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.

Sewer blockages, a recurring issue, lead to backups, overflows, flooding, and environmental contamination. Various factors contribute to these blockages from simple clogs to collapsed sewers and inoperable pumping stations. Improper disposal of products labeled as 'flushable', and the degradation of sewer pipes further increases blockage frequency. Open data initiatives by various government levels provide valuable insights into factors contributing to sewer blockages, aiding in planning and operational management. This study utilized open data from Toronto to identify factors contributing to reported sewer blockages, focusing on physical sewer characteristics, population density, tree density, and precipitation. Geospatial analysis techniques, including hotspot analysis, ordinary least squares regression, and geographically weighted regression, were employed. The results revealed that tree root intrusion and the average age of pipes are significant factors contributing to blockages. These findings offer city managers insights to improve inspection and maintenance planning, refine scheduling, and develop strategies to reduce blockages, ensuring uninterrupted sewer operations.

ABSTRACT The increase in sewer blockages, exacerbated by the COVID-19 pandemic due to improper disposal of non-biodegradable items like wet wipes and sanitary products, along with fats, oils, grease, and the presence of defects in sewers, underscores the need for a more profound understanding of wipes’ contribution to these blockages. This study investigated the probability of wipe snagging on sewer imperfections and their subsequent accumulation and dissipation rates under various conditions through laboratory experiments. Findings indicated a significant variability in the likelihood of wipes snagging (ranging from 93% to 0) and accumulating (ranging from 71% to 0), which was influenced by the defect location and the sewer flow rate. Moreover, the study highlighted a direct relationship between the flow rate in the sewer and the rate at which wipes dissipate. Conversely, an inverse relationship was observed between the size of blockages and the dissipation rate of wipes, with larger blockages typically reducing the speed of this process. Importantly, while dissipation rates at low flow rates are nearly constant regardless of the number of wipes and duration, at medium to high flow rates, dissipation initially increases but levels off after 24 h, demonstrating the persistence of wipe-caused blockages in small-diameter sewer pipes.

Wet wipes are widely used in cleaning and personal hygiene applications. However, they frequently contain plastic components that cause microplastic pollution and, even when marketed as flushable, have, in the case of many tested products, been found to agglomerate and clog pipes and pumps in wastewater collection and treatment systems. To this end, we present a method to create wet wipes using toilet paper infused with biopolymer gels that disintegrate in excess water. By infusing toilet paper with alginate or kappa-carrageenan solutions followed by gelation, we generate wet wipes with tensile strengths that are comparable to those reported for commercial flushable wipes (greater than or similar to 100 N/m wipe width) and can be tailored by varying biopolymer and salt concentrations and numbers of base toilet paper layers. Using this approach, both alcohol wipes (formed through the gelation of alginate solutions in ethanol) and aqueous wet wipes (formed by gelling kappa-carrageenan solutions in room-temperature, aqueous KCl) can be produced. Upon simulated flushing (i.e., immersion of the wipes in excess water, which causes the ethanol or KCl to diffuse out), the gel networks break down within minutes, reducing the tensile strength to that of wet toilet paper, and toilet/drain line clearance and Publicly Available Specification 3 (PAS3) Slosh Box testing confirms their effective dispersion upon flushing. Analyses of how the dispersed wipes affect the performance of wastewater bioreactors indicate that the degradation products from these wipes, at concentrations expected in wastewater treatment, should not disrupt municipal wastewater treatment processes. Collectively, our findings suggest that these toilet paper and biopolymer-based wet wipes could prevent clogging in wastewater pipes and pumps and reduce the accumulation of harmful plastics in the environment.

Considering the rising concern over climate change and the need for local food security, productive blue-green roofs (PBGR) can be an effective solution to mitigate many relevant environmental issues. However, their cost of operation is high because they are intensive, and an economical operation and maintenance approach will render them as more viable alternative. Low-cost sensors with the Internet of Things can provide reliable solutions to the real-time management and distributed monitoring of such roofs through monitoring the plant as well soil conditions. This research assesses the extent to which a low-cost image sensor can be deployed to perform continuous, automated monitoring of a urban rooftop farm as a PBGR and evaluates the thermal performance of the roof for additional crops. An RGB-depth image sensor was used in this study to monitor crop growth. Images collected from weekly scans were processed by segmentation to estimate the plant heights of three crops species. The devised technique performed well for leafy and tall stem plants like okra, and the correlation between the estimated and observed growth characteristics was acceptable. For smaller plants, bright light and shadow considerably influenced the image quality, decreasing the precision. Six other crop species were monitored using a wireless sensor network to investigate how different crop varieties respond in terms of thermal performance. Celery, snow peas, and potato were measured with maximum daily cooling records, while beet and zucchini showed sound cooling effects in terms of mean daily cooling.

