Search Results (22)

Achieving both indoor environmental quality (IEQ) and energy efficiency in school buildings remains a challenge, particularly in older structures where renovation strategies often lack site-specific validation. This study evaluates the impact of energy retrofits on a 1970s primary school in France by integrating in situ measurements with a validated numerical model for forecasting energy demand and IEQ. Temperature, humidity, and CO₂ levels were recorded before and after renovations, which included insulation upgrades and an air handling unit replacement. Results indicate significant improvements in winter thermal comfort (PPD < 20%) with a reduced heating water temperature (65 °C to 55 °C) and stable indoor air quality (CO₂ < 800 ppm), without the need for window ventilation. Night-flushing ventilation proved effective in mitigating overheating by shifting peak temperatures outside school hours, contributing to enhanced thermal regulation. Long-term energy consumption analysis (2019–2022) revealed substantial reductions in gas and electricity use, 15% and 29% of energy saving for electricity and gas, supporting the effectiveness of the applied renovation strategies. However, summer overheating (up to 30 °C) persisted, particularly in south-facing upper floors with extensive glazing, underscoring the need for additional optimization in solar gain management and heating control. By providing empirical validation of renovation outcomes, this study bridges the gap between theoretical predictions and real-world effectiveness, offering a data-driven framework for enhancing IEQ and energy performance in aging school infrastructure. Full article

This article is devoted to the determination of the average daily electric load and the average electric load during the hours of maximum load, taking into account the generalized coefficient A_i, using data on electricity consumption for apartment buildings and individual residential buildings in Chelyabinsk and the cities of Dushanbe and Khorog in the Republic of Tajikistan. The results of modeling the average daily electric load, taking into account the developed generalized coefficient A_i, showed that the specific power values for apartments in apartment buildings and in individual residential buildings in the city of Chelyabinsk and the cities of Dushanbe and Khorog of the Republic of Tajikistan were overestimated, taking into account the applicability in the Republic of Tajikistan of the same standard values of specific electric loads (SELs) for apartments in apartment buildings (ABs) as in the Russian Federation. According to the results of modeling using data on the average monthly electricity consumption for 226 apartments in ABs and for individual residential buildings in Chelyabinsk, and according to the proposed approach, the average daily electric load on days during the month varied in the range of 2–3.5 kW/sq and below, while that for the cities of Dushanbe and Khorog of the Republic of Tajikistan varied in the range of 2–5 kW/sq and below, which did not exceed the SEL given by RB 256.1325800.2016. However, because of the lack of other energy sources (gas supply and hot water supply) in the conditions of the Republic of Tajikistan, on the basis of the obtained maximum load time factor and the generalized coefficient ${\mathrm{A}}_{\mathrm{i}\left(\mathrm{E}\right)}$, the obtained values of actual capacity exceeded the maximum during peak hours by 1.2–2.5 times the SEL given by RB 256.1325800.2016. To increase the durability and serviceability of power supplies and enhance the effectiveness of forecasting, the authors propose an approach based on the clustering of meteorological conditions, where each cluster has its own regression model. The decrease in mean absolute error due to clustering was 0.52 MW (57%). The use of meteorological conditions allowed the forecast error to be reduced by 0.22 MW (27%). High accuracy in electrical consumption forecasting leads to increased quality of power system management in general, including under such key indicators as reliability and serviceability. Full article

As a clean energy source, shale gas plays a crucial role in mitigating the supply–demand imbalance of natural gas and in facilitating the transition to a low-carbon economy. This study employs a system dynamics model to forecast future production trends in shale gas in China, analyze its implications for the natural gas supply–demand structure, and explore pathways for sustainable development. Firstly, by integrating the characteristics of China’s shale gas resources, market dynamics, and policy frameworks, the key factors influencing production are identified, and their interrelationships are systematically analyzed. Subsequently, a causal loop diagram is constructed using the VENSIM software(VENSIM PLE 9.3.5 x64), a set of representative variables is selected, and the logical relationships among these variables are established through a multivariate statistical analysis, culminating in the development of a production forecasting model for China’s shale gas (stock and flow diagram). Finally, based on parameter assumptions, this study predicts the production trends in shale gas in China under multiple scenarios. The forecasting results reveal that China’s shale gas production is expected to grow at an average annual rate of 3.32% to 8.02%, with production under the reference scenario projected to reach 724.22 × 10⁸ m³ by 2040. However, the growth of shale gas production over the next two decades remains limited, accounting for a maximum of 12.07% of the total natural gas consumption, underscoring its transitional role in the low-carbon transformation. To address these challenges, this study proposes four policy recommendations: (1) prioritize the development of shallow, high-quality gas-bearing blocks while gradually transitioning to deeper formations; (2) intensify technological innovation in deep shale gas extraction to enhance recovery rates and mitigate production decline rates; (3) implement flexible production subsidies and moderately increase natural gas sales prices to incentivize production and optimize resource allocation; and (4) strengthen ecological conservation and improve water resource management to ensure the sustainable development of shale gas. Full article

