Copper mining has polluted soils and water, causing a reduction of the microbial diversity and a change in the structure of the resident bacterial communities. In this work, selective isolation combined with MALDI-TOF MS and the 16S rDNA method were used for characterizing cultivable bacterial communities from copper mining samples. The results revealed that MALDI-TOF MS analysis can be considered a reliable and fast tool for identifying copper-resistant bacteria from environmental samples at the genera level. Even though some results were ambiguous, accuracy can be improved by enhancing reference databases. Therefore, mass spectra analysis provides a reliable method to facilitate monitoring of the microbiota from copper-polluted sites. The understanding of the microbial community diversity in copper-contaminated sites can be helpful to understand the impact of the metal on the microbiome and to design bioremediation processes.

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In this study, an effective microbial consortium for the biodegradation of phenol was grown under different operational conditions, and the effects of phosphate concentration (1.4 g L(-1), 2.8 g L(-1), 4.2 g L(-1)), temperature (25 degrees C, 30 degrees C, 35 degrees C), agitation (150 rpm, 200 rpm, 250 rpm) and pH (6, 7, 8) on phenol degradation were investigated, whereupon an artificial neural network (ANN) model was developed in order to predict degradation. The learning, recall and generalization characteristics of neural networks were studied using data from the phenol degradation system. The efficiency of the model generated by the ANN was then tested and compared with the experimental results obtained. In both cases, the results corroborate the idea that aeration and temperature are crucial to increasing the efficiency ofbiodegradation.

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Metal contamination exerts environmental pressure on several lifeforms.