Sediment bacteria in an urban stream: Spatiotemporal patterns in community composition

Sediment bacterial communities play a critical role in biogeochemical cycling in lotic ecosystems. Despite their ecological significance, the effects of urban discharge on spatiotemporal distribution of bacterial communities are understudied. In this study, we examined the effect of urban discharge on the spatiotemporal distribution of stream sediment bacteria in a northeast Ohio stream. Water and sediment samples were collected after large storm events (discharge > 100 m) from sites along a highly impacted stream (Tinkers Creek, Cuyahoga River watershed, Ohio, USA) and two reference streams. Although alpha (α) diversity was relatively constant spatially, multivariate analysis of bacterial community 16S rDNA profiles revealed significant spatial and temporal effects on beta (β) diversity and community composition and identified a number of significant correlative abiotic parameters. Clustering of upstream and reference sites from downstream sites of Tinkers Creek combined with the dominant families observed in specific locales suggests that environmentally-induced species sorting had a strong impact on the composition of sediment bacterial communities. Distinct groupings of bacterial families that are often associated with nutrient pollution (i.e., Comamonadaceae, Rhodobacteraceae, and Pirellulaceae) and other contaminants (i.e., Sphingomonadaceae and Phyllobacteriaceae) were more prominent at sites experiencing higher degrees of discharge associated with urbanization. Additionally, there were marked seasonal changes in community composition, with individual taxa exhibiting different seasonal abundance patterns. However, spatiotemporal variation in stream conditions did not affect bacterial community functional profiles. Together, these results suggest that local environmental drivers and niche filtering from discharge events associated with urbanization shape the bacterial community structure. However, dispersal limitations and interactions among other species likely play a role as well.

Efficiency of blue-green stormwater retrofits for flood mitigation - Conclusions drawn from a case study in Malmö, Sweden

Coupled one-dimensional (1D) sewer and two-dimensional (2D) overland flow hydrodynamic models were constructed to evaluate the flood mitigation efficiency of a renowned blue-green stormwater retrofit, i.e. Augustenborg, in Malmö, Sweden. Simulation results showed that the blue-green stormwater systems were effective in controlling local surface flooding in inner-city catchments, having reduced the total flooded surfaces by about 70%. However, basement flooding could still be a potential problem depending on the magnitude of the inflows through combined sewer from upstream areas. Moreover, interactions between blue-green retrofits and the surrounding pipe-system were studied. It was observed that the blue-green retrofits reduced the peak flows by approximately 80% and levelled out the runoff. This is a substantial advantage for downstream pipe-bound catchments, as they do not receive a cloudburst-equivalent runoff from the retrofitted catchment, but a reduced flow corresponding to a much milder rainfall. Blue-green retrofits are more effective if primarily implemented in the upstream areas of a pipe-bound catchment since the resulting reduced runoff and levelled out discharge would benefit the entire network lying downstream. Implementing blue-green retrofits from upstream towards downstream can be considered as a sustainable approach.

Pin-pointing groundwater infiltration into urban sewers using chemical tracer in conjunction with physically based optimization model

Groundwater infiltration into sanitary sewers increases hydraulic loadings of sewage collection systems and threatens wastewater treatment efficiency. However, cost-effective approach to quantify this important process still needs to be improved in order to better manage this common issue. This paper presents a method for determining the origin and amount of groundwater entering the urban sewer system. On a catchment scale, by measuring and tracking a chemical tracer (i.e., artificial sweetener acesulfame) in the urban sewers, the magnitude of daily groundwater flows in each sub-catchment could be quantified based on a Monte Carlo chemical mass balance approach. For the study site, 7.9% of the sewer length contributed 58% of the total groundwater infiltration. In the identified high-risk sub-catchment, groundwater sources and their spatial-temporal flows could be further pinpointed and elucidated by physically based numerical self-optimization model using microbial genetic algorithm method, which was verified by on-site sewer flow measurements, as well as time-series tracer concentration patterns at the terminal outlet. It was found that the diurnal variations of groundwater seepage into sewer network was linked to the in-pipe water level associated with sewage pumps operation mode, demonstrating the importance of in-pipe water level regulation in controlling groundwater infiltration. Compared with traditional visual inspection or direct flow measurement methods, the proposed approach exhibits distinct advantages in determining groundwater sources and flows in large sewer systems.

