Temporal variations in micropollutant inlet concentrations matter when planning the design and compliance assessment of stormwater control measures.

Stormwater Control Measures (SCMs) contribute to reducing micropollutant emissions from separate sewer systems. SCM planning and design are often performed by looking at the hydrological performance. Assessment of pollutant removal and the ability to comply with discharge concentration limits is often simplified due to a lack of data and limited monitoring resources. This study analyses the impact of using different time resolutions of input stormwater concentrations when assessing the compliance of SCMs against water quality standards. The behaviour of three indicator micropollutants (MP - Copper, Diuron, Benzo[a]pyrene) was assessed in four SCM archetypes, which were defined to represent typical SCM removal processes. High resolution MP data were extrapolated by using high resolution (2 min) measurements of TSS over a long period (343 events). The compliance assessment showed that high resolution input concentrations can result in a different level of compliance with water quality standards, especially when discharged concentrations are close to the limit values. This study underlines the importance of considering the high temporal variability of stormwater micropollutants when planning and designing SCMs to identify the most effective solutions for stormwater pollution management and to ensure a thorough consideration of all the environmental implications.

A decade of monitoring micropollutants in urban wet-weather flows: What did we learn?

Urban wet-weather discharges from combined sewer overflows (CSO) and stormwater outlets (SWO) are a potential pathway for micropollutants (trace contaminants) to surface waters, posing a threat to the environment and possible water reuse applications. Despite large efforts to monitor micropollutants in the last decade, the gained information is still limited and scattered. In a metastudy we performed a data-driven analysis of measurements collected at 77 sites (683 events, 297 detected micropollutants) over the last decade to investigate which micropollutants are most relevant in terms of 1) occurrence and 2) potential risk for the aquatic environment, 3) estimate the minimum number of data to be collected in monitoring studies to reliably obtain concentration estimates, and 4) provide recommendations for future monitoring campaigns. We highlight micropollutants to be prioritized due to their high occurrence and critical concentration levels compared to environmental quality standards. These top-listed micropollutants include contaminants from all chemical classes (pesticides, heavy metals, polycyclic aromatic hydrocarbons, personal care products, pharmaceuticals, and industrial and household chemicals). Analysis of over 30,000 event mean concentrations shows a large fraction of measurements (> 50%) were below the limit of quantification, stressing the need for reliable, standard monitoring procedures. High variability was observed among events and sites, with differences between micropollutant classes. The number of events required for a reliable estimate of site mean concentrations (error bandwidth of 1 around the "true" value) depends on the individual micropollutant. The median minimum number of events is 7 for CSO (2 to 31, 80%-interquantile) and 6 for SWO (1 to 25 events, 80%-interquantile). Our analysis indicates the minimum number of sites needed to assess global pollution levels and our data collection and analysis can be used to estimate the required number of sites for an urban catchment. Our data-driven analysis demonstrates how future wet-weather monitoring programs will be more effective if the consequences of high variability inherent in urban wet-weather discharges are considered.

Classifying pollutant flush signals in stormwater using functional data analysis on TSS MV curves.

Pollution levels in stormwater vary significantly during rain events, with pollutant flushes carrying a major fraction of an event pollutant load in a short period. Understanding these flushes is thus essential for stormwater management. However, current studies mainly focus on describing the first flush or are limited by predetermined flush categories. This study provides a new perspective on the topic by applying data-driven approaches to categorise Mass Volume (MV) curves for TSS into distinct classes of flush tailored to specific monitoring location. Functional Data Analysis (FDA) was used to investigate the dynamics of MV curves in two large data sets, consisting of 343 measured events and 915 modelled events, respectively. Potential links between classes of MV curves and combinations of rain characteristics were explored through a priori clustering. This yielded correct class assignments for 23-63% of the events using different combinations of MV curve clustering and rainfall characteristics. This suggests that while global rainfall characteristics influence flush, they are not sufficient as sole explanatory variables of different flush phenomena, and additional explanatory variables are needed to assign MV curves into classes with a predictive power that is suitable for e.g. design of stormwater control measures. Our results highlight the great potential of the FDA methodology as a new approach for classifying, describing, and understanding pollutant flush signals in stormwater.

