|Modelling metal speciation in the Scheldt Estuary: combining a flexible-resolution transport model with empirical functions|Elskens, M.; Gourgue, O.; Baeyens, W.; Chou, L.; Deleersnijder, E.; Leermakers, M.; de Brauwere, A. (2014). Modelling metal speciation in the Scheldt Estuary: combining a flexible-resolution transport model with empirical functions. Sci. Total Environ. 476-477: 346-358. hdl.handle.net/10.1016/j.scitotenv.2013.12.047
In: Science of the Total Environment. Elsevier: Amsterdam. ISSN 0048-9697; e-ISSN 1879-1026, meer
Modelling; Metal speciation; Scheldt; Estuary; SLIM; Data analysis
|Auteurs|| || Top |
- Elskens, M.
- Gourgue, O.
- Baeyens, W.
- Chou, L.
- Deleersnijder, E.
- Leermakers, M.
- de Brauwere, A.
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.