Modelling faecal contamination in the Scheldt drainage network

Journal of Marine Systems 128 (2013) 77–88

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Modelling faecal contamination in the Scheldt drainage network Nouho Koffi Ouattara a, Anouk de Brauwere b, c, d, Gilles Billen e, Pierre Servais a,⁎ a

Ecologie des Systèmes Aquatiques, Université Libre de Bruxelles, Campus plaine, CP 221, B-1050 Brussels, Belgium Université catholique de Louvain, Institute of Mechanics, Materials and Civil Engineering (IMMC), 4 Avenue G. Lemaître, bte L4.05.02, B-1348 Louvain-la-Neuve, Belgium c Université catholique de Louvain, Georges Lemaître Centre for Earth and Climate Research (TECLIM), 2 Chemin du Cyclotron, B-1348 Louvain-la-Neuve, Belgium d VrijeUniversiteit Brussel, Analytical and Environmental Chemistry, Pleinlaan 2, B-1050 Brussels, Belgium e UMR Sisyphe, Université Pierre & Marie Curie/CNRS, 4 place Jussieu, 75005Paris, France b

a r t i c l e

i n f o

Article history: Received 31 October 2011 Received in revised form 7 May 2012 Accepted 8 May 2012 Available online 15 May 2012 Keywords: Microbiological water quality Escherichia coli Modelling Scheldt river drainage network Scenarios

a b s t r a c t This study developed a model simulating the seasonal and spatial variations of microbiological water quality (expressed in terms of Escherichia coli concentrations) in rivers. The model (SENEQUE-EC) consists of a microbiological module appended to a hydro-ecological model describing the functioning of the entire Scheldt drainage network. The microbiological module describes the sources of E. coli, their transport and the processes responsible for the fate of E. coli once released into the natural environment (mortality, settling and resuspension). This model differentiates the dynamics of three types of E. coli: free-floating E. coli, E. coli attached to suspended solids in the water column and E. coli present in sediments. The model was verified by comparison of its results with temporal and spatial distributions of field data in different stretches of rivers of the Scheldt drainage network. It was then used to test various scenarios involving diverse modifications in wastewater management, which was shown to be the most determining factor of microbiological water quality. Due to its low temporal resolution, the SENEQUE-EC is poorly adapted to describing the microbiological quality in areas under tidal influence. Therefore, the data of the SENEQUE-EC model were used as upstream boundary conditions to run a microbiological model with a high temporal resolution devoted to the tidal Scheldt River and Estuary (the SLIM-EC2 model). © 2012 Elsevier B.V. All rights reserved.

1. Introduction The research presented in this paper was conducted within the scope of the Belgian Interuniversity Attraction Pole (IAP) TIMOTHY project (Lancelot and Gypens, 2013-this issue). This interdisciplinary project is studying and modelling the current sources and fate (transfer, transformation and retention) of key nutrients (nitrogen, phosphorus and silicon) and pollutants (metals, xenobiotics and microbial contaminants) along the land–sea aquatic continuum in response to anthropogenic and natural changes. The Scheldt watershed and the adjacent eastern Channel and Southern Bight of the North Sea (Fig. 1) were chosen as a case study and geographical domain for this study. This paper and another paper (de Brauwere et al., 2013) report on the microbiological water quality and more precisely the modelling of the microbiological contamination level in the Scheldt land– sea continuum. Polluted surface waters can contain a wide variety of pathogenic micro-organisms: viruses, bacteria and protozoa. The main origin of these micro-organisms is the direct and indirect release of human

⁎ Corresponding author. E-mail address: [email protected] (P. Servais). 0924-7963/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jmarsys.2012.05.004

and warm-blooded animal faeces into the aquatic environments. The health risk related to the presence of these pathogens depends on the use of the water (drinking, recreational activities, bathing, irrigation, shellfish harvesting) and on the pathogen concentrations in the water. In aquatic systems, the detection and enumeration of all potentially present pathogenic micro-organisms are very difficult due to the great diversity of pathogens, the low numbers of each species and the absence of standardized methods for detecting some of them. Therefore, the routine monitoring of microbiological water quality is based on the concept of faecal indicator bacteria (FIB). These FIB are groups of bacteria that fulfil the following criteria: they should be universally present in large numbers in human and warm-blooded animal faeces, readily detected by simple methods and they should not grow in natural waters, but persist in water and be removed by water treatment in a similar way as waterborne pathogens (Havelaar et al., 2001). Today, water quality regulations for drinking, irrigation and recreational uses are primarily based on two FIB (Escherichia coli and intestinal enterococci) concentrations. For example, the directive on bathing water quality adopted by the European Parliament and Council in 2006 (Directive 2006/7/EC) is based on the concentration of these two FIB, with different levels of compliance for inland and coastal waters. In the present study, the abundance of E. coli was used to estimate the microbiological quality of surface water in the Scheldt drainage network.

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Fig. 1. Map of the Scheldt river drainage network with the major tributaries and locations of the major cities (Brussels, Ghent, Antwerp, Lille) in the watershed. Locations of the stations for which comparison of modelled and measured E. coli concentrations were performed are also indicated (o).

The Scheldt watershed, ranging from the North of France to Belgium and the South of The Netherlands (Fig. 1), is characterized by a high population density and active industrial and agricultural activities. Due to these anthropogenic pressures, it is an extreme case of a polluted drainage network. Pollution coming from the watershed through the estuary is responsible for marine eutrophication, modification of the ecological functioning (Lancelot et al., 2007), as well as contamination by metals (Baeyens et al., 2005) and organic compounds (Baeyens et al., 2007) of the receiving coastal waters. Studies have been conducted in the last few years to estimate the level of faecal contamination of the Scheldt drainage network, to quantify the sources of microbial contamination and to study the fate of faecal micro-organisms in the rivers (Ouattara et al., 2011). Data showed low microbiological quality in the downstream parts of the main tributaries of the Scheldt River, especially in the Zenne River which crosses the Brussels area (Fig. 1). The quantification of point (outfall of treated and untreated wastewaters) and non-point (surface runoff and soil leaching) sources of faecal contamination of the rivers of the Scheldt drainage network showed that, at the scale of the Scheldt watershed, point sources were largely predominant in comparison to non-point sources (Ouattara et al., 2011). Besides the experimental and field work, the TIMOTHY project included modelling microbiological water quality. In the literature, two fundamentally different approaches are used for FIB modelling in aquatic systems: regression-based (or black box or stochastic) models and mechanistic (or reactive tracer or process-based) models. Regression-based models (Alkan et al., 1995; David and Haggard, 2011; Eleria and Vogel, 2005) make links between a set of input (explanatory) variables and an output in terms of FIB concentration using regression methods. Input variables can include meteorological, hydrological, physico-chemical, land-use, landscape, or previous microbiological quality data. Most of these studies relate microbial water quality to explanatory variables using multivariate linear regression

