Combined Sewer Overflow Volume Research Articles

Estimation of the impact of combined sewer overflows on surface water quality in a sparsely monitored area

Combined sewer overflows (CSOs) can have a severe negative, local impact on surface water systems. To assure good ecological surface water quality and drinking water production that meets the demands, the impact of sewer system overflows on the surrounding water bodies for current and future climate conditions needs to be assessed. Typically, integrated, detailed hydrological and hydrodynamic water quantity and quality models are used for this purpose, but often data and computational resource requirements limit their applicability. Therefore, an alternative computationally efficient, integrated water quantity and quality model of sewer systems and their receiving surface waters is proposed to assess the impact of CSOs on surface water quality in a sparsely observed area. A conceptual model approach to estimate CSO discharges is combined with an empirical model for estimating CSO pollutant concentrations based on waste water treatment plant influent observations. Both methods are compared with observations and independent results of established reference methods as to evaluate their performance. The methodology is demonstrated by modelling the current impact of CSOs on the water abstraction area of a major drinking water production centre in Flanders, Belgium. It is concluded that the proposed conceptual models achieve similar results for daily WWTP effluent and CSO frequency, whereby the accumulated CSO volume is similar to more detailed full hydrodynamic models. Further, the estimated pollutant concentrations correspond with another dataset based on high resolution sampled overflows. As a result, the proposed computational efficient method can give insights in the impact of CSOs on the water quality at a catchment level and can be used for planning monitoring campaigns or performing analyses of e.g. the current and future water availability for a data scarce areas.

Predictive heuristic control: Inferring risks from heterogeneous nowcast accuracy

Urban Drainage Systems can cause ecological and public health issues by releasing untreated contaminated water into the environment. Real-time control (RTC), augmented with rainfall nowcast, can effectively reduce these pollution loads. This research aims to identify key dynamics in the nowcast accuracies and relate those to the performance of nowcast-informed rule-based (RB)-RTC procedures. The developed procedures are tested in the case study of Rotterdam, the Netherlands. Using perfect nowcast data, all developed procedures showed a reduction in combined sewer overflow volumes of up to 14.6%. Considering real nowcast data, it showed a strong ability to predict if no more rain was expected, whilst performing poorly in quantifying rainfall depths. No relation was found in the nowcast accuracy and the consistency of the predicted rainfall using a moving horizon. Using the real nowcast data, all procedures, with the exception of the one predicting the end of the rainfall event, showed a significant risk of operative deterioration (performing worse than the baseline RB-RTC), linked to the relative performance of the nowcast algorithm. Understanding the strengths of a nowcast algorithm can ensure the reliability of the RB-RTC procedure and can negate the need for detailed modelling studies by inferring risks from nowcast data.

Towards coordinated and robust real-time control: a decentralized approach for combined sewer overflow and urban flooding reduction based on multi-agent reinforcement learning

The real-time control (RTC) of urban drainage systems can make full use of the capabilities of existing infrastructures to mitigate combined sewer overflow (CSO) and urban flooding. Despite the benefits of RTC, it may encounter potential risks and failures, which need further consideration to enhance its robustness. Besides failures of hardware components such as sensors and actuators, the RTC performance is also sensitive to communication failures between the devices that are spatially distributed in a catchment-scale system. This paper proposes a decentralized control strategy based on multi-agent reinforcement learning to enhance communication robustness and coordinate the decentralized control agents through centralized training. To investigate different control structures, a centralized and a fully decentralized strategy are also developed based on reinforcement learning (RL) for comparison. A benchmark drainage model and a real-world drainage model are formulated as two cases, and the control agents are trained to control the orifices or pumps for CSO or flooding mitigation in each case. The three RL strategies reduce the CSO volume by 5.62-9.30% compared with a static baseline in historical rainfalls of the benchmark case and reduce the CSO and flooding volume by 14.39-21.36% compared with currently-used rule-based control in synthetic rainfalls of the real-world case. Benefitting from centralized training, the decentralized agents can achieve similar performance to the centralized agent. The decentralized control also enhances the communication robustness with smaller performance loss than the centralized control when observation communication fails, and provides a robust backup at the local level to limit the uncertainties when action commands from the centralized agent are lost. The results and findings indicate that multi-agent RL contributes to a coordinated and robust solution for RTC of urban drainage systems.

