英文题目：Characterizing and Predicting nvPM Size Distributions for Aviation Emission Inventories and Environmental Impact

       中文题目：航空排放清单和环境影响的nvPM粒度分布特征和预测

       作者：Lukas Durdina, Eliot Durand, Jacinta Edebeli, Curdin Spirig, Benjamin T. Brem, Miriam Elser, Frithjof Siegerist, Mark Johnson, Yura A. Sevcenco, and Andrew P. Crayford

       关键词：aviation; non-CO₂ emissions; air pollution; particulate matter; nvPM; particle size distribution

       原文链接：https://doi.org/10.1021/acs.est.4c02538

       期刊名称：Environmental Science & Technology

       期刊分区：JCR Q1

       IF: 11.4

图形摘要

   摘要：Concerns about civil aviation’s air quality and environmental impacts have led to recent regulations on nonvolatile particulate matter (nvPM) mass and number emissions. Although these regulations do not mandate measuring particle size distribution (PSD), understanding PSDs is vital for assessing the environmental impacts of aviation nvPM. This study introduces a comprehensive data set detailing PSD characteristics of 42 engines across 19 turbofan types, ranging from unregulated small business jets to regulated large commercial aircraft. Emission tests were independently performed by using the European and Swiss reference nvPM sampling and measurement systems with parallel PSD measurements. The geometric mean diameter (GMD) at the engine exit strongly correlated with the nvPM number-to-mass ratio (N/M) and thrust, varying from 7 to 52 nm. The engine-exit geometric standard deviation ranged from 1.7 to 2.5 (mean of 2.05). The study proposes empirical correlations to predict GMD from N/M data of emissions-certified engines. These predictions are expected to be effective for conventional rich-burn engines and might be extended to novel combustor technologies if additional data become available. The findings support the refinement of emission models and help in assessing the aviation non-CO₂ climate and air quality impacts.

  英文题目：Impacts of Fuel Stage Ratio on the Morphological and Nanostructural Characteristics of Soot Emissions from a Twin Annular Premixing Swirler Combustor

       中文题目：燃料级比对双环预混旋流燃烧室碳烟排放形态和纳米结构特征的影响

       作者：Longfei Chen, Boxuan Cui, Chenglin Zhang, Xuehuan Hu, Yingying Wang, Guangze Li, Liuyong Chang, and Lei Liu

       关键词：TAPS combustor; soot particle emissions; fuel stage ratio; electron microscopy; Raman spectroscopy

       原文链接：https://doi.org/10.1021/acs.est.4c03478

       期刊名称：Environmental Science & Technology

       期刊分区：JCR Q1

       IF: 11.4

图形摘要

   摘要：Soot particles emitted from aircraft engines constitute a major anthropogenic source of pollution in the vicinity of airports and at cruising altitudes. This emission poses a significant threat to human health and may alter the global climate. Understanding the characteristics of soot particles, particularly those generated from Twin Annular Premixing Swirler (TAPS) combustors, a mainstream combustor in civil aviation engines, is crucial for aviation environmental protection. In this study, a comprehensive characterization of soot particles emitted from TAPS combustors was conducted using scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), and Raman spectroscopy. The morphology and nanostructure of soot particles were examined across three distinct fuel stage ratios (FSR), at 10%, 15%, and 20%. The SEM analysis of soot particle morphology revealed that coated particles constitute over 90% of the total particle sample, with coating content increasing proportionally to the fuel stage ratio. The results obtained from HRTEM indicated that average primary particle sizes increase with the fuel stage ratio. The results of HRTEM and Raman spectroscopy suggest that the nanostructure of soot particles becomes more ordered and graphitized with an increasing fuel stage ratio, resulting in lower oxidation activity. Specifically, soot fringe length increased with the fuel stage ratio, while soot fringe tortuosity and separation distance decreased. In addition, there is a prevalent occurrence of defects in the graphitic lattice structure of soot particles, suggesting a high degree of elemental carbon disorder.

       英文题目：A convolutional neural network prediction model for aviation nitrogen oxides emissions throughout all flight phases

       中文题目：航空氮氧化物全飞行阶段排放的卷积神经网络预测模型

       作者：Longfei Chen; Qian Zhang ; Meiyin Zhu, Guangze Li , Liuyong Chang , Zheng Xu, Hefeng Zhang;Yanjun Wang ;Yinger Zheng; Shenghui Zhong ;Kang Pan; Yiwei Zhao;Mengyun Gao , Bin Zhang

       关键词：Aviation emissions; Nitrogen oxides

       原文链接： https://doi.org/10.1016/j.scitotenv.2024.172432

       期刊名称：Science of the Total Environment

       期刊分区：JCR Q1

       IF：9.8

图形摘要

   摘要：In recent years, there has been an increasing amount of research on nitrogen oxides (NOx) emissions, and the environmental impact of aviation NOx emissions at cruising altitudes has received widespread attention. NOx may play a crucial role in altering the composition of the atmosphere, particularly regarding ozone formation in the upper troposphere. At present, the ground emission database based on the landing and takeoff (LTO) cycle is more comprehensive, while high-altitude emission data is scarce due to the prohibitively high cost and the inevitable measurement uncertainty associated with in-flight sampling. Therefore, it is necessary to establish a comprehensive NOx emission database for the entire flight envelope, encompassing both ground and cruise phases. This will enable a thorough assessment of the impact of aviation NOx emissions on climate and air quality. In this study, a prediction model has been developed via convolutional neural network (CNN) tech­nology. This model can predict the ground and cruise NOx emission index for turbofan engines and mixed turbofan engines fueled by either conventional aviation kerosene or sustainable aviation fuels (SAFs). The model utilizes data from the engine emission database (EEDB) released by the International Civil Aviation Organization (ICAO) and results obtained from several in-situ emission measurements conducted during ground and cruise phases. The model has been validated by comparing measured and predicted data, and the results demonstrate its high prediction accuracy for both the ground (R² > 0.95) and cruise phases (R² > 0.9). This surpasses traditional prediction models that rely on fuel flow rate, such as the Boeing Fuel Flow Method 2 (BFFM2). Furthermore, the model can predict NOx emissions from aircrafts burning SAFs with satisfactory accuracy, facilitating the development of a more complete and accurate aviation NOx emission inventory, which can serve as a basis for aviation environmental and climatic research. Synopsis: The utilization of the ANOEPM-CNN offers a foundation for establishing more precise emission in­ventories, thereby reducing inaccuracies in assessing the impact of aviation NOx emissions on climate and air quality.