Search Results (2,992)

Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine’s design and verify compliance with environmental regulations through the vehicle’s emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle’s temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time. Full article

The transportation sector, a major source of urban air pollution and CO₂ emissions, is the focus of extensive research aimed at developing cleaner and more efficient technologies. In this context, hydrogen–methane blends (HCNG) represent a promising alternative fuel, combining the zero-carbon combustion potential of hydrogen with the availability and cleaner profile of methane. This solution can be implemented in existing internal combustion engines, enabling a technically and economically feasible transition toward more sustainable mobility. This work investigates the use of an HCNG blend in a bus originally powered by natural gas, focusing on pollutant emissions under real driving conditions representative of typical urban operation. Measurements were performed using a Portable Emission Measurement System installed on-board. Two test campaigns were carried out: the first using methane, and the second using an HCNG blend (15% H₂, 85% CH₄ by volume), over identical urban and extra-urban routes with varying drivers and traffic conditions. Results show a reduction in CO₂ emissions with HCNG, along with a more significant decrease in CO, HC, and PN emissions, while NOx exhibited a slight increase due to unchanged engine calibration. The analysis also includes the RPA index, which is related to fuel energy release characteristics, indicating improved vehicle responsiveness and torque delivery with HCNG. Full article

In recent years, strong emphasis has been put on decarbonising the transport and mining sectors in an economically viable manner. To this end, ammonia is presented as a fuel, combining a high energy density (when compared to hydrogen) and zero carbon emissions. In this work, conversion of a mining haul truck engine is simulated for its use with an ammonia/diesel dual-fuel system at up to 70% Ammonia Energy Replacement (AER). The numerical setup is partially validated against engine performance data. The simulations suggest a reduction in CO${}_2$ emissions but an increase in N${}_2$O, which increases the carbon-equivalent emissions of the engine. Nevertheless, NO${}_{\mathrm{x}}$ emissions appear to be reduced, suggesting the use of post-treatment is required to deal with the issue of N${}_2$O. Cylinder temperature control is recommended for its reduction, as temperatures are lower when burning ammonia. On the other hand, the simulations suggest that ammonia slip increases with AER if diesel injection phasing is not optimised. Performance-wise, the engine develops a higher indicated mean effective pressure (IMEP) as AER increases, with a maximum at 40% AER, while combustion is delayed progressively into the engine cycle, as CAD50 values increase from −0.6 CAD ATDC at 0% AER to 20.1 CAD ATDC at 70% AER. Opportunities for further research are discussed, including more extensive experimental work to support or reject what is suggested by the simulations. Full article

The common root characteristic of CO₂ and air pollutants in the power industry, both derived from fossil fuel combustion, provides a natural basis for their synergistic emission reduction. However, existing studies suffer from the lack of a multi-pollutant synergistic evaluation system and an imperfect emission reduction technology database, which hinder their ability to support low-cost and high-efficiency emission reduction practices in the industry. Targeting the minimization of synergistic emission reduction costs and the maximization of emission reduction effects, this study integrated the process and economic parameters of 11 power generation technologies and 55 pollutant control technologies to establish a full-chain energy conservation and emission reduction technology database for the power industry, through literature research, industry surveys, and data mining. Based on the definition of pollution equivalent in the Environmental Protection Tax Law, we innovatively developed an air pollutant equivalent normalization evaluation method and constructed a two-dimensional coordinate system comprehensive evaluation system for CO₂ and air pollutants, enabling quantitative analysis and visual evaluation of the synergistic emission reduction effects of various technologies. The results show that new energy power generation technologies such as nuclear power and wind power, as well as O₂/CO₂ cycle combustion, ammonia-based desulfurization, and SNCR-SCR combined reduction technologies, exhibit excellent synergistic emission reduction performance for CO₂ and multiple pollutants. In contrast, some conventional pollutant control technologies, such as the limestone-gypsum method and traditional electrostatic precipitation, have significant CO₂ emission increase antagonistic effects. This study also completed the two-dimensional classification of 66 emission reduction technologies based on “emission reduction efficiency-economic cost”, identified application scenarios for different types of technologies, and proposed optimized paths for synergistic emission reduction adapted to the development of the power industry. The research findings fill the gap in quantitative standards for multi-pollutant synergistic emission reduction, provide theoretical support and detailed technical references for emission reduction technology selection and environmental policy formulation in the power industry, and help the industry achieve the dual development requirements of the “double carbon” goal and air quality improvement. Full article

