RESEARCH ARTICLE

Simulation of the plant uptake of organophosphatesand other emerging pollutants for greenhouse experimentsand field conditions

Stefan Trapp & Trine Eggen

Received: 16 July 2012 /Accepted: 13 November 2012# Springer-Verlag Berlin Heidelberg 2012

Abstract The uptake of the organophosphates tris(2-chlor-oethyl) phosphate (TCEP), tris(1-chloro-2-propyl) phosphate(TCPP), tributyl phosphate (TBP), the insect repellant N,N-diethyl toluamide (DEET), and the plasticizer n-butyl benze-nesulfonamide (NBBS) into plants was studied in greenhouseexperiments and simulated with a dynamic physiological plantuptake model. The calibrated model was coupled to a tippingbuckets soil transport model and a field scenario with sewagesludge application was simulated. High uptake of the polar,low-volatile compounds TCEP, TCPP, and DEET into plantswas found, with highest concentrations in straw (leaves andstem). Uptake into carrot roots was high for TCPP and TBP.NBBS showed no high uptake but was rapidly degraded.Uptake into barley seeds was small. The pattern and levelsof uptake could be reproduced by the model simulations,which indicates mainly passive uptake and transport (i.e., bythe transpiration stream, with the water) into and within theplants. Also the field simulations predicted a high uptake fromsoil into plants of TCEP, TCPP, and DEET, while TBP is morelikely taken up from air. The BCF values measured andcalculated in the greenhouse study are in most cases compa-rable to the calculated values of the field scenario, whichdemonstrates that greenhouse studies can be suitable for pre-dic t ing the behavior of chemicals in the f ie ld .

Organophosphates have a high potential for bioaccumulationin crops and reach agricultural fields both via sewage sludgeand by atmospheric deposition.

Keywords Bioaccumulation . Emerging contaminants .

Modeling . Organophosphates . Flame inhibitors .

Detergents . Consumer products . Plant uptake

Introduction

In the early years of environmental chemistry, most focus wason organochlorine compounds like DDT, lindane, and poly-chlorinated biphenyls. This was largely due to their persis-tence, bioaccumulation, toxic potential, and global transport.A second factor was that chlorinated compounds were alsoeasy to measure in small amounts due to the invention of theelectron capture detector by James E. Lovelock in 1957.Manyof these “old” organochlorine compounds are now ruled out(Stockholm Convention, REACH). In recent years, novelanalytical and extraction methods have been developed forpolar and ionic compounds present in environmental matrices.HPLC and LC-MS allow measurement of nonvolatile, polarcompounds. Subsequently, it was increasingly recognized thatpolar organic compounds are present in the environment inlarge numbers (Koplin et al. 2002; Meyer and Bester 2004;Bester 2005; Reemtsma et al. 2006).

Examples of polar emerging compounds are organophos-phates that are widely used as flame retardants and plasti-cizers (Reemtsma et al. 2008). Tris(2-chloroethyl)phosphate (TCEP; CAS# 115-96-8) is used as flame retar-dant in the EU with about 1,000 tons usage/year (EuropeanCommission 2009), also tris(1-chloro-2-propyl) phosphate(TCPP; CAS# 13674-84-5) is used for this purpose. Tributylphospate (TBP; CAS# 126-73-8) is used as detergent.Organophosphates (OPE) have been found in sewage sludgein Sweden and Norway with maximum concentrations in the

Responsible editor: Elena Maestri

Electronic supplementary material The online version of this article(doi:10.1007/s11356-012-1337-7) contains supplementary material,which is available to authorized users.

S. Trapp (*)Department of Environmental Engineering,Technical University of Denmark, Miljoevej, Building 113,2800 Kgs. Lyngby, Denmarke-mail: stt@env.dtu.dk

T. EggenBioforsk, Norwegian Institute for Agriculturaland Environmental Research, Postveien 213,Klepp St 4353, Norway

Environ Sci Pollut ResDOI 10.1007/s11356-012-1337-7

milligrams per kilogram range (Marklund et al. 2005,Thomas et al. 2011). OPE were also detected in river waterin relatively high concentrations (Andresen et al. 2004).Wastewater does not seem to be the only source: OPE werealso detected in rain and snow from Germany (Regnery andPüttmann 2009) with concentrations up to micrograms perliter. Moreover, there is evidence for global occurrence inthe atmosphere (Möller et al. 2012). It is thus no surprisethat OPE were also detected in soils that had no history ofsewage sludge application or irrigation (Mihajlovic et al.2011). N,N-diethyl toluamide (DEET; CAS# 134-62-3) ismainly used as an active compound in insect repellents inconsumer products in many countries (Aronson et al. 2011).N-butyl benzenesulfonamide (NBBS; CAS# 3622-84-2) is asulfonamide plasticizer used in polyamide and copolyamideplastic. DEET and NBBS are less analyzed for than OPE,however, they have been detected in wastewater and surfacewater (Weigel et al. 2002; Huppert et al. 1998).

Polar emerging compounds are present in wastewater(Reemtsma et al. 2006), and via application of sewagesludge to agricultural land, or with irrigation water, theymay find their way into crops (Wu et al. 2010; Eggen etal. 2011). Eggen et al. (2012) studied the uptake of TCEP,TCPP, TBP, DEET, and NBBS into plants and found highaccumulation, in particular of TCPP and TCEP. The mea-sured results were compared with dynamic model simula-tions, in order to calibrate the model and to interpret theexperimental data. The model was then applied to simulatethe fate of the five compounds after application of sewagesludge to fields, and the uptake and accumulation in foodcrops under realistic field conditions. Atmospheric deposi-tion was considered, where possible, which allowed toquantify the relative impact of the input processes.

The major objectives of this study can be summarized as

– Can the high uptake of some emerging compounds in thegreenhouse experiments be explained by their physico-chemical properties (i.e., is uptake passive or active)?

