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Assessment of total uncertainty in cocaine and benzoylecgonine wastewater load measurements
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Assessment of total uncertainty in cocaine and benzoylecgonine wastewater load measurements Christoph Mathieu a, Jo¨rg Rieckermann b, Jean-Daniel Berset c, Stefan Schu¨rch d, Rudolf Brenneisen a,* a
Dept. of Clinical Research, University of Berne, Berne, Switzerland Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Du¨bendorf, Switzerland c Water and Soil Protection Laboratory (WSPL), Berne, Switzerland d Dept. of Chemistry and Biochemistry, University of Berne, Berne, Switzerland b
article info
abstract
Article history:
To check the effectiveness of campaigns preventing drug abuse or indicating local effects
Received 11 July 2011
of efforts against drug trafficking, it is beneficial to know consumed amounts of substances
Received in revised form
in a high spatial and temporal resolution. The analysis of drugs of abuse in wastewater
16 September 2011
(WW) has the potential to provide this information. In this study, the reliability of WW drug
Accepted 25 September 2011
consumption estimates is assessed and a novel method presented to calculate the total
Available online 19 October 2011
uncertainty in observed WW cocaine (COC) and benzoylecgonine (BE) loads. Specifically, uncertainties resulting from discharge measurements, chemical analysis and the applied
Keywords:
sampling scheme were addressed and three approaches presented. These consist of (i)
Sewage treatment plant
a generic model-based procedure to investigate the influence of the sampling scheme on
Wastewater
the uncertainty of observed or expected drug loads, (ii) a comparative analysis of two
Cocaine and benzoylecgonine loads
analytical methods (high performance liquid chromatographyetandem mass spectrometry
Analytical uncertainty
and gas chromatographyemass spectrometry), including an extended cross-validation by influent profiling over several days, and (iii) monitoring COC and BE concentrations in WW of the largest Swiss sewage treatment plants. In addition, the COC and BE loads observed in the sewage treatment plant of the city of Berne were used to back-calculate the COC consumption. The estimated mean daily consumed amount was 107 21 g of pure COC, corresponding to 321 g of street-grade COC. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Drug abuse is a widespread problem of the modern community with significant socio-economic consequences, such as health treatment costs and higher incidence of criminality (EMCDDA, 2009). For drug policy makers it is therefore important to correctly identify trends, usage levels, hot spots, and prevalence of drug consumption to design prevention
campaigns or enforcement strategies, which is not trivial given current epidemiological data. Drug abuse is mostly estimated indirectly from population surveys, consumer interviews, medical records, crime statistics, drug production and seizure rates (EMCDDA, 2002). However, traditional methods are costly and the results are often obtained with considerable delay. Therefore, epidemiologists seek to explore novel data sources to improve addiction research or
* Corresponding author. Dep. Clinical Research, University of Berne, Murtenstr. 35, CH-3010 Berne, Switzerland. Tel.: þ41 31 632 8714; fax: þ41 31 632 3297. E-mail address: [email protected] (R. Brenneisen). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.049
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drug early warning systems. Estimating drug consumption from analysis of drugs of abuse in wastewater (WW) has the potential for such improvement, because data could be collected in near-real time, non-intrusively from the majority of the population and without many other limitations of traditional surveys (EMCDDA, 2002). In 2001, Daughton (Daughton, 2001) hypothesized that WW can be regarded as a pooled urine sample of a large population and that mass flows of drugs of abuse in WW reflect the consumed amount. In 2004, for the first time a drug of abuse (3,4-methylenedioxymethamphetamine, MDMA, known as ecstasy) was detected in effluent water from a sewage treatment plant (STP) (Jones-Lepp et al., 2004). In 2005, Zuccato and coworkers (Zuccato et al., 2005) were the first suggesting a model to back-calculate usage figures from WW loads and coined the term “sewage epidemiology”. Consequently, this methodological approach has further been developed and implemented for other drugs of abuse (Castiglioni et al., 2008; Postigo et al., 2008; Van Nuijs, Castiglioni, et al., 2011; Van Nuijs et al., 2011). To further develop WW drug testing as a novel information source in addition to the classical epidemiological tools, Van Nuijs, Castiglioni, et al. (2011) formulated research needs to improve the validity of (i) measured “concentrations of drugs of abuse and/or metabolite(s) in influent wastewater” and (ii) “back-calculations from concentrations in wastewater to an amount of drugs of abuse (g/day)”, which range from errors in discharge measurements, adsorption to particulate matter (Metcalfe et al., 2010) and losses in the urban sewer system (Rutsch et al., 2008) to the variability of excretion rates (Cone et al., 2003) in a population of drug users. Interestingly, while errors in discharge measurements are often being considered in engineering analysis, there has been recent concern that the sampling scheme and frequency also introduce uncertainty in analytical studies. In a recent review, 87 peer-reviewed journal articles were analyzed regarding the fitness for purpose of the applied sampling procedures to monitor drugs of abuse as well as pharmaceuticals and personal care products (PPCPs) in sewers and STP influents (Ort et al., 2010a). One of the major findings was that sampling is mostly carried out according to existing tradition on the STP or standard laboratory protocols. Even for analysis of drugs of abuse the importance of shortterm pollutant variations on observed concentrations or loads is typically not addressed, although a high data quality is mandatory. As errors of up to 100% and more have been reported (Ort et al., 2010b), it remained unclear for the majority of reviewed studies whether observed variations can be attributed to “real” variations or sampling artefacts. In this study, we therefore take a first step towards assessing the reliability of WW drug consumption estimates and present a novel method to assess the total uncertainty in observed WW COC and BE loads based on measurements and stochastic dynamic load modelling. Specifically, we address uncertainties resulting from discharge measurements, chemical analysis and the applied sampling scheme, and present the following three innovative approaches: (i) A generic model-based procedure to investigate the influence of the sampling scheme on the uncertainty of observed or expected drug loads.
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(ii) A comparative analysis of two analytical methods, high performance liquid chromatographyetandem mass spectrometry (HPLCeMS/MS) and gas chromatographyemass spectrometry (GCeMS), including an extended cross-validation by influent profiling over several days. (iii) Monitoring COC and BE concentrations in WW of the largest Swiss STPs. For completeness, the COC and BE loads observed in the STP of Berne were used to back-calculate the COC consumption. However, we deliberately decided not to assess the uncertainty of back-calculated substance abuse figures, because, in our view, the current knowledge (e.g. on degradation) is incomplete.
2.
Methods
2.1. Framework to assess the total uncertainty of COC and BE loads The goal of our inter-disciplinary research project was to develop a methodology to assess the uncertainty resulting from discharge measurements, chemical analysis and sampling procedure. Our guiding principles were that the method should (i) deliver reproducible results, (ii) be simple to apply, (iii) be applicable at reasonable cost, (iv) support the experimental design as well as the a posteriori auditing of existing monitoring data and (v) explicitly account for the influence of the sampling strategy. To this aim, we performed an extensive review on literature regarding environmental sampling, with a focus on the monitoring of continuous WW streams, and identified three candidate procedures: the Theory of Sampling, the Guide to the Expression of Uncertainty in Measurement, and the Eurachem/CITAC procedure, with the latter two being very similar.
2.1.1.
Theory of sampling
The Theory of Sampling (TOS) (Guy, 1998; Petersen et al., 2005), presents a complete methodology for evaluating the total sampling error of process sampling from temporal sampling (integration error), homogeneity of material, etc. However, to assess the influence of the sampling strategy on the observed loads, the so-called “Point Selection Error”, it relies on empirical variographic methods and therefore on speciallytailored monitoring campaigns, has to be estimated. For our purpose, this procedure is not optimal because it only informs about sampling errors under the given conditions (e.g., at that specific morning or night where the sampling was performed). This can be a very strong assumption given the observed daily, weekly (see below) and yearly variability of WW substance loads. In addition, to produce meaningful results, it requires a large dataset at minute resolution. According to Esbensen et al. (2007) 60 or more samples should be available for each variographic analysis, which is prohibitively expensive in most cases.
2.1.2. Guide to the expression of uncertainty in measurement and Eurachem/CITAC guide The Guide to the Expression of Uncertainty (GUM) (JCGM 100, 2008) and the Eurachem guide (Eurachem/Citac, 2000; Ramsey
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and Ellison, 2007), which are conceptually very similar, suggest the estimation of the overall uncertainty of a measurement by summation of individual uncertainty contributions, such as device characteristics, intra-day and intra-operator characteristics, etc. The individual influence factors are considered in an additive or multiplicative fashion, which results in a very generic procedure which can easily be extended. It is rather straight forward and thus meets most of our design principles presented above. Following the terminology and practical recommendations given in the Eurachem guide, the first step is to draw a causeeffect (“fishbone”) diagram which includes all relevant influence factors (Fig. 1). The corresponding mathematical model (Equation (1)) to compute analyte loads from the monitoring data is Z LDTR ¼
Q$CDTR dt$fs
(1)
2.1.4.
tc
where tc is the composition interval during which a composite sample is produced, Q the discharge, CDTR the measured concentration and fs a correction factor to account for uncertainty due to the applied sampling strategy (Eurachem/Citac, 2000). Then, the uncertainty of the observed drug loads (u(LDTR)) is assessed using a linear uncertainty propagation framework with independent components (Equation (2)): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2ffi 2 u fs uðQÞ uðCDTR Þ þ þ uðLDTR Þ ¼ fs Q CDTR
(2)
The variable u(LDTR) describes the uncertainty in discharge (WW flow, Q), concentration of drug target residue (CDTR) and sampling uncertainty ( fs), respectively, which will be discussed in the following sections. While the assessment of uncertainty of discharge and concentration follows standard procedures and is straight forward, the assessment of the sampling error circumvents expensive variographic procedures by instead plugging in the results from a stochastic sewer load model.
2.1.3.
is no standard monitoring equipment, nor standard requirements or techniques of calibration. Therefore, we suggest to develop a meaningful measure of uncertainty together with the operators on the STP or at least consult the manufacturer’s information. Calibration studies would be desirable, especially to eliminate systematic deviations. Relative uncertainties u(Q)/Q should also be expressed as single standard deviation. Note the important assumption that the measurements are unbiased, which is not necessarily the case in practice (Thomann-Haller, 2002). Also, information provided by equipment manufacturers or the operators might rather reflect the uncertainty on an instantaneous value than the uncertainty on summary statistics such as the daily mean. Usually, we expect the uncertainty on the daily mean, which is derived from many hundreds of measurements, to be smaller.
Uncertainty of STP discharge data
WW influent measurements of an STP are crucial for the assessment of plant and process performance. However, there
Discharge (QDTR)
Sampling (fs) Sampling type
Method
Water level Sampling interval
Velocity
Load (LDTR) Calibration
Uncertainty of reported concentration values
The uncertainty of concentration measurements is well defined and intra- as well as inter-day precisions should be assessed. For our purpose, we suggest to define a relative uncertainty (u(CDTR)/CDTR), which should include all influence factors, such as purity of standards, extraction recovery, precision and accuracy of calibration and quantitation, etc. If in doubt, the adopted value should be expanded to avoid overconfidence in the analytical precision. Therefore, taking the single standard deviation is in line with a comparably safe assumption on the uncertainty of discharge measurements.
2.1.5.
Uncertainty due to the sampling scheme
As described above, an assessment based on variographic analysis using high-frequency monitoring data would be desirable, but to date is still prohibitively costly because online sensors are lacking. Therefore, we estimate the correction factor due to sampling uncertainty as suggested by Ort and Gujer (2005). Basically, they suggest to first replace the highfrequent observations needed for variographic analysis with synthetic loads from a physically-based stochastic model, which predicts time series of WW pollutant loads with a resolution of a few minutes or less. In the model, a population of users, distributed over the catchment of interest, is emitting WW pulses which are routed through the sewer network topology to the monitoring point using the wellknown analytical solution of the 1D advectionedispersion equation, which is suitable to model solute transport in gravity-driven sewer systems (Rieckermann et al., 2005). The dynamic load pattern at the monitoring point (LDTR) is then computed as the superposition of all the WW pulses during the simulation period (Equation (3)):
Method Sample prep.
