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.

Quantification and characterization of additives, plasticizers, and small microplastics (5-100 µm) in highway stormwater runoff.

Highway stormwater (HSW) runoff is a significant pathway for transferring microplastics from land-based sources to the other surrounding environmental compartments. Small microplastics (SMPs, 5-100 µm), additives, plasticizers, natural, and nonplastic synthetic fibers, together with other components of micro-litter (APFs), were assessed in HSW samples via Micro-FTIR; oleo-extraction and purification procedures previously developed were optimized to accomplish this goal. The distribution of SMPs and APFs observed in distinct HSW runoff varied significantly since rainfall events may play a crucial role in the concentration and distribution of these pollutants. The SMPs' abundance varied from 11932 ± 151 to 18966 ± 191 SMPs/L. The dominating polymers were vinyl ester (VE), polyamide 6 (PA6), fluorocarbon, and polyester (PES). The APFs' concentrations ranged from 12825 ± 157 to 96425 ± 430 APFs/L. Most APFs originated from vehicle and tire wear (e.g., Dioctyl adipate or 5-Methyl-1H-benzotriazole). Other sources of these pollutants might be pipes, highway signs, packaging from garbage debris, road marking paints, atmospheric deposition, and other inputs. Assessing SMPs in HSW runoff can help evaluating the potential threat they may represent to receiving water bodies and air compartments. Besides, APFs in HSW runoff may be efficient proxies of macro- and microplastic pollution.

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.

The transition of WRRF models to digital twin applications.

Digital Twins (DTs) are on the rise as innovative, powerful technologies to harness the power of digitalisation in the WRRF sector. The lack of consensus and understanding when it comes to the definition, perceived benefits and technological needs of DTs is hampering their widespread development and application. Transitioning from traditional WRRF modelling practice into DT applications raises a number of important questions: When is a model's predictive power acceptable for a DT? Which modelling frameworks are most suited for DT applications? Which data structures are needed to efficiently feed data to a DT? How do we keep the DT up to date and relevant? Who will be the main users of DTs and how to get them involved? How do DTs push the water sector to evolve? This paper provides an overview of the state-of-the-art, challenges, good practices, development needs and transformative capacity of DTs for WRRF applications.

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.

Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning.

Wastewater treatment plants (WWTPs) are designed to eliminate pollutants and alleviate environmental pollution resulting from human activities. However, the construction and operation of WWTPs consume resources, emit greenhouse gases (GHGs) and produce residual sludge, thus require further optimization. WWTPs are complex to control and optimize because of high non-linearity and variation. This study used a novel technique, multi-agent deep reinforcement learning (MADRL), to simultaneously optimize dissolved oxygen (DO) and chemical dosage in a WWTP. The reward function was specially designed from life cycle perspective to achieve sustainable optimization. Five scenarios were considered: baseline, three different effluent quality and cost-oriented scenarios. The result shows that optimization based on LCA has lower environmental impacts compared to baseline scenario, as cost, energy consumption and greenhouse gas emissions reduce to 0.890 CNY/m3-ww, 0.530 kWh/m3-ww, 2.491 kg CO2-eq/m3-ww respectively. The cost-oriented control strategy exhibits comparable overall performance to the LCA-driven strategy since it sacrifices environmental benefits but has lower cost as 0.873 CNY/m3-ww. It is worth mentioning that the retrofitting of WWTPs based on resources should be implemented with the consideration of impact transfer. Specifically, LCA-SW scenario decreases 10 kg PO4-eq in eutrophication potential compared to the baseline within 10 days, while significantly increases other indicators. The major contributors of each indicator are identified for future study and improvement. Last, the authors discussed that novel dynamic control strategies required advanced sensors or a large amount of data, so the selection of control strategies should also consider economic and ecological conditions. In a nutshell, there are still limitations of this work and future studies are required.

Modelling the fate of micropollutants in integrated urban wastewater systems: Extending the applicability to pharmaceuticals.

Pharmaceutical active compounds (PhACs) are a category of micropollutants frequently detected across integrated urban wastewater systems. Existing modelling tools supporting the evaluation of micropollutant fate in such complex systems, such as the IUWS_MP model library (which acronym IUWS stands for Integrated Urban Wastewater System), do not consider fate processes and fractions that are typical for PhACs. This limitation was overcome by extending the existing IUWS_MP model library with new fractions (conjugated metabolites, sequestrated fraction) and processes (consumption-excretion, deconjugation). The performance of the extended library was evaluated for five PhACs (carbamazepine, ibuprofen, diclofenac, paracetamol, furosemide) in two different integrated urban wastewater systems where measurements were available. Despite data uncertainty and the simplicity of the modelling approach, chosen to minimize data requirements, model prediction uncertainty overlapped with the measurements ranges across both systems, stressing the robustness of the proposed modelling approach. Possible applications of the extended IUWS_MP model library are presented, illustrating how this tool can support urban water managers in reducing environmental impacts from PhACs discharges.

