ABSTRACT

Sewer infrastructure is essential to urban functionality but vulnerable to blockages that compromise hydraulic performance and increase maintenance costs. Traditional sewer asset management focuses on structural deterioration, neglecting operational problems such as wipe buildup, a growing source of blockage due to poor disintegration. This research develops a novel algorithm that simulates the formation, growth, and effect of wipe-caused blockages, enhancing urban drainage models by accounting for their hydraulic implications. Capacity loss in sewers due to wipe accumulation was quantified and shown to increase with greater population densities. Results showed the need to adopt structural and operational factors in sewer management. By addressing wipe-caused blockages, this research gives insights into the deterioration of sewer performance that requires better mitigation measures. An improved blockage modeling strategy can be used to enhance asset management, maintain sewer functionality, and provide sustainable urban drainage systems.

1 INTRODUCTION

Sewer systems, the backbone of urban environments, play an important role in maintaining the health, safety, and operational efficiency of cities. Since sewers are prone to blockages and deteriorate over time, both of which can significantly disrupt their functionality, monitoring and managing the performance of these systems is paramount (Tscheikner-Gratl et al. 2019). Sewer systems frequently receive less attention than other infrastructure assets due to their hidden nature. Sewer asset management has become a standard practice in preserving sewer function at an acceptable level and avoiding service disruptions and high maintenance costs (Hukka and Katko 2015). Within the sewer asset management process, models are used to determine the system’s performance. This process involves the assessment of both the physical and operational conditions of the sewer system (Hawari et al. 2017; 2020). Sewer network inspection is a significant task in specifying the condition of assets, which results in data on failures or deterioration. The Closed-Circuit Television (CCTV) inspection technique is the most commonly used technique for the evaluation of the condition of the sewer networks (Wang et al. 2021). Although video quality has been enhanced, CCTV inspections been criticized for giving limited information on the deterioration processes (Dirksen et al. 2013). These inspections give only a snapshot of the pipe condition, and no information about the causes of deterioration and the actual hydraulic capacity loss, which are necessary to assess structural stability and performance. This lack of information inhibits the development of efficient sewer rehabilitation strategies (Ahmadi et al. 2014; Harvey and McBean 2014).

Traditionally, asset management has focused on the physical deterioration of pipes, such as sewer characteristics that directly impact the system's integrity (Ana and Bauwens 2010). This focus is supported by studies from Mohammadi et al. (2020), Hansen et al. (2021), Kabir et al. (2018), Sousa et al. (2014), Salman and Salem (2012), Le Gat (2008), and Lubini and Fuamba (2011). Along with physical characteristics of pipes, environmental and maintenance data including geographical location of pipes, cleaning, backups, root cuts, repairs, and flush reports are considered, as highlighted by Balekelayi and Tesfamariam (2019). However, all the mentioned studies for deterioration modeling have focused on structural deterioration, and often overlook the impact of non-structural factors, such as the accumulation of materials like wet wipes, on sewer performance.

The performance of a wastewater collection system is described as the capability to transport wastewater without hydraulic overload, so to minimize the environmental effects and maintain structural integrity. Hydraulic performance of the sewer depends on factors such as structural deterioration, adequately sized pipe, presence and the effect of blockage, sediment transport, and root intrusion. These issues can reduce capacity of sewers to transport wastewater, resulting in potential flooding and discharge of pollutants into the environment.

Defining the methodology to assess the performance of wastewater collection systems is a challenging task due to the complexity of wastewater collection systems and the presence of various parameters that can affect the system's performance (Tabesh and Madani 2006; Massoud and Zia 2003). Sewer system performance relies heavily on the products transported through them. In the past few decades, the increased utilization of consumer products (wet wipes) has come with immense complications. The widespread use of wet wipes is driven by convenience, hygiene perceptions, and marketing practices that label these products as “flushable,” leading consumers to dispose of them in sewer systems (Joksimovic et al. 2020). The items are non-degradable and can lead to blockages and maintenance problems (Allison et al. 2023; Harter et al. 2021; Mitchell et al. 2017).

