Search Results (213)

The water footprint of grain crop production is a key indicator for assessing agricultural water stress and resource-use efficiency. This study analyzes the dynamic evolution, convergence characteristics, and driving forces of water footprints for major grain crops in China’s primary producing regions from 2011 to 2022. The results show the following: (1) Total water footprints are mainly driven by blue and green water components, while grey water contributes relatively little, and the total footprint follows a fluctuating pattern of “decline–increase–decline–increase–decline.” Rice exhibits the highest water footprint, with an average annual value of 59.8251 million m³, whereas beans and tubers show much lower levels, each with an average annual footprint below 20 million m³. Grey water footprints for all grain crops have declined significantly since 2018, with reductions exceeding 10% by 2022. (2) Significant absolute convergence is observed across provinces, with the absolute convergence rate ultimately approaching 0.1, indicating that inter-provincial differences in water footprints are narrowing and that high-footprint regions are improving more rapidly toward lower-footprint regions. (3) Conditional convergence is also confirmed, with the conditional convergence rate approaching 0.2, suggesting that provinces converge toward their own steady-state levels, though convergence speeds are influenced by heterogeneous factors such as economic development, technological progress, and population size. (4) Generalised Divisia Index Method (GDIM) decomposition reveals that per capita agricultural GDP and mechanization intensity are the core drivers of changes in water footprints, and their synergistic effects produce an amplification impact, with cumulative contributions exceeding 100%. The findings provide important policy implications for optimizing water resource management and promoting sustainable agricultural development in China’s major grain-producing areas. Full article

The lack of knowledge regarding the water footprint (WF) of bananas in the Guayas province of Ecuador, assessed in local terms, creates an information gap concerning the consumptive and sustainable use of water. Therefore, this study aimed to determine the WF of the cultivation and packaging process of this fruit. The Hoekstra methodology was followed, using the evaporation pan procedure for crop evapotranspiration based on a 43-year historical record (1980–2023) and the USDA method for effective precipitation, selecting nine banana farms within the zone. The grey WF was assessed following two approaches: a simple procedure assuming a 10% leaching rate of agrochemicals was followed during the rainy season, and water losses through percolation were accounted for during the dry season. Nitrogen was considered as the pollutant element, while for the grey WF assessment in packaging, active chlorine in wastewater was measured. The WF was determined to be 351.4 m³ t⁻¹, distributed as 45.0% green WF, 49.0% blue WF, and 6.0% grey WF. The grey WF is distributed as 74.7% in the field and 25.3% in the packaging process. Consequently, a moderate impact on groundwater and surface water resources is inferred; however, the irrigation management applied in the zone contributes to reduced contamination of these sources. Full article

Andean high-altitude ecosystems are critical to sustaining biodiversity, agriculture, and the livelihoods of indigenous populations. However, accelerating glacier retreat, irregular precipitation, and intensive water use have exacerbated ecosystem degradation and water insecurity. This study conducts a scoping review (PRISMA-ScR) of peer-reviewed and grey literature (2000–2025) to examine how water footprint (WF) management through its blue, green, and gray components can be operationalized within an Integrated Water Resource Management (IWRM) and Human Rights-Based Approach (HRBA) to ensure equitable access and ecological sustainability in the Andes. Quantitative synthesis from 72 sources shows that agricultural withdrawals account for over 78% of total blue-water use, while glacier-fed runoff has declined by 32% over the past two decades. Empirical evidence from Peru, Ecuador, and Bolivia demonstrates that integrating indigenous irrigation systems with modern efficiency technologies reduces consumptive water use by up to 25% and enhances wetland biodiversity indices by 15–20%. These findings support the development of an Integrated Water-Biodiversity-Rights Framework (IWBRF) that links WF indicators (WFAM and ISO 14046) with ecosystem integrity and social equity metrics. The study advances theory by clarifying how WF indicators inform rather than replace IWRM and HRBA decision processes, offering a practical model for achieving water justice, biodiversity protection, and climate resilience in fragile Andean ecosystems. Full article

