Quantitative structure–property relationships for direct photolysis of polybrominated diphenyl ethers

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Ecotoxicology and Environmental Safety 66 (2007) 348–352 www.elsevier.com/locate/ecoenv

Quantitative structure–property relationships for direct photolysis of polybrominated diphenyl ethers Jingwen Chen, Degao Wang, Shuanglin Wang, Xianliang Qiao, Liping Huang Department of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, People’s Republic of China Received 9 August 2005; received in revised form 6 December 2005; accepted 3 January 2006 Available online 20 February 2006

Abstract Using semiempirical quantum chemical descriptors, by partial least squares (PLS) regression, quantitative structure–property relationships (QSPRs) were established for direct photolysis quantum yields (F) and rate constants (k) of polybrominated diphenyl ether congeners dissolved in water/methanol and methanol solutions, respectively, and irradiated by artificial ultraviolet A light. Q2cum , a parameter indicating robustness and predictive abilities of PLS models, for the significant QSPR models is larger than 0.702. The gap of frontier molecular orbital energies (ELUMO–EHOMO) and the most positive Mulliken atomic charges on a hydrogen atom (qHþ ) are two main molecular structural factors governing the log F values. log F increases with increasing ELUMO–EHOMO and qHþ values. log k is mainly related to bromination degree and pattern which can be characterized by molecular weight (Mw), average molecular polarizability (a), and average Mulliken atomic charges on bromine atoms (qBr). log k increases with bromination degree (Mw, a) and qBr. r 2006 Elsevier Inc. All rights reserved. Keywords: PBDE; QSPR; Photolysis; PLS; Quantum chemical descriptor

1. Introduction Polybrominated diphenyl ethers (PBDEs) are a major class of additive brominated flame retardants that have been widely used in a range of applications including textiles, electronics, and plastics. Because of the extensive use in the past several decades, PBDEs have begun to appear more frequently in various environmental matrixes. An exponential increase of PBDEs in the environment has been observed (de Wit, 2002; Hites, 2004; Oros et al., 2005). Recent toxicology data suggested that some PBDE congeners appear to be endocrine-disrupting chemicals, which have potential to increase hypothyroidism and liver lesion and to induce immunotoxicity and neurotoxicity (McDonald, 2002; Oros et al., 2005). This gives rise to concern about PBDEs as widespread environmental contaminants, and the environmental safety and persistence of PBDEs has prompted studies of the processes that transport and transform these chemicals in the environment. Corresponding author. Fax: +86 411 8470 6269.

E-mail address: [email protected] (J. Chen). 0147-6513/$ - see front matter r 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.ecoenv.2006.01.003

PBDEs belong to the group of organobromine compounds that absorb light in the UV-A spectra. When PBDEs are irradiated by solar light, photochemical degradation of PBDEs may occur in the environment (Hua et al., 2003). Although little is known about the mechanisms and rates of PBDE decay in the natural environment, photochemical transformation is often suggested as a potentially important fate process. Soderstrom et al. (2004) studied photolysis of decabromodiphenyl ether (PBDE-209) in toluene and on silica gel, sand, sediment, and soil. The half-lives (t1=2 ) in toluene and on silica gel were less than 15 min and on other matrices ranged between 40 and 200 h. Debromination appeared to be a major photochemical reaction pathway for PBDEs, although polybrominated dibenzofurans (PBDFs) were also observed in the experiment of Eriksson et al. (2004), who studied photodecomposition of 15 PBDE congeners dissolved in methanol/water, methanol, and tetrahydrofuran, respectively. They found that the degradation rate of PBDEs by UV light in the sunlight region was dependent on the degree of bromination, and lowly brominated diphenyl ethers degrade more slowly than highly brominated congeners. The observed rate difference is up to 700

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times between the slowest- and the fastest-photolyzing PBDE congeners. Thus it seems of interest and importance to explore the relationships between the molecular structure characteristics and the photolysis kinetic parameters for PBDEs. So far, quantitative structure–property relationships (QSPRs) that correlate and predict molecular properties from their molecular structures characterized by appropriate molecular structural descriptors have been developed for photolysis quantum yields of substituted aromatic halides (Chen et al., 2000a), polycyclic aromatic hydrocarbons (PAHs) (Chen et al., 2000b), and polychlorinated dibenzo-p-dioxins (PCDDs) (Chen et al., 2001a) and for photolysis rate constants or half-lives of PAHs (Chen et al., 2001b, c) and PCDD/Fs (Chen et al., 2001d). The purpose of this study was to develop QSPRs on photolysis of PBDE congeners based on the experimental data of Eriksson et al. (2004). Quantum chemical methods and molecular modeling techniques enable the definition of a large number of molecular and local quantities characterizing the reactivity, shape, and binding properties of a complete molecule (Karelson et al., 1996). Since the computational time for semiempirical molecular orbital methods is much shorter than that needed by ab initio methods, semiempirical quantum chemical descriptors were adopted in the previous photolysis QSPR studies (Chen et al., 2001d). Partial least squares (PLS) regression relates two data matrices X and Y by a linear multivariate model but goes beyond traditional regression in that it models also the structure of X and Y. PLS derives its usefulness from its ability to analyze data with many, noisy, collinear, and even incomplete variables in both X and Y. In addition, PLS has the desirable property that the precision of the model parameters improves with the increasing number of relevant variables

