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Evaluation of Simple Treat 3.0 for two hydrophobic and slowly biodegradable chemicals: polycyclic musks HHCB and AHTN
ARTICLE IN PRESS
Water Research 37 (2003) 4377–4384
Evaluation of Simple Treat 3.0 for two hydrophobic and slowly biodegradable chemicals: polycyclic musks HHCB and AHTN Elsa Artola-Garicanoa,*, Joop L.M. Hermensa, Wouter H.J. Vaesb a
Institute for Risk Assessment Sciences, Toxicology Division, University of Utrecht, Yalelaan 2, P.O. Box 80.176, Utrecht 3508 TD, The Netherlands b TNO Nutrition and Food Research, P.O. Box 360, Zeist 3700 AJ, The Netherlands Received 15 October 2002; received in revised form 17 July 2003; accepted 24 July 2003
Abstract In the current study, predictions by Simple Treat 3.0, a fate model for organic chemicals in sewage treatment plants (STPs), are compared with actual measurements in three STPs. Two polycyclic musks, Tonalides (AHTN) and Galaxolides (HHCB), were used for model evaluation. Results show that Simple Treat 3.0 is able to predict the removal efficiency within a factor 4. Predicted concentrations of both chemicals within the different physical compartments of STPs show a high correlation (r2 ¼ 0:80) with experimental values. Although predicted free concentration levels were similar to previously reported experimental data, the trends along the compartments showed an inverse relationship. This bias of the model can be caused by an underestimation of BOD-removal (solids), or an overestimation of bacterial growth, evaporation, or a combination of these three factors. Results show that Simple Treat 3.0 is a valid tool for the risk assessment of slowly biodegradable chemicals, but still some adjustments of the model could be incorporated from a scientific point of view. r 2003 Elsevier Ltd. All rights reserved. Keywords: Polycylclic musks; Model evaluation; Sewage treatment plant; Free concentration
1. Introduction Many industrial and household products are released after use via the sewer, and sewage treatment plants (STPs) into the environment. The two mayor removal pathways in STPs for organic chemicals are sorption to the sludge and biodegradation of the chemical in the aeration tank. The removal efficiency of each chemical is dependent on its physicochemical properties, as well as on the operation efficiency of the plants. Until now, numerous studies have been carried out in which the efficiency of STPs was studied by a comparison of influent and effluent concentrations [1,2]. Nevertheless, the large number of different *Corresponding author. Tel.: +31-30-2535018; fax: +31-302535077. E-mail address: [email protected] (E. Artola-Garicano).
chemicals present in the environment makes it impossible to determine the removal efficiency experimentally for each single chemical that enters the environment via the sewer system. As an alternative, several models have been developed during the last two decades which allow the prediction of the fate of chemicals in STPs [3–6]. These models are based on physical parameters describing the characteristics of the plant, physicochemical and biodegradation parameters describing relevant properties of the compound under study, and thermodynamic equations to combine these parameters. These models are helpful tools and can be used as an early screening tool aimed at selecting the best candidate of a group of new chemicals based on their removal properties. The outcome of these models has been tested for a few chemicals by comparison of predicted versus measured total concentrations in the influent and effluent, the so-called removal efficiency [7] and in some cases, the
0043-1354/$ - see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0043-1354(03)00434-2
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Nomenclature STP Sewage treatment plant AHTN 7-acetyl-1,1,3,4,4,6-hexamethyltetrahydronaphthalene HHCB 7-acetyl, 1,1,3,4,4,6-hexahydro-4,6,6,7,8,8hexamethylcyclopenta(g)-2-benzoapyrene Kow n-octanol-water partition coefficient (dimensionless) H Henry law constant (Pa m3 mol1) kbiodeg Biodegradation rate constant (h1) SPME Solid phase microextraction Primry Settler
Aeration tank
Q N kSLR Kp Koc foc KDOC HRT BOD
Sewage flow (m3 PE1 d1) Number of inhabitants (PE) 1 Sludge loading rate (kgBOD kg1 dwt d ) Solid-water partition coefficient (l kg1) Organic carbon-water partition coefficient (l kg1) Fraction organic carbon (dimensionless) Dissolved organic carbon-water partition coefficient (l kg1) Hydraulic retention time (h) Mass of O2 binding material per person per day (g O2 PE1 d1)
S/L separator
Effluent
Influent
Primary sludge
Waste sludge
Fig. 1. General design of sewage treatment plant that is described as a 9-box model in Simple Treat 3.0.
concentration in sludge was also included [5]. For many applications, e.g., estimation of the release of a chemical to river water after treatment, an estimate of removal efficiency is good enough. Nevertheless, a more thorough validation of these fate models should include the distribution of chemicals along the different compartments of the plant. Generally, model descriptions are based on the physical compartments from which an STP is constructed, e.g., primary settler, aeration tank, and solid/ liquid separator. Within each compartment a distinction is made in an aqueous, organic and gaseous phase. Distribution of chemicals is described by partition and diffusion processes between phases (which mainly depend of compound properties) and advective flows between compartments (which mainly depend on the specific characteristics of an STP). For this study, Simple Treat 3.0 [8] was chosen from the variety of freely available modelling programs because this model supplies estimates of concentrations in each compartment and each phase. The current study evaluates the outcome of Simple Treat 3.0 by a comparison of model predictions with measured data, focussing on (i) removal efficiency, (ii) distribution of chemicals within STPs and (iii) trends of concentration of chemicals in the aqueous phase and in the sludge along the different compartments of the STPs. Two polycyclic musks, AHTN (7-acetyl-1,1,3,4,4,6hexamethyltetrahydronaphthalene) and HHCB (7acetyl-1,1,3,4,4,6-hexahydro-4,6,6,7,8,8-hexamethylcyclopenta(g)-2-benzoapyrane), were selected for the current study. Their frequent use as low cost fragrances in soaps, perfumes, air fresheners, detergents, and other house-
hold cleaning products, results to relatively high concentrations in the environment [9–12], in biota [13,14], as well as in STPs [15–17]. Measured concentrations in grab samples (freely dissolved and total) of AHTN and HHCB in the different compartments of three different STPs in The Netherlands were taken from an earlier study [18]. Although in previous studies it has been discussed that to determine the removal efficiency for a chemical, composite samples should be collected rather than grab samples [16,19], only data from grab samples were available. Chemical properties needed as input parameters (Kow ; Henry law constant (H) and biodegradation rate constants (kbiodeg )) were experimentally determined in this study with the exception of kbiodeg ; which was taken from literature [20]. The plants for which the mathematical model is evaluated comply with the Simple Treat 9-box model structure and consist of influent, primary settler, primary sludge, aeration tank, solid/liquid separator, effluent and waste sludge (Fig. 1).
