In the last decade, the growth of the micro-industry in urban areas has produced an increase in the frequency of xenobiotic polluting discharges in drainage systems. Wastewater treatment plants are usually characterized by low removal efficiencies in respect of such pollutants, which may have an acute or cumulative impact on environmental and public health. To facilitate the early isolation of illicit intrusions, this study aims to develop an approach for positioning water quality sensors based on the Bayesian decision network (BDN). The analysis is focused on soluble conservative pollutants, such as metals. The proposed methodology incorporates several sources of information, including network topology, flows and non-formal ‘grey’ information about the possible locations of contamination sources. The methodology is tested using two sewer systems with increasing complexity: a literature scheme from the Storm Water Management Model (SWMM) manual and a real combined sewer in Italy. In both cases, the approach identifies the optimal sensor location gaining advantage from additional information, which reduces the computational effort needed to obtain the solution. In the real case, the application of the method yielded a better solution with regards to the real position of the implemented sensor network.
Abstract:In integrated urban water systems, energy consumption, and consequently the amount of produced CO 2 , depends on many environmental, infrastructural, and management factors such as supply water quality, on which treatment complexity depends, urban area orography, water systems efficiency, and maintenance levels. An important factor is related to the presence of significant water losses, which result in an increase in the supply volume and therefore a higher energy consumption for treatment and pumping, without effectively supplying users. The current European environmental strategy is committed to sustainable development by generating action plans to improve the environmental performance of products and services. The analysis of carbon footprints is considered one such improvement, allowing for the evaluation of the environmental impact of single production phases. Using this framework, the aim of the study is to apply a Life Cycle Assessment (LCA) methodology to quantify the carbon footprint of an overall integrated urban water system referring to ISO/TS 14067 (2013). This methodology uses an approach known as "cradle to grave" and presumes to conduct an objective assessment of product units, balancing energy, and matter flows along the production process. The methodology was applied to a real case study, i.e., the integrated urban water system of the Palermo metropolitan area in Sicily (Italy). Each process in the system was characterized and globally evaluated from the point of view of water loss, energy consumption, and CO 2 production, and some mitigation strategies are proposed and evaluated to reduce the energy consumption and, consequently, the environmental impact of the system.
Due to urbanization, large portions of vegetated territory have been replaced by waterproof surfaces. The consequences are greater outflows, lower infiltration, and lower evapotranspiration. Pavement systems made with permeable surfaces allow the infiltration of water, ensuring reduction of runoff volume. In this paper, the methods of analysis of the hydrological and environmental performance of the pavement systems are reviewed in the context of urban drainage and regarding their durability. The purpose is to present an overview of the studies published during the last decade in the field. The Pubmed and Web Science Core Collection electronic databases were used to conduct the scientific literature survey. This generated 1238 papers, of which only 17 met the criteria and were included and discussed in this review. The evidence drawn from the knowledge on which the document is based provides useful critical interpretations of existing studies to progress the current understanding on hydrological performance and environment impacts in terms of conventional pollutant removal efficiency and the current permeable pavement systems.
In the urban drainage sector, the problem of polluting discharges in sewers may act on the proper functioning of the sewer system, on the wastewater treatment plant reliability and on the receiving water body preservation. Therefore, the implementation of a chemical monitoring network is necessary to promptly detect and contain the event of contamination. Sensor location is usually an optimization exercise that is based on probabilistic or black-box methods and their efficiency is usually dependent on the initial assumption made on possible eligibility of nodes to become a monitoring point. It is a common practice to establish an initial non-informative assumption by considering all network nodes to have equal possibilities to allocate a sensor. In the present study, such a common approach is compared with different initial strategies to pre-screen eligible nodes as a function of topological and hydraulic information, and non-formal 'grey' information on the most probable locations of the contamination source. Such strategies were previously compared for conservative xenobiotic contaminations and now they are compared for a more difficult identification exercise: the detection of nonconservative immanent contaminants. The strategies are applied to a Bayesian optimization approach that demonstrated to be efficient in contamination source location. The case study is the literature network of the Storm Water Management Model (SWMM) manual, Example 8. The results show that the pre-screening and ‘grey’ information are able to reduce the computational effort needed to obtain the optimal solution or, with equal computational effort, to improve location efficiency. The nature of the contamination is highly relevant, affecting monitoring efficiency, sensor location and computational efforts to reach optimality.
A generic water system consists of a series of works that allow the collection, conveyance, storage and finally the distribution of water in quantities and qualities such as to satisfy the needs of end users. In places characterized by high altitude differences between the intake works and inhabited centres, the potential energy of the water is very high. This energy is attributable to high pressures, which could compromise the functionality of the pipelines; it is therefore necessary to dissipate part of this energy. A common alternative to dissipation is the possibility of exploiting this energy by inserting a hydraulic turbine. The present study aims to evaluate the results obtained from a stochastic approach for the solution of the multi-objective optimization problem of PATs (Pumps As Turbines) in water systems. To this end, the Bayesian Monte Carlo optimisation method was chosen for the optimization of three objective functions relating to pressure, energy produced and plant costs. The case study chosen is the Net 3 literature network available in the EPANET software manual. The same problem was addressed using the NSGA-III (Nondominated Sorting Genetic Algorithm) to allow comparison of the results, since the latter is more commonly used. The two methods have different peculiarities and therefore perform better in different contexts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.