Chemosphere, Vol. 32, No. 7, pp. 1413-1426, 1996 S0045-6535(96)00050-1
Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0045-6535/96 $15.00+0.00
Fate of LAS in Activated Sludge Wastewater Treatment Plants: A Model Verification Study T. Feijtel 1., H. Vits 2, R. Murray-Smith 3, R. van Wijk 4, V. Koch5, R. SchrOder6, R. Birch 7 and W. Ten Berge8
1procter&Gamble, European Technical Center, Temselaan 100, 1583 Stmmbeek-Bever, Belgium 2Shell Research Ltd., Sittingboume, ME9 8AG, Great Britain 3Zeneca, Brixham Environmental Laboratory, Bdxham, Devon TQ5 8BA, Great Britain 4Akzo-Nobel Central Reseamh, P.O. Box 9300, 6800 SB Amhem, The Netherlands 5Hoechst A.G., D-6230 Frankfurt am Main, Germany 6Henkel KGaA, D-40191 D(Jsseldorf, Germany 7Unilever Research, Port Sunlight Laboratory, Bebington, Wirral LG3 3JW, Great Britain 8DSM Corporate Safety, P.O. Box 6500, 6401 JH Heeden, The Netherlands
(Received in Germany 22 May 1995; accepted 20 December 1995)
ABSTRACT Mathematical models that predict the fate of chemicals in Wastewater Treatment Plants (WWTPs) are used as an integral part of risk assessment process in many countdes. In this paper, the predictive power of two mathematical models, SIMPLETREAT and WWTREAT, is checked against linear alkylbenzene sulfonate (I_AS) fate data collected at five WWTPs located across Europe.
The temporal variability in
WWTP streams was identified using statistical methods. Copyright© 1996 Elsevier Science Ltd
The SIMPLETREAT and WVVTREAT models can be used as tools to gain an understanding of the fate and distribution of LAS during wastewater treatment at the screening level of the exposure assessment. Averages for the experimentally observed LAS removal in the five MWVTPs ranged between 98.5 and 99.9%; ranges of predicted removal were respectively 93.8-97.8% for SIPLETREAT and 98.1-99.3% for WWTREAT. Use of SIMPLETREAT and WWTREAT resulted in conservative predictions of LAS removal, and estimated effluent LAS concentrations were up to one order of magnitude higher than monitodng data. Alter calibration against one single measured LAS data set, WWTREAT effluent concentrations differed less than a factor of 5 from monitoring data of the full validation set.
KEYWORDS: Monitoring, Wastewater Treatment, Modelling, Linear Alkylbenzene Sulfonate
(*) To whom all correspondence should be addressed 1413
1414
INTRODUCTION The development of environmental risk assessment procedures for chemicals has received considerable attention from regulators, academia and industry (EEC, 1993a; 1993b; 1993c; ECETOC 1993; OECD 1994).
An iterative and tiered approach in data gathering, evaluation, and decision making has been
generally adopted. The risk assessment process essentially consists of a stepwise comparison of the predicted environmental concentration (PEC) with the predicted no effect concentration (PNEC) for the substance and compartment of interest, with each iteration step reducing uncertainty and improving the PEC and PNEC estimates.
Starting with theoretical considerations, the exposure assessment of a substance becomes more specific and reliable when its release and fate can be quantified on the basis of simple physico-chemical parameters (e.g. solubility, vapour pressure, octanol-water partition) and biodegradation screening results (e.g. OECD ready biodegradability). Laboratory simulation tests provide an important connection between the relevant chemical properties and real-world environmental situations. Standardisation of wastewater treatment or river simulation tests has effectively contributed to the comparison of environmental fate of chemical substances. However, the most relevant information and the yardstick for all theoretical and laboratory-based risk evaluations is field data. A logic-based hierarchy for decision-making based on risk assessment considerations is therefore helpful in setting relative priorities and in determining the necessity for and the scope of monitoring programmes. However, due to analytical, logistical or financial constraints, field monitoring studies cannot be carded out for all existing substances. It is therefore important to develop an understanding in the validity of the currently used predictive models in the area of exposure and dsk assessment.
