Search Results (3,591)

Cutting CO₂ emissions is crucial to face of climate change, and one of the most tried and true means of post-combustion CO₂ capture is by way of chemical absorption. In this work, the effect of titanium dioxide (TiO₂) nanoparticles in a 25 wt% potassium carbonate (K₂CO₃) solution on solvent regeneration is investigated. This research follows the previous work in which the effect of nanofluids was evaluated on CO₂ absorption. Desorption was studied at three different temperatures (343.15, 348.15 and 353.15 K), using the absorbent fluid with and without 0.06 wt% TiO₂ nanoparticles. The results indicate that the nanofluid enhanced the CO₂ release rates, also reducing energy consumption. The mass transfer was intensified by the presence of nanoparticles, which in turn increased CO₂ diffusivity and influenced the liquid boundary layer, resulting in an enhanced desorption rate, because of the higher diffusivity. These enhancements were achieved with negligible modifications to the fluid properties, i.e., viscosity. In summary, application of TiO₂-enhanced K₂CO₃ solutions is a practical approach to enhance CO₂ removal performance and reduce operating costs such that CO₂ capture is beginning to be environmentally and economically more competitive for the existing system retrofit. Full article

Photocatalysis is an emerging technology that harnesses light energy to facilitate chemical reactions. It has garnered considerable attention in the field of catalysis due to its promising applications in environmental remediation and sustainable energy generation. Recently, researchers have been exploring innovative techniques to improve the surface reactivity of ferroelectric materials for catalytic purposes, leveraging their distinct properties to enhance photocatalytic efficiency. With their switchable polarization and improved charge transport capabilities, ferroelectric materials show promise as effective photocatalysts for various reactions, including carbon dioxide (CO₂) reduction. Through a blend of experimental studies and theoretical modeling, researchers have shown that these materials can effectively convert CO₂ into valuable products, contributing to efforts to reduce greenhouse gas emissions and promote a cleaner environment. An artificial neural network (ANN) was employed to analyze parameter relationships and their impacts in this study, demonstrating its ability to manage training data errors and its applications in fields like speech and image recognition. This research also examined changes in charge separation, light absorption, and surface area related to variations in band gap and polarization, confirming prediction accuracy through linear regression analysis. Full article

The dataset presented in this manuscript consists of three distinct sets of data collected during a laboratory experiment aimed at quantifying the emissions of greenhouse gases (GHGs), specifically methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O). The experiment was conducted in three phases, each initiated at different times. The first phase began on 6 June 2022, using a biocover composed of 60% fine-fraction waste, 20% clay soil, and 20% stabilized compost. The second phase commenced on 26 August 2022, with two biocover variants: one composed of 50% fine-fraction waste and 50% clay soil, and the other consisting of 40% fine-fraction waste, 40% clay soil, and 20% shredded paper. The final phase started on 27 October 2022, introducing two biocovers: one containing 25% dried algae, 25% fine-fraction waste, 25% gravel (0–20 mm), and 25% ash, and the other composed of 40% fine-fraction waste, 40% dried algae, and 20% chernozem. Emission assessments were conducted three weeks after the biocover installation to allow for settling and stabilization, followed by weekly measurements two to three days before irrigation with 250 mL of water to simulate field conditions. GHG emission quantification was carried out using the Cavity Ring-Down Spectroscopy gas measurement device, Picarro G2508. This dataset offers substantial scientific value for advancing biocover technologies aimed at reducing GHG emissions in landfill environments, particularly for mitigating methane emissions. In addition to initial experimental use, the dataset offers a wide range of possibilities for reuse, including modeling landfill gas emissions, validating gas flow measurement methods, developing machine learning models, and performing meta-analyses. Its detailed structure facilitates multi-faceted environmental research and supports optimization of landfill management. Full article

