Anthropogenic greenhouse gases (GHGs) and emissions from maritime transport represent a significant challenge for atmospheric monitoring and control. The Istanbul Strait, characterized by its narrow, winding geography and high traffic density, presents a unique chokepoint where these emissions directly impact local air quality. This study proposes a gas-focused integrated framework that combines Sentinel-5 Precursor (Sentinel-5P) TROPOspheric Monitoring Instrument (TROPOMI) satellite observations with Automatic Identification System (AIS) data to analyze atmospheric trace pollutant time series in the Istanbul Strait during 2025. A bottom-up emission methodology based on the IMO 4th GHG Study was employed, yielding annual gaseous pollutant totals of 213,678 tons of carbon dioxide (CO2), 5970 tons of nitrogen oxides (NOx), and 686 tons of sulfur oxides (SOx). Time-series and cross-correlation analyses demonstrated a quantifiable relationship between AIS-derived NOx estimates and TROPOMI NO2 tropospheric column densities (r = 0.76, p < 0.05, n = 12), validating the use of satellite sensors for marine atmospheric monitoring. A decision support system (DSS) proof of concept (PoC) was developed to evaluate emission control scenarios through speed optimization. The results indicate that implementing a 10% speed reduction strategy could reduce CO2 emissions by 18% (38,462 tons) and generate net economic savings of EUR 3.07 million under the European Union Emissions Trading System (EU ETS) carbon pricing framework. Furthermore, a scenario with a 20% speed reduction resulted in a 35% decrease in CO2 emissions. The findings underscore the potential of integrating satellite-based gas remote sensing with AIS data, thereby facilitating real-time atmospheric monitoring and strengthening emission control policy enforcement in maritime chokepoints.
This article aims to examine how the natural gas market evolved following the price shocks observed between 2020 and 2024, paying particular attention to market integration and the persistence of these shocks. The proposed analysis uses daily price data for the Title Transfer Facility (TTF), the main European benchmark traded on the Intercontinental Exchange and quoted in EUR/MWh, as well as Henry Hub (HH), the United States benchmark. These series are combined with a country panel on natural gas production, consumption, and gross domestic product for 59 economies, subject to data availability. The cointegration results show that TTF and HH prices moved together in 2019, but this relationship broke down in 2020 and did not return to its previous pattern in the following years. Granger causality tests point to a one-directional transmission from Henry Hub to Europe. Moreover, GARCH estimates indicate that TTF reacts almost twice as strongly to daily shocks as HH, while volatility remains persistent in both markets. Fixed-effects estimates place the TTF price elasticity of import spending close to 0.5, providing evidence consistent with a causal link between higher natural gas prices and higher domestic energy expenditure. Finally, the clustering analysis complements the econometric modeling by identifying four groups of countries defined by gas import dependency and gas intensity. This classification also offers implications for the global natural gas market since it points to the need for cluster-specific policy approaches rather than a single solution applied to every country.
Understanding how methane moves through nanoporous media is key to predicting performance in unconventional gas reservoirs. At these extremely small scales, pore sizes approach the molecular level, where classical flow assumptions begin to fail and multiple transport mechanisms can occur at the same time. In this work, a unified framework is developed to characterize methane flow regimes using a real-gas corrected Knudsen number. By combining pore size, pressure, and temperature within a single formulation, the approach captures how flow behavior evolves across realistic reservoir conditions. A unified flow regime map is used to characterize the gradual shift in transport behavior—from adsorption-dominated and diffusion-like mechanisms in ultra-tight pores, to transition and slip flow, and eventually to continuum (Darcy) flow in larger pores. The results show that pore size plays the dominant role in determining flow behavior, while pressure introduces a dynamic effect, particularly during reservoir depletion. Sensitivity analysis also highlights that flow regime classification depends not only on thermodynamic conditions but also on molecular-scale parameters such as methane diameter. Comparison with established models and experimental observations shows that the framework captures the expected increase in rarefaction effects at low pressures and small pore sizes. Overall, the results emphasize that gas transport in nanoporous systems is not governed by a single mechanism but evolves over time and across scales. The proposed framework offers a simple, physically grounded tool for identifying dominant transport mechanisms and supporting model selection, while also providing a foundation for more advanced descriptions of gas flow in unconventional reservoirs.
