Online monitoring and conditional regression tree test: Useful tools for a better understanding of combined sewer network behavior
| Autor: | T. Bersinger, Thierry Pigot, Noëlle Bru, I. Le Hécho, Gilles Bareille |
|---|---|
| Přispěvatelé: | Institut des sciences analytiques et de physico-chimie pour l'environnement et les materiaux (IPREM), Université de Pau et des Pays de l'Adour (UPPA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS), Communauté d'Agglomération de Pau Pyrénées), Syndicat d'Eau et d'Assainissement du Pays de Nay, the Adour Garonne Water Agency (Agence de l'Eau Adour Garonne) (UPPA 2014-0863) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Pollution
Environmental Engineering Statistical study media_common.quotation_subject Regression trees 0208 environmental biotechnology Flux 02 engineering and technology 010501 environmental sciences Pollutant 01 natural sciences Continuous monitoring Turbidity Sewerage Environmental Chemistry [CHIM]Chemical Sciences Waste Management and Disposal 0105 earth and related environmental sciences media_common Hydrology Chemical oxygen demand 6. Clean water Combined sewer system 020801 environmental engineering 13. Climate action Environmental science Combined sewer Intensity (heat transfer) |
| Zdroj: | Science of the Total Environment Science of the Total Environment, Elsevier, 2018, 625, pp.336-343. ⟨10.1016/j.scitotenv.2017.12.239⟩ |
| ISSN: | 0048-9697 1879-1026 |
| DOI: | 10.1016/j.scitotenv.2017.12.239⟩ |
| Popis: | A good knowledge of the dynamic of pollutant concentration and flux in a combined sewer network is necessary when considering solutions to limit the pollutants discharged by combined sewer overflow (CSO) into receiving water during wet weather. Identification of the parameters that influence pollutant concentration and flux is important. Nevertheless, few studies have obtained satisfactory results for the identification of these parameters using statistical tools. Thus, this work uses a large database of rain events (116 over one year) obtained via continuous measurement of rainfall, discharge flow and chemical oxygen demand (COD) estimated using online turbidity for the identification of these parameters. We carried out a statistical study of the parameters influencing the maximum COD concentration, the discharge flow and the discharge COD flux. In this study a new test was used that has never been used in this field: the conditional regression tree test. We have demonstrated that the antecedent dry weather period, the rain event average intensity and the flow before the event are the three main factors influencing the maximum COD concentration during a rainfall event. Regarding the discharge flow, it is mainly influenced by the overall rainfall height but not by the maximum rainfall intensity. Finally, COD discharge flux is influenced by the discharge volume and the maximum COD concentration. Regression trees seem much more appropriate than common tests like PCA and PLS for this type of study as they take into account the thresholds and cumulative effects of various parameters as a function of the target variable. These results could help to improve sewer and CSO management in order to decrease the discharge of pollutants into receiving waters. |
| Databáze: | OpenAIRE |
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