Environmental Science and Pollution Research

, Volume 25, Issue 16, pp 15641–15650 | Cite as

Quantitative structure–activity relationship for the partition coefficient of hydrophobic compounds between silicone oil and air

  • Yanfei Qu
  • Yongwen Ma
  • Jinquan Wan
  • Yan Wang
Research Article


The silicon oil-air partition coefficients (KSiO/A) of hydrophobic compounds are vital parameters for applying silicone oil as non-aqueous-phase liquid in partitioning bioreactors. Due to the limited number of KSiO/A values determined by experiment for hydrophobic compounds, there is an urgent need to model the KSiO/A values for unknown chemicals. In the present study, we developed a universal quantitative structure–activity relationship (QSAR) model using a sequential approach with macro-constitutional and micromolecular descriptors for silicone oil-air partition coefficients (KSiO/A) of hydrophobic compounds with large structural variance. The geometry optimization and vibrational frequencies of each chemical were calculated using the hybrid density functional theory at the B3LYP/6-311G** level. Several quantum chemical parameters that reflect various intermolecular interactions as well as hydrophobicity were selected to develop QSAR model. The result indicates that a regression model derived from logKSiO/A, the number of non-hydrogen atoms (#nonHatoms) and energy gap of ELUMO and EHOMO (ELUMOEHOMO) could explain the partitioning mechanism of hydrophobic compounds between silicone oil and air. The correlation coefficient R2 of the model is 0.922, and the internal and external validation coefficient, Q2 LOO and Q2 ext , are 0.91 and 0.89 respectively, implying that the model has satisfactory goodness-of-fit, robustness, and predictive ability and thus provides a robust predictive tool to estimate the logKSiO/A values for chemicals in application domain. The applicability domain of the model was visualized by the Williams plot.


Hydrophobic compounds Silicone oil-air partition coefficients (KSiO/AQuantitative structure–activity relationship (QSAR) Density functional theory (DFT) 



This research has been supported by the National Natural Science Foundation of China (No. 31570568 and No. 31670585), State Key Laboratory of Pulp and Paper Engineering (No. 201535), Science and Technology Planning Project of Guangzhou City, China (No. 201607010079 and No. 201607020007). The authors are grateful to all the anonymous reviewers for their insightful comments and suggestions.

Supplementary material

11356_2018_1705_MOESM1_ESM.doc (598 kb)
ESM 1 (DOC 598 kb)