2. Material and methods 2.1. Chemicals 7-acetyl-1,1,3,4,4,6-hexahydro-4,6,6,7,8,8-hexamethylcyclopenta(g)-2-benzopyrane (HHCB, purity >98%) was obtained from International Flavors and Fragrances (IFF) (Hilversum, The Netherlands) and 7-acetyl1,1,3,4,4,6-hexamethyltetrahydronaphthalene (AHTN, purity >98%) from PFW Aroma Chemical B.V., Hercules Incorporated (Barneveld, The Netherlands).
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n-Octanol and hexachlorobenzene (>99%) were obtained from Fluka Chemika (Buchs, Switzerland), and hexane and cyclohexane from Baker (Deventer, The Netherlands). 2.2. Materials Solid phase microextraction (SPME) fibers of 1-cm length, coated with a 100-mm polydimethylsiloxane layer, were purchased from Supelco (Bellefonte, CA, USA). In all cases, fibers were cut to 1 mm length. New fibers were conditioned for 1 h at 250 C, in a GC split injector to desorb all impurities. 2.3. Model concept Simple Treat 3.0 consists of a mathematical model that was primarily created as a scientific tool to analyse the behaviour of a chemical in an STP, and secondary as a calculation module for risk assessment. The system is divided into boxes and a steady state, non-equilibrium model is applied, also known as ‘‘Mackay level III’’ to predict the concentration in the different phases of each compartment [21]. The most relevant processes the chemical may experience in an STP are: *
*
*
Advective mass flow from the source box to the destination box, as a result of media flow carrying the chemical (irreversible transport processes). Diffusive mass flow of the chemical, driven by nonequilibrium concentrations in two adjacent boxes. Degradation, either abiotic chemical transformations or biodegradation.
Simple Treat 3.0 requires only basic physico-chemical properties of the chemical, the emission scenario and biodegradation rate in activated sludge, as input parameters. This limited set of parameters enables predictions of the distribution of the chemicals in each compartment.
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walled Pyrex flask with a tap on the bottom. Five millilitre of water saturated n-octanol containing the test compound was pipetted carefully on top of the water column. The solution was stirred with a magnetic stirrer creating a vortex smaller than 1.5 cm. After 5 days of stirring, both water and n-octanol samples were collected in triplicate during 5 consecutive days. 15 ml water samples were extracted with 2 ml cyclohexane plus internal standard (IS). Subsequently, the solvent was concentrated under a gentle stream of nitrogen. A subsample of n-octanol was diluted with cyclohexane containing IS. Analysis of all samples was carried out on a Varian Star 3600 CX GC equipped with a split/splitless injector, a 30 m X 0.32 mm fused silica DB 5.625 column with a 0.25 mm film thickness (J&W Scientific, Folson, CA, USA) and a Saturn 2000 ion trap mass spectrometer. Detailed information on the analytical procedure is given in an earlier manuscript [18]. 2.4.2. Henry law constant (H) The Henry law constant of AHTN and HHCB were determined using a method which is an adaptation of the techniques described by Peng and Wan [24] and Dewulf et al. [25]. Two-litre bottles with different volume ratio’s of spiked water (220 mg/l) and air were settled. After agitation of the bottles during 2 min to mix both phases, the bottles were left undisturbed during 24 h at 25 C to reach equilibrium of the chemical between the aqueous and the air phase. Subsequently, 1.5 ml of water was taken from each bottle in triplicate for analysis. Aqueous samples were analysed using SPME on a Varian Star 3600 CX GC equipped with a Varian 8200 CX SPME autosampler with SPME agitation (Varian, Palo Alto, CA) and a flame ionisation detector (FID) [17]. The method was first tested with hexachlorobenzene. The determined value of 123.7 Pa m3 mol1 (121.2– 128.8) for hexachlorobenzene is close to previously reported values of 48.6 to 131.3 for this chemical [26,27]. 2.5. Emission scenario
2.4. Physicochemical parameters For a validation study, it is important to have accurate physicochemical data, because no model can be better than its data it has been based upon. In this study, octanol/water partition coefficients and Henry law constants (H) of AHTN and HHCB were determined experimentally as described below. 2.4.1. n-octanol/water partition coefficient The n-octanol/water partition coefficient for both chemicals was determined according to the slow stirring method described by Brooke et al. [22] and De Bruijn et al. [23]. In short, 240 ml octanol saturated distilled water was transferred to a thermostated (25 C) double-
For all plants, sewage flow (Q), number of inhabitants (N), sludge loading rate (kSLR ) and the mode of aeration were given as input parameters (data shown in Table 1). These parameters were obtained from the corresponding STP management. The emission rate of the chemical was in all cases adjusted in such a way that the predicted and determined total concentration in the influent was equal. 2.6. Biodegradation in activated sludge Biodegradation rate constants for both chemicals were reported earlier by Artola-Garicano et al. [20]. In their study, it is assumed that only the chemical that is freely dissolved in the aqueous phase is available for
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Table 1 Emission parameters and specific characteristics of each plant
3
1
1
Q (m PE d ) N (PE) 1 KSLR (kgBOD kg1 dwt d ) HRT (h)a Mode of aeration a
Nieuwegein
De Bilt
Zeist
0.28 120,000 0.05 13.7 Surface
0.28 68,500 0.15 4.9 Surface
0.34 75,000 0.12 5.1 Bubble
Calculated from Simple Treat.