In the present investigation, carbon dioxide (CO₂), carbon monoxide (CO), hydrocarbons (HC), nitric oxides (NO_X), particulate matter (PM), and fuel consumption were measured in a compression ignition internal combustion engine on a road route cycle in Quito, Ecuador. We used premium diesel and a mixture of diesel and cerium oxide at a concentration of 250 ppm. This research aimed to investigate the impact of cerium oxide on fossil fuels in terms of CO₂, CO, HC, NOx, PM, and fuel consumption. Five repetitions were performed for each fuel, and the results obtained were statistically analyzed using control charts. The experimental results showed a 27.1% reduction in PM, a 24.9% increase in NOx, and a 24.2% increase in HC, along with a 1% decrease in fuel consumption compared to the premium diesel case. We observed that the reduction in PM was due to the catalytic action of CeO₂, which enhances carbon oxidation. On the other hand, the increase in NOx was related to the higher temperature in the combustion chamber resulting from the improved thermal efficiency of the engine. This study provides guidelines for controlling air pollutants originating from vehicle emissions in high-altitude (over 2000 masl) road operations using cerium oxide as an additive. Full article

It is critical to provide proper environmental conditions in poultry houses to maintain birds’ health, boost productivity, and improve the overall economic viability of the poultry industry. Among the myriad of environmental elements, indoor air quality has been a determining factor that directly affects poultry well-being. Elevated concentrations of harmful gases—in particular Carbon Dioxide ($C{O}_2$), Methane ($C{H}_4$), and Ammonia ($N{H}_3$)—decomposition products of poultry litter, feed wastage, and biological processes have draconian effects on bird health, feed efficiency, the growth rate, reproduction efficiency, and mortality rate. Despite their importance, traditional air quality monitoring systems are often operated manually, labor intensive, and cannot detect sudden environmental changes due to the lack of real-time sensing. To overcome these limitations, this paper presents an interdisciplinary approach combining cloud computing, Artificial Intelligence (AI), and Internet of Things (IoT) technologies to measure real-time poultry gas concentrations. Real-time sensor feeds are transmitted to a cloud-based platform, which stores, displays, and processes the data. Furthermore, a machine learning (ML) model was trained using historical sensory data to predict the next-day gas emission levels. A web-based platform has been developed to enable convenient user interaction and display the gas sensory readings on an interactive dashboard. Also, the developed system triggers automatic alerts when gas levels cross safe environmental thresholds. Experimental results of $C{O}_2$ concentrations showed a significant diurnal trend, peaking in the afternoon, followed by the evening, and reaching their lowest levels in the morning. In particular, $C{O}_2$ concentrations peaked at approximately 570 ppm during the afternoon, a value that was significantly elevated (p < 0.001) compared to those recorded in the evening (~560 ppm) and morning (~555 ppm). This finding indicates a distinct diurnal pattern in $C{O}_2$ accumulation, with peak concentrations occurring during the warmer afternoon hours. Full article

In the construction sector, cement and concrete are among the most widely utilized manufactured materials, yet their environmental impact remains a significant concern. The concrete industry is a major contributor to carbon dioxide emissions, accounting for over 8% of global greenhouse gas emissions annually. Several reports have estimated that between 1930 and 2013, a total of 4.5 gigatons of carbon was sequestered through the carbonation of cement-based materials. This process offset approximately 43% of the carbon dioxide (CO₂) emissions resulting from cement production during the same period, excluding emissions related to fossil fuel consumption in the manufacturing process. It is well established that producing one ton of cement results in approximately 0.60–0.98 tons of CO₂ emissions, coupled with substantial energy consumption. To mitigate these environmental effects, developing low-carbon or cement-free binders has become crucial. Alkali-activated binders (AABs), derived from industrial by-products or agricultural waste materials and activated with a low-molarity or one-part activator, are increasingly recommended as sustainable alternatives to reduce greenhouse gas emissions in the cement industry and minimize the consumption of natural resources. The production of alkali-activated concrete (AAC) involves several critical factors that significantly influence its mix design, fresh properties, and compressive strength (CS) performance. This study aims to provide a comprehensive review of the key factors affecting AAC’s mix design, workability, and CS characteristics. Firstly, the study discusses various methods employed for AAC mix design and the factors influencing these designs. Secondly, it examines the impact of binder type, source, chemical, mineralogical, and physical properties, as well as alkaline activator solutions, water content, and fillers on AAC’s workability, setting times, and strength development. Additionally, the study explores the correlation matrix and predictive performance models for fresh and strength properties. Lastly, the relationship between workability and CS is extensively analyzed. The review concludes by highlighting the existing challenges and prospects of AACs as sustainable construction materials to replace traditional cement and reduce carbon emissions. Full article

