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. 2021 Sep 7;9(9):213. doi: 10.3390/toxics9090213

Potential Environmental Risk Characteristics of PCB Transformation Products in the Environmental Medium

Minghao Li 1,2,†, Wei He 1,†, Hao Yang 1, Shimei Sun 2,*, Yu Li 1,*
Editors: Ruijuan Qu, Zunyao Wang
PMCID: PMC8472189  PMID: 34564364

Abstract

The complementary construction of polychlorinated biphenyl (PCB) phytotoxicity and the biotoxicity 3D-QSAR model, combined with the constructed PCB environmental risk characterization model, was carried out to evaluate the persistent organic pollutant (POP) properties (toxicity (phytotoxicity and biotoxicity), bioconcentration, migration, and persistence) of PCBs and their corresponding transformation products (phytodegradation, microbial degradation, biometabolism, and photodegradation). The transformation path with a significant increase in environmental risks was analyzed. Some environmentally friendly PCB derivatives, exhibiting a good modification effect, and their parent molecules were selected as precursor molecules. Their transformation processes were simulated and evaluated for assessing the environmental risks. Some transformation products displayed increased environmental risks. The environmental risks of plant degradation products of the PCBs in the environmental media showed the maximum risk, indicating that the potential risks of the transformation products of the PCBs and their environmentally friendly derivatives could not be neglected. It is essential to further improve the ability of plants to degrade their transformation products. The improvement of some degradation products for environmentally friendly PCB derivatives indicates that the theoretical modification of a single environmental feature cannot completely control the potential environmental risks of molecules. In addition, this method can be used to analyze and evaluate environmentally friendly PCB derivatives to avoid and reduce the potential environmental and human health risks caused by environmentally friendly PCB derivatives.

Keywords: environmental risk, polychlorinated biphenyls, transformation products, three-dimensional quantitative structure-activity relationships, transformation pathways

1. Introduction

Polychlorinated biphenyls (PCBs) are considered persistent organic pollutants (POPs) that spread into the environment in large quantities. The global production of PCBs is estimated to be approximately 1 to 2 million tons, out of which 0.2–0.4 million tons have produced environmental hazards [1]. The degradation or metabolism of PCBs in the environment can occur by using a variety of pathways. For instance, PCBs can be degraded to benzoic acid products by using the expression of dioxygenase degradation genes in tobacco and Arabidopsis plants [2,3,4]. Microorganisms can reduce the dechlorination of PCBs and degrade highly chlorinated PCBs to less-chlorinated ones [5]. In addition, microorganisms can also degrade PCBs by using cytochrome P450 enzymes (CYP450) in vivo to produce hydroxy PCB products with hydroxyl groups (OH-PCBs) [6]. The metabolism of PCBs by organisms can produce polychlorinated biphenyl methane sulfonate (MeSO2-PCB) through reactions such as oxidative substitution [7]. Under natural light radiation conditions, PCBs in the environment can absorb ultraviolet light and undergo direct photodegradation, and optically active chlorine atoms can break bonds in order to produce dechlorination products [8].

Among the multiple pathways of PCB transformation, the degradation or metabolites such as OH-PCBs and MeSO2-PCBs are also persistent and biotoxic [9,10,11,12]. The DNA-damaging effect of PCB-180 on rat liver was due to its metabolite 3′-OH-PCB180, which indicates that the hydroxyl metabolites of PCBs might show a higher potential for toxicity than the parent compound [13]. OH-PCBs exhibit the potential to interfere with estrogen levels in animals and humans and even in infants, which adversely affects the developmental and reproductive functions in animals and humans [14]. In addition, MeSO2 PCBs showed toxic effects and displayed stronger environmental persistence than the parent PCBs and easily enriched the food chain [15]. Therefore, further studies on the environmental risk characteristics of PCB degradation or metabolites can provide theoretical references for PCB pollution control.

This paper evaluates the environmental risks of PCB transformation products by using the following four pathways: plant degradation, microbial degradation, biometabolism, and photodegradation. The international evaluation criteria of the POPs primarily examine the following four properties of pollutants: toxicity, bioconcentration, persistence and migration. Therefore, these four characteristics were selected for evaluating the environmental risk characteristics of PCB degradation and metabolites. The POP evaluation was not defined as the toxicity evaluation receptor of pollutants. Considering that the degradation and metabolites of PCBs show estrogen interference toxicity to organisms and toxicity to plants at the same time, the phytotoxicity and biotoxicity (estrogen toxicity) of the PCBs are selected in the scope of the toxicity evaluation. In addition, the potential risk characteristics of the PCB transformation products in environmental media are primarily evaluated by using the 3D-QSAR model of the environmental risk characteristics of the PCBs.