Table 2 Properties of AHTN and HHCB used in the model calculations
Molecular weight (g/mol) Log Kow a Henry law constant (Pa m3/ mol)a Kbiodeg (h1)b Koc (l/kg)b a b
degradation. This implies that the chemical absorbed to solids needs to desorb in order to be degradable. This assumption is in agreement with model assumptions of Simple Treat 3.0.
3. Results and discussion 3.1. Physicochemical parameters In the current study, octanol/water partition coefficients (Kow ) and Henry law constants (H) were determined experimentally for AHTN and HHCB. For both chemicals, measured values for Kow and H are in agreement with data reported earlier by Plassche and Balk [28]. All physicochemical parameters used as input data are shown in Table 2. Simple Treat 3.0 assumes that the Koc is equal to Kow and, Kp ¼ Koc :foc where Kp is the solid/water partition coefficient, Koc is the organic carbon/water partition coefficient and foc the fraction of organic carbon in the solids. Because experimental Koc values for both AHTN and HHCB [20] are substantially lower than Simple Treat assumes, we have used these data instead of the estimated ones. Future improvement of Simple Treat should include new predictions for Koc : 3.2. Model predictions for the total concentrations in the STPs. The average predicted total concentrations of AHTN and HHCB in the different compartments of three STPs are shown in Figs. 2a and b. Overall, the trends observed in this figure are very similar to experimental data reported earlier by Artola-Garicano et al. [18]. The decrease of the total concentration from the influent to the primary settler observed in all plants is obviously due to the withdrawal of two-thirds of the solids in the primary sedimentation. In the aeration tank, the total concentrations of AHTN and HHCB increase as a result of recycling part of the sludge from the solid/liquid separator to the aeration tank. In this compartment, the sewage that enters from the primary settler into the aeration tank, is mixed with the recycled sludge. The waste sludge contains a high concentration of organic
AHTN
HHCB
258.4 5.4 (70.05) 37.1 (72.7)
258.4 5.3 (70.04) 36.9 (70.8)
0.023 7018
0.071 6681
Standard error of the mean estimate. From Artola-Garicano et al. [20].
carbon and thus, also a high concentration of AHTN and HHCB. Possible removal processes in the aeration tank (evaporation and biodegradation of the chemical) are assumed to occur only from the unbound fraction in the aqueous phase. As a result of the relatively low kbiodeg of both chemicals, a low removal efficiency of the chemicals in the aeration tank can be expected. Simple Treat assumes that the removal processes in the aeration tank depend of the hydraulic retention time (HRT) and sludge-loading rate (kSLR ) in the plant. Thus, an increase of the HRT or a decrease of the kSLR increases the residence time of the chemicals in the aeration tank, and therefore more chemical will then be degraded or evaporate from this compartment. This phenomenon also explains the relatively higher predicted removal efficiency from the aeration tank in the STP of Nieuwegein (28% and 39% for AHTN and HHCB) in comparison with the ones in De Bilt and Zeist (between 12% and 25%). A decrease of the total chemical concentration is obtained from the aeration tank to the effluent, as a result of the removal of the chemical through waste sludge. Clearly, the high total chemical concentration in both primary and waste sludge is related to the high affinity of AHTN and HHCB for the organic carbon. 3.3. Model predictions for the free concentrations in the STPs. In Figs. 2a and b, the average predicted free concentrations of AHTN and HHCB in the different compartments of three STPs are given. In all plants, a similar pattern is observed: the free concentration is constant in the influent, primary settler and primary sludge and decreases slightly in the aeration tank. After that, the concentration stays constant in the effluent from the S/L separator and waste sludge. Analogous to the total concentrations, also the predicted freely dissolved concentrations are similar to previously reported experimental data [18]. The decrease observed in the aeration tank is related to the evaporation and biodegradation processes. In all
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Concentration (µg/l)
100.0
10.0
1.0
0.1 Influent
Primary settler
Primary sludge
Aeration tank
Effluent
Waste sludge
Influent
Primary settler
Primary sludge
Aeration tank
Effluent
Waste sludge
Concentration (µg/l)
100.0
10.0
1.0
0.1
Fig. 2. Predicted free and total concentration of (a) AHTN and (b) HHCB in the different compartments of three STPs. Bars represent average total concentrations and closed symbols the average free concentrations. Note the logarithmic scaling for the Y -axis.