models (David and Haggard, 2011; Frick et al., 2008; Ge and Frick, 2007; Heberger et al., 2008; Nevers and Whitman, 2011; Nevers et al., 2007); their main advantage is that they are easy to implement because they are based on relatively basic statistical concepts. Generally, regression-based models are developed with the aim to use them for real-time predictions (nowcasts) of bathing water quality (Dorevitch et al., 2010; Frick et al., 2008; Nevers and Whitman, 2005; Stidson et al., 2012), in order to include them in early warning systems. The black box nature of regression-based models has a main disadvantage; indeed, they usually do not enable an in-depth understanding of the system, because they do not include mechanistic or causal relationships. Thus, they are not able to predict the effect on microbial water quality of potential future changes in wastewater management. The mechanistic models are based on the coupling of models representing the processes affecting FIB with models describing the hydrodynamics of the system (Bai and Lung, 2005; Bougeard et al., 2011; de Brauwere et al., 2011; Dorner et al., 2006; Gao et al., 2011; Kashefipour et al., 2006; Liu et al., 2006; Pachepsky et al., 2006; Servais et al., 2007a,b; Thupaki et al., 2010 ). It has been shown that using a mechanistic modelling approach in conjunction with laboratory experiments for parameters determination and field observations (for model validation) can help improve the understanding of the fate and transport of FIB in water bodies, and that these results can then be further applied to provide predictive information for effective public health management (Cho et al., 2010). Some recent models using the mechanistic approach (Cho et al., 2010; Gao et al., 2011; Kashefipour et al., 2006; Liu et al., 2006) aims at calculating short term variations of E. coli concentrations in order to replace regular monitoring of the microbiological quality by model simulations. Two models that simulate the temporal and spatial fluctuations of E. coli concentrations were developed in this study. Both are based on a deterministic approach but they differ in their domain of application: the SENEQUE-EC model covers the whole drainage network that

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is not influenced by the tide, while the SLIM-EC2 model has been used to model the microbiological quality in the tidal Scheldt River and Estuary de Brauwere et al., 2013. The SENEQUE-EC model consists of the hydro-ecological SENEQUE/ RIVERSTRAHLER model describing the functioning of large river systems (Ruelland et al., 2007) to which a module describing E. coli dynamics has been appended. A previous coupling of the SENEQUE model with a microbiological module has been used to predict faecal coliforms concentrations in the Seine River drainage network (Servais et al., 2007a,b). In the model proposed by Servais et al. (2007a,b), only one stock of FIB was considered. The SENEQUE model was improved in the present study by considering three stocks of E. coli in the river: free E. coli (ECF), E. coli attached to suspended solids (SS) present in the water column (ECA) and E. coli in the deposited sediments (ECS). The three stocks are affected by a different mortality rate (first-order kinetics). In addition, attached E. coli can settle and deposit in the sediment, while E. coli in the deposited sediments can be resuspended in the water column. The module also takes into account the input of E. coli through point sources (release of treated or untreated wastewaters) and non-point sources (soil leaching and runoff). The main objective of the SENEQUE-EC model developed in this study was to evaluate the impact of modifications in wastewater management in the watershed on the rivers' microbiological quality. Due to its low temporal resolution (average concentrations are calculated for 10-day periods), the SENEQUE-EC model is not well adapted to describe the microbiological quality in areas under tidal influence, nor what happens during extreme events. That is the reason why a second model was developed, the SLIM-EC2 model, designed to model the microbiological quality in the tidal Scheldt River and Estuary with a much higher temporal resolution (15-min time steps). SLIM-EC2 combines the hydrodynamic model SLIM (Secondgeneration Louvain-la-neuve Ice-ocean Model; de Brye et al., 2010) with a module describing the dynamics of E. coli in the aquatic system. This model and its results are presented in another paper (de Brauwere et al., 2013). The upstream boundary concentrations required to run the SLIM-EC2 model are calculated by the SENEQUE-EC model. This offline coupling of the two models thus simulates the microbiological contamination from the upstream headwaters of the drainage network to the coastal zone and the North Sea. This paper presents and validates the SENEQUE-EC model, encompassing the issue of faecal contamination at the scale of the whole (non-tidal) Scheldt drainage network. Then, the SENEQUE-EC model was used to test various scenarios in relation with wastewater management in the watershed. By coupling SENEQUE-EC and SLIMEC2, the impact of these wastewater management scenarios will be traced further downstream throughout the tidally influenced rivers and the estuary, to assess the load of faecal micro-organisms to the coastal zone and the North Sea (de Brauwere et al., 2013). 2. Material and methods 2.1. The Scheldt river watershed The Scheldt watershed (20,000 km2 ranging from the North of France to the Belgian–Dutch border) (Fig. 1) is densely populated, with around 500 inhabitants km− 2. It comprises three main sub-basins: the upperScheldt basin (8125 km2, draining the cities of Ghent and Mons), the Leie river basin (3850 km2, draining Lille in France) and the Rupel basin (6475 km2). The Brussels conurbation, drained by the Zenne River, is a major attraction pole in the watershed, with more than 2 million inhabitants. The Scheldt watershed is characterized by a mosaictype landscape, in which urban zones are mixed with surrounding agricultural and cropland areas. Agriculture is characterized by intensive cattle farming and, especially in the Flemish region, pig breeding. The total length of the rivers of the Scheldt drainage network is 3265 km, among which 1637 km are first-order streams (Thieu et al.,