Evaluating the performance of a simple phenomenological model for online forecasting of ammonium concentrations at WWTP inlets.

A simple model for online forecasting of ammonium (NH4 +) concentrations in sewer systems is proposed. The forecast model utilizes a simple representation of daily NH4 + profiles and the dilution approach combined with information from online NH4 + and flow sensors. The method utilizes an ensemble approach based on past observations to create model prediction bounds. The forecast model was tested against observations collected at the inlet of two wastewater treatment plants (WWTPs) over an 11-month period. NH4 + data were collected with ion-selective sensors. The model performance evaluation focused on applications in relation to online control strategies. The results of the monitoring campaigns highlighted a high variability in daily NH4 + profiles, stressing the importance of an uncertainty-based modelling approach. The maintenance of the NH4 + sensors resulted in important variations of the sensor signal, affecting the evaluation of the model structure and its performance. The forecast model succeeded in providing outputs that potentially can be used for integrated control of wastewater systems. This study provides insights on full scale application of online water quality forecasting models in sewer systems. It also highlights several research gaps which - if further investigated - can lead to better forecasts and more effective real-time operations of sewer and WWTP systems.

Robust model for estimating pumping station characteristics and sewer flows from standard pumping station data.

Flow data represent crucial input for reliable diagnostics of sewer functions and identification of potential problems such as unwanted inflow and infiltration. Flow estimates from pumping stations, which are an integral part of most separate sewer systems, might help in this regard. A robust model and an associated optimization procedure is proposed for estimating inflow to a pumping station using only registered water levels in the pump sump and power consumption. The model was successfully tested on one month of data from a single upstream station. The model is suitable for identification of pump capacity and volume thresholds for switching the pump on and off. These are parameters which are required for flow estimation during periods with high inflows or during periods with flow conditions triggering pump switching on and off at frequencies close to the temporal resolution of monitored data. The model is, however, sensitive within the transition states between emptying and filling to observation errors in volume and on inflow/outflow variability.

Towards model predictive control: online predictions of ammonium and nitrate removal by using a stochastic ASM.

Online model predictive control (MPC) of water resource recovery facilities (WRRFs) requires simple and fast models to improve the operation of energy-demanding processes, such as aeration for nitrogen removal. Selected elements of the activated sludge model number 1 modelling framework for ammonium and nitrate removal were included in discretely observed stochastic differential equations in which online data are assimilated to update the model states. This allows us to produce model-based predictions including uncertainty in real time while it also reduces the number of parameters compared to many detailed models. It introduces only a small residual error when used to predict ammonium and nitrate concentrations in a small recirculating WRRF facility. The error when predicting 2 min ahead corresponds to the uncertainty from the sensors. When predicting 24 hours ahead the mean relative residual error increases to ∼10% and ∼20% for ammonium and nitrate concentrations respectively. Consequently this is considered a first step towards stochastic MPC of the aeration process. Ultimately this can reduce electricity demand and cost for water resource recovery, allowing the prioritization of aeration during periods of cheaper electricity.

Definitions of event magnitudes, spatial scales, and goals for climate change adaptation and their importance for innovation and implementation.

We examine how core professional and institutional actors in the innovation system conceptualize climate change adaptation in regards to pluvial flooding-and how this influences innovation. We do this through a qualitative case study in Copenhagen with interconnected research rounds, including 32 semi-structured interviews, to strengthen the interpretation and analysis of qualitative data. We find that the term "climate change adaptation" currently has no clearly agreed definition in Copenhagen; instead, different actors use different conceptualizations of climate change adaptation according to the characteristics of their specific innovation and implementation projects. However, there is convergence among actors towards a new cognitive paradigm, whereby economic goals and multifunctionality are linked with cost-benefit analyses for adapting to extreme rain events on a surface water catchment scale. Differences in definitions can lead to both successful innovation and to conflict, and thus they affect the city's capacity for change. Our empirical work suggests that climate change adaptation can be characterized according to three attributes: event magnitudes (everyday, design, and extreme), spatial scales (small/local, medium/urban, and large/national-international), and (a wide range of) goals, thereby resulting in different technology choices.

