Classifying pollutant flush signals in stormwater using functional data analysis on TSS MV curves.

Autor: Reinholdt Jensen DM; Department of Environmental and Resource Engineering, Technical University of Denmark (DTU), Bygningstorvet, Bygning 115, 2800 Kongens Lyngby, Denmark; State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences (RCEES), Chinese Academy of Sciences (CAS), 18 Shuangqing Road, Beijing 100085, China; Sino-Danish Center for Education and Research (SDC), Aarhus, Denmark and University of Chinese Academy of Sciences (UCAS), China. Electronic address: dije@env.dtu.dk., Sandoval S; University of Lyon, INSA Lyon, DEEP, EA 7429, F-69621 Villeurbanne cedex, France; University of Applied Sciences and Arts of Western Switzerland (HES-SO), HEIA-Fr, ITEC, Boulevard de Pérolles 80, 1700 Fribourg, Switzerland. Electronic address: santiago.sandoval@hefr.ch., Aubin JB; University of Lyon, INSA Lyon, DEEP, EA 7429, F-69621 Villeurbanne cedex, France. Electronic address: jean-baptiste.aubin@insa-lyon.fr., Bertrand-Krajewski JL; University of Lyon, INSA Lyon, DEEP, EA 7429, F-69621 Villeurbanne cedex, France., Xuyong L; State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences (RCEES), Chinese Academy of Sciences (CAS), 18 Shuangqing Road, Beijing 100085, China. Electronic address: xyli@rcees.ac.cn., Mikkelsen PS; Department of Environmental and Resource Engineering, Technical University of Denmark (DTU), Bygningstorvet, Bygning 115, 2800 Kongens Lyngby, Denmark. Electronic address: psmi@env.dtu.dk., Vezzaro L; Department of Environmental and Resource Engineering, Technical University of Denmark (DTU), Bygningstorvet, Bygning 115, 2800 Kongens Lyngby, Denmark. Electronic address: luve@env.dtu.dk.
Jazyk: angličtina
Zdroj: Water research [Water Res] 2022 Jun 15; Vol. 217, pp. 118394. Date of Electronic Publication: 2022 Apr 04.
DOI: 10.1016/j.watres.2022.118394
Abstrakt: Pollution levels in stormwater vary significantly during rain events, with pollutant flushes carrying a major fraction of an event pollutant load in a short period. Understanding these flushes is thus essential for stormwater management. However, current studies mainly focus on describing the first flush or are limited by predetermined flush categories. This study provides a new perspective on the topic by applying data-driven approaches to categorise Mass Volume (MV) curves for TSS into distinct classes of flush tailored to specific monitoring location. Functional Data Analysis (FDA) was used to investigate the dynamics of MV curves in two large data sets, consisting of 343 measured events and 915 modelled events, respectively. Potential links between classes of MV curves and combinations of rain characteristics were explored through a priori clustering. This yielded correct class assignments for 23-63% of the events using different combinations of MV curve clustering and rainfall characteristics. This suggests that while global rainfall characteristics influence flush, they are not sufficient as sole explanatory variables of different flush phenomena, and additional explanatory variables are needed to assign MV curves into classes with a predictive power that is suitable for e.g. design of stormwater control measures. Our results highlight the great potential of the FDA methodology as a new approach for classifying, describing, and understanding pollutant flush signals in stormwater.
(Copyright © 2022. Published by Elsevier Ltd.)
Databáze: MEDLINE
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