Comments to the Author

The aircraft engine emissions have important impacts on air quality in and around airports and the potential exposure of nearby residential populations. The impact study of aircraft emission relies on the detailed and accurate emission information. This paper provides a detailed information about four-dimensional aircraft emission for landing and takeoff cycle from 2019 to 2023 based on the flight time and trajectory information. It could provide useful basis to further study of the environmental impacts. Overall, this MS is well-structured and is appropriate for the scope of the Earth System Science Data journal. There are several necessary revisions should be made before the manuscript could be considered for publication acceptance.

• Introduction: It should be stated how much China aircraft emissions contributes to global aircraft emissions. This provides a general context for global implications in terms of pollution that emphasizes the importance of better estimates of the specific emissions mentioned in this study.
• What is the content of Section 2.1.2?
• Section 2.1: The time-in-mode was described in detail in emissions calculation, however, other input data such as emission factors for different flight modes (taxi, takeoff, climb, and approach) were not sourced or calculated.
• Provide a table summarizing the emission factors used for key pollutants or cite the references.
• Section 2.2: It is recommended to cite more classical literature on the application of the DBSCAN algorithm in this field.
• While the DBSCAN algorithm is referenced for flight trajectory recognition, the paper does not provide a detailed explanation of its parameters (e.g., minimum points, radius).
• Section 3: What is the basis for determining the high-resolution spatial grid (0.03° × 0.03° × 34 vertical layers)?
• Does this resolution significantly improve the representation of emissions compared to conventional models with fewer layers?
• Section 3.2: The data in this paragraph is messy to show as a list, can it be shown as a table or some other form?
• Section 4: The study area is China, and the literature of the comparative study is better supplemented with more studies of Chinese airports.
• For the result of emission during 2020-2023, while the authors compare results with previous ICAO-based methods, the statistical measures of validation (e.g., R², RMSE) are not clearly presented.
• It should be further clarified the datasets used for validation, including observational data from airports and other inventory results.
• Line 444: The results mention a rebound in emissions by 2023 to 95.3% of 2019 levels, but this observation is not broken down by pollutant or flight mode.
• Apart from NOx, other pollutants such as HC and PM are not discussed in detail, why? and what are their specific temporal and spatial patterns?
• Line 460: The emissions above 915 m account for 24.6%, what is the significance of this finding? Does this altitude range impact local air quality differently than ground-level emissions?
• Health impacts are mentioned in the introduction, but there is no specific health-related discussion in the discussion, specific pollutants such as NOx and PM2.5 are known to cause respiratory and cardiovascular issues, please add or cite references.
• The dataset is established for China, how can the methodology be applied to other regions with different aviation?
• Linking the conclusion to wider global challenges such as climate change or international emissions reduction targets for aviation will be better.

This study presents a four-dimensional aircraft emission inventory of the LTO cycle in China. The author first calculated the air pollutant emissions during the LTO cycles with the ICAO method with modeled running time and aircraft type-specified emission factors. Then flight altitude and horizontal trajectory was identified for emission allocations. The developed methods aim to construct the flight trajectories without using the ADS-B data that have limited availability. This study objective is attractive; however, the descripting of the methods lacks some critical details and the model performance is not fully evaluated. More information is needed for readers to evaluate the quality of the presented emission data.

• To calculate the flight emission, the author collected all aircraft types and proportion of engine types for each aircraft type to get a weighted EI and EF. Thus the accuracy of the engine proportion significantly affect the calculation accuracy. Are the proportion data with complete coverage and varying year by year? If not, please provides the details and add uncertainty discussion.
• The section 2.1.2 Climb and approach time calculation is missing.
• The author constructed a model to estimate the aircraft’s taxi time in order to fill the missingness in actual taxi time data; however, there lacks figures or tables to summarize the coverage and quality of actual taxi time data that were used for model fitting, making it hard to evaluate the representativeness of the model. The consistency of exponential relationship between deltaT and T₀ with N in different years need to be presented by fitting the exponential relationship with each year’s data separately and comparing the fitted parameter. The R² of deltaT and N is 0.1 for taxi in in PEK, indicating poor correlation. The fitting effect of deltaT and T₀ in other airports is nor presented. A summary of the overall performance of the model is critical for readers to consider this method.
• The flight altitude was modeled following a previous study. The author should summarize this model here rather than only provide a citation.
• The AMDAR data were used to identify flight horizontal trajectory, but information on the AMDAR data is missing.
• The description of the application of DBSCAN method is not clear. What are the inputs of DBSCAN? Why the trajectory is high dimension (besides longitude, latitude, and altitude) and the dimension was set to 25?
• The performance of taxi time model is represented by PEK and the performance of trajectory cluster is represented by PVG. Why these two airports are selected? I am wondering the model performance at other airports.
• The uncertainty calculation section is full of uncertainty. How did the author calculate the distribution parameters of each terms with uncertainty and what are these parameters used in the final calculation? The uncertainty ranges were only presented at two airports with PEK shows uncertainty in emission calculation and PVG shows uncertainty in spatial distribution. What is the overall uncertainty of this emission inventory?
• Some previous studies, e.g. Teoh et al., 2024 and Zhang et al., 2022, used ADS-B data to estimate the aviation emissions at a high spatiotemporal resolution. What is the major advantage of this study compared to those previous studies? Please also compare the estimated aviation emissions to other emission inventories, e.g. EDGAR, EMEP, AERO2k et al.