Modelling Faecal Contamination in the Scheldt Drainage Network

Journal of Marine Systems 128 (2013) 77–88

Contents lists available at ScienceDirect

Journal of Marine Systems

journal homepage: www.elsevier.com/locate/jmarsys

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 b 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 article info abstract

Article history: This study developed a model simulating the seasonal and spatial variations of microbiological water quality Received 31 October 2011 (expressed in terms of Escherichia coli concentrations) in rivers. The model (SENEQUE-EC) consists of a Received in revised form 7 May 2012 microbiological module appended to a hydro-ecological model describing the functioning of the entire Accepted 8 May 2012 Scheldt drainage network. The microbiological module describes the sources of E. coli, their transport and Available online 15 May 2012 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 Keywords: fi Microbiological water quality attached to suspended solids in the water column and E. coli present in sediments. The model was veri ed by Escherichia coli comparison of its results with temporal and spatial distributions of field data in different stretches of rivers Modelling of the Scheldt drainage network. It was then used to test various scenarios involving diverse modifications in Scheldt river drainage network wastewater management, which was shown to be the most determining factor of microbiological water quality. Scenarios 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 and warm-blooded animal faeces into the aquatic environments. The health risk related to the presence of these pathogens depends The research presented in this paper was conducted within the on the use of the water (drinking, recreational activities, bathing, scope of the Belgian Interuniversity Attraction Pole (IAP) TIMOTHY irrigation, shellfish harvesting) and on the pathogen concentrations project (Lancelot and Gypens, 2013-this issue). This interdisciplinary in the water. In aquatic systems, the detection and enumeration of project is studying and modelling the current sources and fate (trans- all potentially present pathogenic micro-organisms are very difficult fer, transformation and retention) of key nutrients (nitrogen, phos- due to the great diversity of pathogens, the low numbers of each spe- phorus and silicon) and pollutants (metals, xenobiotics and cies and the absence of standardized methods for detecting some of microbial contaminants) along the land–sea aquatic continuum in re- them. Therefore, the routine monitoring of microbiological water sponse to anthropogenic and natural changes. The Scheldt watershed quality is based on the concept of faecal indicator bacteria (FIB). and the adjacent eastern Channel and Southern Bight of the North Sea These FIB are groups of bacteria that fulfil the following criteria: (Fig. 1) were chosen as a case study and geographical domain for this they should be universally present in large numbers in human and study. This paper and another paper (de Brauwere et al., 2013) report warm-blooded animal faeces, readily detected by simple methods on the microbiological water quality and more precisely the model- and they should not grow in natural waters, but persist in water ling of the microbiological contamination level in the Scheldt land– and be removed by water treatment in a similar way as waterborne sea continuum. pathogens (Havelaar et al., 2001). Today, water quality regulations Polluted surface waters can contain a wide variety of pathogenic for drinking, irrigation and recreational uses are primarily based on micro-organisms: viruses, bacteria and protozoa. The main origin of two FIB (Escherichia coli and intestinal enterococci) concentrations. these micro-organisms is the direct and indirect release of human 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 ⁎ Corresponding author. abundance of E. coli was used to estimate the microbiological quality E-mail address: [email protected] (P. Servais). of surface water in the Scheldt drainage network.

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 models (David and Haggard, 2011; Frick et al., 2008; Ge and Frick, and the South of The Netherlands (Fig. 1), is characterized by a high 2007; Heberger et al., 2008; Nevers and Whitman, 2011; Nevers et al., population density and active industrial and agricultural activities. 2007); their main advantage is that they are easy to implement because Due to these anthropogenic pressures, it is an extreme case of a polluted they are based on relatively basic statistical concepts. Generally, drainage network. Pollution coming from the watershed through the regression-based models are developed with the aim to use them for estuary is responsible for marine eutrophication, modification of the real-time predictions (nowcasts) of bathing water quality (Dorevitch ecological functioning (Lancelot et al., 2007), as well as contamination et al., 2010; Frick et al., 2008; Nevers and Whitman, 2005; Stidson by metals (Baeyens et al., 2005) and organic compounds (Baeyens et et al., 2012), in order to include them in early warning systems. The al., 2007) of the receiving coastal waters. Studies have been conducted black box nature of regression-based models has a main disadvantage; in the last few years to estimate the level of faecal contamination indeed, they usually do not enable an in-depth understanding of the of the Scheldt drainage network, to quantify the sources of microbial system, because they do not include mechanistic or causal relationships. contamination and to study the fate of faecal micro-organisms in Thus, they are not able to predict the effect on microbial water quality of the rivers (Ouattara et al., 2011). Data showed low microbiological potential future changes in wastewater management. The mechanistic quality in the downstream parts of the main tributaries of the Scheldt models are based on the coupling of models representing the processes River, especially in the Zenne River which crosses the Brussels area affecting FIB with models describing the hydrodynamics of the system (Fig. 1). The quantification of point (outfall of treated and untreated (Bai and Lung, 2005; Bougeard et al., 2011; de Brauwere et al., 2011; wastewaters) and non-point (surface runoff and soil leaching) sources Dorner et al., 2006; Gao et al., 2011; Kashefipour et al., 2006; Liu et al., of faecal contamination of the rivers of the Scheldt drainage network 2006; Pachepsky et al., 2006; Servais et al., 2007a,b; Thupaki et al., showed that, at the scale of the Scheldt watershed, point sources were 2010 ). It has been shown that using a mechanistic modelling ap- largely predominant in comparison to non-point sources (Ouattara proach in conjunction with laboratory experiments for parameters et al., 2011). determination and field observations (for model validation) can Besides the experimental and field work, the TIMOTHY project help improve the understanding of the fate and transport of FIB in included modelling microbiological water quality. In the literature, water bodies, and that these results can then be further applied to two fundamentally different approaches are used for FIB modelling provide predictive information for effective public health manage- in aquatic systems: regression-based (or black box or stochastic) ment (Cho et al., 2010). Some recent models using the mechanistic models and mechanistic (or reactive tracer or process-based) models. approach (Cho et al., 2010; Gao et al., 2011; Kashefipour et al., Regression-based models (Alkan et al., 1995; David and Haggard, 2006; Liu et al., 2006) aims at calculating short term variations of E. 2011; Eleria and Vogel, 2005) make links between a set of input coli concentrations in order to replace regular monitoring of the mi- (explanatory) variables and an output in terms of FIB concentration crobiological quality by model simulations. using regression methods. Input variables can include meteorological, Two models that simulate the temporal and spatial fluctuations of hydrological, physico-chemical, land-use, landscape, or previous mi- E. coli concentrations were developed in this study. Both are based crobiological quality data. Most of these studies relate microbial water on a deterministic approach but they differ in their domain of applica- quality to explanatory variables using multivariate linear regression tion: the SENEQUE-EC model covers the whole drainage network that N.K. Ouattara et al. / Journal of Marine Systems 128 (2013) 77–88 79 is not influenced by the tide, while the SLIM-EC2 model has been used 2009). Seventh-order stream is the highest, for the part of the Scheldt to model the microbiological quality in the tidal Scheldt River and Es- downstream from its confluence with the Rupel River. The long-term tuary de Brauwere et al., 2013. annual