Pollutant Emissions Research Articles

Influence of fuel and operation mode on air pollutants emission from pulverized coal-fired power plant: Field experiments and ML predictions

Influence of fuel and operation mode on air pollutants emission from pulverized coal-fired power plant: Field experiments and ML predictions

Does outward foreign direct investment improve environmental quality in home country?——Evidence from Chinese firms’ water pollution emissions

Does outward foreign direct investment improve environmental quality in home country?——Evidence from Chinese firms’ water pollution emissions

Air pollution reduction co-benefits associated with low-carbon transport initiatives for carbon neutrality in China by 2060

Low-carbon transport policy measures will contribute to greenhouse gas emission reductions, as well as improvements in air quality via air pollutant emission reductions. Here, we developed a regional transport energy model by integrating a transport demand model and an energy system model to explore the associated air pollutant emission reductions achieved by low-carbon transport initiatives when projecting a path towards carbon neutrality. Several scenarios were created to identify the effectiveness and feasibility of low-carbon strategies using the Avoid–Shift–Improve (A-S-I) framework including “Technology”, “Regulation”, “Information”, and “Economy” instruments. Significant reductions in air pollutant emissions alongside transport decarbonization in China could be realized by implementing policy measures to establish effective transport demand management, a modal shift in transport use, and technological improvements. In particular, the “Improve” strategy was more effective in delivering significant reductions in air pollutant emissions than were the “Avoid” and “Shift” strategies, but the policy effects of each A-S-I pillar varied greatly for different pollutants and across different regions. An air pollution reduction strategy for the transport sector should be designed in an integrated manner based on a series of different strategies and tools.

Timescales distribution and reactive structures in MILD reactors for different energy carriers

Moderate or Intense Low-oxygen Dilution (MILD) combustion processes are purported to be fuel-flexible, thus offering a real opportunity to use a large palette of fuels. In this respect, the way the oxidative reactive structure may change by changing the specific fuel is a key issue to determine, to continue relying on high efficiency and low pollutant emissions of MILD combustion. In this work, the differences among the reactive structures at the macroscale of the MILD process were analyzed for pure NH3, CH4 and H2, with a peculiar attention to the interplay between chemical (τc) and turbulence (τI) timescales. Reactive structures were analyzed through a combined experimental and CFD approach. To this purpose, a cyclonic MILD burner was considered as reference experimental facility, while macroscale features were resolved through CFD simulations using the Partially Stirred Reactor (PaSR) model. Results were analyzed in terms of Heat Release, reactive cell number distribution and behavior. Experimental results highlighted the existence of a distributed reactive region for the cyclonic burner, independently of the considered fuel, while its spatial distribution and extension were found to be significantly affected by the fuel reactivity. As the fuel reactivity increases, reactive regions tighten and move upstream the burner exit. This results in a uniform heat release region for pure NH3 and more localized areas for H2, while CH4 shows and intermediate behavior. Furthermore, the chemical and fluid-dynamic timescales interplay covers a fundamental role in defining the global behavior of reactive regions. These approach a PSR-like condition as the fuel reactivity decreases (NH3<CH4<H2), as result of the longer chemical times compared to the mixing ones (τc>>τI).

Wildfire smoke and health impacts: a narrative review.

Air pollution emission associated with wildfires is a global concern, contributing to air quality deterioration and severely impacting public health. This narrative review aims to provide an overview of wildfire smoke (WFS) characteristics and associated impacts on adults' and children's health. Literature review based on a bibliographic survey in PubMed (National Library of Medicine, United States), SciELO (Scientific Electronic Library Online), and Google Scholar databases. Observational, cross-sectional, longitudinal, and review studies were considered, prioritizing peer-reviewed articles published in the last 10 years (2014-2024). Wildfire smoke (WFS) contributes to the deterioration of air quality, resulting in increased exposure to air pollution especially in wildland-urban interfaces. WFS contains particulate matter (PM) in a range of sizes and chemical compositions, as well as multiple toxic gasses. The health impacts of WFS are systemic, affecting the respiratory, cardiovascular, and neurological systems. Exposure to WFS is associated with inflammatory and oxidative stress, DNA damage, epigenetic modulations, and stress-disorders in adults and children. Children may be at an increased risk of WFS respiratory impacts, due to their smaller airways and developing lungs. Wildfires are increasing in frequency and intensity, resulting in thousands of premature deaths and hospitalizations worldwide, each year. Preventive measures against wildfire spread must be reinforced, considering the increasing trends of global warming and extreme weather events. Adaptation strategies should be undertaken especially in wildland-urban interface regions, including the improvement of early warning systems, improvement of health care facilities and household preparedness and promotion of risk communication campaigns.