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2009). Seventh-order stream is the highest, for the part of the Scheldt downstream from its confluence with the Rupel River. The long-term annual mean rainfall is 813 mm and average annual flow rate of the Scheldt River at Schelle (downstream from the confluence of the Rupel and upper Scheldt) is 110 m 3 s− 1 (over the 1950–2000 period) with high flows in winter (maximum 500 m 3 s− 1) and low flows in summer (typical summer flow around 50 m 3 s − 1) (Billen et al., 2005). The main branch of the Scheldt, from Ghent to the sea, as well as a number of tributaries, is subjected to the tide, and, given the low river discharge, the salinity increase is already perceptible upstream from Antwerp. Due to its high level of faecal contamination, special attention was paid in this study to the Zenne River (Fig. 1). This tributary of the Dyle River has a watershed of 1011 km2 characterized by agricultural activities in its upstream part and a large urban area in its downstream part. The population density in the watershed is very high (on average 1259 inhabitants km− 2), mostly located in Brussels and its suburbs. The Zenne River crosses this city from South to North over a distance of about 20 km and receives the sewage from two wastewater treatment plants (WWTPs): the Brussels South WWTP (360,000 inhabitantequivalents) and the Brussels North WWTP (1.1 million inhabitantequivalents). The annual average discharge of the Zenne River upstream from Brussels is 4 m 3 s− 1 (average for the 2007–2008 period), while the flow released by the two Brussels WWTPs is on the same order of magnitude. 2.2. Monitoring microbiological water quality in the Scheldt basin In order to collect field data on microbiological water quality in the main rivers of the Scheldt drainage network, a monitoring survey was organized monthly from March 2007 to June 2008. During the monitoring survey, water samples were collected in the downstream part of the main rivers of the Scheldt watershed. During these campaigns, grab water samples were collected in the rivers with a plastic bucket from bridges, halfway between the banks, and E. coli were enumerated. Details on this monitoring survey and its data can be found in Ouattara et al. (2011). The data obtained in some stations outside the area under tidal influence were used in this study for validating the model. These stations are located on the upper Scheldt (Gavere station, Sc), the Leie (St-Martens-Leerne station, Ly), the Dyle (Gastruche station, Dy), the Dender (Gijzegem station, De) and the Zenne (Lot (Ze1), Eppegem (Ze2) and Leest (Ze3) stations) Rivers (see Fig. 1 for the location of these stations). 2.3. Enumeration of E. coli In the present study, E. coli were enumerated in water samples by standard plate counts on Chromocult Coliform Agar (CCA) (Merck KGaA, Darmstadt, Germany). This chromogenic growth medium was shown to be highly specific for E. coli (Prats et al., 2008). CCA plates were incubated at 36 °C for 24 h. Plate counts were expressed as colony-forming units (CFU) per 100 mL of sample. In some samples, the approach proposed by Garcia-Armisen and Servais (2009) was used to estimate the fraction of E. coli attached to suspended matter (SM). It is based on measurements of the β-Dglucuronidase (GLUase) activity (an enzymatic activity specific to E. coli) coupled with size fractionation. GLUase activity was estimated by fluorometry as the production of fluorescent methylumbelliferone (MUF) resulting from the hydrolysis of the substrate 4-methylumbelliferyl-βD-glucuronide (MUGlu) (George et al., 2000). GLUase activity measurements have been shown to be a good surrogate to E. coli enumeration by plate counts in different types of aquatic systems (Garcia-Armisen et al., 2005; Lebaron et al., 2005). GLUase activity retained on a 0.2 μm pore-size membrane was used to quantify the GLUase activity of the total population of E. coli in a sample while the GLUase activity retained on a 5 μm pore-size membrane was used to quantify the activity of the

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fraction of E. coli attached to SM. The ratio of both activities gives an estimate of the proportion of E. coli attached to SM. Measurements of GLUase activities were performed following the protocol proposed by Servais et al. (2005). 3. Microbiological quality model The past few decades have shown an increasing interest in modelling the dynamics of faecal indicator bacteria (FIB) in various types of aquatic environments (coastal areas, lakes and rivers). Models able to predict FIB concentrations are of course valuable tools for the management of aquatic systems. The SENEQUE-EC model results from the coupling of the hydro-ecological SENEQUE/RIVERSTRAHLER model describing the functioning of large river systems (Ruelland et al., 2007) with a module describing E. coli dynamics. 3.1. The SENEQUE/RIVERSTRAHLER model The RIVERSTRAHLER model is a simplified model of the biogeochemical functioning of river systems at the basin scale relating water quality to anthropogenic activity in the watershed (Billen and Garnier, 1999; Billen et al., 1994). RIVERSTRAHLER describes the drainage network of any river system as a combination of basins, idealized as a regular scheme of confluence of tributaries of increasing stream order, each characterized by mean morphologic properties, connected to branches, represented more realistically, with a higher spatial resolution (Billen et al., 2005). The water flows in the hydrographical network are calculated from the specific surface and base flow generated within the watershed of the different sub-basins and branches considered. These are calculated from daily data at gauged stations, using the Eckardt (2005) recursive filter for hydrogram separation. The water flows are then routed along the confluence scheme of the whole drainage network defined by the structure of basins and branches. The seasonal cycle is represented as a succession of ten day periods, each with permanent hydrological conditions which are the average of the observed daily flows. For long-term simulations (one or several years), it is not feasible to document all the time variability of hydrological and meteorological conditions, as well as sources of E. coli. As the objective of the model is to evaluate the impact of modifications in wastewater management in the watershed on the rivers' microbiological quality by performing simulations for long periods of time (typically one or several years) it was not required to develop a model able to simulate hourly or daily fluctuations of E. coli in the natural environment. The essence of the model is to couple these routed water flows with a model describing biological, microbiological and physico-chemical processes occurring within the water masses, according to a Lagrangian calculation scheme. In the SENEQUE software, this model is embedded within a GIS interface, allowing the use of fully distributed geo-databases (Ruelland et al., 2007). Thus, in addition to morphological and climatic constraints, the SENEQUE/RIVERSTRAHLER model takes into account diffuse sources (based on land use) and point sources, typically wastewater discharges (see Section 3.2.2); land use and WWTPs are geo-referenced. The addition of a module describing the dynamics of E. coli to the model (to build the SENEQUE-EC model) allows the inclusion of E. coli concentration as an additional state variable which can be calculated by the model in the whole drainage network for which the suitable database has been assembled. A benthic compartment is also considered all along the drainage network by grid cells of 1 km length over the width of the particular river stretch. 3.2. Modelling E. coli dynamics in rivers In many models describing FIB dynamics in aquatic systems, only one compartment of faecal bacteria is considered. However, several

studies (Cizek et al., 2008; Jamieson et al., 2005) reported that a significant part of FIB in aquatic systems was associated with suspended solids (SS) and that this association influences their survival and transport characteristics. Indeed, FIB associated with SS can settle, although this is not the case of free-floating FIB (Garcia-Armisen and Servais, 2009). Some authors have suggested that FIB attached to SS have a lower decay rate because they are protected from some processes leading to the disappearance of FIB in waters (protection against UV and grazing by some small protozoa) (Hellweger and Masopust, 2008; Liu et al., 2006). When FIB settle in the water column, they can feed the stock of FIB within the sediment; several studies have demonstrated high FIB concentrations in the sediments of aquatic systems (Pachepsky and Shelton, 2011; Roslev et al., 2008). This may be partly due to the prolonged survival of FIB in sediments with regards to the water phase, as reported in the literature (Pachepsky and Shelton, 2011). For these reasons, in the present modelling exercise, three stocks of E. coli were considered in each stretch of the rivers (Fig. 2): free E. coli (ECF), E. coli attached to SS present in the water column (ECA) and E. coli in the deposited sediments (ECS). The three stocks were affected by a different mortality rate (first-order rate kinetics, see below for greater detail). In addition, attached E. coli can settle and deposit in the sediment, while E. coli in the deposited sediments can be resuspended in the water column. Today, there is a growing consensus in the literature about the importance to include the processes of settling and resuspension in mechanistic models of faecal contamination of surface waters (Droppo et al., 2011; Jamieson et al., 2005; Pachepsky and Shelton, 2011). Models making the distinction between free and attached FIB must calculate the proportion of attached and free FIB at each time step and at each location in the modelled domain so that only the attached FIB are affected by settling. For this, some authors consider a constant proportion of attached FIB in time and space. Dorner et al. (2006) considered 30% of attached micro-organisms and Wu et al. (2009) 50%. Other studies considered that the fraction of attached FIB is a function of the SS concentration (Bai and Lung, 2005; Gao et al., 2011). In

