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Online Journal of Chemistry
Editorial Article | Open Access | 10.31586/ojc.2021.010101

Bioconcentration Factor of Polychlorinated Biphenyls and Its Correlation with UV- and IR-Spectroscopic data: A DFT based Study

Sangeeta Sahu1, Vishnu Kumar Sahu2 and Anil Kumar Soni3
1
Department of Chemistry, University of Lucknow, Lucknow-226007 (U.P.), India.
2
Department of Chemistry, Maharani Lal Kunwari Post Graduate College, Balrampur-271201 (U.P.), India.
3
Department of Chemistry, Shia Post Graduate College, Sitapur Road, Lucknow-226020 (U.P.), India.
Received April 11, 2021
Revised May 12, 2021
Accepted May 18, 2021
Published May 20, 2021

Abstract

Polychlorinated biphenyls (PCBs) are important class of persist organic pollutants that were used as a component of paints especially in printings, as plastificator of plastics and insulating materials in transformers and capacitors, heat transfer fluids, additives in hydraulic fluids in vacuum and turbine pumps. There is always a need to establish reliable procedures for predicting the bioconcentration potential of chemicals from the knowledge of their molecular structure, or from readily measurable properties of the substance. Hence, correlation and prediction of biococentration factors (BCFs) based on λmax and vibration frequencies of various bonds viz υ(C-H) and υ(C=C) of biphenyl and its fifty-seven derivatives have been made. For the study, the molecular modeling and geometry optimization of the PCBs have been performed on workspace program of CAChe Pro 5.04 software of Fujitsu using DFT method. UV-visible spectra for each compound were created by electron transition between molecular orbitals as electromagnetic radiation in the visible and ultraviolet (UV-visible) region is absorbed by the molecule. The energies of excited electronic states were computed quantum mechanically. IR spectra of transitions for each compound were created by coordinated motions of the atoms as electromagnetic radiation in the infrared region is absorbed by the molecule. The force necessary to distort the molecule was computed quantum mechanically from its equilibrium geometry and thus frequency of vibrational transitions was predicted. Project Leader Program associated with CAChe has been used for multiple linear regression (MLR) analysis using above spectroscopic data as independent variables and BCFs of PCBs as dependent variables. The reliability of correlation and predicting ability of the MLR equations (models) are judged by R2, R2adj, se, q2L10O and F values. This study reflected clearly that UV and IR spectroscopic data can be used to predict BCFs of a large number of related compounds within limited time without any difficulty.

1. Introduction

In our recent communication, we have employed semiempirical technique to predict the bioconcentration factor (BCF: the ratio of the concentration of a chemical inside an organism to the concentration in the surrounding environment) of organic compounds in fish [1]. In the present work, we present the density functional theory (DFT) [2, 3, 4] based prediction of BCF of polychlorinated biphenyls (PCBs: an important class of persist organic pollutants) [5]. PCBs were either washed down into the soil or into the water bodies [6, 7]. From the soil, these are absorbed by the plants along with water and minerals, and from the water bodies these are taken up by aquatic plants and animals [8]. This is one of the ways in which they enter the food chain. As these chemicals are not degradable, these get accumulated progressively at each tropic level [9, 10, 11]. As human beings occupy the top level in any food chains, the maximum concentration of these chemicals gets accumulated in our bodies [12, 13, 14, 15, 16]. Fish are the principal target organisms of BCF assessment due to their relevance as food for many species including humans and the availability of standardized testing protocols [17, 18]. However, experimental determination of BCFs is expensive and demanding if performed correctly. Hence a number of quantitative structure-activity relationship (QSAR) based study of BCFs of the PCBs were made time to time increase the probability of success and reduce the time and cost in exploring the toxicological and ecological characteristics of molecules [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]. Hence, correlation and prediction of bioconcentration factors (BCFs) based on λmax and vibration frequencies of various bonds viz υ(C-H) and υ(C=C) of biphenyl and its fifty-seven derivatives have been made. Our interest in DFT-based prediction of BCFs was stimulated by the fact that DFT methods [2, 36] are capable of generating UV- and IR-spectra quite accurately.

