Expanding the theory for reducing the CO2 disaster—Hypotheses from partial least-squares regression and machine learning - Xu, Bai-Zhou; Li, Xiao-Liang; Wang, Wen-Feng; Chen, Xi (Frontiers in Earth Science, 2022)
The rapid increase of atmospheric CO2 concentration has caused a climate disaster (here termed as CO2 disaster). This article aims to expand the theory for reducing the disaster by analyzing the possibility to reinforce soil CO2 uptake (Fx) in arid regions by partial least squares regression (PLSR) and machine learning models (ANN). Results highlight that groundwater level is a leading contributor to regulate the dynamics of the main drivers of Fx - air temperature at 10 cm above the soil surface, the soil volumetric water content at 0-5 cm (R2=0.76, RMSE=0.435) and soil pH (R2=0.978, RMSE=0.028) in arid regions. Fx can be reinforced through groundwater sources management (R2=0.692, RMSE=0.03). Some basic hypotheses and evidences for quantitively reinforcing Fx are also presented and discussed.