Search Results (621)

Carbon monoxide (CO) is a major toxic gas emitted from vehicle exhaust, industrial processes, and incomplete fuel combustion, posing serious environmental and health risks. Catalytic oxidation of CO into less harmful CO₂ is an effective strategy to reduce these emissions. In this study, we investigated the catalytic performance of platinum (Pt) single atoms doped on C₃N monolayers with various vacancy defects, including single carbon (C_V) and nitrogen (N_V) vacancies, using density functional theory (DFT) calculations. Our results demonstrate that Pt@N_V-C₃N exhibited the most favorable catalytic properties, with the highest O₂ adsorption energy (−3.07 eV). This performance significantly outperforms Pt atoms doped at other vacancies. It can be attributed to the strong binding between Pt and nitrogen vacancies, which contributes to its excellent resistance to Pt aggregation. CO oxidation on Pt@N_V-C₃N proceeds via the Eley–Rideal (ER2) mechanism with a low activation barrier of 0.41 eV for the rate-determining step, indicating high catalytic efficiency at low temperatures. These findings suggest that Pt@N_V-C₃N is a promising candidate for CO oxidation, contributing to developing cost-effective and environmentally sustainable catalysts. The strong binding of Pt atoms to the nitrogen vacancies prevents aggregation, ensuring the stability and durability of the catalyst. The kinetic modeling further revealed that the ER2 mechanism offers the highest reaction rate constants over a wide temperature range (273–700 K). The low activation energy barrier also facilitates CO oxidation at lower temperatures, addressing critical challenges in automotive and industrial pollution control. This study provides valuable theoretical insights for designing advanced single-atom catalysts for environmental remediation applications. Full article

Progressive research on reducing engine emissions is highly valued due to the emissions’ significant environmental and health impacts. This comprehensive comparative study examines the catalytic efficiency of manganese (Mn) and cerium silica (Ce-Si) synthesis catalyst-based molds in a diesel engine using a selective catalytic reduction (SCR) technique with diesel and diesel–plastic oil blend (DPB) (B50). In addition to Fourier transform infrared spectroscopy (FTIR) studies, X-ray diffraction (XRD), scanning electron microscopy (SEM), and the Brunauer–Emmett–Teller (BET) method are utilized to characterize the produced molds before and after exhaust gas passes. The Ce-Si-based mold demonstrates superior redox capacity, better adsorption capacity, and better thermal stability, attributed to enhanced oxygen storage and structural integrity compared to the Mn-based mold. Under minimum load conditions, nitrogen oxide (NO) reduction efficiency peaks at 80.70% for the Ce-Si-based mold in the SCR treatment with DPB fuel. Additionally, significant reductions of 86.84%, 65.75%, and 88.88% in hydrocarbon (HC), carbon monoxide (CO), and smoke emissions, respectively, are achieved in the SCR treatment under optimized conditions. Despite a wide temperature range, Ce-Si-based mold promotes high surface area and superior gas diffusion properties. Overall, the Ce-Si-based mold provides efficient emission control in diesel engines, which paves a path for developing better environmental sustainability. The outcomes contribute to advancing environmental sustainability by supporting the achievement of SDGs 7, 11, and 13. Full article

Transportation is a major contributor to air pollution, with vehicles emitting around 65% of manmade hydrocarbons, 64% of carbon monoxide, and 40% of nitrogen oxides. These pollutants harm the environment, human health, and materials. With vehicle populations expected to reach 1.3 billion by 2030, emissions will only worsen. This project focuses on enhancing the efficiency of catalytic converters, which help convert harmful tailpipe emissions like unburned hydrocarbons and CO into less harmful substances (CO₂ and H₂O). Using a selective catalyst alongside a catalytic converter, the study aims to significantly reduce toxic emissions from traditional IC engine vehicles. Full article

