Microbial community profiling of arsenic-rich mine tailing and arsenic bioadsorption by indigenous bacteria

Abstract

Arsenic is a common contaminant in gold mine soil and tailings. Moreover, the contamination of water with arsenic is a serious health issue. Microbes present an opportunity to remove arsenic from wastewater via adsorption process, which is distinguished by its low cost and easy technique in comparison with conventional techniques include oxidation, coagulation-flocculation, and membrane techniques. However, the development of existing bio-treatment approaches depends on isolation of arsenic-resistant microbes from arsenic contaminated samples. In this study, a culture-independent approach using Illumina sequencing technology was used to profile the microbial community in situ. This was coupled with a culture-dependent technique to analyse the microbial population in arsenic-laden tailing dam sludge based on the culture-independent sequencing approach. Based on the culture-independent sequencing approach, 4 phyla and 8 genera were identified in a sample from the arsenic-rich goldmine. Firmicutes (92.23%) was the dominant phylum, followed by Proteobacteria (3.21%), Actinobacteria (2.41%), and Bacteroidetes (1.49%). The identified genera included Staphylococcus (89%), Pseudomonas (1.25%), Corynebacterium (0.82%), Prevotella (0.54%), Pseudonocardia (0.39%), Megamonas (0.38%) and Sphingomonas (0.36%). The culture dependent method exposed significant similarities with culture independent methods at the phylum level with Firmicutes, Proteobacteria and Actinobacteria, being common, and Firmicutes was the dominant phylum whereas, at the genus level, only Pseudomonas was presented by both methods. Considering the advantage of the different structures of these bacterial cell walls in adsorption, attempts were made to use individual dried biomass of Bacillus thuringiensis strain WS3 (IDB) and mixed dried biomass of three species B. thuringiensis strain WS3, Pseudomonas stutzeri strain WS9 and Micrococcus yunnanensis strain WS11 (MDB) to achieve highest As (III) and As (V) removal under different conditions. Successively, MDB were found to be efficient in the removal of As (III) and As (V) up to 95 % and 98 %, respectively. The maximum adsorption capacity of As (III) and As (V) increased from 95 mg/g and 145 mg/g for IDB to 217 mg/g and 333 mg/g for MDB as obtained from the Langmuir isotherm. The pattern of adsorption fitted well with the Langmuir isotherm model and kinetic data followed a pseudo-second-order model for both IDB and MDB. The thermodynamic parameters ?G°, ?H° and ?S° revealed that the adsorptions of both As (III) and As (V) were spontaneous, feasible and endothermic in nature. FESEM-EDX analysis established diverse cell morphological changes with significant amounts of arsenic adsorbed onto biomass compared to original biomass. Results from FTIR have shown the involvement of mainly hydroxyl, thiol, amide and amino functional groups in the arsenic adsorption. Batch experimental data were taken into account to create an artificial neural network (ANN) model that mimicked the human brain function. 5-7-1 neurons were in the input, hidden and output layers respectively. The batch data was reserved for training (75%), testing (10%) and validation process (15%). The predicted output of the proposed model showed a good agreement with the batch experiments with reasonable accuracy. This study has demonstrated the potential for using mixed dried non-living biomass as a new biosorbent for arsenic removal.