Urban Water Consumption Research Articles

Irrigation suitability assessment of urban wastewater for sustainable agriculture: a case study of Musi river, India

In urban areas with dense populations and high water demands, effective management of water resources is crucial for sustainable development. It includes optimizing water usage and recycling wastewater to balance supply and demand. Urban rivers, which now carry sewage year-round due to increased urban water consumption, are increasingly used for irrigation in peri-urban regions. This study aims to assess the suitability of an urban stream for irrigation compared to catchment groundwater, using hydrochemical parameters to evaluate water quality indices. Analysis of major ion distribution from upstream to downstream reveals a significant increase of TDS (r2 = 0.65; p < 0.001) in the surface water and a decrease (r2 = 0.93; p < 0.001) in groundwater samples. The calculated Weighted Arithmetic-Water Quality Index (WA-WQI) indicates that around 85% and 75% of surface water and groundwater samples from the Musi river are classified as very poor to unsuitable for any use, highlighting critical pollution levels. Based on irrigation suitability indices, only 16% of the surface water samples and 60% of the groundwater samples are suitable for irrigation. The nitrate concentration increased from less than 10 mg l−1 in the upstream region to over 100 mg l−1 in the urban stretch. Nitrogen-Phosphate-Potassium (NPK) ratios across the basin are exceptionally higher, representing crops irrigated using the Musi river water might not require fertilizer applications. The adoption of fertilizer application concerning supplied water hydrochemistry, particularly the NPK ratio, might limit the excess usage of fertilizer and benefit farmers. Additionally, this practice may also limits the excess input of nitrate to the groundwater in the Musi river basin.

Automatic Detection of Water Consumption Temporal Patterns in a Residential Area in Northen Italy

One of the main challenges for city development is to ensure a sustainable water resource management for the water supply system. A clear identification of the urban water consumption patterns supports policy and decision makers in managing the water resources, satisfying the total demand and, at the same time, reducing losses and identifying potential leakages or other issues in the distribution network. High resolution smart meters have widely shown to be an efficient tool to measure in-pipe water consumption. The collected data can be used to identify water demand patterns at different temporal and spatial scales, reaching the end-uses level. Water consumption patterns at building level can be influenced by multiple factors, such as socio-demographic aspects, seasonality, and house characteristics. The presence of a garden that requires summer irrigation strongly alters the daily consumption pattern. In this framework, we present an innovative approach to automatically detect the presence of garden irrigation, identifying daily average water consumption patterns with and without it. The proposed methodology was tested in a residential area in Northen Italy, where 23 smart meters recorded data at 1-minute resolution for two years. Results show very good performances in distinguishing between days with and without garden irrigation. The derived average normalized water consumption patterns for both scenarios can help decision makers and water managers to regulate the pressure regimes in the distribution network correctly.

Urban water consumption analysis through a spatial panel modeling approach: a case study of Mexico City, 2004–2022

ABSTRACT Urban water demand management has become a paradigm for approaching water scarcity in cities. Consequently, the need to understand urban water consumption has increased. Studies about this topic usually focus on either temporal or spatial dimensions, highlighting the relevance of spatial panel data analysis to conduct spatiotemporal modeling. This technique is useful for performing long-term analysis for water management and decision-making. Mexico City is an urban area where total water consumption and its spatiotemporal evolution are still unknown. We aim to model spatiotemporally measured water consumption during 2004–2022 at the neighborhood level through a spatial panel modeling approach, to select the best model to estimate total water consumption and compare it with urban water supply to Mexico City. Based on the spatiotemporal model coefficients, our estimation of total water consumption is 15.44 m3/s which is close to 18.52 m3/s, calculated by the local water utility. These values, compared to 30.05 m3/s of total urban water supply for the period 2004–2022, suggest the need to focus on promoting water management efficiency actions. Our contribution also addresses intra-urban patterns of water consumption, water users, and meter coverage in Mexico City neighborhoods.

