Emission Estimates Research Articles

Spatiotemporal estimates of anthropogenic NOx emissions across China during 2015-2022 using a deep learning model.

As one of the significant air pollutants, nitrogen oxides (NOx = NO + NO2) not only pose a great threat to human health, but also contribute to the formation of secondary pollutants such as ozone and nitrate particles. Due to substantial uncertainties in bottom-up emission inventories, simulated concentrations of air pollutants using GEOS-Chem model often largely biased from those of ground-level observations. To address this issue, we developed a new deep learning model to simulate the inverse process of the GEOS-Chem model. This framework was applied to improve the total anthropogenic NOx emission intensity over a five-year period (2015-2019), and then to predict anthropogenic NOx emission intensity for 2020-2022. The deep learning model showed higher R2 value (0.94) based on 10-fold cross-validation, indicating that the model effectively captured spatial features and patterns. Then, NOx emission intensity was calibrated based on the new framework using high-resolution NO2 concentration dataset instead of the simulated NO2 levels derived from GEOS-Chem model. Overall, the top-down inversion result was in agreement with the bottom-up emission inventory at the spatial scale. The top-down emission fluxes were lower in high-emission regions such as central China, Beijing, Shanghai, the Pearl River Delta, and the central part of Liaoning, while posterior estimates were higher in regions with lower prior emission intensity. The posterior NOx emission intensity suggested that some major regions such as Beijing-Tianjin-Hebei (BTH) (-56.4 %) and Pearl River Delta (PRD) (-52.5 %) experienced dramatic NOx emission intensity decreases from 2015 to 2022, whereas some remote regions such as Tibetan Plateau remained relatively stable. This research contributes to the timely tracking of changes in pollutant emission and aids in the formulation of more effective and relevant pollution prevention and control policies.

Quantitative study on the environmental impact of Beijing’s urban rail transit based on carbon emission reduction

Urban rail transit, as an efficient and eco-friendly mode of transportation, plays a pivotal role in mitigating traffic congestion and lowering urban carbon emissions. Despite the significant contributions by scholars in this area, debates surrounding the quantification of carbon emissions during the operational phase of urban rail transit persist, particularly in assessing its impact on reducing ground traffic congestion. This study examines the passenger flow during Beijing’s morning and evening peak hours, assuming that all passengers initially using urban rail transit switch to buses and taxis during these periods. A traffic congestion prediction model is developed based on the analysis of actual traffic operation data under this assumption. Through this model, the study calculates the potential congestion times across various scenarios, employing a bottom-up approach to carbon emission estimation to analyze the impact on carbon emissions. Results spanning 2015 to 2021 suggest that substituting urban rail transit with buses could increase congestion by 37–92 min and 46–59 min during morning and evening peaks, respectively, leading to a 24-82% and 27-56% surge in carbon emissions. The conversion of all these vehicles to taxis would result in a direct paralysis of Beijing’s road transport network, with a corresponding increase in carbon emissions of between 289% and 556% and 333% and 614%, respectively.These outcomes emphasize the substantial efficacy of urban rail transit in curbing traffic congestion and carbon emissions.

Effectiveness of the Federal ‘Clean Air’ Project to Improve Air Quality in the Most Polluted Russian Cities

An unavoidable adverse consequence of industrial development is the contamination of urban atmospheres. Deterioration of air quality leads to a decrease in the quality of life of the population, creates a lot of risks of serious diseases, and threatens to increase life expectancy. This phenomenon is particularly evident in many large Russian cities, where historically a powerful industry has developed. In recent decades, the Russian government has acknowledged environmental remediation as a pivotal priority for the National Development Goals. The dedicated funding from the National ‘Ecology’ Project in 2018–2024 allowed for large-scale public and private investments to address the problem of improving the air quality of urban areas in Russia. What is the effectiveness of this spending? In this article, we answer this question by analyzing the effectiveness of the Federal ‘Clean Air’ Project, part of the National ‘Ecology’ Project, which aimed to improve air quality in 12 of the most polluted Russian cities. We show that the project’s key performance indicators (KPIs) underwent significant changes over the 2018–2024 period. The emissions reduction target was lowered from 22% to 20%, the methodology for measuring pollution was revised, and new targets were set. One of the main reasons for this was the suboptimal quality of the data on which the initial plan was based. As a result, the revised emissions estimates produced by the project were found to exceed not only the target benchmarks but also the baseline. The planned targets are largely on track, and it is likely that the target of a 20% reduction in emissions from the 2017 baseline will be met. However, the link between the KPIs and the improvement in urban air quality is questionable. The initial phase of the ‘Clean Air’ Project was a valuable first step, particularly in establishing an air quality monitoring network and conducting detailed pollution assessments in 12 cities. However, to further improve project performance, it is essential to base project KPIs on estimates of air pollution-related health damage and economic losses.

