School of Energy and Power Engineering, Beihang University, Beijing 100191, China; Beihang Hangzhou Innovation Institute Yuhang, Xixi Octagon City, Yuhang District, Hangzhou 310023, China.
Beihang Hangzhou Innovation Institute Yuhang, Xixi Octagon City, Yuhang District, Hangzhou 310023, China.
Sci Total Environ. 2022 Dec 1;850:158089. doi: 10.1016/j.scitotenv.2022.158089. Epub 2022 Aug 18.
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) and number (EI) for a number of high-bypass turbofan engines. The accuracy of predicting EI and EI at ground level is significantly improved (R = 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 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.
航空排放物是高空人为颗粒物污染的唯一直接来源，这些排放物可以形成尾迹和尾迹诱导云，从而对全球辐射强迫产生影响。在本研究中，我们使用卷积神经网络（CNN）技术开发了一种名为 APMEP-CNN 的航空非挥发性颗粒物（nvPM）排放预测模型。该模型是基于新发布的航空排放数据库中的数据集以及地面和巡航运行期间的几项现场研究的测量结果建立的。该模型还考虑了燃料特性，将可持续航空燃料（SAFs）对 nvPM 排放的影响考虑在内。本研究表明，APMEP-CNN 可以预测多种高涵道比涡扇发动机的质量（EI）和数量（EI）的 nvPM 排放指数。与已发表的模型相比，该模型在预测地面 EI 和 EI 方面的准确性有显著提高（R = 0.96 和 0.96）。我们验证了 APMEP-CNN 模型在估算巡航和燃烧 SAFs 和混合燃料时的 nvPM 排放的适用性和适用性，并发现我们对 EI 的预测在巡航时与测量值的偏差在±36.4%以内，燃烧 SAFs 时的偏差在平均水平上为±33.0%。在最坏的情况下，对于 JT3D-3B 发动机，APMEP-CNN 的预测值与巡航时的测量值相差-69.2%。因此，APMEP-CNN 模型可以为建立全球航空排放的准确清单提供新数据，并有助于评估航空排放对人类健康、环境和气候的影响。摘要：本文的研究结果提供了使用中飞机发动机 nvPM 排放的准确预测，这会影响机场的当地空气质量和全球辐射强迫。