Carbon dioxide (CO2) enrichment using fuel combustion is widely applied in greenhouse production. However, its implications for air quality and occupational safety under real operating conditions remain insufficiently characterized. This study evaluates a propane-based CO2 enrichment system in an advanced greenhouse. The analysis integrates CO2 dynamics, combustion-derived pollutants, and occupational exposure. High-resolution monitoring at 5 min intervals was conducted in an enriched module and a control module over a five-month period. Two operational modes were assessed: continuous and diurnal-only enrichment. The system maintained CO2 concentrations within agronomic targets. Mean values reached 1200 ppm and 940 ppm for continuous and diurnal operation, respectively. However, significant CO2 losses were observed due to ventilation. The maximum enrichment efficiency, expressed as the Combustion Efficiency Index (CEI), was 2.67 × 10−3. Combustion-related pollutants (CO, NO, NO2, SO2, and O3) showed transient peaks during burner activation. However, concentrations remained below occupational exposure limits when evaluated using time-weighted averages. The incomplete combustion ratio (ICR) remained stable at approximately 1.9 × 10−3. This indicates predominantly complete combustion. These results provide field-based evidence on the performance and safety of propane-based CO2 enrichment systems. They also highlight the importance of continuous monitoring and improved CO2 retention strategies in semi-confined greenhouse environments.
Flaring is necessary to prevent pressure buildup in the unit. Due to hydrotreatment processes at the refinery, flare gas can contain significant amounts of hydrogen sulfide. Combusting this gas can result in environmental and health issues. One method to reduce hydrogen sulfide is to replace the water in the seal drum with an amine solution. Honeywell UniSIM® process simulation was used to calculate the hydrogen sulfide removal efficiency with 45 wt% MDEA solution. Results show that removal efficiency depends on amine loading and pool height. Removal efficiency of up to 72.5% was achieved with a hydrogen sulfide-to-amine molar loading of 0.2 (4:20 ratio) and a pool effective height of 2.5 m.
The integration of catalytic methane decomposition (CMD) with CO2 gasification (Reverse Boudouard Reaction) offers a promising chemical looping route for carbon-negative hydrogen and syngas production. This work systematically investigates the gasification reactivity of six carbon morphologies, CNTs, CNFs, activated carbon, graphite, graphene, and CMD-derived carbon, with and without Ni addition. First, activity tests and characterization (XRD, XPS, Raman) revealed that CMD-derived carbon outperformed all other benchmarks due to its highly amorphous nature (sp3/sp2 = 0.98), which provides a high density of reactive sites. Second, kinetic analysis showed that the incorporation of 5 wt% Ni on CMD carbon reduced the activation energy (Ea) from 435.3 kJ mol−1 to 114.6 kJ/mol, the lowest among all samples. This 74% reduction confirms that structural defects in CMD carbon act as anchoring sites for Ni, facilitating a strong metal–support interaction (MSI) that promotes CO2 activation. Third, an investigation into structural synergy revealed that higher Ni loadings (>5 wt%) increased the activation energy (up to 171.2 kJ mol−1). This trend is attributed to Ni agglomeration and weakened MSI, which reduces the active catalytic interface. These findings demonstrate that the efficiency of CO2 valorization is highly sensitive to carbon morphology, providing a clear optimization strategy for integrated chemical looping methane-to-syngas energy cycles.
Electronic nose platforms based on metal-oxide (MOX) sensors offer potential for low-power gas classification under dynamic operating conditions. This study evaluates a BME688-based digital nose configured with a temperature-modulated heater profile (HP-354) and reduced duty cycle (RDC-5-10) for binary ethylene presence classification in fruit headspace. Seven climacteric fruit types were sealed in bags to allow natural ethylene accumulation and were sampled across multiple sessions over a two-week period. A structured alternating protocol between fruit headspace (Class A) and neutral air (Class B) generated 21 ethylene sessions and 23 neutral-air sessions, comprising 38,882 individual thermal scan cycles (~10 s per cycle). Each full heater cycle was treated as a training instance within BME AI-Studio. A supervised neural-network classifier trained on 70% of cycle-level data achieved 92.9% overall accuracy with a macro F1 score of 91.9% on validation data. Results demonstrate that temperature-modulated MOX signatures enable robust discrimination of biologically generated ethylene from baseline air under realistic headspace variability. This study demonstrated classification feasibility under naturally accumulated fruit emissions while highlighting the need for future concentration-resolved calibration studies.