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. Full article

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water’s appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region’s WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that Random Forest and Gradient Boosting lead with 95% and 96% accuracy, followed closely by SVM at 92%. KNN exhibits an accuracy rate of 84%, and Decision Trees achieve 77%. Traditional water quality assessment methods are time-consuming and error-prone; a transformative approach using artificial intelligence and machine learning addresses these limitations. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization. Full article

This paper considers bottled water with respect to the three pillars of sustainability: economic viability, environmental impacts, and social equity. Per-capita consumption of bottled water has been growing steadily and is the fastest-growing sector of the packaged beverages industry, with expected annual growth of 10% until 2026. Most bottled water is sold in PET containers, and various impacts are evident along all phases of the product lifecycle. This paper reviews market trends and forecasts, lifecycle estimates of energy consumption, associated air pollution and GHG emissions, water footprint, and waste generation. Concerns around human and ecosystem health due to pollution, land use changes, storage conditions, microplastics, and leaching from containers are described, as well as local environmental benefits from companies’ efforts to preserve the quality of their source water. Growing awareness of the cumulative negative impacts of bottled water have pushed the industry to voluntarily improve its performance. Yet, as growth continues, further actions should focus on stricter regulation and on the provision of more sustainable, affordable, available, and trusted alternatives. Gaps remain in knowledge of the effects of bottled water over its full life cycle. Full article

Groundwater is regarded as the primary source of agricultural and drinking water in semi-arid and arid regions. However, toxic substances released from sources such as landfills, industries, insecticides, and fertilizers from the previous year exhibited extreme levels of groundwater contamination. As a result, it is crucial to assess the quality of the groundwater for agricultural and drinking activities, both its current use and its potential to become a reliable water supply for individuals. The quality of the groundwater is critical in Egypt’s Sohag region because it serves as a major alternative source of agricultural activities and residential supplies, in addition to providing drinking water, and residents there frequently have issues with the water’s suitability for human consumption. This research assesses groundwater quality and future forecasting using Deep Learning Time Series Techniques (DLTS) and long short-term memory (LSTM) in Sohag, Egypt. Ten groundwater quality parameters (pH, Sulfate, Nitrates, Magnesium, Chlorides, Iron, Total Coliform, TDS, Total Hardness, and Turbidity) at the seven pumping wells were used in the analysis to create the water quality index (WQI). The model was tested and trained using actual data over nine years from seven wells in Sohag, Egypt. The high quantities of iron and magnesium in the groundwater samples produced a high WQI. The proposed forecasting model provided good performances in terms of average mean-square error (MSE) and average root-mean-square error (RMSE) with values of 1.6091 × 10⁻⁷ and 4.0114 × 10⁻⁴, respectively. The WQI model’s findings demonstrated that it could assist managers and policymakers in better managing groundwater resources in arid areas. Full article

Water shortage has been an issue for urbanized areas. For the Penang state in Malaysia, it is forecast that there will be a significant increase in water demand in the future. Penang authorities in Malaysia are trying to find an alternative water source to overcome the problem, with one of the options being the Perai River catchment. However, the river water quality was found to be polluted and not suitable to be used for water extraction for domestic consumption. This paper aims to study the pollution level variation due to changes in rainfall during the year in the Perai River Basin, and estimate the TMDL of the river in a particular case for BOD, COD, and NH₃N parameters. A water quality model was developed for the Perai River, Jarak River and Kulim River using InfoWorks ICM. The year 2016 was selected as a model event due to data availability. BOD, COD and NH₃N concentrations were used for TMDL calculation, and the load duration curve approach was used to estimate TMDL. The tidal effect at the downstream of the Perai River was found to impact the data analysis in the river stretch. It was found that pollutant load exceedance was the highest during the rainy season and the problematic pollutant was NH₃N. Thus, local authorities need to focus on tidal and seasonal change factors when developing action plans to manage water quality issues in this basin. Full article

A region’s population growth inevitably results in higher water consumption. This persistent rise in water use increases the region’s wastewater production. Consequently, due to this increase in wastewater (influent), Wastewater Treatment Plants (WWTPs) are required to run effectively in order to handle the huge demand for treated/processed water (effluent). Knowing in advance the influent and effluent parameters increases the operational efficiency and enables cost-effective utilization of diverse resources at wastewater treatment plants. This paper is based on a prediction/forecasting of an influent quality parameter, namely total MLD, as well as effluent quality parameters, namely MPN, BOD, DO, COD and pH for the real-time data collected pre-, during and post-COVID-19 at the Bharwara WWTP in Lucknow, India. It is the largest UASB-based wastewater treatment facility in Uttar Pradesh and the second largest in Asia. In this paper, we propose a novel model namely, wPred comprising extensions of SARIMA with seasonal order and ANN-based ML models to estimate the influent and effluent quality parameters, respectively, and compare it with the existing machine learning models. The lowest sMAPE error for the influent parameters using wPred is 2.59%. The findings of the paper show a strong correlation (R-value), up to 0.99, between the effluent parameters actually measured and predicted. As a result, the model designed in this paper has an acceptable level of accuracy and generalizability which efficiently predicts/forecasts the performance of Bharwara WWTP. Full article

