Global demand for animal-derived foods, particularly beef, continues to rise, intensifying greenhouse gas (GHG) emissions and accelerating climate change. Robust carbon footprint assessments at system and product levels are therefore critical, especially in organic and extensive livestock systems that simultaneously contribute to biodiversity conservation. In this study, we quantified GHG emissions from an organic, extensive Brachycheros cattle farm and its associated beef products using a Tier 2 life cycle assessment (LCA) and a cradle-to-gate boundary. Total farm-level emissions were estimated at 325,821 kg CO2-eq, with enteric methane representing the predominant source. Beef emissions' intensity reached 98.29 kg CO2-eq per kilogram of produced meat, reflecting the lower carcass yields of locally adapted breeds under extensive systems. Product-level emissions were calculated at 20.19 kg CO2-eq and 24.80 kg CO2-eq when applying literature-based and study-specific beef intensity values, respectively. Overall, the findings underscored the strong influence of production system characteristics and indigenous breed performance on carbon footprints, highlighting the importance of farm-specific GHG assessments in traditional livestock systems.
In this research, I analyzed the relationship between the renewable energy transition, financial development (FD), and sustainability in Saudi Arabia over a period covering the years 1990 through 2024. My primary purpose of this research was to determine the impact that financial, environmental, and institutional factors have on the transition to renewable energy under the auspices of Saudi Vision 2030. To achieve this, I utilized time-series data over a span of 30 years and employed advanced econometric methods, including the Autoregressive Distributed Lag (ARDL) Bounds Testing Technique, Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS). All three techniques revealed statistically significant relationships between the dependent and independent variables in the short-run and long-run. Therefore, the results were replicated several times in the empirical section of the study. The findings support the existence of a long-term stable correlation between independent and dependent variables, particularly the positive influence of financial services development, human capital accumulation, improvements in energy efficiency (EE), increased government spending on renewable energy, greater trade openness, and more foreign direct investment (FDI) on the shift to renewable energy. However, carbon dioxide (CO2) emissions and oil rents have a negative impact on the shift to renewable energy due to the reliance on fossil fuels. Moreover, the evidence also demonstrates that the renewable energy transition is a key source of structural diversification of sustainable development within Saudi Arabia's economy. As recommendations for practice, I suggest to strengthen green financing instruments, expand renewable energy investments, enhance institutional quality, and continue to develop human capital to accelerate the renewable energy transition.
Green economy has been attracting attention around the world. The "dual carbon" policy has been proposed to emphasize the pursuit of high-quality economy in China, and improving energy efficiency is considered an effective way to achieve this strategic target. Moreover, the Yangtze River Economic Belt acts as the principal carrier for China's economic development; therefore, analyzing the energy efficiency is crucial. Three phases constituted the analysis in this paper: a) Using the LDA topic model to conduct the evaluation system of energy efficiency based on the "dual carbon" policy texts; b) using the SBM-Undesirable model to measure the energy efficiency of 11 provinces and cities in the Yangtze River Economic Belt between 2011 and 2020; and c) using the fsQCA method to further analyze the configurational impact of influential factors on the energy efficiency of the Yangtze River Economic Belt. Through systematic research, the evolving trends within the Yangtze River Economic Belt could be examined from two perspectives. Chronologically, energy efficiency within the region exhibited fluctuating development patterns. From a geographical perspective, overall energy efficiency exhibited a tiered distribution pattern of "downstream > midstream > upstream", indicating that the midstream and upstream sectors possessed greater potential for improvement compared to the downstream sector. Therefore, four configuration paths that affect energy efficiency were finally identified toward achieving the "dual carbon" target.