2. Materials and Methods

2.1. Data Sources

  • (1)

    The data sources of the PCB environmental characteristics

The Stockholm Convention determines whether a chemical substance can be classified as a POP by using the following four characteristics: toxicity (phytotoxicity and biotoxicity), bioconcentration, migration, and persistence. Each characteristic requires its own characteristic parameters for evaluating the degree of each characteristic. Bioconcentration factors (BCFs) are used to represent biological enrichment. Bioconcentration as a component of risk assessment determines a meaningful BCF value for hazardous substances. It indicates the potential hazardous capacity of a substance and is the basis for assessing environmental and human risks [16]. As the evaluation criterion for the retention time of PCBs in environmental media, the half-life (t1/2) was used. The larger the t1/2 of a PCB is, the longer the retention time in the environmental media will be [17]. The octanol air partition coefficient (KOA) can respond to the migration ability of PCBs to some extent. As the KOA decreases, PCBs easily volatilize into the air [18]. When PCBs enter the plant body, they can cause the oxidation of the cell membrane and several organelles, inhibit peroxidase activity, and damage the health of the plant body [19,20,21]. Therefore, this paper selected the total score of the PCB interactions with peroxidase for characterizing the phytotoxicity of the PCBs and with estrogen receptors for representing the biotoxicity (estrogen toxicity) of the PCBs. The 3D-QSAR models of the PCB bioconcentration [16], migration [17], and persistence [18] refer to the existing models. The 3D-QSAR models of PCB phytotoxicity and biotoxicity were constructed in this paper. The structures of the receptor enzyme for phytotoxicity (1CCK) and estrogen receptor enzyme (3MDJ) for estrogen toxicity were derived from the Protein Data Bank (http://www1.rcsb.org; accessed on 15 February 2021).

  • (2)

    The data sources of transformation pathways and transformation products of PCBs in the environmental media

The literature review summarized the following four transformation pathways of the PCBs [7,22,23,24,25]: plant degradation pathway, microbial degradation pathway, biometabolism pathway, and photodegradation pathway (Figure 1). The pathway for the degradation of PCBs by plants is usually a preferential attack on the non-chlorine-substituted benzene ring by dioxygenases. However, the oxidation reaction can also occur on the chlorine-substituted ring if there is no barrier at the 2 and 3 carbon positions. For instance, first, PCBs are oxidized by dioxygenases at the 2 and 3 carbon positions in order to produce 2,3-dihydro dihydroxy PCB products; then, 2,3-dihydro dihydroxy PCBs undergo dehydrogenation reactions in order to produce 2,3-dihydroxy PCB products; thereafter, 2,3-dihydroxy PCBs undergo the meta-ring opening reaction by oxidation; finally, the resulting meta-ring opening mixture is hydrolyzed in order to produce polychlorinated benzoic acid [26]. In the microbial degradation pathway, PCBs can be oxidized to epoxides through oxidation, primarily by attacking the meta- and para-substitution sites of PCBs. The epoxidation products after meta- and para-oxidation can be directly metabolized into hydroxy PCBs by adding hydroxyl groups. In addition, PCBs can also undergo a reductive dechlorination reaction, in which the main reduction sites are meta and para, and the ortho-reaction is relatively less [27]. First, the biometabolism of PCBs is catalyzed by enzymes to produce epoxidation intermediates and carry out the methylation of PCBs by the nucleophilic reaction, dehydration, and methylation. Finally, PCBs with methyl benzenesulfonic acid were synthesized by catalytic oxidation [7]. The essence of the photodegradation of PCBs is the change of molecular energy under the action of light radiation from a low-energy state to a high-energy state, chemical bond breaking, and chemical reaction. PCBs can absorb ultraviolet light directly [28]. Fifty PCB degradation or metabolism products are summarized in Table 1.

Figure 1.

Figure 1

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Schematic diagram of the four degradation or metabolic pathways of the PCBs.

Table 1.

Summary of the degradation or metabolites of the PCBs [7,24,29,30,31].

NO. Degradation Pathway Parent Molecule Degradation or Metabolites
1 Plant degradation PCB-3 4-CBA
2 PCB-4 2-CBA
3 PCB-5 2,3-CBA
4 PCB-11 3-CBA
5 PCB-31 2,5-CBA
6 Microbial aerobic degradation PCB-97 4′-OH-CB97
7 PCB-101 4′-OH-CB101
8 PCB-107 4-OH-CB107
9 PCB-109 4-OH-CB109
10 PCB-118 3-OH-CB118
11 PCB-148 4-OH-CB148
12 PCB-153 3-OH-CB153
13 PCB-162 4-OH-CB162
14 PCB-172 4′-OH-CB172
15 PCB-187 4-OH-CB187
16 PCB-199 4′-OH-CB199
17 PCB-202 4-OH-CB202
18 Microbial anaerobic degradation PCB-90 PCB-49
PCB-68
19 PCB-91 PCB-51
20 PCB-92 PCB-52
PCB-72
21 PCB-95 PCB-53
22 PCB-99 PCB-47
23 PCB-101 PCB-49
24 PCB-102 PCB-51
25 PCB-130 PCB-90
26 PCB-132 PCB-91
27 PCB-135 PCB-94
28 PCB-137 PCB-90
PCB-99
29 PCB-138 PCB-99
30 PCB-146 PCB-90
31 PCB-147 PCB-91
32 PCB-149 PCB-102
33 PCB-151 PCB-95
34 PCB-153 PCB-99
35 PCB-154 PCB-100
36 PCB-170 PCB-130
PCB-137
PCB-138
37 PCB-174 PCB-149
38 PCB-180 PCB-153
PCB-146
39 PCB-183 PCB-154
40 PCB-187 PCB-149
41 Biometabolism PCB-49 3′-MeSO2-CB49
42 PCB-64 4-MeSO2-CB64
43 PCB-70 3-MeSO2-CB70
44 PCB-110 3-MeSO2-CB110
45 PCB-149 4-MeSO2-CB149
46 PCB-174 4-MeSO2-CB174
47 Photodegradation PCB-47 PCB-15
48 PCB-40 PCB-11
49 PCB-101 PCB-70
50 PCB-171 PCB-35
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2.2. 3D-QSAR Model Construction of PCB Toxicity (Phytotoxicity and Estrogen Toxicity)

