4-D Aviation Emission Inventory Data of China Estimated with QAR Data in 2023 (4D-AEID-2023)

Abstract. Accurate aviation emission inventories are essential for environmental impact assessment and mitigation, yet existing estimates predominantly rely on aircraft performance models and standardized assumptions, introducing significant uncertainties. Here, we present a high-resolution, four-dimensional aviation emission inventory for China's civil aviation sector in 2023, built directly from Quick Access Recorder (QAR) data covering 4.52 million flights (~96.8 % of national commercial movements). By coupling second-level fuel-flow measurements with observed flight-state and meteorological parameters via the Boeing Fuel Flow Method 2 and a dynamic thermodynamic correction, this dataset yields temporally and spatially explicit emission estimates for CO₂, NOₓ, SO₂, PM, CO, and HC across all flight phases. In 2023, China's civil aviation consumed 28.4 Mt of fuel, emitting 89.3 Mt CO₂, 634.6 kt NOₓ, 28.4 kt SO₂, 8.46 kt PM, 44.3 kt CO, and 6.35 kt HC. Monte Carlo analysis demonstrates that direct physical observations substantially constrain inventory uncertainty, reducing coefficients of variation (CV) for major pollutants to 2 %–10 % and shifting the dominant error source from activity-level assumptions to emission index parameterizations. High temporal resolution reveals pronounced phase-specific variations: taxiing accounts for 23.7 % of HC and 19.4 % of CO emissions due to low-thrust incomplete combustion, while the brief high-thrust acceleration segment preceding climb contributes 18.0 % of total PM. Spatially, emissions are highly concentrated, with the top 10 % of grid cells accounting for 74.2 % of national CO₂ emissions, exhibiting significant clustering (Global Moran's I = 0.3389, p < 0.05) along major corridors. Providing second-level and high resolution, this dataset serves as an observation-based benchmark for refining existing inventories and assessing near-airport air quality. The dataset is publicly available at https://doi.org/10.5281/zenodo.20828076 (Lu et al., 2026).