Observations from sewer utilities in North America and Europe indicate the magnitude and importance of this issue. Wet wipes have been reported to account for approximately 94% of the material causing sewer blockages in the United Kingdom (Reynolds and Hardy 2025), 28% of solids in a wastewater treatment plant in Spain (Pérez et al. 2021), 14% of sewer litter in Berlin, Germany (Mitchell et al. 2017), and 47% of solids in wastewater treatment facilities in New York, USA (Fuss & O'Neill 2016). These blockages often result in increased operational costs, maintenance activities, and service disruptions. Wipes are estimated to result in approximately $441 million per year in additional operating costs for water utilities in the United States (NACWA 2020), £200 million per year in the UK (Reynolds and Hardy 2025), and at least $250 million annually in Canada to address blockages associated with wet wipes, which further emphasizes the economic burden of these materials (The Canadian Press 2019).

Although experimental research has led to the development of methodologies to represent wipe-caused blockage dynamics (Kargar and Joksimovic 2024), the frequent occurrence of such blockages in real sewer systems along with their financial implications, has highlighted the need to better account for these effects in asset management modeling. There is a gap in incorporating these operational concerns into current asset management modeling regarding wipes and the presence of sewer imperfections that cause their snagging. Modeling wipe-related blockages is important for quantifying the impact of blockages on sewer capacity and system performance. This helps make more accurate assessments of blockage-related risks and supports informed asset management decisions. Utilities have implemented mitigation measures, such as public education campaigns (Metro Vancouver 2026), specific “Do Not Flush” messaging (LRWRA 2026; OCWA 2014), labelling improvements (INDA and EDANA 2017; IWSFG 2018) and enhanced inspection and maintenance practices, yet these efforts alone remain inadequate to effectively resolve wipe-related blockages.

The objective of this paper is to develop and analyze different approaches for continuous modeling of blockage dynamics in urban drainage systems. By enhancing existing urban drainage models, long-term hydraulic impacts of wipe-caused blockages could be quantified, allowing for explicit inclusion of this phenomenon in sewer asset management planning. This approach quantifies the decline of sewer capacity due to blockage dynamics by accounting for catchment characteristics such as population served, wipe generation rate, pipe initial condition, and flow dynamics. The ongoing operation of sewer systems requires the inclusion of physical and operational factors in deterioration modeling. An in-depth understanding of interactions among materials such as wipes and sewer systems facilitates efficient management strategies to mitigate sewer management issues. An integrated approach is necessary to provide the operational integrity of sewer systems while simultaneously guaranteeing the sustainability and efficiency of urban drainage systems.

2 METHODOLOGY

2.1 Long-term blockage dynamics

Figure 1 shows the process developed in this research to simulate blockage dynamics in a sanitary and combined sewer system, utilizing the SWMM (Storm Water Management Model) and a custom subroutine, SWMMPulse. The process is started by first simulating the flows using the hydraulic model under “clean” conditions (i.e., with no wipes), to generate the necessary flow data. This data is then fed into SWMMPulse to simulate the travel times of wipes from system nodes, where they may be introduced, to the downstream location’s sewer system, where imperfections may exist (e.g., hanging pipe gasket). These locations, chosen as the outlets of the four catchments, are where the flow discharges into a sewer with a larger diameter. These dynamics, probabilistic simulation of snagging, and accumulation and deterministic dissipation, are modeled using probabilities and equations from Kargar and Joksimovic (2024). Once a blockage forms, its size and flow rate is used to update the hydraulic model in SWMM by modifying orifice settings, which model the reduced capacity of the pipe under blockage. The model is rerun to assess the system response to blockage and the resulting HGL changes. This process is conducted in an iterative manner, with orifice settings and flow rates being modified based on the outcome of every iteration, until differences in successive iterations minimize the predefined threshold (ε < 0.01). The ultimate hydraulic results give insights into the effect of blockages on the overall performance of the sewer system under varying conditions, such as various flow rates and blockage sizes. Each simulation examines one blockage at a time, hence allowing close examination of the hydraulic effects of the individual blockages.

