Emission Model Research Articles

Оценка средней энергии потока авроральных электронов по измерению интенсивности излучения эмиссии λ427.8 нм

We propose a method for estimating average energy of precipitating electrons from 427.8 nm emission intensity measurements. This method is based on the experimental dependence of the ratio of λ630.0 and λ427.8 nm emission intensities on the λ427.8 emission intensity and model calculations of the dependence of the average auroral electron energy on the I₆₃₀.₀/I₄₂₇.₈ ratio. We present numerical estimates of the influence of three factors on this dependence: the shape of the auroral electron energy spectrum, the atomic oxygen concentration, and the NO concentration. The dependence of the average energy of the auroral electron flux on the 427.8 nm emission intensity is obtained and its analytical approximation is presented.

Estimating the average energy of auroral electrons from 427.8 nm emission intensity measurements

We propose a method for estimating average energy of precipitating electrons from 427.8 nm emission intensity measurements. This method is based on the experimental dependence of the ratio of λ630.0 and λ427.8 nm emission intensities on the λ427.8 emission intensity and model calculations of the dependence of the average auroral electron energy on the I₆₃₀.₀/I₄₂₇.₈ ratio. We present numerical estimates of the influence of three factors on this dependence: the shape of the auroral electron energy spectrum, the atomic oxygen concentration, and the NO concentration. The dependence of the average energy of the auroral electron flux on the 427.8 nm emission intensity is obtained and its analytical approximation is presented.

Research on Factors Influencing Global Carbon Emissions and Forecasting Models

Investigating the determinants of global carbon emissions and developing carbon emission models are essential to meet the 2050 carbon neutrality goal. This paper initially examines the primary factors shaping global carbon emissions over the past two decades, employing case studies and panel data analysis. Subsequently, a CNN-LSTM carbon emissions prediction model is established using data from Hebei Province, China, spanning from 2005 to 2022. This study reveals that global carbon emissions are predominantly affected by elements such as population, economic growth, industrial activities, energy consumption, environmental conditions, and technological advancements. By incorporating these variables, the CNN-LSTM model proposed in this research significantly enhances the average relative accuracy of carbon emission forecasts, thereby contributing substantially to global efforts in energy conservation and emission reduction.

Research on the development path of Chengdu as a low-carbon city under the dual constraints of carbon emission and economic growth

As the proportion of carbon emissions from urban areas rises, cities like Chengdu have become critical frontiers for emission reduction in China. Consequently, constructing low-carbon cities has emerged as the primary strategy for mitigating carbon emissions. According to the LMDI additive decomposition analysis, Chengdu’s carbon emissions increased by 4,397,700 tons during the 13th Five-Year Plan, primarily due to economic expansion, which alone accounted for an increase of 8,078,200 tons. Meanwhile, shifts in industrial structure and reductions in energy intensity contributed to declines of 657,700 tons and 3,016,500 tons, respectively, while changes in energy structure resulted in a marginal decrease of 640 tons. The LMDI multiplicative decomposition indicates a 10.3% growth in carbon emissions, with economic size amplifying emissions by 1.197 times, while enhanced energy intensity mitigated growth, reducing emissions to 0.935 times 2016 levels. Adjustments in industrial and energy structures exerted minimal impact on emission reductions. By developing a decomposition model for carbon emission influencing factors across various industries, this study identifies challenges to economic growth within the context of carbon reduction constraints and proposes pathways for low-carbon city development, including industrial restructuring, urban optimization, green building initiatives, and comprehensive transportation systems, thereby offering valuable insights for national low-carbon city initiatives.

A hierarchical Bayesian brain parcellation framework for fusion of functional imaging datasets

Abstract Different task-based and resting-state imaging datasets provide complementary information about the organization of the human brain. Brain parcellations based on single datasets will therefore be biased towards the particular type of information present in each dataset. To overcome this limitation, we propose here a hierarchical Bayesian framework that can learn a probabilistic brain parcellation across numerous task-based and resting-state datasets, exploiting their combined strengths. The framework is partitioned into a spatial arrangement model that defines the probability of each voxel belonging to a specific parcel (the probabilistic group atlas), and a set of dataset-specific emission models that define the probability of the observed data given the parcel of the voxel. Using the human cerebellum as an example, we show that the framework optimally combines information from different datasets to achieve a new population-based atlas that outperforms atlases based on single datasets. Furthermore, we demonstrate that using only 10 min of individual data, the framework is able to generate individual brain parcellations that outperform group atlases.

