The most common drinking water clarification technology worldwide is based on precipitation using the floccules of iron(III) oxide-hydroxide FeO(OH). The coagulation sludge obtained is usually mixed with biowaste and composted. Inexperienced farmers are interested in these composts, in the expectation that the smallest fractions of the precipitated organic matter together with the colloidal particles of clay captured during the clarification process might increase the level of soil organic matter as well as cation-exchange capacity (CEC) of soil and increase crop yields. Nevertheless, those who have used these composts observed that plants on treated soil show signs of phosphorus (P) insufficiency, despite having been fertilized appropriately. It has been published recently that the traces of iron (Fe) from the FeO(OH) turn the soil P into Fe phosphates (FeP), which makes P not readily available to agricultural plants. A 6-year field-scale experiment was carried out and was accompanied by robust analyses on soils and yields. It was confirmed that the traces of Fe from the coagulation sludge worsen the availability of soil P to plants. However, for the first time, evidence has been obtained concerning the fact that the Fe also damages the efficiency of P fertilizers applied afterward. A questionnaire was conducted among drinking water treatment plant operators and regulators to identify a reason why similar practices have not been banned yet. The results showed a growing awareness of the issue among experts, but politicians seem to prefer putting off unpopular measures. We recommend abandoning the application of these composts onto arable land and the revitalization of the affected fields using biochar activated by calcium and aluminum phosphates (CaP and AlP) so that the problem is not delayed to such an extent that its solution would become a problem for future generations.

Economic impacts of soil fertility degradation by traces of iron from drinking water treatment

A scenario-based approach for urban water management in the context of the COVID-19 pandemic and a case study for the Tabriz metropolitan area, Iran.

Proper determination of unitary water demand and diurnal distribution of water consumption (water consumption histogram) provides the basis for designing, dimensioning, and all analyses of water supply networks. It is important in the case of mathematical modelling of flows in the water supply network, particularly during the determination of nodal water demands in the context of Extended Period Simulation (EPS). Considering the above, the analysis of hourly water consumption in selected apartment buildings was performed to verify the justification of the application of grouping by means of k-means clustering. The article presents a detailed description of the adopted methodology, as well as the obtained results in the form of synthetic distributions of hourly water consumption, and the effect of the COVID-19 pandemic on their change.

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The Use of Cluster Analysis to Evaluate the Impact of COVID-19 Pandemic on Daily Water Demand Patterns

The aim of this Special Issue is to gather evidence on the impact of price policies (PP) and non-price policies (NPP) in shaping residential water use in a context of increased water scarcity. Indeed, a large body of the empirical economic literature on residential water demand has been devoted to measuring the impact of PP (water price increases, use of block rate pricing or peak pricing, etc.). The consensus is that the residential water demand is inelastic with respect to water price, but not perfectly. Given the low water price elasticity, pricing schemes may not always be effective tools for modifying household water behaviors. This is puzzling since increasing the water price is still viewed by public authorities as the most direct economic tool for inducing water conservation behaviors. Additional evidence regarding the use of PP in shaping residential water use is then required. More recently, it has been argued that residential consumers may react to NPP, such as water conservation programs, education campaigns, or smart metering. NPP are based on the idea that residential water users can implement strategies that will result in water savings via changing their individual behaviors. Feedback information based on smart water metering is an example of approach used by some water utilities. There are still large gaps in the knowledge on the residential water demand, and in particular on the impact of PP and NPP on residential water use, household water affordability and water service performance. These topics are addressed in this Special Issue "Advances in the Economic Analysis of Residential Water Use".

Advances in the Economic Analysis of Residential Water Use: An Introduction

The challenge for city authorities goes beyond managing growing cities, since as cities develop, their exposure to climate change effects also increases. In this scenario, urban water supply is under unprecedented pressure, and the sustainable management of the water demand, in terms of practices including economic, social, environmental, production, and other fields, is becoming a must for utility managers and policy makers. To help tackle these challenges, this paper presents a well-timed review of predictive methods for short-term water demand. For this purpose, over 100 articles were selected from the articles published in water demand forecasting from 2010 to 2021 and classified upon the methods they use. In principle, the results show that traditional time series methods and artificial neural networks are among the most widely used methods in the literature, used in 25% and 20% of the articles in this review. However, the ultimate goal of the current work goes further, providing a comprehensive guideline for engineers and practitioners on selecting a forecasting method to use among the plethora of available options. The overall document results in an innovative reference tool, ready to support demand-informed decision making for disruptive technologies such as those coming from the Internet of Things and cyber–physical systems, as well as from the use of digital twin models of water infrastructure. On top of this, this paper includes a thorough review of how sustainable management objectives have evolved in a new era of technological developments, transforming data acquisition and treatment.

