Prediction the biodegradation rate of soil contaminated with different oil concentrations

Abstract views: 88 / PDF downloads: 57


  • Andrey Lipatov State University of Management
  • Elvira Belyanova Bauman Moscow State Technical University
  • Irina Petunina Federal State Budgetary Educational Institution of Higher Education «Kuban State Agrarian University named after I.T. Trubilin»


biodegradation, oil, pollution, regression, soil, visual and mathematical analysis


A multiple linear regression model is a practical statistical model for estimating relationships between a continuous dependent variable and predictor variables. The model itself is linear in that it consists of additive terms, each representing a predictor multiplied by an estimate of the coefficient. In addition, a constant (free term) is usually added to the model as well. Using two bacterial species (wild and recombinant), this paper develops a multiple model linear regression model to predict the biodegradation rate of soil contaminated with different oil concentrations. Factors such as crude oil concentration, number of days of incubation, and type of microbial strain were discovered to significantly influence the biodegradation rate based on visual and mathematical analysis. Mathematical models were developed to predict the biodegradation rate. The equation developed using multiple linear regression predicted the biodegradation rate with a coefficient of determination R2=0.549. The equation developed using polynomial regression predicted the biodegradation rate with a coefficient of determination R2=0.799. The resulting equations can be used to understand the relationship between the variables and also to predict the biodegradation rate of petroleum products.


Y. Nan, R. Sun, Z. Zhen, C. Fangjing, Measurement of international crude oil price cyclical fluctuations and correlations and correlation with the world economic cyclical changes, Energy, 2022, 260, 124946.

E. Hauptfeld, J. Pelkmans, T.T. Huisman, A. Anocic, B.L. Snoek, F.A.B. von Meijenfeldt, J. Gerritse, J. van Leeuwen, G. Leurink, A. van Lit, R. van Uffelen, M.C. Koster, B.E. Dutilh, A metagenomic portrait of the microbial community responsible for two decades of bioremediation of poly-contaminated groundwater, Water Res., 2022, 221, 118767.

A. Koolivand, R. Saeedi, F. Coulon, V. Kumar, J. Villasenor, F. Asghari, F. Hesampoor, Bioremediation of petroleum hydrocarbons by vermicomposting process bioaugmented with indigenous bacterial consortium isolated from petroleum oily sludge, Ecotoxicol. Environ. Saf., 2020, 198, 110645.

R. Margesin, F. Schinner, Biodegradation and bioremediation of hydrocarbons in extreme environments, Appl. Microbiol. Biotechnol., 2001, 56, 650-663.

M.D. Yuniati, Bioremediation of petroleum-contaminated soil: A review, IOP Conf. Ser.: Earth Environ. Sci., 2018, 118, 1-7.

Y. Lv, J. Bao, Y. Dang, D. Liu, T. Li, S. Li, Y. Yu, L. Zhu, Biochar aerogel enhanced remediation performance for heavy oil-contaminated doil via biostimulation strategy, J. Hazard. Mater., 2023, 443, 130209.

Z. Wang, M. Fingas, S. Blenkinsopp, G. Sergy, M. Landriault, L. Sigouin, J. Foght, K. Semple, D.W.S. Westlake, Comparison of oil composition changes due to biodegradation and physical weathering in different oils, J. Chromatogr. A, 1998, 809, 89-107.

USEPA, EPA Test Methods for Evaluating Solid Wastes. OSW Web Site Manager, 2001.

F. Benyahia, M. Abdulkarim, A. Zekri, O. Chaalal, H. Hasanain, Bioremediation of crude oil contaminated soils a black art or an engineering challenge?, Process Saf. Environ. Prot., 2005, 83, 364-370.

T. Kheirkhah, P. Hejazi, A. Rahimi, Effects of utilising sawdust on non-ligninolytic degradation of high concentration of n-hexadecane by white-rot fungi: kinetic analysis of solid-phase bioremediation, Environ. Technol. Innovat, 2020, 19, 100887.

L. Zhu, J.P. O’Dwyer, V.S. Chang, C.B. Granda, M.T. Holtzapple, Multiple linear regression model for predicting biomass digestibility from structural features, Bioresour. Technol, 2010, 101, 4971-4979.

W. Tang, Y. Li, Y. Yu, Z. Wang, T. Xu, J. Chen, J. Lin, X. Li, Development of models predicting biodegradation rate rating with multiple linear regression and support vector machine algorithms, Chemosphere 2020, 253, 110645.

T. Dragomir, A. Pana, V. Ordodi, V. Gherman, G. Dumitrel, S. Nanu, An empirical model for predicting biodegradation profiles of glycopolymers, Polymers 2021, 13, 1-13.

