The expansion of cities escalates the demand on water utilities amid global water scarcity, making leakage management a critical challenge for water sector sustainability. On this subject, the study introduces an advanced practical approach for estimating nodal leakage through the calibration of decentralized networks using available data. The approach includes conducting a pressure step-test for data collection, employing the DBSCAN algorithm for outlier detection, and employing Bayesian optimization for effective calibration. Hydraulic leakage models, particularly the power and modified orifice equations, are innovatively applied to model and identify existing leaks at each network node through deep comparison. The computational analysis demonstrated efficiency and accuracy in overcoming vast computational complexity and time constraints, with the calibration of a real-life network resulting in a cumulative MSE of 0.892 and an average R² and NSE values near 0.98. Additionally, realistic leakage modeling revealed inaccuracies in the acknowledged connection between the modified orifice equation and the leakage power equation. This study also provides key insights for enhancing water loss management and conducting on-site inspections in an environmentally conscious manner, especially crucial for budget-constrained water utilities and authorities experiencing revenue declines.

Accurate water demand forecasting can help understand water usage dynamics, which has a potential application in water saving and demand management. Despite extensive research, most exiting methods cannot capture long-range dependency of water demand and maintain high-accuracy in multi-horizon forecasting continuously. To address these issues, the focus of this research is primarily on the temporal model for multi-horizon water demand forecasting using deep learning. This research proposes a signal enhancement strategy and develops an attention-based deep learning model. Firstly, the Fourier transform is used for sparse approximation of water demand data, which helps represent the signal more compactly. To enhance the performance of multi-horizon forecasting model, the complex water demand time series is decomposed into trend and seasonal components. Then, an attention mechanism is utilized to learn temporal dependencies within the data, providing assists for multi-horizon water demand forecasting. Furthermore, a comparative study is carried out between the proposed model and the state-of-the-art methods using the water demand data from four usage scenarios. Experimental results suggest that the present model has the ability to produce accurate and robust forecasting, especially for multi-horizon water demand. Therefore, this research has the potential to enhance water resource management in a socioecological context.

This study utilizes year-round temperature data observed in 15 cities around the United States (U.S.) to investigate the relationships between UHI and urban design attributes, and answer the following questions: (1) Whether and to what extent these relationships vary with the diurnal cycle and across seasons?, (2) What is the spatial extent within which UHI is affected by different urban variables?, and (3) Which climatic factors determine the correlations between UHI and urban variables? Our analysis revealed that while surface reflectance, vegetation and building height are more influential on winter UHI, the effect of anthropogenic heat and building density is independent of the season. Also, tree canopy cover, impervious area and built-up area were more dominant on nighttime UHI, and albedo, surface reflectance, vegetation and building height had stronger influence on daytime UHI. With regards to the spatial extent, while the effect of 3D urban variables and imperviousness was prominent in the immediate vicinity (250 m–1 km), the effect of vegetation, surface reflectance and anthropogenic factors extend to a larger surrounding area (4 km–8 km). Lastly, the relationships between UHI and urban variables were noted to be strongly driven by humidity in each location.

Understanding the impact of global land surface temperature (LST) changes on gross primary production (GPP) is crucial for addressing global sustainability challenges effectively. This article explores the effects of winter and summer temperature variations on GPP from 2001 to 2020 on a global scale, employing a range of modeling techniques to investigate the complex relationship between temperature changes and GPP. The study employs various modeling approaches, including linear regression models, artificial neural networks, Random Forest (RF) models, and the XGBoost algorithm to examine both linear and non-linear relationships between global temperature changes and GPP. The results reveal that the RF and XGBoost models effectively capture the non-linear relationship between LST and GPP during both summer and winter, demonstrating a high level of statistical significance (P < 0.01) and achieving R2 values of 0.598. Furthermore, the study applies a Sen+MK trend analysis model to identify six distinct trend patterns in LST and GPP during both summer and winter seasons. In summer, the area exhibiting a non-significant increasing trend (NSI) for GPP covers 65,066,274.77 km2, whereas in winter, a strong significant decreasing trend (SSD) for GPP spans 64,537,108.31 km2. Notably, GPP patterns closely mirror those of LST, suggesting that rising high-temperature conditions during summer lead to reduced GPP, while increased low temperatures during winter promote GPP growth. Robust correlations between LST and GPP are observed under various trend conditions in both summer and winter, displaying strong statistical significance (P < 0.01), with SSD achieving a maximum R2 of 0.594. These findings contribute significantly to our comprehensive understanding of the dynamic relationship between LST and GPP and offer valuable insights for addressing the sustainable development of global climate change challenges.