Concentration (CDTR) Fig. 1 e Aggregated cause-effect (“fishbone”) diagram to assess the total uncertainty in observed benzoylecgonine (BE) loads in the STP influent due to uncertainty in discharge measurement, chemical analysis and sampling scheme. Factors in grey are examples for individual influence factors that do not necessarily apply to each STP.
LDTR ðtÞ ¼
NP X i¼1
m:pulsei
2 ! 1 S 1 pffiffiffiffiffiffiexp t Ti þ i n s2s;i ss;i 2p
(3)
with t ¼ simulation time, Np ¼ number of pulses discharged per hour, m.pulsei ¼ substance mass contained in the ith pulse, v ¼ mean velocity, Ti ¼ release time of the ith pulse, and si ¼ flow distance. The spread of substance pulse at monitoring station (ss,i) is calculated as follows (Equation (4)):
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si 1 þ s2ini s2s;i ¼ 2Dx $ v v2
(4)
with s ¼ single standard deviation of a normal distribution, Dx ¼ longitudinal dispersion coefficient, sini ¼ spread of pulse at the house connection. As several parameters, such as m.pulsei, Ti, si, and sini are described by (empirical) probability distributions (Table 1), this results in a truly stochastic behaviour of the model (Fig. 2). Secondly, they estimate the sampling error by applying the original sampling procedure of the STP to the simulated loads. The relative sampling error S is then defined as the relative difference from the load of the obtained sample (ls) to the simulated reference load (lr) (Equation (5)). S¼
ls lr lr
Fig. 2 e Simulated benzoylecgonine (BE) time series for the STPs of Basel (upper black and grey profiles) and Lucerne (lower black and dark grey profiles).
(5)
2.2.
S: relative sampling error; ls: load of the obtained sample; lr: simulated reference load. To estimate the empirical distribution of fs, a Monte Carlo framework is suggested, where this procedure is repeated a large number of times with varying input factors of pulse masses, voiding times and sewer transport parameters (Table 2). Here, we suggest to use the mean of the obtained sampling errors for fs and the standard deviation of the obtained sample of S as a measure for u( fs). The original procedure has been developed on observed benzotriazole loads, which is an anticorrosive used for silver protection in dishwasher detergents (Ort et al., 2005). While it has been subsequently applied to a variety of other substances, such as gadolinium, our study is the first application to drug target residues (DTR), such as COC and BE. Here, we therefore adapt their procedure to drugs of abuse loads, which requires prior information on population, substance use, catchment characteristics, sewer topology and flow to predict realistic substance load patterns in our case study catchments. Details on this procedure, including important underlying assumptions and suggested simplifications are described together with the case studies in Section 3.
Analytical methods
HPLCeMS/MS and GCeMS were used to determine COC and BE in influent and effluent STP water samples. For experimental details concerning materials, calibration, and validation see Section 2 of Electronic supplementary material. Briefly, HPLCeMS/MS analysis was performed by direct injection reversed-phase liquid chromatography followed by electrospray ionization (ESI) tandem mass spectrometry (MS/ MS) detection using atmospheric pressure ionization (API) and a triple-quadrupole MS-MS system (Agilent 1200 HPLC and AB Sciex API 5000 triple-quadrupole mass spectrometer) (Berset et al., 2010). Quantitation was performed using labelled internal standards for each compound and applying the multiple reaction monitoring mode (MRM). For GCeMS analysis, after adding deuterated internal standards, 500 mL of an STP sample were filtered through a glass microfiber filter and then extracted on a manual SPE unit by using a mixed-mode cation-exchange column. The evaporated extract was then silylated and analyzed by GCeMS operated in the selected ion monitoring (SIM) mode. Peak assignment was achieved by retention times, the characteristic ions of COC and BE, and their ion ratios vs. those of control samples. Calibration was performed by using
Table 1 e Details on the sampling schemes and flow meters of the investigated STPs and the resulting sampling error. STP
Sampling
Sampling interval 3
dV[m ] Berne Basel Geneva Lucerne Zurich
vol. vol. vol. vol. vol.
Prop. Prop. Prop. Prop. Prop.
500 1000 2100 ca. 200 ca. 600
Composition interval
Flow measuring principle
dt[min] 10 18 20 5 8
8:00 8:00 8:00 8:00 8:00
a.m.e8:00 a.m.e8:00 a.m.e8:00 a.m.e8:00 a.m.e8:00
a.m. a.m. a.m. a.m. a.m.
MID Venturi Ultrasonic MID Venturi
u(Q)
Np,BE
u(fs)
[%]
[pulses per day]
[%]
4992 9564 8144 3382 25,469
1.0 2.5 3.1 1.1 0.8
a
1 1.5b 2c 5d 5e10b
STP: sewage treatment plant; vol. Prop.: volume-proportional; dV: volumetric sampling interval; dt: corresponding temporal sampling interval based on measured flow data; MID: MagneticeInductive Flowmeter. a Volumetric calibration. b Expert opinion. c Manufacturer information. d Assumption; u(Q): uncertainty in discharge; Np,BE: number of wastewater pulses containing benzoylecgonine; u(fs): estimated uncertainty due to the applied sampling scheme.
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Table 2 e Parameters for stochastic load model and estimation of sampling error by Monte Carlo simulation. Parameter n S Dx n Np,BE sini sampling interval composite interval n.MCS
Unit
Distribution
counts m m2s1 ms1 counts s min h counts
uniform uniform uniform e uniform e e e
Berne
Basel
Geneva
Lucerne
Zurich
139,050 [500; 10,000]
174,635 [500; 9000]
114,766 [500; 12,000]
273,360 [500; 15,000]
4992
9564
3382
25,469
10 24 5000
18 24 5000
242,764 [500; 12,000] [0.03; 0.3] [0.5; 1] 8144 [10; 300] 20 24 5000
5 24 5000
10 24 5000
n: population; S: sewer travel distance; Dx: longitudinal dispersion coefficient; v: velocity; Np,BE: number of wastewater pulses containing benzoylecgonine; sini: initial spread of wastewater pulse at discharge point; n.MCS: number of Monte Carlo Simulations for error propagation.
a spiked blank WW sample. Quantification was based on peak target ion ratios of non-deuterated to deuterated analytes abundance, and linear regression analysis (least squares model).
2.3.
Back-calculation of COC consumption
For the back-calculation of COC consumption we used the original model suggested by Zuccato et al. (2005) (see Equation (6)). They calculated COC by multiplying the BE concentration by the WW flow (Q) and a factor of 2.33. This factor takes into account the COC-to-BE molar mass ratio (1.05) and the midrange excretion percentage of 45% of a COC dose excreted as BE (Cone et al., 1998, 2003; Baselt, 2008; Postigo et al., 2008; Van Nuijs, Castiglioni, et al., 2011). Van Nuijs et al. (2011) recently applied a factor of 3 for the estimation of LCOC from BE concentrations assuming that only 35% of the COC dose is excreted as BE. Similar COC loads were calculated by them using formulae taking into account ecgonine methyl ester (EME) alone or together with BE. COCðg=dayÞ ¼ CBE ðg=LÞ FðL=dayÞ 2:33
3.2.
Modelling COC and BE loads in major Swiss STPs
(6)
3. Case studies: monitoring and modelling COC and BE loads in major Swiss STPs 3.1.
Further 24-h samples (08:00 a.m. to 08:00 a.m., Sunday and Wednesday) were obtained from each of the largest STPs in Switzerland, i.e. Zurich-Werdho¨lzli (Zurich; Table 3: QeS), Geneva-Aı¨re (Geneva, AeD), Basel-Pro Rheno (Basel, GeH), and Lucerne-Region (Lucerne, EeF). The samples were on-site acidified to pH 2 and stored at 4 C in the dark until sent by courier to the laboratory. For Berne and Zurich, the weekend samples (sample P and R) covered an important local 3-days music open-air festival (about 0.1 million participants, July 16e19, 2009) and the annual 1-day mass rave event “Street Parade” (about 0.5 million participants, taking place on August 8, 2009), respectively. Furthermore, the observations from the 14-days monitoring period enabled us to compare the performance of the HPLCeMS/MS and GCeMS methods, focussing on extraction, sensitivity, and general handling. In addition, we also collected effluent samples in the STP of Geneva (sample CeD) in parallel to the influent samples on two days to check the elimination efficiency of STPs.
Monitoring campaigns
For development and validation of the analytical procedures, 1-L WW samples were collected in June and July 2009 (Table 3: sample M) at the STP Berne-Neubru¨ck (Berne), the main WW treatment facility of the city of Berne. Volume-proportional 24-h composite samples were taken from 08:00 a.m. to 08:00 a.m. of the next day, in intervals of 500 m3 with the routine autosampler, which corresponds to an average temporal interval of 10 min (for details see Table 1). The pH of the samples was immediately set to 2 by adding 18% hydrochloric acid. The samples were stored light-protected at 4 C based on own stability experiments and according to Gheorghe et al. (2008). A blank WW sample (see Fig. S2, Electronic supplementary material) was collected from the STP of Da¨rligen, a small Swiss rural community of 400 inhabitants. End of August and beginning of September 2009, 24-h samples were taken from the STP Berne for a period of 14 days (Fig. 3).
As described above, we estimated the correction factor due to sampling uncertainty with a stochastic sewer load model. To simulate realistic load patterns, meaningful input variables and parameter values of the model had to be chosen. While we expected that there reasonable information about catchment characteristics and population is generally available, some stronger assumptions and results from recent research were necessary to specify those parameters which determine substance masses in excreted WW pulses and depend on drug use behaviour and pharmacokinetics.
3.2.1. Population figures, STP catchment characteristics and sewer transport parameters Population figures (n) (Table 2) for the age group 14e65 years were taken from regional statistical offices. For Basel and Geneva they were estimated from total population values applying a proportional scaling procedure. Sewer system topology and flow distances s were estimated based on analyses of the bounding polygon of the STP catchment and the position of the STP, using a Geographic Information System (Maurer and Merlyn, 2006). Sewer transport parameters, such as Dx, n and sini, were taken from Ort et al. (2005) and Rieckermann et al. (2005).
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Table 3 e Concentrations (C, ng/L), and loads (L, g/day) of cocaine (COC) and benzoylecgonine (BE) in wastewater collected from major Swiss sewage treatment plants (STP) on different sampling dates and determined by GCeMS; Q: WW flow rate (L/day). Sample A B C D E F G H I K L M N O P Q R S
STP
Day, date
CCOC[ng/L]
LCOC [g/day]
CBE [ng/L]
LBE [g/day]
Q [m3/day]
Geneva Geneva Genevaa Genevaa Lucerne Lucerne Basel Basel Berne Bernec Bernec Berne Berne Berne Bernec Zurich Zurichh Zurich
Wed 2.9.09 Sun 6.9.09 Wed 2.9.09 Sun 6.9.09 Sun 30.8.09 Wed 2.9.09 Wed 26.8.09 Sun 30.8.09 Sun 12.7.09 Sat 18.7.09 Sun 19.7.09 JuneeJulyd JuneeJuly, SateSune JuneeJuly, MoneFrif SateSun, 18.e19.7.09 Wed 5.8.09g Sun 9.8.09 Sun 23.8.09
e 215 e e 16 e 25 11 18 28 17 16 18 14 22 e 216 134
269 1339 101 249 663 244 872 1299 1022 721 1132 655 944 522 927 e >2000i 1622
70 189 26 35 48 29 82 83 59 83 87 52 57 46 89 e 5657 219
260,247 141,236 260,247b 141,236b 71,885 120,263 94,300 63,700 57,800 114,800 77,000 79,226 60,167 88,023 95,900 180,000 195,000 135,000
a Effluent. b Qeffluent approx. corresponding to Qinfluent. c Three-days open-air music festival. d Mean (N ¼ 19). e Mean (N ¼ 6). f Mean (N ¼ 13). g Not available, not analyzed. h Day after mass rave event (“Street Parade”). i >ULOQ.