– Is plant uptake also relevant under (simulated) fieldconditions?

– Is experimental and simulated plant uptake from green-house conditions relevant for field scenarios?

– What levels can be expected for field crops, and what isthe major source (sewage sludge versus atmosphericdeposition)?

Methods

Experimental approach

Pot growth experiments were performed in greenhouse atBioforsk Vest Særheim, Norway. Detailed information of the

design and performance of growth experiment is presentedelsewhere (Eggen et al. 2011, 2012), briefly: agricultural soilfrom Western Norway was used; loamy sand with low or-ganic content at 0.7 % organic carbon, pH 5.4, and cationexchange capacity at 46.6 mmolckg

−1. The soil was spikedwith solutions containing the mix of test compounds(50 mL to each pot), mixed carefully by hand, and pouredinto each pot. Seeds were sown, kept at 14 °C untilgermination and transferred to greenhouse and temperaturewas set at 20/14 °C (day/night 16 hday−1 length). Thepots were placed on individual trays, irrigated with fer-tilized water (pH 7.4, EC 1.5 mScm−1) and excess ofwater was poured back into the pots. Uptake into fiveplant species was investigated in Eggen et al. (2012).Here, data of experiments with barley (Hordeum vulgarecv. Edel) and carrot (Daucus carota cvs. Napoli, Amagar,Rothild, and Nutri Red) were selected for the simula-tions. Structures, properties, abbreviations, and chemicalparameters of the test compounds are given in Table 1.

Model approach

Cascade model The simulation of the plant uptake experi-ments from pots in greenhouses was done using the newdynamic plant uptake model based on the multicascadeapproach (Rein et al., 2011; Legind et al., 2011). Fourdifferential equations describe uptake and loss in roots,stem, leaves, and fruits (see box). Transport processes con-sidered inside the plant are partitioning (diffusion) and ad-vection with water, i.e., purely passive transport processesonly. Loss from leaves and fruits to air is by volatilization.Deposition of OPE from air to plants is by particle deposi-tion, to soil also with rain. Soil particle resuspension withsubsequent deposition on plant surfaces is considered withan additional, fixed amount of 0.01 (leaves) and 0.001(seeds) kg soil/kg plant material. The equations are de-scribed in detail in Rein et al. (2011) and have been usedfor the simulation of a pesticide application in Legind et al.(2011).

Box. Mass balance equations for change of compoundmass m in root, stem, leaves, and fruits (indices R, St, L, andF) with time t. “Fruits” of cereals is seeds.

where C is concentration (in milligrams per kilogram); Q iswater flux (transpiration; in liters per day); A is the surfacearea (in square meters); P is permeability, exchange velocity(in meters per day); KWS is partition coefficient between

Root dmRdt ¼ QKWSCS � Q

KRWCR � kR;degmR

Stem dmStdt ¼ Q

KRWCR � Q

KStWCSt � kSt;degmSt

Leaf dmLdt ¼ QL

KStWCSt þ vdepALfPCAir � 1; 000 ALPL

KLWCL � kL;degmL

Fruit dmFdt ¼ QF

KStWCSt þ vdepAFfPCAir � 1; 000 AFPF

KFWCF � kF;degmF

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water and soil (in kilograms per liter); KiW is partitioncoefficients (in liters per kilogram) between plant com-partment i and water; ki,deg is first-order degradationrate constant (in days); factor 1,000 converts cubicmeters to liter; vdep is deposition velocity of particles (inmeters per day); and fP is fraction at particles in air (set to 1).Index S is soil.

Partition coefficients between plant tissue (roots, stem,leaves, or fruit) and water, KPW (in liters per kilogram freshweight (fw)) are all calculated from the octanol–water par-tition coefficient, KOW (in liters per liter), the lipid content L

(in kilograms per kilogram fw), and the water content W (inliters per kilogram fw) of the respective tissue:

KPW ¼ W þ L� 1:22� KbOW

The exponent b considers differences between plant lip-ids and octanol and is 0.77 for barley roots and 0.95 foraerial plant parts (Briggs et al. 1982; Trapp et al. 1994).

The differential equations can be rewritten in matrix formand solved analytically by known solutions for various typesof input (Rein et al. 2011). The simulation is divided into n

Table 1 Structures, properties, and abbreviations of test compounds

Test compounds Structure M log KOW

pKa

KAW

Tris(2-chloroethyl)phosphate (TCEP)Flame retardant

285.5

a) 1.44b) 1.72c) 1.4d) 1.7

a) 1.38 x10-4

b) 3.37x10-7

c) 1.8x10-8

d) 1.06x10-6

e) 1.37x10-4

Tris(1-chloro-2-propyl) phosphate (TCPP)Flame retardant

327.6

a) 2.59b) 2.59, 2.53-2.75c) 2.6d) 2.6

a) 2.4 x10-6

b) 6.7210-7

c) 1.57x10-7

d) 2.48x10-6

e) 3.02x10-7

Tri-n-butyl phospate (TBP)Detergent

266.3

a) 4.0b) 3.83c) 3.8d) 4.0

a) 5.76x10-5

b) 5.4 x10-5

c) 6.07 x10-4

d) 1.33 x10-4

N-butyl benzenesulfon-amide (NBBS)

213.3

a) 2.31b)1.89-2.24

b) pKa 10.1 (acid)

a) 8.87x10-5

b) 2.91x10-6

e) 1.23x10-6

N.N-diethyl toluamide (DEET)Insecticide

191

a) 2.18b) 2.42-2.46

b) pKa -0.4 (base)

a) 8.5x10-7

b) 1.18x10-4

e) 2.29x10-5

P OO

O

O

H3C

H3C

H3C

Cl

Cl

Cl

P

O

O

H3C

O

CH3

O

CH3

Entries underlined were selected for subsequent simulations by calibration, see text

M molar mass (in grams per mole), KOW n-octanol–water partition coefficient, KAW partition coefficient air–water (in liters per liter; also known asnondimensional Henry’s law constant), pKa –log of the dissociation constanta Eggen et al. (2012), log KOW experimentally determined from ChemIDPlus Advanced (http://chem.sis.nlm.nih.gov/chemidplus/chemidheavy.jsp) and KAW from EPIsuite 4.0b ACD Advanced Chemistry Development (2010)cMihajlovic et al. (2011)d Regnery and Püttmann (2009)e From estimated vapor pressure and water solubility in Eggen et al. (2012)

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periods, where each period can have its own set of inputdata. The final concentration course is calculated using thesuperposition principle (Rein et al. 2011).

