A risk-based approach to sanitary sewer pipe asset management

Global perspectives on groundwater infiltration to sewer networks: A threat to urban sustainability

While existing studies on sewer networks have explored topics such as surface water inflow, limited research has delved into groundwater infiltration (GWI). This study aims to fill this void by providing a comprehensive overview of quantitative analyses of GWI in sewer networks plus current status, limitations and future perspectives, considering the most relevant peer-reviewed research, including 83 studies. We propose dividing the existing research into two main groups: (1) phreatic zone, and (2) vadose zone. Most research has focused on the latter, mainly considering Rainfall-Derived Inflow and Infiltration (RDII), including surface water inflow and GWI. The ratio of each is not frequently separated; otherwise, there may be some assumptions, e.g. in dry weather and assuming zero surface water inflow. We also divided the employed approaches in different categories from physically-based numerical models, to simpler ones, e.g. water budget analysis. In fact, a combination of approaches may be applied to find the intricate characteristics of 'urban groundwater' or 'urban karst.' The findings revealed a heightened vulnerability of sewer networks to GWI, due to climate change (CC) and its associated repercussions, e.g. sea level rise (SLR), making the coastal cities the most vulnerable regions. In future research, the criticality of pre-emptive measures and monitoring of networks, especially near the coastline, is emphasised to ensure the resilience and adaptability of sewer networks in the context of GWI amid the potential impacts of CC. However, current monitoring practices lack widespread evidence for spatiotemporal analysis of GWI quantity.

Water, Water Everywhere, but Every Drop Unique: Challenges in the Science to Understand the Role of Contaminants of Emerging Concern in the Management of Drinking Water Supplies

The protection and management of water resources continues to be challenged by multiple and ongoing factors such as shifts in demographic, social, economic, and public health requirements. Physical limitations placed on access to potable supplies include natural and human-caused factors such as aquifer depletion, aging infrastructure, saltwater intrusion, floods, and drought. These factors, although varying in magnitude, spatial extent, and timing, can exacerbate the potential for contaminants of concern (CECs) to be present in sources of drinking water, infrastructure, premise plumbing and associated tap water. This monograph examines how current and emerging scientific efforts and technologies increase our understanding of the range of CECs and drinking water issues facing current and future populations. It is not intended to be read in one sitting, but is instead a starting point for scientists wanting to learn more about the issues surrounding CECs. This text discusses the topical evolution CECs over time (Section 1), improvements in measuring chemical and microbial CECs, through both analysis of concentration and toxicity (Section 2) and modeling CEC exposure and fate (Section 3), forms of treatment effective at removing chemical and microbial CECs (Section 4), and potential for human health impacts from exposure to CECs (Section 5). The paper concludes with how changes to water quantity, both scarcity and surpluses, could affect water quality (Section 6). Taken together, these sections document the past 25 years of CEC research and the regulatory response to these contaminants, the current work to identify and monitor CECs and mitigate exposure, and the challenges facing the future.

Deep learning-assisted automated sewage pipe defect detection for urban water environment management

A healthy sewage pipe system plays a significant role in urban water management by collecting and transporting wastewater and stormwater, which can be assessed by hydraulic model. However, sewage pipe defects have been observed frequently in recent years during regular pipe maintenance according to the captured interior videos of underground pipes by closed-circuit television (CCTV) robots. In this case, hydraulic model constructed based on a healthy pipe would produce large deviations with that in real hydraulic performance and even be out of work, which can result in unanticipated damages such as blockage collapse or stormwater overflows. Quick defect evaluation and defect quantification are the precondition to achieve risk assessment and model calibration of urban water management, but currently pipe defects assessment still largely relies on technicians to check the CCTV videos/images. An automated sewage pipe defect detection system is necessary to timely determine pipe issues and then rehabilitate or renew sewage pipes, while the rapid development of deep learning especially in recent five years provides a fantastic opportunity to construct automated pipe defect detection system by image recognition. Given the initial success of deep learning application in CCTV interpretation, the review (i) integrated the methodological framework of automated sewage pipe defect detection, including data acquisition, image pre-processing, feature extraction, model construction and evaluation metrics, (ii) discussed the state-of-the-art performance of deep learning in pipe defects classification, location, and severity rating evaluation (e.g., up to ~96 % of accuracy and 140 FPS of processing speed), and (iii) proposed risk assessment and model calibration in urban water management by considering pipe defects. This review introduces a novel practical application-oriented methodology including defect data acquisition by CCTV, model construction by deep learning, and model application, provides references for further improving accuracy and generalization ability of urban water management models in practical application.