Pressure on water consumption by the expansion of fruit crops in Melipilla, Metropolitan Region of Chile

This study investigated the expansion of the surface of five fruit crop species between 2004 and 2020 and the magnitude of their water consumption, for three communes in the Province of Melipilla, Metropolitan Region of Chile. The statistical information about the crops was obtained from the fruit crop cadasters of the Region, and their spatial information was taken from the Rural Territorial Information System (SIT), while various sources of secondary information were used to approximate the water requirements of each species of interest. The results detail the cartography of expansion and increase in water consumption of the species on the dates of interest, which, in a context of mega-drought persisting since 2010, translates into a strong inverse correlation between consumption and water supply in the lower basin of the Maipo River. On the other hand, the magnitude of these water consumptions represents a significant proportion of water uses within the Maipo River basin, surpassing the combined uses of rural drinking water, forestry, livestock, and mining, and encompassing 22 % of urban drinking water consumption in the Metropolitan Region. As the relationship between availability and water deficit narrows, the absence of sustainable water management that could ensure water security within the country's most populous basin becomes palpable.

Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences

ObjectiveThis study delves into the impact of urban meteorological elements—specifically, air temperature, relative humidity, and atmospheric pressure—on water consumption in Kamyaran city. Data on urban water consumption, temperature (in Celsius), air pressure (in hectopascals), and relative humidity (in percent) were used for the statistical period 2017–2023. Various models, including the correlation coefficient, generalized additive models (GAM), generalized linear models (GLM), and support vector machines (SVM), were employed to scrutinize the data.ResultsWater consumption increases due to the influence of relative humidity and air pressure when the temperature variable is controlled. Under specific air temperature conditions, elevated air pressure coupled with high relative humidity intensifies the response of water consumption to variations in these elements. Water consumption exhibits heightened sensitivity to high relative humidity and air pressure compared to low levels of these factors. During winter, when a western low-pressure air mass arrives and disrupts normal conditions, causing a decrease in pressure and temperature, urban water consumption also diminishes. The output from the models employed in this study holds significance for enhancing the prediction and management of water resource consumption.

Uncovering urban water consumption patterns through time series clustering and entropy analysis

Sustainable urban water management is crucial for meeting the growing demands of urban populations. This study presents a novel approach that combines time series clustering, seasonal analysis, and entropy analysis to uncover residential water consumption patterns and their drivers. Using a three-year dataset from the SmartH2o project, encompassing 374 households, we identify nine distinct water consumption patterns through time series clustering, leveraging Dynamic Time Warping (DTW) as the optimal similarity measure. Multiple linear regression reveals key household characteristics influencing water usage behaviors, such as the number of bathrooms and appliance efficiency ratings. Seasonal analysis uncovers temporal dynamics, highlighting shifts towards lower consumption during summer months and increased variability in transitional seasons. Entropy analysis quantifies the diversity and complexity of water consumption at both cluster and household levels, informing targeted interventions. This comprehensive, granular approach enables the development of personalized water conservation strategies and policies, empowering water utilities to optimize resource management and contribute to sustainable urban water practices.

Predicting Urban Water Consumption and Health Using Artificial Intelligence Techniques in Tanganyika Lake, East Africa

Water quality has significantly declined over the past few decades due to high industrial rates, rapid urbanization, anthropogenic activities, and inappropriate rubbish disposal in Lake Tanganyika. Consequently, forecasting water quantity and quality is crucial for ensuring sustainable water resource management, which supports agricultural, industrial, and domestic needs while safeguarding ecosystems. The models were assessed using important statistical variables, a dataset comprising six relevant parameters, and water use records. The database contained electrical conductivity, pH, dissolved oxygen, nitrate, phosphates, suspended solids, water temperature, water consumption records, and an appropriate date. Furthermore, Random Forest, K-nearest Neighbor, and Support Vector Machine are the three machine learning methodologies employed for water quality categorization forecasting. Three recurrent neural networks, namely long short-term memory, bidirectional long short-term memory, and the gated recurrent unit, have been specifically designed to predict urban water consumption and water quality index. The water quality classification produced by the Random Forest forecast had the highest accuracy of 99.89%. The GRU model fared better than the LSTM and BiLSTM models with values of R2 and NSE, which are 0.81 and 0.720 for water consumption and 0.78 and 0.759 for water quality index, in the prediction results. The outcomes showed how reliable Random Forest was in classifying water quality forecasts and how reliable gated recurrent units were in predicting water quality indices and water demand. It is worth noting that accurate predictions of water quantity and quality are essential for sustainable resource management, public health protection, and ecological preservation. Such promising research could significantly enhance urban water demand planning and water resource management.

