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Article

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

by
Minghao Li
1,2,†,
Wei He
1,†,
Hao Yang
1,
Shimei Sun
2,* and
Yu Li
1,*
1
The Moe Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
2
School of Emergency Science and Engineering, Jilin Jianzhu University, Changchun 130119, China
*
Authors to whom correspondence should be addressed.
†
These authors have contributed equally to the study and they receive equal credit.
Toxics 2021, 9(9), 213; https://doi.org/10.3390/toxics9090213
Submission received: 29 July 2021 / Revised: 2 September 2021 / Accepted: 3 September 2021 / Published: 7 September 2021
(This article belongs to the Special Issue The Transformation Mechanism and Environmental Effects of Emerging Pollutants)
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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.

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.

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.
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.
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].

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.
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.
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.
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%.
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%.
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.

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.

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Toxics 09 00213 g001 550
Figure 1. Schematic diagram of the four degradation or metabolic pathways of the PCBs.
Figure 1. Schematic diagram of the four degradation or metabolic pathways of the PCBs.
Toxics 09 00213 g001
Toxics 09 00213 g002 550
Figure 2. The plot of the observed vs. estimated phytotoxicity values of PCBs by using the 3D-QSAR models.
Figure 2. The plot of the observed vs. estimated phytotoxicity values of PCBs by using the 3D-QSAR models.
Toxics 09 00213 g002
Toxics 09 00213 g003 550
Figure 3. The plot of the observed vs. estimated estrogen interference values by the 3D-QSAR models.
Figure 3. The plot of the observed vs. estimated estrogen interference values by the 3D-QSAR models.
Toxics 09 00213 g003
Toxics 09 00213 g004 550
Figure 4. Environmental risk characteristics of the PCB transformation products under different degradation pathways.
Figure 4. Environmental risk characteristics of the PCB transformation products under different degradation pathways.
Toxics 09 00213 g004
Toxics 09 00213 g005 550
Figure 5. Schematic diagram of the phytodegradation products of three PCBs and their environmentally friendly derivatives.
Figure 5. Schematic diagram of the phytodegradation products of three PCBs and their environmentally friendly derivatives.
Toxics 09 00213 g005
Toxics 09 00213 g006 550
Figure 6. Changes in the environmental characteristics of PCB conversion products.
Figure 6. Changes in the environmental characteristics of PCB conversion products.
Toxics 09 00213 g006
Table
Table 1. Summary of the degradation or metabolites of the PCBs [7,24,29,30,31].
Table 1. Summary of the degradation or metabolites of the PCBs [7,24,29,30,31].
NO.Degradation PathwayParent MoleculeDegradation or Metabolites
1Plant degradationPCB-34-CBA
2PCB-42-CBA
3PCB-52,3-CBA
4PCB-113-CBA
5PCB-312,5-CBA
6Microbial aerobic degradationPCB-974′-OH-CB97
7PCB-1014′-OH-CB101
8PCB-1074-OH-CB107
9PCB-1094-OH-CB109
10PCB-1183-OH-CB118
11PCB-1484-OH-CB148
12PCB-1533-OH-CB153
13PCB-1624-OH-CB162