A novel modelling framework to prioritize estimation of non-point source pollution parameters for quantifying pollutant origin and discharge in urban catchments

Stormwater runoff in urban catchments contains heavy metals (zinc, copper, lead) and suspended solids (TSS) which can substantially degrade urban waterways. To identify these pollutant sources and quantify their loads the MEDUSA (Modelled Estimates of Discharges for Urban Stormwater Assessments) modelling framework was developed. The model quantifies pollutant build-up and wash-off from individual impervious roof, road and car park surfaces for individual rain events, incorporating differences in pollutant dynamics between surface types and rainfall characteristics. This requires delineating all impervious surfaces and their material types, the drainage network, rainfall characteristics and coefficients for the pollutant dynamics equations. An example application of the model to a small urban catchment demonstrates how the model can be used to identify the magnitude of pollutant loads, their spatial origin and the response of the catchment to changes in specific rainfall characteristics. A sensitivity analysis then identifies the key parameters influencing each pollutant load within the stormwater given the catchment characteristics, which allows development of a targeted calibration process that will enhance the certainty of the model outputs, while minimizing the data collection required for effective calibration. A detailed explanation of the modelling framework and pre-calibration sensitivity analysis is presented.

Modelling transitions in urban water systems

Long term planning of urban water infrastructure requires acknowledgement that transitions in the water system are driven by changes in the urban environment, as well as societal dynamics. Inherent to the complexity of these underlying processes is that the dynamics of a system's evolution cannot be explained by linear cause-effect relationships and cannot be predicted under narrow sets of assumptions. Planning therefore needs to consider the functional behaviour and performance of integrated flexible infrastructure systems under a wide range of future conditions. This paper presents the first step towards a new generation of integrated planning tools that take such an exploratory planning approach. The spatially explicit model, denoted DAnCE4Water, integrates urban development patterns, water infrastructure changes and the dynamics of socio-institutional changes. While the individual components of the DAnCE4Water model (i.e. modules for simulation of urban development, societal dynamics and evolution/performance of water infrastructure) have been developed elsewhere, this paper presents their integration into a single model. We explain the modelling framework of DAnCE4Water, its potential utility and its software implementation. The integrated model is validated for the case study of an urban catchment located in Melbourne, Australia.

Integrated hydro-environmental impact assessment and alternative selection of low impact development practices in small urban catchments

Attention is increasingly being paid to low impact development (LID) practices in urban stormwater management. Because LID practices offer a wide variety of hydro-environmental benefits, it is often necessary to account for these benefits collectively in cost-benefit analysis and LID alternative selection. The conventional methods of quantifying these benefits, however, can hardly incorporate the preferences of decision makers, and commonly involve tedious parameter estimations. To address these shortcomings, this study adopts a relative performance evaluation method to assess the various hydro-environmental impacts of LID alternatives in small urban catchments. This study considers several categories of hydro-environmental impacts, including water balance impact, surface pollutant load abatement, and combined sewer overflow and flood risk mitigation. Several performance indicators are used for each impact category. The system-wide effectiveness of an LID alternative is then derived by the weighted aggregation of its indicator scores, which are obtained by comparing its performance with that of all of the other alternatives. The hydro-environmental impact of green roofs and bioretention cells of varying areas in New York City, U.S. are investigated in detail. The results suggest that a green roof that covers the whole catchment is as effective as a bioretention cell that covers 3%-5% of the catchment in terms of stormwater management, and that the effectiveness of a bioretention cell doubles when its surface area increases from 2% to 10% of the catchment area. These assessment results are influenced by catchment-specific assessment criteria (e.g., the high flow threshold) and management interests, which suggests that design guidelines for different catchments should be tailored to their natural and drainage characteristics. The framework used in this study allows stakeholders' interests to be reflected in LID alternative selections and the implications of different design guidelines to be thoroughly investigated.

