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

Q: What data source is used to construct the 4D aviation emission inventory for China in 2023?

The inventory is built directly from Quick Access Recorder (QAR) data covering about 4.52 million flights (~96.8% of national commercial movements).

Q: Which emissions and flight phases does the dataset provide estimates for?

It provides temporally and spatially explicit estimates for CO2, NOx, SO2, PM, CO, and HC across all flight phases.

Q: How does the method reduce uncertainty compared with conventional approaches?

Monte Carlo analysis shows direct physical observations constrain inventory uncertainty, reducing coefficients of variation for major pollutants to about 2%–10% and shifting dominant error sources toward emission index parameterizations.

Oct 3, 2026

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Accurate aviation emission inventories are essential for environmental impact assessment yet existing estimates rely heavily on aircraft performance models and standardized assumptions, creating substantial uncertainty. A high-resolution four-dimensional aviation emission inventory is presented for China’s civil aviation sector in 2023, derived directly from Quick Access Recorder data covering 4.52 million flights (~96.8% of national commercial movements). Coupling second-level fuel-flow measurements with observed flight-state and meteorological parameters yields temporally and spatially explicit emissions for CO2, NOx, SO2, PM, CO, and HC across all flight phases.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages29

Reading Time1 h 13 min

AccessFree to download

Abstract

Introduction

[https://doi.org/10.5194/essd-2026-530](https://doi.org/10.5194/essd-2026-530) Preprint. Discussion started: 12 August 2026 􀀍c Author(s) 2026 . CC BY 4.0 License. 4-D Aviation Emission Inventory Data of China Estimated with QAR Data in 2023 (4D-AEID-2023) Binbin Lu1, Lingkun Shi 1, Chun Wang2*, Huabo Sun2, Qinli Guo3, Xinbo Wang3, Xuhui Wang2* 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China 5 2China Academy of Civil Aviation Science and Technology, Beijing, 100028, China 3Capital Airports Holdings Co., Ltd. Beijing Construction Project Management Headquarters, Beijing, 100028, China Correspondence to: Chun Wang ([wangchun@castc.org.cn](wangchun@castc.org.cn)); Xuhui Wang ([wangxh@castc.org.cn](wangxh@castc.org.cn)) 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 10 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 15 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 20 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](https://doi.org/10.5281/zenodo.20828076) (Lu et al., 2026). 25 1 Introduction The robust recovery of aviation demand has positioned the air transport industry as a key driver of economic development in the post-pandemic era. However, its expanding environmental footprint presents a formidable challenge to achieving global carbon neutrality goals (Grewe et al., 2021; Johansson et al., 2025). Aircraft engines release a variety of emissions, including carbon dioxide (CO₂), nitrogen oxides (NOₓ), carbon monoxide (CO), and non-volatile particulate matter (nvPM) . These 30 emissions may alter the Earth's radiation balance through the greenhouse effect and contrail cirrus (Schumann et al., 2012), [https://doi.org/10.5194/essd-2026-530](https://doi.org/10.5194/essd-2026-530) Preprint. Discussion started: 12 August 2026 􀀍c Author(s) 2026 . CC BY 4.0 License. significantly contributing to anthropogenic radiative forcing and climate change (Dannet et al., 2025; Lee et al., 2021) . Furthermore, they may pose serious threats to public health by deteriorating regional air quality (Yim et al., 2013) . Statistics indicate that global aviation emissions at cruise altitudes cause approxi

FAQs

What data source is used to construct the 4D aviation emission inventory for China in 2023?

The inventory is built directly from Quick Access Recorder (QAR) data covering about 4.52 million flights (~96.8% of national commercial movements).

Which emissions and flight phases does the dataset provide estimates for?

It provides temporally and spatially explicit estimates for CO2, NOx, SO2, PM, CO, and HC across all flight phases.

How does the method reduce uncertainty compared with conventional approaches?

Monte Carlo analysis shows direct physical observations constrain inventory uncertainty, reducing coefficients of variation for major pollutants to about 2%–10% and shifting dominant error sources toward emission index parameterizations.

Oct 3, 2026

Upload by นรินทร์