Combined Sewer Systems Research Articles

Application of time series and multivariate statistical models for water quality assessment and pollution source apportionment in an Urban River, New Jersey, USA

Water quality monitoring reveals changing trends in the environmental condition of aquatic systems, elucidates the prevailing factors impacting a water body, and facilitates science-backed policymaking. A 2020 hiatus in water quality data tracking in the Lower Passaic River (LPR), New Jersey, has created a 5-year information gap. To gain insight into the LPR water quality status during this lag period and ahead, water quality indices computed with 16-year historical data available for 12 physical, chemical, nutrient, and microbiological parameters were used to predict water quality between 2020 and 2025 using seasonal autoregressive moving average (ARIMA) models. Average water quality ranged from good to very poor (34 ≤ µWQI ≤ 95), with noticeable spatial and seasonal variations detected in the historical and predicted data. Pollution source tracking with the positive matrix factorization (PMF) model yielded significant R2 values (0.9 < R2 ≤ 1) for the input parameters and revealed four major LPR pollution factors, i.e., combined sewer systems, surface runoff, tide-influenced sediment resuspension, and industrial wastewater with pollution contribution rates of 23–30.2% in the upstream and downstream study areas. Significant correlation of toxic metals, nutrients, and sewage indicators suggest similarities in their sources.Graphical

Capacity Assessment of a Combined Sewer Network under Different Weather Conditions: Using Nature-Based Solutions to Increase Resilience

Severe weather conditions and urban intensification are key factors affecting the response of combined sewer systems, especially during storm events. In this regard, the capacity assessment of combined sewer networks under the impact of rainfall storm events of different return periods was the focus of this work. The selected case study area was a mixed-use catchment in the city centre of Thessaloniki, Greece. The hydraulic performance of the examined sewer network was assessed using an InfoWorks ICM model. The results indicated that mitigation strategies, such as the application of nature-based solutions (NBSs) or low-impact developments (LIDs) are considered essential for controlling combined sewer overflows. A multicriteria analysis was conducted to select the most appropriate NBSs/LIDs to be located in the study area to enhance the system’s capacity. The results of this multicriteria analysis were used to propose a combined sewer overflow mitigation scenario, based on the installation of green roofs, as the most highly ranked solution in the analysis performed. Incorporating the proposed NBS/LID in the hydrologic-hydraulic model significantly increased the performance of the studied combined sewer network.

Optimizing the Activation of WWTP Wet-Weather Operation Using Radar-Based Flow and Volume Forecasting with the Relative Economic Value (REV) Approach

Wastewater treatment plants (WWTPs) connected to combined sewer systems must cope with high flows during wet-weather conditions, often leading to bypass and thus pollution of water bodies. Radar rainfall forecasts coupled with a rainfall-runoff model provides flow and volume forecasts that can be used for deciding when to switch from normal to wet-weather operation, which temporarily allows for higher inflow. However, forecasts are by definition uncertain and may lead to potential mismanagement, e.g., false alarms and misses. Our study focused on two years of operational data from the Damhuså sewer catchment and WWTP. We used the Relative Economic Value (REV) framework to optimize the control parameters of a baseline control strategy (thresholds on flow measurements and radar flow prognosis) and to test new control strategies based on volume instead of flow thresholds. We investigated two situations with different objective functions, considering higher negative impact from misses than false alarms and vice versa, and obtained in both cases a reduction of the rate of false alarms, higher flow thresholds and lower bypass compared to the baseline control. We also assess a new control strategy that employs thresholds of predicted accumulated volume instead of predicted flow and achieved even better results.

The impact of blue-green infrastructure on trace contaminants: A catchment-wide assessment

