Search Results (125)

Global warming is attributed to anthropogenic emissions of CO₂ and the contribution from the transport sector is significant. Estimating on-road vehicle CO₂ emission factors is essential for guiding carbon-reduction efforts in transportation. In order to accurately calculate carbon emission factors from vehicles, this study built a multi-scenario model for open, semi-enclosed, and enclosed road environments based on Fick’s second law and the law of conservation of mass. During the model optimization phase, it was found that the model’s applicability domain effectively encompassed most urban roadway scenarios, making it suitable for estimating urban traffic CO₂ emissions. The spatiotemporal heterogeneity analysis of field measurements indicated that this method can effectively distinguish variations in CO₂ emission factors across different road types and time periods. The method proposed in this study offers an effective solution for the real-time monitoring of large-scale on-road vehicle carbon emissions. Full article

Over 15 billion tonnes year⁻¹ of biomass is used globally, yet 14% is downcycled for energy, forfeiting billions in potential revenue for higher-value products. Robust metrics that couple cascading use with cradle-to-gate greenhouse gas (GHG) emissions and economic value are essential for identifying superior biomass pathways. The aim of this review is to systematically map biomass utilization indicators published between 2010 and 2025; compare their treatment regarding circularity, climate, and economic value; and introduce the enhanced Bioresource Utilization Index (eBUI). A PRISMA-aligned search of Scopus and Web of Science yielded 80,808 records, of which 33 met the eligibility criteria. Each indicator was scored on cascading, data intensity, and environmental and economic integration, as well as computational complexity and sector scope. The Material Circularity Indicator, Biomass Utilization Efficiency, the Biomass Utilization Factor, and legacy BUI satisfied no more than two criteria simultaneously, and none directly linked mass flows to both GHG emissions and net revenue. The eBUI concept integrates mass balance, lifecycle carbon intensity, and value coefficients into a single 0–1 score. An open-access calculator and data quality checklist accompany the metric, enabling policymakers and industry to prioritize biomass pathways that are circular, climate-smart, and economically attractive. Full article

Blast furnace (BF) ironmaking remains one of the most efficient countercurrent processes; however, achieving further CO₂ emission reductions through conventional methods is increasingly challenging. Currently, BF ironmaking emits approximately 2.33 tonnes of fossil-derived CO₂ per tonne of crude steel cast. Integrating briquettes composed of biochar and in-plant fines into the BF process offers a promising short- to medium-term strategy for lowering emissions. This approach enables efficient recycling of fine residues and the substitution of fossil reductants with bio-based alternatives, thereby improving productivity while reducing energy and carbon intensity. This study investigates the reduction behavior of (i) biochar mixed with pellet fines, (ii) various in-plant residues individually, and (iii) briquettes composed of biochar and in-plant fines. The reduction rate of biochar–pellet fine mixtures was found to depend on biochar type, with pyrolyzed pine sawdust exhibiting the highest reactivity, and pyrolyzed contorta wood chips the lowest. A correlation between reduction rate and the alkali index of each char was established, although other factors such as char origin and physical properties also influenced reactivity. The effect of biochar addition (0, 5, and 10 wt.%) on the reduction of steelmaking residues was also studied. In general, biochar enhanced the reduction degree and shifted the reaction onset to lower temperatures. The produced briquettes maintained high mechanical integrity during and after reduction, regardless of biochar origin. Thermogravimetric and XRD analyses revealed that mass loss initiates with the dehydroxylation of cement phases and release of volatiles, followed by carbonate decomposition and reduction of higher oxides above 500 °C. At temperatures ≥ 850 °C, the remaining iron oxides were further reduced to metallic iron. Full article