Exploring critical pathways for urban water management to identify robust strategies under deep uncertainties

Long-term projections for key drivers needed in urban water infrastructure planning such as climate change, population growth, and socio-economic changes are deeply uncertain. Traditional planning approaches heavily rely on these projections, which, if a projection stays unfulfilled, can lead to problematic infrastructure decisions causing high operational costs and/or lock-in effects. New approaches based on exploratory modelling take a fundamentally different view. Aim of these is, to identify an adaptation strategy that performs well under many future scenarios, instead of optimising a strategy for a handful. However, a modelling tool to support strategic planning to test the implication of adaptation strategies under deeply uncertain conditions for urban water management does not exist yet. This paper presents a first step towards a new generation of such strategic planning tools, by combing innovative modelling tools, which coevolve the urban environment and urban water infrastructure under many different future scenarios, with robust decision making. The developed approach is applied to the city of Innsbruck, Austria, which is spatially explicitly evolved 20 years into the future under 1000 scenarios to test the robustness of different adaptation strategies. Key findings of this paper show that: (1) Such an approach can be used to successfully identify parameter ranges of key drivers in which a desired performance criterion is not fulfilled, which is an important indicator for the robustness of an adaptation strategy; and (2) Analysis of the rich dataset gives new insights into the adaptive responses of agents to key drivers in the urban system by modifying a strategy.

Importance of anthropogenic climate impact, sampling error and urban development in sewer system design

Urban drainage design relying on observed precipitation series neglects the uncertainties associated with current and indeed future climate variability. Urban drainage design is further affected by the large stochastic variability of precipitation extremes and sampling errors arising from the short observation periods of extreme precipitation. Stochastic downscaling addresses anthropogenic climate impact by allowing relevant precipitation characteristics to be derived from local observations and an ensemble of climate models. This multi-climate model approach seeks to reflect the uncertainties in the data due to structural errors of the climate models. An ensemble of outcomes from stochastic downscaling allows for addressing the sampling uncertainty. These uncertainties are clearly reflected in the precipitation-runoff predictions of three urban drainage systems. They were mostly due to the sampling uncertainty. The contribution of climate model uncertainty was found to be of minor importance. Under the applied greenhouse gas emission scenario (A1B) and within the period 2036-2065, the potential for urban flooding in our Swiss case study is slightly reduced on average compared to the reference period 1981-2010. Scenario planning was applied to consider urban development associated with future socio-economic factors affecting urban drainage. The impact of scenario uncertainty was to a large extent found to be case-specific, thus emphasizing the need for scenario planning in every individual case. The results represent a valuable basis for discussions of new drainage design standards aiming specifically to include considerations of uncertainty.

Effect of Artificial Solar Radiation on the Die-Off of Pathogen Indicator Organisms in Urban Floods

In the last decade, flooding has caused the death of over 60,000 people and affected over 900 million people globally. This is expected to increase as a result of climate change, increased populations and urbanisation. Floods can cause infections due to the release of water-borne pathogenic microorganisms from surcharged combined sewers and other sources of fecal contamination. This research contributes to a better understanding of how the occurrence of water-borne pathogens in contaminated shallow water bodies is affected by different environmental conditions. The inactivation of fecal indicator bacteria Escherichia coli was studied in an open stirred reactor, under controlled exposure to simulated sunlight, mimicking the effect of different latitudes and seasons, and different concentrations of total suspended solids (TSS) corresponding to different levels of dilution and runoff. While attachment of bacteria on the solid particles did not take place, the decay rate coefficient, k (d^-1), was found to depend on light intensity, I (W m^-2), and duration of exposure to sunlight, T (h d^-1), in a linear way (k = k _D+ 0.03·I and k = k _D+ 0.65·T, respectively) and on the concentration of TSS (mg L^-1), in an inversely proportional exponential way (k = k _D+ 14.57·e^-0.02·[TSS] ). The first-order inactivation rate coefficient in dark conditions, k _D= 0.37 d^-1, represents the effect of stresses other than light. This study suggests that given the sunlight conditions during an urban flood, and the concentration of indicator organisms and TSS, the above equations can give an estimate of the fate of selected pathogens, allowing rapid implementation of appropriate measures to mitigate public health risks.