Plant mass as a function of time was calculated by inte-grating the logistic growth function

MðtÞ ¼ Mmax

1þ MmaxM0

� 1� �

� e�kt

where M0 (in kilograms) is the initial plant mass and k (indays) is the growth rate. The final plant mass at harvest,Mmax (in kilograms) was measured. Transpiration Q (inliters per day) of plants is closely related to growth via thetranspiration coefficient TC (in liters per kilogram)

Q ¼ TC � dM

dt¼ TC � k �M 1� M

Mmax

� �

where Q is the water flux through the roots upwards, relatedvia the transpiration coefficient TC to the change of totalplant mass.

Buckets model Modeling of compound transport in the soil–air–plant system was done by coupling a model for waterand solute transport in soil based on the “tipping buckets”approach (Trapp and Matthies 1998; Legind et al. 2012)with the plant uptake model (Fig. 1). The tipping bucketssoil water and substance transport model was chosen be-cause its step-wise, periodic simulation mode makes it eas-ily compatible to the step-wise solution method of theanalytical multicascade solution of the plant model. A sim-ilar approach could recently successfully describe leachingand plant uptake of two nonessential heavy metals under

real-field conditions (Legind et al. 2012). The model fororganic compounds consists of five soil layers (each 20 cm)and four plant compartments (root, stem, leaf, and fruit/seed). In each time period, the water and substance balancein the five soil layers is solved iteratively considering pre-cipitation, evaporation from soil surface, infiltration, leach-ing, and transpiration (i.e., water uptake from soil bygrowing plants). Precipitation will lead to a refilling of theupper soil layer first. If this layer is filled up with water, i.e.,the field capacity is reached, the water infiltrates to the nextdeeper layer, etc. Chemical input is to the top layer bydeposition from air and with soil amendments. Chemicalsmove with the water to deeper layers and into the roots. Thedissolved concentration is calculated from the Kd (dis-tribution coefficient) of the chemical in soil. Uptake ofchemical into plants is with the water taken up by theroots at various depths. It is assumed that the plantswill always try to satisfy their water demand by uptakefrom the highest soil layer. If the roots do not findsufficient water in that layer (i.e., if the water contentfalls below the permanent wilting point), then theremaining water required for transpiration is taken fromthe next deeper layer, etc. This formulation avoids thecalculation of rooting depth and root density: it isassumed that the roots grow to where the water is.The detailed equations for water and solute transportcan be found in Legind et al. (2012) and in the SI. The coupledsoil water and solute transport and plant uptake model wasrealized as Microsoft Excel™ spreadsheet and is availablefrom the first author’s Web site.

Model parameterization for greenhouse experiment

Soil and plant input data (soil mass, soil organic carboncontent, plant mass at harvest, and water content) weredetermined in the greenhouse study (Table SI 1a in theElectronic supplementary material (ESM)). Missing data(growth rates, plant surface area, and lipid content) weretaken from the default dataset derived in Rein et al. (2011).The simulation covered 17 weeks (119 days). Initial chem-ical concentrations in soil and concentrations of chemicalsin plants at harvest were measured (Table 2). For carrots,four cultivars were studied (cv. Napoli, Amager, Rothild,and Nutri Red) and the table shows minimum and maximumconcentration in roots (whole root and peel plus core).Degradation rates of OPE in soil were determined fromlosses in nonvegetated control pots. The degradation ratesof DEET and NBBS in soil were not measured, and valueswere estimated with EPIsuite. Some parameters were un-known or uncertain and obtained by calibration: the waterflux to seeds was set to 1 % of the water flux to leaves; thedegradation rate of TCEP in soil of the barley experimentshad to be set to 0 day−1, otherwise there was too little TCEP

Fig. 1 Coupled model for soil water and solute transport and plantuptake of organic chemicals. Soil compartments in grey, and plantcompartments in white

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remaining for plant uptake. Metabolism rates from plantwere set to threefold degradation rates in soil. The physico-chemical parameters given in literature vary widely(Table 1), in particular for the KAW, and those values werechosen that gave best agreement between simulated andmeasured concentrations.

Field simulation study and model parameterization

Required input data for the water balance were taken from a10-year field experiment in Feucherolles, France (Legind etal. 2012), from August 1998 to July 2008. The data fromyear August 1999 to July 2000 were selected for the simu-lations because this year had rainfalls above average andwinter wheat as crop. The model default dataset is for wheat(Rein et al. 2011). The simulation ends with harvest (July2000). Temperature in air was used to correct degradationrates in the top soil layer and in plants from 20° (in green-house) to ambient temperature by using an Arrhenius con-stant of 1.047. For deeper soil layers, the average of theannual air temperature was used.

Water balance Data were averaged to give one precipitationvalue per half month (Table SI 2 in the ESM). The simula-tion started with “empty” soil, i.e., the water content of allsoil layers was set to the permanent wilting point,corresponding to the typical situation towards the end ofthe summer (July). Precipitation, evaporation, transpiration,and leaching are shown in Fig. SI 2 in the ESM.

