CN113917101A - Method for predicting chronic toxicity of copper in watershed water environment and deriving long-term reference - Google Patents
Method for predicting chronic toxicity of copper in watershed water environment and deriving long-term reference Download PDFInfo
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- 230000007774 longterm Effects 0.000 title claims abstract description 64
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 title claims abstract description 60
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Abstract
The invention provides a method for predicting chronic toxicity of copper in a watershed water environment and deriving a long-term reference, belonging to the technical field of water quality reference. The invention introduces three water body environmental parameters of water quality parameter hardness, pH value and DOC value which obviously influence the toxicity of copper, establishes an optimally distributed species sensitivity distribution curve, reflects the environmental behavior and toxicity effect of copper in a real water environment by a water quality parameter model correction method, and scientifically, perfectly and simply deduces the water quality standard of the characteristics of the freshwater aquatic organism system.
Description
Technical Field
The invention relates to the technical field of water quality reference, in particular to a method for predicting chronic toxicity of copper in watershed water environment and deriving a long-term reference.
Background
The long-term water quality benchmark is the maximum dose or level at which a water environmental factor (pollutant or detrimental element) does not have a chronic detrimental effect on the ecosystem under specific conditions and uses. From the aspect of revealing objective laws, the water quality standard has universality, but the difference in aspects such as natural geography, ecosystem composition and the like can cause the objective laws to present certain regional specificity, and the targeted research needs to be carried out in each country and each region according to actual conditions.
Due to the difference of the long-term water quality benchmark derivation method and the species use, the copper water quality benchmarks established in different countries and even the same country at different periods have great difference. The developed countries consider the influence of various water body environmental parameters and the biological effectiveness of different forms of copper on the toxicity of aquatic organisms, and recommend forms such as a Biological Ligand Model (BLM) or a hardness correction equation to be adopted in the water quality standard derivation guideline of copper, so as to reflect the environmental behavior and the toxicity effect of copper in a real water environment. Under the condition that the conditions allow, relevant research of the benchmark should be developed according to different ecological environment characteristics of each region, and the water quality benchmark is established.
Copper has the characteristics of high accumulation, easy migration, durability, capability of being enriched through a food chain and the like, has obvious toxic effect on aquatic organisms, is one of basic items of surface water environment quality standards (GB 3838-2002), and is also one of constraint indexes for controlling the total emission amount of main pollutants of a water environment. Copper can destroy important macromolecular substances such as lipid, protein and DNA in cells, and can cause poisoning of organisms. Excessive copper can cause the activity reduction of ceruloplasmin, glutathione peroxide and the like, influence the antioxidation of the organism, cause the accumulation of free radicals in the body, damage the membrane system and the membrane structure, cause the increase of the fragility of red blood cells, reduce the damage and the functions of the structure, and cause the degeneration, the necrosis and the dysfunction of tissue cells.
The toxicity of copper is obviously different due to various physicochemical actions of exposed water, and DOC value, suspended particles, pH value, various inorganic cations and anions of soluble organic compounds are important factors influencing the toxicity of copper and the water quality standard. The impact of physicochemical factors on copper toxicity is due to the effects on copper morphology and bioavailability.
In the existing standard, a single-value system is adopted, the influence of regional difference and environmental background is not reflected, and the combination with the actual surface water body monitoring data, water quality evaluation results and other local water quality characteristics is neglected. Therefore, it is necessary to develop a corresponding model to characterize different geological features of a typical watershed or water body.
The prior art has at least the following disadvantages:
1. the existing copper standard value adopts a single-value system and does not reflect the influence of regional difference and environmental background, and the combination of the existing copper standard value and the local water quality characteristics such as actual surface water body monitoring data, water quality evaluation results and the like is neglected.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for predicting the chronic toxicity of copper in watershed water environment and deducing a long-term benchmark, which provides a method for correcting three parameters of hardness H (Hardness) value, pH value of acid and alkali and soluble organic carbon DOC (DissolvedOrganic carbon) value to obtain the long-term water quality benchmark of copper, a plurality of groups of researchers obtain toxicity data in a database and a document library, correct the water ecological toxicity by the environmental parameters at least comprising the three parameters of hardness, pH value and DOC value, obtain the average chronic value of each species after correction, sort the average chronic values of each species, obtain cumulative probability, perform species sensitivity distribution model fitting, select an optimal fitting model from a normal distribution model, a lognormal distribution model, a logistic distribution model and a logistic distribution model through index evaluation, and acquiring the long-term water quality reference of the copper through the fitting model. The invention reasonably introduces three water body environmental parameters of water quality parameter hardness, pH value and DOC value which obviously affect the toxicity of copper, establishes an optimally distributed species sensitivity distribution curve, reflects the environmental behavior and toxicity effect of copper in a real water environment by a water quality parameter model correction method, and scientifically, perfectly and simply derives the water quality reference of the characteristics of the freshwater aquatic organism system.
The invention provides a method for predicting the chronic toxicity of copper in a watershed water environment and deducing a long-term benchmark, which comprises the following steps:
acquiring and processing long-term water ecological toxicity data through data retrieval and data screening, wherein the water ecological toxicity data at least comprise water body environment parameters such as hardness H value, pH value of acidity and alkalinity and DOC value of soluble organic carbon;
carrying out chronic toxicity correction through water quality parameters such as a hardness H value, an acid-base pH value and a soluble organic carbon DOC value;
obtaining a species average chronic value of each species;
sequencing the average chronic values of the seeds to obtain cumulative probability;
determining a fitting model of the average chronic value SMCV of the seeds selected under different water quality conditions through model fitting and evaluation;
predicting the harmful concentration of species from HC by using the selected fitting model5And acquiring a final long-term reference value.
Preferably, data retrieval and data screening is done independently by multiple groups of researchers from toxicity databases and literature repositories;
the toxicity data retrieval comprises retrieval of compound types, species types, toxicity effect test endpoint types and water body environment parameters;
the literature database comprises Chinese knowledge infrastructure engineering, a universal knowledge service platform, a Uppur network and a scientific network.
Preferably, the compound types include copper chloride, copper nitrate, copper sulfate, copper acetate, copper oxychloride, copper hydroxide, copper sulfate hydroxide, cuprous oxide, cupric oxide, and cuprous nitrate;
the species types include at least 1 fish of the family Osmanthus carpio, 1 fish of the family Osmanthus non-Cyprinus carpio, 1 zooplankton, 1 zoobenthos, 1 animal of the other phyla not mentioned above, 1 phytoplankton or aquatic vascular bundle.
The chronic toxicity value CTV comprises values of various toxicity effect test end point typesPoint types include No Observed Effect Concentration (NOEC), minimum Observed Effect Concentration (LOEC), No Observed Effect Level (NOEL), minimum Observed Effect Level (LOEL), Maximum allowable Concentration (MATC), and 20% Effect Concentration (EC)20(20％of Effective Concentration，EC20) (ii) a MATC is the geometric mean of NOEC and LOEC (or NOEL and LOEL);
the hardness H value in the water body environment parameters comprises calcium ion concentration and magnesium ion concentration, and the DOC value of the soluble organic carbon comprises fulvic acid, fulvic acid and humic acid.
Preferably, the chronic toxicity correction comprises:
fitting the chronic toxicity with the water body environment parameters;
and correcting the chronic toxicity value through the water body environmental parameters.