Sewerage infrastructure asset management based on a consumer-centric approach

In most developed countries, such as the USA, the E.U., and East Asia, the importance of public infrastructure asset management has been stressed for a long time. Among the various types of public infrastructure, sewerage systems are one of the most cost-intensive facilities to manage. Sewerage systems are considered highly difficult to manage due to the undetermined level of service needed, different standards of user satisfaction, and the large gap of service understanding between experts and users. To address these issues, this study aims to define the appropriate target level of service improvement by combining consumers' expected level of service and complaint data. In this study, the case of the inland flood management project in South Korea is investigated because of the global trend of increasing flood damage. The complaint data represent the frequency of flood damage in the area. Using the contingent valuation method, we found that people want to use 25% of their current monthly sewage bill on the management project. In addition, the results of this study demonstrate that people prefer to deal with the problems caused by old service infrastructure when it can be handled at a lower cost during early stages.

Characterising Penetrometer Tip Contact during Concrete Condition Assessment

Concrete condition-assessing penetrometers need to be able to distinguish between making contact with a hard (concrete) surface as opposed to a semi-solid (corroded concrete) surface. We investigated whether different shaped tips of a cylindrical penetrometer were better than others at maintaining contact with concrete and not slipping. We designed a range of simple symmetric tip shapes, controlled by a single superellipse parameter. We performed a finite element analysis of these parametric models in SolidWorks before machining in stainless steel. We tested our penetrometer tips on a concrete paver cut to four angles at 20∘ increments. The results indicate that the squircle-shaped tip had the least slippage when used for concrete condition assessment.

A Two-Stage Data-Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

Risk-informed asset management is key to maintaining optimal performance and efficiency of urban sewer systems. Although sewer system failures are spatiotemporal in nature, previous studies analyzed failure risk from a unidimensional aspect (either spatial or temporal), not accounting for bidimensional spatiotemporal complexities. This is owing to the insufficiency of good-quality data, which ultimately leads to under-/overestimation of failure risk. Here, we propose a generalized methodology/framework to facilitate a robust spatiotemporal analysis of urban sewer system failure risk, overcoming the intrinsic challenges of data imperfections-e.g., missing data, outliers, and imbalanced information. The framework includes a two-stage data-driven modeling technique that efficiently models the highly right-skewed sewer system failure data to predict the failure risk, leveraging a bidimensional space-time approach. We implemented our analysis for Bogotá, the capital city of Colombia. We train, test, and validate a battery of machine learning algorithms-logistic regression, decision trees, random forests, and XGBoost-and select the best model in terms of goodness-of-fit and predictive accuracy. Finally, we illustrate the applicability of the framework in planning/scheduling sewer system maintenance operations using state-of-the-art optimization techniques. Our proposed framework can help stakeholders to analyze the failure-risk models' performance under different discrimination thresholds, and provide managerial insights on the model's adequate spatial resolution and appropriateness of decentralized management for sewer system maintenance.

Is it possible developing reliable prediction models considering only the pipe’s age for decision-making in sewer asset management?

Purpose

The purpose of this paper was exploring and comparing different deterioration models based on statistical and machine learning approaches. These models were chosen from their successful results in other case studies. The deterioration models were developing considering two scenarios: (i) only the age as covariate (Scenario 1); and (ii) the age together with other available sewer characteristics as covariates (Scenario 2). Both were evaluated to achieve two different management objectives related to the prediction of the critical condition of sewers: at the network and the sewer levels.