Figure 1  Flowchart of the overall methodology.

2.2 Study site

Catchment description/Location selection

The study area is in north Toronto, Canada, and is servicing a population of 60,000 via a drainage area of 11.3 km². It has 84.2 km of gravity sanitary pipes with diameters from 250 mm to 2,100 mm and has 1,363 maintenance holes. The serviced area contains a medium-density residential development with several subcatchments and population densities and is therefore an ideal place for the study of wipe generation, sewer blockages, and their corresponding hydraulic effects (König et al. 2023). The research concentrated on pipes with diameters of 250 mm, given that they are particularly susceptible to blockage. For instance, imperfections such as hanging gaskets resulting from specific construction techniques, create snag points where wipes and debris accumulate. Additionally, these pipes are often buried at shallow depths, making them more susceptible to root penetration through joints and cracks. Figure 2 shows the four selected catchments for the study, highlighting locations with varying upstream populations. Yellow crosses in each area represent points where flow discharges into a larger-diameter sewer. The upstream population contributing to each pipe was estimated, as population size directly correlates with wipe generation rates and blockage risks. Pipes that service the 10th, 50th, 90th, and 99th population percentiles were selected to ensure a diverse representation of flow conditions and system characteristics. Table 1 provides more information on each catchment area, including serviced population, serviced area, sewer density, population density, and average slope. An initial condition grade of 2 was used as the baseline, consistent with the PACP rating system (NASSCO 2015), where hanging gaskets, considered protruding materials, are assumed to be grade 2 as they occupy less than 20% of the pipe capacity.

Figure 2  Selected study areas.

Table 1  Characteristics of each catchment area.

Study area	Serviced population	Serviced area (ha)	Sewer density (m/ha)	Population density (person/ha)	Average slope (%)
A	27	0.5	481	54	3.2
B	176	4.9	219.6	36.1	0.7
C	581	10.7	283.9	54.3	1.3
D	1084	14	168.8	77.4	2.9

Model calibration

A hydraulic model was developed using sanitary asset information and calibrated for both dry and wet weather flow. The hydraulic sewer model was built in PCSWMM (CHI 2025) using sewer assets and attribute information obtained from the City of Toronto. The dry-weather flow calibration was previously performed by König et. al (2023), using flow data from July 2020. The dry-weather inflows were calculated based on flow measurements at the catchment outlet and weighted by the connected upstream population derived from census and building data (Statistics Canada 2016; City of Toronto 2020).

For the current study, the model was further calibrated for wet-weather flow. The Rainfall-Derived Inflow and Infiltration (RDII) component, representing the additional flow entering the system during and after rainfall events, was modeled using the RTK unit hydrograph method. This method employs three key parameters: R, T, and K, which define the hydrograph generated by rainfall events. The RDII parameters were calibrated by analyzing rainfall and flow data over a four-month period (April to July 2020), adjusting the RTK values iteratively to match the observed flow patterns at the catchment outlet.

2.3 Blockage dynamics–implementation

Key factors to be considered in long-term blockage dynamics include the generation of wipes, their dynamics (snagging, accumulation, and dissipation) and the associated impacts. These aspects are explained below.

Wipe generation and transport in the model

The introduction and routing of wipes through the sanitary sewer system were modeled using a modified pulse-load approach provided in SWMMPulse. Wipes were introduced into the system as pulse loads, representing individual flush events at various nodes based on the spatial distribution of the population within the study catchment. Population data from census records and building information (Statistics Canada 2016; City of Toronto 2020) was used to determine the number of residents at each model node.

