Laboratory tests for simulating attenuation processes of aromatic amines in riverbank filtration

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1 Journal of Hydrology 266 (2002) Laboratory tests for simulating attenuation processes of aromatic amines in riverbank filtration Eckhard Worch*, Thomas Grischek, Hilmar Börnick, Petra Eppinger Institute of Water Chemistry, Dresden University of Technology, D Dresden, Germany Received 23 March 2001; revised 24 October 2001; accepted 4 January 2002 Abstract Based on a two-step laboratory test including biodegradation and adsorption, it is possible to derive a prognosis of the behaviour of organic compounds during riverbank filtration and to prioritise the substances with regard to drinking water quality. It is shown for aromatic amines, used as an example of organics found in River Elbe water, Germany, how the simulation methods provide basic information about rate constants of biological degradation and adsorption equilibrium constants under conditions that are as realistic as possible. Biodegradation of nitroanilines and higher chlorinated anilines is relatively slow and adsorption onto the sandy aquifer material is weak. Accordingly, occurrence of these compounds in the production wells of the waterworks cannot be excluded. q 2002 Elsevier Science B.V. All rights reserved. Keywords: Bank filtration; Aromatic amines; Biodegradation; Adsorption 1. Introduction In Germany, about 30% of drinking water is produced from surface water (water from lakes, reservoirs and rivers). Main rivers, such as the Rhine and the Elbe, are important drinking water sources. In drinking water production from polluted river water, riverbank filtration is commonly used as a first pretreatment step. For instance, in Saxony (a federal state in eastern Germany), about 18% of drinking water is produced from riverbank filtrate (Grischek et al., 1995). Also in other European countries riverbank filtration is a frequently used low-cost water treatment technology. Processes during river water infiltration and underground transport result in a significant improvement * Corresponding author. address: eworch@rcs.urz.tu-dresden.de (E. Worch). in raw water quality. Riverbank filtration can remove a substantial percentage of natural organic matter (NOM), synthetic organic chemicals (SOCs) and also harmful pathogens. In this way bank filtration is able to replace or support technical water treatment steps, thus leading to reduced treatment expenditure (Kuehn and Mueller, 2000). The aim of this paper is to present a two-step laboratory test, which gives an insight into the efficiency of riverbank filtration processes with regard to the attenuation of SOCs. 2. Main processes during riverbank filtration The main processes operating during riverbank filtration are dispersion, filtration, biodegradation, adsorption and mixing with groundwater. Mechanical filtration leads to a removal of suspended material, /02/$ - see front matter q 2002 Elsevier Science B.V. All rights reserved. PII: S (02)

2 260 E. Worch et al. / Journal of Hydrology 266 (2002) including hydrophobic organic substances adsorbed on suspended solids. In the riverbed and at the beginning of the groundwater flow path, aerobic conditions are frequently found and a relatively high microbial activity that can lead to mineralization or transformation of degradable organics. The content of dissolved organic carbon (DOC) decreases and even SOCs can be degraded. Biodegradation of xenobiotic organic substances is possible by co-metabolism (Fritsche, 1985). During subsurface transport, the filtrate is in contact with the solid aquifer material, and so adsorption of organic compounds onto solid material can take place. The degree of adsorption varies depending on the nature of the compounds and the kind of solid material present. In general, contact with a large surface area and long flowpaths between the river and the production wells increase adsorption. Furthermore, mixing processes between groundwater and infiltrate can cause a decrease in pollutant concentrations and a reduction of concentration peaks depending on the groundwater quality landside of the production wells. The simulation tests presented in the following paragraphs are focussed on the main processes biodegradation and adsorption. In principle, the tests are applicable to all classes of organic substances found in river water. In this paper the application of the tests is demonstrated taking the substance group of aromatic amines as an example. This substance class was found to be of relevance for the River Elbe and therefore it was studied in detail. 