In this paper, we used SYBYL-X 2.0 software for molecular structure mapping. The PCBs were studied by using the Minimize module of SYBYL-X 2.0 software. The energy convergence was limited to 0.005 kJ/mol by using the Powell conjugate gradient method with the Gasteiger-Huckel charge, and the Tripos force field was selected for 10,000 iterations. The optimized molecules were stored in the database, and the PCBs with the highest environmental risk values in the PCB samples were used as the common skeleton for superposition.

StockholOpen was used as the molecular library of the training set. The environmental risk values of some PCBs were input into the database in turn, and the model parameters were automatically calculated using the calculate properties function. In order to establish the relationship between the structure and biological activity of the target compounds, the partial least-squares (PLS) analysis was used. By using the leave-one-out (L-O-O) method, the training set compounds were cross-validated, and the cross-validation coefficient q2 and the best principal component n were calculated. Then, by using the non-cross-validation function (No Validation), the regression analysis was performed. Finally, to ensure a reliable 3D-QSAR estimation model for the PCB risk characteristics, the non-cross-validation coefficient r2, standard deviation SEE, and the test value F were calculated [16].

3. Results

3D-QSAR Model Construction and Evaluation of PCB Toxicity (Phytotoxicity and Estrogen Toxicity)

Based on the CoMFA method using the total score of 70 PCBs docked with the 1CCK enzyme as the dependent variable and their molecular structures as the independent variables, the 3D-QSAR model for the phytotoxicity of the PCBs was constructed. In this process, 60 PCBs were randomly selected as the training set, and the remaining 10 molecules were selected as the test set. Based on the CoMFA method, the results showed that the best principal component n and the cross-validation coefficient q2 of the constructed 3D-QSAR model were 8 and 0.695 (q2 > 0.5), respectively, which indicates that the model exhibited good estimation ability. The non-cross-validation coefficient R2, the standard deviation SEE, and the test value F were estimated as 0.914 (R2 > 0.9), 2.854, and 67.952, respectively, which indicates that the constructed model fulfilled the stability requirements and exhibited good fitting and estimation abilities [32]. Based on the CoMFA method, Figure 2 shows the linear fit plots of the experimental and estimated values of the 3D-QSAR model for the phytotoxicity of PCBs. The results showed that all the data were concentrated around the trend line, and the R-value was 0.956, which indicates that the linear fit between the experimental and estimated values was good. The model exhibited a high internal estimative power [32]. This model can be used for estimating the phytotoxicity values of PCBs and their derivatives.

Figure 2.

Figure 2

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The plot of the observed vs. estimated phytotoxicity values of PCBs by using the 3D-QSAR models.

Based on the CoMFA method, the constructed 3D-QSAR model of phytotoxicity in the PCBs estimated 70 PCBs with known experimental values. The results showed that the relative error between the experimental and estimated values of the phytotoxicity in 70 PCBs was less than 10% [16]. The estimated values of the phytotoxicity in 209 PCBs are shown in Table 2.

Table 2.

Estimated phytotoxicity and estrogen interference values of the PCBs by using the 3D-QSAR models.