The timing and frequency of wipe flush events were based on an hourly distribution pattern reflecting typical human defecation habits (Heaton et al. 1992). The total number of wipes flushed per day was determined using an average wipe generation rate of 0.1 wipes flushed per person per day, as indicated by industry data (Powling 2024). This estimate assumes that all purchased wipes for sanitary use will ultimately be flushed, without considering regional variations in consumer behavior or disposal practices. The growth in sales was assumed to directly translate into a proportional increase in flushed wipes. A growth rate of 5.6% per annum, derived from market research of the non-woven industry (Deep 2025), was applied to the wipes generation rate to represent more wipes being flushed throughout the simulation. The generated pulse loads were passed through the sewer system using SWMMPulse. Flow rates generated by the simulation were used to trace the wipes’ movement and arrival within the system. A five-year duration of the simulation under both wet and dry weather was set with an aim of encompassing numerous flow conditions. The code used in generating and routing the flush events was made available on GitHub to achieve reproducibility and future research (https://pypi.org/project/swmmRouting/).

Simulation of blockage dynamics

Blockage dynamics were modeled considering the processes of snagging, accumulation, and dissipation. These processes were quantified using empirical equations developed based on the results of Kargar and Joksimovic (2024). The snagging probability (Ps) and accumulation probability (Pa) were derived as probabilistic linear functions of flow rate Q (l/s), while the dissipation rate (dp) was expressed as a deterministic exponential function influenced by blockage size (BP), flow rate (Q), and blockage age (t).

The probability of snagging and accumulation for high-risk scenario was defined using Equations 1 and 2, by Kargar and Joksimovic (2024):

(1)

(2)

The dissipation rate coefficients were derived using regression analysis based on experimental data from Kargar and Joksimovic (2024). The coefficients were optimized to fit the observed data, ensuring that the model accurately captures the relationship between blockage size, flow rate, and blockage age in predicting dissipation behavior.

The dissipation rate (dp), which describes the rate at which the size of the blockages decreases over time, was modeled using Equation 3:

(3)

Where:

BP	=	blockage size (number of wipes),
Q	=	flow rate (L/s),
t	=	age of the blockage (time),
b1	=	-0.5121,
b2	=	-23.7678,
b3	=	-0.4641,
b4	=	0.1510,
b5	=	3.519,
b6	=	0.3810, and
b7	=	0.9839 are the calibrated non-dimensional coefficients for the dissipation rate.

This model represents blockage reduction under varying conditions by capturing steady-state dissipation dynamics at different stages of the system's evolution.

Different conceptual models for dissipation were provided to model the reduction of blockages over time:

Bulk model: This model was developed based on the experimental results of Kargar and Joksimovic (2024), where blockages of various sizes were created under low, medium, and high flow rates, and dissipation was measured at different time intervals. The model calculates the dissipation rate based on the age of the first wipe that snagged at the blockage. It simplifies the blockage by treating it as a single entity, as the laboratory setup did not account for the dynamic nature of real-world conditions. This experimental model does not consider the diurnal flushing patterns or the staggered arrival of wipes at different times, which would more accurately reflect field conditions.

Layered model: To overcome the limitations of the bulk model, layered models were conceptualized, which provide more detailed and plausible analysis by considering the specific characteristics of individual layers within the blockage. Each layer represents the accumulation of wipes snagged during a single simulation timestep (1 hour), allowing for a more refined representation of blockage formation. This approach can be further divided into the following sub-approaches:

• Layer weighted averaged age: the calculation of dissipation rate is determined by using the weighted average age of all accumulated hourly layers of snagged wipes. As such, the age of the blockage does not increase linearly with time, as newer wipes are continuously added to the pile. Older wipes will not dissipate at the same rate as new ones because they become covered by new wipes, reducing their exposure to water flow, shear stress. Therefore, the model accounts for the individual ages of the accumulated wipe layers. While the model accounts for the layered structure of wipe-induced sewer blockages, it oversimplifies the dissipation process by assuming that all layers contribute to dissipation in proportion to their average age. Equation 4 is used to formulate this conceptual model:

(4)

Where:

ā(t)	=	layer-weighted average age of the pile at time t.
Si(t)	=	size (number of wipes) of the i-th layer at time t,
ai(t)	=	age of the i-th layer at time t,
N(t)	=	total number of layers at time t, and