  1. Arriaga S, Muñoz R, Hernández S, Guieysse B, Revah S (2006) Gaseous hexane biodegradation by Fusarium solani in two liquid phase packed-bed and stirred-tank bioreactors. Environ Sci Technol 40:2390–2395. CrossRefGoogle Scholar
  2. Chen J, Quan X, Zhao Y, Yan YYF (2001) Quantitative structure-property relationship studies on n-octanol/water partitioning coefficients of PCDD/Fs. Chemosphere 44:1369–1374. CrossRefGoogle Scholar
  3. Chen J, Xue X, Schramm KW, Quan X, Yang F, Kettrup A (2002) Quantitative structure-property relationships for octanol-air partition coefficients of polychlorinated biphenyls. Chemosphere 48:535–544CrossRefGoogle Scholar
  4. Cox HH, Deshusses MA (1998) Biological waste air treatment in biotrickling filters. Curr Opin Biotechnol 9:256–262. CrossRefGoogle Scholar
  5. Daugulis AJ (2001) Two-phase partitioning bioreactors: a new technology platform for destroying xenobiotics. Trends Biotechnol 19:457–462. CrossRefGoogle Scholar
  6. Dearden JC, Cronin MTD, Kaiser KLE (2009) How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR QSAR Environ Res 20:241–266. CrossRefGoogle Scholar
  7. Dumont E, Darracq G, Couvert A, Couriol C, Amrane A, Thomas D, Andrès Y, le Cloirec P (2010) Determination of partition coefficients of three volatile organic compounds (dimethylsulphide, dimethyldisulphide and toluene) in water/silicone oil mixtures. Chem Eng J 162:927–934. CrossRefGoogle Scholar
  8. Dumont E, Darracq G, Couvert A, Couriol C, Amrane A, Thomas D, Andrès Y, le Cloirec P (2013) Volumetric mass transfer coefficients characterising VOC absorption in water/silicone oil mixtures. Chem Eng J 221:308–314. CrossRefGoogle Scholar
  9. Eguchi A, Hanazato M, Suzuki N, Matsuno Y, Todaka E, Mori C (2015) Maternal–fetal transfer rates of PCBs, OCPs, PBDEs, and dioxin-like compounds predicted through quantitative structure–activity relationship modeling. Environ Sci Pollut Res.
  10. Famini GR, Penski CA, Wilson L (1992) Using theoretical descriptors in quantitative structure activity relationships: some physicochemical properties. J Phys Org Chem 5:395–408. CrossRefGoogle Scholar
  11. Gao Y, Tian X, Li J, Shang S, Song Z, Shen M (2016) Study on amphipathic modification and QSAR of volatile turpentine analogues as value-added botanical fungicides against crop-threatening pathogenic fungi. ACS Sustain Chem Eng 4:2741–2747. CrossRefGoogle Scholar
  12. García-Galán MJ, Uggetti E, Garfi M, Olguín EJ, García J, Puigagut J (2018) Biotechnology: a highly efficient tool for the current environmental challenges. Sci Total Environ 616–617:1664–1667. CrossRefGoogle Scholar
  13. Guieysse B, Mattiasson CB (2001) Microbial degradation of phenanthrene and pyrene in a two-liquid phase-partitioning bioreactor. Appl Microbiol Biotechnol 56:796–802. CrossRefGoogle Scholar
  14. Huang H, Xiao X, Shi J, Chen Y (2014) Structure-activity analysis of harmful algae inhibition by congeneric compounds: case studies of fatty acids and thiazolidinediones. Environ Sci Pollut Res 21:7154–7164. CrossRefGoogle Scholar
  15. Jaworska J, Nikolova N, Aldenberg T (2005) QSAR applicabilty domain estimation by projection of the training set descriptor space: a review. Altern Lab Anim 33(5):445–459Google Scholar
  16. Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 96:1027–1044. CrossRefGoogle Scholar
  17. Kraakman NJR, Rocha-Rios J, Van Loosdrecht MCM (2011) Review of mass transfer aspects for biological gas treatment. Appl Microbiol Biotechnol 91:873–886. CrossRefGoogle Scholar
  18. Lee JY, Kwon TS, Lee YC (2017) Removal of polycyclic aromatic hydrocarbons from contaminated soil in a two-phase partitioning bioreactor. Korean J Chem Eng 34:2418–2422. CrossRefGoogle Scholar
