Taxonomy of influential factors for predicting pollutant first flush in urban stormwater runoff

被引:51
|
作者
Perera, Thamali [1 ,2 ]
McGree, James [1 ]
Egodawatta, Prasanna [1 ]
Jinadasa, K. B. S. N. [3 ]
Goonetilleke, Ashantha [1 ]
机构
[1] Queensland Univ Technol, Sci & Engn Fac, GPO Box 2434, Brisbane, Qld 4001, Australia
[2] Univ Srijayewardenepura, Dept Math, Nugegoda 10250, Sri Lanka
[3] Univ Peradeniya, Dept Civil Engn, Peradeniya 20400, Sri Lanka
关键词
Pollutant first flush; Stormwater treatment design; Classification and regression tree; Random forest; Stormwater quality; Stormwater pollutant processes; VOLUME-SIZING METHOD; WASH-OFF; REGRESSION; 1ST-FLUSH; BUILDUP; SOLIDS; TREES;
D O I
10.1016/j.watres.2019.115075
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Pollutant first flush in urban stormwater runoff is an important phenomenon influenced by a range of rainfall and catchment related variables. Even though numerous studies have been undertaken to mathematically define the first flush and the influential variables of first flush, limited research have been carried out to rank such variables in terms of their level of importance in generating first flush. Identifying the degree of importance of the variables is critical for accurate predictions of first flush occurrence and understanding the main drivers of first flush. This research study undertook a comprehensive analysis of the variables influencing the predictions of first flush occurrence and their relative importance. The study results are expected to contribute to more accurate predictions of first flush by affording greater importance to the highly ranked factors and their impacts. The study outcomes confirmed that total rainfall depth was the most important variable influencing the prediction of first flush events while the maximum intensity was the second. Rain duration, runoff depth, runoff peak and average intensity were the next four most important variables. Antecedent dry period and effective impervious area fraction had relatively low ranking while the time of concentration and the event mean concentration were found to be the least important variables. Furthermore, the study outcomes highlight that the use of a combination of variables and due consideration of their interactions can yield better results than considering their individual roles. (C) 2019 Elsevier Ltd. All rights reserved.
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页数:10
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