Preferably, fitting the chronic toxicity to the water body environment parameters comprises the following steps:
taking the logarithm value with the base of 10 of the hardness H value, the pH value and the DOC value of the water quality as an independent variable, taking the logarithm value with the base of 10 of the chronic toxicity value corresponding to each group of water quality parameters as a dependent variable, and performing multivariate linear fitting by adopting the following formula to obtain KA、KB、KCAnd CA：
lg(CTVi)＝KA*lg(Hi)+KB*lg(pHi)+KC*lg(DOCi)+CA
In the formula:
CTVi: the chronic toxicity value before the correction of the water quality parameters of the ith group is mu g/L;
KA、KB、KC: chronic toxicity: the coefficients corresponding to the hardness, the pH value and the DOC after the water quality parameters are fitted are dimensionless;
CA: chronic toxicity constant, intercept, dimensionless;
Hi: the hardness value of the water body before the correction of the ith group of water quality parameters is mg/L;
pHi: the pH value of the water body before the correction of the water quality parameters of the ith group is dimensionless;
DOCi: before the correction of the water quality parameter of the ith group, the water body solubility organic carbon concentration value is mg/L;
and (5) carrying out multivariate linear goodness of fit test.
Preferably, the watershed water environment parameter correction chronic toxicity value adopts the following formula:
in the formula:
CTViand CTV is the chronic toxicity value before and after the correction of the water quality parameter of the i-th group, mu g/L respectively;
Hiand H is the hardness value of the water body before and after the correction of the water quality parameter of the i-th group, mg/L respectively;
pHiand the pH is the water pH value before and after the correction of the i-th group of water quality parameters respectively, and is dimensionless;
DOCiand DOC is the water body solubility organic carbon concentration value before and after the i group water quality parameter correction, mg/L respectively;
KA、KB、KC: the corresponding coefficients of hardness, pH and DOC after the chronic toxicity and water quality parameters are fitted are dimensionless.
Preferably, the average chronic value SMCV of each species under the specified water body environmental parameters is obtained according to the following formulai，
In the formula (I), the compound is shown in the specification,
SMCVirepresenting the species average chronic value of the species i under the specified water body environmental parameter, mu g/L;
CTV represents the chronic toxicity value of the water body after environmental parameter correction, mu g/L;
n represents the number of CTV of the species i;
i represents the species number, dimensionless.
Preferably, the sorting of the seed average chronic values and the obtaining of the cumulative probability comprises the steps of:
arranging and combining different watershed water environment parameters to obtain a plurality of water environment parameter combinations;
SMCV treating the above water quality conditionsiThe log values of the two groups are respectively sorted from small to large, and the serial number R of the toxicity sorting is determined; the cumulative probability F of each species is calculated according to the following formulaR，
In the formula (I), the compound is shown in the specification,
FRrepresents cumulative probability,%;
r-the serial number of the species sequence, dimensionless;
n-total number of species, dimensionless.
Preferably, the determination of the fitted model of the mean chronic value of the seeds SMCV selected under different water quality conditions by model fitting and evaluation comprises the following steps:
average chronic value SMCV of seeds under the condition of multiple groups of water qualityiTaking the logarithm of base 10, and dividing lg (SMCV)i) As an independent variable in the model fitting, lg (SMCV)i) Corresponding cumulative probability FRFitting a species sensitivity distribution model for the dependent variable, the species sensitivity distribution model comprising: a normal distribution model, a lognormal distribution model, a logistic distribution model and a loglogistic distribution model; wherein, the SMCV isiIs the species average chronic value of species i;
determining coefficient R according to model fitting2The root mean square RMSE and the K-S test result determine a fitting model of the average chronic value SMCV of the seeds under different water quality conditions;
respectively taking the average chronic value of the species as an independent variable during model fitting and the cumulative probability corresponding to the average chronic value of the species as a dependent variable under the condition of specified water quality, and performing species sensitivity distribution model fitting;
and (5) carrying out model fitting goodness test, and determining a fitting model of the selected species average chronic value under the given water body environment parameter condition.
Preferably, environmental parameters at least including three parameters of hardness, pH value and DOC value are selected to correct water ecotoxicity, and the harm concentration of species is predicted through a selected fitting model, and HC is used for predicting the harm concentration of species5Obtaining a final long-term baseline value, comprising: determining species average chronic value corresponding to 5% of cumulative probability through species sensitivity distribution curve fitted by selected fitting model, and determining HC with chronic 5% of species hazard concentration5，HC5Divided by the evaluation factor to be used as the long-term water quality benchmark of copper.
Compared with the prior art, the invention has the following beneficial effects:
(1) the invention reasonably introduces three water body environmental parameters of water quality parameter hardness, pH value and DOC value which obviously affect the toxicity of copper, establishes an optimally distributed species sensitivity distribution curve, reflects the environmental behavior and toxicity effect of copper in a real water environment by a water quality parameter model correction method, and scientifically, perfectly and simply derives the long-term water quality reference of the characteristics of the freshwater aquatic organism system.
Drawings
FIG. 1 is a flow chart of a method for predicting chronic toxicity of copper and deriving a long-term baseline according to an embodiment of the present invention;
FIG. 2a, FIG. 2b and FIG. 2c are residual graphs obtained by linear fitting lg (CTV) with lg (H), lg (pH) and lg (DOC), respectively, according to an embodiment of the present invention;
FIG. 3 is a fitted curve of the species sensitivity distribution according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
The invention provides a method for predicting the chronic toxicity of copper in a watershed water environment and deducing a long-term benchmark, which comprises the following steps:
acquiring and processing long-term water ecological toxicity data through data retrieval and data screening, wherein the water ecological toxicity data at least comprise water body environment parameters such as hardness H value, pH value of acidity and alkalinity and DOC value of soluble organic carbon;
carrying out chronic toxicity correction through water quality parameters such as a hardness H value, an acid-base pH value and a soluble organic carbon DOC value;
obtaining a species average chronic value of each species;
sequencing the average chronic values of the seeds to obtain cumulative probability;
determining a fitting model of the average chronic value SMCV of the seeds selected under different water quality conditions through model fitting and evaluation;
predicting the harmful concentration of species from HC by using the selected fitting model5And acquiring a final long-term reference value.
According to one embodiment of the present invention, data retrieval and data screening is performed independently by multiple groups of researchers from toxicity databases and literature libraries;
the toxicity data retrieval comprises retrieval of compound types, species types, toxicity effect test endpoint types and water body environment parameters;
the literature database comprises Chinese knowledge infrastructure engineering, a universal knowledge service platform, a Uppur network and a scientific network.
According to a specific embodiment of the present invention, the compound types include copper chloride, copper nitrate, copper sulfate, copper acetate, basic copper chloride, copper hydroxide, basic copper sulfate, cuprous oxide, cupric oxide, and cuprous nitrate;
the species types include at least 1 fish of the family Osmanthus carpio, 1 fish of the family Osmanthus non-Cyprinus carpio, 1 zooplankton, 1 zoobenthos, 1 animal of the other phyla not mentioned above, 1 phytoplankton or aquatic vascular bundle.
The chronic toxicity value CTV includes values of various toxicity effect test endpoint types including non-observed effect concentration NOEC, lowest observed effect concentration LOEC, non-observed effect level NOEL, lowest observed effect level LOEL, maximum allowable concentration MATC and 20% effect concentration EC20；
The hardness H value in the water body environment parameters comprises calcium ion concentration and magnesium ion concentration, and the DOC value of the soluble organic carbon comprises fulvic acid, fulvic acid and humic acid.
According to a particular embodiment of the invention, the chronic toxicity correction comprises:
fitting the chronic toxicity with the water body environment parameters;
and correcting the chronic toxicity value through the water body environmental parameters.