14PCB-1724′-OH-CB172
15PCB-1874-OH-CB187
16PCB-1994′-OH-CB199
17PCB-2024-OH-CB202
18Microbial anaerobic degradationPCB-90PCB-49
PCB-68
19PCB-91PCB-51
20PCB-92PCB-52
PCB-72
21PCB-95PCB-53
22PCB-99PCB-47
23PCB-101PCB-49
24PCB-102PCB-51
25PCB-130PCB-90
26PCB-132PCB-91
27PCB-135PCB-94
28PCB-137PCB-90
PCB-99
29PCB-138PCB-99
30PCB-146PCB-90
31PCB-147PCB-91
32PCB-149PCB-102
33PCB-151PCB-95
34PCB-153PCB-99
35PCB-154PCB-100
36PCB-170PCB-130
PCB-137
PCB-138
37PCB-174PCB-149
38PCB-180PCB-153
PCB-146
39PCB-183PCB-154
40PCB-187PCB-149
41BiometabolismPCB-493′-MeSO2-CB49
42PCB-644-MeSO2-CB64
43PCB-703-MeSO2-CB70
44PCB-1103-MeSO2-CB110
45PCB-1494-MeSO2-CB149
46PCB-1744-MeSO2-CB174
47PhotodegradationPCB-47PCB-15
48PCB-40PCB-11
49PCB-101PCB-70
50PCB-171PCB-35
Table
Table 2. Estimated phytotoxicity and estrogen interference values of the PCBs by using the 3D-QSAR models.
Table 2. Estimated phytotoxicity and estrogen interference values of the PCBs by using the 3D-QSAR models.
PCBs (IUPAC)PhytotoxicityEstrogen ToxicityPCBs (IUPAC)PhytotoxicityEstrogen Toxicity
Estd.Obs.Relative Error (%)Estd.Obs.Relative Error (%)Estd.Obs.Relative Error (%)Estd.Obs.Relative Error (%)
080.694 71.424 10591.091 65.655
180.32575.426−6.5070.958 10681.145 69.353
284.36381.624−3.3670.67469.396−1.8410784.562 70.567
383.94982.426−1.8567.88965.720 a−3.3010889.361 69.117
473.80273.8220.0363.97763.512−0.7310967.583 57.749
579.09080.0071.1571.672 11068.63371.019 a3.3661.73660.469−2.10
683.63283.7660.1671.588 11182.886 73.23568.404−7.06
783.25983.7860.6367.399 11263.621 62.04262.049 a0.01
885.74987.006 a1.4468.852 11370.030 64.786
978.35980.980 a3.2472.131 11483.185 66.746
1071.72972.3980.9264.205 11558.217 55.372
1183.11887.5335.0473.81473.504−0.4211661.924 58.568
1288.28188.3230.0567.752 11767.104 57.67257.287−0.67
1389.93888.073−2.1267.21967.002−0.3211888.52485.897−3.0665.011
1481.876 72.630 11967.740 57.761
1589.55086.282−3.7965.043 12086.663 68.555
1675.27469.318−8.5964.841 12171.060 55.813
1772.967 60.820 12290.103 68.87068.399−0.69
1872.08971.252−1.1763.389 12393.99195.6201.7064.795
1969.084 56.327 12488.268 69.449
2082.35479.668−3.3772.22870.152−2.9612575.464 60.670
2182.154 68.549 12699.156105.1655.7166.748
2284.490 69.539 12792.611 71.565
2376.21282.0077.0772.676 12868.32866.586−2.6258.37259.4881.88
2463.846 64.83469.6216.8812971.759 62.271
2586.49389.4033.2567.989 13074.54872.689−2.5662.57459.408 a−5.33
2681.368 72.709 13162.701 52.006
2774.870 64.884 13266.789 54.858
2888.61289.893 a1.4265.302 13373.554 65.404
2980.237 67.876 13462.993 56.028
3070.770 60.77856.932−6.7613564.861 59.012
3183.42086.3923.4470.012 13664.381 50.250
3265.574 61.050 13767.115 58.501
3389.35790.1960.9368.14267.742−0.5913868.88969.9001.4557.865
3487.497 71.727 13968.238 50.422
3586.138 69.104 14060.491 48.887
3691.98691.012−1.0773.519 14174.464 61.583
3791.435 64.331 14270.237 52.663
3892.285 69.47766.772−4.0514366.244 54.364
3991.51190.431−1.1969.204 14464.648 55.168
4073.32773.154−0.2465.302 14574.108 61.47763.476 a3.15
4168.109 61.187 14675.031 60.764
4274.542 61.114 14765.471 52.282
4374.076 64.081 14870.797 52.261
4466.061 65.927 14968.510 54.15654.2400.16
4560.125 56.469 15079.539 46.136
4666.15365.282−1.3354.948 15167.629 57.03056.600−0.76
4765.818 57.19860.0104.6915277.851 63.524
4865.849 60.330 15366.58567.7311.6957.174
4971.324 61.95665.967 a6.0815467.694 50.534
5068.467 53.53151.652−3.6415574.516 64.116
5163.434 52.778 15686.50589.3603.1966.754
5267.20367.7510.8164.519 15793.025 65.739
5368.356 66.008 15869.996 60.48355.534 a−8.91
5466.777 60.121 15984.79882.536−2.7469.399
