Search Results (2,645)

The construction industry plays a major role in the consumption of natural resources and the generation of waste. Construction and demolition waste (CDW) is produced in substantial volumes globally and is widely available. Its accumulation poses serious challenges related to storage and disposal, highlighting the need for effective strategies to mitigate the associated environmental impacts of the sector. This investigation intends to evaluate the influence of mixed CDW on the physical, mechanical, and durability properties of mortars with CDW partially replacing Portland cement, and allow performance comparisons with mortars produced with fly ash, a commonly used supplementary binder in cement-based materials. Thus, three mortar formulations were developed (reference mortar, mortar with 25% CDW, and mortars with 25% fly ash) and several characterization tests were carried out on the CDW powder and the developed mortars. The work’s principal findings revealed that through mechanical grinding processes, it was possible to obtain a CDW powder suitable for cement replacement and with good indicators of pozzolanic activity. The physical properties of the mortars revealed a decrease of about 10% in water absorption by immersion, which resulted in improved performance regarding durability, especially with regard to the lower carbonation depth (−1.1 mm), and a decrease of 51% in the chloride diffusion coefficient, even compared to mortars incorporating fly ash. However, the mechanical performance of the mortars incorporating CDW was reduced (25% in terms of flexural strength and 58% in terms of compressive strength), but their practical applicability was never compromised and their mechanical performance proved to be superior to that of mortars incorporating fly ash. Full article

As a typical grain base with a dense population and high-level urbanization, the North China Plain (NCP) faces a serious threat to its sustainable development due to water shortage. Surface water area (SWA) is a key indicator for continuously measuring the trends of regional water resources and assessing their current status. Therefore, a deep understanding of its changing patterns and driving forces is essential for achieving the sustainable management of water resources. In this study, we examined the interannual variability and trends of SWA in the NCP from 1990 to 2023 using annual 30 m water body maps generated from all available Landsat imagery, a robust water mapping algorithm, and the cloud computing platform Google Earth Engine (GEE). The results showed that the SWA in the NCP has significantly increased over the past three decades. The continuous emergence of artificial reservoirs and urban lakes, along with the booming aquaculture industry, are the main factors driving the growth of SWA. Consequently, the expansion of artificial water bodies resulted in a significant increase in water evaporation (0.16 km³/yr). Moreover, the proportion of water evaporation to regional evapotranspiration (ET) gradually increased (0–0.7%/yr), indicating that the contribution of water evaporation from artificial water bodies to ET is becoming increasingly prominent. Therefore, it can be concluded that the ever-expanding artificial water bodies have become a new hidden danger affecting the water security of the NCP through evaporative loss and deserve close attention. This study not only provides us with a new perspective for deeply understanding the current status of water resources security in the NCP but also provides a typical case with great reference value for the analysis of water resources changes in other similar regions. Full article

Environmental protection has become one of the key challenges of our time. This has led to an increase in pro-environmental activities in the field of cosmetics and household chemicals, where manufacturers are increasingly trying to meet the expectations of consumers who are aware of the potential risks associated with the production of cosmetics and household chemistry products. This is one of the most important challenges of today’s industry, given that some of the raw materials still commonly used, such as surfactants, may be toxic to aquatic organisms. Many companies are choosing to use natural raw materials that have satisfactory performance properties but are also environmentally friendly. In addition, modern products are also characterized by reduced consumption of water, resources, and energy in production processes. These measures reduce the carbon footprint and reduce the amount of plastic packaging required. In the present study, seven formulations of environmentally friendly car shampoo concentrates were developed, based entirely on mixtures of bio-based surfactants. The developed formulations were tested for application on the car body surface, allowing the selection of the two best products. For these selected formulations, an in-depth physicochemical analysis was carried out, including pH, density, and viscosity measurements. Comparison of the results with commercial products available on the market was also performed. Additionally, using the multiple light scattering method, the foamability and foam stability were determined for the car shampoos developed. The results obtained indicate the very high application potential of the products under study, which combine high performance and environmental concerns. Full article