Point sources

Free E. coli (ECF)

Mortality 1

Non-Point sources

Attached E. coli (ECA)

Mortality 2

Suspended solids

Sedimentation Resuspension

Sediments

E. coli in sediments (ECS)

Mortality 3

Fig. 2. Schematic representation of the structure of the module describing the dynamics of E. coli in the rivers included in the SENEQUE-EC model. Three stocks of E. coli are considered in each stretch of the rivers: free E. coli, E. coli attached to SS present in the water column and E. coli in the deposited sediments. The three stocks are affected by a different mortality rate (first-order rate kinetics with the first-order rate constant depending on temperature). In addition, attached E. coli can settle while E. coli in the deposited sediments can be resuspended in the water column. The sources of E. coli considered in the model are the point sources and the diffuse sources: the E. coli concentration in wastewaters (point sources) depends on the type of treatment applied, while the concentration in runoff (non-point sources) depends on land use in all elementary sub-basins of the watershed.

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fact, these approaches consider that FIB can be rapidly exchanged from one form to the other by fast adsorption and desorption processes. However, it is known from the general literature on bacterial adhesion onto surfaces that bacteria, after a first reversible binding to particles by adsorption, can synthesise exopolymers that strengthen their bindings to the surface (Fletcher, 1996). With such biological binding, the reversible character of the attachment disappears. Thus, some authors considered the attachment of E. coli to SS as irreversible. For example, Jamieson et al. (2005), when simulating the injection of sediment associated E. coli during an artificial high flow event, considered the transport of free-floating and attached bacteria separately. Garcia-Armisen et al. (2006) when modelling sources, transport and fate of faecal coliforms in an estuary considered that a part of the FIB enters in the modelled domain as free and another part as attached and that no exchange between free and attached E. coli occurs. This approach that gave satisfying results for modelling free and attached FIB (Garcia-Armisen et al., 2006) was used in the present study. In addition to the description of the transport and fate of E. coli in the river waters, the E. coli module developed in this study has taken into account the inputs of E. coli to rivers; these inputs are controlled by activities in the watershed. In urban areas, faecal microorganisms are mainly brought to aquatic environments through the discharge of domestic wastewater (treated or not treated in WWTPs). In rural areas, faecal pollution can also be brought to rivers through non-point sources (surface runoff and soil leaching); its origin can be wild life and grazing livestock faeces as well as cattle manure spread on cultivated fields. In the input of E. coli to rivers, the bacteria present as free cells were distinguished from the cells attached to SS. 3.2.1. Transport and fate of E. coli in rivers 3.2.1.1. Free E. coli (ECF). In the SENEQUE-EC model, ECF are transported from upstream to downstream with the flowing water masses. It is well known that after their release in natural aquatic environments, FIB tend to decrease quite rapidly. The decay of culturable E. coli in rivers results from the combined actions of various biological and physico-chemical processes (grazing by protozoa; virus-induced cell lysis and autolysis; stress due to nutrient depletion and sunlight irradiation inducing mortality or loss of culturability)(Barcina et al., 1997). To the best of our knowledge, in all models describing the fate of FIB in aquatic systems, the overall decay is usually described by a first-order kinetics (Collins and Rutherford, 2004; Kashefipour et al., 2002; Tian et al., 2002; Wilkinson et al., 1995). This approach was adopted in the present study with the following equation describing the fate of ECF: d½ECF=dt ¼ kECF  ½ECF

ð1Þ

with kECF = first-order decay rate of free E. coli (h− 1); t = time (h); and [ECF] = Free E. coli concentration (E. coli 100 mL− 1). Numerous laboratory and in situ studies reported that increasing temperature in the range usually found in surface waters results in an increase of the decay rate (Barcina et al., 1986; Craig et al., 2004; Flint, 1987). Better survival at low temperature can be explained by lower energy costs for bacteria due to reduced metabolic activities (direct effect) while higher mortality rates at high temperature can be explained by increased predation (indirect effect) (Servais et al., 1985; Menon et al., 2003). Indeed at high temperature the abundance of protozoa, the main bacterial grazers, is usually higher than at low temperature (Servais et al., 2000) and the grazing rate per protozoa also increases with temperature (Marasse et al., 1992). In the model, the impact of temperature on the decay process was taken into account using the following sigmoid relationship as already done in several

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studies (Beaudeau et al., 2001; de Brauwere et al., 2011; Servais et al., 2007a,b):

kECFðTÞ

  Þ2 e ðT25 400 ¼ kECF20∘C 
e 25 400

ð2Þ

with T = temperature (°C); kECF (T) = first-order decay rate of ECF (h− 1) at temperature T; and kECT20 °C = decay rate of ECT at 20 °C (h− 1). A value of 45× 10− 3 h− 1 for kECT20 °C was used in the present study. This decay rate value was already successfully used to model faecal coliforms dynamics in the Seine River drainage network (Servais et al., 2007b) and in the Seine Estuary (Garcia-Armisen et al., 2006). 3.2.1.2. E. coli attached to SS (ECA). In the SENEQUE-EC model, the fate of E. coli attached to SS was described by the following equation that takes into account decay (first-order kinetics as for ECF) and settling: d½ECA=dt ¼ −kECA  ½ECA–ðVSET =dÞ  ½ECA þ resuspension=d

