João Marques

This paper deals with the optimal design-for-control of water distribution networks (WDNs) with the objectives of minimizing pressure-induced background leakage and maximizing resilience. This problem entails defining locations for installing valves and/or pipes and for simultaneously determining valve settings and belongs to the class of non-convex mixed-integer nonlinear problems. Solving highly complex infrastructure problems, such as WDNs, raises a fundamental question about the accuracy of the solutions to be implemented for sound water management. Therefore, two kinds of optimization methods are applied and assessed on two case studies. While the first is an exact global optimization method, the second is the metaheuristic based on the concept of simulated annealing. This paper proposes an innovative methodological analysis to interpret and discuss the results provided by both methods, as well as to identify their impact on the performance of the WDN. This type of analysis may help in highlight how the integration of the best features of both solution methods can promote a step forward in solving WDN problems.

Historically, water utilities have relied on tried-and-true practices in the design and operation of their infrastructure, tapping new resources and expanding networks as needed. However, as the effects of climate change and/or urbanization increasingly impact both water supply and demand, utilities need new, holistic planning and management approaches. Integrated planning approaches must account for changing policies, technological progress, and unique, setting-specific operating conditions. Based on this notion, an international web seminar with faculty, researchers, and students from nine universities across five continents was conducted. In the 3-month seminar, participants were split into groups and tasked with developing future-proof, sustainable water management solutions for fictitious settings with unique resource availability, climate change predictions, demographic, and socioeconomic constraints. The goal of the seminar was to combine participants’ unique perspectives to tackle challenges in developing future water infrastructure, while forming lasting relationships. Water management concepts became more daring or “out-of-the-box” as the seminar progressed. Most groups opted for a holistic approach, optimizing existing infrastructure, integrating decentralized water management, furthering digitization, and fostering the adoption of innovative policy and planning strategies. To gauge their impact on the evolution of ideas, group dynamics and communication were observed throughout the seminar. As a result, the findings serve not only as a compendium of ideas and concepts for holistic design in the water sector, but also facilitate international collaboration, improve communication in cross-cultural teams or guide the development of training programs in water management for researchers, professional engineers, or water utilities.

Demand in a water distribution network (WDN) is an aleatory variable, owing to the unpredictable behaviors of water users. Therefore, it is one of the main reasons for uncertainty in the design of this infrastructure. The increasing number of water demand data sets offers opportunities to improve the traditional deterministic design approaches of WDNs by combining statistical and optimization methods. Robust optimization (RO) takes demand uncertainty into account by studying solutions that perform well under any possible demand scenario, that is, any possible realizations of this variable in the lifetime of a WDN. The right choice of scenario is therefore essential to ensure the reliability of the designed network. This paper presents a statistical methodology for generating scenarios to be used to solve a robust design optimization problem. It involves three steps: (a) descriptive analytics of historical data to derive the marginal distributions of peak hour demand in each node of the WDN, (b) generation of a very large number of snapshots by stratified sampling from the correlated marginal distributions of nodal peak demand, (c) generation of the peak demand scenarios by reducing the number of snapshots. Two heuristic techniques are proposed to reduce the number of snapshots, and for each of them, two different numbers of scenarios are derived. Two multi‐objective RO models are solved: the first model includes cost minimization and a mean‐variance Generalized Resilience and Failure index maximization objectives, and the second one additionally considers the minimization of the maximum undelivered demand, formalized using a regret function.

This paper is a systematic review of studies that used multicriteria decision analysis (MCDA) to address plastic waste management. A literature search for scientific articles in online databases (Web of Science and Scopus) enabled us to identify 20 relevant papers from 2008 to 2021, spanning case studies in three continents. These studies focus on: plastics as a resource (material), plastics as a product (reverse logistics), and plastics as a problem (pollution). Content analysis methodology was used, with the focus being on how the authors used MCDA for managing plastic waste, which has relevance for researchers and practitioners. Alternative solutions were found for the selection of disposal methods for almost all types of plastic categorized in this review. The most popular method was AHP, followed by TOPSIS, outranking methods, MAUT/MAVT and simple weighted sums, with some studies including more than one method. The choice of criteria spanned operational (mostly), but also environmental and economic aspects to evaluate the alternatives. Less frequently, one finds criteria related to social, managerial, and political aspects. The weighting of the criteria was performed mainly by consulting experts, followed by decision makers. Representatives of the affected population or other stakeholders have been consulted only on a few occasions. The authors of the studies consider their application of MCDA was successful, highlighting mainly the importance of being able to encompass different dimensions in the evaluation of the alternatives and the transparency of the process. In most cases, a winning alternative emerged clearly, which sometimes was a combination of multiple strategies. We also report other recommendations of these authors concerning marine and terrestrial plastic waste management.