Dynamic mechanism of biodiesel-ethanol-graphene droplets during micro-explosion and puffing: droplet evaporation, distribution and velocity characteristics

Adding graphene to biodiesel and ethanol blends can significantly improve the combustion efficiency, which can significantly reduce pollutant emissions when applied to alternative engine fuels. The study of the droplet dynamic properties of biodiesel-ethanol-graphene blends can help to aid in a deeper understanding of the properties of the fuel, which in turn can provide a theoretical basis for the application of the fuel. In this paper, a modified tube furnace experimental platform was used to investigate the evaporation, micro-explosion, and dynamic properties of parent and child droplets of biodiesel-ethanol-graphene. The micro-explosion and dynamics mechanisms of the parent droplets and child droplets of the blended fuel were further investigated in terms of graphene content, ambient temperature and biodiesel/ethanol blend proportion. During the research, three modes of bubble growth in droplet were proposed, the number and velocity of child droplets and the intensity of micro-explosion were found to have a positive correlation trend. It is found that BD50E50(1%G) droplets show high micro-explosion effect and evaporation rate, and can be considered as a sustainable alternative fuel for airlines or oil companies. The movement law of mixed droplets is analyzed, and the internal conditions and external characterization parameters of micro-explosion are summarized.

Developing airport management practices towards net zero emissions: Experiences from the Australian aviation industry.

Emissions from airport sources degrade air quality impacting community health. While some airports assess air pollution, others assess broader environmental effects, including CO2 emissions and noise. Utilising a transition management approach, this paper examines Australian airport practices and develops key sustainable strategies to reduce environmental impacts. After reviewing environmental policies and reports from eight major airports and conducting in-depth interviews with 18 sustainable aviation experts, five key strategies are proposed: 1) Collaborating data-sharing among stakeholders, including airport operators, ground handlers, airlines and air traffic controllers; 2) Evaluating emissions from aircraft ground idling delays; 3) Advancing in the Airport Carbon Accreditation program; 4) Assessing air pollutant emissions directly emitted from airport sources; 5) Maintaining an air pollutant emissions inventory. Airports should integrate these strategies into their environmental policies to support their long-term sustainable goal of reaching net zero emissions by 2050.

Economic and Environmental Assessment of Ammonia as a Sustainable Fuel for Tugboat Operations at the Port of Texas

This study assesses the feasibility of ammonia as a sustainable alternative fuel for tugboat operations in the Port of Texas, a major U.S. maritime, oil and gas hub. Tugboats, while critical to port logistics, contribute significantly to localized air pollution and greenhouse gas emissions, impacting both environmental quality and public health in surrounding communities. Given the International Maritime Organization’s (IMO) goals to reduce carbon emissions by 40% by 2030 and 70% by 2050, exploring alternative fuels has become imperative. Ammonia, a carbon-free fuel, presents both opportunities and challenges. While ammonia combustion produces no direct CO₂ emissions, making it attractive from a decarbonization standpoint, its lower energy density results in higher fuel volumes and increased operational costs compared to marine fuel oil (MFO). This paper presents a comparative analysis based on real-world data, mathematical modeling, and emission trade-offs, examining the economic implications, environmental impact, and safety concerns of adopting ammonia as a tugboat fuel. Results indicate that while ammonia could enable significant CO₂ reductions, challenges related to fuel storage, cost, and infrastructure must be addressed. The findings suggest that with appropriate policy support and investment in handling infrastructure, ammonia can become a viable fuel option for sustainable maritime operations.

