaminant Hydr

Modelling assessment of regional groundwater contaminationdue to historic smelter emissions of heavy metals

Bas van der Grift ⁎, Jasper Griffioen

TNO Geological Survey of the Netherlands P.O. Box 80.015, 3508 TA Utrecht, The Netherlands

Received 22 March 2005; received in revised form 5 October 2007; accepted 10 October 2007Available online 18 October 2007

Abstract

Historic emissions from ore smelters typically cause regional soil contamination. We developed a modelling approach to assessthe impact of such contamination on groundwater and surface water load, coupling unsaturated zone leaching modelling with 3Dgroundwater transport modelling. Both historic and predictive modelling were performed, using a mass balance approach for threedifferent catchments in the vicinity of three smelters. The catchments differ in their hydrology and geochemistry. The historicmodelling results indicate that leaching to groundwater is spatially very heterogeneous due to variation in soil characteristics, inparticular soil pH. In the saturated zone, cadmium is becoming strongly retarded due to strong sorption at neutral pH, even thoughthe reactivity of the sandy sediments is low. A comparison between two datasets (from 1990 to 2002) on shallow groundwater andmodelled concentrations provided a useful verification on the level of statistics of “homogeneous areas” (areas with comparableland use, soil type and geohydrological situation) instead of comparison at individual locations. While at individual locationsobservations and the model varies up to two orders of magnitude, for homogeneous areas, medians and ranges of measuredconcentrations and the model results are similar.

A sensitivity analysis on metal input loads, groundwater composition and sediment geochemistry reveals that the best availableinformation scenario based on the median value of input parameters for the model predicts the range in observed concentrationsvery well. However, the model results are sensitive to the sediment contents of the reactive components (organic matter, clayminerals and iron oxides). Uncertainty in metal input loads and groundwater chemistry are of lesser importance.

Predictive modelling reveals a remarkable difference in geochemical and hydrological controls on subsurface metal transport atcatchment-scale. Whether the surface water load will peak within a few decades or continue to increase until after 2050 depends onthe dominant land use functions in the areas, their hydrology and geochemical build-up.© 2007 Elsevier B.V. All rights reserved.

Keywords: Soil; Groundwater flow; Metal transport; Modelling; Catchment-scale

1. Introduction

There are various reasons why the soil becomescontaminated with heavy metals and these leach to

⁎ Corresponding author. Tel.: +31 30 2564720.E-mail address: [email protected] (B. van der Grift).

0169-7722/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.jconhyd.2007.10.001

groundwater: impact of acid rain (Edmunds et al., 1992;Frei et al., 2000; Kjøller et al., 2004), long-term manure-and fertilizer-borne input to agricultural soils (McBrideet al., 1997; Keller et al., 2001; Miller et al., 2003; Xueet al., 2003), ore mining (Brown et al., 1998), dispersionand deposition from smelters (Wilkens and Loch, 1997;Fernandeze-Turiel et al., 2001; Seuntjens et al., 2002;

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http://dx.doi.org/10.1016/j.jconhyd.2007.10.001

49B. van der Grift, J. Griffioen / Journal of Contaminant Hydrology 96 (2008) 48–68

Burt et al., 2003), forestation of cultivated soils(Römkens, 1998; Strobel et al., 2001), and diffuseatmospheric deposition (Tiktak, 1999). The fate of thecontaminating heavy metals in the subsoil needs to bepredicted, so that soil and water can be managedproperly. For example, when the contaminant load isunacceptably high, it may be necessary to take measuresto safeguard the abstraction of drinking water or thedischarge to surface water. Reactive transport modelsare useful tools for the management of contaminatedareas.

Few studies have addressed the reactive transport ofheavy metals in the field. For these studies, a cleardistinction can be made between studies that addresstransport within the unsaturated zone, where cropuptake is relevant and transport is dominantly vertical,and studies of transport in the saturated zone wheretransport is three-dimensional.

In the case of the unsaturated zone, Wilkens (1995)modelled transport of Cd and Zn in several field plotsusing a finite-difference approach with verticallyvariable distribution coefficients. Tiktak (1999) used amass budget model to model accumulation of Cd in thetop horizon in a regional context, as well as a one-dimensional reactive transport model for the unsaturatedzone of three field plots. Seuntjens et al. (2002) did asensitivity analysis in order to rank the importance ofspatially variable water flow and solute transportparameters affecting field-scale cadmium flux in alayered sandy soil. Keller et al. (2001) applied anempirical stochastic model to the plowed layer ofagricultural soils, which considers heavy metal inputsfrom agricultural management and outputs from cropremoval and leaching on a regional-scale.

Brown et al. (1998) and Kjøller et al. (2004) modelledreactive transport of trace metals in the saturated zoneone-dimensionally using multicomponent geochemicalformulations for the hydrogeochemical reactions. Walteret al. (1994) used a two-dimensional vertical cross-section addressing both the major compounds as well astrace metals by taking into account multicomponentgeochemical formulations for the hydrogeochemistry.Kent et al. (2000) and Curtis et al. (2006) applied two-dimensional reactive groundwater transport models,Kent et al. (2000) incorporated semi-empirical surfacecomplexation models to describe sorption, Curtis et al.(2006) used a semi-mechanistic surface complexationmodel.

So far, regional-scale metal transport simulationshave not been conducted. The objective of the study wasto develop a regional approach, using reactive transportmodels, that addresses geochemical and hydrological

controls on subsurface transport of the trace metals Cdand Zn. The study was developed for three differentcatchments in the Kempen region in the Netherlands(Fig. 1), where the soil had been severely contaminatedby atmospheric emissions from three Zn smelters. Withthis study we want to assess the history and to predictthe future in terms of: (1) temporal and spatial variableleaching of metals from the unsaturated zone togroundwater; (2) developments in Cd and Zn concen-trations in shallow and deeper groundwater, and (3) themetal loads of the surface water drainage network inrelation to the hydrology and geology.

2. Site characterization

2.1. Study area

In the study area (see Fig. 1), which is near theDutch–Belgian border, there is heavy metal contami-nation from three zinc-ore smelters less than 10 kmapart. The zinc ore most often used in these smelters issphalerite (ZnS), which contains a wide range of othermetals, the most abundant being manganese, cadmium,copper, arsenic, tin, gallium, antimony, and thallium(Levinson, 1977). From 1880 to 1974 the chimneys ofthese smelters emitted oxides of heavy metals thatreached the soil either by dry deposition or with rainfall.The plants switched to an electrolytic process in 1974;since then, atmospheric emissions have diminisheddrastically. The historic input of heavy metals on the soilthrough atmospheric deposition has resulted in anexcessive accumulation in the topsoil in the Kempenregion, accompanied by increased leaching to thegroundwater (Harmsen, 1977; Boekhold, 1992; Tiktak,1999; Seuntjens et al., 2002, Sonke et al., 2002; CSO,2001).

