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Stormwater pollutants can have deleterious impacts on the aquatic life of receiving water bodies. This paper presents the findings of a stormwater quality monitoring program performed in the town of Jasper in Alberta, Canada. This is one of the very few studies done on a small urban settlement to identify key pollutants of concern, characterize stormwater and identify unique pollutant sources in the town. A total of 14 stormwater quality parameters were found to be of high concern to aquatic life. The most prominent pollutants were total suspended solids (TSS), metals and phosphorus which compared well with studies conducted in large urban settlements. Tourist influx contributed to high metal and petroleum hydrocarbon loads during the peak season due to high vehicular activity. Elk were found to reside in the town during summer and their fecal droppings resulted in elevated fecal coliform concentrations. It was found that winter road salting was responsible for excessive chloride concentrations observed during the spring melt. TSS concentrations were statistically correlated with various metals as well as phosphorus using Spearman's rank correlation. It was found that the current street sweeping schedule in the town coincided with lower TSS and metal loads in stormwater.

The present study is an attempt to model the stormwater quality of a stream located in Pune, India. The city is split up into twenty-three basins (named A to W) by the Pune Municipal Corporation. The selected stream lies in the haphazardly expanded peri-urban G basin. The G basin has constructed stormwater drains which open up in this selected open stream. The runoff over the regions picks up the non-point source pollutants which are also added to the selected stream. The study becomes more complex as the stream is misused to dump trash materials, garbage and roadside litter, which adds to the stormwater pollution. Experimental investigations include eleven distinct locations on a naturally occurring stream in the G basin. Stormwater samples were collected for twenty-two storm events, for the monsoon season over four years from 2018–2021, during and after rainfall. The physicochemical characteristics were analyzed for twelve water quality parameters, including pH, Conductivity, Turbidity, Total solids (TS), Total Suspended Solids (TSS), Total Dissolved Solids (TDS), Bio-chemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Phosphate, Ammonia and Nitrate. The Water Quality Index (WQI) ranged from 46.9 to 153.9 and from 41.20 to 87.70 for samples collected during and immediately after the rainfall, respectively. Principal Component Analysis was used to extract the most significant stormwater quality parameters. To understand the non-linear complex relationship of rainfall characteristics with significant stormwater pollutant parameters, a Support Vector Regression (SVR) model with Radial Basis Kernel Function (RBF) was developed. The Support Vector Machine is a powerful supervised algorithm that works best on smaller datasets but on complex ones with the help of kernel tricks. The accuracy of the model was evaluated based on normalized root-mean-square error (NRMSE), coefficient of determination (R2) and the ratio of performance to the interquartile range (RPIQ). The SVR model depicted the best performance for parameter TS with NRMSE (0.17), R2 (0.82) and RPIQ (2.91). The unit increase or decrease in the coefficients of rainfall characteristics displays the weighted deviation in the values of pollutant parameters. Non-linear Support Vector Regression models confirmed that both antecedent dry days and rainfall are correlated with significant stormwater quality parameters. The conclusions drawn can provide effective information to decision-makers to employ an appropriate treatment train approach of varied source control measures (SCM) to be proposed to treat and mitigate runoff in an open stream. This holistic approach serves the stakeholder’s objectives to manage stormwater efficiently. The research can be further extended by selecting a multi-criteria decision-making tool to adopt the best SCM and its multiple potential combinations.

This study investigated the potential effect of climate changes on stormwater pollution runoff characteristics and the treatment efficiency of a stormwater retention pond in a 95 ha catchment in Denmark. An integrated dynamic stormwater runoff quality and treatment model was used to simulate two scenarios: one representing the current climate and another representing a future climate scenario with increased intensity of extreme rainfall events and longer dry weather periods. 100-year long high-resolution rainfall time series downscaled from regional climate model projections were used as input. The collected data showed that total suspended solids (TSS) and total copper (Cu) concentrations in stormwater runoff were related to flow, rainfall intensity and antecedent dry period. Extreme peak intensities resulted in high particulate concentrations and high loads but did not affect dissolved Cu concentrations. The future climate simulations showed an increased frequency of higher flows and increased total concentrations discharged from the catchment. The effect on the outlet from the pond was an increase in the total concentrations (TSS and Cu), whereas no major effect was observed on dissolved Cu concentrations. Similar results are expected for other particle bound pollutants including metals and slowly biodegradable organic substances such as PAH. Acute toxicity impacts to downstream surface waters seem to be only slightly affected. A minor increase in yearly loads of sediments and particle-bound pollutants is expected, mainly caused by large events disrupting the settling process. This may be important to consider for the many stormwater retention ponds existing in Denmark and across the world.