To perform a tier-one or screening exposure assessment, a calculation routine is needed to estimate emissions based on accepted release factors, use categories, anticipated tonnages, and physico-chemical and biodegradability data. For many chemicals of domestic and industrial use, these emission estimates need to account specifically for removal in a WWTP, as this treatment step can have a major impact on the actual release into the receiving environment. If neither laboratory simulation tests nor monitoring data are available, a robust model is needed for estimating elimination dudng waste-water treatment.
This is
particularly true for new chemicals, but is also valid for numerous existing substances already in use.
In this paper, the performance of two WWTP models applicable at the screening phase - namely SIMPLETREAT (Struijs et al., 1991) and WWTREAT (Cowan et al., 1993) - are compared to WWTP monitoring data for linear alkylbenzene sulfonate (I_AS), a major anionic surfactant used in household detergents.
The WWTP data set is dedved from a wider study on the environmental fate of IAS,
sponsored by the European Chemical Industry and carried out by a joint industry Task Force operated by the Association Internationale de la Savonnerie et la D6tergence (AIS) and the Comite Europeen des Agents de Surface et Interm6diares Organiques (CESIO).
Data was collected at 5 different WWTPs
1415
across Europe (Holt et al., 1995; Feijtel et al., 1995; Di Corcia et al., 1994; Schoberl el al., 1994; SanchezLeal et al., 1994; Waters and Feijtet, 1995).
SIMPLETREAT and WVVTREAT are steady state models. Field observations sample a time-dependent, space-dependent and treatment-type specific distribution of concentrations. Due consideration of the variability in influent and effluent is essential for a meaningful comparison of monitoring data and model predictions. A full field validation program requires an extensive and costly set of monitoring observations to allow a solid statistical evaluation of relevant sources of variation.
Logistical constraints, such as a
limited availability of personnel and equipment, and financial constraints usually dictate a compromise.
METHODS S t a t i s t i c a l D a t a Analysis The monitoring data from the five VWVTPs across Europe (Holt et al., 1994; Feijtel et al., 1994; Di Corcia et al., 1994; Schoberl et al., 1994; S&nchez-Leal, 1994) are analysed and charactedsed for sources of variability, for the precision of the derived summary statistics (e.g. mean values and percentiles), and for any influent and effluent correlations. Generally, the monitoring data was acquired at two-hourly or threehourly intervals over one or two days. The monitoring data and model predictions are tested statistically, to determine the overall suitability of the models in representing the measured data set. Daily averages were collected for a pedod up to one week. 'Day-to-day' variations are referred to as 'daily' variations while the 'within-day' variations are called 'diurnal', in what follows.
Characterisation
of Variability
The diumal and daily variability of influent and effluent concentration in each WWTP are estimated from the respective sample standard deviations. Overall standard deviations (Ellis and Lacey, 1980 ) are given by: 2
2
a ~,r,,n = ( ~ a~,r,,,,l + ~ a,,i~
)½
(1)
The relative contribution of the diurnal and daily variances is quantified by a variance ratio.
Mean and Percentiles
A parametric analysis assuming log-normal and normal distributions (Montgomery and Hart, 1974) is carded out. For a normal distribution, estimators for the precision (p) of the mean and percentiles are derived from
p = .f t a / n ½
(2)
where f is an estimation factor dependent on the percentile, t is Student's t-level of desired significance and n is the sample size.
]4]6
Time Series Analysis Similarities in time signatures of infiuent and effluent are established using correlation methods (e.g. Box and Jenkins, 1975).
A 'signal-to-noise' F-test is performed to establish the significance of a linear
correlation between series.
Comparison of Measured Effluent Results with Model Simulations A matched-pair test (e.g. Green, 1984) was applied to determine the differences between the monitoring results and model simulations using Student's t. The null hypothesis assumes a mean of differences, between model and observations, equal to zero.
Wastewater Treatment Plant Models SIMPLETREAT and WWTREAT are two environmental fate models developed to account for the fate and distribution processes in a wastewater treatment plant, and to predict the concentration of substance in effluent, sludge and air.
SIMPLETREAT (Struijs et al. 1991) was developed as a box model to assess the probable fate of chemicals from the so-called base-set data, as requested by the EU upon notification of new chemicals. SIMPLETREAT can be used as a diagnostic tool, providing regulatory authorities with a quick impression of the emission patterns of a chemical in a municipal WW'I'P.