Evolution in Patterns of Urban Water Consumption Accompanying Socio-Economic Development

To explore the evolution in patterns of urban water consumption (UWC) accompanying socio-economic development, historical data on socio-economic conditions and water consumption in developed cities worldwide were collected. stages of evolution and patterns in UWC were identified through Pettitt tests. Through correlation analysis, the main socio-economic indicators influencing UWC were identified, and their threshold values for different trends in the evolution of UWC were statistically analyzed. The use of these patterns of evolution for UWC prediction was explored taking Changsha, China as a case study. Results indicate the following: (1) UWC typically increases in the early stage of development and then stabilizes or decreases later; (2) when UWC stabilizes or decreases, socio-economic indicators, namely per capita GDP, tertiary industry’s contribution to GDP, and urbanization rate, range from [USD 10,000, 60,000], [60%, 80%], and [85%, 95%], respectively, entering or approaching the developed economy stage. (3) Using patterns in this evolution, Changsha’s UWC is predicted to increase until the turning point, which will occur between 2027 and 2035 (earlier than that predicted using genetic programming) and then decrease earlier than that of Hunan Province or the entirety of China. Predicted values are lower than those obtained using the quota method. This study provides a new perspective on relationships between UWC and socio-economic conditions, supporting water resources planning.

Urban Water Consumption: A Systematic Literature Review

The study and analysis of urban water consumption habits in different regions contribute to the development of strategies aimed at secure water reduction and distribution. Within this context, knowledge of global water availability and the analysis of factors that influence consumption in different regions in distinct situations become extremely important. Several studies have been carried out in a number of countries and describe different approaches. The objective of this article is to learn about the strategies used in water consumption forecast and analysis. Most of the studies analysed seek to understand the factors influencing consumption in different building types. When it comes to residential buildings, the number of residents and the influence of economic issues on water consumption have an important role in this matter. In this context, pieces of research present the use of awareness campaigns as a strategy towards water use reduction. As a contribution, this article presents a systemic view of the pieces of research conducted and their contribution to forecasting water consumption in different regions. In conclusion, one observes the importance of analysing the factors influencing water consumption in different regions and scenarios, such as during the COVID-19 pandemic. This article can help managers and researchers understand the main factors that influence water consumption and how this consumption takes place in different regions.

Study on Carbon Emissions from an Urban Water System Based on a Life Cycle Assessment: A Case Study of a Typical Multi-Water County in China’s River Network Plain

Revealing the inherent relationship between urban water systems and carbon emissions has important theoretical and practical significance for promoting “water conservation and carbon reduction” in cities. This study utilizes the 2021 social water cycle statistical data of Yiwu City to quantify the carbon emissions of urban water systems. It analyzes the “water–carbon” related characteristics and carbon emission intensities under different water sources and stages and explores the carbon reduction potential of urban water systems under different scenarios. The results show that the operational phase is the main contributor to the carbon emissions of the urban water system in the research area, accounting for approximately 86% of the total carbon emissions. Over the entire process, the carbon emissions from the water supply and drainage stages are the largest, accounting for 39% and 31% of the total carbon emissions, respectively. In terms of carbon emission intensity, the carbon footprint of the water cycling process using reclaimed water as the water source is higher than that of high-quality water and conventional water sources. This is primarily due to the significantly higher carbon emission intensity in the reclaimed water phase compared with the other phases. In terms of influencing factors, the differences in the “water–carbon” correlation characteristics of different links in the water system in the research area are mainly affected by changes in urban water consumption, water treatment methods and processes, and other related factors. For the coordinated development of “water conservation and carbon reduction” in urban areas, future efforts should focus on improving the reuse rate of reclaimed water in urban life and industry, reducing the leakage rate of water distribution networks, and enhancing water treatment processes. These measures aim to increase water efficiency in urban water systems and reduce carbon emissions.