The study investigates the impact of data normalization on the prediction of electricity consumption in buildings using four multilayer Artificial Neural Networks (ANN) algorithms: Long Short-Term Memory Networks (LSTM), Levenberg-Marquardt Back-propagation (LMBP), Recurrent Neural Networks (RNN), and General Regression Neural Network (GRNN). Four data normalization approaches, Min-Max Scaling, Mean, Z-score, and Gaussian function were assessed on experimental datasets. The LSTM algorithm, when combined with Min-Max normalization, showed the most favorable predictive capabilities, with a low Coefficient of Variation of the Root Mean Square Error (CVRMSE) of 10.3 and Normalized Mean Bias Error (NMBE) of 0.6. The remaining three normalization approaches showed satisfactory concordance with empirical data, but with slight disparities in precision. The LMBP model, when using Z-score normalization, had favorable performance in forecasting electricity consumption, but the discrepancies across the models were not significant. The Recurrent Neural Network (RNN) model, when used with Gaussian normalization, exhibited the most favorable performance, with the lowest Coefficient of Variation of Root Mean Square Error (CVRMSE) at 11.8 and Normalized Mean Biased Error (NMBE) at 0.6. The Generalized Regression Neural Network (GRNN) model, trained on unprocessed data, exhibited superior performance, with the lowest Coefficient of Variation of Root Mean Square Error (CVRMSE) at 19.2 and NMBE at 1.0. In conclusion, the study highlights the significant influence of data normalization on the predictive capabilities of various ANN models, suggesting that careful use of data normalization techniques can significantly improve the accuracy of electricity consumption forecasting in buildings.

Vegetation cooling effects can effectively improve the outdoor microclimate. To explore the potential of vegetation transpiration cooling, we validated the feasibility of a new coupled porous medium and solar radiation model to simulate the interactions between vegetation transpiration and other physical processes in CFD. To emphasize the spatial extent of vegetation's influence, we compared and quantified the flow-adjustment distances(LFA) for wind speed, the temperature difference between simulated temperature and background temperature(ΔT(°C)), and water vapor mass fraction(ΔMw(g/kg)) between shrub(100m) and tree(200m) canopies: 1)For both vegetations, the LFA required for ΔT and ΔMw exceeds that of wind speed achieve fully-developed, indicating the momentum adjustment occurs more rapidly than energy and mass transport processes. 2)Vegetation drag effect depends on both leaf area density and vegetation canopy's height and width.3)Vegetation exhibits a significantly higher transpiration cooling effect under ambient relative humidity(RH)=0% than RH=60%(the mean value of ΔT, ΔTfd), for the tree(z=1.5m): ΔT fd is -5.4°C (-1.4∼-1.5°C) at RH=0%/60%. 4)At RH=60%, both shrubs and trees exhibit a notable warming phenomenon near the vegetation canopies tops(ΔTMax=1.3°C/0.9°C for shrubs/trees). This study provides and confirms a reliable numerical method for simulating the interactions of vegetation transpiration and other physical processes in urban or rural areas.