3.2.2.
Fig. 3 e Loads (L, g/day) of cocaine (COC) and benzoylecgonine (BE) in wastewater from the sewage treatment plant of Berne, collected on 14 consecutive days (AugusteSeptember 2009), and determined by high performance liquid chromatographyetandem mass spectrometry (HPLCeMS/MS). Weekends are marked by grey bars.
DTR masses in excreted WW pulses and numbers
The distribution of drug target residues of interest (DTR) mass in a WW pulse depends among other parameters on the intake route, the drug use behaviour, the voiding behaviour of each user, and the purity of the substance. To obtain uncertainty estimates that are on the safe side, we assumed that the data reported by Cone et al. (2003), represent a reasonable estimate of the variability of pulse masses of an entire population of drug users. The study, which investigated the excretion of COC and major metabolites following different routes of administration, provides detailed amounts of excreted masses and is to the best of our knowledge the most complete dataset available to date (for details see Section 1, Electronic supplementary material). We further assumed that a common COC dose consists of 100 mg/day, the maximum excretion rate is about 45% for BE (Cone et al., 2003; Baselt, 2008), and 5 toilet uses per day is an average value (Boedker et al., 1989). Based on these assumptions we computed the number of pulses (Np,BE) contained in the minimum values of the observed loads. We used BE for the computation of Np,BE, because the variability of the individual pulse masses is much larger (see Section 1, Electronic supplementary material) and consequently leads to larger sampling errors in comparison to COC. To simulate realistic load variations, we chose a model output temporal resolution of 1 min and a simulation time of 3 weeks. To estimate the distribution of fs we simulated 100 realizations of the influent load pattern for each STP with varying input variable of pulse masses, voiding times and
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sewer transport parameters. Examples of output from the stochastic model are given for a 1-day window in Fig. 2.
3.3. Assessing the correction factor due to sampling uncertainty ( fs) From each of the 100 patterns fifty 24-h composite samples were computed based on the real-world sampling scheme of the respective STP, using varying starting times. Thus, for each observation, 5000 different realizations of S were computed. As described above, u( fs) was taken as the standard deviation of the empirical histogram.
4.
Results and discussion
4.1.
Analytical methods
For HPLCeMS/MS, correlation coefficients r2 were >0.999, showing excellent linearity for both analytes in the calibration range of 20e1000 ng/L. The lower (LLOQ) and upper limit of quantification (ULOQ) were 20 ng/L (signal-to-noise ratio 1300 and 510 for COC and BE) and 1000 ng/L, corresponding to the lowest and highest calibrator, respectively. Recoveries at the LLOQ were 105 and 117% and at 10 times the LLOQ were 95 and 97% for COC and BE, respectively. COC concentrations of 169 6 and 168 ng/L for water-based and standard addition calibration, respectively, show that comparable values were obtained using both quantification modes. With 453 12 and 453 ng/L, respectively, the BE concentrations were very similar, too. Therefore, stable-isotope-labelled internal standards efficiently compensate for WW matrix effects when analyzing COC and BE in STP samples. In conclusion, validation data clearly demonstrate the suitability of the direct injection HPLCeMS/MS approach for measuring COC and BE in WW. For GCeMS, correlation coefficients r2 0.999 for COC and BE indicate good linearity of the calibration graphs ranging
from 80 to 2000 ng/L. With 80 ng/L (signal-to-noise ratio, S/ N ¼ 3 and 6 for COC and BE, respectively), LLOQ was set at the lowest calibrator concentration. With intra- and inter-assay accuracies of 1 to 8 and 3 to 4% (deviation to target concentration, N ¼ 3) for COC and BE, respectively, the GCeMS method was precise. With intra- and inter-assay precisions of 3e12 and 1e11% (r.s.d., N ¼ 3) for COC and BE, respectively, the assay was also repeatable and reproducible. The overall recovery for COC was 88, 100 and 73% at the high (4 ng/mL), medium (2 ng/mL) and low (0.2 ng/mL) concentration level, respectively. For BE it was 34, 39 and 57%, respectively. When stored at 4 C and pH 2, the COC concentration dropped to 66%, whereas BE showed a loss of only 3% after 29 days. Apparently, the formation of BE by hydrolysis of COC is compensated by the degradation of BE when stored at pH 2 and 4 C in WW (our study) or in pond water (Gheorghe et al., 2008). Compared to HPLCeMS/MS the sensitivity of the GCeMS is lower, thus requiring larger sample volumes and efficient analyte enrichment by SPE. Even when performing an appropriate clean-up by SPE, in rare cases of very dirty WW samples the residual matrix still present in the derivatized extracts is slightly impairing the selectivity of GCeMS. Cleaner extracts may be obtained by repeated SPE washing steps, however resulting in lower analyte recoveries, or using 500 mg SPE cartridges. Due to the lower sensitivity of GCeMS, in some samples (Table 3: A, C, D, F, Q) COC and/or BE could only be quantified by HPLCeMS/MS. In some samples (Tables 3 and 4: D, E, F) considerably lower CBE were found by GCeMS compared to HPLCeMS/MS.
4.2.
Profiling of COC and BE in WW
A 14-days profiling of STP Berne WW was performed by HPLCeMS/MS and GCeMS from Saturday 22.8.2009 to Friday 4.9.2009. With 36 and 66 g/day the highest COC and BE loads (L) determined by HPLCeMS/MS were observed in Berne on Sunday 23.8.2009 and Saturday 29.8.2009, respectively (Fig. 3).
Table 4 e Concentrations (C, ng/L), and loads (L, g/day) of cocaine (COC) and benzoylecgonine (BE) in wastewater collected from major Swiss sewage treatment plants (STP) on different sampling dates and determined by HPLCeMS/MS; Q: WW flow rate (L/day). Sample A B C D E F I K L P Q R a b c d
STP
Day, date
CCOC[ng/L]
LCOC [g/day]
CBE [ng/L]
LBE [g/day]
Q [m3/day]
Geneva Geneva Genevaa Genevaa Lucerne Lucerne Berne Bernec Bernec Bernec Zurich Zurichd
Wed 2.9.09 Sun 6.9.09 Wed 2.9.09 Sun 6.9.09 Sun 30.8.09 Wed 2.9.09 Sun 12.7.09 Sat 18.7.09 Sun 19.7.09 SateSun, 18.e19.7.09 Wed 5.8.09 Sun 9.8.09
114 1928
30 272 e 15 21 15 19 37 23 30 73 195
280 1788 166 425 1040 350 954 694 853 774 802 2400
73 253 43 60 75 42 55 80 66 74 144 468
260,247 141,236 260,247b 141,236b 71,885 120,263 57,800 114,800 77,000 95,900 180,000 195,000
Effluent. Qeffluent approx. corresponding to Qinfluent. Three-days open-air music festival. Day after mass rave event (“Street Parade”).
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The remarkable fluctuation of the STP WW profiles is characterized by minimum levels during the first half of the week, e.g. MondayeTuesday, and maximum levels towards the end of the week (SaturdayeSunday). The loads for the 14-days sampling campaign in Berne were normally distributed (data not shown). There is a significant difference between loads on MondayeThursday and FridayeSunday ( p-value >0.01). This confirms our expectation and observations of others (Van Nuijs et al., 2008; Van Nuijs et al., 2011), that recreational use of COC is predominantly occurring on weekends, especially on Saturdays. Note that the low concentrations observed on 24.8., 25.8. as well as 1.e4.9.2009 are also due to dilution from precipitation, which in addition might have caused losses of WW due to combined WW overflows (daily precipitation > 4 mm). The results from the nation-wide monitoring campaigns at the STPs Geneva, Lucerne, Basel, Berne and Zurich are shown in Fig. 4 and summarized in Table 3 (for GCeMS) and Table 4 (for HPLCeMS/MS). With 272 and 468 g/day the highest COC and BE loads measured by HPLCeMS/MS were in Sunday influent samples from the STP Geneva and Zurich (sample B and R). It can be assumed that the extremely high BE level of the Zurich Sunday sample R is due to the “Street Parade”, which with about 0.5 million participants is one of the biggest annual European mass rave events taking place on that weekend. The average COC-to-BE ratios (mean s.d.) was 0.4 0.1 (HPLCeMS/MS). Factors for the unusually high ratio of 1.1 in the Geneva Sunday sample (B) could be (i) a reduced degradation of COC to BE in WW, (ii) disposal of COC to WW due to a police pursuit or (iii) seasonal and temperature variables (Van Nuijs et al., 2009). On Sunday 19.7.2009 (sample L), at the end of a 3-days event in Berne (open-air music festival, about 0.1 million participants), LCOC was surprisingly lower compared to the Sunday before (sample I), whereas LBE was slightly elevated. However, the COC load LCOC was almost 50% higher on this particular event than on a regular Sunday. The results of the elimination efficiency of the STP Geneva differ slightly, depending on the analytical method used (Tables 3 and 4: sample C and D). Overall, 0e6% of COC and 19e59% of BE were still present in the effluent. Therefore, although diluted, a significant amount of BE is reaching the
Fig. 4 e Loads (L, g/day) of cocaine (COC) and benzoylecgonine (BE) in wastewater collected from major Swiss sewage treatment plants (STP) on different sampling dates, determined by GCeMS and HPLCeMS/MS.
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surface water. This can be due to incomplete biological treatment, release of untreated WW during heavy rainfall and/or adhesion of BE to sludge particles. With 94e100% the removal efficiency for COC of the STP Geneva is very similar to that observed by Postigo et al. (2010), whereas for BE (41e81% vs. 88%) the elimination rate is significantly lower.
4.3. loads
Assessing the total uncertainty in observed DTR
4.3.1.
Discharge (Q)
In the present study, the different STPs operated flow meters based on electromagnetic induction, ultrasonic velocity and level measurements, and the venturi effect (see Table 1). For the STP Berne, reliable information on the uncertainty of the influent measurement (u(Q)/Q) is available, because it is checked experimentally by comparison to volumetric measurements in the primary clarifier basin on a routine basis. For the other STPs, we relied on the expert opinion of the operators or manufacturer information (see Table 1). From our experience, uncertainties of about 1e10% reflect the quality of very well maintained stationary flow monitoring stations and might very well be larger (Thomann-Haller, 2002). However, the real concern is a bias and not a random uncertainty component.
4.3.2.
Concentrations (C )
The validation of the analytical procedures showed intra- and inter-day precisions to be <20% for the lowest and <15% for medium and high concentrations. To avoid underestimation of the measurement uncertainty we choose u(CDTR)/ CDTR ¼ 0.20 for both COC and BE.
4.3.3.
