Toxicity impact of hydrogen peroxide on the fate of zebrafish and antibiotic resistant bacteria.

Hydrogen peroxide (H2O2) is applied in various environments. It could be present at concentrations ranging from nanomolar to micromolar in a water system. It is produced through pollutants and natural activities. Since few studies have been conducted about the impact of naturally produced H2O2 on aquatic organisms, the objective of the present study was to monitor changes in responses of aquatic model organisms such as zebrafish and antibiotic-resistant bacteria to different exogenous H2O2 exposure. Increases in exposure concentration and time induced decreases in the perception of zebrafish larvae (up to 69%) and movement of adult zebrafish (average speed, average acceleration, movement distance, and activity time) compared to the control (non-exposed group). In addition, as a function of H2O2 exposure concentration (0-100,000 nM) and time, up to 20-fold increase (p = 5.00*10-6) of lipid peroxidation compared to control was observed. For microorganisms, biofilm, an indirect indicator of resistance to external stressors, was increased up to 68% and gene transfer was increased (p = 2.00*10-6) by more than 30% after H2O2 exposure. These results imply that naturally generated H2O2 could adversely affect aquatic environment organisms and public health. Thus, more careful attention is needed for H2O2 production in an aquatic system.

Learning Bayesian networks based diagnosis system for wastewater treatment process with sensor data.

Contemporary technical capabilities allow an operator to easily monitor and control several remote wastewater treatment processes simultaneously but an on-line automatic diagnostic system has not yet been installed. In this paper, an on-line diagnostic system is proposed, designed and implemented for the lab-scale five-stage step-feed Enhanced Biological Phosphorus Removal plant based upon a learning Bayesian network. In order to practically diagnose wastewater treatment processes, a lab-scale pilot plant was built and the proposed on-line diagnostic method was applied to evaluate the performance of the algorithm. In experimental results, real abnormal conditions occurred 21 times in a three month period. The suggested on-line diagnosis system made correct predictions 14 times and incorrect predictions 7 times. Moreover, a comparison of the prediction results of the Bayesian model and learning Bayesian model clearly show that learning algorithm became more effective as time passed.