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.