As the global imperative for climate neutrality intensifies, hydrogen (H2) from fossil fuels remains central to decarbonizing hard-to-abate sectors. Conventional production via steam methane reforming (SMR), however, is carbon-intensive and, even with carbon capture and storage (CCS), incurs energy penalties and long-term storage constraints. This review develops a harmonized well-to-gate, market-oriented framework to evaluate methane pyrolysis (MP) relative to SMR and autothermal reforming (ATR), with or without CCS, moving beyond reactor-focused assessments toward system-level commercialization analysis. MP decomposes methane into hydrogen and solid carbon, avoiding direct CO2 formation and the need for CCS infrastructure. Integrating with the reverse water–gas shift (RWGS) reaction enables flexible syngas production with adjustable H2:CO ratios for methanol and chemical synthesis. A central finding is the dominant role of the “carbon lever”: MP generates approximately 3 kg of solid carbon per kg of H2, making the carbon market’s absorptive capacity the primary scalability constraint. While carbon monetization can reduce levelized hydrogen costs, large-scale deployment would rapidly saturate existing carbon black and specialty carbon markets. Techno-economic evidence indicates that carbon prices above $500/ton are required to achieve parity with gray hydrogen, whereas $150–200/ton enables competitiveness with blue hydrogen. Lifecycle assessments further show that climate superiority over SMR or ATR with CCS requires upstream methane leakage below 0.5% and very low-carbon electricity. Commercial readiness varies, with plasma MP at TRL 8–9 and thermal, catalytic, and molten-media pathways remaining at the pilot or demonstration stage. Parametric decision-space analysis under harmonized boundary assumptions shows that MP is not a universal substitute for reforming but a conditional pathway competitive only under aligned conditions of low-leakage gas supply, low-carbon electricity, credible carbon monetization, and supportive policy incentives. The review concludes with a roadmap that highlights standardized carbon certification, end-of-life accounting, and long-duration operational data as priorities for commercialization.
Methane emissions from end-use installations in residential natural gas systems remain poorly quantified, despite their importance to both safety and climate policies worldwide. While distribution networks and appliances have received research attention, interior piping between the meter and appliances represents a critical knowledge gap. To address this gap, a systematic survey of 473 residential systems in Saarlouis, Germany, was conducted using standardized pressure decay tests (DVGW G 600). Measurements were performed during the installation of gas regulators necessitated by a grid pressure increase from 23 mbar to 55 mbar above ambient. This provided a unique opportunity to assess whole-system leakage under controlled conditions without installation modifications. Leak rates were standardized to reference pressure and converted to methane emissions using measured gas composition, using a linear pressure scaling as a provisional approximation valid for the small pressure differences in the applied test conditions. A total of 411 (86.9%) installations showed no detectable leak rate (LDL: 0.2 Lh−1). However, seven systems (1.5%) exceeded 1 Lh−1, and one surpassed the unacceptable threshold of 5 Lh−1. Mean emissions across all systems were 0.067 [0.041, 0.098] gh−1, with smaller installations showing higher volume-normalized rates. Critically, fewer than 1.48% of systems contributed more than 46% of total emissions, demonstrating a strongly skewed, heavy-tailed distribution. Scaled nationally using Monte Carlo methods accounting for sampling uncertainty and skewed distributions, residential interior piping contributes 12.30 [8.11, 18.55] Ggyear−1 to Germany’s methane emissions. These results emphasize the need to include residential leak rates in emission inventories and highlight the efficiency potential of targeted mitigation strategies focused on high-emitting installations under evolving EU methane regulations.