ð3Þ

with kECA = first-order decay rate of E. coli attached to SS (h− 1); t = time -(h); [ECA] = concentration of E. coli attached to SS (E. coli 100 mL− 1); VSET = settling rate of ECA (m h− 1); d = depth of the river; see the next section for explanation on the resuspension term. As experimental data on the comparison of the decay rate of freefloating and attached E. coli showed that the decay rate of attached E. coli was roughly half than that of free-floating E. coli (GarciaArmisen and Servais, 2009), a value of 22.5 × 10 − 3 h − 1 at 20 °C was considered in the model for the decay rate of ECA; this decay rate value was previously used by Garcia-Armisen et al. (2006) to model faecal coliforms attached to SS in the Seine estuary. The impact of temperature on the ECA decay rate was considered similar to the ECF decay rate (Eq. 2). In the model, a settling rate value for ECA equal to 0.1 m h − 1 was used; this value is similar to the settling rate value for organic particles used in the SENEQUE/RIVERSTRAHLER model. This value is also within the range of values mentioned in studies specifically devoted to the estimation of settling rates of E. coli attached to SS (Auer and Niehaus, 1993; Garcia-Armisen and Servais, 2009; Jamieson et al., 2005). 3.2.1.3. E. coli in sediments (ECS). The stock of ECS in the model is fed by the settling of ECA. A low baseline content of 5000 E. coli g − 1 was assumed over the whole benthic phase of the drainage network as an initial condition. This arbitrary but plausible value only influences the dynamics of the system during the first 10 days of the simulation. In some conditions, sediment can be resuspended in the overlying water column, increasing the concentration of ECA in the water phase. Resuspension is considered by several authors (Cho et al., 2010; Crabill et al., 1999; Roslev et al., 2008; Smith et al., 2008) as a significant source of E. coli, in some situations leading to river water column pollution. In SENEQUE-EC, this occurs when the suspended loading (i.e., the suspended solid concentration) of the water column is lower than the transport capacity of the river body (CapSS), calculated as an empirical cubic function of the river flow velocity, until this transport capacity is reached (Celik and Rodi, 1991; Martin, 2001; Thouvenot et al., 2009). 3

CapSS ¼ C0 þ C1  v

ð4Þ

with C0 = 20 mg L − 1 and C1 = 100 mg L− 1 m− 3 s 3; v = cross-sectional average river flow velocity (m s− 1). The values of Co and C1, applied all over the drainage network, have been obtained by adjustment on the observed suspended solid concentration at different station of the river network (Thouvenot et al, 2009). This makes it possible to define a resuspension rate (rs) of the sediment stock at each time step, which also applies to all sedimentary

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variables, including ECS, which is assumed to be homogeneously distributed within the upper sediment layer subject to resuspension. resuspension ¼ rs  ECS

ð5Þ

with rs = resuspension rate (h − 1). The sediment is also assumed to be slowly and continuously compacted, leading to the burial of a part of the benthic stocks, at a bu rate of 0.0005 h − 1 (Thouvenot et al., 2007, 2009). The overall dynamics of the processes affecting ECS stock is thus described by the following equation: d½ECS=dt ¼kECS  ½ECSþðVSET Þ  ½ECA  10–rs  ½ECS–bu  ½ECS ð6Þ with kECS = first-order decay rate of E. coli in the sediment (h − 1); t = time (h); [ECS] = concentration of E. coli in the sediment (10 3 E. coli m − 2); rs = resuspension rate (h − 1); bu = burial rate (h − 1). The factor 10 is related to the conversion of concentration units of E. coli in the water column (E. coli 100 mL− 1) to E. coli in the sediments (103 E. coli m− 2). Concerning the decay rate in sediments, numerous studies have reported prolonged survival in sediment. This may be because sediments offer a more favourable chemical and biological environment, supplying osmoprotecting substances and protecting them from UV irradiation and predation by protozoa (Pachepsky and Shelton, 2011). In their extensive review on FIB in sediments of freshwater systems, Pachepsky and Shelton (2011) concluded that E. coli and faecal coliforms inactivation rates in sediment are one order of magnitude lower than those for the water column. Based on this information, a decay rate of 4.5 × 10− 3 h− 1 at 20 °C was used in the model for ECS. The impact of temperature on the ECS decay rate was considered similar to the ECF decay rate (Eq. 2). 3.2.2. Sources of E. coli to rivers In order to estimate the release of FIB through wastewaters, E. coli were enumerated in samples collected in raw and treated waters of various wastewater treatment plants (WWTPs) located in the Scheldt watershed (Ouattara et al., 2011). As expected (Garcia-Armisen and Servais, 2007; Servais et al., 2007a), the E. coli concentrations in wastewaters vary depending on the type of treatment (Table 1): the more complete the treatment process, the lower the E. coli concentrations. In order to quantify the E. coli input by point sources, Table 1 Concentrations of E. coli in point sources and non-point sources of faecal bacteria to rivers. Point sources WWTP types

the WWTPs present in the Scheldt basin were identified and classified following their treatment type. The daily E. coli load discharged into the rivers by each WWTP was calculated by multiplying the concentration of E. coli (E. coli 100 mL − 1) (depending on the treatment type) by the daily average treated volume. It should be noted that in the large Brussels North WWTP, two treatment lines can function in parallel when the discharge reaching the WWTP is high: a biological treatment line (including activated sludge with removal of N and P) and a treatment line in which only primary settling is applied. The latter is devoted to treating the excess volume which cannot be treated by the biological line; on average, the volume treated in the biological line accounts for roughly 90% of the total volume reaching the WWTP. The E. coli loads from both treatment lines are taken into account individually in the modelling exercise. Partitioning of E. coli in treated wastewaters was studied and the data showed that on average 50% of E. coli were present as free E. coli and 50% were attached to SS (Fig. 3); no significant differences were found in the partitioning of E. coli between the different types of treatment investigated. Based on these data, in the model half of the E. coli brought by WWTP effluents is considered free and the other half attached to SS. In addition, in Brussels, due to an insufficient capacity of some stretches of the old sewer system, combined sewer overflows (CSOs) can occur during rain events. In these cases, a mixture of untreated wastewater and urban surface runoff water is released into the Zenne River in the Brussels area. In order to take this into account in the model, the daily average volume of CSOs discharged into the Zenne River has been calculated and multiplied by an average E. coli concentration in CSOs (Table 1) to obtain the load of E. coli per CSO. The partitioning of E. coli into CSOs is considered similar to WWTPs effluents. This load is considered in the model as an additional point source of E. coli. The E. coli input by non-point sources to the model is calculated according to Servais et al. (2007a). Four major land uses were identified in each elementary sub-basin of the watershed: forested, pastured, cultivated and urbanized areas. The model considered two origins of E. coli brought by non-point sources: the E. coli brought by the runoff water (dependent on the land use) and by the base flow. The contribution of runoff water was calculated using the surface runoff specific to each type of land use in each sub-basin multiplied by the concentration of E. coli in runoff waters determined experimentally (Table 1). The contribution of base flow was calculated by multiplying the specific base flow associated to each type of land use in each sub-basin with a base level of E. coli due to soil leaching (Table 1). The proportion of these two flows varies from one decade to another which means, as the E. coli concentrations are different in base flow and runoff water, that