Halogen Emissions from Coal-Fired Power Plants in China: Evolutions, Driving Forces, and Future Trends.

Atmospheric halogens, including fluorine (F), chlorine (Cl), bromine (Br), and iodine(I), significantly impact atmospheric chemistry and climate change. Containing all types of halogens, coal fired power plants (CFPPs) are among the major anthropogenic sources of atmospheric halogens. However, comprehensive estimates of halogen emissions from CFPPs in China remain limited, despite significant advancements in scale and pollution control. This study developed a detailed emissions inventory for all halogens from CFPPs using multisource data and the mass balance method, analyzing their spatiotemporal variations, driving forces, and future trends under climate goals. Results showed fluctuating halogen emissions from 2018 to 2022, with F, Cl, Br, and I reaching 6,875.7 t, 24,872.4 t, 1,127.9 t, and 476.7 t in 2022, respectively. Emissions were predominately concentrated in key coal resource areas and high-energy-consuming regions. Increased coal consumption was the primary driver of emissions growth, while improvements in pollution control and power generation technology contributed to reductions. Under air pollution control and climate goals, halogen emissions are expected to peak before 2030 and decline rapidly thereafter, with near-elimination by 2050. Combining strict air pollutants and carbon control technologies would offer the greatest reduction potential.

Benchmarking data-driven inversion methods for the estimation of local CO2 emissions from synthetic satellite images of XCO2 and NO2

Abstract. The largest anthropogenic emissions of carbon dioxide (CO2) come from local sources, such as cities and power plants. The upcoming Copernicus CO2 Monitoring (CO2M) mission will provide satellite images of the CO2 and NO2 plumes associated with these sources at a resolution of 2 km × 2 km and with a swath of 250 km. These images could be exploited using atmospheric-plume inversion methods to estimate local CO2 emissions at the time of the satellite overpass and their corresponding uncertainties. To support the development of the operational processing of satellite imagery of the column-averaged CO2 dry-air mole fraction (XCO2) and tropospheric-column NO2, this study evaluates data-driven inversion methods, i.e., computationally light inversion methods that directly process information from satellite images, local winds, and meteorological data, without resorting to computationally expensive dynamical atmospheric transport models. We designed an objective benchmarking exercise to analyze and compare the performance of five different data-driven inversion methods: two implementations with different complexities for the cross-sectional flux approach (CSF and LCSF), as well as one implementation each for the integrated mass enhancement (IME), divergence (Div), and Gaussian plume (GP) model inversion approaches. This exercise is based on pseudo-data experiments with simulations of synthetic true emissions, meteorological and concentration fields, and CO2M observations across a domain of 750 km × 650 km, centered on eastern Germany, over 1 year. The performance of the methods is quantified in terms of the accuracy of single-image emission estimates (from individual images) or annual-average emission estimates (from the full series of images), as well as in terms of the number of instant estimates for the city of Berlin and 15 power plants within this domain. Several ensembles of estimations are conducted using different scenarios for the available synthetic datasets. These ensembles are used to analyze the sensitivity of performance to (1) data loss due to cloud cover, (2) uncertainty in the wind, or (3) the added value of simultaneous NO2 images. The GP and LCSF methods generate the most accurate estimates from individual images. The deviations between the emission estimates and the true emissions from these two methods have similar interquartile ranges (IQRs), ranging from ∼ 20 % to ∼ 60 % depending on the scenario. When taking cloud cover into account, these methods produce 274 and 318 instant estimates, respectively, from the ∼ 500 daily images, which cover significant portions of the plumes from the sources. Filtering the results based on the associated uncertainty estimates can improve the statistics of the IME and CSF methods but does so at the cost of a large decrease in the number of estimates. Due to a reliable estimation of uncertainty and, thus, a suitable selection of estimates, the CSF method achieves similar, if not better, accuracy statistics for instant estimates compared to the GP and LCSF methods after filtering. In general, the performance of retrieving single-image estimates improves when, in addition to XCO2 data, collocated NO2 data are used to characterize the structure of plumes. With respect to the estimates of annual emissions, the root mean square errors (RMSEs) for the most realistic benchmarking scenario are 20 % (GP), 27 % (CSF), 31 % (LCSF), 55 % (IME), and 79 % (Div). This study suggests that the Gaussian plume and/or cross-sectional approaches are currently the most efficient tools for providing estimates of CO2 emissions from satellite images, and their relatively light computational cost will enable the analysis of the massive amount of data to be provided by future satellite XCO2 imagery missions.