This study presents experimental adsorption–desorption data of CH4 and CO2 on shale samples from the Cesar-Ranchería Basin, Colombia, a region with limited characterization of gas–rock interactions under reservoir-relevant conditions. The work addresses the behavior of early-mature shales, contributing to the understanding of gas retention mechanisms in tropical basins. Adsorption–desorption isotherms were obtained using a high-pressure manometric system at 50 °C and 80 °C, with pressures up to 3 MPa, and were fitted using the Langmuir model. The results show a consistently higher adsorption capacity for CO2 compared to CH4 across all conditions, along with a clear decrease in adsorption capacity with increasing temperature, confirming the exothermic nature of the process. No hysteresis was observed, indicating fully reversible adsorption dominated by physisorption mechanisms. The integration of adsorption data with mineralogical, BET surface area, and geochemical characterization provides insight into the factors controlling gas retention in early-mature shales. The results highlight the combined influence of surface area, organic matter, and clay mineralogy on adsorption performance, and demonstrate that CO2 exhibits a stronger affinity for the shale matrix under all tested conditions. These findings contribute experimental evidence of gas adsorption behavior in an underexplored basin and provide a reference framework for evaluating gas storage potential in similar geological settings.
During natural gas production and transportation, multi-stage pressure regulation is often required to meet downstream pressure demands, resulting in substantial waste of residual pressure energy at high-pressure wellheads. This study focuses on high-pressure natural gas at the wellhead of the XC gas well in western Sichuan. Based on thermodynamic and exergy analysis, Aspen HYSYS was employed to simulate residual pressure power generation processes, and a systematic comparison was conducted between single-stage and multi-stage expansion schemes. Under operating conditions of an inlet pressure of 20 MPa, an inlet temperature of 70 °C, and a flow rate of 50 × 104 m3/d, the influence of operating parameters on power generation performance was analyzed. The results indicate that power output increases with increasing natural gas flow rate and inlet temperature but decreases with increasing outlet pressure. Under large pressure differential conditions, single-stage expansion is unable to meet the requirements of high-pressure wellhead residual pressure power generation due to excessive temperature drop and limitations in existing expander performance. On this basis, two-stage, three-stage, and four-stage expansion power generation processes were further developed, and the effects of intermediate pressure selection on power output, heating demand, and pressure energy recovery efficiency were systematically examined. The results show that operating under equal expansion ratio conditions enhances pressure energy utilization. By comprehensively comparing power generation performance, heating power requirements, and economic feasibility, the two-stage expansion scheme was identified as the most favorable option under the investigated operating conditions, providing a practical reference for process design and engineering applications of high-pressure natural gas wellhead residual pressure power generation.
In megacities, where conventional mitigation strategies exhibit variable and environment-dependent performance, urban air pollution continues to be a significant public health concern. To methodically assess the operational reliability of urban smog mitigation systems under dynamic atmospheric conditions, this study proposes a data-driven failure analysis approach. A machine learning architecture based on Random Forest and XGBoost algorithms is developed using integrated meteorological and air quality metrics from Lahore, Pakistan, such as temperature, wind speed, and relative humidity. AQI is used as an integrated pollution indicator alongside meteorological variables to enhance the model’s ability to capture overall atmospheric pollution impact and improve the accuracy of smog mitigation failure prediction. This study presents a data-driven framework for predicting the failure of smog mitigation methods based on meteorological conditions. Unlike existing approaches that primarily focus only on air quality prediction, this work identifies specific environmental conditions, along with AQI as an input feature, to determine when mitigation strategies become ineffective. This enables proactive decision-making to maintain healthy indoor air quality. A threshold-controlled indoor air purification system that self-activates when the model predicts mitigation failure using real-time sensor inputs is introduced to address outdoor mitigation restrictions. PM2.5 reduction efficiency, clean air delivery rate, and energy consumption indicators are used to evaluate the purifier’s optimized performance. Predicting mitigation failure rather than just pollution levels and connecting it with an intelligent interior reaction mechanism is what makes this research novel. In a comparative analysis, Random Forest outperforms XGBoost with an accuracy of 95.5% as opposed to 94.5%, as well as higher precision (96.9%), recall (96.1%), and F1-score (96.5%). The purifier lowered indoor AQI from dangerous to safe levels within 30–40 min.