Non-point sources E. coli 100 mL− 1 7

Untreated wastewater CSOs PT PT + AS PT + AS + Nit

1.0 × 10 5.0 × 106 4.0 × 106 1.6 × 105 7.9 × 104

PT + AS + Nit–Denit PT + AS + Nit–Denit + P PT + trickling filter Stabilisation pond

5.0 × 104 2.0 × 104 5.4 × 105 2.1 × 104

Land uses

E. coli 100 mL− 1

Forested areas Cultivated areas Pastured areas Urbanized areas Groundwater (base flow)

9.6 × 101 4.4 × 102 2.2 × 103 5.0 × 103 2.0 × 101

CSOs: combined sewer overflows; PT: primary treatment (settling); AS: activated sludge; Nit: nitrification; Denit: denitrification; P: biological or physico-chemical phosphorus removal. Values indicated in the table for WWTPs are geometric means; for each of the treatment type mentioned in the table, samples were collected in a minimum of two WWTPs located in the Scheldt basin. Three sampling campaigns or more were performed in the different WWTPs investigated. These data are from Ouattara et al. (2011) and Servais et al. (unpublished data). For the concentration of E. coli in CSO, as no values were available for the Brussels CSO, the geometric mean value reported by Servais et al. (2011) for CSOs in Paris area was considered.

Fig. 3. Box plots of the partitioning of E. coli in point sources (treated wastewaters) and in non-point sources (surface runoff waters) expressed in fraction of E. coli attached to SS. Box plots represent the median (horizontal line in the box), the lower and upper quartiles (bottom and top box lines), the 10th and 90th percentiles (bottom and top whiskers) and the outliers (circles).

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the E. coli concentration in the non-point sources is not constant. Based on data from Fig. 3, 20% of E. coli brought by runoff and soil leaching were considered attached to SS and 80% were considered free. 4. Modelling microbiological quality in Scheldt drainage network 4.1. Model validation In order to validate the model, calculations using the SENEQUE-EC model were compared to field E. coli data in different stretches of various rivers in the Scheldt drainage network. Due to its low temporal resolution (10 days), the SENEQUE-EC model is not able to simulate short time variations of FIB concentration such as those due to the tidal impact or extreme conditions. For this reason, the stations chosen for validation were located outside the area influenced by the tide. It should be noted that no step of parameter calibration was performed as the value of all parameters is issued from experimental results reported in the literature, and independent on the observations with which the model results are compared. 4.1.1. Temporal variations of E. coli concentrations in a few rivers of the Scheldt drainage network Fig. 4 presents the temporal variations of E. coli concentrations calculated by the SENEQUE-EC model and the field data obtained during the monitoring performed by Ouattara et al. (2011) at four stations located in the upper Scheldt, the Leie, the Dyle and the Dender. In the upper Scheldt and in the Leie, calculated E. coli concentrations are higher in winter during high-flow periods, whereas in the Dyle and the Dender, the simulated concentrations are rather constant throughout the period studied. The river flow rate is often reported in the literature as a factor affecting the level of faecal contamination (Schilling et al. 2009). The E. coli concentrations at the validation stations were usually high (in the range 5.0 × 10 3 to

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2.0 × 10 4 E. coli 100 mL − 1); such concentrations are not compatible with bathing activities given that the minimal quality requirement of the new EU Directive for bathing waters stipulates that the 90th percentile of the E. coli concentrations measured at a given station should be lower than 9.0 × 10 2 E. coli 100 mL − 1 (EU, 2006). Comparison between the measured and simulated E. coli concentrations (in log scale) at the four stations shows that the SENEQUEEC model is able to satisfactorily predict the average level of E. coli concentrations in the rivers of the Scheldt drainage network. The model performance was evaluated using two statistical parameters: the Root Mean Square Error (RMSE) to measure the difference between values predicted by a model and observed values and the Nash Sutcliffe efficiency (NSE) to indicate how well the plot of observed versus simulated data fits the equivalence line. The statistical parameters were calculated based on log transformed values (as done previously by Thupaki et al., 2010) of all measured and calculated E. coli concentrations at the four stations considered in Fig. 4. RMSE value found here (0.59) is in the range of RMSE values reported by Liu et al. (2006) and Thupaki et al. (2010) (RMSE from 0.41 to 0.80) which were considered by these authors as demonstrating the good performance of their models. NSE ranges between −∞ and 1.0, with NSE= 1 being the optimal value; values between 0.0 and 1.0 are generally viewed as acceptable levels of performance (Moriasi et al., 2007). The NSE value was positive (0.57) demonstrating that the model gives an acceptable level of performance. The fact that such a deterministic model with no parameter adjustment is able to correctly predict the average of the observations is by itself already a non-trivial result, which indicates that the major processes are correctly represented. However, the fluctuations of the E. coli concentrations measured at the four stations over the 2 years were much greater than those of the simulated E. coli concentrations. Two main reasons can explain this observation. Firstly, the SENEQUE-EC model considers average and constant loads of E. coli in the rivers through point and non-point

Fig. 4. Seasonal variations of E. coli concentrations calculated by the SENEQUE-EC model for the years 2007 and 2008 (bold line) in the upper Scheldt (Gavere station), the Leie (St-Martens-Leerne station), the Dyle (Gastruche station) and the Dender (Gijzegem station) Rivers. Field data (black dots) are also plotted.