The soils are mainly Spodosols and Earth soil whichhave developed in poor, fluvio-eolian, Pleistocenesands, and which are vulnerable to leaching becauseof the acidifying conditions and low contents of organicmatter and clay (De Bakker and Schelling, 1989;Wilkens and Loch, 1997). In the brook valleys thatintersect the sandy regions the soil varies from peatyvia loamy to sandy. The Kempen region is flat lowlandwith surface levels decreasing from south to north, fromabout 40 m to 20 m above sea level. The differencein surface level between the brook valleys and thehigher parts of the area is in the order of a few to 10 m.The climate of the area is humid and temperate withmean July and January temperatures of 19 and 4 °C,respectively, and annual precipitation averaging700 mm. Geologically, the area can be divided into

Fig. 1. Map showing the location of the Kempen area in the Netherlands, the Beekloop–Keersop, Buulder Aa and Tungelroijsche Beek catchments,the geological profile along the dotted line with the important Feldbiss Fault and Peel Boundary Fault, the locations of the drillings for geochemicalanalysis and the interpolated cadmium content in the topsoil for the year 1995 (CSO, 2001).

50 B. van der Grift, J. Griffioen / Journal of Contaminant Hydrology 96 (2008) 48–68

two: the southwestern part is the Kempen High boundedby the Feldbiss Fault with the Roer Valley Graben. Thisactive tectonic subsidence area contains the thickestterrestrial fine-grained deposits with most completerecord in the Netherlands (Schokker, 2003; De Mulderet al., 2003). The Late Pleistocene Boxtel Formation that

covers the Roer Valley Graben is a fine-grained fluvialand eolian sandy deposit intercalated with heteroge-neous layers of loam and peat, and has a maximumthickness of 35 m. In the Kempen High, the top unit isthe middle Pleistocene Sterksel Formation, whichconsists mainly of medium and coarse sand and some


gravel deposits from the rivers Rhine and Meuse.Deeper geological formations in the area with increasingage are: the lower and middle Pleistocene StramproyFormation of eolian and local terrestrial origin, the lowerPleistocene Waarle Formation, and the Late Mioceneand Pliocene Kiezeloöliet Formation. The latter two aredeposits from predecessors of the river Rhine. The RoerValley Graben is bounded on the northeast by the PeelBoundary Fault which marks the transition to the PeelHorst (which lies outside the study area).

This paper focuses on three catchments that differ intheir hydrogeological and geochemical characteristics:the Beekloop–Keersop on the Kempen High andBuulder Aa and Tungelroijsche Beek catchments inthe Roer Valley Graben. The upper streams of thesecatchments are located in immediate vicinity of thesmelters. The three catchments encompass an area that isover 330 km2 in size.

2.2. Groundwater quality

In the period 2003–2004 groundwater in the Kempenarea was sampled and analyzed. At 62 sampling fieldsthe uppermost meter of groundwater was sampled usingtemporary monitoring wells. At each of the samplingfields, four wells were drilled in a square plot of about100⁎100 m to a depth of 1 m below the actualgroundwater level. These groundwater levels varybetween 0.2 and 4.5 m depth. Groundwater samples ofthe four wells at one location were collected, mixed andanalyzed for macro-chemistry (pH, electrical conductiv-ity, alkalinity, Cl, NO3, SO4, PO4, NH4, Na, K, Mg, Ca,Al, Fe), heavy metals (As, Cd, Ni, Cr, Cu, Pb, Zn) andDOC using routine techniques (ICP-MS, Ion chroma-tography, Element Analyser, TOC Analyzer). Themonitoring wells were removed after sampling. Addi-tionally, 51 samples were taken from existing permanentmonitoringwells; these ranged in depth from 1.5 to 30m.These data were augmented with data from a monitoringprogram performed in 1990 in the Kempen area, inwhich the uppermost groundwater had been sampled andanalyzed for Cd, Zn and pH (Tauw, 1991).

We used the major groundwater chemistry data asinput variables for the Cd and Zn sorption isotherms ingroundwater environment, as explained later. Thegroundwater data on Cd and Zn were not input into themodel but were instead used for model evaluation. Here,the groundwater chemistry data are grouped according tothe concept of “areas of homogeneous groundwatercomposition”, i.e., areas within which the chemicalcomposition of groundwater varies less than it doesbetween such areas (Broers, 2002). Therefore, dominant

controls on the chemical groundwater composition hadto be assigned. In the Netherlands, these dominantcontrols are input loads, which are directly related to landuse, and hydrologic regimes (Broers, 2002). Weestablished the homogeneous areas for two land uses(agriculture and nature areas) and three hydrologicregimes (infiltration, intermediate and discharge areas).Nature areas are areas without an intensive agriculturalor residential function and exist mainly of pine forest andmoor land. About 25% of the land use in the study area isnature, rising to 29% within 5 km of the smelters.Infiltration areas are higher-lying parts of the landscapebetween the brooks. The water table is deep (on average,2–6 m) and there are no drainage ditches. Dischargeareas correspond with the brook valleys in the lowerparts of the landscape; here, groundwater exfiltrates andthe water table is close to the surface. In between are theintermediate areas, which are characterized by thepresence of locally recharged flow systems thatdischarge into the local ditches and small brooks, andin which the water table is at about 1–3 m depth (Broersand Van der Grift, 2004; Broers, 2004).

2.3. Sediment geochemistry

Data about the aquifer geochemistry was obtainedfrom 11 deep drillings to about 100 m depth and 6shallow drillings in the Boxtel Formation in the RoerValley Graben (see Fig. 1). From the deep drillings, anumber of samples were selected for analysis on thebasis of the lithostratigraphy. Thus, within everygeological formation we selected a series of samples at10 m intervals, and also at the transition between twoformations. From the Boxtel Formation drillings,samples were taken every meter (Schokker, 2003).

In total, 298 sediment samples were analyzed forbulk geochemistry (X-ray Fluorescence Spectrometryand Thermogravimetric Analyzer) to determine thecontents of reactive components: organic matter, totalAl2O3 (as proxy for clay fraction), carbonates, Stotal (asproxy for pyrite), Fereactive (as proxy for iron oxides andhydroxides and siderite). The results for the reactivecomponents were averaged per geologic unit and usedas input variables for sorption affinity of Cd and Zn inthe saturated zone model (Table 1).

3. Modelling approach

For this study, we developed a modelling approachcoupling an unsaturated zone leaching model with agroundwater flow and transport model (Fig. 2). With theunsaturated zone model we calculate the spatially and

Table 1Median values of reactive component in the geological formations derived from analyzed samples from 17 drillings in the Kempen area

Origin N Al2O3 (%) Stotal (%) Fereactive (%) OM (%) Carbonates (%)

Boxtel Formation Local and eolian 204 4.24 0.11 0.03 0.94 0.30Beegden Formation Fluvial 5 1.96 0.03 0.00 0.25 0.24Sterksel Formation Fluvial 45 3.33 0.10 0.07 0.34 1.34Stramproy Formation Local and eolian 31 5.15 0.18 0.41 2.55 2.29Waalre Formation Fluvial 1 6.73 0.09 0.00 1.33 0.75Breda Formation Marine 12 4.15 0.80 1.63 1.55 1.53


temporally variable leaching of heavy metals to ground-water. We subsequently applied a fully distributed three-dimensional reactive transport model to the saturated zoneto assess subsurface transport and surface water load.