Blue-green infrastructure (BGI) reduce urban combined sewer overflows (CSOs) and stormwater outlets (SWOs). However, most conventional BGI are not designed to remove trace organic contaminants. Little is known about the potential of conventional BGI to improve surface water quality by reducing the discharge of trace organic contaminants. We derived wash-off loads for street runoff (6PPD-q, DPG, and HMMM), construction materials (diuron), and wastewater-derived contaminants (diclofenac) based on measurements in the combined sewer system. Subsequently, the performance of four BGI types (bioretention cells, green roofs, porous pavements, and urban wetlands) to reduce the discharge of trace organic contaminants via SWOs and CSOs was quantified with a hydrodynamic SWMM model. Moreover, the catchment-wide impact of SWOs and CSOs on surface water was assessed using risk quotients. We found that the annually discharged load can be considerably reduced by implementing BGI. Among the studied BGI types, bioretention cells are the most effective, with a load reduction of up to 80% to surface waters, mainly due to a larger suitable implementation area and a substantial stormwater infiltration. BGI implemented in the separate sewer system are more effective in reducing stormwater contaminant loads than BGI in the combined system. The assessment of the risk quotient in the surface water showed that the concentrations during SWO and CSO discharges exceed the acute environmental threshold in the surface water for 6PPD-q, DPG, diuron, and diclofenac during several events. The implementation of BGI reduced the hours of exceeded risk quotient in the surface water by 93% for bioretention cells. These findings underscore the need for a catchment-wide assessment of future BGI implementations to quantify, manage, and mitigate the impacts of urban pollution.

Can blue–green infrastructure counteract the effects of climate change on combined sewer overflows? Study of a swiss catchment

Abstract Combined sewer overflows (CSOs), the discharge of untreated sewage mixed with stormwater into surface waters, are expected to increase under climate change as a result of more extreme rainfall. Blue–green infrastructure (BGI), such as bioretention cells and porous pavements, can help to reduce the amount of stormwater entering combined sewer systems, thus reducing CSO discharge. However, our understanding of the potential for BGI to mitigate CSOs in a future climate is still lacking, as performance is typically evaluated for individual BGI elements with fixed implementation areas under historical climate conditions or limited future scenarios. In response, this study investigates the performance of 30 combinations of BGI elements and implementation rates to prevent increases in CSOs under a range of future climate scenarios in an urban catchment near Zurich, Switzerland. Median total annual rainfall, projected to increase by as much as 46%, could double the median annual CSO volume and increase median annual CSO frequency by up to 52%. Four BGI combinations that include bioretention cells show the most promise to prevent increases in CSO volume and frequency in a future climate; and given the diverse responses of BGI elements to distinct rainfall patterns, their combinations can enhance CSO discharge reduction across varying climate patterns. BGI is also likely to become more cost-effective under future climatic conditions as projected increases in total rainfall led to larger CSO volume reductions obtained through BGI. However, there is a trade-off between robustness to climate change and cost-effectiveness, since CSO volume reduction capacity scales with BGI implementation rate but cost-effectiveness declines. Our study illustrates the effectiveness of various BGI combinations to prevent increases in CSOs in a future climate, calling for a range of BGI elements and implementation areas to be considered for urban drainage adaptation.

Occurrence of specific pollutants in a mixture of sewage and rainwater from an urbanized area

Urban runoff appears to be a pathway for transferring new emerging pollutants from land-based sources to the aquatic environment. This paper aimed to identify and describe the groups of pollutants present in rainwater surface runoff as well as their mixture with wastewater in the combined sewer system from urbanized catchments and to determine the correlations between these pollutants. Four leading groups of new emerging pollutants have been identified that may be present in rainwater and municipal wastewater mixtures. The samples were tested for microplastics, phthalic acid esters, pesticides, and polycyclic aromatic hydrocarbons as well as basic parameters. The pilot site was Słupsk (northwestern Poland). We conducted nine sampling campaigns at three points. The results of the present study revealed that (i) polycyclic aromatic hydrocarbons were not present in the tested samples; (ii) the selected organochlorine pesticides were detected during one campaign in the dry season and therefore were not of critical importance; (iii) out of the 11 analyzed phthalic acid esters, five selected substances released from commonly used plastic products were present; and (iv) the number of microplastics contained in the tested samples ranged from 1,400 to 14,036 pcs/L and even occurred during pure rainfall.

Urban Stormwater Management: A Sustainable Approach

Climate change is impacting urban areas, especially through extreme rainfall that stresses conventional water management systems. Rainwater resulting from impervious runoffs, stormwater leads to an increase in the amount of wastewater that requires treatment and an overflow of the combined sewer system. Therefore, untreated wastewater is released into the surroundings or, in some cases, causes damage to urban systems. Nevertheless, many cities in the world are in the process of establishing a sustainable approach to urban water management. Sustainable stormwater management practices are essential for overcoming various environmental challenges and promoting community sustainability and resilience. The purpose of this paper is to communicate Oslo’s success in implementing sustainable stormwater management in urban areas. By analyzing successful cases, researchers may be able to record patterns that offer potential answers to the stormwater management challenge. The present case study presents an approach that can enlighten urban planners and policymakers about the potential outcomes of sustainable stormwater management under the conditions presented.

