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Modelling Faecal Contamination in the Scheldt Drainage Network

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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. 0924-7963/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jmarsys.2012.05.004 78 N.K. Ouattara et al. / Journal of Marine Systems 128 (2013) 77–88 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;
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