Evaluation of pollutant removal efficiency of urban stormwater wet ponds and the application of machine learning algorithms

Yang Yang; David Z Zhu; Mark R Loewen; Sherif S Ahmed; Wenming Zhang; Haibin Yan; Bert van Duin; Khizar Mahmood

doi:10.1016/j.scitotenv.2023.167119

Evaluation of pollutant removal efficiency of urban stormwater wet ponds and the application of machine learning algorithms

Sci Total Environ. 2023 Dec 20:905:167119. doi: 10.1016/j.scitotenv.2023.167119. Epub 2023 Sep 17.

Authors

Yang Yang¹, David Z Zhu², Mark R Loewen¹, Sherif S Ahmed¹, Wenming Zhang¹, Haibin Yan¹, Bert van Duin³, Khizar Mahmood⁴

Affiliations

¹ Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
² Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; School of Civil and Environmental Engineering, Ningbo University, Zhejiang 315211, China. Electronic address: dzhu@ualberta.ca.
³ Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; City & Regional Planning, City of Calgary, Calgary, AB T2P 2M5, Canada.
⁴ Climate & Environment, City of Calgary, Calgary, AB T2P 2M5, Canada.

PMID: 37717762
DOI: 10.1016/j.scitotenv.2023.167119

Abstract

Wet ponds have been extensively used for controlling stormwater pollutants, such as sediment and nutrients, in urban watersheds. The removal of pollutants relies on a combination of physical, chemical, and biological processes. It is crucial to assess the performance of wet ponds in terms of removal efficiency and develop an effective modeling scheme for removal efficiency prediction to optimize water quality management. To achieve this, a two-year field program was conducted at two wet ponds in Calgary, Alberta, Canada to evaluate the wet ponds' performance. Additionally, machine learning (ML) algorithms have been shown to provide promising predictions in datasets with intricate interactions between variables. In this study, the generalized linear model (GLM), partial least squares (PLS) regression, support vector machine (SVM), random forest (RF), and K-nearest neighbors (KNN) were applied to predict the outflow concentrations of three key pollutants: total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP). Generally, the concentrations of inflow pollutants in the two study ponds are highly variable, and a wide range of removal efficiencies are observed. The results indicate that the concentrations of TSS, TN, and TP decrease significantly from the inlet to outlet of the ponds. Meanwhile, inflow concentration, rainfall characteristics, and wind are important indicators of pond removal efficiency. In addition, ML algorithms can be an effective approach for predicting outflow water quality: PLS, GLM, and SVM have shown strong potential to capture the dynamic interactions in wet ponds and predict the outflow concentration. This study highlights the complexity of pollutant removal dynamics in wet ponds and demonstrates the potential of data-driven outflow water quality prediction.

Keywords: Machine learning; Removal efficiency; Total nitrogen; Total phosphorus; Total suspended sediment; Wet pond.