3. Simulation of processes in laboratory tests 3.1. The assessment scheme In the last ten to fifteen years, laboratory tests have been developed to assess experimentally the effects of riverbank filtration (Sontheimer, 1988; Mälzer et al., 1993; Börnick et al., 2001). In these laboratory simulations, particular emphasis has been given to SOCs, principally because field investigations are difficult to realise. To assess the efficiency of riverbank filtration for removal of dissolved SOCs from river water, two questions in particular have to be answered: 1. How fast is the substance biologically degradable? 2. To what degree is the substance bound to soil and aquifer materials and retarded in comparison to water transport? Non-degradable or poorly degradable substances can be transported to the production wells of the waterworks. For these substances the effect of adsorption along the flowpath is of central importance. Transport time to the production well depends strongly on the sorption coefficients of the transported substances. According to the questions above, an assessment scheme (Fig. 1) was developed. From a two-step laboratory test, including biodegradation and adsorption, it is possible to give a prognosis on the behaviour of organic compounds during riverbank filtration and to prioritise the substances with regard to drinking water quality. The simulation methods give basic information about rate constants of biological degradation and adsorption equilibrium constants under conditions that are as realistic as possible. Moreover, the laboratory tests are neither time consuming nor expensive. The aims of the tests are to obtain general indications of contaminant behaviour or worst case results, rather than an exact description of complicated microprocesses The example of aromatic amines Relevance Aromatic amines aniline (aminobenzene) and its derivatives were found in the River Elbe at concentrations of up to 10 mgl 21 (Börnick, 1998). Besides aniline, which is most frequently found, several chloro-, chloromethyl- and nitroanilines have been determined in the river water. Aromatic amines can occur as constituents of industrial waste waters (e.g. from dye production) and also as degradation products (metabolites) from pesticides. Table 1 lists aromatic amines investigated in the simulation tests presented in this paper Analytical methods The water taken from the River Elbe was filtered through a 0.5 mm glass fibre filter and spiked with single substances or mixtures in concentrations of

3 E. Worch et al. / Journal of Hydrology 266 (2002) Fig. 1. Relevance of synthetic organic compounds (SOCs) to riverbank filtrate quality (assessment scheme). 3 mg l 21 to 30 mg l 21 per substance. HPLC and GC/MS methods were used for analytical determination of the amines. Most of the investigated amines were determined by a HPLC/DAD-method. Supplementary examinations of the substituted amines 4-methyl-2-nitroaniline and 2-chloro-5-nitroaniline were carried out with GC/MS. For HPLC, 100 ml of the filtered water sample were enriched on a conditioned SDB1-solid phase (Fa. Baker, 200 mg, self-filled glass cartridge). After drying in nitrogen gas the cartridge was flushed with acetonitrile (4 0.6 ml). The extract was concentrated to 0.5 ml under nitrogen gas and made up to 1 ml with ammonium acetate buffer (0.04 mol l 21 ). The final solution was then analyzed with a HPLC/DAD (DAD L-4500, Merck) using a C-18- column (ABZ þ Plus, Supelco) with acetonitrile and ammonium acetate buffer solution (0.025 mol l 21 ), a Table 1 Aromatic amines investigated in simulation tests flow rate of 1 ml min 21 and the gradient technique. The wavelengths for measurements of aniline, o- toluidine and 2-nitroaniline were 231 nm and for all other aromatic amines 241 nm. The peak identification was found by comparison of the retention time with that of the standard solutions and by comparison of spectra (wavelength range nm). For GC the enrichment on SDB-1 was undertaken according to the HPLC-method. After extraction with 2 ml ethylacetate the extract was concentrated to a volume of 1 ml. 2 ml of the extract were injected into the GC (5890, Series II, Hewlett Packard). The GC parameters were as follows: column HP-5-MS (30 