Effect of climate change on stormwater runoff characteristics and treatment efficiencies of stormwater retention ponds: a case study from Denmark using TSS and Cu as indicator pollutants.

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.

Modelling the impact of retention-detention units on sewer surcharge and peak and annual runoff reduction.

Stormwater management using water sensitive urban design is expected to be part of future drainage systems. This paper aims to model the combination of local retention units, such as soakaways, with subsurface detention units. Soakaways are employed to reduce (by storage and infiltration) peak and volume stormwater runoff; however, large retention volumes are required for a significant peak reduction. Peak runoff can therefore be handled by combining detention units with soakaways. This paper models the impact of retrofitting retention-detention units for an existing urbanized catchment in Denmark. The impact of retrofitting a retention-detention unit of 3.3 m³/100 m² (volume/impervious area) was simulated for a small catchment in Copenhagen using MIKE URBAN. The retention-detention unit was shown to prevent flooding from the sewer for a 10-year rainfall event. Statistical analysis of continuous simulations covering 22 years showed that annual stormwater runoff was reduced by 68-87%, and that the retention volume was on average 53% full at the beginning of rain events. The effect of different retention-detention volume combinations was simulated, and results showed that allocating 20-40% of a soakaway volume to detention would significantly increase peak runoff reduction with a small reduction in the annual runoff.

A new settling velocity model to describe secondary sedimentation.

Secondary settling tanks (SSTs) are the most hydraulically sensitive unit operations in biological wastewater treatment plants. The maximum permissible inflow to the plant depends on the efficiency of SSTs in separating and thickening the activated sludge. The flow conditions and solids distribution in SSTs can be predicted using computational fluid dynamics (CFD) tools. Despite extensive studies on the compression settling behaviour of activated sludge and the development of advanced settling velocity models for use in SST simulations, these models are not often used, due to the challenges associated with their calibration. In this study, we developed a new settling velocity model, including hindered, transient and compression settling, and showed that it can be calibrated to data from a simple, novel settling column experimental set-up using the Bayesian optimization method DREAM(ZS). In addition, correlations between the Herschel-Bulkley rheological model parameters and sludge concentration were identified with data from batch rheological experiments. A 2-D axisymmetric CFD model of a circular SST containing the new settling velocity and rheological model was validated with full-scale measurements. Finally, it was shown that the representation of compression settling in the CFD model can significantly influence the prediction of sludge distribution in the SSTs under dry- and wet-weather flow conditions.

Significance of settling model structures and parameter subsets in modelling WWTPs under wet-weather flow and filamentous bulking conditions.

Current research focuses on predicting and mitigating the impacts of high hydraulic loadings on centralized wastewater treatment plants (WWTPs) under wet-weather conditions. The maximum permissible inflow to WWTPs depends not only on the settleability of activated sludge in secondary settling tanks (SSTs) but also on the hydraulic behaviour of SSTs. The present study investigates the impacts of ideal and non-ideal flow (dry and wet weather) and settling (good settling and bulking) boundary conditions on the sensitivity of WWTP model outputs to uncertainties intrinsic to the one-dimensional (1-D) SST model structures and parameters. We identify the critical sources of uncertainty in WWTP models through global sensitivity analysis (GSA) using the Benchmark simulation model No. 1 in combination with first- and second-order 1-D SST models. The results obtained illustrate that the contribution of settling parameters to the total variance of the key WWTP process outputs significantly depends on the influent flow and settling conditions. The magnitude of the impact is found to vary, depending on which type of 1-D SST model is used. Therefore, we identify and recommend potential parameter subsets for WWTP model calibration, and propose optimal choice of 1-D SST models under different flow and settling boundary conditions. Additionally, the hydraulic parameters in the second-order SST model are found significant under dynamic wet-weather flow conditions. These results highlight the importance of developing a more mechanistic based flow-dependent hydraulic sub-model in second-order 1-D SST models in the future.

Urban drainage models--simplifying uncertainty analysis for practitioners.