349

and observations (Svante et al., 2001). Thus, PLS regression and semiempirical quantum chemical descriptors were used in the current study. 2. Materials and methods 2.1. Data set The direct photolysis rate constants (k) for PBDEs dissolved in methanol/water (80:20) and methanol and the quantum yields (F) in methanol/water (Eriksson et al., 2004) are reproduced in Table 1, which constitute the base of the current study.

2.2. Descriptor generation PM3 Hamiltonian (Stewart, 1989) contained in MOPAC 2000 of the CS Chem3D Ultra (Chemoffice Ultra 2004, CambridgeSoft) was used to compute semiempirical quantum chemical descriptors. The molecular structures were optimized using eigenvector following (Baker, 1986), a geometry optimization procedure within MOPAC 2000. The geometry optimization criterion GNORM was set at 0.01. A total of 11 theoretical descriptors were selected, which are summarized in Table 2. In addition, two combinations of frontier molecule orbital energies, ELUMO+EHOMO and ELUMOEHOMO, were included as predictor variables for screening purposes. The molecular absolute electronegativity, which measures the escaping tendency of electrons from a molecule, can be defined as 1/2(ELUMO+EHOMO) (Parr and Pearson, 1983). The ELUMOEHOMO gap, defining the necessary energy to elevate an electron from the highest occupied molecular orbital to the lowest unoccupied molecular orbital, reflects a measure of energy stabilization in chemical systems. Values of selected quantum chemical descriptors are listed in Table 3, where units of energy, charge, and polarizability are electron volts, atomic charge units, and atomic units, respectively.

2.3. PLS analysis PLS analysis was carried out using the Simca-S package (Umetrics AB, Sweden). The conditions for computation were as follows: cross validation rounds ¼ 7, maximum iteration ¼ 200, missing data tolerance ¼ 50%,

Table 1 The PBDEs and their experimental and predictive log k or log F values PBDEs

209 208 207 206 203 190 183 181 155 154 139 138 99 77 47

log k (s1), Model (2) Methanol/water (80:20)

log F, Model (1) Methanol/water (80:20)

log k (s1), Model (3) Methanol

Observed

Predicted

Observed

Predicted

Observed

Predicted

0.85 1.00 1.05 0.77 0.92

0.84 0.94 1.01 0.89 0.85 0.84 0.73 0.95 0.89 0.80 0.83 0.77 0.88 0.56 0.66

3.40 3.77 3.72 4.08 4.43 4.52 5.17 4.50 5.39 5.48 5.40 5.21 5.52 6.22 6.16

3.12 3.99 3.73 3.97 4.37 4.60 5.18 4.40 5.36 5.47 5.21 5.17 5.59 6.20 6.36

3.19 3.52 3.50 3.89 4.23

2.70 3.75 3.52 3.81 4.26 4.29 4.91 4.18 4.90 4.95 4.98 5.00 5.02 6.04 6.07

0.80 1.00 0.85 0.85

0.54 0.66

4.96 4.25

5.17

5.92

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and significance level (p) limit ¼ 0.05. Simca-S adopts cross validation to determine the number of significant PLS components. By cross validation, the prediction error sum of squares (PRESS), that is the squared differences between observed Y (Yim) and predicted values (Yim) when the observations were kept out of the model, is computed. Based on PRESS, Q2 (the fraction of the total variation of the dependent variables that can be predicted by a component) and Q2cum (cumulative Q2) can be calculated as Q2 ¼ 1:0  PRESS=SS, Q2cum ¼ 1:0  PðPRESS=SSÞa;

ða ¼ 1; 2; . . . ; AÞ,

where SS is the residual sum of squares of the previous dimension and P(PRESS/SS)a is the product of PRESS/SS for each individual component a. When PRESS=SSp0:952 or Q2 Xð1  0:952 Þ ¼ 0:097, the tested PLS component is considered significant. Q2cum is thus a good measure of the predictive power and robustness of a model. When Q2cum is larger than 0.5, the model is considered to have a good predictive ability. In addition, a general standard error (SE) defined previously (Chen et al., 2004) was adopted to indicate prediction precision of models. In PLS modeling, a summary of the importance of an X variable for both Y and X is given by a parameter variable importance for the projection (VIP). If irrelevant descriptors are included in a PLS model, the quality of prediction and robustness of the PLS model may decrease, and the interpretation of the model becomes difficult. So it is necessary to eliminate redundant predictor variables and identify important predictor variables. This is so-called variable selection. A variable selection procedure described in a previous study (Chen et al., 2004), which is based on VIP, was adopted in the present study.