The hydrologic performance of permeable interlocking concrete pavers (PICP) and a dome concrete forming system (DCFS) was quantified and evaluated at a retrofitted residential laneway in Toronto, Ontario. The quantification and evaluation were performed by comparing their monitored performance over 14 months with an adjacent concrete pavement. Their monitored performance was then used to assess their ability to mimic predevelopment hydrologic behavior, estimated through modeling. The PICP and DCFS achieved (1) an average runoff volume reduction of 33% and 85%; (2) an average runoff coefficient of 0.48 and 0.12; and (3) an average peak flow reduction of 43% and 89%, respectively. The outflow duration from the DCFS was extended 4.1 times greater than the inflow duration, indicating its potential to attenuate flashy events. The peak flows produced by the DCFS were delayed by nearly 2.5 h from those occurring on the PICP. Correlation analyses indicated a more significant influence of rainfall depth and intensity on the performance of PICP than that of the DCFS. Compared with predevelopment levels, PICP did not match runoff volumes for smaller events (<10 mm) but produced less runoff for larger events (>30 mm). However, the PICP did not match predevelopment peak flows in 100% of occurrences. The DCFS reduced runoff volumes and peak flows to magnitudes lower than all predevelopment levels. Surface infiltration testing and subsurface hydraulic conductivity estimations indicated that infiltration capability in both systems could be reduced over time. The study demonstrated that the DCFS might need to be prioritized over permeable pavements, especially when installed over low-permeability soils.

A faecal transport model was applied to a 11.3 km2 wastewater servicing area in Toronto, Ontario, Canada to explore the role that different wastewater sampling campaigns have on estimating the prevalence of SARS-CoV-2 in a population of 60,000. A stochastic wastewater and water quality model was used to evaluate the effectiveness of 11 sampling campaigns during periods of high and low COVID-19 infection among the population, tested using virtual sampling during dry-weather flow. The virtual sampling campaigns were based on the most common automatic sampler programming capabilities and widely used wastewater-based epidemiology (WBE) sampling campaigns reported in the literature. Sampling campaigns differ in weighting method (time, volume, or flow-weighted sampling), sample count, collection period, or sample time. Results suggest that grab samples should be avoided and/or that sampling campaigns with the greatest sample counts and durations are the most robust at capturing COVID-19 infection among the population. Most surprisingly, changes to the weighting method were negligible indicating that a greater number of samples, and larger sample volumes are preferred. This work suggests that investment in flow monitoring equipment for flow- or volume-weighted sampling will not improve WBE results, and that standard time based sampling is sufficient.

Blue-green and blue roofs are increasingly promoted to adapt to climate change by providing multiple benefits. However, uncer-tainties about their design and how they differ from conventional green roofs hinder their implementation. This studyinvestigates the potential of green, blue-green, and blue roofs to control urban stormwater and improve microclimate by moni-toring their performance in Toronto, Ontario, Canada. Experimental setups were built and varied with the following designfactors: substrate type and thickness, drainage layer thickness and orifice size. The results revealed that blue-green roofswith organic and FLL (blended according to the German Forschungsgesellschaft Landschaftsentiwicklung Landschaftsbau) sub-strates significantly improved detention compared to green roofs with similar substrates. The organic blue-green roof achievedmaximum retention, but FLL blue-green roof did not have higher retention than FLL green roof. The blue roof with smaller ori-fices had comparable hydrologic performance to vegetated roofs but suffered from long water standing durations. Organicsubstrates followed by FLL substrates result in the highest air cooling in the noon, but blue roofs had the highest air coolingin the evening. In-substrate temperatures in blue-green roofs were lower than those in green roofs. Trade-offs between thebenefits and drawbacks need to be considered in future designs.

Urban agriculture is receiving increased attention not only for food security and public health but for mitigating the impacts of urbanization and climate change. In cities, rooftop urban farms provide a solution for the limited space at the ground level. However, rooftop urban farming poses several challenges, including an increased need for workforce and site visits and a demand for efficient water use. Recent advancements in information and communication technology (ICT) and the Internet of Things (IoT) have enabled a tremendous suite of low-cost, wireless sensor nodes. In this work, an IoT-enabled approach is introduced to improve water management in an urban rooftop farm in downtown Toronto, Canada. Low-cost resistive water level sensors were calibrated and integrated into wireless sensor nodes to send data through LoRaWAN, an IoT protocol, to The Things Network (TTN) console, after which the processed data are visualized in the user dashboard. This paper addresses the main design stages, field deployment, and suggestions for maintenance learned through monitoring the growing season of 2021. The combination of low-cost sensors, user-friendly microcontrollers and open-source platforms provides an opportunity to improve decision-making, lower costs and reduce reliance on labor.

