Characteristics Of Emissions Research Articles

Non-destructive evaluation of failure mechanisms in multilayered coal-rock specimens using acoustic emission characterisation

ABSTRACT This study presents a multi-parameter Acoustic Emission (AE) framework to evaluate failure mechanisms in coal-rock composites. Using uniaxial compression tests with varying coal-seam dip angles (0°–60°) and thickness ratios (1:3 to 3:1), we developed an AE-based methodology that integrates three key analyses: (a) stress–strain relationships, (b) cumulative AE energy/count evolution, and (c) b-value transitions. Results show that structural anisotropy significantly influences failure modes. Increased dip angles (45°/60°) enhanced strength by 18.7–23.4% due to better stress redistribution, while higher coal-layer ratios decreased load-bearing capacity by 27.3% due to interfacial decohesion. The b-value transition sequence (fluctuation → stabilisation → critical minimum → recovery → sudden decay) provides reliable precursors for predicting failure, correlating 82–94% with macroscopic fracture initiation. This research advances AE-based non-destructive testing by quantifying interfacial damage through energy dissipation patterns and predicting instability using b-value trajectories. The proposed method enables real-time monitoring of structural integrity in complex geological environments, with applications in mining support and early-warning systems.

Characterization of Cold-Start Gaseous Pollutant Emissions from Diesel Engine Fueled Biodiesel at Low Plateau Temperatures

ABSTRACT As a key power source in construction machinery and transportation, diesel engine cold start performance and emissions under high-altitude, low-temperature conditions directly affect reliability and environmental adaptability. However, research on diesel cold start behavior below 0°C and above 3000 m remains limited. This study develops a specialized cold start experimental system and systematically analyzes emissions from B10 biodiesel and pure diesel across altitudes (0 m, 3000 m, 4000 m) and temperatures (0°C, −10°C, −20°C), focusing on unburned hydrocarbons (HC) and nitrogen oxides (NOx). Results show that increasing altitude and decreasing temperature significantly prolong cold start delay, reduce start success rates, and elevate HC and NOx emissions. At 3000 m and −20°C, HC emissions from B10 reached 64.72%, 179.78 times higher than at 0 m and 0°C, while NOx emissions increased 103.21 times. Compared to pure diesel, B10’s higher oxygen content reduced HC emissions (down to 38%) but slightly increased NOx emissions. This study provides novel insights into biodiesel and diesel cold start emissions under extreme conditions, offering critical data for optimizing engine performance and emission control strategies.

Characteristics of soil carbon emissions from different forest types and regions in China

Characteristics of soil carbon emissions from different forest types and regions in China

Trends and characteristics of global CH4 emissions: Insights from UNFCCC greenhouse gas inventories

Trends and characteristics of global CH4 emissions: Insights from UNFCCC greenhouse gas inventories

Strain rate effect on splitting tensile behavior and failure mechanisms of geopolymeric recycled aggregate concrete: Insights from acoustic emission characterization

Strain rate effect on splitting tensile behavior and failure mechanisms of geopolymeric recycled aggregate concrete: Insights from acoustic emission characterization

Analysis of Air Pollution Emission and Asthma Hotspots Considering Spatiotemporal Characteristics

It is necessary to propose a method to identify the regional distribution characteristics of air pollution and its health impacts as an important capacity to achieve an inclusive and sustainable community by considering the environment and health in a balanced manner. In this study, hotspot areas were identified by considering the spatial and spatiotemporal characteristics of air pollutant emissions and asthma in 250 cities over a period of 5 years (2015-2019). In the search for spatial hotspot areas of air pollutant emissions and asthma prevalence using Hotspot Analysis Comparison, some cities in South Chungcheong Province were found to be hotspot areas where air pollutant emissions and asthma prevalence were clustered together and showed high spatial values for five years. The SaTScan search for hotspot areas considering spatiotemporal characteristics indicated that the Chungnam region included in Cluster 1 had the highest asthma relative risk ratio (2.64) in the top 25% of air pollutant emissions compared with the bottom 25%, which was statistically significant. Although the national averages of air pollutant emissions and asthma prevalence rates are decreasing, some regions have emerged as hotspots where air pollutant emissions and asthma prevalence rates are consistently higher than those in other regions. Identifying and managing hotspot areas by considering environmental hazards and health-impact factors is one way to efficiently utilize limited resources and manpower. Additionally, the results of this study can be used as scientific evidence in areas where regional environmental health monitoring and intensive management are required.

