Predicting aviation non-volatile particulate matter emissions at cruise via convolutional neural network

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 (EI_m) and number (EI_n) for a number of high-bypass turbofan engines. The accuracy of predicting EI_m and EI_n 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 EI_m 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.