Analysis of the characteristics and influencing factors of water ecological security in Beijing-Tianjin-Hebei region: A perspective of industrial agglomeration

Water ecological security (WES) is crucial for promotion of economic and social development characterized by high-quality, while exploring the characteristics of WES and its response mechanism to industrial agglomeration can help formulate and implement relevant policies. In this study, a WES evaluation index system tailored for the Beijing-Tianjin-Hebei (BTH) region was constructed based on the Pressure-State-Response (PSR) framework. Additionally, building on the assessment of the degree of industrial agglomeration within the BTH region, the research employs a spatial Durbin model to investigate the impact of industrial agglomeration on WES in the BTH region. The main results are as follows: (1) The level of WES in the BTH region has steadily increased from 2005 to 2021, consistent with the trend of industrial agglomeration in the study area. (2) Influenced by regional economic development and natural conditions, the WES in the BTH region exhibits significant regional disparities and spatial autocorrelation, with levels decreasing from the central area to the east, northwest, and south. (3) Industrial agglomeration enhances WES by improving resource utilization efficiency, reducing pollutant emissions, and providing regional governments with financial resources to improve public services and water ecological protection, leading to better water ecological quality. (4) The impact of different industries on WES varies. As the proportion of the primary industry in the BTH region is relatively small, the agglomeration of the secondary and tertiary industries is more effective in promoting improvements in WES. Moreover, the spatial spillover effects of industrial agglomeration in the BTH region are significant. To enhance the level of WES in the region, this study recommends strengthening water ecological protection, promoting industrial structure adjustment, and enhancing collaborative development.

Quantitative study on carbon emissions of modified recycled asphalt mixture based on life cycle assessment method

The recycled asphalt technology is considered to have environmental sustainability prospects due to the resource conservation of old material recycling. Nonetheless, how to quantitatively evaluate the environmental impact of the entire life cycle of recycled asphalt pavement (RAP) still needs to be sorted out. Based on the theory of life cycle management, through the Life-cycle assessment (LCA) method, this study establishes a quantitative assessment model for the environmental impact of recycled asphalt pavement. A quantitative assessment model is established for the full life cycle environmental impact of recycled asphalt pavement. The model can output a list of environmental impacts for each stage of the life cycle, and can also conduct characteristic impact assessments based on five major impact categories: energy consumption (EC), global warming potential (GWP), acidification potential (AP), human health hazards (HTP), and particulate matter emissions. The results indicate that the acquisition of raw materials is the dominant stage for the environmental impact of cold recycled asphalt pavement, with a proportion of over 50% for each major impact category. In the construction of highways, using recycled modified asphalt mixture can reduce the total emissions by 12,976 kg per kilometer. In addition, the life cycle inventory (LCI) analysis shows that the environmental impact of recycled asphalt pavement is mainly quantified by energy consumption and various pollutant emissions, such as CO2, CH4, SO2, CO, N2O, NMVOC, particulate matter, and asphalt smoke. The raw material extraction stage has been identified as the stage with the greatest environmental impact, making significant contributions in energy consumption, global warming potential, acidification potential, human health hazards, and particulate matter emissions. This indicates that utilizing cold recycling technology and increasing the use of recycled RAP materials are efficient ways to promote energy conservation, reduce emissions, and minimize the environmental impact of asphalt pavement throughout its lifecycle.

Property Prediction of Bio-Derived Block Copolymer Thermoplastic Elastomers Using Graph Kernel Methods.

Increasing the diversity of bio-based polymers is needed to address the combined problems of plastic pollution and greenhouse gas emissions. The magnitude of the problems necessitates rapid discovery of new materials; however, identification of appropriate chemistries maybe slow using current iterative methods. Machine learning (ML) methods could significantly expedite new material discovery and property identification. Here, PolyAGM, a ML algorithm using graph kernel methods, is introduced and used to predict the properties of block copolymers and identify the responsible structural 'motifs'. It applies a "fingerprinting" method to convert Graph representations of polymers into numerical vectors. The Graphs explicitly encode the entire copolymer of atoms and bonds such that the sequencing of chemical features and polymer chain length are included, alongside relevant stereochemical information. PolyAGM gives predictions for both thermal and mechanical properties that are in good agreement with experimental measurements. This work focuses on predicting the properties of bio-derived ABA-block polymer thermoplastic elastomers, but the general fingerprinting technique of PolyAGM should be relevant to other application fields.