m in length, inner diameter 0.25 mm, film thickness 0.25 mm); flow rate 0.8 ml min 21 ; injector temperature 200 8C; and oven-programme from 60 8C (1 min isotherm) with 10 8C min 21 ramp to 250 8C (5 min isotherm). For detection, an MSD (5917, Hewlett Packard) was used under the following conditions: detector temperature 280 8C; solvent delay 3 min; scan range amu; and EMV-voltage with Scan 2000 V and SIM 2500 V. Further analytical details are given by Börnick (1998) and Eppinger (2001). Using an enrichment factor of 100 the limits of detection ranged from 0.28 (o-toluidine) to 1.2 mg l 21 (2-chloro-5-nitroaniline), the recoveries from 73 (4- chloroaniline) to 102% (3-chloro-4-methylaniline) and the relative standard deviations of method from 2.2 (o-toluidine) to 12% (4-methyl-2-nitroaniline) (Börnick et al., 2001). 4. Simulation of biodegradation processes 4.1. Method Aniline a 2-Nitroaniline b 3-Nitroaniline b 4-Nitroaniline b 2-Chloroaniline d 3-Chloroaniline b 4-Chloroaniline d 4-Bromoaniline b o-toluidine (2-methylaniline) b a Laborchemie Apolda. b Aldrich. c Riedel-de Haen; purity of all substances.97%. d Fluka. 2,5-Dichloroaniline b 3,4-Dichloroaniline c 2,4,5-Trichloroaniline d 2,4,6-Trichloroaniline b 4-Methyl-2-nitroaniline b 2-Chloro-5-nitroaniline b 3-Chloro-4-methylaniline b N,N-dimethylaniline b Biological degradation of SOCs can be studied in the laboratory using a biologically active test filter. The test filter concept was developed by Sontheimer (1988), mainly to investigate the degradation of dissolved organic matter (DOC) and single organic compounds. Fig. 2 shows a typical test filter set-up. The glass filter column is filled with an inert solid material. To exclude adsorption processes the solid material should have no or very low adsorption capacity. In our investigations we have used the commercial product Hydrofilt (Akdolit GmbH), which is a pumice stone

4 262 E. Worch et al. / Journal of Hydrology 266 (2002) Fig. 2. Biologically active test filter apparatus. material. As a first step, the filter is conditioned with fresh river water. For this, river water is percolated through the fixed bed for about one month to form a biofilm of typical microorganism populations. After conditioning, the reservoir bottle is filled with a solution of river water containing the test compound. The spiked river water is percolated through the filter material. Biological degradation takes place in the biofilm and the decrease in concentration can be determined by taking samples from the reservoir after defined time intervals. Design parameters of the biologically active test filter set-up are shown in Fig. 2. The experimental parameters were chosen according to the following conditions: The concentration in the test should be in the same order of magnitude as in the river water. The flow rate has to be low to avoid an abrasion of the biofilm. The ratio between pore volume and reservoir should enable a long contact time in the test filter Results and discussion Typical concentration decay curves for several aromatic amines are shown in Fig. 3. The rates of degradation vary depending on the chemical structure of the amines. In particular, highly chlorinated anilines and nitroanilines are poorly degradable, whereas unsubstituted aniline is completely degraded after an effective contact time of about 3 h. Although biological degradation is very complex in nature, several investigations have shown that the overall kinetics can be described by the pseudo-first order rate law (Mälzer, 1993; Börnick et al., 2001; Paul et al., 2001): 2 dc ¼ kc ð1þ dt where c is the concentration (M/L 3 ); t, the time (T); and k is the rate constant (T 21 ). Using the integrated form of the rate law c ¼ e 2kt ð2þ c 0 rate constants may be determined using a non-linear regression analysis. Fig. 3 shows the fitted kinetic curves for aromatic amines. Taking into account the possible analytical errors, from a first approximation it can be assumed that the degradation curves follow a first order rate law. The rate constants estimated in this series of experiments range from about 2 h 21 for aniline to about 0.03 h 21 for 2,4,5-trichloroaniline. The halflives ranged from 20 min for aniline to more than 20 h for 2,4,5-trichloroaniline. Except for 3-chloroaniline, 3-chloro-4-methylaniline, and 2,4,5-trichloroaniline, between 2 and 20 parallel runs were carried out. In other parallel experimental series the constants were found to be slightly different, but in the same order of