Table 2 Molecular structural descriptors Descriptor

Definitions

Mw a EHOMO ELUMO qC

Molecular weight Average molecular polarizability Energy of the highest occupied molecular orbit Energy of the lowest unoccupied molecular orbit Average Mulliken atomic charges on carbon atoms linking bromine atoms Average Mulliken atomic charges on bromine atoms Most positive Mulliken atomic charges on hydrogen atoms Negative Mulliken atomic charge on oxygen atoms

qBr qHþ qO

3. Results and discussion 3.1. Photolysis quantum yields The quantum yield (F) characterizes the efficiency of photon utilization for photochemical reactions, which can be defined as the ratio between the number of reacted molecules and the total number of photons absorbed. Based on the F values for PBDEs dissolved in methanol/ water, using the aforementioned PLS analysis procedure, the following QSPR equation that is a pseudo-analytical form transformed from PLS results was obtained: log F ¼ 3:657  101 ðE LUMO  E HOMO Þ þ 3:546  101 qHþ  1:349  103 a  4:075q O  9:275, n ¼ 11; A ¼ 2; Q2cum ¼ 0:702, R2 ¼ 0:861; SE ¼ 0:065; po0:0001.

ð1Þ

where n stands for number of PBDE congeners, A is the number of PLS components, R2 is the square of correlation coefficient between the observed and the fitted values, and p is the significance level (p). The predictor variables in Eq. (1) and subsequent QSPR equations are arranged in the order of decreasing VIP values. As A ¼ 2, although there are four predictor variables, in essence only two independent variables (latent variables) were used in the regression analysis. Eq. (1) can be regarded as statistically significant since the Q2cum values are larger than 0.50. As shown in Fig. 1, the observed log F values are in good agreement with the predicted values. The VIP values for the two predictors, ELUMOEHOMO and qHþ , are larger than 1.0, indicating that they are more significant in explaining log F than the other predictors. The frontier molecular orbital energies were also found significant in previous QSPR studies on photolysis quantum yields of PAHs, substituted aromatic halides, and PCDDs (Chen et al., 2000a, b, 2001a). PBDE and PAH (Chen et al., 2001b)

Table 3 Values of selected semiempirical quantum chemical descriptors PBDE

Mw

a

EHOMO

ELUMO

qC

q O

qBr

q+ H

209 208 207 206 203 190 183 181 155 154 139 138 99 77 47

959.171 880.275 880.275 880.275 801.379 722.483 722.483 722.483 643.587 643.587 643.587 643.587 564.691 485.795 485.795

222.158 204.871 207.923 208.889 199.186 185.191 186.815 186.383 175.529 177.185 174.866 175.868 169.435 150.896 152.317

9.957 9.754 9.772 9.793 9.850 9.925 9.852 9.813 9.878 9.821 9.761 9.840 9.481 9.787 9.726

1.275 1.228 1.251 1.273 1.257 1.282 1.092 1.222 0.865 0.864 1.036 0.917 0.734 0.611 0.585

0.146 0.152 0.151 0.147 0.156 0.151 0.164 0.157 0.189 0.173 0.167 0.163 0.159 0.153 0.175

0.086 0.093 0.093 0.101 0.103 0.108 0.100 0.104 0.074 0.087 0.105 0.113 0.103 0.145 0.116

0.128 0.118 0.118 0.122 0.111 0.115 0.097 0.106 0.076 0.084 0.089 0.098 0.077 0.081 0.061

0.144 0.142 0.145 0.145 0.143 0.146 0.141 0.142 0.143 0.143 0.142 0.141 0.140 0.141

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-0.5

351

-2.5 Model (1)

Model (2) 77

Model (3) -3.5

47 log (Predicted)

logk (Predicted)

183

-0.7

139 203 -0.9

209

208

155

-4.5

PBDE-183 PBDE-181

206

-5.5

PBDE-190

207 181 -1.1 -1.1

-0.9

-0.7

-0.5

log (Observed)

-6.5 -6.5

-5.5

-4.5 logk (Observed)

-3.5

-2.5

Fig. 2. Observed versus predicted log k by Eqs. (2) and (3).