PCBs (IUPAC) Phytotoxicity Estrogen Toxicity PCBs (IUPAC) Phytotoxicity Estrogen Toxicity
Estd. Obs. Relative Error (%) Estd. Obs. Relative Error (%) Estd. Obs. Relative Error (%) Estd. Obs. Relative Error (%)
0 80.694 71.424 105 91.091 65.655
1 80.325 75.426 −6.50 70.958 106 81.145 69.353
2 84.363 81.624 −3.36 70.674 69.396 −1.84 107 84.562 70.567
3 83.949 82.426 −1.85 67.889 65.720 a −3.30 108 89.361 69.117
4 73.802 73.822 0.03 63.977 63.512 −0.73 109 67.583 57.749
5 79.090 80.007 1.15 71.672 110 68.633 71.019 a 3.36 61.736 60.469 −2.10
6 83.632 83.766 0.16 71.588 111 82.886 73.235 68.404 −7.06
7 83.259 83.786 0.63 67.399 112 63.621 62.042 62.049 a 0.01
8 85.749 87.006 a 1.44 68.852 113 70.030 64.786
9 78.359 80.980 a 3.24 72.131 114 83.185 66.746
10 71.729 72.398 0.92 64.205 115 58.217 55.372
11 83.118 87.533 5.04 73.814 73.504 −0.42 116 61.924 58.568
12 88.281 88.323 0.05 67.752 117 67.104 57.672 57.287 −0.67
13 89.938 88.073 −2.12 67.219 67.002 −0.32 118 88.524 85.897 −3.06 65.011
14 81.876 72.630 119 67.740 57.761
15 89.550 86.282 −3.79 65.043 120 86.663 68.555
16 75.274 69.318 −8.59 64.841 121 71.060 55.813
17 72.967 60.820 122 90.103 68.870 68.399 −0.69
18 72.089 71.252 −1.17 63.389 123 93.991 95.620 1.70 64.795
19 69.084 56.327 124 88.268 69.449
20 82.354 79.668 −3.37 72.228 70.152 −2.96 125 75.464 60.670
21 82.154 68.549 126 99.156 105.165 5.71 66.748
22 84.490 69.539 127 92.611 71.565
23 76.212 82.007 7.07 72.676 128 68.328 66.586 −2.62 58.372 59.488 1.88
24 63.846 64.834 69.621 6.88 129 71.759 62.271
25 86.493 89.403 3.25 67.989 130 74.548 72.689 −2.56 62.574 59.408 a −5.33
26 81.368 72.709 131 62.701 52.006
27 74.870 64.884 132 66.789 54.858
28 88.612 89.893 a 1.42 65.302 133 73.554 65.404
29 80.237 67.876 134 62.993 56.028
30 70.770 60.778 56.932 −6.76 135 64.861 59.012
31 83.420 86.392 3.44 70.012 136 64.381 50.250
32 65.574 61.050 137 67.115 58.501
33 89.357 90.196 0.93 68.142 67.742 −0.59 138 68.889 69.900 1.45 57.865
34 87.497 71.727 139 68.238 50.422
35 86.138 69.104 140 60.491 48.887
36 91.986 91.012 −1.07 73.519 141 74.464 61.583
37 91.435 64.331 142 70.237 52.663
38 92.285 69.477 66.772 −4.05 143 66.244 54.364
39 91.511 90.431 −1.19 69.204 144 64.648 55.168
40 73.327 73.154 −0.24 65.302 145 74.108 61.477 63.476 a 3.15
41 68.109 61.187 146 75.031 60.764
42 74.542 61.114 147 65.471 52.282
43 74.076 64.081 148 70.797 52.261
44 66.061 65.927 149 68.510 54.156 54.240 0.16
45 60.125 56.469 150 79.539 46.136
46 66.153 65.282 −1.33 54.948 151 67.629 57.030 56.600 −0.76
47 65.818 57.198 60.010 4.69 152 77.851 63.524
48 65.849 60.330 153 66.585 67.731 1.69 57.174
49 71.324 61.956 65.967 a 6.08 154 67.694 50.534
50 68.467 53.531 51.652 −3.64 155 74.516 64.116
51 63.434 52.778 156 86.505 89.360 3.19 66.754
52 67.203 67.751 0.81 64.519 157 93.025 65.739
53 68.356 66.008 158 69.996 60.483 55.534 a −8.91
54 66.777 60.121 159 84.798 82.536 −2.74 69.399
55 85.426 69.076 160 65.245 58.509
56 88.107 68.780 161 71.927 56.126 57.228 1.93
57 79.168 73.179 75.837 3.51 162 86.262 69.840
58 86.389 72.286 163 66.042 64.367
59 66.140 65.444 164 73.207 73.038 −0.23 59.661
60 87.500 66.423 67.218 1.18 165 66.114 62.462
61 78.246 68.885 166 60.991 54.165
62 65.854 60.866 167 90.001 89.597 −0.45 65.217
63 81.240 70.529 168 77.170 57.249
64 64.290 57.249 169 95.088 67.929 69.672 2.50
65 60.271 62.106 64.739 4.07 170 73.365 72.234 −1.57 58.643
66 92.187 64.582 70.824 a 8.81 171 70.282 49.261
67 83.173 68.420 172 69.300 61.413
68 90.326 68.134 173 70.549 53.322 55.317 3.61
69 74.005 61.429 174 70.357 67.737 −3.87 55.179
70 86.766 69.266 68.144 −1.65 175 82.557 56.668
71 68.635 61.179 58.979 −3.73 176 71.336 72.986 2.26 63.146
72 84.899 72.836 77.695 6.25 177 66.333 67.823 a 2.20 52.939 50.357 −5.13
73 73.877 64.293 178 67.363 57.564
74 85.224 85.467 0.28 65.771 179 66.274 48.973
75 64.592 57.676 180 68.687 72.489 5.24 57.952 55.123 −5.13
76 91.184 68.336 181 73.058 49.108
77 97.273 99.318 2.06 64.961 182 65.665 51.597
78 95.861 70.370 183 71.546 71.487 −0.08 50.906 52.807 a 3.60
79 88.812 69.859 184 73.752 43.131
80 88.972 83.873 −6.08 74.719 185 75.222 53.835
81 95.415 99.705 a 4.30 66.087 70.222 5.89 186 73.291 75.368 2.76 58.614
82 75.659 83.729 9.64 61.660 64.109 3.82 187 71.067 52.759
83 75.123 64.490 188 77.819 65.671
84 60.484 58.008 189 88.147 84.203 −4.68 66.052
85 67.346 64.546 −4.34 57.494 190 67.586 59.613 58.962 −1.10
86 67.811 60.494 191 75.088 77.172 2.70 50.567 51.685 2.16
87 68.117 62.251 192 67.662 58.945
88 60.852 53.986 193 69.188 69.360 0.25 58.711
89 61.130 52.143 194 73.294 76.926 a 4.72 57.909
90 73.421 60.341 195 69.996 71.502 2.11 50.303
91 62.968 53.689 196 73.869 51.909
92 64.058 65.180 197 76.361 74.519 −2.47 61.428
93 62.641 55.883 198 74.917 54.322
94 71.336 55.614 199 73.260 72.533 −1.00 53.729
95 65.125 70.561 a 7.70 58.455 200 73.676 76.580 3.79 62.805 61.690 −1.81
96 79.861 66.412 201 68.497 70.293 2.56 58.049
97 76.133 61.494 59.222 −3.84 202 68.262 65.828 −3.70 49.883
98 65.547 51.651 52.414 1.46 203 78.701 78.053 −0.83 49.585
99 65.061 56.699 204 72.909 71.865 a −1.45 60.753
100 62.776 50.011 205 70.706 73.702 4.07 53.985
101 72.904 71.570 −1.86 61.497 65.198 a 5.68 206 76.912 55.815 59.438 a 6.10
102 68.287 53.276 207 79.669 76.815 a −3.72 60.496
103 73.465 54.758 208 74.789 75.332 0.72 55.552 55.405 −0.27
104 69.305 62.315 209 79.286 79.575 0.36 59.179 59.612 0.73
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a Test set.