Stormwater is one of the last major untapped urban water resources that can be exploited as an alternative water source in Australia. The information in the current Australian Guidelines for Water Recycling relating to stormwater harvesting and reuse only emphasises on a limited number of stormwater quality parameters. In order to supply stormwater as a source for higher value end-uses, a more comprehensive assessment on the potential public health risks has to be undertaken. Owing to the stochastic variations in rainfall, catchment hydrology and also the types of non-point pollution sources that can provide contaminants relating to different anthropogenic activities and catchment land uses, the characterisation of public health risks in stormwater is complex, tedious and not always possible through the conventional detection and analytical methods. In this study, a holistic approach was undertaken to assess the potential public health risks in urban stormwater samples from a medium-density residential catchment. A combined chemical–toxicological assessment was used to characterise the potential health risks arising from chemical contaminants, while a combination of standard culture methods and quantitative polymerase chain reaction (qPCR) methods was used for detection and quantification of faecal indicator bacteria (FIB) and pathogens in urban stormwater. Results showed that the concentration of chemical contaminants and associated toxicity were relatively low when benchmarked against other alternative water sources such as recycled wastewater. However, the concentrations of heavy metals particularly cadmium and lead have exceeded the Australian guideline values, indicating potential public health risks. Also, high numbers of FIB were detected in urban stormwater samples obtained from wet weather events. In addition, qPCR detection of human-related pathogens suggested there are frequent sewage ingressions into the urban stormwater runoff during wet weather events. Further water quality monitoring study will be conducted at different contrasting urban catchments in order to undertake a more comprehensive public health risk assessment for urban stormwater.

Stormwater runoff is a leading cause of nitrogen (N) transport to water bodies and hence one means of water quality deterioration. Stormwater runoff was monitored in an urban residential catchment (drainage area: 3.89 hectares) in Florida, United States to investigate the concentrations, forms, and sources of N. Runoff samples were collected over 22 storm events (May to September 2016) at the end of a stormwater pipe that delivers runoff from the catchment to the stormwater pond. Various N forms such as ammonium (NH4-N), nitrate (NOx-N), dissolved organic nitrogen (DON), and particulate organic nitrogen (PON) were determined and isotopic characterization tools were used to infer sources of NO3-N and PON in collected runoff samples. The DON was the dominant N form in runoff (47%) followed by PON (22%), NOx-N (17%), and NH4-N (14%). Three N forms (NOx-N, NH4-N, and PON) were positively correlated with total rainfall and antecedent dry period, suggesting longer dry periods and higher rainfall amounts are significant drivers for transport of these N forms. Whereas DON was positively correlated to only rainfall intensity indicating that higher intensity rain may flush out DON from soils and cause leaching of DON from particulates present in the residential catchment. We discovered, using stable isotopes of NO3-, a shifting pattern of NO3- sources from atmospheric deposition to inorganic N fertilizers in events with higher and longer duration of rainfall. The stable isotopes of PON confirmed that plant material (oak detritus, grass clippings) were the primary sources of PON in stormwater runoff. Our results demonstrate that practices targeting both inorganic and organic N are needed to control N transport from residential catchments to receiving waters.

This paper reviews the current status of permeable pavement research and limitations of its applicability. This discusses the influence of design factors such as permeable pavement type, mix design of porous concrete/asphalt, aggregate materials, particle size and distribution, sub-base depth, and layer setting on hydraulic, structural, and environmental performances of the pavement. Findings of this review demonstrate that the uptake of permeable pavement systems as a stormwater best management practice is relatively limited and slow due to lack of in-depth scientific understanding and economic uncertainties. It confirms the necessity of undertaking further research to fill the knowledge gap by providing practical solutions supported by new knowledge and innovations on permeable pavemen systerms. Followings have been identified as challengers and needs for future research on permeable pavement systems: (a) unavailability of cost data and difficulties of estimation of intangible benefits; (b) co-optimising environmental, hydraulic, and structural performances by modifying design; (c) difficulties of simulating actual field condition to investigate the clogging phenomena via laboratory experiments; (d) modelling the relationship of design variations with structural, hydraulic, and environmental performance; (e) developing a standard maintenance procedure to restore infiltration capacity; and (f) improving the bearing capacity of the structure to withstand higher vehicular loads and speeds.