Techno-economic assessment of a green liquid hydrogen supply chain for ship refueling

Maritime transportation is an essential component of international trade and global economic growth: according to the 2021 Review of Maritime Transport, transportation by sea is responsible for more than 80% of the global product flow. To facilitate decarbonization and reduce pollutant emissions in the maritime sector, the International Maritime Organization (IMO) has set very ambitious targets, and hydrogen is one of the most promising energy vectors capable of supporting the required low-carbon energy transition. Recently, therefore, more attention has been paid to finding technical solutions for the development of hydrogen-based alternative propulsion systems and, at the same time, a hydrogen distribution infrastructure suitable for shipping. In this scenario, this study aims to analyze, through design, modeling and optimization approaches, the energy and economic performance of an offshore wind power plant integrated with a liquid hydrogen production system to be used for refueling ships far from ports. This study focuses on the development of an optimization procedure dedicated to finding the best technical solution in terms of component sizes and management strategies that ensure the minimum Levelized Cost of Hydrogen (LCOH). Resultshave highlighted that the proposed innovative plant configuration for the offshore liquid hydrogen production is able to produce 317 tons per year of green hydrogen with a LCOH of 16.77 €/kg by using 19 MW offshore wind farm and 5 MW PEM electrolyzer.

Impact of hard and soft digital infrastructure on pollutant emissions: evidence from China

ABSTRACT The ‘dual-track transition’ of digitalisation and environmental sustainability brings both opportunities and challenges. A deeper understanding of the relationship between these two aspects is essential to ensuring the success of this transition. This study fully considers the high-energy consumption characteristics and layered nature of digital infrastructure, using city-level sample data from China from 2006 to 2018 to empirically examine the actual impact and underlying mechanisms of digital infrastructure’s energy consumption on pollutant emissions. The results indicate that digital infrastructure directly increases pollutant emissions due to energy consumption, and there is an overlapping effect between hard and soft digital infrastructure. Mechanistically, stakeholders’ environmental behaviours mitigate the negative impact of digital infrastructure energy consumption on pollutant emissions; these behaviours include government environmental regulation, public environmental appeals, and corporate green innovation. Additionally, when considering the inter-regional coal power output and input relationships, the development of digital infrastructure exhibits a cross-regional pollutant transfer effect. These insights are crucial for supporting the success of the ‘dual-track transition’.

Bioaugmentation strategy for mitigating pollutant gas emissions in food waste composting using fermented mixtures

The fermented mixtures at different composting stages exhibit varying physicochemical and microbiological properties, adding them to compost, which may show different biological enhancement abilities. This study analyzed the efficiency of fermented mixtures in mitigating pollutant gas emissions during composting, through the co-composting of food waste and sawdust, supplemented with 20 % fermented mixtures from different composting stages: thermophilic (TS), cooling (CS), and mature (MS). Results revealed that fermented mixtures affected the composting process and pollutant gas emissions by regulating bacterial communities. The TS treatment increased the abundance of high-temperature resistant organic matter degrading bacteria, thereby effectively promoting temperature and reaching the highest peak temperature (> 70 °C) in just 2 days. Among all the treatments, TS exhibited the most pronounced reduction in N2O emissions, inhibiting the proliferation of bacteria engaged in nitrite respiration, which resulted in reducing overall N2O emissions by 20.7 %. Furthermore, TS outperformed both CK and MS treatments regarding CO2 reduction, achieving declines of 11.2 % and 20.2 %, respectively. The MS treatment diminished the abundance of acidogens and methanogens bacteria, such as Lactobacillus, Weissella, and Leuconostoc, resulting in a 24.2 % reduction in methane emissions compared to CK. In contrast, the CS treatment had minimal effect, reducing methane emissions by 4.2 %. In conclusion, thermophilic compost demonstrated superior effectiveness in reducing pollutant gas emissions compared to other fermented mixtures. Additionally, owing to its economic advantages, thermophilic compost can be produced significantly faster than the others. Therefore, thermophilic compost is recommended as a bioaugmentation technology that synergistically offers environmental benefits and cost-effectiveness.

