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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). [ABSTRACT FROM AUTHOR]

Inter-provincial trade leads to changes in CO2 and air pollutant emissions. However, there is a research gap regarding the coordinated effects (co-effects) between embodied CO2 and air pollutant emissions in trade. Understanding co-effects in inter-provincial trade is a prerequisite for driving the green transformation of trade and achieving coordination between pollution and carbon reduction. Here, we calculated provincial-level CO2 and air pollutant emission leakage in 2012 and 2017 based on a modified input–output model and, for the first time, investigated the co-effects between CO2 and air pollutant emission leakage caused by emissions transfers in China. Three types of co-effects, categorized as co-benefits, trade-offs, and co-damage, were discovered and defined to reveal the provincial differences. Furthermore, combined with structural decomposition analysis (SDA), we calculated the interannual variation in trade-induced emissions and identified the key driving factors of provincial-level co-effects from 2012 to 2017. Optimizing the energy structure has led to the greatest co-benefits, while changes in the industrial structure and emission coefficients have led to limited co-benefits in specific provinces. Variations in trade volume have led to co-damages across all provinces, and changes in emission coefficients have led to trade-offs in the majority of provinces. The case analysis confirmed that identifying and adjusting the key driving factors of co-effects can promote the transformation from co-damage and trade-offs to co-benefits. The findings implied a new approach for the reduction in pollution and carbon through inter-provincial trade. [ABSTRACT FROM AUTHOR]

Air pollution has become a great challenge to achieving sustainable development. Among the pollutants, aerosols significantly affect human health and play an important role in global climate change. The concentration of aerosols in the ambient air is influenced strongly by the regional transport of pollutants and their precursors and may vary considerably under different meteorological conditions in different years. This inter-annual variation in meteorology may yield conflicting results in the quantification of the contribution from regional transport of air pollutants. It creates uncertainty for local governments to develop pollution control measures to reduce the challenges to sustainable development. Previous studies on this issue are often year-specific or cover short time spans, and the inter-city transport of air pollutants in the long term is still not fully understood. Therefore, in this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality (CMAQ) model was used to assess inter-annual variations in the contribution of inter-city transport to the PM2.5 concentration in the Beijing–Tianjin–Hebei region from 2010 to 2019. To highlight the impact of inter-annual variations in meteorology, the authors used the same emission inventory and the same model configurations for the 10-year simulation. The major findings can be summarized as follows: (1) Both PM2.5 concentration and inter-city transport in the Beijing–Tianjin–Hebei (BTH) region were influenced by the inter-annual variation in meteorological conditions. (2) The simulated annual average concentrations in 13 cities in BTH are highly variable, with fluctuations ranging from 30.8% to 54.1%, and more evident variations were found in seasonal results. (3) Seven out of thirteen cities have a contribution from regional transport exceeding 50%, which are located in the eastern half of the Beijing–Tianjin–Hebei region. (4) The magnitude of the regional transport contribution varies significantly among the cities of BTH, on an annual basis, from a minimum inter-annual fluctuation of 8.9% to a maximum of 37.2%, and seasonal fluctuation is even more strongly evident. These results indicate that, when formulating pollution control strategies, inter-annual changes in meteorological conditions should not be ignored. [ABSTRACT FROM AUTHOR]