Impacts of projected future changes in precipitation on wastewater treatment plant influent volumes connected by combined sewer collection systems

Climate change induced precipitation changes can impact wastewater influent volumes and, in turn, the design and operation of wastewater treatment plants (WWTPs). As influent volumes approach or exceeds the design capacity of the WWTP, the likelihood of poor treatment or untreated discharges that result in environmental damage, increases. To date, there has been a lack of research analysing the impact changes in precipitation may have on influent volumes of WWTPs with combined sewerage systems. This study leverages data driven models of observed precipitation and influent volumes for 14 Irish WWTPs with combined sewerage systems, to project monthly wastewater influent volumes in 2041 – 2060 using Ireland’s most up-to-date high resolution multi-model RCM projections under RCPs 4.5 and 8.5. With changing monthly average daily precipitation, influent volumes for all the WWTPs demonstrated maximum decreases during summer months for both RCP 4.5 and RCP 8.5. Winter months showed increasing trends in influent volumes, particularly under RCP 8.5. The results indicate that with projected increase in high and very high precipitation days in Ireland (under CMIP5), the return periods for influent volumes that exceed the hydraulic capacity of 5 of the WWTPs will reduce. This work also presents a framework by which newer high-resolution downscaled climate models can be used to developed updated impact analysis of changing rainfall patterns on wastewater. It likely that wastewater treatment infrastructure will need to adapt to more intense precipitation to minimise the occurrence of combined sewer overflows.

The influence of road network topology on street flooding in New York City—A social media data approach

Urban road network is crucial stormwater infrastructure as city roads serve as paths for stormwater runoff and urban flooding. However, it remains unclear how different road networks influence urban flooding. While past studies have examined high-magnitude and high-intensity flooding events at the large river basin scale, they neglected smaller and more frequent street flooding events (SFEs) which could have a higher cumulative cost. Hence, this study used urban road network topology (RNT) factors and social media big data related to street flooding to investigate their associations with data-driven methods for 557 sewer catchments across New York City (NYC). 11,042 SFEs were taken from raw street flooding complaints (SFCs) recorded on the NYC 311 sewer complaints platform between January 1, 2010, and April 28, 2022. We then used generalized linear mixed models to identify the relationship between RNT factors and street flooding risk (SFR) and found that the characteristics of SFR periodically changed with spatial–temporal heterogeneity. While impervious factor was previously found important, RNT emerged as a new significant factor influencing SFR, with the effects being larger in combined sewer systems and heavy precipitation events. We discerned that planning a dendric road network pattern, increasing RNT connectivity in downstream areas, and having wider roads with more intersections could reduce SFR. Decreasing RNT connectivity in upstream areas, road length, the number of roads, and sewer catchment area also mitigated SFR. Our analysis provided informed street flooding mitigation and adaptation strategies to promote a more resilient, healthy, and sustainable urban environment.

Intelligent control of combined sewer systems using PySWMM—A Python wrapper for EPA’s Stormwater Management Model

Wastewater utilities face competing priorities as they work to protect human health and water quality, and to maintain infrastructure in their communities. Budgetary constraints can be especially pronounced among small to medium-sized utilities. Utilities are increasingly turning to so-called intelligent water approaches as a cost-effective alternative to upgrading aging infrastructure. Intelligent water encompasses automated control and real-time decision support technologies and can be applied at scale to large and small utilities alike accommodating differences in needs, capabilities, and funds. Intelligent water upgrades can be designed to optimize existing conveyance, storage, and treatment during storms to help mitigate flooding and combined sewer overflows. The most promising real-time control algorithms coordinate control of upstream and downstream assets and are designed using urban hydrologic and hydraulic modeling software. The capabilities of legacy software, however, can sometimes inhibit the creation of sophisticated control algorithms. In this paper, we present PySWMM — an open-source Python wrapper developed for the EPA Storm Water Management Model (SWMM). PySWMM enables runtime interactions with the SWMM computational engine to flexibly read, modify system parameters, and control digital infrastructure during a simulation. Crucially, it allows modelers to easily combine SWMM with the rich set of scientific computing, big data, and machine learning modules found in the Python ecosystem. We highlight two real-world intelligent water case studies utilizing PySWMM in the cities of Cincinnati and Columbus, Ohio where it has helped to eliminate tens of millions of gallons of combined sewer overflows annually.