This paper introduces the record-based transmuted Rayleigh distribution of order 3 (rbt-R), a three-parameter extension of the classical Rayleigh model designed to address data characterized by high skewness and heavy tails. While traditional generalizations of the Rayleigh distribution enhance model flexibility, they often lack sufficient adaptability to capture the complexity of empirical distributions encountered in applied statistics. The rbt-R model incorporates two additional shape parameters, a and b, enabling it to represent a wider range of distributional shapes. Parameter estimation for the rbt-R model is performed using the maximum likelihood method. Simulation studies are conducted to evaluate the asymptotic properties of the estimators, including bias and mean squared error. The performance of the rbt-R model is assessed through empirical applications to four datasets: nicotine yields and carbon monoxide emissions from cigarette data, as well as breaking stress measurements from carbon-fiber materials. Model fit is evaluated using standard goodness-of-fit criteria, including AIC, AIC_c, BIC, and the Kolmogorov–Smirnov statistic. In all cases, the rbt-R model demonstrates a superior fit compared to existing Rayleigh-based models, indicating its effectiveness in modeling highly skewed and heavy-tailed data. Full article

Carbon monoxide (CO) is a key reactant in industries like chemicals, pharmaceuticals, and metallurgy, with a projected global market of $8.2 billion by 2032. A novel method of CO production is biowaste composting, but the impact of organic matter content (OMC) on CO yield remains unexplored. Since OMC affects composting costs, optimizing it is crucial for economic feasibility. This study aimed to identify the optimal OMC in bioreactors for CO production during food waste composting. A laboratory process was conducted in bioreactors with forced aeration. Food waste (FW) was mixed with gravelite (G) at ratios of 1:0, 1:1, and 1:2 (FW:G), corresponding to 95%, 40%, and 20% dry OMC. Bioreactors were incubated at 45 °C, 60 °C, and 70 °C with ~5% oxygen. The highest CO levels were at 70 °C for FW:G 1:2, with an average of 655 ppm and a maximum of 2000 ppm. Daily CO emissions were highest at 70 °C, reaching up to 1.25 mg. Therefore, the study demonstrated that even a low organic matter content allows for CO production during composting under thermophilic conditions (~70 °C) with limited oxygen. Industrial modeling estimated daily CO yield from 39.25 to 670.61 g, with a 7-day market value between USD 28.89 and USD 175.86. Further studies are needed for large-scale feasibility. Full article

It is a well-known fact that proper nutrition is essential for human beings to live healthy lives. For thousands of years, it has been considered that dates are one of the best nutrient providers. To have better-quality dates and to enhance the shelf life of dates, it is vital to preserve dates in optimal conditions that contribute to food security. Hence, it is crucial to know the shelf life of different types of dates. In current practice, shelf life assessment is typically based on manual visual inspection, which is subjective, error-prone, and requires considerable expertise, making it difficult to scale across large storage facilities. Traditional cold storage systems, whilst being capable of monitoring temperature and humidity, lack the intelligence to detect spoilage or predict shelf life in real-time. In this study, we present a novel IoT-based shelf life estimation system that integrates multichannel gas sensors and a lightweight machine learning model deployed on an edge device. Unlike prior approaches, our system captures the real-time emissions of spoilage-related gases (methane, nitrogen dioxide, and carbon monoxide) along with environmental data to classify the freshness of date fruits. The model achieved a classification accuracy of 91.9% and an AUC of 0.98 and was successfully deployed on an Arduino Nano 33 BLE Sense board. This solution offers a low-cost, scalable, and objective method for real-time shelf life prediction. This significantly improves reliability and reduces postharvest losses in the date supply chain. Full article