Environmental flow assessment frameworks have begun to consider changes to flow regimes resulting from land-use change. Urban stormwater runoff, which degrades streams through altered volume, pattern and quality of flow, presents a problem that challenges dominant approaches to stormwater and water resource management, and to environmental flow assessment. We used evidence of ecological response to different stormwater drainage systems to develop methods for input to environmental flow assessment. We identified the nature of hydrologic change resulting from conventional urban stormwater runoff, and the mechanisms by which such hydrologic change is prevented in streams where ecological condition has been protected. We also quantified the increase in total volume resulting from urban stormwater runoff, by comparing annual streamflow volumes from undeveloped catchments with the volumes that would run off impervious surfaces under the same rainfall regimes. In catchments with as little as 5-10% total imperviousness, conventional stormwater drainage, associated with poor in-stream ecological condition, reduces contributions to baseflows and increases the frequency and magnitude of storm flows, but in similarly impervious catchments in which streams retain good ecological condition, informal drainage to forested hillslopes, without a direct piped discharge to the stream, results in little such hydrologic change. In urbanized catchments, dispersed urban stormwater retention measures can potentially protect urban stream ecosystems by mimicking the hydrologic effects of informal drainage, if sufficient water is harvested and kept out of the stream, and if discharged water is treated to a suitable quality. Urban stormwater is a new class of environmental flow problem: one that requires reduction of a large excess volume of water to maintain riverine ecological integrity. It is the best type of problem, because solving it provides an opportunity to solve other problems such as the provision of water for human use.

Storm water quality models are useful tools in storm water management. Interest has been growing in analyzing existing data for developing models for urban storm water quality evaluations. It is important to select appropriate model inputs when many candidate explanatory variables are available. Model calibration and verification are essential steps in any storm water quality modeling. This study investigates input variable selection and calibration data selection in storm water quality regression models. The two selection problems are mutually interacted. A procedure is developed in order to fulfil the two selection tasks in order. The procedure firstly selects model input variables using a cross validation method. An appropriate number of variables are identified as model inputs to ensure that a model is neither overfitted nor underfitted. Based on the model input selection results, calibration data selection is studied. Uncertainty of model performances due to calibration data selection is investigated with a random selection method. An approach using the cluster method is applied in order to enhance model calibration practice based on the principle of selecting representative data for calibration. The comparison between results from the cluster selection method and random selection shows that the former can significantly improve performances of calibrated models. It is found that the information content in calibration data is important in addition to the size of calibration data.

Stormwater management modeling tools have been utilized to enhance stormwater operating systems, assess modeling system efficiency, and evaluate the impacts of urban growth on stormwater runoff and water quality. This review explores the potential of stormwater management strategies and Artificial Intelligence modeling tools in enhancing water quality. The study focuses on evaluating stormwater modeling tools for planning and improving stormwater systems, assessing modeling efficiency, and understanding the impacts of new development on stormwater runoff and water quality. Various stormwater modeling tools are compared to aid in water management in urban and rural settings, which is crucial due to increasing storm intensity from climate change. The review debates the advantages and limitations of different modeling tools, particularly in modeling stormwater quantity and quality under different scenarios. It also examines tools used for predicting and analysing stormwater runoff during storm events in diverse locations. The assessment of modeling tools is centred on their support for Green Infrastructure (GI) practices, considering factors like modeling accuracy, data availability, and requirements. The study highlights the importance of these tools in managing water in urban areas and safeguarding water sources during stormwater events. Notably, the accuracy and efficacy of stormwater modeling tools are influenced by input data quality, calibration methods, and standardization metrics, with the widely used Stormwater Management Model (SWMM) being a common modeling tool.

Urban waterlogging and the deterioration of receiving water quality caused by stormwater runoff have become increasingly significant problems. Based on the concept of combining grey and green infrastructure, a combined permeable concrete pavement (PCP) and constructed wetland (CW) system has been developed to treat stormwater runoff and enable on-site reuse. The results showed that the removal rate of suspended solids (SS) by PCP ranged from 96.61 to 99.20%; however, the chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) concentrations in the effluent did not meet the standards required for rainwater reuse. For the combined PCP-CW system, the removal rates of COD, TN and TP by the CW were 48.45–75.12%, 47.26–53.05%, and 59.04–75.28%, respectively, under different hydraulic loading (HL) rates; thus, the effluent TN concentrations did not consistently meet the reuse standards. Further optimization of aeration in different parts of the CW revealed that aeration in the middle and front sections of the wetland had the most significant effect on pollutant removal, under which the TN concentrations in the effluent met the standard required for reuse. The effluent from the combined PCP-CW system was able to fully meet the stormwater reuse standards under these optimized conditions, and the reuse of urban stormwater runoff can therefore be realized.