• Individual layer: This model treats each layer of wipes as an individual entity, where the time-dependent dissipation rate was applied to each layer independently once it snagged, where Equation 3 is applied to each layer separately throughout the simulation period. However, this model assumes that layers dissipate in isolation and does not account for the shielding effect, where newer layers may cover older ones and reduce their exposure to dissipation forces. By neglecting these effects, this model simplifies the dissipation process and may not fully represent real-world blockage behavior.
• Shielded layer: This model builds upon the individual layer model by introducing a shielding factor to represent the effect of newer layers covering older ones, thereby altering their dissipation rates. As blockages form, existing (older) layers of wipes become buried under newly accumulated material, reducing their exposure to dissipation forces. Observations from lab experiments (Kargar and Joksimovic 2024) supported this effect, as wipes within experimental blockages showed slower breakdown when covered by additional layers. These findings indicated that dissipation was not uniform across all layers, highlighting the need to incorporate a shielding factor in the model. The shielding factor accounts for this effect by modifying the dissipation rate of older layers based on their position within the blockage. A shielding factor was applied using the formula (index/total layers)^p, where the "index" represents the position of a layer within the blockage.

After accounting for snagging, accumulation, and dissipation, blockages were presented using orifice settings (S, dimensionless) to simulate the capacity loss caused by wipe accumulation. The orifice setting (Equation 5) is determined from the results of lab experiments based on blockage size (BP, number of wipes) and flow rate (Q, L/s).

(5)

Where:

c	=	c1 · Q, and
b0, b1, and c1	=	unitless parameters determined through model training.

The parameters were calibrated to values of b₀ = -0.55, b₁ = -1.02, and c₁ = 0.24.

2.4 Blockage impacts

The impact of blockages was assessed by simulating the change in pipe capacity over a 5-year period, using recorded precipitation at a nearby climate station from 2019 to 2023, considering varying upstream populations serviced in four catchments, and assumed wipe generation rates. This simulation-based analysis assesses the impact of various loading scenarios on the pipe's operational status, showing how it moves from good to fair or poor condition ratings (NASSCO 2015). To better understand the relationship between wipe accumulation in a blockage and the subsequent capacity reduction, simulations were conducted to analyze how pipes responded to loading conditions over time. Through the identification of degradation trends under various population densities and wipe generation rates, the research documents the evolution of pipe performance over time. This thorough analysis helps prioritize upcoming inspection and maintenance tasks by providing insights on how pipes move from a good condition grade to fair or poor.

3 RESULTS AND DISCUSSION

Based on the methodology presented in Figure 1, a calibrated hydraulic model was required to initiate the analysis. The hydraulic model was calibrated for both dry and wet weather with a Nash-Sutcliffe Efficiency (NSE) of 0.82, showing a strong correlation between measured and simulated data. For the selected areas under clean pipe conditions, the hydraulic model produced ranges of flow rates as shown in Figure 3, influenced by population size (dry weather), and serviced area and rainfall (wet weather). The box plots in Figure 3 highlight variations in flow rates across these areas. Area A exhibits the lowest flow rates, indicating a low-density residential zone, while areas C and D show higher and more variable flow rates, primarily driven by population density, though some variability in area C is attributed to non-residential contributions (commercial and industrial use).

Figure 3  Flow rate distribution for the selected areas (A, B, C, and D).

Acquired flow rates from the SWMM model were used as the baseline to model wipe travel times in the system and blockage formation, considering catchment outlets as hypothetical locations of sewer imperfections. Using SWMMPulse, wipes were routed through the system with their travel times determined from flow velocities obtained from the hydraulic model.

Figure 4 illustrates the frequency distribution of wipe arrivals and hourly accumulations as a percentage of the total duration across different study areas. The observed variation in wipe arrivals is influenced by population size, defecation patterns, and wipe generation rates of serviced population, resulting in differences among the areas. In all cases, the percentage of time during which wipes arrived is consistently higher than the percentage of time wipes accumulated, reflecting the probabilistic nature captured by Equation 1. A comparison of different areas highlights variations in wipe arrival patterns. In Area A, the variability in the number of wipes arriving per hour is relatively low, whereas in Area C, the range of wipe arrivals is significantly higher, spanning from 1 to 27 wipes per hour. However, higher arrival rates occur less frequently, with smaller wipe counts being more common. This suggests that while extreme events of high wipe arrivals do occur, they are seldom.