Research on a Carbon Emission Prediction Model for Deep Foundation Pit Construction Based on the BP-GA Algorithm

To address the challenges posed by the multi-source heterogeneous characteristics of carbon emissions in deep foundation pit construction and the insufficient generalization capability of traditional prediction models, this study proposes a hybrid model based on a genetic algorithm-optimized BP (Backpropagation) neural network. The model employs an elite retention strategy and a single-point crossover mechanism to optimize the initial weights of the network. Additionally, it utilizes a two-layer neural network structure with 16 and 8 nodes, respectively, to achieve a nonlinear mapping from input features to carbon emissions. Validation using a dataset of over 1,000 engineering cases demonstrates the models strong predictive performance, achieving a Mean Squared Error (MSE) of 2856.34 and a Mean Absolute Error (MAE) of 43.11. These results highlight the models reliability as a prediction tool for carbon emissions in deep foundation pit projects, offering a promising approach for improving the accuracy of carbon footprint assessments in construction engineering.

Pollution Characterization and Environmental Impact Evaluation of Atmospheric Intermediate Volatile Organic Compounds: A Review.

Atmospheric intermediate volatile organic compounds (IVOCs) are important precursors of secondary organic aerosols (SOAs), and in-depth research on them is crucial for atmospheric pollution control. This review systematically synthesizes global advancements in understanding IVOC sources, emissions characterization, compositional characteristics, ambient concentrations, SOA contributions, and health risk assessments. IVOCs include long-chain alkanes (C12~C22), sesquiterpenes, polycyclic aromatic hydrocarbons, monocyclic aromatic hydrocarbons, phenolic compounds, ketones, esters, organic acids, and heterocyclic compounds, which originate from primary emissions and secondary formation. Primary emissions include direct emissions from anthropogenic and biogenic sources, while secondary formation mainly results from radical reactions or particulate surface reactions. Recently, the total IVOC emissions have decreased in some countries, while emissions from certain sources, such as volatile chemical products, have increased. Ambient IVOC concentrations are generally higher in urban rather than in rural areas, higher indoors than outdoors, and on land rather than over oceans. IVOCs primarily generate SOAs via oxidation reactions with hydroxyl radicals, nitrate radicals, the ozone, and chlorine atoms, which contribute more to SOAs than traditional VOCs, with higher SOA yields. SOA tracers for IVOC species like naphthalene and β-caryophyllene have been identified. Integrating IVOC emissions into regional air quality models could significantly improve SOA simulation accuracy. The carcinogenic risk posed by naphthalene should be prioritized, while benzo[a]pyrene requires a combined risk assessment and hierarchical management. Future research should focus on developing high-resolution online detection technologies for IVOCs, clarifying the multiphase reaction mechanisms involved and SOA tracers, and conducting comprehensive human health risk assessments.

Carbon Emissions and Innovation Cities: A SHAP-Model-Based Study on Decoupling Trends and Policy Implications in Coastal China

This study investigates the spatiotemporal distribution of carbon emissions and the decoupling relationship between emissions and innovation-driven urban development in six coastal provinces and municipalities in China from 2008 to 2022. The main questions of this paper are as follows: What are the spatial and temporal distribution characteristics of carbon emissions in the study area? What is the decoupling relationship between carbon emissions and innovation-driven urban development? What key variables contribute significantly to carbon emissions and urban development? Carbon emissions increased overall, with higher levels in northern regions such as Shandong, northern Jiangsu, and the Yangtze River Delta. Meanwhile, innovation levels rose but disparities widened, with northern cities leading and those in western Fujian and Guangdong lagging behind. The green economy and industrial transformation were key drivers of rapid development in some cities. To identify the driving factors, the SHAP (SHapley Additive exPlanations) model was employed to quantify the contributions of key variables, including energy structure, technological innovation, and industrial upgrading, to both carbon emissions and urban development. This study found that decoupling between carbon emissions and smart city development improved, transitioning from negative to strong decoupling, particularly in coastal cities. These insights can assist governments in formulating sustainable development strategies. High-emission cities should focus on integrating low-emission measures to mitigate their carbon footprint. High-carbon cities need to transition to low-carbon pathways, enhancing resource efficiency and reducing emissions. Low-emission cities should prioritize improving carbon sinks. Cities with weak economies but rich ecological resources should develop tertiary and ecological economies. Developed cities should optimize resource allocation, digitize industries, and pursue low-carbon growth. Additionally, adjustments in transportation and industry can further boost innovation and urbanization.