Soil data The soil was divided into five layers of thicknessof 20 cm. For the top soil layer, the measured organic carboncontent of the greenhouse experiments was taken. For thelower layers, half of this value was taken. As data fordensity, porosity, thickness of soil layers, field capacity,and permanent wilting point of the soils typical values werechosen (Table SI 3 in the ESM).

Plant data Crop-specific parameters are transpiration coef-ficient, growth rates, and initial and final plant mass. Winterwheat mass at harvest was measured at Feucherolles (TableSI 4 in the ESM); other data were chosen according to Reinet al. (2011).

Chemical data Chemical was applied with sewage sludgeamendment 1st of September. The input (in milligrams persquare meter) of the compounds via sewage sludge was cal-culated from measured concentrations of the organophos-phates in Norwegian sewage sludge (Thomas et al. 2011)and simulated as pulse input by the model. For NBBS andDEET, concentrations in sludge were calculated using KOC

from measured sewage concentrations (Table 3). Norwegianregulations allow a maximum of 40 t/ha sewage sludge (dw)in 10 years (VKM Norwegian Scientific Committee for FoodSafety 2009), and it is common to apply the allowed amount atonce. This amount (4 kg/m2) mixed into the 20 cm top soil(density, 1.3 kg/L) leads to a dilution ratio of 65 kg soil/kgsludge. Table 3 gives the measured (median) concentrations insewage sludge and the resulting concentrations in top soil(20 cm). The highest concentration in top soil due to sewagesludge amendment is calculated for TCPP with 159 μg/kg dw.Concentrations of OPE in air and rain were found in literature(Table 3). According to Möller et al. (2012), OPE are mostlyadsorbed to particles in air. Wet atmospheric deposition wascalculated by the product of measured concentration in rainwith precipitation. Dry atmospheric deposition was calculatedusing a dry deposition velocity of 1 mm/s (default value of theGerman TA-Luft for fine particles, and default value for gasdeposition on leaves, Trapp and Matthies 1998). The back-ground concentrations of organophosphates in top soil result-ing from one year atmospheric deposition without applicationof sewage sludge were calculated by model simulations andused as initial top soil concentration (Table 3). Atmosphericand soil background concentrations of NBBS and DEETcould not be found and were neglected.

Table 2 Measured concentrations (in milligrams per kilogram dw) and degradation rates obtained from the greenhouse experiments (Eggen et al.2012)

Parameter Soilinitial

Barleyroots

Barleystem

Barleygrains

Carrot root Carrotleaves

Deg. rate exp.(day−1)

Deg. rate est.(day−1)

TCEP 0.85 0.523 21.65 0.029 0.22–0.58 35.7 0.01 0.0058

TCPP 0.717 0.533 4.573 0.079 6.6–14.5 12.5 0.0038 0.0058

TBP 0.62 0.86 0.76 <DL 0.23–2.83 0.345 0.0034 0.041

NBBS 1.02 <DL 0.077 <DL 0.21–0.46;mean, 0.36

0.53 nd 0.023

DEET 1.02 0.82 7.6 <DL 0.36–2.67;mean, 1.89

4.18 nd 0.009

“Stem” barley is leaves and stem; “root” carrot is peel and core. Entries set in italics were used as input for subsequent model simulations, exceptTCEP 0 day−1 fitted for barley experiments; 0.001 day−1 rate was used for field simulations

Deg. rate exp. experimentally obtained degradation rate, Deg. rate est. estimated degradation rate (EPIsuite 4.0), DL detection limit (0.01 mg/kg dw)

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Results

Simulation of greenhouse experiments

Figure 2a, b shows the measured and the simulated concen-trations in barley and carrot for the greenhouse experiments.

DEET, TCEP, and TCPP show very high uptake into barleyand carrot leaves (above 5 mg/kg dw). The model predictsthis quite accurately, indicating that passive uptake andtranslocation with the xylem stream are the relevant pro-cesses for the high accumulation of these compounds.Comparatively low is the uptake of NBBS, both measuredand simulated. NBBS is rapidly degraded (Table 2). Uptakeinto carrot roots is generally higher than uptake into barleyroots, and this holds for both measurements and simulation.For carrots, measurements from four cultivars were avail-able, and Fig. 2b shows minimum and maximum concen-trations. The difference in calculation between barley rootsand carrot roots is due to a higher lipid content of carrots(5 %, twice as high than in barley which is 2.5 %), thehigher final root mass and the lower total plant mass (andthus less transpiration) of carrots. Surprisingly, there is a bigdifference between accumulation in carrot root (peel andcore) of TCEP (measured 0.22 to 0.58 mg/kg, simulated0.38 mg/kg) and of TCPP (measured 6.6 to 14.5 mg/kg,simulated 3.9 mg/kg). Uptake into seeds is small and wasmeasurable only for TCEP and TCPP at low concentrations(<0.1 mg/kg dw). Model predictions for concentrations inbarley seeds range from 0.001 mg/kg dw for NBBS to0.09 mg/kg dw for TCPP. The outcome of the simulatedconcentrations in leaves and seeds depends strongly on theloss to air, which is controlled by the partition coefficientair–water KAW, and the optimal data were chosen fromTable 1. All measured concentrations in control samples(without spiked chemical) were below detection limit(<0.01 mg/kg dw), the only exception was TCPP in rapeseeds (0.011–0.014 mg/kg dw, not simulated here). Thisshows that uptake from air in the greenhouse experiments

Table 3 Measured median concentrations of emerging pollutants inNorwegian sewage sludge (Thomas et al. 2011), calculated amountsapplied to fields using 40 t/ha sludge application rate, and calculated

resulting concentration in top soil; calculated background concentrationin top soil; measured concentrations in rain (Regnery and Püttmann2009) and air (Möller et al. 2012)