Sampling uncertainty ( fs)
From our analysis we found that the expected sampling uncertainty for all investigated catchments and applied sampling schemes on each STP varies between 0.8 and about 3%, which is generally small (Table 1). This is mostly due to the relatively large estimated number of WW pulses containing drugs of abuse and the rather small variability of substance masses per pulse in comparison to other substance, such as gadolinium or benzotriazole. In Fig. 5A and B we present 2 histograms as examples of the empirical error distributions obtained from the Monte Carlo Simulations. With an average sampling interval of 20 min the largest uncertainty (about 3%) was computed for the catchment of Geneva (Fig. 5B), the smallest for STP Zurich. Fig. 5C depicts the individual error contributions for the Geneva data. Our results are consistent with Ort and Gujer (2005) in demonstrating that catchments with short sampling intervals on the STP and large number of discharged pulses in the catchment exhibit the smallest error. This is, because the expected short-time fluctuations are less pronounced for patterns which contain many similar WW pulses. In addition, we observe that the distributions are symmetric, which is another sign of less fluctuating pollutant concentrations.
4.3.4.
Total uncertainty
The total uncertainty from discharge, analytics, and sampling is estimated to be <20% for all observed samples (not shown).
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A
B
C
24-h composite sample, dt: 20 min
24-h composite sample, dt: 18 min
1000
Q
600
Frequency
400
fs
200
200
Frequency
600
CBE
0
0
u(Load)
-5
0
5
10
Rel. error [%]
-10
-5
0
5
10
0
5000
10000
15000
u(y,xi) [mg/day]
Rel. error [%]
Fig. 5 e Assessment of uncertainty shown by the distribution of the relative sampling error from the STP Basel (A, s.d. [ 0.025) and Geneva (B, s.d. [ 0.031). By the total uncertainty of influent loads of STP Geneva (C) and individual error contributions it is clearly seen that the analytical uncertainty is the dominant factor.
As such, it is dominated by the analytical uncertainty (Fig. 5C). Flow measurement errors are in the same order of magnitude than sampling errors, but vary in importance for the individual STP and catchment.
4.4.
Swiss persons at the age of 15e30 years have at least once consumed COC, with 6.1% being men and 2.6% women (Addiction Info Switzerland, 2010). Taking into account that COC consumption is decreasing with age, this survey correlates reasonably with our estimation for Berne.
Back-calculation of COC consumption
As expected, with 132 and 95 g/day the estimated amounts of used COC (calculated according to Zuccato et al. (2005)) on weekends were in Berne higher compared to those on working days (Table 3: samples N and O; mean, N ¼ 6 and 13). For the period of JuneeJuly 2009 (sample M; mean, N ¼ 19) the mean daily COC consumption is estimated to be 107 21 g. During a 3-days open-air festival with about 0.1 million visitors the estimated weekend COC consumption was 198 g/day (sample P), which is 66 g/day higher than for other weekends in JuneeJuly 2009 (sample N). To calculate the approximate percentage of the population consuming COC, one has to make some assumptions. Firstly, it is estimated that, as an average, the consumed single dose of COC is 100 mg (United Nations Office on Drugs and Crime, 2004; Eve and Rave, 2011), corresponding to 10 doses per gram. Ten doses per gram multiplied with the grams of COC estimated to be consumed per day in Berne during JuneeJuly 2009, which is 107 21 g (sample M), result in an average of 1070 210 doses per day. In 2009, 92,290 of 130,290 inhabitants in the city of Berne were between 16 and 64 years old corresponding to 71% of the population. Seventy-one percent of the 196,711 people living in the STP Berne drainage area correspond to 139,665 people at the age of 16e64 years. This would mean that 0.76 0.15% of the 139,665 people in this particular age group is consuming 1 dose COC per day. However, this estimation is based on the assumption, that street COC is of 100% purity, i.e. not adulterated or diluted, which in reality is not the case. If we consider the median COC content to be 33% (Swiss Society of Legal Medicine, 2009), we estimate a daily COC consumption in Berne of 321 g, which is equivalent to 2.3% of the population living in the STP Berne drainage area consuming COC. According to a telephone survey, 4.4% of the interviewed
5.
Conclusions
In this study, we presented a novel method to assess the total uncertainty in observed WW COC and BE loads resulting from discharge measurements, chemical analysis and the applied sampling scheme and demonstrated its usefulness on data from the largest Swiss STPs. In summary we can draw the following conclusions: The monitoring of drugs or drug target residues in WW has the potential to provide supplementary information on short- and long-term, local, national, and international COC use, if the monitoring data are evaluated critically and accompanied by an estimate of uncertainty. This should take into account all relevant influence factors, such as discharge and chemical measurements as well as the applied sampling protocol. To reliably assess all influence factors, we suggest a linear uncertainty propagation framework which is based on the Eurachem guide and has been extended with a stochastic model-based assessment of sampling uncertainty. Thus, we avoid variographic analysis which requires an expensive set of preliminary data. Instead, the model-based assessment derives an empirical distribution of the sampling error from prior information on population, substance use and excretion, catchment characteristics, sewer topology and discharge, which are generally available. Results for the 5 largest Swiss STPs suggest that the total uncertainty in observed loads is smaller than 20% and that the analytical uncertainty is the dominating influence factor. We compute the sampling uncertainty in daily loads to 1e3%, which is about the same order of magnitude of
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stationary flow measurements. For the sampling uncertainty, we identified the number of excreted substance pulses and the sampling interval at the STP as important influence factors. These results could be generally valid for communities of more than 100,000 inhabitants and gravitydriven sewers, but most probably do not hold for small communities and systems with large WW pumps. The highest COC concentrations were found in Geneva influent water, resulting from a Sunday WW sample. The highest BE concentrations were measured in WW of the Zurich STP collected on a Sunday after a mass rave event. Back-calculations with a standard model suggest that, in Berne, about 2.3% of the people between the age of 16 and 64 years are estimated to consume 1 dose of COC per day. This is higher compared to data of The World Drug Report 2008, which estimated for Switzerland a 1.1% annual prevalence of COC consumption in the age group 15e64 years (United Nations Office on Drugs and Crime, 2008). For the first time, two analytical approaches using GCeMS and HPLCeMS/MS were compared on the same WW samples to monitor COC and BE over a longer time period. We found that, except for sensitivity and recovery (GCeMS < HPLCeMS/MS), the validation data showed a similar performance and that, at least for COC and BE quantification in WW, GCeMS can be recommended as an efficient and less expensive alternative. This was recently also stated by Gonzalez-Marino et al. (2010) using GCeMS/ MS. However, when available in a lab, HPLCeMS/MS should be the method of first choice for WW analysis. We deliberately did not tackle to assess the uncertainty of back-calculated user figures itself, because in our view today there is still a lack of understanding of important influence factors such as degradation and surface adsorption in the WW system, sewer leakage, short-term population fluctuations in the catchment and usage habits, which could introduce potential biases. Nevertheless, we believe that our method is useful to audit past and design future monitoring campaigns. Therefore we hope that our approach helps to ensure a high data quality and can serve as a first step towards providing better information on substance abuse in the future.
Acknowledgements Special thanks go to the following STP technicians for assisting in sampling, providing samples, and supporting the project: Peter Wyss and Tanja van der Heijden from the STP Berne, Mr. Caflisch and colleagues from the STP Da¨rligen, Mr. Wahl from the STP Geneva, Mr. Kopf from the STP Basel, Mr. Zumbach from the STP Lucerne, Mr. Langenegger and Mr. Pfund from the STP Zurich.
Appendix. Supplementary data Supplementary data related to this article can be found online at doi:10.1016/j.watres.2011.09.049.
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references
Addiction Info Switzerland, 2010. Use of Illegal Substances in Switzerland: Cocaine Lausanne, Switzerland. Baselt, R.C., 2008. Disposition of Toxic Drugs and Chemicals in Man. Biomedical Publications, Foster City, CA. Berset, J.D., Brenneisen, R., Mathieu, C., 2010. Analysis of llicit and illicit drugs in waste, surface and lake water samples using large volume direct injection high performance liquid chromatographyeelectrospray tandem mass spectrometry (HPLCeMS/MS). Chemosphere 81 (7), 859e866. Boedker, A., Lendorf, A., Nielsen, A.H., Glahn, B., 1989. Micturition pattern assessed by the frequency/volume chart in a healthy population of men and women. Neurourol. Urodyn. 8, 4212. Castiglioni, S., Zuccato, E., Chiabrando, C., Fanelli, R., Bagnati, R., 2008. Mass spectrometric analysis of illicit drugs in wastewater and surface water. Mass Spectrom. Rev. 27 (4), 378e394. Cone, E.J., Tsadik, A., Oyler, J., Darwin, W.D., 1998. Cocaine metabolism and urinary excretion after different routes of administration. Ther. Drug Monit. 20 (5), 556e560. Cone, E.J., Sampson-Cone, A.H., Darwin, W.D., Huestis, M.A., Oyler, J.M., 2003. Urine testing for cocaine abuse: metabolic and excretion patterns following different routes of administration and methods for detection of false-negative results. J. Anal. Toxicol. 27 (7), 386e401. Daughton, C.G., 2001. In: Daughton, C.G., Jone-Lepp, T. (Eds.), Pharmaceuticals and Personal Care Products in the Environment: Scientific and Regulatory Issues. American Chemical Society, Washington, DC, pp. 348e364. European Monitoring Centre for Drugs and Drug Addiction (EMCDDA). EMCDDA Project CT.99.EP.08 B, 2002 EMCDDA, 2002. Handbook for Surveys on Drug Use Among the General Population. EMCDDA, Lisbon. Available at: http://www. emcdda.eu.int/?fuseaction¼public. AttachmentDownload&nNodeID¼1390. European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) EMCDDA, 2009. The State of the Drug Problem in the European Union and Norway. Annual Report 2009. EMCDDA, Lisbon. Available at: http://www.emcdda.europa. eu/publications/annual-report/2009. Esbensen, K.H., Friis-Petersen, H.H., Petersen, L., Holm-Nielsen, J. B., Mortensen, P.P., 2007. Representative process sampling e in practice: variographic analysis and estimation of total sampling errors (TSE). Chemometrics Intell. Lab. Syst. 88, 41e59. Eurachem/Citac, 2000. Quantifying uncertainty in analytical measurement. Tech. Rep. Guide CG4, EU-RACHEM/CITEC EURACHEM/CITAC Guide. Eve & Rave, 2011. DrugseSubstance Informations. Eve & Rave, Switzerland. Available at: http://www.eve-rave.ch/drugs. Gheorghe, A., van Nuijs, A., Pecceu, B., Bervoets, L., Jorens, P.G., Blust, R., Neels, H., Covaci, A., 2008. Analysis of cocaine and its principal metabolites in waste and surface water using solidphase extraction and liquid chromatography-ion trap tandem mass spectrometry. Anal. Bioanal. Chem. 391 (4), 1309e1319. Gonzalez-Marino, I., Quintana, J.B., Rodriguez, I., 2010. Determination of drugs of abuse in water by solid-phase extraction, derivatisation and gas chromatography-ion traptandem mass spectrometry. J. Chromatogr. A. 1217 (11), 1748e1760. Guy, P., 1998. Sampling for Analytical Purposes. John Wiley & Sons, Chichester. JCGM 100, 2008. Evaluation of Measurement Data e Guide to the Expression of Uncertainty in Measurement. Available at: http://www.bipm.org/utils/common/documents/jcgm/JCGM_ 100_2008_E.pdf.