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sources. For point sources, the FIB concentrations in the outlet of WWTPs are known to vary depending on the moment of the day and on the meteorological conditions (dry versus wet weather). For nonpoint sources, the FIB concentrations in runoff waters depend on the meteorological conditions, with an increase of FIB abundance during rain events (Servais et al., 2007a). Thus the model's use of constant loads of E. coli leads to reducing the variability of the simulated concentrations with regards to concentrations measured in field samples collected in the rivers. Secondly, the model calculates average discharges and average contributions of base flow and runoff to the total discharge over the 10-day periods, meaning that the short-term variability of the discharge due to punctual rain events is not taken into account by the model. For these two reasons, the model did not simulate the observed variability displayed by the field measurements while the calculated average E. coli concentrations are in agreement with field data. The SENEQUE-EC model provides a smoothed description of the seasonal variations in the different sectors of the drainage network. Although it is obvious that E. coli concentrations are much more variable in time than simulated by this model, we consider that this ‘scatter’ has to be considered as a random signal superimposed to the general calculated trend. In order to determine how the quite simplified approach adopted to simulate the E. coli contribution from the non-point sources could influence the model output, a sensitivity run was performed. We compared the simulated results without diffuse sources to the reference simulation (with non-point sources dependent on the land use as described in this section) at the four sampling stations presented in Fig. 4. Results confirmed that the non-point sources are negligible compared to point sources as the simulated E. coli concentrations decreased by less than 3% on average when the non-point sources of E. coli are not considered in the calculation (data not shown). This is in complete agreement with the comparison of E. coli loads by point versus non-point sources presented by Ouattara et al. (2011). A sensitivity analysis was performed to see if modifying the partitioning of E. coli in the point sources could ameliorate the simulations. For this, simulations were calculated with the model using three different partitionings (25%, 50% and 75% of attached E. coli in the point sources). The NSE value for a partitioning of E. coli in point sources of 50% of attached bacteria was positive (0.57) while they were negative for the two other tested partitionings. In our case, it means that increasing or decreasing the fraction of attached E. coli in point sources did not improve the modelling results. Also, the lower RMSE value (i.e., the best performance) is obtained for the partitioning of 50%. 4.1.2. Longitudinal profile of the Zenne River The E. coli concentrations calculated using the SENEQUE-EC model and field data were compared for the Zenne River. Fig. 5 presents the longitudinal profile of calculated E. coli concentrations and data of field measurements at three sampling stations (Lot, Eppegem and Leest stations). The SENEQUE-EC model predicts a high level of E. coli concentrations upstream from Brussels in agreement with field measurements. The high level of E. coli concentrations observed in the Zenne River upstream from Brussels is related to the high population density and agricultural activities in the upstream part of the Zenne watershed. At kilometric point (KP) 42, the Zenne River receives the discharge of Brussels South WWTP effluents and the highly contaminated tributary, the Zuunbeek. These inputs are responsible for the increase of the E. coli concentration observed directly downstream from this kilometric point. Between KP 49 and KP 56, the increase of E. coli concentration is due to the combined action of six CSO outfalls located in this stretch of the river. At KP 57, the discharge of the treated effluents of the Brussels North WWTP leads to an additional increase of E. coli abundance levels. The E. coli concentrations observed downstream from Brussels are very high (between 4.3 × 104 and 3.80 × 10 6 E. coli 100 mL− 1). Comparison between the calculated

Fig. 5. Longitudinal fluctuations of E. coli concentrations in the Zenne River calculated by the SENEQUE-EC model for the years 2007 and 2008. The bold line is the median of the calculated values over the 2 years and the dashed lines are the maximum and minimum values calculated by the model for the years 2007 and 2008. Vertical bars indicate the values measured at three sampling stations (o: median, top of the vertical bar: 75th percentile, bottom of the vertical bar: 25th percentile). The x‐axis is a kilometric unit that is set at zero at the Zenne River source and increases from upstream to downstream.

and measured E. coli concentrations shows that the median values of both data sets are in good agreement. NSE was calculated using the log transformed predicted and observed E. coli concentrations at three stations of the Zenne River (Fig. 5). The NSE value greater than 0.5 indicated that the SENEQUE-EC model can well predict the E. coli concentrations in the Zenne River. The RMSE value found for the Zenne River data is close to the value reported by Thupaki et al. (2010) (RMSE = 0.41) which was considered by these authors as demonstrating the good performance of their model. As already observed in Fig. 4, the measured values show greater variability than the calculated data. The longitudinal profile clearly highlights the impact of the wastewaters of Brussels and its suburbs (more than 1 million inhabitants) on the microbiological water quality of a river with a low discharge. The high E. coli concentrations downstream from Brussels result primarily from the fact that the Zenne River water is composed, on average, of more than 50% of treated wastewaters and CSOs, this proportion being even higher during the river's low-flow periods. 4.1.3. Prediction of the fraction of free and attached E. coli To validate the prediction calculated by the SENEQUE-EC model for free and attached E. coli in river waters, the calculated and the measured values of the concentrations of attached E. coli at the validation stations presented above were compared. Fig. 6 presents boxplots of

Fig. 6. Concentrations of E. coli attached to SS simulated by the SENEQUE-EC model plotted (in Log–Log scale) against concentrations of E. coli attached to SS measured at the seven validation stations. Black dots are the median of the measured and simulated concentrations, vertical and horizontal bars indicate the 25th and 75th percentiles.

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the measured and calculated concentrations of E. coli attached to SS at the validation stations. The calculated and measured fractions of attached E. coli are in good agreement, indicating that the SENEQUEEC model is able to adequately predict the fraction of attached E. coli in river waters. However, as already mentioned for the total E. coli concentrations, the measured concentrations of attached E. coli were highly variable compared to the calculated values of attached E. coli. 4.2. Microbiological contamination of the rivers of the Scheldt drainage network Fig. 7 presents the distribution of E. coli in the whole Scheldt drainage network calculated by the SENEQUE EC model for the summer 2007 period (low-flow period). This presentation easily locates the rivers presenting good quality and the hot spots of faecal contamination. If we consider that an acceptable E. coli concentration should be lower than 1000 per 100 mL, Fig. 7 shows that the stretches of rivers presenting acceptable microbiological quality (blue on the map) account for only a small part of the total length of rivers in the Scheldt watershed. This completely confirms the observation-based conclusion of Ouattara et al. (2011) stating the poor microbiological quality of the rivers in this watershed. In most areas of the watershed, the small rivers in their upstream stretches present high E. coli concentrations in the range 1 × 10 3 to 1 × 10 5 E. coli 100 mL − 1. These levels of contamination are explained by the considerable urbanization and agriculture activities in the watershed. In some rivers, such as in the upper Scheldt, quality improvement can be observed from upstream to downstream, demonstrating that in some stretches the decay processes (mortality and settling) overtake the input of FIB in the river. As expected, the most contaminated river is the Zenne River downstream from Brussels with concentrations values above 1 × 10 5 E. coli 100 mL − 1.

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4.3. Impact of wastewater management scenarios on microbiological quality Once validated, the SENEQUE-EC model can be a useful tool to investigate the impact of modifications in wastewater management on the microbiological water quality of the rivers of the drainage network. In this study, the impact of wastewater management on the microbiological water quality of the Zenne River was investigated by testing two scenarios. An optimistic scenario resulting in a significant improvement of the microbiological water quality of the Zenne River obtained by reducing the faecal contamination in the effluents of the Brussels WWTPs and a worst case scenario corresponding to the release of the whole volume of wastewaters from Brussels and its suburbs in the Zenne River with no treatment. 4.3.1. Optimistic scenario The Zenne River is highly polluted by microbial contaminants due to the load of faecal pollution released by both Brussels WWTPs and by CSO outfalls in downtown Brussels (Fig. 5). To improve the microbiological water quality of the Zenne River downstream from Brussels, the faecal contaminants released by the Brussels WWTPs effluents should be reduced and the CSO should be avoided. Thus, in our optimistic scenario, we considered that: (i) the sewer system will be improved to increase its transport capacity so that it will be able to transport the entire volume of water to the WWTPs, whatever the intensity of the rain event; (ii) the treatment capacity of the biological treatment line of Brussels North WWTP will be increased so that it can treat the entire volume reaching the WWTP even during intense rain events; and (iii) a disinfection stage will be added at the end of the treatment line in both Brussels WWTPs. UV irradiation is the most common treatment used in Belgium for treated wastewater disinfection; it has been demonstrated that the addition

Fig. 7. Map of the distribution of E. coli concentrations in the rivers of the Scheldt drainage network for the summer 2007 situation (the first 10-days of August), as calculated by the SENEQUE-EC model.

