Particle size distribution of total suspended sediments in urban stormwater runoff: Effect of land uses, precipitation conditions, and seasonal variations.

Understanding particle size distribution (PSD) of total suspended sediments in urban runoff is essential for pollutant fate and designing effective stormwater treatment measures. However, the PSDs from different land uses under different weather conditions have yet to be sufficiently studied. This research conducted a six-year water sampling program in 15 study sites to analyze the PSD of total suspended sediments in runoff. The results revealed that the median particle size decreased in the order: paved residential, commercial, gravel lane residential, mixed land use, industrial, and roads. Fine particles less than 125 µm are the dominant particles (over 75%) of total suspended sediments in runoff in Calgary, Alberta, Canada. Roads have the largest percentage of particles finer than 32 µm (49%). Gravel lane residential areas have finer particle sizes than paved residential areas. The results of PSD were compared with previous literature to provide more comprehensive information about PSD from different land uses. The impact of rainfall event types can vary depending on land use types. A long antecedent dry period tends to result in the accumulation of fine particles on urban surfaces. High rainfall intensity and long duration can wash off more coarse particles. The PSD in spring exhibits the finest particles, while fall has the largest percentage of coarse particles. Snowmelt particles are finer for the same land use than that during rainfall events because the rainfall-runoff flows are usually larger than the snowmelt flows.

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.

Impact of rainfall characteristics on urban stormwater quality using data mining framework.

Understanding the impact of rainfall characteristics on urban stormwater quality is important for stormwater management. Even though significant attempts have been undertaken to study the relationship between rainfall and urban stormwater quality, the knowledge developed may be difficult to apply in commercial stormwater management models. A data mining framework was proposed to study the impacts of rainfall characteristics on stormwater quality. A rainfall type-based calibration approach was developed to improve water quality model performance. Specifically, the relationship between rainfall characteristics and stormwater quality was studied using principal component analysis and correlation analysis. Rainfall events were classified using a K-means clustering method based on the selected rainfall characteristics. A rainfall type-based (RTB) model was independently calibrated for each rainfall type to obtain optimal parameter sets of stormwater quality models. The results revealed that antecedent dry days, average rainfall intensity, and rainfall duration were the most critical rainfall characteristics affecting the event mean concentrations (EMCs) of total suspended solids, total nitrogen, and total phosphorus, while total rainfall was found to be of negligible importance. The K-means method effectively clustered the rainfall events into four types that could represent the rainfall characteristics in the study areas. The rainfall type-based calibration approach can considerably improve water quality model accuracy. Compared to the traditional continuous simulation model, the relative error of the RTB model was reduced by 11.4 % to 16.4 % over the calibration period. The calibrated stormwater quality parameters can be transferred to adjacent catchments with similar characteristics.

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.

Land cover based simulation of urban stormwater runoff and pollutant loading.

Urban stormwater models such as PCSWMM are important tools for evaluating urban stormwater quantity and quality. However, due to the lack of consideration of land covers, traditional catchment delineation methods have defects in model precision, parameter transferability and assessment of contribution from individual land cover types. This paper used PCSWMM model as a foundation, built a new land-cover based (LCB) model and made a systematic comparison with the traditional watershed delineation tool (WDT) model to study the impacts of land cover on the simulation of stormwater runoff and pollutant loading. The models were applied to two urban catchments in Calgary, Canada. The results revealed that the LCB model performed better than the WDT model in hydrological simulation, and land cover consideration can considerably improve model accuracy. The two models showed comparable performances in simulation of total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) loading. The LCB model parameters could be regionalized based on land cover types. The hydrologic-hydraulic parameters can be satisfactorily transferred from neighboring gauged catchments to similar ungauged catchments. The transferring of water quality parameters did not perform as satisfactory. The LCB model could quantitively evaluate the contribution to runoff and pollutant loads of different land covers. Roads and roofs were found to be the major contributors to urban runoff and pollutants in the two urban catchments. Green space became important only during large storms events and its contribution could be ignored during dryer years.