Synthetic dyes contained in industrial wastewater have become major sources of pollution for the environment and human health owing to their persistence and difficulty in degradation through traditional wastewater treatment processes. In this study, ozonation was studied as a method for the oxidation of methylene blue (MB) dye in water as an advanced oxidation process (AOP). This research is a combination of process optimization, kinetic modeling, energy requirement assessment, and ecotoxicological evaluation. To determine the optimal conditions (effect of O3 dose, pH, and contact time) for MB removal, mineralization (chemical oxygen demand-COD; total organic carbon-TOC), residual ozone level, and toxicity toward Daphnia magna and Vibrio fischeri, a Taguchi L9 (33) experimental design was utilized. Optimal conditions for MB removal achieved 98%, COD removal 88%, TOC removal 82%, a minimal level of residual ozone (0.32 mg L-1), and low ecotoxicological risk. Kinetic analysis confirmed the pseudo-first-order reaction rate with k = 0.112 min-1 (R2 = 0.987). The energy cost of ozonation (electrical energy per order (EEO)) was 7.06 kWh m-3 order-1, which made it energy-efficient among AOPs. According to the results obtained, the key parameters for optimizing the direct ozone oxidation and OH-radical oxidation pathways included the contact time and O 3 dose under neutral pH values.
Genomic data on multidrug (MDR) airborne bacteria from nonclinical settings in Tunisia, particularly childcare facilities, remain scarce. We aimed to characterize the genome of an MDR Serratia marcescens (S. marcescens) isolate recovered from indoor air and assess its relevance for public health surveillance. Phenotypic characterization included antibiotic susceptibility testing (disc diffusion and broth microdilution) and quantification of biofilm formation. The underlying genetic basis was investigated via whole-genome sequencing (WGS), followed by bioinformatic analysis to characterize the resistome, virulome, and plasmid content. The S. marcescens strain EA1 exhibited an MDR phenotype, with resistance to 10 of the 23 tested antibiotics, including amoxicillin-clavulanic acid. Phenotypically, it displayed a strong biofilm-forming capacity and a-hemolytic activity. In silico resistome analysis supported the phenotypic profile by revealing antibiotic resistance determinants such as the AmpC beta-lactamase gene (blaSRT-2) and efflux systems belonging to all four major families (ABC, MFS, RND, and SMR). The detection of genes associated with disinfectant tolerance (e.g., qacG) indicates the presence of genetic determinants that, if expressed, could contribute to persistence under hygiene-related pressures; however, functional studies are needed to confirm this phenotype. Additionally, genomic islands characterized by insertion sequences and transposons were detected, suggesting a high potential for horizontal gene transfer. These elements were also present on the single pSM22 plasmid, highlighting a highly mobile and adaptable genome. In this study, we report the first characterization of an MDR airborne S. marcescens strain (EA1) within the framework of environmental surveillance of indoor air quality in a childcare facility in Tunisia. These findings highlight the presence of genetic determinants associated with antimicrobial resistance, virulence, and disinfectant tolerance. Based on a single sentinel isolate, larger-scale sampling is required to assess generalizability.
As cities grow and intelligent urban areas expand, effectively managing escalating waste volumes, from their generation and sorting to their final disposal, becomes increasingly essential. This investigation introduces a sophisticated deep-learning (DL) approach for categorizing solid waste into multiple waste categories, including glass, metal, paper, plastic, cardboard, and trash. Our model addresses issues overlooked in previous studies, including class imbalance and sensitivity to hyperparameters in classification tasks. It uses wide, dilated convolutional layers that adeptly identify and combine key features for precise classification. Addressing class imbalance, we implement a reinforcement learning (RL) approach, where the agent evaluates each sample individually and classifies it. For every accurate classification, the agent earns rewards, whereas inaccuracies lead to penalties, with greater penalties/rewards applied to the less prevalent class. This system enables the agent to develop an optimal strategy guided by specific reward functions and a well-defined learning environment. To enhance hyperparameter optimization, the proposed approach improves the differential evolution (DE) algorithm using a clustering-guided mutation strategy based on k-means clustering. This strategy clusters the candidate hyperparameter population using k-means, selects the cluster with the lowest average objective value, and uses the best candidate within that cluster to guide the mutation process. A unique method revitalizes the candidate solutions throughout the population, advancing the hyperparameter adjustment process. Exhaustive evaluations on the TrashNet and Trash datasets demonstrate the efficacy of our model, achieving a high classification accuracy of 89.908% on TrashNet and 87.438% on Trash. These findings highlight the system's capacity to properly handle particular difficulties in solid waste classification, especially data imbalance and hyperparameter sensitivity.