Categorization of precipitation for predicting combined sewer overflows. Application to the City of Montréal

Combined sewer systems are widespread in America and Europe. They often face limitations in transport or treatment capacity, especially during heavy rain events or thaw periods, resulting in combined sewer overflows (CSOs). Predictive modeling for CSOs is essential in a risk management context, and some studies have presented methods to categorize precipitations based on their potential to generate overflows. However, the precipitation classification is usually based on a few characteristics, and its predictive power is limited. The objective of this study is to present a simple yet powerful method to categorize precipitation for predicting CSO occurrences. A prediction model, based on an optimized classification tree, is proposed to predict CSO occurrences as a function of publicly accessible precipitation data. We fit the model on 9 overflow outlets in Montréal city from 2013 to 2019 and use this model to predict CSOs in 2020. The results showed a very good predictive power of overflows, with a prediction rate of 89%, a sensitivity rate of 83%, and a specificity rate of 91%. The method is also more accurate than the 5-category classification currently used by the City of Montréal. The proposed method could be easily applied to another region where CSO data are available, providing a simple and rigorous method for predicting CSOs across urban drainage networks containing many overflow outlets.

Association between Combined Sewer Overflow Events and Gastrointestinal Illness in Massachusetts Municipalities with and without River-Sourced Drinking Water, 2014-2019.

Combined sewer overflow (CSO) events release untreated wastewater into surface waterbodies during heavy precipitation and snowmelt. Combined sewer systems serve people in the United States, primarily in urban and suburban municipalities in the Midwest and Northeast. Predicted increases in heavy precipitation events driven by climate change underscore the importance of quantifying potential health risks associated with CSO events. The aims of this study were to a) estimate the association between CSO events (2014-2019) and emergency department (ED) visits for acute gastrointestinal illness (AGI) among Massachusetts municipalities that border a CSO-impacted river, and b) determine whether associations differ by municipal drinking water source. A case time-series design was used to estimate the association between daily cumulative upstream CSO discharge and ED visits for AGI over lag periods of 4, 7, and 14 days, adjusting for temporal trends, temperature, and precipitation. Associations between CSO events and AGI were also compared by municipal drinking water source (CSO-impacted river vs. other sources). Extreme upstream CSO discharge events (th percentile by cumulative volume) were associated with a cumulative risk ratio (CRR) of AGI of 1.22 [95% confidence interval (CI): 1.05, 1.42] over the next 4 days for all municipalities, and the association was robust after adjusting for precipitation [1.17 (95% CI: 0.98, 1.39)], although the CI includes the null. In municipalities with CSO-impacted drinking water sources, the adjusted association was somewhat less pronounced following 95th percentile CSO events [ 1.05 (95% CI: 0.82, 1.33)]. The adjusted CRR of AGI was 1.62 in all municipalities following 99th percentile CSO events (95% CI: 1.04, 2.51) and not statistically different when stratified by drinking water source. In municipalities bordering a CSO-impacted river in Massachusetts, extreme CSO events are associated with higher risk of AGI within 4 days. The largest CSO events are associated with increased risk of AGI regardless of drinking water source. https://doi.org/10.1289/EHP14213.

Design Issue Analysis and Operation Effect Evaluation of Large-Scale Storage Tank

In order to address the issue of combined sewer overflows (CSOs), W city has constructed a large-scale storage tank with a volume of 220,000 m3. The storage tank is planned for CSO control in the near term and stormwater runoff pollution control in the long term. However, the actual operation of the storage tank is unsatisfactory. This paper elucidates the design scheme and operation mode of the tank and analyzes the challenges encountered during its design and operation. A storm water management model (SWMM) model was constructed to simulate the effect of the storage tank working in a combined sewer system (CSS), a separate sewer system (SSS) and a decentralized storage situation. This study determined that during the 2022 rainy season, the actual reduction in pollutants by the storage tank was only about 60% of the designed value. As a result, the inadequate treatment capacity of the downstream wastewater treatment plant (WWTP) resulted in the water being retained in the tank for a long time, leading to unsatisfactory operation outcomes. If the storage tank works in SSS and the problem of water retention can be solved, it could reduce the total runoff volume by 30% and the total amount of pollutants by 40% during the same rainy season. At the same time, under the premise of constant total storage volume, if decentralized storage tanks were used to control runoff pollution, the reduction effect can be increased by up to 11.6% compared with that of the centralized storage.

Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches.

Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer flow prediction and RDII estimation based on field monitoring data. The study implemented feature engineering for extracting physically significant features in sewer flow modelling and investigated the importance of the relevant features. The results from two case studies indicated the superior capability of machine learning models in RDII estimation in the combined and separated sewer systems, and LSTM model outperformed the two models. Compared to traditional methods, machine learning models were capable of simulating the temporal variation in RDII processes and improved prediction accuracy for peak flows and RDII volumes in storm events.

Investigating First Flush Occurrence in Agro-Urban Environments in Northern Italy

The first flush (FF) phenomenon is commonly associated with a relevant load of pollutants, raising concerns about water quality and environmental management in agro-urban areas. An FF event can potentially transport contaminated water into a receiving water body by activating combined sewer overflow (CSO) systems present in the drainage urban network. Therefore, accurately characterizing FF events is crucial for the effective management of sewer systems and for limiting environmental degradation. Given the ongoing controversy in the literature regarding the delineation of FF event occurrences, there is an unavoidable necessity for further investigations, especially experimental-based ones. This study presents the outcomes of an almost two-year field campaign focused on assessing the water quantity and quality of two combined sewer systems in Northern Italy. For this purpose, various hydro-meteorological variables, including precipitation, flow rate, temperature, and solar radiation, in addition to water quality analytics, were measured continuously to capture stormwater events. Throughout the monitoring period, sixteen stormwater events were identified and analyzed using five indices usually adopted in the literature to identify FF occurrences. The results indicate that there is a strong positive correlation between the mass first flush ratios calculated for nutrients and three factors, including maximum rainfall intensity, maximum flow rate, and antecedent dry weather period. Furthermore, rainfall duration was found to possess a strong negative correlation with the mass first flush ratios calculated for nutrients. However, for the same rainfall event, the occurrence of FF has never been unanimously confirmed by the indices examined in this study. Moreover, different macro-groups of pollutants can behave differently. Thus, it becomes apparent that relying solely on a priori analyses, without the support of data from experimental monitoring campaigns, poses a risk when designing actions for the mitigation of FF occurrences.

Water depth prediction in combined sewer networks, application of generative adversarial networks

This paper addresses the pressing issue of combined sewer overflows (CSOs) in urban areas, which pose significant environmental and public health threats. CSOs occur when combined sewer systems become overwhelmed during heavy rainfall, leading to untreated sewage and stormwater being discharged into nearby water bodies. To effectively manage and mitigate CSO effects, accurate predictions of CSOs are crucial for real-time control measures. This study introduces an innovative approach that utilizes Generative Adversarial Networks (GANs) to augment data and improve the accuracy of data-driven models for predicting water depth in combined sewer systems. Apart from data augmentation, the paper addresses scenarios where rare patterns, such as extreme events, are infrequently observed regardless of dataset size. It proposes a specialized generative model based on GANs designed to augment datasets targeting these rare patterns. This innovation involves adding a tail-focused loss function to the adversarial objective of the GAN training pipeline. The research is exemplified through a case study in Kaiserslautern, Germany. The results indicate an improvement in water depth predictions. Also, it was found that the model trained solely with synthetic data is comparable to that trained with real data.Graphical

The role of macroinvertebrate taxonomic resolution in bioassessment of urban streams: A case study from the City of Lodz, Poland