By 2050, global population growth will lead to a significant increase in demand for animal-based products, including seafood. Aquaculture is a key solution to meet these needs while reducing pressure on wild aquatic stocks. However, its environmental footprint and energy demand remain open concerns. This study explores the co-location of offshore aquaculture with a wave energy converter—WaveRoller—as a renewable power source. Using a 44-year dataset from the Portuguese coast near Peniche, the analysis evaluates the survivability and operation of the WaveRoller, long-term percentile trends, seasonal energy production, extrapolated extreme events using probabilistic modeling, and confidence intervals for energy costs. A scenario-based range of energy demand is constructed from a baseline blue mussel production of over 400 tons/yr. The K-Means clustering method is applied to reduce data size while maintaining its representativeness. Results show that a 600 kW WaveRoller is similarly suited to operational wave conditions compared to a 1000 kW device, though it excels when aquaculture energy demand peaks in Summertime. The probability that a single WaveRoller fails to meet annual aquaculture energy needs is nearly zero, though, during Summer, it can become statistically significant. The opposite is verified on survivability during Winter, under harsher wave conditions. The Levelized Cost of Energy is calculated for different expenditure scenarios, with minimum values slightly under 200 EUR/MWh being reported only under ideal conditions. Future work should include climate change scenarios and life cycle assessments to better evaluate environmental impacts and techno-economic viability. Full article

Water scarcity is predicted to intensify due to climate change, population growth, and industrial expansion. This poses serious problems for long-term food safety and agricultural sustainability. This field experiment involved varying the sowing times three times (advance, normal and delayed seed sowing) and the irrigation water levels three times, with three replications. The result shows the maximum height of the plant, length of the tip, number of grains per tip, and grain production (5063 kg ha⁻¹) were achieved via regular planting with sufficient irrigation (T1). Although the output was somewhat reduced (2–3%), the treatment of water irrigation with the deficit (T2, T3) improved the plant level water use efficiency by 10–23%and reduced the trace of blue water by 12–28%. T3 had the lowest blue water track (736 m³ t⁻¹). Conversely, advance and delayed seed sowing combined with deficit irrigation significantly reduced yield up to 14% and increased blue water footprint under full irrigation. Economic analysis revealed that T1 provided the highest net income (Rs: 376,284 ha⁻¹), while T2 and T3 retained 97–98% as compared to the advance seed sowing with well water, while improving water productivity. Therefore, it is concluded that the normal sowing with a 15–30% irrigation deficit enhances water productivity without substantial yield losses, providing a climate-adaptive approach for wheat production in water-scarce regions. Full article

There has been an increasing demand for high-resolution image sensing technologies in recent years due to their diverse and advanced optical applications. With recent advances in nanofabrication technologies, this can be achieved through the realization of high-density pixels. However, the development of high-density and miniaturized pixels introduces challenges to the conventional color filters, which generally transmit and absorb different spectral components of light. A significant portion of the incident light is inherently lost using conventional color filters. Moreover, as the pixel size is shrunk, optical losses appear to be substantial. To address these fundamental limitations, a novel nanophotonic optical router is proposed in this work. Our router utilizes a single-layer, all-dielectric metasurface as a spectral router. The metasurface is designed through an inverse design approach that exploits adjoint sensitivity analysis. A novel figure of merit is developed and incorporated in the inverse design process, enabling the metasurface design to effectively sort and route the incoming light into four targeted channels, each corresponding to a distinct spectral component—red, green, blue, and near-infrared. We demonstrate that the proposed quad-spectral metasurface router, having a compact footprint of $2 \mathsf{\mu }\mathrm{m}\times 2 \mathsf{\mu }\mathrm{m}$, achieves an average optical efficiency of approximately 39% across the broad spectral range, i.e., 400–850 nm, with each spectral channel exceeding an efficiency of 25%. This surpasses the maximum efficiency attainable by the conventional four-channel color filters. Our proposed quad-spectral metasurface router offers a wide range of applications in low-light imaging, image fusion, computational photography, and computer vision. In addition, this work highlights the applicability of an adjoint-based inverse design approach to accelerate the development of compact, efficient, and high-performance nanophotonic devices for the next generation of imaging and sensing systems. Full article