Response Study of Streamflow and Sediment Reduction in the Northeast Region of the Loess Plateau under Changing Environment

The Sanggan River Basin in the loess hill and gully area is the primary water source for the establishment of the capital water conservation function area and the ecological environment support zone. Against the backdrop of global warming, population growth, and accelerated urbanization, water consumption in the Sanggan River Basin has persistently increased in recent years, leading to a series of issues, such as river pollution, surface streamflow reduction, groundwater over-exploitation, soil erosion, and decreased vegetation coverage. In this research, we employed the DMC approach, SWAT model, InVEST model, and CA-Markov model to quantitatively analyze the correlation between alterations in streamflow and sediment within this area and three influential factors: climate variations, LUCC, and other human interventions. Furthermore, we clarified the relative contributions of climate factor elements, land-use types, and human activities to streamflow and sediment in this region. The findings indicate a decline in the annual streamflow and sediment quantities observed within this region from 1960 to 2020, with reduction rates of 1.27 × 108 m3/10 a and 129.07 × 104 t/10 a, respectively, and an abrupt change year in 1983 and 1982. Compared to the annual streamflow, the annual sediment volume demonstrates more substantial variation. Based on the constructed model, three scenario simulation periods: P0 (1962–1981/1982), P1 (1983/1982–1999), and P2 (2000–2020) were set. Compared with P0, the respective contribution rates of climatic variation, land use, and other human intervention to streamflow (sediment volume) were 15.247% (19.601%), −0.03% (−1.349%), and 84.783% (81.748%) in P1. In P2, the contribution rates of these three factors on streamflow (sediment volume) were 9.160% (9.128%), 0.211% (3.053%), and 90.629% (84.818%), respectively. Through a quantitative analysis of climatic factors on streamflow and sediment, we found a positive correlation between precipitation and both streamflow and sediment. Additionally, there is an inverse relationship between temperature and streamflow, but streamflow will increase when temperature rises by 10%. Under three different future land-use scenarios, the variations in streamflow and sediment exhibited as ecological protection scenario &lt; urban expansion scenario &lt; natural development scenario. Changes in streamflow in this region are primarily caused by human intervention that alters the underlying surface. The increase in check dams and silted land could lead the runoff and sediment to decrease. Moreover, compared with the increase in industrial and agricultural water consumption, the increase in urban water consumption is the main factor for human water consumption.

A segmentation approach to understanding water consumption behavioral patterns among households in Saudi Arabia for a sustainable future

Urban water consumption has significant implications for both human welfare and the environment, especially in arid regions. In Saudi Arabia, where 60% of the urban water supply relies on energy-intensive desalination processes, the average per capita water consumption rate of 300 L/day is one of the highest worldwide. This study investigates potential behavioral patterns in residential water consumption among households in Saudi Arabia. A segmentation approach is employed to classify residents into clusters and evaluate their potential water consumption behavioral patterns. Data from 618 households were collected via a structured questionnaire and analyzed using descriptive statistics, principal component analysis (PCA), and cluster analysis (CA). Participants were categorized into six segments based on the relationship between their behaviors and water consumption derived from the factor analysis. The outcomes of PCA and CA analyses indicate a relationship between respondents’ socio-demographic factors and their potential behavioral patterns in residential water consumption. The findings highlight that education, household size, income, housing type, age, and nationality, in increasing order of significance, are the key factors influencing household water consumption and conservation tendencies. The paper concludes that comprehending residential water consumption patterns is crucial for effective interventions, social marketing strategies, and communication campaigns for behavioral changes. The study can inform water conservation policies and programs that promote sustainable water consumption practices, benefiting agencies, policymakers, and scholars in the water sector.

