CN113917101B - Method for predicting chronic toxicity of copper in watershed water environment and deducing long-term reference - Google Patents
Method for predicting chronic toxicity of copper in watershed water environment and deducing long-term reference Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 76
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- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 title claims abstract description 57
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- 150000001875 compounds Chemical class 0.000 claims description 13
- PUKLDDOGISCFCP-JSQCKWNTSA-N 21-Deoxycortisone Chemical compound C1CC2=CC(=O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@@](C(=O)C)(O)[C@@]1(C)CC2=O PUKLDDOGISCFCP-JSQCKWNTSA-N 0.000 claims description 12
- JJLJMEJHUUYSSY-UHFFFAOYSA-L Copper hydroxide Chemical compound [OH-].[OH-].[Cu+2] JJLJMEJHUUYSSY-UHFFFAOYSA-L 0.000 claims description 12
- QPLDLSVMHZLSFG-UHFFFAOYSA-N Copper oxide Chemical compound [Cu]=O QPLDLSVMHZLSFG-UHFFFAOYSA-N 0.000 claims description 12
- 241000252233 Cyprinus carpio Species 0.000 claims description 12
- FCYKAQOGGFGCMD-UHFFFAOYSA-N Fulvic acid Natural products O1C2=CC(O)=C(O)C(C(O)=O)=C2C(=O)C2=C1CC(C)(O)OC2 FCYKAQOGGFGCMD-UHFFFAOYSA-N 0.000 claims description 12
- 210000000988 bone and bone Anatomy 0.000 claims description 12
- OPQARKPSCNTWTJ-UHFFFAOYSA-L copper(ii) acetate Chemical compound [Cu+2].CC([O-])=O.CC([O-])=O OPQARKPSCNTWTJ-UHFFFAOYSA-L 0.000 claims description 12
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- 238000012545 processing Methods 0.000 claims description 7
- QJZYHAIUNVAGQP-UHFFFAOYSA-N 3-nitrobicyclo[2.2.1]hept-5-ene-2,3-dicarboxylic acid Chemical compound C1C2C=CC1C(C(=O)O)C2(C(O)=O)[N+]([O-])=O QJZYHAIUNVAGQP-UHFFFAOYSA-N 0.000 claims description 6
- BHPQYMZQTOCNFJ-UHFFFAOYSA-N Calcium cation Chemical compound [Ca+2] BHPQYMZQTOCNFJ-UHFFFAOYSA-N 0.000 claims description 6
- 238000001276 Kolmogorov–Smirnov test Methods 0.000 claims description 6
- JLVVSXFLKOJNIY-UHFFFAOYSA-N Magnesium ion Chemical compound [Mg+2] JLVVSXFLKOJNIY-UHFFFAOYSA-N 0.000 claims description 6
- 229910002651 NO3 Inorganic materials 0.000 claims description 6
- NHNBFGGVMKEFGY-UHFFFAOYSA-N Nitrate Chemical compound [O-][N+]([O-])=O NHNBFGGVMKEFGY-UHFFFAOYSA-N 0.000 claims description 6
- 229910001424 calcium ion Inorganic materials 0.000 claims description 6
- ARUVKPQLZAKDPS-UHFFFAOYSA-L copper(II) sulfate Chemical compound [Cu+2].[O-][S+2]([O-])([O-])[O-] ARUVKPQLZAKDPS-UHFFFAOYSA-L 0.000 claims description 6
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- BERDEBHAJNAUOM-UHFFFAOYSA-N copper(I) oxide Inorganic materials [Cu]O[Cu] BERDEBHAJNAUOM-UHFFFAOYSA-N 0.000 claims description 5
- KRFJLUBVMFXRPN-UHFFFAOYSA-N cuprous oxide Chemical compound [O-2].[Cu+].[Cu+] KRFJLUBVMFXRPN-UHFFFAOYSA-N 0.000 claims description 5
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Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/18—Water
- G01N33/186—Water using one or more living organisms, e.g. a fish
Abstract
The invention provides a method for predicting chronic toxicity and deducing a long-term standard of copper in a watershed water environment, which belongs to the technical field of water quality standard. According to the method, three water body environment parameters of water quality parameters of hardness, pH value and DOC value which obviously influence copper toxicity are introduced, an optimally distributed species sensitivity distribution curve is established, the environmental behavior and toxic effect of copper in a real water environment are reflected by a water quality parameter model correction method, and the water quality standard of the characteristics of the freshwater aquatic biological system is scientifically, perfectly and simply deduced.
Description
Technical Field
The invention relates to the technical field of water quality references, in particular to a method for predicting chronic toxicity of copper in a watershed water environment and deducing a long-term reference.