2. Materials and Methods

Biphenyl and fifty-seven PCB congeners (Table 1) are the study materials for the present study. These compounds were taken from the literature with their experimental logarithmic BCF values (LogBCFexp) fish species [30]. For the study, the 3D modeling and geometry optimization of all the fifty-eight compounds were performed on workspace program, active document window of CAChe pro software of Fujitsu(www.CACheSoftware.com), using the B88-PW91 GGA energy function with the DZVP basis set [37, 38, 39, 40, 41]. The energies of excited electronic states were computed quantum mechanically. UV-visible spectrum for each compound was created by electron transition between molecular orbitals as electromagnetic radiation in the visible and ultraviolet (UV-visible) region is absorbed by the molecule. After obtaining approximate minimum energy structures, the UV-visible electronic transitions of the each compound were generated by a ZINDO configuration interaction. IR spectrum of transitions for each compound was created by coordinated motions of the atoms as electromagnetic radiation in the infrared region is absorbed by the molecule. The force necessary to distort the molecule was computed quantum mechanically from its equilibrium geometry and thus frequency of vibrational transitions was obtained [42].

Table 1
Biphenyl and PCB congeners with their experimental logarithmic BCF [30]

3. Results and Discussion

The general chemical structure of PCBs [5] is based upon the skeleton of biphenyl, which containing one to ten atoms of chlorine and has 12 sites viz. 1,2,3,4,5,6,1’,2’,3’,4’,5’ and 6’(Figure 1). The biphenyl and its fifty seven chloro-derivatives i.e. mono-, di-, tri, tetra-, penta-, hexa-, hepta-, nona- and decachlorobiphenyl are one, eight, four, ten, nine, twelve, six, five and one in number, respectively. Benzene shows three absorption bands at 184 nm (intense band, due to an allowed transition), 203.5 nm (weaker K-band, due to forbidden transition) and 254 nm (weaker B-band, also due to forbidden transition). When one of the hydrogen atoms of benzene (C6H5-H or Ph-H) is replaced by phenyl group, then we get biphenyl molecule (C6H5-C6H5 or Ph-Ph). Although biphenyl is symmetrical molecule (dipole moment, μ =0 and dihedral angle between both ring planes through the interring bond = 0˚), therefore, conjugation between the ring is not affected. Biphenyl thus shows a very intense absorption bands at 252 nm (K-band). Biphenyl derivative with chloro substitutions in ortho positions are more stable in twisted conformations (compound no. 11, 15-22, 25-29, 31, 32, 34-53, 46-58) than in planar conformation (compound no. 1-10, 12-14, 23, 24, 30, 33, 45 and 45). These suffer serious non-bonded compressions from the juxtaposed substitutions. The compounds with twisted conformations have HOMO-LUMO gap greater than 3.590 eV and their dihedral angle is also greater than 53.3˚. While those having planar structures have HOMO-LUMO gap lower than 3.590 eV and their dihedral angle is either zero or equal to 1. To correlate the effect of number and position of chlorine atoms on UV-visible spectra, we have presented UV-visible spectra of all the compounds in Scheme 1. A close look at the scheme 1 also sports the loss of conjugation in the twisted conformations of compounds.

Figure 1
Structure of PCB
(A)
(A)
(B)
(B)
Scheme 1
(a) UV-visible spectra of compound no. 1-30; (b) UV-visible spectra of compound no. 31-58

The calculated vibrational spectrum has proved to be a powerful tool to investigate changes in molecular aggregation, orientation, and structure at the level of functional groups [42]. Biphenyl and PCBs in the ground state have 60 fundamentals with various symmetries. Since almost all the vibrational modes are delocalized over whole molecule, they cannot be ascribed to several local vibrational motions. These are (IR tors), (IR str + R ip def), (R oop def), (R ip def + IR str + CC str), (CH oop bend), (R ip def), (CC str), (CH ip bend), (IR str + CH ip bend), (CC str + R ip def) and (CH str). This is the characteristic feature of aromatic compounds. For comparative study the IR spectra of all the compounds are placed in scheme 2. Among the above modes, we have extracted CC str + R ip def i.e. υ(C=C) and CH str i.e. υ(C-H) for correlation with BCF and are tabulated in Table 2.

Table 2
Calculated HOMO-LUMO gap, λmax from UV-visible spectra and frequencies from IR spectra, experimental BCF and predicted BCF of PCBs
(A)
(A)
(B)
(B)
Scheme 2
(a) IR spectra of compound no. 1-30; (b) IR spectra of compound no. 31-58

To correlate BCFs of the compounds with UV- and IR spectral data, we have performed multiple linear regression (MLR) analysis. For this electronic spectral data and vibration frequencies were used as independent variables and the experimental BCFs of the compounds as dependent variables. To test the predictive ability of the model, we have performed external cross validation model with leave-ten-out (L10O) technique, where ten compounds are removed at a time and a number of models were generated and among them following model was found reliable.

logBCFpre= ‒0.0319944 × υ(C=C) +0.00151573 × λmax +55.3602

R2L10O = 0.650, R2adj = 0.642, se = 0.60, F = 111.86, q2 L10O = 0.535

where υ(C=C) is the first descriptor and λmax is the second descriptors. υ(C=C) has negative descriptor coefficient magnitudes that shows indirect relationship with BCF, while λmax has positive descriptor coefficient magnitudes that shows direct relationship with BCF. The predicted BCF values as obtained from this model are also given in Table 2, while the trend of experimental and predicted BCF is shown in Figure 2.

