Search Results (2,903)

The objective of this study is to create an integrated and sustainability-centered framework to identify optimal locations for wind energy projects in Indonesia. This research employs a novel two-phase multi-criteria decision-making (MCDM) framework that combines the strengths of Data Envelopment Analysis (DEA), Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy Combined Compromise Solution (F-CoCoSo). Initially, DEA is utilized to pinpoint the most promising sites based on a variety of quantitative factors. Subsequently, these sites are evaluated against qualitative criteria such as technical, economic, environmental, and socio-political considerations using FAHP for criteria weighting and F-CoCoSo for ranking the sites. Comprehensive sensitivity analysis of the criteria weights and a comparative assessment of methodologies substantiate the robustness of the proposed framework. The results converge on consistent rankings across methods, highlighting the effectiveness of the integrated approach. Notably, the results consistently identify Lampung, Aceh, and Riau as the top-ranked provinces, showcasing their strategic suitability for wind plant development. This framework provides a systematic approach for enhancing resource efficiency and strategic planning in Indonesia’s renewable energy sector. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article

One hundred presumptive Pseudomonas isolates, recovered from 15 sites impacted by anthropogenic activity in the Foggia district (Italy), were screened for key adaptive and functional traits important for environmental applications. The isolates were phenotypically characterized for their ability to grow under combined pH (5.0–8.0) and temperature (15–37 °C) conditions, to produce proteolytic enzymes, pigments, and exopolysaccharides, and to tolerate SDS. Moreover, the resistance to six environmentally relevant heavy metals (Cd, Co, Cu, Ni, Zn, As) was qualitatively assessed. The results highlighted wide inter-strain variability, with distinct clusters of isolates showing unique combinations of stress tolerance, enzymatic potential, and resistance profile. PERMANOVA analysis revealed significant effects of both the isolation site and the metal type, as well as their interaction, on the observed resistance patterns. A subset of isolates showed co-tolerance to elevated temperatures and heavy metals. These findings offer an initial yet insightful overview of the adaptive diversity of soil-derived Pseudomonas, laying the groundwork for the rational selection of strains for bioaugmentation in contaminated soils. Full article

With the advance of digital transformation, the assessment of the environmental impact of digital tools and technologies grows more relevant. Considering the inflated expectations of environmental responsibility in higher education, this study analyses how websites of Polish universities conform to sustainable web design criteria. The sustainability audit employed a methodology encompassing carbon emissions measurement, technical website analysis, and SEO evaluation. The author analysed 63 websites of public universities in Poland using seven independent audit tools, including an original AI Custom GPT agent preconfigured in the ChatGPT ecosystem. The results revealed a substantial differentiation in CO₂ emissions and website optimisation, with an average EcoImpact Score of 66.41/100. Nearly every fourth website exhibited a significant carbon footprint and excessive component sizes, which indicates poor asset optimisation and energy-intensive design techniques. The measurements exposed considerable variability in emission intensities and resource intensity among the university websites, suggesting the need for standardised digital sustainability practices. Regulations on the carbon footprint of public institutions’ websites and mobile applications could become vital strategic components for digital climate neutrality. Promoting green hosting, “Green SEO” practices, and sustainability audits could help mitigate the environmental impact of digital technologies and advance sustainable design standards for the public sector. The proposed auditing methodology can effectively support the institutional transition towards sustainable management of digital infrastructure by integrating technical, sustainability, and organisational aspects. Full article

Nano- and microplastic pollution, together with the ongoing rise in global temperatures driven by climate change, represent increasingly critical environmental challenges. Although these stressors often co-occur in the environment, their combined effects on plant systems remain largely unexplored. To test the hypothesis that their interaction may exacerbate the effects observed under each stressor individually, we investigated the response of seedlings of Triticum turgidum to treatments with fluorescent polystyrene nanoplastics under optimal (25 °C) and elevated (35 °C) temperature conditions. We evaluated seedling growth, photosynthetic pigment content, and oxidative stress markers using both biochemical and histochemical techniques. In addition, we assessed enzymatic and non-enzymatic antioxidant responses. The use of fluorescently labeled nanoplastics enabled the visualization of their uptake and translocation within plant tissues. Elevated temperatures negatively affect plant growth, increasing the production of proline, a key protective molecule, and weakly activating secondary defense mechanisms. Nanoplastics disturbed wheat seedling physiology, with these effects being amplified under high temperature conditions. Combined stress enhances nanoplastic uptake in roots, increases oxidative damage, and alters antioxidant responses, reducing defense capacity in leaves while triggering compensatory mechanisms in roots. These findings underscore a concerning interaction between plastic pollution and climate warming in crop plants. Full article