plants, a relatively higher decrease of HHCB is predicted. This is related to the higher value for kbiodeg of this chemical than for AHTN. In addition, in the aeration tank of the STP of Nieuwegein, analogous to the total concentration, a relatively big difference in the free concentration between the primary settler and the aeration tank is predicted. In the aeration tank of Nieuwegein, 40% and 55% of AHTN and HHCB, respectively, are predicted to decrease, while in De Bilt and Zeist the predicted decreases of AHTN are 27% and 17%, and of HHCB 36% and 30%, respectively. 3.4. Evaluation of simple treat 3.0 For a detailed validation of STP models, many measurements are required. Therefore, it has become common practice to only compare predicted influent and effluent concentrations with measured concentrations [7]. Clearly, this approach is not sufficient to determine
the distribution of the chemical in the different compartments of the plants. Nevertheless, it can still be sufficient when the model is only used to predict residual concentrations of the chemical in the aqueous phase or sludge entering the environment. Table 3 shows the removal efficiency of the total concentration as well as the predicted and experimentally determined free concentrations in the influent and effluent of three STPs. The current study is based on grab samples which means that variation within and between days is not taken into account. Therefore, the experimentally determined removal efficiencies should be interpreted as indicative. In the STP of Zeist and Nieuwegein, predicted removal of the total concentration compares favourably with the measured data (ofactor 1.3), while in the plant of De Bilt, the removal efficiency of the total concentration is overestimated by a factor of 4 and 2 for AHTN and HHCB, respectively. Regarding the free concentration, in contrast to what is
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Table 3 Removal efficiency of AHTN and HHCB in the three STPs Predicted
Measured
Conc. (mg/l)
Nieuwegein De Bilt
HHCB Zeist Nieuwegein De Bilt
Conc. (mg/l)
Removal
Influent
Effluent
%
Influent
Effluent
%
Total Free Total Free Total Free
1.76 1.13 1.24 0.74 1.40 0.84
0.91 0.83 0.43 0.40 0.60 0.55
48
1.76 0.48 1.24 0.36 1.40 0.38
0.77 0.45 0.57 0.48 1.20 0.55
56
Total Free Total Free Total Free
4.30 2.81 3.56 2.16 3.26 1.99
1.91 1.77 0.95 0.88 1.23 1.14
4.30 1.63 3.56 1.55 3.26 1.18
1.84 1.83 1.43 1.97 2.22 1.44
experimentally determined, a decrease of the concentration from the influent to the effluent is predicted by the model in all the STPs. From these data, and considering that predictions are compared to measurements of grab samples, it can be concluded that Simple Treat 3.0 is able to predict relatively well (within a factor 4) the removal efficiency of total chemical concentration while it systematically overestimates the decrease of the free chemical concentrations. To study not only the removal efficiency but also the processes involved in the removal, a more elaborated evaluation of the model is required. Therefore, the model predictions in the different compartments have been correlated with experimentally determined concentrations (free and total) of both chemicals in all STPs [18]. This comparison is given in Fig. 3. Results show that in a two order of magnitude range, almost an ideal correlation (slope of unity and intercept of zero) is obtained. Altogether, the similarity of the predicted values with previously reported experimental data implies that the mass flows are well accounted for. Of course, it should be noted here that this evaluation concerns two chemicals that are hydrophobic, and, for which the flow of organic materials (the solids) is more crucial than the aqueous phase. Additionally, predicted concentrations of chemicals in the different phases (water, solids) of the STP compartments were compared with previously reported experimental data [18]. In Fig. 4 as well as in Table 3, predicted and measured free and total chemical concentrations in the STP of Nieuwegein are shown separately, for each compound. Similar correlations
65 57
56 73 62
54 14
57 60 32
2
log (Cmeasured)
AHTN Zeist
Removal
1
0
-1 -1
0
1
2
log (Cpredicted)
Fig. 3. Correlation between measured free and total concentration values of AHTN and HHCB in all compartments in three STPs in The Netherlands, and their corresponding predictions by Simple Treat 3.0. (r2 ¼ 0:80; N ¼ 58; y ¼ 1:158 (7 0.078) X 0.017 (70.035)).
were obtained for the other two STPs. Results show a good model prediction of the trend of the total chemical concentration along the STP compartments, while a negative correlation is observed between predicted and experimentally determined free concentrations. In reality, no decrease and sometimes even a slight increase of the free concentration is observed. In our opinion, the constant or even increasing free concentration is, at least partly, an effect of mineralisation of the sludge in the aeration tank combined with a low biodegradation rate of the chemical. The aeration tank of an STP is characterised by two mutually dependent processes with an opposite effect on the concentration in particulate matter. On one hand, the solids are being mineralised by
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Acknowledgements
1.5
The authors thank Dr. J. Struijs for the fruitful discussions.
log (Cmeasured)
1.0
0.5
0.0
References
-0.5
-1.0 -1.0
-0.5
0.0
0.5
1.0
1.5
log (Cpredicted)
Fig. 4. Correlation between predicted and measured free and total concentration of AHTN (&,J, respectively) and of HHCB (’,K, respectively) in the STP of Nieuwegein.
bacteria to decrease the BOD content of the sewage. On the other hand, this mineralisation gives the bacteria the energy to grow. Netto, there will be a decrease of organic matter and thus a decrease of the fugacity capacity along the aeration tank. For chemicals which are slowly (or not at all) biodegraded, this will lead to an increase of the free concentration. Especially for slowly biodegrading chemicals, a small deviation in the description of this process compared to reality will have a relatively large impact on the prediction of the free concentration. Thus, the underestimation of the predicted free concentration in the aeration tank can be caused by an underestimation of BOD-removal (solids), an overestimation of bacterial growth or evaporation, or a combination of all these processes. Future improvements should be focussed at least on refining the description of these processes.