Despite increasing evidence that cigarette smoke is a significant source of indoor fine particulate matter (PM_2.5), quantitative emission factors (EFs) for PM_2.5 and its toxic chemical composition in mainstream (MS) and sidestream (SS) smoke are still not well defined. In this study, we employed a custom-designed chamber to separately collect MS (intermittent puff) and SS (continuous sampling) smoke from eleven cigarette models, representing six brands and two product types, under controlled conditions. PM_2.5 was collected on quartz-fiber filters and analyzed for carbon fractions (using the thermal–optical IMPROVE-A protocol), nine water-soluble inorganic ions (by ion chromatography), and twelve trace elements (via ICP-MS). SS smoke exhibited significantly higher mass fractions of total analyzed species (84.7% vs. 65.9%), carbon components (50.6% vs. 44.2%), water-soluble ions (17.1% vs. 13.7%), and elements (17.0% vs. 7.0%) compared to MS smoke. MS smoke is characterized by a high proportion of pyrolytic organic carbon fractions (OC₁–OC₃) and specific elements such as vanadium (V) and arsenic (As), while SS smoke shows elevated levels of elemental carbon (EC1), water-soluble ions (NH₄⁺, NO₃⁻), and certain elements like zinc (Zn) and cadmium (Cd). The toxicity-weighted distribution indicates that MS smoke primarily induces membrane disruption and pulmonary inflammation through semi-volatile organics and elements, whereas SS smoke enhances oxidative stress and cardiopulmonary impairment via EC-mediated reactions and secondary aerosol formation. The mean OC/EC ratio of 132.4 in SS smoke is an order of magnitude higher than values reported for biomass or fossil-fuel combustion, indicative of extensive incomplete combustion unique to cigarettes and suggesting a high potential for oxidative stress generation. Emission factors (µg/g cigarette) revealed marked differences: MS delivered higher absolute EFs for PM_2.5 (422.1), OC (8.8), EC (5.0), Na⁺ (32.6), and V (29.2), while SS emitted greater proportions of NH₄⁺, NO₃⁻, Cl⁻, and carcinogenic metals (As, Cd, Zn). These findings provide quantitative source profiles suitable for receptor-oriented indoor source-apportionment models and offer toxicological evidence to support the prioritization of comprehensive smoke-free regulations. Full article

The results of strain-induced crystallization (SIC) studies on natural rubber compounds containing different amounts of carbon black and silica are reported. Two-dimensional wide-angle X-ray diffraction (2D WAXD) experiments were performed to quantify the degree of SIC at ambient and enlarged temperatures. The influence of temperature and filler system on the degree of crystallinity of natural rubber was investigated, since the estimated temperatures in truck tire treads are in the range 60–80 °C. Interestingly, the degree of crystallinity for silica-filled natural rubber compounds was commonly at least similar or higher compared to carbon black-filled compounds with identical filler mass fraction. In addition, it was demonstrated that the temperature dependence of natural rubber compounds containing silica and carbon black is also similar. In both cases the SIC disappeared slightly above 100 °C. Hence, it was concluded that the SIC behavior is most likely not the decisive factor for the different abrasion resistance of silica- and carbon black-reinforced natural rubber compounds for truck tire treads. This is an important insight considering the rising demand for sustainable rubber compounds for truck tire treads with low CO₂ emissions as well as reduced abrasion. Full article

The measurement and evaluation of the atmospheric background levels of greenhouse gases (GHGs) and aerosols are useful to determine long-term tendencies and variabilities, and pinpoint peaks attributable to anthropogenic emissions and exceptional natural emissions such as volcanoes. At the Lamezia Terme (code: LMT) World Meteorological Organization–Global Atmosphere Watch (WMO/GAW) observation site located in the south Italian region of Calabria, the “Proximity” methodology based on photochemical processes, i.e., the ratio of tropospheric ozone (O₃) to nitrogen oxides (NO_x) has been used to discriminate the local and remote atmospheric concentrations of GHGs. Local air masses are heavily affected by anthropogenic emissions while remote air masses are more representative of atmospheric background conditions. This study applies, to eight continuous years of measurements (2016–2023), the Proximity methodology to sulfur dioxide (SO₂) for the first time, and also extends it to equivalent black carbon (eBC) to assess whether the methodology can be applied to aerosols. The results indicate that SO₂ follows a peculiar pattern, with LOC (local) and BKG (background) levels being generally lower than their N–SRC (near source) and R–SRC (remote source), thus corroborating previous hypotheses on SO₂ variability at LMT by which the Aeolian Arc of volcanoes and maritime traffic could be responsible for these concentration levels. The anomalous behavior of SO₂ was assessed using the Proximity Progression Factor (PPF) introduced in this study, which provides a value representative of changes from local to background concentrations. This finding, combined with an evaluation of known sources on a regional scale, has been used to provide an estimate on the spatial resolution of proximity categories, which is one of the known limitations of this methodology. Furthermore, the results confirm the potential of using the Proximity methodology for aerosols, as eBC shows a pattern consistent with local sources of emissions, such as wildfires and other forms of biomass burning, being responsible for the observed peaks. Full article