mean rainfall is 813 mm and average annual flow rate of The SENEQUE-EC model consists of the hydro-ecological SENEQUE/ the Scheldt River at Schelle (downstream from the confluence of the RIVERSTRAHLER model describing the functioning of large river sys- Rupel and upper Scheldt) is 110 m3 s−1 (over the 1950–2000 period) tems (Ruelland et al., 2007) to which a module describing E. coli dynam- with high flows in winter (maximum 500 m3 s−1)andlowflows in ics has been appended. A previous coupling of the SENEQUE model with summer (typical summer flow around 50 m3 s−1)(Billen et al., 2005). a microbiological module has been used to predict faecal coliforms con- The main branch of the Scheldt, from Ghent to the sea, as well as a centrations in the Seine River drainage network (Servais et al., 2007a,b). number of tributaries, is subjected to the tide, and, given the low river In the model proposed by Servais et al. (2007a,b), only one stock of FIB discharge, the salinity increase is already perceptible upstream from was considered. The SENEQUE model was improved in the present Antwerp. study by considering three stocks of E. coli in the river: free E. coli Due to its high level of faecal contamination, special attention was (ECF), E. coli attached to suspended solids (SS) present in the water paid in this study to the Zenne River (Fig. 1). This tributary of the Dyle column (ECA) and E. coli in the deposited sediments (ECS). The three River has a watershed of 1011 km2 characterized by agricultural activi- stocks are affected by a different mortality rate (first-order kinetics). ties in its upstream part and a large urban area in its downstream part. In addition, attached E. coli can settle and deposit in the sediment, The population density in the watershed is very high (on average 1259 while E. coli in the deposited sediments can be resuspended in the inhabitants km−2), mostly located in Brussels and its suburbs. The water column. The module also takes into account the input of E. coli Zenne River crosses this city from South to North over a distance of through point sources (release of treated or untreated wastewaters) about 20 km and receives the sewage from two wastewater treatment and non-point sources (soil leaching and runoff). The main objective plants (WWTPs): the Brussels South WWTP (360,000 inhabitant- of the SENEQUE-EC model developed in this study was to evaluate the equivalents) and the Brussels North WWTP (1.1 million inhabitant- impact of modifications in wastewater management in the watershed equivalents). The annual average discharge of the Zenne River upstream on the rivers' microbiological quality. from Brussels is 4 m3 s−1 (average for the 2007–2008 period), while Due to its low temporal resolution (average concentrations are the flow released by the two Brussels WWTPs is on the same order of calculated for 10-day periods), the SENEQUE-EC model is not well magnitude. adapted to describe the microbiological quality in areas under tidal influence, nor what happens during extreme events. That is the reason 2.2. Monitoring microbiological water quality in the Scheldt basin why a second model was developed, the SLIM-EC2 model, designed to model the microbiological quality in the tidal Scheldt River and In order to collect field data on microbiological water quality in Estuary with a much higher temporal resolution (15-min time the main rivers of the Scheldt drainage network, a monitoring survey steps). SLIM-EC2 combines the hydrodynamic model SLIM (Second- was organized monthly from March 2007 to June 2008. During the generation Louvain-la-neuve Ice-ocean Model; de Brye et al., 2010) monitoring survey, water samples were collected in the downstream with a module describing the dynamics of E. coli in the aquatic system. part of the main rivers of the Scheldt watershed. During these This model and its results are presented in another paper (de campaigns, grab water samples were collected in the rivers with a Brauwere et al., 2013). The upstream boundary concentrations re- plastic bucket from bridges, halfway between the banks, and E. coli quired to run the SLIM-EC2 model are calculated by the SENEQUE-EC were enumerated. Details on this monitoring survey and its data model. This offline coupling of the two models thus simulates the can be found in Ouattara et al. (2011). The data obtained in some microbiological contamination from the upstream headwaters of the stations outside the area under tidal influence were used in this study drainage network to the coastal zone and the North Sea. for validating the model. These stations are located on the upper Scheldt This paper presents and validates the SENEQUE-EC model, (Gavere station, Sc), the Leie (St-Martens-Leerne station, Ly), the Dyle encompassing the issue of faecal contamination at the scale of the (Gastruche station, Dy), the Dender (Gijzegem station, De) and the whole (non-tidal) Scheldt drainage network. Then, the SENEQUE-EC Zenne (Lot (Ze1), Eppegem (Ze2) and Leest (Ze3) stations) Rivers (see model was used to test various scenarios in relation with wastewater Fig. 1 for the location of these stations). management in the watershed. By coupling SENEQUE-EC and SLIM- EC2, the impact of these wastewater management scenarios will be 2.3. Enumeration of E. coli traced further downstream throughout the tidally influenced rivers and the estuary, to assess the load of faecal micro-organisms to the In the present study, E. coli were enumerated in water samples by coastal zone and the North Sea (de Brauwere et al., 2013). standard plate counts on Chromocult Coliform Agar (CCA) (Merck KGaA, Darmstadt, Germany). This chromogenic growth medium was 2. Material and methods 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 2.1. The Scheldt river watershed colony-forming units (CFU) per 100 mL of sample. In some samples, the approach proposed by Garcia-Armisen and The Scheldt watershed (20,000 km2 ranging from the North of France Servais (2009) was used to estimate the fraction of E. coli attached to the Belgian–Dutch border) (Fig. 1) is densely populated, with around to suspended matter (SM). It is based on measurements of the β-D- 500 inhabitants km−2. It comprises three main sub-basins: the upper- glucuronidase (GLUase) activity (an enzymatic activity specifictoE. coli) Scheldt basin (8125 km2, draining the cities of Ghent and Mons), the coupled with size fractionation. GLUase activity was estimated by fluo- Leie river basin (3850 km2, draining Lille in France) and the Rupel rometry as the production of fluorescent methylumbelliferone (MUF) basin (6475 km2). The Brussels conurbation, drained by the Zenne resulting from the hydrolysis of the substrate 4-methylumbelliferyl-β- River, is a major attraction pole in the watershed, with more than 2 mil- D-glucuronide (MUGlu) (George et al., 2000). GLUase activity measure- lion inhabitants. The Scheldt watershed is characterized by a mosaic- ments have been shown to be a good surrogate to E. coli enumeration type landscape, in which urban zones are mixed with surrounding by plate counts in different types of aquatic systems (Garcia-Armisen agricultural and cropland areas. Agriculture is characterized by intensive et al., 2005; Lebaron et al., 2005). GLUase activity retained on a 0.2 μm cattle farming and, especially in the Flemish region, pig breeding. pore-size membrane was used to quantify the GLUase activity of the The total length of the rivers of the Scheldt drainage network is total population of E. coli in a sample while the GLUase activity retained 3265 km, among which 1637 km are first-order streams (Thieu et al., on a 5 μm pore-size membrane was used to quantify the activity of the 80 N.K. Ouattara et al. / Journal of Marine Systems 128 (2013) 77–88 fraction of E. coli attached to SM. The ratio of both activities gives an esti- studies (Cizek et al., 2008; Jamieson et al., 2005) reported that a signif- mate of the proportion of E. coli attached to SM. Measurements of GLUase icant part of FIB in aquatic systems was associated with suspended activities were performed following the protocol proposed by Servais solids (SS) and that this association influences their survival and trans- et al. (2005). port characteristics. Indeed, FIB associated with SS can settle, although this is not the case of free-floating FIB (Garcia-Armisen and Servais, 3. Microbiological quality model 2009). Some authors have suggested that FIB attached to SS have a lower decay rate because they are protected from some processes The past few decades have shown an increasing interest in modelling leading to the disappearance of FIB in waters (protection against UV the dynamics of faecal indicator bacteria (FIB) in various types of aquatic and grazing by some small protozoa) (Hellweger and Masopust, 2008; environments (coastal areas, lakes and rivers). Models able to predict Liu et al., 2006). When FIB