Method for deducing long-term water quality standard of phthalate

Abstract

The invention discloses a method for deducing a long-term water quality standard of phthalate, and belongs to the technical field of water quality standard, and the method comprises the following steps: collecting and screening toxicity effect data according to the toxicity effect data of phthalate on aquatic organisms; converting the obtained toxicity effect data by using an uncertain factor, and normalizing the toxicity effect data into a concentration without an observation effect; obtaining an average chronic value of the species, and performing sorting to obtain a cumulative probability; through model fitting and evaluation, a fitting model of the selected seed average chronic value is determined; the hazard concentration of species is predicted through a selected fitting model, and a final long-term reference value is obtained through HC5. According to the method, a toxicological test with long period, high cost and large uncertainty does not need to be carried out; a probabilistic species sensitivity distribution method is used, a method for processing uncertainty and inter-laboratory variability is developed, and the method is simple, convenient and easy to understand, has good robustness, reliability and predictive ability, and is convenient for practical application.

Description

Technical Field

[0001] The invention mainly relates to the technical field of water quality benchmarks, and in particular to a method for deriving long-term water quality benchmarks for phthalates. Background Art

[0002] Phthalates are colorless or slightly yellowish, odorless, oily, viscous liquids at room temperature. They are slightly soluble in water and easily soluble in organic solvents. Phthalates are often used as plasticizers to improve the mechanical properties of plastic materials, increase product plasticity and strength, and are widely used to improve the durability, elasticity and plasticity of polyvinyl chloride (PVC) and non-PVC products. Their use has increased dramatically in the past few decades. The widespread use of phthalates makes them ubiquitous in rivers, lakes, reservoirs, wastewater, air, soil and oceans, posing potential risks to human health and ecological safety. Studies have shown that phthalates are endocrine disrupting chemicals and new pollutants, and have attracted much attention due to their immunotoxic endocrine disrupting effects. These substances have immunotoxicity, metabolic toxicity, endocrine toxicity, neurotoxicity, genotoxicity, developmental toxicity and other harmful effects, which can cause organ damage and behavioral changes in aquatic organisms, and therefore have attracted much attention.

[0003] Water quality benchmarks are established to protect aquatic ecosystems and ensure water resource safety. They serve as guidelines or standards that define acceptable levels of various physical, chemical and biological parameters in water bodies. These benchmarks are based on scientific research and are used by regulatory agencies to assess and regulate water resource quality. Water quality indicators take into account various water needs, including drinking water supply, recreational activities and the maintenance of aquatic life. They provide a framework for monitoring and managing pollutants in water bodies to maintain a strong and sustainable environment. The goal of water quality benchmarks is to protect commercially and recreationally important aquatic organisms, as well as other important species, including fish, invertebrates and zooplankton, from the negative effects caused by short-term exposure to high levels and long-term exposure to low levels of substances. Long-term water quality benchmarks are the maximum doses or levels of water environmental factors (pollutants or harmful elements) that do not produce chronic harmful effects on ecosystems under specific conditions and uses. From the perspective of revealing objective laws, water quality benchmarks are universal, but differences in natural geography and ecosystem composition will also make this objective law present certain regional specificities, requiring countries and even regions to carry out targeted research based on actual conditions.

[0004] The existing technology lacks relevant technology for deriving long-term water quality benchmarks for phthalates. Summary of the invention

[0005] The technical solution of the present invention aims at the technical problem that the existing technical solution is too single, provides a solution that is significantly different from the existing technology, and mainly provides a method for deriving the long-term water quality benchmark for phthalates.

[0006] The technical solution adopted by the present invention to solve the above technical problems is:

[0007] A method for deriving a long-term water quality benchmark for phthalates comprises the following steps:

[0008] S1. Based on the toxic effect data of phthalates on aquatic organisms, select appropriate test organisms, appropriate toxic effect endpoints, and collect screening toxic effect data;

[0009] S2. Use uncertainty factors to transform the acquired toxic effect data and normalize them to no-observed-effect concentrations;

[0010] S3, obtaining the average chronicity value of the species, and sorting the average chronicity values ​​of the species to obtain the cumulative probability;

[0011] S4. Determine the fitting model of the species average chronic value SMCV selected under different water quality conditions through model fitting and evaluation;

[0012] S5. Predict the hazard concentration of species through the selected fitting model, and calculate the HC 5 Get the final long-term baseline value.