The acceleration of industrialization and urbanization worldwide has dramatically improved living standards but has also introduced serious environmental and public health challenges. One of the most critical challenges is air pollution, particularly indoors, where individuals typically spend over 90% of their time. Ensuring good Indoor Air Quality (IAQ) is essential, especially in heavily frequented public spaces such as shopping malls. This study focuses on assessing IAQ in a large shopping mall located in Tabuk, Saudi Arabia, covering retail zones as well as an attached underground parking area. Monitoring is conducted over a continuous two-month period using calibrated instruments placed at representative locations to capture variations in pollutant levels. The investigation targets key contaminants, including carbon monoxide (CO), carbon dioxide (CO₂), fine particulate matter (PM_2.5), total volatile organic compounds (TVOCs), and formaldehyde (HCHO). The data are analyzed and compared against international and national guidelines, including World Health Organization (WHO) standards and Saudi environmental regulations. The results show that concentrations of CO, CO₂, and PM_2.5 in the shopping mall are generally within acceptable limits, with values ranging from approximately 7 to 15 ppm, suggesting that ventilation systems are effective in most areas. However, the study identifies high levels of TVOCs and HCHO, particularly in zones characterized by poor ventilation and high human occupancy. Peak concentrations reach 1.48 mg/m³ for TVOCs and 1.43 mg/m³ for HCHO, exceeding recommended exposure thresholds. These findings emphasize the urgent need for enhancing ventilation designs, prioritizing the use of low-emission materials, and establishing continuous air quality monitoring protocols within commercial buildings. Improving IAQ is not only crucial for protecting public health but also for enhancing occupant comfort, satisfaction, and overall building sustainability. This study offers practical recommendations to policymakers, building managers, and designers striving to create healthier indoor environments in rapidly expanding urban centers. Full article

As road traffic in Turkey is a significant source of emissions due to the increasing number of vehicles on the road, the goal of this study is to calculate greenhouse gas emissions from Turkey’s roads between 2010 and 2020, create an inventory, and estimate possible emissions until 2050. In the study, both greenhouse gases (carbon dioxide (CO₂) and nitrous oxide (N₂O) and co-emitting air pollutants that indirectly contribute to climate change (ammonia—NH₃, nitrogen oxide—NO_X, sulfur dioxide—SO₂, carbon monoxide—CO, non-methane volatile organic compounds—NMVOC, and particulate matter—PM) were investigated. The study revealed that the total number of vehicles using state roads in Turkey increased by 60% between 2010 and 2020. As a result, emissions of CO₂, N₂O, NH₃, NO_X, SO₂, CO, NMVOC, and PM increased by 29.6%, 24.2%, 0.5%, 19.9%, 9.9%, 18.2%, 21.5%, and 39.7%, respectively. When emissions were analyzed on a provincial basis, particular attention was drawn to provinces with high levels of urbanization. Based on forecast studies, the total number of vehicles registered for traffic will increase by 105% by 2050. Due to this increase, CO₂, N₂O, NH₃, NO_X, SO₂, CO, NMVOC, and PM emissions are estimated to increase by 149.17%, 151.78%, 154.39%, 138.95%, 150.97%, 153.09%, 152.09%, and 151.47%, respectively. Full article

The combustion of gaseous fuels in condensing boilers contributes to the greenhouse gas and toxic compound emissions in exhaust gases. Hydrogen, as a clean energy carrier, could play a key role in decarbonizing the residential heating sector. However, its significantly different combustion behavior compared to hydrocarbon fuels requires thorough investigation prior to implementation in heating systems. This study presents experimental and theoretical analyses of the co-combustion of natural gas with hydrogen in low-power-output condensing boilers (second and third generation), with hydrogen content of up to 50% by volume. The results show that mixtures of hydrogen and natural gas contribute to increasing heat transfer in boilers through convection and flue gas radiation. They also highlight the benefits of using the heat from the condensation of vapors in the flue gases. Other studies have observed an increase in efficiency of up to 1.6 percentage points compared to natural gas at 50% hydrogen content. Up to a 6% increase in the amount of energy recovered by water vapor condensation was also recorded, while exhaust gas losses did not change significantly. Notably, the addition of hydrogen resulted in a substantial decrease in the emission of nitrogen oxides (NOx) and carbon monoxide (CO). At 50% hydrogen content, NO_x emissions decreased several-fold to 2.7 mg/m³, while CO emissions were reduced by a factor of six, reaching 9.9 mg/m³. All measured NO_x values remained well below the current regulatory limit for condensing gas boilers, which is 33.5 mg/m³. These results highlight the potential of hydrogen blending as a transitional solution on the path toward cleaner residential heating systems. Full article

