Land use and rainfall influences on bacterial levels and sources in stormwater ponds.

Urban stormwater runoff is a known source of microbial contamination of stormwater ponds. However, less is known about the influences of land use and rainfall on microbial quality over time in these receiving waters. In this study, two fecal indicator bacteria (FIB), namely Escherichia coli and thermotolerant coliforms, were monitored in three stormwater ponds in Calgary, Alberta, Canada. The stormwater ponds were selected due to their potential as water sources for beneficial uses such as irrigation, which requires lower water quality than drinking water, thereby alleviating the pressure on the city's potable water demands. The selected stormwater ponds vary in size and shape, contribution catchment size, and percentages of several primary land use types. Microbial source tracking for human, dog, seagull, Canada goose, ruminant, and muskrat was also conducted to determine sources of bacterial contamination in the stormwater ponds. Sampling was conducted near the pond surface and adjacent to the shoreline, specifically near the outfalls that discharge stormwater runoff into the ponds and the inlets that convey water out of the ponds. Overall, the FIB concentrations in the vicinity of pond outfalls were significantly or relatively higher than those near pond inlets. The contamination in the McCall Lake and the Country Hills stormwater ponds showed higher amounts of human markers (40 to 60%) compared to the Inverness stormwater pond (< 20%), which coincided with their higher FIB concentration medians. The results revealed that stormwater drained from catchments with a higher percentage of commercial land use was more contaminated than those with primary residential land use, while the impacts of residential development on the FIB levels in the Inverness stormwater pond were not obvious. Furthermore, FIB concentrations in the ponds increased in response to both rain events and inter-event dry periods, with human-specific markers being predominant despite the high levels of animal markers during inter-event dry periods. Human-origin sources might be among the main microbial loading contributors in the pond catchments in general. All these findings can inform the development or improvement of measures for mitigating microbial pollution, strategies for reusing stormwater, and maintenance programs.

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.

A new LID spatial allocation optimization system at neighborhood scale: Integrated SWMM with PICEA-g using MATLAB as the platform.

Despite the growing interest, limited studies have been conducted on LID spatial allocation optimization (SAO) at neighborhood scale, and no study has applied modifications to the optimization algorithm to improve its performance. In this study, such a new LID SAO system was proposed, which integrated a hydrological computing engine (SWMM) with an optimization algorithm (PICEA-g) using a programming language (MATLAB) as the platform. The specific modifications to the PICEA-g algorithm include: new methodologies for initializing candidate solutions, defining goal vector boundaries and enhanced genetic operators. The new LID SAO system was tested in a typical urban residential neighborhood in western Canada, and optimal solutions for LID implementation (bioretention, rain garden, permeable pavement and green roof) were obtained. The results showed that promising hydrologic benefits of reducing peak flow rate and total volume of stormwater runoff from the catchment, can be achieved with a relatively low cost. The LID SAO system provides users with flexibility and feasibility for a variety of drainage locations, scales and objectives (e.g., water quality).

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.

Nutrient leaching behavior of green roofs: Laboratory and field investigations.

Despite the benefits of green roofs in managing stormwater quality, green roofs especially at their early age might leach nutrients. Research in this regard is still very limited. Therefore, this paper conducted both the laboratory and field observations to characterize and model the leaching of nutrients including nitrogen (N) and phosphorus (P) and to examine the discrepancy in knowledge produced from these two settings. The experiment revealed that the higher the initial nutrient contents of media were, the higher the degree of nutrient leaching was. The nutrient leaching from both the laboratory cells and the field green roof declined temporally, which was largely explained by the cumulative inflow. The semi-physically based nutrient leaching model generally captured the nutrient leaching from both the laboratory cells (R2 in the range of 0.87-0.98) and the field green roof (R2 in the range of 0.28-0.86). The mass balance analysis for the laboratory cells demonstrated that the masses of nutrients leached in outflow were 85-112% of the nutrients reduced in media in general (except P of two laboratory cells). The analysis and modeling results supported that media was the primary source for nutrients leached and the pattern of nutrient leaching was consistent with wash-off being the dominant process. The results also revealed the difference in the P leaching between the laboratory cells and the field green roof. Apart from the wash-off, other chemical and biological processes and/or nutrient sources might play non-negligible roles on the P leaching of the field green roof, implied by the relatively low performance of the models (R2 of approximately 0.30 in both the regression analysis and the nutrient leaching model). The difference observed between the laboratory experiment and the field observation also calls into attention when translating knowledge derived from laboratory experiments into real practice.

The impact of media, plants and their interactions on bioretention performance: A review.

Bioretention systems have gained considerable popularity as a more natural approach to stormwater management in urban environments. The choice of bioretention media is frequently cited as one of the critical design parameters with the ultimate impact on the performance of the system. The goal of this review is to highlight data that challenge the importance of media as being the dominant design parameter and argue that the long-term performance is shaped by the interactions between media and the living components of a bioretention system, especially vegetation. Some of the key interactions are related to the impact of plant roots on media pore structure, which has implications on infiltration, storage capacity, and treatment. Another relevant interaction pertains to evapotranspiration and the associated impacts on the water balance and the water quality performance of bioretention systems. The impacts of vegetation on the media are highlighted and actual, as well as potential, impacts of plant-media interactions on bioretention performance are presented.

Response of green roof performance to multiple hydrologic and design variables: a laboratory investigation.

Multiple factors affect green roof performance and their effects might vary at different stages of operation. This paper aimed to link green roof performance to hydrologic variables (antecedent moisture condition (AMC) and rainfall intensity) and design variables (growing medium (GM) type and depth) under multiple dimensions at the early stage of operation using laboratory experiment data. The results showed that the AMC is the most influential factor of hydrologic performance, whereas the GM type appeared to primarily affect the nutrient levels of the outflow. The significant main effects of other variables and interaction effects between two variables point to challenges in green roof design.

Note on sediment removal efficiency in oil-grit separators.

Oil-grit separators (OGSs) are one type of best management practice, designed to remove oil and grit from stormwater runoff (e.g., from parking lots and paved roads). This note examines scaling parameters for OGS removal efficiency. Three dimensionless parameters are chosen as scaling parameters: Hazen number (Ha), Reynolds number (Re) and Froude number (Fr). The Hazen number is a ratio of hydraulic residence time to particle settling time. The Reynolds number measures the surrounding turbulence effects on sediment removal efficiency. The Froude number represents the ratio of inertial and gravitational forces, which indicates the influence of gravity on fluid motion. The collected data from the literature on sediment removal in OGSs can be represented by a single curve when the Hazen, Reynolds, and Froude numbers are combined into a new scaling parameter (HRF = Ha(Re/Fr)). A general form is proposed to correlate the sediment removal efficiency with this new parameter. This generalized prediction method can be used as a preliminary performance indicator for OGS units. The obtained curve can also be used to adjust raw laboratory and field measurement data to improve the evaluation of the performance of various OGSs.

Experimental study of hydraulics and sediment capture efficiency in catchbasins.

Catchbasins (also known as gully pot in the UK and Australia) are used to receive surface runoff and drain the stormwater into storm sewers. The recent interest in catchbasins is to improve their effectiveness in removing sediments in stormwater. An experimental study was conducted to examine the hydraulic features and sediment capture efficiency in catchbasins, with and without a bottom sump. A sump basin is found to increase the sediment capture efficiency significantly. The effect of inlet control devices, which are commonly used to control the amount of flow into the downstream storm sewer system, is also studied. These devices will increase the water depth in the catchbasin and increase the sediment capture efficiency. Equations are developed for predicting the sediment capture efficiency in catchbasins.