  19. Li L, Wang Q, Qiu X, Dong Y, Jia S, Hu J (2014) Field determination and QSPR prediction of equilibrium-status soil/vegetation partition coefficient of PCDD/Fs. J Hazard Mater 276:278–286. CrossRefGoogle Scholar
  20. Liu H, Liu H, Sun P, Wang Z (2014) QSAR studies of bioconcentration factors of polychlorinated biphenyls (PCBs) using DFT, PCS and CoMFA. Chemosphere 114:101–105. CrossRefGoogle Scholar
  21. Liu H, Wei M, Yang X, Yin C, He X (2017) Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water. Sci Total Environ 574:1371–1378. CrossRefGoogle Scholar
  22. Lu Q, de Toledo RA, Xie F, Li J, Shim H (2015) Combined removal of a BTEX, TCE, and cis-DCE mixture using Pseudomonas sp. immobilized on scrap tyres. Environ Sci Pollut Res 22:14043–14049. CrossRefGoogle Scholar
  23. Luo S, Wei Z, Dionysiou DD, Spinney R, Hu WP, Chai L, Yang Z, Ye T, Xiao R (2017a) Mechanistic insight into reactivity of sulfate radical with aromatic contaminants through single-electron transfer pathway. Chem Eng J 327:1056–1065. CrossRefGoogle Scholar
  24. Luo S, Wei Z, Spinney R, Yang Z, Chai L, Xiao R (2017b) A novel model to predict gas–phase hydroxyl radical oxidation kinetics of polychlorinated compounds. Chemosphere 172:333–340. CrossRefGoogle Scholar
  25. Luo S, Wei Z, Spinney R, Villamena FA, Dionysiou DD, Chen D, Tang CJ, Chai L, Xiao R (2018a) Quantitative structure–activity relationships for reactivities of sulfate and hydroxyl radicals with aromatic contaminants through single–electron transfer pathway. J Hazard Mater 344:1165–1173. CrossRefGoogle Scholar
  26. Luo S, Wei Z, Spinney R, Zhang Z, Dionysiou DD, Gao L, Chai L, Wang D, Xiao R (2018b) UV direct photolysis of sulfamethoxazole and ibuprofen: an experimental and modelling study. J Hazard Mater 343:132–139. CrossRefGoogle Scholar
  27. Ma G, Yuan Q, Yu H, Lin H, Chen J, Hong H (2017) Development and evaluation of predictive model for bovine serum albumin-water partition coefficients of neutral organic chemicals. Ecotoxicol Environ Saf 138:92–97. CrossRefGoogle Scholar
  28. Moradkhani H, Izadkhah MS, Anarjan N, Abdi A (2017) Oxygen mass transfer and shear stress effects on Pseudomonas putida BCRC 14365 growth to improve bioreactor design and performance. Environ Sci Pollut Res 24:22427–22441. CrossRefGoogle Scholar
  29. Ohara H (2003) Biorefinery. Appl Microbiol Biotechnol 62:474–477. CrossRefGoogle Scholar
  30. Ordaz A, López JC, Figueroa-González I, Muñoz R, Quijano G (2014) Assessment of methane biodegradation kinetics in two-phase partitioning bioreactors by pulse respirometry. Water Res 67:46–54. CrossRefGoogle Scholar
  31. Organisation for Economic Co-operation and Development (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. Paris, France. http://www.OECD.Org/env/ehs/risk-assessment/guenvironment
  32. Patel MJ, Popat SC, Deshusses MA (2017) Determination and correlation of the partition coefficients of 48 volatile organic and environmentally relevant compounds between air and silicone oil. Chem Eng J 310:72–78. CrossRefGoogle Scholar
  33. Poggi-Varaldo HM, Devault DA, Macarie H, Sastre-Conde I (2017) Environmental biotechnology and engineering: crucial tools for improving and caring for the environment and the quality of life of modern societies. Environ Sci Pollut Res 24:25483–25487. CrossRefGoogle Scholar
  34. Poleo EE, Daugulis AJ (2013) Simultaneous biodegradation of volatile and toxic contaminant mixtures by solid-liquid two-phase partitioning bioreactors. J Hazard Mater 254–255:206–213. CrossRefGoogle Scholar
  35. San-Valero P, Gabaldón C, Penya-roja JM, Quijano G (2017) Enhanced styrene removal in a two-phase partitioning bioreactor operated as a biotrickling filter: towards full-scale applications. Chem Eng J 309:588–595. CrossRefGoogle Scholar