Climate change is significantly affecting water availability, emphasising the need for sustainable strategies such as wastewater reuse. While this represents a promising alternative resource, insufficiently treated wastewater may pose health risks, particularly through aerosol formation during irrigation, which can facilitate Legionella transmission. This study aimed to evaluate the presence of Legionella across various stages in a wastewater treatment plant (WWTP) that reuses effluent for agricultural purposes. Samples from the influent, four treatment phases, and the final effluent were analysed using both culture-based methods and quantitative PCR (qPCR) for Legionella spp. and L. pneumophila. qPCR detected Legionella spp. in all samples and L. pneumophila in 66% of them. In contrast, the culture-based analysis showed much lower detection levels, with only one positive sample at the influent stage—likely due to microbial interference or growth inhibition. Although contamination decreased in the final effluent, Legionella was still detected in water designated for reuse (Legionella spp. in 100% and L. pneumophila in 17% of samples). No treatment stage appeared to promote Legionella proliferation, likely due to WWTP characteristics, in addition to wastewater temperature and COD. These findings underscore the importance of monitoring Legionella in reclaimed water and developing effective control strategies to ensure the safe reuse of treated wastewater in agriculture. Full article

For effective groundwater resource management, it is essential to model the dynamic behaviour of aquifers in response to rainfall. Here, a methodological approach using a recurrent neural network, specifically a Long Short-Term Memory (LSTM) network, is used to model groundwater levels of the shallow porous aquifer in Southern Italy. This aquifer is recharged by local rainfall, which exhibits minimal variation across the catchment in terms of volume and temporal distribution. To gain a deeper understanding of the complex interactions between precipitation and groundwater levels within the aquifer, we used water level data from six wells. Although these wells were not directly correlated in terms of individual measurements, they were geographically located within the same shallow aquifer and exhibited a similar hydrogeological response. The trained model uses two variables, rainfall and groundwater levels, which are usually easily available. This approach allowed the model, during the training phase, to capture the general relationships and common dynamics present across the different time series of wells. This methodology was employed despite the geographical distinctions between the wells within the aquifer and the variable duration of their observed time series (ranging from 27 to 45 years). The results obtained were significant: the global model, trained with the simultaneous integration of data from all six wells, not only led to superior performance metrics but also highlighted its remarkable generalization capability in representing the hydrogeological system. Full article

Drought is one of the most complex natural hazards, characterized by its slow onset, persistent nature, diverse sectoral impacts (e.g., agriculture, water resources, ecosystems), and dependence on meteorological, hydrological, and socioeconomic factors. Over the years, significant scientific effort has been devoted to developing methodologies that address its multifaceted nature, reflecting the interdisciplinary challenges of drought analysis. However, previous reviews have typically focused on individual methods, while this study presents a unified, multidisciplinary framework that integrates multiple drought analysis methods and links them to key factors guiding method selection. To address this gap, five widely used methods—index-based, remote sensing, threshold-level methods (TLM), impact-based methods, and the storyline approach—are critically evaluated from a multidisciplinary perspective. In addition, the study examines spatial and temporal trends in scientific publications, illustrating how the application of these methods has evolved over time and across regions. The primary objective of this review is twofold: (1) to provide a holistic, state-of-the-art synthesis of these methods, their applications, and their limitations; and (2) to evaluate and prioritize the critical decision-making factors, including drought type, data type/availability, study scale, and management objectives that influence method selection. By bridging this gap, the paper offers a conceptual decision-support framework for selecting context-appropriate drought analysis methods. However, challenges remain, including the vast diversity of methods beyond the scope of this review and the limited consideration of less influential factors such as user expertise, computational resources, and policy context. The paper concludes with insights and recommendations for optimizing method selection under varying circumstances, aiming to support both drought research and effective policy implementation. Full article

