Environmental Impact Research Articles

Tribological and environmental impact of silicon dioxide nanoparticles in algae biodiesel fuels for diesel engines

The research evaluates the impact of Sargassum algae methyl ester (SAME20) with 50 and 100 ppm silicon dioxide (SiO 2 ) nanoparticles on the performance and emissions of a single-cylinder, four-stroke diesel engine. SiO 2 nanoparticles were characterized using FTIR, SEM, and TEM. Diesel, SAME20, SAME20 + 50 ppm of SiO 2 , and SAME20 + 100 ppm of SiO 2 blends were tested at 1500 rpm under loads from 25% to 100%. The SAME20 + 100 ppm of SiO 2 blend outperformed others, improving brake thermal efficiency by 1.43% and reducing brake-specific fuel consumption by 5.35% compared to diesel. It also decreased HC, CO, and smoke emissions by 11.12%, 18.1%, and 16.2%, respectively, with a slight NO x increase. Tribological analysis using a four-ball tribometer showed the SAME20 + 100 ppm of SiO 2 blend reduced the coefficient of friction by 76.89% and wear scar diameter by 59.19%. SEM analysis confirmed reduced surface wear and damage, highlighting improved engine and tribological performance.

US grass-fed beef is as carbon intensive as industrial beef and ≈10-fold more intensive than common protein-dense alternatives

The high resource intensity of industrial beef in high-income economies has prompted growing interest in alternative, potentially lower environmental impact beef production pathways. Of those, grass feeding is promoted by some as one such alternative, but rigorous quantification of this claim is required. Motivated to bridge this knowledge gap, we integrate empirical evidence with a model based on authoritative equations governing beef cattle performance to quantify the greenhouse gas emissions of producing grass-fed beef. Because geographical specificity and dependence on agricultural intensity are key, we model widely varied herds, from extensive operations on semiarid, marginal rangelands to partially industrial, intensive ones in lusher, more accommodating settings. We find that emissions per kg protein of even the most efficient grass-fed beef are 10 to 25% higher than those of industrial US beef and 3- to over 40-fold higher than a wide range of plant and animal alternatives. Soil sequestration enhancement by rangeland grazing reduces these emissions from 280-390 to 180-290 kg CO2eq (kg protein)-1, still somewhat above industrial beef's 180 to 220 kg CO2eq (kg protein)-1, and well above nonbeef alternatives' 10 to 70 kg CO2eq (kg protein)-1. These differences prove robust across a broad set of combinations of grass-fed beef operation types, management practices, and ration qualities. Consequently, even with maximal credit for putative sequestration enhancement, grass-fed beef is still no less carbon intensive than industrial beef, and severalfold more intensive than nonbeef alternatives.

FEASIBILITY AND DEVELOPMENT PROSPECTS OF ELECTRIC ROAD TRANSPORT IN EUROPEAN COUNTRIES BASED ON THE INTEGRAL EMISSIONS INDEX

The aim of this article is to review the development of electric road transport and assess its feasibility in European countries based on an analysis of CO₂ emissions using an integral index. Electric road transport is evolving in three main directions: battery-powered vehicles, hydrogen fuel cell vehicles, and road electrification. Battery electric vehicles are widely used, but their future may be constrained by issues related to cost, charging infrastructure, and raw material availability. Hydrogen-powered vehicles offer fast refueling; however, they require substantial investments and strict safety standards. Road electrification includes both contact and wireless energy transfer systems. Contact-based systems, in particular, provide high power and low energy losses, making them optimal for vehicles with significant energy demands. The environmental efficiency of electric vehicles largely depends on the source of electricity. Countries with a high share of renewable energy, such as Norway and France, demonstrate greater environmental efficiency in electric vehicle adoption. Special attention should be given to road electrification, which reduces the duration of battery-powered autonomous driving and allows for smaller battery sizes, thereby extending their lifespan. This approach not only increases battery longevity but also has a positive environmental impact by reducing emissions associated with battery production and disposal.

