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This paper presents hydrochemical data of an AMD stream, Poderosa Creek, in the Iberian Pyrite Belt, obtained between its source, in the Poderosa Mine portal, and its confluence with the Odiel River. The main objective is to establish potential interdependent relationships between sulphate and metal loads and the following physico-chemical variables: pH, electrical conductivity (EC), redox potential (EH) and dissolved oxygen (DO). All the parameters show an overall increasing tendency from the tunnel exit to the confluence at the Odiel River. The TDS and EC are two relevant exceptions. They behave similarly, showing a decreasing trend and a strong inflection that describes a minimum immediately after the discharging point. Spatial analysis combined with statistical tools reveal typical AMD processes and the respective physico-chemical implications. Inputs with distinctive hydrochemical signatures impose relevant modifications in the Poderosa Creek waters. This indicates low hydrochemical inertia and high vulnerability to external stimuli.

Poderosa Mine is an abandoned pyrite mine, located in the Iberian Pyrite Belt which pours its acid mine drainage (AMD) waters into the Odiel river (South-West Spain). This work focuses on establishing possible reasons for interdependence between the potential redox and pH, with the load of metals and sulfates, as well as a set of variables that define the physical chemistry of the water-conductivity, temperature, TDS, and dissolved oxygen-transported by a channel from Poderosa mine affected by acid mine drainage, through the use of techniques of artificial intelligence: fuzzy logic and data mining. The sampling campaign was carried out in May of 2012. There were a total of 16 sites, the first inside the tunnel and the last at the mouth of the river Odiel, with a distance of approximately 10 m between each pair of measuring stations. While the tools of classical statistics, which are widely used in this context, prove useful for defining proximity ratios between variables based on Pearson's correlations, in addition to making it easier to handle large volumes of data and producing easier-to-understand graphs, the use of fuzzy logic tools and data mining results in better definition of the variations produced by external stimuli on the set of variables. This tool is adaptable and can be extrapolated to any system polluted by acid mine drainage using simple, intuitive reasoning.