Carbon monoxide (CO) is a toxic pollutant emitted by municipal solid waste incineration (MSWI), which has a strong correlation with dioxins. In terms of the sustainable development of an ecological environment, CO emission concentration is strictly controlled by the environmental departments of various countries in the world. The construction of its prediction model is conducive to pollution reduction control. The MSWI process is affected by multi-factors such as MSW component fluctuation, equipment wear and maintenance, and seasonal change, and has complex nonlinear and time-varying characteristics, which makes it difficult for the CO prediction model based on offline historical data to adapt to the above changes. In addition, the continuous emission monitoring system (CEMS) used for conventional pollutant detection has unavoidable misalignment and failure problems. In this article, a novel prediction model of CO emission from the MSWI process based on semi-supervised concept drift (CD) detection in kernel feature space is proposed. Firstly, the CO emission deep prediction model and the kernel feature space detection model are constructed based on offline batched historical data, and the historical data set for the real-time construction of the pseudo-labeling model is obtained. Secondly, the drift detection for the CO emission prediction model is carried out based on real-time data by using unsupervised kernel principal component analysis (KPCA) in terms of feature space. If CD occurs, the pseudo-label model is constructed, the pseudo-truth value is obtained, and the drift sample is confirmed and selected based on the Page–Hinkley (PH) test. If no CD occurs, the CO emission concentration is predicted based on the historical prediction model. Then, the updated data set of the CO emission prediction model and kernel feature space detection is obtained by combining historical samples and drift samples. Finally, the offline history model is updated with a new data set when the preset conditions are met. Based on the real data set of an MSWI power plant in Beijing, the validity of the proposed method is verified. Full article

Marine transportation contributes approximately 2.5% of global greenhouse gas emissions. While previous studies have examined biodiesel effects on automotive engines, research on marine applications reveals critical gaps: (1) existing studies focus on single-parameter analysis without considering the complex interactions between biodiesel ratio, engine load, and operating conditions; (2) most research lacks comprehensive lifecycle assessment integration with real-time operational data; (3) previous optimization models demonstrate insufficient accuracy (R² < 0.80) for practical marine applications; and (4) no adaptive algorithms exist for dynamic biodiesel ratio adjustment based on operational conditions. These limitations prevent effective biodiesel implementation in maritime operations, necessitating an integrated multi-parameter optimization approach. This study addresses this research gap by proposing an integrated optimization model for fuel efficiency and emissions of marine diesel engines using biodiesel mixtures under diverse operating conditions. Based on extensive experimental data from two representative marine engines (YANMAR 6HAL2-DTN 200 kW and Niigatta Engineering 6L34HX 2471 kW), this research analyzes correlations between biodiesel blend ratios (pure diesel, 20%, 50%, and 100% biodiesel), engine load conditions (10–100%), and operating temperature with nitrogen oxides, carbon dioxide, and carbon monoxide emissions. Multivariate regression models were developed, allowing prediction of emission levels with high accuracy (R² = 0.89–0.94). The models incorporated multiple parameters, including engine characteristics, fuel properties, and ambient conditions, to provide a comprehensive analytical framework. Life cycle assessment (LCA) results show that the B50 biodiesel ratio achieves optimal environmental efficiency, reducing greenhouse gases by 15% compared to B0 while maintaining stable engine performance across operational profiles. An adaptive optimization algorithm for operating conditions is proposed, providing detailed reference charts for ship operators on ideal biodiesel ratios based on load conditions, ambient temperature, and operational priorities in different maritime zones. The findings demonstrate significant potential for emissions reduction in the maritime sector through strategic biodiesel implementation. Full article