settle in the water column, they can feed FIB concentrations are of course valuable tools for the management of the stock of FIB within the sediment; several studies have demonstrated aquatic systems. The SENEQUE-EC model results from the coupling of high FIB concentrations in the sediments of aquatic systems (Pachepsky the hydro-ecological SENEQUE/RIVERSTRAHLER model describing the and Shelton, 2011; Roslev et al., 2008). This may be partly due to the functioning of large river systems (Ruelland et al., 2007) with a module prolonged survival of FIB in sediments with regards to the water describing E. coli dynamics. phase, as reported in the literature (Pachepsky and Shelton, 2011). For these reasons, in the present modelling exercise, three stocks of E. coli 3.1. The SENEQUE/RIVERSTRAHLER model 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 The RIVERSTRAHLER model is a simplified model of the biogeo- in the deposited sediments (ECS). The three stocks were affected by a chemical functioning of river systems at the basin scale relating water different mortality rate (first-order rate kinetics, see below for greater quality to anthropogenic activity in the watershed (Billen and Garnier, detail). In addition, attached E. coli can settle and deposit in the sedi- 1999; Billen et al., 1994). RIVERSTRAHLER describes the drainage ment, while E. coli in the deposited sediments can be resuspended in network of any river system as a combination of basins, idealized as the water column. a regular scheme of confluence of tributaries of increasing stream Today, there is a growing consensus in the literature about the order, each characterized by mean morphologic properties, connected importance to include the processes of settling and resuspension in to branches, represented more realistically, with a higher spatial resolu- mechanistic models of faecal contamination of surface waters (Droppo tion (Billen et al., 2005). The water flows in the hydrographical network et al., 2011; Jamieson et al., 2005; Pachepsky and Shelton, 2011). are calculated from the specific surface and base flow generated within Models making the distinction between free and attached FIB must the watershed of the different sub-basins and branches considered. calculate the proportion of attached and free FIB at each time step These are calculated from daily data at gauged stations, using the and at each location in the modelled domain so that only the attached Eckardt (2005) recursive filter for hydrogram separation. The water FIB are affected by settling. For this, some authors consider a constant flows are then routed along the confluence scheme of the whole proportion of attached FIB in time and space. Dorner et al. (2006) con- drainage network defined by the structure of basins and branches. sidered 30% of attached micro-organisms and Wu et al. (2009) 50%. The seasonal cycle is represented as a succession of ten day periods, Other studies considered that the fraction of attached FIB is a function each with permanent hydrological conditions which are the average of the SS concentration (Bai and Lung, 2005; Gao et al., 2011). In 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 Point Free Mortality 1 in wastewater management in the watershed on the rivers' microbio- sources E. coli logical quality by performing simulations for long periods of time (ECF) (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. Non-Point Attached The essence of the model is to couple these routed water flows with sources E. coli Mortality 2 a model describing biological, microbiological and physico-chemical Suspended (ECA) processes occurring within the water masses, according to a Lagrangian solids calculation scheme. In the SENEQUE software, this model is embedded within a GIS in- Sedimentation terface, allowing the use of fully distributed geo-databases (Ruelland Resuspension 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 E. coli in (see Section 3.2.2); land use and WWTPs are geo-referenced. The addi- sediments Mortality 3 tion of a module describing the dynamics of E. coli to the model (to build Sediments (ECS) 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 Fig. 2. Schematic representation of the structure of the module describing the dynamics been assembled. A benthic compartment is also considered all along 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 the drainage network by grid cells of 1 km length over the width of water column and E. coli in the deposited sediments. The three stocks are affected by the particular river stretch. 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 3.2. Modelling E. coli dynamics in rivers 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 In many models describing FIB dynamics in aquatic systems, only the concentration in runoff (non-point sources) depends on land use in all elementary one compartment of faecal bacteria is considered. However, several sub-basins of the watershed. N.K. Ouattara et al. / Journal of Marine Systems 128 (2013) 77–88 81 fact, these approaches consider that FIB can be rapidly exchanged from studies (Beaudeau et al., 2001; de Brauwere et al., 2011; Servais et al., one form to the other by fast adsorption and desorption processes. 2007a,b): However, it is known from the general literature on bacterial adhesion 2 onto surfaces that bacteria, after a first reversible binding to particles ðÞT25 e 400 by adsorption, can synthesise exopolymers that strengthen their k ðÞ¼ k ∘ ÀÁ ð2Þ ECF T ECF20 C e 25 bindings to the surface (Fletcher, 1996). With such biological binding, 400 the reversible character of the attachment disappears. Thus, some with T = temperature (°C); kECF (T) = first-order decay rate of ECF authors considered the attachment of E. coli to SS as irreversible. − (h 1) at temperature T; and k = decay rate of ECT at 20 °C For example, Jamieson et al. (2005), when simulating the injection ECT20 °C (h−1). A value of 45×10−3 h−1 for k was used in the present of sediment associated E. coli during an artificial high flow event, con- ECT20 °C study. This decay rate value was already successfully used to model sidered the transport of free-floating and attached bacteria separately. faecal coliforms dynamics in the Seine River drainage network Garcia-Armisen et al. (2006) when modelling sources, transport and (Servais et al., 2007b) and in the Seine Estuary (Garcia-Armisen et al., fate of faecal coliforms in an estuary considered that a part of the 2006). FIB enters in the modelled domain as free and another part as attached and that no exchange between free and attached E. coli 3.2.1.2. E. coli attached to SS (ECA). In the SENEQUE-EC model, the fate occurs. This approach that gave satisfying results for modelling free of E. coli attached to SS was described by the following equation that and attached FIB (Garcia-Armisen et al., 2006) was used in the pre- takes into account decay (first-order kinetics as for ECF) and settling: sent study. In addition to the description of the transport and fate of E. coli ½= ¼ − ½–ðÞ= ½þ = ð Þ d ECA dt kECA ECA VSET d ECA resuspension d 3 in the river waters, the E. coli module developed in this study has −1 taken into account the inputs of E. coli to rivers; these inputs are with kECA = first-order decay rate of E. coli attached to SS (h ); t = controlled by activities in the watershed. In urban areas, faecal micro- time -(h); [ECA] = concentration of E. coli attached to SS (E. col- −1 −1 organisms are mainly brought to aquatic environments through i 100 mL ); VSET = settling rate of ECA (m h ); d = depth of the the discharge of domestic wastewater (treated or not treated in river; see the next section for explanation on the resuspension term. WWTPs). In rural areas, faecal pollution can also be brought to rivers As experimental data on the comparison of the decay rate of free- through non-point sources (surface runoff and soil leaching); its floating and attached E. coli showed that the decay rate of attached origin can be wild life and grazing livestock faeces as well as cattle E. coli was roughly half than that of free-floating E. coli (Garcia- manure spread on cultivated fields. In the input of E. coli to rivers, the Armisen and Servais, 2009), a value of 22.5×10− 3 h− 1 at 20 °C was bacteria present as free cells were distinguished from the cells attached considered in the model for the decay rate of ECA; this decay rate to SS. value was previously used by Garcia-Armisen et al. (2006) to model faecal coliforms attached to SS in the Seine estuary. The impact of tem- 3.2.1. Transport and fate of E. coli in rivers perature 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 3.2.1.1. Free E. coli (ECF). In the SENEQUE-EC model, ECF are trans- 0.1 m h− 1 was used; this value is similar to the settling rate value ported from upstream to downstream with the flowing water masses. for organic particles