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of a UV treatment at the final stage of wastewater treatment after biological treatment increased the Log removal by 2–3 units (Servais et al., 2007a). In addition, in this scenario, we have taken into account recent improvements that have been made in the treatment processes of three small WWTPs located downstream from Brussels. The concentration of E. coli along the Zenne River was calculated by the SENEQUE-EC model considering this optimistic scenario. Fig. 8 shows a comparison between the optimistic scenario and the reference situation (situations 2007 and 2008, see Fig. 5). Logically, upstream from Brussels, the concentrations of E. coli calculated by the SENEQUE model are similar for the optimistic scenario and the reference situation. In the optimistic scenario, the E. coli concentration decreases after KP 42 (outfall of the Brussels South WWTP) because UV-treated effluents are less contaminated than the river water. A similar decrease can also be observed at KP 57 (outfall of the Brussels North WWTP). This scenario shows, downstream from Brussels, an improvement of about two Log units in E. coli concentration with regards to the reference situation (2007–2008). 4.3.2. Worst case scenario The worst case scenario corresponds to a situation where the wastewaters of Brussels and its suburbs are released into the Zenne River with no treatment. This scenario was imagined because in December 2009, due to technical problems, the treatment was interrupted at the Brussels North WWTP for 10 days and thus the whole volume of wastewaters reaching the WWTP was released into the Zenne River. This situation was responsible for an important decrease of quality (physico-chemical and microbiological) in the downstream part of the Zenne River (Brion, pers. com. and Ouattara, unpublished data). In the worst case scenario, simultaneous interruptions of the treatment in both Brussels WWTPs were considered. This scenario explores the impact of the release of raw wastewaters from Brussels and its suburbs on the microbiological water quality of the Zenne River. Fig. 7 provides an easy comparison of the worst case scenario and the reference situation. Upstream from Brussels, the E. coli concentrations calculated by the SENEQUE-EC model are similar for the scenario and the reference situation. At KP 42, after the release of Brussels South WWTP effluents, the worst case scenario showed a very high increase of E. coli concentration with values reaching 1.0 × 10 6 E. coli 100 mL − 1. This increase is followed by a slow decrease during the crossing of the Brussels region (between KP 43 and KP 56). After the outfall of Brussels North WWTP effluents at KP 57, an additional increase of E. coli concentration was observed. The average

concentrations of E. coli in the worst case scenario after KP 42 are ten times higher than in the reference situation. In the current situation, this indicates that the WWTPs of the Brussels region, even without a disinfection system contributed to reducing the level of faecal contamination in the Zenne River. Note that this worst case scenario was the real situation until the year 2000. Indeed, the implementation of WWTPs in Brussels is quite recent: the Brussels South WWTP has been in operation since 2000 and Brussels North only since 2007. 5. Conclusions The SENEQUE-EC model, the combination of a module describing the dynamics of E. coli and a hydro-ecological model, is to our knowledge one of the first models able to simulate spatial and seasonal variations of microbial contamination (E. coli concentration) at the scale of the whole drainage network of a large regional river basin resulting from point and diffuse sources of faecal bacteria generated by human activities. This model is also different from most models describing faecal contamination in that it differentiates the dynamics of three types of E. coli: free-floating E. coli, E. coli attached to SS in the water column and E. coli present in sediments. The predictions of the SENEQUE-EC model in terms of E. coli concentrations were validated by comparison with seasonal and spatial distributions of field data in different stretches of rivers of the Scheldt drainage network. Thus, the SENEQUE-EC model appears to be a very useful tool to test the impact of wastewater management strategies on the level of microbial contamination in the rivers of the whole drainage network. The final objective of these modelling exercises was to investigate how the microbiological pollution generated by human activities on the watershed can affect the level of contamination in the coastal zone, or, in other words, to see how microbial contaminants are transported in the Scheldt land–sea continuum. Due to its low temporal resolution, the SENEQUE-EC was considered to be poorly adapted to describing the microbiological quality in the areas under tidal influence. We therefore combined the SENEQUE-EC and the SLIM-EC2 models, which is, owing to its high temporal resolution, better adapted to describing the processes occurring in the tidal Scheldt River and Estuary. Thus, we used the SENEQUE-EC for the areas of the drainage network not influenced by the tide and the E. coli concentrations calculated by the SENEQUE-EC model were used as upstream boundary concentrations required to run the SLIM-EC2 model in the tidal Scheldt River and Estuary. This offline coupling of the two models allows one to simulate the microbiological contamination from the upstream headwaters of the drainage network to the coastal zone, performed for the reference situation (2007–2008) as well as for the optimistic and worst cases scenarios; the resulting data are presented in de Brauwere et al. (2013). Acknowledgements

Fig. 8. Comparison of the longitudinal profiles of E. coli concentrations in the Zenne River calculated by the SENEQUE-EC model for the years 2007 and 2008. The bold line represents the median of E. coli concentrations for the reference situation and the dashed lines represent the medians of E. coli concentrations for the worst case (black) and optimistic scenarios (grey). The x-axis is a kilometric unit that is set at zero at the Zenne River source and increases from upstream to downstream.

This study was mainly conducted within the scope of the “Tracing and Integrated Modelling of Natural and Anthropogenic effects on Hydrosystems: The Scheldt River Basin and Adjacent Coastal North Sea” (TIMOTHY) project, an Interuniversity Attraction Pole (IAP6.13) funded by the Belgian Federal Science Policy Office. A part of the work was also performed in the scope of the GESZ research project (Towards the Good Ecological Status of River Zenne: Reevaluating Brussels wastewater management) from the “Impulse Environment” programme of the Brussels Institute for Research and Innovation (Innoviris). Anouk de Brauwere had a post-doctoral fellowship from the FNRS (Fonds de la Recherche Scientifique, Belgium). N.K. Ouattara had a doctoral grant from the Ivory Coast Government and benefited of a doctoral fund from “Fonds Van Buuren”. The authors wish to thank Julie Callens (Université Pierre et Marie Curie, France) for her assistance in preparing the WWTP files and for providing the maps of Scheldt drainage network.

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