According to a specific embodiment of the present invention, the fitting of the chronic toxicity to the water body environment parameters comprises the following steps:
taking the logarithm value with the base of 10 of the hardness H value, the pH value and the DOC value of the water quality as an independent variable, taking the logarithm value with the base of 10 of the chronic toxicity value corresponding to each group of water quality parameters as a dependent variable, and performing multivariate linear fitting by adopting the following formula to obtain KA、KB、KCAnd CA：
lg(CTVi)＝KA*lg(Hi)+KB*lg(pHi)+KC*lg(DOCi)+CA
In the formula:
CTVi: the chronic toxicity value before the correction of the water quality parameters of the ith group is mu g/L;
KA、KB、KC: chronic toxicity: the coefficients corresponding to the hardness, the pH value and the DOC after the water quality parameters are fitted are dimensionless;
CA: chronic toxicity constant, intercept, dimensionless;
Hi: the hardness value of the water body before the correction of the ith group of water quality parameters is mg/L;
pHi: the pH value of the water body before the correction of the water quality parameters of the ith group is dimensionless;
DOCi: before the correction of the water quality parameter of the ith group, the water body solubility organic carbon concentration value is mg/L;
and (5) carrying out multivariate linear goodness of fit test.
According to a specific embodiment of the invention, the watershed water environment parameter correction chronic toxicity value adopts the following formula:
in the formula:
CTViand CTV is the chronic toxicity value before and after the correction of the water quality parameter of the i-th group, mu g/L respectively;
Hiand H is the hardness value of the water body before and after the correction of the water quality parameter of the i-th group, mg/L respectively;
pHiand the pH is the water pH value before and after the correction of the i-th group of water quality parameters respectively, and is dimensionless;
DOCiand DOC is the water body solubility organic carbon concentration value before and after the i group water quality parameter correction, mg/L respectively;
KA、KB、KC: the corresponding coefficients of hardness, pH and DOC after the chronic toxicity and water quality parameters are fitted are dimensionless.
According to an embodiment of the invention, the average chronic value SMCV of each species under the specified water body environmental parameters is obtained according to the following formulai，
In the formula (I), the compound is shown in the specification,
SMCVirepresenting the species average chronic value of the species i under the specified water body environmental parameter, mu g/L;
CTV represents the chronic toxicity value of the water body after environmental parameter correction, mu g/L;
n represents the number of CTV of the species i;
i represents the species number, dimensionless.
According to a specific embodiment of the present invention, the step of ranking the seed average chronic values and obtaining the cumulative probability comprises the steps of:
arranging and combining different watershed water environment parameters to obtain a plurality of water environment parameter combinations;
SMCV treating the above water quality conditionsiThe logarithmic values of (A) go from small to large respectivelySequencing rows and determining the serial number R of the toxicity sequencing; the cumulative probability F of each species is calculated according to the following formulaR，
In the formula (I), the compound is shown in the specification,
FRrepresents cumulative probability,%;
r-the serial number of the species sequence, dimensionless;
n-total number of species, dimensionless.
According to a specific embodiment of the invention, the fitting model for determining the mean chronic value SMCV of the seeds selected under different water quality conditions through model fitting and evaluation comprises the following steps:
average chronic value SMCV of seeds under the condition of multiple groups of water qualityiTaking the logarithm of base 10, and dividing lg (SMCV)i) As an independent variable in the model fitting, lg (SMCV)i) Corresponding cumulative probability FRFitting a species sensitivity distribution model for the dependent variable, the species sensitivity distribution model comprising: a normal distribution model, a lognormal distribution model, a logistic distribution model and a loglogistic distribution model; wherein, the SMCV isiIs the species average chronic value of species i;
determining coefficient R according to model fitting2The root mean square RMSE and the K-S test result determine a fitting model of the average chronic value SMCV of the seeds under different water quality conditions;
respectively taking the average chronic value of the species as an independent variable during model fitting and the cumulative probability corresponding to the average chronic value of the species as a dependent variable under the condition of specified water quality, and performing species sensitivity distribution model fitting;
and (5) carrying out model fitting goodness test, and determining a fitting model of the selected species average chronic value under the given water body environment parameter condition.
According to one embodiment of the invention, environmental parameters including at least three of hardness, pH and DOC are selected to provide water ecotoxicityCorrecting, predicting harmful concentration of species by selected fitting model, and determining from HC5Obtaining a final long-term baseline value, comprising: determining species average chronic value corresponding to 5% of cumulative probability through species sensitivity distribution curve fitted by selected fitting model, and determining HC with chronic 5% of species hazard concentration5，HC5Divided by the evaluation factor to be used as the long-term water quality benchmark of copper.
Example 1
According to an embodiment of the invention, the method for predicting the chronic toxicity of copper in the watershed water environment and deducing the long-term benchmark are described in detail by combining the attached drawings.
The invention provides a method for predicting the chronic toxicity of copper in a watershed water environment and deducing a long-term benchmark, which comprises the following steps:
acquiring and processing long-term water ecological toxicity data through data retrieval and data screening, wherein the water ecological toxicity data at least comprise water body environment parameters such as hardness H value, pH value of acidity and alkalinity and DOC value of soluble organic carbon;
carrying out chronic toxicity correction through water quality parameters such as a hardness H value, an acid-base pH value and a soluble organic carbon DOC value;
obtaining a species average chronic value of each species;
sequencing the average chronic values of the seeds to obtain cumulative probability;
determining a fitting model of the average chronic value SMCV of the seeds selected under different water quality conditions through model fitting and evaluation;
predicting the harmful concentration of species from HC by using the selected fitting model5And acquiring a final long-term reference value.
Example 2
According to an embodiment of the invention, the method for predicting the chronic toxicity of copper in the watershed water environment and deducing the long-term benchmark are described in detail by combining the attached drawings.
The invention provides a method for predicting the chronic toxicity of copper in a watershed water environment and deducing a long-term benchmark, which comprises the following steps:
acquiring and processing long-term water ecological toxicity data through data retrieval and data screening, wherein the water ecological toxicity data at least comprise water body environment parameters such as hardness H value, pH value of acidity and alkalinity and DOC value of soluble organic carbon;
data retrieval and data screening are independently completed by a plurality of groups of researchers from a toxicity database and a document library;
the literature database comprises Chinese knowledge infrastructure engineering, a universal knowledge service platform, a Uppur network and a scientific network;
the toxicity data retrieval comprises retrieval of compound types, species types, toxicity effect test endpoint types and water body environment parameters;
the compound types include copper chloride, copper nitrate, copper sulfate, copper acetate, basic copper chloride, copper hydroxide, basic copper sulfate, cuprous oxide, cupric oxide, and cuprous nitrate;
the species types include at least 1 fish of the family Osmanthus carpio, 1 fish of the family Osmanthus non-Cyprinus carpio, 1 zooplankton, 1 zoobenthos, 1 animal of the other phyla not mentioned above, 1 phytoplankton or aquatic vascular bundle.
The chronic toxicity value CTV includes values of various toxicity effect test endpoint types including non-observed effect concentration NOEC, lowest observed effect concentration LOEC, non-observed effect level NOEL, lowest observed effect level LOEL, maximum allowable concentration MATC and 20% effect concentration EC20；
The hardness H value in the water body environment parameters comprises calcium ion concentration and magnesium ion concentration, and the DOC value of the soluble organic carbon comprises fulvic acid, fulvic acid and humic acid.
The chronic toxicity correction is carried out through water quality parameters such as hardness H value, pH value of acidity and alkalinity and DOC value of soluble organic carbon, and comprises the following steps:
fitting the chronic toxicity with the water body environment parameters;
and correcting the chronic toxicity value through the water body environmental parameters.