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Jones-Lepp, T.L., Alvarez, D.A., Petty, J.D., Huckins, J.N., 2004. Polar organic chemical integrative sampling and liquid chromatography-electrospray/ion-trap mass spectrometry for assessing selected prescription and illicit drugs in treated sewage effluents. Arch. Environ. Contam. Toxicol. 47 (4), 427e439. Maurer, M., Merlyn, A., 2006. State, Cost and Investment Needs into Swiss Wastewater Infrastructure. (Zustand, Kosten und Investitionsbedarf der schweizersichen Abwasserentsorgung). in German, Du¨bendorf, Switzerland. Metcalfe, C., Tindale, K., Li, H., Rodayan, A., Yargeau, V., 2010. Illicit drugs in Canadian municipal wastewater and estimates of community drug use. Environ. Pollut. 158 (10), 3179e3185. Ort, C., Gujer, W., 2005. Sampling for representative micropollutant loads in sewer systems. Water Sci. Technol. 54 (6e7), 169e176. Ort, C., Schaffner, C., Giger, W., Gujer, W., 2005. Modeling stochastic load variations in sewer systems. Water Sci. Technol. 52 (5), 113e122. Ort, C., Lawrence, M.G., Reungoat, J., Mueller, J.F., 2010a. Sampling for PPCPs in wastewater systems: comparison of different sampling modes and optimization strategies. Environ. Sci. Technol. 44 (16), 6289e6296. Ort, C., Lawrence, M.G., Rieckermann, J., Joss, A., 2010b. Sampling for pharmaceuticals and personal care products (PPCPs) and illicit drugs in wastewater systems: are your conclusions valid? A critical review. Environ. Sci. Technol. 44 (16), 6024e6035. Petersen, L., Minkkinen, P., Esbensen, K.H., 2005. Representative sampling for reliable data analysis: theory of sampling. Chemometrics Intell. Lab. Syst. 77, 261e277. Postigo, C., Lopez de Alda, M.J., Barcelo, D., 2008. Analysis of drugs of abuse and their human metabolites in water by LCeMS/MS: a non-intrusive tool for drug abuse estimation at the community level. Trends Anal. Chem. 27 (11), 1053e1069. Postigo, C., Lopez de Alda, M.J., Barcelo, D., 2010. Drugs of abuse and their metabolites in the Ebro River basin: occurrence in sewage and surface water, sewage treatment plants removal efficiency, and collective drug usage estimation. Environ. Int. 36 (1), 75e84. Ramsey, M.H., Ellison, S.L.R., 2007. Eurachem/EUROLAB/CITAC/ Nordtest/AMC Guide: Measurement Uncertainty Arising from
Sampling: A Guide to Methods and Approaches. Eurachem. Available at: http://www.eurachem.org/guides/UfS_2007.pdf. Rieckermann, J., Neumann, M., Ort, C., Gujer, W., 2005. Dispersion coefficients of sewers from tracer experiments. Water Sci. Technol. 52, 123e133. Rutsch, M., Rieckermann, J., Cullmann, J., Ellis, J.B., Vollertsen, J., Krebs, P., 2008. Towards a better understanding of sewer exfiltration. Water Res. 42 (10e11), 2385e2394. Swiss Society of Legal Medicine, 2009. Personal Communication. Thomann-Haller, M., 2002. Datenkontrolle von Abwasserreinigungsanlagen mit Massenbilanzen, Experimenten und statischen Methoden, Dissertation Nr. 14824 ETH Zu¨rich. United Nations Office on Drugs and Crime, 2004. World Drug Report 2004. United Nations, Vienna. Available at: http://www. unodc.org/unodc/en/data-and-analysis/WDR-2004.html. United Nations Office on Drugs and Crime, 2008. World Drug Report 2008. United Nations, Vienna. Available at: http://www. unodc.org/unodc/en/data-and-analysis/WDR-2008.html. Van Nuijs, A.L., Mougel, J.F., Tarcomnicu, I., Bervoets, L., Blust, R., Jorens, P.G., Neels, H., Covaci, A., 2011. Sewage epidemiology e A real-time approach to estimate the consumption of illicit drugs in Brussels, Belgium. Environ. Int. 37 (3), 612e621. Van Nuijs, A.L., Castiglioni, S., Tarcomnicu, I., Postigo, C., de Alda, M.L., Neels, H., Zuccato, E., Barcelo, D., Covaci, A., 2011. Illicit drug consumption estimations derived from wastewater analysis: a critical review. Sci. Total Environ. 409 (19), 3564e3577. Van Nuijs, A.L., Pecceu, B., Theunis, L., Dubois, N., Charlier, C., Jorens, P.G., Bervoets, L., Blust, R., Neels, H., Covaci, A., 2008. Cocaine and metabolites in waste and surface water across Belgium. Environ. Pollut. 157 (1), 123e129. Van Nuijs, A.L., Pecceu, B., Theunis, L., Dubois, N., Charlier, C., Jorens, P.G., Bervoets, L., Blust, R., Meulemans, H., Neels, H., Covaci, A., 2009. Can cocaine use be evaluated through analysis of wastewater? A nation-wide approach conducted in Belgium. Addiction 104 (5), 734e741. Zuccato, E., Chiabrando, C., Castiglioni, S., Calamari, D., Bagnati, R., Schiarea, S., Fanelli, R., 2005. Cocaine in surface waters: a new evidence-based tool to monitor community drug abuse. Environ. Health 4, 14.
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Assessment of total uncertainty in cocaine and benzoylecgonine wastewater load measurements Christoph Mathieu a, Jo¨rg Rieckermann b, Jean-Daniel Berset c, Stefan Schu¨rch d, Rudolf Brenneisen a,* a
Dept. of Clinical Research, University of Berne, Berne, Switzerland Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Du¨bendorf, Switzerland c Water and Soil Protection Laboratory (WSPL), Berne, Switzerland d Dept. of Chemistry and Biochemistry, University of Berne, Berne, Switzerland b
article info
abstract
Article history:
To check the effectiveness of campaigns preventing drug abuse or indicating local effects
Received 11 July 2011
of efforts against drug trafficking, it is beneficial to know consumed amounts of substances
Received in revised form
in a high spatial and temporal resolution. The analysis of drugs of abuse in wastewater
16 September 2011
(WW) has the potential to provide this information. In this study, the reliability of WW drug
Accepted 25 September 2011
consumption estimates is assessed and a novel method presented to calculate the total
Available online 19 October 2011
uncertainty in observed WW cocaine (COC) and benzoylecgonine (BE) loads. Specifically, uncertainties resulting from discharge measurements, chemical analysis and the applied
Keywords:
sampling scheme were addressed and three approaches presented. These consist of (i)
Sewage treatment plant
a generic model-based procedure to investigate the influence of the sampling scheme on
Wastewater
the uncertainty of observed or expected drug loads, (ii) a comparative analysis of two
Cocaine and benzoylecgonine loads
analytical methods (high performance liquid chromatographyetandem mass spectrometry
Analytical uncertainty
and gas chromatographyemass spectrometry), including an extended cross-validation by influent profiling over several days, and (iii) monitoring COC and BE concentrations in WW of the largest Swiss sewage treatment plants. In addition, the COC and BE loads observed in the sewage treatment plant of the city of Berne were used to back-calculate the COC consumption. The estimated mean daily consumed amount was 107 21 g of pure COC, corresponding to 321 g of street-grade COC. ª 2011 Elsevier Ltd. All rights reserved.
1.
Introduction
Drug abuse is a widespread problem of the modern community with significant socio-economic consequences, such as health treatment costs and higher incidence of criminality (EMCDDA, 2009). For drug policy makers it is therefore important to correctly identify trends, usage levels, hot spots, and prevalence of drug consumption to design prevention
campaigns or enforcement strategies, which is not trivial given current epidemiological data. Drug abuse is mostly estimated indirectly from population surveys, consumer interviews, medical records, crime statistics, drug production and seizure rates (EMCDDA, 2002). However, traditional methods are costly and the results are often obtained with considerable delay. Therefore, epidemiologists seek to explore novel data sources to improve addiction research or
* Corresponding author. Dep. Clinical Research, University of Berne, Murtenstr. 35, CH-3010 Berne, Switzerland. Tel.: þ41 31 632 8714; fax: þ41 31 632 3297. E-mail address: [email protected] (R. Brenneisen). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.09.049
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drug early warning systems. Estimating drug consumption from analysis of drugs of abuse in wastewater (WW) has the potential for such improvement, because data could be collected in near-real time, non-intrusively from the majority of the population and without many other limitations of traditional surveys (EMCDDA, 2002). In 2001, Daughton (Daughton, 2001) hypothesized that WW can be regarded as a pooled urine sample of a large population and that mass flows of drugs of abuse in WW reflect the consumed amount. In 2004, for the first time a drug of abuse (3,4-methylenedioxymethamphetamine, MDMA, known as ecstasy) was detected in effluent water from a sewage treatment plant (STP) (Jones-Lepp et al., 2004). In 2005, Zuccato and coworkers (Zuccato et al., 2005) were the first suggesting a model to back-calculate usage figures from WW loads and coined the term “sewage epidemiology”. Consequently, this methodological approach has further been developed and implemented for other drugs of abuse (Castiglioni et al., 2008; Postigo et al., 2008; Van Nuijs, Castiglioni, et al., 2011; Van Nuijs et al., 2011). To further develop WW drug testing as a novel information source in addition to the classical epidemiological tools, Van Nuijs, Castiglioni, et al. (2011) formulated research needs to improve the validity of (i) measured “concentrations of drugs of abuse and/or metabolite(s) in influent wastewater” and (ii) “back-calculations from concentrations in wastewater to an amount of drugs of abuse (g/day)”, which range from errors in discharge measurements, adsorption to particulate matter (Metcalfe et al., 2010) and losses in the urban sewer system (Rutsch et al., 2008) to the variability of excretion rates (Cone et al., 2003) in a population of drug users. Interestingly, while errors in discharge measurements are often being considered in engineering analysis, there has been recent concern that the sampling scheme and frequency also introduce uncertainty in analytical studies. In a recent review, 87 peer-reviewed journal articles were analyzed regarding the fitness for purpose of the applied sampling procedures to monitor drugs of abuse as well as pharmaceuticals and personal care products (PPCPs) in sewers and STP influents (Ort et al., 2010a). One of the major findings was that sampling is mostly carried out according to existing tradition on the STP or standard laboratory protocols. Even for analysis of drugs of abuse the importance of shortterm pollutant variations on observed concentrations or loads is typically not addressed, although a high data quality is mandatory. As errors of up to 100% and more have been reported (Ort et al., 2010b), it remained unclear for the majority of reviewed studies whether observed variations can be attributed to “real” variations or sampling artefacts. In this study, we therefore take a first step towards assessing the reliability of WW drug consumption estimates and present a novel method to assess the total uncertainty in observed WW COC and BE loads based on measurements and stochastic dynamic load modelling. Specifically, we address uncertainties resulting from discharge measurements, chemical analysis and the applied sampling scheme, and present the following three innovative approaches: (i) A generic model-based procedure to investigate the influence of the sampling scheme on the uncertainty of observed or expected drug loads.
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(ii) A comparative analysis of two analytical methods, high performance liquid chromatographyetandem mass spectrometry (HPLCeMS/MS) and gas chromatographyemass spectrometry (GCeMS), including an extended cross-validation by influent profiling over several days. (iii) Monitoring COC and BE concentrations in WW of the largest Swiss STPs. For completeness, the COC and BE loads observed in the STP of Berne were used to back-calculate the COC consumption. However, we deliberately decided not to assess the uncertainty of back-calculated substance abuse figures, because, in our view, the current knowledge (e.g. on degradation) is incomplete.
2.
Methods
2.1. Framework to assess the total uncertainty of COC and BE loads The goal of our inter-disciplinary research project was to develop a methodology to assess the uncertainty resulting from discharge measurements, chemical analysis and sampling procedure. Our guiding principles were that the method should (i) deliver reproducible results, (ii) be simple to apply, (iii) be applicable at reasonable cost, (iv) support the experimental design as well as the a posteriori auditing of existing monitoring data and (v) explicitly account for the influence of the sampling strategy. To this aim, we performed an extensive review on literature regarding environmental sampling, with a focus on the monitoring of continuous WW streams, and identified three candidate procedures: the Theory of Sampling, the Guide to the Expression of Uncertainty in Measurement, and the Eurachem/CITAC procedure, with the latter two being very similar.