Optimization of in-pipe storage capacity use in urban drainage systems with improved DP considering the time lag of flow routing

The active control of urban drainage systems (UDSs) is playing an increasingly important role in the world threatened by urban flooding and associated disasters caused by insufficient drainage capacity. However, little research has recognized the importance of the optimal use of in-pipe storage space. To address this issue, the use of the in-pipe storage capacity was optimized in this study. A novel approach, that is, dynamic programming with successive approximation considering the time lag of flow routing (DPSA-TL), was developed to determine the control policies, in addition to the commonly used passive, rule-based control (RBC), and evolutionary algorithm (EA) strategies. A real-life urban catchment considering flooding control and combined sewer overflow (CSO) reduction was used as the case study. First of all, the potential benefit of maximizing the use of in-pipe storage space was tested using the four control strategies in three storm events, including a 3-year, 2-hour design (46.5 mm), a 5-year, 2-hour design (56.0 mm) and a 7-h historical (152.5 mm) storm events. Results indicate that DPSA-TL performed best in all cases. Without compromising the goal of flooding control, it provided 16.5%, 12.6%, and 3.0% reductions in CSO volume for the three storm events when compared with the passive strategy. Due to the limited capacity of in-pipe storage, the relative improvement diminished as the total rainfall depth increased. Then, control strategies were further applicated to the real-time operation. DPSA-TL was found to be the best alternative for CSO control, with the CSO volume reduced by 14.7%, 11.4%, and 2.5% in the three storm events, respectively. The findings suggest that the performance of UDS can be significantly improved by optimizing the use of in-pipe storage capacity, and the proposed method is effective in the offline optimization and real-time control of UDSs.

Regression modeling of combined sewer overflows to assess system performance.

Combined sewer overflows (CSOs) occur when untreated raw sewage mixed with rainwater, runoff, or snowmelt is released during or after a storm in any community with a combined sewer system (CSS). Climate change makes CSOs worse in many locales; as the frequency and severity of wet weather events increases, so do the frequency and volume of CSO events. CSOs pose risks to humans and the environment, and as such, CSS communities are under regulatory pressure to reduce CSOs. Yet, CSS communities lack the tools needed, such as performance indicators, to assess CSS performance. Using the city of Cumberland, Maryland as a case study, we use public data on CSOs and precipitation over a span of 16 years to identify a new critical rainfall intensity threshold that triggers likely CSO incidence, and a multiple linear regression model to predict CSO volume using rainfall event characteristics. Together, this indicator and modeling approach can help CSS communities assess the performance of their CSS over time, especially to evaluate the effectiveness of efforts to reduce CSOs.

Costs and benefits of combined sewer overflow management strategies at the European scale.

Combined sewer overflows (CSOs) may represent a significant source of pollution, but they are difficult to quantify at a large scale (e.g. regional or national), due to a lack of accessible data. In the present study, we use a large scale, 6-parameter, lumped hydrological model to perform a screening level assessment of different CSO management scenarios for the European Union and United Kingdom, considering prevention and treatment strategies. For each scenario we quantify the potential reduction of CSO volumes and duration, and estimate costs and benefits. A comparison of scenarios shows that treating CSOs before discharge in the receiving water body (e.g. by constructed wetlands) is more cost-effective than preventing CSOs. Among prevention strategies, urban greening has a benefit/cost ratio one order of magnitude higher than grey solutions, due to the several additional benefits it entails. We also estimate that real time control may bring on average a CSO volume reduction of just above 20%. In general, the design of appropriate CSO management strategies requires consideration of context-specific conditions, and is best made in the context of an integrated urban water management plan taking into account factors such as other ongoing initiatives in urban greening, the possibility to disconnect impervious surfaces from combined drainage systems, and the availability of space for grey or nature-based solutions.