The synergistic role of sludge conditioner FeCl3/Rice husk on co-combustion with coal gangue: Thermaldynamic behavior, gases pollutants control and bottom ash stabilization

Coal processing invariably generates substantial quantities of low calorific value waste, specifically coal gangue (CG), which can be advantageously utilized by combusting to yield valuable electrical energy. However, CG incur poor ignition and flame instability, and consequently is not suited to separate combustion. The co-combustion with sewage sludge (SS) has demonstrated positive impacts on energy recovery, whereas the SS dewatering may significantly influence the co-combustion behavior. Therefore, this study systematically investigated the impact of two typical sludge conditioner, namely FeCl3·6H2O and rice husk (RH), which functions as flocculant and skeletal builder, on their synergistic role on co-combustion with CG. The thermal dynamic combustion behavior, pollutant emissions, slag tendency and bottom ash stability and toxicity were systematically studied. A robust positive synergism is observed, attributed to heat compensation and the formation of alkali metal aluminosilicates from Rh during the ignition phase. Concurrently, the temperature dependent iron oxides evolution enhances the acceleration of O2 loop, thereby promoting the char combustion. After being jointly conditioned with Rh and FeCl3, the co-combustion with CG resulted in CCi being 3.46 times higher than that of C1S3, and the average activation energy in each stage was reduced by 49.1 %. Significantly, the sludge conditioner also contributes to the reduced exhausted gases such as CO2, SO2 and NO. The Rh in SS has been found to mitigate slagging and fouling tendencies, while the retention of Cr, Cu, Ni, and Pb is greatly improved due to the stabilization of silicate minerals in CG. The Artificial Neural Network (ANN) models were established to predict the thermogravimetric experimental data of CG-SS-Rh/Fe, which aims to provide a basis for the selection of optimal operating conditions in real industrial applications.

The Assessment of the Ecological State of Leachate from the Largest landfill of Georgia and Its Impact on Climate Change

Over the past decade the municipal solid waste management status in Georgia remained at the stage of mixed waste collection and disposal in landfills having negative impacts on entire ecosystem. It results the formation of large amounts of leachate, methane gas, potential resource depletion and is potential to health hazards. Landfill leads to groundwater contamination, soil &amp; land pollution, harm to animals exposing to harmful materials &amp; foods. Landfills are vulnerable to accidental fires due to the methane produced by decomposing organic waste, financial costs that can be expensive for municipalities or city councils and taxpayers. The aim of this study is to assess the adversee impact of leachate management technology on water resources, as well as on climate change. For utilisation of leachate in this research, the technology of distributing the collected leachate over the surface of the landfill is used. The main objectives include, the study of physical-chemical and microbiological parameters of leachate and the calculation of GHG (methane) emissions from sedimentation systems/tanks/reservoirs of leachate. Physical-chemical and microbiological analysis of leachate water indicates that the concentrations of organic, biogenic and microbiological parameters in research samples exceed limits of discharge by ten times. In this study, for the first time an index of water pollution and greenhouse gas emissions was calculated for leachate of the biggest landfill in Georgia. Leachates belong to class VII according to water pollution index. Emission of methane from leachate for 2024-year estimated as 5% additionally to total methane emissions from the studied landfill.

Synergistic effect evaluation method of atmospheric emission reduction based on deep learning fusion model

Currently, the absence of a nonlinear response between air quality and industrial emissions, which accurately captures the complex relationship between pollution levels and emission sources, poses a significant challenge in the formulation of effective control policies. For the purpose of effectively managing industrial emissions and mitigating severe pollution peaks, a new method for evaluating the synergistic effects of atmospheric emission reduction was proposed. The objective of this method is to simulate the impact of emissions on air quality. In particular, we have developed a deep learning fusion model, GR-BILSTM, which integrates a generative adversarial network for data enhancement and a ResNet-BILSTM model to effectively address the issue of gradient disappearance in deep networks and capture high-dimensional data features, thereby improving the model's prediction accuracy. This model is used to study the relationship between emissions and air pollution. Subsequently, a perturbation analysis method is employed to provide a quantitative assessment of the impact of varying types and scales of industrial park emissions on PM2.5 concentrations. By modifying the emission reduction ratio, the PM2.5 concentration in the subsequent moment is predicted, and the correlation between the predicted value and the measured value is examined. This provides a vital point of reference for the formulation of targeted emission policies in industrial parks. In comparison to the LSTM, BILSTM, and CNN-BILSTM models that are commonly employed in this field, the GR-BILSTM model has been demonstrated to exhibit superior performance in terms of fitting accuracy. Our research indicates that the greatest contributions of SO2, NOx, and TSP to PM2.5 pollution in industrial parks are 16 %, 15 %, and 18 %, respectively.