Due to the growing scarcity of water resources, wastewater reuse has become one of the most effective solutions for industrial consumption. However, various factors can detrimentally affect the performance of a wastewater treatment plant (WWTP), which is considered a risk of not fulfilling the effluent requirements. Thus, to ensure the quality of treated wastewater, it is essential to analyze system failure causes and their potential outcomes and mitigation measures through a systematic dynamic risk assessment approach. This work shows how a dynamic Bayesian network (DBN) can be effectively used in this context. Like the conventional Bayesian network (BN), the DBN can capture complex interactions between failure contributory factors. Additionally, it can forecast the upcoming failure likelihood using a prediction inference. This proposed methodology was applied to a WWTP of the Moorchekhort Industrial Complex (MIC), located in the center of Iran. A total of 15 years’ time frame (2016–2030) has been considered in this work. The first six years’ data have been used to develop the DBN model and to identify the crucial risk factors that are further used to reduce the risk in the remaining nine years. The risk increased from 21% to 42% in 2016–2021. Applying the proposed risk mitigation measures can decrease the failure risk from 33% to 9% in 2022–2030. The proposed model showed the capability of the DBN in risk management of a WWTP system which can help WWTPs’ managers and operators achieve better performance for higher reclaimed water quality. Full article

Increased concentrations of chemicals in surface waters affect the development of fish and the state of water bodies in general. In turn, the human consumption of fish that have accumulated heavy metals can cause toxicological hazards and endanger health. The importance of this area and the lack of water quality assessment methods in Ukraine based on the fluctuating asymmetry level of fish and the chemical parameters of water informed the object and aim of the current research. The object of this study was the use of fish populations as a bioindicator of water quality. The study had three purposes: (1) the determination of the dominant fish species and a comparison of their fluctuating asymmetry in the studied rivers; (2) the evaluation of the sensitivity/tolerance of the selected fish populations for assessing water quality; and (3) the creation of a model for assessing the water quality of the studied rivers based on the determined fluctuating asymmetry of the typical fish populations. Each of the studied fish populations had different frequency of fluctuating asymmetry (FFA) levels: the common roach had the highest value, and the silver crucian carp had the lowest. The final stage of the study was building an artificial neural network (ANN) model for predicting water quality based on the FFA of meristic features. Optimal results were obtained for the ANN model with the ReLU activation function and SGD optimization algorithm (MAPE = 6.7%; R² = 0.97187). Such values for the MAPE and R² indicators demonstrated that the level of agreement between the target and forecast data was satisfactory. The novelty of this research lay in the development of a model for assessing water quality based on the comparison of the fluctuating asymmetry values of the typical fish populations in the studied rivers. Full article

Globally, cities are feeling the effects of climate change: rising temperatures, droughts, heatwaves, and more extreme storms are impacting water quantity and water quality. Roughly half of the world’s population are now living in cities. By the year 2050, that figure is anticipated to rise to as much as 80 per cent. If we want our cities to be sustainable both locally and globally, we need to make sure that we use fewer natural resources and generate less trash. The built environment of a smart city incorporates digital, human, and physical components. It is critical to discover appropriate assessment procedures given the rapid urbanization and wide-ranging innovations. Due to impending scarcity, we must move now to increase water treatment and distribution efficiency while decreasing consumption. Risk assessment, mitigation, warning, and forecasting are all critical components of flood risk management going forward. Institutional and governance measures are also important. By providing fresh insights, this research contributes to the corpus of knowledge on smart city mobility. A smart water city improves the quality of life of citizens by solving existing urban water problems based on various technologies and ICT technologies throughout the urban water cycle. It not only provides individual solutions for conventional water management, such as drainage, water treatment, and wastewater treatment, but also improves comprehensive water management through the restoration of the urban water cycle, waterfront usage, and intelligent water management. Furthermore, in a smart water city, ICT-based intelligent technologies complement and improve existing infrastructure and technologies for water management within the whole urban system. They are a supportive tool for the different functions of water in urban settings. This understanding highlights that smart water cities concern not only the provision of drinking water and sanitation services for urban water users, but also other urban water functions such as urban water restoration, waterfront usage, and integrated water management. This paper examined the use of integrated, real-time information and ICT solutions, such as sensors, monitors, geographic information system (GIS), satellite mapping, and other contactless, intelligent tools in both urban and agriculture water management. The paper presents evidence of how SWM has provided solutions at different scales and across various urban and rural contexts, and how they have impacted the social, economic, environmental, governance, and technological spheres. Full article