The spatiotemporal dynamic evolution characteristics of carbon emissions in less developed resource-based cities: evidence from Pingliang City, China

This study estimates the carbon emissions of Pingliang City, a resource-based city in less developed areas of China, from 2010 to 2020, based on nighttime light data and county-level carbon emission data from the CEADs database. By employing visualization methods such as standard deviation ellipses and exploratory spatial analysis, the spatiotemporal evolution and spatial agglomeration characteristics of carbon emissions in Pingliang City are depicted. The results indicate: (1) From 2010 to 2020, the total carbon emissions in Pingliang City exhibited a phased characteristic of initial increase, followed by a period of stability, and then a further increase; spatially, the central counties of Pingliang City had higher carbon emissions, mainly around Liuhu Town and Baimiao Township in Kongtong District. (2) The distribution center of carbon emissions had gradually shifted towards the northwest, indicating that the carbon emissions in the northwest of the research area had increased more significantly than those in other regions; moreover, carbon emissions initially diverged and then agglomerated in the “southeast-northwest”direction, and initially agglomerated and then diverged in the“northeast-southwest”direction, with the overall direction of emission divergence or agglomeration being relatively stable. (3) Carbon emissions performed a positive spatial autocorrelation, manifesting as an agglomeration effect, and overall presented a distribution characteristic of“single core-multiple scatter points,”with the degree of carbon emission agglomeration in each county having increased to some extent during the period under review.

Deep-Learning Image-Processing Model Uses Optical Gas-Imaging Camera To Detect Leaks

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 34756, “Temporal Deep-Learning Image-Processing Model for Natural Gas Leak Detection Using OGI Camera,” by Mehdi Korjani, David Conley, and Mark Smith, Clean Connect. The paper has not been peer reviewed. _ The authors have developed a novel deep-learning (DL) image-processing model that uses videos captured by a specialized optical gas imaging (OGI) camera to detect natural gas leaks. The temporal DL algorithm is designed to identify patterns associated with gas leaks and improve its performance through supervised learning. Modeling This study was designed to exploit the technical strengths of OGI technology in tandem with temporal DL techniques. The central objective of the study was to engineer a robust model capable of accurate and efficient detection and quantification of methane leaks from video data. This resulted in the inception of the temporal DL image-processing model (TDLP-NG). The operational phase commenced with the strategic deployment of OGI cameras at select locations throughout natural gas facilities. Equipped with specialized infrared sensors sensitive to the spectral signature of methane gas, these cameras served as the eyes of the study. Their arrangement considered a multitude of variables—distance, angle, and environmental factors—to calibrate the instrumentation for optimal gas-leak visualization. TDLP-NG. The cornerstone of the methodology is the discussed DL architecture crafted to detect and analyze methane emissions within OGI video data. The model harnesses the combined power of convolutional neural networks (CNNs) and long short-term memory (LSTM) units to deliver spatial and temporal analysis capabilities. The model’s CNN component was designed to extract high-level feature representations from individual frames. The temporal aspect was addressed by integrating LSTM units following CNN feature extraction. LSTMs are an ideal choice for capturing the dynamic evolution of methane patterns over time. The LSTM layers analyzed the sequence of CNN-derived features to predict the presence and characteristics of emissions. A custom loss function was designed to simultaneously optimize the spatial precision of detected plumes and the temporal correlation across frames. The model was trained on a curated data set using backpropagation through time. Validation and Testing. The validity of the TDLP-NG’s detection was comprehensively assessed through a validation phase involving a set of reserved video data. A comparison between model predictions and expert annotations served to fine-tune the model parameters and solidify its detection efficacy. A battery of tests also was conducted on entirely unseen data to challenge the TDLP-NG’s generalization capabilities. Rate Estimation. To estimate gas-leak rate using an optical flow-based model, various factors were considered, including the properties of the plume captured by OGI cameras, the distance of the camera from the leak, and the camera’s lens angle. Each pixel of the image represents larger areas for more-distant leaks vs. close ones. A workflow for estimating gas-leak rates with an optical flow-based model is provided in the complete paper. By integrating optical flow with machine learning and accounting for camera specifications, the model provides accurate and robust leak-rate estimations. Gas properties such as temperature, pressure, and methane composition also are integrated to further refine the estimation of the leak rate.