Although the transition to electric vehicles (EVs) is well underway and expected to continue in global car markets, most vehicles on the world’s roads will be powered by internal combustion engine vehicles (ICEVs) and fossil fuels for the foreseeable future, possibly well past 2050. Thus, good environmental performance and effective emission control of ICE vehicles will continue to be of paramount importance if the world is to achieve the stated air and climate pollution reduction goals. In this study, we review 228 publications and identify four main issues confronting these objectives: (1) cheating by vehicle manufacturers, (2) tampering by vehicle owners, (3) malfunctioning emission control systems, and (4) inadequate in-service emission programs. With progressively more stringent vehicle emission and fuel quality standards being implemented in all major markets, engine designs and emission control systems have become increasingly complex and sophisticated, creating opportunities for cheating and tampering. This is not a new phenomenon, with the first cases reported in the 1970s and continuing to happen today. Cheating appears not to be restricted to specific manufacturers or vehicle types. Suspicious real-world emissions behavior suggests that the use of defeat devices may be widespread. Defeat devices are primarily a concern with diesel vehicles, where emission control deactivation in real-world driving can lower manufacturing costs, improve fuel economy, reduce engine noise, improve vehicle performance, and extend refill intervals for diesel exhaust fluid, if present. Despite the financial penalties, undesired global attention, damage to brand reputation, a temporary drop in sales and stock value, and forced recalls, cheating may continue. Private vehicle owners resort to tampering to (1) improve performance and fuel efficiency; (2) avoid operating costs, including repairs; (3) increase the resale value of the vehicle (i.e., odometer tampering); or (4) simply to rebel against established norms. Tampering and cheating in the commercial freight sector also mean undercutting law-abiding operators, gaining unfair economic advantage, and posing excess harm to the environment and public health. At the individual vehicle level, the impacts of cheating, tampering, or malfunctioning emission control systems can be substantial. The removal or deactivation of emission control systems increases emissions—for instance, typically 70% (NO_x and EGR), a factor of 3 or more (NO_x and SCR), and a factor of 25–100 (PM and DPF). Our analysis shows significant uncertainty and (geographic) variability regarding the occurrence of cheating and tampering by vehicle owners. The available evidence suggests that fleet-wide impacts of cheating and tampering on emissions are undeniable, substantial, and cannot be ignored. The presence of a relatively small fraction of high-emitters, due to either cheating, tampering, or malfunctioning, causes excess pollution that must be tackled by environmental authorities around the world, in particular in emerging economies, where millions of used ICE vehicles from the US and EU end up. Modernized in-service emission programs designed to efficiently identify and fix large faults are needed to ensure that the benefits of modern vehicle technologies are not lost. Effective programs should address malfunctions, engine problems, incorrect repairs, a lack of servicing and maintenance, poorly retrofitted fuel and emission control systems, the use of improper or low-quality fuels and tampering. Periodic Test and Repair (PTR) is a common in-service program. We estimate that PTR generally reduces emissions by 11% (8–14%), 11% (7–15%), and 4% (−1–10%) for carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NO_x), respectively. This is based on the grand mean effect and the associated 95% confidence interval. PTR effectiveness could be significantly higher, but we find that it critically depends on various design factors, including (1) comprehensive fleet coverage, (2) a suitable test procedure, (3) compliance and enforcement, (4) proper technician training, (5) quality control and quality assurance, (6) periodic program evaluation, and (7) minimization of waivers and exemptions. Now that both particulate matter (PM, i.e., DPF) and NO_x (i.e., SCR) emission controls are common in all modern new diesel vehicles, and commonly the focus of cheating and tampering, robust measurement approaches for assessing in-use emissions performance are urgently needed to modernize PTR programs. To increase (cost) effectiveness, a modern approach could include screening methods, such as remote sensing and plume chasing. We conclude this study with recommendations and suggestions for future improvements and research, listing a range of potential solutions for the issues identified in new and in-service vehicles. Full article