Figure 2
(a) UV-visible spectra of compound no. 1-30; (b) UV-visible spectra of compound no. 31-58

Previously, we have also predicted the BCF of these compounds using DFT-method and the descriptors used were atomic in nature viz., partial atomic charges and HOMO densities [43]. The values of above descriptors were calculated at twelve sites of PCBs. And the study reflected that partial atomic charges have more reliable predicting power than the HOMO densities. The ability of these models was r2 between 0.645 and 0.916 for atomic charges and r2 between 0.638 and 0.840 for HOMO densities. And the r2CV for these models was 0.639 to 0.916 (atomic charge) and 0.614 to 0.693 (HOMO density). The s values for these models were 0.482 to 0.288 for atomic charges and 0.532 to 0.397 for HOMO densities. The reliable model with respect to atomic charge was:

logBCFpre=1.65 × Q6 +3.82 × Q5 +3.56 × Q4 +4.68 × Q3 +4.326 × Q2 +0.425 × Q1 +1.25 × Q5’ +7.78

r2 = 0.917, r2CV = 0.879, s = 0.270, n = 56, k = 7

While, the reliable model with respect to HOMO density was:

logBCFpre= ‒5.76 × ρ6H ‒ 15.398 × ρ5H ‒ 1.764 × ρ4H ‒ 17.15 × ρ3H ‒ 11.205 × ρ2H ‒ 11.828 × ρ1’H ‒ 21.27 × ρ2’H ‒ 11.26 × ρ3’H ‒ 12.95 × ρ6’H + 12.318

r2 = 0.902, r2CV = 0.837, s = 0.307, n = 52, k = 10

Further, we have also made DFT based prediction of BCF these compounds using molecular descriptors. The descriptors used were dipole moment (μ), ionization energy (IE), electron affinity (EA), electronegativity (χ), hardness (η), softness (S) and electrophilicity index (ω) [44]. Here, the study reflected that μ and IP are reliable descriptors for correlation of BCF of PCBs with their electronic structures. The resulted model as shown below has r2 = 0.914, r2CV = 0.898, s = 0.267, n = 52, k = 2

logBCFpre= 0.0319265 × µ +3.68314 × IP -17.7294

A comparative study of these results shows that atomic and molecular properties are reliable descritors to predict the BCF of PCBs and can also made valuable contribution to descovered the methodology that help to degradate these persistants pollutants. While the spectroscopic data based descriptors help to find out the route and the mechanism that help to reduce the wealth and time for descovering the new pathway of the degradation of these persistant organic pollutants.

4. Conclusion

On the basis of present study it has been concluded that DFT-based υ(C=C) and λmax have sufficient reliability to relate the bioconcentration factors of polychlorinated biphenyls with their structures. Thus UV-visible and IR-spectroscopic properties can be used to predict BCFs of a large number of related compounds within limited time without any difficulty and help to find out the route and the mechanism that help to reduce the wealth and time for descovering the new pathway of the degradation of these persistant organic pollutants.

Acknowledgement

The authors are thankful to Dr. O.P. Mishra, Ex.Principal M.L.K. (P.G.) College; Balrampur, for providing the laboratory facilities to conduct the calculation.