Real-time model predictive and rule-based control with green infrastructures to reduce combined sewer overflows

The impact of integrating large-scale distribution of green infrastructures (GIs) with different real-time control strategies on combined sewer overflows (CSOs) is assessed for the southern area of the City of Montreal's combined sewer system (Canada). An iterative process involving a synthetic design rainfall event and model predictive control (MPC) of the sewer system is developed to distribute GIs according to cost-efficiency and spatial analysis criteria. The distributed GIs are alternatively integrated with static, rule-based control (RBC) and MPC, for which model simulations are performed for a two-month period. The performance of the three strategies is compared in terms of CSO volume and frequency reductions, fulfillment of the outfall environmental priorities and transfer of runoff capture to CSO volume reduction. A gradual increase in GI implementation levels and an alternative scenario of GIs distribution are also considered to assess the performance of the two real-time control (RTC) strategies. By comparing the scenarios where GIs are uniformly distributed with those where no GIs are implemented and omitting the most extreme rainfall event, average CSO volume reduction is about 65%, 82% and 92%, respectively, for static control, RBC and MPC. Moreover, the scenario integrating GIs with MPC is the only one permitting to avoid almost all CSO events and the fulfilment of the outfall environmental priorities. GIs efficiency performance (the transferability between global runoff capture and CSO volume reduction) is also the highest under MPC, even when considering varying GI implementation levels and spatial distribution schemes.

Spatial design strategies and performance of porous pavements for reducing combined sewer overflows

The installation of Green Infrastructure (GI) is a popular strategy for reducing stormwater runoff that contributes to Combined Sewer Overflows (CSOs). However, quantifying the impact of proposed GI systems on CSO discharges is a difficult task that requires the simulation of runoff in a complex network of land parcels, drainage controls, and sewer pipes. In this study, the performance of Porous Pavement (PP) was examined, with a focus on how the spatial design of PP features affects predicted CSO volume. Numerical experiments were performed to explore how simulation-based designs can identify specific subcatchments for cost-effective PP implementation subject to budgetary constraints. Among the alternative design strategies considered, simulation–optimization was effective at finding cost-effective solutions. The experiments showed that PP can achieve substantial CSO reductions across a range of rainfalls, budgets, and CSO drainage characteristics, but the performance is sensitive to the spatial configuration of the features. Good-performing systems were also identified by generating multiple realizations via a randomized clustering strategy, with additional improvement provided by the more computationally expensive optimization process.

Optimization of Real‐Time Control With Green and Gray Infrastructure Design for a Cost‐Effective Mitigation of Combined Sewer Overflows

AbstractAn innovative optimization‐simulation framework is applied to a case study of the Province of Quebec, Canada, to optimize the spatial distribution of green infrastructure (GI), the capacity and location of gray infrastructure, and the parameters specific to real‐time control (RTC) operating rules of a sewer system for reducing combined sewer overflows (CSOs) frequency and volume. GI, gray infrastructure, and RTC are applied either individually or in integration through eight optimization scenarios which are simulated over a nine‐year period of historical rainfall data. Among all scenarios, spatial optimization of GI with RTC leads to maximal CSO volume reduction (98%) and is the most cost‐effective option analyzed (70$/m3 of seasonal average CSO reduction compared to 140$/m3 for the scenario involving gray infrastructure alone). However, it requires a high GI implementation level and the CSO frequency under this scenario is sensitive to varying GI design parameters. The findings suggest that the best alternative for CSO control is the integration of the optimization of green and gray infrastructures with RTC as it still provides high CSO volume reduction (95%) and remains a cost‐effective solution (90$/m3 of CSO reduction), while providing robustness under cost and design uncertainties.

Comparing methods to place adaptive local RTC actuators for spill volume reduction from multiple CSOs

Abstract The selection of flow control device (FCD) location is an essential step for designing real-time control (RTC) systems in sewer networks. In this paper, existing storage volume-based approaches for location selection are compared with hydraulic optimisation-based methods using genetic algorithm (GA). A new site pre-screening methodology is introduced, enabling the deployment of optimisation-based techniques in large systems using standard computational resources. Methods are evaluated for combined sewer overflow (CSO) volume reduction using the CENTAUR autonomous local RTC system in a case study catchment, considering overflows under both design and selected historic rainfall events as well as a continuous 3-year rainfall time series. The performance of the RTC system was sensitive to the placement methodology, with CSO volume reductions ranging between −6 and 100% for design and lower intensity storm events, and between 15 and 36% under continuous time series. The new methodology provides considerable improvement relative to storage-based design methods, with hydraulic optimisation proving essential in relatively flat systems. In the case study, deploying additional FCDs did not change the optimum locations of earlier FCDs, suggesting that FCDs can be added in stages. Thus, this new method may be useful for the design of adaptive solutions to mitigate consequences of climate change and/or urbanisation.