Analytical formulations for nitrogen oxides emissions estimation of a hydrogen fueled Synergetic Air-Breathing Rocket Engine (SABRE) in air-breathing mode

The space sector is undergoing rapid expansion due to the transformations it is experiencing, evolving from government-funded traditional space initiatives to commercially driven NewSpace activities. The surge of government and private investments in the space domain has positioned it as one of the world's fastest-growing sectors, making it crucial to assess the impact of already operational launch assets and to adopt design-to-sustainability strategies for under-development and future launchers. To actively contribute to this transition, this paper proposes a methodology for deriving novel analytical formulations to estimate nitrogen oxide (NOx) emissions of a hydrogen-fueled reusable access-to-space vehicle. Throughout the paper, the Skylon spaceplane and its Synergetic Air Breathing Rocket Engine are used as a case study. In particular, the SABRE engine presents a highly integrated dual propulsion architecture supporting both air-breathing and rocket modes. This study specifically focuses on the former, enabling the Single Stage To Orbit (SSTO) Skylon reaching the hypersonic speed regime before transitioning to rocket mode. This paper proposes novel analytical formulations to be integrated into the Fuel Flow (FF) and P3-T3 methods for estimating NOx Emission Index (EINOx), since the early design phase. These estimation techniques currently enable the calculation of the emission index of pollutants and GreenHouse Gases (GHGs) produced by any subsonic aero-engines powered by traditional fuels (i.e. kerosene) only. The methodology presented in this paper allows for upgrading the original mathematical formulations of the methods to ensure the applicability to high-speed flight and hydrogen fuel conditions. This involves the propulsive modelling of the engine and the chemical-kinetic modelling of the combustion process, representative of various on-ground and in-flight operating conditions, to generate updated and reliable propulsive and emissive databases for the engine. To this end, 0D chemical-kinetic air/hydrogen combustion numerical simulations are employed. The results of the performance analysis and emission modeling of the SABRE engine are reported and discussed. Regarding the calculated EINOx trend for the air-breathing phase of the engine operation, it reaches a peak of 45 gNOx/kgH2burnt under hypersonic conditions. Through the analysis of correlations between the propulsive characteristics of the SABRE and its NOx production, a series of parameters (such as Mach Number, Fuel to Air Ratio, Water to Fuel Ratio, and others) are selected to be integrated into the original formulations of the P3-T3 and FF methods via mathematical interpolation to adapt them to the case study. Finally, the EINOx trends resulting from the application of the new H2-P3T3 and H2-FF methods to the SABRE are graphically reported, along with the corresponding estimation errors relative to the reference trend from the engine emission database. Additionally, a discussion regarding the chemical-physical justifications for the mathematical contributions to the formulations for EINOx of the newly introduced parameters is provided.

Does regional economic development drive sustainable grain production growth in China? Evidence from spatiotemporal perspective on low-carbon total factor productivity

Contrary to conventional expectations, the impact of economic development on food green efficiency exhibits pronounced regional heterogeneity. This study utilizes total factor productivity (TFP) as a key metric, establishing a model for carbon emissions from grain production and developing a framework for measuring grain low-carbon TFP growth. Employing the undesirable super-slacks-based measure (SBM) and the Malmquist-Luenberger (ML) index, we assess the low-carbon TFP growth across 31 Chinese provinces from 2000 to 2021. The Dagum–Gini coefficient and variance decomposition are applied to analyze disparities from both regional and structural perspectives. An econometric model further validates the influence of economic development on low-carbon TFP growth. Our findings reveal that China’s technical efficiency (TE) and low carbon TE initially show a declining trend, with the gap between them narrowing over time. The eastern region consistently demonstrates high low-carbon TE, while the northeastern region lags. All regions exhibit positive low-carbon TFP growth, with efficiency change (EC) significantly contributing to this growth. Although economic development generally fosters low-carbon TFP growth, marked regional heterogeneity persists. Interestingly, certain traditionally economically developed regions, such as the eastern region, exert a significantly negative impact on low-carbon TFP growth. Balancing economic development with low-carbon TFP growth remains an ongoing challenge that necessitates effective management strategies.

