Search Results (1,690)

The transport sector plays a significant role in air pollution, and real-world emissions measurements are becoming increasingly important. In this study, emissions from a turbocharged, direct-injection gasoline internal combustion engine (ICE) vehicle and a port fuel injection (PFI) hybrid electric vehicle (HEV) were compared using a portable emissions measurement system (PEMS) under real-world driving conditions. The CO₂, CO, NO_x, and PN emissions of the two vehicles were measured in urban, rural, and motorway sections. HEV CO₂ emissions were ~20% lower than ICE emissions in the entire Real Driving Emissions (RDE) cycle, while in urban operation, they were almost 50% lower. PN emissions were lower for HEV in rural and motorway sections than for ICE, but significant PN peaks occurred during the early urban phase, attributable to the slower engine warm-up of the HEV. Machine learning analysis (Random Forest and Extra Trees Regressor) indicated that coolant temperature was the dominant driver of HEV PN emissions. The results indicate that powertrain characteristics and thermal management strongly influence real-world driving emissions, highlighting their importance for the further development of hybrid vehicles. Full article

Metal oxide nanoparticles serve as crucial drivers in modern biomedical, catalytic, environmental, and energy technologies due to their high surface-to-volume ratios and quantum confinement properties. Traditional chemical and physical synthesis methods remain limited by significant energy footprints, high costs, and the use of hazardous reagents. To address these challenges, bioinspired (“green”) synthesis has emerged as a sustainable paradigm that employs biological systems as nature nanofactories. This critical review provides a provides a comprehensive and systematic analysis of the green synthesis of major metal oxide systems (ZnO, TiO₂, Fe₃O₄/Fe₂O₃, CuO, Co₃O₄, CeO_2, and MnO₂) using diverse biological templates, including plant extracts, bacteria, fungi, algae, and biopolymers. Moving beyond simple descriptive summaries, we critically evaluate the foundational electron-transfer and nucleation mechanism, systematically correlate processing parameters with physical outcomes, and offer a rigorous comparative analysis across different biological kingdoms. Finally, we directly address the underlying challenges facing the field: reproducibility bottlenecks, scalability limits, environmental safety variations, and regulatory hurdles necessary for industrial translation. Full article

Background: The research question explored in this study revolves around the quantitative evaluation of the carbon footprint of cranioplasty surgery, a neurosurgical intervention meant to reconstruct skull defects. Methods: Following a calculation of the emissions pertaining to Scope 1 to 3 of the Greenhouse Gas (GHG) Protocol, the authors engaged with various stakeholders to identify possible interventions meant to drive the carbon efficiency of a cranioplasty pathway. The service improvement project (SIP) that ensued was aimed at reducing the volume and weight of the packaging materials for cranioplasty shipping boxes, and decreasing the paper consumption relative to the preparation of user manuals without compromising patients’ safety. Results: Our analysis indicates a cumulative carbon footprint of 104.35 kg CO₂e for a single unilateral cranioplasty operation, where packaging corresponds to 6.4% of Scope 3 emissions and 1.41% of its total emissions. Of note, our SIP led to an overall 76.53% decrease in the number of emissions generated by the packaging equivalent required for a unilateral titanium implant. Conclusions: This study demonstrates the effectiveness of a partnership between public institutions and medtech companies in driving carbon net efficiency of a cranioplasty pathway, and we suggest that such approach is scalable to other surgical specialties. Full article