The integration of renewable energy into industrial processes is crucial for reducing the carbon footprint of conventional hydrogen production. This work presents detailed design, optical–thermal simulation, and performance analysis of a solar parabolic dish (SPD) system for supplying high-temperature steam to a Steam Methane Reforming (SMR) plant. A 5 m diameter dish with a focal length of 3 m was designed and optimized using COMSOL Multiphysics (version 6.2) and MATLAB (version R2023a). Optical ray tracing confirmed a geometric concentration ratio of 896×, effectively focusing solar irradiation onto a helical cavity receiver. Thermal–fluid simulations demonstrated the system’s capability to superheat steam to 551 °C at a mass flow rate of 0.0051 kg/s, effectively meeting the stringent thermal requirements for SMR. The optimized SPD system, with a 5 m dish diameter and 3 m focal length, was designed to supply 10% of the total process heat (≈180 GJ/day). This contribution reduces natural gas consumption and leads to annual fuel savings of approximately 141,000 SAR (Saudi Riyal), along with a substantial reduction in CO₂ emissions. These quantitative results confirm the SPD as both a technically reliable and economically attractive solution for sustainable blue hydrogen production. Full article

Efficient water management is critical for sustainable crop production in arid and semi-arid regions. This study investigated the effects of two irrigation regimes—25% and 50% Management Allowable Depletion (MAD) and two planting patterns (single-row and double-row) on evapotranspiration (ET) partitioning, water use efficiency (WUE), and water footprint (WF) in drip-irrigated faba bean (Vicia faba L.). Field data were combined with a leaf area index (LAI)-based model to estimate the relative contributions of transpiration (T) and evaporation (E) to total ET. The highest grain yield (6171 kg ha⁻¹) and the lowest blue (570 m³ ton⁻¹) and green (68 m³ ton⁻¹) water footprints were recorded under the 25% MAD with double-row planting. This treatment also achieved the highest proportion of transpiration in ET (70%), indicating a shift toward productive water use. In contrast, the lowest-performing treatment (50% MAD, single-row) had the highest total water footprint (792 m³ ton⁻¹) and the lowest transpiration share (44%). Although high-density planting slightly reduced WUE based on transpiration, it improved overall water efficiency when total input (ETc) was considered (1.57 kg m⁻³ for total input WUE, 4.17 kg/m⁻³ for T-based WUE). These findings highlight the importance of integrating irrigation scheduling and planting pattern to improve both physiological and agronomic water productivity. The approach offers a practical strategy for sustainable faba bean production in water-scarce environments and supports climate-resilient irrigation planning aligned with Iraq’s National Water Strategy. Full article

The processing of freshly cut fruits and vegetables represents an important niche for implementing circular economy principles, particularly through the reuse of washing water. This is especially relevant as post-wash water is often treated as wastewater and discarded without reuse. One promising research avenue is the use of plant-derived extracts in water sanitation processes. Their antimicrobial properties offer a natural alternative to conventional disinfectants while reducing the formation of harmful disinfection by-products. The aim of this study was to evaluate the effectiveness of different filter bed configurations in removing pathogens from water. These configurations included a hydrogel saturated with natural plant extracts, an ion exchange resin layer, and an activated carbon layer. The most effective composite was also tested using real process water from a fruit washing line. The test materials included concentrated extracts from oak bark (Quercus robur), willow (Salix alba), birch (Betula pendula), raspberry shoots (Rubus idaeus), tea leaves (Camellia sinensis), and linden flowers (Tilia cordata), all immobilized in hydrogel, along with activated carbon and ion-exchange resin. Water samples were artificially inoculated with six opportunistic pathogens and collected process water was also used. Samples were analyzed microbiologically at six time intervals. The composite filter (hydrogel–resin–carbon) achieved a reduction of over 2 log₁₀ in heavily inoculated water (~10⁸ CFU mL⁻¹) and maintained at least a 1 log₁₀ reduction in real process effluents. The proposed solution supports blue water footprint reduction strategies (as the system aims to decrease the demand for freshwater resources through the reuse of treated wastewater) and aligns with the principles of green processing. Full article

