Advertisement

Journal of Soils and Sediments

, Volume 17, Issue 1, pp 35–46 | Cite as

Theoretical investigation of congener-specific soil sorption of polychlorinated biphenyls by DFT computation and potent QSAR analyses

  • Mengrong Zhu
  • Chenggang Gu
  • Yinwen Cheng
  • Xuehai Ju
  • Yongrong Bian
  • Xinglun Yang
  • Yang Song
  • Mao Ye
  • Fang Wang
  • Xin Jiang
Soils, Sec 1 • Soil Organic Matter Dynamics and Nutrient Cycling • Research Article

Abstract

Purpose

Few studies have been conducted to understand well the underlying soil sorptive mechanism due to the limited experimental determination for the enormous number of polychlorinated biphenyl (PCB) congeners. The objective of this paper was to obtain further insights into the soil sorption behavior of PCBs with exploration of the sorptive mechanism at the molecular level for sorption affinity, which could be anticipated to help explore the migration fates and assess the bioavailability of PCBs in soil.

Materials and methods

Soil sorption coefficients of 52 kinds of PCB congeners were collected in this paper. The geometries of PCBs were fully optimized within Gaussian 03 suite of programs, and 27 molecular descriptors which describe the electronic and thermodynamic properties were finally determined with optimized structures after optimization. The quantitative structure–activity relationships (QSARs) for predicting the soil sorption of PCBs were developed by the combination of density functional theory (DFT) computation and partial least squares analyses, which maximized the correlation between the DFT-calculated properties and soil sorption of PCBs. The QSAR was critically validated with better performance in sensitivity, robustness, interpretation and prediction, and specific description of the applicability domain.

Results and discussion

For the successfully developed QSAR, R y,cum(adj) 2 and Q cum 2 were respectively recorded as 0.922 and 0.896, and R EXT 2 and Q EXT 2 were respectively recorded as 0.905 and 0.914, which demonstrated the stability and predictability of the model. The molecular electronegativity of PCBs by DFT was significantly indicative of a positive correlation with sorption potency, while polarizability had a negative correlation with it. QSAR analyses also revealed the favorable structural requirement of more chlorination at meta/para sites for soil sorption. It was implied that the soil sorption should be largely ascribed to the electrostatic interaction between PCBs and soil organic matter. Nevertheless, the thermodynamic stability and hydrophobicity related to the molecular entropy increment of PCBs were also beneficial to enhancing soil sorption.

Conclusions

QSAR analyses particularly indicated the strong dependence of variation of soil sorption on molecular electronic properties, such as electronegativity and polarizability, which suggested the predominance of electrostatic interaction with soil organic matter. Meta/para chlorination was illustrated as a preferable structural requirement for soil sorption. In addition, the thermodynamic stability and hydrophobicity driven by entropy increment were also effective for soil sorption. These results contributed to predict the migration and fate of PCBs in soil system.

Keywords

Congener specificity DFT PCBs QSARs Soil sorption 

Abbreviations

AD

Applicability domain

DFT

Density functional theory

HOMO

Highest occupied molecular orbital

KOC

Soil organic carbon-normalized sorption coefficient

LUMO

Lowest unoccupied molecular orbital

OECD

Organization for Economic Cooperation and Development

PCBs

Polychlorinated biphenyls

PLS

Partial least squares

QSAR

Quantitative structure–activity relationship

VIP

Variable importance in projection

Notes

Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (21377138), National Basic Research Program (973) of China (2014CB441105), Frontier Program of “135” Plans and Knowledge Innovation Engineering Field of CAS (ISSASIP1618), and National Natural Science Foundation of China (41271464 and 41271327).

Compliance with ethical standards

Conflicts of interest

The authors declare no conflict of interests.

Supplementary material

11368_2016_1487_MOESM1_ESM.docx (68 kb)
ESM 1 (DOCX 68 kb)

