Por: Fernando Sancho, Virginia Sebastián, Carlos Garrido-Allepuz, José María Lezcano, Ángela Acosta y Marcos Garayar, de Helix BioS y Pedro Delvasto, de la Universidad Industrial de Santander (España).

Abstract

Solid mining waste may contain pyrites and other sulfides, which are unstable in the presence of air, water and microorganisms, leading to the generation of acid mine drainage (AMD). In rehabilitation processes, it is common to adopt eminently physical-chemical approaches, despite the fact that microbiological aspects are known to be relevant, since they catalyze the alteration of sulfide minerals, thus accelerating contamination in areas affected by mining. 

In these scenarios, the interaction of microorganisms (e.g. bacteria, fungi and microalgae) with mining environmental liabilities must be analyzed in greater depth, since their role is fundamental in the reclamation of soils and waters. Thanks to metagenomics, these communities can be studied and analyzed from their genetic traces, which allows monitoring of the populations of an ecosystem, both at certain times and areas as well as throughout the entire process. These population data are used as markers for control to establish the proper progress of the land rehabilitation plan or environmental mitigation actions. 

In the present work, genetic data from environmental samples, available in the repository of the European Nucleotide Archive (ENA) were used to re-evaluate the samples and carry out bioinformatic and biostatistical studies, with our own methods and procedures, in order to establish the diversity microbial in sites affected by mining processes. 

Two groups of data were chosen from mining sites with common problems at a global level. The first group corresponded to the monitoring of contamination in an acid water mitigation system of a copper mine in China and its subsequent discharge into a river. The second group chosen corresponded to coal mining sites in South Africa, whose soils were rehabilitated at different times for 24 years. 

It is thus intended to show a new approach, from computational genomics, which allows a better understanding of how the ecosystems affected by mining activity are and how this information can allow fine-tuning decision-making when selecting or monitoring environmental control systems or processes of closure or rehabilitation of mining sites.