5 E. Worch et al. / Journal of Hydrology 266 (2002) Fig. 3. Experimental data and fitted kinetic curves of biodegradation of aromatic amines (c 0 ¼ 6mgl 21 for each component); for test filter parameters see Fig. 2. magnitude. Due to the typical variability in biological systems, the standard deviations of the kinetic constants were relatively high and ranged from 31% up to 81% with a mean value of 49%. More details are given by Börnick et al. (2001). Table 2 summarises the mean values of rate constants and half-lives from all experiments. Because of the strong influence of the substituent groups on the kinetic coefficients it should be possible to correlate the coefficients with the acidity constants of the protonated amines, which are also influenced by the substituent groups. Such correlations are known from chemical kinetics as Hammet equations or linear free energy relationships. In the case of aromatic amines, the linear correlation shown in Fig. 4 was obtained by plotting the logarithms of the mean values of first order rate constants from all parallel runs, k, versus the negative logarithms of the acidity constants, pk a. Correlations like this can be used to predict the degradation behaviour of substances other than those investigated. The results from test filter experiments give an impression of biological degradation in the case of a single dose of SOC (e.g. 2,4,6-trichloroaniline). This can be considered as the worst case, since the microorganisms have no time for adapting to the new substance. In practice, such a situation is found in the case of shock loads resulting from accidents. On the other hand, if the SOC is in long contact with the microorganisms, the biological degradation often improves because of an adaptation effect. This case is typical for substances that are continuously present in river water. This can be simulated in test filter experiments by repeating the dosage of SOCs in the circulating water. As an example, Fig. 5 shows the effect of adaptation of the biofilm to the poorly degradable 2,4,6-trichloroaniline. In a long-term experiment, the solution of 2,4,6-trichloroaniline was renewed to its initial concentration every week. The biological degradation is very slow in the first week, but becomes faster as shown in Fig. 5 for the third and the sixth weeks. In this way, the test filter experiments can be adapted to the practical situation of interest. Table 2 Acidity constants (given as pk a ¼ 2log K a ) and biodegradation parameters of aromatic amines Aromatic amine pk a Rate constant K (h 21 ) Half-life t 1/2 (h) o-toluidin (2-methylaniline) Chloroaniline Chloroaniline Aniline Bromoaniline Chloro-4-methylaniline Chloroaniline ,4-Dichloroaniline Chloro-5-nitroaniline ,5-Dichloroaniline ,4,6-Trichloroaniline Nitroaniline Nitroaniline ,4,5-Trichloroaniline

6 264 E. Worch et al. / Journal of Hydrology 266 (2002) Fig. 4. log k pk a amines. correlation for biodegradation of aromatic 5. Simulation of sorption processes 5.1. Method Fig. 5. Effect of adaptation on biodegradation of 2,4,6-trichloroaniline. Fig. 6. Adsorption column apparatus. The simulation of sorption processes during riverbank filtration can be carried out in a manner analogous to well-known GAC (granular activated carbon) column experiments (Kümmel and Worch, 1990). The main difference consists of using original solid material from the aquifer as sorbent instead of activated carbon (Fig. 6). A river water solution containing the substance of interest is continuously fed through a fixed-bed column filled with sediment material. Samples are taken from the column outlet and the measured concentration is plotted versus time to get a breakthrough curve, which is the basis for estimating the sorption coefficient. The parameters of the soil column used in the experiments are given in Fig. 6. The boundary conditions for the choice of the parameters were: The concentrations in the tests should be comparable to those in the river. A flow rate typical for groundwater flow velocity should be used in the experiments. The column design has to ensure that full breakthrough curves can be measured (e.g. breakthrough not too fast, enough sample volume for trace analysis). To understand the estimation method for sorption coefficients, it is necessary to consider the mass balance of the sorption process. In general, it is possible to use the differential form as well as the integral form of the balance equation. Here, the integral form is used. Until ideal breakthrough time, the amount of sorbate fed to the adsorption column must be equal to the amount adsorbed and the amount accumulated in the bed voids (condition: no losses by biodegradation and/or evaporation): amount adsorbed þ amount accumulated in voids ¼ amount applied m A q 0 þ c 0 e B V A ¼ c 0 _Vt id b ð3þ where m A is the adsorbent mass (M); q 0, the amount

