RESUMO
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.
RESUMO
Aviation emissions are the only direct source of anthropogenic particulate pollution at high altitudes, which can form contrails and contrail-induced clouds, with consequent effects upon global radiative forcing. In this study, we develop a predictive model, called APMEP-CNN, for aviation non-volatile particulate matter (nvPM) emissions using a convolutional neural network (CNN) technique. The model is established with data sets from the newly published aviation emission databank and measurement results from several field studies on the ground and during cruise operation. The model also takes the influence of sustainable aviation fuels (SAFs) on nvPM emissions into account by considering fuel properties. This study demonstrates that the APMEP-CNN can predict nvPM emission index in mass (EIm) and number (EIn) for a number of high-bypass turbofan engines. The accuracy of predicting EIm and EIn at ground level is significantly improved (R2 = 0.96 and 0.96) compared to the published models. We verify the suitability and the applicability of the APMEP-CNN model for estimating nvPM emissions at cruise and burning SAFs and blend fuels, and find that our predictions for EIm are within ±36.4 % of the measurements at cruise and within ±33.0 % of the measurements burning SAFs in average. In the worst case, the APMEP-CNN prediction is different by -69.2 % from the measurements at cruise for the JT3D-3B engine. Thus, the APMEP-CNN model can provide new data for establishing accurate emission inventories of global aviation and help assess the impact of aviation emissions on human health, environment and climate. SYNOPSIS: The results of this paper provide accurate predictions of nvPM emissions from in-use aircraft engines, which impact airport local air quality and global radiative forcing.