4. Conclusions *
*
*
*
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The comparison between predicted and measured concentrations in the compartments of STPs shows that Simple treat 3.0 is a valid tool for the risk assessment of slowly biodegradable chemicals. The model is able to predict the removal efficiency for hydrophobic slowly biodegradable chemicals. Levels of the free concentration were similar to experimentally determined data. Nevertheless, the predicted trends along the compartments were sometimes inversely related to the measured free concentrations. Data suggest that further improvements of the model should be focused on refining the description of the processes involved in the organic carbon cycles and on the prediction of Koc :
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Water Research 37 (2003) 4377–4384
Evaluation of Simple Treat 3.0 for two hydrophobic and slowly biodegradable chemicals: polycyclic musks HHCB and AHTN Elsa Artola-Garicanoa,*, Joop L.M. Hermensa, Wouter H.J. Vaesb a
Institute for Risk Assessment Sciences, Toxicology Division, University of Utrecht, Yalelaan 2, P.O. Box 80.176, Utrecht 3508 TD, The Netherlands b TNO Nutrition and Food Research, P.O. Box 360, Zeist 3700 AJ, The Netherlands Received 15 October 2002; received in revised form 17 July 2003; accepted 24 July 2003
Abstract In the current study, predictions by Simple Treat 3.0, a fate model for organic chemicals in sewage treatment plants (STPs), are compared with actual measurements in three STPs. Two polycyclic musks, Tonalides (AHTN) and Galaxolides (HHCB), were used for model evaluation. Results show that Simple Treat 3.0 is able to predict the removal efficiency within a factor 4. Predicted concentrations of both chemicals within the different physical compartments of STPs show a high correlation (r2 ¼ 0:80) with experimental values. Although predicted free concentration levels were similar to previously reported experimental data, the trends along the compartments showed an inverse relationship. This bias of the model can be caused by an underestimation of BOD-removal (solids), or an overestimation of bacterial growth, evaporation, or a combination of these three factors. Results show that Simple Treat 3.0 is a valid tool for the risk assessment of slowly biodegradable chemicals, but still some adjustments of the model could be incorporated from a scientific point of view. r 2003 Elsevier Ltd. All rights reserved. Keywords: Polycylclic musks; Model evaluation; Sewage treatment plant; Free concentration
1. Introduction Many industrial and household products are released after use via the sewer, and sewage treatment plants (STPs) into the environment. The two mayor removal pathways in STPs for organic chemicals are sorption to the sludge and biodegradation of the chemical in the aeration tank. The removal efficiency of each chemical is dependent on its physicochemical properties, as well as on the operation efficiency of the plants. Until now, numerous studies have been carried out in which the efficiency of STPs was studied by a comparison of influent and effluent concentrations [1,2]. Nevertheless, the large number of different *Corresponding author. Tel.: +31-30-2535018; fax: +31-302535077. E-mail address: [email protected] (E. Artola-Garicano).
chemicals present in the environment makes it impossible to determine the removal efficiency experimentally for each single chemical that enters the environment via the sewer system. As an alternative, several models have been developed during the last two decades which allow the prediction of the fate of chemicals in STPs [3–6]. These models are based on physical parameters describing the characteristics of the plant, physicochemical and biodegradation parameters describing relevant properties of the compound under study, and thermodynamic equations to combine these parameters. These models are helpful tools and can be used as an early screening tool aimed at selecting the best candidate of a group of new chemicals based on their removal properties. The outcome of these models has been tested for a few chemicals by comparison of predicted versus measured total concentrations in the influent and effluent, the so-called removal efficiency [7] and in some cases, the
0043-1354/$ - see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0043-1354(03)00434-2
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Nomenclature STP Sewage treatment plant AHTN 7-acetyl-1,1,3,4,4,6-hexamethyltetrahydronaphthalene HHCB 7-acetyl, 1,1,3,4,4,6-hexahydro-4,6,6,7,8,8hexamethylcyclopenta(g)-2-benzoapyrene Kow n-octanol-water partition coefficient (dimensionless) H Henry law constant (Pa m3 mol1) kbiodeg Biodegradation rate constant (h1) SPME Solid phase microextraction Primry Settler
Aeration tank
Q N kSLR Kp Koc foc KDOC HRT BOD
Sewage flow (m3 PE1 d1) Number of inhabitants (PE) 1 Sludge loading rate (kgBOD kg1 dwt d ) Solid-water partition coefficient (l kg1) Organic carbon-water partition coefficient (l kg1) Fraction organic carbon (dimensionless) Dissolved organic carbon-water partition coefficient (l kg1) Hydraulic retention time (h) Mass of O2 binding material per person per day (g O2 PE1 d1)
S/L separator
Effluent
Influent
Primary sludge
Waste sludge
Fig. 1. General design of sewage treatment plant that is described as a 9-box model in Simple Treat 3.0.
concentration in sludge was also included [5]. For many applications, e.g., estimation of the release of a chemical to river water after treatment, an estimate of removal efficiency is good enough. Nevertheless, a more thorough validation of these fate models should include the distribution of chemicals along the different compartments of the plant. Generally, model descriptions are based on the physical compartments from which an STP is constructed, e.g., primary settler, aeration tank, and solid/ liquid separator. Within each compartment a distinction is made in an aqueous, organic and gaseous phase. Distribution of chemicals is described by partition and diffusion processes between phases (which mainly depend of compound properties) and advective flows between compartments (which mainly depend on the specific characteristics of an STP). For this study, Simple Treat 3.0 [8] was chosen from the variety of freely available modelling programs because this model supplies estimates of concentrations in each compartment and each phase. The current study evaluates the outcome of Simple Treat 3.0 by a comparison of model predictions with measured data, focussing on (i) removal efficiency, (ii) distribution of chemicals within STPs and (iii) trends of concentration of chemicals in the aqueous phase and in the sludge along the different compartments of the STPs. Two polycyclic musks, AHTN (7-acetyl-1,1,3,4,4,6hexamethyltetrahydronaphthalene) and HHCB (7acetyl-1,1,3,4,4,6-hexahydro-4,6,6,7,8,8-hexamethylcyclopenta(g)-2-benzoapyrane), were selected for the current study. Their frequent use as low cost fragrances in soaps, perfumes, air fresheners, detergents, and other house-
hold cleaning products, results to relatively high concentrations in the environment [9–12], in biota [13,14], as well as in STPs [15–17]. Measured concentrations in grab samples (freely dissolved and total) of AHTN and HHCB in the different compartments of three different STPs in The Netherlands were taken from an earlier study [18]. Although in previous studies it has been discussed that to determine the removal efficiency for a chemical, composite samples should be collected rather than grab samples [16,19], only data from grab samples were available. Chemical properties needed as input parameters (Kow ; Henry law constant (H) and biodegradation rate constants (kbiodeg )) were experimentally determined in this study with the exception of kbiodeg ; which was taken from literature [20]. The plants for which the mathematical model is evaluated comply with the Simple Treat 9-box model structure and consist of influent, primary settler, primary sludge, aeration tank, solid/liquid separator, effluent and waste sludge (Fig. 1).