A study on urban household water consumption behavior under drought conditions

The increasing frequency of drought events has intensified the risk of water scarcity, posing significant challenges for urban domestic water supply. Reducing urban household water consumption is an important way to alleviate water stress during drought periods. However, due to various factors, it is difficult to determine a water-saving target that is within the residents' capacity. Here, taking Beijing, China as an example, we explored the socio-psychological factors behind urban household water use behaviors under drought conditions, and further quantified the compressible ratio of water quotas for flexible water use behaviors. Therefore, the present study was based on the theory of planned behavior (TPB) and extended TPB (ETPB) by adding drought risk perception as a variable to the theoretical framework. With the help of questionnaire method and structural equation modeling (SEM), the explanatory power of TPB and ETPB in predicting people's water saving intention and behavior was compared. Meanwhile, mathematical statistical analysis methods were employed to calculate the water quota for elastic water consumption behavior and the compressible proportion of urban residents' elastic water consumption under drought conditions. The results showed that drought risk perception has a significant positive correlation with subject norms and water reduction behavior under drought conditions. Furthermore, ETPB was more effective in analyzing water use intentions and behaviors. The predictive explanatory power of SEM for reducing water use increased from 44% to 50% after adding drought risk perception variable. In terms of quantification of elastic water use behavior, the average total water consumption in summer and winter were 71.3L/(p.d) and 52.9L/(p.d) under drought conditions, while it were 124.3 L/(p.d) and 108.9 L/(p.d) under normal conditions. And the compressible proportions of the total water quota for summer and winter elastic water use were 46.7% and 56.8%, respectively. The calculation results can provide a reference for the government to make emergency water supply decisions against drought.

Poisson rectangular pulse (PRP) model establishment based on uncertainty analysis of urban residential water consumption patterns

The commonly used Poisson rectangular pulse (PRP) model, employed for simulating high-resolution residential water consumption patterns (RWCPs), relies on calibration via medium-resolution RWCPs obtained from practical measurements. This introduces inevitable uncertainty stemming from the measured RWCPs, which consequently impacts the precision of model simulations. Here we enhance the accuracy of the PRP model by addressing the uncertainty of RWCPs. We established a critical sampling size of 2000 household water consumption patterns (HWCPs) with a data logging interval (DLI) of 15 min to attain dependable RWCPs. Through Genetic Algorithm calibration, the optimal values of the PRP model's parameters were determined: pulse frequency λ = 91 d−1, mean of pulse intensity E(I) = 0.346 m3 h−1, standard deviation of pulse intensity STD(I) = 0.292 m3 h−1, mean of pulse duration E(D) = 40 s, and standard deviation of pulse duration STD(D) = 55 s. Furthermore, validation was conducted at both HWCP and RWCP levels. We recommend a sampling size of ≥2000 HWCPs and a DLI of ≤30 min for PRP model calibration to balance simulation precision and practical implementation. This study significantly advances the theoretical foundation and real-world application of the PRP model, enhancing its role in urban water supply system management.

Urban Water Consumption Prediction Based on CPMBNIP

Urban Water Consumption Prediction Based on CPMBNIP

Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia

Accurate prediction of short-term water demand, especially, in the case of extreme weather conditions such as flood, droughts and storms, is crucial information for the policy makers to manage the availability of freshwater. This study develops a hybrid model for the prediction of monthly water demand using the database of monthly urban water consumption in Melbourne, Australia. The dataset consisted of minimum, maximum, and mean temperature (°C), evaporation (mm), rainfall (mm), solar radiation (MJ/m2), maximum relative humidity (%), vapor pressure (hpa), and potential evapotranspiration (mm). The dataset was normalized using natural logarithm and denoized then by employing the discrete wavelet transform. Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. Post-optimization ANN model was trained using eleven different leaning algorithms. Models were trained several times with different configuration (nodes in hidden layers) to achieve better accuracy. The final optimum learning algorithm was selected based on the performance values (regression; mean absolute, relative and maximum error) and Taylor diagram.