2.1.1.
Theory of sampling
The Theory of Sampling (TOS) (Guy, 1998; Petersen et al., 2005), presents a complete methodology for evaluating the total sampling error of process sampling from temporal sampling (integration error), homogeneity of material, etc. However, to assess the influence of the sampling strategy on the observed loads, the so-called “Point Selection Error”, it relies on empirical variographic methods and therefore on speciallytailored monitoring campaigns, has to be estimated. For our purpose, this procedure is not optimal because it only informs about sampling errors under the given conditions (e.g., at that specific morning or night where the sampling was performed). This can be a very strong assumption given the observed daily, weekly (see below) and yearly variability of WW substance loads. In addition, to produce meaningful results, it requires a large dataset at minute resolution. According to Esbensen et al. (2007) 60 or more samples should be available for each variographic analysis, which is prohibitively expensive in most cases.
2.1.2. Guide to the expression of uncertainty in measurement and Eurachem/CITAC guide The Guide to the Expression of Uncertainty (GUM) (JCGM 100, 2008) and the Eurachem guide (Eurachem/Citac, 2000; Ramsey
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and Ellison, 2007), which are conceptually very similar, suggest the estimation of the overall uncertainty of a measurement by summation of individual uncertainty contributions, such as device characteristics, intra-day and intra-operator characteristics, etc. The individual influence factors are considered in an additive or multiplicative fashion, which results in a very generic procedure which can easily be extended. It is rather straight forward and thus meets most of our design principles presented above. Following the terminology and practical recommendations given in the Eurachem guide, the first step is to draw a causeeffect (“fishbone”) diagram which includes all relevant influence factors (Fig. 1). The corresponding mathematical model (Equation (1)) to compute analyte loads from the monitoring data is Z LDTR ¼
Q$CDTR dt$fs
(1)
2.1.4.
tc
where tc is the composition interval during which a composite sample is produced, Q the discharge, CDTR the measured concentration and fs a correction factor to account for uncertainty due to the applied sampling strategy (Eurachem/Citac, 2000). Then, the uncertainty of the observed drug loads (u(LDTR)) is assessed using a linear uncertainty propagation framework with independent components (Equation (2)): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2ffi 2 u fs uðQÞ uðCDTR Þ þ þ uðLDTR Þ ¼ fs Q CDTR
(2)
The variable u(LDTR) describes the uncertainty in discharge (WW flow, Q), concentration of drug target residue (CDTR) and sampling uncertainty ( fs), respectively, which will be discussed in the following sections. While the assessment of uncertainty of discharge and concentration follows standard procedures and is straight forward, the assessment of the sampling error circumvents expensive variographic procedures by instead plugging in the results from a stochastic sewer load model.
2.1.3.
is no standard monitoring equipment, nor standard requirements or techniques of calibration. Therefore, we suggest to develop a meaningful measure of uncertainty together with the operators on the STP or at least consult the manufacturer’s information. Calibration studies would be desirable, especially to eliminate systematic deviations. Relative uncertainties u(Q)/Q should also be expressed as single standard deviation. Note the important assumption that the measurements are unbiased, which is not necessarily the case in practice (Thomann-Haller, 2002). Also, information provided by equipment manufacturers or the operators might rather reflect the uncertainty on an instantaneous value than the uncertainty on summary statistics such as the daily mean. Usually, we expect the uncertainty on the daily mean, which is derived from many hundreds of measurements, to be smaller.
Uncertainty of STP discharge data
WW influent measurements of an STP are crucial for the assessment of plant and process performance. However, there
Discharge (QDTR)
Sampling (fs) Sampling type
Method
Water level Sampling interval
Velocity
Load (LDTR) Calibration
Uncertainty of reported concentration values
The uncertainty of concentration measurements is well defined and intra- as well as inter-day precisions should be assessed. For our purpose, we suggest to define a relative uncertainty (u(CDTR)/CDTR), which should include all influence factors, such as purity of standards, extraction recovery, precision and accuracy of calibration and quantitation, etc. If in doubt, the adopted value should be expanded to avoid overconfidence in the analytical precision. Therefore, taking the single standard deviation is in line with a comparably safe assumption on the uncertainty of discharge measurements.
2.1.5.
Uncertainty due to the sampling scheme
As described above, an assessment based on variographic analysis using high-frequency monitoring data would be desirable, but to date is still prohibitively costly because online sensors are lacking. Therefore, we estimate the correction factor due to sampling uncertainty as suggested by Ort and Gujer (2005). Basically, they suggest to first replace the highfrequent observations needed for variographic analysis with synthetic loads from a physically-based stochastic model, which predicts time series of WW pollutant loads with a resolution of a few minutes or less. In the model, a population of users, distributed over the catchment of interest, is emitting WW pulses which are routed through the sewer network topology to the monitoring point using the wellknown analytical solution of the 1D advectionedispersion equation, which is suitable to model solute transport in gravity-driven sewer systems (Rieckermann et al., 2005). The dynamic load pattern at the monitoring point (LDTR) is then computed as the superposition of all the WW pulses during the simulation period (Equation (3)):
Method Sample prep.
Concentration (CDTR) Fig. 1 e Aggregated cause-effect (“fishbone”) diagram to assess the total uncertainty in observed benzoylecgonine (BE) loads in the STP influent due to uncertainty in discharge measurement, chemical analysis and sampling scheme. Factors in grey are examples for individual influence factors that do not necessarily apply to each STP.
LDTR ðtÞ ¼
NP X i¼1
m:pulsei
2 ! 1 S 1 pffiffiffiffiffiffiexp t Ti þ i n s2s;i ss;i 2p
(3)
with t ¼ simulation time, Np ¼ number of pulses discharged per hour, m.pulsei ¼ substance mass contained in the ith pulse, v ¼ mean velocity, Ti ¼ release time of the ith pulse, and si ¼ flow distance. The spread of substance pulse at monitoring station (ss,i) is calculated as follows (Equation (4)):
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si 1 þ s2ini s2s;i ¼ 2Dx $ v v2
(4)
with s ¼ single standard deviation of a normal distribution, Dx ¼ longitudinal dispersion coefficient, sini ¼ spread of pulse at the house connection. As several parameters, such as m.pulsei, Ti, si, and sini are described by (empirical) probability distributions (Table 1), this results in a truly stochastic behaviour of the model (Fig. 2). Secondly, they estimate the sampling error by applying the original sampling procedure of the STP to the simulated loads. The relative sampling error S is then defined as the relative difference from the load of the obtained sample (ls) to the simulated reference load (lr) (Equation (5)). S¼
ls lr lr
Fig. 2 e Simulated benzoylecgonine (BE) time series for the STPs of Basel (upper black and grey profiles) and Lucerne (lower black and dark grey profiles).
(5)
2.2.
S: relative sampling error; ls: load of the obtained sample; lr: simulated reference load. To estimate the empirical distribution of fs, a Monte Carlo framework is suggested, where this procedure is repeated a large number of times with varying input factors of pulse masses, voiding times and sewer transport parameters (Table 2). Here, we suggest to use the mean of the obtained sampling errors for fs and the standard deviation of the obtained sample of S as a measure for u( fs). The original procedure has been developed on observed benzotriazole loads, which is an anticorrosive used for silver protection in dishwasher detergents (Ort et al., 2005). While it has been subsequently applied to a variety of other substances, such as gadolinium, our study is the first application to drug target residues (DTR), such as COC and BE. Here, we therefore adapt their procedure to drugs of abuse loads, which requires prior information on population, substance use, catchment characteristics, sewer topology and flow to predict realistic substance load patterns in our case study catchments. Details on this procedure, including important underlying assumptions and suggested simplifications are described together with the case studies in Section 3.
Analytical methods
HPLCeMS/MS and GCeMS were used to determine COC and BE in influent and effluent STP water samples. For experimental details concerning materials, calibration, and validation see Section 2 of Electronic supplementary material. Briefly, HPLCeMS/MS analysis was performed by direct injection reversed-phase liquid chromatography followed by electrospray ionization (ESI) tandem mass spectrometry (MS/ MS) detection using atmospheric pressure ionization (API) and a triple-quadrupole MS-MS system (Agilent 1200 HPLC and AB Sciex API 5000 triple-quadrupole mass spectrometer) (Berset et al., 2010). Quantitation was performed using labelled internal standards for each compound and applying the multiple reaction monitoring mode (MRM). For GCeMS analysis, after adding deuterated internal standards, 500 mL of an STP sample were filtered through a glass microfiber filter and then extracted on a manual SPE unit by using a mixed-mode cation-exchange column. The evaporated extract was then silylated and analyzed by GCeMS operated in the selected ion monitoring (SIM) mode. Peak assignment was achieved by retention times, the characteristic ions of COC and BE, and their ion ratios vs. those of control samples. Calibration was performed by using
Table 1 e Details on the sampling schemes and flow meters of the investigated STPs and the resulting sampling error. STP
Sampling
Sampling interval 3
dV[m ] Berne Basel Geneva Lucerne Zurich
vol. vol. vol. vol. vol.
Prop. Prop. Prop. Prop. Prop.
500 1000 2100 ca. 200 ca. 600
Composition interval
Flow measuring principle
dt[min] 10 18 20 5 8
8:00 8:00 8:00 8:00 8:00
a.m.e8:00 a.m.e8:00 a.m.e8:00 a.m.e8:00 a.m.e8:00
a.m. a.m. a.m. a.m. a.m.
MID Venturi Ultrasonic MID Venturi
u(Q)
Np,BE
u(fs)
[%]
[pulses per day]
[%]
4992 9564 8144 3382 25,469
1.0 2.5 3.1 1.1 0.8
a
1 1.5b 2c 5d 5e10b
STP: sewage treatment plant; vol. Prop.: volume-proportional; dV: volumetric sampling interval; dt: corresponding temporal sampling interval based on measured flow data; MID: MagneticeInductive Flowmeter. a Volumetric calibration. b Expert opinion. c Manufacturer information. d Assumption; u(Q): uncertainty in discharge; Np,BE: number of wastewater pulses containing benzoylecgonine; u(fs): estimated uncertainty due to the applied sampling scheme.
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Table 2 e Parameters for stochastic load model and estimation of sampling error by Monte Carlo simulation. Parameter n S Dx n Np,BE sini sampling interval composite interval n.MCS
Unit
Distribution
counts m m2s1 ms1 counts s min h counts
uniform uniform uniform e uniform e e e
Berne
Basel
Geneva
Lucerne
Zurich
139,050 [500; 10,000]
174,635 [500; 9000]
114,766 [500; 12,000]
273,360 [500; 15,000]
4992
9564
3382
25,469
10 24 5000
18 24 5000
242,764 [500; 12,000] [0.03; 0.3] [0.5; 1] 8144 [10; 300] 20 24 5000
5 24 5000
10 24 5000
n: population; S: sewer travel distance; Dx: longitudinal dispersion coefficient; v: velocity; Np,BE: number of wastewater pulses containing benzoylecgonine; sini: initial spread of wastewater pulse at discharge point; n.MCS: number of Monte Carlo Simulations for error propagation.
a spiked blank WW sample. Quantification was based on peak target ion ratios of non-deuterated to deuterated analytes abundance, and linear regression analysis (least squares model).
2.3.
Back-calculation of COC consumption
For the back-calculation of COC consumption we used the original model suggested by Zuccato et al. (2005) (see Equation (6)). They calculated COC by multiplying the BE concentration by the WW flow (Q) and a factor of 2.33. This factor takes into account the COC-to-BE molar mass ratio (1.05) and the midrange excretion percentage of 45% of a COC dose excreted as BE (Cone et al., 1998, 2003; Baselt, 2008; Postigo et al., 2008; Van Nuijs, Castiglioni, et al., 2011). Van Nuijs et al. (2011) recently applied a factor of 3 for the estimation of LCOC from BE concentrations assuming that only 35% of the COC dose is excreted as BE. Similar COC loads were calculated by them using formulae taking into account ecgonine methyl ester (EME) alone or together with BE. COCðg=dayÞ ¼ CBE ðg=LÞ FðL=dayÞ 2:33
3.2.
