CN110993019B - Biological enrichment factor prediction method for establishing water quality benchmark and risk assessment - Google Patents

Biological enrichment factor prediction method for establishing water quality benchmark and risk assessment Download PDF

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CN110993019B
CN110993019B CN201911025512.2A CN201911025512A CN110993019B CN 110993019 B CN110993019 B CN 110993019B CN 201911025512 A CN201911025512 A CN 201911025512A CN 110993019 B CN110993019 B CN 110993019B
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何佳
张璇
杜鹏
殷国栋
郝芳华
付永硕
郝增超
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Beijing Normal University
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Abstract

The invention belongs to the field of ecological risk evaluation test strategies, and particularly relates to a biological enrichment factor prediction method for establishing a water quality standard and risk assessment. The method comprises the following steps: (1) selecting biological enrichment factor data; (2) selection of alternative species; (3) establishing a biological enrichment factor interspecies relation model; (4) the steps of screening prediction data and the like can avoid time-consuming and labor-consuming biological experiments, biological enrichment factors of more species, particularly blank of rare and endangered species biological enrichment factor data, are predicted for the biological enrichment factors of known species through chemicals, the condition that China is faced with extreme shortage of local species data at present is improved, the requirement of China water quality standard on biological diversity of the biological enrichment factor data is met, the research and development of the water quality standard are accelerated, and basic data support can be provided for ecological risk assessment of the chemicals.

