Turbidity informed real-time control of a dry extended detention basin: a case study. Issue 10 (20th January 2022)

• Record Type: Journal Article
• Authors: Akin, Aaron A.; Hathaway, Jon M.; Khojandi, Anahita
• Is Part Of: Environmental science. Volume 8:Issue 10(2022)
• Page Start: 2040
• Page End: 2051
• Publication Date: 2022-01-20
• Subjects: Water-supply -- Periodicals; Water security -- Periodicals; Water resources development -- Periodicals; Water chemistry -- Periodicals; 553.705
• DOI: 10.1039/d1ew00654a
• Languages: English
• ISSNs: 2053-1400

Abstract

Abstract : This research investigated the impact and use of real-time water quality data in a dry extended detention basin retrofitted with a controllable valve and a turbidity sensor with the goal of more consistently meeting water quality objectives. Abstract : Dry extended detention basins are static stormwater infrastructure, unable to adapt to shifts in water quality caused by urbanization in their source watersheds or long-term changes in rainfall patterns. As a potential solution to these problems, this research investigated the impact and use of real-time water quality data in a dry extended detention basin retrofitted with a controllable valve and a turbidity sensor with the goal of more consistently meeting water quality objectives. When rainfall was detected, the basin's valve would close and detain all water until either a maximum allowable detention time was reached, or turbidity values fell below a predetermined threshold. This method was shown to produce highly variable detention times after rainfall events which highlights the advantages an adaptive system has over a traditional static system or one which uses predetermined detention times to meet water quality objectives. To investigate if turbidity-based controls could operate effectively in the future if the turbidity sensor were to be removed, an advantage for economical resource allocation, several modeling approaches were evaluated to estimate the detention time of the system based on observed basin stage and precipitation data. Two of these models, a logistic regression model and a long short-term memory (LSTM) model, proved accurate in estimating the necessary detention time of the system. With this system's ability to meet water quality objectives more consistently when real-time water quality data were integrated into the decision framework, this study lays the groundwork for other applications where improved water quality is the goal.