The Constrained Application Protocol (CoAP) is widely adopted to ensure end-to-end reliability in resource-constrained Artificial Intelligence of Things (AIoT) and Wireless Sensor Networks (WSNs). However, CoAP’s default retransmission timeout (RTO) mechanism lacks algorithmic responsiveness under volatile channel conditions, and state-of-the-art benchmarks like CoCoA+ and FASOR often suffer from over-conservative backoff states or destabilizing retransmission storms. To overcome these operational bottlenecks, this paper proposes a novel dual-adaptive Dynamic RTO algorithm specifically engineered for heterogeneous IoT deployment scales. The proposed framework dynamically adjusts its parameter inspection cycle (N) based on instantaneous round-trip time (RTT) variance while simultaneously scaling its tuning coefficient (α) in response to real-time packet loss indicators. To rigorously validate the algorithmic resilience, performance evaluations were conducted within a highly volatile network environment governed by the Gilbert–Elliott dynamic loss model across multi-hop linear (1 × 6) and grid (3 × 6, 5 × 6) topologies. Experimental results demonstrate that the proposed Dynamic RTO consistently optimizes the throughput–latency trade-off, achieving a total communication time of 25.92 s in complex grids—outperforming CoCoA+ and FASOR by 14.28% and 8.89%, respectively. Furthermore, the proposed mechanism significantly curtails transmission overhead, restricting the cumulative retransmission footprint to just 59 counts under severe localized impairments, thereby establishing a scalable, resource-efficient, and empirically robust transport-layer solution for next-generation edge-computing infrastructures. Full article

Environmental monitoring in hazardous indoor zones conventionally relies on fixed-sensor networks or manual inspections, both of which suffer from spatial blind spots and increased human exposure risks. This paper addresses the problem of transforming sparse, mobile sensor measurements into spatially resolved risk assessments in GPS-denied environments. We propose a Hazard Index (HI) framework that normalizes environmental parameters against established safety thresholds into a unified, graduated risk metric with $O\left(N\right)$ computational complexity, where N is the number of monitored parameters. The framework is designed for multi-parameter hazard assessment; the present work validates the computational pipeline, spatial mapping methodology, and classification logic through single-parameter CO₂ detection ($N=1$) deployed on a LiDAR-guided robotic platform integrating an MQ-135 gas sensor interfaced via a NodeMCU ESP8266 microcontroller. Experimental validation across a 144 sq ft indoor area achieved a trajectory-following RMSE of 0.54 ft relative to planned waypoints using Hector SLAM without odometry, detected CO₂ concentrations ranging from 0.02% to 0.25%, and identified a hazardous region encompassing eight measurement points ($\mathrm{HI}\ge 1.0$) using a three-tier classification scheme (Safe, Elevated, Hazardous) within 225 s of active mapping. The framework provides a lightweight computational footprint suitable for real-time evaluation on an NVIDIA Jetson Nano. The proposed approach establishes a cost-effective, reproducible methodology for autonomous indoor environmental monitoring, with the modular architecture designed for future expansion to multi-parameter sensing. Full article

This study presents a gate-to-gate life cycle assessment (LCA) of an industrial sweet cherry sorting and packing facility in Greece, directly addressing environmental sustainability in agri-food supply chains through data-driven impact quantification and improvement pathways in post-harvest operations. The assessment focuses on a gate-to-gate system boundary encompassing all processes inside the cherry sorting and packing facility, while upstream cherry production and downstream waste management are modeled and reported separately to provide system-level context. Core-stage hotspots are then analyzed in detail in the Results section, highlighting the dominant role of electricity use compared with packaging materials. The functional unit is defined as 1 kg of packed, market-ready cherries at the factory gate. Primary data are obtained from high-resolution, batch-level measurements of mass flows, energy use, water consumption, packaging materials and waste streams over a full processing season, structured as virtual sensor outputs. These sensor-informed operational data are combined with secondary life cycle inventory information from established databases to quantify climate change impacts and identify environmental hotspots across materials, energy, water, and waste, thereby delivering a quantified picture of environmental performance in the post-harvest stage. The results show that corrugated cardboard and associated packaging components are among the main contributors within the facility-level, gate-to-gate system, while the Core stage accounts for 28.43% of total GWP100. Upstream cherry production dominates the overall Upstream–Core–Downstream climate footprint with 70.61% of total impacts. Moreover, practical mitigation scenarios are modeled, including packaging optimization, partial substitution of grid electricity with photovoltaic generation, and increased water recirculation. Ιn the combined mitigation scenario, where packaging optimization, low-carbon electricity and improved water management are implemented simultaneously, total GWP100 decreases from 114,207.32 to 92,500.27 kg CO₂-eq (−19.0%) relative to the baseline, providing actionable sustainability improvements for industry stakeholders and supporting Sustainable Development Goals (SDGs) related to climate action and resource efficiency. In addition, the proposed virtual sensor architecture and data workflow support continuous monitoring, eco-efficiency management and near-real-time LCA implementation in post-harvest agri-food systems, enabling operational sustainability. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