Reconstruction of corrupted datasets from ammonium-ISE sensors at WRRFs through merging with daily composite samples.

Long-term, continuous datasets of high quality are important for instrumentation, control, and automation efforts of wastewater resources recovery facility (WRRFs). This study presents a methodology to increase the reliability of measurements from ammonium ion-selective electrodes (ISEs). This is done by correcting corrupted ISE data with a data source that often is available at WRRFs (volume-proportional composite samples). A yearlong measurement campaign showed that the existing standard protocols for sensor maintenance might still create corrupted dataset, with poor sensor recalibrations responsible for abrupt and unrealistic jumps in the measurements. The proposed automatic correction methodology removes both recalibration jumps and signal drift by using information from composite samples that already are taken for reporting to legal authorities. Results showed that the developed methodology provided a continuous, high-quality time series without the major data quality issues of the original signal. In fact, the signal was improved for 87% of days when a reference sample was available. The effect of correcting the data before use in a data-driven software sensor was also investigated. The corrected dataset led to noticeably smaller day-to-day variations in estimated NH4+ loads, and to large improvements on both median estimates and prediction bounds. The long time series allowed for an investigation of how much training data that is required to fit a software sensor, which provides estimates that are representative for the entire study period. The results showed that 8 weeks of data allowed for a good median estimate, while 16 weeks are required for obtaining good 80% prediction bounds. Overall, the proposed method can increase the applicability of relatively cheaper ISE sensors for ICA application within WRRFs.

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.

Risk assessment of contaminants of emerging concern in the context of wastewater reuse for irrigation: An integrated modelling approach.

Direct reuse of reclaimed wastewater (RWW) in agriculture has recently received increasing attention as a possible solution to water scarcity. The presence of contaminants of emerging concern (CECs) in RWW can be critical, as these chemicals can be uptaken in irrigated crops and eventually ingested during food consumption. In the present study, an integrated model was developed to predict the fate of CECs in water reuse systems where RWW is used for edible crops irrigation. The model was applied to a case study where RWW (originating from a municipal wastewater treatment plant) is discharged into a water channel, with subsequent irrigation of silage maize, rice, wheat and ryegrass. Environmental and human health risks were assessed for 13 CECs, selected based on their chemical and hazard characteristics. Predicted CEC concentrations in the channel showed good agreement with available measurements, indicating potential ecotoxicity of some CECs (estrogens and biocides) due to their limited attenuation. Plant uptake predictions were in good agreement with existing literature data, indicating higher uptake in leaves and roots than fruits. Notably, high uncertainties were shown for weakly acidic CECs, possibly due to degradation in soil and pH variations inside plants. The human health risk due to the ingestion of wheat and rice was assessed using the threshold of toxicological concern and the hazard quotient. Both approaches predicted negligible risk for most CECs, while sulfamethoxazole and 17α-ethinylestradiol exhibited the highest risk for consumers. Alternative scenarios were evaluated to identify possible risk minimization strategies (e.g., adoption of a more efficient irrigation system).

Modelling micropollutant fate in sewer systems - A new systematic approach to support conceptual model construction based on in-sewer hydraulic retention time.

Conceptual sewer models are useful tools to assess the fate of micropollutants (MPs) in integrated wastewater systems. However, the definition of their model structure is highly subjective, and obtaining a realistic simulation of the in-sewer hydraulic retention time (HRT) is a major challenge without detailed hydrodynamic information or with limited measurements from the sewer network. This study presents an objective approach for defining the structure of conceptual sewer models in view of modelling MP fate in large urban catchments. The proposed approach relies on GIS-based information and a Gaussian mixture model to identify the model optimal structure, providing a multi-catchment conceptual model that accounts for HRT variability across urban catchment. This approach was tested in a catchment located in a highly urbanized Italian city and it was compared against a traditional single-catchment conceptual model (using a single average HRT) for the fate assessment of reactive MPs. Results showed that the multi-catchment model allows for a successful simulation of dry weather flow patterns and for an improved simulation of MP fate compared to the classical single-catchment model. Specifically, results suggested that a multi-catchment model should be preferred for (i) degradable MPs with half-life lower than the average HRT of the catchment and (ii) MPs undergoing formation from other compounds (e.g. human metabolites); or (iii) assessing MP loads entering the wastewater treatment plant from point sources, depending on their location in the catchment. Overall, the proposed approach is expected to ease the building of conceptual sewer models, allowing to properly account for HRT distribution and consequently improving MP fate estimation.

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.

Pollution levels of stormwater discharges and resulting environmental impacts.