used in the SENEQUE/RIVERSTRAHLER model. It is well known that after their release in natural aquatic environ- This value is also within the range of values mentioned in studies ments, FIB tend to decrease quite rapidly. The decay of culturable specifically devoted to the estimation of settling rates of E. coli attached E. coli in rivers results from the combined actions of various biological to SS (Auer and Niehaus, 1993; Garcia-Armisen and Servais, 2009; and physico-chemical processes (grazing by protozoa; virus-induced Jamieson et al., 2005). cell lysis and autolysis; stress due to nutrient depletion and sunlight irradiation inducing mortality or loss of culturability)(Barcina et al., 3.2.1.3. E. coli in sediments (ECS). The stock of ECS in the model is fed by 1997). To the best of our knowledge, in all models describing the fate the settling of ECA. A low baseline content of 5000 E. coli g− 1 was of FIB in aquatic systems, the overall decay is usually described by a assumed over the whole benthic phase of the drainage network as first-order kinetics (Collins and Rutherford, 2004; Kashefipour et al., an initial condition. This arbitrary but plausible value only influences 2002; Tian et al., 2002; Wilkinson et al., 1995). This approach was the dynamics of the system during the first 10 days of the simulation. adopted in the present study with the following equation describing In some conditions, sediment can be resuspended in the overlying the fate of ECF: 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)asa d½ ECF =dt ¼k ½ECF ð1Þ ECF significant source of E. coli, in some situations leading to river water column pollution. In SENEQUE-EC, this occurs when the suspended fi −1 with kECF = rst-order decay rate of free E. coli (h ); t = time (h); and loading (i.e., the suspended solid concentration) of the water column −1 [ECF] = Free E. coli concentration (E. coli 100 mL ). is lower than the transport capacity of the river body (CapSS), calcu- Numerous laboratory and in situ studies reported that increasing lated as an empirical cubic function of the river flow velocity, until temperature in the range usually found in surface waters results in this transport capacity is reached (Celik and Rodi, 1991; Martin, an increase of the decay rate (Barcina et al., 1986; Craig et al., 2004; 2001; Thouvenot et al., 2009). Flint, 1987). Better survival at low temperature can be explained by ¼ þ 3 ð Þ lower energy costs for bacteria due to reduced metabolic activities CapSS C0 C1 v 4 (direct effect) while higher mortality rates at high temperature can −1 −1 −3 3 be explained by increased predation (indirect effect) (Servais et al., with C0 =20mgL and C1 =100 mg L m s ; v = cross-sectional −1 1985; Menon et al., 2003). Indeed at high temperature the abundance average river flow velocity (m s ). The values of Co and C1,applied of protozoa, the main bacterial grazers, is usually higher than at low all over the drainage network, have been obtained by adjustment on temperature (Servais et al., 2000) and the grazing rate per protozoa the observed suspended solid concentration at different station of the also increases with temperature (Marasse et al., 1992). In the model, river network (Thouvenot et al, 2009). the impact of temperature on the decay process was taken into account This makes it possible to define a resuspension rate (rs) of the using the following sigmoid relationship as already done in several sediment stock at each time step, which also applies to all sedimentary 82 N.K. Ouattara et al. / Journal of Marine Systems 128 (2013) 77–88 variables, including ECS, which is assumed to be homogeneously the WWTPs present in the Scheldt basin were identified and classified distributed within the upper sediment layer subject to resuspension. following their treatment type. The daily E. coli load discharged into the rivers by each WWTP was calculated by multiplying the concen- resuspension ¼ rs ECS ð5Þ tration of E. coli (E. coli 100 mL− 1) (depending on the treatment type) by the daily average treated volume. It should be noted that − 1 with rs = resuspension rate (h ). in the large Brussels North WWTP, two treatment lines can function The sediment is also assumed to be slowly and continuously in parallel when the discharge reaching the WWTP is high: a biological compacted, leading to the burial of a part of the benthic stocks, at a treatment line (including activated sludge with removal of N and P) − 1 bu rate of 0.0005 h (Thouvenot et al., 2007, 2009). and a treatment line in which only primary settling is applied. The overall dynamics of the processes affecting ECS stock is thus The latter is devoted to treating the excess volume which cannot be described by the following equation: treated by the biological line; on average, the volume treated in the biological line accounts for roughly 90% of the total volume reaching ½= ¼ ½þðÞ ½ – ½– ½ d ECS dt kECS ECS VSET ECA 10 rs ECS bu ECS the WWTP. The E. coli loads from both treatment lines are taken into ð6Þ account individually in the modelling exercise. Partitioning of E. coli in treated wastewaters was studied and the data showed that on − 1 with kECS = first-order decay rate of E. coli in the sediment (h ); t = average 50% of E. coli were present as free E. coli and 50% were time (h); [ECS] = concentration of E. coli in the sediment attached to SS (Fig. 3); no significant differences were found in the (103 E. coli m− 2); rs = resuspension rate (h− 1); bu = burial rate partitioning of E. coli between the different types of treatment investi- (h− 1). The factor 10 is related to the conversion of concentration gated. Based on these data, in the model half of the E. coli brought units of E. coli in the water column (E. coli 100 mL−1)toE. coli in the by WWTP effluents is considered free and the other half attached to sediments (103 E. coli m−2). SS. In addition, in Brussels, due to an insufficient capacity of some Concerning the decay rate in sediments, numerous studies have stretches of the old sewer system, combined sewer overflows (CSOs) reported prolonged survival in sediment. This may be because sedi- can occur during rain events. In these cases, a mixture of untreated ments offer a more favourable chemical and biological environment, wastewater and urban surface runoff water is released into the supplying osmoprotecting substances and protecting them from UV Zenne River in the Brussels area. In order to take this into account in irradiation and predation by protozoa (Pachepsky and Shelton, the model, the daily average volume of CSOs discharged into the 2011). In their extensive review on FIB in sediments of freshwater Zenne River has been calculated and multiplied by an average E. coli systems, Pachepsky and Shelton (2011) concluded that E. coli and concentration in CSOs (Table 1) to obtain the load of E. coli per CSO. faecal coliforms inactivation rates in sediment are one order of magni- The partitioning of E. coli into CSOs is considered similar to WWTPs tude lower than those for the water column. Based on this information, effluents. This load is considered in the model as an additional point a decay rate of 4.5×10−3 h−1 at 20 °C was used in the model for ECS. source of E. coli. The impact of temperature on the ECS decay rate was considered similar The E. coli input by non-point sources to the model is calculated to the ECF decay rate (Eq. 2). according to Servais et al. (2007a). Four major land uses were identified in each elementary sub-basin of the watershed: forested, pastured, 3.2.2. Sources of E. coli to rivers cultivated and urbanized areas. The model considered two origins of In order to estimate the release of FIB through wastewaters, E. coli E. coli brought by non-point sources: the E. coli brought by the runoff were enumerated in samples collected in raw and treated waters of water (dependent on the land use) and by the base flow. The contribu- various wastewater treatment plants (WWTPs) located in the Scheldt tion of runoff water was calculated using the surface runoff specificto watershed (Ouattara et al., 2011). As expected (Garcia-Armisen and each type of land use in each sub-basin multiplied by the concentration Servais, 2007; Servais et al., 2007a), the E. coli concentrations of E. coli in runoff waters determined experimentally (Table 1). The con- in wastewaters vary depending on the type of treatment (Table 1): tribution of base flow was calculated by multiplying the specificbase the more complete the treatment process, the lower the E. coli con- flow associated to each type of land use in each sub-basin with a base centrations. In order to quantify the E. coli input by point sources, 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 fl Table 1 E. coli concentrations are different in base ow and runoff water, that Concentrations of E. coli in point sources and non-point sources of faecal bacteria to rivers.