References

  1. Ahmed AA, Kühn O, Aziz SG, Hilal RH, Leinweber P (2014) How soil organic matter composition controls hexachlorobenzene–soil-interactions: adsorption isotherms and quantum chemical modeling. Sci Total Environ 476–477:98–106CrossRefGoogle Scholar
  2. ATSDR (2000) Toxicological profile for polychlorinated biphenyls (PCBs). U.S. Department of Health and Human Services, Public Health Service, AtlantaGoogle Scholar
  3. Becke AD (1993) Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 98:5648–5652CrossRefGoogle Scholar
  4. Bohac M, Loeprecht B, Damborsky J, Schuurmann G (2002) Impact of orthogonal signal correction (OSC) on the predictive ability of CoMFA models for the ciliate toxicity of nitrobenzenes. Quant Struct-Act Relat 21:3–11CrossRefGoogle Scholar
  5. Calvel R (1989) Adsorption of organic chemicals in soils. Environ Sci Technol 83:145–177Google Scholar
  6. Chen G, White PA (2004) The mutagenic hazards of aquatic sediments: a review. Mutat Res 567:151–225CrossRefGoogle Scholar
  7. Chen JW, Xu XY, Schramm KW, Quan X, Yang FL, Kettrup A (2002) Quantitative structure–property relationships for octanol-air partition coefficients of polychlorinated biphenyls. Chemosphere 48:535–544CrossRefGoogle Scholar
  8. Dalla VM, Jurado E, Dachs J, Sweetman AJ, Jones KC (2005) The maximum reservoir capacity of soils for persistent organic pollutants: implications for global cycling. Environ Pollut 134:153–164CrossRefGoogle Scholar
  9. dos Reis RR, Sampaio SC, de Melo EB (2014) An alternative approach for the use of water solubility of nonionic pesticides in the modeling of the soil sorption coefficients. Water Res 53:191–199CrossRefGoogle Scholar
  10. Doucette WJ (2003) Quantitative structure–activity relationships for predicting soil-sediment sorption coefficients for organic chemicals. Environ Toxicol Chem 22:1771–1788CrossRefGoogle Scholar
  11. Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 111:1361–1375CrossRefGoogle Scholar
  12. Faroon O, Jones D, De Rosa C (2000) Effects of polychlorinated biphenyls on the nervous system. Toxicol Ind Health 16:305–333CrossRefGoogle Scholar
  13. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Montgomery Jr, JA, Vreven T, Kudin KN, Burant JC, Millam JM, Iyengar SS, Tomasi J, Barone V et al. (2003) In: Gaussian 03, revision B.03. Gaussian Inc., PittsburghGoogle Scholar
  14. Giesy JP, Kannan K (1998) Dioxin-like and non-dioxin-like toxic effects of polychlorinated biphenyls (PCBs): implications for risk assessment. Crit Rev Toxicol 28:511–569CrossRefGoogle Scholar
  15. Golbraikh A, Tropsha A (2002a) Beware of q 2! J Mol Graph Model 20:269–276CrossRefGoogle Scholar
  16. Golbraikh A, Tropsha A (2002b) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J Comput Aided Mol Des 16:357–369CrossRefGoogle Scholar
  17. Goudarzi N, Goodarzi M, Araujo MCU, Galvāo RKH (2009) QSPR modeling of soil sorption coefficients (K OC) of pesticides using SPA-ANN and SPA-MLR. J Agric Food Chem 57:7153–7158CrossRefGoogle Scholar
  18. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701CrossRefGoogle Scholar
  19. Gramatica P, Giani E, Papa E (2007) Statistical external validation and consensus modeling: a QSPR case study for K OC prediction. J Mol Graph Model 25:755–766CrossRefGoogle Scholar
  20. Grathwohl P (1990) Influence of organic matter from soils and sediments from various origins on the sorption of some chlorinated aliphatic hydrocarbons: implications on K oc, correlations. Environ Sci Technol 24:1678–1693CrossRefGoogle Scholar
  21. Grimm FA, Hu DF, Korwel IK, Lehmler HJ, Ludewig G, Hornbuckle KC, Duffel MW, Bergman Å, Robertson LW (2015) Metabolism and metabolites of polychlorinated biphenyls. Crit Rev Toxicol 45:245–272CrossRefGoogle Scholar
  22. Hemmateenejad B (2004) Optimal QSAR analysis of the carcinogenic activity of drugs by correlation ranking and genetic algorithm-based PCR. J Chemom 18:475–485CrossRefGoogle Scholar
  23. Huuskonen J (2003) Prediction of soil sorption coefficient of a diverse set of organic chemicals from molecular structure. J Chem Inf Comput Sci 43:1457–1462CrossRefGoogle Scholar