Fig. 1. Observed versus predicted log F values for Eq. (1).

molecules with high ELUMOEHOMO values tend to have high F values. This may be because the molecules with higher ELUMOEHOMO values have greater probabilities of intersystem crossing from excited singlet states into excited triplet states, and triplet excited states are far more likely to take part in chemical reactions than singlet excited states are. According to Eq. (1), log F increases with increasing qHþ values. This can be due to the higher probabilities of excited PBDEs to obtain electrons in photoreductive debromination reactions for the molecules with more positive qHþ values. 3.2. Photolysis rate constants Based on the log k values of 15 PBDEs dissolved in methanol/water and methanol, the following two QSPR models were established: methanol/water log k ¼ 2:214  103 Mw þ 1:562  102 a þ 6:999qBr  3:427  101 E LUMO  5:671  101 ðE LUMO  E HOMO Þ  1:532 101 ðE LUMO þ E HOMO Þ  1:126  102 qHþ  2:129qC þ 8:675, n ¼ 15; A ¼ 3; Q2cum ¼ 0:973; R2 ¼ 0:982, SE ¼ 0:149; po0:0001,

ð2Þ

methanol log k ¼ 1:849  101 qC þ 1:096  103 Mw þ 6:715qBr þ 7:088  103 a þ 2:026  101 q O  2:774  101 E LUMO  1:851  101 ðE LUMO þ E HOMO Þ  3:745  101 ðE LUMO  E HOMO Þ

 9:580  101 qHþ þ 1:236  101 n ¼ 9; A ¼ 2; Q2cum ¼ 0:958; R2 ¼ 0:958, SE ¼ 0:249; po0:0001

ð3Þ

The Q2cum values of models (2) and (3) are much higher than 0.5, indicating good robustness and high predictive power of the two models. The predicted log k values are consistent with the corresponding observed values (Fig. 2). Thus the two models can be used to estimate direct photolysis rate constants of PBDEs dissolved in water/ methanol. The information carried by the molecular structural descriptors included in models (2) and (3) were actually condensed into three and two variables used for regression analysis. VIP values for the predictors, Mw, qBr, and a, in both models (2) and (3) are larger than 1.0, implying that the variables are the most significant predictor variables. The increase of log k with molecular weight (Mw) and/or average molecular polarizability (a) is consistent with the observations in previous QSPR studies on photolysis halflives of PAHs irradiated by sunlight in natural water (Chen et al., 2001b), photolysis half-lives of PAHs in aerosols (Chen et al., 2001c), and photolysis rate constants of PCDD/Fs dissolved in cuticular waxes of laurel cherry (Chen et al., 2001d). a correlates with Mw significantly (r ¼ 0:99, po0:001, n ¼ 15). Thus the photolysis rate constants increase with the bromination degree of PBDE congeners. The increase of log k with Mw or a can be explained by the absorbance behavior of PBDEs since the higher brominated diphenyl ethers absorb at longer wavelengths, and the incident light intensities were high at longer wavelengths (Eriksson et al., 2004). The increase of log k with the average Mulliken atomic charges on bromine atoms (qBr) implies that PBDE molecules with

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high qBr values tend to get electrons quickly in the process of the photoreductive debromination reaction. Eriksson et al. (2004) observed and explained the differences of k for three heptaBDEs, PBDE-190, 181, and 183 (Fig. 2). The three PBDE congeners have the same Mw and similar a values. According to models (2) and (3), the variation of k for the three PBDEs is thus determined by their qBr values, with qBr for PBDE-183 being the lowest, and the other two PBDEs having similar qBr values. The experimental k value for PBDE-139 is slightly lower than that for PBDE-138 (Eriksson et al., 2004), which is due to lower a and qBr values of PBDE-139 than of PBDE138, according to QSPR Eq. (2). Similarly, PBDE-138 (a hexaBDE) and PBDE-183 (a heptaBDE with the same substitution pattern as that of BDE-138) have similar experimental k values, which is due to their different Mw and a values and similar qBr values. 4. Conclusions The direct photolysis quantum yields (F) and rate constants (k) of PBDE congeners can be fitted by QSPR models. The gap of frontier molecular orbital energies (ELUMOEHOMO) and the most positive Mulliken atomic charges on a hydrogen atom (qHþ ) are two main molecular structural factors governing the log F values. log F increases with increasing ELUMOEHOMO and qHþ values. log k is mainly related with bromination degree and pattern that can be characterized by Mw, a, and qBr. log k increases with bromination degree (Mw, a) and qBr. Acknowledgments This research was supported by the National Basic Research Program of China (No. 2003CB415006) and the National Natural Science Foundation of P. R. China (NO. 20337020). Shuanglin Wang gratefully acknowledges the support from the student’s innovation foundation of Dalian University of Technology. References Baker, J., 1986. An algorithm for the location of transition states. J. Comput. Chem. 7, 385–395. Chen, J.W., Peijnenburg, W.J.G.M., Quan, X., Chen, S., Zhao, Y.Z., Yang, F.L., 2000a. The use of PLS algorithms and quantum chemical

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