This study examines when green innovation contributes to pollution abatement by analyzing how financial globalization and different forms of innovation jointly shape ecological pressure across European Union (EU) countries over the period 1992–2021. The findings show that financial globalization consistently increases ecological pressure, with stronger effects at upper quantiles (0.8–0.9). Technological innovation exhibits a nonlinear pattern: general RD increases ecological pressure at lower quantiles (0.1–0.4), but this effect becomes insignificant and then negative at higher quantiles (0.7–0.9). In contrast, environmental innovation (EI) reduces CO₂ emissions at middle and upper quantiles (0.5–0.8), suggesting a stronger environmental contribution under medium-to-high ecological pressure conditions. Overall, the results demonstrate that the environmental impact of innovation depends on both the type of innovation and the prevailing level of ecological pressure. Specifically, general R&D and environmental innovation exhibit different environmental effects across lower and upper quantiles, suggesting that environmentally oriented innovation policies may be more effective under higher ecological pressure conditions. Full article

This paper presents an exploratory operational assessment of the carbon footprint of an LNG tanker using real operational data collected by a continuous emission monitoring system over a ten-month period of vessel operation. The analysis included carbon dioxide (CO₂) and methane (CH₄) emissions from the main engines and diesel generators, the calculation of CO₂-equivalent using the GWP₁₀₀ and GWP₂₀ global warming potential factors, and a comparison with a hypothetical heavy fuel oil (HFO) operating scenario. The methodology is based on a Tier III approach, that is, on real operational data, which allows a more realistic assessment of emissions than approaches based on standard emission factors. The results show that CO₂ emissions make up the largest share of total emissions, but including methane emissions significantly increases the ship’s overall climate impact. Total methane slip was 3.62%, with diesel generators exhibiting higher slip than the main engines. When GWP₂₀ was applied, total emissions expressed as CO₂-equivalent were, in some periods, comparable to or higher than those estimated for the HFO scenario, despite lower direct CO₂ emissions. The emission distribution indicated that the main engines dominated CO₂ emissions, while methane emissions were more evenly distributed between the main engines and the auxiliary generators, with generators making a significant contribution to total CO₂-equivalent emissions due to their higher methane slip. The results confirm that any assessment of the climate performance of LNG-fueled operation must include methane emissions and should be based on real operational data; otherwise, the overall climate impact may be underestimated. Full article

A predictive gate-to-gate life cycle assessment (LCA) of plasma-assisted ammonia synthesis at TRL 4 is presented according to ISO 14040/44 standards. General plasma-assisted synthesis was evaluated through a mini-review‚ sensitivity analysis‚ and predictive LCA. The specific DBD needle-to-plate configuration LCA is performed using previously published experimental data. Two distinct scenarios were investigated. In the literature-based baseline scenario derived from sensitivity analysis, electricity consumption was 533 kWh/kg NH₃, giving a carbon footprint of 26.65–639.60 kg CO₂-eq/kg NH₃; electricity contributed 98.5% of total emissions, and impacts remained about 2.05 times higher than conventional Haber–Bosch. In contrast, the experimental DBD case study required 63,450 kWh/kg NH₃, showing reactor efficiency as the dominant driver of environmental performance. The BCS (≈1.39 kWh/kg NH₃) suggests that optimized plasma systems could potentially surpass conventional ammonia synthesis in energy efficiency. The environmental performance of plasma-assisted ammonia synthesis is affected by NH₃, NO_x, N₂O, and hydrogen emissions due to impacts on climate, air quality, water systems, and biodiversity. Future improvements may come from reactor and electrode optimization, catalyst integration, alternative plasma sources, and better process and heat integration, although deployment will likely depend on major efficiency gains and may be limited to niche decentralized applications. Full article