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Sahu, S., Kumar Sahu, V., & Kumar Soni, A. (2021). Bioconcentration Factor of Polychlorinated Biphenyls and Its Correlation with UV- and IR-Spectroscopic data: A DFT based Study. Online Journal of Chemistry, 1(1), 1–12. Retrieved from https://www.scipublications.com/journal/index.php/ojc/article/view/2
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  1. V. K. Sahu and R. K. Singh, Prediction of bioconcentration factor of organic compounds in fish, Clean-soil, air, water, 3 (2009), pp. 850-857.[CrossRef]
  2. R. G. Parr and W. Yang, Density functional theory of atoms and molecules, Oxford University Press, New York, p. 127, 1989.
  3. P. Geerlings, F. D. Proft and J. M. L. Martin, In Recent Developments in Density Functional Theory (Theoretical and Computational Chemistry) Seminario, S., Ed.; Elsevier: Amsterdam, vol. 5, pp.773-809, 1996.[CrossRef]
  4. M. Karelson, V. S. Lobanov and A. R. Katritzky, Quantum-chemical descriptors in QSAR/QSAR research, Chem. Rev. 96 (1996), pp. 1027-1044.[CrossRef] [PubMed]
  5. I. Grabowska, Polychlorinated Biphenyls (PCBs) in Poland: Occurance, Determination and Degradation, Polish J. of Environ. Stud. 19 (2010), pp. 7-13.
  6. R. F. Herrick, D. J. Lefkowitz, G. A. Weymouth, Soil contamination from PCB containing buildings, Environ. Health Persp. 115 (2007), pp. 173-175.[CrossRef] [PubMed]
  7. S. K. Sahu, P. Y. Ajmal, G. G. Pandit and V. D. Puranik, Vertical distribution of polychlorinated biphenyl congeners in sediment core from Thane Creek area of Mumbai, India, J. Hazard Mater. 164 (2009), pp.1573–1579.[CrossRef] [PubMed]
  8. K. Senthilkumar, K. Kannan, A. Subramanian and S. Tanabe, Accumulation of organochlorine pesticides and polychlorinated biphenyls in sediments, aquatic organisms, birds, bird eggs and bat collected from South India, Environ. Sci. Pollut. Res. 8 (2001), pp.35–47.[CrossRef] [PubMed]
  9. B. Hope, S. Scatolini, E. Titus, Bioconcentration of chlorinated biphenyls in biota from the North Pacific Ocean, Chemosphere 36 (1938), pp. 1247-1261.[CrossRef]
  10. T. Kunisue, M. Watanabe, A. Subramanian, A.M. Titenko, S. Tanabe, Congener-specific patterns and toxic assessment of polychlorinated biphenyls in resident and migratory birds from Southern India and Lake Baikal in Russia, Arch. Environ. Contam. Toxicol. 45 (2003), pp.547–561.[CrossRef] [PubMed]
  11. G.M. Swanson, H.E. Ratcliffe and L.J. Fischer, Human exposure to policlorinated biphenyls (PCBs): A critical assessment of the evidence for adverse health effects, Regul. Toxicol. Pharmacol. 21 (1995), pp.136–150.[CrossRef] [PubMed]
  12. G. Schade and B. Heinzow, Organochlorine pesticides and polychlorinated biphenyls in human milk of mothers living in northern Germany: current extent of contamination, time trend from 1986 to 1997 and factors that influence the levels of contamination, Sci. Total Environ. 215 (1998), pp.31–39.[CrossRef]
  13. O. Faroon, D. Jones and C. De Rosa, Effects of polychlorinated biphenyls on the nervous system, Toxicol. Ind. Health, 16 (2000), pp.305–333.[CrossRef] [PubMed]
  14. G. Devanathan, T. Isobe, A. Subramanian, K.A. Asante, S. Natrajan, P. Palanlappan, S. Takahashi and S. Tanabe, Contamination Status of Polychlorinated Biphenyls and Brominated Flame Retardants in Environmental and Biota Samples from India, Interdisciplinary Studies on Environmental Chemistry—Environmental Pollution and Ecotoxicology, Eds., M. Kawaguchi, K. Misaki, H. Sato, T. Yokokawa, T. Itai, T. M. Nguyen, J. Ono and S. Tanabe, pp. 269–277, 2012.
  15. G. Devanathan, A. Subramanian, M. Someya, A. Sudaryanto, T. Isobe, S. Takahashi, P. Chakraborty and S. Tanabe, Persistent organochlorines in human breast milk from major metropolitan cities in India, Environ. Pollut. 157 (2009), pp.148–154.[CrossRef] [PubMed]
  16. I. Cok, A. Bilgili, M. Ozdemir, H. Ozbek, N. Bilgili, S. Burgaz, Organo-chlorine pesticides residues in human breast milk from agricultural regions of Turkey, 1995–1996, Bull. Environ. Contam. Toxicol. 59 (1997), pp.577–582.[CrossRef] [PubMed]
  17. S. E. Bysshe, Bioconcentration Factor in Aquatic Organisms, in Handbook of Chemical Property Estimation Methods (Eds: W. J. Lyman, W. F. Reehl, D. H. Rosenblatt), McGraw Hill, New York, pp.5-30, 1982.
  18. G. Schüürmann and W. Klein, Advances in bioconcentration prediction, Chemosphere, 17 (1988), pp. 1551–1574.[CrossRef]
  19. R. P. Davies and A. J. Dobbs, The prediction of bioconcentration in fish, Water Res. 18 (1984), pp. 1253-1262.[CrossRef]
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