Assessing the system performance of an evolving and integrated urban drainage system to control combined sewer overflows using a multiple-layer based coupled modeling approach

Many earlier developed and densely populated metropolises served by combined sewer systems have been implementing additional stormwater control measures to reduce the impacts of combined sewer overflows (CSO) driven by severe rainstorms. These mitigation measures call for robust modeling strategies to assess the overall performance before development and to predict vulnerabilities, and examine the underlying mechanisms causing them during or after development in a systematic manner. This paper first introduces a hydrologic and hydraulic coupled modeling program-MetroFlow that interacts and integrates multiple models built via diverse methods and tools. A typical integrated urban drainage system (IUDS) in the greater land area of Chicago in the US was investigated as a testbed. In addition, a large-scale Tunnel and Reservoir Plan in construction was evaluated as a mitigation measure to alleviate CSO risks. In this effort, three models were coupled into MetroFlow to account for the best available data of the multiple structural layers involved in this evolving system. Hydrological responses were analyzed under existing and projected system conveyance and storage capacities considering the full spectrum of rainfall conditions employing synthetic storms and real-time simulations of four historical extreme events to account for the anomalies exemplified by individual natural events. The overall effectiveness and resilience of the system were further assessed by the evaluation of normalized performance indicators (PIs) quantifying the reduction of total volume, duration and spread of CSOs. The cluster of simulation results collectively suggests that: (a) the proposed structural layer-based modeling approach is applicable in reproducing distributed rainstorm-driven CSOs for an evolving IUDS based on emerging digital-physical data; (b) CSO characteristics are rather multivariate functions of the rainfall tempo-spatial characteristics as well as the hydraulic performance of the collection system and (c) consistent improvement of CSO abatement was predicted with increasing conveyance and storage provided by the deep tunnel system. The projected system is predicted to capture 24-h storms of return period up to 10 years. MetroFlow’s framework may also promote collaborative modeling, in which community-scale models can be utilized and merged to support extended-scale water and wastewater infrastructure planning, design and management.

The Impacts of Climate Change and Porous Pavements on Combined Sewer Overflows: A Case Study of the City of Buffalo, New York, USA

Combined sewer overflows (CSOs) release pollutants collected in urban runoff into local waterways, impacting both aquatic life and human health. The impact of climate change on precipitation may result in an increase in the frequency and magnitude of heavy precipitation events, with a corresponding increase in CSO discharges. The installation of Green Infrastructure (GI) such as Porous Pavements (PP) is a resilient approach to mitigate CSO events. However, an understanding of the impact of climate change on CSO events and the effectiveness of GI practices is crucial for designing sustainable urban stormwater management systems. Using the Storm Water Management Model (SWMM), the performance of PP as a CSO abatement strategy was studied for the city of Buffalo, New York, USA. This paper used the Intensity-Duration-Frequency (IDF) curves for current (1970–1999) and future (2070–2099) design rainfall scenarios, with four rainfall durations (1, 6, 12, and 24 hours) and four return periods (2, 10, 50, and 100 years). The simulation results show that (1) current 100-year events generate CSO volumes similar to predicted 50-year events; (2) CSO volumes could increase by 11–73% in 2070–2099 compared to 1970–1999 when no GI intervention is performed; and (3) the installation of PP can reduce 2–31% of future CSO volume. This case study demonstrates the regional CSO challenges posed by climate change and supports the use of GI as a mitigation strategy.

Chance-constrained stochastic MPC of Astlingen urban drainage benchmark network

In urban drainage systems (UDS), a proven method for reducing the combined sewer overflow (CSO) pollution is real-time control (RTC) based on model predictive control (MPC). MPC methodologies for RTC of UDSs in the literature rely on the computation of the optimal control strategies based on deterministic rain forecast. However, in reality, uncertainties exist in rainfall forecasts which affect severely accuracy of computing the optimal control strategies. Under this context, this work aims to focus on the uncertainty associated with the rainfall forecasting and its effects. One option is to use stochastic information about the rain events in the controller; in the case of using MPC methods, the class called stochastic MPC is available, including several approaches such as the chance-constrained MPC(CC-MPC) method. In this study, we apply CC-MPC to the UDS. Moreover, we also compare the operational behavior of both the classical MPC with perfect forecast and the CC-MPC based on different stochastic scenarios of the rain forecast. The application and comparison have been based on simulations using a SWMM model of the Astlingen urban drainage benchmark network. From the simulations, it was found that CSO volumes were larger when CC-MPC had overestimating forecast biases, while for MPC they increased with any presence of forecast biases.