Laboratory-based certification cycles systematically underestimate real-world fuel consumption and CO₂ emissions. On-board diagnostics (OBD-II) telemetry offers a low-cost alternative, yet most published approaches rely on mass air flow (MAF) sensors absent from many modern vehicles. This study validates a speed-density air-mass estimation method on a naturally aspirated RON 95 gasoline passenger car (1368 cm³, Euro 6) across seven drive cycles recorded over three measurement days in northwestern Türkiye, covering 609.6 km of highway, urban, and mixed conditions. Instantaneous air mass flow was estimated from four standard OBD-II PIDs—manifold absolute pressure, engine speed, intake air temperature, and fuel trim corrections—using the ideal gas law applied to actual engine displacement. Results were validated against pump-measured fill-up volumes. The speed-density model achieved errors of −3.6% to +4.3% across individual segments (combined error: −0.5%), outperforming the vehicle’s onboard trip computer, which exhibited errors of −10.6% to +14.6%. Derived CO₂ intensities ranged from 125.0 to 166.4 g/km, with a combined average of 147.2 g/km (pump reference: 147.9 g/km). Urban driving produced approximately 15% higher specific emissions than highway driving. These results demonstrate that a physics-based speed-density model can achieve within ±5% trip-level accuracy across diverse real-world conditions without machine learning, bespoke calibration, or a physical MAF sensor. Full article

To address the global water crisis, desalination technologies contribute about 1% of the global freshwater supply. Membrane-based desalination technologies offer high performance, operational ease, cost-effectiveness and high scalability compared to conventional thermal desalination modes. Among all membrane-based technologies, reverse osmosis is prevailing globally. However, the high energy demand of the reverse osmosis process and fouling in case of hypersaline feed streams motivate the exploration of alternative technologies, i.e., pervaporation. Pervaporation desalination involves dense hydrophilic polymer membranes to deal with high salt streams at low cost, along with less fouling than a few other membrane processes, i.e., reverse osmosis and membrane distillation. Mass transport through pervaporation desalination membranes is well-explained by solution-diffusion theory involving a tri-stage transfer, i.e., sorption, diffusion and evaporation. Since the last few decades, a green approach in all domains has offered chemical products and processes with the least hazards and minimal waste production. Application of biodegradable materials like poly(lactic acid) in combination with suitable green solvents, e.g., ethyl lactate, methyl lactate, cyrene, dimethyl isosorbide and gamma valerolactone for pervaporation desalination would be a good roadmap to meet the sustainability criterion. Some intrinsic features of poly(lactic acid) that make it a ‘material of choice’ for pervaporation desalination include hydrophilicity imparted by the presence of polar ester groups, high salt rejection, biodegradability with simple mineralization products, i.e., H₂O and CO₂, sustainable production, low toxicity, low carbon footprint, ease of processing and versatility. Poly(lactic acid) undergoes four interrelated degradation mechanisms: hydrolytic degradation, biodegradation, thermal degradation and photodegradation. The concern for poly(lactic acid) based pervaporation desalination is increased hydrolytic cleavage of poly(lactic acid) at high temperatures, which requires some modifications, e.g., nanoenhancement, additions of crosslinkers, surface modifications, addition of other polymers to prepare blends and post-treatments. These modifying strategies result in an increased stability and better performance of poly(lactic acid) films. However, optimization of various parameters relevant to such modifications leaves room for further research. This review offers a critical analysis of the need for biodegradable polymers with special focus on poly(lactic acid) rather than their fossil fuel-based alternatives, the environmental and health effects of all these polymers, cost estimation and possible performance-efficient, green and eco-friendly solutions. Full article

