Anouk de Brauwere

Modeling Fecal Indicator Bacteria Concentrations in Natural Surface Waters: A Review

Microbiological quality of waters must be assessed to ensure that no health risk due to pathogenic microorganisms is associated with its use. As it is impossible to measure the abundance of all possible pathogens, it is general practice to quantify the abundance only of one or a few fecal indicator bacteria (FIB), organisms which are selected to be indicative of fecal pollution and can therefore serve as general indicators of microbiological water quality. Yet, even by focusing on this limited number of indicator organisms, it is still unfeasible to experimentally monitor their levels at the high spatiotemporal resolution often needed in real applications. Therefore, direct FIB measurements are increasingly combined with the use of models. The aim of this review is to present and evaluate the wide variety of models used so far in the scientific literature to simulate and predict FIB concentrations in natural surface waters. First, the distinction is made between regression-based and mechanistic models. While the first are particularly useful in operational contexts and indeed can produce reliable short-term predictions, they do not allow an in-depth understanding of the processes. Because the sources and processes are not modeled explicitly, they cannot be used to test the effect of changes in these internal and external forcings, e.g., to evaluate the impact of different management options. These questions can only be addressed by the use of mechanistic models, which are often based on rather complex computational methods. These models explicitly consider the effect on FIB concentrations due to horizontal transport, external sources, decay, and/or sediment-related processes. It is not possible to make statements about “best” practices, given the broad range of study domains and questions. Instead, we attempted to compile the modeling approaches published so far in a comprehensive and transparent way, hoping that the resulting overview would help to better understand current models and more efficiently set up future ones.

Modelling Escherichia coli concentrations in the tidal Scheldt river and estuary

Recent observations in the tidal Scheldt River and Estuary revealed a poor microbiological water quality and substantial variability of this quality which can hardly be assigned to a single factor. To assess the importance of tides, river discharge, point sources, upstream concentrations, mortality and settling a new model (SLIM-EC) was built. This model was first validated by comparison with the available field measurements of Escherichia coli (E. coli, a common fecal bacterial indicator) concentrations. The model simulations agreed well with the observations, and in particular were able to reproduce the observed long-term median concentrations and variability. Next, the model was used to perform sensitivity runs in which one process/forcing was removed at a time. These simulations revealed that the tide, upstream concentrations and the mortality process are the primary factors controlling the long-term median E. coli concentrations and the observed variability. The tide is crucial to explain the increased concentrations upstream of important inputs, as well as a generally increased variability. Remarkably, the wastewater treatment plants discharging in the study domain do not seem to have a significant impact. This is due to a dilution effect, and to the fact that the concentrations coming from upstream (where large cities are located) are high. Overall, the settling process as it is presently described in the model does not significantly affect the simulated E. coli concentrations.

Integrated modelling of faecal contamination in a densely populated river-sea continuum (Scheldt River and Estuary)

In order to simulate the long-term (months-years) median Escherichia coli distributions and variations in the tidal Scheldt River and Estuary, a dedicated module was developed for the Second-generation Louvain-la-Neuve Ice-ocean Model (SLIM, www.climate.be/slim). The resulting model (SLIM-EC2) presents two specific and new features compared to the older SLIM-EC model version. The first is that the E. coli concentrations in the river are split in three fractions: the free E. coli in the water column, the ones attached to suspended solids and those present in the bottom sediments, each with their own transport, decay and settling-resuspension dynamics. The bacteria attached to particles can settle and survive on the bottom, where they can be brought back in the water column during resuspension events. The second new feature of the model is that it is coupled to the catchment model SENEQUE-EC, which thus provides upstream boundary conditions to SLIM-EC2. The result is an integrated and multi-scale model of the whole Scheldt drainage network from its source down to the Belgian/Dutch coastal zone. This new model reproduces the long-term median E. coli concentration along the Scheldt River and Estuary. An extensive sensitivity study is performed demonstrating the relative robustness of the model with respect to the chosen parameterisations. In addition to reproducing the observed E. coli concentrations in 2007-2008 at various stations, two extreme wastewater management scenarios were considered. Overall, there is no doubt that the Scheldt Estuary acts as a cleaning filter of faecal contamination originating from large Belgian cities. As a result, at the mouth of the Scheldt Estuary E. coli concentration is negligible in all investigated conditions.