7 E. Worch et al. / Journal of Hydrology 266 (2002) adsorbed in equilibrium with c 0 (M/M); c 0, the inlet concentration (M/L 3 ); e B, the bed porosity (dimensionless); V A, the adsorbent bed volume (L 3 ); _V; the volumetric flow rate (L 3 /T); and tb id is the ideal breakthrough time (T). The time tb id is the breakthrough time of an ideal breakthrough curve with no hydrodynamic dispersion and no mass transfer resistances (a sharp concentration step from c=c 0 ¼ 0toc=c 0 ¼ 1). It is equal to the breakthrough time of the half-concentration breakthrough ðc=c 0 ¼ 0:5Þ in the case of a symmetrical curve with dispersion and/or slow mass transfer. For the practical use of this equation, several expressions for process and sorbate parameters have to be introduced. In particular, these expressions concern the adsorption coefficient, bed density, volumetric flow rate, adsorbent mass and velocity of the concentration profile. Adsorption coefficient K (L 3 /M): K ¼ q 0 c 0 Bed bulk density r B (M/L 3 ): r B ¼ m A V A Volumetric flow rate _V (L 3 /T): _V ¼ v F A ¼ v l e B A Adsorbent mass m A (M): m A ¼ r B Ah ð4þ ð5þ ð6þ ð7þ Concentration profile velocity v c (L/T): v c ¼ h tb id ð8þ where v F is the flow velocity (L/T); v l, the pore water velocity (L/T); A, the cross-sectional area (L 2 ); and h is the bed height (L). Introducing these expressions into the balance equation and re-arranging leads to the so-called retardation equation: Kr B e B þ 1 ¼ v l v c ¼ v tracer v adsorbate ¼ t b;adsorbate t b;tracer ¼ R d ð9þ where R d is the retardation coefficient (dimensionless); and t b is the breakthrough time (T) at c=c 0 ¼ 0:5: It should be noted that a linear isotherm is assumed to be valid. In most cases this is a good approximation for soil or sediment adsorption, in particular if the sorbate concentration is relatively low. As can be seen from the retardation equation, the ratio of the velocity of water and the velocity of the sorbate has a constant value, which is the retardation coefficient, R d. The velocity of water can be experimentally estimated by measuring the breakthrough curve of a nonadsorbable tracer, for instance chloride. Taking c=c 0 ¼ 0:5 as the midpoint of the breakthrough curve, R d can also be expressed as the ratio of breakthrough times of sorbate and tracer. With the knowledge of the tracer breakthrough curve, the time axis of the sorbate breakthrough curve can be transformed to a dimensionless time t=t b ; tracer : Alternatively, the time axis can be written as the ratio of feed volume and pore volume (the number of pore volume flushes), V=V P : In so doing, the retardation coefficient can be read directly from the dimensionless breakthrough time of sorbate at c=c 0 ¼ 0:5: 5.2. Results and discussion In our investigations, we have used sand aquifer material from a Elbe riverbank filtration site near the town of Torgau in Saxony. This material has only a low organic carbon content of about 0.015%. As an example, Fig. 7 shows breakthrough curves for 2-nitroaniline and 2,4,6-trichloroaniline. It should be noted that deviations from the sigmoid form of the curves as well as concentration values higher than c=c 0 ¼ 1 result from experimental errors. Neverthelesss, from these breakthrough curves it can be found that the retardation coefficient, R d, for 2- nitroaniline is about 1.1, whereas for 2,4,6-trichloroaniline it is about 1.5, indicating a stronger adsorption of the latter component. Because the retardation coefficient, R d, is correlated directly to the adsorption coefficient, K, it is easy to calculate the latter from the known R d. Table 3 lists the sorption coefficients for the amines under consideration. The given coefficients are mean values from up to 9 parallel runs. The standard deviations of R d ranged from 3.4% for o- toluidine up to 13.6% for 2,4,6-trichloroaniline. R d or K can be used in a transport model to calculate the transport of organic substances in the subsurface.