Data-Science-Informed Operation, Maintenance Practices Reduce Emissions

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3866061, “Reducing Methane Emissions: Implementing Data-Science-Informed Operation and Maintenance Work Practices Using Continuous Monitoring Technology,” by Kathleen Jenkins, Carol A. Brereton, and Alex MacGregor, Qube Technologies, et al. The paper has not been peer reviewed. _ Diverse methods are used by oil and gas operators for methane leak detection and repair (LDAR). Deploying continuous monitoring (CM) point-sensor technologies to an oil and gas facility allows operators to implement novel operation and maintenance work practices to respond efficiently to methane emissions. The authors examine how data-science-driven work practices can result in substantial reduction in methane emissions compared with other LDAR methods. CM Technology CM point sensors track changes in parts per million of methane in the air. When several fixed sensors are deployed at a site, a large area can be surveilled. Using proprietary algorithms based on a plume-dispersion model, the volume and site rate of the methane emission are calculated. The probable location of the emission is estimated and, together with the volume, is shown on a Web-based dashboard that displays the changes in site rate through time. Alerts generated by the CM system allow the operator to respond to leaks more quickly than when waiting for data from an intermittent inspection to provide emission data. CM was deployed by a Permian Basin operator with a desire to operate cleanly, with transparency. The devices were installed with the operator beginning in March 2022 and continue to be active at the time of writing. Alerts generated by the system are delivered to operations personnel, including control-room operators, and investigated by field operators. When the source is identified, repairs are scheduled or completed on the spot and recorded for review. The operator has expanded the deployments throughout the field and uses the data to identify and repair leaks. Theory and Methods The CM devices were deployed around the fence line at several production facilities in the Permian Basin. The optimal number and placement of sensors was determined by analyzing publicly available wind data and sources of emissions at the facility. The CM devices have an inlet port that allows the ambient air to enter the device and flow across a metal oxide sensor. Metal oxide sensors measure the change in conductivity as the air flows across them as a result of the methane adhering to the metal. The change in conductivity, directly correlated with the parts per million of methane in the air, is recorded. Also measured are temperature and wind data, read by temperature sensors and anemometers on each device. This data is sent through a mobile telecommunications network to cloud-based servers that apply an emission-estimation model based on a plume dispersion and a localization algorithm to the data. Cloud services facilitate the transfer of information from devices to Web-based software. Each methane sensor is calibrated in the laboratory before installation by exposing it to known levels of methane. In this process, the temperature and humidity are systematically varied while the methane concentrations are changed and the reported conductivity is recorded. The resulting coefficients of conductivity are stored with the serial number of the device and sent to the device through the mobile network when it is deployed in the field.

Elevation-Dependent Snow Cover Dynamics and Associated Topo-Climate Impacts in Upper Indus River Basin

In the present study, Improved Moderate Resolution Imaging Spectro-radiometer (MODIS) snow cover product (MOYDGL06*) has been used to evaluate the snow cover area (SCA) in Kabul, Jhelum, and Indus river basins for the time period of 2003 – 2020 with available MODIS land surface temperature (LST), and CHIRPS (precipitation) with objectives to evaluate the spatio-temporal SCA, and climate variables with respect to different elevations analyzed from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 3 (GDEM v3) and also to correlate the climatic variables with SCA. The results presented average annual SCA is around 50.7% – 64.7% in sub-basins of UIB, further it has been observed that SCA is decreasing on annual and seasonal timescale in all three basins. Elevation-dependent SCA, temperature, and precipitation presented a mix of trend on annual, seasonal, and monthly timescale at lower and higher altitude in all selected basins. Moreover, it was noticed that topography (slope, & aspect) also influences the SCA in the region. Furthermore, it has been examined that temperature has significant inverse relationship with SCA at middle and higher altitude in Indus, while in Kabul, and Jhelum no significant relationship observed at extreme lower and higher altitudes. It is also evident from relationship between SCA and climate variable that temperature is significantly responsible for decreasing trend of SCA rather than intense precipitation in all three river basins. Thus, all these elevation-dependent changes can improve our hydrological understanding which can have a considerable implication for hydrology, climate science, water resource management and socio-economic activities.