Description

Biological enrichment factor prediction method for establishing water quality benchmark and risk assessment
Technical Field
The invention belongs to the field of ecological risk evaluation test strategies, and particularly relates to a biological enrichment factor prediction method for establishing a water quality standard and risk assessment.
Background
The water quality standard is a scientific basis for establishing a water quality standard, and plays a very important role in water environment protection. The chemical ecological risk assessment is an important component and scientific support of environmental risk management, is a key and difficult point in the field of current environmental management, helps environmental management departments to understand and predict the relationship between external ecological influence factors and ecological consequences, and finally provides decision support for the risk management. However, at present, the relative scarcity of native species to chemical bio-enrichment factor (BCF) data is one of the key factors that restrict water quality benchmarks and chemical ecological risk assessment studies. Early studies thought that the migration of pollutants in the environment was primarily due to physical effects (e.g., atmospheric, ocean currents, etc.), but recent studies highlighted the key role that organisms play in migrating and transporting organic pollutants. Therefore, it is crucial to understand the enrichment effect of chemicals on organisms. With the rapid development of modern industry, new synthetic chemicals are put into the market in hundreds of quantities every year, and if experimental detection is performed on the biological enrichment effect of each compound, a large amount of manpower, material resources and financial resources are consumed, a huge amount of experimental expenses are also required, and the principles of OECD organization on animal experiment reduction are not met.
The chinese patent application CN 103761431a also discloses a technology for predicting biological enrichment factors of organic chemicals fish by quantitative structure activity relationship, which applies a model QSAR model, currently, in predicting biological enrichment factors by applying a model extrapolation method, the most common international model is a quantitative structure-activity relationship (QSAR) model, and the QSAR model establishes a correlation between biological enrichment factors and structures by calculating their structure descriptors according to the structural properties of compounds. The united states Environmental Protection Agency (EPA) also develops an EPISuite software for predicting biological enrichment factors according to QSAR model integration, and predicts the enrichment capacity of chemicals in a specific species by using related structure descriptors such as octanol water partition coefficient (logKow) of the chemicals. However, each QSAR model can only predict the prediction of a single species on multiple chemical biological enrichment factors, and most of the enrichment factor data are concentrated in model organisms, and the QSAR model needs to be established based on a large amount of data, so that the QSAR model is mostly established based on the model organisms and cannot meet the requirements of water quality reference and risk assessment research on species diversity.
The lack of the data of the biological enrichment factors of the chemicals is a common problem of water quality benchmark and ecological risk assessment of the chemicals, and particularly for some new synthetic chemicals, the lack of the biological enrichment factors more restricts the development requirements of chemical safety management and environmental risk management. Therefore, how to develop relevant research on water quality benchmark and chemical ecological risk assessment under the condition of data deficiency is a very challenging problem, and at the moment, a model prediction method is particularly important.
Therefore, it is highly desirable to provide an accurate, rapid, simple and convenient method for predicting the bio-enrichment factor.
Disclosure of Invention
In order to overcome the technical problems, the invention provides a biological enrichment factor prediction method for establishing a water quality benchmark and risk assessment, which can be used for accurately, quickly and simply predicting the biological enrichment factor and can be used for appointing the water quality benchmark and risk assessment.
In order to achieve the above purpose, the technical scheme provided by the invention is as follows:
a biological enrichment factor prediction method for establishing a water quality standard and evaluating ecological risks of chemicals comprises the following steps:
(1) selecting biological enrichment factor data: taking a predicted species, screening actual measurement data of biological enrichment factors of different species, wherein the data needs to meet the same enrichment organ, the development stage of a tested organism, an experimental method, experimental conditions, an exposure way and the purity grade of chemicals, and taking a geometric average value of all biological enrichment factors of the same species corresponding to the same chemicals;
(2) selection of surrogate species: selecting representative species as substitute species according to the screened biological enrichment factor data set, wherein the experiment data of the substitute species is more sufficient;
(3) establishing a biological enrichment factor interspecific relation (BCF-ICE) model, matching the biological enrichment factor data between the substitute species and the predicted species one by one according to the same chemical names, and establishing a correlation relation model between the biological enrichment factors of the substitute species and the predicted species based on a partial least squares normal regression equation, wherein the formula is as follows:
Log10(Predict taxes BCF)＝a+b×Log10(Surrogate taxes BCF)
in the formula, a and b are respectively the intercept and the slope of a linear equation;
(4) screening prediction data, and judging the accuracy and precision of model prediction according to statistical parameters;
wherein the statistical parameters include Mean Square Error (MSE), cross validation rate, confidence interval, degree of freedom, and correlation coefficient (R)2) Significance level (p-value).
Preferably, in step (2), the substitute species needs to satisfy the following two conditions: (1) at least 5 of the same surrogate species; (2) at least 10 interspecies relationship models are established for the same substitute species;
preferably, in step (4), R is2＞0.6，p＜0.05；
Preferably, the biological enrichment factor prediction method for establishing the water quality benchmark and the chemical ecological risk assessment further comprises the following steps:
classifying the hydrophobicity of the chemicals, matching the selected alternative species with the biological enrichment factor data among the predicted species one by one according to the same chemical name, classifying the hydrophobicity of the matched chemicals, selecting BCF data according to classification, and establishing a biological enrichment interspecific relation model;