The study involved pyrolyzing biomass from Parthenium hysterophorus at temperatures of 300, 500, and 700 degrees C to generate charcoal adsorbents (PTC 300, PTC 500, and PTC 700) at reduced prices. Subsequently, the PTCs were evaluated for their efficacy in eliminating Pb (II) from industrial effluent. Through the manipulation of operational parameters and the analysis of mathematical models, PTC 500 exhibited the highest adsorption capacity among the synthesized adsorbents, achieving 20.40 mg Pb (II)/g. The major mechanisms for Pb (II) adsorption onto biochar derived from P. hysterophorus are ion exchange with inherent mineral cations and surface complexation with oxygenated functional groups (-COOH,-OH). The enhanced immobilization efficacy is due to electrostatic attraction and lead precipitation within the pores of biochar. The pseudo-first order and Langmuir models provided the most precise characterization of Pb (II) adsorption onto PTCs in the batch adsorption study. An increased Pb (II) concentration, reduced flow velocity, and elevated bed height collectively enhanced Pb (II) sorption in the fixed bed column. The Clark model most accurately represented the adsorption process and aligned with the experimental data in comparison to the other column models (Thomas, Yoon-Nelson, Clark, and Bohart-Adams models). The supplemental experimental results were consistent with the breakthrough curves. The maximum adsorption capacity achieved using the column approach was 8.16 mg/g. In contrast to rival adsorbents, PTCs exhibited significant reusability potential and enhanced adsorption efficiency.
Microplastic (MP) pollution has emerged as a growing environmental concern in freshwater ecosystems, yet data from northeastern Thailand, a key hydrological region within the Mekong River Basin, remain limited. In this study, we investigated the abundance, characteristics, and seasonal variations of microplastics in six tributary rivers and four Mekong mainstream sites in Ubon Ratchathani Province. Surface-water samples were collected during the dry (April 2021) and wet (October 2021) seasons using a surface-trawl microplastic sampler, and particles were isolated through density separation and oxidative digestion before identification by FTIR spectroscopy. Nine polymer types were detected across all sites and seasons, with polypropylene (PP) and polyethylene (PE) as the predominant polymers. Seasonal differences were evident, with fragment-shaped MPs more abundant in the wet season, reflecting intensified hydrodynamic fragmentation, while fibers and sheets were more prevalent during the dry season. Tributary sites exhibited consistently higher MP abundances than Mekong mainstream sites, highlighting the importance of localized land-based inputs such as municipal wastewater, stormwater runoff, and urban activities. Overall, the findings demonstrate that hydrological conditions and land-use characteristics influence microplastic distribution within the region. This study provides baseline data for northeastern Thailand and contributes to understanding microplastic distribution in the Mekong River Basin.
To improve the complex dynamic transmission lines monsoon weather environment risk early warning and refinement management capability, this paper proposes a digital twin and multi-source data fusion-driven transmission lines monsoon risk early warning method. First, a multi-source heterogeneous data system is constructed, which integrates meteorological, geographic, line ontology, and real-time monitoring data. Based on high-precision three-dimensional modelling and physical attribute binding technology, the digital twin of transmission lines is established and the bidirectional dynamic mapping between physical entity and virtual model at the geometry, attribute and state levels is realized. Beyond the one-way mapping from physical entity to virtual model, a bidirectional dynamic feedback mechanism is designed: real-time monitoring data continuously update the twin state, while the twin's simulation results (e.g., predicted wind-induced responses) are fed back to guide online sensor calibration and inspection strategies, thereby closing the loop between physical and digital spaces at geometry, attribute, and state levels. Next, the temporal and spatial heterogeneity of multi-source data, which are designed based on the deep learning framework of multimodal data fusion model, realize the weather forecast, the geographical environment, and collaborative analysis and dynamic structural response line deduction. Further, by integrating the dynamic mechanical response of the line with its electrical insulation characteristics, the critical state under monsoon conditions and the corresponding dynamic safety thresholds are defined. A real-time probabilistic risk assessment model is then established, enabling a paradigm shift from static threshold-based early warning to dynamic, evolution-based risk early warning. Finally, selecting typical typhoon influence area on the southeastern coast of China's 220 kv transmission line, the presented method is introduced in detail, from the front-end data integration, twin model driven, fusion algorithm operation to the early warning information to generate the whole process of application, and through comparing analysis of early warning effectiveness, more groups of data form. The results show that the warning accuracy of the proposed method is 92.3% and the average effective warning advance time is 98 minutes. Compared with the traditional warning method based on wind speed at meteorological stations, the spatial accuracy and time resolution of the proposed method are significantly improved, which provides more accurate and reliable decision support for the disaster prevention and mitigation and intelligent operation and maintenance of the power grid under extreme weather.