Methods to model particulate matter clarification of unit operations subject to unsteady loadings

Stormwater, and also wastewater unit operations (UOs) to a much lower extent, are subject to unsteady hydrodynamic and particulate matter (PM) fluxes. Simulating fully transient clarification of hetero-disperse PM requires much greater computational expense compared to steady simulations. An alternative to fully unsteady methods are stepwise steady (SS) methods which use stepwise steady flow transport and fate to approximate unsteady PM clarification of a UO during transient hydraulic loadings such as rainfall-runoff. The rationale is reduced computational effort for computational fluid dynamics (CFD) compared to simulating continuous unsteadiness of such events. An implicit solution stepwise steady (IS³) method is one approach which builds upon previous SS methods. The IS³ method computes steady flows that are representative of unsteady PM transport throughout an unsteady loading. This method departs from some previous SS methods that assume PM fate can be simulated with an instantaneous clarifier (basin) influent flowrate coupled with a PM input. In this study, various SS methods were tested for basins of varying size and residence time to examine PM fate. Differences between SS methods were a function of turnover fraction indicating the role of unsteady flowrates on PM transport for larger basins of longer residence times. The breakpoint turnover fraction was between two and three. The IS³ method best approximated unsteady behavior of larger basins. These methods identified limitations when utilizing standard event-based loading analysis for larger basins. For basins with a turnover fraction less than two, the majority of effluent PM did not originate from the event-based flow; originating from previous event loadings or existing storage. Inter- and multiple event processes and interactions, that are dependent on this inflow turnover fraction, are not accounted for by single event-based inflow models. Results suggest the use of long-term continuous modeling combined with the IS³ method for hydraulic, PM and chemical loadings to a UO when the turnover fraction is less than three.

Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning

We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. The surrogates are trained against a limited set of simulation results from a hydrodynamic (HiFi) model. Our approach reduces simulation times by one to two orders of magnitude compared to a HiFi model. It is thus slower than e.g. conceptual hydrological models, but it enables simulations of water levels, flows and surcharges in all nodes and links of a drainage network and thus largely preserves the level of detail provided by HiFi models. Comparing time series simulated by the surrogate and the HiFi model, R² values in the order of 0.9 are achieved. Surrogate training times are currently in the order of one hour. However, they can likely be reduced through the application of transfer learning and graph neural networks. Our surrogate approach will be useful for interactive workshops in initial design phases of urban drainage systems, as well as for real time applications. In addition, our model formulation is generic and future research should investigate its application for simulating other water systems.

Determination of hydrocarbons transported by urban runoff in sediments of São Gonçalo Channel (Pelotas - RS, Brazil)

A high concentration of hydrocarbons in the environment is indicative of pollution. To evaluate the effect of hydrocarbons transported by urban runoff, the present study analyzed total petroleum hydrocarbons (TPHs), aliphatic hydrocarbons (AHs), unresolved complex mixture (UCM), and n-alkanes of the sediments of the canal that cross the urban area of Pelotas, Rio Grande do Sul, Brazil. The carbon preference index (CPI), terrigenous/aquatic ratio (TAR), and pristane/phytane ratio were determined. The TPH content ranged from 177,043.7μg·kg^-1±13.4% to 5,892,667.0μg·kg^-1±5.9%. The total aliphatic content ranged from 116,268.8μg·kg^-1±11.1% to 2,393,592.6μg·kg^-1±7.7%, indicating chronic contamination of n-alkanes petrogenic and biogenic sources. The levels of hydrocarbons (TPH, AHs, and n-alkanes) were considered relatively high, confirming the effect of urban runoff on the drainage system of cities and their consequent effect on the estuarine region of Patos Lagoon and other water resources.

Unravelling the metabolism black-box in a dynamic wetland environment using a hybrid model framework: Storm driven changes in oxygen budgets

Estimating gross primary production and ecosystem respiration from oxygen data is performed widely in aquatic systems, yet these estimates can be challenged by high advective fluxes of oxygen. In this study, we develop a hybrid framework linking data-driven and process-based modelling to examine the effect of storm events on oxygen budgets in a constructed wetland. After calibration against measured flow and water temperature data over a two-month period with three storm events, the model was successfully validated against high frequency dissolved oxygen (DO) data exhibiting large diurnal fluctuations. The results demonstrated that pulses of high-DO water injected into the wetland during storm events were able to dramatically change the wetland oxygen budget. A shift was observed in the dominant oxygen inputs, from benthic net production during non-storm periods, to inflows of oxygen during storm events, which served to dampen the classical diurnal oxygen signature. The model also demonstrated the changing balance of pelagic versus benthic production and hypoxia extent in response to storm events, which has implications for the nutrient attenuation performance of constructed wetlands. The study highlights the benefit of linking analysis of high-frequency oxygen data with process-based modelling tools to unravel the varied responses of components of the oxygen budget to storm events.

