阅读说明：本技术 一种预测有机污染物在聚乙烯型微塑料和水相之间分配平衡常数的方法 (Method for predicting distribution equilibrium constant of organic pollutants between polyethylene type micro plastic and water phase ) 是由 尉小旋 李淼 于海瀛 王一飞 张加根 马广才 于 2020-02-19 设计创作，主要内容包括：本发明提供了一种预测有机污染物在聚乙烯型微塑料和水相之间分配平衡常数的方法,属于面向环境生态风险评价的定量结构-性质关系技术领域。本发明使用描述符ε<Sub>α</Sub>(ε<Sub>α</Sub>＝E<Sub>LUMO</Sub>－E<Sub>HOMO-water</Sub>,E<Sub>LUMO</Sub>为最低未占分子轨道能,E<Sub>HOMO</Sub>为最高占据分子轨道能)、ε<Sub>β</Sub>(ε<Sub>β</Sub>＝E<Sub>LUMO-water</Sub>－E<Sub>HOMO</Sub>)和log D(不同pH条件下的正辛醇/水分布系数),分别构建了可用于预测有机污染物在PE和海水、PE和淡水、PE和纯水之间K<Sub>d</Sub>值的QSPR模型。所建模型可用于预测多氯联苯、氯苯、多环芳烃、抗生素、芳烃、脂肪烃、六氯环己烷等有机污染物的K<Sub>d</Sub>值,且具有良好的拟合能力、预测能力和稳健性。(The invention provides a method for predicting a distribution equilibrium constant of organic pollutants between polyethylene type micro-plastic and a water phase, and belongs to the technical field of quantitative structure-property relation oriented to environmental ecological risk evaluation. The invention uses descriptors α ( α ＝E LUMO －E HOMO‑water ，E LUMO At the lowest unoccupied molecular orbital energy, E HOMO To occupy the highest molecular orbital energy) β ( β ＝E LUMO‑water －E HOMO ) And log D (n-octanol/water distribution coefficient under different pH conditions), respectively constructing a model for predicting organic pollutants in PE and seawater, PE and fresh water, PE and pure waterK between water d QSPR model of value. The established model can be used for predicting K of organic pollutants such as polychlorinated biphenyl, chlorobenzene, polycyclic aromatic hydrocarbon, antibiotic, aromatic hydrocarbon, aliphatic hydrocarbon, hexachlorocyclohexane and the like d The method has good fitting ability, prediction ability and robustness.)

1. A method for predicting the partition equilibrium constant of organic contaminants between a polyethylene-based microplastic and an aqueous phase, comprising the steps of:

(a) obtaining K of 37 organic pollutants between PE/seawater through an existing database and documents_dK between PE/fresh water for 24 organic pollutants_dK between PE/pure water for 48 organic pollutants_dA value;

(b) based on an analysis of the mechanism of partitioning of organic contaminants between the microplastics and the aqueous phase, the following descriptors were screened for the construction of log K_dAnd (3) prediction model:_α，_βand log D;

wherein the content of the first and second substances,_α＝E_LUMO-E_HOMO-water，_β＝E_LUMO-water-E_HOMO；E_LUMOat the lowest unoccupied molecular orbital energy, E_HOMOIs the highest occupied molecular orbital energy;

log D is the n-octanol/water distribution coefficient under different pH conditions;

(c) establishing K by adopting multiple linear regression method_dAnd_α、_βand a regression model between log D, the specific process being performed by SPSS 21.0; using the square of the correlation coefficient r²And the root mean square error rms is taken as a statistical index to characterize the fitting performance of the model, and the square q of the prediction correlation coefficient is used²Characterizing the predictive performance of the model; the regression model was obtained as follows:

PE/seawater: log K_d＝0.725×log D-23.169×_β-36.236×_α+17.856 (1)；

PE/fresh water: log K_d＝0.667×log D+1.714 (2)；

PE/pure water: log K_d＝0.486×log D+2.420 (3)。

2. The method of predicting the partition equilibrium constant of an organic contaminant between a polyethylene-based microplastic and an aqueous phase of claim 1 wherein E_HOMO-waterAnd E_LUMOAccording to the molecular structure of the organic matter to be predicted, performing structure optimization and frequency analysis on the molecules by using a Density Functional Theory (DFT) B3L YP/6-31G (d, p) algorithm of Gaussian 09 software, and extracting E of the organic matter to be predicted from an output file_HOMO-waterAnd E_LUMOThe value is obtained.