Design/methodology/approach

Six statistical and machine learning methods [logistic regression (LR), random forest (RF), multinomial logistic regression, ordinal logistic regression, linear discriminant analysis and support vector machine] were explored considering two kinds of predictor variables (independent variables in the model). The main propose of these models was predicting the structural condition at network and pipe level evaluated from deviation analysis and performance curve techniques. Further, the deterioration models were exploring for two case studies: the sewer systems of Bogota and Medellin. These case studies were considered because of both counts with their own assessment standards and low inspection rate.

Findings

The results indicate that LR models for both case studies show higher prediction capacity under Scenario 1 (considering only the age) for the management objective related to the network, such as annual budget plans; and RF shows the highest success percentage of sewers in critical condition (sewer level) considering Scenario 2 for both case studies.

Practical implications

There is not a deterioration method whose predictions are adaptable for achieving different management objectives; it is important to explore different approaches to find which one could support a sewer asset management objective for a specific case study.

Originality/value

The originality of this paper consists of there is not a paper in which the prediction of several statistical and machine learning-based deterioration models has been compared for case studies with different local assessment standard. The above to find which is adaptable for each one and which model is adaptable for each management objective.

Coliphages as a Complementary Tool to Improve the Management of Urban Wastewater Treatments and Minimize Health Risks in Receiving Waters

Even in countries with extensive sanitation systems, outbreaks of waterborne infectious diseases are being reported. Current tendencies, such as the growing concentration of populations in large urban conurbations, climate change, aging of existing infrastructures, and emerging pathogens, indicate that the management of water resources will become increasingly challenging in the near future. In this context, there is an urgent need to control the fate of fecal microorganisms in wastewater to avoid the negative health consequences of releasing treated effluents into surface waters (rivers, lakes, etc.) or marine coastal water. On the other hand, the measurement of bacterial indicators yields insufficient information to gauge the human health risk associated with viral infections. It would therefore seem advisable to include a viral indicator—for example, somatic coliphages—to monitor the functioning of wastewater treatments. As indicated in the studies reviewed herein, the concentrations of somatic coliphages in raw sewage remain consistently high throughout the year worldwide, as occurs with bacterial indicators. The removal process for bacterial indicators and coliphages in traditional sewage treatments is similar, the concentrations in secondary effluents remaining sufficiently high for enumeration, without the need for cumbersome and costly concentration procedures. Additionally, according to the available data on indicator behavior, which is still limited for sewers but abundant for surface waters, coliphages persist longer than bacterial indicators once outside the gut. Based on these data, coliphages can be recommended as indicators to assess the efficiency of wastewater management procedures with the aim of minimizing the health impact of urban wastewater release in surface waters.

An Efficient Framework for Multi-Objective Risk-Informed Decision Support Systems for Drainage Rehabilitation

Combining multiple modules into one framework is a key step in modelling a complex system. In this study, rather than focusing on modifying a specific model, we studied the performance of different calculation structures in a multi-objective optimization framework. The Hydraulic and Risk Combined Model (HRCM) combines hydraulic performance and pipe breaking risk in a drainage system to provide optimal rehabilitation strategies. We evaluated different framework structures for the HRCM model. The results showed that the conventional framework structure used in engineering optimization research, which includes (1) constraint functions; (2) objective functions; and (3) multi-objective optimization, is inefficient for drainage rehabilitation problem. It was shown that the conventional framework can be significantly improved in terms of calculation speed and cost-effectiveness by removing the constraint function and adding more objective functions. The results indicated that the model performance improved remarkably, while the calculation speed was not changed substantially. In addition, we found that the mixed-integer optimization can decrease the optimization performance compared to using continuous variables and adding a post-processing module at the last stage to remove the unsatisfying results. This study (i) highlights the importance of the framework structure inefficiently solving engineering problems, and (ii) provides a simplified efficient framework for engineering optimization problems.