Point sources Non-point sources

WWTP types E. coli100 mL− 1 Land uses E. coli 100 mL− 1

Untreated wastewater 1.0×107 Forested areas 9.6 ×101 CSOs 5.0×106 Cultivated areas 4.4 ×102 PT 4.0×106 Pastured areas 2.2 ×103 PT+AS 1.6×105 Urbanized areas 5.0 ×103 PT+AS+Nit 7.9×104 Groundwater 2.0 ×101 (base ﬂow) PT+AS+Nit–Denit 5.0×104 PT+AS+Nit–Denit+P 2.0×104 PT+trickling ﬁlter 5.4×105 Stabilisation pond 2.1×104

CSOs: combined sewer overflows; PT: primary treatment (settling); AS: activated sludge; Nit: nitrification; Denit: denitrification; P: biological or physico-chemical phos- phorus 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 Fig. 3. Box plots of the partitioning of E. coli in point sources (treated wastewaters) and were performed in the different WWTPs investigated. These data are from Ouattara in non-point sources (surface runoff waters) expressed in fraction of E. coli attached to et al. (2011) and Servais et al. (unpublished data). For the concentration of E. coli in SS. Box plots represent the median (horizontal line in the box), the lower and upper CSO, as no values were available for the Brussels CSO, the geometric mean value quartiles (bottom and top box lines), the 10th and 90th percentiles (bottom and top reported by Servais et al. (2011) for CSOs in Paris area was considered. whiskers) and the outliers (circles). N.K. Ouattara et al. / Journal of Marine Systems 128 (2013) 77–88 83 the E. coli concentration in the non-point sources is not constant. Based 2.0×104 E. coli 100 mL− 1); such concentrations are not compatible on data from Fig. 3,20%ofE. coli brought by runoff and soil leaching with bathing activities given that the minimal quality requirement were considered attached to SS and 80% were considered free. of the new EU Directive for bathing waters stipulates that the 90th percentile of the E. coli concentrations measured at a given station 4. Modelling microbiological quality in Scheldt drainage network should be lower than 9.0×102 E. coli 100 mL− 1 (EU, 2006). Comparison between the measured and simulated E. coli concen- 4.1. Model validation trations (in log scale) at the four stations shows that the SENEQUE- EC model is able to satisfactorily predict the average level of E. coli con- In order to validate the model, calculations using the SENEQUE-EC centrations in the rivers of the Scheldt drainage network. The model model were compared to field E. coli data in different stretches of var- performance was evaluated using two statistical parameters: the ious rivers in the Scheldt drainage network. Due to its low temporal Root Mean Square Error (RMSE) to measure the difference between resolution (10 days), the SENEQUE-EC model is not able to simulate values predicted by a model and observed values and the Nash Sutcliffe short time variations of FIB concentration such as those due to efficiency (NSE) to indicate how well the plot of observed versus the tidal impact or extreme conditions. For this reason, the stations simulated data fits the equivalence line. The statistical parameters chosen for validation were located outside the area influenced by were calculated based on log transformed values (as done previously the tide. It should be noted that no step of parameter calibration by Thupaki et al., 2010) of all measured and calculated E. coli concen- was performed as the value of all parameters is issued from experi- trations at the four stations considered in Fig. 4. RMSE value found mental results reported in the literature, and independent on the here (0.59) is in the range of RMSE values reported by Liu et al. observations with which the model results are compared. (2006) and Thupaki et al. (2010) (RMSE from 0.41 to 0.80) which were considered by these authors as demonstrating the good perfor- 4.1.1. Temporal variations of E. coli concentrations in a few rivers of the mance of their models. NSE ranges between −∞ and 1.0, with NSE=1 Scheldt drainage network being the optimal value; values between 0.0 and 1.0 are generally Fig. 4 presents the temporal variations of E. coli concentrations viewed as acceptable levels of performance (Moriasi et al., 2007). The calculated by the SENEQUE-EC model and the field data obtained NSE value was positive (0.57) demonstrating that the model gives an during the monitoring performed by Ouattara et al. (2011) at four acceptable level of performance. The fact that such a deterministic stations located in the upper Scheldt, the Leie, the Dyle and the model with no parameter adjustment is able to correctly predict the Dender. In the upper Scheldt and in the Leie, calculated E. coli concen- average of the observations is by itself already a non-trivial result, trations are higher in winter during high-flow periods, whereas in the which indicates that the major processes are correctly represented. Dyle and the Dender, the simulated concentrations are rather con- However, the fluctuations of the E. coli concentrations measured at stant throughout the period studied. The river flow rate is often the four stations over the 2 years were much greater than those of the reported in the literature as a factor affecting the level of faecal simulated E. coli concentrations. Two main reasons can explain this contamination (Schilling et al. 2009). The E. coli concentrations at observation. Firstly, the SENEQUE-EC model considers average and the validation stations were usually high (in the range 5.0×103 to 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. 84 N.K. Ouattara et al. / Journal of Marine Systems 128 (2013) 77–88 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 non- point 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 Fig. 5. Longitudinal fluctuations of E. coli concentrations in the Zenne River calculated SENEQUE-EC model provides a smoothed description of the seasonal by the SENEQUE-EC model for the years 2007 and 2008. The bold line is the median variations in the different sectors of the drainage network. Although it of the calculated values over the 2 years and the dashed lines are the maximum and is obvious that E. coli concentrations are much more variable in time minimum values calculated by the model for the years 2007 and 2008. Vertical bars than simulated by this model, we consider that this ‘scatter’ has to be 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 kilo- considered as a random signal superimposed to the general calculated metric unit that is set at zero at the Zenne River source and increases from upstream trend. to downstream. 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 and measured E. coli concentrations shows that the median values of compared the simulated results without diffuse sources to the refer- both data sets are in good agreement. NSE was calculated using the ence simulation (with non-point sources dependent on the land use log transformed predicted and observed E. coli concentrations at three as described in this section) at the four sampling stations presented stations of the Zenne River (Fig. 5). The NSE value greater than 0.5 indi- in Fig. 4. Results confirmed that the non-point sources are negligible cated that the SENEQUE-EC model can well predict the E. coli concentra- compared to point sources as the simulated E. coli concentrations tions in the Zenne River. The RMSE value found for the Zenne River data decreased by less than 3% on average when the non-point sources of is close to the value reported by Thupaki et al. (2010) (RMSE=0.41) E. coli are not considered in the calculation (data not shown). This is which was considered by these authors as demonstrating the good per- in complete agreement with the comparison of E. coli loads by point formance of their model. versus non-point sources presented by Ouattara et al. (2011). As already observed in Fig. 4, the measured values show greater A sensitivity analysis was performed to see if modifying the par- variability than the calculated data. The longitudinal profile clearly titioning of E. coli in the point sources could ameliorate the simula- highlights the impact of the wastewaters of Brussels and its suburbs tions. For this, simulations were calculated with the model using (more than 1 million inhabitants) on the microbiological water quality three different partitionings (25%, 50% and 75% of attached E. coli in of a river with a low discharge. The high E. coli concentrations down- the point sources). The NSE value for a partitioning of E. coli in point stream from Brussels result primarily from the fact that the Zenne sources of 50% of attached bacteria was positive (0.57) while they River water is composed, on average, of more than 50% of treated were negative for the two other tested partitionings. In our case, it wastewaters and CSOs, this proportion being even higher during the means that increasing or decreasing the fraction