3. The method for predicting the partition equilibrium constant of organic pollutants between polyethylene-based micro-plastics and an aqueous phase as claimed in claim 1, wherein the log D is calculated by ACD L abs 6.0 software according to the molecular structure of the organic substance to be predicted.

4. The method of predicting the partition equilibrium constant of organic contaminants between polyethylene-based microplastics and an aqueous phase of claim 1, wherein the organic compounds include polychlorinated biphenyls, chlorobenzenes, antibiotics, aromatic hydrocarbons, aliphatic hydrocarbons, hexachlorocyclohexanes.

5. The method of predicting the partition equilibrium constant of an organic contaminant between a polyethylene-based microplastic and an aqueous phase of claim 4 wherein the aromatic hydrocarbons comprise monocyclic aromatic hydrocarbons, polycyclic aromatic hydrocarbons, fused ring aromatic hydrocarbons, and substitutes thereof.

Technical Field

The invention relates to the technical field of ecological safety evaluation, in particular to the technical field of quantitative structure-property relationship (QSPR) for environmental ecological risk evaluation, and particularly relates to a method for predicting a distribution equilibrium constant of organic pollutants between polyethylene type micro-plastics and a water phase.

Background

Microplastics (particle size < 5mm) are a new class of global environmental pollutants and are of great interest for their bioaccumulation, ecotoxicity and adsorption behavior. After entering the water body, the micro plastic interacts with organic pollutants in the water environment, so that the toxic effect of the organic pollutants on organisms is enhanced, even the organic pollutants are transmitted along a food chain, the exposure risk of the organisms and human bodies is increased, and the ecological environment safety and the human health are further harmed.

K_dIs an important parameter describing the partitioning behavior of organic contaminants between the microplastic and the water. K_dThe value can influence the migration, transformation and tendency of organic pollutants in the environment, and the understanding of the interaction between the micro-plastic and the organic pollutants in the water body is necessary for the subsequent research on the toxicological effects of the micro-plastic and the organic pollutants. However, the related research of the current micro-plastics is still at the initial stage, and K reported in experiments_dThe number of values is very limited and completely fails to meet the requirements of subsequent studies. Meanwhile, K to be measured is caused by various organic pollutants, dissociable organic pollutants with different dissociation forms, obvious difference of interaction strength between the organic pollutants and the micro-plastic in different water environment media and the like_dThe number of values increases greatly. Determination of K by experiment_dThe problems that the micro plastic is not easy to be uniformly dispersed in the solution, so that experimental errors are caused and the like exist in the value process. Therefore, the prediction K with good development performance_dThe method of value is particularly important.

Quantitative Structure-Property relationship (Quantitative Structure)re-Property Relationship, QSPR) can avoid experimental error and is not limited by a plurality of factors such as experimental conditions, the development cost is low, the QSPR is convenient and quick, the QSPR is successfully applied to predicting physicochemical properties, environmental behaviors and ecotoxicity parameters of organic pollutants, and the QSPR can predict K of organic matters according to molecular structure information_dThe value is obtained. However, the currently reported information about K_dThe QSPR model of (a) has some disadvantages in terms of the type and number of organics, the predictive power of the model, and the adsorption mechanism disclosed.

Smedes et al (Smedes F, Geertsma R W, Zande T, et al. Polymer-Water adsorption coefficients of hydrolytic components for passive adsorption: Application of solvent modules for evaluation. environmental Science & Technology,2009,43(18): 7047. 7054.) established adsorption models for 26 polycyclic aromatic hydrocarbons based on molecular weight and n-octanol/water partition coefficients, respectively, and compared the optimal methods for describing the equilibrium partition coefficients. However, the model does not distinguish different types of micro-plastics, and the applicability of the model application domain is poor, so that the model application domain cannot be used for predicting the adsorption capacity of different types of micro-plastics in different water media on organic pollutants.

H ü ffer et al (H ü ffer T, Hofmann T. reflection of non-polar organic compounds micro-sized plastic particles in aqueous solution, 2016,214: 194-201.) use molar volume, octanol/water partition coefficient, hexadecane/water partition coefficient, etc. to establish K of non-polar organic compounds (including seven of n-hexane, cyclohexane, benzene, toluene, chlorobenzene, ethyl benzoate, naphthalene, etc.)_dA value prediction model. However, the number of the model compounds is small, the model is not checked and characterized, and the model is limited by an application domain and cannot be used for predicting more classes of compounds (such as polychlorinated biphenyl).