In addition, the total-score of 48 PCBs docked with the 3GZX enzyme were selected for representing the estrogen toxicity, and 38 PCBs and 10 PCBs were randomly selected in the training and test sets of the model for constructing the 3D-QSAR model of estrogen toxicity. Based on the CoMFA method, the results showed that the 3D-QSAR model of estrogen toxicity in PCBs showed a good estimation ability by using the best principal component n with a value of 7 and the cross-validation coefficient q2 with a value of 0.671 (q2 > 0.5). The constructed model fulfilled the stability requirements and exhibited a good fitting ability (the non-cross-validation coefficient R2 of 0.90 (R2 > 0.9), the standard deviation SEE, and test values F of 2.509 and 38.578, respectively) [32]. The experimental and estimated values of the test and training sets of the estrogen toxicity model in PCBs were linearly fitted (Figure 3). As shown in Figure 3, all the data were concentrated near the trend line with an R-value of 0.949, which indicated a high correlation coefficient and estimate capability for the linear fit of the relationship between the experimental and estimated values [14]. This model can be used for estimating the estrogen toxicity values of PCBs and their derivatives (Figure 3). The 3D-QSAR model of estrogen toxicity in PCBs was used for estimating the estrogen toxicity values of 209 PCBs. The relative errors between the experimental and estimated values of the estrogen toxicity in 48 PCBs were less than 10% [16].

Figure 3.

Figure 3

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The plot of the observed vs. estimated estrogen interference values by the 3D-QSAR models.

4. Discussion

4.1. The Estimation of the Environmental Risk Characteristics of PCB Transformation Products in Environmental Media

To determine the degradation path of PCB degradation and transformation products with the greatest environmental risk, a total of 50 PCB transformation products were estimated by using the phytodegradation pathway, microbial degradation pathway, biometabolism pathway, and photodegradation pathway of the PCBs. Five kinds of environmental risk characteristics (phytotoxicity, estrogen toxicity, bioconcentration, persistence, and migration) of the PCB transformation products were evaluated. As shown in Table S1, the ranges of the phytotoxicity, estrogen toxicity, bioconcentration, persistence, and migration for the different PCB transformation products were as follows: for PCB phytodegradation products: −1.36% to 22.92%, −11.01% to 8.32%, 22.08% to 61.26%, 56.87% to 421.71%, and 1.22% to 32.27%, respectively; for PCB microbial aerobic degradation products: −14.46% to 17.93%, −10.48% to 15.48%, −5.40% to 9.50%, −37.71% to 12.31%, and −19.68% to 18.51%, respectively; for PCB microbial anaerobic degradation products: −8.82% to 32.53%, −5.76% to 12.92%, −13.09% to 4.05%, −25.45% to 9.64%, and −19.10% to −3.99%, respectively; for PCB biometabolism products: −8.97% to 19.40%, −8.74% to 1.89%, −4.45% to 19.39%, −5.69% to 42.10%, and −2.92% to 20.42%, respectively; for PCB photodegradation products: 13.35% to 36.06%, 12.63% to 40.28%, −22.93% to −11.85%, −60.70% to −15.36%, and −16.20% to −1.89%, respectively.

Figure 4 is a heat map of the environmental risk characteristics (phytotoxicity, estrogen toxicity, bioconcentration, persistence, and migration) of PCB transformation products under different degradation pathways (the phytodegradation pathway, the microbial degradation pathway, the biometabolism pathway, and the photodegradation pathway). The color of the heat map is divided into 10 levels. The higher the color level, the greater the variation range of environmental risk characteristics of PCBs degradation products. As shown in Figure 4, the region of PCBs plant degradation products has the darkest color. Combined with the data of Table S1, the environmental risk characteristics of the PCB degradation products showed a maximum increase of 421.71%. Therefore, the environmental risk of the PCB plant degradation products was the highest. Improving the degradation of PCBs by plants is of great significance for environmental health. In addition, the change of environmental risk of the microbial products in all the PCB degradation products is relatively small, indicating that microbial degradation methods have little impact on secondary environmental pollution. The microbial anaerobic degradation method has certain advantages over the microbial aerobic degradation method. The phytotoxicity of anaerobic degradation products was higher than that of aerobic degradation products, and other properties were improved than that of the aerobic degradation products.