Figure 4  Frequency of number of wipe arrivals and accumulation per hour for each area.

The wipe generation rate of 0.1 wipes/person/day, along with the assumed defecation patterns, further shaped the accumulation process. For example, in area A, wipe accumulation was minimal, with occasional peaks rarely exceeding 2 units/hour, while in area D, wipe accumulation frequently exceeded 20 units/hour. The increased wipe generation and snagging in higher populated areas highlight the strong influence of population size and behavior on blockage formation. Higher populations generate more wipes and higher flow rates, while lower population areas have fewer wipes and lower flow rates. These dynamics underscore the importance of both wipe generation and hydraulic flow conditions in determining blockage risks, where excessive wipe loads and inadequate flow contribute to system vulnerabilities.

The probabilistic nature of the simulation introduced year-to-year variability in wipe accumulation, even for the same population size. This variability was driven by the probabilistic distribution of wipe snagging, accumulation, and user behavior (defecation pattern). Additionally, an annual growth rate of flushed wipes, set at 5.6%, further contributed to the observed variability (Figure 5). Some years exhibited higher peaks and faster accumulation rates than others, underscoring the importance of continuous monitoring and adaptive maintenance strategies.

Figure 5  Time series of wipe accumulation.

Following the process of generating wipes and creating blockages, Equation 3 was applied to simulate the reduction of blockage size over time. Multiple conceptual dissipation models, as described in the methodology, were compared based on their underlying assumptions and their applicability to blockage reduction in sewer system. For this purpose, an average constant flow rate of 5 L/s and an average of 2.42 wipes flushed per hour (average values for Area C) were used to test each conceptual model over a simulation duration of 100 hours. This duration was selected as an example to provide a consistent framework for comparing the behavior of each model under identical conditions. By standardizing the inputs and time frame, the evaluation aimed to highlight the differences in blockage growth and dissipation dynamics determined by different conceptual dissipation models.

The bulk model, which calculates dissipation based solely on the age of the first wipe snagged, resulted in a linear growth of blockage size over the 100-hour simulation period, as shown in Figure 6a. This occurs because the model ignores the gradual accumulation of new layers over time, instead treating the blockage as a single entity with uniform dissipation. Consequently, the model predicts continuous growth without stabilization, and it fails to incorporate the complexity of real-world blockage dynamics. These limitations suggest that while the model is computationally straightforward, it may not be suitable for accurately modeling long-term blockage behavior in sewer systems. Building on the limitations of the bulk model, the weighted average age model was tested, producing a similar linear growth of blockage size, as shown in Figure 6b. This model showed slight improvement by considering the average age of all layers for dissipation calculations, but it still oversimplified blockage dynamics. By assuming that all layers contribute equally to dissipation, regardless of their position or exposure to shear forces, it failed to account for variability in dissipation rates within the layered structure of blockages.

Figure 6  a) Bulk approach, b) Layer-based (weighted average age), c) Layer-based (individual layer), and d) Layer-based (shielded old layers).

To address these limitations, the individual layer model was developed, which treated each layer independently by applying dissipation separately to each layer, as shown in Figure 6c. This model improved upon previous models by considering individual layer dynamics but did not account for the position of layers. As a result, it overestimated dissipation rates, predicting that the blockage size would reach a steady state over time. These limitations highlight the need for a model that accounts for layer attachment and its effects on blockage behavior under varying conditions. The shielded layer model addressed this need by incorporating a shielding factor to account for layer attachment, as shown in Figure 6d. This factor reduced the dissipation rate of older layers as they became covered by newer ones, reflecting the reduced exposure of older layers to flow forces. This resulted in a blockage growth pattern where the size initially increased, reached a maximum point, and then gradually decreased as dissipation began to outpace accumulation. By incorporating the layered structure and the effects of layer attachment, this model provided a more detailed representation of blockage evolution compared to previous models.