This study develops an integrated environmental–economic assessment framework to evaluate the life cycle environmental impacts and economic costs of CO₂ geological storage and produced water treatment in saline formations in China. Using a case study of a saline aquifer carbon storage project in the Ordos Basin, eight full-chain carbon capture, utilization, and storage (CCUS) scenarios were analyzed. The results indicate that environmental and cost performance are primarily influenced by technology choices across carbon capture, transport, and storage stages. The scenario employing potassium carbonate-based capture, pipeline transport, and brine reinjection after a reverse osmosis treatment (S5) achieved the most balanced outcome. Breakeven analyses under three carbon price projection models revealed that carbon price trajectories critically affect project viability, with a steadily rising carbon price enabling earlier profitability. By decoupling CCUS from power systems and focusing on unit CO₂ removal, this study provides a transparent and transferable framework to support cross-sectoral deployment. The findings offer valuable insights for policymakers aiming to design effective CCUS support mechanisms under future carbon neutrality targets. Full article

Asphalt plant reclaimed powder is a common solid waste in road engineering. Reusing reclaimed powder as filler holds significant importance for environmental protection and resource conservation. The key factors affecting the feasibility of reclaimed powder reuse are its acidity/alkalinity and cleanliness. Traditional evaluation methods, such as the methylene blue test and plasticity index, can assess reclaimed powder properties to guide its recycling. However, these methods suffer from inefficiency, strong empirical dependence, and high variability. To address these limitations, this study proposes a rapid and precise evaluation method for reclaimed powder properties based on Fourier transform infrared spectroscopy (FTIR). To do so, five field-collected reclaimed powder samples and four artificial samples were evaluated. Scanning electron microscopy (SEM), X-ray fluorescence spectroscopy (XRF), and X-ray diffraction (XRD) were employed to characterize their microphase morphology, chemical composition, and crystal structure, respectively. Subsequently, FTIR was used to establish correlations between key acidity/alkalinity, cleanliness, and multiple characteristic peak intensities. Representative infrared characteristic peaks were selected, and a quantitative functional group index (Is) was proposed to simultaneously evaluate acidity/alkalinity and cleanliness. The results indicate that reclaimed powder primarily consists of tiny, crushed stone particles and dust, with significant variations in crystal structure and chemical composition, including calcium carbonate, silicon oxide, iron oxide, and aluminum oxide. Some samples also contained clay, which critically influenced the reclaimed powder properties. Since both filler acidity/alkalinity and cleanliness are affected by clay (silicon/carbon ratio determining acidity/alkalinity and aluminosilicate content affecting cleanliness), this study calculated four functional group indices based on FTIR absorption peaks, namely the Si-O-Si stretching vibration (1000 cm⁻¹) and the CO₃²⁻ asymmetric stretching vibration (1400 cm⁻¹). These indices were correlated with conventional testing results (XRF for acidity/alkalinity, methylene blue value, and pull-off strength for cleanliness). The results show that the Is index exhibited strong correlations (R² = 0.89 with XRF, R² = 0.80 with methylene blue value, and R² = 0.96 with pull-off strength), demonstrating its effectiveness in predicting both acidity/alkalinity and cleanliness. The developed method enhances reclaimed powder detection efficiency and facilitates high-value recycling in road engineering applications. Full article