K.A. Ani, E.C. Chukwuma, Kinetics and statistical analysis of the bio-stimulating effects of goat litter in crude oil biodegradation process, Beni-Suef Univ. J. Basic Appl. Sci., 2020, 9, 29.

R. Idroes, T.R. Noviandy, A. Maulana, R. Suhendra, N.R. Sasmita, M. Muslem, G.M. Idroes, P. Kemala, I. Irvanizam, Application of genetic algorithm-multiple linear regression and artificial neural network determinations for prediction of Kovats Retention Index, Int. Rev. Model. Simul., 2021, 14, 137-145.

R. Idroes, T.R. Noviandy, A. Maulana, R. Suhendra, N.R. Sasmita, M. Muslem, G.M. Idroes, I. Irvanizam, Retention index prediction of flavour and fragrance by multiple linear regression and the genetic algorithm, Int. Rev. Model. Simul., 2019, 12, 373-380.

S. Benkachcha, J. Benhra, H. El Hicham, Demand forecasting in supply Chain: Comparing multiple linear regression and artificial neural networks approaches, Int. Rev. Model. Simul., 2014, 7, 279-286.

M. Ajona, P. Vasanthi, Bio-remediation of crude oil contaminated soil using recombinant native microbial strain, Environ. Technol. Innov., 2021, 23, 101635.

S. Sharma, R. Gupta, R. Bhatia, A.P. Toor, H. Setia, Predicting microbial response to anthropogenic environmental disturbances using artificial neural network and multiple linear regression, Int. J. Cogn. Comput. Eng., 2021, 2, 65-70.

Y. Wang, S. Wu, H. Wang, Y. Dong, X. Li, S. Wang, H. Fan, X. Zhuang, Optimisation of conditions for a surfactant-producing strain and application to petroleum hydrocarbon-contaminated soil bioremediation, Colloids Surf. B: Biointerfaces, 2022, 213, 112428.

X. An, B. Zhong, G. Chen, W. An, X. Xia, H. Li, F. Lai, Q. Zhang, Evaluation of bioremediation and detoxification potentiality for papermaking black liquor by a new isolated thermophilic and alkali-tolerant Serratia sp. AXJ-M, J. Hazard. Mater., 2021, 406, 124285.

E.E. Raimondo, J.D. Aparicio, A.L. Bigliardo, M.S. Fuentes, C.S. Benimeli, Enhanced bioremediation of lindane-contaminated soils through microbial bioaugmentation assisted by biostimulation with sugarcane filter cake, Ecotoxicol. Environ. Saf., 2020, 190, 110143.

Y.W. Chang, C.J. Hsieh, K.W. Chang, M. Ringgaard, C.J. Lin, Training and testing low-degree polynomial data mappings via linear SVM, J. Mach. Learn. Res., 2010, 11, 1471-1490.

S.M. Stigler, Gergonne’s 1815 paper on the design and analysis of polynomial regression experiments, Hist. Math., 1974, 1, 431-439.

G. Jiang, A. Thummala, M.V.S. Wadhwa, Applications of statistical regression and modelling in fill-finish process development of structurally related proteins, J. Pharm. Sci., 2011, 100, 464-481.

O.B. Said, C. Cravo-Laureau, F. Armougom, S. Cipullo, M.B. Khelil, M.B.H. Yahiya, A. Douihech, H. Beyrem, F. Coulon, R. Duran, Enhanced pilot bioremediation of oily sludge from petroleum refinery disposal under hot-summer Mediterranean climate, Environ. Technol. Innov., 2021, 24, 102037.

H.S. Hussein, R.A. Abdula, Multiple linear regression approach for the vitrinite reflectance estimation from well logs: A case study in Sargelu and Naokelekan Formations - Shaikhan-2 Well, Shaikhan oil field, Iraq, Egypt. J. Pet., 2018, 27, 1095-1102.

M.J. Lerma-Garcia, E.F. Simo-Alfonso, A. Bendini, L. Cerretani, Rapid evaluation of oxidised fatty acid concentration in virgin olive oil using Fourier-transform infrared spectroscopy and multiple linear regression, Food Chem., 2011, 124, 679-684.

O.B. Ozturk, E. Basar, Multiple linear regression analysis and artificial neural networks based decision support system for energy efficiency in shipping, Ocean Eng., 2022, 243, 110209.




How to Cite

Lipatov, A., Belyanova, E., & Petunina, I. (2023). Prediction the biodegradation rate of soil contaminated with different oil concentrations. Results in Nonlinear Analysis, 7(1), 24–. Retrieved from