Compound Csludgea (μg/kg dw)

measuredAmount applied(μg/m2) calculated

Resulting CSoilb

(μg/kg dw) calculatedCSoil

c background(μg/kg dw) calculated

CRaind (ng/L)

measuredCAir

e (pg/m3)measured

TCEP 128 512 7.88 0.026 73 1,450<200

TCPP 2,580 10,320 158.8 0.67 743 490<200

TBP 94 376 5.78 0.24 203 570<200

NBBS 20f 80 1.23 nd nd nd

DEET 1.35g 5.4 0.083 nd nd nd

nd no dataa Thomas et al. (2011) if not stated otherwiseb Due to sewage sludge application to the top soil layer of 20 cm thicknessc Calculated concentration in top soil after 1 year deposition from air by wet and dry depositiond Regnery and Püttmann (2009), data for urban area (Frankfurt/Germany)e Green et al. (2008; cited in Möller et al. 2012), upper number median for Oslo (urban) and lower number for Birkenes, Norway (remote)f Calculated from concentration in wastewater (1.7 μg/L, Huppert et al. 1998)g Calculated from concentration in wastewater (0.15 μg/L, Reemtsma et al. 2006) and Kd0OC×KOC, OC012 %

0.001

0.010

0.100

1.000

10.000

100.000

DEET NBBS TBP TCEP TCPP

Con

c. m

g/kg

dw a) Barley

Root meas Root sim Leaf+stem meas

Leaf+stem sim Grains meas Grains sim

0.10

1.00

10.00

100.00

DEET NBBS TBP TCEP TCPP

Co

nc.

mg

/kg

dw b) Carrot

Carrot meas min Carrot meas max Carrot sim

Leaf+stem meas Leaf+stem sim

Fig. 2 Measured and simulated concentrations (in milligrams per kilo-gram dw, log scale) in a) barley leaves, stem, roots, and seeds and b)carrots (carrot is peel and core, measured minimum andmaximum of fourvarieties are shown) and leaves (only cv. Napoli was measured) fromgreenhouse experiments. Measured concentrations of DEET, NBBS, andTBP in barley seeds<DL, 0.5 DL (0.005 mg/kg dw) is depicted

Environ Sci Pollut Res

can indeed be neglected. The outcome of a dynamic simu-lation is shown in Fig. SI 1 (ESM). Concentrations in soiland plants change over time, with maxima of concentrationsin roots and leaves in the exponential growth phase, anddecline in the ripening phase of the plants.

Simulation of the field scenario

The simulation of the field scenario gave results to thesource and loss processes of the compounds, the waterbalance, the water distribution in soil layers over time, andthe concentrations in soil and plant over time.

Time pattern for TCPP Figure 3 shows the simulated timecourse of the concentration pattern in soil and plant ofTCPP. Application of sewage sludge is 1st of Septemberon the top soil layer. TCPP transfers relatively quickly todeeper soil layers (Fig. 3 top). At the end of the simulationperiod, the highest concentration of TCPP is in soil layer 3(3.68 μg/kg dw; Table 4). Concentrations in leaves were notcalculated before March (no growth before 1st of March)and increase until May with increasing transpiration, thendecline in the ripening phase (Fig. 3 bottom).

Concentrations Table 4 shows the calculated concentrations(in micrograms per kilogram dw) in soil (maximum at theend of the simulation) and in roots, stem, leaves, and seedsat harvest (31st of July 2000) of all compounds. The con-centrations of the organophosphates are in the microgramsper kilogram range and highest for TCEP with above500 μg/kg dw in leaves and 14 μg/kg in seeds. Calculated

concentrations of NBBS are throughout very low (≤11 ng/kg dw). Simulated concentrations of DEET are also low,with maximally 0.57 μg/kg dw in leaves. One reason is thelow adsorption to sewage sludge of these two compoundsand thus the small input to fields, and perhaps the neglectionof atmospheric deposition. All samples would have finalconcentrations in seeds close to or below the detection limitof the analytical method (10 μg/kg dw) and would thusrarely have been found in samples from field studies underrealistic conditions (i.e., realistic soil concentrations), de-spite the high uptake into plants. Table 4 gives also finalmaximum concentration in soil, and the layer, in which thismaximum is located. Despite deposition from air, the con-centrations after a 1-year simulation period are highest in thelower soil layers (layer 3 or 5), except for TBP.

Leaching Details of the water balance (precipitation, evap-oration, transpiration, leaching, water content of soil layers,and plant water uptake from soil layers, all over time) areshown in the SI (Fig. SI 2 in the ESM). Briefly, the precip-itation in the considered period was 928 L/m2, leaching togroundwater of 266 L/m2 (28.7 %) and transpiration of 622(67 %) and 40 L/m2 (4.3 %) were stored in the soil. The soilis water saturated at field capacity over the winter, andleaching to lower soil or groundwater occurs until spring.Then, high transpiration empties all soil layers until end ofMay. When plants cease water uptake end of June, the soillayers fill up again. The calculated total amount of waterleaching from layer 5 to deeper soil (>1 m depth) over thewhole simulation period is 266 L/m2, with the highestleaching, 144 L/m2, in December (Fig. SI 2a in the ESM).The total amount of chemical leaching to groundwater in theone-year period is highest for TCEPwith 182μg/m2, followedby TCPP with 74.4 μg/m2 and DEET with 0.33 μg/m2

(Table 4). Leaching is only 59 ng/m2 for NBBS because thecompound is degraded before it reaches the lowest soil layer.Calculated leaching of TBP is only 40 pg/m2 because 1 yearsimulation is too short for this compound to reach lower soillayers. Themaximum concentration in leachate is 1.1 μg/L forTCPP, 1.5 μg/L for TCEP, 6 ng/L for NBBS, 4.3 ng/L forDEET, and <<1 ng/L for TBP (not shown).