Figure 4.

Figure 4

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Environmental risk characteristics of the PCB transformation products under different degradation pathways.

In summary, the environmental risk characteristics of PCB degradation products were increased in different degrees under different degradation pathways, which, for the future, indicates that the environmental risk characteristics of PCB degradation products should not be neglected.

4.2. The Estimation of the Environmental Risk Characteristics of Environmentally Friendly PCB Transformation Products in Plants

In this study, the environmental risk of the PCB phytodegradation products was the highest. Environmentally friendly PCB derivatives refer to those PCB molecules whose functions remain unchanged and environmental risk characteristics (such as the representative characteristics of persistent organic pollutants) are improved by the design method of molecular modification. Considering the phytodegradation pathway as an example, some environmentally friendly PCB derivatives were designed in different studies [16,32,33], and their parent molecules (low migration environmentally friendly derivative P1 and the parent molecule PCB-52, low bioconcentration environmentally friendly derivative P2, and the parent molecule PCB-189, and low toxicity environmentally friendly derivative P3 and the parent molecule PCB-209) were selected for analyzing the environmental risks. The specific path inference of the molecules is shown in Figure 5.

Figure 5.

Figure 5

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Schematic diagram of the phytodegradation products of three PCBs and their environmentally friendly derivatives.

The higher the parameter value of migration is, the lower the risk of physical environment will be. The other four environmental risk characteristic parameters showed contrary characteristics. As shown in Table 3, the ranges of phytotoxicity, estrogen toxicity, bioconcentration, persistence, and mobility of the phytodegradation products of the three PCBs (PCB-52, PCB-189, and PCB-209) were −63.71% to 34.98%, −13.84 to 29.36%, −74.97% to 16.03%, −184.02% to 11.43%, and −61.55 to 18.42%, respectively. Though the environmental risk of most of the PCB degradation products has been reduced, the environmental risk of some products is still increasing. The target organism of phytotoxicity, estrogen toxicity, and bioconcentration of the PCBs is focused on the human body. The increased phytotoxicity represents that the transformation products of PCBs, for example, they may enter the human body through the food chain and, finally, increase the threat to human health [34]. The increase of estrogen toxicity also represents that the transformation products of PCBs interfere with the health of the human endocrine system and affect the normal expression of estrogen [35]. The bioconcentration also represents the enrichment ability of the transformation products of PCBs in the human body. The greater the enrichment degree, the stronger the harm to the human body [36]. The persistence effect of the transformation products of PCBs is also implied in the human body and organism in the environment. The long-term existence of the transformation products of PCBs will damage the health of the human body or organism exposed to the environment of PCB metabolites [37], and the migration effect of the transformation products of PCBs also includes the human body or organism far away from the contaminated environment, which represents the long-distance mobility of the transformation products of PCBs in the atmosphere. The research shows that, in Arctic seabirds and Greenland sharks, PCBs were detected at certain concentrations [38,39], indicating that, once the transformation products of PCB conversion products flow into the environment, they will have long-distance migration and cause risks to the environment health. Therefore, as compared to the parent compounds of PCBs, the migration characteristics of phytodegradation products of the three PCBs showed the highest increase in environmental risks, and the variation range was up to 61.55%, which indicates that, when the migration ability of PCB degradation products is improved once they flow into the environment, there is a risk of long-distance migration. The potential risk of long-range migration of the transformation products of PCBs cannot be completely overcome by controlling their parent’s migration capacities.

Table 3.

The environmental risk statistics of the three PCBs and their environmentally friendly derivative phytodegradation products.