  36. San-Valero P, Dorado AD, Quijano G, Álvarez-Hornos FJ, Gabaldón C (2018) Biotrickling filter modeling for styrene abatement. Part 2: simulating a two-phase partitioning bioreactor. Chemosphere 191:1075–1082. CrossRefGoogle Scholar
  37. Su P, Zhu H, Shen Z (2016) QSAR models for removal rates of organic pollutants adsorbed by in situ formed manganese dioxide under acid condition. Environ Sci Pollut Res 23:3609–3620. CrossRefGoogle Scholar
  38. Sudhakaran S, Amy GL (2013) QSAR models for oxidation of organic micropollutants in water based on ozone and hydroxyl radical rate constants and their chemical classification. Water Res 47:1111–1122. CrossRefGoogle Scholar
  39. Tandjaoui N, Abouseoud M, Couvert A, Amrane A, Tassist A (2016) A new combined green method for 2-Chlorophenol removal using cross-linked Brassica rapa peroxidase in silicone oil. Chemosphere 148:55–60. CrossRefGoogle Scholar
  40. Thanikaivelan P, Subramanian V, Rao JR, Nair BU (2000) Application of quantum chemical descriptor in quantitative structure activity and structure property relationship. Chem Phys Lett 323:59–70. CrossRefGoogle Scholar
  41. Tropsha A, Gramatica P, Gombar V (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77. CrossRefGoogle Scholar
  42. Volckaert D, Ebude DEL, Van Langenhove H (2016) SIFT-MS analysis of the removal of dimethyl sulphide, n-hexane and toluene from waste air by a two phase partitioning bioreactor. Chem Eng J 290:346–352. CrossRefGoogle Scholar
  43. Wiener H (1947) Structural determination of paraffin boiling points. J Am Chem Soc 69:17–20. CrossRefGoogle Scholar
  44. Xiao R, Ye T, Wei Z, Luo S, Yang Z, Spinney R (2015) Quantitative structure-activity relationship (QSAR) for the oxidation of trace organic contaminants by sulfate radical. Environ Sci Technol 49:13394–13402. CrossRefGoogle Scholar
  45. Xiao R, Gao L, Wei Z, Spinney R, Luo S, Wang D, Dionysiou DD, Tang C–J, Yang W (2017) Mechanistic insight into degradation of endocrine disrupting chemical by hydroxyl radical: an experimental and theoretical approach. Environ Pollut 231:1446–1452. CrossRefGoogle Scholar
  46. Yang F, Qu R, Wang M, Tang Y, Feng M, Wang Z (2013) Experimental and QSPR study of sorption properties of polychlorinated diphenyl sulfides (PCDPSs) in Yangtze River plain soil. Geoderma 193–194:140–148. CrossRefGoogle Scholar
  47. Yang Z, Luo S, Wei Z, Ye T, Spinney R, Chen D, Xiao R (2016) Rate constants of hydroxyl radical oxidation of polychlorinated biphenyls in the gas phase: a single− descriptor based QSAR and DFT study. Environ Pollut 211:157–164. CrossRefGoogle Scholar
  48. Yang Z, Su R, Luo S, Spinney R, Cai M, Xiao R, Wei Z (2017) Comparison of the reactivity of ibuprofen with sulfate and hydroxyl radicals: an experimental and theoretical study. Sci Total Environ 590–591:751–760. CrossRefGoogle Scholar
  49. Ye T, Wei Z, Spinney R, Tang CJ, Luo S, Xiao R, Dionysiou DD (2017a) Chemical structure-based predictive model for the oxidation of trace organic contaminants by sulfate radical. Water Res 116:106–115. CrossRefGoogle Scholar
  50. Ye T, Wei Z, Spinney R, Dionysiou DD, Luo S, Chai L, Yang Z, Xiao R (2017b) Quantitative structure–activity relationship for the apparent rate constants of aromatic contaminants oxidized by ferrate (VI). Chem Eng J 317:258–266. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yanfei Qu
    • 1
  • Yongwen Ma
    • 1
    • 2
    • 3
  • Jinquan Wan
    • 1
    • 2
    • 3
  • Yan Wang
    • 1
    • 2
  1. 1.College of Environment and EnergySouth China University of TechnologyGuangzhouChina
  2. 2.The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of EducationSouth China University of TechnologyGuangzhouChina
  3. 3.State Key Laboratory of Pulp and Paper EngineeringSouth China University of TechnologyGuangzhouChina

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