Modelling COC and BE loads in major Swiss STPs
(6)
3. Case studies: monitoring and modelling COC and BE loads in major Swiss STPs 3.1.
Further 24-h samples (08:00 a.m. to 08:00 a.m., Sunday and Wednesday) were obtained from each of the largest STPs in Switzerland, i.e. Zurich-Werdho¨lzli (Zurich; Table 3: QeS), Geneva-Aı¨re (Geneva, AeD), Basel-Pro Rheno (Basel, GeH), and Lucerne-Region (Lucerne, EeF). The samples were on-site acidified to pH 2 and stored at 4 C in the dark until sent by courier to the laboratory. For Berne and Zurich, the weekend samples (sample P and R) covered an important local 3-days music open-air festival (about 0.1 million participants, July 16e19, 2009) and the annual 1-day mass rave event “Street Parade” (about 0.5 million participants, taking place on August 8, 2009), respectively. Furthermore, the observations from the 14-days monitoring period enabled us to compare the performance of the HPLCeMS/MS and GCeMS methods, focussing on extraction, sensitivity, and general handling. In addition, we also collected effluent samples in the STP of Geneva (sample CeD) in parallel to the influent samples on two days to check the elimination efficiency of STPs.
Monitoring campaigns
For development and validation of the analytical procedures, 1-L WW samples were collected in June and July 2009 (Table 3: sample M) at the STP Berne-Neubru¨ck (Berne), the main WW treatment facility of the city of Berne. Volume-proportional 24-h composite samples were taken from 08:00 a.m. to 08:00 a.m. of the next day, in intervals of 500 m3 with the routine autosampler, which corresponds to an average temporal interval of 10 min (for details see Table 1). The pH of the samples was immediately set to 2 by adding 18% hydrochloric acid. The samples were stored light-protected at 4 C based on own stability experiments and according to Gheorghe et al. (2008). A blank WW sample (see Fig. S2, Electronic supplementary material) was collected from the STP of Da¨rligen, a small Swiss rural community of 400 inhabitants. End of August and beginning of September 2009, 24-h samples were taken from the STP Berne for a period of 14 days (Fig. 3).
As described above, we estimated the correction factor due to sampling uncertainty with a stochastic sewer load model. To simulate realistic load patterns, meaningful input variables and parameter values of the model had to be chosen. While we expected that there reasonable information about catchment characteristics and population is generally available, some stronger assumptions and results from recent research were necessary to specify those parameters which determine substance masses in excreted WW pulses and depend on drug use behaviour and pharmacokinetics.
3.2.1. Population figures, STP catchment characteristics and sewer transport parameters Population figures (n) (Table 2) for the age group 14e65 years were taken from regional statistical offices. For Basel and Geneva they were estimated from total population values applying a proportional scaling procedure. Sewer system topology and flow distances s were estimated based on analyses of the bounding polygon of the STP catchment and the position of the STP, using a Geographic Information System (Maurer and Merlyn, 2006). Sewer transport parameters, such as Dx, n and sini, were taken from Ort et al. (2005) and Rieckermann et al. (2005).
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Table 3 e Concentrations (C, ng/L), and loads (L, g/day) of cocaine (COC) and benzoylecgonine (BE) in wastewater collected from major Swiss sewage treatment plants (STP) on different sampling dates and determined by GCeMS; Q: WW flow rate (L/day). Sample A B C D E F G H I K L M N O P Q R S
STP
Day, date
CCOC[ng/L]
LCOC [g/day]
CBE [ng/L]
LBE [g/day]
Q [m3/day]
Geneva Geneva Genevaa Genevaa Lucerne Lucerne Basel Basel Berne Bernec Bernec Berne Berne Berne Bernec Zurich Zurichh Zurich
Wed 2.9.09 Sun 6.9.09 Wed 2.9.09 Sun 6.9.09 Sun 30.8.09 Wed 2.9.09 Wed 26.8.09 Sun 30.8.09 Sun 12.7.09 Sat 18.7.09 Sun 19.7.09 JuneeJulyd JuneeJuly, SateSune JuneeJuly, MoneFrif SateSun, 18.e19.7.09 Wed 5.8.09g Sun 9.8.09 Sun 23.8.09
e 215 e e 16 e 25 11 18 28 17 16 18 14 22 e 216 134
269 1339 101 249 663 244 872 1299 1022 721 1132 655 944 522 927 e >2000i 1622
70 189 26 35 48 29 82 83 59 83 87 52 57 46 89 e 5657 219
260,247 141,236 260,247b 141,236b 71,885 120,263 94,300 63,700 57,800 114,800 77,000 79,226 60,167 88,023 95,900 180,000 195,000 135,000
a Effluent. b Qeffluent approx. corresponding to Qinfluent. c Three-days open-air music festival. d Mean (N ¼ 19). e Mean (N ¼ 6). f Mean (N ¼ 13). g Not available, not analyzed. h Day after mass rave event (“Street Parade”). i >ULOQ.
3.2.2.
Fig. 3 e Loads (L, g/day) of cocaine (COC) and benzoylecgonine (BE) in wastewater from the sewage treatment plant of Berne, collected on 14 consecutive days (AugusteSeptember 2009), and determined by high performance liquid chromatographyetandem mass spectrometry (HPLCeMS/MS). Weekends are marked by grey bars.
DTR masses in excreted WW pulses and numbers
The distribution of drug target residues of interest (DTR) mass in a WW pulse depends among other parameters on the intake route, the drug use behaviour, the voiding behaviour of each user, and the purity of the substance. To obtain uncertainty estimates that are on the safe side, we assumed that the data reported by Cone et al. (2003), represent a reasonable estimate of the variability of pulse masses of an entire population of drug users. The study, which investigated the excretion of COC and major metabolites following different routes of administration, provides detailed amounts of excreted masses and is to the best of our knowledge the most complete dataset available to date (for details see Section 1, Electronic supplementary material). We further assumed that a common COC dose consists of 100 mg/day, the maximum excretion rate is about 45% for BE (Cone et al., 2003; Baselt, 2008), and 5 toilet uses per day is an average value (Boedker et al., 1989). Based on these assumptions we computed the number of pulses (Np,BE) contained in the minimum values of the observed loads. We used BE for the computation of Np,BE, because the variability of the individual pulse masses is much larger (see Section 1, Electronic supplementary material) and consequently leads to larger sampling errors in comparison to COC. To simulate realistic load variations, we chose a model output temporal resolution of 1 min and a simulation time of 3 weeks. To estimate the distribution of fs we simulated 100 realizations of the influent load pattern for each STP with varying input variable of pulse masses, voiding times and
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sewer transport parameters. Examples of output from the stochastic model are given for a 1-day window in Fig. 2.
3.3. Assessing the correction factor due to sampling uncertainty ( fs) From each of the 100 patterns fifty 24-h composite samples were computed based on the real-world sampling scheme of the respective STP, using varying starting times. Thus, for each observation, 5000 different realizations of S were computed. As described above, u( fs) was taken as the standard deviation of the empirical histogram.
4.
Results and discussion
4.1.
Analytical methods
For HPLCeMS/MS, correlation coefficients r2 were >0.999, showing excellent linearity for both analytes in the calibration range of 20e1000 ng/L. The lower (LLOQ) and upper limit of quantification (ULOQ) were 20 ng/L (signal-to-noise ratio 1300 and 510 for COC and BE) and 1000 ng/L, corresponding to the lowest and highest calibrator, respectively. Recoveries at the LLOQ were 105 and 117% and at 10 times the LLOQ were 95 and 97% for COC and BE, respectively. COC concentrations of 169 6 and 168 ng/L for water-based and standard addition calibration, respectively, show that comparable values were obtained using both quantification modes. With 453 12 and 453 ng/L, respectively, the BE concentrations were very similar, too. Therefore, stable-isotope-labelled internal standards efficiently compensate for WW matrix effects when analyzing COC and BE in STP samples. In conclusion, validation data clearly demonstrate the suitability of the direct injection HPLCeMS/MS approach for measuring COC and BE in WW. For GCeMS, correlation coefficients r2 0.999 for COC and BE indicate good linearity of the calibration graphs ranging
from 80 to 2000 ng/L. With 80 ng/L (signal-to-noise ratio, S/ N ¼ 3 and 6 for COC and BE, respectively), LLOQ was set at the lowest calibrator concentration. With intra- and inter-assay accuracies of 1 to 8 and 3 to 4% (deviation to target concentration, N ¼ 3) for COC and BE, respectively, the GCeMS method was precise. With intra- and inter-assay precisions of 3e12 and 1e11% (r.s.d., N ¼ 3) for COC and BE, respectively, the assay was also repeatable and reproducible. The overall recovery for COC was 88, 100 and 73% at the high (4 ng/mL), medium (2 ng/mL) and low (0.2 ng/mL) concentration level, respectively. For BE it was 34, 39 and 57%, respectively. When stored at 4 C and pH 2, the COC concentration dropped to 66%, whereas BE showed a loss of only 3% after 29 days. Apparently, the formation of BE by hydrolysis of COC is compensated by the degradation of BE when stored at pH 2 and 4 C in WW (our study) or in pond water (Gheorghe et al., 2008). Compared to HPLCeMS/MS the sensitivity of the GCeMS is lower, thus requiring larger sample volumes and efficient analyte enrichment by SPE. Even when performing an appropriate clean-up by SPE, in rare cases of very dirty WW samples the residual matrix still present in the derivatized extracts is slightly impairing the selectivity of GCeMS. Cleaner extracts may be obtained by repeated SPE washing steps, however resulting in lower analyte recoveries, or using 500 mg SPE cartridges. Due to the lower sensitivity of GCeMS, in some samples (Table 3: A, C, D, F, Q) COC and/or BE could only be quantified by HPLCeMS/MS. In some samples (Tables 3 and 4: D, E, F) considerably lower CBE were found by GCeMS compared to HPLCeMS/MS.
4.2.
Profiling of COC and BE in WW
A 14-days profiling of STP Berne WW was performed by HPLCeMS/MS and GCeMS from Saturday 22.8.2009 to Friday 4.9.2009. With 36 and 66 g/day the highest COC and BE loads (L) determined by HPLCeMS/MS were observed in Berne on Sunday 23.8.2009 and Saturday 29.8.2009, respectively (Fig. 3).
Table 4 e Concentrations (C, ng/L), and loads (L, g/day) of cocaine (COC) and benzoylecgonine (BE) in wastewater collected from major Swiss sewage treatment plants (STP) on different sampling dates and determined by HPLCeMS/MS; Q: WW flow rate (L/day). Sample A B C D E F I K L P Q R a b c d
STP
Day, date
CCOC[ng/L]
LCOC [g/day]
CBE [ng/L]
LBE [g/day]
Q [m3/day]
Geneva Geneva Genevaa Genevaa Lucerne Lucerne Berne Bernec Bernec Bernec Zurich Zurichd
Wed 2.9.09 Sun 6.9.09 Wed 2.9.09 Sun 6.9.09 Sun 30.8.09 Wed 2.9.09 Sun 12.7.09 Sat 18.7.09 Sun 19.7.09 SateSun, 18.e19.7.09 Wed 5.8.09 Sun 9.8.09
114 1928
30 272 e 15 21 15 19 37 23 30 73 195
280 1788 166 425 1040 350 954 694 853 774 802 2400
73 253 43 60 75 42 55 80 66 74 144 468
260,247 141,236 260,247b 141,236b 71,885 120,263 57,800 114,800 77,000 95,900 180,000 195,000
Effluent. Qeffluent approx. corresponding to Qinfluent. Three-days open-air music festival. Day after mass rave event (“Street Parade”).