5585.426 69.076 16065.245 58.509
5688.107 68.780 16171.927 56.12657.2281.93
5779.168 73.17975.8373.5116286.262 69.840
5886.389 72.286 16366.042 64.367
5966.140 65.444 16473.20773.038−0.2359.661
6087.500 66.42367.2181.1816566.114 62.462
6178.246 68.885 16660.991 54.165
6265.854 60.866 16790.00189.597−0.4565.217
6381.240 70.529 16877.170 57.249
6464.290 57.249 16995.088 67.92969.6722.50
6560.271 62.10664.7394.0717073.36572.234−1.5758.643
6692.187 64.58270.824 a8.8117170.282 49.261
6783.173 68.420 17269.300 61.413
6890.326 68.134 17370.549 53.32255.3173.61
6974.005 61.429 17470.35767.737−3.8755.179
7086.766 69.26668.144−1.6517582.557 56.668
7168.635 61.17958.979−3.7317671.33672.9862.2663.146
7284.899 72.83677.6956.2517766.33367.823 a2.2052.93950.357−5.13
7373.877 64.293 17867.363 57.564
7485.22485.4670.2865.771 17966.274 48.973
7564.592 57.676 18068.68772.4895.2457.95255.123−5.13
7691.184 68.336 18173.058 49.108
7797.27399.3182.0664.961 18265.665 51.597
7895.861 70.370 18371.54671.487−0.0850.90652.807 a3.60
7988.812 69.859 18473.752 43.131
8088.97283.873−6.0874.719 18575.222 53.835
8195.41599.705 a4.3066.08770.2225.8918673.29175.3682.7658.614
8275.65983.7299.6461.66064.1093.8218771.067 52.759
8375.123 64.490 18877.819 65.671
8460.484 58.008 18988.14784.203−4.6866.052
8567.34664.546−4.3457.494 19067.586 59.61358.962−1.10
8667.811 60.494 19175.08877.1722.7050.56751.6852.16
8768.117 62.251 19267.662 58.945
8860.852 53.986 19369.18869.3600.2558.711
8961.130 52.143 19473.29476.926 a4.7257.909
9073.421 60.341 19569.99671.5022.1150.303
9162.968 53.689 19673.869 51.909
9264.058 65.180 19776.36174.519−2.4761.428
9362.641 55.883 19874.917 54.322
9471.336 55.614 19973.26072.533−1.0053.729
9565.12570.561 a7.7058.455 20073.67676.5803.7962.80561.690−1.81
9679.861 66.412 20168.49770.2932.5658.049
9776.133 61.49459.222−3.8420268.26265.828−3.7049.883
9865.547 51.65152.4141.4620378.70178.053−0.8349.585
9965.061 56.699 20472.90971.865 a−1.4560.753
10062.776 50.011 20570.70673.7024.0753.985
10172.90471.570−1.8661.49765.198 a5.6820676.912 55.81559.438 a6.10
10268.287 53.276 20779.66976.815 a−3.7260.496
10373.465 54.758 20874.78975.3320.7255.55255.405−0.27
10469.305 62.315 20979.28679.5750.3659.17959.6120.73
a Test set.
Table
Table 3. The environmental risk statistics of the three PCBs and their environmentally friendly derivative phytodegradation products.
Table 3. The environmental risk statistics of the three PCBs and their environmentally friendly derivative phytodegradation products.
MolecularPhytotoxicityChange Rate (%)Estrogen ToxicityChange Rate (%)BioconcentrationChange Rate (%)PersistenceChange Rate (%)MigrationChange Rate (%)
PCB-5267.203 64.519 4.63 0.989 8.538
52-190.70834.9865.5761.643.879−16.221.0112.229.0616.13
52-275.43812.2564.8840.575.0348.731.09510.7210.11118.42
52-358.492−12.9655.954−13.285.37216.031.10211.439.43110.46
52-439.965−40.5369.9288.381.159−74.97−0.831−184.023.449−59.60
P165.259 69.296 5.477 0.811 9.686
P1-165.134−0.1954.331−21.605.258−4.000.665−18.008.575−11.47
P1-270.8688.5973.9926.784.416−19.370.8818.638.494−12.31
P1-379.05221.1468.901−0.574.214−23.060.78−3.828.634−10.86
P1-476.85017.7656.281−18.784.596−16.090.89310.118.852−8.61
PCB-18988.147 66.052 5.440 1.567 11.517
189-184.575−4.0562.105−5.985.4460.111.274−18.7010.565−8.27
189-279.545−9.7658.150−11.965.6493.841.5992.0410.912−5.25
189-384.176−4.5068.6073.875.9399.171.299−17.1010.702−7.08
189-431.989−63.7159.291−10.242.873−47.19−0.558−135.614.428−61.55
P271.373 49.398 3.446 1.311 4.642
P2-175.6015.9269.55540.814.43928.820.694−47.067.40659.54