To elucidate the food–energy–water (FEW) nexus, in this paper, a food–energy–water–carbon (FEWC) measurement method is established, and the evolutionary mechanisms within the nexus are determined to optimize crop production systems (CPSs). A quantitative assessment of the trade-offs and synergies among the constituent sub-nexuses is presented. This assessment is achieved through carbon footprint analysis of CPSs. In addition to examining FEW resource interactions, we employ the logarithmic mean divisia index methodology—a tool well-suited for practical energy decomposition—to explore the nexus interrelationships. This research further accounts for anthropogenic inputs in CPSs, specifically using blue water and energy consumption as key indicators for characterizing water and energy dynamics, respectively. Five crops are selected for CPS carbon emissions analysis to inform cropping structure optimization. The results show that during 2000–2022, greenhouse gas (GHG) emissions from China’s CPSs exhibited significant fluctuations characterized by a concentrated–dispersed–concentrated distribution pattern: the food system’s carbon footprint decreased notably, the food–energy (FE) system’s impact increased substantially, and the food–water (FW) system’s footprint fluctuated before decreasing. The spatial diversity in the FE system’s provincial carbon footprint increased over time, while the FW nexus exhibited fluctuating yet significant efficiency gains, indicating movement toward more balanced spatial distribution along the Hu Huanyong Line and Botai Line. The net effect of the FEW nexus interactions on GHG emissions exhibited a slight mitigating influence. Full article

The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO₂) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization. Traditional Extreme Learning Machines (ELMs) offer rapid learning but often yield unstable performance due to random parameter initialization. This study introduces a novel hybrid model, Red-Billed Blue Magpie Optimizer-tuned ELM (RBMO-ELM) which harnesses the intelligent foraging behavior of red-billed blue magpies to optimize input-to-hidden layer weights and biases. The RBMO algorithm is first benchmarked on 15 functions from the CEC2015 test suite to validate its optimization effectiveness. Subsequently, RBMO-ELM is applied to predict Indonesia’s CO₂ emissions using a multidimensional dataset that combines economic, technological, environmental, and globalization-driven indicators. Empirical results show that the RBMO-ELM significantly surpasses several state-of-the-art hybrid models in accuracy (higher R²) and convergence efficiency (lower error). A permutation-based feature importance analysis identifies social globalization, GDP, and ecological footprint as the strongest predictors underscoring the socio-economic influences on emission patterns. These findings offer both theoretical and practical implications that inform data-driven Artificial Intelligence (AI) and Machine Learning (ML) applications in environmental policy and support sustainable governance models. Full article

Urban blue–green space (UBGS) plays a critical role in mitigating the urban heat island (UHI) effect and reducing land surface temperatures (LSTs). However, existing research has not sufficiently explored the optimization of UBGS spatial configurations or their interactions with urban morphology. This study takes New York City as a case and systematically investigates small-scale urban cooling strategies by integrating multiple factors, including adjustments to the blue–green ratio, spatial layouts, vegetation composition, building density, building height, and layout typologies. We utilize multi-source geographic data, including LiDAR derived land cover, OpenStreetMap data, and building footprint data, together with LST data retrieved from Landsat imagery, to develop a prediction model based on generative adversarial networks (GANs). This model can rapidly generate visual LST predictions under various configuration scenarios. This study employs a combination of qualitative and quantitative metrics to evaluate the performance of different model stages, selecting the most accurate model as the final experimental framework. Furthermore, the experimental design strictly controls the study area and pixel allocation, combining manual and automated methods to ensure the comparability of different ratio configurations. The main findings indicate that a blue–green ratio of 3:7 maximizes cooling efficiency; a shrub-to-tree coverage ratio of 2:8 performs best, with tree-dominated configurations outperforming shrub-dominated ones; concentrated linear layouts achieve up to a 10.01% cooling effect; and taller buildings exhibit significantly stronger UBGS cooling performance, with super-tall areas achieving cooling effects approximately 31 percentage points higher than low-rise areas. Courtyard layouts enhance airflow and synergistic cooling effects, whereas compact designs limit the cooling potential of UBGS. This study proposes an innovative application of GANs to address a key research gap in the quantitative optimization of UBGS configurations and provides a methodological reference for sustainable microclimate planning at the neighborhood scale. Full article

