Efficiency in Water and Sanitation Sector. A Survey on Empirical Literature

In this paper, it was made an exhaustive survey of the literature related with cost and production frontiers in the water and sanitation sector. The survey shed light in order to determine the variables to choose in the model to be estimated in a further empirical estimation developed for the Latin American Region by the authorsfrontiers; water and sanitation sector; empirical estimation

Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review

There is an increasing demand to enhance infrastructure asset management within the drinking water sector. A key factor for achieving this is improving the accuracy of pipe failure prediction models. Machine learning-based models have emerged as a powerful tool in enhancing the predictive capabilities of water distribution network models. Extensive research has been conducted to explore the role of explanatory variables in optimizing model outputs. However, the underlying mechanisms of incorporating explanatory variable data into the models still need to be better understood. This review aims to expand our understanding of explanatory variables and their relationship with existing models through a comprehensive investigation of the explanatory variables employed in models over the past 15 years. The review underscores the importance of obtaining a substantial and reliable dataset directly from Water Utilities databases. Only with a sizeable dataset containing high-quality data can we better understand how all the variables interact, a crucial prerequisite before assessing the performance of pipe failure rate prediction models.EF-O acknowledges the financial support provided by the “Agencia de Gestió d’Ajust Universitaris I de Recerca” (https:// agaur. gencat. cat/ en/) through the Industrial Doctorate Plan of the Secretariat for Universities and Research of the Department of Business and Knowledge of the Government of Catalonia, under the Grant DI 093-2021. Additionally, EF-O appreciates the economic support received from the Water Utility Aigües de Barcelona, Empresa Metropolitana de Gestió del Cicle Integral de l'Aigua.Peer ReviewedPostprint (published version

Using Gas Chromatography to Investigate Volatile Organics in Drinking Water

Trihalomethanes (THMs) are a major class of disinfection by-products found in chlorinated drinking water. They are an unfortunate side effect of the chlorination process. Due to possible adverse health effects, the United States Environmental Protection Agency has set a maximum contaminant level of 0.080 milligrams per liter for Total Trihalomethanes in drinking water. Recently, the way in which utilities report their trihalomethane levels has changed. This has renewed interest in on-line, near real time monitoring of trihalomethane concentrations. The focus of this research was the development of a fully automated instrument capable of on-line near real time measurement of THMs concentrations in drinking water distribution systems and its application to real world problems. A commercial instrument that was shown to be both rugged and robust was developed. This instrument was used to collect unprecedented on-line THMs data in multiple distribution systems. This data was then used for treatment process optimization in a functioning water treatment plant. Comparison to empirical models showed that it is possible to use on-line monitoring data to calibrate the models for a particular system. This is a possible alternative to the expensive process of developing an entirely new empirical model. Additional studies used the rate of formation of THMs to detemrine the time to the first tap for a particular treatment system. This determined amount of time was used with the rate of ormation for haloacetic acids to distinguish between concentrations resulting from formation and thos resulting from the use of bulk hypochlorite solution

Vulnerability assessment to trihalomethane exposure in water distribution system.

Chlorination is an effective and cheap disinfectant for preventing waterborne diseases-causing microorganisms, but its compounds tend to react with the natural organic matter (NOM), forming potentially harmful and unwanted disinfection by-products (DBPs) such as trihalomethanes (THMs), haloacetic acids (HAAs), and others. The present paper proposes a methodology for estimating the vulnerability with respect to users' exposure to DPBs in water distribution systems (WDSs). The presented application considers total THMs (TTHMs) concentration, but the methodology can be used also for other types of DPBs. Five vulnerability indexes are adopted that furnish different kinds of information about the exposure. The methodology is applied to five case studies, and the results suggest that the introduced indexes identify different critical areas in respect to elevated concentrations of TTHMs. In this way, the use of the proposed methodology allows identifying the higher risk nodes with respect to the different kinds of exposure, whether it is a short period of exposure to high TTHMs values, or chronic exposure to low concentrations. The application of the methodology furnishes useful information for an optimal WDS management, for planning system modifications and district sectorization taking into account water quality

Applications of natural organic matter optical properties for assessing drinking water disinfection and distribution