This study investigates the environmental and energy performance of rice straw-based energy pathways in Egypt, combining life cycle assessment (LCA) with supply chain optimization to improve system efficiency. The analysis covers thirteen governorates producing over 4.45 million tons of rice straw annually. It examines the whole supply chain from paddy farming, straw collection, and transport to electricity generation and ash disposal. Total energy consumption was 11,287 TJ, dominated by farming (5673 TJ) and transport (5490 TJ). Greenhouse gas (GHG) emissions were estimated at 12,007.5 million kg CO₂-eq, with significant contributions from farming (5158 million), combustion (3630 million), and natural gas use (3039 million). Gross electricity output was 5525 GWh, yielding a net of 4973 GWh, equivalent to 1116.5 kWh per ton of straw. Scenario analysis highlighted that the optimized multi-hub system, prioritizing Cluster 1 in the Nile Delta, which contributes over 92% of straw production and 4607 GWh of net electricity, achieved a reduction of more than 25% in transport distances and an 18% decrease in diesel consumption and related emissions. Sensitivity analysis further indicated that delivered electricity and GHG intensity are more sensitive to conversion efficiency and transmission and distribution losses than to moderate changes in transport assumptions. In addition to environmental improvements, the optimized scenario indicates potential social co-benefits, including rural employment generation, additional income opportunities for farmers, and improved air quality associated with reduced open-field burning. These outcomes are presented as indicative qualitative insights. Findings confirm rice straw as a strategic, scalable, and sustainable energy resource aligned with Egypt’s Vision 2030 and the UN Sustainable Development Goals (SDGs). Full article

Internal combustion engines fueled by fossil fuels are major contributors to greenhouse gas (GHG) emissions. This study analyzes and predicts GHG emissions from hydrogen-enriched compressed natural gas (HCNG)-fueled spark-ignition (SI) engines. Experiments were conducted under stoichiometric conditions, and emissions before and after the three-way catalytic converter (TWC) were analyzed by varying hydrogen fraction (0–50%), EGR ratio (0–23%), engine speed (900 rpm–1500 rpm), engine load (25–75%), and spark timing (8–49 °CA bTDC). Before the TWC, increasing the hydrogen fraction from HCNG0% to HCNG40% at 1500 rpm, 50% load, and 23% EGR reduced total GHG emissions from 184.3 to 65.17 g/kWh. Similarly, for HCNG20% at 900 rpm and 30% load, the TWC reduced the CO₂-equivalent emissions of CO, CH₄, and NOx from 18.531, 8.149, and 9.057 g${\mathrm{C}\mathrm{O}}_{2eq}$/kWh to 7.013, 1.626, and 0.429 g${\mathrm{C}\mathrm{O}}_{2eq}$/kWh, respectively. Pearson correlation analysis revealed strong linear relationships between operating parameters and GHG emissions. Furthermore, emissions were predicted using four Gaussian process regression (GPR) models: Squared, Exponential, Matern 5/2, and Rational. Among these, the Exponential GPR demonstrated the highest predictive accuracy, achieving RMSE values of 0.098, 0.022, and 0.035, with corresponding R² values of 0.999, 0.807, and 0.996 for CO, CH₄, and NOx, respectively. The findings of this study offer valuable insights into GHG emissions and support the development of cleaner, more efficient HCNG engines. Full article

As aviation faces growing pressure to reduce its climate impact, the exFAN project investigates a hydrogen fuel cell aircraft concept equipped with a heat recuperation system that reuses waste thermal energy to improve efficiency and lower fuel demand. This study compares the exFAN configuration with five major propulsion pathways, kerosene, bio-fuel, e-fuel, hydrogen combustion, and standard fuel cell systems, through an integrated well-to-wake (WTT + TTW) assessment including both CO₂ and non-CO₂ effects. The exFAN results are preliminary and based on analytical estimations regarding potential efficiency gains and fuel savings, providing an indicative view of hydrogen aviation’s lowest achievable climate footprint. Full article

Direct-fired supercritical CO₂ cycles are considered a promising way to reduce CO₂ emissions in the energy sector. One of the key elements of such cycles is a combustor, in which natural gas is burned at supercritical pressures up to 300 atm in an O₂/CO₂ environment. Understanding the chemical combustion kinetics of C1–C4 alkanes, the main components of natural gas, in a supercritical CO₂-diluted medium is important for designing such combustors. This article provides an overview of studies on the chemical kinetics of C1–C4 alkanes combustion in CO₂ at ultra-high pressures. It has been established that with increasing pressure, regardless of the diluent, CH₃O₂ and HO₂ chemistries start to significantly influence the combustion of alkanes, but at the moment this influence is not sufficiently understood. Influence of CO₂ dilution on kinetics is mainly thermal, but the chemical effect is also significant. At the same time, the direct chemical effect of CO₂ is more important for the laminar burning velocity, while the indirect third-body effect is more important for the ignition delay time. However, the available literature lacks experimental measurements of the laminar burning velocity in a CO₂ environment at pressures above 70 atm, which limits the current understanding of chemical kinetics at supercritical pressures. Full article