Bayesian network-based methodology for selecting a cost-effective sewer asset management model

This paper presents a methodology based on Bayesian networks (BN) to prioritize and select the minimal number of variables that allows predicting the structural condition of sewer assets to support the strategies in proactive management. The integration of BN models, statistical measures of agreement (Cohen's Kappa coefficient) and a statistical test (Wilcoxon test) were useful for a robust and straightforward selection of a minimum number of variables (qualitative and quantitative) that ensure a suitable prediction level of the structural conditions of sewer pipes. According to the application of the methodology to a specific case study (Bogotás sewer network, Colombia), it found that with only two variables (age and diameter) the model could achieve the same capacity of prediction (Cohen's Kappa coefficient = 0.43) as a model considering several variables. Furthermore, the methodology allows finding the calibration and validation percentage subsets that best fit (80% for calibration and 20% for validation data in the case study) in the model to increase the capacity of prediction with low variations. Furthermore, it found that a model, considering only pipes in critical and excellent conditions, increases the capacity of successful predictions (Cohen's Kappa coefficient from 0.2 to 0.43) for the proposed case study.

Spatiotemporal Assessment of COVID-19 Spread over Oman Using GIS Techniques

Coronavirus disease (COVID-19), caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide challenge effecting millions of people in more than 210 countries, including the Sultanate of Oman (Oman). Spatiotemporal analysis was adopted to explore the spatial patterns of the spread of COVID-19 during the period from 29th April to 30th June 2020. Our assessment was made using five geospatial techniques within a Geographical Information System (GIS) context, including a weighted mean centre (WMC), standard deviational ellipses, Moran's I autocorrelation coefficient, Getis-Ord General-G high/low clustering, and Getis-Ord G i ∗ statistic. The Moran's I-/G- statistics proved that COVID-19 cases in datasets (numbers of cases) were clustered throughout the study period. The Moran's I and Z scores were above the 2.25 threshold (a confidence level above 95%), ranging from 2274 cases on 29th April to 40,070 cases on 30th June 2020. The results of G i ∗ showed varying rates of infections, with a large spatial variability between the different wilayats (district). The epidemic situation in some wilayats, such as Mutrah, As-Seeb, and Bowsher in the Muscat Governorate, was more severe, with Z score higher than 5, and the current transmission still presents an increasing trend. This study indicated that the directional pattern of COVID-19 cases has moved from northeast to northwest and southwest, with the total impacted region increasing over time. Also, the results indicate that the rate of COVID-19 infections is higher in the most populated areas. The findings of this paper provide a solid basis for future study by investigating the most resolute hotspots in more detail and may help decision-makers identify targeted zones for alleviation plans.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41748-020-00194-2.

GIS-based MCDM modeling for landfill site suitability analysis: A comprehensive review of the literature

One of the cheapest and proper methods for the ultimate disposal of Municipal Solid Waste (MSW) is landfilling. However, determining the location of landfill sites is a difficult and complex task due to depending on social, environmental, technical, economic, and legal factors. To solve the aforementioned challenges related to the landfill site suitability analysis, the combinations of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) have been studied by academia and applied by experts over the years. This notice is apparent by the large number of academic papers which have been announced in the near future. To provide a framework of the existing literature, and to guide colleagues, a state-of-the-art of recent papers is crucial. The goal of this study is to review all scientific papers in GIS-based MCDM modeling for landfill site suitability analysis in academic journals. A total of 106 studies published between 2005 and 2019 are recorded and surveyed. The studies are then investigated and classified by a generated taxonomy including following categories: GIS software, application area, uncertainty, MCDM techniques, cell sizes in GIS, and criteria. Based on the review conducted, it is observed that while Analytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC) are the most widely used MCDM methods for weighting the criteria and ranking the alternatives, respectively. On the other hand, while environmental dimension is the most commonly preferred main criteria, surface water comes first in the sub-criteria pool. Criteria analysis shows that surface and ground water, geology, land use, distance to fault zone, distance to urban areas, and distance to road and slope are the most commonly used criteria groups among others. These classifications and observations are helpful for identifying research gaps in the current literature and provide insights for future modeling and research efforts in the field.