  24. Iwata H, Tanabe S, Ueda K et al (1995) Persistent organochlorine residues in air, water, sediments, and soils from the Lake Baikal region, Russia. Environ Sci Technol 29:792–801CrossRefGoogle Scholar
  25. Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 96:1027–1043CrossRefGoogle Scholar
  26. King CM, King RB, Bhattacharyya NK, Newton MG (2000) Organonickel chemistry in the catalytic hydrodechlorination of polychlorobiphenyls (PCBs): ligand steric effects and molecular structure of reaction intermediates. J Organomet Chem 600:63–70CrossRefGoogle Scholar
  27. Kojima H, Takeuchi S, Uramaru N, Sugihara K, Yoshida T, Kitamura S (2009) Nuclear hormone receptor activity of polybrominated diphenyl ethers and their hydroxylated and methoxylated metabolites in transactivation assays using Chinese hamster ovary cells. Environ Health Perspect 117:1210–1218CrossRefGoogle Scholar
  28. Koopmans T (1934) Über die zuordnung von wellenfunktionen und eigenwerten zu den einzelnen elektronen eines atoms. Physica 1:104–113CrossRefGoogle Scholar
  29. Lee C, Yang W, Parr RG (1988) Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 37:785–789CrossRefGoogle Scholar
  30. Leijs MM, ten Tusscher GW, Olie K, van Teunenbroek T, van Aalderen WM, deVoogt P, Vulsma T, Bartonova A, Krayer von Krauss M, Mosoiu C (2012) Thyroid hormone metabolism and environmental chemical exposure. Environ Health 11(1):S10CrossRefGoogle Scholar
  31. Lewis DFV (1989) The calculation of molar polarizabilities by the CNDO/2 method: correlation with the hydrophobic parameter, LogP. J Comput Chem 10:145–151CrossRefGoogle Scholar
  32. Liu P, Zhu DQ, Zhang H et al (2008) Sorption of polar and nonpolar aromatic compounds to four surface soils of eastern China. Environ Pollut 156:1053–1060CrossRefGoogle Scholar
  33. Lovley DR, Elizabeth JP, Phillips (1986) Organic matter mineralization with reduction of ferric iron in anaerobic sediments. Appl Environ Microbiol 51:683–689Google Scholar
  34. Luco JM (1999) Prediction of the brain–blood distribution of a large set of drugs from structurally derived descriptors using partial least-squares (PLS) modeling. J Chem Inf Comput Sci 39:396–404CrossRefGoogle Scholar
  35. Maqueda C, Perez Rodriguez JL, Eugenio MP (1986) Interaction of chlordimeform with the clay fraction of a variable-charge soil. Soil Sci 141:138–143CrossRefGoogle Scholar
  36. Muller N (1990) Search for a realistic view of hydrophobic effects. Acc Chem Res 23:23–28CrossRefGoogle Scholar
  37. Nguyen TH, Goss KU, Ball PW (2005) Polyparameter linear free energy relationships for estimating the equilibrium partition of organic compounds between water and the natural organic matter in soils and sediments. Environ Sci Technol 39:913–924CrossRefGoogle Scholar
  38. OECD (2007) Guidance document on the validation of (quantitative) structure–activity relationship [(Q)SAR] models. OECD Publishing, ParisGoogle Scholar
  39. Parr RG, Szentpály LV, Liu S (1999) Electrophilicity index. J Am Chem Soc 121:1922–1924CrossRefGoogle Scholar
  40. Rahman MS, Payá-Pérez A, Skejø-Andreasen H, Larsen B (1994) Surfactant solubilization of hydrophobic compounds in soil and water. Environ Sci Pollut Res 1:131–139CrossRefGoogle Scholar
  41. Reed JL (1994) Electronegativity-proton affinity. J Phys Chem 98:10477–10483CrossRefGoogle Scholar
  42. Rodríguez-Valdez LM, Martínez-Villafañe A, Glossman-Mitnik D (2005) CHIH-DFT theoretical study of isomeric thiatriazoles and their potential activity as corrosion inhibitors. J Mol Struct (Theochem) 716:61–65CrossRefGoogle Scholar
  43. Sabljic A, Güsten H, Verhaar H, Hermens J (1995) QSAR modeling of soil sorption. Improvements and systematics of logK OC vs. logK OW correlations. Chemosphere 31:4489–4514, Corrigendum (1996) Chemosphere 33:2577CrossRefGoogle Scholar
  44. Schüürmann G, Ebert RU, Kühne R (2006) Prediction of the sorption of organic compounds into soil organic matter from molecular structure. Environ Sci Technol 40:7005–7011CrossRefGoogle Scholar