3.1. Unsaturated zone model

HYDRUS-1D was used to model the leaching ofcadmium and zinc to the water table for two periods:1880 – present and present – 2050. The first periodserved for historic modelling and verification purposes,and the second for prediction and assessment ofvulnerability. Breakthrough curves of Zn and Cdwere calculated for unique combinations, considering76 soil types, 2 land use functions, 10 classes of depthsof water tables and 11 classes of metal input loads. Inall there were 16,720 unique combinations of these

Fig. 2. Structure of the

variables. The modelling was performed for yearlyaveraged conditions of the precipitation excess andgroundwater level, and free drainage as bottom boun-dary condition.

Soil chemical and physical parameters are needed tomodel metal leaching from topsoil to groundwater. Notethat the sandy soils in the Kempen area are mainlyaerobic and mildly acid (pH 4–6.5) and thereforeprecipitation of Cd or Zn as carbonates or oxides playsno role in this region. We, therefore, described leachingby sorption together with advective/dispersive transport.In this area, sorption on organic matter and clay mineralsmust be key processes controlling reactive transport inthe unsaturated zone, because oxide contents arerelatively low (Wilkens and Loch, 1997). We used anempirical-partitioning model from Römkens et al.(2004) to obtain adsorption isotherms for the individual

coupled model.


soils horizons in the area. The individual soil types werederived from Steur and Heijink (1991). The Freundlich-type partition equations have been derived frommultiple-linear regression analysis of 1450 soil–soilsolution data of 60 very diverse Dutch soils, includingthe soils in the study area:

log QCd=C0;54Cd

h i¼ �5:01þ 0:27 log k clay½ �

þ 0:65 log k SOM½ �þ 0:29 pH ð1Þ

R2 ¼ 0:77 se Yð Þ ¼ 0:37

log QZn=C0;78Zn

h i¼ �4:96þ 0:36 log k clay½ �

þ 0:51 log k SOM½ �þ0:52 pH

ð2Þ

R2 ¼ 0:85 se Yð Þ ¼ 0:41

where QMe is the metal content in soil (mol/kg) using0.43 N HNO3 destruction, CMe is the total metalconcentration in the soil solution (mol/m3), SOM issedimentary organic matter and pH is the acidity of thesoil (0.01 M CaCl2 extraction). Input parameters for theempirical-partitioning equations are pH, organic matterand clay content. We used national data on the physical–chemical characterization of the soil types in theNetherlands (De Vries, 1999) as input. This character-ization provides the characteristics of the most commonsoil horizons to a depth of 120 cm, including the soils inthe study area.

Groundwater levels were derived from the regionalgroundwater flowmodel (Buma et al., 2002) (see Section2.3). The Van Genuchten parameters for modelling theunsaturated water flow in the 76 soil profiles werecalculated with pedo-transfer functions from the soiltexture (clay, loam, organic matter content and themedian of the sand fraction) and bulk density (Wöstenet al., 2001). These parameters were also available fromthe physical–chemical characterization of Dutch soiltypes (De Vries, 1999). A precipitation excess of259 mm/year for nature areas and 277 mm/year foragricultural land was used for the unsaturated zonemodel, which takes annual precipitation and land usedependent evapotranspiration into account.

3.2. Soil surface load

The following three sources of Cd and Zn wereconsidered: (1) atmospheric deposition, (2) fertilizers

and (3) animal manure. Rozemeijer (2002) made areconstruction of the historic atmospheric depositiondata for Cd and Zn originating from the smelters acrossthe Kempen region. Rozemeijer (2002) reconstructedthe time-dependent historic atmospheric deposition rateof Cd and Zn in the period 1880–1975 from presentmeasured contents of these metals in forest soilssamples at 19 locations in the study area with varyingdistance from the smelters, taking developments inhistoric zinc production into account. As it can beassumed that in the 20th century the yearly atmosphericemission and deposition of Cd and Zn increasedconcomitantly with the zinc production rate of thesmelters, the temporal trend in annual deposition rate inthe period 1880–1975, has been linearly correlated tothe zinc production of the Budel smelter (Makaskeet al., 1995). Rozemeijer (2002) used HYDRUS-1D(Šimůnek et al., 1998) to calibrate the level of the1880–1975 atmospheric deposition to the analyzedcadmium and zinc contents in the soil. The metalcontent ranged from b0.2 mg/kg Cd to b20 mg/kg Znat locations over 30 km from the smelters to 5.3 mg/kgCd and 733 mg/kg Zn at a location within two km fromthe Budel smelter. Non-linear Freundlich adsorptionisotherm coefficients (KF) for the HYDRUS modelwere calculated from the pH, organic matter and claycontent of the analyzed soil samples, as describedabove. The results of Rozemeijer (2002) indicated thatthe historic atmospheric deposition of the metals due toemission from the smelters decreased strongly withdistance from the smelters. For cadmium, the modelledaverage atmospheric deposition at the 19 locations inthe period 1880–1975 varied between 15 and 642 g/(hayr) depending on the distance from the smelters. Forzinc, the levels varied between 730 and 25000 g/(ha yr).Using the distance from the smelters and the predom-inant wind direction as input variables, we spatiallyinterpolated the deposition at the 19 locations ascalculated by Rozemeijer (2002) to a map of atmo-spheric deposition rates (Fig. 3). For the period 1975–2005 regional values for atmospheric deposition ofmetals were used (RIVM/CBS, 2002).

The load from agriculture sources was determinedfrom data on local and national use of manure andfertilizer salts in the period 1950–2000 (Broers and Vander Grift, 2004). These data were augmented with dataon the metal composition of manure and fertilizers(Hotsma 1997; Raven and Loeppert 1997; Moolenaarand Lexmond, 1998), in order to calculate the historiccadmium and zinc loads. Far less heavy metals originatefrom agriculture than from the historic atmosphericdeposition in the vicinity of the smelters. The average

Fig. 3. Average cadmium deposition in period 1880–1975 as used for the unsaturated zone model (in g/ha/year).


agricultural cadmium and zinc loads in the 1880–2000period were 1.5 g/(ha yr) and 606 g/(ha yr), respectively.Peak levels around 1985 were 4.3 and 1830 g/(ha yr),respectively.

The soil surface load of Cd and Zn expected in 2005–2050 was calculated from data on the current values ofatmospheric deposition of metals (RIVM/CBS, 2002)and the statutory norms for the use of manure andfertilizers in the Netherlands (NMP-RIVM, 2002).

3.3. Saturated zone groundwater flow model

The saturated zone transport model was based on aMODFLOW finite-difference groundwater flow model(McDonald and Harbaugh, 1988) and an MT3DMStransport model (Zheng and Wang, 1999). By use oftelescopic mesh refinement, detailed models for thethree individual catchments were clipped out from anexisting supraregional groundwater flow model (Bumaet al., 2002) that covers over 13,000 km2 and thusextends over the entire province of Noord–Brabant(Fig. 1). This model has natural boundaries located insurrounding provinces and in Belgium. The supraregio-nal model has 9 geohydrological layers covering thegeological formations and contains over 9⁎106 gridcells. It has been calibrated on transmissivity andhydraulic layer resistance using a representer-basedinverse method for groundwater flow (Valstar et al.,

2004). All groundwater abstractions above 1000 m3/yrwere included in the model, plus their exact depths ofabstraction.