The task of providing safe drinking water requires proper monitoring of water quality and treatment performance from source to tap. Accordingly, the demand for online monitoring is increasing both at treatment plants a nd within distribution networks. Some of the available techniques use correlations between the optical properties of dissolved organic matter (DOM), mainly absorbance, and other water quality parameters. Fluorescence spectroscopy is significantly more sensitive than absorbance spectroscopy and gives comprehensive information about the composition and concentration of organic matter, so it has a strong potential for online monitoring applications. In this thesis, the application of fluorescence spectroscopy was investigated for two locations in the drinking water treatment system: the ultraviolet (UV) disinfection chambers and the distribution network. With respect to UV254disinfection, fluorescence spectroscopy was investigated as a proxy of potential unwanted by-products (assimilable organic carbon (AOC) and disinfection by products (DBPs), in addition to the administered UV dose. The UV254 irradiation increased assimilable organic carbon (AOC) concentration, with the estimated AOC production induced by UV254irradiation at a dose of 40 mJ cm-2being 0.4 % of the dissolved organic carbon. The concentration of adsorbable organic chlorine (AOCl), a subset of adsorbable organic halogens (AOX), generally increased following UV254 irradiation at the typical disinfection dose. Weak but statistically-significant correlations were found between the UV-induced fluorescence reduction and increases in both AOC and AOCl concentrations. Compared to absorbance, greater reductions were observed in fluorescence intensity following sequential UV irradiation and chlorination and these correlated more strongly with AOCl production. Following UV254disinfection, a linear relationship was observed between UV254dose and changes in long-wavelength fluorescence (> 400 nm) intensities at doses up to 200 mJ cm-2. However, the application of fluorescence as a proxy of the UV254dose is limited due to the relatively small and unpredictable direction of change of fluorescence intensity. In the distribution network, the sensitivity of fluorescence to detect contamination caused by entrainment was compared to the sensitivity of other common water quality parameters including several trace elements and microbial indicator species abundances. Of these, fluorescence was the most sensitive tracer for distinguishing contamination from natural variation, followed by absorbance. The relationship between fluorescence and microbial regrowth was also examined; however, no correlation was observed. The results of this thesis imply that although fluorescence might not always correlate with the chemical and microbial water parameters, its prompt response to treatment-induced modifications and fluctuations, together with high analytical precision and sensitivity of fluorescence measurements, make it a useful parameter for real-time monitoring of water quality changes in drinking water treatment plants and distribution systems

Forecasting Chlorine Residuals in a Water Distribution System Using a General Regression Neural Network

Abstract: In a water distribution system (WDS), chlorine disinfection is important in preventing the spread of waterborne diseases. By strictly controlling residual chlorine throughout the WDS, water quality managers can ensure the satisfaction and safety of their customers. However, due to the travel time of water between the chlorine dosing point and any strategic monitoring points, water treatment plant (WTP) operators often receive information too late for their responses to be effective. Given the ability to forecast the chlorine residual at strategic points in a WDS, it would be possible to have superior control over the chlorine dose, thereby preventing incidents of under-and over-chlorination. In this research, a general regression neural network (GRNN) has been developed for forecasting chlorine residuals in the Myponga WDS to the south of Adelaide, South Australia, 24 hours in advance. A number of critical model issues are addressed including: selection of an appropriate forecasting horizon; division of the available data into subsets for modelling; and, the determination of the inputs that are relevant to the chlorine forecasts. In order to determine if the GRNN is able to capture any nonlinear relationships that may be present in the data set, a comparison is made between the GRNN model and a multiple linear regression (MLR) model. When tested on an independent validation set of data, the GRNN models were able to forecast chlorine levels to a high level of accuracy, up to 24 hours in advance. The GRNN also significantly outperformed the MLR model, thereby providing evidence for the existence of nonlinear relationships in the data set

Developing Multi-Scale Models for Water Quality Management in Drinking Water Distribution Systems