Multi-scale physical model simulation of particle filtration using computational fluid dynamics

Clarifiers integrating radial cartridge filtration (RCF) are a combined unit operation variant of millennia-old sedimentation-filtration systems. Similarly, RCF is a primarily horizontal flow variant with flow orthogonal to gravity and a radial velocity gradient, in contrast to traditional deep-bed vertical filtration. These granular filters function at lower finite granular Reynolds numbers. A proposed computational fluid dynamics framework, implementing the Navier-Stokes equations, couples a pore-scale filter model with a macroscopic scale sedimentation-filtration model to create a tool examining non-Brownian particle separation. Validation is conducted using previous physical testing from a full-scale sedimentation-filtration system under steady flow and particulate loads. Model results illustrate a two-zone filtration structure with respect to particle diameter, similar to vertical filtration. The computational tool predicts particulate matter separation of 86.1% compared to 87.8% for physical testing. The physical-based computational framework does not need high-level calibration as compared to analytical, lumped, or empirical models; conferring direct extensibility to similar unit operation systems. The novel multi-scale tool simulates particulate matter fate in a modern re-incarnation of a sedimentation-filtration unit operation. The tool functions as an adjuvant that complements regulatory or certification testing. The tool can provide guidance for design or maintenance as well as system management with respect to particle fate in, and breakthrough from, granular filters in a combined unit operation.

Site selection for nature-based solutions for stormwater management in urban areas: An approach combining GIS and multi-criteria analysis

In recent years, particularly following the definition of the UN Sustainable Development Goals (SDGs) for 2030, Nature-Based Solutions (NBS) have gained considerable attention, capturing the interest of both the scientific community and policymakers committed to addressing urban environmental issues. However, the need for studies to guide decision-makers in identifying suitable locations for NBS implementation within urban stormwater management is evident. To address this gap, the present study employs a methodological approach grounded in multi-criteria analysis integrated with Geographic Information Systems (GIS) to identify areas with potential for NBS implementation. In this process, ten NBS were proposed and tested in the drainage area of a shallow tropical urban lake in Londrina, southern Brazil. Additionally, the study investigates areas hosting lower-income populations, a relevant aspect for public managers given the diverse economic subsidies required to implement NBS. Furthermore, the study incorporates a preliminary analysis that evaluates the potential ecosystem benefits to determine the most suitable NBS for a specific site. The result shows that all the ten analyzed NBS were deemed suitable for the study area. Rain barrels had the highest percentage coverage in the study area (37.1%), followed by tree pits (27.9%), and rain gardens (25.4%). Despite having the highest distribution in the basin area, rain barrels exhibited only moderate ecosystem benefits, prompting the prioritization of other NBS with more significant ecological advantages in the final integrated map. In summary, the methodology proposed showed to be a robust approach to selecting optimal solutions in densely populated urban areas.

Enhanced method for estimation of flow intercepted by drainage grate inlets on roads

Determining appropriate road drainage grate installation intervals requires an equation for estimating the flow intercepted by each grate inlet and its interception efficiency. In this study, 720 experiments were performed using a hydraulic model to estimate the flow intercepted by a grate inlet on a road. The flow calculation considered the number of lanes (2-4), longitudinal slope of the road (2-10%), transverse slope of the gutter (2-10%), and design capacity (up to 30 years of rainfall). The experimental results revealed that the flow intercepted by a grate inlet increased with increasing transverse slope of the gutter or the inlet length, thereby increasing its interception efficiency. Intercepted flow estimation equation was derived by regression analysis, and the derived equation was found to be more accurate than an existing empirical equation. The derived equation can thus be used to determine the installation of drainage grate inlets more effectively.

