(620v) Eutrophication Control In Lakes and Reservoirs Through Integrated 3D Ecological and Hydrodynamic Models

Eutrophication in most water bodies constitutes an important environmental problem. It is caused by increasing nutrient loading associated to urban, industrial and agricultural activities. Eutrophication is associated to accelerated growth of phytoplankton (algal blooms). The presence of certain species of cyanobacteria within the blooms is of special care, as they may produce hepato- and neurotoxins that can severely compromise human and animal health. To address nutrient point sources, such as urban and industrial, much effort has been devoted to the development of wastewater treatment processes and facilities (Gernaey  et al., 2004; Karuppiah and Grossmann, 2008). However, nonpoint nutrient sources, such as those associated to agricultural activities, have not received much attention (Estrada et al., 2009). In particular, increased eutrophication can be a key feature associated to the large-scale production of biofuels from energy crops when compared to fossil fuels. The life cycle wide emissions of nutrients depend on the application and losses of fertilisers during the agricultural production of biofuel feedstocks. External restoration techniques for nonpoint sources include the construction of artificial wetlands nearby the water bodies to remove nutrients and, in this way, decrease nutrient loading. But the application of external restoration techniques is not enough to control algae growth in the middle term. Within in-lake restoration strategies, biomanipulation is based on the trophic chain theory and it has been applied to control phytoplankton growth in lakes and reservoirs. The basic idea is to keep a high grazing pressure on the phytoplankton community by the herbivore zooplankton by performing zooplanktivorous fish removal (Sondegaard et al., 2007). Another in-lake restoration strategy is hypolimniom aeration, so as to precipitate phosphorous compounds to the sediment, not becoming available for phytoplankton production. Three dimensional eutrophication models have been proposed for large lakes with horizontal uneven distributions (Hu et al., 2006) and for costal systems (Moll and Radach, 2003).

In this work, we formulate a three-dimensional eutrophication model, based on a previous one- dimensional model (Estrada et al.; 2010) for a reservoir providing drinking water to two cities. Continuity and momentum equations have been formulated for the lake, considering a stretching vertical coordinate that allows for spatially and temporally varying depth. The model is based on first principles, with ecological parameters that have been tuned with collected data from the specific reservoir under study (Estrada et al., 2009). Dynamic mass balances for three phytoplankton groups and main nutrients have also been included. The distributed parameter model is formulated within a control vector parametrization dynamic optimization framework. Numerical results provide quantitative information for planning restoration strategies over a one year horizon, as well as its effect on algae growth and nutrient concentration.

 References

V. Estrada, E. Parodi, M.S. Diaz, 2009, Determination of biogeochemical parameters in eutrophication models as large scale dynamic parameter estimation problems, Computers and Chemical Engineering, 33, 1760-1769.

V. Estrada, M.S. Diaz, 2010, Global sensitivity analysis and dynamic parameter estimation in eutrophication models, Environmental Modelling and Software, 2010, 25,1539-1551.

K.V. Gernaey, M. van Loosdrecht, M. Henze, M. Lind, S. B. Jørgensen, 2004, Activated sludge wastewater treatment plant modelling and simulation: state of the art, Environmental Modelling & Software 19, 763–783.

W. Hu, Jørgensen, S. E. and Zhang, F., 2006. A vertical-compressed three-dimensional ecological model in Lake Taihu, China. Ecol. Model. 190, 367-398

R. Karuppiah, I.E. Grossmann, 2008, Global optimization of multiscenario mixed integer nonlinear programming models arising in the synthesis of integratedwater networks under uncertainty. Computers&Chemical Engineering, 32, 145–216.

A. Moll, G. Radach, 2003, Review of three-dimensional ecological modelling related to the North Sea shelf system. Part 1: models and their results, Progress in Oceanography 57, 175–217.

E. Parodi, V. Estrada, N. Trobbiani, G. Argañaraz Bonini, 2004,  Análisis del estado trófico del Embalse Paso de las Piedras. Ecología en tiempos de Cambio. 178..

PSEnterprise, 2009,  gPROMS User guide.

M. Søndergaard, E. Jeppesen, T. L. Lauridsen, C. Skov, E. H. van Nes, R. Roijackers, E. Lammens, and R. Portielje, 2007, Lake restoration: successes, failures and long-term effects. J. Appl. Ecol., 44, 1095–1105.