Growing ozone (O₃) pollution in industrial cities urgently requires in-depth mechanistic research. This study utilized multi-year observational data from Datong City, China, from 2020 to 2024, integrating time trend diagnostics, correlation dynamics analysis, Environmental Protection Agency Positive Matrix Factorization 5.0 (EPA PMF 5.0) model simulations, and a grey prediction model (GM (1,1)) projection method to reveal the coupling mechanisms among O₃ precursors. Key breakthroughs include the following: (1) A ratio of volatile organic compounds (VOCs) to nitrogen oxides (NO_x) of 1.5 clearly distinguishes between NO_x-constrained (winter) and VOC-sensitive (summer) modes, a conclusion validated by the strong negative correlation between O₃ and NO_x (r = −0.80, p < 0.01) and the dominant role of NO titration. (2) Aromatic compounds (toluene, xylene) used as solvents in industrial emissions, despite accounting for only 7.9% of VOC mass, drove 37.1% of ozone formation potential (OFP), while petrochemical and paint production (accounting for 12.2% of VOC mass) contributed only 0.3% of OFP. (3) Quantitative analysis of OFP using PMF identified natural gas/fuel gas use and leakage (accounting for 34.9% of OFP) and solvent use (accounting for 37.1% of OFP) as key control targets. (4) The GM (1,1) model predicts that, despite a decrease in VOC concentrations (−15.7%) and an increase in NO_x concentrations (+2.4%), O₃ concentrations will rise to 169.7 μg m⁻³ by 2025 (an increase of 7.4% compared to 2024), indicating an improvement in photochemical efficiency. We have established an activity-oriented prioritization framework targeting high-OFP species from key sources. This provides a scientific basis for precise O₃ emission reductions consistent with China’s 15th Five-Year Plan for synergistic pollution/carbon governance. Full article

As cities densify, deep underground infrastructure construction such as mass rapid transit (MRT) systems increasingly demand smarter, digitalized, and more sustainable approaches. RC diaphragm walls, essential to these systems, present challenges due to complex rebar configurations, spatial constraints, and high material usage and waste, factors that contribute significantly to carbon emissions. This study presents an AI-assisted rebar optimization framework to improve constructability and reduce waste in MRT-related diaphragm wall construction. The framework integrates the BIM concept with a custom greedy hybrid Python-based metaheuristic algorithm based on the WOA, enabling optimization through special-length rebar allocation and strategic coupler placement. Unlike conventional approaches reliant on stock-length rebars and lap splicing, this approach incorporates constructability constraints and reinforcement continuity into the optimization process. Applied to a high-density MRT project in Singapore, it demonstrated reductions of 19.76% in rebar usage, 84.57% in cutting waste, 17.4% in carbon emissions, and 14.57% in construction cost. By aligning digital intelligence with practical construction requirements, the proposed framework supports smart city goals through resource-efficient practices, construction innovation, and urban infrastructure decarbonization. Full article

Photovoltaic thermal (PVT) hybrid systems offer a promising approach to maximizing solar energy utilization by combining electricity generation with thermal energy recovery. This study presents an experimental evaluation of a commercially available PVT panel, focusing on its thermal performance under varying inlet temperatures and flow rates. The work addresses a gap in the literature regarding the real-world behavior of integrated systems, particularly in residential settings where space constraints and energy efficiency are crucial. Experimental tests were conducted at three mass flow rates and five inlet water temperatures, demonstrating that lower inlet temperatures and higher flow rates consistently improve thermal efficiency. The best-performing condition was achieved at 0.012 kg/s and 10 °C. These findings deepen our understanding of the panel’s thermal behavior and confirm its suitability for practical applications. The experimental platform developed in this study also enables standardized PVT testing under controlled conditions, supporting consistent evaluation across different settings and contributing to global optimization efforts for hybrid solar technologies. Full article

