Modelling and Optimizing the Effect of pH on Remediation of Crude Oil Polluted Soil with Biochar Blend: RSM Approach

Abstract

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.