The agri-food sector in Navarra, Spain, is exploring alternative protein sources like pea protein due to concerns regarding the environmental impacts and allergenic properties of traditional options like soy. This study aimed to evaluate a pea-based snack using carbon footprint and water footprint methodologies to assess the environmental performance of pea extrusion. The carbon footprint of the pea snacks was found to be 0.12 kg of CO₂e per 100 g of packaged product. The water footprint was 174 L per 100 g of packaged product, with the blue water footprint accounting for the largest share (52%), followed by green (47%) and grey (1%) water footprints. Strategies such as minimizing ingredient loss and switching to renewable electricity could potentially reduce greenhouse gas emissions by 17% and green water consumption by 3%. Regarding alternative protein matrices, pea extrusion utilized 225 L of water per 150 g of extrudate, primarily as green water, demonstrating a lower dependence on blue and grey water compared to soy-based alternatives, suggesting its suitability for blue water-scarce regions. The carbon and water footprint assessments highlight the potential of pea protein as a regionally suitable, low-impact alternative to soy in terms of both carbon and water use. Full article

The determination of the actual crop water requirement (CWR) today represents an important prerogative for combating climate change. A three-year trial was conducted to ad-dress the need to provide adequate support to processing tomato growers in defining the correct amounts of water to be supplied. In fact, the objective of this work was to calculate the water requirement of processing tomatoes, specifically analyzing their irrigation needs using the CROPWAT 8.0 software and through capacitive and tensiometric probes. Furthermore, for both methods, the tomato yield was evaluated both by supplying 100% of its water requirement and by supplying, through regulated deficit irrigation (RDI), 70% of its water requirement. Subsequently, for each irrigation strategy employed and for each CWR calculation method, the water footprint was calculated by analyzing the blue, green, and grey components. In the years 2022 and 2023, there was an overestimation of CWR of 13.5% for IR100 and 13.94% for IR70, and 14.53% for IR100 and 11.65% for IR70, respectively, while in 2024 there was an underestimation, with values of 9.17% and 5.22% for the IR100 and IR70 treatments compared to the values obtained with the probes. The total WF of tomatoes varied between 33.42 and 51.91 m³ t⁻¹ with the CROPWAT model and between 35.82 and 47.19 m³ t⁻¹ with the probes for IR100, while for RDI70, the values ranged between 38.72 and 59.44 m³ t⁻¹ with the CROPWAT method and between 35.81 and 53.95 m³ t⁻¹ with the probe method. In water-scarce regions, integrating the CROPWAT 8.0 model (enhanced with real-world data) and implementing smart systems can significantly improve water management, refine decision-making processes, and mitigate environmental impacts. This approach directly addresses the urgent need for water security within sustainable agriculture. Full article

The arid and semi-arid regions of Northwest China, as major agricultural production zones, have long faced dual challenges: increasing water resource pressure and severe supply–demand imbalances caused by the expansion of cultivated land. The crop water footprint, an effective indicator for quantifying agricultural water use, plays a crucial role in supporting sustainable development in the region. This study adopted a multi-scale spatiotemporal analysis framework, combining the CROPWAT model with Geographic Information System (GIS) techniques to investigate the spatiotemporal evolution of crop water footprints in Northwest China from 2000 to 2020. The Logarithmic Mean Divisia Index (LMDI) model was used to analyze spatial variations in the driving forces. A multidimensional evaluation system—encompassing structural, economic, ecological, and sustainability dimensions—was established to comprehensively assess agricultural water resource utilization in the region. Results indicated that the crop water footprint in Northwest China followed a “decline-increase-decline” trend, it increased from 90.97 billion m³ in 2000 to a peak of 133.49 billion m³ in 2017, before declining to 129.30 billion m³ in 2020. The center of the crop water footprint gradually shifted northward—from northern Qinghai to southern Inner Mongolia—mainly due to rapid farmland expansion and increasing water consumption in northern areas. Policy and institutional effect, together with economic development effect, were identified as the primary drivers, contributing 49% in total. Although reliance on blue water has decreased, the region continues to experience moderate water pressure, with sustainable use achieved in only half of the study years. Water scarcity remains a pressing concern. This study offers a theoretical basis and policy recommendations to enhance water use efficiency, develop effective management strategies, and promote sustainable water resource utilization in Northwest China. Full article