The fitting of the chronic toxicity and the water body environment parameters comprises the following steps:
using the logarithmic value of the hardness H value, the pH value and the DOC value of the water quality with the base of 10 as an independent variable and using the water quality of each groupTaking a logarithmic value of the chronic toxicity value corresponding to the parameter with the base 10 as a dependent variable, and carrying out multivariate linear fitting by adopting the following formula to obtain KA、KB、KCAnd CA：
lg(CTVi)＝KA*lg(Hi)+KB*lg(pHi)+KC*lg(DOCi)+CA
In the formula:
CTVi: the chronic toxicity value before the correction of the water quality parameters of the ith group is mu g/L;
KA、KB、KC: chronic toxicity: the coefficients corresponding to the hardness, the pH value and the DOC after the water quality parameters are fitted are dimensionless;
CA: chronic toxicity constant, intercept, dimensionless;
Hi: the hardness value of the water body before the correction of the ith group of water quality parameters is mg/L;
pHi: the pH value of the water body before the correction of the water quality parameters of the ith group is dimensionless;
DOCi: before the correction of the water quality parameter of the ith group, the water body solubility organic carbon concentration value is mg/L;
and (5) carrying out multivariate linear goodness of fit test.
The water environment parameter correction chronic toxicity value of the watershed adopts the following formula:
in the formula:
CTViand CTV is the chronic toxicity value before and after the correction of the water quality parameter of the i-th group, mu g/L respectively;
Hiand H is the hardness value of the water body before and after the correction of the water quality parameter of the i-th group, mg/L respectively;
pHiand the pH is the water pH value before and after the correction of the i-th group of water quality parameters respectively, and is dimensionless;
DOCiand DOC is the water body solubility organic carbon concentration value before and after the i group water quality parameter correction, mg/L respectively;
KA、KB、KC: the corresponding coefficients of hardness, pH and DOC after the chronic toxicity and water quality parameters are fitted are dimensionless.
Obtaining a species average chronic value of each species; obtaining the average chronic value SMCV of each species under the specified water body environmental parameters according to the following formulai，
In the formula (I), the compound is shown in the specification,
SMCVirepresenting the species average chronic value of the species i under the specified water body environmental parameter, mu g/L;
CTV represents the chronic toxicity value of the water body after environmental parameter correction, mu g/L;
n represents the number of CTV of the species i;
i represents the species number, dimensionless.
Sequencing the average chronic values of the seeds to obtain cumulative probability;
determining a fitting model of the average chronic value SMCV of the seeds selected under different water quality conditions through model fitting and evaluation;
predicting the harmful concentration of species from HC by using the selected fitting model5And acquiring a final long-term reference value.
Example 3
According to an embodiment of the invention, the method for predicting the chronic toxicity of copper in the watershed water environment and deducing the long-term benchmark are described in detail by combining the attached drawings.
According to an embodiment of the invention, the method for predicting the chronic toxicity of copper in the watershed water environment and deducing the long-term benchmark are described in detail by combining the attached drawings.
The invention provides a method for predicting the chronic toxicity of copper in a watershed water environment and deducing a long-term benchmark, which comprises the following steps:
acquiring and processing long-term water ecological toxicity data through data retrieval and data screening, wherein the water ecological toxicity data at least comprise water body environment parameters such as hardness H value, pH value of acidity and alkalinity and DOC value of soluble organic carbon;
data retrieval and data screening are independently completed by a plurality of groups of researchers from a toxicity database and a document library;
the literature database comprises Chinese knowledge infrastructure engineering, a universal knowledge service platform, a Uppur network and a scientific network;
the toxicity data retrieval comprises retrieval of compound types, species types, toxicity effect test endpoint types and water body environment parameters;
the compound types include copper chloride, copper nitrate, copper sulfate, copper acetate, basic copper chloride, copper hydroxide, basic copper sulfate, cuprous oxide, cupric oxide, and cuprous nitrate;
the species types include at least 1 fish of the family Osmanthus carpio, 1 fish of the family Osmanthus non-Cyprinus carpio, 1 zooplankton, 1 zoobenthos, 1 animal of the other phyla not mentioned above, 1 phytoplankton or aquatic vascular bundle.
The chronic toxicity value CTV includes values of various toxicity effect test endpoint types including non-observed effect concentration NOEC, lowest observed effect concentration LOEC, non-observed effect level NOEL, lowest observed effect level LOEL, maximum allowable concentration MATC and 20% effect concentration EC20；
The hardness H value in the water body environment parameters comprises calcium ion concentration and magnesium ion concentration, and the DOC value of the soluble organic carbon comprises fulvic acid, fulvic acid and humic acid.
The chronic toxicity correction is carried out through water quality parameters such as hardness H value, pH value of acidity and alkalinity and DOC value of soluble organic carbon, and comprises the following steps:
fitting the chronic toxicity with the water body environment parameters;
and correcting the chronic toxicity value through the water body environmental parameters.
The fitting of the chronic toxicity and the water body environment parameters comprises the following steps:
the logarithm value of the hardness H value, the pH value and the DOC value of the water quality with the base of 10 is taken as an independent variable, and the logarithm value of the chronic toxicity value with the base of 10 corresponding to each group of water quality parameters is taken as a logarithmTaking the value as a dependent variable, and performing multivariate linear fitting by adopting the following formula to obtain KA、KB、KCAnd CA：
lg(CTVi)＝KA*lg(Hi)+KB*lg(pHi)+KC*lg(DOCi)+CA
In the formula:
CTVi: the chronic toxicity value before the correction of the water quality parameters of the ith group is mu g/L;
KA、KB、KC: chronic toxicity: the coefficients corresponding to the hardness, the pH value and the DOC after the water quality parameters are fitted are dimensionless;
CA: chronic toxicity constant, intercept, dimensionless;
Hi: the hardness value of the water body before the correction of the ith group of water quality parameters is mg/L;
pHi: the pH value of the water body before the correction of the water quality parameters of the ith group is dimensionless;
DOCi: before the correction of the water quality parameter of the ith group, the water body solubility organic carbon concentration value is mg/L;
and (5) carrying out multivariate linear goodness of fit test.
The water environment parameter correction chronic toxicity value of the watershed adopts the following formula:
in the formula:
CTViand CTV is the chronic toxicity value before and after the correction of the water quality parameter of the i-th group, mu g/L respectively;
Hiand H is the hardness value of the water body before and after the correction of the water quality parameter of the i-th group, mg/L respectively;
pHiand the pH is the water pH value before and after the correction of the i-th group of water quality parameters respectively, and is dimensionless;
DOCiand DOC is the water body solubility organic carbon concentration value before and after the i group water quality parameter correction, mg/L respectively;
KA、KB、KC: the corresponding coefficients of hardness, pH and DOC after the chronic toxicity and water quality parameters are fitted are dimensionless.
Obtaining a species average chronic value of each species; obtaining the average chronic value SMCV of each species under the specified water body environmental parameters according to the following formulai，
In the formula (I), the compound is shown in the specification,
SMCVirepresenting the species average chronic value of the species i under the specified water body environmental parameter, mu g/L;
CTV represents the chronic toxicity value of the water body after environmental parameter correction, mu g/L;
n represents the number of CTV of the species i;
i represents the species number, dimensionless.
Sorting the average chronic values of the seeds to obtain the cumulative probability, comprising the following steps:
arranging and combining different watershed water environment parameters to obtain a plurality of water environment parameter combinations;
SMCV treating the above water quality conditionsiThe log values of the two groups are respectively sorted from small to large, and the serial number R of the toxicity sorting is determined; the cumulative probability F of each species is calculated according to the following formulaR，
In the formula (I), the compound is shown in the specification,
FRrepresents cumulative probability,%;
r-the serial number of the species sequence, dimensionless;
n-total number of species, dimensionless.