Methane emissions from the Nord Stream subsea pipeline leaks.

The amount of methane released to the atmosphere from the Nord Stream subsea pipeline leaks remains uncertain, as reflected in a wide range of estimates1-18. A lack of information regarding the temporal variation in atmospheric emissions has made it challenging to reconcile pipeline volumetric (bottom-up) estimates1-8 with measurement-based (top-down) estimates8-18. Here we simulate pipeline rupture emission rates and integrate these with methane dissolution and sea-surface outgassing estimates9,10 to model the evolution of atmospheric emissions from the leaks. We verify our modelled atmospheric emissions by comparing them with top-down point-in-time emission-rate estimates and cumulative emission estimates derived from airborne11, satellite8,12-14 and tall tower data. We obtain consistency between our modelled atmospheric emissions and top-down estimates and find that 465 ± 20 thousand metric tons of methane were emitted to the atmosphere. Although, to our knowledge, this represents the largest recorded amount of methane released from a single transient event, it is equivalent to 0.1% of anthropogenic methane emissions for 2022. The impact of the leaks on the global atmospheric methane budget brings into focus the numerous other anthropogenic methane sources that require mitigation globally. Our analysis demonstrates that diverse, complementary measurement approaches are needed to quantify methane emissions in support of the Global Methane Pledge19.

A CO2–Δ14CO2 inversion setup for estimating European fossil CO2 emissions

Abstract. Independent estimation and verification of fossil CO2 emissions on a regional and national scale are crucial for evaluating the fossil CO2 emissions and reductions reported by countries as part of their nationally determined contributions (NDCs). Top-down methods, such as the assimilation of in situ and satellite observations of different tracers (e.g., CO2, CO, Δ14CO2, XCO2), have been increasingly used for this purpose. In this paper, we use the Lund University Modular Inversion Algorithm (LUMIA) to estimate fossil CO2 emissions and natural fluxes by simultaneously inverting in situ synthetic observations of CO2 and Δ14CO2 over Europe. We evaluate the inversion system by conducting a series of observing system simulation experiments (OSSEs). We find that in regions with a dense sampling network, such as western/central Europe, adding Δ14CO2 observations in an experiment where the prior fossil CO2 and biosphere fluxes are set to zero allows LUMIA to recover the time series of both categories. This reduces the prior-to-truth root mean square error (RMSE) from 1.26 to 0.12 TgC d−1 in fossil CO2 and from 0.97 to 0.17 TgC d−1 in biosphere fluxes, reflecting the true total CO2 budget by 91 %. In a second set of experiments using realistic prior fluxes, we find that in addition to retrieving the time series of the optimized fluxes, we are able to recover the true regional fossil CO2 budget in western/central Europe by 95 % and in Germany by 97 %. In all experiments, regions with low sampling coverage, such as southern Europe and the British Isles, show poorly resolved posterior fossil CO2 emissions. Although the posterior biosphere fluxes in these regions follow the seasonal patterns of the true fluxes, a significant bias remains, making it impossible to close the total CO2 budget. We find that the prior uncertainty of fossil CO2 emissions does not significantly impact the posterior estimates, showing similar results in regions with good sampling coverage like western/central Europe and northern Europe. Finally, having a good prior estimate of the terrestrial isotopic disequilibrium is important to avoid introducing additional noise into the posterior fossil CO2 fluxes.