Liguangyu et al (Liguangyu, Chengjing, Lisnowflake, et al. linear dissolution energy relationship model of micro-plastic/water partition coefficients of several classes of organic pollutants [ J]Ecological toxicology report, 2017.) a linear solution energy parameter was used to construct organic pollutants (polychlorinated biphenyls, polycyclic aromatic hydrocarbons, hexachlorocyclohexanes, and chlorobenzenes) in polypropylene type micro-plastics/seasK between water, polyethylene type micro plastic/seawater, polyethylene type micro plastic/fresh water_dThe model performance of the prediction model of the value is good, but the model parameters depend on experimental measurement, the data set is less, the dissociation form difference is not considered, and the use of the model is limited to a certain extent.

Therefore, the existing model can not simply and quickly predict K of various organic pollutants in different water medium environments_dThe value is obtained.

Therefore, K with excellent development performance, simple and transparent algorithm and strong practicability_dA prediction model, which can be used for predicting K of different organic pollutants such as polychlorinated biphenyl, chlorobenzene, polycyclic aromatic hydrocarbon and the like_dThe method can effectively make up for the problem of basic data loss in risk evaluation and management of organic chemicals, and provides data support and theoretical guidance for ecological safety and health risk evaluation.

Disclosure of Invention

The invention aims to provide a method for predicting the partition equilibrium constant K of organic pollutants between polyethylene micro-plastic and a water phase_dThe method can evaluate the adsorption capacity of organic pollutants in different water environment media on the surface of polyethylene type micro Plastic (PE), and has the advantages of high efficiency, rapidness, low cost and strong practicability.

In order to solve the technical problems, the technical scheme adopted by the invention is as follows:

(a) obtaining K of 37 organic pollutants between PE/seawater through an existing database and documents_dK between PE/fresh water for 24 organic pollutants_dK between PE/pure water for 48 organic pollutants_dA value;

(b) based on an analysis of the mechanism of partitioning of organic contaminants between the microplastics and the aqueous phase, the following descriptors were screened for the construction of log K_dAnd (3) prediction model:

_α(_α＝E_LUMO-E_HOMO-water，E_LUMOat the lowest unoccupied molecular orbital energy, E_HOMOThe highest occupied molecular orbital energy),

_β(_β＝E_LUMO-water-E_HOMO) And are and

log D (n-octanol/water distribution coefficient at different pH);

(c) k is established by adopting a multiple linear regression (M L R) method_dAnd_α、_βand a regression model between log D, the specific process being performed by SPSS 21.0; using the square of the correlation coefficient (r)²) And root mean square error (rms) as a statistical indicator to characterize the fit performance of the model, using the square of the predicted correlation coefficient (q)²) Characterizing the predictive performance of the model;

the regression model obtained by the M L R method was as follows:

PE/seawater: log K_d＝0.725×log D-23.169×_β-36.236×_α+17.856 (1)；

PE/fresh water: log K_d＝0.667×log D+1.714 (2)；

PE/pure water: log K_d＝0.486×log D+2.420 (3)。

The organic pollutants in the step (a) cover seven categories of polychlorinated biphenyl, chlorobenzene, polycyclic aromatic hydrocarbon, antibiotics, aromatic hydrocarbon, aliphatic hydrocarbon and hexachlorocyclohexane.

Further, said E_HOMO-waterAnd E_LUMOAccording to the molecular structure of the organic matter to be predicted, performing structure optimization and frequency analysis on the molecules by using a Density Functional Theory (DFT) B3L YP/6-31G (d, p) algorithm of Gaussian 09 software, and extracting E of the organic matter to be predicted from an output file_HOMO-waterAnd E_LUMOThe value is obtained.

Further, the log D is calculated by ACD L abs 6.0 software according to the molecular structure of the organic matter to be predicted.

Further, the organic compound includes polychlorinated biphenyl, chlorobenzene, aromatic hydrocarbons such as monocyclic aromatic hydrocarbon, polycyclic aromatic hydrocarbon and polycyclic aromatic hydrocarbon, antibiotic, aliphatic hydrocarbon, hexachlorocyclohexane.