Determining a fitting model of the mean chronic value SMCV of the selected species under different water quality conditions through model fitting and evaluation, comprising the following steps of:
for multiple groups of water quality conditionsAverage chronic value SMCV of breedingiTaking the logarithm of base 10, and dividing lg (SMCV)i) As an independent variable in the model fitting, lg (SMCV)i) Corresponding cumulative probability FRFitting a species sensitivity distribution model for the dependent variable, the species sensitivity distribution model comprising: a normal distribution model, a lognormal distribution model, a logistic distribution model and a loglogistic distribution model; wherein, the SMCV isiIs the species average chronic value of species i;
determining coefficient R according to model fitting2The root mean square RMSE and the K-S test result determine a fitting model of the average chronic value SMCV of the seeds under different water quality conditions;
respectively taking the average chronic value of the species as an independent variable during model fitting and the cumulative probability corresponding to the average chronic value of the species as a dependent variable under the condition of specified water quality, and performing species sensitivity distribution model fitting;
and (5) carrying out model fitting goodness test, and determining a fitting model of the selected species average chronic value under the given water body environment parameter condition.
Selecting environmental parameters at least including three parameters of hardness, pH value and DOC value to correct water ecotoxicity, predicting harmful concentration of species by selected fitting model, and determining the risk of water5Obtaining a final long-term baseline value, comprising: determining species average chronic value corresponding to 5% of cumulative probability through species sensitivity distribution curve fitted by selected fitting model, and determining HC with chronic 5% of species hazard concentration5，HC5Divided by the evaluation factor to be used as the long-term water quality benchmark of copper.
Example 4
According to an embodiment of the invention, the method for predicting the chronic toxicity of copper in the watershed water environment and deducing the long-term benchmark are described in detail by combining the attached drawings.
According to an embodiment of the invention, the method for predicting the chronic toxicity of copper in the watershed water environment and deducing the long-term benchmark are described in detail by combining the attached drawings.
The invention provides a method for predicting the chronic toxicity of copper in a watershed water environment and deducing a long-term benchmark, which comprises the following steps:
acquiring and processing long-term water ecological toxicity data through data retrieval and data screening, wherein the water ecological toxicity data at least comprise water body environment parameters such as hardness H value, pH value of acidity and alkalinity and DOC value of soluble organic carbon;
data retrieval and data screening are independently completed by a plurality of groups of researchers from a toxicity database and a document library;
the toxicity database comprises the environmental protection agency database of a certain country;
the literature database comprises Chinese knowledge infrastructure engineering, a universal knowledge service platform, a Uppur network and a scientific network;
the toxicity data retrieval comprises retrieval of compound types, species types, toxicity effect test endpoint types and water body environment parameters;
the compound types include copper chloride, copper nitrate, copper sulfate, copper acetate, basic copper chloride, copper hydroxide, basic copper sulfate, cuprous oxide, cupric oxide, and cuprous nitrate;
the exposure time of the chronic toxicity is more than or equal to 21 days, and the experimental exposure time at least spans one generation or life sensitive stage; the reference test species covers at least 3 nutritional levels of the producer, primary consumer and secondary consumer; the reference test species covers at least 3 nutritional levels for the producer, primary consumer and secondary consumer;
the species types include at least 1 fish of the family Osmanthus carpio, 1 fish of the family Osmanthus non-Cyprinus carpio, 1 zooplankton, 1 zoobenthos, 1 animal of the other phyla not mentioned above, 1 phytoplankton or aquatic vascular bundle.
The chronic toxicity value CTV includes values of various toxicity effect test endpoint types including non-observed effect concentration NOEC, lowest observed effect concentration LOEC, non-observed effect level NOEL, lowest observed effect level LOEL, maximum allowable concentration MATC and 20% effect concentration EC20；
The hardness H value in the water body environment parameters comprises calcium ion concentration and magnesium ion concentration, and the DOC value of the soluble organic carbon comprises fulvic acid, fulvic acid and humic acid.
The chronic toxicity correction is carried out through water quality parameters such as hardness H value, pH value of acidity and alkalinity and DOC value of soluble organic carbon, and comprises the following steps:
fitting the chronic toxicity with the water body environment parameters;
and correcting the chronic toxicity value through the water body environmental parameters.
The fitting of the chronic toxicity and the water body environment parameters comprises the following steps:
taking the logarithm value with the base of 10 of the hardness H value, the pH value and the DOC value of the water quality as an independent variable, taking the logarithm value with the base of 10 of the chronic toxicity value corresponding to each group of water quality parameters as a dependent variable, and performing multivariate linear fitting by adopting the following formula to obtain KA、KB、KCAnd CA：
lg(CTVi)＝KA*lg(Hi)+KB*lg(pHi)+KC*lg(DOCi)+CA
In the formula:
CTVi: the chronic toxicity value before the correction of the water quality parameters of the ith group is mu g/L;
KA、KB、KC: chronic toxicity: the coefficients corresponding to the hardness, the pH value and the DOC after the water quality parameters are fitted are dimensionless;
CA: chronic toxicity constant, intercept, dimensionless;
Hi: the hardness value of the water body before the correction of the ith group of water quality parameters is mg/L;
pHi: the pH value of the water body before the correction of the water quality parameters of the ith group is dimensionless;
DOCi: before the correction of the water quality parameter of the ith group, the water body solubility organic carbon concentration value is mg/L;
and (5) carrying out multivariate linear goodness of fit test.
The water environment parameter correction chronic toxicity value of the watershed adopts the following formula:
in the formula:
CTViand CTV is the chronic toxicity value before and after the correction of the water quality parameter of the i-th group, mu g/L respectively;
Hiand H is the hardness value of the water body before and after the correction of the water quality parameter of the i-th group, mg/L respectively;
pHiand the pH is the water pH value before and after the correction of the i-th group of water quality parameters respectively, and is dimensionless;
DOCiand DOC is the water body solubility organic carbon concentration value before and after the i group water quality parameter correction, mg/L respectively;
KA、KB、KC: the corresponding coefficients of hardness, pH and DOC after the chronic toxicity and water quality parameters are fitted are dimensionless.
Obtaining a species average chronic value of each species; obtaining the average chronic value SMCV of each species under the specified water body environmental parameters according to the following formulai，
In the formula (I), the compound is shown in the specification,
SMCVirepresenting the species average chronic value of the species i under the specified water body environmental parameter, mu g/L;
CTV represents the chronic toxicity value of the water body after environmental parameter correction, mu g/L;
n represents the number of CTV of the species i;
i represents the species number, dimensionless.
Sorting the average chronic values of the seeds to obtain the cumulative probability, comprising the following steps:
arranging and combining different watershed water environment parameters to obtain a plurality of water environment parameter combinations;
SMCV treating the above water quality conditionsiThe log values of the two groups are respectively sorted from small to large, and the serial number R of the toxicity sorting is determined; the lowest toxicity value is ranked in order of 1, the next rank of 2, if there are two or more species toxicIf the values are the same, randomly arranging the serial numbers into continuous sequencing serial numbers, wherein the number of the articles under each sequencing serial number is 1;
the cumulative probability F of each species is calculated according to the following formulaR，
In the formula (I), the compound is shown in the specification,
FRrepresents cumulative probability,%;
r-the serial number of the species sequence, dimensionless;
n-total number of species, dimensionless.