Background: Container terminals are crucial nodes in global supply chains, but they also contribute significantly to environmental pollution. The analysis of sustainability in port logistics through carbon footprint offers crucial knowledge on how to reduce environmental impact in logistics. Methods: This systematic review uses a PRISMA-based research flow to extract key facts about energy consumption and greenhouse gas emissions, particularly CO₂, which are still prevalent in terminal operations and logistics. Results: The paper analyses strategies and technologies adopted to reduce the carbon footprint, such as efficient infrastructure, electrification, automation, digitalisation, and AI-powered port logistics. It highlights the potential of sustainable logistics solutions, such as real-time cargo tracking, intelligent robotics and data analytics, to make container terminals more eco-friendly. Conclusions: Beyond analysing sustainability assessment models for the ecological efficiency and operational performance of container terminals, this paper highlights the need for future applied research into how investments in sustainable practices, as demonstrated by the most successful Asian port examples, can further reduce container terminal environmental footprint. Full article

In this study, we aimed to assess the availability and environmental impact of a selection of widespread agro-industrial by-products in Europe and their potential to reduce the environmental impact of small ruminant diets by replacing conventional feed ingredients. Grape, olive, and tomato pomaces and spent grains of beer were considered. The carbon footprint of products (CFP) was used to quantify the environmental impact of agro-industrial by-products, according to the ISO 14067:2018 standard. The system boundary was defined as gate-to-gate, and 1 kg of dried by-product was chosen as the functional unit (FU). The system included the relevant stages of agro-industrial by-product production, from the process of drying agro-industrial by-products to the treatments carried out in the feed industry (e.g., milling, mixing, and pelleting). The CFPs of grape, olive, and tomato pomaces and spent beer grains were 0.26, 0.22, 0.31, and 0.21 kg CO₂ equivalents (CO₂e)/FU, respectively. Under the assumptions adopted in this scenario-based assessment, reusing grape, olive, tomato, and brewery industry by-products as partial replacements for conventional feed ingredients in sheep and goat diets reduced diet CFP by an average of 23%, with an estimated mitigation potential of 5.15 Mt CO₂e. These values should be interpreted as model-based estimates rather than as directly generalizable mitigation outcomes, because they depend on the selected system boundary, drying assumptions, emission factors, transport exclusion, and the practical feasibility of the dietary substitutions considered. Overall, the results suggest that the recovery of selected agro-industrial by-products may contribute to reducing the environmental impact of conventional feed ingredients when appropriate preservation, logistics, and diet formulation conditions are met. Full article

Community-based tourism (CBT) destinations are increasingly expected to align visitor experiences with climate responsibility, yet local managers often lack product-level carbon evidence that can guide practical route redesign and service decisions. This study addresses this aggregation-to-action gap by developing a route-level carbon footprint baseline for a CBT itinerary in Ban Boonjaem, Phrae Province, Thailand. Using an exploratory and applied case study design, the study treats one completed six-hour, non-overnight itinerary as the functional unit and applies a life-cycle-informed operational boundary covering transportation, food and beverage consumption, and solid waste generated during the route test. Primary activity data were collected from one organized route test involving 20 Thai domestic volunteer tourists and were matched with relevant emission factors to estimate total and per-tourist emissions. The tested itinerary generated 0.2234 tCO2e, equivalent to 223.4 kgCO2e in total and approximately 11.2 kgCO2e per tourist per trip. Transportation was the largest emission domain, accounting for 55.89% of total route emissions, followed by food and beverage consumption at 38.55%, while waste contributed 5.56%. Together, transportation and food and beverage represented 94.44% of measured emissions, indicating that the route’s carbon profile was shaped mainly by mobility arrangements and service provisioning rather than waste generation alone. The study contributes a transparent, route-specific operational baseline for low-carbon CBT management. The findings should be interpreted as case-specific decision-support evidence rather than as a destination-wide carbon inventory or statistically generalizable estimate. Full article