2. Material and methods 2.1. Chemicals 7-acetyl-1,1,3,4,4,6-hexahydro-4,6,6,7,8,8-hexamethylcyclopenta(g)-2-benzopyrane (HHCB, purity >98%) was obtained from International Flavors and Fragrances (IFF) (Hilversum, The Netherlands) and 7-acetyl1,1,3,4,4,6-hexamethyltetrahydronaphthalene (AHTN, purity >98%) from PFW Aroma Chemical B.V., Hercules Incorporated (Barneveld, The Netherlands).
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n-Octanol and hexachlorobenzene (>99%) were obtained from Fluka Chemika (Buchs, Switzerland), and hexane and cyclohexane from Baker (Deventer, The Netherlands). 2.2. Materials Solid phase microextraction (SPME) fibers of 1-cm length, coated with a 100-mm polydimethylsiloxane layer, were purchased from Supelco (Bellefonte, CA, USA). In all cases, fibers were cut to 1 mm length. New fibers were conditioned for 1 h at 250 C, in a GC split injector to desorb all impurities. 2.3. Model concept Simple Treat 3.0 consists of a mathematical model that was primarily created as a scientific tool to analyse the behaviour of a chemical in an STP, and secondary as a calculation module for risk assessment. The system is divided into boxes and a steady state, non-equilibrium model is applied, also known as ‘‘Mackay level III’’ to predict the concentration in the different phases of each compartment [21]. The most relevant processes the chemical may experience in an STP are: *
*
*
Advective mass flow from the source box to the destination box, as a result of media flow carrying the chemical (irreversible transport processes). Diffusive mass flow of the chemical, driven by nonequilibrium concentrations in two adjacent boxes. Degradation, either abiotic chemical transformations or biodegradation.
Simple Treat 3.0 requires only basic physico-chemical properties of the chemical, the emission scenario and biodegradation rate in activated sludge, as input parameters. This limited set of parameters enables predictions of the distribution of the chemicals in each compartment.
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walled Pyrex flask with a tap on the bottom. Five millilitre of water saturated n-octanol containing the test compound was pipetted carefully on top of the water column. The solution was stirred with a magnetic stirrer creating a vortex smaller than 1.5 cm. After 5 days of stirring, both water and n-octanol samples were collected in triplicate during 5 consecutive days. 15 ml water samples were extracted with 2 ml cyclohexane plus internal standard (IS). Subsequently, the solvent was concentrated under a gentle stream of nitrogen. A subsample of n-octanol was diluted with cyclohexane containing IS. Analysis of all samples was carried out on a Varian Star 3600 CX GC equipped with a split/splitless injector, a 30 m X 0.32 mm fused silica DB 5.625 column with a 0.25 mm film thickness (J&W Scientific, Folson, CA, USA) and a Saturn 2000 ion trap mass spectrometer. Detailed information on the analytical procedure is given in an earlier manuscript [18]. 2.4.2. Henry law constant (H) The Henry law constant of AHTN and HHCB were determined using a method which is an adaptation of the techniques described by Peng and Wan [24] and Dewulf et al. [25]. Two-litre bottles with different volume ratio’s of spiked water (220 mg/l) and air were settled. After agitation of the bottles during 2 min to mix both phases, the bottles were left undisturbed during 24 h at 25 C to reach equilibrium of the chemical between the aqueous and the air phase. Subsequently, 1.5 ml of water was taken from each bottle in triplicate for analysis. Aqueous samples were analysed using SPME on a Varian Star 3600 CX GC equipped with a Varian 8200 CX SPME autosampler with SPME agitation (Varian, Palo Alto, CA) and a flame ionisation detector (FID) [17]. The method was first tested with hexachlorobenzene. The determined value of 123.7 Pa m3 mol1 (121.2– 128.8) for hexachlorobenzene is close to previously reported values of 48.6 to 131.3 for this chemical [26,27]. 2.5. Emission scenario
2.4. Physicochemical parameters For a validation study, it is important to have accurate physicochemical data, because no model can be better than its data it has been based upon. In this study, octanol/water partition coefficients and Henry law constants (H) of AHTN and HHCB were determined experimentally as described below. 2.4.1. n-octanol/water partition coefficient The n-octanol/water partition coefficient for both chemicals was determined according to the slow stirring method described by Brooke et al. [22] and De Bruijn et al. [23]. In short, 240 ml octanol saturated distilled water was transferred to a thermostated (25 C) double-
For all plants, sewage flow (Q), number of inhabitants (N), sludge loading rate (kSLR ) and the mode of aeration were given as input parameters (data shown in Table 1). These parameters were obtained from the corresponding STP management. The emission rate of the chemical was in all cases adjusted in such a way that the predicted and determined total concentration in the influent was equal. 2.6. Biodegradation in activated sludge Biodegradation rate constants for both chemicals were reported earlier by Artola-Garicano et al. [20]. In their study, it is assumed that only the chemical that is freely dissolved in the aqueous phase is available for
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Table 1 Emission parameters and specific characteristics of each plant
3
1
1
Q (m PE d ) N (PE) 1 KSLR (kgBOD kg1 dwt d ) HRT (h)a Mode of aeration a
Nieuwegein
De Bilt
Zeist
0.28 120,000 0.05 13.7 Surface
0.28 68,500 0.15 4.9 Surface
0.34 75,000 0.12 5.1 Bubble
Calculated from Simple Treat.