Molecular Phytotoxicity Change Rate (%) Estrogen Toxicity Change Rate (%) Bioconcentration Change Rate (%) Persistence Change Rate (%) Migration Change Rate (%)
PCB-52 67.203 64.519 4.63 0.989 8.538
52-1 90.708 34.98 65.576 1.64 3.879 −16.22 1.011 2.22 9.061 6.13
52-2 75.438 12.25 64.884 0.57 5.034 8.73 1.095 10.72 10.111 18.42
52-3 58.492 −12.96 55.954 −13.28 5.372 16.03 1.102 11.43 9.431 10.46
52-4 39.965 −40.53 69.928 8.38 1.159 −74.97 −0.831 −184.02 3.449 −59.60
P1 65.259 69.296 5.477 0.811 9.686
P1-1 65.134 −0.19 54.331 −21.60 5.258 −4.00 0.665 −18.00 8.575 −11.47
P1-2 70.868 8.59 73.992 6.78 4.416 −19.37 0.881 8.63 8.494 −12.31
P1-3 79.052 21.14 68.901 −0.57 4.214 −23.06 0.78 −3.82 8.634 −10.86
P1-4 76.850 17.76 56.281 −18.78 4.596 −16.09 0.893 10.11 8.852 −8.61
PCB-189 88.147 66.052 5.440 1.567 11.517
189-1 84.575 −4.05 62.105 −5.98 5.446 0.11 1.274 −18.70 10.565 −8.27
189-2 79.545 −9.76 58.150 −11.96 5.649 3.84 1.599 2.04 10.912 −5.25
189-3 84.176 −4.50 68.607 3.87 5.939 9.17 1.299 −17.10 10.702 −7.08
189-4 31.989 −63.71 59.291 −10.24 2.873 −47.19 −0.558 −135.61 4.428 −61.55
P2 71.373 49.398 3.446 1.311 4.642
P2-1 75.601 5.92 69.555 40.81 4.439 28.82 0.694 −47.06 7.406 59.54
P2-2 67.157 −5.91 52.344 5.96 5.459 58.42 1.038 −20.82 9.928 113.87
P2-3 80.525 12.82 50.002 1.22 4.725 37.12 1.49 13.65 9.046 94.87
P2-4 86.936 21.81 58.514 18.45 5.536 60.65 1.212 −7.55 10.088 117.32
PCB-209 79.408 54.982 6.136 2.191 11.805
209-1 85.844 8.10 50.643 −7.89 4.726 −22.98 1.85 −15.56 10.003 −15.26
209-2 77.534 −2.36 51.355 −6.60 6.204 1.11 2.079 −5.11 9.816 −16.85
209-3 61.850 −22.11 47.372 −13.84 6.079 −0.93 1.606 −26.70 10.169 −13.86
209-4 50.929 −35.86 71.124 29.36 3.190 −48.01 −0.487 −122.23 7.964 −32.54
P3 75.056 57.487 5.500 1.863 11.102
P3-1 63.598 −15.27 48.786 −15.14 5.815 5.73 1.700 −8.75 9.383 −15.48
P3-2 79.836 6.37 58.633 1.99 5.703 3.69 1.478 −20.67 9.948 −10.39
P3-3 61.300 −18.33 58.633 1.99 5.367 −2.42 1.402 −24.75 8.608 −22.46
P3-4 79.556 6.00 52.530 −8.62 5.065 −7.91 0.949 −49.06 10.965 −1.23
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The variations of phytotoxicity, estrogenic toxicity, bioconcentration, persistence, and migration of the three environmentally friendly PCB derivatives (P1, P2, and P3) of the phytodegradation products were estimated to range from −18.33% to 21.81%, −21.60 to 40.81%, −23.06% to 60.65%, −49.06% to 13.65%, and −22.46 to 117.32%, respectively. Similarly, the environmental risk of some degradation products was observed to increase. As compared to the parent molecules of the environmentally friendly PCB derivatives, their phytodegradation products showed the best bioconcentration performance. The results showed that the risk of bioconcentration of the PCB degradation products was increased. The bioconcentrations of PCBs in breast milk in urban areas in China were 2.66–3.90 pg/g [36] and, in the adipose tissue of Belgians, were 490-ng/g lipid weight [40]. The accumulation of PCBs in the human body increases with age and, hence, can indirectly cause visceral [41], endocrine [42], and reproductive diseases [43]. Therefore, the control of the bioconcentration ability of environmentally friendly PCB derivatives should not be neglected.

Figure 6 is an effect diagram that represents the changes in the environmental characteristics of PCB conversion products. In Figure 6, the size of the sphere represents the activity value of each molecule. Comparing the size of the sphere, the final phytodegradation product of P1 represents the low mobility derivative of PCB-52 (Figure 6). As compared to the final phytodegradation product of PCB-52, the migration of the final P1 phytodegradation product is still lower. This result is found to be consistent with the design concept [16,32,33]. In addition, the estrogenic toxicity and the migration of the final P1 phytodegradation product also showed a significant improvement as compared to the final PCB-52 phytodegradation product. The improvement in estrogen toxicity and migration were estimated as 19.52% and 156%, respectively. As compared to the final degradation product of low bioconcentration derivative P2, the final PCB-189 degradation products showed no improvement in terms of the bioconcentration properties and improvement in the estrogen toxicity and migration properties. It was observed that the phytotoxicity of P3 was improved compared to PCB-209, but the environmental risk of the final product was increased. The environmental risk of P3 was higher as compared to PCB-209 in biotoxicity, but the environmental risk of the final product was significantly improved by 26.14%. In addition, the migration of the final P3 product was improved up to 37.68%.

Figure 6.

Figure 6

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Changes in the environmental characteristics of PCB conversion products.

In summary, the environmental risks of the final degradation products of environmentally friendly PCB derivatives P1 and P2 showed improvements, agreeing with the modification results. However, some degradation products still showed an increase in environmental risks, indicating that the environmental risk control of the PCB degradation products and their environmentally friendly derivatives cannot be neglected. The potential environmental risk of PCBs cannot be completely controlled by the theoretical modification of single environmental characteristics. Therefore, the environmental risks of the transformed products of the environmentally friendly PCB derivatives are also required to be considered.

4.3. The Validation of the Total Score and Its Estimated Value of PCBs and Their Products Containing Different Chemical Structure

The phytotoxicity value and estrogen toxicity value of parent PCBs are derived from the total score after docking with the corresponding enzymes. Taking the phytotoxicity and estrogen toxicity as examples, we calculated the total score of the PCBs and their metabolites containing different chemical structures (such as −OH, −SO2CH3, etc.) in the manuscript and analyzed the correlation and relative error between the total score and the estimated value by the 3D-QSAR model (Table 4). The results showed that the correlation coefficient r between the total cost of 33 molecular estrogen toxicities and their predicted values was 0.547, which met the correlation coefficient test standard (i.e., when p = 0.001, the correlation limit value r0 is 0.539). However, the correlation coefficient r between the total cost of the phytotoxicity of 33 molecules and their estimated values was only 0.369, the correlation was relatively lower, which only met the correlation coefficient test standard when p = 0.05 (the correlation limit value r0 is 0.339). Most of the relative errors were within the allowable range, only one-third of the molecules having a relative error more than 10%.