Modelling metal speciation in the Scheldt Estuary: combining a flexible-resolution transport model with empirical functions

Predicting metal concentrations in surface waters is an important step in the understanding and ultimately the assessment of the ecological risk associated with metal contamination. In terms of risk an essential piece of information is the accurate knowledge of the partitioning of the metals between the dissolved and particulate phases, as the former species are generally regarded as the most bioavailable and thus harmful form. As a first step towards the understanding and prediction of metal speciation in the Scheldt Estuary (Belgium, the Netherlands), we carried out a detailed analysis of a historical dataset covering the period 1982-2011. This study reports on the results for two selected metals: Cu and Cd. Data analysis revealed that both the total metal concentration and the metal partitioning coefficient (Kd) could be predicted using relatively simple empirical functions of environmental variables such as salinity and suspended particulate matter concentration (SPM). The validity of these functions has been assessed by their application to salinity and SPM fields simulated by the hydro-environmental model SLIM. The high-resolution total and dissolved metal concentrations reconstructed using this approach, compared surprisingly well with an independent set of validation measurements. These first results from the combined mechanistic-empirical model approach suggest that it may be an interesting tool for risk assessment studies, e.g. to help identify conditions associated with elevated (dissolved) metal concentrations.

Design of a sampling strategy to optimally calibrate a reactive transport model: Exploring the potential for Escherichia coli in the Scheldt Estuary

For the calibration of any model, measurements are necessary. As measurements are expensive, it is of interest to determine beforehand which kind of samples will provide maximal information. Using a criterion related to the Fisher information matrix as a measure for information content, it is possible to design a sampling scheme that will enable the most precise parameter estimates. This approach was applied to a reactive transport model (based on the Second-generation Louvain-la-Neuve Ice-ocean Model, SLIM) of Escherichia coli concentrations in the Scheldt Estuary. As this estuary is highly influenced by the tide, it is expected that careful timing of the samples with respect to the tidal cycle can have an effect on the quality of the data. The timing and also the positioning of samples were optimised according to the proposed criterion. In the investigated case studies the precision of the estimated parameters could be improved by up to a factor of ten, confirming the usefulness of this approach to maximize the amount of information that can be retrieved from a fixed number of samples. Precise parameter values will result in more reliable model simulations, which can be used for interpretation, or can in turn serve to plan subsequent sampling campaigns to further constrain the model parameters.

Identification of a periodic time series from an environmental proxy record

The past environment is often reconstructed by measuring a certain proxy (e.g. @d^1^8O) in an environmental archive, i.e. a species that gradually accumulates mass and records the current environment during this mass formation (e.g. corals, shells, trees, etc.). When such an environmental proxy is measured, its values are known on a distance grid. However, to relate the data to environmental variations, the date associated with each measurement has to be known too. This transformation from distance to time is not straightforward to solve, since species usually do not grow at constant or known rates. In this paper, we investigate this problem for environmental archives exhibiting a certain periodicity. In practice, the method will be applicable to most annually resolved archives because these contain a seasonal component, e.g. clams, corals, sediment cores or trees. Due to variations in accretion rate the data along the distance axis have a disturbed periodic profile. In this paper, a method is developed to extract information about the accretion rate, such that the original (periodic, but further unknown) signal as a function of time can be recovered. The final methodology is quasi-independent of choices made by the investigator and is designed to deliver the most precise and accurate result. Every step in the procedure is described in detail, the results are tested on a Monte-Carlo simulation, and finally the method is exemplified on a real world example.

Organic Carbon in the Ocean's Twilight Zone: Controls on Organic Carbon Export and Twilight Zone Remineralization; Brussels, Belgium, 28–30 May 2008

Today, the key role played by oceans in the Earths carbon cycle is widely recognized. The important boundary to consider is not the ocean-atmosphere interface, across which the carbon dioxide (CO2) concentration gradient is small, but the seasonal and permanent thermoclines, whose positions range from about 20 to 300 meters in depth. Once CO2 is delivered to depths beyond these thermoclines, the sequestration efficiency for CO2 is increased. Both physical and biological pumps act to increase the amount of carbon stored in the deep sea. For the latter process, more than 90% of the organic carbon exported (10–12 gigatons per year) to the deeper mesopelagic waters (i.e., the region below the upper mixed layer down to about 1000 meters, also called the twilight zone) is again respired to form CO2. Only a very small fraction of this organic carbon (<1%) eventually reaches the seafloor, where it is sequestered on longer timescales. The mechanisms controlling carbon export and its fate are still insufficiently understood to allow an informed assessment of its variability.