Radon (222Rn) is a noble, radioactive gas and tends to be accumulated in poorly ventilated enclosed spaces. Mainly due to its radioactive daughters and the α-particles emitted, 222Rn poses a risk of cancer and therefore its concentration in air and water should be kept under certain reference levels. Several methods have been developed to accurately measure 222Rn concentration in water, using α, β or γ counting. A well-established, but not the only, method involves γ-spectroscopy using a High-Purity Germanium (HPGe) detector to identify the 222Rn decay isotopes 214Pb and 214Bi, assuming they are in secular equilibrium with 222Rn. This technique requires costly, bulky equipment due to the HPGe’s operation at −196 °C and the need for substantial shielding. The present study introduces a more affordable and compact device, utilizing CdZnTe (CZT) crystals, which provide exceptional energy resolution in the 300 to 600 keV range, with nearly eight times the Full Width at Half Maximum (FWHM) of HPGe. Four stacked CZT detectors, each containing a 0.5 cm3 crystal, were compared with measurements from an HPGe detector. Water samples were collected from boreholes and taps in a region where radon concentration in water ranged from 10 to 900 Bq/L. The results are promising for samples around 100 Bq/L, considering the potential advancements of the device with larger CZT detectors. Additionally, the method has the potential for in situ use due to its handheld capability.
Forensic ethanol gas standards are used for, among other things, the calibration and metrological verification of evidential breath analysers as described in OIML-R126 Evidential breath analysers. A correction for the amount fraction of ethanol in forensic gas standards due to cylinder wall adsorption is described. The correction was developed for both the national primary measurement standards as well as for derived primary reference materials. A novel method based on the well-known decanting principle was developed and assessed using two suites of gas mixtures with ethanol amount fractions between 50 μmol mol−1 to 1000 μmol mol−1 in nitrogen. From the results, it is inferred that the initial adsorption loss is a function of the amount fraction, and an interpolation formula was developed accordingly. To account for differences in adsorption between cylinders, a mixed-effects model was used to describe the adsorption loss data with an excess standard deviation to account for between-cylinder effects.
Maritime transport accounts for around 3% of global anthropogenic greenhouse gas (GHG) emissions, a share expected to grow without effective technological and regulatory intervention. Recent policy developments, including the IMO Revised GHG Strategy (2023), the extension of the EU Emissions Trading System to maritime transport, and the FuelEU Maritime Regulation, require ports and shipping stakeholders to evaluate multiple decarbonization technologies under complex and often conflicting constraints. These decisions involve trade-offs across economic, technical, environmental, social, and cyber–physical security dimensions, which are not adequately addressed by conventional decision-support tools. This paper introduces XAI–MCDA-HoDEM, an explainable multi-criteria decision framework integrating Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and SHAP-based explainability. The framework explicitly incorporates cyber–physical security as a core evaluation criterion and provides transparent, criterion-level explanations of decision outcomes. Using real-world data, the methodology is demonstrated through an illustrative case study and empirically validated at the Port of Rotterdam. Results show stable and robust rankings, alignment with observed port decarbonization strategies, and improved interpretability of decision drivers. The proposed framework supports transparent, policy-relevant decision-making for the maritime energy transition.
Metal-organic frameworks (MOFs), particularly ELM-11, are promising sorbents for CO2 capture due to their gate-opening phenomenon and excellent reusability. Since actual exhaust gases contain impurities such as NO2, in this study, the effect of NO2 on the CO2 sorption performance of ELM-11 was investigated. ELM-11 was exposed to 1000 ppm NO2 for varying durations, ranging from short to long, and subsequent CO2 sorption was evaluated using several methods: gravimetric analysis (TG-DTA), volumetric analysis (sorption isotherms), FT-IR spectroscopy (to detect chemical bond changes), TG-MS (to analyze decomposition products), and PXRD (to observe structural changes). The TG-DTA results indicated that long-term NO2 exposure (e.g., 20 h) generally reduced CO2 sorption, whereas short-term exposure (3 h) could enhance it. This finding was supported by volumetric sorption isotherm measurements. FT-IR and TG-MS analyses revealed that NO2 underwent both physical and chemical sorption in small amounts, with chemical sorption occurring through reactions with Cu2+ ions. Consequently, 20 h of NO2 exposure resulted in approximately a 6 or 10% reduction in CO2 recovery capacity. However, since the degradation was only 6 or 10% despite exposure to a relatively high concentration of NO2 (1000 ppm), these results suggest that ELM-11 exhibits high resistance to NO2, making it suitable for practical applications.