8 266 E. Worch et al. / Journal of Hydrology 266 (2002) Fig. 7. Column breakthrough curves of 2-nitroaniline ðc 0 ¼ 6 mgl 21 Þ and 2,4,6-trichloroaniline ðc 0 ¼ 6 mgl 21 Þ; for column parameters see Fig. 6. Under strongly simplifying assumptions, e.g no further biodegradation, one-dimensional transport, no heterogeneities in the soil, no mixing with groundwater, R d can be used to estimate approximately the transport velocity of the retarded substance in comparison to the water flow (Eq. (9)). In order to get a quick assessment of the adsorption behaviour of SOCs and to minimise the experimental work it is useful to look for empirical correlations between the adsorption coefficient and known substance properties. In general, sorption increases with decreasing Table 3 n-octanol water partition coefficients and adsorption coefficients of aromatic amines Aromatic amine log P OW K (cm 3 g 21 ) 2,4,6-Trichloroaniline ,4,5-Trichloroaniline ,4-Dichloroaniline ,5-Dichloroaniline Chloroaniline Methyl-2-nitroaniline Chloro-5-nitroaniline Nitroaniline Bromoaniline N,N-Dimethylaniline Chloroaniline Chloroaniline Nitroaniline Nitroaniline o-toluidine (2-methylaniline) Aniline affinity of the sorbate for the water phase. This means that the higher the hydrophobicity of the sorbate, the higher the adsorption. Therefore, it is useful to correlate the adsorption coefficient with the n-octanol/water partition coefficient, P OW, which is a measure of hydrophobicity. A number of such correlations are given in the literature (e.g. Karickhoff et al., 1979; Baker et al., 1997). A selection of empirical correlations is shown in Table 4. As a rule, these correlations were determined for K OC, the organic carbon/water partition coefficient. This means that the adsorption coefficient, K, is normalised to the organic carbon content of the sediment, assuming that adsorption of hydrophobic substances takes place preferentially on the organic matter. K OC ¼ K ð10þ f OC where f OC is the organic carbon (OC) content of the solid in kg OC kg 21 solid. Several investigators proposed non-class specific correlations, which should be applicable to many types of SOCs. The parameters of the non classspecific correlations differ significantly and it is not clear which correlation is the most reliable. On the other hand, more exact class-specific correlations exist only for a limited number of substance classes. Fig. 8 shows the correlation between log K and log P OW found for the aromatic amines investigated in this study. There is a good linear correlation, that can be described by the equation log K ¼ 0:42 log P OW 2 2:33 ð11þ It must be noted that this equation is not only substance class-specific but also solid-specific. To find a more general correlation independent of soil material and comparable to the published equations, the K values were normalised to the organic carbon content. For the sand aquifer material used, with a f OC ¼ 1: kg OC kg 21 solid Eq. (11) can be rewritten as log K OC ¼ 0:42 log P OW þ 1:49: ð12þ Eq. (11) or (12) can be used to predict the adsorption behaviour of amines other than the experimentally investigated compounds. A comparison of log K OC values calculated from Eq. (12) and from the equations shown in Table 4, for

9 E. Worch et al. / Journal of Hydrology 266 (2002) Table 4 log K OC log P OW correlations Non-class specific (a) log K OC ¼ 0:544 log P OW þ 1:377 Kenaga and Goring (1980) (b) log K OC ¼ 0:679 log P OW þ 0:663 Gerstl (1990) (c) log K OC ¼ 0:909 log P OW þ 0:088 Hassett et al. (1983) (d) log K OC ¼ 0:903 log P OW þ 0:094 Baker et al. (1997) Class-specific Chloro and methyl benzenes (e) log K OC ¼ 0:72 log P OW þ 0:49 Schwarzenbach and Westall (1981) Benzene, PAHs (f) log K OC ¼ 1:00 log P OW 2 0:21 Karickhoff et al. (1979) Polychlorinated Biphenyls (g) log K OC ¼ 1:0 log P OW 2 0:21 Girvin and Scott (1997) the minimum and maximum log P OW values of the investigated amines (0.8, 3.7), is given in Table 5. Whereas in the case of the higher log P OW the results of all correlations (including the class-specific correlations) are in the same order of magnitude, the calculated log K OC values for the lower log P OW differ considerably, up to a factor of about one hundred in K OC. It can be concluded that, in general, the published non class-specific correlations or classspecific correlations for other substance groups can be used only as a first approximation. For more exact K OC estimations in each case, an experimental determination of the log P OW log K OC correlation for the substance class of interest is recommended. 6. Summary The laboratory-scale tests presented here give important information on the behaviour of organic compounds during river bank filtration. From a twostep laboratory test, including biodegradation and adsorption, it is possible to predict the behaviour of organic compounds during riverbank filtration and to prioritise specific substances. In general, the experimental tests presented are a helpful tool to assess the relevance of synthetic organic substances for bank filtrate quality. In the case of aromatic amines, biodegradation (without adaptation) of nitroanilines and higher chlorinated anilines is relatively slow and adsorption Table 5 Comparison of log P OW log K OC correlations (Eqs. (a) (g) see Table 4; Eq. (12) this work) Equation log K OC (for log P OW ¼ 0.8) log K OC (for log P OW ¼ 3.7) (a) (b) (c) (d) (e) (f) (g) (12) Fig. 8. log K log P OW correlation for aromatic amines. Note: The Eqs. (e) (g) were developed for other substance classes (class-specific correlations, see Table 4).