Although amine-based post-combustion carbon capture is among the most established routes for CO₂ capture, it suffers from the high energy demand associated with amine regeneration. Recent research proposals suggest that microwave or frequency-tuned infrared heating may lead to more efficient amine regeneration processes. However, such approaches inherently introduce oscillating electromagnetic fields whose non-thermal effects on reaction pathways and energetics remain poorly understood. In this series paper, we employ high-accuracy quantum computational chemistry calculations to quantify the non-thermal effects of external electric fields on CO₂ absorption and desorption in monoethanolamine (MEA) and triethanolamine (TEA) under both aqueous and non-aqueous conditions. In this first part, we focus on static electric fields in order to establish a mechanistic reference framework helpful for interpreting non-thermal effects arising from frequency-tuned infrared laser excitation, which are addressed in Part II of this series. Our results show that static electric fields stabilize CO₂–amine reaction products, lowering absorption barriers, while consistently increasing both activation energies and reaction enthalpies associated with the amine regeneration process. This effect is particularly pronounced for MEA, where carbamate species become progressively more resistant to conversion to zwitterion as the field strength increases. These findings demonstrate that non-thermal static electric field effects counter the fundamental requirement for low-energy amine regeneration. By defining this intrinsic mechanistic limitation, the present study provides a useful baseline for assessing infrared laser-assisted carbon capture and underscores the importance of carefully selecting excitation frequencies to avoid adverse non-thermal stabilization effects. Full article

Under the dual-carbon goals, adopting renewable alternative fuels in transportation is crucial. Alcohol-based fuels, produced via biomass fermentation or green electricity-powered CO₂ hydrogenation, offer benefits like renewability, engine compatibility, and long driving range. Bio-butanol, with an energy density close to gasoline, can power SI engines directly, but its high production costs due to low fermentation efficiency limit its viability. In contrast, IBE (a butanol fermentation intermediate) avoids costly separation steps, making it more competitive than pure butanol. Existing research on IBE in spark ignition engines mainly focuses on fixed-ratio IBE-gasoline blends, restricting real-time fuel adjustment. Building on prior findings that IBE outperforms ABE and butanol, this study examines the combustion and emission characteristics of a gasoline port injection + IBE direct injection engine under varying direct injection timings, IBE ratios, and excess air ratios. Research indicates that early direct injection timings with pure IBE provide optimal performance at stoichiometric conditions. As the excess air ratio rises, an 80% IBE direct injection ratio becomes more advantageous. IBE shows great promise as an alternative fuel, enhancing combustion performance and reducing gaseous and particulate emissions. Full article

To advance the design of novel clean coal-fired boilers, this study integrates artificial intelligence with numerical simulations to optimize a 130 MW conceptual boiler based on Moderate or Intense Low-oxygen Dilution (MILD) and oxy-coal combustion technologies. First, mathematical models for pulverized-coal MILD-oxy combustion are validated using experimental data from a 0.58 MW pilot-scale boiler and then applied to the full-scale 130 MW boiler. An orthogonal experimental design with four factors and five levels is employed to generate 25 simulation cases, evaluating the effects of burner nozzle configuration and furnace geometry on boiler performance. Based on the simulation dataset, mutual information analysis is conducted to identify key influencing features, guiding nine additional simulations to refine samples in critical design areas. Finally, using the complete 34 simulation data, an optimal boiler structure is identified using support vector machine and multi-objective optimization algorithms. The results indicate that both the burner circumferential diameter and the O₂/CO₂ inlet diameter are positively correlated with nitrogen oxide (NOx) emissions, whereas the former is negatively correlated with the wall thermal non-uniformity. After optimization, the average char burnout rate increased by 1.4%, NOx emissions decreased by 4%, and wall heat non-uniformity coefficient reduced by 1.1%, demonstrating the effectiveness of the proposed approach. Full article