Benthic macroinvertebrates were sampled at 28 sites situated on ten urban streams (Łódź, Poland) representative of a wide range of anthropogenic modifications of water chemistry and bed structure. A total of 5,043 individuals, comprising 121 species belonging to 37 families and 15 orders, were collected. Diptera (35.8 %) and Ephemeroptera (31.8 %) were dominant.The studied sites were grouped into clusters based on separately-analyzed environmental parameters, various levels of taxonomic diversity, and bioassessment variables. All analyses demonstrated similar trends of anthropogenic modification, intensifying concentrically from the suburbs towards the city centre. The first cluster included the upper section of streams; less polluted, richer in ecotone zones and natural stream bed substrate. The second cluster comprised streams demonstrating the influence of moderate human pressure. The third cluster included streams flowing through the city center in concreted beds and which were connected to a combined sewage system in which sewage and rain water are transported through a single canal system and enters the nearest stream. Modification of stream beds was significantly negatively correlated with the number of species, families and orders, but not with total abundance.The number of species, the number of families and the number of orders were highly mutually correlated. Moreover, they were similarly correlated with biodiversity and biotic indices, which are used to assess the ecological status of the aquatic environment. Hence, different levels of taxonomic identification demonstrate similar effectiveness in assessment, i.e. higher taxa of aquatic macroinvertebrates may be suitable for simplified ecological monitoring of urban watercourses.

Opportunities for Leveraging Existing Hydrologic and Hydraulic Models Developed for Water Quantity Management to Mitigate Flooding Due to Extreme Precipitation

With a focus on a specific flood-prone community in Camden, NJ, this research utilizes a detailed hydraulic and hydrologic (H&amp;H) model to assesses the impacts of climate change on Combined Sewer Overflows (CSOs) and localized flooding under two different infrastructure scenarios. In the US, the Clean Water Act compels regulated utilities to develop Long-Term Control Plans to reduce combined sewer overflows (CSOs), but there is no parallel mandate to simultaneously reduce flooding within the associated service areas. With different control measures in place, H&amp;H models are frequently used to evaluate CSO volumes and frequencies under historical climate conditions. However, precipitation intensification and sea level rise (SLR) will also modify CSO volumes. This study uses a calibrated and validated 1D and 2D Personal Computer Stormwater Management Model (PCSWMM) simulation to predict both CSO discharges and flooding under different climate and infrastructure scenarios. A total of ten climate change scenarios comprising a range of plausible climate futures are considered. The infrastructure scenario that is tested would divert stormwater generated in an upstream municipality (Pennsauken, NJ) away from Camden’s combined sewer system. Without the disconnection, increases in precipitation will increase CSOs, whereas SLR primarily increases flooding. The proposed mitigation strategy can immediately reduce both CSOs and flooding, but with diminishing effectiveness over time, as climate change demonstrates the need for supplemental measures. Areas for further analysis regarding alternative mitigation methods and future research are outlined.

Side-by-Side Testing of Grit Separation Units

Two grit removal systems were tested side-by-side for the treatment of wastewater from a combined-sewer system. The two units were operated at constant flows and variable grit loads ranging from 1.2 to 745 Kg/m3 /h that covered the typical range of dry and wet weather conditions experienced at the treatment facility. The average mass grit removal for the multi-tray free vortex unit was 92.6% and it was 85.4% for the forced vortex system. Both units consistently failed to remove particles with sand equivalent sizes, which their respective overflow rates would otherwise predict. This observation can be explained by uneven force distributions over non-spherical grit particles with lumpy grease deposits and variable curvature that effect motion traverse to flow, increase buoyancy and cause particle lift.

Analysis of suspended solids emissions from a combined sewage system using the Stormwater Management Model (SWMM)

The protection of water bodies requires the reduction of pollutant emissions from all major sources. In urbanized areas, these include: wastewater treatment plants (WWTPs) and (depending on the type of sewage system) combined sewer overflows (CSOs) and stormwater drainage outlets. WWTPs are usually monitored and emitted pollutant loads are known, but it is more difficult to assess the pollutant load discharged by CSOs and stormwater drainage systems. The article attempts to use the Stormwater Management Model (SWMM) to assess emissions of suspended solids from a large urban combined catchment. Suspended solids are the main pollutant of stormwater runoff in urban areas, and the dynamics of their emission from catchments is very diverse. The amount of suspended solids discharged by CSOs functioning in the given city was assessed in comparison with emissions from a wastewater treatment plant. The results show that CSOs discharge a pollutant load to the receiver which is comparable to WWTPs, but in a much shorter time and in a violent manner which can lead to the severe deterioration of receiving water quality. The modelling took into account the quality of dry weather sewage, the build-up of suspended solids, wash-off processes in the catchment area, and local precipitation characteristics. Factors affecting the quality of the obtained model and the accuracy of the emission level assessment were analysed.