Soil smoldering in temperate forests: a neglected contributor to fire carbon emissions revealed by atmospheric mixing ratios

Abstract. Fire is regarded as an essential climate variable, emitting greenhouse gases in the combustion process. Current global assessments of fire emissions traditionally rely on coarse remotely sensed burned-area data, along with biome-specific combustion completeness and emission factors (EFs). However, large uncertainties persist regarding burned areas, biomass affected, and emission factors. Recent increases in resolution have improved previous estimates of burned areas and aboveground biomass while increasing the information content used to derive emission factors, complemented by airborne sensors deployed in the tropics. To date, temperate forests, characterized by a lower fire incidence and stricter aerial surveillance restrictions near wildfires, have received less attention. In this study, we leveraged the distinctive fire season of 2022, which impacted western European temperate forests, to investigate fire emissions monitored by the atmospheric tower network. We examined the role of soil smoldering combustion responsible for higher carbon emissions, locally reported by firefighters but not accounted for in temperate fire emission budgets. We assessed the CO/CO2 ratio released by major fires in the Mediterranean, Atlantic pine, and Atlantic temperate forests of France. Our findings revealed low modified combustion efficiency (MCE) for the two Atlantic temperate regions, supporting the assumption of heavy smoldering combustion. This type of combustion was associated with specific fire characteristics, such as long-lasting thermal fire signals, and affected ecosystems encompassing needle leaf species, peatlands, and superficial lignite deposits in the soils. Thanks to high-resolution data (approximately 10 m) on burned areas, tree biomass, peatlands, and soil organic matter (SOM), we proposed a revised combustion emission framework consistent with the observed MCEs. Our estimates revealed that 6.15 Mt CO2 (±2.65) was emitted, with belowground stock accounting for 51.75 % (±16.05). Additionally, we calculated a total emission of 1.14 Mt CO (±0.61), with 84.85 % (±3.75) originating from belowground combustion. As a result, the carbon emissions from the 2022 fires in France amounted to 7.95 MtCO2-eq (±3.62). These values exceed by 2-fold the Global Fire Assimilation System (GFAS) estimates for the country, reaching 4.18 MtCO2-eq (CO and CO2). Fires represent 1.97 % (±0.89) of the country's annual carbon footprint, corresponding to a reduction of 30 % in the forest carbon sink this year. Consequently, we conclude that current European fire emission estimates should be revised to account for soil combustion in temperate forests. We also recommend the use of atmospheric mixing ratios as an effective monitoring system of prolonged soil fires that have the potential to re-ignite in the following weeks.

Improving spatial disaggregation of vehicular emission inventories

ABSTRACT Precise estimates of vehicular emissions at fine spatial scales are essential for effective emission reduction strategies. Achieving high-resolution vehicular emission inventories necessitates detailed data on traffic flow, driving patterns, and vehicle speeds for each road network segment. However, in developing countries, the lack of comprehensive traffic data, limited infrastructure, and insufficient monitoring systems constrains the development of high-resolution inventories. This gap poses significant challenges for accurately quantify emissions in regions that often experience rapid urbanisation and traffic growth. Here, we propose a novel method to enhance the spatial disaggregation of large-scale vehicular emission inventories. By analysing road-level emissions data from 63 Brazilian municipalities, we developed a model that predicts weighting factors to disaggregate vehicular emissions into a gridded format, based on the proportion of primary road lengths. Our findings indicate that the predicted weighting factors significantly improve the spatial disaggregation of vehicular emissions compared to the traditional road density method by reasonably increasing the emissions in high vehicular activity areas. This approach not only provides more accurate representations of vehicular emissions for urban planning in Brazil but also offers a solution that can be adapted to enhance top-down vehicular emissions inventories globally. Our study offers a valuable tool that can be tailored to various regions, enabling more precise urban planning and policy-making for air quality management worldwide.