High levels of sewage contamination released from urban areas after storm events: A quantitative survey with sewage specific bacterial indicators

BACKGROUND

Past studies have demonstrated an association between waterborne disease and heavy precipitation, and climate change is predicted to increase the frequency of these types of intense storm events in some parts of the United States. In this study, we examined the linkage between rainfall and sewage contamination of urban waterways and quantified the amount of sewage released from a major urban area under different hydrologic conditions to identify conditions that increase human risk of exposure to sewage.

METHODS AND FINDINGS

Rain events and low-flow periods were intensively sampled to quantify loads of sewage based on two genetic markers for human-associated indicator bacteria (human Bacteroides and Lachnospiraceae). Samples were collected at a Lake Michigan estuary and at three river locations immediately upstream. Concentrations of indicators were analyzed using quantitative polymerase chain reaction (qPCR), and loads were calculated from streamflow data collected at each location. Human-associated indicators were found during periods of low flow, and loads increased one to two orders of magnitude during rain events from stormwater discharges contaminated with sewage. Combined sewer overflow (CSO) events increased concentrations and loads of human-associated indicators an order of magnitude greater than heavy rainfall events without CSO influence. Human-associated indicator yields (load per km2 of land per day) were related to the degree of urbanization in each watershed. Contamination in surface waters were at levels above the acceptable risk for recreational use. Further, evidence of sewage exfiltration from pipes threatens drinking water distribution systems and source water. While this study clearly demonstrates widespread sewage contamination released from urban areas, a limitation of this study is understanding human exposure and illness rates, which are dependent on multiple factors, and gaps in our knowledge of the ultimate health outcomes.

CONCLUSIONS

With the prediction of more intense rain events in certain regions due to climate change, sewer overflows and contamination from failing sewer infrastructure may increase, resulting in increases in waterborne pathogen burdens in waterways. These findings quantify hazards in exposure pathways from rain events and illustrate the additional stress that climate change may have on urban water systems. This information could be used to prioritize efforts to invest in failing sewer infrastructure and create appropriate goals to address the health concerns posed by sewage contamination from urban areas.

Sewage loading and microbial risk in urban waters of the Great Lakes

Despite modern sewer system infrastructure, the release of sewage from deteriorating pipes and sewer overflows is a major water pollution problem in US cities, particularly in coastal watersheds that are highly developed with large human populations. We quantified fecal pollution sources and loads entering Lake Michigan from a large watershed of mixed land use using host-associated indicators. Wastewater treatment plant influent had stable concentrations of human Bacteroides and human Lachnospiraceae with geometric mean concentrations of 2.77 × 10⁷ and 5.94 × 10⁷ copy number (by quantitative PCR) per 100 ml, respectively. Human-associated indicator levels were four orders of magnitude higher than norovirus concentrations, suggesting that these human-associated bacteria could be sensitive indicators of pathogen risk. Norovirus concentrations in these same samples were used in calculations for quantitative microbial risk assessment. Assuming a typical recreational exposure to untreated sewage in water, concentrations of 7,800 copy number of human Bacteroides per 100 mL or 14,000 copy number of human Lachnospiraceae per 100 mL corresponded to an illness risk of 0.03. These levels were exceeded in estuarine waters during storm events with greater than 5 cm of rainfall. Following overflows from combined sewer systems (which must accommodate both sewage and stormwater), concentrations were 10-fold higher than under rainfall conditions. Automated high frequency sampling allowed for loads of human-associated markers to be determined, which could then be related back to equivalent volumes of untreated sewage that were released. Evidence of sewage contamination decreased as ruminant-associated indicators increased approximately one day post-storm, demonstrating the delayed impact of upstream agricultural sources on the estuary. These results demonstrate that urban areas are a diffuse source of sewage contamination to urban waters and that storm-driven release of sewage, particularly when sewage overflows occur, creates a serious though transient human health risk.