There is increasing awareness about uncertainties in the modelling of urban drainage systems and, as such, many new methods for uncertainty analyses have been developed. Despite this, all available methods have limitations which restrict their widespread application among practitioners. Here, a modified Monte-Carlo based method is presented that reduces the subjectivity inherent in typical uncertainty approaches (e.g. cut-off thresholds), while using tangible concepts and providing practical outcomes for practitioners. The method compares the model's uncertainty bands to the uncertainty inherent in each measured/observed datapoint; an issue that is commonly overlooked in the uncertainty analysis of urban drainage models. This comparison allows the user to intuitively estimate the optimum number of simulations required to conduct uncertainty analyses. The output of the method includes parameter probability distributions (often used for sensitivity analyses) and prediction intervals. To demonstrate the new method, it is applied to a conceptual rainfall-runoff model (MOPUS) using a dataset collected from Melbourne, Australia.

Modelling and monitoring of integrated urban wastewater systems: review on status and perspectives.

While the general principles and modelling approaches for integrated management/modelling of urban water systems already present a decade ago still hold, in recent years aspects like model interfacing and wastewater treatment plant (WWTP) influent generation as complements to sewer modelling have been investigated and several new or improved systems analysis methods have become available. New/improved software tools coupled with the current high computational capacity have enabled the application of integrated modelling to several practical cases, and advancements in monitoring water quantity and quality have been substantial and now allow the collecting of data in sufficient quality and quantity to permit using integrated models for real-time applications too. Further developments are warranted in the field of data quality assurance and efficient maintenance.

Model-based monitoring of stormwater runoff quality.

Monitoring of micropollutants (MP) in stormwater is essential to evaluate the impacts of stormwater on the receiving aquatic environment. The aim of this study was to investigate how different strategies for monitoring of stormwater quality (combining a model with field sampling) affect the information obtained about MP discharged from the monitored system. A dynamic stormwater quality model was calibrated using MP data collected by automatic volume-proportional sampling and passive sampling in a storm drainage system on the outskirts of Copenhagen (Denmark) and a 10-year rain series was used to find annual average (AA) and maximum event mean concentrations. Use of this model reduced the uncertainty of predicted AA concentrations compared to a simple stochastic method based solely on data. The predicted AA concentration, obtained by using passive sampler measurements (1 month installation) for calibration of the model, resulted in the same predicted level but with narrower model prediction bounds than by using volume-proportional samples for calibration. This shows that passive sampling allows for a better exploitation of the resources allocated for stormwater quality monitoring.

State-space adjustment of radar rainfall and skill score evaluation of stochastic volume forecasts in urban drainage systems.

Merging of radar rainfall data with rain gauge measurements is a common approach to overcome problems in deriving rain intensities from radar measurements. We extend an existing approach for adjustment of C-band radar data using state-space models and use the resulting rainfall intensities as input for forecasting outflow from two catchments in the Copenhagen area. Stochastic grey-box models are applied to create the runoff forecasts, providing us with not only a point forecast but also a quantification of the forecast uncertainty. Evaluating the results, we can show that using the adjusted radar data improves runoff forecasts compared with using the original radar data and that rain gauge measurements as forecast input are also outperformed. Combining the data merging approach with short-term rainfall forecasting algorithms may result in further improved runoff forecasts that can be used in real time control.

Comparing the impact of time displaced and biased precipitation estimates for online updated urban runoff models.

When an online runoff model is updated from system measurements, the requirements of the precipitation input change. Using rain gauge data as precipitation input there will be a displacement between the time when the rain hits the gauge and the time where the rain hits the actual catchment, due to the time it takes for the rain cell to travel from the rain gauge to the catchment. Since this time displacement is not present for system measurements the data assimilation scheme might already have updated the model to include the impact from the particular rain cell when the rain data is forced upon the model, which therefore will end up including the same rain twice in the model run. This paper compares forecast accuracy of updated models when using time displaced rain input to that of rain input with constant biases. This is done using a simple time-area model and historic rain series that are either displaced in time or affected with a bias. The results show that for a 10 minute forecast, time displacements of 5 and 10 minutes compare to biases of 60 and 100%, respectively, independent of the catchments time of concentration.

Comparison of short-term rainfall forecasts for model-based flow prediction in urban drainage systems.