Review on the Application of Remote Sensing Data and Machine Learning to the Estimation of Anthropogenic Heat Emissions

Review on the Application of Remote Sensing Data and Machine Learning to the Estimation of Anthropogenic Heat Emissions

Combination of anaerobic digestion and sludge biochar for bioenergy conversion: Estimation and evaluation of energy production, CO2 emission, and cost analysis.

Waste-to-energy technologies involve the conversion of several wastes to useful energy forms like biogas and biochar, which include biological and thermochemical processes, as well as the combination of both systems. Assessing the economic and environmental impacts is an important step to integrate sustainability and economic viability at anaerobic digestion systems and its waste management. Energy production, CO2 emissions, cost analysis, and an overall process evaluation were conducted, relying on findings from both laboratory and pilot-scale experiments. The digestate generated during anaerobic digestion proved to be an effective approach for mitigating some CO2 emissions while managing sludge waste within the anaerobic digestion and CHP system. Incorporating biochar production and application in soil into the process led to a 3.5 % reduction in CO2 emissions, which contributed to a more sustainable form of energy production while offering the potential for the generation of carbon credits through a carbon-negative process. The employment of digestate biochar for energy production seems a feasible way to reduce the amount of residue to final disposal in landfill with a minimal reduction of profit per GWh and a slight increase in the CO2 emissions by 2.7 %.

Green energy systems for powering electric vehicles considering telecommunication system with case study of Pakistan

The objective is to analyze the sustainability and efficiency of Pakistan’s telecommunication sector by developing a framework for base transceiver stations integrating renewable energy and charging stations. Various renewable energy sources such as solar, wind, biomass and hydropower were considered as the object of research. The following methodological steps were implemented in this work: site analysis; determination of optimal sizing of plants, energy storage systems and electric vehicle charging stations; cost-benefit analysis methods; greenhouse gas emissions estimation; and system design methods for integrating selected renewable energy sources and energy storage solutions, taking into account the operational requirements of the base transceiver stations. It is found that switching to hybrid renewable energy systems can significantly reduce dependence on diesel generators. It is shown that operating costs can be reduced by more than 80% compared to conventional diesel-fueled systems. Also, the introduction of hybrid renewable energy sources can lead to significant reductions in CO2 emissions. The integration of battery storage systems has been shown to improve the reliability of energy supply by ensuring uninterrupted operation during periods of high demand and blackouts. The proposed structure scheme for base transceiver stations is designed to accommodate future growth in the share of electric vehicles and technological advancements in renewable energy and electric vehicle charging. By prioritizing the integration of renewable technologies along with charging station infrastructure, telecom service providers in Pakistan can reduce their carbon footprint and operational costs. This approach not only addresses the unpredictability of the electricity grid, especially in rural areas, but also positions the telecoms sector as an active participant in global efforts to combat climate change.

A Multisource Data Approach for Change and Disturbance Mapping of Ontario's Clay Belt Toward More Accurate Carbon and Emissions Estimation

A Multisource Data Approach for Change and Disturbance Mapping of Ontario's Clay Belt Toward More Accurate Carbon and Emissions Estimation

Summer CH4 ebullition strongly determines year-round greenhouse gas emissions from agricultural ditches despite frequent dredging.

Recent studies indicate that greenhouse gas (GHG) emissions from agricultural drainage ditches can be significant on a per-unit area basis, but spatiotemporal investigations are still limited. Additionally, the impact of dredging - a common management in such environments - on ditch GHG emissions is largely unknown. This study presents year-round GHG emissions from nine ditches on a dairy farm in the center of the Netherlands, where each year, approximately half of the ditches are dredged in alternating cycles. We measured monthly diffusive fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), along with ebullitive CH4 emissions, supplemented by diel flux measurements (i.e., 24-h measurements) conducted in summer and winter. Our findings indicate that while diffusive GHG fluxes exhibited low spatiotemporal variation, ebullitive CH4 emissions were significantly higher during warmer periods and marginally elevated at ditch intersections. CH4 ebullition was the dominant pathway of ditch GHG emissions, accounting for 58% of the total annual emissions, followed by CO2 (39%), and N2O (3%). Approximately 80% of the total CH4 emissions occurred through ebullition during spring and summer. The average CH4 emission factor estimated for our ditches (574kgha-1 year-1) is ∼40% higher than the Tier-1 value suggested by the IPCC for ditches on mineral soils (416kgha-1 year-1). Based on two 24-h measurement campaigns, we concluded that neglecting nighttime diffusive CO2 and CH4 emissions may lead to inaccurate estimates of annual ditch GHG emissions, with ∼12% underestimation in this study. Although dredging led to subtle increases in water-to-atmosphere GHG emissions immediately after the activity, it reduced overall annual GHG emissions by ∼35%. This study highlights the importance of CH4 ebullition and of capturing diel cycles of diffusive emissions to accurately assess ditch GHG emissions and underscores the importance of considering seasonal variations and dredging practices when budgeting ditch GHG emissions.