The exponential growth of lithium-ion battery consumption has amplified the urgency of identifying sustainable and economically viable recycling solutions. This study proposes an integrated decision-making framework based on the T-Spherical Fuzzy Einstein Interaction Aggregator DEMATEL-CoCoSo approach to comprehensively evaluate and rank battery recycling technologies under uncertainty. Ten key evaluation criteria—encompassing environmental, economic, and technological dimensions—were identified through expert consultation and literature synthesis. The T-Spherical Fuzzy DEMATEL method was first applied to analyze the causal interdependencies among criteria and determine their relative weights, revealing that environmental drivers such as energy consumption, greenhouse gas emissions, and waste generation exert the most systemic influence. Subsequently, six recycling alternatives were assessed and ranked using the CoCoSo method enhanced by Einstein-based aggregation, which captured the complex interactions present in the experts’ evaluations and assessments. Results indicate that Direct Recycling is the most favorable option, followed by the Hydrometallurgical and Bioleaching methods, while Pyrometallurgical Recycling ranked lowest due to its high energy demands and environmental burden. The proposed hybrid model effectively handles linguistic uncertainty, expert variability, and interdependent evaluation structures, offering a robust decision-support tool for sustainable technology selection in the circular battery economy. The framework is adaptable to other domains requiring structured expert-based evaluations under fuzzy environments. Full article

(1) The beach areas of Galveston, Texas, USA are heavily used for recreational activities and often experience elevated fecal indicator bacteria levels, representing a potential threat to ecosystem services, human health, and tourism-based economies that rely on suitable water quality. (2) During the span of 15 months (March 2022–May 2023), water samples that exceeded the U.S. Environmental Protection Agency-accepted alternative Beach Action Value (BAV) for enterococci of 104 MPN/100 mL were analyzed via microbial source tracking (MST) through quantitative polymerase chain reaction (qPCR) assays. The Bacteroides HF183 and DogBact as well as the Catellicoccus LeeSeaGull markers were used to detect human, dog, and gull fecal sources, respectively. The qPCR MST data were then utilized in a quantitative microbial risk assessment (QMRA) to assess human health risks. Additionally, samples collected in July and August 2022 were sequenced for 16S rRNA and matched with fecal sources through the Bayesian SourceTracker2 program. (3) Overall, 26% of the 110 samples with enterococci exceedances were positive for at least one of the MST markers. Gull was revealed to be the primary source of identified fecal contamination through qPCR and SourceTracker2. Human contamination was detected at very low levels (<1%), whereas dog contamination was found to co-occur with human contamination through qPCR. QMRA identified Campylobacter from canine sources as being the primary driver for human health risks for contact recreation for both adults and children. (4) These MST results coupled with QMRA provide important insight into water quality in Galveston that can inform future water quality and beach management decisions that prioritize public health risks. Full article

Biochar has garnered considerable attention as a soil amendment due to its unique physicochemical properties. Its application not only enhances soil carbon sequestration but also improves nutrient availability. Incorporating biochar into soil is regarded as a promising strategy for mitigating global climate change while delivering substantial environmental and agricultural benefits. In this study, biochar was extracted from Siraitia grosvenorii and subsequently modified through alkali treatment. A laboratory incubation experiment was conducted to assess the effects of unmodified (JMC) and modified (GXC) biochar, applied at different rates (1%, 2%, and 4%), on organic carbon mineralization and soil nutrient dynamics. Results indicated that, at equivalent application rates, JMC-treated soils exhibited lower CO₂ emissions than those treated with GXC, with emissions increasing alongside biochar dosage. After the incubation, the 1% JMC treatment exhibited a mineralization rate of 17.3 mg·kg⁻¹·d⁻¹, which was lower than that of the control (CK, 18.8 mg·kg⁻¹·d⁻¹), suggesting that JMC effectively inhibited organic carbon mineralization and reduced CO₂ emissions, thereby contributing positively to carbon sequestration in Siraitia grosvenorii farmland. In contrast, GXC application significantly enhanced soil nutrient levels, particularly increasing available phosphorus (AP) by 14.33% to 157.99%. Furthermore, partial least squares structural equation modeling (PLS-SEM) identified application rate and pH as the key direct factors influencing soil nutrient availability. Full article