[0013] Furthermore, in step S1, the toxic effect data of phthalates on aquatic organisms are obtained through information retrieval and data screening, and the toxicity data retrieval includes the retrieval of compound type, species type, and toxic effect test endpoint type.

[0014] Further, the compound types include dimethyl phthalate, diethyl phthalate, diisobutyl phthalate, di-n-butyl phthalate, di(2-methoxyethyl)phthalate, dipentyl phthalate, di(2-ethoxyethyl)phthalate, butyl phthalate, diphenyl phthalate, dicyclohexyl phthalate, di(4-methyl-2-pentyl)phthalate, dihexyl phthalate, di(2-butoxyethyl)phthalate, di(2-ethylhexyl)phthalate, di-n-octyl phthalate, dinonyl phthalate, dibutyl phthalate and dioctyl phthalate.

[0015] Furthermore, the species types include at least one bony carp, one bony non-carp, one zooplankton, one benthic animal, one animal of other phyla not mentioned above, and one phytoplankton or aquatic vascular plant;

[0016] and / or, toxic effect test endpoint types include no observed effect concentration (NOEC), lowest observed effect concentration (LOEC), no observed effect level (NOEL), lowest observed effect level (LOEL), maximum allowable concentration (MATC), and X% effect concentration (EC) X , X=10,20.

[0017] Furthermore, in step S2, the toxicity data is normalized using the following formula:

[0018] Chronic NOEC = toxic effect value / (UF t ×UF d )

[0019] Where:

[0020] NOEC stands for no observed effect concentration;

[0021] UF t represents the time uncertainty factor;

[0022] UF d represents the concentration uncertainty factor.

[0023] Further, in step S3, the species average chronic value SMCV of each species is obtained according to the following formula: i ,

[0024]

[0025] Where:

[0026] SMCV i represents the average chronic value of species i, μg / L;

[0027] CTV stands for chronic toxicity value, μg / L; CTV includes the values ​​of each toxic effect test endpoint type;

[0028] n represents the number of CTVs of species i, pieces;

[0029] i represents the species number and is dimensionless.

[0030] Furthermore, in step S3, the logarithmic values ​​of the average chronic values ​​of the species are sorted from small to large to determine the sequence number R of their toxicity ranking; then the cumulative probability F of each species is calculated according to the following formula R :

[0031]

[0032] Where:

[0033] F R represents cumulative probability, %;

[0034] R—the ordinal number of the species, dimensionless;

[0035] N—the total number of species, dimensionless.

[0036] Further, step S4 includes:

[0037] (1) Average chronic value SMCV i Take the logarithm with base 10 respectively, and convert lg(SMCV i ) as the independent variable in model fitting, with lg(SMCV i ) corresponds to the cumulative probability F R As the dependent variable, the species sensitivity distribution model was fitted;

[0038] (2) Based on the determination coefficient R of the model fitting 2 , root mean square RMSE and AD test results, determine the fitting model of the average chronic value SMCV;

[0039] (3) Perform a model fit goodness-of-fit test to determine the fitting model for the average chronicity value of the selected species;

[0040] (4) Based on the goodness of fit evaluation results and combined with professional judgment, the model with the smallest RMSE was selected as the optimal fitting model among the fitting models with probability P>0.05.

[0041] Further, step S5 includes:

[0042] (1) The species sensitivity distribution curve is fitted by the selected fitting model, and the species average chronic value corresponding to the cumulative probability of 5% is determined, which is the HC value of the chronic 5% species hazard concentration. 5 ;

[0043] (2) HC 5 Divide by the assessment factor to provide a long-term water quality benchmark for phthalates.