This study presents the design, fabrication, and performance assessment of a novel, small-scale (30–70 W), hybrid ocean energy system that captures energy from wave-induced heave motion using a point-absorber buoy and from ocean currents via a vertical axis water turbine (VAWT). Key innovations include a custom designed and built dual-rotor generator that accepts independent mechanical input from both subsystems without requiring complex mechanical coupling and a bi-directional mechanical motion rectifier with an overdrive. Numerical simulations using ANSYS AQWA (2024R2) and QBLADE(2.0.4) guided the design optimization of the buoy and turbine, respectively. Wave resource assessment for the Khobar coastline, Saudi Arabia, was conducted using both historical data and field measurements. The prototype was designed and built using readily available 3D-printed components, ensuring cost-effective construction. This mechanically simple system was tested in both laboratory and outdoor conditions. Results showed reliable operation and stable power generation under simultaneous wave and current input. The performance is comparable to that of existing hybrid ocean wave–current energy converters that employ more complex flywheel or dual degree-of-freedom systems. This work provides a validated pathway for low-cost, compact, and modular hybrid ocean energy systems suited for remote coastal applications or distributed marine sensing platforms. Full article

Selecting an appropriate model for discharge simulation remains a fundamental challenge in modeling. While artificial neural networks (ANNs) have been widely accepted due to detecting streamflow patterns, they require large datasets for efficient training. However, when short-term datasets are available, training ANNs becomes problematic. Autoregressive integrated moving average (ARIMA) models offer a promising alternative; however, severe volatility, nonlinearity, and trends in hydrological time series can still lead to significant errors. To address these challenges, this study introduces a new adaptive hybrid model, ARIMA-iGARCH, designed to account volatility, variance inconsistency, and nonlinear behavior in short-term hydrological datasets. We apply the model to four hourly discharge time series from the Schwarzbach River at the Nauheim gauge in Hesse, Germany, under the assumption of normally distributed residuals. The results demonstrate that the specialized parameter estimation method achieves lower complexity and higher accuracy. For the four events analyzed, R² values reached 0.99, 0.96, 0.99, and 0.98; RMSE values were 0.031, 0.091, 0.023, and 0.052. By delivering accurate short-term discharge predictions, the ARIMA-iGARCH model provides a basis for enhancing water resource planning and flood risk management. Overall, the model significantly improves modeling long memory, nonlinear, nonstationary shifts in short-term hydrological datasets by effectively capturing fluctuations in variance. Full article

Evapotranspiration (ET) crucially regulates water storage dynamics and is an essential component of the terrestrial water cycle. Understanding ET dynamics is fundamental for sustainable water resource management, particularly in regions facing increasing drought risks under climate change. In regions like southwestern China, where extreme drought events are prevalent due to complex terrain and climate warming, ET becomes a key factor in understanding water availability and drought dynamics. Using the SWAT model, this study investigates ET dynamics and influencing factors in the Jizi Basin, Yunnan Province, a small basin with over 71% forest coverage. The model calibration and validation results demonstrated a high degree of consistency with observed discharge data and ERA5, confirming its reliability. The results show that the annual average ET in the Jizi Basin is 573.96 mm, with significant seasonal variations. ET in summer typically ranges from 70 to 100 mm/month, while in winter, it drops to around 20 mm/month. Spring ET exhibits the highest variability, coinciding with the occurrence of extreme hydrological events such as droughts. The monthly anomalies of ET effectively reproduce the spring and early summer 2019 drought event. Notably, ET variation exhibits significant uncertainty under scenarios of +1 °C temperature and −20% precipitation. Furthermore, although land use changes had relatively small effects on overall ET, they played crucial roles in promoting groundwater recharge through enhanced percolation, especially forest cover. The study highlights that, in addition to climate and land use, soil moisture and groundwater conditions are vital in modulating ET and drought occurrence. The findings offer insights into the hydrological processes of small forested basins in southwestern China and provide important support for sustainable water resource management and effective climate adaptation strategies, particularly in the context of increasing drought vulnerability. Full article