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

Leptospirosis is a globally significant zoonosis affecting animal health, productivity, and the environment. While typically associated with tropical climates, its persistence in semi-arid regions such as La Laguna, Mexico—characterized by low humidity, high temperatures, and limited water sources—remains poorly understood. Although these adverse environmental conditions theoretically limit the survival of Leptospira, high livestock density and synanthropic reservoirs (e.g., rodents) may compensate, facilitating transmission. In this cross-sectional study, blood sera from 445 dairy cows (28 herds: 12 intensive [MI], 16 semi-intensive [MSI] systems) were analyzed via microscopic agglutination testing (MAT) against 10 pathogenic serovars. Urine samples were cultured for active Leptospira detection. Risk factors were assessed through epidemiological surveys and multivariable analysis. This study revealed an overall apparent seroprevalence of 27.0% (95% CI: 22.8–31.1), with significantly higher rates in MSI (54.1%) versus MI (12.2%) herds (p < 0.001) and an estimated true seroprevalence of 56.3% (95% CI: 50.2–62.1) in MSI and 13.1% (95% CI: 8.5–18.7) in MI herds (p < 0.001). The Sejroe serogroup was isolated from urine in both systems, confirming active circulation. In MI herds, rodent presence (OR: 3.6; 95% CI: 1.6–7.9) was identified as a risk factor for Leptospira seropositivity, while first-trimester abortions (OR:10.1; 95% CI: 4.2–24.2) were significantly associated with infection. In MSI herds, risk factors associated with Leptospira seropositivity included co-occurrence with hens (OR: 2.8; 95% CI: 1.5–5.3) and natural breeding (OR: 2.0; 95% CI: 1.1–3.9), whereas mastitis/agalactiae (OR: 2.8; 95% CI: 1.5–5.2) represented a clinical outcome associated with seropositivity. Despite semi-arid conditions, Leptospira maintains transmission in La Laguna, particularly in semi-intensive systems. The coexistence of adapted (Sejroe) and incidental serogroups underscores the need for targeted interventions, such as rodent control in MI systems and poultry management in MSI systems, to mitigate both zoonotic and economic impacts. Full article

Nanofluids are an innovative working medium in solar hot water installations (DHWs), thanks to their increased thermal conductivity and heat transfer coefficient. The aim of this work was to assess the effect of Al₂O₃ nanofluids in a water–ethylene glycol base (40:60%) and with the addition of Tween 80 surfactant (0.2 wt%) on thermal efficiency (ε) and exergy (η_ex) in a plate heat exchanger at DHW flows of 3 and 12 L/min. The numerical NTU–ε model was used with dynamic updating of thermophysical properties of nanofluids and the solution of the ODE system using the ode45 method, and the validation was carried out against the literature data. The results showed that the nanofluids achieved ε ≈ 0.85 (vs. ε ≈ 0.87 for the base fluid) and η_ex ≈ 0.72 (vs. η_ex ≈ 0.74), with higher entropy generation. The addition of Tween 80 reduced the viscosity by about 10–15%, resulting in a slight increase of Re and h-factor; however, the impact on ε and η_ex was marginal. The environmental analysis with an annual demand of Q = 3000 kWh/year and an emission factor of 0.2 kg CO₂/kWh showed that for ε < 0.87 the nanofluids increased the emissions by ≈16 kg CO₂/year, while at ε ≈ 0.92, a reduction of ≈5% was possible. This paper highlights the need to optimize nanofluid viscosity and exchanger geometry to maximize energy and environmental benefits. Nowadays, due to the growing problems of global warming, the analysis of energy efficiency and carbon footprint related to the functioning of a building seems to be crucial. Full article

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

Silvopastoral systems, though ecologically beneficial, remain underrepresented in the European Union’s Common Agricultural Policy and are seldom studied in Mediterranean contexts. The current study assesses both the environmental and economic aspects of five typical silvopastoral systems in central Greece, encompassing cattle, sheep, and goat farming. A Life Cycle Assessment approach was implemented to quantify greenhouse gas emissions using economic allocation, distributing impacts between milk and meat outputs. Enteric fermentation was the major emission source, accounting for up to 65.14% of total emissions in beef-based systems, while feeding and soil emissions were more prominent in mixed and small ruminant systems. Total farm-level emissions ranged from 60,609 to 273,579 kg CO₂eq per year. Economically, only beef-integrated systems achieved an average annual profitability above EUR 20,000 per farm, based on financial data averaged over the last five years (2020–2024) from selected case studies in central Greece, while the remaining systems fell below the national poverty threshold for an average household, underscoring concerns about their economic viability. The findings underline the dual challenges of economic viability and policy neglect, stressing the need for targeted support if these multifunctional systems are to add value to EU climate goals and rural sustainability. Full article