Hydraulic heads from the supraregional model wereused as boundary conditions for the three catchmentflow models. These models were adjusted to performsolute transport simulations for each individual catch-ment and have there boundaries outside the naturalboundaries of the catchment. The Tungelroijsche Beekcatchment does not cross the Belgian boarder and onlysmall parts of the Beekloop–Keersop and Buulder Aacatchments are situated in Belgium. The catchmentmodels contain only the first geohydrological layers ofthe original supraregional model to a depth of about 70to 100 m, because Zn and Cd are not transported beyondthese depths. In order to minimize numerical dispersion,the catchment models have 13 layers with increasingthickness from surface: 3 m for the upper 5 layers, 5 mfor the next 4 layers to about 15 m for the deepest layers.Transmissivity of each layer of the new layers wasassigned on basis of there depth towards the originallayers and the conductivity and resistance of theselayers. The horizontal grid discretization of the modelsis 100⁎100 m. The calculation time steps were in theorder of 13 days, fulfilling the Courant condition. Forexample, the Buulder Aa catchment groundwater modelcontains 546,000 grid cells encompassing an area of24⁎17.5 km.

Table 3Characteristics of sorbents considered

Cation Carboxyl group Phenol group

log KMe Non-ideality log KMe Non-ideality

Zn2+ 1.55 0.48 4.40 0.64Al3+ 0.51 0.36AlOH2+ 0.95 0.32Al(OH)2

+ 3.11 0.32

Table 2Median values of groundwater composition in homogeneous areas atthree depth intervals derived from the groundwater quality monitoringnetwork in the area

Clay mineralsCation-exchange capacity 10 meq/kg

Amorphous Fe-hydroxide (Dzombak and Morel, 1990)Capacity, strongly selective 200 mmol/mol FeCapacity, weakly selective 5.0 mmol/mol FeSurface area 600 m2/g FeOOH

Humic acidSorption capacity, carboxyl 2.54 mol/kg humic acidSorption capacity, phenol 3.75 mol/kg humic acidHeterogeneity 0.54


3.4. Saturated zone groundwater transport model

We applied the transport model on grid cells insidethe natural boundaries of catchment. Grid cells outsidethis natural boundary and grid cells inside the catchmentboundary in Belgium were set to inactive. For retardingsolutes like Cd and Zn neglecting the lateral cross-boarder transport is not a serious limitation. Due to longgroundwater travel times, metals that leached to thegroundwater in Belgium will not reach the Netherlandsgroundwater and surface water in relevant amounts inthe timeframe our model.

We used results of the unsaturated zone HYDRUSmodel runs at the groundwater table as rechargeconcentrations for the reactive groundwater transportmodelling of Cd and Zn. This output was averaged per5 years to match input for the groundwater transportmodel. The transport of cadmium and zinc in ground-water was calculated for the period from 1950 to 2050;note that prior to 1950 no breakthrough at the watertable was simulated with the unsaturated zone model.Initial groundwater concentrations for Cd and Zn in1950 were set at zero.

Sorption of Zn or Cd was described for eachindividual grid cell by general Freundlich isothermstype curves. However, the empirical Freundlich iso-therms used for the unsaturated zone could not be usedfor the groundwater transport model. This unsaturatedzone approach was developed to understand therelationship between the soil and the soil solution.This empirical model could not be extrapolated to sandyaquifer material, which contains significantly lowercontents of sorbents. Therefore, we used Freundlichisotherm functions derived by a meta-model fromGriffioen et al. (1998). This approach was speciallydeveloped for aquifer environments. These functionscalculate the fraction of the sorbent occupied by thetrace metal of interest as a function of pH. First, the

activity of the free trace metal (e.g. Cd2+) was calculatedfrom a groundwater composition, taking into accountthe major inorganic aqueous complexes and complex-ation with dissolved organic acids (Griffioen et al.,1998). The median concentrations of macro-chemicalcomponents and the pH of the groundwater were inputin the aqueous complexation and sorption models.These medians were derived per homogeneous area, asexplained before. We distinguished four depth intervalswhen interpreting groundwater quality data: uppermostgroundwater (0–6 m), moderately deep groundwater(6–15 m), deep groundwater (15–35 m) and very deepgroundwater (N35 m) and they were assumed to beconstant over the period modelled. For the pH, whichcan be considered as the dominant factor controllingaqueous complexation and sorption of metals, thisassumption is justified. As soil pH changes are bufferedby several chemical weathering reactions, the excessatmospheric acid input of the last decades has not lead tolower groundwater pH values but to higher concentra-tions of elements like aluminium, calcium and magne-sium (Appelo and Postma, 1993; Kros and Mol, 2001;Broers, 2002).

Second, sorption to heterogeneous sediment wasconsidered using a meta-model approach. We consid-ered three types of sorbents and assumed that thesorption to these three individual sorbents was additive(Fest et al., 2005). The three sorbents were clayminerals, iron hydroxides and organic matter. Thesorption capacities for the three different sorbents arepresented in Table 2 together with other relevant data.The Gaines–Thomas selectivity coefficients for cation-exchange to clay minerals refer to values for CampBerteau montmorillonite (Bruggenwert and Kamphorst,1982). The intrinsic binding constants for amorphousFe-hydroxide were obtained from Dzombak and Morel(1990). The intrinsic binding constants of H+, Ca2+ andCd2+ for sorption to humic acids as organic matter wereobtained from Kinniburgh et al. (1996); non-publisheddata were used for Zn2+, Al3+, AlOH2

+ and Al(OH)22+

(Table 3). Cation-exchange to clay minerals and surface

Table 4Process parameters used in NICA-DONNAN model for sorption of Zn and Al to humic acid (where KMe refers to ion-specific, average intrinsic binding constant; cf. Milne et al., 2003)

Type of homogeneousarea

Depth interval N pH Ec(μS/cm)

Alk(mmol/l)

Cl(mmol/l)

NO3

(mmol/l)SO4

(mmol/l)Na(mmol/l)

K(mmol/l)

Mg(mmol/l)

Ca(mmol/l)

Al(mmol/l)

Fe(mmol/l)

DOC(mmol C/l)

Nature recharge Uppermost(0–1 m)

40 4.31 167 0.00 0.25 0.24 0.29 0.26 0.02 0.04 0.05 0.21 0.00 0.43

Moderately deep(7–10 m)

16 4.51 180 0.00 0.40 0.17 0.60 0.37 0.07 0.17 0.23 0.04 0.00 0.21

Deep (20–25 m) 20 5.71 208 0.10 0.38 0.01 0.47 0.38 0.04 0.09 0.25 0.00 0.07 0.13Nature intermediate Uppermost

(0–1 m)18 4.25 288 0.00 0.51 0.27 0.69 0.42 0.05 0.06 0.17 0.31 0.01 0.73


5 5.26 300 0.06 0.60 0.01 0.77 0.92 0.07 0.21 0.63 0.00 0.21 0.38

Deep (20–25 m) 1 5.81 97 0.10 0.27 0.01 0.19 0.27 0.01 0.03 0.17 0.00 0.02 0.07Agriculture recharge Uppermost