preferably, the chemical is subjected to hydrophobic classification by using an octanol/water partition coefficient logKow as a classification index of the hydrophobicity of the chemical;
preferably, the matched chemical bio-enrichment factors are classified according to three groups of indices logKow <1, 1< logKow <5 and logKow > 5.
Preferably, the selected BCF data is: the data of the group 1< logKow <5 are selected, and the data of the group 1< logKow <5 are selected to establish a biological enrichment interspecific relation model, so that the inaccuracy of model prediction can be effectively reduced.
Compared with the prior art, the invention has the technical advantages that:
(1) according to the invention, through establishing the BCF-ICE model, time-consuming and labor-consuming biological experiments can be avoided, biological enrichment factors of more species, especially blank of rare and endangered species biological enrichment factor data, are predicted for the biological enrichment factors of known species through chemicals, the condition of extremely short native species data at present is improved, the requirement of water quality standard on biological enrichment factor data biodiversity is met, the research and development of water quality standard are accelerated, and basic data support can be provided for ecological risk assessment of chemicals.
(2) The prediction method of the invention not only can fully utilize the actual measurement data of the existing biological enrichment factor of the native species, but also can utilize the relatively perfect chemical biological enrichment factor database of developed countries to establish a biological enrichment factor inter-species prediction model suitable for the native species, expand the data volume of the native species biological enrichment factor, and is a main way for solving the problem of restricting the water quality benchmark and the ecological risk evaluation development.
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FIG. 1: a correlation graph among biological enrichment factors;
the invention will now be further illustrated with reference to the accompanying drawings and examples:
Detailed Description
Basic embodiment
The biological enrichment factor prediction method for establishing a water quality standard and evaluating ecological risks of chemicals comprises the following steps of:
(1) selecting biological enrichment factor data: taking a predicted species, screening actual measurement data of biological enrichment factors of different species, wherein the data needs to meet the same enrichment organ, the development stage of a tested organism, an experimental method, experimental conditions, an exposure way and the purity grade of chemicals, and taking a geometric average value of all biological enrichment factors of the same species corresponding to the same chemicals;
(2) selection of surrogate species: selecting representative species as substitute species according to the screened biological enrichment factor data set, wherein the experiment data of the substitute species is more sufficient; the surrogate species needs to satisfy the following two conditions: (1) at least 5 of the same surrogate species; (2) at least 10 interspecies relationship models are established for the same substitute species;
(3) establishing a biological enrichment factor interspecific relation (BCF-ICE) model, matching the biological enrichment factor data between the substitute species and the predicted species one by one according to the same chemical names, and establishing a correlation relation model between the biological enrichment factors of the substitute species and the predicted species based on a partial least squares normal regression equation, wherein the formula is as follows:
Log10(Predict taxes BCF)＝a+b×Log10(Surrogate taxes BCF)
in the formula, a and b are respectively the intercept and the slope of a linear equation;
(4) screening prediction data, and judging the accuracy and precision of model prediction according to statistical parameters;
wherein the statistical parameters include Mean Square Error (MSE), cross validation rate, confidence interval, degree of freedom, and correlation coefficient (R)2) Significance level (p-value); satisfy R2＞0.6，p＜0.05；
Besides the basic method, the biological enrichment factor prediction method for establishing the water quality benchmark and the chemical ecological risk assessment further comprises the following steps:
classifying the hydrophobicity of the chemicals, matching the biological enrichment factor data between the selected substitute species and the predicted species one by one according to the same chemical name, classifying the hydrophobicity of the matched chemicals, and establishing a biological enrichment interspecific relation model according to classification selection data;
when the chemical is subjected to hydrophobic classification, the octanol/water distribution coefficient logKow is used as a classification index of the hydrophobicity of the chemical; the biological enrichment factors of the matched chemicals are classified according to three groups of indexes of logKow <1, 1< logKow <5 and logKow > 5.
Example 1
(1) The method comprises the steps of taking the primula forbesii as a predicted species and the palygorskite as a substitute species, and screening out biological enrichment factors of the primula forbesii and the palygorskite with the same purity levels of enrichment organs, development stages, experimental methods, experimental conditions, exposure ways and chemicals through literature research and database search. Geometric mean values of all biological enrichment factors corresponding to the same chemical in the same species are obtained, and the biological enrichment factor data of 12 different structural chemicals are obtained after matching of the same compounds in the two species, as shown in table 1.
Table 1: BCF-ICE model BCF data, unit (L/kg) established based on Gubiyu and blackfish
Figure BDA0002248497520000041
Figure BDA0002248497520000051
(2) Establishing a BCF-ICE model between two species based on biological enrichment factor data of 12 different structural chemicals, wherein the BCF-ICE model is established as shown in the attached drawing 1, and the linear relation between the two species obtained from the attached drawing 1 is as follows:
Log10(blackhead fish). + 0.14+0.92log10(Gubi fish)
Wherein R is2＝0.88，DF＝10，P＜0.05，AE＝0，AAE＝0.19，RMSE＝0.24；
Among the above statistical parameters: r2Denotes the correlation coefficient, R2A value of 0.88 greater than 0.6 indicates that the degree of fit is statistically significant; DF represents the degree of freedom, and the number of variables with unlimited values is 10; a P value of 0 less than 0.05 indicates significant correlation; AE represents average residual, AAE represents average absolute residual, RMSE represents root mean square error, AE is 0, AAE is 0.19 and RMSE is 0.24, and the parameters indicate that the BCF-ICE model has stable prediction capability and can be used for establishing water quality reference and risk assessment.
The above detailed description is specific to one possible embodiment of the present invention, and the embodiment is not intended to limit the scope of the present invention, and all equivalent implementations or modifications without departing from the scope of the present invention should be included in the technical scope of the present invention.