Accurate, high-resolution emission inventories are essential for air quality modeling and policy evaluation, yet national-scale inventories for Thailand remain limited in spatial and temporal detail. This study develops a comprehensive national emission inventory for Thailand in 2019 (EI–TH 2019), covering 12 major air pollutants and greenhouse gases across key sectors, including energy, transport, industry, agriculture, waste, and residential activities. The inventory is constructed using country-specific activity data from official statistics and sectoral surveys, combined with GAINS-consistent emission factors and control assumptions. Emissions are resolved at 1 × 1 km spatial resolution and monthly temporal resolution to capture Thailand-specific emission dynamics. The results show that emissions across major pollutants are dominated by a limited number of source groups, with biomass burning and residential solid-fuel use driving particulate matter, transport dominating NO_x and CO emissions, large-scale combustion and industry controlling SO₂ emissions, and agriculture contributing the majority of NH₃ emissions. Strong seasonal variability is observed in PM_2.5, CO, and NH₃, primarily driven by dry-season biomass burning, whereas NO_x and SO₂ exhibit relatively stable temporal patterns. The reliability of EI–TH 2019 is supported by a multi-dimensional evaluation framework. Temporal consistency is demonstrated through strong agreement between modeled PM_2.5 emissions and ground-based observations, as well as between NO_x emissions and satellite-derived TROPOMI NO₂ (r = 0.93; ρ = 0.96). Biomass burning timing is further validated using satellite fire activity (VIIRS), showing consistent seasonal patterns. Comparisons with global inventories (EDGAR v8.1, HTAP v3.2, and GFED5.1) reveal systematic differences in sectoral contributions, temporal profiles, and emission magnitudes, particularly for biomass burning, reflecting the importance of country-specific data and assumptions. Overall, EI–TH 2019 provides a robust, high-resolution, and policy-relevant emission dataset that improves the representation of emission processes in Thailand. The results highlight key priority sectors—biomass burning, transport, industry, and agriculture—for targeted emission-reduction strategies and support applications in chemical transport modeling, exposure assessment, and integrated air-quality and climate-policy analysis. Full article

Given the challenges associated with the transferability of specific emission modeling tools between different regions, developing accurate local emission models utilizing field measurements has become increasingly relevant for effectively reflecting local conditions. In this study, we employed a comprehensive benchmarking approach, drawing on an extensive set of on-road experiments encompassing various vehicle types. More specifically, this study aims to (1) conduct a thorough review of alternative modeling techniques used for modeling second-by-second fuel consumption and emission measures across different vehicle categories and (2) assess and compare the performance of identified modeling methods, employing either internal (OBD) or external (GPS) variables, and evaluate the impact of lag effects. Moreover, (3) we make available the collected data, preprocessing codes, and an implementation example as open-source resources for the research community to facilitate reproducibility. The outcomes of this research are expected to offer guidelines for both practical modeling applications and for future work. Full article

Syngas (synthesis gas) is a promising gaseous biofuel for small-scale power generation, but its highly variable composition, which depends on the biomass source and gasification process, poses challenges for engine optimization. This study investigates syngas–diesel dual-fuel combustion in an optically accessible engine using chemiluminescence imaging of OH*, CH*, and CH₂O* to characterize ignition and flame development. Three representative syngas compositions—Downdraft, Updraft, and Fluidbed—were examined. The Fluidbed composition exhibited the weakest OH* signal, approximately one-third of that observed for the other two, primarily due to its higher CO₂ dilution and lower H₂ content. Ignition delay trends were strongly correlated with dilution level: Downdraft and Updraft showed similar delays despite different H₂/CO ratios, while larger CO₂ shares led to longer delays and flattened heat-release rates. CH* and CH₂O* chemiluminescence showed better agreement with combustion timing than OH*. Methane enrichment enhanced flame propagation and reduced ignition delay, partially offsetting CO₂ dilution effects. Full article

The identification and characterization of air pollutants in metropolitan environments are of paramount global concern due to their significant implications for air quality and public health. This study investigates the chemical composition of fine particulate matter (PM_2.5) at two strategically selected urban sites in Seoul, South Korea, during 2020: Gwanghwamun Plaza, representing a high-density central location, and Bokjeong Station, situated in the metropolitan periphery. A key aspect of this research is the detection of terephthalic acid (TPA)—a distinct marker of polyethylene terephthalate (PET) combustion—using high-resolution liquid chromatography–time-of-flight tandem mass spectrometry (LC-ToF-MS/MS). Results from the simultaneous measurement campaign demonstrate that nighttime conditions strongly influence PM_2.5 at both sites, with increases observed not only in absolute concentrations (levoglucosan, TPA, As, CO, and NH₃) but also in OC-normalized ratios (levoglucosan/OC and TPA/OC). The consistent nighttime enhancement of these ratios suggests that the observed increases cannot be explained solely by reduced planetary boundary layer height but instead indicate relatively stronger emission contributions. These increases are likely influenced by waste incineration activities, wherein PET-based plastics and wood materials are combusted. Furthermore, assessment of the dithiothreitol assay-derived oxidative potential (DTT-OP) underscores the heightened oxidative stress associated with these emissions, posing substantial health risks. Full article