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.

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.

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.

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 (NH ) concentrations in sewer systems is proposed. The forecast model utilizes a simple representation of daily NH profiles and the dilution approach combined with information from online NH and flow sensors. The method utilizes an ensemble approach based on past observations to create model prediction bounds.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.