Extrapolating performance indicators for annual overflow volume reduction of system-wide real time control strategies

ABSTRACT Model-based evaluation of real time control and model predictive control strategies is often performed by estimating the yearly reduction in combined sewer overflow (CSO) volumes. This requires sufficiently long data series, but the necessary input data are often lacking in the case of complex control strategies. This article compares a new method to extrapolate yearly CSO volume reduction by simulating a limited number of CSO events. The method showed a good accuracy (2–3% deviation) when applied to a synthetic example. When applied to a real case study in Copenhagen (Denmark), the method showed a tendency to overestimate the performance of the control. The results underline how performance of real time control strategies is strongly affected by yearly variation, non-linearity and interactions among the elements of the system. It is thus suggested to use a mix of different performance indicators when evaluating control performance in conditions of data scarcity.

Efficient integration of IoT-based micro storages to improve urban drainage performance through advanced control strategies

The smart rain barrel (SRB) consists of a conventional RB with storage volumes between 200 and 500 L, which is extended by a remotely (and centrally) controllable discharge valve. The SRB is capable of releasing stormwater prior to precipitation events by using high-resolution weather forecasts to increase detention capacity. However, as shown in a previous work, a large-scale implementation combined with a simultaneous opening of discharge valves clearly reduced the effectiveness. The aim of this work was to systematically investigate different control strategies for wet weather by evaluating their impact on sewer performance. For the case study, an alpine municipality was hypothetically retrofitted with SRBs (total additional storage volume of 181 m3). The results showed that combined sewer overflow (CSO) volume and subsequently pollution mass can be reduced by between 7 and 67% depending on rain characteristics (e.g., rain pattern, amount of precipitation) and an applied control strategy. Effectiveness of the SRBs increases with lower CSO volume, whereas more advanced control strategies based on sewer conditions can clearly improve the system's performance compared to simpler control strategies. For higher CSO volume, the SRBs can postpone the start of an CSO event, which is important for a first-flush phenomenon.

Assessment of combined sewer overflows impacts under flooding in coastal cities

Abstract Wastewater treatment plants (WWTPs) are among the most important infrastructures, especially in coastal cities with a risk of flooding. During intense floods, runoff volume may exceed the capacity of a WWTP causing plant failures. This paper investigates the impacts of flooding on combined sewer overflows (CSOs) in a WWTP in New York City. The impacts of CSOs after flooding are classified into four categories of health, economic, social, and environmental factors. Different factors are defined to evaluate the impacts of CSOs using multi-criteria decision-making of Preference Ranking Organization Method For Enrichment Evaluation and fuzzy technique for order performance by similarity to ideal solution. Since volume and depth were found to be the most significant factors for the CSO impact assessment, the Gridded Surface Subsurface Hydrologic Analysis model was run to compute flood depth and CSO volume under three treatment plant failure scenarios considering the Hurricane Sandy information. Sensitivity analysis revealed that the Total Suspended Solids (TSS), Biochemical Oxygen Demand (BOD), and dissolved oxygen have the highest impacts on CSO. Uncertainty analysis was applied to investigate CSO impact variation. Results show that evaluating the impacts of CSOs in different aspects can help improve the efficiency of flood planning and management during storms.

Not all SuDS are created equal: Impact of different approaches on combined sewer overflows

Sustainable urban drainage systems (SuDS) help in stormwater management by reducing runoff volume, increasing runoff concentration time and thereby improving the drainage system capacity. This study investigated the potential and cost-effectiveness of SuDS in reducing combined sewer overflows (CSOs). We simulated the performance of four SuDS techniques (bioretention cell, permeable pavement, rain barrel and green roof) at incremental levels of spatial coverage for a small urban catchment with a combined sewer system. We also used an Analytic Hierarchy Process (AHP) considering end-point CSO, land use, imperviousness, slope and elevation criteria to identify priority areas for SuDS deployment. Results showed that CSO volume attenuation ranged a maximum of 50–99% for the catchment, depending on the deployment strategy and underlying mechanisms of each technology. We also found that deployment of SuDS in AHP-selected sub-catchments improved CSO reduction only for rain barrels and green roofs, but not for bioretention cells and permeable pavements. SuDS were also a cost-effective retrofit option: for a 40% volume reduction, the SuDS cost, at most, 25% of the equivalent cost required for a large CSO tank. Outcomes of this study demonstrate the efficacy of SuDS in controlling CSOs, adding yet another tangible benefit to their increasingly recognised multi-functionality.