Determining a fitting model of the mean chronic value SMCV of the selected species under different water quality conditions through model fitting and evaluation, comprising the following steps of:
average chronic value SMCV of seeds under the condition of multiple groups of water qualityiTaking the logarithm of base 10, and dividing lg (SMCV)i) As an independent variable in the model fitting, lg (SMCV)i) Corresponding cumulative probability FRFitting a species sensitivity distribution model for the dependent variable, the species sensitivity distribution model comprising: a normal distribution model, a lognormal distribution model, a logistic distribution model and a loglogistic distribution model; wherein, the SMCV isiIs the species average chronic value of species i;
determining coefficient R according to model fitting2The root mean square RMSE and the K-S test result determine a fitting model of the average chronic value SMCV of the seeds under different water quality conditions;
in addition to the above-mentioned test standards, the test standards may also include Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC);
the smaller the value is, the more the statistical significance is; r2The closer to 1, the greater the goodness of fit of the toxicity data is, and the more accurate the model fitting is; RMSE is the square root of the ratio of the square of the deviation of the observed and true values to the number of observations, and this statistical parameter is also called the fitted standard deviation of the regression systemThe RMSE can reflect the precision of the model in a statistical sense, and the closer the RMSE is to 0, the higher the accuracy of model fitting is; the index of the F test is the correlation probability p calculated by multi-factor variance analysis; testing by adopting a P value corresponding to the F statistic; in order to prevent problems such as overfitting in multivariate regression analysis (not only making model explanatory strong, but also having little tension), AIC and BIC are used as the criteria for regression model selection; in the regression model, the smaller both values the better; calculated according to the following formula,
AIC nln (sum of squares of residuals) +2(p +1) -nln (n)
BIC nln (sum of squares of residuals) + (p +1) in (n) -nln (n)
Wherein n is the sample size (number of individual cases, number of sample parties, etc.) of the logarithm operator; and p is the number of independent variables in the regression equation.
Respectively taking the average chronic value of the species as an independent variable during model fitting and the cumulative probability corresponding to the average chronic value of the species as a dependent variable under the condition of specified water quality, and performing species sensitivity distribution model fitting;
and (4) carrying out model fitting goodness test by adopting the above test standards, and determining a fitting model of the average chronic value of the selected species under the given water body environmental parameter condition.
Predicting the harmful concentration of species from HC by using the selected fitting model5Obtaining a final long-term baseline value, comprising: determining cumulative probabilities of 5%, 10%, 25%, 50%, 75%, 9% by fitting species sensitivity distribution curves to selected fitting modelsLg (SMCV) corresponding to 0% and 95%i) Value, inverse logarithm of obtained SMCViThat is, the chronic species hazard concentration HC of 5%, 10%, 25%, 50%, 75%, 90%, 95%5、HC10、HC25、HC50、HC75、HC90、HC95(ii) a The average chronic value of species corresponding to the cumulative probability of 5 percent is HC with the chronic 5 percent species hazard concentration5，HC5Divided by the evaluation factor to be used as the long-term water quality benchmark of copper. Taking an evaluation factor of 3 when the number of the species samples is less than 15; the number of species samples is greater than 15, and the evaluation factor is 2.
Example 5
According to an embodiment of the invention, the method for predicting the chronic toxicity of copper in the watershed water environment and deducing the long-term benchmark are described in detail by combining the attached drawings.
The invention calculates the mean chronic value (SMCV) and predicts chronic toxicity based on the toxicity endpoint value of growth and reproductive toxicity effects as CTV. The method is a method for correcting chronic toxicity-water environment parameters based on different geological feature characteristics of a typical watershed or a water body and applying the chronic toxicity-water environment parameters to copper chronic toxicity prediction and long-term reference derivation.
The three water quality parameters most significantly affecting copper toxicity, hardness, DOC and pH, are exemplified below. As shown in fig. 1, it is a schematic flow chart of the method for predicting the chronic toxicity of copper in watershed water environment and deriving the long-term reference, and the specific process is as follows:
step a, acquiring, screening and summarizing toxicity data;
step a1, data acquisition process;
step a2, data screening process;
the data screening satisfies the following conditions:
1) chronic toxicity data was reported from the last 10 years literature and from the national Bureau database;
2) each toxicity data must include scientific, complete experimental condition parameters, preferably hardness, DOC and pH parameters.
3) Chronic toxicity values include NOECLOEC, NOEL, LOEL, MATC and EC20MATC is the geometric mean of NOEC and LOEC (or NOEL and LOEL);
4) toxicity testing must be performed under a range of environmental conditions with standard procedures;
step a3, data summarization process:
the final data set includes the species latin name, the tested organism type, the toxic effect type, the end point index, the test condition, the exposure time, the data source.
The detailed toxicity data acquisition procedure is as follows:
the modeled chronic toxicity data is preferably collected from the toxicity database of the environmental protection agency of a certain country. If the toxicity data is insufficient, the effective data (ISI Web of Knowledge) of SCI scientific citation index query in the last 10 years is used as a supplement. And inputting keywords such as a metal name, a name of a species to be detected, chronic toxicity and the like through a database and a document retrieval engine, and deriving a toxicity data set meeting the conditions. And screening qualified toxicity data on the premise of meeting the condition of the step a 2. In the data compiling process, information such as compound types, tested organism types, toxic effect types, end point indexes, test conditions, exposure time, data sources and the like is recorded and is organized into a modeling basis set.
Data were screened and summarized as the chronic toxicity endpoint of daphnia fuzz (ceriodophnia dubia), and the results are shown in table 1.
TABLE 1 fuzzy Scorzonera toxicity data screening, calculation and summary examples
B, firstly setting a baseline water quality condition during data correction, and setting the baseline water quality conditions of water hardness, pH and DOC as hardness 50mg/L, pH 7 and DOC 5mg/L according to the water quality condition of surface water and the suitable condition for aquatic organism survival;
b1, constructing different multiple regression equations, and fitting the chronic toxicity and the water body environment parameters;
respectively taking logarithm with the base of 10 for the CTV of each piece of data after screening and summarizing in the step a and the pH, hardness and DOC value of the corresponding water body, and performing linear fitting by using a formula (1) and a formula (2) to respectively obtain a formula (3) and a formula (4):
lg(CTV)＝K1lg(H)+K2lg(pH)+K3lg(DOC)+C1(1)
lg(CTV)＝K4lg(H)+K5lg(pH)+K6lg(DOC)+K7lg(H)×lg(pH)×lg(DOC)+C2(2)
lg(CTV)＝0.2795×lg(H)+1.0929×lg(pH)+0.7447×lg(DOC)-0.3701(3)
lg(CTV)＝0.2307lg(H)+0.7800lg(pH)+0.3493lg(DOC)+0.2034lg(H)×lg(pH)×
lg(DOC)+0.00359(4)
in the formula (I), the compound is shown in the specification,
K1、K2、K3: the linear combination formula (1) is the coefficient corresponding to lg (H), lg (pH) and lg (DOC) after the chronic toxicity-water quality parameter fitting, and is dimensionless;
K4、K5、K6、K7the coefficients corresponding to lg (H), lg (pH), lg (DOC) and lg (H) lg (DOC) lg (pH) are dimensionless after the linear combination (2) is fitted with the chronic toxicity-water quality parameters;
C1、C2the chronic toxicity constants of the linear combination (1) and the linear combination (2) are intercept and dimensionless;
h represents water hardness value (as CaCO)3Calculated), the values are respectively 50 mg/L; the pH value represents the pH value of the water body, the values are 7 respectively, and no dimension exists; DOC represents the water body solubility organic carbon concentration value, and the values are respectively 5 mg/L;
step b2, performing multiple linear fitting goodness test and significance test of the regression equation, selecting the optimal linear fitting equation, and adopting F test:
the test indexes of the goodness of fit of the toxicity-water environment parameters are as follows: square of correlation coefficient R2Root mean square RMSE, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC);
the smaller the value is, the more the statistical significance is; r2The closer to 1, the greater the goodness of fit of the toxicity data is, and the more accurate the model fitting is; RMSE is the square root of the ratio of the square of the deviation of an observed value and a true value to the observation frequency, the statistical parameter is also called the fitting standard deviation of the regression system, the RMSE can reflect the precision of the model in the statistical sense, and the closer the RMSE is to 0, the higher the precision of model fitting is; the index of the F test is the correlation probability p calculated by multi-factor variance analysis; testing by adopting a P value corresponding to the F statistic; in order to prevent problems such as overfitting in multivariate regression analysis (not only making model explanatory strong, but also having little tension), AIC and BIC are used as the criteria for regression model selection; in the regression model, the smaller both values are the better.