of attached E. coli river's low-flow periods. in point sources did not improve the modelling results. Also, the lower RMSE value (i.e., the best performance) is obtained for the 4.1.3. Prediction of the fraction of free and attached E. coli partitioning of 50%. To validate the prediction calculated by the SENEQUE-EC model for free and attached E. coli in river waters, the calculated and the mea- 4.1.2. Longitudinal profile of the Zenne River sured values of the concentrations of attached E. coli at the validation The E. coli concentrations calculated using the SENEQUE-EC model stations presented above were compared. Fig. 6 presents boxplots of 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 addi- Fig. 6. Concentrations of E. coli attached to SS simulated by the SENEQUE-EC model plotted tional increase of E. coli abundance levels. The E. coli concentrations (in Log–Log scale) against concentrations of E. coli attached to SS measured at the seven 4 observed downstream from Brussels are very high (between 4.3×10 validation stations. Black dots are the median of the measured and simulated concentra- 6 −1 and 3.80×10 E. coli 100 mL ). Comparison between the calculated tions, vertical and horizontal bars indicate the 25th and 75th percentiles. N.K. Ouattara et al. / Journal of Marine Systems 128 (2013) 77–88 85 the measured and calculated concentrations of E. coli attached to SS 4.3. Impact of wastewater management scenarios on microbiological at the validation stations. The calculated and measured fractions of quality attached E. coli are in good agreement, indicating that the SENEQUE- EC model is able to adequately predict the fraction of attached E. coli Once validated, the SENEQUE-EC model can be a useful tool to in river waters. However, as already mentioned for the total E. coli investigate the impact of modifications in wastewater management concentrations, the measured concentrations of attached E. coli were on the microbiological water quality of the rivers of the drainage highly variable compared to the calculated values of attached E. coli. network. In this study, the impact of wastewater management on the microbiological water quality of the Zenne River was investigated 4.2. Microbiological contamination of the rivers of the Scheldt drainage by testing two scenarios. An optimistic scenario resulting in a signifi- network cant improvement of the microbiological water quality of the Zenne River obtained by reducing the faecal contamination in the effluents Fig. 7 presents the distribution of E. coli in the whole Scheldt drainage of the Brussels WWTPs and a worst case scenario corresponding to network calculated by the SENEQUE EC model for the summer 2007 the release of the whole volume of wastewaters from Brussels and period (low-flow period). This presentation easily locates the rivers its suburbs in the Zenne River with no treatment. presenting good quality and the hot spots of faecal contamination. If we consider that an acceptable E. coli concentration should be 4.3.1. Optimistic scenario lower than 1000 per 100 mL, Fig. 7 shows that the stretches of rivers The Zenne River is highly polluted by microbial contaminants presenting acceptable microbiological quality (blue on the map) ac- due to the load of faecal pollution released by both Brussels WWTPs count for only a small part of the total length of rivers in the Scheldt and by CSO outfalls in downtown Brussels (Fig. 5). To improve the watershed. This completely confirms the observation-based conclu- microbiological water quality of the Zenne River downstream from sion of Ouattara et al. (2011) stating the poor microbiological quality Brussels, the faecal contaminants released by the Brussels WWTPs of the rivers in this watershed. In most areas of the watershed, the effluents should be reduced and the CSO should be avoided. small rivers in their upstream stretches present high E. coli concentra- Thus, in our optimistic scenario, we considered that: (i) the sewer tions in the range 1×103 to 1×105 E. coli 100 mL− 1. These levels of system will be improved to increase its transport capacity so that it contamination are explained by the considerable urbanization and will be able to transport the entire volume of water to the WWTPs, agriculture activities in the watershed. In some rivers, such as in whatever the intensity of the rain event; (ii) the treatment capacity the upper Scheldt, quality improvement can be observed from up- of the biological treatment line of Brussels North WWTP will be stream to downstream, demonstrating that in some stretches the increased so that it can treat the entire volume reaching the WWTP decay processes (mortality and settling) overtake the input of FIB in even during intense rain events; and (iii) a disinfection stage will be the river. As expected, the most contaminated river is the Zenne added at the end of the treatment line in both Brussels WWTPs. UV River downstream from Brussels with concentrations values above irradiation is the most common treatment used in Belgium for treated 1×105 E. coli 100 mL− 1. 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. 86 N.K. Ouattara et al. / Journal of Marine Systems 128 (2013) 77–88 of a UV treatment at the final stage of wastewater treatment after concentrations of E. coli in the worst case scenario after KP 42 are biological treatment increased the Log removal by 2–3 units (Servais ten times higher than in the reference situation. In the current situa- et al., 2007a). In addition, in this scenario, we have taken into account tion, this indicates that the WWTPs of the Brussels region, even with- recent improvements that have been made in the treatment processes out a disinfection system contributed to reducing the level of faecal of three small WWTPs located downstream from Brussels. contamination in the Zenne River. Note that this worst case scenario The concentration of E. coli along the Zenne River was calculated was the real situation until the year 2000. Indeed, the implementation by the SENEQUE-EC model considering this optimistic scenario. of WWTPs in Brussels is quite recent: the Brussels South WWTP has Fig. 8 shows a comparison between the optimistic scenario and the been in operation since 2000 and Brussels North only since 2007. reference situation (situations 2007 and 2008, see Fig. 5). Logically, upstream from Brussels, the concentrations of E. coli calculated by 5. Conclusions the SENEQUE model are similar for the optimistic scenario and the reference situation. In the optimistic scenario, the E. coli concentra- The SENEQUE-EC model, the combination of a module describing tion decreases after KP 42 (outfall of the Brussels South WWTP) the dynamics of E. coli and a hydro-ecological model, is to our knowl- because UV-treated effluents are less contaminated than the river edge one of the first models able to simulate spatial and seasonal water. A similar decrease can also be observed at KP 57 (outfall of variations of microbial contamination (E. coli concentration) at the the Brussels North WWTP). This scenario shows, downstream from scale of the whole drainage network of a large regional river basin Brussels, an improvement of about two Log units in E. coli concentra- resulting from point and diffuse sources of faecal bacteria generated tion with regards to the reference situation (2007–2008). by human activities. This model is also different from most models describing faecal contamination in that it differentiates the dynamics 4.3.2. Worst case scenario of three types of E. coli: free-floating E. coli, E. coli attached to SS in the The worst case scenario corresponds to a situation where the water column and E. coli present in sediments. The predictions of the wastewaters of Brussels and its suburbs are released into the Zenne SENEQUE-EC model in terms of E. coli concentrations were validated River with no treatment. This scenario was imagined because in by comparison with seasonal and spatial distributions of field data in December 2009, due to technical problems, the treatment was inter- different stretches of rivers of the Scheldt drainage network. Thus, rupted at the Brussels North WWTP for 10 days and thus the whole the SENEQUE-EC model appears to be a very useful tool to test the im- volume of wastewaters reaching the WWTP was released into the pact of wastewater management strategies on the level of microbial Zenne River. This situation was responsible for an important decrease contamination in the rivers of the whole drainage network. The final of quality (physico-chemical and microbiological) in the downstream objective of these modelling exercises was to investigate how the part of the Zenne River (Brion, pers. com. and Ouattara, unpublished microbiological pollution generated by human activities on the water- data). In the worst case scenario, simultaneous