Background
The long term water quality benchmark is the maximum dosage or level of water environmental factors (pollutants or harmful elements) that do not produce chronic harmful effects on the ecosystem under specific conditions and uses. From the aspect of disclosing objective rules, the water quality standard has universality, but the differences in the aspects of natural geography, ecological system constitution and the like can also lead the objective rules to present certain regional specificity, and the targeted research is required to be carried out according to actual conditions in various countries and even various regions.
Because of the difference of the long-term water quality standard deducing method and species use, copper water quality standards formulated in different countries and even the same country in different periods also have larger difference. The developed nations consider the influence of various water environmental parameters and the biological effectiveness of copper in different forms on the toxicity of aquatic organisms, and the biological ligand model (Biotic Ligand Model, BLM) or hardness correction equation and other forms are recommended to be adopted in the water quality standard derivation guideline of copper, so that the environmental behaviors and toxic effects of copper in a real water environment are reflected. Under the condition of allowable conditions, standard related researches are developed according to different ecological environment characteristics of each region, and a water quality standard is formulated.
Copper has the characteristics of high accumulation, easy migration, durability, capability of enriching through a food chain and the like, has obvious toxicity effect on aquatic organisms, is one of basic projects of the quality standard (GB 3838-2002) of the surface water environment, and is one of constraint indexes for controlling the total emission amount of main pollutants of the water environment. Copper can destroy important macromolecular substances such as lipid, protein, DNA and the like in cells, and poison organisms. Excessive copper can cause the activity reduction of ceruloplasmin, glutathione peroxide and the like, influence the antioxidation of organisms, cause the accumulation of free radicals in the bodies, damage membrane systems and membrane structures, cause the increase of erythrocyte brittleness, and cause the structural damage and the decrease of functions, thereby causing the degeneration necrosis and dysfunction of tissue cells.
The toxicity of copper is significantly different due to various physical and chemical effects of the exposed water body, and the DOC value, suspended particles, pH value and various inorganic cations and anions of the soluble organic compound are all important factors influencing the toxicity of copper and the water quality standard. The effect of physicochemical factors on copper toxicity is due to the effect on copper morphology and bioavailability.
In the current standard, a single-value system is adopted, the influences of regional differences and environmental background are not reflected, and the combination of the current standard with the local water quality characteristics such as actual surface water body monitoring data, water quality evaluation results and the like is ignored. Therefore, there is a need to develop corresponding models to characterize different geographic geologic features of a typical watershed or body of water.
The prior art has at least the following disadvantages:
1. the current copper reference value adopts a single-value system, does not reflect the influences of regional differences and environmental background, and neglects the combination of the current copper reference value with the local water quality characteristics such as actual surface water body monitoring data, water quality evaluation results and the like.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for predicting the chronic toxicity and deducing a long-term standard of copper in a watershed water environment, which provides a method for correcting three parameters based on a Hardness H (Hardness) value, an acid-base pH value and a soluble organic carbon DOC (DissolvedOrganicCarbon) value and obtaining a copper long-term water quality standard, wherein a plurality of groups of researchers acquire toxicity data in a database and a literature library, the toxicity data are corrected through environmental parameters at least comprising the three parameters of the Hardness, the pH value and the DOC value, the average chronic value of each species is acquired after correction, the average chronic values of the species of each species are sequenced, the cumulative probability is acquired, species sensitivity distribution model fitting is carried out, a best fitting model is selected from a normal distribution model, a lognormal distribution model, a logic-standard distribution model and a loglogic-standard distribution model, and the copper long-term water quality standard is acquired through the fitting model. According to the method, three water body environment parameters of water quality parameters of hardness, pH value and DOC value which obviously influence copper toxicity are reasonably introduced, an optimally distributed species sensitivity distribution curve is established, the environmental behavior and toxic effect of copper in a real water environment are reflected by a water quality parameter model correction method, and the water quality standard of the characteristics of the freshwater aquatic biological system is scientifically, perfectly and simply deduced.
The invention provides a method for predicting 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 comprises a water body environment parameter hardness H value, an acid-base pH value and a soluble organic carbon DOC value;
carrying out chronic toxicity correction through the water quality parameter hardness H value, the pH value of acid and alkali and the DOC value of soluble organic carbon;
obtaining a seed average chronicity value of each species;
sorting the average chronic values of the seeds to obtain the cumulative probability;
determining a fitting model of the SMCV (mean average chronic value) of the seeds selected under different water quality conditions through model fitting and evaluation;
predicting the hazard concentration of species by using a selected fitting model, and determining the hazard concentration of the species by HC 5 Obtaining final long-term referenceValues.