Said step b2 is calculated according to the following formula, for comparing two linear combinations,
AIC nln (sum of squares residual) +2(p +1) -nln (n) (8)
BIC nln (sum of squared residuals) + (p +1) ln (n) -nln (n) (9)
Wherein n is the number of samples (number of individual cases, number of samples, etc.) as the logarithm operator, and p is the number of independent variables in the regression equation.
After calculation, the ratio 273.0 of the standard error to the predicted value in the formula (2) is far higher than 2.392 in the formula (1), the statistics of the second group are considered to be poor, and the coefficient R is determined20.5531 and 0.5525 respectively, the p values are both less than 0.001, and the comparison shows that the first group of linear combination is selected as the optimal solutionRMSE 0.3787, F61.23, AIC 5.996, BIC 10.54, and the residuals after fitting are shown in fig. 2a, 2b, and 2 c;
step b3, correcting each toxicity data according to water hardness, pH and DOC according to a formula (10) to obtain CTV under the condition of baseline water quality, and correcting the CTV:
in the formula:
CTViand CTV is the chronic toxicity value before and after the correction of the water quality parameter of the i-th group, mu g/L respectively;
Hiand H is the hardness value of the water body before and after the correction of the water quality parameter of the i-th group, mg/L respectively;
pHiand the pH is the water pH value before and after the correction of the i-th group of water quality parameters respectively, and is dimensionless;
DOCiand DOC is the water body solubility organic carbon concentration value before and after the i group water quality parameter correction, mg/L respectively;
KA、KB、KC: the coefficients corresponding to the hardness, the pH and the DOC after the fitting of the chronic toxicity and the water quality parameters are dimensionless;
taking the fuzzy cheilopsis (ceriodophnia dubia) as an example, table 2 shows the CTV obtained under the baseline water quality condition according to the water hardness, pH, DOC correction.
TABLE 2 CTV paradigm before and after Correct Moire flea
| Name of species | CTVi(μg/L) | CTV (μ g/L) at baseline Water quality conditions |
| Fuzzy reticulate flea | 7.897 | 8.170 |
| Fuzzy reticulate flea | 19.36 | 27.18 |
| Fuzzy reticulate flea | 24.50 | 110.67 |
| Fuzzy reticulate flea | 34.60 | 156.29 |
| Fuzzy reticulate flea | 9.170 | 25.98 |
| Fuzzy reticulate flea | 12.00 | 52.42 |
| Fuzzy reticulate flea | 34.00 | 33.83 |
| Fuzzy reticulate flea | 40.00 | 24.10 |
| Fuzzy reticulate flea | 59.00 | 35.83 |
| Fuzzy reticulate flea | 41.00 | 16.46 |
| Fuzzy reticulate flea | 1.800 | 8.080 |
Step c, calculating the average chronic value SMCV of the species under the base line condition according to the formula (11)i：
In the formula (I), the compound is shown in the specification,
SMCVirepresenting the species average chronic value of the species i under the specified water body environmental parameter, mu g/L; CTV represents the chronic toxicity value of the water body after environmental parameter correction, mu g/L;
n represents the number of CTV of the species i;
i represents a species, dimensionless;
the SMCVs for each species were obtained under baseline water quality conditions, as shown in table 3.
TABLE 3 SMCV under Baseline Water quality conditions
Step d, calculating the cumulative probability of different species;
step d1, selecting a water body environment parameter combination with hardness of 50mg/L, pH 7 and DOC of 5mg/L under the baseline condition;
step d2, testing the SMCV at baseline conditionsiRespectively from small to smallOrdering until large, determining toxicity rank R (the rank of the minimum toxicity value is 1, the rank of the next is 2, arranging in sequence, if the toxicity values of two or more species are the same, randomly arranging them into continuous rank, and the number of the species under each rank is 1);
step d3, respectively calculating the cumulative probability F of the species according to the formula (12)R，
In the formula (I), the compound is shown in the specification,
FRrepresents cumulative probability,%;
r-the serial number of the species sequence, dimensionless;
n-total number of species, dimensionless.
The cumulative probabilities of the species under baseline conditions were obtained from step d 1-step d3, in descending order, as shown in Table 4.
TABLE 4 cumulative probability under baseline Water quality conditions
E, fitting and evaluating the model; SMCV (surface acoustic wave device)iTaking the logarithm of base 10, and dividing lg (SMCV)H,i) As an independent variable in the model fitting, lg (SMCV)i) Corresponding FRPerforming SSD model fitting (including normal distribution model, lognormal distribution model, logistic distribution model, and logarithmic logistic distribution model) for dependent variable, and determining coefficient (R) according to formula (6), formula (7), and formula (8)2) The results of the Root Mean Square (RMSE) and K-S tests (p values) are shown in Table 5, and by comparison, the lognormal distribution model is the best fit model of the SMCV under the baseline condition, and the SSD fit curve of the model isThe lines are shown in figure 3.
And (3) checking the goodness of fit of the model: for parametric models, p-values>In the case of 0.05 (the reflection model is in accordance with the theoretical distribution), the coefficient R is determined2The larger the root mean square RMSE, the more consistent the distribution is with some theoretical distribution.
TABLE 5 Long-term Water quality benchmark model fitting results under baseline conditions
| Fitting model | R2 | RMSE | p(K-S) |
| Normal distribution model | 0.9394 | 0.0681 | >0.05 |
| Lognormal distribution model | 0.9573 | 0.0571 | >0.05 |
| Logical stetty model | 0.9417 | 0.0667 | >0.05 |
| Logarithmic logistic model | 0.9542 | 0.0591 | >0.05 |
Step f, according to the SSD curve which is optimally fitted by the fitting model under the condition of the baseline water quality and is determined by the method in the steps a to e, determining lg (SMCV) corresponding to 5%, 10%, 25%, 50%, 75%, 90% and 95% of cumulative probabilityi) Value, inverse logarithm of obtained SMCViThat is, the chronic species hazard concentration HC of 5%, 10%, 25%, 50%, 75%, 90%, 95%5、HC10、HC25、HC50、HC75、HC90、HC95(ii) a See table 6;
TABLE 6 Long term species hazard concentrations
HC5And dividing by an evaluation factor (according to the national standard HJ 831-2017, the number f of the species contained in the effective toxicity data is more than 15 and covers enough nutritional levels, and the value of the evaluation factor is 2), so that the chronic toxicity of the copper can be predicted, and the copper long-term reference value LWQC in the water environment is calculated. Baseline HC in Table 75Divided by an evaluation factor, namely a long-term water quality standard under the water quality conditions of 50mg/L, pH 7 hardness and 5mg/L DOC, the maximum concentration (in any 1-hour arithmetic mean concentration) of copper in the water body which does not produce chronic harmful effects on 95% of Chinese freshwater aquatic organisms and ecological functions thereof is shown.
