Kinetic Studies and Development of Maximum Remediation Time Prediction Models for the Bioremediation of Crude Petroleum Hydrocarbon Polluted Soil

Abstract

This paper evaluates the kinetics of bioremediation of petroleum
hydrocarbon polluted soil using dead cells microorganism such as yeast,
organic substrate such as poultry manure, decayed domestic waste
manure and inorganic substrate such as N.P.K fertilizer. Hydrocarbon
polluted soil was simulated on a laboratory scale and characterized to
determine the particle size distribution, pH, moisture content, total
organic carbon, total nitrogen and total hydrocarbon content. For
effective treatment, 2:1 mixture (w/w) of the polluted soil and the
treatment substrate in addition to 25ml of nutrient agar was utilized. The
entire experiment was monitored for a period of twelve (12) weeks and
triplicate samples were collected from each setup on a weekly basis and
analyzed using Atomic Adsorption Spectrophotometer (AAS) to
determine the residual hydrocarbon content. The amount of
hydrocarbon removed per time was computed from simple mass balance
equation. Results obtained reveal a gradual decrease in total
hydrocarbon content (THC) with remediation time. The decrease in THC
was also observed to vary with the type of substrate as follows. For
yeast; THC reduces from 11.533 to 3.18mg/l. For poultry manure; THC
reduces from 11.533 to 3.54mg/l. For NPK; THC reduces from 11.533
to 2.02mg/l. For decomposed waste; THC reduces from 11.533 to
9.17mg/l. The gradual decrease in THC can be traced to the fact that;
as the microorganism utilizes the available nitrogen and organic carbon
present in the soil and increase in population; they again react with and
break down the petroleum hydrocarbon. Based on the model developed,
it was observed that; the non-linear regression models perform better
than the linear regression method in predicting the rate of hydrocarbon
loss with time for the different substrate. More also, the second order
kinetic model was observed to have a better fitting of the experimental
data.