International Journal of Environmental Science and Development

Abstract—The aim of this research work is to report the CO₂ measurements of a short-term period carried out in the geothermal zone of Acoculco, Puebla (Mexico). CO₂ measurements were logged using a micrometeorological station Eddy Covariance. According to the complex geochemical phenomena involved in these measurements, artificial neural networks have been used for reproducing the CO₂ measurements, and to fill the gap issues during the monitoring period. This survey was also performed for the identification of CO₂ anomalies in the zone, and also to determine the natural emission baseline of CO₂ at the early exploration stage of this promissory geothermal system. Details of this evaluation study are outlined.

Index Terms—Enhanced geothermal systems, environmental sustainability, geothermal energy, hot-dry rock, soil-gas emission.

E. Santoyo and M. Guevara are with the Institute for Renewable Energy (UNAM), Temixco, Mexico (e-mails: esg@ier.unam.mx, mygg@ier.unam.mx).
A. Acevedo-Anicasio is with the PhD Programme of Computational Sciences, Faculty of Sciences (UAEMor), 62209 Mexico (e-mail: agustin.acevedoa@uaem.edu.mx).
D. Perez-Zarate is with the Institute of Geophysics (UNAM), CONACyT Catedra, 04510 Mexico (e-mail: depez@igeofisica.unam.mx).

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Cite: E. Santoyo, A. Acevedo-Anicasio, D. Pérez-Zarate, and M. Guevara, "Evaluation of Artificial Neural Networks and Eddy Covariance Measurements for Modelling the CO₂ Flux Dynamics in the Acoculco Geothermal Caldera (Mexico)," International Journal of Environmental Science and Development vol. 9, no. 10, pp. 298-302, 2018.