Characterization of emissions from a turbojet engine running on sustainable aviation fuels, blends and conventional jet A1

Characterization of emissions from a turbojet engine running on sustainable aviation fuels, blends and conventional jet A1

Emission characteristics of VOCs from the typical spent lithium-ion battery recycling industry.

Emission characteristics of VOCs from the typical spent lithium-ion battery recycling industry.

Characteristics of agricultural carbon emissions in arid zones, drivers and decoupling effects: evidence from Xinjiang, China

Characteristics of agricultural carbon emissions in arid zones, drivers and decoupling effects: evidence from Xinjiang, China

Characterisation of supraharmonic emissions based on phase angle representation methods

Characterisation of supraharmonic emissions based on phase angle representation methods

Characterization of greenhouse gas emissions and water requirement of farmland in China's main grain-producing areas under future climate scenarios

Characterization of greenhouse gas emissions and water requirement of farmland in China's main grain-producing areas under future climate scenarios

Energy evolution and constitutive model of flawed coal failure based on acoustic emission and electric potential characterisation

Energy evolution and constitutive model of flawed coal failure based on acoustic emission and electric potential characterisation

Transport Carbon Emission Measurement Models and Spatial Patterns Under the Perspective of Land–Sea Integration–Take Tianjin as an Example

The goal of “double carbon” puts forward higher requirements for the control of transport carbon emissions, and the exploration of transport carbon emission modelling driven by big data is an important attempt to reduce carbon accurately. Based on the land Vehicle Miles Traveled data (VMT) and the sea Automatic Identification System (AIS) data, this study establishes a refined, high-resolution carbon emission measurement model that incorporates the use of motor vehicles and ships from a bottom-up approach and analyzes the spatial distribution characteristics of land and sea transport carbon emissions in Tianjin using geospatial analysis. The results of the study show that (1) the transportation carbon emissions in Tianjin mainly come from land road traffic, with small passenger cars contributing the most to the emissions; (2) high carbon emission zones are concentrated in economically developed, densely populated, and high road network density areas, such as the urban center Binhai New Area, and the marine functional zone of Tianjin; (3) carbon emission values are generally higher in the segments where ports, airports, and interchanges are connected. The transportation carbon emission measurement model developed in this study provides practical, replicable, and scalable insights for other coastal cities.

Analysis of Nitrogen Oxide Emissions Generated by the F100-PW-229 Turbine Aircraft Engine, Performed Using the GasTurb Software

The article discusses various aspects related to determining the composition of exhaust gases (i.e. nitrogen oxides) generated by a turbine aircraft engine. The paper highlights the problem of ozone depletion caused by toxic components of aircraft engine exhaust gases. The study is concerned with an engine used on the F-16 aircraft, i.e. F100-PW-229. GasTurb 12 software was used to calculate the engine’s operating parameters and to determine nitrogen oxides emission levels. The calculations were also performed analytically. The calculated thrust value was compared with data published in scientific articles (thrust of the engine). Characteristics of NOx emissions were obtained as a function of high-pressure rotor speed, temperature at the combustion chamber outlet, temperature downstream of the compressor, and engine thrust. The results obtained seem to correlate closely with data available in the literature.

A Novel Ship Fuel Sulfur Content Estimation Method Using Improved Gaussian Plume Model and Genetic Algorithms

Maritime transportation plays a vital role in global economic development but is also a significant contributor to air pollution, especially through emissions of SO2, NOx, and CO2. Identifying non-compliance with fuel sulfur content regulations is crucial for mitigating these environmental impacts, yet current methods face challenges, particularly in the absence of reliable CO2 concentration data. This study proposes a novel inverse calculation framework to estimate ship fuel sulfur content without relying on CO2 measurements. An improved Gaussian plume line source model was tailored to the dispersion characteristics of ship emissions, with influencing factors evaluated under varying wind field conditions. The emission source intensity inversion was formulated as an unconstrained multi-dimensional optimization problem, solved using genetic algorithms. By incorporating ship fuel consumption data derived from basic ship information, the sulfur content of ship fuels was effectively estimated. Experimental evaluations using 30 days of monitoring data revealed that the method successfully identified 2743 ships, with an overall detection rate of 82.72%. Among them, 131 ships were flagged as suspected of using high-sulfur fuel, and 111 were confirmed to be non-compliant via sampling and laboratory testing, achieving an accuracy of 84.73%. These results demonstrate that the proposed approach offers a reliable and efficient solution for real-time fuel sulfur content monitoring and enforcement under diverse atmospheric conditions, contributing to improved environmental management of maritime transport emissions.