Aerobic denitrifying bacteria are often inhibited by inorganic ions during wastewater treatment, and elucidating their regulatory mechanisms is crucial for enhancing nitrogen removal efficiency. In this study, we systematically investigated the effects of varying concentrations of inorganic ions (0-200 mg/L) on the growth, denitrification performance, and metabolic activity of Pseudomonas mosselii H6. Based on enzyme kinetics, energy metabolism indicators, and measurements of electron transport chain activity, CO32-was found to significantly enhance denitrification capacity. At 200 mg/L, electron transport system activity (ETSA) increased by 26.42%, ATP synthesis peaked at 1.25 mu mol/mg prot, and the NADH/NAD+ ratio rose to 22.69. Nitrate reductase (NR) and nitrite reductase (NiR) activities reached 28.37 and 17.76 U/mg prot, respectively. In contrast, Cl-and NO3-at 200 mg/L markedly suppressed cellular metabolism, reducing NH4+-N removal efficiency to 21.41% and 16.42%, respectively. These findings revealed a concentration-dependent and ion-specific regulatory mechanism. CO32-enhances nitrogen removal by optimizing electron transfer and improving enzyme stability. This study provides a theoretical foundation for optimizing the biological treatment of high-salinity wastewater.
Background: Laundry wastewater contains a range of hazardous substances, including phosphates, surfactants, BOD (Biochemical Oxygen Demand), COD (Chemical Oxygen Demand), and TSS (To-tal Suspended Solids), which can pollute the environment. Thus, effective laundry wastewater management is crucial to reducing negative impacts on water quality and aquatic ecosystems. Objective: We aimed to analyse the effectiveness of small-scale laundry wastewater treatment using electrocoagulation technology combined with a bioball media biofilter. Methods: A quantitative experiment with a one-group pretest-posttest design was used. The tests were conducted on three reactors: Electrocoagulation, a bioball biofilter, and a combination of both, with measurements of physical and chemical wastewater parameters, namely TSS, BOD, COD, and phosphate. Results: The results showed that the combination of electrocoagulation and bioball bio filter technology produced a significant reduction in all parameters: TSS (82.5%), BOD (83.91%), COD (82.27%), and phosphate (97.27%) after 12 hours of treatment. The ANOVA test showed significant differences in TSS (P = 0.000) and BOD (P = 0.036), but not in COD (P = 0.290) or phosphate (P = 0.619). Conclusion: The combination of electrocoagulation and bioball biofilters is highly effective for treating laundry wastewater, achieving significant reductions in TSS, BOD, COD, and phosphate levels and meeting stricter wastewater quality standards.
Due to micro-deformation, wide spatial distribution, and high randomness of instability, stability assessment and early warning of slope rock mass remain challenging. To identify highly sensitive and robust indicators of rock mass damage, we investigated time-frequency dynamic differences between unstable rock mass and bedrock during failure, focusing on micro-vibration characteristics. We further examined their correlation with structural plane constraint strength. Our results showed that the degree of structural plane damage in unstable rock mass is positively correlated with the amplitude ratio between rock mass and bedrock, and negatively correlated with the frequency ratio. By integrating these dynamic indicators with classification algorithms, a dynamics-PSO-SVMbased stability analysis method for unstable rock mass was proposed. This model enabled rapid classification of rock mass stability states based on dynamic indicators. Laboratory-scale similarity experiments analyzed the evolution of amplitude and frequency ratios during rock mass instability, revealing stage-specific patterns in unstable rock mass. The method achieved 100% classification accuracy between stable and unstable states. Moreover, it effectively reduced interference from complex environmental excitation and equipment temperature drift in vibration monitoring data, demonstrating strong noise immunity. These findings enhance the practical applicability of dynamic evaluation methods for assessing the stability of unstable rock mass.