(0–1 m)15 4.98 446 0.03 0.47 1.87 0.57 0.35 0.49 0.42 1.00 0.04 0.01 1.16


17 5.31 596 0.05 0.96 0.27 0.90 0.68 0.32 0.40 1.14 0.01 0.00 0.32

Deep (20–25 m) 8 5.55 646 0.10 1.08 0.02 1.67 0.89 0.09 0.82 1.48 0.00 0.15 0.16Agriculture

intermediateUppermost(0–1 m)

58 5.67 554 0.24 0.64 1.33 0.75 0.62 0.70 0.59 1.11 0.02 0.01 2.11


59 5.76 554 0.24 0.95 0.06 0.89 0.88 0.10 0.42 1.12 0.00 0.09 0.46

Deep (20–25 m) 21 5.98 421 0.41 1.20 0.01 0.80 0.68 0.06 0.24 0.90 0.00 0.18 0.27Discharge Uppermost

(0–1 m)18 6.05 477 0.79 0.60 0.52 0.63 0.65 0.50 0.46 1.06 0.02 0.02 2.38


6 6.75 646 3.31 0.91 0.01 0.56 0.51 0.04 0.24 1.85 0.00 0.12 0.38

Deep (20–25 m) 3 7.00 469 4.00 0.41 0.17 0.27 0.47 0.07 0.26 1.88 0.00 0.09 0.32

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complexation to amorphous Fe-hydroxide were mod-elled using PHREEQC (Parkhurst and Appelo, 1999).Surface complexation to humic acid was modelled usingECOSAT (Keijzer and Van Riemsdijk, 1998). Generalsingle-solute isotherm type curves were establishedfor these three sorbents from a representative range offresh groundwater systems. For example, sorption toiron oxides and organic matter was described by aFreundlich isotherm:

bMe ¼ K Me2þ� �n ð3Þ

with

logK ¼ aþ b pHþ c pH2 þ d pH3

n ¼ aVþ bVpHþ cVpH2 þ dVpH3

where βMe is the fraction of the sorbent occupied by themetal, [Me2+] is the free activity of Me2+, K is the sortionaffinity and n is an exponential term. For each of the threesorbents, the fraction occupied by a trace metal wascalculated for a series of fresh water compositions: (1)from pH 4 to 7, (2) from unpolluted (soft, slightlymineralized water) to hard, mineralized but still freshgroundwater, (3) one tenth of the maximum tolerableconcentration for drinking water for the trace metal ofinterest, to ten times that value. This yielded a series of 63groundwater compositions that is representative forgroundwater composition in Pleistocene, sandy areas inthe Netherlands (Van den Brink et al., 2007). For each pHvalue in the range of 4 to 7 with an interval of a half pHvalue, we fitted a Freundlich isotherm (log k and n)through the set of trace metal free activities and calculatedfractions occupied. This resulted in 7 pH-dependentisotherms. For each sorbent, the values for the third-orderpolynomial pH-function were subsequently obtainedfrom a fit through the series of 7 log k and n valuesusing GENSTAT 5 (1987).

The third step was to calculate the total amountsorbed as the sum of the products of the fraction sorbedand the amounts of individual sorbents present for agiven activity of a trace metal and a given sedimentcomposition:

SMe ¼ SclaybMe;clay þ SoxbMe;ox þ SSOMbMe;SOM ð4Þ

where SMe refers to the amount of sorbent present in ageological unit and βMe is the fraction of the sorbentoccupied by Me. As amount of sorbent present we usedthe medians per geological unit of measured sorbentcontents (Table 1). The Freundlich isotherm used in thegroundwater transport model was calculated per grid cell

from an empirical fit of data points that relate the totalsorbed amount, SMe, to the aqueous concentration of thetrace metal. Four trace metal concentrations wereconsidered: the actual concentration, the drinking waterlimit, and one tenth and ten times the value of the latter.Now, total concentrations instead of the associatedaqueous activity of Zn or Cd are used in order to obtainsingle-solute sorption isotherms that can be used inMT3DMS (Zheng and Wang, 1999). A typical range ofFreundlichK values for Zn in the uppermost groundwateris 2.36E-04 to 6.13E-03 (based on concentration in waterin μg/l and adsorbed on soil in mg/kg), n values variesbetween 0.82 and 0.99.

4. Results and discussion

4.1. Regional groundwater contamination

Our field assessment shows that in the nature areasthere is regional contamination of groundwater at thewater table. In the 2003 monitoring program, 31 of the 60samples of uppermost groundwater exceeded the statutoryDutch intervention limit for cadmium in groundwater(6 μg/l) and 33 exceeded the intervention limit for zinc(800 μg/l). A clear pH-dependency of groundwatercontamination with Cd and Zn is observed, which ishere not presented. The monitoring programs in theKempen revealed that the median concentration of Cd isstatistically significantly higher than those calculated forsurrounding sandy soils areas in the province Noord–Brabant: 6.64 μg Cd/l versus 1.3 μg Cd/l. Note that thesevalues are also anthropogenically influenced.

Table 4 presents the median values of the macro-chemical components and pH for the homogeneousareas. In all areas, the pH rises with increasing depth.Agricultural areas show higher concentrations of majorcations and anions than nature areas. The higher pH inagricultural areas is attributable to the use of manure andlime fertilizers, which boosts the concentrations of themajor cations and anions compared with those observedin nature areas.

4.2. Transport through the unsaturated zone andleaching to groundwater

Fig. 4 shows the modelled cadmium concentration inthe uppermost groundwater (0–3 m depth) in 2005. Thedominant pattern is that after starting with very highvalues close to the smelters, cadmium concentrationdecreases with increasing distance. The concentrationsin the uppermost groundwater are more variable andmore scattered than the metal contents in the topsoil

Fig. 4. Simulation results of cadmium concentration (μg/l) in uppermost groundwater (0–3 m depth) in 2005.


(Fig. 1) and atmospheric deposition pattern that is usedas upper boundary for the model (Fig. 3). The factorsmainly responsible for the short scale spatial variation inconcentrations in cadmium content are differencesin retardation for various soil types, and also depth ofwater table. The spatial variability in loading at thewater table was found to be high. Fig. 5 shows the

Fig. 5. Median, 25 and 75 percentile breakthrough curves of cadmium (μg/l)areas derived from all modelled breakthrough curves in those areas.

median, 25 and 75 percentile breakthrough curves ofcadmium at groundwater level in nature infiltrationareas and agricultural infiltration areas, i.e., areashomogeneous in terms of their land use groundwaterregime. The median breakthrough curve for the natureinfiltration areas peaks about 30 years earlier than for theagricultural infiltration areas, but there is large variation:

at the phreatic surface in nature infiltration and agriculture infiltration


the 25 percentile breakthrough curve of agriculturalinfiltration areas occurs before the 75 percentile curve ofthe nature infiltration areas. The variation in break-through curves within homogenous areas is mainly

Fig. 6. Measured and modelled cadmium concentrations (μg/l) in the uppeexfiltration areas as boxplots with median values, P25–P75 and non-outinfiltration=33, 1990 nature intermediate=7, 2003 nature intermediate=46,

attributed to variation of soil pH and groundwater levelswithin homogeneous areas. Other factors controlling thevariation are differences in soil organic matter andcontent and differences in the metal load.

r meter of groundwater in nature infiltration, nature intermediate andlier range (N monitoring: 1990 nature infiltration=13, 2003 nature1990 discharge=6, 2003 discharge=15).