For all the conceptual models evaluated, their ability to accurately represent real-world blockage dynamics remains uncertain due to the lack of validation data. Further investigation, supported by experimental or field studies, is required to assess the applicability of these methods for real-world sewer system conditions.

Based on the testing of conceptual models under constant flow and accumulation conditions, the bulk, weighted average age, and individual layer models were excluded from long-term simulations. The layered shielding approach was selected for the dynamic modeling of blockages, as it offers a more detailed and potentially realistic representation of blockage evolution by accounting for layer attachment and its effects.

To support the development of the shielded layer model, an 8-hour laboratory experiment was conducted to refine Equation 3, originally derived from the bulk model. The experiment aimed to incorporate the effects of shielding into the dissipation formulation. Wipes were introduced hourly, depending on the average flushing rate per person in each catchment area (A, B, C, and D) to replicate the gradual formation of blockages over time. To calculate the rate of dissipation and the quantity of wipes left in the system, the collected wipes were dried at 105 °C for 24 hours, and their weight was measured at the end of the experiment (Kargar and Joksimovic 2024).

Using experimental data collected at all four sites, Equation 3 was refined by adding a shielding factor to account for the older wipes' reduced exposure to dissipation forces. By assuming more realistic wipe accumulation and dissipation, this is a step toward better blockage modeling; nonetheless, validation data are needed to ensure model accuracy for field applications. To verify these developments and improve dissipation models, field or experimental data collection should be the main focus of future study.

The layered shielding model was applied to estimate the blockage size over time, generating a time series of blockage size. The time series was then merged into the hydraulic model by simulating the blockage as an orifice setting, where a setting of 1 represents a fully open pipe without any blockage, and 0 represents a fully blocked pipe. The orifice equation (Equation 5) used the calculated blockage size and flow rate to dynamically change the orifice setting, hence simulating the impact of the blockage on the hydraulic performance of the system by changing the orifice opening. The hydraulic model was run iteratively to simulate the flow variations due to the blockage, and the runs were repeated until the difference between two successive runs was below 0.01. This iterative method ensured convergence and provided a clear picture of the impact of blockages on system performance under varying flow rates and blockage sizes.

The findings showed that system performance was significantly impacted by wipe-caused blockages. Three wipe generation rates were taken into consideration to assess the impact of various disposal behaviors: 0.1, 0.05, and 0.01 wipes per person per day. The only estimate in the literature from industry data (Powling 2024) was 0.1 wipes/person/day. Lower rates (0.05 and 0.01 wipes/person/day) were considered to evaluate the possible effects of decreased wipe disposal due to behavioral changes or public awareness efforts.

Figure 7 shows the extent of capacity reduction in four areas of study under different wipe generation rates. The results showed that a higher wipe generation rate (0.1) produces more frequent and longer duration capacity reductions, while lower rates (0.01) yield less intensive declines and shorter periods of restricted flow conditions. The extent of these declines varies by study area, with larger population areas having greater capacity loss.

Figure 7  Capacity reduction for 4 study areas under 3 wipe generation rates.

At all wipe generation rates, the extent of capacity loss is greater in more densely populated places. For instance, at the wipe generation rate of 0.1, system capacity decreases to about 0.75 in Area A, 0.6 in Area B, 0.4 in Area C, and less than 0.2 in Area D. Similarly, at the rate of 0.05, capacity diminishes to 0.75 in Area A, 0.7 in Area B, 0.55 in Area C, and 0.4 in Area D. When the lowest generation rate of wipes is 0.01, capacity drops to 0.85 in Area A, 0.8 in Area B, 0.75 in Area C, and 0.65 in Area D. This suggests larger populations deposit a larger quantity of wipes within the system, which enhances chances for blockage formation and has more abrupt declines in sewer capacity.