It requires a minimal data input to calculate
air-water and sludge solids-water partition coefficients. Distribution coefficients are calculated from solubility, vapour pressure, and octanol-water partition coefficients. To account for degradation and speciation changes of the chemical in the water phase, this model requires biodegradability information and the degree of dissociation or protonation of the substance.
The biodegradation rate constant used in
SlMPLETREAT is either set to 'zero' for non-biodegradable chemicals or to 3 h-1 in the aqueous phase of the activated sludge compartment.
The latter value is applicable if the chemical passes one of the
stringent OECD tests for 'ready biodegradability (OECD, 1981). Plant operating conditions are pre-set on high, medium and low sludge loading conditions. Plant-specific hydraulic and sludge residence times can not be specified by the user.
WWTREAT
(Cowan et al., 1993) was developed to predict the degree of removal and distribution of
consumer product chemicals among air, treated liquid effluent, and sludge for primary and activated sludge WWTPs, using independently determined distribution coefficients and biodegradation rate constants. The major difference between this model and previous models (Namkung and Rittman, 1987; Stmijs et al., 1991) is that both the sorbed and dissolved fractions of a chemical are considered to be available for biodegradation in the biological aerator. In addition, all operating conditions can be independently changed to simulate any specific WWTP. The rate constants used in WW'I-REAT are experimentally determined in a batch activated sludge (BAS) system with a solids concentration of 2500 mg/L, and initial chemical concentration of 0,1 to 1 mg/L, as described by Games et al. (1982). The biodegradation rate constants for the dissolved and sorbed chemical are assumed to be the same, and equal to the overall rate constant
1417 measured in the BAS system.
At the base-set level, however, these biodegradation rate data will be
lacking, and the information on biodegradation will be limited to screening test data.
The standard output of both models shows the relative amounts of chemical degraded and emitted via effluent, sludge and air. To evaluate the influence of model structure and input data, a comparison of the prediction of both models will be presented for the five monitoring data sets.
RESULTS AND DISCUSSION Influent and Effluent Variability Insights into the applicability of a steady state WWTP model can be gained from assessing the variability in concentrations and flows from monitoring data. The variabUity of LAS concentrations is assessed from a comparison of the diurnal and the overall variances. For example, the UK monitoring exercise (Holt et al, 1995) showed that a large proportion of the overall variability in LAS concentrations corresponded to the diurnal component (Table 1).
In the same study, the diurnal variability of the effluent concentration
changed significantly from day 1 to day 5. Table 1: Variability of LAS concentration (mg/L) in influent and effluent (UK monitoring data). Day-1 Data
Day-5 Data
Daily Mean Data
6-Day Data
(mg/L)
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
Mean
15.5
0.006
16.9
0.012
15.1
0.010
15.1
0.010
Standard Deviation
9.4
0.003
7.7
0.009
2.6
0.003
9.8
0.009
A dominance of the diurnal variance was also established in the Italian monitoring study (Di Corcia et al., 1994).
The German study (Schoberl et al., 1994) showed that the variability in the effluent can be
dominated by the daily variation (Table 2), likely due to sudden changes in operating conditions. Table 2: Summary of LAS concentration variability in European monitoring study.
Country
2 /(~2 (~ diurnal/ overall
2 / 2 (~ diurnal~ (~ overall
Influent
Effluent
Germany
0.54
0.10
Italy (11)
0.91
0.91
UK
0.92
0.92
(¶) : Data of day 1 in Italian monitoring study not included (Di Corcia et al., 1994). The Dutch data set was not considered as the two-hourly monitoring followed a heavy rain event that significantly perturbed the operation of the WWTP (Feijtel et al. 1995). In the Spanish study (S&nchez-Leal et al., 1994), the two-hourly monitoring did not overlap with the daily mean data acquisition period.
1418 Overall, the analysis of LAS concentration variability shows that it is not possible to draw a general conclusion on the relative magnitude of the diurnal and daily variances. The variability reflects largely the local use pattern of consumer products, water consumption, and possibly the fluctuations in the ratio of industrial to domestic waste flows into the WWI-Ps. The temporal variability shows that the VWEIPs under study are not in a true steady state, as could be expected considering the variability in influent concentrations. variations.