Table 2 Properties of AHTN and HHCB used in the model calculations
Molecular weight (g/mol) Log Kow a Henry law constant (Pa m3/ mol)a Kbiodeg (h1)b Koc (l/kg)b a b
degradation. This implies that the chemical absorbed to solids needs to desorb in order to be degradable. This assumption is in agreement with model assumptions of Simple Treat 3.0.
3. Results and discussion 3.1. Physicochemical parameters In the current study, octanol/water partition coefficients (Kow ) and Henry law constants (H) were determined experimentally for AHTN and HHCB. For both chemicals, measured values for Kow and H are in agreement with data reported earlier by Plassche and Balk [28]. All physicochemical parameters used as input data are shown in Table 2. Simple Treat 3.0 assumes that the Koc is equal to Kow and, Kp ¼ Koc :foc where Kp is the solid/water partition coefficient, Koc is the organic carbon/water partition coefficient and foc the fraction of organic carbon in the solids. Because experimental Koc values for both AHTN and HHCB [20] are substantially lower than Simple Treat assumes, we have used these data instead of the estimated ones. Future improvement of Simple Treat should include new predictions for Koc : 3.2. Model predictions for the total concentrations in the STPs. The average predicted total concentrations of AHTN and HHCB in the different compartments of three STPs are shown in Figs. 2a and b. Overall, the trends observed in this figure are very similar to experimental data reported earlier by Artola-Garicano et al. [18]. The decrease of the total concentration from the influent to the primary settler observed in all plants is obviously due to the withdrawal of two-thirds of the solids in the primary sedimentation. In the aeration tank, the total concentrations of AHTN and HHCB increase as a result of recycling part of the sludge from the solid/liquid separator to the aeration tank. In this compartment, the sewage that enters from the primary settler into the aeration tank, is mixed with the recycled sludge. The waste sludge contains a high concentration of organic
AHTN
HHCB
258.4 5.4 (70.05) 37.1 (72.7)
258.4 5.3 (70.04) 36.9 (70.8)
0.023 7018
0.071 6681
Standard error of the mean estimate. From Artola-Garicano et al. [20].
carbon and thus, also a high concentration of AHTN and HHCB. Possible removal processes in the aeration tank (evaporation and biodegradation of the chemical) are assumed to occur only from the unbound fraction in the aqueous phase. As a result of the relatively low kbiodeg of both chemicals, a low removal efficiency of the chemicals in the aeration tank can be expected. Simple Treat assumes that the removal processes in the aeration tank depend of the hydraulic retention time (HRT) and sludge-loading rate (kSLR ) in the plant. Thus, an increase of the HRT or a decrease of the kSLR increases the residence time of the chemicals in the aeration tank, and therefore more chemical will then be degraded or evaporate from this compartment. This phenomenon also explains the relatively higher predicted removal efficiency from the aeration tank in the STP of Nieuwegein (28% and 39% for AHTN and HHCB) in comparison with the ones in De Bilt and Zeist (between 12% and 25%). A decrease of the total chemical concentration is obtained from the aeration tank to the effluent, as a result of the removal of the chemical through waste sludge. Clearly, the high total chemical concentration in both primary and waste sludge is related to the high affinity of AHTN and HHCB for the organic carbon. 3.3. Model predictions for the free concentrations in the STPs. In Figs. 2a and b, the average predicted free concentrations of AHTN and HHCB in the different compartments of three STPs are given. In all plants, a similar pattern is observed: the free concentration is constant in the influent, primary settler and primary sludge and decreases slightly in the aeration tank. After that, the concentration stays constant in the effluent from the S/L separator and waste sludge. Analogous to the total concentrations, also the predicted freely dissolved concentrations are similar to previously reported experimental data [18]. The decrease observed in the aeration tank is related to the evaporation and biodegradation processes. In all
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Concentration (µg/l)
100.0
10.0
1.0
0.1 Influent
Primary settler
Primary sludge
Aeration tank
Effluent
Waste sludge
Influent
Primary settler
Primary sludge
Aeration tank
Effluent
Waste sludge
Concentration (µg/l)
100.0
10.0
1.0
0.1
Fig. 2. Predicted free and total concentration of (a) AHTN and (b) HHCB in the different compartments of three STPs. Bars represent average total concentrations and closed symbols the average free concentrations. Note the logarithmic scaling for the Y -axis.