This research evaluates the carbon dioxide emission-reduction efficiency of new energy vehicles (NEVs) in China from 2018 to 2023 by applying a three-stage super-SBM data envelopment analysis (DEA) model that incorporates undesirable outputs. This model offers significant advantages over traditional DEA models, as it effectively disentangles the influences of external environmental factors and stochastic noise, thereby providing a more accurate and robust assessment of true efficiency. Its super-efficiency characteristic also allows for effective ranking of all decision-making units (DMUs) on the efficiency frontier. The empirical findings reveal several key insights. (1) The NEV industry’s carbon-reduction efficiency in China between 2018 and 2023 displayed an upward trend accompanied by pronounced fluctuations. Its mean super-efficiency score was 0.353, indicating substantial scope for improvements in scale efficiency. (2) Significant interprovincial disparities in efficiency appear. Unbalanced coordination between production and consumption in provinces such as Shaanxi, Beijing, and Liaoning has produced correspondingly high or low efficiency values. (3) Although accelerated urbanization has reduced the capital and labor inputs required by the NEV industry and has raised energy consumption, the net effect enhances carbon-reduction efficiency. Household consumption levels and technological advancement exerts divergent effects on efficiency. The former negatively relates to efficiency, whereas the latter is positively associated. Full article

As an important part of global carbon neutrality strategies, carbon dioxide (CO₂) capture, utilization, and storage technologies have emerged as critical solutions for reducing carbon emissions. However, conventional CO₂ applications, including food preservation, industrial synthesis, and enhanced oil recovery, face inherent limitations such as suboptimal gas–liquid mass transfer efficiency and inadequate long-term stability. Recent advancements in CO₂ micro-nanobubbles (CO₂ MNBs) have demonstrated remarkable potential across multidisciplinary domains, owing to their distinctive physicochemical characteristics encompassing elevated internal pressure, augmented specific surface area, exceptional stability, etc. In this review, we try to comprehensively explore the unique physicochemical properties of CO₂ MNBs and their emerging applications, including industrial, agricultural, environmental, and energy fields. Furthermore, we provide a prospective analysis of how these minuscule bubbles can emerge as pivotal in future technological innovations. We also offer novel insights and directions for research and applications across related fields. Finally, we engage in predicting their future development trends as a promising technological pathway for advancing carbon neutrality objectives. Full article

Cement production is a carbon-intensive process that contributes significantly to global greenhouse gas emissions. Approximately 50–60% of these emissions result from limestone calcination, while 30–40% result from fossil fuel combustion in kilns. This study presents a multi-objective optimization (MOO) framework that integrates raw mix design and alternative fuel blending to simultaneously reduce production costs and carbon dioxide (CO₂) emissions while maintaining clinker quality. A hybrid Genetic Algorithm–Linear Programming (GA-LP) model was developed to navigate the balance between economic and environmental objectives under stringent chemical and operational constraints. The approach models the impact of raw materials and fuel ash on critical clinker quality indices: the Lime Saturation Factor (LSF), Silica Modulus (SM), and Alumina Modulus (AM). It incorporates practical constraints such as maximum substitution rates and specific fuel compositions. A case study inspired by a medium-sized African cement plant demonstrates the utility of the model. The results reveal a Pareto front of optimal solutions, highlighting that a 20% reduction in CO₂ emissions from 928 to 740 kg/ton clinker is achievable with only a 24% cost increase. Optimal strategies include 10% fly ash and 30–50% alternative fuels, such as biomass, tire-derived fuel (TDF), and dynamic raw mix adjustments based on fuel ash contributions. Sensitivity analysis further illustrates how biomass cost and LSF targets affect clinker performance, emissions, and fuel shares. The GA-LP hybrid model is validated through process simulation and benchmarked against African case studies. Overall, the findings provide cement producers and policymakers with a robust decision-support tool to evaluate and adopt sustainable production strategies aligned with net-zero targets and emerging carbon regulations. Full article

