AUTHOR=Xu Bai-Zhou , Li Xiao-Liang , Wang Wen-Feng , Chen Xi 

TITLE=Expanding the theory for reducing the CO2 disaster—Hypotheses from partial least-squares regression and machine learning

JOURNAL=Frontiers in Earth Science

VOLUME=10

YEAR=2022

URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.1004920

DOI=10.3389/feart.2022.1004920

ISSN=2296-6463

ABSTRACT=<p>The rapid increase in atmospheric CO<sub>2</sub> concentration has caused a climate disaster (CO<sub>2</sub> disaster). This study expands the theory for reducing this disaster by analyzing the possibility of reinforcing soil CO<sub>2</sub> uptake (F<sub>x</sub>) in arid regions using partial least-squares regression (PLSR) and machine learning models such as artificial neural networks. The results of this study demonstrated that groundwater level is a leading contributor to the regulation of the dynamics of the main drivers of F<sub>x</sub>–air temperature at 10 cm above the soil surface, the soil volumetric water content at 0–5 cm (<italic>R</italic><sup>2</sup>=0.76, RMSE=0.435), and soil pH (<italic>R</italic><sup>2</sup>=0.978, RMSE=0.028) in arid regions. F<sub>x</sub> can be reinforced through groundwater source management which influences the groundwater level (<italic>R</italic><sup>2</sup>=0.692, RMSE=0.03). This study also presents and discusses some basic hypotheses and evidence for quantitively reinforcing F<sub>x</sub>.</p>