Stormwater carries pollutants that potentially cause negative environmental impacts to receiving water bodies, which can be quantified using life cycle impact assessment (LCIA). We compiled a list of 20 metals, almost 300 organic compounds, and nutrients potentially present in stormwater, and measured concentrations reported in literature. We calculated mean pollutant concentrations, which we then translated to generic impacts per litre of stormwater discharged, using existing LCIA characterisation factors. Freshwater and marine ecotoxicity impacts were found to be within the same order of magnitude (0.72, and 0.82 CTUe/l respectively), while eutrophication impacts were 3.2E-07 kgP-eq/l for freshwater and 2.0E-06 kgN-eq/l for marine waters. Stormwater discharges potentially have a strong contribution to ecotoxicity impacts compared to other human activities, such as human water consumption and agriculture. Conversely, contribution to aquatic eutrophication impacts was modest. Metals were identified as the main contributor to ecotoxicity impacts, causing >97% of the total impacts. This is in line with conclusions from a legal screening, where metals showed to be problematic when comparing measured concentrations against existing environmental quality standards.

Revisiting conceptual stormwater quality models by reconstructing virtual state variables.

This work proposes a Bayesian non-informative reconstruction of virtual state variables in the representation of stormwater total suspended solids pollutographs by the traditional wash-off models, based on 255 rainfall events measured in a 185 ha French urban catchment. Results from event-based analyses revealed the missing representation of an essential process in the traditional rating curve (RC) model (simplest wash-off model) for 56% of the rainfall events. The unsatisfactory performances of the RC model are found to be not necessarily linked to antecedent dry weather conditions, as assumed by a great number of accumulation/wash-off models. Statistical tests suggest that non-representable rainfall events by the RC model are randomly distributed in time. The proposed Bayesian reconstructions of a potential process missed by the RC model exhibit a suitable identifiability at an intra-event scale. However, these reconstructions are not interpretable from the traditional accumulation/wash-off notions, i.e. in terms of a unique state of virtual available mass over the catchment that is decreasing over time, due to their high unrepeatability regarding their shape and their low prediction capacity for other rainfall events.

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.

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.

Velocity dependent passive sampling for monitoring of micropollutants in dynamic stormwater discharges.

Micropollutant monitoring in stormwater discharges is challenging because of the diversity of sources and thus large number of pollutants found in stormwater. This is further complicated by the dynamics in runoff flows and the large number of discharge points. Most passive samplers are nonideal for sampling such systems because they sample in a time-integrative manner. This paper reports test of a flow-through passive sampler, deployed in stormwater runoff at the outlet of a residential-industrial catchment. Momentum from the water velocity during runoff events created flow through the sampler resulting in velocity dependent sampling. This approach enables the integrative sampling of stormwater runoff during periods of weeks to months while weighting actual runoff events higher than no flow periods. Results were comparable to results from volume-proportional samples and results obtained from using a dynamic stormwater quality model (DSQM). The paper illustrates how velocity-dependent flow-through passive sampling may revolutionize the way stormwater discharges are monitored. It also opens the possibility to monitor a larger range of discharge sites over longer time periods instead of focusing on single sites and single events, and it shows how this may be combined with DSQMs to interpret results and estimate loads over extended time periods.

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.

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.

Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling.

Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessment of the parameters of water models. This paper compares a number of these techniques: the Generalized Likelihood Uncertainty Estimation (GLUE), the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), an approach based on a multi-objective auto-calibration (a multialgorithm, genetically adaptive multi-objective method, AMALGAM) and a Bayesian approach based on a simplified Markov Chain Monte Carlo method (implemented in the software MICA). To allow a meaningful comparison among the different uncertainty techniques, common criteria have been set for the likelihood formulation, defining the number of simulations, and the measure of uncertainty bounds. Moreover, all the uncertainty techniques were implemented for the same case study, in which the same stormwater quantity and quality model was used alongside the same dataset. The comparison results for a well-posed rainfall/runoff model showed that the four methods provide similar probability distributions of model parameters, and model prediction intervals. For ill-posed water quality model the differences between the results were much wider; and the paper provides the specific advantages and disadvantages of each method. In relation to computational efficiency (i.e. number of iterations required to generate the probability distribution of parameters), it was found that SCEM-UA and AMALGAM produce results quicker than GLUE in terms of required number of simulations. However, GLUE requires the lowest modelling skills and is easy to implement. All non-Bayesian methods have problems with the way they accept behavioural parameter sets, e.g. GLUE, SCEM-UA and AMALGAM have subjective acceptance thresholds, while MICA has usually problem with its hypothesis on normality of residuals. It is concluded that modellers should select the method which is most suitable for the system they are modelling (e.g. complexity of the model's structure including the number of parameters), their skill/knowledge level, the available information, and the purpose of their study.