Advancing Sustainable Aviation: Evaluating the Role of Cryogenic Engines in Reducing Emissions and Enhancing Fuel Efficiency in Commercial Aircraft

This paper explores the feasibility of implementing cryogenic engines in commercial aircraft as a potential solution to reduce greenhouse gas emissions and improve fuel efficiency. Cryogenic engines, which utilize liquid hydrogen (LH2) as fuel and liquid oxygen (LOX) as an oxidizer, offer exceptional efficiency and performance due to their high specific impulse and low environmental impact. The study assumes that the necessary infrastructure for producing, storing, and distributing cryogenic fuels can be developed and scaled to meet the demands of the aviation industry2. Through a combination of theoretical analysis and simulation, this paper evaluates the technical and economic challenges associated with the adoption of cryogenic engines in aircraft, as well as the potential benefits in terms of reduced carbon emissions and operational costs. The findings suggest that while significant hurdles remain, the integration of cryogenic engines into aircraft propulsion systems could play a crucial role in achieving sustainable aviation in the future.

Noncoding RNAs as Tools for Advancing Translational Biology in Plants.

Noncoding RNAs (ncRNAs), once considered the "dark matter" of the genome, have emerged as critical regulators of gene expression in plants. Research initially focused on model organisms has laid the groundwork for harnessing the potential of ncRNAs in agriculture, particularly for crop protection, improvement and modulation. This review explores the role of long and small ncRNAs in plant biology, highlighting their application as powerful tools in agricultural biotechnology. We examine the latest strategies for ncRNA expression and delivery in crops, including transgenic and non-transgenic approaches, as well as emerging technologies that enable precise and efficient modulation of gene activity in plants and pathogens. Additionally, we provide a comprehensive overview of the current state-of-the-art in the regulation of RNA-based products, addressing the challenges and opportunities for integrating these innovations into sustainable agricultural practices. As the regulatory landscape evolves, understanding the safety, efficacy, and environmental impact of ncRNA-based technologies will be crucial for their successful deployment. By leveraging the advances in plant science research, long and small ncRNAs hold promise for designing highly specific tools to boost crop productivity while preserving genetic diversity, contributing to global food security and sustainable agriculture.

Exploring Green Practices: a Review of Carbon Dot-Based Sustainable Sensing Approaches.

The increasing demand for sustainable and eco-friendly technologies has driven significant interest in carbon dots (CDs) due to their unique optical properties, low toxicity, and versatile applications in sensing. The aim of this review is to provide a comprehensive analysis of the current advancements in CD-based sensing approaches, with a focus on their environmental sustainability based on greenness evaluation tools. We begin by discussing the principles and methodologies of greenness evaluation, including various assessment tools and metrics used to measure the environmental impact of CD synthesis and applications. Key applications of CD-based sensors in detecting pollutants, biomolecules, and other analytes are examined, emphasizing their potential in environmental monitoring, biological, and food analysis. The review concludes with a discussion on future research directions aimed at overcoming these challenges and enhancing the sustainability of CD-based sensing technologies. Through this detailed exploration, we aim to provide valuable insights into the greenness of CDs, fostering their development as a cornerstone of sustainable sensing technologies. The evaluation tools applied for future probes confirmed their superior environmental friendliness.

Eco-Efficient Fertilizer Optimization for Enhanced Crop Productivity using Random Forest Algorithm

Agricultural productivity is crucial for global food security, and optimizing fertilizer usage is a key factor in enhancing crop yield while maintaining environmental sustainability. This study proposes an eco-efficient fertilizer optimization model using the Random Forest algorithm, a powerful machine learning technique, to improve fertilizer application strategies. The model analyzes various soil properties, crop requirements, and environmental factors to predict the optimal fertilizer composition and quantity. By leveraging historical agricultural data, the algorithm identifies patterns that lead to increased crop productivity with minimal environmental impact. The proposed approach enhances efficiency by reducing excessive fertilizer use, mitigating soil degradation, and minimizing greenhouse gas emissions. Experimental results demonstrate that the model outperforms traditional fertilizer application methods by improving yield prediction accuracy and resource utilization. This research provides a data-driven framework for precision agriculture, ensuring sustainable farming practices and enhanced food production.

Limited efficiency of wet scrubbers in reducing the environmental impact of ship-emitted particles

Limited efficiency of wet scrubbers in reducing the environmental impact of ship-emitted particles

The Effect of Implementing Green Construction on Project Performance: A Comprehensive Study Using Structural Equation Models (S.E.M.)