Solar energy enhances the energy and environmental performance of coal gasification by lowering carbon emissions and increasing the yield and quality of synthesis gas. This patent review surveys recent global advances in solar thermochemical reactors for coal gasification, focusing on key innovations disclosed in patent applications and grants, with particular attention to technologies that improve process efficiency and sustainability. The novelty of the review is that unlike most patent reviews that focus primarily on statistical indicators such as application counts, geography, and classification, this work integrates qualitative analysis of specific technical solutions alongside statistical evaluation. This combined approach enables a deeper assessment of technological maturity and practical applicability. Fifteen patents from different countries were reviewed. The largest number (8, 53%) belongs to the United States. China has the second place with 4 (27%). The remaining countries (the EU, Korea, and Russia) hold 1 patent (7% each). The present work emphasises the technological and engineering solutions associated with the integration of solar energy into gasification processes. The author’s design is free of the disadvantages of its counterparts and is a simplified design with a high degree of adaptability to various types of fuel, including brown coal, biomass, and other carbon-containing materials.
This study examines how different sustainability assessment approaches influence climate-policy choices when evaluating greenhouse gas (GHG) emissions across industrial value chains. Using Spain as an empirical setting, we apply Environmentally Extended Input–Output Tables combined with Production Layer Decomposition to quantify Scope 1–2–3 emissions and assess economic and employment impacts. The results show that indirect emissions dominate most value chains, revealing structural dependencies that are not captured by sector-level inventories. Incorporating social and economic dimensions highlights the need for transition pathways that minimise employment disruption while maximising environmental gains. Although public procurement can enhance the uptake of emerging low-carbon and circular-economy technologies, it has limited quantitative influence on total value-chain emissions. The findings demonstrate that value-chain-based sustainability assessments provide a more comprehensive basis for designing coherent, equitable, and effective decarbonisation strategies.
This study examines the determinants of U.S. CO2 emissions and provides evidence to inform more effective carbon-reduction policies. Using Autoregressive Distributed Lag (ARDL) and Nonlinear ARDL (NARDL) models, the analysis covers January 1997 to February 2022 across four end-use sectors: Residential, Commercial, Industrial, and Transportation. The models capture both long-run equilibria and short-run adjustments between emissions and key drivers, including industrial production, interest rates, climate policy uncertainty (CPU), and energy prices. Results indicate a long-run asymmetric relationship in which economic growth and interest rates differentially affect total emissions, while CPU exerts a significant negative influence only in the transportation sector. Methodologically, the combined ARDL–NARDL approach offers robust evidence of nonlinear and asymmetric effects of macroeconomic and policy variables on emissions. These findings underscore the need to integrate economic and financial conditions into climate policy design and suggest that sector-specific measures—particularly targeting transportation—may substantially improve the effectiveness of carbon-mitigation strategies.
This study provides a comprehensive analysis of the impact of different marine fuels such as heavy fuel oil (HFO), methane, methanol, ammonia, or hydrogen, on energy efficiency and pollutant emissions in maritime transport, using a combined application of the Energy Efficiency Design Index (EEDI), Energy Efficiency Operational Indicator (EEOI), and Carbon Intensity Indicator (CII). The results show that methane offers the most balanced alternative, reducing CO2 by more than 30% and improving energy efficiency, while methanol provides an intermediate performance, eliminating sulfur and partially reducing emissions. Ammonia and hydrogen eliminate CO2 but generate NOx (nitrogen oxides) emissions that require mitigation, demonstrating that their environmental impact is not negligible. Unlike previous studies that focus on a single fuel or only on CO2, this work considers multiple pollutants, including SOx (sulfur oxides), H2O, and N2, and evaluates the economic cost of emissions under the European Union Emissions Trading System (EU ETS). Using a representative model ship, the study highlights regulatory gaps and limitations within current standards, emphasizing the need for a global system for monitoring and enforcing emissions rules to ensure a truly sustainable and decarbonized maritime sector. This integrated approach, combining energy efficiency, emissions, and economic evaluation, provides novel insights for the scientific community, regulators, and maritime operators, distinguishing itself from previous multicriteria studies by simultaneously addressing operational performance, environmental impact, and regulatory gaps such as unaccounted NOx emissions.
