Prediction of volcanic CO2 flux based on random simulation: Taking the Mount Etna, Italy as an example

doi:10.13745/j.esf.sf.2023.11.66

Abstract

Abstract:

Volcanic activity, as a significant source of geological carbon emissions and contributor to deep carbon cycles, transports carbon from the Earth’s interior to the atmosphere. Greenhouse gases, particularly CO₂, emitted by volcanic regions, exert a profound influence on global climate dynamics. Against the backdrop of global warming and initiatives such as the “carbon neutral” program, accurately estimating the flux of greenhouse gases from volcanic regions and assessing their impact on global carbon budgets are imperative. This paper elucidates the primary characteristics and survey methodologies for quantifying greenhouse gas emissions in volcanic areas. It proposes employing geostatistical methods to simulate CO₂ sampling data from volcanoes, exemplified by Mount Etna, Italy. Additionally, the feasibility of incorporating covariates for cokriging interpolation simulations is analyzed, with comparisons drawn against ordinary kriging interpolation methods. Our findings reveal a correlation between CO₂ release flux and soil temperature in volcanic regions, indicating that integrating soil temperature into cokriging interpolation simulations can mitigate error indices in the results. This research offers critical insights for quantitatively assessing the impact of volcanic activity on climate change and enhancing early warning systems for volcanic hazards.

Key words: volcanic activity, greenhouse gas, carbon emission, stochastic simulation, Kriging interpolation method

CLC Number:

P317

SUN Haoran, DOU Jiale, LI Nan, WU Peng, DU Cong, DUAN Xianzhe. Prediction of volcanic CO₂ flux based on random simulation: Taking the Mount Etna, Italy as an example[J]. Earth Science Frontiers, 2024, 31(4): 429-437.

Figures/Tables 8

Fig.1 Schematic diagram of the distribution of sampling points for CO2 gas flux test data in the Etna Volcano region of Italy

Fig.2 Histogram of primary variable data

Fig.3 Quantile-quantile plot of primary variable data

Fig.4 Prediction diagram using the ordinary kriging method

Table 1 Error table for ordinary kriging interpolation

平均误差	标准平均误差	均方根误差	标准均方根误差
22	0.10	3 741	1.70

Fig.5 Prediction graph using the cokriging interpolation method (covariate: soil temperature)

Fig.6 Prediction graph using the cokriging interpolation method (covariate: air temperature)

Table 2 Error table for the cokriging interpolation method

协变量	平均误差	标准平均误差	均方根误差	标准均方根误差
土壤温度/℃	69	0.05	3 586	1.65
气体温度/℃	280	0.24	4 683	2.56

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Prediction of volcanic CO₂ flux based on random simulation: Taking the Mount Etna, Italy as an example

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