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The evaluation of rainfall influence on combined sewer overflows characteristics: the Berlin case study

机译:降雨对联合下水道溢流特征的影响评估:柏林案例研究

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摘要

The present study aims to explore the relationship between rainfall variables and water quality/ quantity characteristics of combined sewer overflows (CSOs), by the use of multivariate statistical methods and online measurements at a principal CSO outlet in Berlin (Germany). Canonical correlation results showed that the maximum and average rainfall intensities are the most influential variables to describe CSO water quantity and pollutant loads whereas the duration of the rainfall event and the rain depth seem to be the most influential variables to describe CSO pollutant concentrations. The analysis of partial least squares (PLS) regression models confirms the findings of the canonical correlation and highlights three main influences of rainfall on CSO characteristics: (i) CSO water quantity characteristics are mainly influenced by the maximal rainfall intensities, (ii) CSO pollutant concentrations were found to be mostly associated with duration of the rainfall and (iii) pollutant loads seemed to be principally influenced by dry weather duration before the rainfall event. The prediction quality of PLS models is rather low (R~2<0.6) but results can be useful to explore qualitatively the influence of rainfall on CSO characteristics.
机译:本研究旨在通过使用多元统计方法和柏林(德国)主要CSO出口处的在线测量方法,探讨降雨变量与下水道溢流(CSO)的水质/水量特征之间的关系。典型的相关结果表明,最大和平均降雨强度是描述CSO水量和污染物负荷的最有影响力的变量,而降雨事件的持续时间和降雨深度似乎是描述CSO污染物浓度的最有影响力的变量。对偏最小二乘(PLS)回归模型的分析证实了典范相关性的发现,并强调了降雨对CSO特征的三个主要影响:(i)CSO水量特征主要受最大降雨强度的影响;(ii)CSO污染物污染物浓度主要与降雨持续时间有关,并且(iii)污染物负荷似乎主要受降雨事件发生之前的干旱天气持续时间影响。 PLS模型的预测质量较低(R〜2 <0.6),但结果可用于定性探讨降雨对CSO特征的影响。

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