Nitrogen (N) legacies have built up in anthropogenic landscapes over decades of agricultural intensification, and these legacies lead to time lags in water quality change measurable even beyond the moment of application of N. It is important to understand these legacies to quantify the relationship between N inputs and N concentrations in streams and implement best management practices for water quality improvement; however, little is known about the magnitude of legacies in various landscape elements like soils and groundwater. Here, we have used the ELEMeNT (Exploration of Long-tErM Nutrient Trajectories) model to explore the buildup and depletion of N legacies over a 216-year period, across the Mondego River Basin, a 6645-km² watershed in Portugal, where human interventions have considerably changed the characteristics of the basin to prevent floods and improve farming conditions in recent decades. The results show that the increase in the amount of inorganic fertilizer applied was the main driver for the anthropogenic N loads in the watershed from 1950 until the beginning of the 1990s. The N inputs have been decreasing since then, but N loads in the river did not document any decrease till the 1990s; after which there was a decline. This time lag between the N inputs to the watershed and the N loads in the river (about two decades) is a function of accumulation of N legacy.

The Low Mondego (Baixo Mondego, in Portuguese) river basin has been considerably modified by human interventions to prevent floods and to improve agricultural conditions over recent decades. This work analyzes the main impacts arising from those interventions and the socio-economic context in which they occurred. The development and application of a framework to compute the variation of the nitrogen surplus between 1986 and 2018 in the Low Mondego watershed in the central part Portugal is presented. The nitrogen mass balances take into consideration different land use types, inputs and outputs, thereby making it possible to understand how human interventions have impacted the variation of the surplus. It is noticeable that the major nitrogen sources are related to the agricultural sector. However, factors such as the implementation of the Code of Good Agricultural Practices, as well as social conditions, and regulations to reduce nitrogen emissions to the atmosphere helped to cut the nitrogen surplus. This work shows how improving the agricultural conditions has served to increase the crop productivity in improved areas. Very particular social circumstances led to the reduction in anthropogenic nitrogen sources, which has been accompanied by a decline in the nitrogen that is exported at the river outlet.

The LEAP project (Legacies of Agricultural Pollutants), supported by ERA-NET COFUND WATERWORKS 2015, aims at developing an “Integrated Assessment of Biophysical and Socioeconomic Controls on Water Quality in Agroecosystems”.The impacts of agriculture intensification gave rise to an excess of nitrogen (N) and phosphorus (P) accumulation in soils and their leaching to water bodies (surface and groundwater bodies). Even though various actions have been undertaken through different institutions and the publication of the Water Framework Directive in the EU, it is clear that better management practices have to be implemented. The objectives should be twofold. First, the amount of fertilizers used should be reduced and the legacies remaining from past practices must start to decrease. There are many options to try to fulfil these objectives, and different perspectives from which to evaluate the consequences of each option. Identifying appropriate solutions is a considerable challenge, and therefore the choices to be made should be structured using appropriate approaches. Given the multiple issues at stake, multicriteria decision analysis (MCDA) will be very valuable in developing a framework to help decision makers. The exploration of different MCDA methods described in Cinelli et. al (2014) confirmed the importance and the broad applicability of these tools in several fields to provide sustainable solutions that take social, economic and environmental dimensions into account.In this paper different alternatives for implementing best management practices that not only involve the amount of fertilizers to be used, but also the timing of their application over the plant growth cycle, through various climate scenarios, are considered. Criteria such as N-NO3 exports, ecological indicators, willingness to pay, variation of production, and variation of gross margin are taken into account. Weights are established to express the importance given to each criterion.After launching the main components to perform an MCDA evaluation, the PROMETHEE  method (Mareschal and De Smet, 2009) is used to identify the best ranked solutions to be implemented according to different weights ascribed to criteria. The application of the methodology is illustrated through a case study located in the Vouga river basin, in Portugal.AcknowledgmentsThis research was supported by LEAP (LEgacies of Agricultural Pollutants) project (WaterJPI/005/2015).The authors also thank to the Portuguese Foundation for Science and Technology through the project grant UIDB/00308/2020 granted to INESCC. ReferencesCinelli, M., Coles, S. R., and Kirwan, K. (2014). Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment. Ecological Indicators, 46, 138–148.Mareschal, B. and De Smet, Y. (2009). Visual PROMETHEE: Developments of the PROMETHEE & GAIA multicriteria decision aid methods, In Industrial Engineering and Engineering Management, 2009. IEEM 2009.IEEE International Conference (pp. 1646–1649) 

This paper presents a simulated annealing multiobjective algorithm developed to include novel features for promoting the convergence toward the best Pareto front that shows diversity and uniformity in the distribution of solutions. The new algorithm, MultiObjective Simulated Annealing with new Generation and Reannealing procedures (MOSA‐GR), is a trajectory‐based algorithm, where, in a first phase, diversified generation strategies are used to define candidate solutions at different stages of the search procedure. In a second phase a reannealing process starting at low temperature is implemented to intensify the search based on the last solutions of the first phase. An extensive comparison with solutions obtained by various multiobjective evolutionary algorithms (MOEAs) described in the literature is provided. Using a limited budget for the number of function evaluations, the results show that MOSA‐GR has a very good performance and gives rise to better fronts, even compared with those obtained by merging the results from different MOEAs implemented in similar conditions.