A drainage network-based impact matrix to support targeted blue-green-grey stormwater management solutions

Urban floods will continue to be an alarming issue worldwide due to climate change and urban expansion. The costly and less environmentally friendly grey infrastructure is not always the most adequate solution to resolve urban pluvial flooding issues. The combination of grey and blue-green infrastructures, also called hybrid infrastructure, has been considered a promising solution for urban stormwater management. Existing approaches for identifying suitable hybrid solutions frequently rely on global multi-objective optimization algorithms. We developed a pre-screening method that decomposes a drainage network into clusters of pipes connected to sub-catchments, based on pipe hydraulic characteristic that allows for the impact of infrastructure combinations (blue-green and grey) to be mapped. Four impact matrices are proposed to map the total, local, upstream, and downstream flood reduction of all possible blue-green, grey, and hybrid solutions. Using an urban catchment in Guangzhou (China) as a case study, results showed that such an exercise could identify prime candidate locations for blue-green and grey infrastructure while filtering out ineffective locations for flood reduction. Furthermore, the impact matrices enabled the identification of flood zones where blue-green infrastructure could handle flood mitigation without the need of local grey infrastructure upgrades. As such, they are not only useful for quick screening of suitable interventions for each flooded zone, but can also potentially serve as a priori knowledge before diving into the data and computationally expensive process of finding the most effective flood mitigation solutions.

When does infrastructure hybridisation outperform centralised infrastructure paradigms? - Exploring economic and hydraulic impacts of decentralised urban wastewater system expansion

We explore the dynamics of centralised and decentralised wastewater infrastructure across various scenarios and introduce novel insights into their performance regarding structural vulnerability, hydraulic capacity, and costs. This study determines circumstances under which infrastructure hybridisation outperforms traditional centralised infrastructure paradigms. We combined system analysis to map out the modelling problem with the model-based exploration of the transition space using the novel TURN-Sewers model. System diagramming was used to identify the parameters or combinations of parameters that significantly influence the performance indicators being assessed. This allowed the creation of relevant simulation scenarios to identify circumstances where a decentralised sewer system could outperform a centralised one. TURN-Sewers was applied to model the infrastructure maintenance and generation of new infrastructure over 20 years for a municipality on the Swiss Plateau, considering a population growth rate of 0.03 a-1. Results show that decentralisation in expansion areas with higher densification can outperform the hydraulic performance and structural vulnerability of expanding centralised sanitary wastewater infrastructure. Decentralised systems can also offer economic advantages when capital expenditure costs for small-scale wastewater treatment plants are significantly reduced compared to current costs, particularly at higher discount rates, e.g. reaping effects of economies of scale. The findings of this study emphasise the potential of transition pathways towards decentralisation in urban water infrastructures and the value of models that allow the exploration of this transition space.

Automated screening of control potential with spatially explicit results to support dialogue about sewer overflow reduction and beyond

For real-time control to become a standard measure for upgrading urban drainage systems, control potential screenings need to be easily integrated into the early planning processes that already take place. However, current screening methods are either not aligned with the present planning process, unrelatable for water managers or too time-consuming. We therefore developed an automated screening methodology through a co-design process with six Danish utilities. The process started out from a literature review, included interviews and workshops, and resulted in the control potential screening tool COPOTO. In the co-design process, utilities generally responded that indicators based solely on an assessment of static system attributes are insufficient. Thus, COPOTO instead post-processes the results of urban drainage simulation models that are commonly available. The decision context considered in initial planning phases was found to include environmental, economic, social and technical objectives that were highly case-dependent. When presenting CSO reduction potentials, the utilities therefore generally preferred interactive, spatially explicit visualisations that link the CSO reduction at a particular location to the storages and actuators that need to be activated. This enables water managers to discuss, for example, operational constraints of a considered control location. COPOTO provides such assessments with very limited manual and computational effort and thus facilitates the integration of real-time control into standard planning workflows of utilities.