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A Critical Review of Short-Term Water Demand Forecasting Tools—What Method Should I Use?

The COVID-19 lockdown has instigated significant changes in household behaviours across a variety of categories including water consumption, which in the south and east regions of England is at an all-time high. We analysed water consumption data from 11,528 households over 20 weeks from January 2020, revealing clusters of households with distinctive temporal patterns. We present a data-driven household water consumer segmentation characterising households’ unique consumption patterns and we demonstrate how the understanding of the impact of these patterns of behaviour on network demand during the COVID-19 pandemic lockdown can improve the accuracy of demand forecasting. Our results highlight those groupings with the highest and lowest impact on water demand across the network, revealing a significant quantifiable change in water consumption patterns during the COVID-19 lockdown period. The implications of the study to urban water demand forecasting strategies are discussed, along with proposed future research directions.

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Quantifying the impact of the COVID-19 lockdown on household water consumption patterns in England

A review of household water demand management and consumption measurement

Characterising individual households’ consumption patterns reliably and ascertaining the extent to which these patterns change and how they underpin aggregate demand continues to present a challenge. This paper presents an empirical characterisation of household water consumption patterns, based on consumer segmentation, to improve the accuracy of demand forecasting and to develop both proactive and responsive water conservation strategies. Medium resolution smart metre data for 2019 for 10,000 households were analysed using Machine Learning (ML), revealing four household clusters whose significant differences are underpinned by a variety of indicators in their temporal consumption patterns. The clusters, labelled according to the predominant peak demand times of constituent households, are ‘Evening Peaks’ (EP), ‘Late Morning’ (LM), ‘Early Morning’ (EM) and ‘Multiple Peaks’ (EP). Some of the significant findings include the fact that on average households in EM only record one peak event in 24 h, compared with the MP clusters’ four peak events, with 2 in every 5 households in MP having a confirmed internal leak compared with 1 in every 5 for the other three clusters. A total of 31,788 Cubic metres (m3) was consumed, constituting a monthly mean of 2,649m3, equating to a per household consumption (PHC) of ~270 litres per household per day (l/h/d). Results also revealed the clusters’ distributed dominance of hourly demand and the most active clusters in different seasons. The paper concludes that identifying the significant differences characterising consumption patterns and their concomitant impact on network demand will not only serve to enhance demand forecasting and the prediction of geographical consumption hotspots but will also allow the delivery of targeted intervention measures according to households’ shared characteristics.

An empirical water consumer segmentation and the characterisation of consumption patterns underpinning demand peaks

Water utilities strive to achieve a sustainable reduction in per capita consumption (PCC) by optimising their peak demand management strategies. Socioeconomic (SE) and socio-demographic (SD) characteristics have been proven to correlate with PCC. However, the full extent to which these characteristics underpin peak demand and PCC is yet to be fully understood. Previous work used medium resolution smart meter data from 10,000 households to discover and characterise temporal consumption patterns that underpin peak demand, identifying four distinct clusters of households, namely "Evening Peak" (EP), Late Morning Peak (LM), Early Morning Peak (EM) and Multiple Peak (MP). Using survey results, "Acorn household classification", household occupancy and UK population and household attribute data, this study attempts to draw a correlation between the four clusters and known variables of the participating households. Results have revealed a strong correlation between many endogenous attributes (particularly housing, occupancy, age, number of children and household income) and households' consumption patterns underpinning peak demand. Some 56% of families in privately rented housing show EP characteristics compared with 22% owner-occupiers and 9% social renters. EP households with teenage boys have 37% higher per household consumption (PHC) than average, while EM families with teenage girls use 47% more water in early morning showers than average. 

Halidu Abu-Bakar

Papers

Quantifying the impact of the COVID-19 lockdown on household water consumption patterns in England

A review of household water demand management and consumption measurement

An empirical water consumer segmentation and the characterisation of consumption patterns underpinning demand peaks

Contextualising household water consumption patterns in England: A socio-economic and socio-demographic narrative