4.3. Verification of historic modelling

The model results were evaluated by comparing themeasured and modelled Cd and Zn concentrations forgroundwater at the water table. The two datasets ofshallow groundwater samples from 1990 to 2003/2004were used. Input variables for the transport model werederived from regional maps and surveys and hydrologicmodels. Therefore, since the input parameters wereincorporated per area that is homogeneous in terms ofland use, soil type and hydrogeology, it is most useful tocompare the modelled and measured concentrations perhomogeneous area statistically. Fig. 6 shows the medianand range of modelled and measured concentrations for1990 and 2003/2004 as boxplots for the nature infil-tration areas, nature intermediate areas and dischargeareas. Here, the statistics for modelled concentrationswere obtained from all layer-1 grid cells within ahomogeneous area. In both the measured and modelledconcentrations there is a clear increase from 1990 to2003. It seems that the medians, the range of concentra-tions and its temporal changes have been well simulated.Here, it must be noted that the reactive transport modelwas developed without any calibration.

The comparison also reveals that the highest medianvalues and the largest range in Cd and Zn concentrationsoccur in the uppermost groundwater in nature infiltra-tion areas. In 2003 the modelled and measured medianZn concentrations were 1799 and 1713 μg/l in theseareas, compared with 1249 and 900 μg/l in “interme-diate” nature areas and 155 and 61 in discharge areas.The high concentration in the nature infiltration areas isattributable to the unsaturated zone being prone toleaching because of its low soil pH. Furthermore, inthese areas, the shallow groundwater is not influencedby presence of old pristine exfiltrating groundwater.

Large differences of up to two orders of magnitude forindividual measurement locations should also be noted,which are attributed to local-scale variation in soil pH, etc.When we compare our modelling fit with other studies ascited below, we note that a match within 50% of observedvalues for most observation points has not been reached inany 3D field modelling study, even those where processparameters were calibrated. The difference betweenmeasured and modelled values is often several factorswhen individual points are compared, a regional approachcannot cope with individual locations. The discrepancy isattributed to e.g. errors in mass estimate (Schirmer et al.,2000) or problems with specifying the contaminantboundary condition in the source area (Brun et al., 2002).

Ideally, physically-based distributed models areapplied and all parameters are independently obtained

in field application of transport models. This can bereached for systems that are rather homogenous in theirspatial characteristics, have a temporally constant inputand where a limited amount of processes is operational.However, when models are used at a regional or largerscale this can seldom be reached, especially in cases of a3D modelling approach as in this study. Two kinds ofapproaches are now recognized. First, values for processparameters are obtained by means of calibration.Schäfer and Therrien (1995), Keating and Bahr(1998), Brun et al. (2002) and Prommer and Stuyfzand(2005) used this approach in 3D, local field studieswhere groundwater transport happened within a singlegeological stratum and a limited amount of processparameters needed to be calibrated. Mostly, one or a fewbiodegradation constants were calibrated. A problemwith calibration is that non-unique calibration solutionsmay exist even for these relatively simple field systems(Schäfer and Therrien, 1995; Saaltink et al., 2003).Another problem is that objective calibration criteriahave often not been used and the reproduction ofpatterns is generally felt satisfactory. A minimumrequisite for calibration would be use of optimizationsoftware and even this software needs subjectiveweighing criteria for the calibration parameters involved(Van Breukelen et al., 2004).

The second kind of approach is based upon use ofbest available information (Brusseau, 1991). Historicalmodelling with a predictive approach is performed usingindependently estimated or measured parameters to testthe performance of the model (Kent et al., 2000;Schirmer et al., 2000; Schreiber et al., 2004; Curtis et al.,2006; Thorsen et al., 2001). Sensitivity analysis isperformed: 1. to investigate model sensitivity toparameters and 2. to explore the influence of measure-ments error or system spatial and temporal variability.After this, the model is suitable for future scenariopredictions. This approach is more useful for under-constrained problems, where non-unique calibrationsolutions will certainly exist and unrealistic values forparameters may become obtained.

We performed a sensitivity analysis on historicsurface loads, groundwater composition and sedimentgeochemistry. These are the most dominant inputparameters controlling the subsurface metal concentra-tion. We modelled a scenario with half of the calculatedsurface input load and a double surface input load. Thestatistics of modelled zinc concentrations in natureinfiltration areas are plotted in Fig. 7. The differencebetween modelled median, 25 percentile and 75percentile of the basis scenario (respectively, 1799,859 and 2867 μg/l) and those measured (1713, 878 and

Fig. 7. Boxplots with median values, P25–P75 and non-outliner range of modelled zinc concentrations in uppermost groundwater in natureinfiltration areas as a result of a sensitivity analysis on Zn surface load.


3784 μg/l) is smaller than that of the double surface loadscenario (4003, 2493 and 6049 μg/l) and half surfaceload scenario (765, 293 and 1328 μg/l).

The second and third sensitivity scenarios were donewith the 25 percentile and 75 percentiles concentrations ofthe individual macro-chemical groundwater componentsand the sorbent contents of the geological units,respectively. As explained before these input valueswere derived by taking statistics per homogeneous area ofgroundwater quality and for sediment geochemistry pergeologic unit. The best available information scenariowasdonewithmedian values fromTables 1 and 4. Because the

Fig. 8. Modelled zinc retardation factor in uppermost groundwater inagricultural area as result of a sensitivity analysis on groundwaterchemistry (hydrochemistry) and sediment composition (geochemis-try). The retardation was modelled using median, 25 percentile and 75percentile values of individual macro-chemical groundwater element(including pH) and sediment sorbents (clay minerals, organic matterand iron oxides).

inverse relation between ionic strength of a solution andthe relative sorption of an individual trace metal to soilparticles, the 25 percentile scenario was done with asolution that has a lower pH than the basis scenario andhigher concentrations of macro-chemical elements. Fig.8 gives the result for these sensitivity scenarios ascalculated retardation of zinc in shallow groundwater inagricultural areas. Not surprisingly, the mobility of metalsis higher in the 25 percentile scenario and lower in the 75percentile scenario. However, there is an obviousdifference in model sensitivity towards groundwatercomposition and the sediment geochemistry. As a resultof variation in groundwater chemistry the retardationvaries between 23 and 34. This is a minor change inmobility comparing to sediment geochemistry scenario.The retardation in this scenario varies between 1 and 131.There are two reasons for this difference in sensitivity.First, the large uncertainty in sediment compositionwithin a geological formation compared to the uncertaintyin groundwater concentrations within a homogeneousarea: the 25 percentile of the clay content in the BoxtelFormation is lower than the detection limit of 0.1% whilethe 75 percentile is 12.6%, the 25 percentile of the pH isthe uppermost groundwater in agricultural areas is 4.84and the 75 percentile is 5.45. Second, Cd and Zn are notstrong complex-forming metals in fresh groundwatersystems. This results in a behavior in which the free metalactivity is not strongly dependent on the aqueouscomposition. Curtis et al. (2006) came to the oppositeconclusion for uranium(VI) sorption due to formationof U(VI)-carbonate complexes in groundwater with ahigher alkalinity and pH.