Role and impact of urban water prices on the management of groundwater consumption due to Covid-19 outbreaks

The main objective of this study is to investigate the environmental role and impact of urban water prices, and hence its consumption in Tehran on its groundwater level drawdown. The issue gets vital in times of drought when the scarcity value of water exceeds benefits of groundwater environmental flow. Hence, the argument is that urban water tariffs may play a role in managing groundwater flow.In this regard, the harmful impact of urban water consumption on the environmental flow of groundwater resources is distinguished. Then the objective would be to identify determinants of urban water consumption. To do so, an urban water's demand function, including important key regressors such as price, income, temperature, and rainfall is estimated. Moreover, the role of Covid-19 in urban water consumption is highlighted.Results show a demand elasticity of −0.29 ± 0.1 for urban water consumption, or that a 10 percent increase in average price results in a decline in urban water consumption of 1.9–3.9 percent. In addition, one percent lower urban water consumption causes 1.3 ± 0.1 percent less groundwater extraction, or that response of a 10 percent decrease in the consumption could cause a decline in groundwater extraction of 12–14 percent. Last but not least, a 1 percent decrease in underground extraction for urban water raises 13.2 ± 6.7 cm of GRACE water level after three months.The results show that groundwater exploitation for urban drinking water is a responsible contributor to the water level drawdown in Tehran. In addition, the significant elasticity of 0.29 shows that price mechanisms are useful to manage hydrological scarcity and the consequent disasters. Moreover, observing significant effects of covid-related determinants for calming down the upward trend of groundwater extraction, we alert for post-covid deterioration of subsidence areas due to regrowth of water consumption.

Convergence and influencing factors of urban domestic water consumption in the Yangtze River Delta under urbanization

ABSTRACT This study employs panel data from 41 cities from 2010 to 2019 in the Yangtze River Delta. The entropy weight method is used to calculate the urbanization level. Then, the convergence, panel spatial econometric, and threshold regression models are adopted to examine the convergence and to identify the factors affecting water consumption. The following conclusions may be drawn: Spatial differences in the level of domestic water consumption show a trend of gradual reduction over time; there exists mutual influence of neighboring cities; urbanization positively affects domestic water consumption significantly; the spatial spillover effect of urbanization on domestic water consumption is significantly negative; the promotion effect of urbanization on domestic water consumption is nonlinear, and the promotion effect increases with the increase of the proportion of domestic water and the proportion of public service water. Then, targeted countermeasures are brought forward.

Urban residential water and electricity consumption behavior: A systematic literature review

Consumer behavior is an understudied determinant of residential water and electricity demand and, when studied, capturing, measuring, and quantifying attitudes and behaviors is often problematic. This research explores behavioral determinants of the demand for water and electricity, contributing a set of categories and items that may be useful for understanding knowledge, attitudes, and practices related to consumption and constructing valid scales. These results may help researchers capture the complexity of consumer behavior regarding water and electricity and decision-makers in designing tailored demand management strategies.

ANN-based prediction of ammonia nitrogen for wastewater discharge indicators under carbon neutral trend

IntroductionWith the rapid development of society and urbanization, greenhouse gas emissions have increased, leading to environmental problems such as global warming. The rise in urban water consumption has also resulted in increased sewage discharge, exacerbating freshwater scarcity and water pollution. Understanding the current status and spatial distribution of greenhouse gas emissions in China's sewage treatment industry is crucial for emission reduction measures and controlling ammonia nitrogen pollution.MethodsThis study comprehensively investigates greenhouse gas emissions from sewage treatment plants, analyzing influencing factors and predicting future spatial and temporal distributions. The uncertainty of ammonia nitrogen emissions is calculated using the IPCC's error propagation method, considering uncertainty ranges of variables. Additionally, an artificial neural network is employed to predict ammonia nitrogen content in sewage discharge, aiming to prevent excessive levels in wastewater.Results and discussionThe proposed model outperforms others with an R-Squared score of 0.926, demonstrating its superior accuracy in predicting ammonia content in wastewater. These findings contribute to better emission reduction strategies and control of ammonia nitrogen emissions. This model can effectively prevent excessive ammonia nitrogen content in discharged wastewater, contributing to water pollution control. In conclusion, this study highlights the importance of understanding greenhouse gas emissions from sewage treatment plants and their impact on water pollution. The research provides valuable insights into emission reduction measures, emission prediction, and technological innovations suitable for China's specific conditions. By effectively managing ammonia nitrogen emissions and adopting the proposed predictive model, the goals of carbon neutrality and environmental sustainability can be better achieved.