ENVIRONMENTAL ASPECTS OF DESIGNING TURBOCOMPRESSOR UNITS OF COMPRESSOR STATIONS OF MAIN GAS PIPELINES

The article examines the environmental aspects of creating turbo-compressor units for compressor stations of main gas pipelines. The aim of the study is to identify and analyze the main sources of environmental pollution arising from the operation of turbo-compressor units, as well as to analyze modern technical solutions aimed at reducing their negative impact on the environment. This goal is achieved through an analysis of the operational processes of turbo-compressor units, a review of literature sources and applicable regulations, as well as the systematization of many years of experience gained at JSC “SMNPO-Engineering” (Sumy, Ukraine) in the design and testing of similar equipment. The most important results of the work are as follows: sources of chemical, acoustic, and thermal pollution generated during the operation of turbo-compressor units with gas turbine drives and centrifugal compressors have been identified; a conclusion has been drawn about the priority of developing dry fuel combustion methods to reduce emissions of pollutants. It is noted that the application of catalytic exhaust gas purification systems is advisable for modernizing existing compressor stations to ensure compliance with environmental requirements without replacing or significantly reworking engine designs. Using a 16 MW gas turbine compressor unit as an example, an assessment of thermal pollution in the environment has been carried out. It has been concluded that reducing thermal pollution can be achieved by increasing the energy efficiency of turbo-compressor units, particularly through the application of complex working cycles of the drive, utilizing waste heat from exhaust gases, and creating energy technology complexes based on compressor stations of main gas pipelines for the production of electricity, heat, and cooling. The results of this work can be used in the development of environmentally more efficient turbo-compressor units, which is especially relevant in the context of global climate change and increasing requirements of international environmental regulations. Moreover, increasing the energy efficiency of turbo-compressor units will not only reduce environmental pollution but also lower energy consumption and improve the cost-effectiveness of gas transportation.

Does industrial robot adoption inhibit environmental pollution in China? An empirical study on energy consumption and green technology innovation

In this research, we develop a theoretical model to examine the relationship between the adoption of industrial robots and environmental pollution. Additionally, we explore the mechanisms through which the implementation of industrial robots influences pollutant emissions. Our findings indicate that the adoption of industrial robots has a negative effect on environmental pollution. This conclusion is drawn from a panel data analysis of 283 cities in China from 2006 to 2020, utilizing a double fixed effect model. Further mechanism tests reveal that the use of industrial robots contributes to a reduction in pollution emissions by optimizing the energy consumption structure and enhancing energy efficiency. Meanwhile, industrial robot usage decreases pollution emissions by enhancing pollution treatment capabilities and boosting green technological innovation. Furthermore, regions characterized by heightened environmental awareness demonstrate a more significant improvement in environmental pollution levels when industrial robots are applied to their production process. This study ultimately demonstrates that integrating industrial robots can lead to a reduction in pollutant emissions, thereby yielding sustainable benefits for the environment.

Prediction of Pollutant Emissions from a Low-Speed Marine Engine Based on Harris Hawks Optimization and Lightgbm

With the rapid development of data science, machine learning has been widely applied to research on pollutant emission prediction in internal combustion engines due to its excellent responsiveness and generalization ability. This article introduces Lightgbm (LGB), which belongs to ensemble learning, to predict the pollutant emissions from a low-speed two-stroke marine engine. The dataset used to train LGB was derived from a one-dimensional performance simulation model of the engine, which was rigorously verified for its reliability by experimental data. To further improve the forecast performance of the LGB model, we used Harris Hawks Optimization (HHO) to automatically optimize the hyperparameters of the model, and finally, we analyzed the importance of the model features. The results show that changes in engine control parameters have significant influences on NOx and soot emissions from the engine, which can serve as the basis for the selection of the LGB model features; the LGB model was able to accurately predict pollutant concentrations from the engine with much higher accuracy than a single decision tree (DT) model; combining with HHO, the predictive ability of the LGB model was significantly improved, such as for the validation set prediction results, the mean absolute error (MAE) was reduced by about 20%, the mean squared error (MSE) was reduced by about 30%, and the coefficient of determination (R2) was increased by about 0.005; and the importance analysis of the model features indicated that the combustion condition of the fuel was highly correlated with the generation of the pollutants, and the fuel injection phases can be adjusted in practice to achieve highly efficient and low-emission processes of combustion. The results of this study can provide references for the development of a new generation of highly efficient and low-pollution marine engines.