Dislocation Emission Model of Kinked Cracks in Aluminum Crystal under the Condition of Hydrogen Filling

Dislocation Emission Model of Kinked Cracks in Aluminum Crystal under the Condition of Hydrogen Filling

Challenges for Fast Radio Bursts as Multimessenger Sources from Binary Neutron Star Mergers

Fast radio bursts (FRBs) are millisecond-duration transients from extragalactic sources, with their origins remaining a topic of active debate. Among the proposed progenitors, binary neutron star (BNS) mergers are compelling candidates for some nonrepeating FRBs. However, associating FRBs with BNS mergers cannot be based solely on low chance coincidence probability. This study delineates necessary criteria for associating FRBs with BNS mergers, focusing on the postmerger ejecta environment. To underscore the significance of these criteria, we scrutinise the proposed association between GW190425 and FRB 20190425A, considering the requirement for the FRB signal to traverse the dense merger ejecta without significant attenuation to remain detectable at 400 MHz. Our investigation reveals that if the FRB is linked to the gravitational-wave (GW) event, the GW data support a highly off-axis configuration, with a probability of the BNS merger viewing angle p(θ v > 30°) being ≈99.99%. This strongly excludes an on-axis system, which is required for this FRB to be detectable. We also find faraway FRB emission models inadequate to explain the FRB 20190425A–GW190425 connection. Thus, we conclude that GW190425 is not related to FRB 20190425A. We discuss the implications for future multimessenger observations, suggesting that BNS merger remnants are unlikely to account for more than 1% of FRB sources. This finding implies that short gamma-ray bursts, which are expected to occur in only a fraction of all BNS mergers, cannot account for the overall characteristics of the FRB host population.

Development of Measurement Tool for Energy Efficiency Operational Indicator (EEOI)

Abstract The Energy Efficiency Operational Index (EEOI) is a value that represents the energy efficiency of a ship’s operations in relation to CO2 emissions. Improving energy efficiency is one of the important ways to achieve cleaner shipping. Statistical methods are used to predict a number of parameters that determine the value of the EEOI. The aim of this research is an EEOI measurement tool. The analysis was carried out with 100 container ships. The Ship Traffic Emission Model (STEAM) and Multiple Linear Regression (MLR) methods were used in this research. STEAM modeling is based on estimated fuel oil consumption and MLR generated from STEAM EEOI results. This modeling also considers the following parameters overall length, width, draft, depth, service speed, container capacity, average speed of last trip, average draft of last trip, installed main engine power, and travel time. Furthermore, EEOI measurement tools were created in the Android-based version which represent EEOI values based on the ship’s last trip per voyage.

ML-ERV: A machine learning-based CO2 emissions model designed for rental vehicles

ML-ERV: A machine learning-based CO2 emissions model designed for rental vehicles