Additional sources of variability not included in this study are weekly and seasonal
This implies that model predictions are only expected to approach mean measured effluent
concentrations, which are considered to be sufficient for risk assessment purposes.
Concentration means and percentiles - with their associated precisions - are summarised in Table 3. The underlying number of 'spot' samples for each influent/effluent can be established from the procedures for compositing. For example, in the UK study (Holt et al, 1995), 24 spot samples were systematically taken at hourly intervals, each day. Two successive spot samples were collected in the same vessel flask, leading to a total number of 72 samples, over six days, for estimating the precision of the mean and percentiles. A similar assumption was made for the German and Italian data sets. The analysis of the Dutch data did not include diurnal data, for the reasons given above. Thus, the calculations on the Dutch data set (and also on the Spanish data set) only reflect the daily variability and, accordingly, the number of samples is much smaller (Table 3). Table 3:
Summary of influent and effluent I_AS concentrations (mg/L) in all WWTPs. Influent (mglL) Country
Mean *
90% Percentile *
Standard
-+95% Confidence
+ 95% Confidence
Deviation*'l"
5.4 (6.1) + 0.47
8.2 (10.0) + 0.82
Italy¶
4.6 (5.1) + 050
6.8 (8.5)_+ 0.86
1.8 (2.7)
Netherlands
4.0 (4.0) +1.7
5.5 (5.6) + 2.9
1.2 (1.2)
UK
15.1 (19.2) _+2.3
27.6 (37.4) + 3.9
9.7 (15.8)
Spain~
9.6 (9.6) ~ 3.5
Germany
0.067 (0.076) + 0.008
Italy¶ Netherlands UK
Infiuent Germany
11.4 (11.5)_+ 5.9
2.2 (3.0)
1.4 (1.4)
Effluent (rag/L)
Spain~
0.11 (0.13) + 0.013
0.034 (0.041)
0.043 (0065) +_0.034
0.19 (0.15) _+0 058
0.12 (0.13)
0009 (0.008) +- 0.003
0012 (0.016) + 0.005
0.002 (0.008)
0.010 (0.014) + 0.002
0.022 (0.027) + 0.004
0.009 (0.012)
0.14 (0.14) _+0.19
0.23 (0.25) + 0.32
0.080 (0.09)
(*) : Bracketed values correspond to estimates using the log-normal distribution. Confidence intervals were calculated using a normal distribution assumption. (1-) : Sample standard deviation (S.D) calculated for the logarithm of concentration values. Log-normal S.D. obtained using standard conversion formulae. (¶) : Data of day 1 in monitoring study not included due to major plant disturbance (Di Corcia et al., 1994) (5) : Daily variability only (N=3).
1419 In view of the limited amount of data, it was decided to evaluate the LAS concentration frequency distributions in influent and effluent using parametric statistics. The concentrations were assumed to be either normal or log-normal distributed.
The confidence intervals for means and percentiles were
calculated for a normal distribution (Table 3).
The relative uncertainty in the influent concentration was highest for the Dutch and Spanish data set. This reflects the smaller number of underlying samples being considered. effluent was highest in the Spanish study.
The relative uncertainty for the
In general, the log-normal values for mean and percentiles
were higher than those from the normal distribution. The highest average effluent concentrations were found in the Spanish and German studies.
Time Series Analysis A limited attempt at cross-correlating LAS concentration in effluent and influent, time series analysis was performed by 'cycling' the data. The UK LAS monitoring exercise (Holt et al., 1995) reported two-hourly measurements for two non-consecutive days (day 1 and day 5 in the monitoring programme) allowing for a preliminary auto-correlation evaluation (Figure 1).
Figure 1: Correlation analysis for LAS concentrations in influent/effluent, UK monitoring study (Holt et al., 1994). (O) correlation between influent and effluent on day 1 ;(A) correlation between influent and effluent on day 5.
I,.
._~
0.5
0
~¢,.
0
0
P h. O
-o.5
-1 0
8
16
24
LAG (h)
The cross-correlation between influent and effluent concentration gave maximum values of r = 0.53 (,o<0.08) and 0.79 (,o<0.003), at a lag of 14 hours (Table 4).
A calculation of the effective hydraulic
residence time from the average influent flow rates to the VWVTP (7450 m3d-1 and 8250 m3d-1,
1420 respectively) yields 15.8 h and 14.3 h for days 1 and 5 of the UK study. These residence times are in good agreement with the lag value of highest cross-correlation.