Carbon dioxide capture, utilization, and storage for enhanced oil recovery (CCUS-EOR) represents an effective strategy for reducing CO₂ emissions while improving oil recovery efficiency. However, harsh environmental conditions during the process can induce a supercritical state in captured CO₂, which may undermine the structural integrity of tubular components through corrosion. This study systematically investigated the corrosion behaviors of two tubing steels (P110 and Super 13Cr) in 20 MPa supercritical CO₂ containing trace H₂S/O₂ impurities at 60–120 °C using weight loss tests and surface analysis. The results demonstrate that in water-unsaturated supercritical CO₂ with ≤500 ppmv H₂S, both steels exhibited low general corrosion rates (P110: 0.03 mm/y; S13Cr: 0.01 mm/y), with incomplete surface films partially covering grinding traces. However, S13Cr suffered pitting corrosion at >500 ppmv H₂S. Oxygen introduction triggered severe general/localized corrosion characterized by cracked, non-protective surface films. Reducing O₂ to 500 ppm yielded thin, continuous protective films, eliminating pitting. Temperature critically influenced S13Cr corrosion: decreasing from 120 °C to 60 °C increased the corrosion rates from 0.0031 mm/y to 0.08 mm/y due to enhanced water precipitation and impurity gas dissolution. These findings establish impurity thresholds to ensure acceptable corrosion performance. Full article

Background: The growing demand for fresh food leads to extensive use of cold chain logistics (CCL) that significantly contributes to greenhouse gas (GHG) emissions due to its dependence on energy-intensive transport refrigeration units (TRUs). Understanding the need to balance food preservation with environmental sustainability, this paper explores practical strategies for reducing GHG emissions in CCL, focusing on fresh food products. Methods: The quantitative and qualitative analyses are applied to analyse data from Transport for London and Transport Scotland. Emission data were assessed to evaluate the impact of alternative TRU technologies and route optimisation practices. Results: The findings reveal that electric and cryogenic TRUs, along with improved route planning and operational practices, can significantly reduce the emissions of carbon dioxide, nitrogen oxides and particulate matter. These results highlight the potential strategy for industry-led emission reductions without compromising food quality. Conclusions: This paper recommends the coordination of government policy and industry to support technological adaptation and infrastructure upgrades and to research into real-time monitoring and renewable energy integration in CCL systems. Full article

Carbon dioxide (CO₂) is the primary component contributing to anthropogenic greenhouse gas emissions, necessitating the adoption of effective mitigation strategies to promote environmental sustainability. Among the various carbon capture methodologies, chemical absorption is acknowledged as the most scalable solution for post-combustion applications. This investigation presents a thorough, comparative, and scenario-based evaluation of both singular and blended amine solvents for CO₂ capture within packed absorption–desorption columns. A validated rate-based model employing monoethanolamine (MEA) functions as the benchmark for executing process simulations. Three sequential scenarios are meticulously examined to switch the solvents and see the results. In the preliminary scenario, baseline performance is assessed by applying MEA to achieve the designated 73% removal target. Then the implementation of alternative solvents is examined—piperazine (PZ), a combination of methyldiethanolamine (MDEA) and PZ, and a blend of MEA and PZ—under uniform design parameters to ascertain their relative effectiveness and performance. In the second scenario, the design of the system is changed to reach a CO₂ removal efficiency for MEA of 90%, and then MEA is switched to other solvents. In the final scenario, critical design parameters, including column height and diameter, are adjusted for each solvent system that did not meet the 90% capture efficiency in Scenario 2 to achieve 90% CO₂ capture. A comprehensive sensitivity analysis is subsequently conducted on the adjusted systems to evaluate the influence of critical operational variables such as temperature, flue gas and solvent flow rates, and concentrations. Importantly, the MEA + PZ blend also demonstrated the lowest specific reboiler duty, as low as 4.28 MJ/kg CO₂, highlighting its superior energy efficiency compared to other solvents in the condition that the system in this study is pilot-scale, not commercial-scale, and due to this reason, the energy consumption of the system is slightly higher than the reported value for the commercial-scale systems. The results yield invaluable insights into the performance trade-offs between singular and blended amines, thereby facilitating the development of more efficient CO₂ capture systems that function within practical constraints. Full article

