Insights into the Pollutant Removal Performance of Stormwater Green Infrastructures: A Case Study of Detention Basins and Retention Ponds

Seol Jeon, Siyeon Kim, Moonyoung Lee, Heejin An, Kichul Jung, Myoung-Jin Um, Kyungjin An and Daeryong Park
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Seol Jeon: Department of Civil, Environmental and Plant Engineering, Konkuk University, Seoul 05029, Korea
Siyeon Kim: Department of Civil, Environmental and Plant Engineering, Konkuk University, Seoul 05029, Korea
Moonyoung Lee: Department of Civil, Environmental and Plant Engineering, Konkuk University, Seoul 05029, Korea
Heejin An: Department of Civil, Environmental and Plant Engineering, Konkuk University, Seoul 05029, Korea
Kichul Jung: Division for Integrated Water Management, Korea Environment Institute, Sejong 30147, Korea
Myoung-Jin Um: School of Smart City Engineering, Kyonggi University, Suwon 16227, Korea
Kyungjin An: Department of Forestry and Landscape Architecture, Konkuk University, Seoul 05029, Korea
Daeryong Park: Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Korea

IJERPH, 2021, vol. 18, issue 19, 1-28

Abstract: The quality of water has deteriorated due to urbanization and the occurrence of urban stormwater runoff. To solve this problem, this study investigated the pollutant reduction effects from the geometric and hydrological factors of green infrastructures (GIs) to more accurately design GI models, and evaluated the factors that are required for such a design. Among several GIs, detention basins and retention ponds were evaluated. This study chose the inflow, outflow, total suspended solids (TSS), total phosphorus (TP), watershed area, GI area (bottom area in detention basins and permanent pool surface area in retention ponds), and GI volume (in both detention basins and retention ponds) for analysis and applied both ordinary least squares (OLS) regression and multiple linear regression (MLR). The geometric factors do not vary within each GI, but there may be a bias due to the number of stormwater events. To solve this problem, three methods that involved randomly extracting data with a certain range and excluding outliers were applied to the models. The accuracies of these OLS and MLR models were analyzed through the percentage bias (PBIAS), Nash-Sutcliffe efficiency (NSE), and RMSE-observations standard deviation ratio (RSR). The results of this study suggest that models which consider the influent concentration combined with the hydrological and GI geometric parameters have better correlations than models that consider only a single parameter.

Keywords: green infrastructure (GI); total suspended solids (TSS); total phosphorous (TP); ordinary least squares (OLS); multilinear regression (MLR) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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