Water is a fundamental resource for human survival but the consumption of water that is unfit for drinking leads to serious diseases. Access to high–resolution satellite imagery provides an opportunity for innovation in the techniques used for water quality monitoring. With remote sensing, water quality parameter concentrations can be estimated based on the band combinations of the satellite images. In this study, a hybrid remote sensing and deep learning approach for forecasting multi–step parameter concentrations was investigated for the advancement of the traditionally employed water quality assessment techniques. Deep learning models, including a convolutional neural network (CNN), fully connected network (FCN), recurrent neural network (RNN), multi–layer perceptron (MLP), and long short term memory (LSTM), were evaluated for multi–step estimations of an optically active parameter, i.e., electric conductivity (EC), and an inactive parameter, i.e., dissolved oxygen (DO). The estimation of EC and DO concentrations can aid in the analysis of the levels of impurities and oxygen in water. The proposed solution will provide information on the necessary changes needed in water management techniques for the betterment of society. EC and DO parameters were taken as independent variables with dependent parameters, i.e., pH, turbidity, total dissolved solids, chlorophyll–$\alpha$, Secchi disk depth, and land surface temperature, which were extracted from Landsat–8 data from the years 2014–2021 for the Rawal stream network. The bi–directional LSTM obtained better results with a root mean square error (RMSE) of 0.2 (mg/L) for DO and an RMSE of 281.741 ($\mathsf{\mu }$S/cm) for EC, respectively. The results suggest that a hybrid approach provides efficient and accurate results in feature extraction and evaluation of multi–step forecast of both optically active and inactive water quality parameters. Full article

Iraqi greenhouses require an active microcontroller system to ensure a suitable microclimate for crop production. At the same time, reliable and timely Water Consumption Rate (WCR) forecasts provide an essential means to reduce the amount of water loss and maintain the environmental conditions inside the greenhouses. The Arduino micro-controller system is tested to determine its effectiveness in controlling the WCR, Temperature (T), Relative Humidity (RH), and Irrigation Time (IT) levels and improving plant growth rates. The Arduino micro-controller system measurements are compared with the traditional methods to determine the quality of the work of the new control system. The development of mathematical models relies on T, RH, and IT indicators. Based on the results, the new system proves to reliably identify the amount of WCR, IT, T, and RH necessary for plant growth. A t-test for the values from the Arduino microcontroller system and traditional devices for both conditions show no significant difference. This means that there is solid evidence that the WCR, IT, T, and RH levels for these two groups are no different. In addition, the linear, two-factor interaction (2FI), and quadratic models display acceptable performance very well since multiple coefficients of determination (R²) reached 0.962, 0.969, and 0.977% with IT, T, and RH as the predictor variables. This implies that 96.9% of the variability in the WCR is explained by the model. Therefore, it is possible to predict weekly WCR 14 weeks in advance with reasonable accuracy. Full article

Water, an essential resource for crop production, is becoming increasingly scarce, while cropland continues to expand due to the world’s population growth. Proper irrigation scheduling has been shown to help farmers improve crop yield and quality, resulting in more sustainable water consumption. Soil Moisture (SM), which indicates the amount of water in the soil, is one of the most important crop irrigation parameters. In terms of water usage optimization and crop yield, estimating future soil moisture (forecasting) is an essentially valuable task for crop irrigation. As a result, farmers can base crop irrigation decisions on this parameter. Sensors can be used to estimate this value in real time, which may assist farmers in deciding whether or not to irrigate. The soil moisture value provided by the sensors, on the other hand, is instantaneous and cannot be used to directly compute irrigation parameters such as the best timing or the required water quantity to irrigate. The soil moisture value can, in fact, vary greatly depending on factors such as humidity, weather, and time. Using machine learning methods, these parameters can be used to predict soil moisture levels in the near future. This paper proposes a new Long-Short Term Memory (LSTM)-based model to forecast soil moisture values in the future based on parameters collected from various sensors as a potential solution. To train and validate this model, a real-world dataset containing a set of parameters related to weather forecasting, soil moisture, and other related parameters was collected using smart sensors installed in a greenhouse in Chiang Mai province, Thailand. Preliminary results show that our LSTM-based model performs well in predicting soil moisture with a 0.72% RMSE error and a 0.52% cross-validation error (LSTM), and our Bi-LSTM model with a 0.76% RMSE error and a 0.57% cross-validation error. In the future, we aim to test and validate this model on other similar datasets. Full article