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Research on AI-based algorithms for atmospheric carbon dioxide data imputation and reconstruction
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Tao-feng GU1, Hai-yan YUE2, Zi-li XIONG1, Jian-ping LIANG3, Yan-ling WU4, Li-xin LIU5, *
China Environmental Science | 2026, 46(3) : 1288 - 1297
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China Environmental Science | 2026, 46(3): 1288-1297
• Air Pollution Control •
Research on AI-based algorithms for atmospheric carbon dioxide data imputation and reconstruction
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Tao-feng GU1, Hai-yan YUE2, Zi-li XIONG1, Jian-ping LIANG3, Yan-ling WU4, Li-xin LIU5, *
Affiliations
  • 1.Guangzhou Meteorological Comprehensive Guarantee Center, Guangzhou 511430, China
  • 2.Guangzhou Meteorological Observatory, Guangzhou 511430, China
  • 3.Guilin Meteorological Observatory, Guilin 541000, China
  • 4.Longfengshan Regional Atmospheric Backbone Station, Wuchang 150001, China
  • 5.China Meteorological Bureau Meteorological Detection Center, Beijing 100081, China
Published: 2026-03-20
Outline
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To address the challenge of partial missing data in atmospheric CO2 concentration observations, this study systematically evaluated the performance of 12 mainstream machine learning algorithms for data imputation, utilizing CO2 measurement data collected at the 30m (L1 layer) and 80m (L2 layer) heights of the Longfengshan Regional Atmospheric Background Station over the period from January 2009 to June 2022. An optimized ensemble machine learning method was proposed, with the distance-weighted K-Nearest Neighbors (KNeighborsDist) algorithm as its core framework. This method reconstructs missing data by leveraging synchronous observational records from the two co-located height layers. Validation analysis of the reconstructed dataset yielded excellent performance (R2≥0.98), which confirms a strong correlation between the reconstructed greenhouse gas data and in-situ measurements, as well as the effectiveness and accuracy of the proposed model in greenhouse gas data reconstruction tasks. The method successfully filled the data gaps in the original dataset of Longfengshan Station, generating a continuous 5minute interval CO2 dataset with a completeness rate of 100%. Additionally, it well preserved the long-term trends and seasonal, monthly, and diurnal variation characteristics of CO2. The proposed AI-based data reconstruction algorithm is applicable to other atmospheric background stations and can provide high-quality data support for regional climate change analysis, carbon emission accounting, and inversion research.

carbon dioxide  /  machine learning algorithm  /  data filling  /  data set  /  Longfengshan
顾桃峰, 岳海燕, 熊梓立, 梁建平, 吴艳玲, 刘立新. 基于AI的大气二氧化碳数据填补与重构算法研究. 中国环境科学, 2026 , 46 (3) : 1288 -1297 .
Tao-feng GU, Hai-yan YUE, Zi-li XIONG, Jian-ping LIANG, Yan-ling WU, Li-xin LIU. Research on AI-based algorithms for atmospheric carbon dioxide data imputation and reconstruction[J]. China Environmental Science, 2026 , 46 (3) : 1288 -1297 .
Year 2026 volume 46 Issue 3
92
17
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Article Info
  • Receive Date:2025-08-09
  • Online Date:2026-10-08
  • Published:2026-03-20
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  • Received:2025-08-09
Funding
Affiliations
    1.Guangzhou Meteorological Comprehensive Guarantee Center, Guangzhou 511430, China
    2.Guangzhou Meteorological Observatory, Guangzhou 511430, China
    3.Guilin Meteorological Observatory, Guilin 541000, China
    4.Longfengshan Regional Atmospheric Backbone Station, Wuchang 150001, China
    5.China Meteorological Bureau Meteorological Detection Center, Beijing 100081, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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