[0044] Furthermore, when the number of species samples is less than 15, the evaluation factor is 3; when the number of species samples is greater than or equal to 15, the evaluation factor is 2.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] (1) The present invention provides a method for deriving a long-term water quality benchmark for phthalates, and normalizes the data of short-term exposure time and other toxic effect endpoints to a chronic no-observed-effect concentration value, without the need to conduct a toxicological test with a long cycle, high cost, and large uncertainty. The shortcomings of traditional toxicological experiments, such as long cycles, high costs, time-consuming and labor-intensive, and a large waste of costs, can be overcome; a large amount of chronic toxicity data can be quickly obtained for deriving a long-term water quality benchmark; there is no need to conduct experimental tests on these various types of compounds one by one, and sufficient ecotoxicity data can be efficiently and quickly provided to derive a water quality benchmark.

[0047] (2) The present invention determines the selected fitting model of the species average chronic value based on the species average chronic value and cumulative probability through model fitting and evaluation; then the selected fitting model is used to predict the hazard concentration of the species, and finally the HC 5 The present invention develops methods to handle uncertainty and inter-laboratory variability, resolving the difficulty of the occasional limited availability of ecotoxicity data.

[0048] In summary, the method provided by the present invention is simple, easy to understand, and has good robustness, reliability and predictive ability, and is convenient for practical application.

[0049] The present invention will be explained in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flow chart of a method for deriving a long-term water quality benchmark for phthalates according to an embodiment of the present invention;

[0051] Figure 2 A probabilistic species sensitivity distribution fitting curve (normal model distribution) of an embodiment of the present invention;

[0052] Figure 3 This is a probabilistic species sensitivity distribution fitting curve (logistic model distribution) of an embodiment of the present invention. DETAILED DESCRIPTION

[0053] To facilitate understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings, but the present invention can be implemented in different forms and is not limited to the embodiments described in the text. On the contrary, these embodiments are provided to make the content disclosed in the present invention more thorough and comprehensive.

[0054] Example 1: Please refer to the attached Figure 1 The present invention provides a method for deriving a long-term water quality benchmark for phthalates, the method comprising the following steps:

[0055] Step 1: Based on the toxic effect data of phthalates on aquatic organisms, select appropriate test organisms, appropriate toxic effect test endpoints, and collect screening toxic effect data;

[0056] Toxic effect test endpoints included population growth, mortality, reproduction (including embryonic growth and development), photosynthesis, and behavior (including larval photolocomotor response and immobilization).

[0057] When both acute and chronic data are reported in a toxicity study, only the chronic data that are relevant to the hazard assessment should be collected.

[0058] If different endpoints were available for the same species, the most sensitive endpoint was selected; if multiple toxicity values ​​were available for the same species and endpoint, the geometric mean was taken as the average toxicity value for the species.

[0059] Step 2: Use uncertainty factors to transform the acquired toxicity data and normalize them to no-observed-effect concentrations;

[0060] The toxicity data were normalized using the following formula:

[0061] Chronic NOEC = toxic effect value / (UF t ×UF d )

[0062] Where:

[0063] NOEC stands for no observed effect concentration;

[0064] UF t represents the time uncertainty factor;

[0065] UF d represents the concentration uncertainty factor.

[0066] Step 3: Obtain the average chronicity value of each species, sort the average chronicity values ​​of each species, and obtain the cumulative probability;

[0067] (1) The average chronic value (SMCV) of each species is obtained according to the following formula: i ,

[0068]

[0069] Where:

[0070] SMCV i represents the average chronic value of species i, μg / L;

[0071] CTV stands for chronic toxicity value, μg / L; CTV includes the values ​​of each toxic effect test endpoint type, using the above chronic NOEC data;

[0072] n represents the number of CTVs of species i, pieces;

[0073] i represents the species number and is dimensionless.

[0074] (2) Sort the average chronicity values ​​of the species by: SMCV i The logarithmic values ​​of the toxicity are sorted from small to large to determine the order R of their toxicity ranking;

[0075] (3) Calculate the cumulative probability F of each species according to the following formula R ,

[0076]

[0077] Where:

[0078] F R represents cumulative probability, %;

[0079] R—the ordinal number of the species, dimensionless;

[0080] N—the total number of species, dimensionless.