Claims (3)

1. A biological enrichment factor prediction method for establishing a water quality standard and evaluating ecological risks of chemicals comprises the following steps:
(1) selecting biological enrichment factor data: taking a predicted species, screening actual measurement data of biological enrichment factors of different species, wherein the data needs to meet the same enrichment organ, the development stage of a tested organism, an experimental method, experimental conditions, an exposure way and the purity grade of chemicals, and taking a geometric average value of all biological enrichment factors of the same species corresponding to the same chemicals;
(2) selection of surrogate species: selecting representative species as substitute species according to the screened biological enrichment factor data set;
(3) establishing a biological enrichment factor interspecies relation model, matching biological enrichment factor data between the substitute species and the predicted species one by one according to the same chemical names, and establishing a correlation relation model between the biological enrichment factors of the substitute species and the predicted species based on a partial least squares linear regression equation, wherein the formula is as follows:
Log10(Predict taxes BCF)＝a+b×Log10(Surrogate taxes BCF)
in the formula, a and b are respectively the intercept and the slope of a linear equation;
(4) screening prediction data, and judging the accuracy and precision of model prediction according to statistical parameters;
wherein the statistical parameters include mean square error, cross validation rate, confidence interval, degree of freedom, R2、p；
The biological enrichment factor prediction method for establishing the water quality benchmark and the chemical ecological risk assessment further comprises the following steps:
classifying the hydrophobicity of the chemicals, matching the biological enrichment factor data between the selected substitute species and the predicted species one by one according to the same chemical name, classifying the hydrophobicity of the matched chemicals, selecting BCF data according to the classification, and establishing a biological enrichment interspecies relation model;
when the chemical is subjected to hydrophobic classification, the octanol/water distribution coefficient logKow is used as a classification index of the hydrophobicity of the chemical;
classifying the biological enrichment factors of the matched chemicals according to three groups of indexes of logKow <1, 1< logKow <5 and logKow > 5;
the selected BCF data is as follows: data for group 1< logKow <5 were selected.
2. The method for predicting bio-enrichment factors according to claim 1, wherein in the step (2), the substitute species needs to satisfy the following two conditions: at least 5 pollutant data of the same substitute species; at least 10 interspecies relation models established by the same substitute species.
3. The method for predicting bio-enrichment factor according to claim 1, wherein in the step (4), R is2＞0.6，p＜0.05。
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014071742A (en) * 2012-09-28 2014-04-21 Fujifilm Corp Prediction device and prediction method of bio-concentration factor and program
CN103761431A (en) * 2014-01-10 2014-04-30 大连理工大学 Method for predicting fish bio-concentration factors of organic chemicals by quantitative structure-activity relationship
CN105205322A (en) * 2015-09-17 2015-12-30 中国环境科学研究院 Model-estimation-based biological toxicity estimation method

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* Cited by examiner, † Cited by third party
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CN107085672B (en) * 2017-05-24 2018-05-29 中国环境科学研究院 A kind of toxicity prediction method for being applied to formulate water quality benchmark
CN109214701A (en) * 2018-09-20 2019-01-15 中国环境科学研究院 Pyrethroid pesticide water quality benchmark derivation method and water quality security evaluation method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014071742A (en) * 2012-09-28 2014-04-21 Fujifilm Corp Prediction device and prediction method of bio-concentration factor and program
CN103761431A (en) * 2014-01-10 2014-04-30 大连理工大学 Method for predicting fish bio-concentration factors of organic chemicals by quantitative structure-activity relationship
CN105205322A (en) * 2015-09-17 2015-12-30 中国环境科学研究院 Model-estimation-based biological toxicity estimation method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
An interspecies correlation model to predict acute dermal toxicity of plant protection products to terrestrial life stages of amphibians using fish acute toxicity and bioconcentration data;LennartWeltje et al;《Chemosphere》;20171231;第189卷;第619-626页 *
Interspecies Correlation Estimates Predict Protective Environmental Concentrations;Scott D. Dyer et al;《Environ. Sci. Technol. 》;20060328;第40卷(第9期);第3102-3111页 *
宁夏贺兰山自然保护区蝴蝶群落多样性及其环境影响因子;李欣芸等;《环境昆虫学报》;20190719;第660-673页 *

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