Four-dimensional aircraft emission inventory dataset of Landing and takeoff cycle in China (2019–2023)

Abstract. The rapid growth of the aviation industry has resulted in aircraft emissions during landing and takeoff (LTO), which have direct and increasingly adverse impacts on air quality and human health. An accurate and high-resolution LTO emission inventory is crucial for investigating these adverse effects, with the LTO emission having unique three-dimensional spatial characteristics and typical hourly temporal variations. This study integrated the emission calculation and flight trajectory recognition methods to establish a four-dimensional aircraft emission inventory dataset of China’s LTO cycle (4D-LTO emission inventory dataset) from 2019 to 2023. The dataset has a high spatial-temporal resolution (hourly, 0.03° × 0.03° × 34 height layers) and incorporates calculation emissions accurately. Moreover, the actual taxi out/in time for each flight was determined by a statistical model of taxi time and some aircraft in schedule based on 38,000,000 flights. Each flight’s climb/approach time was also obtained based on mixing layer height (MLH) and the height-time nonlinear relationship. Additionally, we calculated the LTO emission for China’s flight, establishing the hourly emission inventory based on each mode’s running time, emission index, and fuel flow. We obtained the flight trajectory core of each airport based on measured flight trajectories and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to depict the spatial distribution. Then, each flight’s takeoff/landing direction and trajectory were identified from the wind direction and relative departure/arrival airport position. The findings indicate that the impact of COVID-19 has reduced the LTO number in 2020–2022 to 73.1 %, 77.6 %, and 48.7 % of 2019 levels, respectively. However, by 2023, the LTO number has rapidly bounced back to 95.3 % of 2019 levels. The recovery rate during daytime (6:00–23:00) was 41.6 % higher than night-time (0:00–5:00). The emissions of various pollutants were measured as follows: HC, CO, NOx, PM, and SO₂ are 3.2 Gg, 46.1 Gg, 62.3 Gg, 1.1 Gg and 18.4 Gg. LTO emissions’ horizontal characteristic is the distance along the runway and spread. This elongated distribution will be hidden if a rough grid (e.g., 0.36°×0.36°) and the emissions are evenly distributed. Moreover, LTO emissions height characteristic ‘decreases with height,’ and the maximum height varies with MLH. Emissions above the standard height set by the International Civil Aviation Organization standard height (~915 m) are not estimated. For example, NOx emissions above 915 m during various months make up an average of 24.6 % (9.9 %–37.5 %) in the LTO cycle, indicating the emissions are significantly underestimated when using the ICAO method. Compared with conventional spatial allocation methods, our dataset provides a more accurate representation of the actual LTO situation in both horizontal and height at different times. Our 4D-LTO emission inventory dataset and its adaptable methodology are valuable resources for researching temporal and spatial variations, air quality, and health impacts of aircraft emissions in the LTO cycle. The dataset can be accessed from https://doi.org/10.5281/zenodo.13908440 (Lang et al., 2024).