Drinking water supply systems belong to the group of critical infrastructure systems that support the socioeconomic development of our modern societies. In addition, drinking water infrastructure plays a key role in the protection of public health by providing a common access to clean and safe water for all our municipal, industrial, and firefighting purposes. Yet, in the United States, much of our national water infrastructure is now approaching the end of its useful life while investments in its replacement and rehabilitation have been consistently inadequate. Furthermore, the aging water infrastructure has often been operated empirically, and the embracement of modern technologies in infrastructure monitoring and management has been limited. Deterioration of the water infrastructure and poor water quality management practices both have serious impacts on public health due to the increased likelihood of contamination events and waterborne disease outbreaks. Water quality reaching the consumers’ taps is largely dependent on a group of physical, chemical, and biological interactions that take place as the water transports through the pipes of the distribution system and inside premise plumbing. These interactions include the decay of disinfectant residuals, the formation of disinfection by-products (DBPs), the corrosion of pipe materials, and the growth and accumulation of microbial species. In addition, the highly dynamic nature of the system’s hydraulics adds another layer of complexity as they control the fate and transport of the various constituents. On the other hand, the huge scale of water distribution systems contributes dramatically to this deterioration mainly due to the long transport times between treatment and consumption points. Hence, utilities face a considerable challenge to efficiently manage the water quality in their aging distribution systems, and to stay in compliance with all regulatory standards. By integrating on-line monitoring with real-time simulation and control, smart water networks offer a promising paradigm shift to the way utilities manage water quality in their systems. Yet, multiple scientific gaps and engineering challenges still stand in the way towards the successful implementation of such advanced systems. In general, a fundamental understanding of the different physical, chemical, and biological processes that control the water quality is a crucial first step towards developing useful modeling tools. Furthermore, water quality models need to be accurate; to properly simulate the concentrations of the different constituents at the points of consumption, and fast; to allow their implementation in real-time optimization algorithms that sample different operational scenarios in real-time. On-line water quality monitoring tools need be both reliable and inexpensive to enable the ubiquitous surveillance of the system at all times. The main objective of this dissertation is to create advanced computational tools for water quality management in water distribution systems through the development and application of a multi-scale modeling framework. Since the above-mentioned interactions take place at different length and time scales, this work aims at developing computational models that are capable of providing the best description of each of the processes of interest by properly simulating each of its underlying phenomena at its appropriate scale of resolution. Molecular scale modeling using tools of ab-initio quantum chemical calculations and molecular dynamics simulations is employed to provide detailed descriptions of the chemical reactions happening at the atomistic level with the aim of investigating reaction mechanisms and developing novel materials for environmental sensing. Continuum scale reactive-transport models are developed for simulating the spatial and temporal distributions of the different compounds at the pipe level considering the effects of the dynamic hydraulics in the system driven by the spatiotemporal variability in water demands. System scale models are designed to optimize the operation of the different elements of the system by performing large-scale simulations coupled with optimization algorithms to identify the optimal operational strategies as a basis for accurate decision-making and superior water quality management. In conclusion, the computational models developed in this study can either be implemented as stand-alone tools for simulating the fundamental processes dictating the water quality at different scales of resolution, or be integrated into a unified framework in which information from the small scale models are propagated into the larger scale models to render a high fidelity representation of these processes

Anticipating and Adapting to Increases in Water Distribution Infrastructure Failure Caused by Interdependencies and Heat Exposure from Climate Change

abstract: This dissertation advances the capability of water infrastructure utilities to anticipate and adapt to vulnerabilities in their systems from temperature increase and interdependencies with other infrastructure systems. Impact assessment models of increased heat and interdependencies were developed which incorporate probability, spatial, temporal, and operational information. Key findings from the models are that with increased heat the increased likelihood of water quality non-compliances is particularly concerning, the anticipated increases in different hardware components generate different levels of concern starting with iron pipes, then pumps, and then PVC pipes, the effects of temperature increase on hardware components and on service losses are non-linear due to spatial criticality of components, and that modeling spatial and operational complexity helps to identify potential pathways of failure propagation between infrastructure systems. Exploring different parameters of the models allowed for comparison of institutional strategies. Key findings are that either preventative maintenance or repair strategies can completely offset additional outages from increased temperatures though-- improved repair times reduce overall duration of outages more than preventative maintenance, and that coordinated strategies across utilities could be effective for mitigating vulnerability.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Performance assessment in water supply and distribution

Abstract unavailable please refer to PD