Discussion

Bioaccumulation in field simulation versus greenhousestudies

Greenhouse Several compounds showed high uptake intoroots and leaves. The measured bioconcentration factors(BCF), defined as concentration in plant at harvest relatedto initial concentration in soil, reached values above 40 kg/

0.00

0.01

0.02

0.03

0.04

0.05

Aug

Aug

Sep

Sep Oct

Oct

Nov

Nov

Dec

Dec Jan

Jan

Feb

Feb

Mar

…M

ar…

Apr

ilA

pril

May

May

June

June

July

July

C s

oil

mg

/L

Layer 1 Layer 2 Layer 3Layer 4 Layer 5

0.00

0.50

1.00

1.50

2.00

C le

aves

mg

/kg

dw

Fig. 3 Simulated concentration of TCPP versus time in (top) soil (inmilligrams per liter, ρdry01.3 kg/L) and (bottom) leaves (in milligramsper kilogram dw) field scenario

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kg dw for TCEP in carrot leaves (Table SI 5ab in the ESM).Also TCEP, TBP (carrot roots) and DEET (carrot leaves)showed BCF-values above 1. The high uptake was alsofound in the model simulations, which means that it canbe explained solely by passive transport with the waterstreams. The physicochemical properties of TCEP, DEET,and TCPP, i.e., low to medium lipophilicity (log KOW) andlow volatility (low KAW) (Table 1) together with relativepersistence are the reasons for the high BCF values inleaves. For comparison, partition properties of TCPP (logKOW 2.53 to 2.75 and KAW 10−6 to 10−7; Table 1) are verysimilar to those of the xylem-mobile herbicide atrazine (logKOW 2.58 and KAW 8×10−8; Rippen 1991). NBBS hassimilar properties (Table 1) but does not accumulate inplants due to the higher degradation rate.

We expected lower uptake in the field simulation, due tothe dilution over a larger soil volume, the dissipation togroundwater and air and the longer time period of thesimulation (Trapp and Eggen 2011). BCF values for thefield simulation are difficult to define, because the concen-trations of the chemicals differ in the five soil layers and arenot constant over time, see discussion in the ESM. Theconcentration ratio plant to soil (BCF) can be related tothe maximum concentration in soil (Table SI 6a in theESM), to the concentration in soil when plants start to grow(Table SI 6b in the ESM), or to the concentration in soil atharvest (Table SI 6c in the ESM). The method that leads tothe lowest BCF values but is most comparable to the resultsfrom the greenhouse study is the first one: BCF is theconcentration in plants at harvest divided by the maximumconcentration in soil (which occurs in soil layer 1 aftersewage sludge application; Table SI 6a in the ESM).TCEP shows high BCF values in the field simulation, evenhigher than those from the greenhouse study. Also TCPP,DEET, and TBP reach BCF of >1 kg/kg. The BCF of TBPfor the field scenario is lower for roots, but higher for stem,leaves and seeds than that from the greenhouse. This indi-cates a strong uptake from air which is not a contribution ingreenhouse studies. NBBS has much lower BCF in the fieldstudy than in the greenhouse, because it is rapidly degradedbefore the plants start to grow. Overall, the comparison

shows that greenhouse experiments can be a good indicatorfor uptake of chemicals into crops from soil grown underfield conditions (however, it usually neglects the depositionfrom air and soil erosion by rain splashing and wind). Animportant finding for a risk assessment of these substancesis that the organophosphates and DEET under realistic fieldconditions seem to accumulate quite well in harvest prod-ucts. In the simulations, we used average—not maximum—measured sewage sludge concentrations for the organophos-phate compounds, and there may be events where sludgewith higher loading is applied on agricultural fields.

Mass balance

An advantage of model simulations is that not only concen-trations but also fluxes of input and loss can be calculatedvia the mass balance.

Soil The mass balance for the substance input is shown inTable SI 7 in the ESM. The dominating input of OPE issewage sludge. However, for TBP, calculated deposition byrain is almost half of that with sewage sludge, and for TCEPit is more than one tenth. Dry particle deposition from airplays a minor role in the simulated scenarios. Sewage sludgeapplication of 40 tons/ha is only once in 10 years, accordingto Norwegian rules, while atmospheric deposition is perma-nent. Thus, in the long run, deposition with rain is the mostimportant source of TBP in top soil, and of similar relevanceas sewage sludge application for TCEP and TCPP. Thecalculation of dry deposition is based on measurements ofurban air concentrations. With data of the rural site,Birkenes (Table 3), dry deposition would be even lessimportant.

Loss Loss from soil can be due to four processes: viadegradation, by plant uptake, by leaching, and by volatili-zation. The relative contributions of these processes vary foreach soil layer and also with time of the year. The processesare not independent: if plants would not transpire, morewater remained for leaching. Compounds being degradedcannot be lost by other processes and vice versa. The

Table 4 Calculated concentration (in micrograms per kilogram dw) in soil and in harvest products (31st of July) for the field scenario

Compound Max Csoil end(μg/kg dw)

Soillayer

Croot

(μg/kg dw)Cstem

(μg/kg dw)Cleaves

(μg/kg dw)Cseeds

(μg/kg dw)Leaching(μgm−2a−1)

TCEP 0.24 5 1.66 17.7 551 13.9 182

TCPP 3.68 3 53.3 38.6 195 12.6 74.4

TBP 0.36 1 1.1 12.9 26.7 0.2 40×10−6

NBBS 453×10−6 3 1.22×10−3 1.47×10−3 11×10−3 0.5×10−3 59×10−3

DEET 3.4e10−3 3 18×10−3 30×10−3 566×10−3 38×10−3 0.33

Concentration in soil—maximum of all layers; second column gives layer with maximum soil concentration at the end of the simulation

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calculated loss of TCEP from top soil over the entire simu-lation period is 92.5 % due to leaching, 5.3 % due to rootuptake, 0.25 % loss to air, and 1.5 % due to degradation. Theproblem is that loss by leaching from soil layer 1 is gain forsoil layer 2, in other words, not really a loss for the soil butonly a transport to the next lower layer. In order to be able tocompare the loss due to leaching with the loss due to plantuptake, we did the following calculation: both losses are inthe model proportional to the amount of water; water loss byleaching is factor 2.33 lower than water uptake due totranspiration. The loss via leaching was thus set to 2.33times lower than loss by plant uptake (Table 5). Loss byvolatilization was seen as independent from the deposition.