Abstract The construction industry plays a crucial role in development but also faces serious challenges related to environmental impact and occupational safety. The sector contributes significantly to producing solid waste, air, water, soil pollution and emissions of hazardous gases. In addition, high noise levels from construction activities can harm public health. Workplace accidents at construction sites are also a major problem, with impacts ranging from worker safety to project costs. Green construction offers a comprehensive solution to address these issues. By adopting sustainable practices such as efficient waste management, using environmentally friendly materials, and implementing modern construction technologies, projects can reduce their negative impact on the environment. This study uses the Structural Equation Model (S.E.M.) to analyze green construction implementation’s effect on project performance. This study identifies 20 green construction indicators and evaluates their impact on five aspects of project performance: cost, time, quality, Occupational Health and Safety (O.H.S.), and Green Building performance. The analysis results show that green construction has a significant and robust effect on cost performance and a moderate impact on time, quality, and O.H.S. performance. Project documentation is identified as the most influential green construction indicator, while conservation and energy efficiency have the lowest effects. Another interesting finding is that implementing 86% of green indicators is optimal for Green Building Performance, indicating a balance between sustainability and project efficiency. This study provides valuable insights for practitioners and policymakers in optimizing sustainable construction practices and developing more effective regulations for the green construction industry.

A Bibliometric Analysis of Multi-Criteria Decision-Making Techniques in Disaster Management and Transportation in Emergencies: Towards Sustainable Solutions

Disaster management minimizes potential harm and protects populations across four phases: preparedness, mitigation, response, and recovery. Diverse scientific approaches could be applied at each phase, among which Multi-Criteria Decision-Making (MCDM) methods are widely recognized and utilized. Their integration provides a systematic framework for prioritizing disaster-related criteria, optimizing resource use, and minimizing environmental impact, ultimately enhancing community resilience. This study conducts a bibliometric analysis to identify pioneering researchers, leading institutions, contributing countries, and interaction levels working on MCDM methods in disaster management and emergency transportation, as well as to reveal key trends. 365 Web of Science and Scopus publications (2000–2024) were analyzed using the Bibliometrix tool in R. As a significant outcome, three important clusters emerged: Disaster Planning and Logistics, Risk and Resilience, and Crisis Response and Decision Support. The interplay between these clusters and the methodologies shaping them was highlighted, alongside insights from the most recent studies. This study could serve as a roadmap for future research, guiding efforts to address gaps such as real-time applications, multi-hazard integration, and scalability. It contributes to the limited body of research on MCDM in disaster management and emergency transportation, laying the groundwork for upcoming studies that could enhance resilience and promote sustainable development.

A BIM-Based Framework for Life Cycle, Cost, and Circularity Data Integration in Environmental Impact Assessment

A BIM-Based Framework for Life Cycle, Cost, and Circularity Data Integration in Environmental Impact Assessment

Institutional Environmental Impact on the Use of a Performance Measurement System: The Mediation Effect From Organizational Renewal

Institutional Environmental Impact on the Use of a Performance Measurement System: The Mediation Effect From Organizational Renewal

Sustainable Dependent Sub-Tasks Orchestration at Extreme Edge Computing: A Partitioning-based Deep Reinforcement Learning Approach

Extreme Edge Computing (EEC) promotes sustainable computing by reducing reliance on centralized data centres and decreasing their environmental impact. By using extreme edge devices to handle computing requests, the EEC reduces the energy demands for data transmission and execution, thereby reducing carbon footprints. However, EEC introduces challenges due to the mobile, heterogeneous, and resource-limited nature of these devices. Additionally, tasks are often complex and interdependent, complicating offloading and workload orchestration. The dynamicity of EEC systems, where both task generation and resources can be mobile, alongside task inter-dependencies, escalates the complexity of task offloading and workload management. To tackle these complexities, task partitioning emerges as a viable strategy. Moreover, in dynamic edge computing scenarios, resource demand remains unpredictable, emphasizing the critical need to optimize resource utilization efficiently. In this paper, we investigate the problem of tasks with inter-dependencies offloading in an EEC environment where mobile and resource-constrained edge devices are employed as computing resources. In this regard, a partitioning-based Deep Reinforcement Learning (DRL) for Dependent sub-Task Orchestration (DeTOrch) model is proposed. DeTOrch uses a state-of-the-art partitioning method for decomposing tasks and proposes a novel mobility task-orchestration mechanism to minimize the task completion time and maximize the use of edge devices’ resource. The simulation results show that the proposed model can significantly improve the task success rate and decrease task completion time. In addition, in various scenarios with different levels of mobility, the proposed model outperforms the baselines while utilizing the resource of edge devices.