Table 4.

The total score and its estimated value of PCBs and their metabolites containing different chemical structures.

NO. Phytotoxicity Estrogen Toxicity
Total Cost Estimated Relative Error (%) Total Cost Estimated Relative Error (%)
1 4′-OH-CB97 75.468 71.34 −5.47 66.793 55.05 −17.58
2 4′-OH-CB101 74.627 62.36 −16.44 65.252 59.60 −8.66
3 4-OH-CB107 78.598 84.60 7.64 67.165 67.39 0.34
4 4-OH-CB109 75.046 67.77 −9.70 65.372 59.92 −8.34
5 3-OH-CB118 86.550 83.53 −3.49 62.666 63.70 1.65
6 4-OH-CB148 68.485 66.70 −2.61 71.272 55.92 −21.54
7 3-OH-CB153 72.700 78.53 8.02 66.310 59.69 −9.98
8 4-OH-CB162 97.896 85.20 −12.97 61.408 64.24 4.61
9 4′-OH-CB172 80.308 66.42 −17.29 65.140 55.88 −14.22
10 4-OH-CB187 75.115 72.69 −3.23 63.185 52.85 −16.36
11 4′-OH-CB199 79.422 85.35 7.46 60.988 62.00 1.66
12 4-OH-CB202 77.024 69.99 −9.13 55.911 56.65 1.32
13 3′-MeSO2-CB49 72.948 67.66 −7.25 67.760 56.88 −16.06
14 4-MeSO2-CB64 71.831 62.72 −12.68 61.242 52.25 −14.68
15 3-MeSO2-CB70 86.872 78.98 −9.08 72.756 66.08 −9.18
16 3-MeSO2-CB110 80.192 67.57 −15.74 78.504 62.03 −20.98
17 4-MeSO2-CB149 83.809 81.80 −2.40 58.452 54.16 −7.34
18 4-MeSO2-CB174 80.324 72.89 −9.26 54.488 55.18 1.27
19 P1 66.228 65.26 −1.46 70.507 69.30 −1.72
20 P1-1 104.434 65.13 −37.63 58.480 54.33 −7.10
21 P1-2 78.201 70.87 −9.38 73.265 73.99 0.99
22 P1-3 85.788 79.05 −7.85 67.833 68.90 1.57
23 P1-4 78.552 76.85 −2.17 52.126 56.28 7.97
24 P2 77.873 71.37 −8.35 60.337 49.40 −18.13
25 P2-1 83.167 75.60 −9.10 77.099 69.56 −9.78
26 P2-2 73.239 67.16 −8.30 72.490 52.34 −27.79
27 P2-3 88.294 80.53 −8.80 65.488 50.00 −23.65
28 P2-4 80.868 86.94 7.50 64.082 58.51 −8.69
29 P3 77.512 75.06 −3.17 63.670 57.49 −9.71
30 P3-1 78.478 63.60 −18.96 53.482 48.79 −8.78
31 P3-2 76.342 79.84 4.58 63.270 58.63 −7.33
32 P3-3 76.683 61.30 −20.06 72.835 58.63 −19.50
33 P3-4 77.896 79.56 2.13 55.943 52.53 −6.10
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The above results indicate that, indeed, the 3D-QSAR model constructed by the parent PCBs data is slightly lesser accurate in estimating PCBs with different chemical structures and their metabolites. Most of the relative errors are negative, indicating that the 3D-QSAR model has indeed underestimated the toxicity of PCBs with different chemical structures and their metabolites, which should actually be higher than those estimated values. However, since the purpose is to measure the environment risk of PCB metabolites on a relative scale, the overall trend of toxicity of the PCB metabolites is consistent with the estimation in this study. The overall analysis of the results is reasonable in the manuscript.

5. Conclusions

In this paper, the transformation pathways of PCBs (phytodegradation, microbial degradation, biometabolism, and photodegradation) were derived. The constructed 3D-QSAR models were used for estimating the POP characteristics (toxicity (phytotoxicity and biotoxicity), bioconcentration, migration, and persistence) of PCBs and their transformed products. In addition, for the environmental risk evaluation of PCBs and their environmentally friendly derivative transformation products, the plant degradation pathway with the highest environmental risk increase was selected. The environmental risk of some PCBs and their derivative degradation products was observed to be increased, which indicated that the environmental risk control of PCBs and their environmentally friendly derivative degradation products could not be neglected. The potential environmental risk of PCBs cannot be completely controlled by theoretical modification considering single environmental characteristics. Therefore, the environmental risks of the transformed products of environmentally friendly PCBs are also required to be considered.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/toxics9090213/s1: Table S1: Estimated statistics of the environmental risk characteristics of PCBs and their transformation products.

Click here for additional data file. (156.5KB, zip)

Author Contributions

Conceptualization, Data curation, Software, and Writing-Original Draft: M.L.; Investigation and Methodology: W.H.; Investigation and Methodology: H.Y.; Writing—Review and Editing: S.S.; and Supervision and Writing—Review and Editing: Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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