Accordingly, degradation is responsible for between0.3 % (DEET) and 97.6 % (TBP) of the loss of compoundsfrom soil and is the most important removal process for TBPand TCPP (67.8 %). Loss to air can be relevant for the topsoil layer, accounting for 48.7 % loss of NBBS and 19.7 %loss of DEET. For all compounds except TBP, leaching isrelevant as loss process, removing up to 24 % (DEET) of thecompound mass. For TCEP and DEET, the most relevantremoval process is, according to the simulations, plant up-take, which is responsible for about half of the total loss.

Input The chemicals in soil and plant at harvest may orig-inate from sewage sludge, or from air. Directly after thesewage sludge application, 99.7 % of TCEP, 99.6 % ofTCPP, and 95.8 % of TBP stem from the sewage sludge(calculated from Table 3). For NBBS and DEET, no con-centrations in air were available, and deposition from air hadto be neglected. With time, the compound fraction originat-ing from sludge is eliminated from soil, and deposition fromair gets more relevant.

The calculated fraction of OPE that originates from air atthe end of the simulation period (11 months after sludgeapplication) is shown in Table 6. The actual amount ofTCEP in soil at harvest is almost 100 % from air, while77.1 % of TCEP in roots, 51.9 % in leaves, and 27 % inseeds stem from aerial sources. Atmospheric deposition isless relevant for TCPP, with 31 (soil) to 14.5 % (seeds) ofthe final concentrations depending on dry and wet deposi-tion. But most TBP in leaves and seeds originates from air,

with dry deposition being the major uptake route, accordingto the model simulation. TBP is the most lipophilic and alsothe most volatile of the test substances. High uptake from airinto plants of lipophilic, semivolatile compounds was pre-dicted earlier (Trapp 2007) and has been observed repeat-edly for chlorinated pesticides and persistent organicpollutants POPs (e.g., Mikes et al. 2009).

Other findings

Möller et al. (2012) measured organophosphorus flameretardants and plasticizers in airborne particles over theNorthern Pacific and Indian Ocean toward the PolarRegions and found evidence for global occurrence of thesecompounds. Their data are generally up to factor 10 lowerthan those for Oslo cited in their study and used as inputdata here (Table 3). Also concentrations in rain, measuredby Regnery and Püttmann (2009), were taken from an urbanarea and are among the highest of all measured concentra-tions. This means, deposition from atmosphere in rural areasis less than that predicted by the simulations made here.

Organophosphates have been measured in top soil(Mihajlovic et al. 2011, Fries and Mihajlovic 2011).Concentrations at urban sites (Osnabrück, Frankfurt) ofTCEP were 5 and 13–18 μg/kg dw, at rural sites between<LOQ (0.6 μg/kg dw) and 2.5 μg/kg dw. For TCPP at urbansites, 1.2 and 2.6 to 8.3 μg/kg dw were found and 0.6 to6.3 μg/kg dw at rural sites. Concentrations of TBP inOsnabrück were <LOQ (9 μg/kg dw). The authors reportthat their sampling sites were neither affected by irrigationwith river water nor by spreading of sewage sludge, and thatthe results demonstrate the relevance of atmospheric depo-sition processes for organophosphate pollution of soil. Thecalculated concentrations in top soil due to aerial depositionof TCEP, TCPP, and TBP were 0.026, 0.67, and 0.24 μg/kgdw, which is too low for TCEP but within the measuredrange for TCPP.

OPE were screened in biota sampled in the vicinity ofNorwegian wastewater treatment plants and at a referencesite in 2010 (Norwegian Climate and Pollution Agency2011). TCEP was found in the range 0.1 to 1.6 μg/kg fwin crabs, mussels, and bird eggs, and TCPP in the range 0.68to 5.4 μg/kg fw. The levels of TBP in biota samples were all

Table 6 Calculated fraction of chemical (in percent) that originatesfrom air at the end of the simulation period

TCEP TCPP TBP

Soil (layer 1) 100.0 31.0 69.9

Roots 77.1 24.7 63.4

Leaves 51.9 29.3 99.7

Seeds 27.0 14.5 98.3

Table 5 Calculated loss processes (in percent of the total loss) fromtop soil layer 1

Loss process TCEP TCPP TBP NBBS DEET

Leaching (%) 19.7 9.5 0.7 11.6 24.0

Plant uptake (%) 46.1 22.2 1.7 27.2 56.0

Loss to air (%) 2.2 0.5 0.0 48.7 19.7

Degradation (%) 32.0 67.8 97.6 12.6 0.3

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below the detection limit (<0.43 to <56 μg/kg fw). Theseconcentration ranges in mostly aquatic biota are below thelevels predicted in crops (Table 4). Bioaccumulation isusually considered to be a partition process occurring pref-erably for lipophilic compounds (Travis and Arms 1988).Bioaccumulation from soil into crops and in particular intofoliage is due to up-concentration with the water flux. Thisis more likely for polar compounds and may thus be relevantfor many emerging contaminants.

