La mentalidad del grupo de trabajo y las premisas de la base

The complexity of the physical processes driving the dispersion of effluents discharged into the marine environment makes it difficult to assess the behaviour of the PFWs in the near- and far-field zones only by means of in situ observations. Relevant works (e.g., Petrenko et al.1998; Washburn et al., 1999; Nedwed et al., 2004; Cianelli et al., 2008) comparing model results with field measurements of effluent dilution showed that the numerical approach represents an efficient alternative to conducting difficult and expensive field observations in every discharge scenario.

A numerical model may be defined as any tool describing a physical, chemical or biological problem by means of a set of equations solved by using different numerical methods. There is no ―best model‖, but rather a broad range of models that can be applied to simulate several different cases. The selection of a numerical model for a particular pollutant transport scenario requires considering various issues:

 Model aims: as a first natural step, the investigator has to define detailed model aims taking into account the physical processes involved in the system to be modelled. It is worth underlining that a model may target scientific as well as operational goals.

 Model features: starting from the model goals, a list of the required model characteristics in terms of input-output flexibility has to be formulated.

 Available models: the investigator may choose to apply an existing model or to develop a new one. Several models are available as public-domain, which allows to use widely tested and applied tools. If existing models are not suitable for a specific issue, a new model has to be devised and implemented.

 Model choice: choosing a model requires an optimal compromise between the model aims, availability, cost/ease of use and accuracy. In the case of a new model, the cost and difficulty due to the implementation of the numerical tool depend on the available software and the complexity of the parameterized processes. The computational cost required to perform the calculation depends on the computer resources required (e.g., workstation, personal computer, etc.) and on the time and skills needed to run the simulation. The model accuracy may be determined comparing the model results with experimental data and is a fundamental step allowing to identify and estimate numerical errors.

After selecting the model, a scale analysis is necessary to determine the relevant scales of the problem and the complexity of the model in order to adequately reproduce the studied system. A rigorous procedure to test a numerical model ensures that it is appropriate to simulate the functioning of our natural system. The following tests have to be carried out:

 verify the model consistency in terms of conserved quantities (e.g., mass);

 test the model in idealized cases and compare the results with known analytical solutions;

 calibrate and validate the model by comparing the numerical results with experimental data.

A broad range of numerical models varying in complexity, accuracy and other features has been conceived for pollutant transport problems. It is worth underlining that accurate model implementation, calibration and testing are imperative to ensure the reliability of model results.

The models may be classified into different groups according to the problem description adopted; transport processes can be described using the Eulerian or, equivalently, the Lagrangian approach. The difference lies in the expected output: the Eulerian approach will result in pollutant concentration maps, whereas the Lagrangian one will yield trajectories of pollutant particles.

Following a general classification, the models simulating the dynamics of an effluent discharged into the fluid can also be divided into two main groups: empirical and theoretical models (MacIntyre et al., 1995; Glenn, 1997).

The most frequently applied theoretical models are: the UM3 model (Three-dimensional Update Merge), the DKHW model (Davis, Kannberg, Hirst model for Windows) and the JETLAG (Lagrangian Jet) model (Baumgartner et al., 1994; Frick et al., 2002). Some of the most widespread empirical models are the CORMIX (Cornell Mixing Zone Expert System) and the RSB (Roberts-Snyder-Baumgartner) models (Baumgartner et al., 1994; Glenn, 1997;

Frick et al., 2002).

The better understanding of the physical processes involved in the pollutants transport problem has yielded a growing development of these advanced numerical models which accurately reproduce the dispersion processes and may provide an estimate of pollutants

concentration generated by the effluent discharges. In particular, in the case of PFW releases, numerical modelling allows to simulate the dispersion process taking into account both the discharge and receiving environment conditions.

For such reasons since the 1990s several transport models simulating the initial mixing process as well as the effect of the ambient currents and turbulence in the far- field zone on an effluent discharged into the sea have been successfully developed. Here we present a brief review of some of the most representative modelling studies on the dispersion of PFW into marine environment.

Some of the previously cited numerical models have been successfully applied to evaluate the fate of PFWs discharged in coastal areas (e.g. Washburn et al, 1999; Berry, 2005;

Cianelli et al, 2008). Washburn et al. (1999) used the RSB model to perform a field and modelling study around a diffuser located in California; they demonstrated that a crucial factor controlling the exposure of organisms to PFWs around the discharge point is the depth of the effluent in the water column. Berry (2005) developed an analysis of potential environmental effects associated with PFW discharge adopting an integrated modelling approach. In particular, the CORMIX model was applied to describe the dispersion of PFW released at Sable Island Bank (Canada); the results of this work suggested that the potential risks for the environment are low due to the rapid dilution of the wastewater plume. In the following section we will summarize the results of a case study (Cianelli et al., 2008) on the dispersion of PFW discharged in the Adriatic Sea (Mediterranean Sea), where the initial mixing has been simulated by means of the UM3 model.

At present several modelling studies using various approaches have also been implemented and applied to PFWs discharged from platforms located in the main oil and gas extraction areas.

In the North Sea, where the discharge of PFW from oil and gas production reached an annual volume of almost 400 million m³/year in 2003 (e.g. Durell et al., 2006), monitoring programmes have been conducted since the mid-1990s. These local and regional field studies have been used to optimize the monitoring plan as well as to implement and validate the numerical models simulating the dispersion and fate of the PFW chemical compounds released into the marine environment.

The CHARM (Chemical Hazard Assessment and Risk Management) model (Stagg et al., 1996) was developed to predict the potential risks due to the chemicals released offshore and was validated with field measurements of concentration of selected PFW compounds.

The DREAM (Dose related Risk and Effect Assessment Model) model was applied in the Norwegian sector of the North Sea to estimate the dispersion of PAH (polycyclic aromatic hydrocarbons) (Durell et al., 2006) and to predict the ecological risks associated with PFW discharges (Neff et al., 2006). A comparison with field measurements showed that the DREAM model results complement the in situ and laboratory data and that the numerical approach represents an useful tool for PFW discharges and impact assessment (Durell et al., 2006; Neff et al., 2006).

The dispersion and dilution of PFW discharged from 95 oil platforms operating in the North Sea have also been simulated by means of the PROVANN (Produced Water in Norwegian) model (Rye et al., 1998). PROVANN is a three-dimensional model simulating the transport, dilution and degradation of chemical compounds released into the marine environment from one or more simultaneous discharge points (Reed et al., 1996). The

numerical predicted concentrations were compared with the field data; the model provided useful results in terms of potential exposures to marine biota (Rye et al., 1998).

Estimates of the PFW concentration in the Gulf of Mexico, the North Sea and the Bass Strait (Australia) were computed by means of the OOC (Offshore Operators Committee) Mud and Produced Water Discharge model (Brandsma and Smith, 1996). In a more recent work Smith et al. (2004) validated the OOC model using field data on mud and PFW discharges from platforms located respectively in California and in the Gulf of Mexico. In both studies, the model predicted plume depth and trajectory were in good agreement with field observations for a wide range of discharge and receiving environment conditions. In particular, in the near field zone the simulated PFW concentrations matched very accurately the measured data (Smith et al., 2004).

Independently on the numerical approach followed, all the previously described works demonstrate that, at present, modelling PFW dispersion both in near- and far- field zones may play a crucial role in a ―prevention first― policy and represents an important first step in the design of a decision-making action.