While Battery Electric Vehicles (BEVs) offer environmental benefits by reducing carbon emissions during use, their range remains limited compared to conventionally fuelled vehicles. This paper focuses on identifying factors that directly influence BEV range and explores strategies to mitigate range anxiety among potential users. Specifically, it reviews the impact of battery cell characteristics and vehicle lightweighting. Using the WLTP Class 3B drive cycle, energy consumption and Depth of Discharge (DoD) were evaluated across various battery capacities. Multiple Lithium-Ion battery models were simulated to analyse discharge behaviour, while vehicle mass composition was examined to assess the effectiveness of lightweighting in extending driving range. A lower initial State of Charge (SoC) and a standard discharge rate were used to estimate the remaining range, highlighting an approximate gain of up to 6 km at lower DoD levels. This work aims to accurately demonstrate how battery technology and structural weight impact energy consumption and usable range in BEVs. Current modelling approaches often overlook the relationship between driver discomfort and battery performance metrics. The main contribution is to address the gap by integrating Li-ion discharge modelling with vehicle dynamics to estimate range and compare cell characteristics. The ultimate goal is to support cost-effective strategies for increasing BEV usability, aligning them more closely with conventional vehicle expectations and enhancing journey flexibility. Full article

This study investigates the implementation of new drying schedules for chickpeas, a significant pulse, incorporating a techno-economic analysis. The research also explores the reduction in anti-nutritional factors, such as trypsin inhibitors, through fluidized-bed drying with an air recycling system. The processing cost per unit mass of chickpeas is predicted to decrease with an increasing recycling ratio, from over AUD 1.32/kg of chickpeas with no recycling down to AUD 0.0885/kg of chickpeas at a ratio of 99%. With no air recycling, the lowest inlet air temperature (40 °C) gives the lowest cost, but near the optimum recycling ratio, the highest inlet air temperature (80 °C) is best. This pattern is followed when considering equivalent carbon dioxide emissions, with the lowest emissions (over 0.259 kg CO₂ (kg chickpeas)⁻¹) corresponding to high recycling ratios and high inlet air temperatures. The use of air recycling should cause no significant challenges when implementing a drying schedule for trypsin inhibitor reduction in chickpeas. Full article

This study aims to evaluate and optimize the influences of operational factors, including the engine’s rotational speed, methane mass, diesel mass, and the duration of injected diesel fuel on the methane/diesel reactivity-controlled compression ignition (RCCI) light-duty engine’s performance and emissions by executing the Nondominated Sorting Genetic Algorithm-II (NSGAII). The optimizations aimed to minimize peak firing pressure simultaneously, decrease indicated specific fuel consumption, and reduce tailpipe emissions. It is found that the excess air ratios of (2.22 to 2.37) are the range of feasible results of the RCCI engine, and the power should be less than 0.89 from the maximum design load of the diesel engine when it works without it after treatment. The methane/diesel RCCI engine achieves an indicative thermal efficiency of 51%. The Pareto results from the NSGA algorithm occur on multiple fronts, and there is a tradeoff between power and nitrogen oxide (NOx) in addition to unburned hydrocarbons (UHCs) and carbon monoxide (CO) with NOx emissions. Moreover, EURO IV emissions regulations can occur when using a start of injection (SOI) of −35 CA, a diesel mass of 1.82 mg, a methane mass of 9.74 mg, a diesel injection duration of 2.63 CA, and a rotational speed of 2540 rpm. This accomplished a reduction in indicative specific fuel consumption by 27.8%, higher indicative efficiency by 21.9%, and emissions reductions compared to a conventional diesel engine. Full article