Table 4: Approximate cross-correlation analysis for LAS concentrations in influent and effluent. Country
r Influent/Effluent ( p <)
Lag Time t (h)
Germany
0.75 (0.18)
12
Italy
0.72 (0.04)
3
UK (Day 1)
0.53 (0.08)
14
UK (Day 5)
0.79 (0.003)
14
At a 90% significance level, only the Italian and UK cross-correlations were acceptable (Table 4). The Italian lag time, obtained by cross-correlation does not relate to the hydraulic residence time.
This
inconsistency may reflect the upset of the plant.
WW'FP M o d e l P r e d i c t i o n s Both WW'I'REAT and SIMPLETREAT were run with the same chemical input data file. The physicochemical properties and biodegradation data for LAS are taken from Cowan et al, (1993) and given in Table 5. Table 5: SIMPLETREAT and WWTREAT input data. Partition Coefficient (L/Kg)
Henry's Law Constant (Pa m3/mol)
Ready Biodegradable
Measured Biodegradation Rate Constant (d-1)
3500
< 10-1°
yes
1.04:1:0.22
WWTREAT was run using biodegradation rate constants determined in BAS tests which use biomass and chemical concentrations that mimic those of an Activated Sludge Reactor (Cowan et al., 1993). SIMPLETREAT was run with a default value of 3 h-1 ( 72 d-1 ) because LAS is ready biodegradable in the OECD test system (OECD, 1981). A comparison of SIMPLETREAT and WWTREAT model predictions with measured data is presented in Table 6. Generally, SIMPLETREAT predicts a lower I_AS removal than WWTREAT. Predicted effluent concentrations are up to one order of magnitude higher than the monitoring data. Both models give conservative estimates of I_AS removal. The relative importance of sorption and biodegradation processes is affected by the combination of chemical properties of the material, i.e. the input data. Experimentally derived input data are the most reliable source of the sorption and rate data for LAS. For example, Holysh et al. (1986) used laboratory and field data on LAS to parameterise Mackay fugacity models. A Monte-Carlo two-parameter (sorption
]42! coefficient and biodegradation rate) sensitivity analysis of I_AS removal was performed with WWTREAT (Figure 2). Table 6: Comparison of measured and predicted values for LAS removal in 5 European WWTPs. Monitoring
(*) Primary Removal
(%)
Data
14 - 38
SIMPLETREAT
WWTREAT
25.8
26.1
Removal by sorption - Total
(%)
40 - 44
30 - 37
Removal by Biodegradation
(%)
50 - 57
61 - 69
Total Removal
(%)
98.5 - 99.9
93.8 - 97.8
98.1 - 99.3
Effluent Concentration
(p.g/L)
9 - 140
88 - 339
29 -180
99.1 - 99.7 #
12- 83 # Primary Sludge Concentration
(g/kg)
4.3 - 8.3
7.9 - 10.6
Combined Sludge Concentration
(g/kg)
6 - 9.4
4.8 - 6.5
(*) Range of averages in monitoring studies. # WWTREAT calibrated with Monitoring data set for The Netherlands
Fi,qure 2: Two-parameter w W ' r R E A T sensitivity analysis of I_AS removal.
10080-
k =0.5 - 4.0 (day -1)
Average LAS Removal =S8 %
Kp =1(3(]0 - 4000 (L/kg)
m
¢0
>
O
60-
E
40.
r-] biodegradation
20-
[ ] sorptJon
O-
50
99
Number of Runs
This analysis showed that the LAS removal predicted by VWVTREAT was rather insensitive to sorption and biodegradation within the specified range (Kp = 1 000 - 4 000 (L/kg); k = 0.5 - 4.0 day-l). The total predicted I_AS removal averaged 98.0 + 1.7 %.
1422 To gain additional insights into WWTREAT predictions, the input data was calibrated on one of the monitoring data sets. The other monitoring data sets were then used for validation and compared with the model predictions.
With the Dutch data set used as calibration set, a biodegradation rate constant of 3
day -1 and a sorption coefficient of 3000 L/kg were found to be appropriate (Figure 3). The biodegradation rate constant used in the model is about 3 times higher than the experimentally derived mineralisation rate constant used by Cowan et al. (1993). The primary biodegradability rate for LAS is expected to be significantly higher than the mineralisation rate, possibly by a factor 2-6.