A mass abatement scalable system through managed aquifer recharge (MAR-MASS) improves mass extraction from groundwater with a variable-density flow. This method is superior to conventional injection systems because it promotes uniform mass displacement, reduces density gradients, and increases mass extraction efficiency over time. Simulations of various scenarios involving hydrogeologic variables, including hydraulic conductivity, vertical anisotropy, specific yield, mechanical dispersion, molecular diffusion, and mass concentration in aquifers, have identified critical variables and parameters influencing mass transport interactions to optimize the system. MAR-MASS is adaptable across hydrogeologic conditions in aquifers that are 25–75 m thick, comprising unconsolidated materials with hydraulic conductivities between 5 and 100 m/d. It is effective in scenarios near coastal areas or in aquifers with variable-density flows within the continent, with mass concentrations of salts or solutes ranging from 3.5 to 35 kg/m³. This system employs a modular approach that offers scalable and adaptable solutions for mass extraction at specific locations. The integration of programming tools, such as Python 3.13.2, along with technological strategies utilizing parallelization techniques and high-performance computing, has facilitated the development and validation of MAR-MASS in mass extraction with remarkable efficiency. This study confirmed the utility of these tools for performing calculations, analyzing information, and managing databases in hydrogeologic models. Combining these technologies is critical for achieving precise and efficient results that would not be achievable without them, emphasizing the importance of an advanced technological approach in high-level hydrogeologic research. By enhancing groundwater quality within a comparatively short time frame, expanding freshwater availability, and supporting sustainable aquifer recharge practices, MAR-MASS is essential for improving water resource management. Full article

The production of bio-based products for different purposes has become an increasingly common strategy over the last few decades, both in Europe and worldwide. This trend seeks to contribute to mitigating the impacts associated with climate change and to cope with the ambitious objectives established at European level. Over recent decades, agro-industries have shown significant potential as biomass suppliers, triggering the development of robust logistical supply chains and the valorization of by-products to obtain bio-based products that can be marketed at competitive prices. However, this transformation may, in some cases, involve restructuring traditional business model to incorporate the biorefinery concept. In this sense, the first step in developing a bio-based value chain involves assessing the resource’s availability and characterizing the feedstock to select the valorization pathway and the bio-application with the greatest potential. The paper incorporates inputs from a case study on PATURPAT, a company commercializing a wide range of ready-prepared potato products, which has commissioned a starch extraction facility to process the rejected pieces of potatoes and water from the process to obtain starch that can be further valorized for different bio-applications. This study aims to comprehensively review current trends and frameworks for potatoes processing agro-industries and define the most suitable bio-applications to target, as well as identify opportunities and challenges. Full article

Precipitation, as a key input in the water cycle, directly influences the formation and change process of runoff. Meanwhile, the return runoff intuitively reflects the available quantity of water resources in a river basin. An in-depth analysis of the evolution laws and response relationships between precipitation and return runoff over a long time scale serves as an important support for exploring the evolution of hydrometeorological conditions and provides an accurate basis for the scientific planning and management of water resources. Taking Lanzhou Station on the upper Yellow River as a typical case, this study proposes the VSSL (LSTM Fusion Method Optimized by SSA with VMD Decomposition) deep learning precipitation element series extension method and the SSVR (SVR Fusion Method Optimized by SSA) machine learning runoff element series extension method. These methods achieve a reasonable extension of the missing data and construct 100-year precipitation and return runoff series from 1921 to 2020. The research results showed that the performance of machine learning and deep learning methods in the precipitation and return runoff test sets is better than that of traditional statistical methods, and the fitting effect of return runoff is better than that of precipitation. The 100-year precipitation and return runoff series of Lanzhou Station from 1921 to 2020 show a non-significant upward trend at a rate of 0.26 mm/a and 0.42 × 10⁸ m³/a, respectively. There is no significant mutation point in precipitation, while the mutation point of return runoff occurred in 1991. The 100-year precipitation series of Lanzhou Station has four time-scale alternations of dry and wet periods, with main periods of 60 years, 20 years, 12 years, and 6 years, respectively. The 100-year return runoff series has three time-scale alternations of dry and wet periods, with main periods of 60 years, 34 years, and 26 years, respectively. During the period from 1940 to 2000, an approximately 50-year cycle, precipitation and runoff not only have strong common-change energy and significant interaction, but also have a fixed phase difference. Precipitation changes precede runoff, and runoff responds after a fixed time interval. Full article

