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Roxarsone is an organoarsenical compound used as food additive in the poultry industry. Roxarsone has the potential risk to contaminate the environment, mainly by the use of poultry industry manure as fertilizer, releasing inorganic arsenic to the soil and water. The aim of this work was to isolate and characterize a bacterial consortium capable to degrade roxarsone under aerobic conditions. A bacterial consortium was cultured from a soil sample obtained from a field fertilized with poultry litter containing roxarsone. The consortium was cultured in the presence or absence of roxarsone. Roxarsone degradation and growth kinetics were determined by incubation of the bacterial consortium in the presence of roxarsone at room temperature and under aerobiosis. Both consortiums were characterized molecularly by denaturing gradient gel electrophoresis analysis and metabolically using Biolog Ecoplates. Inorganic arsenic was assessed by precipitation with silver nitrate. The consortium was also analyzed by scanning electron microscopy. The results showed that growth rate of the bacterial consortium was 1.4-fold higher in presence of roxarsone and 81.04 % of the roxarsone was transformed after 7 days of incubation. Molecular characterization revealed the presence of different bacterial groups, being alphaproteobacteria and firmicutes the groups that showed the highest count in both consortiums. The metabolic profile of the consortium did not change in the presence of roxarsone, but it showed a greater ability to oxidize amines, suggesting production of functional amines to decrease the stability of the aromatic ring resonance energy, the principal problem associated with aromatic compounds degradation.

Roxarsone is included in chicken food as anticoccidial and mainly excreted unchanged in faeces. Microorganisms biotransform roxarsone into toxic compounds that leach and contaminate underground waters used for human consumption. This study evaluated roxarsone biotransformation by underground water microorganisms and the toxicity of the resulting compounds. Underground water from an agricultural field was used to prepare microcosms, containing 0.05 mM roxarsone, and cultured under aerobic or anaerobic conditions. Bacterial communities of microcosms were characterized by PCR-DGGE. Roxarsone degradation was measured by HPLC/HG/AAS. Toxicity was evaluated using HUVEC cells and the Toxi-ChromoTest kit. Roxarsone degradation analysis, after 15 days, showed that microcosms of underground water with nutrients degraded 90 and 83.3% of roxarsone under anaerobic and aerobic conditions, respectively. Microcosms without nutrients degraded 50 and 33.1% under anaerobic and aerobic conditions, respectively. Microcosms including nutrients showed more roxarsone conversion into toxic inorganic arsenic species. DGGE analyses showed the presence of Proteobacteria, Firmicutes, Actinobacteria, Planctomycetes and Spirochaetes. Toxicity assays showed that roxarsone biotransformation by underground water microorganisms in all microcosms generated degradation products toxic for eukaryotic and prokaryotic cells. Furthermore, toxicity increased when roxarsone leached though a soil column and was further transformed by the bacterial community present in underground water. Therefore, using underground water from areas where roxarsone containing manure is used as fertilizer might be a health risk.

Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes. Graphical abstract ᅟ.