Field observations of stratification in stormwater wet ponds.

Stratification is one of the fundamental physical processes that may have a significant impact on water quality in stormwater wet ponds. However, the role of thermal and chemical stratifications in governing water quality processes is not fully understood. This is in part due to the lack of detailed field measurements of sufficient governing parameters over time periods that span a wide range of environmental conditions. To fill this gap, a comprehensive 2-year field program was undertaken in two stormwater wet ponds in Calgary, Alberta, Canada, during the ice-free season from May to November in 2018 and 2019. At different locations in each pond, thermal and chemical stratifications were observed, thermocline depth and strength were determined, and continuous water velocity profiles were measured. In addition, the effect of local weather conditions on stratification, thermocline, and hydrodynamics was investigated. The results showed that the ponds had vertical water temperature differences >1 °C for 99% of the time, May to August. In addition, salt-laden inflows from road deicing salts led to strong chemical stratification up to five times stronger in the sediment forebays than in the main cells in spring. Wind-induced surface currents were insignificant, scaling at 0.3% of the wind speed with negligible impact on vertical mixing in the ponds. Our results demonstrate that the ponds' strong and prolonged stratification decreased pollutant retention capacity and caused the water at depth to become anoxic, degrading the quality of the water discharged downstream. Hence, additional consideration of stratification is required when designing new stormwater ponds.

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

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.

Stratification and its consequences in two constructed urban stormwater wetlands.

Stratification in constructed urban stormwater wetlands is one of the fundamental physical processes that affect hydrodynamics, transport and fate of stormwater pollutants. Adverse effects of stratification include decreasing pollutant retention capacity, causing the water at lower depths to become anoxic, degrading water quality and increasing stress on the downstream aquatic communities. The current study reports on a comprehensive field monitoring program of stratification and hydrodynamics in two ice-free seasons (May - October) in two constructed urban stormwater wetlands in Calgary, Canada, with different inlet, outlet, morphometric and vegetation designs. Despite their small sizes of 0.5 and 1.2 ha and shallow water depths of 0.8 m, stratification was strong and persistent in the wetlands. The response of stratification and mixing to atmospheric forcings (e.g., air temperature, atmospheric instability, rainfall depth, wind speed) and the impact of design characteristics (inlet/outlet design, water depth, surface area and aquatic vegetation) were examined and discussed. Thermal stratification, defined as a vertical temperature gradient >1 °C/m, was found to be significantly higher (up to ten times) near the inlets and last longer (up to twice) than in the main cells and the outlet basins due to the relatively cold summer inflows. The wetland with twice the permanent water volume and surface area and half the length-to-width ratio had denser submerged aquatic vegetation, higher (by up to 2 °C) water temperature and more severe (up to eight times) thermal stratification. Strong densimetric stratification and low wind stress on the water surface caused hypoxic conditions near the bed, potentially adversely affecting water quality and downstream aquatic communities.