Preferably, the data retrieval and data screening are accomplished independently by groups of researchers from toxicity databases and literature libraries;
the toxicity data retrieval comprises the retrieval of compound types, species types, toxicity effect test end point types and water environment parameters;
the literature database comprises China knowledge infrastructure engineering, a universal knowledge service platform, a Wipe network and a scientific network.
Preferably, the compound types include cupric chloride, cupric nitrate, cupric sulfate, cupric acetate, cupric hydroxide, cupric oxide and cuprous nitrate;
species types include at least 1 hard bone carp, 1 hard bone non-carp, 1 zooplankton, 1 benthic animal, 1 other phylum of the above mentioned non-involvement, 1 phytoplankton or hydrovascular plant.
The chronic toxicity values CTV included the values for each toxicity effect test endpoint type, including no observed effect concentration (No Observed Effect Concentration, NOEC), lowest observed effect concentration (Lowest Observed Effect Concentration, LOEC), no observed effect level (No Observed Effect Level, NOEL), lowest observed effect level (Lowest Observed Effect Concentration, LOEL), maximum allowable concentration (Maximum Acceptable Toxicant, MATC), and 20% effect concentration EC 20 (20％of Effective Concentration，EC 20 ) The method comprises the steps of carrying out a first treatment on the surface of the MATC is the geometric mean of NOEC and LOEC (or NOEL and LOEL);
the hardness H value in the water environment parameter comprises the concentration of calcium ions and the concentration of magnesium ions, 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 water environmental parameters;
and correcting the chronic toxicity value through the water environment parameters.
Preferably, the fitting of the chronic toxicity to the water environmental parameters comprises the steps of:
taking the logarithmic values of the hardness H value, the pH value and the DOC value of water quality as independent variables and taking the logarithmic values of the chronic toxicity values corresponding to various groups of water quality parameters as dependent variables, and adopting the following formula to perform multi-element linear fitting to obtain K A 、K B 、K C And C A ：
lg(CTV i )＝K A *lg(H i )+K B *lg(pH i )+K C *lg(DOC i )+C A
Wherein:
CTV i : chronic toxicity value before the correction of the water quality parameters of the ith group, mug/L;
K A 、K B 、K C : chronic toxicity: after fitting the water quality parameters, the hardness, the pH and the DOC respectively correspond to coefficients without dimension;
C A : chronic toxicity constant, intercept, dimensionless;
H i : the hardness value of the water body before the correction of the water quality parameters of the ith group is mg/L;
pH i : the water pH value before the i-th group of water quality parameters are corrected is dimensionless;
DOC i : the water solubility organic carbon concentration value of the water body before the i-th group of water quality parameters are corrected is mg/L;
and (5) performing a multi-element linear fitting goodness test.
Preferably, the watershed water environment parameter correction chronic toxicity value adopts the following formula:
wherein:
CTV i CTV is the chronic toxicity value before and after the correction of the water quality parameter of the ith group, and mu g/L;
H i and H is the water hardness value before and after the correction of the water quality parameter of the ith group and mg/L respectively;
pH i And the pH value is the pH value of the water body before and after the correction of the water quality parameters of the ith group respectively, and the water body is dimensionless;
DOC i and DOC are the water solubility organic carbon concentration values before and after the correction of the ith group of water quality parameters, and mg/L respectively;
K A 、K B 、K C : the coefficient corresponding to the hardness, pH and DOC after the chronic toxicity is fitted with the water quality parameters is dimensionless.
Preferably, the species average chronicity value SMCV for each species under the specified water environmental parameters is obtained according to the following formula i ，
In the method, in the process of the invention,
SMCV i represents the species average chronicity value of species i in a specified water environmental parameter, μg/L;
CTV represents chronic toxicity value after water environmental parameter correction, mug/L;
n represents the number of CTVs of species i, one;
i represents the species number, dimensionless.
Preferably, the sorting of the seed average chronicity values, the obtaining the cumulative probability comprises the steps of:
arranging and combining water environment parameters of different watercourses to obtain a plurality of water environment parameter combinations;
SMCV under the condition of multiple groups of water quality i The values of the toxicity sequences are respectively sequenced from small to large, and the sequence number R of toxicity sequencing is determined; the cumulative probability F of each species is calculated according to the following formula R ，
In the method, in the process of the invention,
F R representing cumulative probability,%;
r is the serial number of the species order, and is dimensionless;
n-total number of species, dimensionless.