Gaseous pollutants and aerosols resulting from anthropic activities and natural phenomena require adequate source apportionment methodologies to be fully assessed. Furthermore, it is crucial to differentiate between fresh anthropogenic emissions and the atmospheric background. The proximity method based on the O₃/NO_x (ozone to nitrogen oxides) ratio has been used at the Lamezia Terme (code: LMT) World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) regional station in Italy to determine the variability of CO (carbon monoxide), CO₂ (carbon dioxide), CH₄ (methane), SO₂ (sulfur dioxide), and eBC (equivalent black carbon), thus allowing the differentiation between local and remote sources of emission. Prior to this work, all O₃/NO_x ratios lower than 10 were grouped under the LOC (local) proximity category, thus including very low ratios (≤1), which are generally attributed by the literature to “urban” air masses, particularly enriched in anthropogenic emissions. This study, aimed at nine continuous years of measurements (2015–2023), introduces the URB category in the assessment of CO, CO₂, CH₄, SO₂, and eBC variability at the LMT site, highlighting patterns and peaks in concentrations that were previously neglected. The daily cycle, which is locally influenced by wind circulation and Planetary Boundary Layer (PBL) dynamics, is particularly susceptible to urban-scale emissions and its analysis has allowed the highlighting of notable peaks in concentrations that were previously neglected. Correlations with wind corridors and speeds indicate that most evaluated parameters are linked to northeastern winds at LMT and wind speeds under 5.5 m/s. Weekly cycle analyses, i.e., differences between weekdays (MON-FRI) and weekends (SAT-SUN), have also highlighted tendencies driven by seasonality and wind corridors. The results highlight the potential of the URB category as a tool necessary to access a given area’s anthropogenic output and its impact on air quality and the environment. 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

The revitalization of post-industrial areas has emerged as a critical strategy for sustainable urban development, achieving a balance between economic, social, and environmental priorities. This study assesses the transformative capacity of revitalization strategies by conducting a comprehensive case analysis of “Radex Park Marywilska” in Warsaw, Poland. The analysis quantifies the benefits of revitalization in comparison to demolition and new construction methodologies. An examination of the revitalization initiative demonstrates that it yielded a total of PLN 41.15 million in benefits, with PLN 28.13 million attributed to direct cost savings and another PLN 13.02 million resulting from environmental improvements. In practical terms, this equates to a return of PLN 1.93 for every PLN 1 invested—a notably efficient outcome. The project transformed four industrial buildings, significantly increasing usable space in some (e.g., Building L1 by 345% and K1 by 21.6%) while slightly reducing it in others (B1 by 4.7% and I1 by 10.5%). From an environmental impact perspective, the success was staggering: 48,217 tons of carbon dioxide emissions were prevented, and 72,315 tons of building waste were diverted from landfills. To these figures, the study further adds a return in economic activity, the generation of new jobs, and improvement in local infrastructure. The retrofitting of historical buildings to contemporary standards has encountered numerous challenges; nonetheless, the implementation of circular economy principles has succeeded in negating such challenges. Generally, the results show economic, environmental, and social benefits of revitalization projects compared to new, greenfield projects. The case study provides valuable lessons to policymakers and urban planners, rendering adaptive reuse a fundamental approach in achieving sustainable urban development. Full article