10 268 E. Worch et al. / Journal of Hydrology 266 (2002) onto sandy aquifer material is low (R d, 2). Accordingly, occurrence of these compounds in production wells of the waterworks cannot be excluded. Acknowledgments This work was financially supported by the German Ministry for Education and Research (grant 02WT9347/5) and the Deutsche Bundesstiftung Umwelt (grant 1000/179). References Baker, J.R., Mihelcic, J.R., Luehrs, D.C., Hickey, J.P., Evaluation of estimation methods for organic carbon normalized sorption coefficients. Water Environ. Res. 69, Börnick, H., Aromatische Amine in der Elbe Entwicklung von Analysenverfahren und Untersuchungen zum Verhalten bei der Trinkwasseraufbereitung (Aromatic amines in the River Elbe Development of analytical methods and investigations into the behaviour during drinking water treatment). PhD Thesis. Dresden University of Technology, 245 p. Börnick, H., Eppinger, P., Grischek, T., Worch, E., Simulation of biological degradation of aromatic amines in river bed sediments. Water Res. 35, Eppinger, P., Aromatische Amine in der Elbe und ihr Verhalten bei der Trinkwasseraufbereitung (Aromatic amines in the River Elbe and their behaviour during drinking water treatment). PhD Thesis. Dresden University of Technology, 213 p. Fritsche, W., Umwelt-Mikrobiologie, Akademie, (Environmental Microbiology), Berlin, 195 p. Gerstl, Z., Estimation of organic chemical sorption by soils. J. Contam. Hydrol. 6, Girvin, D.C., Scott, A.J., Polychlorinated biphenyl sorption by soils: measurement of soil water partition coefficients at equilibrium. Chemosphere 35, Grischek, T., Dehnert, J., Nestler, W., Neitzel, P., Trettin, R., Groundwater flow and quality in an alluvial aquifer recharged from river bank infiltration, Torgau Basin, Germany. In: Brown, A.G., (Ed.), Geomorphology and groundwater, Wiley, Chichester, 213 p. Hassett, J.J., Banwart, W.L., Griffen, R.A., Correlation of compound properties with sorption characteristics of nonpolar compounds by soil and sediments: concepts and limitations. In: Francis, C.W., Auerbach, S.I. (Eds.), Environmental and solid wastes characterization, treatment and disposal, Butterworth, Newton, MA, 498 p. Karickhoff, S.W., Brown, D.S., Scott, T.A., Sorption of hydrophobic pollutants on natural sediments. Water Res. 13, Kenaga, E.E., Goring, C.A.I., Relationship between water solubility, soil sorption, octanol water partitioning, and concentration of chemicals in biota. In: Eaton, J.G., Parrish, P.R., Hendricks, A.C. (Eds.), Aquatic Toxicology ASTM STP 707, American Society for Testing and Materials, Philadelphia, PA, 405 p. Kuehn, W., Mueller, U., Riverbank filtration: an overview. Journal AWWA 92, Kümmel, R., Worch, E., Adsorption aus wässrigen Lösungen. Deutscher Verlag für Grundstoffindustrie, Leipzig, (Adsorption from Aqueous Solutions), 293 p. Mälzer, H.-J., Untersuchungen zu Transport und Abbauvorgängen bei der Uferfiltration in Hinblick auf die Auswirkungen von Stobbelastungen (Investigations of transport and degradation processes during riverbank filtration with regard to effects of shock loads). PhD Thesis. University of Karlsruhe, 203 p. Mälzer, H.-J., Gerlach, M., Gimbel, R., Effects of shock loads on bank filtration and their prediction by control filters. Water Supply 11, Paul, S., Börnick, H., Worch, E., Untersuchungen zur Wasserwerksrelevanz aliphatischer Amine bei der Trinkwasseraufbereitung aus Elbeuferfiltrat. Teil 1: Mikrobiologische Abbaubarkeit (Investigations of the relevance of aliphatic amines for drinking water production from Elbe riverbank filtrate. Part 1: Microbiological degradation). Vom Wasser 96, Schwarzenbach, R.P., Westall, J., Transport of nonpolar organic compounds from surface water to groundwater. Laboratory sorption studies. Environ. Sci. Technol. 15, Sontheimer, H., Das Testfilterkonzept, eine Methode zur Beurteilung von Wässern (the testfilter concept a method for the characterisation of waters). DVGW-Schriftenreihe Wasser Nr. 60,