This work systematically explores the impact of spark plug electrode number on engine performance and environmental effects, including noise, vibration, fuel consumption, and exhaust emissions. Indicators of combustion efficiency and mechanical health are engine vibration and noise; emissions directly affect ecological sustainability. Four-electrode spark plugs reduce vibration by 10%, noise by 5%, and fuel economy by 15%, according to experimental results showing they outperform single-electrode designs. Especially four-electrode designs also lower harmful hydrocarbon (HC) and carbon monoxide (CO) emissions by up to 20%, indicating more complete combustion and providing significant environmental benefits through lower air pollution and greenhouse gas emissions. Reduced exhaust temperatures of surface discharge plugs indicate better combustion efficiency and perhaps help with decarbonization. With poorer emission profiles, two- and three-electrode configurations raise fuel consumption, noise, and vibration. Reduced quenching effects, improved spark distribution, and accelerated flame propagation all help to explain enhanced combustion efficiency in multi-electrode designs and so affect the fundamental combustion chemistry. These results highlight the possibilities of four-electrode spark plugs to improve engine performance and reduce environmental impact, providing information for automotive engineers and legislators aiming at strict emissions standards (e.g., Euro 7) and sustainability targets. With an eye toward the chemical processes involved, additional study is required to investigate electrode geometry, material innovations, and lifetime environmental impacts. Full article

This study aims to investigate the impact of combustion chamber geometry and biodiesel on the performance of diesel engines under various load conditions. Simulations were conducted using AVL FIRE software, followed by experimental validation to compare the performance of the prototype Omega combustion chamber with the optimized TCD combustion chamber (T for turbocharger, C for charger air cooling, and D for diesel particle filter). This study utilized four types of fuels: D100, B10, B20, and B50, and was conducted under different load conditions at a rated speed of 1800 revolutions per minute (rpm). The results demonstrate that the TCD combustion chamber outperforms the Omega chamber in terms of indicated thermal efficiency (ITE), in-cylinder pressure, and temperature, and also exhibits a lower indicated specific fuel consumption (ISFC). Additionally, the TCD chamber shows lower soot and carbon monoxide (CO) emissions compared to the Omega chamber, with further reductions as the load increases and the biodiesel blend ratio is raised. The high oxygen content in biodiesel helps to reduce soot and CO formation, while its lower sulfur content and heating value contribute to a decrease in combustion temperature and a reduction in nitrogen oxide (NOx) production. However, the NOx emissions from the TCD chamber are still higher than those from the Omega chamber, possibly due to the increased in-cylinder temperature resulting from its combustion chamber structure. The findings provide valuable insights into diesel engine system design and the application of oxygenated fuels, promoting the development of clean combustion technologies. Full article

Effectively managing carbon monoxide (CO) pollution in complex industrial cities like Jubail remains challenging due to the diversity of emission sources and local environmental dynamics. This study analyzes spatiotemporal CO patterns and builds accurate predictive models using five years (2018–2022) of data from ten monitoring stations, combined with meteorological variables. Exploratory analysis revealed distinct diurnal and moderate weekly CO cycles, with prevailing northwesterly winds shaping dispersion. Spatial correlation of CO was low (average 0.14), suggesting strong local sources, unlike temperature (0.92) and wind (0.5–0.6), which showed higher spatial coherence. Seasonal Trend decomposition (STL) confirmed stronger seasonality in meteorological factors than in CO levels. Low wind speeds were associated with elevated CO concentrations. Key predictive features, such as 3-h rolling mean and median values of CO, dominated feature importance. Spatiotemporal analysis highlighted persistent hotspots in industrial areas and unexpectedly high levels in some residential zones. A range of models was tested, with ensemble methods (Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost)) achieving the best performance ($R^2>0.95$) and XGBoost producing the lowest Root Mean Squared Error (RMSE) of 0.0371 ppm. This work enhances understanding of CO dynamics in complex urban–industrial areas, providing accurate predictive models ($R^2>0.95$) and highlighting the importance of local sources and temporal patterns for improving air quality forecasts. Full article