L. Boithias, R. Marcé, V. Acuña, J. Aldekoa, V. Osorio, M. Petrović, A. Ginebreda, F. Francés, S. Pérez, S. Sabater
4th SCARCE International Conference25‐26 November 2013, Cádiz, Spain
Assessment of the water purification ecosystem service
regarding in‐stream pharmaceutical residues:
Exploring the GREAT‐ER model parameters
based on data uncertainty
Barcelona
Igualada
Manresa
Anoia river
Llobregat river
Cardener river
N
Drinking water treatment facilityWaste water treatment plants
Pharmaceuticals in the Llobregat basin
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• 5000 km2
• High industrial, agricultural and urban activity: 60 WWTP
• PCP = 700 mm . At outlet, discharge is 19 m3 s‐1
• The basin supplies drinking water for the 3 million inhabitants Barcelona area
• Throughout the basin, discharge may be provided by the WWTP
• High concentrations of pollutants, including pharmaceuticals
• Water purification service from WWTP and ecosystems is a major issue
Modelling the fate of pharmaceuticals
• Pharmaceuticals are ubiquitous in densely urbanized areas
• Attenuation of pharmaceutical contamination in the aquatic environment depends on:
• Few studies about the in‐stream attenuation of pharmaceuticals
• Objective: assess the ability of the spatially explicit GREAT‐ER model to simulate the concentration of 13 pharmaceuticals in two contrasted hydrological conditions
• Uncertainty analysis approach
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‐ Physico‐chemical properties‐ Hydrology (sediments)
The GREAT‐ER model
• Steady‐state spatially explicit model (Boeije and Koorman, 2003) for personal care and pharmaceutical compounds
• Previously applied over several catchments
– UK: triclosan (Sabaliunas et al., 2003), LAS (Price et al., 2009), diclofenac and propranolol (Johnson et al., 2007)
– Austria: LAS, EDTA, triclosan (Wind et al., 2004)
– Germany: carbamazepine and diclofenac (Heberer et al., 2005)
– Swiss: Estrogens (Vermeirssen et al., 2006)
– Spain: Diclofenac in the Llobregat (Aldekoa et al., 2013)
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The GREAT‐ER model
• 3levels of complexity ‐> simplest one
• Inputs:
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Attenuation = Degradation / Sorption
Catchment descriptionPharmaceuticals
properties
‐ River stretches location (confluences, dams, WWTP, gauging stations)‐ River stretches annual discharges, velocity, depth
‐WWTP location ‐WWTP annual discharge
‐ Annual pharmaceutical emissions to sewage (kg cap‐1 yr‐1)
‐WWTP removal rates (percentage ‐ %)
‐ River removal rate (decay ‐ h‐1)
Outputs: annual average concentration in each river stretch
LLO1
LLO4
LLO3
LLO2
LLO5LLO6
LLO7
CAR3
CAR2
CAR1
CAR4
ANO1ANO2
ANO3
Data ‐ Pharmaceuticals of interest
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• Discharge : ACA• Pharmaceuticals in‐stream
concentrations: 14 sampling points of SCARCE– 2 campaigns : high flow (2010) and low
flow (2011) conditions• Selected pharmaceuticals based on:
– Medical use (point source through WWTP)– At least 8 out of 14 samples with
concentration > LOQ for both campaigns– Availability of WWTP removal efficiencies
values (Gros et al., 2010; Jelic et al., 2011)– Availability of river removal efficiencies
values (literature review – 30 references)– 13 selected pharmaceuticals
• Calculated percentiles to provide a 95% confidence interval
Input data uncertainty
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• Uncertainty is high, depends on:– Molecules– Number of available data
• Model minimal, median and maximal scenarios
• Simulation of the statistical distribution of observed data ‐> avoiding the calibration step
• Is the simplest model of GREAT‐ER able to model pharmaceutical fate in the Llobregat?
1<n<27
6<n<38
7<n<40
Results
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Nonsteroidal anti‐inflammatory‐ 9 < RMSEmedian < 65‐Min & max encompass the 1:1 line‐Well simulated for both low flow and high flow
Results
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Antiepileptic‐ 7 < RMSEmedian < 29‐Min & max encompass the 1:1 line‐Well simulated for both low flow and high flow
Antibiotic‐ 2 < RMSEmedian < 3‐Min & max encompass the 1:1 line‐Well simulated for both low flow and high flow
Sim. C
onc. (n
g L‐1)
Sim. C
onc. (n
g L‐1)
Results
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Lipid regulator‐ 126 < RMSEmedian < 507‐Min & max encompass the 1:1 line‐Well simulated for both low flow and high flow
Sim. C
onc. (n
g L‐1)
Results
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Sim. C
onc. (n
g L‐1)
Sim. C
onc. (n
g L‐1)
Antihistaminic‐ 4 < RMSEmedian < 5‐Min & max encompass the 1:1 line‐ Badly simulated for low flow
Beta‐blocker‐ 49 < RMSEmedian < 84‐Min & max encompass the 1:1 line‐ Badly simulated for low flow
Results
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Beta‐blocker‐ 1 < RMSEmedian < 217‐ Overestimated during high flow
Lipid regulator‐ 5 < RMSEmedian < 18‐ Overestimated during low flow
Results
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Sim. C
onc. (n
g L‐1)
Analgesic‐ 2 < RMSEmedian < 6‐ Underestimated during both low flow and high flow
Analgesic‐ 43 < RMSEmedian < 62‐ Underestimated during high flow and ovestimated during low flow
Discussion
• For some molecules, the simplest model is enough to describe the fate, when using the median of the available data– Non‐steroidal anti‐inflammatory drugs were well simulated– A simple statistical analysis showed that the RMSE was lower for
lower Henry low constants ‐> more volatilizable molecules
• Some molecules are badly simulated during low flow: REWWTPand RERivers may increase during low flow depending on the pharmaceutical concentration ‐> this was not simulated:
– future work: simulate 27 Med/Min/max combinations to check thisassumption
• Other uncertainty source : emissions may change spatially, but not depending on the hydrological conditions
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Conclusions and future work
• Well simulated molecules could already be used to assess the water purification service, i.e. the contaminants removal, at basin scale
• Next step: run the 27 scenarios of Min/Max/Median combinations, to get more details about the effect of the hydrological conditions on the removal efficiencies
• Ongoing work: automatic sensitivity analysis and an automatic calibration (GREAT‐ER more complex tiers)
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