P2-267.157−5.9152.3445.965.45958.421.038−20.829.928113.87
P2-380.52512.8250.0021.224.72537.121.4913.659.04694.87
P2-486.93621.8158.51418.455.53660.651.212−7.5510.088117.32
PCB-20979.408 54.982 6.136 2.191 11.805
209-185.8448.1050.643−7.894.726−22.981.85−15.5610.003−15.26
209-277.534−2.3651.355−6.606.2041.112.079−5.119.816−16.85
209-361.850−22.1147.372−13.846.079−0.931.606−26.7010.169−13.86
209-450.929−35.8671.12429.363.190−48.01−0.487−122.237.964−32.54
P375.056 57.487 5.500 1.863 11.102
P3-163.598−15.2748.786−15.145.8155.731.700−8.759.383−15.48
P3-279.8366.3758.6331.995.7033.691.478−20.679.948−10.39
P3-361.300−18.3358.6331.995.367−2.421.402−24.758.608−22.46
P3-479.5566.0052.530−8.625.065−7.910.949−49.0610.965−1.23
Table
Table 4. The total score and its estimated value of PCBs and their metabolites containing different chemical structures.
Table 4. The total score and its estimated value of PCBs and their metabolites containing different chemical structures.
NO. PhytotoxicityEstrogen Toxicity
Total CostEstimatedRelative Error (%)Total CostEstimatedRelative Error (%)
14′-OH-CB9775.46871.34−5.4766.79355.05−17.58
24′-OH-CB10174.62762.36−16.4465.25259.60−8.66
34-OH-CB10778.59884.607.6467.16567.390.34
44-OH-CB10975.04667.77−9.7065.37259.92−8.34
53-OH-CB11886.55083.53−3.4962.66663.701.65
64-OH-CB14868.48566.70−2.6171.27255.92−21.54
73-OH-CB15372.70078.538.0266.31059.69−9.98
84-OH-CB16297.89685.20−12.9761.40864.244.61
94′-OH-CB17280.30866.42−17.2965.14055.88−14.22
104-OH-CB18775.11572.69−3.2363.18552.85−16.36
114′-OH-CB19979.42285.357.4660.98862.001.66
124-OH-CB20277.02469.99−9.1355.91156.651.32
133′-MeSO2-CB4972.94867.66−7.2567.76056.88−16.06
144-MeSO2-CB6471.83162.72−12.6861.24252.25−14.68
153-MeSO2-CB7086.87278.98−9.0872.75666.08−9.18
163-MeSO2-CB11080.19267.57−15.7478.50462.03−20.98
174-MeSO2-CB14983.80981.80−2.4058.45254.16−7.34
184-MeSO2-CB17480.32472.89−9.2654.48855.181.27
19P166.22865.26−1.4670.50769.30−1.72
20P1-1104.43465.13−37.6358.48054.33−7.10
21P1-278.20170.87−9.3873.26573.990.99
22P1-385.78879.05−7.8567.83368.901.57
23P1-478.55276.85−2.1752.12656.287.97
24P277.87371.37−8.3560.33749.40−18.13
25P2-183.16775.60−9.1077.09969.56−9.78
26P2-273.23967.16−8.3072.49052.34−27.79
27P2-388.29480.53−8.8065.48850.00−23.65
28P2-480.86886.947.5064.08258.51−8.69
29P377.51275.06−3.1763.67057.49−9.71
30P3-178.47863.60−18.9653.48248.79−8.78
31P3-276.34279.844.5863.27058.63−7.33
32P3-376.68361.30−20.0672.83558.63−19.50
33P3-477.89679.562.1355.94352.53−6.10
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Li, M.; He, W.; Yang, H.; Sun, S.; Li, Y. Potential Environmental Risk Characteristics of PCB Transformation Products in the Environmental Medium. Toxics 2021, 9, 213. https://doi.org/10.3390/toxics9090213

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Li M, He W, Yang H, Sun S, Li Y. Potential Environmental Risk Characteristics of PCB Transformation Products in the Environmental Medium. Toxics. 2021; 9(9):213. https://doi.org/10.3390/toxics9090213

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Li, Minghao, Wei He, Hao Yang, Shimei Sun, and Yu Li. 2021. "Potential Environmental Risk Characteristics of PCB Transformation Products in the Environmental Medium" Toxics 9, no. 9: 213. https://doi.org/10.3390/toxics9090213

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Li, M., He, W., Yang, H., Sun, S., & Li, Y. (2021). Potential Environmental Risk Characteristics of PCB Transformation Products in the Environmental Medium. Toxics, 9(9), 213. https://doi.org/10.3390/toxics9090213

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