Air pollution, specifically PM10, is a critical challenge in Saudi Arabia, where levels often exceed World Health Organization (WHO) guidelines due to industrial activities and arid conditions, posing serious risks to human health. A key barrier is obtaining accurate PM10 data, as estimations are limited by the few and unevenly distributed air quality stations. Notably, despite its severity, research on PM10 estimation in the region remains scarce. Atmospheric reanalysis datasets like MERRA-2 offer complementary data, but their model-based nature, lacking actual measurements, introduces potential biases. To bridge this gap, this study developed a machine learning framework to estimate daily and monthly PM10 concentrations in three climatically distinct Saudi cities. The framework integrates ground-based PM10 data, meteorological parameters, and MERRA-2 reanalysis data. To our knowledge, this study represents the first application of MERRA-2 for PM10 estimation in Saudi Arabia. The proposed AtmoStack is a stacked machine learning model, and we compared it against individual models (RF, HGB, CatBoost, and MLP) and state-of-the-art models, including LightGBM, ANN, and LSTM. Moreover, the framework incorporates feature-importance analysis to identify the most influential factors, helping to interpret the model. AtmoStack outperformed all baselines; in the dust-dominated environment of Buraidah, it achieved a daily R2 of 0.73 and a monthly R2 of 0.96. In Taif, it achieved a daily R2 of 0.63 and a monthly R2 of 0.94, indicating that AtmoStack effectively captures realistic distribution characteristics. These results support effective air-quality management and public health decisions.
This study is centered on the measurement of carbon emissions at the provincial level in China and the analysis of their spatial distribution. We address the quantification of regional disparities and the dynamic evolution of emission patterns, which are critical for informing climate governance. Moreover, we aim to systematically measure carbon emissions across Chinese provinces and to investigate the regional disparities and the dynamic evolution in their distribution. Our goal is to provide an empirical basis for formulating differentiated and well-targeted emission reduction policies. The modified carbon emission factor method was employed for accounting. The Theil index and its decomposition were used to quantify regional disparities, while kernel density estimation was applied to characterize the dynamic evolution trends of the emission distribution. The results revealed significant regional imbalances in China's carbon emission distribution, with inter-regional differences identified as the primary source of overall disparity. Kernel density curves further showed distinct heterogeneity in distribution shapes and dynamic evolution across regions, reflecting deep-seated differences in emission structures and development stages. Our findings provide critical data for designing differentiated regional carbon reduction strategies and can directly support policy-making for coordinated emission reduction. They offer practical insights for industrial green transition planning at national and provincial levels, aiding in the alignment of economic development with climate targets.
Flash floods are among the most critical natural hazards in arid regions, where limited infiltration capacity and intense episodic rainfall generate rapid runoff and severe flooding. Egypt's Northwest Coast, particularly the Sallum-Alexandria corridor, faces growing flood risks under changing climatic conditions, posing challenges to sustainable water and land management. In this study, we present an integrated assessment of flood hazard severity and geomorphological susceptibility across seven major arid coastal basins (>500 km2) using an integrated framework of Geographic Information Systems (GIS), remote sensing (RS), and hydrological and hydraulic modeling. Basin morphometry was derived from SRTM-based DEMs and analyzed through WMS 11.0 to extract 45 parameters describing geometry, drainage, relief, and texture. Rainfall trend analysis (1979-2020) demonstrated a significant increase in precipitation intensity over the last decade, with HYFRAN-PLUS employed to estimate 100-year return periods. Hydrological simulations revealed Basin 2 (Wadi El-Harika) produced the highest peak discharge (800 m3/s), while Basins 6 and 7 generated minimal runoffs. Conversely, hydraulic modeling indicated that Basins 5, 6, and 7 experienced the greatest inundation depths (up to 30 m), highlighting a disconnect between runoff volume and flood severity due to local topographic and drainage conditions. The results emphasized the need for basin-specific analysis and demonstrated that flood hazard severity could not be inferred from runoff magnitude alone. The proposed integrated framework provided a robust physical basis for flood hazard and geomorphological susceptibility assessment in arid coastal basins and offers a foundation for future flood risk or vulnerability analyses through the incorporation of exposure and adaptive capacity indicators.