Advantageous and limitations

Model concept Despite its dynamic nature, the model con-cept is still simplistic in many aspects: transport of waterand solutes in soil is approached by a tipping bucketmodel, and uptake into plants is calculated by lineardifferential equations which are solved analytically. In anearlier study, with metals and persistent organic pollutants,the validity of the model concept both for solute leachingand plant uptake could be documented by comparison toresults from a 10-year field study (Legind et al. 2012).The approach can be seen as a compromise betweenpracticability and complexity. Compared to the less so-phisticated steady-state plant uptake models used in chem-ical risk assessment (Ryan et al. 1988; Trapp and Matthies1995; Trapp 2007; Legind and Trapp 2009), the majorstep is the combination with the soil transport model. Thisallows studying the plant uptake under real environmentalconditions and leads to new insights on the substancedynamics and behavior, as it is impacted by environmentalfactors such as precipitation, transpiration and vertical soilstructure.

Model complexity Running complex model simulationsrequires high efforts and costs. This is not so much due tothe mathematical difficulties. Once the model code is imple-mented and verified, math provides little trouble. But themeasurement and/or collection of the large datasets is timeconsuming. In the simulations above, we used data fromvarious sources: meteorological data as input for the waterbalance from a study in France (Legind et al. 2012); soilproperties from the greenhouse study; chemical concentra-tions in Norwegian sewage sludge and air were available inliterature; and concentrations in rain and in background soilwere only found for German sites. The authors are awarethat the mix of data does not represent any real sites and thatthe simulations are illustrative only. Moreover, the many inputdata lead to large uncertainty concerning the output. Theresults should therefore not be over-interpreted but may serveas an estimate of relevant processes in the environment.

But also results from experimental plant uptake studiesusually show a high variation. Eggen et al. (2012) presentdata for five crops, of which only two were simulated here.

They found high variations between crop types (e.g., TCEPof <0.01 mg/kg dw in wheat and in the range of 0.064–0.116 mg/kg dw in rape seeds) and even between cultivarsof the same species (e.g., BCF for TBP for four carrotcultivars ranged from 0.37 to 4.56 kg/kg; Table SI 5b inthe ESM). Variations may be a consequence of analyticaluncertainties and small differences in experimental condi-tions, such as growth of plants and soil inhomogeneities, butmay also reflect individual differences of the plants. Suchlarge variations and uncertainties are not uncommon inexperiments of plant uptake. In their review of the plantuptake of the explosive hexahydro-1,3,5-trinitro-1,3,5-tri-azine, which is polar and nonvolatile like the compoundsin our study, McKone and Maddalena (2007) found the BCFvalues varying factor 56 for roots, factor 79 for fruits, andfactor 93 for leaves (Trapp and Legind 2011). Also themodel output can vary widely, depending on the data usedas input. The most uncertain and sensitive chemical data—partition coefficient air–water and degradation rates—couldbe calibrated using the experimental results from the green-house study.

Risk assessment

The European Commission (2009) performed a risk assess-ment on TCEP. It was assumed that “since TCEP does notpossess a bioaccumulation potential, the derivation ofPNECoral is not necessary” and “since there is no indicationof bioaccumulation of TCEP, a risk characterization forexposure via the food chain is not necessary.” Our resultsshow that TCEP has a high potential for bioaccumulation incrops and reaches agricultural fields both via sewage sludgeand by atmospheric deposition.

Conclusions

The uptake of the organophosphates TCEP, TCPP, TBP, theinsect repellant DEET, and the plasticizer NBBS into plantswas studied in greenhouse experiments and simulated with adynamic physiological plant uptake model. The calibratedmodel was coupled to a tipping buckets soil transport mod-el, and a field scenario with sewage sludge application wassimulated.

In the growth experiments rather high uptake of the polar,low-volatile compounds TCEP, TCPP, and DEET into plantswas found, with highest concentrations in straw (leaves andstem). Uptake into carrot roots was high for TCPP and TBP.NBBS showed no high uptake but was rapidly degraded.The pattern and levels of uptake could be reproduced by themodel simulations, which indicates mainly passive uptakeand transport (i.e., by the transpiration stream, with thewater) into and within the plants. However, both

Environ Sci Pollut Res

experimental results and model simulations show large var-iation, due to many influencing parameters.

Also the field simulations predicted a high uptake from soilinto plants of TCEP, TCPP, and DEET, while TBP is morelikely taken up from air (which was not relevant as exposurepathway in the greenhouse experiments). The BCF valuesmeasured and calculated in the greenhouse study are in mostcases comparable to the calculated values of the field scenario,which demonstrates that greenhouse studies can be suitablefor predicting the behavior of chemicals in the field.

Plant uptake is rarely seen as relevant removal process ofchemicals from soil, but the combination of slow degradationand high plant uptake favors this process, and we predictedplant uptake to be the most relevant loss process for TCEP andDEET both in the greenhouse study and in the field simulation.

Bioaccumulation in fish, fat and milk is due to lipophilicpartitioning (Travis and Arms 1988) and was thus a majorconcern for chloro-organic pollutants. On the contrary, bio-accumulation in crops from soil is a process that occursmore likely for polar compounds and is thus of high rele-vance for many polar, emerging pollutants, in particularwhen these are nonvolatile and persistent in the soil–plantsystem.

Acknowledgments This study was funded partly by the EuropeanUnion, project PHARMAS (grant agreement No. 265 346 (modelingwork)) and the Norwegian Research Council, the Food Program(1848339/I10 to TE; plant uptake experiments). We also thank HansRagnar Norli, Bioforsk, for analytical work and Isak Drozdik, MetteHjermann, Henk Maessen, and Hans Martin Hanslin for their contri-bution throughout the growth experiment. Thanks to Sabine Houot,Philippe Cambier, Claire-Sophie Haudin (INRA), Jeanne Serre andViolaine Brochier (Veolia Environnement) for their cooperation(Feucherolles study, Legind et al. 2012). Thanks also to Arno Reinand Charlotte N. Legind for assistance with model implementationand parameterization and for Figure 1.

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