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    Although sewage diversion outside of a lake's watershed is now ordinary practice in the restoration of eutrophic lakes, often the observed recovery is slower than expected and the internal load from the lake anoxic sediments is identified as a possible reason. However, in the case of combined sewer, the quantification of the residual nutrient load discharged from sewer spillways must also be questioned. In this paper, the diversion efficiency of the sewer system along the east coast of Lake Iseo, a prealpine Italian lake where eutrophication effects are still severe, is investigated. To this purpose, a representative part of the sewer system was modelled by PCSWMM and calibrated by using an extensive series of discharge measurements. Water quality monitoring during wet weather periods reveals that the first flush is common in tributary sewers, whereas it is absent along the main collector. Moreover, flow discharges are strongly affected by infiltration waters, which are controlled by the lake water level. The calibrated model, including infiltration modeling, was used to assess the annual overflow volumes and the nutrient load through a continuous 10-year simulation. Simulations were conducted both with regard to the current conditions and to a climate change scenario. Results show that the discharged residual load is at least 7 times larger than the design value, with the water infiltration contributing to 17% to the overflow volume and that non-structural practices could considerably reduce the overall impact of the sewer. This research thus provides important insight into the potential impact of combined sewer overflows on lacustrine environments and addresses effective mitigation measures in similar contexts. Copyright © 2020. Published by Elsevier Ltd.

    Citation

    Marco Pilotti, Laura Barone, Matteo Balistrocchi, Giulia Valerio, Luca Milanesi, Daniele Nizzoli. Nutrient delivery efficiency of a combined sewer along a lake challenged by incipient eutrophication. Water research. 2021 Feb 15;190:116727

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    PMID: 33333436

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