[0081] Step 4: Determine the average chronic value (SMCV) of the species selected under different water quality conditions through model fitting and evaluation i The fitted model of

[0082] (1) Average chronic value SMCV i Take the logarithm with base 10 respectively, and convert lg(SMCV i ) as the independent variable in model fitting, with lg(SMCV i ) corresponds to the cumulative probability F R The species sensitivity distribution model is fitted by using SMCV as the dependent variable. The species sensitivity distribution model includes: normal distribution model, lognormal distribution model, logistic distribution model and loglogistic distribution model; among them, SMCV i is the average chronicity value of species i;

[0083] (2) Based on the determination coefficient R of the model fitting 2 , root mean square RMSE and AD test results, determine the fitting model of the average chronic value SMCV;

[0084] The coefficient of determination R of the model fitting 2 , the calculation formula of root mean square RMSE is:

[0085]

[0086] The ratio of the standard error to the predicted value, the smaller the value, the more statistically significant it is; R 2The closer it is to 1, the greater the goodness of fit of the toxicity data and the more accurate the model fit; RMSE is the square root of the ratio of the square of the deviation between the observed value and the true value to the number of observations. This statistical parameter is also called the fitting standard deviation of the regression system. RMSE can reflect the precision of the model in a statistical sense. The closer RMSE is to 0, the higher the accuracy of the model fit; probability P value (AD test), P>0.05, indicates that the fit passes the AD test and the model conforms to the theoretical distribution.

[0087] (3) Perform a model fit goodness-of-fit test to determine the fitting model for the average chronic value of the selected species.

[0088] (4) Based on the goodness of fit evaluation results and professional judgment, the model with the smallest RMSE is selected as the optimal fitting model among the fitting models with P>0.05. The curve obtained by the optimal fitting model should be in good agreement with the data points involved in the fitting, ensuring that the water quality benchmark obtained by extrapolating the fitted SSD curve is statistically reasonable and reliable.

[0089] Step 5: Predict the hazard concentration of the species using the selected fitting model, using HC 5 Get the final long-term baseline value.

[0090] (1) The species sensitivity distribution curve is fitted by the selected fitting model, and the species average chronic value corresponding to the cumulative probability of 5% is determined, which is the HC value of the chronic 5% species hazard concentration. 5 (the concentration of a chemical that causes harmful effects in 5% of the individuals of a test species in a toxicity test);

[0091] (2) HC 5 Divide by the assessment factor to provide a long-term water quality benchmark for phthalates.

[0092] Embodiment 2: This embodiment differs from Embodiment 1 in that:

[0093] Step 1: Obtain and process long-term water ecotoxicity data through information retrieval and data screening;

[0094] Information retrieval and data screening were independently completed by multiple groups of researchers from toxicity databases and literature libraries; toxicity data retrieval included retrieval of compound types, species types, and toxic effect test endpoint types; literature databases included China National Knowledge Infrastructure, Wanfang Knowledge Service Platform, VIP, Web of Science, and the US Environmental Protection Agency ECOTOX database.

[0095] The compound types include dimethyl phthalate (DMP), diethyl phthalate (DEP), diisobutyl phthalate (DiBP), di-n-butyl phthalate (DBP), di(2-methoxyethyl) phthalate (DMEP), dipentyl phthalate (DPP), di(2-ethoxyethyl) phthalate (DEEP), butyl phthalate (BBP), diphenyl phthalate (DPHP), di-n-butyl phthalate (DNBP ... Phthalate (DCHP), di(4-methyl-2-pentyl) phthalate (BMPP), dihexyl phthalate (DHXP), di(2-butoxyethyl) phthalate (DBEP), di(2-ethylhexyl) phthalate (DEHP), di-n-octyl phthalate (DnOP), dinonyl phthalate (DNP), dibutyl phthalate (DnBP) and dioctyl phthalate (DOP).

[0096] The species types include at least one bony carp, one bony non-carp, one zooplankton, one benthic animal, one animal from other phyla not mentioned above, and one phytoplankton or aquatic vascular plant;

[0097] The chronic toxicity value CTV includes the values ​​of each toxic effect test endpoint type, which includes no observed effect concentration NOEC, lowest observed effect concentration LOEC, no observed effect level NOEL, lowest observed effect level LOEL, maximum allowable concentration MATC and X% effect concentration ECx (X=10,20).

