Published June 24, 2026 | Version v1

High-Resolution Gridded Aviation Emission Inventory for China (2023)

  • 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 2. China Academy of Civil Aviation Science and Technology, Beijing, 100028, China
  • 3. Capital Airports Holdings Co., Ltd. Beijing Construction Project Management Headquarters, Beijing, 100028, China

Description

Accurate quantification of civil aviation carbon emissions and their non-CO₂ effects is a critical component for atmospheric modeling and regional air quality management. Current inventories predominantly rely on generic performance models and standard operational assumptions, leading to systematic biases due to the lack of measured thermodynamic parameters. In order to overcome these limitations, this study integrated observation-constrained emission calculation methods to establish a real-world flight trajectory-based high-resolution spatial emission inventory for China’s civil aviation in 2023. Utilizing Quick Access Recorder (QAR) telemetry from 4.52 million flights, this measurement-driven approach integrated second-level Fuel Flow (FF) rates and environmental parameters recorded by airborne sensors. By quantifying phase-specific emissions with physical high fidelity, the continuous flight trajectories were mathematically mapped onto a discretized geographic grid system, providing a highly robust national aviation emission benchmark.

Species: CO₂, NOx, SO₂, CO, HC, and PM.

Temporal information: 2023 (Annual aggregated total mass).

Spatial information: The horizontal resolution of the aviation emission inventory is 0.1° × 0.1° under the WGS84 (EPSG:4326) coordinate system, covering the territorial airspace of China. The total mass of emissions falling within each grid cell was summed up for the entire year, with the unit of measurement being grams (g) per grid cell.

Data structure information: The dataset is provided as discrete GeoTIFF (.tif) files for each specific pollutant (e.g., CO2_2023_China.tif, NOx_2023_China.tif).

Files

NOx_SUM_2023_China.tif

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