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The remarkable fluctuation of the STP WW profiles is characterized by minimum levels during the first half of the week, e.g. MondayeTuesday, and maximum levels towards the end of the week (SaturdayeSunday). The loads for the 14-days sampling campaign in Berne were normally distributed (data not shown). There is a significant difference between loads on MondayeThursday and FridayeSunday ( p-value >0.01). This confirms our expectation and observations of others (Van Nuijs et al., 2008; Van Nuijs et al., 2011), that recreational use of COC is predominantly occurring on weekends, especially on Saturdays. Note that the low concentrations observed on 24.8., 25.8. as well as 1.e4.9.2009 are also due to dilution from precipitation, which in addition might have caused losses of WW due to combined WW overflows (daily precipitation > 4 mm). The results from the nation-wide monitoring campaigns at the STPs Geneva, Lucerne, Basel, Berne and Zurich are shown in Fig. 4 and summarized in Table 3 (for GCeMS) and Table 4 (for HPLCeMS/MS). With 272 and 468 g/day the highest COC and BE loads measured by HPLCeMS/MS were in Sunday influent samples from the STP Geneva and Zurich (sample B and R). It can be assumed that the extremely high BE level of the Zurich Sunday sample R is due to the “Street Parade”, which with about 0.5 million participants is one of the biggest annual European mass rave events taking place on that weekend. The average COC-to-BE ratios (mean s.d.) was 0.4 0.1 (HPLCeMS/MS). Factors for the unusually high ratio of 1.1 in the Geneva Sunday sample (B) could be (i) a reduced degradation of COC to BE in WW, (ii) disposal of COC to WW due to a police pursuit or (iii) seasonal and temperature variables (Van Nuijs et al., 2009). On Sunday 19.7.2009 (sample L), at the end of a 3-days event in Berne (open-air music festival, about 0.1 million participants), LCOC was surprisingly lower compared to the Sunday before (sample I), whereas LBE was slightly elevated. However, the COC load LCOC was almost 50% higher on this particular event than on a regular Sunday. The results of the elimination efficiency of the STP Geneva differ slightly, depending on the analytical method used (Tables 3 and 4: sample C and D). Overall, 0e6% of COC and 19e59% of BE were still present in the effluent. Therefore, although diluted, a significant amount of BE is reaching the
Fig. 4 e Loads (L, g/day) of cocaine (COC) and benzoylecgonine (BE) in wastewater collected from major Swiss sewage treatment plants (STP) on different sampling dates, determined by GCeMS and HPLCeMS/MS.
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surface water. This can be due to incomplete biological treatment, release of untreated WW during heavy rainfall and/or adhesion of BE to sludge particles. With 94e100% the removal efficiency for COC of the STP Geneva is very similar to that observed by Postigo et al. (2010), whereas for BE (41e81% vs. 88%) the elimination rate is significantly lower.
4.3. loads
Assessing the total uncertainty in observed DTR
4.3.1.
Discharge (Q)
In the present study, the different STPs operated flow meters based on electromagnetic induction, ultrasonic velocity and level measurements, and the venturi effect (see Table 1). For the STP Berne, reliable information on the uncertainty of the influent measurement (u(Q)/Q) is available, because it is checked experimentally by comparison to volumetric measurements in the primary clarifier basin on a routine basis. For the other STPs, we relied on the expert opinion of the operators or manufacturer information (see Table 1). From our experience, uncertainties of about 1e10% reflect the quality of very well maintained stationary flow monitoring stations and might very well be larger (Thomann-Haller, 2002). However, the real concern is a bias and not a random uncertainty component.
4.3.2.
Concentrations (C )
The validation of the analytical procedures showed intra- and inter-day precisions to be <20% for the lowest and <15% for medium and high concentrations. To avoid underestimation of the measurement uncertainty we choose u(CDTR)/ CDTR ¼ 0.20 for both COC and BE.
4.3.3.
Sampling uncertainty ( fs)
From our analysis we found that the expected sampling uncertainty for all investigated catchments and applied sampling schemes on each STP varies between 0.8 and about 3%, which is generally small (Table 1). This is mostly due to the relatively large estimated number of WW pulses containing drugs of abuse and the rather small variability of substance masses per pulse in comparison to other substance, such as gadolinium or benzotriazole. In Fig. 5A and B we present 2 histograms as examples of the empirical error distributions obtained from the Monte Carlo Simulations. With an average sampling interval of 20 min the largest uncertainty (about 3%) was computed for the catchment of Geneva (Fig. 5B), the smallest for STP Zurich. Fig. 5C depicts the individual error contributions for the Geneva data. Our results are consistent with Ort and Gujer (2005) in demonstrating that catchments with short sampling intervals on the STP and large number of discharged pulses in the catchment exhibit the smallest error. This is, because the expected short-time fluctuations are less pronounced for patterns which contain many similar WW pulses. In addition, we observe that the distributions are symmetric, which is another sign of less fluctuating pollutant concentrations.
4.3.4.
Total uncertainty
The total uncertainty from discharge, analytics, and sampling is estimated to be <20% for all observed samples (not shown).
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A
B
C
24-h composite sample, dt: 20 min
24-h composite sample, dt: 18 min
1000
Q
600
Frequency
400
fs
200
200
Frequency
600
CBE
0
0
u(Load)
-5
0
5
10
Rel. error [%]
-10
-5
0
5
10
0
5000
10000
15000
u(y,xi) [mg/day]
Rel. error [%]
Fig. 5 e Assessment of uncertainty shown by the distribution of the relative sampling error from the STP Basel (A, s.d. [ 0.025) and Geneva (B, s.d. [ 0.031). By the total uncertainty of influent loads of STP Geneva (C) and individual error contributions it is clearly seen that the analytical uncertainty is the dominant factor.
As such, it is dominated by the analytical uncertainty (Fig. 5C). Flow measurement errors are in the same order of magnitude than sampling errors, but vary in importance for the individual STP and catchment.
4.4.
Swiss persons at the age of 15e30 years have at least once consumed COC, with 6.1% being men and 2.6% women (Addiction Info Switzerland, 2010). Taking into account that COC consumption is decreasing with age, this survey correlates reasonably with our estimation for Berne.
Back-calculation of COC consumption
As expected, with 132 and 95 g/day the estimated amounts of used COC (calculated according to Zuccato et al. (2005)) on weekends were in Berne higher compared to those on working days (Table 3: samples N and O; mean, N ¼ 6 and 13). For the period of JuneeJuly 2009 (sample M; mean, N ¼ 19) the mean daily COC consumption is estimated to be 107 21 g. During a 3-days open-air festival with about 0.1 million visitors the estimated weekend COC consumption was 198 g/day (sample P), which is 66 g/day higher than for other weekends in JuneeJuly 2009 (sample N). To calculate the approximate percentage of the population consuming COC, one has to make some assumptions. Firstly, it is estimated that, as an average, the consumed single dose of COC is 100 mg (United Nations Office on Drugs and Crime, 2004; Eve and Rave, 2011), corresponding to 10 doses per gram. Ten doses per gram multiplied with the grams of COC estimated to be consumed per day in Berne during JuneeJuly 2009, which is 107 21 g (sample M), result in an average of 1070 210 doses per day. In 2009, 92,290 of 130,290 inhabitants in the city of Berne were between 16 and 64 years old corresponding to 71% of the population. Seventy-one percent of the 196,711 people living in the STP Berne drainage area correspond to 139,665 people at the age of 16e64 years. This would mean that 0.76 0.15% of the 139,665 people in this particular age group is consuming 1 dose COC per day. However, this estimation is based on the assumption, that street COC is of 100% purity, i.e. not adulterated or diluted, which in reality is not the case. If we consider the median COC content to be 33% (Swiss Society of Legal Medicine, 2009), we estimate a daily COC consumption in Berne of 321 g, which is equivalent to 2.3% of the population living in the STP Berne drainage area consuming COC. According to a telephone survey, 4.4% of the interviewed
5.
Conclusions
In this study, we presented a novel method to assess the total uncertainty in observed WW COC and BE loads resulting from discharge measurements, chemical analysis and the applied sampling scheme and demonstrated its usefulness on data from the largest Swiss STPs. In summary we can draw the following conclusions: The monitoring of drugs or drug target residues in WW has the potential to provide supplementary information on short- and long-term, local, national, and international COC use, if the monitoring data are evaluated critically and accompanied by an estimate of uncertainty. This should take into account all relevant influence factors, such as discharge and chemical measurements as well as the applied sampling protocol. To reliably assess all influence factors, we suggest a linear uncertainty propagation framework which is based on the Eurachem guide and has been extended with a stochastic model-based assessment of sampling uncertainty. Thus, we avoid variographic analysis which requires an expensive set of preliminary data. Instead, the model-based assessment derives an empirical distribution of the sampling error from prior information on population, substance use and excretion, catchment characteristics, sewer topology and discharge, which are generally available. Results for the 5 largest Swiss STPs suggest that the total uncertainty in observed loads is smaller than 20% and that the analytical uncertainty is the dominating influence factor. We compute the sampling uncertainty in daily loads to 1e3%, which is about the same order of magnitude of
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 6 5 0 e6 6 6 0
stationary flow measurements. For the sampling uncertainty, we identified the number of excreted substance pulses and the sampling interval at the STP as important influence factors. These results could be generally valid for communities of more than 100,000 inhabitants and gravitydriven sewers, but most probably do not hold for small communities and systems with large WW pumps. The highest COC concentrations were found in Geneva influent water, resulting from a Sunday WW sample. The highest BE concentrations were measured in WW of the Zurich STP collected on a Sunday after a mass rave event. Back-calculations with a standard model suggest that, in Berne, about 2.3% of the people between the age of 16 and 64 years are estimated to consume 1 dose of COC per day. This is higher compared to data of The World Drug Report 2008, which estimated for Switzerland a 1.1% annual prevalence of COC consumption in the age group 15e64 years (United Nations Office on Drugs and Crime, 2008). For the first time, two analytical approaches using GCeMS and HPLCeMS/MS were compared on the same WW samples to monitor COC and BE over a longer time period. We found that, except for sensitivity and recovery (GCeMS < HPLCeMS/MS), the validation data showed a similar performance and that, at least for COC and BE quantification in WW, GCeMS can be recommended as an efficient and less expensive alternative. This was recently also stated by Gonzalez-Marino et al. (2010) using GCeMS/ MS. However, when available in a lab, HPLCeMS/MS should be the method of first choice for WW analysis. We deliberately did not tackle to assess the uncertainty of back-calculated user figures itself, because in our view today there is still a lack of understanding of important influence factors such as degradation and surface adsorption in the WW system, sewer leakage, short-term population fluctuations in the catchment and usage habits, which could introduce potential biases. Nevertheless, we believe that our method is useful to audit past and design future monitoring campaigns. Therefore we hope that our approach helps to ensure a high data quality and can serve as a first step towards providing better information on substance abuse in the future.
Acknowledgements Special thanks go to the following STP technicians for assisting in sampling, providing samples, and supporting the project: Peter Wyss and Tanja van der Heijden from the STP Berne, Mr. Caflisch and colleagues from the STP Da¨rligen, Mr. Wahl from the STP Geneva, Mr. Kopf from the STP Basel, Mr. Zumbach from the STP Lucerne, Mr. Langenegger and Mr. Pfund from the STP Zurich.
Appendix. Supplementary data Supplementary data related to this article can be found online at doi:10.1016/j.watres.2011.09.049.
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