Risk assessment model to prioritize sewer pipes inspection in wastewater collection networks

In wastewater systems as one of the most important urban infrastructures, the adverse consequences and effects of unsuitable performance and failure event can sometimes lead to disrupt part of a city functioning. By identifying high failure risk areas, inspections can be implemented based on the system status and thus can significantly increase the sewer network performance. In this study, a new risk assessment model is developed to prioritize sewer pipes inspection using Bayesian Networks (BNs) as a probabilistic approach for computing probability of failure and weighted average method to calculate the consequences of failure values. Finally to consider uncertainties, risk of a sewer pipe is obtained from integration of probability and consequences of failure values using a fuzzy inference system (FIS). As a case study, sewer pipes of a local wastewater collection network in Iran are prioritized to inspect based on their criticality. Results show that majority of sewers (about 62%) has moderate risk, but 12%of sewers are in a critical situation. Regarding the budgetary constraints, the proposed model and resultant risk values are expected to assist wastewater agencies to repair or replace risky sewer pipelines especially in dealing with incomplete and uncertain datasets.

Spatial Models of Sewer Pipe Leakage Predict the Occurrence of Wastewater Indicators in Shallow Urban Groundwater

Twentieth century municipal wastewater infrastructure greatly improved U.S. urban public health and water quality. However, sewer pipes deteriorate, and their accumulated structural defects may release untreated wastewater to the environment via acute breaks or insidious exfiltration. Exfiltrated wastewater constitutes a loss of potentially reusable water and delivers a complex and variable mix of contaminants to urban shallow groundwater. Yet, predicting where deteriorated sewers impinge on shallow groundwater has been challenging. Here we develop and test a spatially explicit model of exfiltration probability based on pipe attributes and groundwater elevation without prior knowledge of exfiltrating defect locations. We find that models of exfiltration probability can predict the probable occurrence in underlying shallow groundwater of established wastewater indicators including the artificial sweetener acesulfame, tryptophan-like fluorescent dissolved organic matter, nitrate, and a stable isotope of water (δ¹⁸O). The strength of the association between exfiltration probability and indicators of wastewater increased when multiple pipe attributes, distance weighting, and groundwater flow direction were considered in the model. The results prove that available sanitary sewer databases and groundwater digital elevation data can be analyzed to predict where pipes are likely leaking and contaminating groundwater. Such understanding could direct sewer infrastructure reinvestment toward water resource protection.

Assessing urban potential flooding risk and identifying effective risk-reduction measures

Flood protection is one of the traditional functions of any drainage system, and it remains a major issue in many cities because of economic and health impact. Heavy rain flooding has been well studied and existing simulation software can be used to predict and improve level of protection. However, simulating minor flooding remains highly complex, due to the numerous possible causes related to operational deficiencies or negligent behaviour. According to the literature, causes of blockages vary widely from one case to another: it is impossible to provide utility managers with effective recommendations on how to improve the level of protection. It is therefore vital to analyse each context in order to define an appropriate strategy. Here we propose a method to represent and assess the flooding risk, using GIS and data gathered during operation and maintenance. Our method also identifies potential management responses. The approach proposed aims to provide decision makers with clear and comprehensible information. Our method has been successfully applied to the Urban Community of Bordeaux (France) on 4895 interventions related to flooding recorded during the 2009-2011 period. Results have shown the relative importance of different issues, such as human behaviour (grease, etc.) or operational deficiencies (roots, etc.), and lead to identify corrective and proactive. This study also confirms that blockages are not always directly due to the network itself and its deterioration. Many causes depend on environmental and operating conditions on the network and often require collaboration between municipal departments in charge of roads, green spaces, etc.