interruptions of the shed can affect the level of contamination in the coastal zone, or, in treatment in both Brussels WWTPs were considered. This scenario other words, to see how microbial contaminants are transported in explores the impact of the release of raw wastewaters from Brussels the Scheldt land–sea continuum. Due to its low temporal resolution, and its suburbs on the microbiological water quality of the Zenne the SENEQUE-EC was considered to be poorly adapted to describing River. the microbiological quality in the areas under tidal influence. We Fig. 7 provides an easy comparison of the worst case scenario therefore combined the SENEQUE-EC and the SLIM-EC2 models, and the reference situation. Upstream from Brussels, the E. coli which is, owing to its high temporal resolution, better adapted to de- concentrations calculated by the SENEQUE-EC model are similar for scribing the processes occurring in the tidal Scheldt River and Estuary. the scenario and the reference situation. At KP 42, after the release Thus, we used the SENEQUE-EC for the areas of the drainage network of Brussels South WWTP effluents, the worst case scenario showed not influenced by the tide and the E. coli concentrations calculated by a very high increase of E. coli concentration with values reaching the SENEQUE-EC model were used as upstream boundary concentra- 1.0×106 E. coli 100 mL− 1. This increase is followed by a slow decrease tions required to run the SLIM-EC2 model in the tidal Scheldt River during the crossing of the Brussels region (between KP 43 and KP 56). and Estuary. This offline coupling of the two models allows one to After the outfall of Brussels North WWTP effluents at KP 57, an addi- simulate the microbiological contamination from the upstream head- tional increase of E. coli concentration was observed. The average waters 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

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 Fig. 8. Comparison of the longitudinal profiles of E. coli concentrations in the Zenne had a doctoral grant from the Ivory Coast Government and benefited River calculated by the SENEQUE-EC model for the years 2007 and 2008. The bold of a doctoral fund from “Fonds Van Buuren”. The authors wish to line represents the median of E. coli concentrations for the reference situation and thank Julie Callens (Université Pierre et Marie Curie, France) for her the dashed lines represent the medians of E. coli concentrations for the worst case fi (black) and optimistic scenarios (grey). The x-axis is a kilometric unit that is set at assistance in preparing the WWTP les and for providing the maps zero at the Zenne River source and increases from upstream to downstream. of Scheldt drainage network. N.K. Ouattara et al. / Journal of Marine Systems 128 (2013) 77–88 87

References Gao, G., Falconer, R.A., Lin, B., 2011. Numerical modelling of sediment-bacteria interaction processes in surface waters. Water Res. 45, 1951–1960. Garcia-Armisen, T., Servais, P., 2007. Respective contributions of point and non-point Alkan, U., Elliott, D.J., Evison, L.M., 1995. Survival of enteric bacteria in relation to sources of E. coli and enterococci in a large urbanized watershed (the Seine river, simulated solar radiation and other environmental factors in marine waters. France). J. Environ. Manage. 82 (4), 512–518. Water Res. 29 (9), 2071–2080. Garcia-Armisen, T., Servais, P., 2009. Partitioning and fate of particle-associated E. coli Auer, M.T., Niehaus, S.L., 1993. Modeling fecal-coliform bacteria.1. Field and laboratory in river waters. Wat. Environm. Res. 81 (1), 21–28. determination of loss kinetics. Water Res. 27 (4), 693–701. Garcia-Armisen, T., Lebaron, P., Sevais, P., 2005. β-D-glucuronidase activity assay to Baeyens, W., Leermakers, M., De Gieter, M., Nguyen, H.L., Parmentier, K., Panutrakul, S., assess viable Escherichia coli abundance in freshwaters. Lett. Appl. Microbiol. 40, Elskens, M., 2005. Overview of trace metal contamination in the Scheldt estuary 278–282. and effect of regulatory measures. Hydrobiologia 540, 141–154. Garcia-Armisen, T., Thouvenin, B., Servais, P., 2006. Modelling faecal coliforms dynamics Baeyens, W., Leermakers, M., Elskens, M., Van Larebeke, N., De Bont, R., Vanderperen, in the Seine estuary, France. Water Sci. Technol. 54 (3), 177–184. H., Fontaine, A., Degroodt, J.M., Goeyens, L., Hanot, V., Windal, I., 2007. PCBs and Ge, Z., Frick, W.E., 2007. Some statistical issues related to multiple linear regression PCDD/FS in fish and fish products their impact on the human body burden in modeling of beach bacteria concentrations. Environ. Res. 103 (3), 358–364. Belgium. Arch. Environ. Contam. Toxicol. 52 (4), 563–571. George, I., Petit, M., Servais, P., 2000. Use of enzymatic methods for rapid enumeration Bai, S., Lung, W.-S., 2005. Modeling sediment impact on the transport of fecal bacteria. of coliforms in freshwaters. J. Appl. Microbiol. 88, 404–413. Water Res. 39 (20), 5232–5240. Havelaar, A., Blummenthal, U.J., Strauss, M., Kay, D., Bartram, J., 2001. Guidelines the Barcina, I., Arana, I., Iriberri, J., Egea, L., 1986. Factors affecting the survival of E. coli in a current position. In: Fewtrell, L., Bartram, J. (Eds.), Water quality: Guidelines, river. Hydrobiologia 141, 249–253. Standards and Health. World Health Organ. Water Ser. IWA Publishing, London Barcina, I., Lebaron, P., VivesRego, J., 1997. Survival of allochthonous bacteria in aquatic (U.K.), pp. 17–41. systems: a biological approach. FEMS Microbiol. Ecol. 23, 1–9. Heberger, M.G., Durant, J.L., Oriel, K.A., Kirshen, P.H., Minardi, L., 2008. Combining real- Beaudeau, P., Tousset, N., Bruchon, F., Lefèvre, A., Taylor, H.D., 2001. In situ measure- time bacteria models and uncertainty analysis for establishing health advisories for ment and statistical modelling of Escherichia coli decay in small rivers. Water Res. recreational waters. J. Water Resour. Plan. Manag. 134 (1), 73–82. 35 (13), 3168–3178. Hellweger, F.L., Masopust, P., 2008. Investigating the fate and transport of Escherichia Billen, G., Garnier, J., 1999. Nitrogen transfer through the Seine drainage network: a budget coli in the Charles River, Boston, using high-resolution observation and modeling. based on the application of the RIVERSTRAHLER Model. Hydrobiologia 410, 139–150. J. Am. Water Resour. Assoc. 44 (2), 509–522. Billen, G., Garnier, J., Hanset, P., 1994. Modelling phytoplankton development in whole Jamieson, R.C., Joy, D.M., Lee, H., Kostaschuk, R., Gordon, R.J., 2005. Transport and deposi- drainage networks: the RIVERSTRAHLER model applied to the Seine river system. tion of sediment-associated Escherichia coli in natural streams. Water Res. 39 (12), Hydrobiologia 289, 119–137. 2665–2675. Billen, G., Garnier, J., Rousseau, V., 2005. Nutrient fluxes and water quality in the drain- Kashefipour, S.M., Lin, B., Harris, E., Falconer, R.A., 2002. Hydro-environmental modelling age network of the Scheldt basin over the last 50 years. Hydrobiologia 540, 47–67. for bathing water compliance of an estuarine basin. Water Res. 36 (7), 1854–1868. Bougeard, M., Le Saux, J.-C., Pérenne, N., Baffaut, C., Robin, M., Pommepuy, M., 2011. Kashefipour, S.M., Lin, B., Falconer, R.A., 2006. Modelling the fate of faecal indicators in Modeling of Escherichia coli fluxes on a catchment and the impact on coastal a coastal basin. Water Res. 40 (7), 1413–1425. water and shellfish quality. J. Am. Water Resour. Assoc. 47 (2), 350–366. Lancelot, C., Gypens, N., 2013. Tracing and integratedmodelling of natural and anthro- Celik, I., Rodi, W., 1991. Suspended sediment-transport capacity for open channel flow. pogenic effects on hydrosystems: The Scheldt River Basin and adjacent coastal J. Hydraul. Eng. ASCE 117 (2), 191–204. North as a case study. J. Mar. Syst. 128, 1–2 (in this issue). Cho, K.H., Cha, S.M., Kang, J.-H., Lee, S.W., Park, Yongeun, Kim, J.-W., Kim, J.H., 2010. Lancelot, C., Gypens, N., Billen, G., Garnier, J., Roubeix, V., 2007. Testing an integrated Meteorological effects on the levels of fecal indicator bacteria in an urban stream: river–ocean mathematical tool for linking marine eutrophication to land use: the a modeling approach. Water Res. 44 (7), 2189–2202. Phaeocystis-dominated Belgian coastal zone (Southern North Sea) over the past Cizek, A.R., Characklis, G.W., Krometis, L.A., Hayes, J.A., Simmons, O.D. Di, Lonardo, S., 50 years. J. Mar. Syst. 64 (14), 216–228. Alderisio, K.A., Sobsey, M.D., 2008. Comparing the partitioning behavior of Giardia Lebaron, P., Henry, A., Lepeuple, A.