Reconstructing annual XCO2 at a 1 km×1 km spatial resolution across China from 2012 to 2019 based on a spatial CatBoost method

Chao Wu; Yuechuang Ju; Shuo Yang; Zhenwei Zhang; Yixiang Chen

doi:10.1016/j.envres.2023.116866

Reconstructing annual XCO₂ at a 1 km×1 km spatial resolution across China from 2012 to 2019 based on a spatial CatBoost method

Environ Res. 2023 Nov 1;236(Pt 2):116866. doi: 10.1016/j.envres.2023.116866. Epub 2023 Aug 9.

Authors

Chao Wu¹, Yuechuang Ju², Shuo Yang², Zhenwei Zhang³, Yixiang Chen⁴

Affiliations

¹ School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China; Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
² School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
³ School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, No.219, NingLiu Road, Nanjing, China.
⁴ School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China; Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China. Electronic address: chenyixiang@njupt.edu.cn.

PMID: 37567384
DOI: 10.1016/j.envres.2023.116866

Abstract

Long-time-series, high-resolution datasets of the column-averaged dry-air mole fraction of carbon dioxide (XCO₂) have great practical importance for mitigating the greenhouse effect, assessing carbon emissions and implementing a low-carbon cycle. However, the mainstream XCO₂ datasets obtained from satellite observations have coarse spatial resolutions and are inadequate for supporting research applications with different precision requirements. Here, we developed a new spatial machine learning model by fusing spatial information with CatBoost, called SCatBoost, to fill the above gap based on existing global land-mapped 1° XCO₂ data (GLM-XCO₂). The 1-km-spatial-resolution dataset containing XCO₂ values in China from 2012 to 2019 reconstructed by SCatBoost has stronger and more stable predictive power (confirmed with a cross-validation (R² = 0.88 and RSME = 0.20 ppm)) than other traditional models. According to the estimated dataset, the overall national XCO₂ showed an increasing trend, with the annual mean concentration rising from 392.65 ppm to 410.36 ppm. In addition, the spatial distribution of XCO₂ concentrations in China reflects significantly higher concentrations in the eastern coastal areas than in the western inland areas. The contributions of this study can be summarized as follows: (1) It proposes SCatBoost, integrating the advantages of machine learning methods and spatial characteristics with a high prediction accuracy; (2) It presents a dataset of fine-scale and high resolution XCO₂ over China from 2012 to 2019 by the model of SCatBoost; (3) Based on the generated data, we identify the spatiotemporal trends of XCO₂ in the scale of nation and city agglomeration. These long-term and high resolution XCO₂ data help understand the spatiotemporal variations in XCO₂, thereby improving policy decisions and planning about carbon reduction.

Keywords: China; High resolution; SCatBoost; Spatial characteristics; Temporal variations; XCO(2).

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

China
Environmental Monitoring* / methods