Fig. 9. Depth profiles of average values of modelled cadmium and zinc concentrations in homogeneous areas and measured concentration for all areas.


After leaching from the unsaturated zone, the fate ofmetals depends on the flow of the groundwater andsorption capacity within the groundwater-saturatedzone. In infiltration areas metal-contaminated ground-water was found at a depth of 18 m but in intermediateor exfiltration areas clean pristine groundwater was alsofound at depths near surface. Fig. 9 presents the depthprofiles of average Cd and Zn concentrations from allgroundwater samples in the area. Unfortunately, therewere insufficient samples to allow us to statisticallyanalyze the groundwater samples grouped according tothe different homogeneous areas. Concentrations dropstrongly from 3 to 13 m below surface. The averagegroundwater quality is influenced by contaminantsleaching from the surface down to a depth of 13.5 m.Increased concentrations were occasionally foundbelow this depth in the monitoring survey as well asin the model. The model predicted no increased metalconcentration deeper that 18 m for the present situation.This is in agreement with the results of the monitoringsurvey.

There is a clear difference in the depths of the Cd andZn front between the homogeneous areas. As expected,the metal front is much deeper in the nature areas than inthe agricultural areas, due to difference in pH andassociated retardation factor in the shallow subsurface.The average values in nature infiltration areas between0–3 and 3–6 m depth are almost the same (2210 and1965 μg/l for Zn, and 29 and 24 μg/l for Cd), indicatingthat leaching to shallow groundwater is at momentaround its maximum in these areas.

4.4. Predictive transport modelling: hydrogeologicaland geochemical controls

As described before, sorption of metals on clayminerals, iron oxides and organic matter is the dominantprocess controlling reactive transport at shallow depths.The pH of the infiltrating groundwater increases to nearneutral with increasing depth (see Table 1), resulting inan increase of sorption. Therefore, cadmium and zincbecome strongly retarded in the saturated zone, despitethe low reactivity of the sandy sediments. Acidificationof groundwater in the past decades has also been limitedto no more than several tenths of pH units at shallowdepths, and thus no strong mobilization has occurred atregional-scale. The relative importance of sorptivecontrol versus groundwater travel time is thus ofprime interest to relate future Cd and Zn surface waterload to leaching and transport in groundwater zone.

Table 5 provides the mass balances of cadmium andzinc for the three catchments. The mass balances werecalculated with the following equations:

Storedunsat þ Storedsatð Þt¼ Int � Outt ð5Þ

Rcht ¼ Int � Storedunsat þ Storedsat þ Outt ð6Þ

Storedunsat ¼ Storedsoil moisture þ Storedsorbed ð7Þ

Storedsat ¼ Storedgroundwater þ Storedsorbed ð8Þ

Table 5Calculated mass budgets for cadmium and zinc of the Beekloop–Keersop, Buulder Aa and Tungelroijsche Beek catchment (soil surface load in1000 kg and other budgets in percentage of the total soil surface load)

Cadmium Zinc

Soilsurfaceload

Storedin soil

Cumulativerecharge flux

Stored insaturated zone

Cumulativedischarge flux

Soilsurfaceload

Storedin soil

Cumulativerecharge flux

Stored insaturated zone

Cumulativedischarge flux

Solute Sorbed Solute Sorbed

1000 kg % 1000 kg %

Beekloop–Keersop Beekloop–Keersop1965 33.9 98.8 0.9 0.1 0.7 0.1 1932 93.6 6.4 0.9 4.6 1.01990 51.7 95.7 4.2 0.8 2.7 0.8 3079 84.5 15.5 2.0 10.0 3.62005 52.1 86.2 13.8 2.3 9.3 2.3 3219 71.6 28.4 3.7 18.1 6.72050 52.5 52.6 47.3 4.9 32.3 11.0 3342 29.6 70.4 7.2 44.0 19.3

Buulder Aa Buulder Aa1965 44.7 98.7 1.3 0.3 1.0 0.1 2949 94.6 5.4 1.4 3.4 0.61990 68.2 90.6 9.4 2.6 5.9 0.9 4614 83.2 16.8 4.0 10.2 2.62005 68.6 80.2 19.9 4.1 13.7 2.1 4750 69.9 30.1 5.8 19.6 4.72050 69.1 53.1 46.8 4.6 33.7 8.6 4896 30.0 70.0 6.8 48.7 14.6

Tungelroijsche Beek Tungelroijsche Beek1965 33.9 98.5 1.4 0.4 0.8 0.3 2010 94.2 5.9 1.1 3.4 1.31990 51.9 92.7 7.3 1.9 3.6 1.8 3218 83.7 16.4 2.8 8.7 4.92005 52.3 86.2 13.9 2.3 7.7 3.8 3378 73.8 26.2 3.1 14.5 8.62050 52.8 65.2 34.9 3.3 17.0 14.6 3576 39.0 61.0 2.9 29.6 28.6


where Int is the soil surface load, Outt is the cumulativedischarge flux, Rcht is the cumulative recharge flux,Storedunsat is the metal amount in the unsaturated zoneand Storedsat is the amount in the saturated zone.

All three catchments show an increase of the ratiosorbed to dissolved in the saturated zone over time,which is a result of the downward movement of thecontamination front with associated near neutral pHconditions in the deeper subsurface. The modelpredicted that in 2050, for all the three catchments,87% of the cadmium in the saturated zone will be sorbedon the soil particles (compared with 70% in 1990).However, there is a substantial increase in the totalamount of cadmium and zinc present in the saturatedzone in the period 1990–2050.

The model predictions for the three catchments needfurther attention to illustrate the differences on hydro-geological and geochemical controls. In Beekloop–Keersop intensive leaching of metals to the groundwaterstarts a few years later than in Buulder Aa andTungelroijsche Beek. In 1990 4% of the total cadmiumload was leached to the groundwater in Beekloop–Keersop; in Buulder Aa and Tungelroijsche Beek thiswas 9 and 7%. From 1990 to 2005 the leaching inBeekloop–Keersop increases by more than a factor 3.3,compared with comparable factors of 2.1 and 1.9 for

