Modeling Reaction Rate of Dispersion Coefficient and Oxygen Deficit on Nitrobacteria Transport in Onu-Imo River

Abstract

The migration rate of nitrobacteria in the Onu-Imo River was monitored based on some variables that determine the predictive model for such pollution transport. These parameters were factors applied in developing the derived model for the study. The variables were integrated into the system to monitor their various rates of impact on the transport process in the study environment. This was monitored by applying a one-dimensional flow transport system at different points of discharge. The study from every point of discharge was monitored to investigate the variation rate of concentration influenced by dispersions and oxygen deficit. The predictive values experienced an exponential growth rate in most figures that represent the different station points of discharge. Modeling the reaction rate of dispersion coefficient and oxygen deficit impact on bacteria were carried out in Onu-Imo River based on the factors. The study was carried out based on a thorough investigation through microbial analysis and physiochemical evaluations. These concepts predict the transport rate of the contaminant at different station points. Dispersion coefficient and oxygen deficit were predominant parameters monitored to determine their various rates of impact on the transport process of the contaminant in the Onu-Imo River. It was observed that there is a tremendous increase with the change of distance in all the figures. This implies that the system was influenced by the regeneration of constant discharge in the station point that caused an increase in concentration. Other factors could be the deposition of microelements in the river. It also includes a variation of velocity of flow in the river. These are some of the parameters that develop a negative impact as is observed from the exponential growth rate of the contaminant. The derived solution from its simulation generated a predictive model that was compared with experimental values and both parameters developed a best- fits correlation.