Conventional green roofs have been widely accepted as a climate change adaptation strategy. However, little is known about the potential of blue–green roofs and rooftop farms to control urban stormwater and improve microclimates. This study evaluates a farmed blue–green roof’s hydrologic and thermal performance over an entire growing season in Toronto, Ontario, Canada. The runoff discharge from three plots planted with various crops was monitored. The substrate and air temperatures at two elevations of different cultivated and self-sowing plant species were collected and compared to a control roof. Results indicate that planting and harvesting activities impacted the hydrologic performance. Mean values for retention ranged from 85–88%, peak attenuation ranged from 82–85%, and peak delay ranged from 7.7 to 8 h. At the lower elevation, the mean air temperature difference above okra, tobacco, and beet was 2.5 °C, whereas, above squash, potato, and milkweed, it was 1.4 °C. Maximum and moderate air-cooling effects were observed in the afternoon and evening, but a warming effect was observed in the early morning. Farmed blue–green roof evaluated in this study provides a runoff control and microclimate improvement comparable to or better than conventional green roofs, in addition to other benefits such as improving food security.

Blue-Green Infrastructure (BGI) consists of natural and semi-natural systems implemented to mitigate climate change impacts in urban areas, including elevated air temperatures and flooding. This study is a state-of-the-art review that presents recent research on BGI by identifying and critically evaluating published studies that considered urban heat island mitigation and stormwater management as potential benefits. Thirty-two records were included in the review, with the majority of studies published after 2015. Findings indicate that BGI effectively controls urban runoff and mitigates urban heat, with the literature being slightly more focused on stormwater management than urban heat island mitigation. Among BGI, the studies on blue- and blue-green roofs focused on one benefit at a time (i.e. thermal or hydrologic performance) and did not consider promoting multiple benefits simultaneously. Two-thirds of the selected studies were performed on a large urban scale, with computer modelling and sensor monitoring being the predominant assessment methods. Compared with typical Green Infrastructure (GI), and from a design perspective, many crucial questions on BGI performance, particularly on smaller urban scales, remain unanswered. Future research will have to continue to explore the performance of BGI, considering the identified gaps.

Determining whether or not a consumer product is flushable is often a matter of some assessment of the package labeling. Unfortunately, consumers are sometimes unaware of whether or not a product can be safely disposed of via flushing. Differences in the composition of fibers and in how products are made can be used to determine if a specific product is flushable. Importantly, consumer awareness should be raised as to how different products can be disposed of, either via flushing or other means.

Roadside bio-retention (RBR) facilities are low impact development practices, which control urban runoff primarily from road pavements. Using hydrologic models, such as the US EPA Storm Water Management Model (SWMM), RBR are typically designed with some fundamental assumptions, including where runoff completely enters the facilities and fully utilizes the whole surface area for percolation, detention, filtration, and infiltration to the surrounding soils. This paper highlights the importance of inlet hydraulics and the spatial distribution of inflow along a RBR, and proposes an integrated hydraulic and hydrologic modelling approach to simulate its overall runoff control performance. The integrated hydraulic/hydrologic modelling approach consists of three components: (1) A dual drainage hydrologic model to simulate runoff generation, runoff hydrographs entering and bypassing a storm inlet, and the outflow hydrograph from a fully utilized RBR; (2) a computational fluid dynamic model to determine the inflow distribution along a RBR; and (3) an overall runoff control performance analysis of RBR by considering the inlet efficiency, and the partially and fully utilized RBR during a storm event. A case study of an underground RBR in the City of Toronto was used to demonstrate the integrated modelling approach. It is concluded that; (1) inlet efficiency of a RBR will determine the overall runoff control performance; and (2) the inflow distribution will dictate the effective length of a RBR, which may affect the overall runoff control performance.

Inappropriate disposal of wipes and other products that are either explicitly labelled or assumed by the consumers to be flushable via toilets is increasingly being cited as the cause of a range of sewer systems issues. In the rapidly growing and diverse market for these consumer products, there are significant variations in consumer information provided by manufacturers, product composition and behaviour in different components of wastewater system. This paper summarizes the results of assessing the labelling, drainline clearance and disintegration testing of 101 consumer products, adopting the International Water Services Flushability Group flushability specifications. None of the products tested satisfy the product labelling Code of Practice, and all products other than bathroom tissue failed the disintegration test, including the 23 products that were labelled ‘flushable’. The need for a global definition of a ‘flushable’ product exists and it is vital that it is brought into legislation in an effort to combat misconceptions around consumer products that may exist internationally.