[0098] In step 5, the species sensitivity distribution curve is fitted by the selected fitting model to determine the lg (SMCV) corresponding to the cumulative probability of 5%. i ) value, and the SMCV obtained by taking the antilogarithm i , which is the chronic 5% species hazard concentration HC 5 HC 5 Divide by the assessment factor to serve as the long-term water quality benchmark for phthalates. In this example, the number of species samples is less than 15, and the assessment factor is 3;

[0099] The rest is the same as the first embodiment.

[0100] Embodiment 3: This embodiment differs from Embodiment 2 in that:

[0101] In step 5, if the number of species samples meets the condition of being greater than or equal to 15, the evaluation factor is 2.

[0102] The rest is the same as the second embodiment.

[0103] Embodiment 4: This embodiment differs from Embodiment 1 in that:

[0104] Obtain and process long-term water ecotoxicity data through information retrieval and data screening;

[0105] (1) The physical and chemical properties of some phthalates were collected and sorted from relevant websites, as shown in Table 1.

[0106] Table 1 Physicochemical properties of phthalates

[0107]

[0108] The following is a detailed description taking diethyl phthalate (DEP) as an example.

[0109] (2) The water ecotoxicity data of DEP were retrieved and screened in the literature database. The results are shown in Table 2.

[0110] Table 2 Water ecotoxicity data of DEP

[0111]

[0112]

[0113] The above toxicity data were normalized according to the uncertainty factors in Tables 3 and 4;

[0114] Table 3 Time uncertainty factors

[0115]

[0116] Table 4 Dose uncertainty factors

[0117]

[0118] Calculate the average chronic value of each species and sort them from small to large to determine their toxicity rank R (the rank of the smallest toxicity value is 1, the rank of the second largest is 2, and so on. If two or more species have the same toxicity value, they will be randomly arranged into consecutive ranks, and the number of species in each rank is 1). Calculate the cumulative probability F of the species R , and obtain Table 5.

[0119] Table 5 Average chronic values ​​and cumulative probability

[0120]

[0121]

[0122] Model fitting and evaluation: SMCV i Take the logarithm with base 10 respectively, and convert lg(SMCV i ) as the independent variable in model fitting, with lg(SMCV i) corresponding to F R As the dependent variable, the SSD model was fitted (including normal distribution model, lognormal distribution model, logistic distribution model, loglogistic distribution model), and the determination coefficient (R 2 ), root mean square (RMSE) and AD test (P value) results are shown in Table 6.

[0123] Table 6 Model fitting results and evaluation parameters

[0124]

[0125] After comparison, among the four models, only the normal distribution model and the logistic model have fitting results, and their SSD fitting curves are as follows: Figure 2 , Figure 3 shown.

[0126] The basis for testing the goodness of fit of the model: among the fitting models with P>0.05, the model with the smallest RMSE was selected as the best fitting model.

[0127] Based on this, the distributed optimal fitting model of the logistic model can be obtained.

[0128] HC 5 After dividing by the assessment factor (according to the national standard HJ831-2022, the number of species included in the effective toxicity data is greater than 15 and covers sufficient trophic levels, and the assessment factor is 2), the chronic toxicity of DEP can be predicted, and the long-term benchmark value of DEP in the water environment can be calculated (Table 7), which represents the maximum concentration of DEP in water bodies that does not produce chronic harmful effects on 95% of China's freshwater aquatic organisms and their ecological functions (calculated as the arithmetic mean concentration in any 1 hour).

[0129] Table 7 DEP long-term water quality benchmarks

[0130]

[0131] The above is an exemplary description of the present invention in combination with the accompanying drawings. It is obvious that the specific implementation of the present invention is not limited to the above-mentioned method. As long as such non-substantial improvements are made by adopting the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. A method for deriving a long-term water quality benchmark for phthalates, characterized in that: The steps include: S1. Based on the toxic effect data of phthalates on aquatic organisms, select appropriate test organisms, appropriate toxic effect endpoints, and collect screening toxic effect data; S2. Use uncertainty factors to transform the acquired toxic effect data and normalize them to no-observed-effect concentrations; S3, obtaining the average chronicity value of the species, and sorting the average chronicity values ​​of the species to obtain the cumulative probability; S4. Determine the fitting model of the species average chronic value SMCV selected under different water quality conditions through model fitting and evaluation; S5. Predict the hazard concentration of species through the selected fitting model and obtain the final long-term benchmark value from HC5.

