Vegetation plays a major role in controlling the fate of contaminants in natural and constructed wetlands. Estimating the efficiency of contaminant removal of a wetland requires separate knowledge of the residence time statistics in the main flow channels, where the flow velocity is relatively higher, and in the more densely vegetated zones, where the velocity is smaller and most of the biochemical transformations occur. A conceptual wetland characterized by a main flow channel (MFC) and lateral vegetated zones (LVZs) is modeled here using a two-dimensional depth-averaged hydrodynamic and advection–dispersion model. The effect of vegetation is described as a flow resistance represented in the hydrodynamic model as a function of the stem density. Simulations are performed for a given flow discharge and for increasing values of the ratio between the vegetation density in the LVZs and in the MFC. Residence time distributions (RTDs) of a nonreactive tracer are derived from numerical simulations of the solute breakthrough curves (BTCs) resulting from a continuous concentration input. Results show that increasing vegetation densities produce an increasingly pronounced bimodality of the RTDs. At longer times, the RTDs decrease exponentially, with different timescales depending on the stem density ratio and other system parameters. The overall residence time distribution can be decomposed into a first component associated with the relatively fast transport in the MFC, and a second component associated with the slower transport in the LVZs. The weight of each temporal component is related to the exchange flux at the MFC-LVZ interface. A one-dimensional transport model is proposed that is capable to reproduce the RTDs predicted by the depth-averaged model, and the relationship between model and system parameters is investigated using a combination of direct and inverse modeling approaches
Wetlands are artificial ponds, designed to filter and purify running water through the contact with plant stems and roots. Wetland layouts are traditionally designed by experts through a laborious and time-consuming procedure: in principle, small patches of vegetation with purifying properties are tentatively placed, then the resulting water flow is verified by fluid dynamics simulators and when a satisfying outcome is reached, the wetland final layout is decided. This paper proposes to automate wetland design exploiting an evolutionary algorithm: a population of candidate solutions is cultivated by the evolutionary core, and their efficiency is evaluated using a state-of-the-art fluid-dynamics simulation framework. Experimental results show that the results obtained by the proposed approach are qualitatively comparable with those provided by experts, despite the complete absence of human intervention during the optimization process
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