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This research modelled the effect of pH on the remediation of crude oil-polluted soil using biochar blend. The biochar blends, PL-500, pW-500, and RS-400, were made by pyrolyzing poultry litter, pine wood, and rice straw at varied temperatures and times. The pH of the crude oil polluted soil was 4.72. Response surface experimental design mixed biochar to remediate total petroleum hydrocarbons (TPH). Following 30 days of bioremediation, 15g PL-500, 3g PW-500 and 6g RS-400, removed a maximum of 46% TPH. The experimental data were statistically modelled and optimized using design expert software and response surface methods. Analysis of variance (ANOVA) was used to determine the significance of each regression coefficient. Biochar blend improved soil pH to 6.9 following remediation. ANOVA indicated that PL-500 was significant for predicting TPH % degradation at p =0.0290, suggesting that its high pH, nutrient, and soil water conservation values made it more effective in remediating TPH. The quadratic model predicts  with R2 =0.8567. A model fit statistics were used to examine soil pH influence on TPH remediation. RSM study indicated a good positive association between statistical model and experiment with R2 = 0.7612. The model fits experimental data and predicts that  . Remediation requires soil pH and biochar's alkalinity raised soil pH to 6.9, which promoted hydrocarbon-utilizing bacteria.

The study focused on development of mathematical modeling and numerical simulation technique for selected heavy metal transport in Uyo municipal solid waste dumpsite in Akwa Ibom State to investigate the level in depth to which leachate from the dumpsite extends and the quantity of leachate at various depth of the dumpsite soil. Uyo waste dumpsite is operating open dumping system where provisions are not made for preservation and conservation of soil and water quality, hence, the need for this study. Three monitoring pits within Uyo waste dumpsite were constructed and infiltration runs were measured, and soil samples were collected beside infiltration points from nine designated depths ranging from 0 to 0.9 m for modeling heavy metal transport in the soil. Data collected were subjected to descriptive and inferential statistics while the COMSOL Multiphysics software 6.0 was used to simulate the movement of pollutants in the soil. It was observed that heavy metal contaminant transport in soil of the study area is in the power functional form. The transport of heavy metals in the dumpsite can be described by a power model from linear regression and a numerical model based on finite element. Their validation equations showed that the predicted and the observed concentrations yielded a very high R2 value of over 95%. The power model and the COMSOL finite element model show very strong correlation for all selected heavy metals. Findings from the study has identified level in depth to which leachate from the dumpsite extends and the quantity of leachate at various depth of the dumpsite soil which can be accurately predicted using leachate transport model of this study.

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