Estimation of Energy Demand and Emissions from the Tricycle Sector in Quezon City, Philippines

Estimation of Energy Demand and Emissions from the Tricycle Sector in Quezon City, Philippines

Thirty years of global warming potential evolution for the Italian dairy cow sector measured by two different metrics

This study aimed to provide a comprehensive evaluation of the greenhouse gases (GHG) emission between 1991 and 2021 of both the entire Italian dairy cattle sector (ITA_POP) and the registered Holstein Friesian population only (HF). An attributional cradle-to-farm gate Life Cycle Assessment was performed, including emissions (methane–CH4, nitrous oxide–N2O, carbon dioxide–CO2) due to animal and manure management, feed production, and farm materials. The functional units were 1 year and 1 kg of fat- and protein-corrected milk. We applied 100-year global warming potential (GWP) metric. For 2011–2021 period, we also applied GWP star (GWP*). Data originated from Italian GHG accounting and official milk recording systems. From 1991 to 2021, annual GHG emission of ITA_POP decreased from 20.4 ± 2.7 to 15.5 ± 2.1 Mt CO2e and emission intensity (EI) from 1.90 ± 0.25 to 1.17 ± 0.16 kg CO2e/kg corrected milk. The share of officially registered HF cows in ITA_POP increased from 36 to 70%. The annual emission of HF increased by 32% to 12.2 ± 1.8 Mt CO2e, but its EI decreased by 34% to 1.09 ± 0.16 kg CO2e/kg corrected milk. The accumulation in GHGs in 2011–2021 period due to ITA_POP (100-year GWP) continuously increased. When considering the short lifetime of CH4 (GWP*), the rate of increase in the same years was quite lower, with a flat pattern in the last 5 years. The decrease in CH4 emission is compensating the accumulation of N2O and CO2 from fossil fuels, highlighting the need of considering GHGs lifetime in the estimation of dairy cow sector emissions.

Descriptive analysis of the environmental impact of intensive rabbit production

This descriptive literature review presents some elements that allow us to quantify the main contributions of rabbit farming to global warming, pollution (mostly nitrogen losses, airborne particulate matter), water footprint and biodiversity loss. As the majority of meat rabbits farmed in the world are raised in indoor cage systems, most studies only cover this production system. A single attempt has been made to quantify the environmental impact of an alternative system, based on rabbits grazing under photovoltaic panels. Although it provides some insights into possible alternatives, the results obtained are not based on real data. Regarding the contribution of rabbit production to global warming, the estimations of greenhouse gas emissions ranged from 3.13 to 3.25 kg of CO2 eq. per growing rabbit over a 35-d period. No estimates are available for the whole system (all animal categories). Pollution associated with nitrogen losses varied between 40.1 and 59.1 g of N per kg of liveweight gain. Air pollution related to the airborne particulate matter (10 micron) varied from 0.082 to 0.045 mg per m3, and there was no data available on the water footprint, which is likely to be between those observed for poultry and pig production. For biodiversity loss, there are no studies on the impact of rabbit production on wild life. This communication ends with a brief discussion of the possible alternatives and presents some technical perspectives for the rabbit sector.