Smart grids offer numerous possibilities for developing and executing sustainable and efficient electricity supply chains that exhibit increased resilience to disturbances. The exploration of the Electric Supply Chain Network (ESCND) design problem using smart grids has been limited. This article aims to tackle this challenge through the application of a robust multi-objective optimization approach, encompassing three economic goals (maximizing profit), environmental goals (minimizing greenhouse gas emissions), and resilience goals (maximizing network resilience). Additionally, the optimization process takes into account the dual objectives of maximizing efficiency and minimizing energy loss by incorporating relevant cost considerations. The proposed methodology incorporates distinctive features of smart grids, such as demandside management programs, microgrid structures, bidirectional distribution lines, and consumer–supplier nodes. It addresses various interconnected decisions, including location placement, capacity expansion, load allocation, and pricing. To solve the formulated model, a potent combination of multi-objective optimization methods based on cutting-plane algorithms and AUGMECON2 is introduced. Subsequently, the proposed model and solution approach are applied to a practical case study, and the obtained results are comprehensively examined. The findings suggest that, despite potential contradictions between economic and environmental objectives, the implementation of smart grids can concurrently improve environmental performance and network flexibility.

When the process of urbanization has brought economic benefits in Yangtze River Economic Belt (YREB), the environmental issues become increasingly prominent. The evolutionary coupled coordination degree (CCD) between urban social economy and ecological environments was explored in 41 cities of YREB. Results indicated that the overall CCD between urban social economy and ecological environments showed a fluctuating upward trend from 2010 to 2020 in YREB. The CCD of Shanghai was the best among these of 41 cities, while those of three provinces were lower than the average CCD of YREB. There were significant differentiated spatial clusters of high and low among these YREB cities in both the global and local spatial autocorrelation analyses during 2010–2020. The CCD estimations indicated a fitted spatial distribution of "high in the east and low in the west" in YREB. Furthermore, CCD was positively influenced by the urban density of population, economic acceleration and technical advancement (P < 0.01), while it was negatively influenced by the urban industrial structures and energy consumption (P < 0.01). Subsequently, corresponding policy implications and recommendations, including enhancing policy innovation and promoting technological progress, were proposed to facilitate the formulation of evidence-based developmental strategies and policies of sustainable cities and society in YREB.

Nowadays, the consumption of natural resources is increasing due to population growth, climate change, and development. Considering the crucial impacts of the regional characteristics, this study develops an energy-water-emissions (EWE) nexus model using a system dynamics (SD) framework to assess the pattern of greenhouse gas (GHG) emissions. The model simulates regional GHG emissions (including indirect emissions of the electricity sector) under climate change and development scenarios from 2011 to 2031. The results indicate that total emissions are projected to increase in the future, while water-related emissions are expected to decrease. The study highlights the limitations of global assumptions at the regional scale, emphasizing the need for planning based on specific regional scale conditions. Through evaluating different development scenarios, the energy-saving scenario had the greatest impact on reducing emissions and resource consumption. Additionally, the results showed that development scenarios had varying effects in each region, highlighting the importance of defining region-specific development scenarios separately. This study offers policymakers valuable insights to enhance their understanding of the climate change effects to evaluate a range of development scenarios more effectively.

The development of the biomass economy contributes to economic growth and social development. The study of biomass energy economy from the perspective of industrial structure type has the significance of promoting the optimization and upgrading of industrial structure, promoting the development of green industry and other aspects. It helps to promote the sustainable development of the economy and society. And industrial structure is a major component of the socio-economic system. Therefore, it is necessary to study the impact of material energy supply and demand from the perspective of regional industrial structure type. The objective of this study is to explore the relationship between the type of industrial structure and the development of biomass energy from the perspective of the economic direction, so as to fill the gap in this research perspective. Based on the three major industries generally regarded to constitute GDP - agriculture, industry, and service - the regression results of STATA software were used to calculate biomass potential according to crop distribution status. Using the ArcGIS analysis tool, the biomass break-even model related to resource supply-demand has been used to obtain the break-even status of the research area under six industrial structures in the past 20 years. According to research results, the break-even degree of biomass energy supply is between 0.109 and 1.236 when the industrial structure types of counties and cities are service-agriculture-industry and industry-agriculture-service. This is suitable for biomass energy development. When the industrial structure type of counties and cities is service-industry-agriculture, the break-even degree of biomass energy supply is between -0.428 and 0.277, which is not suitable for biomass energy development.