Catalytic hydrogenation of agricultural residues to green diesel: Process optimization with FeMo/Al2O3 catalyst

ABSTRACT Limitations of converting agricultural residues into green diesel include high production costs, particularly for catalysts and hydrogen, and technical viability. Safety risks also arise from high-pressure hydrogenation, requiring precautions and specialized equipment. This research evaluates green diesel from agricultural residues via hydrogenation with FeMo/Al2O3 under optimized conditions. Utilizing FeMo/Al2O3 heterogeneous catalysts, the process demonstrates high precision and efficiency. The catalytic hydrogenation reactors, specifically designed for converting cellulosic biomass into green diesel, ensure both effectiveness and safety, particularly when managing the high pressures and temperatures required. Notably, the highest yield occurs at a specific temperature, with returns decreasing when operating beyond this optimal temperature range. Outcomes derived from experimentation and MATLAB data analysis, exploring catalyst efficiency, green diesel production, catalyst stability, reusability, techno-economic assessment, cost and environmental impact assessment, and life cycle analysis. Petroleum diesel has the highest emissions at 0.88 kg CO2/MJ, followed by biodiesel at 0.8 kg CO2/MJ, and green diesel with the lowest emissions at 0.1 kg CO2/MJ. This indicates that green diesel emits the least amount of greenhouse gases, with petroleum diesel emitting nearly nine times more than green diesel. .

Decoding grinding dynamics using experimental investigations of wet and dry conditions on D3 tool steel

The study reports an important research gap, as identified from literature surveys revealed, where minimal studies about dry cylindrical grinding processes which require excessive specific energy. The present investigation intends to explore the effect of grinding variables on D3 tool steel in two diverse settings: dry condition and ideas of wet condition. Experimental research has evaluated heat generation levels in dry workpiece grinding compared to the wet grinding process. The Taguchi method has contributed to deriving optimal parameters for achieving favorable tool steel responses during dry and wet grinding processes. Scanning electron microscope evaluation has shown the ground surface of D3 tool steel displays a well-maintained topography. Investigative outcomes demonstrate dry grinding can produce precise surface finishes while maintaining the integrity of workpiece surfaces. The method minimizes adverse effects on the working environment by reducing the need for grinding fluids. When performing dry grinding operations proper feed rates vary from 5 mm/min to 25 mm/min. A feed rate lower than 25 mm/min protects the workpiece surface from thermal harm. Dry grinding with optimal parameters achieved a surface roughness of 0.25 µm, comparable to wet grinding, whereas in the case of roundness error wet grinding yield least value of 0.001 mm and returning a maximum material removal rate of 154.95 g/min. Wet grinding operation requires a coolant flow rate between 5 l/min and 7.5 l/min for optimal results. Wet grinding operations using lower coolant levels or adopting dry grinding methods both allow companies to decrease chemical waste exposure to the working environment, which will reduce the environmental impact.

Sunlight-sensitive carbon dots for plant immunity priming and pathogen defence.