plants, a relatively higher decrease of HHCB is predicted. This is related to the higher value for kbiodeg of this chemical than for AHTN. In addition, in the aeration tank of the STP of Nieuwegein, analogous to the total concentration, a relatively big difference in the free concentration between the primary settler and the aeration tank is predicted. In the aeration tank of Nieuwegein, 40% and 55% of AHTN and HHCB, respectively, are predicted to decrease, while in De Bilt and Zeist the predicted decreases of AHTN are 27% and 17%, and of HHCB 36% and 30%, respectively. 3.4. Evaluation of simple treat 3.0 For a detailed validation of STP models, many measurements are required. Therefore, it has become common practice to only compare predicted influent and effluent concentrations with measured concentrations [7]. Clearly, this approach is not sufficient to determine
the distribution of the chemical in the different compartments of the plants. Nevertheless, it can still be sufficient when the model is only used to predict residual concentrations of the chemical in the aqueous phase or sludge entering the environment. Table 3 shows the removal efficiency of the total concentration as well as the predicted and experimentally determined free concentrations in the influent and effluent of three STPs. The current study is based on grab samples which means that variation within and between days is not taken into account. Therefore, the experimentally determined removal efficiencies should be interpreted as indicative. In the STP of Zeist and Nieuwegein, predicted removal of the total concentration compares favourably with the measured data (ofactor 1.3), while in the plant of De Bilt, the removal efficiency of the total concentration is overestimated by a factor of 4 and 2 for AHTN and HHCB, respectively. Regarding the free concentration, in contrast to what is
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Table 3 Removal efficiency of AHTN and HHCB in the three STPs Predicted
Measured
Conc. (mg/l)
Nieuwegein De Bilt
HHCB Zeist Nieuwegein De Bilt
Conc. (mg/l)
Removal
Influent
Effluent
%
Influent
Effluent
%
Total Free Total Free Total Free
1.76 1.13 1.24 0.74 1.40 0.84
0.91 0.83 0.43 0.40 0.60 0.55
48
1.76 0.48 1.24 0.36 1.40 0.38
0.77 0.45 0.57 0.48 1.20 0.55
56
Total Free Total Free Total Free
4.30 2.81 3.56 2.16 3.26 1.99
1.91 1.77 0.95 0.88 1.23 1.14
4.30 1.63 3.56 1.55 3.26 1.18
1.84 1.83 1.43 1.97 2.22 1.44
experimentally determined, a decrease of the concentration from the influent to the effluent is predicted by the model in all the STPs. From these data, and considering that predictions are compared to measurements of grab samples, it can be concluded that Simple Treat 3.0 is able to predict relatively well (within a factor 4) the removal efficiency of total chemical concentration while it systematically overestimates the decrease of the free chemical concentrations. To study not only the removal efficiency but also the processes involved in the removal, a more elaborated evaluation of the model is required. Therefore, the model predictions in the different compartments have been correlated with experimentally determined concentrations (free and total) of both chemicals in all STPs [18]. This comparison is given in Fig. 3. Results show that in a two order of magnitude range, almost an ideal correlation (slope of unity and intercept of zero) is obtained. Altogether, the similarity of the predicted values with previously reported experimental data implies that the mass flows are well accounted for. Of course, it should be noted here that this evaluation concerns two chemicals that are hydrophobic, and, for which the flow of organic materials (the solids) is more crucial than the aqueous phase. Additionally, predicted concentrations of chemicals in the different phases (water, solids) of the STP compartments were compared with previously reported experimental data [18]. In Fig. 4 as well as in Table 3, predicted and measured free and total chemical concentrations in the STP of Nieuwegein are shown separately, for each compound. Similar correlations
65 57
56 73 62
54 14
57 60 32
2
log (Cmeasured)
AHTN Zeist
Removal
1
0
-1 -1
0
1
2
log (Cpredicted)
Fig. 3. Correlation between measured free and total concentration values of AHTN and HHCB in all compartments in three STPs in The Netherlands, and their corresponding predictions by Simple Treat 3.0. (r2 ¼ 0:80; N ¼ 58; y ¼ 1:158 (7 0.078) X 0.017 (70.035)).
were obtained for the other two STPs. Results show a good model prediction of the trend of the total chemical concentration along the STP compartments, while a negative correlation is observed between predicted and experimentally determined free concentrations. In reality, no decrease and sometimes even a slight increase of the free concentration is observed. In our opinion, the constant or even increasing free concentration is, at least partly, an effect of mineralisation of the sludge in the aeration tank combined with a low biodegradation rate of the chemical. The aeration tank of an STP is characterised by two mutually dependent processes with an opposite effect on the concentration in particulate matter. On one hand, the solids are being mineralised by
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Acknowledgements
1.5
The authors thank Dr. J. Struijs for the fruitful discussions.
log (Cmeasured)
1.0
0.5
0.0
References
-0.5
-1.0 -1.0
-0.5
0.0
0.5
1.0
1.5
log (Cpredicted)
Fig. 4. Correlation between predicted and measured free and total concentration of AHTN (&,J, respectively) and of HHCB (’,K, respectively) in the STP of Nieuwegein.
bacteria to decrease the BOD content of the sewage. On the other hand, this mineralisation gives the bacteria the energy to grow. Netto, there will be a decrease of organic matter and thus a decrease of the fugacity capacity along the aeration tank. For chemicals which are slowly (or not at all) biodegraded, this will lead to an increase of the free concentration. Especially for slowly biodegrading chemicals, a small deviation in the description of this process compared to reality will have a relatively large impact on the prediction of the free concentration. Thus, the underestimation of the predicted free concentration in the aeration tank can be caused by an underestimation of BOD-removal (solids), an overestimation of bacterial growth or evaporation, or a combination of all these processes. Future improvements should be focussed at least on refining the description of these processes.
4. Conclusions *
*
*
*
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The comparison between predicted and measured concentrations in the compartments of STPs shows that Simple treat 3.0 is a valid tool for the risk assessment of slowly biodegradable chemicals. The model is able to predict the removal efficiency for hydrophobic slowly biodegradable chemicals. Levels of the free concentration were similar to experimentally determined data. Nevertheless, the predicted trends along the compartments were sometimes inversely related to the measured free concentrations. Data suggest that further improvements of the model should be focused on refining the description of the processes involved in the organic carbon cycles and on the prediction of Koc :
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