Buulder Aa and Tungelroijsche Beek. In the end,Tungelroijsche Beek shows the slowest leaching (65%of cadmium still present in the unsaturated zone in 2050,this is both 53% for Beekloop–Keersop and BuulderAa) and least metals present in the saturated zone (only58% of the leached cadmium is present in the saturatedzone in 2050, this is 79 and 82% for Beekloop–Keersopand Buulder Aa). The soils in the Beekloop–Keersopcatchment are most vulnerable to leaching: the soilparent material in the Kempen High area consists ofmedium and coarse sand and some gravel depositscharacterized by lower pH and smaller contents oforganic matter and clay fractions than in the Roer ValleyGraben. Less intensive leaching in the period before1990 is explained by the fact that the zinc smelters arenot located within the catchment. This catchment doesnot contain the areas with the highest level ofcontamination around the smelters. Because non-linearsorption behavior, the retardation factor is inverselyrelated to increasing concentrations, leaching is lessintensive. Despite the higher reactivity of the soil parentmaterial, the leaching in the Buulder Aa catchment is asintensive as in the Beekloop–Keersop catchment. TheBuulder Aa catchment contains the largest proportion ofnature areas. As mentioned before, retardation withinthe unsaturated zone is greater in agricultural soils than

Fig. 10. Simulated cadmium load by seepage of groundwater to surface waters in the Beekloop–Keersop and Tungelroijsche Beek catchments, flux inkg/year and concentration in μg/l.


in nature soils because the pH is higher due to liming.The Tungelroijsche Beek catchment has the highestretardation of cadmium in the unsaturated zone. Due tothe parent material and agriculture as dominant land use,this catchment is less vulnerable for metal leaching togroundwater.

Fig. 10 provides the simulated cadmium load ofsurface water by groundwater exfiltration and drainagein the Beekloop–Keersop and Tungelroijsche Beekcatchments as flux in kg/year and average concentrationin μg/l. Ditches and drains – particularly the latter – arewidespread in the catchments studied and part of thesurplus precipitation drains away rapidly via them. Thesurface water load was calculated by multiplying theconcentration in a grid cell by the river and/or drainageflux for that specific grid cell and summation of all gridcell in a catchment.

Qj;t ¼Xni¼1

Ci;j;td Frivi;j;t ð9Þ

Where Qt,j is the surface water load in catchment j andat time t (kg yr−1), Ci,j,t is the metal concentration ingrid cell i (kg m3) and Friv, i,j,t is the river or drainageflux in grid cell i (m3 yr−1).

The concentrations were derived by dividing themass flux (kg yr−1) by the water flux (m3 yr−1) of

exfiltrating groundwater. Here, the concentrations wereused without considering geochemical transformationprocesses in the stream sediment. The modelledconcentrations of exfiltrating groundwater are in thesame range of several μg/l in both catchments, and willincrease in the coming decades. The simulations showthat for the Beekloop–Keersop catchment a peak will bereached at 2030, whereas the flux and concentration willgradually increase until 2050 for Tungelroijsche Beek.

To establish the hydrogeological control, Fig. 11presents the relative concentration of a conservativesolute in the surface water system as a result ofgroundwater exfiltration. In this scenario the rechargegroundwater contains a contamination block front with aconcentration of 1 during 10 years. In the remaining90 years the recharge concentration was set at 0. Themaximum concentration in exfiltrating groundwater isreached in both catchments after 10 years, which is atthe end of the block front when the concentration of therecharge water switched from 1 to 0. This means thatthere is an instantaneous response of the surface waterload to changes in groundwater recharge concentration.Note that transport in the unsaturated zone was notconsidered in this conservative simulation, but for aconservative solute this takes about 1 to 2 years.

The two main differences between the Beekloop–Keersop and Tungelroijsche Beek catchments are the

Fig. 11. Simulated surface water concentration as result of transport of a non-reactive contamination block front input (recharge concentration of 1during 10 years followed by 0 during 90 years).


maximum relative concentration at 10 years after the startof the simulation and the concentrations from 30 years on.The maximum concentration for the Tungelroijsche Beekcatchment is 0.56 and that for Beekloop–Keersopcatchment is 0.45, which is a difference of 23%. After30 years the concentration of the Tungelroijsche Beek hasdecreased to almost zero. For the Beekloop–Keersop, therelative concentration increases slightly to a second peakof 0.068 at 50 years. Short streamlines feeding thedrainage system are more important for the Tungel-roijsche Beek than for the Beekloop–Keersop. As well asbeing fed by the drainage system, the Beekloop–Keersopis also fed by baseflow, which impacts on the surfacewater system for more than 100 years.

Regarding exfiltration of solutes from groundwaterto surface water, the results of the reactive transportsimulation are opposite to the results of conservativetransport (compare Figs. 10 and 11). For a non-reactivetracer, the Tungelroijsche Beek is the quickest respond-ing system, whereas the Beekloop–Keersop is thequickest for reactive transport modelling. This reversalis caused by differences in soil type, dominant land useand geology between the areas: due to dominantlynature land use combined with the low reactivity of thesoil parent material, the Beekloop–Keersop catchmentis more vulnerable to leaching of metals to the surfacewater than the Tungelroijsche Beek catchment.

5. Conclusions

Using coupled unsaturated and saturated zone flow andreactive transport models, a remarkable difference wasfound in the behavior of both cadmium and zinc withinthree catchments studied in the Cd- and Zn-contaminatedKempen area in the south of theNetherlands. This resultedin a difference in surface water load due to exfiltration ofcontaminated groundwater between the catchments. It isattributable to differences in the geohydrologic andgeologic structure, soil type and dominant land use. Theintensively drained Tungelroijsche Beek catchment,which in terms of its hydrogeology should be the mostvulnerable for solute discharge from groundwater to thesurface water, turns out to be the least vulnerable formetals. This is due to its soil parent material that isstrongly correlated to geological build-up of the RoerValley Graben and dominant agricultural land use thatretards metal leaching to groundwater for a long time. TheBeekloop–Keersop catchment which in terms of itshydrogeology should be the least vulnerable turns out tobe the most vulnerable for the metals. This is mainly dueto the soil parentmaterial in this catchment on theKempenHigh. The subsurface is as a result of its low pH andorganic matter content extremely vulnerable for leaching.Overall, it can be concluded that geochemical controls aredominating the subsurface metal transport.


Comparison of statistical results of the historicalmodelling with those of actual data at the level ofhomogenous areas is a proper way for evaluation of aregional subsurface transport model: the modellingapproach predicts for homogeneous areas cadmium andzinc concentrations in shallow groundwater that are in thesame order of magnitude and have the same range inconcentrations as measured values. Depth profiles ofconcentrations are also comparable between modelresults and field measurement. As concentration depthprofiles typically include all the important features ofsubsurface solute transport that are soil input load,groundwater flow and geochemical processes, theseare very useful for verification of regional subsurfacetransport models.

A sensitivity analysis on metal surface input loads,groundwater composition and sediment geochemistryreveals that the best available information scenario predictsthe range in observed concentrations very well. Due to thewide range inmeasured organicmatter, clay and iron oxidecontent of the sediment the model is sensitive to theparamerisation of the sediment geochemistry.

Acknowledgements

The authors are grateful to ‘Actief Bodembeheer deKempen’, for funding a major part of the research. EbelSmidt is gratefully acknowledged for stimulating discus-sions. The research was co-financed by TNO researchprogram for the Ministry of Environment. Peter Venemais thanked for providing non-published data for bindingconstants to humic acids. Joy Burrough advised on theEnglish. The authors thank the editor-in-chief, Dr. E.O.Frind, and three anonymous reviewers for their usefulcritical comments on the draft versions of this paper.

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