Stochastic evaluation of annual micropollutant loads and their uncertainties in separate storm sewers

Abstract : This article describes a stochastic method to calculate the annual pollutant loads and its application over several years at the outlet of three catchments drained by separate storm sewers. A stochastic methodology using Monte Carlo simulations is proposed for assessing annual pollutant load, as well as the associated uncertainties, from a few event sampling campaigns and/or continuous turbidity measurements (representative of the total suspended solids concentration (TSS)). Indeed, in the latter case, the proposed method takes into account the correlation between pollutants and TSS. The developed method was applied to data acquired within the French research project “INOGEV” (innovations for a sustainable management of urban water) at the outlet of three urban catchments drained by separate storm sewers. Ten or so event sampling campaigns for a large range of pollutants (46 pollutants and 2 conventional water quality parameters: TSS and total organic carbon (TOC)) are combined with hundreds of rainfall events for which, at least one among three continuously monitored parameters (rainfall intensity, flow rate, and turbidity) is available. Results obtained for the three catchments show that the annual pollutant loads can be estimated with uncertainties ranging from 10 to 60%, and the added value of turbidity monitoring for lowering the uncertainty is demonstrated. A low inter-annual and inter-site variability of pollutant loads, for many of studied pollutants, is observed with respect to the estimated uncertainties, and can be explained mainly by annual precipitation.
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Soumis le : jeudi 27 septembre 2018 - 18:07:41
Dernière modification le : mercredi 4 septembre 2019 - 13:52:21



Ali Hannouche, Ghassan Chebbo, Claude Joannis, Johnny Gasperi, Marie-Christine Gromaire, et al.. Stochastic evaluation of annual micropollutant loads and their uncertainties in separate storm sewers. Environmental Science and Pollution Research, Springer Verlag, 2017, 24 (36), pp.28205 - 28219. ⟨10.1007/s11356-017-0384-5⟩. ⟨hal-01883198⟩



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