Sustainable urban drainage systems in established city developments: Modelling the potential for CSO reduction and river impact mitigation

Sustainable urban drainage systems (SUDS) can significantly reduce runoff from urban areas. However, their potential to mitigate acute river impacts of combined sewer overflows (CSO) is largely unknown. To close this gap, a novel coupled model approach was deployed that simulates the effect of realistic SUDS strategies, developed for an established city quarter, on acute oxygen depressions in the receiving river. Results show that for an average rainfall year the SUDS strategies reduce total runoff by 28%–39% and peak runoff by 31%–48%. Resulting relative reduction in total CSO volume ranges from 45%–58%, exceeding annual runoff reduction from SUDS by a factor of 1.5. Negative impacts in the form of fish-critical dissolved oxygen (DO) conditions in the receiving river (<2 mg DO L−1) can be completely prevented with the SUDS strategies for an average rainfall year. The realistic SUDS strategies were compared with a simpler simulation approach which consists in globally downscaling runoff from all impervious areas. It indicates that such a simple approach does not completely account for the positive effect of SUDS, underestimating CSO volumes for specific rain events by up to 13%. Accordingly, global downscaling is only recommended for preliminary planning purposes.

Assessment of Climate, Sizing, and Location Controls on Green Infrastructure Efficacy: A Timescale Framework

AbstractWe investigate how the effectiveness of green infrastructure (GI) to mitigate the frequency and magnitude of significant discharge events and combined sewer overflows (CSOs) depend on both climate and sewershed characteristics and propose a theoretical framework for a holistic assessment of GI's efficacy. The framework is based on the comparison of three characteristic timescales that control the production of peak discharge: rainfall duration (tr), travel time in the sewer network (tn), and the duration of rain that would be required to fill the GI's storage (tGI). Storm events can then be characterized by two ratios of these timescales: Tn = tn/tGI and Tr = tr/tGI. A third dimensionless number characterizes critical storms during which adverse events (such as CSOs) occur and allows us to identify the combinations of Tn and Tr for which GI may substantially mitigate those events. The results of numerical experiments with the model demonstrate that the storms for which GI can substantially reduce peak discharge and CSO volume typically occur in a narrow band of Tn and Tr. Within that band, the efficacy of GI may depend on the location of GI within the sewershed if network routing substantially affects the timing and magnitude of flood peaks. The proposed framework is applied to examine the efficacy of GI using historical precipitation data from two major U.S. cities: Philadelphia, PA, and Seattle, WA, and the results of this comparative analysis suggest that GI location is an important control on catchment‐scale GI efficacy in Philadelphia, but less so in Seattle.

Estimated discharge of antibiotic-resistant bacteria from combined sewer overflows of urban sewage system

Yearly discharge of antibiotic-resistant bacteria (ARB) from combined sewer overflow (CSO) was estimated. The volume of CSO was estimated from operating data of the pumping station. In the target sewer catchment, 23% of the total of the volume of combined sewage was discharged untreated as CSO. Combined sewage contained 3-log larger E. coli than secondary treatment effluent although the abundance of antibiotic-resistant E. coli was not significantly different. In the target-combined sewer catchment, a yearly total of 4.8 × 1016 CFU of E. coli was discharged from 6.1 × 106 m3 of CSO, while 1.3 × 1012 CFU of E. coli from 2.1 × 107 m3 of effluent from the wastewater treatment plant (WWTP). This E. coli discharge was equivalent to 7.9 × 109 CFU/m3 from CSO, and 6.2 × 104 CFU/m3 from WWTP effluent. Consequently, a yearly total discharge of antibiotic-resistant E. coli from CSO was 3.7-log larger than the WWTP effluent. The small-flow CSO events, which had hourly flow rate smaller than five times of the average dry-weather flow, accounted for 43% of the total CSO volume, but 79% of the total discharge of antibiotic-resistant E. coli due to a small dilution factor with stormwater and frequent discharge. Reduction of small-flow CSO events would be important for effective reduction of ARB discharge from CSO.