Predicting transient performance of a heavy-duty gaseous-fuelled engine using combined phenomenological and machine learning models

Decarbonizing long-haul goods transportation poses a substantial challenge. High-efficiency natural gas (NG) engines, which retain the efficiency of a diesel engine but reduce the carbon content of the fuel, offer substantial potential for near-term greenhouse gas (GHG) reductions. A fast-running model that can predict engine performance, GHG and air pollutant emissions is critical to assessing this approach for different applications and vehicle drivetrain configurations. This paper presents the development, validation and application of an engine system model that adapts GT-SUITE™’s phenomenological DI-Pulse predictive model to predict the performance and emissions of a 6-cylinder NG engine using a high pressure direct-injection combustion process. The model includes the engine air exchange system, enabling the prediction of the engine and in-cylinder conditions and overall performance over transient drive cycles. The engine model with a fixed set of calibration parameters captures the complex high-pressure direct injection combustion process and generates time-resolved parameters that are fed into a coupled machine learning model to predict emissions, including nitrogen oxide (NOx) and methane (CH4) emissions. While the 1-D model’s predictions for CH4 were not accurate, coupling the 1-D engine model with a machine learning model has been shown to substantially improve the estimation of CH4 emissions and allow accurate prediction of engine total GHG emissions over different duty cycles. The model has been validated using transient engine dynamometer data and is then applied to assess performance and emissions over several regulatory and real-world long-haul drive cycles. The model showed an average error of less than 5% in steady operation. Cumulative errors of NOx and CH4 emissions in studied cycles were also less than 10%. The results showed that CH4 share in total GHG emissions ranges from 0.2% to 1.4% over various drive cycles. By predicting engine performance and emissions, the developed combined model has considerable potential for use in engine evaluation studies, especially when combined with new technologies across different duty cycles.

Power and emission estimation of plastic waste pyrolysis-derived fuel blends in internal combustion engines

Energy, especially from fossil fuels, is essential for everyday life, while plastic waste is an increasing environmental threat. Plastic waste disposal methods such as landfilling and burning cause pollution. Therefore, a process is needed that converts plastic waste into fuel. The object of the study is the engine performance. The problem to be solved is the relationship between the use of a mixture of fossil fuels and pyrolysis fuel on the performance of internal combustion engines. This research uses a systematic data collection process to obtain accurate and reliable results. The necessary equipment, including a dynamometer and gas analyzer, was prepared, and the engine was warmed up to a stable operating temperature of 80 °C. The motorbike is then positioned on the dynamometer with the rear tires aligned and the front tires secured to prevent movement. Data collection was carried out at engine speeds of 2000, 3000, 4000, 5000, and 6000 rpm, using three fuel mixtures: 10 % plastic pyrolysis fuel with 90 % RON 90, 20 % plastic pyrolysis fuel with 80 % 90 RON, and 30 % plastic pyrolysis fuel with 70 % RON 90. Each test was repeated three times, with the output power measured using a dynamometer and exhaust emissions (CO and HC levels) recorded using a gas analyzer. The test results show that the optimal fuel mixture to produce maximum engine power is a PE-RON 90 mixture with a ratio of 20:80, providing the best performance at medium to high engine speeds (3000–6000 rpm) with low CO emissions. The highest power output (1.05) occurs at 4000 rpm, while the PE-RON 90 30:70 alloy produces the best power performance at 6000 rpm (0.78 % CO). Additionally, the pyrolysis fuel blend significantly reduces CO and HC emissions, with the PE-RON 90 30:70 blend showing the lowest CO (0.78 % at 6000 rpm) and consistently reducing HC emissions across the rpm range

Mitigation potential and state support for forest climate projects in Russia

Russia, which has significant potential to increase the absorption capacity of ecosystems, capable of providing it with the status of a global climate donor, currently does not use such a mitigation tool as forest climate projects effectively enough. The reasons for this are the immaturity of the domestic regulatory framework and the mechanism of state support for forest climate projects, as well as the low activity of Russian companies in the national and international voluntary carbon markets. The article considers estimates of greenhouse gas emissions and absorption in Russia and the world. The work defines the role of forestry in achieving the key long-term goal of the national climate policy - achieving a balance between anthropogenic greenhouse gas emissions and their absorption by 2060. The authors disclose the content of forest climate projects, show their importance for the socio-ecological-economic system of the regions and substantiate the need to provide state support to priority projects. The article proposes an approach to the assessment and selection of forest climate projects for state support in the regions.