Figure 3: Verification of predicted LAS removal (%) with SIMPLETREAT and WWTREAT calibrated with full monitoring data set (NL = Netherlands, I = Italy, SP = Spain, UK = United Kingdom, D = Germany; day 1 data in Italian monitoring study included).
100.0 99.0 98.0 97.0 Measured
96.0
[ ] WVWREAT
% 95.0 94.0
•
SIMPLETREAT
93.0 92.0 91.0 90.0 D
UK
NL
E
I
The predicted removal for the other European locations are either under- or overestimated. However, the calibrated WWTREAT model does yields effluent concentrations varying by less than a factor of 5 from monitoring data, for all 5 WWTPs studied (Figure 4).
It must be noted however, that similar results would be expected with SIMPLETREAT - provided that the model would allow the same flexibility as provided with WW-IREAT.
Plant-specific hydraulic and sludge
residence time have been entered for each WVVTP, and calibration of the biodegradation rate constant on one specific data-set provided additional reassurance.
1423 Figure 4: Measured and WWTREAT-predicted LAS effluent concentrations for different operating
conditions (NL = Netherlands, I = Italy, SP = Spain, UK = United Kingdom, D -- Germany; day 1 data in Italian monitoring study included; VVVVTREAT calibrated with Dutch dataset).
140
c
.s 120 c
100
o .~
80
C "="
60
(SP)
(UK) (D) (I) , III I
•
Measured
•
Predicted
i
-=
40
14-q.,
i L
', •
uJ
u)
20
,<
(NL)
l
0
i
0
5
,
'
10
I
t5
S R T (days)
A matched pair t-test allows an additional statistical comparison of model and monitoring results (Table 7). The null hypothesis in a single-tailed t-test corresponds to identical results from model predictions and monitoring data. At 95% significance this test could not discriminate between WWTREAT and monitoring LAS effiuen[ data. However, the null hypothesis could not be validated for SIMPLETREAT and monitoring data. This suggests that SIMPLETREAT over predicts I_AS effluent concentrations, whereas VWVTREAT predicted effluent concentration which did not significantly differ from measured effluent concentrations.
Table 7: Matched-pair test comparing monitoring effluent data with WWTREAT and SIMPLETREAT
predictions ( WWTREAT calibrated with Dutch data set ).
Country
Netherlands Italyll Spain UK Germany
Average I_AS Effluent (p.g/L) 8.9 68 136.1 10.4 67.1
VWVTREAT effluent (p.g/L) 12.1 17.1 83.2 67.0 22.7
SIMPLETREAT effluent (p.g/L) 88.3 100.2 594.1 331.1 176
d (WWTREAT Measured ) 3.2 -50.9 -52.9 56.6 -44.4 d = -17.7 s = 47.4
d
: difference between model prediction and average of monitoring data.
S
: standard deviation.
~)
: day 1 data in Italian monitoring study included.
d (SIMPLETREAT Measured) 79.4 32.2 458.0 320.6 108.9 d -- 199.8 s = 181.7
1424
CONCLUSIONS Statistical characterisation of the AIS/CESIO monitoring programme for LAS has shown that:
(1) The relative contributions of diurnal and daily variations to the overall variability in influent and effluent concentrations is a function of the local VMWTP characteristics and operating conditions. It was not possible to conclude that, in all cases, the diurnal variability was more significant than the daily variability. The results did suggest however, that if a characterisation of the overall variability of the influent / effluent would be needed, then it is important to quantify the diurnal variability. (2) Time series analysis can be utilised as a 'black-box' approach to test correlations and patterns for the influent and effluent. This is particularly helpful to evaluate the data sets for model calibration and model verification.
The model verification study has shown that:
(1) Predicted total LAS removal is underestimated in both SIMPLETREAT and ~WVTREAT. Effluent concentrations are up to one order of magnitude higher than measured data, and both models seem to be conservative in respect to its potential use within the Risk Assessment context. (2) WWTREAT model yields reasonably accurate predictions for all operating conditions considered, with effluent concentrations which vary by less than a factor 5, i.e. not significantly different from the validation data set.
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