K in collecting organic pollutants between PE/seawater, PE/fresh water and PE/pure water through existing database and literature_dIn the course of the value, the organic pollutants collected are covered moreChlorobiphenyl, chlorobenzene, polycyclic aromatic hydrocarbon, antibiotic, aliphatic hydrocarbon and hexachlorocyclohexane, log K_dThe value range is 0.79-8.84, and spans 8 orders of magnitude.

For the three models (1), (2) and (3) analyzed by the invention, r is²0.87, 0.90 and 0.81 respectively, which shows that the model has good fitting capability and no dependency between prediction error and experimental value. The stability and predictive power of the model were evaluated by two methods:

the method I comprises the following steps of simulating external verification: randomly dividing the original data set into two subsets (containing 70% of compounds and 30% of compounds respectively), rebuilding a model by using one subset (containing 70% of compounds) and the descriptors screened by the model, and fitting the result r²0.86, 0.90 and 0.80, respectively, applied to another subset (containing 30% of compound) to obtain a prediction q²0.89, 0.91 and 0.84 respectively. The statistical properties of the two subsets are very close to those of the data full set, which shows that the three models are all based on K_dThe essential correlation between descriptors, rather than the accidental correlation, is statistically stable;

a second method and a first removing method are adopted for cross validation: q. q.s² _CVThe results were 0.89, 0.94 and 0.88, respectively, again demonstrating good stability and predictive power of the model.

The application domain of the model was characterized by a Williams diagram. H in a compound descriptor matrix_iValues were plotted on the abscissa and the ordinate on a Williams plot against standard residuals (SE) to determine high-impact compounds and delocalized points. Williams plots show that the alert values h for models (1), (2), (3) are 0.32, 0.25 and 0.13, respectively, where h is_i>h are 3 compounds in the model (1) and 1 compound in the model (3), and the compounds are far away from the center of the descriptor matrix, but the prediction effect is better, and the accuracy and the extensibility of the model can be enhanced. The standard residuals for all compounds fell within ± 3, indicating that the model had no outliers. In summary, the application domain of the model is defined as: polychlorinated biphenyls, chlorobenzenes, polycyclic aromatic hydrocarbons, antibiotics, aromatic hydrocarbons, aliphatic hydrocarbons, hexachlorocyclohexanes and compounds similar in structure to the latterOther compounds.

The prediction method provided by the invention has the following advantages:

(1) the model has wide application range and can be used for quickly predicting K of polychlorinated biphenyl, chlorobenzene, polycyclic aromatic hydrocarbon, antibiotic, aromatic hydrocarbon, aliphatic hydrocarbon, hexachlorocyclohexane and other compounds with similar structures_dThe method can provide important basic data for ecological risk evaluation of the compounds by evaluating the adsorption capacity of organic pollutants in different water environment media on the surface of polyethylene type micro Plastic (PE).

(2) The molecular structure descriptor used by the model is easy to obtain, the regression analysis is simple and easy to realize, and the practical application capability of the model is strong; the prediction method provided by the invention has the advantages of convenience, rapidness, low cost, convenience in use and the like.

(3) The modeling process strictly follows the guidance of the economic cooperation and development Organization (OECD) on the construction and use of QSAR models, and the constructed models have good fitting capacity (r)²0.81 to 0.90) and prediction ability (q)²＝0.84～0.91，RMSE_ext0.47-0.75) and robustness (q)_cv ²＝0.88～0.94)。

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. In the drawings:

FIG. 1 shows model (1) prediction K_dValue and K in literature_dA comparison graph of values;

FIG. 2 is model (2) prediction K_dValue and K in literature_dA comparison graph of values;

FIG. 3 is model (3) prediction K_dValue and K in literature_dA comparison graph of values;

FIG. 4 shows the model (1) prediction error value and K_dA fitted plot of values;

FIG. 5 shows the model (2) prediction error value and K_dA fitted plot of values;

FIG. 6 shows the prediction error values of model (3)And K_dA fitted plot of values;

FIG. 7 is a Williams diagram characterizing the model (1) high-impact compounds and the delocalization point;

FIG. 8 is a Williams diagram characterizing the high-impact compounds and the delocalization points of model (2);

FIG. 9 is a Williams diagram characterizing the high-impact compounds and the delocalization points of model (3).

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.