Realistic exposure scenarios in combined sewer overflows: how temporal resolution and selection of micropollutants impact risk assessment

Organic micropollutants in combined sewer overflows (CSOs) pose a potential risk to aquatic ecosystems. Previous studies mainly reported event mean concentrations (EMCs) and often focused on a small number of substances. This study presents realistic exposure scenarios using high-temporal resolution (10-minute) data from 24 events at two CSO sites. We analyzed 49 dissolved organic micropollutants for all events and 198 for four events, including pharmaceuticals, pesticides, and road-related compounds, of which we detected 83 substances at least once. From these, we assessed the mixed chemical risk by applying acute quality criteria and evaluated how the risk assessment outcome changes for two aspects: temporal resolution and selection of substances. Our results reveal that total risk quotients (RQtot) can vary greatly within CSO events, with 10-minute data capturing peak concentrations that are missed with EMCs. Using EMCs underestimates the maximum RQtot of an event by a median factor of 4.9, up to a maximum factor of 6.9. When comparing a selection of 20 substances from the Swiss Waters Protection Ordinance to a broader list of 49 substances commonly detected at CSOs and a comprehensive list of 198 substances, the estimated RQtot increases between 1.1 to 2.3-fold. RQtot values exceed the threshold of 1 in 75 % of the events, requiring further dilution in the receiving water body. All three pollutant classes (pharma, pesticide, road) drive the total risk, and no specific phase during overflow events consistently poses higher risk than other phases, which challenges the design of effective mitigation measures. Furthermore, the exposure scenarios presented here offer essential input for future ecotoxicological research as they reveal high short-term fluctuations in RQtot whose ecological significance is still largely unknown.

Rooftop water harvesting for managed aquifer recharge and flood mitigation in tropical cities: Towards a strategy of co-benefit evaluations in João Pessoa, northeast Brazil

Intense urbanisation in many coastal areas has led to intensification of groundwater consumption, while reducing permeable areas and increasing the frequency and magnitude of flooding. Among the potential strategies to compensate for these adverse effects, which are expected to become worse as a result of climate change, rooftop rainwater harvesting (RWH) in combination with managed aquifer recharge (MAR), may be indicated. This work investigated the performance of different configurations of such a system, tested as a twofold sustainable stormwater and domestic water management tool in a tropical metropole (João Pessoa, Brazil). This area located over a sedimentary aquifer system illustrates the water security challenges of densely urbanised areas in southern cities. To that end, several configurations of rooftop catchments and storage volumes were evaluated, by simulating a MAR-RWH system connected to the regional unconfined aquifer (Barreiras Formation) through a 6″ diameter injection well. Rainfall-runoff-recharge processes and water balances were simulated using monitored high-temporal resolution rainfall data. The results showed that catchments ranging from 180 to 810 m², connected to tanks from 0.5 to 30.0 m³, are the optimal solutions in terms of efficient rainwater retention and peak flow reduction. These solutions provided mean annual estimates of aquifer recharge between 57 and 255 m³/yr from 2004 to 2019. The results of this study highlight the opportunity for MAR schemes to reconcile stormwater management and water supply goals.

Source-specific dynamics of organic micropollutants in combined sewer overflows

Combined sewer overflows (CSOs) discharge organic micropollutants (MPs) into open water bodies, posing potential environmental threats. Knowledge of the numbers, sources, and dynamics of MPs during CSOs is scarce but crucial for assessing their impact and developing mitigation strategies. To shed light on the dynamics of dissolved organic MPs in CSOs, we conducted high-temporal-resolution sampling (10 min composite samples) followed by liquid chromatography high-resolution mass spectrometry analysis, both target (60 substances) and nontarget, at two CSO sites in a small [17 hectares reduced (hared)] and a large (368 hared) catchment for over 10 events each. We observe similar patterns among indoor substances in the large catchment and among tire-associated compounds in both catchments, indicating source-specific behavior. Due to high and diverse concentration variability, no temporal correlations were found among indoor substances in the small catchment or among pesticides in either catchment. A random forest classifier was applied to assign nontarget time series to indoor and road sources in the large catchment. The results indicate that CSOs discharge several thousand substances from indoor sources, followed by a few hundred from outdoor sources with continuous leaching. These high numbers substantially surpass the scope of traditional target lists and underscore the importance of broad-spectrum screening methods when assessing MP contamination.

