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Mapping high-resolution monthly XCO2 across China and its relationship with human activities

Yan, Xuqian ; Cui, Guangxin ; Sun, Yanwei ; Gao, Yuhe ; Duan, Zheng LU ; Ruan, Tian and Gao, Chao (2026) In Environmental Research Communications 8(3).
Abstract

Monitoring atmospheric CO2 concentration changes across China is crucial for reducing carbon emissions and tackling global warming. Due to monitoring gap in satellite observations, this paper uses the Extremely Randomized Trees (ERT) model alongside OCO-2 satellite data and predictor variables to develop a 0.1° high-resolution monthly dataset of column-averaged dry air CO2 mole fraction (XCO2) in China between 2015 and 2023. The model demonstrates high precision, achieving a sample cross-validation with R2 of 0.988 and RMSE of 0.732 ppm, while validation at Hefei and Xianghe stations yielded R2 (RMSE) of 0.951 (1.339 ppm) and 0.865 (1.840 ppm), respectively. Therefore, this dataset... (More)

Monitoring atmospheric CO2 concentration changes across China is crucial for reducing carbon emissions and tackling global warming. Due to monitoring gap in satellite observations, this paper uses the Extremely Randomized Trees (ERT) model alongside OCO-2 satellite data and predictor variables to develop a 0.1° high-resolution monthly dataset of column-averaged dry air CO2 mole fraction (XCO2) in China between 2015 and 2023. The model demonstrates high precision, achieving a sample cross-validation with R2 of 0.988 and RMSE of 0.732 ppm, while validation at Hefei and Xianghe stations yielded R2 (RMSE) of 0.951 (1.339 ppm) and 0.865 (1.840 ppm), respectively. Therefore, this dataset is suitable for high-resolution studies. The results show that XCO2 across China is increasing at a rate of 2.46 ppmyr−1 during 2015–2023, with XCO2 highest in spring and lowest in summer. There is a significant spatial heterogeneity in XCO2 distribution during the period, characterized by a gradient of ‘higher in the east and lower in the west’. Among the factors analyzed, the most influential were population density, transportation infrastructure level and energy intensity, which emerged as the strongest human activity-related drivers of XCO2 variability, with population density exhibiting a positive correlation with XCO2, while the latter two show a negative correlation with XCO2. This paper can provide a scientific basis for policymaking to reduce carbon emissions, promote sustainable development and address climate change.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
ERT model, human activities, OCO-2 XCO2, spatiotemporal distribution, XCO2 concentration
in
Environmental Research Communications
volume
8
issue
3
article number
035020
publisher
IOP Publishing
external identifiers
  • scopus:105033694979
ISSN
2515-7620
DOI
10.1088/2515-7620/ae48ad
language
English
LU publication?
yes
id
d5631ca9-415d-4b4e-8d90-14a3839e41cf
date added to LUP
2026-06-10 10:28:44
date last changed
2026-06-10 10:28:51
@article{d5631ca9-415d-4b4e-8d90-14a3839e41cf,
  abstract     = {{<p>Monitoring atmospheric CO<sub>2</sub> concentration changes across China is crucial for reducing carbon emissions and tackling global warming. Due to monitoring gap in satellite observations, this paper uses the Extremely Randomized Trees (ERT) model alongside OCO-2 satellite data and predictor variables to develop a 0.1° high-resolution monthly dataset of column-averaged dry air CO<sub>2</sub> mole fraction (XCO<sub>2</sub>) in China between 2015 and 2023. The model demonstrates high precision, achieving a sample cross-validation with R<sup>2</sup> of 0.988 and RMSE of 0.732 ppm, while validation at Hefei and Xianghe stations yielded R<sup>2</sup> (RMSE) of 0.951 (1.339 ppm) and 0.865 (1.840 ppm), respectively. Therefore, this dataset is suitable for high-resolution studies. The results show that XCO<sub>2</sub> across China is increasing at a rate of 2.46 ppmyr<sup>−1</sup> during 2015–2023, with XCO<sub>2</sub> highest in spring and lowest in summer. There is a significant spatial heterogeneity in XCO<sub>2</sub> distribution during the period, characterized by a gradient of ‘higher in the east and lower in the west’. Among the factors analyzed, the most influential were population density, transportation infrastructure level and energy intensity, which emerged as the strongest human activity-related drivers of XCO<sub>2</sub> variability, with population density exhibiting a positive correlation with XCO<sub>2</sub>, while the latter two show a negative correlation with XCO<sub>2</sub>. This paper can provide a scientific basis for policymaking to reduce carbon emissions, promote sustainable development and address climate change.</p>}},
  author       = {{Yan, Xuqian and Cui, Guangxin and Sun, Yanwei and Gao, Yuhe and Duan, Zheng and Ruan, Tian and Gao, Chao}},
  issn         = {{2515-7620}},
  keywords     = {{ERT model; human activities; OCO-2 XCO2; spatiotemporal distribution; XCO2 concentration}},
  language     = {{eng}},
  month        = {{03}},
  number       = {{3}},
  publisher    = {{IOP Publishing}},
  series       = {{Environmental Research Communications}},
  title        = {{Mapping high-resolution monthly XCO<sub>2</sub> across China and its relationship with human activities}},
  url          = {{http://dx.doi.org/10.1088/2515-7620/ae48ad}},
  doi          = {{10.1088/2515-7620/ae48ad}},
  volume       = {{8}},
  year         = {{2026}},
}