China’s carbon emission forecasts in different development models

China is the world's largest carbon emitter, its carbon peaking and carbon neutrality goals significantly reduce climate change and global warming problems. Although the Chinese government proposed carbon emissions will peak by 2030, how do different development models affect carbon emissions in the future? Here, we analyzed the spatial and temporal trends of carbon emissions over the past two decades. Then we constructed the STIRPAT carbon emissions model based on ridge regression analysis. Finally, one national STIRPAT model and eight STIRPAT models at the sub-regional scales were generated because the effects of population, economy, and energy consumption vary significantly across regions and periods. Meanwhile, we established nine different future development models based on various population, economy, and energy consumption levels, and forecast their carbon emissions from 2020 to 2060 by eight sub-region STIRPAT models. Our results documented that (1) China's carbon emissions significantly increased by 445.79 million-ton/yr between 2000 and 2019. Meanwhile, population, total energy consumption, and GDP were growing at a rate of 7.60 million people/yr, 185.37 million-ton/yr, and 4791.74 billion-yuan/yr, respectively. (2) Ridge regression results indicated that carbon emissions are positively influenced by population, economic growth, and energy consumption in all regions, but the degree of influence varies across sub-regions. (3) In 2060, carbon emissions will be lowest across all variables at low levels of development and highest at high levels of development. Meanwhile, low energy consumption and population levels are possibly the main directions for controlling carbon emissions in the future. The findings indicate that the carbon peak target could be achieved by 2030 by controlling population and energy consumption alone. However, relying solely on these strategies may pose significant challenges in meeting the dual carbon targets, which emphasizes the need for a scientific foundation to inform low-carbon development policies. The findings provide a scientific basis and reference for the low-carbon sustainable development in China.

Multiwavelength Emission from Jets and Magnetically Arrested Disks in Nearby Radio Galaxies: Application to M87

Radio galaxies are a subclass of active galactic nuclei that drive relativistic jets from their center and are observed in radio to very-high-energy gamma rays. The emission mechanisms and regions are still unknown. High-energy gamma rays can be explained by the emission from the magnetically arrested disks (MADs) around the central supermassive black hole (BH), for which the magnetic flux threading the BH is in a saturation level, although the emission from the MADs does not explain the optical and X-ray data. We construct a two-zone multiwavelength emission model in which optical and X-rays come from jets, while millimeter/submillimeter and gamma rays come from MADs. Our model takes into account the particle injection by the magnetic reconnection at the jet base close to the BH and particle entrainment from the ambient gas at the jet emission zone. We apply our model to M87 and find that our model can explain the simultaneous multiwavelength data, except for the radio data, which could be explained if we extend our one-zone emission model to a one-dimensional one. We also find that the strong plasma entrainment is necessary to explain the multiwavelength data. Our model will be tested by variability analysis among the multiwavelength data.

Assessment of CO2 Emissions for Light-Duty Vehicles Using Dynamic Perturbation Additive Regression Trees

Effective predictive modeling is crucial for assessing and mitigating energy consumption and CO2 emissions in light-duty vehicles (LDVs) throughout the whole value chain of an organization. This study enhances the modeling of LDV CO2 emissions by developing novel approaches to analyzing vehicle feature datasets. New tree-based machine learning models are developed to increase the accuracy and interpretability in modeling the CO2 emissions in LDVs. In particular, this study develops a new algorithm called dynamic perturbation additive regression trees (DPART). This new algorithm integrates dynamic perturbation within an iterative boosting framework. DPART progressively adjusts prediction values and explores various tree structures to improve predictive performance with reduced computation time. The effectiveness of the new ensemble-tree-based models is compared to that of other models for the vehicle emission data. The results demonstrate the new models’ capability to significantly improve predicting accuracy and reliability compared to other models. The new models also enable identifying key vehicle features affecting emissions, and thus provide valuable insights into the complex relationships among vehicle features in the dataset.

Optimal Speed Ranges for Different Vehicle Types for Exhaust Emission Control

Controlling vehicle speed is crucial for reducing exhaust emissions and ensuring the sustainable development of road transportation. Currently, speed limits on expressways are primarily set from a safety perspective, with limited research addressing speed limits from an environmental protection standpoint. In this study, based on real-world vehicle experiments and a vehicle flow exhaust emission model, we investigated the exhaust emission characteristics of light passenger vehicles (categorized as M1) and freight vehicles (categorized as N, including N1-minivans, N2-light heavy-duty vehicles, N3-medium heavy-duty vehicles, and N4-large heavy-duty vehicles) both individually and in traffic flows at varying speeds. We take carbon monoxide (CO), nitrogen oxides (NOx), particular matter (PM), and hydrocarbons (HCs) as representative emission components. The emission rate ranking of typical exhaust factors differs between M1-light passenger vehicles and N-freight vehicles. For M1-light passenger vehicles, the order is CO > HC > NOx > PM2.5, while for N-freight vehicles, it is NOx > CO > PM2.5 > HC. Conversely, for freight vehicles, higher speeds correlate with increased exhaust emissions in general, although carbon emissions specifically decrease as the speed increases. The results indicate the following speed limits conducive to sustainable road transportation development and low exhaust and carbon emissions: 90–110 km/h for light passenger vehicles and 80–100 km/h for freight vehicles.