Real-time control of urban drainage systems using neuro-evolution

With climate change and urbanization, existing urban drainage systems are being stressed beyond their design capacity in many parts of the world. Real-time control (RTC) can improve the performance of these systems and reduce the need for system upgrades. However, developing optimal control policies for RTC is a challenging research area due to computational demands, high uncertainties and system dynamics. This study presents a new RTC method using neuro-evolution for controlling combined sewer overflow (CSO) in urban drainage systems. Neuro-evolution is an approach to neural network research by evolutionary algorithms. Neuro-evolution realizes RTC by training the control policy in advance, thus avoiding the online optimization process in the application period. The simulation results of the benchmark Astlingen network indicate that the trained control policy outperforms the equal filling degree strategy in terms of CSO volume reduction and robustness in the face of tank level uncertainty. The performance analysis of the typical CSO events shows that the control policy mainly makes positive contributions during 'small' CSO events rather than 'large' ones. In particular, the effectiveness of the control policy in 'small' CSO events is more prominent in the initial phase of the events compared with the final phase. This work stands to support a foundation for future studies in the control of urban water systems based on neuro-evolution.

Assimilating flow and level data into an urban drainage surrogate model for forecasting flows and overflows

It is crucial to be able to forecast flows and overflows in urban drainage systems to build good and effective real-time control and warning systems. Due to computational constraints, it may often be unfeasible to employ detailed 1D hydrodynamic models for real-time purposes, and surrogate models can be used instead. In rural hydrology, forecast models are usually built or calibrated using long historical time series of, for example, flow or level observations, but such series are typically not available for the ever-changing urban drainage systems. In the current study, we therefore used a fast, reservoir-based surrogate forecast model constructed from a 1D hydrodynamic urban drainage model. Thus, we did not rely directly on historical time series data. Forecast models should preferably be able to update their internal states based on observations to ensure the best initial conditions for each forecast. We therefore used the Ensemble Kalman filter to update the surrogate model before each forecast. Water level or flow observations were assimilated into the model either directly, or indirectly using rating curves. The model forecasts were validated against observed flows and overflows. The results showed that model updating improved the forecasts up to 2 h ahead, but also that updating using water level observations resulted in better flow forecasts than assimilation based on flow data. Furthermore, updating with water level observations was insensitive to changes in the noise formulation used for the Ensemble Kalman filter, meaning that the method is suitable for operational settings where there is often little time and data for fine-tuning.

Hydrological and water quality performance of Waste Tire Permeable Pavements: Field monitoring and numerical analysis

Permeable pavements can reduce the amount of surface runoff and peak flow rate and delay the occurrence of peak flow by allowing water to infiltrate underground similar to natural undeveloped catchments. Such suite of benefits of permeable pavements have made them one of the preferred stormwater control measures in most of the integrated land and water programs. Waste tire permeable pavements (WTPPs), as a relatively new permeable pavement technology, are designed with a surface layer made of up to 50% recycled tire particles. This study aims to investigate the hydrological performance of WTPPs to divert surface runoff and their impact on water quality. A large-scale trial in Australia was constructed and a comprehensive field performance monitoring program including double-ring infiltrometer tests and water quality testing was conducted to evaluate the performance of WTPP in real field conditions. Quality assurance tests on samples of the WTPP surface layer were conducted for permeability in the laboratory, and numerical simulations were done to estimate the surface runoff and investigate the sensitivity of the results to important design parameters. The physically-based models used for numerical simulations were developed in MUSIC X by replicating the layers of the constructed permeable pavement system as well as the impervious part of the trial site. The results indicated that the constructed system is capable of mitigating the surface runoff from the studied site, although only 25% of the discharge area was covered with WTPP. The infiltration rate of the WTPP over nine months with and without maintenance was studied. The results revealed that the infiltration rates even in areas without maintenance after nine months were found to be above the recommended values from ASCE permeable pavements task committee, but lower than the areas that were regularly maintained highlighting the importance of a regular maintenance regime for permeability recovery over time. Water quality tests were done on samples taken over a 17 month-long period indicating that the WTPP system successfully reduced most of the studied pollutants and chemical indicators, including most of the heavy metals, total suspended solids (69%) and turbidity (88%) by physically filtering the water.