Abstract
In order to reveal the dolomite strata developed cave system in the cave water gas CO2 partial pressure of cave water hydro chemical process and its dynamic characteristics and control factors. The dynamic monitoring for a period of year from the water chemistry index of Suiyang Shuanghe cave water from June 2015 to Mar 2016. Results show that: 1. Hydro chemical types of Shuanghedong cave water is mainly \( \rm{HCO}_{3}^{ - } \hbox{-} \rm{Ca}^{2 + } \hbox{-} \rm{Mg}^{2 + } \) and \( \rm{SO}_{4}^{2 - } \hbox{-} \rm{HCO}_{3}^{ - } \hbox{-} \rm{Ca}^{2 + } \hbox{-} \rm{Mg}^{2 + } \) type water and got overlying gypsum strata and \( \rm{SO}_{4}^{2 - } \) become dominant ions and can greatly improve the solution Ca2+ ion content. 2. Cave drip water - gas phase CO2 partial pressure difference (ΔPCO2) between SIc and has good negative correlation at the same time, two important mineral saturation index of SIc, between SId exist a good positive correlation. 3. The analysis of the measured data proves the objective existence of the “actual degassing and deposition” and the difference between the theory and practice of the classical degassing and deposition theory. 4. The liquid phase CO2 (PCO2(water)) in the cave water showed a significant negative correlation with the pH value of the solution, and the CO2 partial pressure of the cave air in the undeveloped natural closed cavity had little influence on the water of the cave.
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Acknowledgments
In this paper, the research was sponsored by National Natural Science Foundation of China (Grant No. 41361081); Guizhou Province Science and Technology Plan (Guizhou S&T Contract G 2014-4004-2); National Natural Science Foundation of China Youth Science Foundation Project (Grant No. 41301504); Major application based research project of Guizhou (Guizhou S&T Contract JZ 2014-0201).
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Zhang, J., Zhou, Z., Cao, M., Pan, Y. (2017). Characteristics and Environmental Significance and Physical and Chemical Properties of Karst Cave Water in Shuanghe Cave, Guizhou Province (in China). In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-10-3966-9_62
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DOI: https://doi.org/10.1007/978-981-10-3966-9_62
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