-S., Pena, G., Servais, P., 2005. An operational method and Cryptosporidium with that of indicator organisms in stormwater runoff. Water for real-time monitoring of E. coli in bathing waters. Mar. Pollut. Bull. 50, 652–659. Res. 42 (17), 4421–4438. Liu, L., Phanikumar, M.S., Molloy, S.L., Whitman, R.L., Shively, D.A., Nevers, M.B., Schwab, Collins, R., Rutherford, K., 2004. Modelling bacterial water quality in streams draining D.J., Rose, J.B., 2006. Modeling the transport and inactivation of E. coli and Enterococci pastoral land. Water Res. 38 (3), 700–712. in the near-shore region of Lake Michigan. Environ. Sci. Technol. 40, 5022–5028. Crabill, C., Donald, R., Snelling, J., Foust, R., Southam, G., 1999. The impact of sediment Marasse, C., Lim, E.L., Caron, D.A., 1992. Seasonal and daily changes in bacterivory in a fecal coliform reservoirs on seasonal water quality in Oak Creek, Arizona. Water coastal plankton community. Mar. Ecol. Prog. Ser. 82, 281–289. Res. 33 (9), 2163–2171. Martin L., 2001. Fonctionnement écologique de la Seine à l'aval de la station Craig, D.L., Fallowfield, H.J., Cromar, N.J., 2004. Use of microcosms to determine persis- d'épuration d'Achères: données expérimentales et modélisation bidimensionnelle. tence of Escherichia coli in recreational coastal water and sediment and validation PhD thesis Ecole Nationale Supérieure des Mines de Paris, 279 p. with in situ measurements. J. Appl. Microbiol. 96 (5), 922–930. Menon, P., Billen, G., Servais, P., 2003. Mortality rates of autochthonous and fecal bacteria David, M.M., Haggard, B.E., 2011. Development of regression-based models to predict in natural aquatic ecosystems. Water Res. 37, 4151–4158. fecal bacteria numbers at select sites within the Illinois River Watershed, Arkansas Moriasi, D.N., Arnold, J.G., Liew, M., Van, W., Bingner, R.L., Harmel, R.D., Veith, T.L., 2007. and Oklahoma, USA. Water Air Soil Pollut. 215, 525–547. Model evaluation guidelines for systematic quantification of accuracy in watershed de Brauwere, A., De Brye, B., Servais, P., Passerat, J., Deleersnijder, E., 2011. Modelling simulations. Am. Soc. Agric. Biol. Eng. 50 (3), 885–900. Escherichia coli concentration in the tidal Scheldt river and estuary. Water Res. 45 Nevers, M.B., Whitman, R.L., 2005. Nowcast modeling of Escherichia coli concentrations at (9), 2724–2736. multiple urban beaches of southern Lake Michigan. Water Res. 39 (20), 5250–5260. de Brauwere, A., Gourgue, O., de Brye, B., Servais, P., Ouattara, K.N., Deleersnijder, E., Nevers, M.B., Whitman, R.L., 2011. Efficacy of monitoring and empirical predictive 2013. Integrated modelling of faecal contamination in a densely populated river- modeling at improving public health protection at Chicago beaches. Water Res. sea continuum (Scheldt River and Estuary). Sci. Total Environ. 468–469, 31–45. 45 (4), 1659–1668. de Brye, B., de Brauwere, A., Gourgue, O., Kärnä, T., Lambrechts, J., Comblen, R., Nevers, M.B., Whitman, R.L., Frick, W.E., Ge, Z.F., 2007. Interaction and influence of Deleersnijder, E., 2010. A finite-element, multiscale model of the Scheldt tribu- two creeks on Escherichia coli concentrations of nearby beaches: exploration of taries, river, estuary and ROFI. Coast. Eng. 57, 850–863. predictability and mechanisms. J. Environ. Qual. 36 (5), 1338–1345. Dorevitch, S., Ashbolt, N.J., Ferguson, C.M., Fujioka, R., McGee, C.D., Soller, J.A., Ouattara, K.N., Passerat, J., Servais, P., 2011. Faecal contamination of the water and sediment Whitman, R.L., 2010. Meeting report: knowledge and gaps in developing microbial intheriversoftheScheldtdrainagenetwork.Environ.Monit.Assess.183,243–257. criteria for inland recreational waters. Environ. Health Perspect. 118 (6), 871–876. Pachepsky, Y.A., Shelton, D.R., 2011. Escherichia coli and fecal coliforms in freshwater Dorner, S.M., Anderson, W.B., Slawson, R.M., Kouwen, N., Huck, P.M., 2006. Hydrologic and estuarine sediments. Crit. Rev. Environ. Sci. Technol. 41, 1067–1110. modeling of pathogen fate and transport. Environ. Sci. Technol. 40, 4746–4753. Pachepsky, Y.A., Sadeghi, A.M., Bradford, S.A., Shelton, D.R., Guber, A.K., Dao, T., 2006. Droppo, I.G., Krishnappan, B.G., Liss, S.N., Biberhofer, M.J., 2011. Modeling sediment- Transport and fate of manure-borne pathogens: modeling perspective. Agric. microbial dynamics in the South Nation River, Ontario, Canada: towards the pre- Water Manag. 86 (1–2), 81–92. diction of aquatic and human health risks. Water Res. 45, 3797–3809. Prats, J., Garcia-Armisen, T., Larrea, J., Servais, P., 2008. Comparison of culture-based Eckardt, K., 2005. How to construct recursive digital filters for baseflow separation. methods to enumerate Escherichia coli in tropical and temperate freshwaters. Hydrol. Process. 19, 507–515. Lett. Appl. Microbiol. 46, 243–246. Eleria, A., Vogel, R.M., 2005. Predicting fecal coliform bacteria levels in the Charles Roslev, P., Bastholm, S., Iversen, N., 2008. Relationship between fecal indicators in River, Massachusetts, USA. J. Am. Water Resour. Assoc. 41 (5), 1195–1209. sediment and recreational waters in Danish estuary. Water Air Soil Pollut. 194 (1–4), EU, 2006. Directive 2006/7/EC of the European Parliament and of the Council of 15 13–21. February 2006 concerning the management of bathing water quality. Off. J. Eur. Ruelland, D., Billen, G., Brunstin, D., Garnier, J., 2007. SENEQUE: a multi-scaling GIS inter- Union 64, 37–51. face to the Riverstrahler model of the biogeochemical functioning of rivers systems. Fletcher, M., 1996. Bacteria attachment in aquatic environments: a diversity of surfaces Sci. Total. Environ. 375, 257–273. and adhesion strategies. In: Fletcher, M. (Ed.), Bacterial adhesion: molecular and Schilling, K.E., Zang, Y.K., Hill, D.R., Jones, C.S., Wolter, C.F., 2009. Temporal variations of ecological diversity. John Wiley Publication, New York. Escherichia coli concentrations in large Midwestern river. J. Hydrol. 365 (1-2), 79–85. Flint, K.P., 1987. The long-term survival of Escherichia coli in river water. J. Appl. Bacteriol. Servais, P., Billen, G., Vives-Rego, J., 1985. Rate of bacterial mortality in aquatic environ- 63, 261–270. ment. Appl. Environ. Microbiol. 49, 1448–1454. Frick, W.E., Ge, Z.F., Zepp, R.G., 2008. Nowcasting and forecasting concentrations of bio- Servais, P., Gosselain, V., Joaquim-Justo, C., Becquevort, S., Thome, J.P., Descy, J.P., 2000. logical contaminants at beaches: a feasibility and case study. Environ. Sci. Technol. Trophic relationships between planktonic microorganisms in the river Meuse 42 (13), 4818–4824. (Belgium): a carbon budget. Arch. Hydrobiol. 149 (4), 625–653. 88 N.K. Ouattara et al. / Journal of Marine Systems 128 (2013) 77–88

Servais, P., Garcia Armisen, T., Lepeuple, A.S., Lebaron, P., 2005. An early warning method Thieu, V., Billen, G., Garnier, J., 2009. Nutrient transfer in three contrasting NW European to detect fecal contamination of river waters. Ann. Microbiol. 55 (2), 67–72. watersheds: the Seine, Somme, Scheldt Rivers. A comparative application of the Servais, P., Garcia-Armisen, T., George, I., Billen, G., 2007a. Fecal bacteria in the rivers Seneque/Riverstrahler model. Water Res. 43 (6), 1740–1754. of the Seine drainage network (France): sources, fate and modelling. Sci. Total. Thouvenot, M., Billen, G., Garnier, J., 2007. Modelling nutrient exchange at the sedi- Environ. 375, 152–167. ment–water interface of river systems. J. Hydrol. 341, 55–78. Servais, P., Billen, G., Goncalves, A., Garcia-Armisen, T., 2007b. Modelling microbiological Thouvenot, M., Billen, G., Garnier, J., 2009. Modelling benthic denitriﬁcation processes water quality in the Seine river drainage network: past, present and future situations. over a whole drainage network. J. Hydrol. 379, 239–250. Hydrol. Earth Syst. Sci. 11, 1581–1592. Thupaki, P., Phanikumar, M.S., Beletsky, D., Schwab, D.J., Nevers, M.B., Whitman, R.L., Servais, P., Poulin, M., Flipo, N., Lesage, L., Goncalves, A., Rocher, V., Masnada, S., Lucas, 2010. Budget analysis of Escherichia coli at a Southern Lake Michigan beach. Environ. F., Mouchel, J.M., 2011. Vers un modèle de la contamination par temps de pluie Sci. Technol. 44 (3), 1010–1016. en Seine. Report PIREN-Seine Research Program. (http://www.sisyphe.upmc.fr/ Tian, Y.Q., Gong, P., Radke, J.D., Scarborough, J., 2002. Spatial and temporal modeling of piren/webfm_send/996). microbial contaminants on grazing farmlands. J. Environ. Qual. 31 (3), 860–869. Smith, J., Edwards, J., Hilger, H., Steck, R.T., 2008. Sediment can be a reservoir for Wilkinson, R.J., Jenkins, A., Wyer, M., Kay, D., 1995. Modelling faecal coliform dynamics coliform bacteria released into streams. J. Gen. Appl. Microbiol. 54 (3), 173–179. in streams and rivers. Water Res. 29 (3), 847–855. Stidson, R.T., Gray, C.A., McPhail, C.D., 2012. Development and use of modelling tech- Wu, J., Rees, P., Storrer, S., Alderisio, K., Dorner, S., 2009. Fate and transport modeling of niques for real-time bathing water quality predictions. Water Environ. J. 26 (1), potential pathogens: the contribution from sediments. J. Am. Water Resour. Assoc. 7–18. 45 (1), 35–44.