TABLE 7 Long-term Water quality benchmark
| HC5 | Evaluation factor | Long term basis (. mu.g/L) (hardness 50mg/L, pH 7 and DOC 5mg/L) |
| 10.84 | 2 | 5.4 |
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (10)
1. A method for predicting the chronic toxicity of copper in a watershed water environment and deducing a long-term benchmark is characterized by comprising the following steps:
acquiring and processing long-term water ecological toxicity data through data retrieval and data screening, wherein the water ecological toxicity data at least comprise water body environment parameters such as hardness H value, pH value of acidity and alkalinity and DOC value of soluble organic carbon;
carrying out chronic toxicity correction through water quality parameters such as a hardness H value, an acid-base pH value and a soluble organic carbon DOC value;
obtaining a species average chronic value of each species;
sequencing the average chronic values of the seeds to obtain cumulative probability;
determining a fitting model of the average chronic value SMCV of the seeds selected under different water quality conditions through model fitting and evaluation;
predicting the harmful concentration of species from HC by using the selected fitting model5And acquiring a final long-term reference value.
2. The method for prediction and long-term baseline derivation of chronic toxicity of copper in watershed aqueous environments as claimed in claim 1, wherein data retrieval and data screening is done independently by multiple groups of researchers from toxicity databases and literature banks;
the toxicity data retrieval comprises retrieval of compound types, species types, toxicity effect test endpoint types and water body environment parameters;
the literature database comprises Chinese knowledge infrastructure engineering, a universal knowledge service platform, a Uppur network and a scientific network.
3. The method for prediction and long-term baseline derivation of chronic toxicity of copper in watershed aqueous environments of claim 2, wherein the compound types comprise cupric chloride, cupric nitrate, cupric sulfate, cupric acetate, basic cupric chloride, cupric hydroxide, basic cupric sulfate, cuprous oxide, cupric oxide, and cuprous nitrate;
species types include at least 1 Osteichthyidae, 1 zooplankton, 1 zoobenthos, 1 animal of other phyla not mentioned above, 1 phytoplankton or aquatic vascular bundle;
the chronic toxicity value CTV includes values of various toxicity effect test endpoint types including non-observed effect concentration NOEC, lowest observed effect concentration LOEC, non-observed effect level NOEL, lowest observed effect level LOEL, maximum allowable concentration MATC and 20% effect concentration EC20；
The hardness H value in the water body environment parameters comprises calcium ion concentration and magnesium ion concentration, and the DOC value of the soluble organic carbon comprises fulvic acid, fulvic acid and humic acid.
4. The method for prediction and long-term baseline derivation of chronic toxicity of copper in watershed aqueous environment as claimed in claim 3, wherein the chronic toxicity correction comprises:
fitting the chronic toxicity with the water body environment parameters;
and correcting the chronic toxicity value through the water body environmental parameters.
5. The method for predicting the chronic toxicity of copper in the watershed water environment and deriving the long-term reference as claimed in claim 4, wherein the fitting of the chronic toxicity and the water environment parameters comprises the following steps:
taking the logarithm value with the base of 10 of the hardness H value, the pH value and the DOC value of the water quality as an independent variable, taking the logarithm value with the base of 10 of the chronic toxicity value corresponding to each group of water quality parameters as a dependent variable, and performing multivariate linear fitting by adopting the following formula to obtain KA、KB、KCAnd CA：
lg(CTVi)＝KA*lg(Hi)+KB*lg(pHi)+KC*lg(DOCi)+CA
In the formula:
CTVi: the chronic toxicity value before the correction of the water quality parameters of the ith group is mu g/L;
KA、KB、KC: chronic toxicity: the coefficients corresponding to the hardness, the pH value and the DOC after the water quality parameters are fitted are dimensionless;
CA: chronic toxicity constant, intercept, dimensionless;
Hi: the hardness value of the water body before the correction of the ith group of water quality parameters is mg/L;
pHi: the pH value of the water body before the correction of the water quality parameters of the ith group is dimensionless;
DOCi: before the correction of the water quality parameter of the ith group, the water body solubility organic carbon concentration value is mg/L;
and (5) carrying out multivariate linear goodness of fit test.
6. The method for predicting and deriving a long-term basis for chronic toxicity of copper in watershed water environment according to claim 5, wherein the formula for correcting the chronic toxicity value of parameters of the watershed water environment is as follows:
in the formula:
CTViand CTV is the chronic toxicity value before and after the correction of the water quality parameter of the i-th group, mu g/L respectively;
Hiand H isWater hardness values of mg/L before and after the correction of the ith group of water quality parameters;
pHiand the pH is the water pH value before and after the correction of the i-th group of water quality parameters respectively, and is dimensionless;
DOCiand DOC is the water body solubility organic carbon concentration value before and after the i group water quality parameter correction, mg/L respectively;
KA、KB、KC: the corresponding coefficients of hardness, pH and DOC after the chronic toxicity and water quality parameters are fitted are dimensionless.
7. The method for predicting the chronic toxicity of copper in watershed water environment and deriving the long-term reference as claimed in claim 1, wherein the average chronic value SMCV of species of each species under the specified water environment parameters is obtained according to the following formulai，
In the formula (I), the compound is shown in the specification,
SMCVirepresenting the species average chronic value of the species i under the specified water body environmental parameter, mu g/L;
CTV represents the chronic toxicity value of the water body after environmental parameter correction, mu g/L;
n represents the number of CTV of the species i;
i represents the species number, dimensionless.
8. The method for predicting the chronic toxicity of copper in the watershed water environment and deriving the long-term reference as claimed in claim 1, wherein the step of ranking the average chronic values of species and obtaining the cumulative probability comprises the following steps:
arranging and combining different watershed water environment parameters to obtain a plurality of water environment parameter combinations;
SMCV under the condition of multi-group water qualityiThe log values of the two groups are respectively sorted from small to large, and the serial number R of the toxicity sorting is determined;
calculating each species according to the following formulaCumulative probability of (F)R，
In the formula (I), the compound is shown in the specification,
FRrepresents cumulative probability,%;
r-the serial number of the species sequence, dimensionless;
n-total number of species, dimensionless.
9. The method for predicting the chronic toxicity of copper in the watershed water environment and deriving the long-term reference according to claim 1, wherein the step of determining the fitted model of the mean chronic value SMCV of the selected species under different water quality conditions through model fitting and evaluation comprises the following steps:
average chronic value SMCV of seeds under the condition of multiple groups of water qualityiTaking the logarithm of base 10, and dividing lg (SMCV)i) As an independent variable in the model fitting, lg (SMCV)i) Corresponding cumulative probability FRFitting a species sensitivity distribution model for the dependent variable, the species sensitivity distribution model comprising: a normal distribution model, a lognormal distribution model, a logistic distribution model and a loglogistic distribution model; wherein, the SMCV isiIs the species average chronic value of species i;
determining coefficient R according to model fitting2The root mean square RMSE and the K-S test result determine a fitting model of the average chronic value SMCV of the seeds under different water quality conditions;
respectively taking the average chronic value of the species as an independent variable during model fitting and the cumulative probability corresponding to the average chronic value of the species as a dependent variable under the condition of specified water quality, and performing species sensitivity distribution model fitting;
and (5) carrying out model fitting goodness test, and determining a fitting model of the selected species average chronic value under the given water body environment parameter condition.
10. Stream according to any one of claims 1-9The method for predicting the chronic toxicity of copper in the water environment and deriving the long-term reference is characterized in that environmental parameters at least comprising three parameters of hardness, pH value and DOC value are selected to correct the water ecotoxicity, the harm concentration of species is predicted through a selected fitting model, and HC is used for predicting the harm concentration of the species5Obtaining a final long-term baseline value, comprising: determining species average chronic value corresponding to 5% of cumulative probability through species sensitivity distribution curve fitted by selected fitting model, and determining HC with chronic 5% of species hazard concentration5，HC5Divided by the evaluation factor to be used as the long-term water quality benchmark of copper.
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