Water pollution represents a critical global challenge affecting human health and natural ecosystems. Among the most hazardous contaminants are heavy metals, particularly arsenic (As), which is frequently detected at elevated concentrations in water bodies as a consequence of anthropogenic activities such as agriculture, mining, and metallurgical processes. In this study, magnetic iron oxide nanoparticles synthesized via a green route using tangerine (Citrus reticulata) peel extract (FeNPs-CR) were developed and evaluated for As removal from aqueous solutions. The nanoparticles were synthesized through a coprecipitation method employing 75 g of pulverized peel extract and characterized by scanning electron microscopy (SEM), FT-IR spectroscopy, UV-Vis spectroscopy, and dynamic light scattering (DLS). SEM analysis revealed quasi-spherical particles with primary sizes ranging from 70 to 300 nm, while DLS measurements indicated larger hydrodynamic diameters due to particle agglomeration in aqueous media. Adsorption experiments were conducted using hydride generation atomic absorption spectroscopy, evaluating the effects of contact time, adsorbent dosage, pH, temperature, and initial As concentration. Removal efficiencies exceeding 99% were achieved at 20 mg/L As within 10-30 min, while concentrations of 50-120 mg/L showed removal efficiencies above 95%. Adsorption kinetics were best described by the pseudo-second-order model, indicating a physicochemical adsorption mechanism. Equilibrium data were better fitted by the Freundlich isotherm (R2 = 0.97), suggesting heterogeneous multilayer adsorption, with a maximum adsorption capacity (qmax) of 227.27 mg/g. Thermodynamic analysis revealed negative Gibbs free energy (Delta G degrees = -7.66 to-11.14 kJ/mol), confirming the spontaneous and exothermic nature of the adsorption process. Optimal performance was observed under acidic conditions (pH 1-3) and temperatures below 35 degrees C. These findings demonstrated that FeNPs-CR synthesized from citrus waste constitute an efficient, sustainable, and low-cost adsorbent for As removal, with promising potential for large-scale water treatment applications.
Stochastic modeling and data analysis, which address uncertainty and complexity in business and industry, are important tools for optimizing decision-making processes. When it comes to providing access to healthy food, the importance of decision-making becomes even more prominent. In this work, the aim was to determine the criteria that healthy food businesses consider when choosing business-to-consumer (B2C) e-marketplaces and to evaluate the performance of alternative platforms. Nine main criteria were determined through a literature review and interviews with ten decision-makers (DMs), as sellers. To overcome the limitations of traditional fuzzy sets in handling uncertainty and inconsistent information, a hybrid decision-making method integrating interval-valued neutrosophic sets (IVNSs) with the stepwise weight assessment ratio analysis (SWARA) and evaluation based on distance from average solution (EDAS) methods was applied. Unlike existing approaches, this methodology better addresses expert uncertainty and inconsistency by simultaneously handling membership in truthiness, uncertainty, and falsity within an interval. Interval-valued neutrosophic (IVN)-SWARA was used to weight the selection criteria for e-marketplace platforms, and these weights were used to rank the platforms using IVN-EDAS. The findings show that integration capacity, site traffic density, and cost-effectiveness are the most important criteria in emarketplace selection, while ease of membership and platform design usability were the least important. Theoretically, this research fills a gap in the literature by addressing B2C e-marketplace selection from the seller's perspective and better handles inconsistencies compared to standard fuzzy sets using IVN fuzzy sets. In addition, the proposed approach offers special solutions for healthy food businesses and sheds light on advanced research for other sectors and regions.