Determining the electrochemical, thermal, and mass transfer dynamics embedded in an alkaline electrolysis (AEL) system provides important information about the application of ancillary services provided by hydrogen energy for the elimination of carbon emissions. Therefore, there is an urgent need to develop methodologies for evaluating key parameters, such as overvoltage coefficients, stack transfer capacity, diaphragm thickness, and permeability, to accurately capture the system’s fluctuating characteristics. However, limited by the lack of superior sensor technology, some significant variables cannot be measured directly. In this context, comprehensively accurate parameters of an estimation strategy offer a novel alternative to characterize the system’s corresponding intrinsic nature. This paper was motivated by this arduous challenge and aims to address the large branching factors with irregular properties. Specifically, the associated mathematical models reflecting the transient operating parameters in terms of electrochemical, heat transfer, and mass transfer are first established. Subsequently, k-means clustering analysis is conducted to deduce the similarity of distribution of the measured variables, which can function as proxies of the separator to distinguish the working status. Furthermore, online reinforcement learning (RL), renowned for its ability to operate without extensive predefined datasets, is employed to conduct dynamic parameter estimation, thereby approximating the robust nonlinear and stochastic behaviors within AEL components. Finally, the experimental results verify that the proposed model achieves significant improvements in estimation errors compared to existing parameter estimation methods (such as EKF and UKF). The enhancements are 76.7%, 54.96%, 51.84%, and 31% in terms of RMSE, NRMSE, PCC, and MPE, respectively. Full article

This study explores the co-pelletization of sludge with landfill-mined plastic waste as a method to create high-energy refuse-derived fuel (RDF), addressing both plastic and sludge waste streams. Key variables used in RDF pelletization included sludge-to-plastic mixing ratios (50:50, 75:25, and 100:0 wt%), mold temperatures (100 °C and 120 °C), and compression pressures (60–80 MPa). Results showed that the characteristics of pellets improved considerably as the mass percentage of plastic waste increased. The 75% sludge mixture produced pellets with high compressive strength (15.9–16.4 MPa), indicating rigid and ductile properties, and achieved a calorific value of up to 33.4 MJ/kg. Mercury levels of the RDF (0.02–0.04 mg/MJ) met solid recovered fuel standards. However, the elevated chlorine content (>3 wt%_db) highlighted the necessity of removing PVC from the plastic waste before pelletization. Carbon emission factors for the pellets (23–25 kg CO₂/GJ) were comparable to commercial RDFs and notably lower than coal, demonstrating their potential as a sustainable alternative fuel source. An assessment of the entire production and utilization chain, including sludge drying, plastic sorting, pelletization, and combustion, revealed that co-pelletization reduces greenhouse gas emissions by more than 24.3% compared to current practices. Full article

This study categorizes 45 cities into four types based on population dynamics using census data (2000–2020). Methods such as ArcGIS10.8, carbon emission estimation, LISA clustering, and association analysis are employed to explore the spatiotemporal distribution of shrinking cities and carbon emissions. This study analyzes carbon emission patterns and influencing factors for the four city types and provides policy recommendations. The findings are as follows: (1) Lasting-growth cities show a “two-end mass, middle-point” pattern, while stage-growth and stage-shrinking cities are “point” distributed. Lasting-shrinking cities are mainly distributed in the middle and lower reaches of the Yangtze River. (2) Total carbon emissions are rising, showing two clusters of high-value areas. Carbon emission intensity is falling quickly, being higher in the west and lower in the east. (3) Lasting-growth cities have the fastest direct carbon emission growth rate, stage-growth cities have the fastest energy-related indirect emission growth rate, and cities undergoing population increase have the fastest growth rate for other indirect carbon emissions. In terms of carbon reduction, lasting-growth cities perform best, whereas stage-growth cities perform worst. (4) Regional GDP, per capita regional GDP, urban construction area, and hospital beds per 10,000 people promote carbon emission reduction in the four city types, while a higher number of industrial enterprises inhibits it. Birth rate, aging rate, and mortality rate have no significant impact. This study addresses the gaps in previous research on shrinking cities and carbon emission reduction by considering the dynamic nature of shrinking processes and analyzing carbon emission patterns. Full article