Forecast-based flow prediction in drainage systems can be used to implement real-time control of drainage systems. This study compares two different types of rainfall forecast - a radar rainfall extrapolation-based nowcast model and a numerical weather prediction model. The models are applied as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real-time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 h. The best performance of the system is found using the radar nowcast for the short lead times and the weather model for larger lead times.

Partitioning of fluoranthene between free and bound forms in stormwater runoff and other urban discharges using passive dosing.

Partitioning of fluoranthene in stormwater runoff and other urban discharges was measured by a new analytical method based on passive dosing. Samples were collected at the inlet (n = 11) and outlet (n = 8) from a stormwater retention pond in Albertslund (Denmark), and for comparison samples were also obtained at a municipal wastewater treatment plant, a power plant, a contaminated site and a waste deposit in Copenhagen (n = 1 at each site). The freely dissolved concentration of (14)C-fluoranthene in the samples was controlled by equilibrium partitioning from a pre-loaded polymer and the total sample concentration measured. The measurements yielded free fractions of fluoranthene in stormwater in the range 0.04-0.15 in the inlet during the first part of the runoff events increasing to 0.3-0.5 at the end of the events and in the outlet from the retention pond. The enhanced capacity of the different stormwater samples for carrying fluoranthene was 2-23 relative to pure water and decreasing during rain events. The enhanced capacity of stormwater showed a different relationship with suspended solid concentrations than the other types of urban discharges. Partitioning of fluoranthene to dissolved organic carbon was lower than partitioning to particulate organic carbon. Partitioning of fluoranthene to particulate organic matter in the 19 stormwater samples yielded a log K(POM) of 5.18. The presented results can be used in stormwater quality modeling and assessment of efficiency of stormwater treatment systems. This work also shows the potential of the passive dosing method to obtain conversion factors between total concentrations, which are needed for comparison with water quality criteria, and freely dissolved concentrations, which are more related to toxicity and obtained by the use of most passive samplers.

A source classification framework supporting pollutant source mapping, pollutant release prediction, transport and load forecasting, and source control planning for urban environments.

PURPOSE: Implementation of current European environmental legislation such as the Water Framework Directive requires access to comprehensive, well-structured pollutant source and release inventories. The aim of this work was to develop a Source Classification Framework (SCF) ideally suited for this purpose. METHODS: Existing source classification systems were examined by a multidisciplinary research team, and an optimised SCF was developed. The performance and usability of the SCF were tested using a selection of 25 chemicals listed as priority pollutants in Europe. RESULTS: The SCF is structured in the form of a relational database and incorporates both qualitative and quantitative source classification and release data. The system supports a wide range of pollution monitoring and management applications. The SCF functioned well in the performance test, which also revealed important gaps in priority pollutant release data. CONCLUSIONS: The SCF provides a well-structured approach for European pollutant source and release classification and management. With further optimisation and demonstration testing, the SCF has the potential to be fully implemented throughout Europe.

Passive dosing to determine the speciation of hydrophobic organic chemicals in aqueous samples.

A new analytical approach to determine the speciation of hydrophobic organic analytes is presented. The freely dissolved concentration in a sample is controlled by passive dosing from silicone (poly(dimethylsiloxane)), and the total sample concentration at equilibrium is measured. The free fraction is determined as the ratio between measured concentrations in pure water and sample. (14)C-labeled fluoranthene served as model analyte, and total sample concentrations were easily measured by liquid scintillation counting. The method was applied to surface water, stormwater runoff, and wastewater. In the untreated wastewater, 61% of the fluoranthene was bound to suspended solids, 28% was associated to dissolved organic matter, and 11% was freely dissolved, while in treated wastewater, the speciation was 16% bound to suspended solids, 4% bound to dissolved organic matter, and 80% freely dissolved. The free fraction in roof runoff (85%) and surface water (91%) was markedly higher than in runoff from paved areas, which ranged from 27 to 36%. A log K(DOC) value of 5.26 was determined for Aldrich humic acid, which agrees well with reported values obtained by fluorescence quenching and solid phase microextraction (SPME). This analytical approach combines simplicity with high precision, and it does not require any phase separation steps.