2. A method for deriving a long-term water quality benchmark for phthalates according to claim 1, characterized in that: In step S1, the toxic effect data of phthalates on aquatic organisms are obtained through information retrieval and data screening. The toxicity data retrieval includes the retrieval of compound type, species type, and toxic effect test endpoint type.

3. A method for deriving a long-term water quality benchmark for phthalates according to claim 2, characterized in that: The compound types include dimethyl phthalate, diethyl phthalate, diisobutyl phthalate, di-n-butyl phthalate, di(2-methoxyethyl)phthalate, dipentyl phthalate, di(2-ethoxyethyl)phthalate, butyl phthalate, diphenyl phthalate, dicyclohexyl phthalate, di(4-methyl-2-pentyl)phthalate, dihexyl phthalate, di(2-butoxyethyl)phthalate, di(2-ethylhexyl)phthalate, di-n-octyl phthalate, dinonyl phthalate, dibutyl phthalate and dioctyl phthalate.

4. A method for deriving a long-term water quality benchmark for phthalates according to claim 2, characterized in that: The species types include at least one bony carp, one bony non-carp, one zooplankton, one benthic animal, one animal from other phyla not mentioned above, and one phytoplankton or aquatic vascular plant; and / or, toxic effect test endpoint types include no observed effect concentration (NOEC), lowest observed effect concentration (LOEC), no observed effect level (NOEL), lowest observed effect level (LOEL), maximum allowable concentration (MATC), and X% effect concentration (EC) X , X=10,20.

5. The method for deriving a long-term water quality benchmark for phthalates according to claim 1, characterized in that: In step S2, the toxicity data is normalized using the following formula: Chronic NOEC = toxic effect value / (UF t ×UF d ) Where: NOEC stands for no observed effect concentration; UF t represents the time uncertainty factor; UF d represents the concentration uncertainty factor.

6. A method for deriving long-term water quality standards for phthalates according to claim 1, characterized in that: In step S3, the species average chronic value SMCV of each species is obtained according to the following formula: i , Where: SMCV i represents the average chronic value of species i, μg / L; CTV stands for chronic toxicity value, μg / L; CTV includes the values ​​of each toxic effect test endpoint type; n represents the number of CTVs of species i, pieces; i represents the species number and is dimensionless.

7. A method for deriving long-term water quality standards for phthalates according to claim 1, characterized in that: In step S3, the logarithmic values ​​of the average chronic values ​​of the species are sorted from small to large to determine the toxicity ranking number R; then the cumulative probability F of each species is calculated according to the following formula R : Where: F R represents cumulative probability, %; R—the ordinal number of the species, dimensionless; N—the total number of species, dimensionless.

8. The method for deriving long-term water quality standards for phthalates according to claim 1, characterized in that: Step S4 includes: (1) Average chronic value SMCV i Take the logarithm with base 10 respectively, and convert lg(SMCV i ) as the independent variable in model fitting, with lg(SMCV i ) corresponds to the cumulative probability F R As the dependent variable, the species sensitivity distribution model was fitted; (2) Based on the determination coefficient R of the model fitting 2 , root mean square RMSE and AD test results, determine the fitting model of the average chronic value SMCV; (3) Perform a model fit goodness-of-fit test to determine the fitting model for the average chronicity value of the selected species; (4) Based on the goodness of fit evaluation results and combined with professional judgment, the model with the smallest RMSE was selected as the optimal fitting model among the fitting models with probability P>0.

05.

9. The method for deriving a long-term water quality benchmark for phthalates according to claim 1, characterized in that: Step S5 includes: (1) Determine the species average chronic value corresponding to the cumulative probability of 5% by fitting the species sensitivity distribution curve using the selected fitting model, which is the HC5 of the chronic 5% species hazard concentration; (2) Divide HC5 by the assessment factor to obtain the long-term water quality benchmark for phthalates.

10. A method for deriving long-term water quality standards for phthalates according to claim 9, characterized in that: When the number of species samples is less than 15, the evaluation factor is 3; when the number of species samples is greater than or equal to 15, the evaluation factor is 2.

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