Evaluating Google Maps’ Eco-Routes: A Metaheuristic-Driven Microsimulation Approach

Eco-routing, as a key strategy for mitigating urban pollution, is gaining prominence due to the fact that minimizing travel time alone does not necessarily result in the lowest fuel consumption. This research focuses on the challenge of selecting environmentally friendly routes within an urban street network. Employing microsimulation modelling and a computer-generated mirror of a small traffic network, the study integrates real-world traffic patterns to enhance accuracy. The route selection process is informed by fuel consumption and emissions data from trajectory parameters obtained during simulation, utilizing the Comprehensive Modal Emission Model (CMEM) for emission estimation. A comprehensive analysis of specific origin–destination pairs was conducted to assess the methodology, with all vehicles adhering to routes recommended by Google Maps. The findings reveal a noteworthy disparity between microsimulation results and Google Maps recommendations for eco-friendly routes within the University of Pittsburgh Campus street network. This incongruence underscores the necessity for further investigations to validate the accuracy of Google Maps’ eco-route suggestions in urban settings. As urban areas increasingly grapple with pollution challenges, such research becomes pivotal for refining and optimizing eco-routing strategies to effectively contribute to sustainable urban mobility.

A Self-consistent Model of Shock-heated Plasma in Nonequilibrium States for Direct Parameter Constraints from X-Ray Observations

X-ray observations of shock-heated plasmas, such as those found in supernova remnants (SNRs), often exhibit features of temperature and ionization nonequilibrium. For accurate interpretation of these observations, proper calculations of the equilibration processes are essential. Here, we present a self-consistent model of thermal X-ray emission from shock-heated plasmas that accounts for both temperature and ionization nonequilibrium conditions. For a given pair of shock velocity and initial electron-to-ion temperature ratio, the temporal evolution of the temperature and ionization state of each element was calculated by simultaneously solving the relaxation processes of temperature and ionization. The resulting thermal X-ray spectrum was synthesized by combining our model with the AtomDB spectral code. Comparison between our model and the nei model, a constant-temperature nonequilibrium ionization model available in the XSPEC software package, reveals a 30% underestimation of the ionization timescale in the nei model. We implemented our model in XSPEC to directly constrain the shock wave’s properties, such as the shock velocity and collisionless electron heating efficiency, from the thermal X-ray emission from postshock plasmas. We applied this model to archival Chandra data of the SNR N132D, providing a constraint on the shock velocity of ∼800 km s−1, in agreement with previous optical studies.

Emission location affects impacts on atmosphere and climate from alternative fuels for Norwegian domestic aviation

Aviation emissions contribute to climate change and local air pollution, with important contributions from non-CO2 emissions. These exhibit diverse impacts on atmospheric chemistry and radiative forcing (RF), varying with location, altitude, and time. Assessments of local mitigation strategies with global emission metrics may overlook this variability, but detailed studies of aviation emissions in areas smaller than continents are scarce. Integrating the AviTeam emission model and OsloCTM3, we quantify CO2, NOx, BC, OC, and SOx emissions, tropospheric concentration changes, RF, region-specific metrics, and assess alternative fuels for Norwegian domestic aviation. Mitigation potentials for a fuel switch to LH2 differ by up to 3.1×108kgCO2-equivalents (GWP20) when using region-specific compared to global metrics. These differences result from a lower, region-specific contribution of non-CO2 emissions, particularly related to NOx. This study underscores the importance of accounting for non-CO2 variability in regional assessments, whether through region-specific metrics or advanced atmospheric modelling techniques.