Global food production faces persistent threats from environmental challenges and pathogenic attacks, leading to significant yield losses. Conventional strategies to combat pathogens, such as fungicides and disease-resistant breeding, are limited by environmental contamination and emergence of pathogen resistance. Herein, we engineered sunlight-sensitive and biodegradable carbon dots (CDs) capable of generating reactive oxygen species (ROS), offering a novel and sustainable approach for plant protection. Our study demonstrates that CDs function as dual-purpose materials: priming plant immune responses and serving as broad-spectrum antifungal agents. Foliar application of CDs generated ROS under light, and the ROS could damage the plant cell wall and trigger cell wall-mediated immunity. Immune activation enhanced plant resistance against pathogens without compromising photosynthetic efficiency or yield. Specifically, spray treatment with CDs at 240 mg/L (2 mL per plant) reduced the incidence of grey mould in N. benthamiana and tomato leaves by 44% and 12%, respectively, and late blight in tomato leaves by 31%. Moreover, CDs (480 mg/L, 1 mL) combined with continuous sunlight irradiation (simulated by xenon lamp, 9.4 × 105 lux) showed a broad-spectrum antifungal activity. The inhibition ratios for mycelium growth were 66.5% for P. capsici, 8% for S. sclerotiorum and 100% for B. cinerea, respectively. Mechanistic studies revealed that CDs effectively inhibited mycelium growth by damaging hyphae and spore structures, thereby disrupting the propagation and vitality of pathogens. These findings suggest that CDs offer a promising, eco-friendly strategy for sustainable crop protection, with potential for practical agricultural applications that maintain crop yields and minimize environmental impact.

Deep Learning Models for Predicting and Mitigating Environmental Impact of Industrial Processes in Real-Time

Deep Learning Models for Predicting and Mitigating Environmental Impact of Industrial Processes in Real-Time

Experimental investigation on the effect of geometrical configuration of suction and discharge tube across the sub-units of propane-based vapor compression air-conditioning unit

Researchers across the globe are looking forward to find alternative solutions from the perspective of environmental impact and sustainability relative to conventional refrigerants. In this regard, propane appears to be a feasible choice. This work experimentally investigates the propane-based vapor compression system for the air-conditioning. The performance of the modified cycle employing propane has been investigated under various operating conditions (geometrical configuration of suction and discharge tubes across cycle sub-units). For a given range of temperature at evaporator outlet, coefficient of performance (COP) varies between 3.2–4.4 (finned spiral tube) as compared to 2.6–3.4 (bend tube) and 2.6–3.0 (spiral tube). Notably, the arrangement of a straight tube at the suction line and a finned spiral tube at the discharge line provides the lowest COP (4.36), whereas the arrangement that is precisely the opposite, yields highest COP (4.94). Furthermore, from the perspective of HVAC industry this study demonstrates that fins added to the suction line will improve performance and may increase the overall cooling performance of the set-up.

Assessing the Environmental Impact of Busan New Port Construction in Korea: A Comprehensive Analysis of Water Quality Changes and Suspended Solids in Jinhae Bay

Assessing the Environmental Impact of Busan New Port Construction in Korea: A Comprehensive Analysis of Water Quality Changes and Suspended Solids in Jinhae Bay

Outdoor Time Could Regulate the Effects of Green Environment on Myopia in Chinese Children and Adolescents

ABSTRACT Purpose To explore the relationship between myopia and green space, and the mediation effects of outdoor time. Methods We used large-scale, cross-sectional observational data from Shanghai, China. Participants were aged 3~20 years and underwent visual acuity and non-cycloplegic refraction in 2021. Using GIS-based data on environments surrounding participants’ schools and kindergartens, we examined the association between green space (measured by NDVI) and myopia. We employed a logistic mixed-effects model to assess the impact of school-level green environments on individual myopia, reporting odds ratios (OR) and their 95% confidence intervals. Mediation analysis was conducted to explore whether outdoor activity time mediates the relationship between green environments and myopia. Additionally, we conducted age- and gender-stratified analyses and validated the robustness of the model through sensitivity analysis. Results Complete case sample sizes were 1,727,709 from 3,399 schools and kindergartens [899,817 (52.082%) boys; mean age 10.065 years (SD: 3.633)]. NDVI within 1000 m of schools was independently associated with lower odds of myopia (OR: 0.299, 95% CI: 0.249 ~ 0.357, p < 0.001), with 2.7% of the total effects attributed to the mediation effects of outdoor time. The effect sizes for NDVI were comparatively more protective in boys and those aged 7~12 years (OR: 0.223, 95% CI:0.156 ~ 0.319, p < 0.001). Sensitivity analysis with a 500-meter NDVI radius and alternative outcome variable of wearing glasses confirmed consistency with the previous results. Conclusions This study demonstrates a significant impact of green environments on myopia in children and adolescents aged 3~20 years, revealing the potential mediating effect of outdoor activities. Policymakers should consider implementing intervention measures to enhance green spaces in schools to promote eye-health-friendly environments.