Skip to Main content Skip to Navigation

Modélisation semi-distribuée de la production et du transfert des MES, HAPs et métaux dans les eaux urbaines de temps de pluie

Abstract : Urban runoff contamination is recognized as a major source of the deterioration of the quality of surface water. Commonly used stormwater quality models have poor performance in predicting the pollutant dynamics at the surface outlet, mainly due to the lack of precise knowledge on the governing processes and the difficulties of acquiring representative and continuous databases on real sites. The main purpose of this Ph.D. thesis is to improve the state of stormwater quality modeling. It aims in particular to develop a conceptual modeling tool for stormwater quality prediction at the scale of a city district catchment, based on a deep understanding of the build-up and the wash-off. The application of commonly used stormwater build-up/wash-off models to simulate the dynamics of total suspended solids (TSS) at the outlet of the road catchment suggests that the models poorly replicate the temporal variability of the TSS concentrations unless short periods are considered. The unpredictable nature of the accumulation is largely responsible for the model failure. The evaluation of the contribution of atmospheric dry deposition to stormwater loads for polycyclic aromatic hydrocarbons (PAHs) and metals shows that atmospheric deposition is not a major source of contaminants in stormwater runoff. Thus, linking the air and water compartment in a modeling chain to have more accurate estimates of pollutant loads in stormwater runoff may not be relevant unless the direct traffic emissions are accounted for. The investigation of the wash-off process on elementary surfaces shows that the fine particles are the most likely to be mobilized and transported during a rainfall event. Stormwater samples were collected for this study using an innovative rainfall simulator that allows continuous, on-site monitoring of instantaneous flow and turbidity measurements and that can be easily transported and used on real sites. The new knowledge acquired on the build-up and wash-off processes underlines the great variability of these processes and calls into question their modeling with deterministic approaches. Hence, this knowledge is incorporated into developing a new conceptual stormwater quality model based on the stochastic drawing of event mean concentrations (EMC) of TSS and water quality parameters. The model is integrated within the hydrological model URBS. The application of this approach accounts for the spatial and temporal variability of pollutant emissions by distinguishing the contributions of each land use separately. The obtained results are promising in terms of estimating the concentration levels of TSS at the outlet of the city district catchment and replicating the general behavior of the TSS dynamics. However, improvements can be envisaged to consolidate the approach and improve its predictions. Comparison of this model with global empirical, semi-distributed conceptual and distributed physical modeling approaches shows that in terms of predictive power and stability, the stochastic-URBS and the physically distributed approaches are the most efficient. However, in terms of ease of implementation and best fit between observations and simulations, the global empirical and semi-distributed conceptual modeling approaches are the most powerful. This comparison shows that the perfect model that covers all aspects of stormwater quality modeling does not exist. The choice of the most appropriate modeling approach should mainly be driven by modeling objectives
Complete list of metadatas
Contributor : Abes Star :  Contact
Submitted on : Tuesday, September 18, 2018 - 4:10:08 PM
Last modification on : Sunday, September 27, 2020 - 5:21:13 AM


Version validated by the jury (STAR)


  • HAL Id : tel-01876672, version 1


Saja Al Ali. Modélisation semi-distribuée de la production et du transfert des MES, HAPs et métaux dans les eaux urbaines de temps de pluie. Chimie analytique. Université Paris-Est; École Doctorale des Sciences et de Technologie (Beyrouth), 2018. Français. ⟨NNT : 2018PESC1012⟩. ⟨tel-01876672⟩



Record views


Files downloads