The understanding of the behavior of a wind turbine is difficult due to changes in weather conditions. To obtain the response of a wind turbine influenced by changes in both wind speed and its direction, using the meteorological station data is often preferred to using the real turbine data. Furthermore, simulated data can be easily extrapolated to varied turbine hub heights. In order to estimate the most effective power output in this study, a wind turbine simulation was developed. The simulation depends on the real meteorological data. For the purpose, three modeling techniques, namely Multi-Nonlinear Regression (MNLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and support vector machines (SVM) were used. In SVM learning process, polynomial and radial basis kernel functions were used. Models were compared to wind turbine measurement values in the same region for similar data. MNLR was used to determine quantify the strength of the relationship between parameters and to eliminate the ineffective parameters. Efficient parameters preferred for training and testing phases of the SVM and ANFIS. The Subtractive Clustering and Grid Partitioning methods were used to identify the inference parameters of ANFIS. According to performance evaluations, MNLR-ANFIS modeling based on Subtractive Clustering gave better results than Grid Partitioning. The results showed that proposed collaborative model could be applied to wind power estimation problems.

Wastewater is an underutilized and readily available source of carbon free thermal energy. The energy derived from wastewater can be augmented using heat pumps to supply thermal energy to buildings. Due to favorable temperatures, wastewater sourced heat pumps are able to operate more efficiently than air and ground sourced heat pumps. This paper evaluates the potential of using wastewater heat recovery (WWHR) to provide heating and cooling to a mid-sized university campus located in the urban center of Toronto, Canada.

Wastewater is an abundant and an underutilized thermal energy source that experiences steady temperatures and predictable flow rates year-round. These characteristics make it an excellent candidate to serve as the heat source and sink for heat pump based HVAC (Heating, Ventilation, and Air Conditioning) systems capable of providing both heating and cooling. The potential for wastewater heat recovery is evaluated for a large hospital in the greater Toronto area, in Canada. A model was developed to calculate the operational savings and benefits of the proposed system, and the results from that analysis were used to carry out an economic analysis.

A numerical tool was developed to calculate the stormwater runoff total suspended solids (TSS) removal efficiency of bioretention cells to assist engineers in obtaining credit and approval for bioretention cell facilities.Numerical models for filtration were used in developing this tool, as they have previously been successfully used for bioretention cells.The equations were adapted to integrate with the widely used USEPA SWMM, through its Add-in Tools feature.The tool was first tested to ensure the model matched the monitored performance of a bioretention cell and, second, benchmarked against the TSS removal predicted by another modeling tool (WinSLAMM).The capability of the tool to accurately simulate the TSS reduction performance of the monitored bioretention cell supports its suitability for use in designing bioretention facilities.This research, model development, and verification are the first steps towards the complete development of a stormwater runoff TSS removal model capable of continuous simulation, which will aid in bioretention cell design and installation.

The revitalization of Toronto’s waterfront presents the largest urban redevelopment project currently underway in North America. With respect to planning the waterfront’s urban water systems (UWS), a number of studies considered a range of criteria in search for sustainable alternatives. However, a comprehensive assessment of the integrated source-drinking-wastewater-stormwater systems over their life cycles has not been developed. According to the main postulates of the integrated approach, hybrid water systems can offer potentially more sustainable solutions than traditional centralized systems. This paper discusses the development process of a decision support tool designed to facilitate evaluation of alternatives based on UWS metabolism concept while addressing some typical challenges of hydroinformatics. This decision-making support tool analyses and compares the sustainability performance of alternative decentralized solutions against a baseline conventional approach on a neighbourhood level. The tool uses a set of criteria, adopted by the large group of stakeholders involved in the development process, that are not typically considered in the decision-making process, such as energy savings, greenhouse gas (GHG) emissions, climate change resiliency, chemical use, and nutrient recovery.

Due to advancements in instrumentation and communication technologies, monitoring of water infrastructure is experiencing a significant growth worldwide and water managers are increasingly deploying monitoring equipment for decision-making purposes. Hydrological events and relevant datasets including rainfall data are of a complex nature and are potentially susceptible to errors from various sources. Hence, it is essential to develop efficient methods for the quality control of the acquired data. The present work introduces an artificial neural network-based approach for real-time quality control and infilling of rain gauge data. Available rainfall measurements from neighboring rain gauges are employed to train and develop the neural network model. Trained artificial neural network model was able to validate up to about 97% of the data using 95% confidence intervals. This finding suggests that artificial neural networks can be successfully implemented for erroneous data identification/correction and reconstruction of missing data points. Given its short processing time and reportedly superior performance to traditional quality control strategies, neural network methodology can be deployed as an efficient tool for the processing and control of large sets of timeseries with complex natures including precipitation data.