The impact of wipe-induced blockages varies across areas, at a 0.1 wipe generation rate, the system operates below 0.8 capacity (Grade 2) for less than 5% of the time in Area A, 10% in Area B, 15% in Area C, and 20% in Area D. The transition to Grade 3 (0.7 capacity) occurs in Areas A and B for less than 5% of the time, while in Areas C and D, it occurs in 10% of cases. Grade 4 (0.6 capacity) and Grade 5 (below 0.5 capacity) are observed in less than 5% of cases, exclusively in Areas C and D. At a 0.05 wipe generation rate, the system spends less time in lower-capacity states, operating below 0.8 capacity for a shorter duration compared to 0.1. Reductions to Grade 3 (0.7 capacity) and Grade 4 (0.6 capacity) occur less frequently across all areas, and the likelihood of reaching Grade 5 conditions (below 0.5 capacity) remains minimal. At the lowest 0.01 wipe generation rate, the system remains at near-full capacity (≥0.8, primarily Grade 2) for nearly the entire duration across all areas, indicating significantly reduced blockage risks. These findings demonstrate the importance of considering both population size and wipe disposal behavior when assessing sewer capacity risks.

4 LIMITATIONS

The limitations of this study highlight several areas for further investigation. The layered approach used here could benefit from experimental validation to better understand dissipation dynamics. A practical approach would involve flushing wipes marked with distinct colors at separate times, and then isolating and drying each layer to assess dissipation rates over extended periods. This method would provide a more detailed understanding of layer-specific dissipation behavior and help confirm the assumptions made in this study.

In addition, this research examined dissipation only in a "midpoint" scenario, with blockages in the center of the pipe. However, the location of blockage would probably have an impact on the rate of dissipation, with non-central blockages having different rates of dissipation due to variation in velocity and shear forces across the pipe. Midpoint blockages are subjected to larger velocities and larger shear forces, which leads to larger dissipation rates when compared to off-centre blockages (MW and W cases). Given these conditions, off-centre blockages may undergo smaller dissipation rates as a consequence of lower velocities and shear forces.

5 CONCLUSIONS

A blockage simulation framework was established to model blockage dynamics in sewer systems and evaluate the effects of wipe-caused blockages on sewer capacity. The results indicate that population density, disposal habits, and structural defects play an important role in blockage formation. Higher wipe generation rates along with larger populations lead to increased blockage events. Moreover, the presence of fat, oil, and grease (FOG) contributes to the problem by sticking to the wipes and creating large solid masses (fatbergs) that significantly reduce the capacity of the sewer and increase maintenance costs. These findings support the need to consider both structural and operational factors in sewer asset management, as high wipe generation rates coupled with structural deficiencies, accelerate capacity reduction and enhance the probability of system failure.

The stress on wastewater systems is expected to increase as urban populations continue to grow, making it imperative to mitigate the risk of wipe-caused blockage. The results of this study highlighted the importance of the need for a proactive and data-driven approach that combines operational interventions with structural assessment. CCTV inspection data, which can pinpoint high-risk spots for defects like tree root intrusion and hanging gaskets that allow wipe buildup, is a crucial tool in the identification of structural imperfections. Regular inspections and targeted infrastructure improvement based on these evaluations can strengthen sewers and save maintenance costs.

The study underlines the importance of reducing the wipe generation rate to minimize sewer capacity loss. For instance, the decrease in wipe generation from an initial rate of 0.1 to 0.05 wipes per person per day has the impact of reducing the duration for which sewer networks operate at reduced capacities by 10–15%. Such an operational improvement not only enhances the overall performance of the sewer system but also reduces the risk of blockage formation in the network. It is also necessary that public education campaigns be carried out alongside regulation-based measures that involve education programs, more stringent disposal regulations, and enhancement of product labeling. These measures are essential for developing responsible disposal habits among the public and subsequently alleviating the pressure being exerted on sewer networks.

To improve the accuracy and reliability of the current simulation model, future research should be aimed at model validation using actual blockage data. In addition, there is a need to give priority to the incorporation of advanced machine learning techniques for predictive maintenance that would enable the prediction of probable problems before their occurrence and evaluate the long-term efficacy of the different mitigation methods that may be used. Through the extension and improvement of blockage modeling techniques, this study contributes to the development of urban drainage systems that are sustainable and reliable.

ACKNOWLEDGMENTS

This work was financially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).

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