Drylands cover approximately 41% of Earth’s land surface, supporting about 500 million people and 45% of global agriculture. Groundwater is essential in drylands and is crucial for maintaining ecosystem services and offering numerous benefits. This article, for the first time, analyses and valuates the hydrological ecosystem services (HESs) provided by groundwater in a region of the Colorado River Delta in Mexico, an area with uncertain economic impact due to water scarcity. The main water sources are the Colorado River and groundwater from the Mexicali and San Luis Rio Colorado valley aquifers, both of which are overexploited. Valuation techniques include surrogate and simulated market methods for agricultural, industrial, urban, and domestic uses, the shadow project approach for water conservation and purification cost avoidance, and the contingent valuation method for recreation. Data from 2013 to 2015 and 2020 were used as they are the most reliable sources available. The annual value of HESs provided by groundwater was USD 883,520 million, with water conservation being a key factor. The analyzed groundwater uses reflect differences in efficiency and economic value, providing key information for decisions on governance, allocation, conservation, and revaluation of water resources. These results suggest reorienting crops, establishing differentiated rates, and promoting payment for environmental services programs. Full article

Water scarcity poses a formidable challenge around the world, especially in arid regions where limited availability of freshwater resources threatens both human well-being and ecosystem sustainability. Membrane-based desalination technologies offer a viable solution to address this issue by providing access to clean water. This work ultimately aims to develop a novel permselective polymeric membrane material to be employed in an electrochemical desalination system. This part of the study addresses the optimization, preparation, and characterization of a polyvinylidene difluoride (PVDF) polymeric membrane using the electrospinning technique. The membranes produced in this work were fabricated under specific operational, environmental, and material parameters. Five different additives and nano-additives, i.e., graphene oxide (GO), carbon nanotubes (CNTs), zinc oxide (ZnO), activated carbon (AC), and a zeolitic imidazolate metal–organic framework (ZIF-8), were used to modify the functionality and selectivity of the prepared PVDF membranes. Each membrane was synthesized at two different levels of additive composition, i.e., 0.18 wt.% and 0.45 wt.% of the entire PVDF polymeric solution. The physiochemical properties of the prepared membranes were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), zeta potential, contact angle, conductivity, porosity, and pore size distribution. Based on findings of this study, PVDF/GO membrane exhibited superior results, with an electrical conductivity of 5.611 mS/cm, an average pore size of 2.086 µm, and a surface charge of −38.33 mV. Full article

This study aimed to analyse the spatiotemporal evolution of phytoplankton community dynamics and its underlying mechanisms in the Liaoning section of the Liao River Basin in 2010, 2015, and 2020. Phytoplankton species diversity increased significantly, with an increase from three phyla and 31 species in 2010 to six phyla and 74 species in 2020. Concurrent increases in α-diversity indicated continuous improvements in habitat heterogeneity. The community structure shifted from a diatom-dominated assemblage to a green algae–diatom co-dominated configuration, contributing to an enhanced water purification capacity. The upstream agricultural zone (Tieling section) had elevated biomass and low diversity, indicating persistent non-point-source pollution stress. The midstream urban–industrial zone (Shenyang–Anshan section) emerged as a phytoplankton diversity hotspot, likely due to expanding niche availability in response to point-source pollution control. The downstream wetland zone (Panjin section) exhibited significant biomass decline and delayed diversity recovery, shaped by the dual pressures of resource competition and habitat filtering. The driving mechanism of community succession shifted from nutrient-dominated factors (NH₃-N, TN) to redox-sensitive factors (DO, pH). These findings support a ‘zoned–graded–staged’ ecological restoration strategy for the Liao River Basin and inform the use of phytoplankton as bioindicators in watershed monitoring networks. Full article