  45. Shao YH, Liu J, Wang MX, Shi LL, Yao XJ, Gramatica P (2014) Integrated QSPR models to predict the soil sorption coefficient for a large diverse set of compounds by using different modeling methods. Atmos Environ 88:212–218CrossRefGoogle Scholar
  46. Taft RW, Abraham MH, Famini GR, Doherty RM, Abboud JLM, Kamlet MJ (1985) Solubility properties in polymers and biological media 5: an analysis of the physicochemical properties which influence octanol-water partition coefficients of aliphatic and aromatic solutes. J Pharm Sci 74:807–814CrossRefGoogle Scholar
  47. Tanabe S (1988) PCB problems in the future: foresight from current knowledge. Environ Pollut 50:5–28CrossRefGoogle Scholar
  48. Tao S, Piao HS, Dawson R, Lu XX, Hu HY (1999) Estimation of organic carbon normalized sorption coefficient (K OC) for soils using the fragment constant method. Environ Sci Technol 33:2719–2725CrossRefGoogle Scholar
  49. Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77CrossRefGoogle Scholar
  50. Van der Voet H (1994) Comparing the predictive accuracy of models using a simple randomization test. Chemometr Intel Lab 25:313–323CrossRefGoogle Scholar
  51. Wang B, Chen JW, Li XH, Wang YN, Chen L, Zhu M, Yu HY, Kühne R, Schüürmann G (2009) Estimation of soil organic carbon normalized sorption coefficient (K OC) using least squares-support vector machine. QSAR Combust Sci 28:561–567CrossRefGoogle Scholar
  52. Wang Y, Chen JW, Yang XH, Lyakurwa F, Li XH, Qiao XL (2015) In silico model for predicting soil organic carbon normalized sorption coefficient (K OC) of organic chemicals. Chemosphere 119:438–444CrossRefGoogle Scholar
  53. Wen Y, Su LM, Qin WC, Fu L, He J, Zhao YH (2012) Linear and non-linear relationships between soil sorption and hydrophobicity: model, validation and influencing factors. Chemosphere 86:634–640CrossRefGoogle Scholar
  54. Wise A, Parham F, Axelrad DA, Guyton KZ, Portier C, Zeise L, Zoeller RT, Woodruff TJ (2012) Upstream adverse effects in risk assessment: a model of polychlorinated biphenyls, thyroid hormone disruption and neurological outcomes in humans. Environ Res 117:90–99CrossRefGoogle Scholar
  55. Wold S (1978) Cross-validation estimation of the number of components in factor and principal components analysis. Technometrics 24:397–405CrossRefGoogle Scholar
  56. Wold S (1994) Exponentially weighted moving principal components analysis and projections to latent structures. Chemometr Intel Lab 23:149–161CrossRefGoogle Scholar
  57. Wold S, Ruhe A, Wold H, Dunn WJ (1984) The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J Sci Stat Comput 5:735–743CrossRefGoogle Scholar
  58. Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intel Lab 58:109–130CrossRefGoogle Scholar
  59. Wong MW, Wiberg KB, Frisch MJ (1995) Ab initio calculation of molar volumes: comparison with experiment and use in solvation models. J Comput Chem 16:385–394CrossRefGoogle Scholar
  60. Zeng X, Freeman PK, Vasil’ev YV, Voinov VG, Simonich SL, Barofsky DF (2005) Theoretical calculation of thermodynamic properties of polybrominated diphenyl ethers. J Chem Eng Data 50:1548–1556CrossRefGoogle Scholar
  61. Zhu DQ, Hyun S, Pignatello JJ, Lee LS (2004) Evidence for π-π electron donor-acceptor interactions between π-donor aromatic compounds and π-acceptor sites in soil organic matter through pH effects on sorption. Environ Sci Technol 38:4361–43CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Mengrong Zhu
    • 1
    • 2
  • Chenggang Gu
    • 1
  • Yinwen Cheng
    • 1
    • 2
  • Xuehai Ju
    • 3
  • Yongrong Bian
    • 1
  • Xinglun Yang
    • 1
  • Yang Song
    • 1
  • Mao Ye
    • 1
  • Fang Wang
    • 1
  • Xin Jiang
    • 1
  1. 1.Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil ScienceChinese Academy of SciencesNanjingPeople’s Republic of China
  2. 2.University of the Chinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.Department of ChemistryNanjing University of Science and TechnologyNanjingPeople’s Republic of China

Personalised recommendations