Preferably, the method for determining the fitting model of the average seed chronic value SMCV under different water quality conditions by model fitting and evaluation comprises the following steps:
average chronic value SMCV for multiple groups of water quality conditions i Taking the base 10 logarithms, we will get lg (SMCV i ) As an independent variable at the time of model fitting, the value of lg (SMCV i ) Corresponding cumulative probability F R For a dependent variable, fitting a species sensitivity distribution model, the species sensitivity distribution model comprising: a normal distribution model, a lognormal distribution model, a logistic distribution model, and a log logistic distribution model; wherein, SMCV i Species average chronicity value for species i;
determining coefficient R according to model fitting 2 Determining a fitting model of the average chronic value SMCV of the seeds under different selected water quality conditions according to the root mean square RMSE and the K-S test result;
respectively taking the seed average chronic value as an independent variable when the model is fitted under the specified water quality condition, and taking the accumulated probability corresponding to the seed average chronic value as a dependent variable to perform the model fitting of the seed sensitivity distribution;
and (3) carrying out model fitting goodness test, and determining a fitting model of the average chronic value of the seeds selected under the given water body environment parameter condition.
Preferably, environmental parameters including at least three parameters of hardness, pH value and DOC value are selected to correct the toxicity of water ecology, and the hazard concentration of the species is predicted by a selected fitting model, and HC is used for the water ecology 5 Obtaining a final long-term reference value includes: determining the average species chronicity value corresponding to the cumulative probability of 5% as HC of the chronicity 5% species hazard concentration through the species sensitivity distribution curve fitted by the selected fitting model 5 ，HC 5 Divided by the evaluation factor, as a copper long-term water quality benchmark.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the method, three water body environment parameters of water quality parameters of hardness, pH value and DOC value which obviously influence copper toxicity are reasonably introduced, an optimally distributed species sensitivity distribution curve is established, the environment behavior and toxic effect of copper in a real water environment are reflected by a water quality parameter model correction method, and a long-term water quality standard of characteristics of a freshwater aquatic biological system is scientifically, perfectly and simply deduced.
Drawings
FIG. 1 is a flow chart of a method for chronic toxicity prediction and long term baseline derivation of copper according to one embodiment of the present invention;
FIGS. 2a, 2b and 2c are graphs of residuals obtained by linear fitting lg (CTV) to lg (H), lg (pH) and lg (DOC), respectively, in one embodiment of the invention;
FIG. 3 is a species sensitivity distribution fitting curve of one embodiment of the invention.
Detailed Description
The following describes the embodiments of the present invention in detail with reference to the drawings.
The invention provides a method for predicting 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 comprises a water body environment parameter hardness H value, an acid-base pH value and a soluble organic carbon DOC value;
carrying out chronic toxicity correction through the water quality parameter hardness H value, the pH value of acid and alkali and the DOC value of soluble organic carbon;
obtaining a seed average chronicity value of each species;
sorting the average chronic values of the seeds to obtain the cumulative probability;
determining a fitting model of the SMCV (mean average chronic value) of the seeds selected under different water quality conditions through model fitting and evaluation;
predicting the hazard concentration of species by using a selected fitting model, and determining the hazard concentration of the species by HC 5 And acquiring a final long-term reference value.
According to one embodiment of the invention, data retrieval and data screening are accomplished independently by groups of researchers from toxicity databases and literature libraries;
the toxicity data retrieval comprises the retrieval of compound types, species types, toxicity effect test end point types and water environment parameters;
the literature database comprises China knowledge infrastructure engineering, a universal knowledge service platform, a Wipe network and a scientific network.
According to a specific embodiment of the present invention, the compound types include cupric chloride, cupric nitrate, cupric sulfate, cupric acetate, cupric hydroxide, cuprous oxide, cupric oxide and cuprous nitrate;
species types include at least 1 hard bone carp, 1 hard bone non-carp, 1 zooplankton, 1 benthic animal, 1 other phylum of the above mentioned non-involvement, 1 phytoplankton or hydrovascular plant.
The chronic toxicity value CTV includes the values of each toxicity effect test endpoint type including 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 20% effect concentration EC 20 ；
The hardness H value in the water environment parameter comprises the concentration of calcium ions and the concentration of magnesium ions, and the DOC value of the soluble organic carbon comprises fulvic acid, fulvic acid and humic acid.
According to one embodiment of the invention, the chronic toxicity correction comprises:
fitting the chronic toxicity with water environmental parameters;
and correcting the chronic toxicity value through the water environment parameters.
According to one embodiment of the invention, the fitting of the chronic toxicity to the environmental parameters of the body of water comprises the steps of:
Taking the logarithmic values of the hardness H value, the pH value and the DOC value of water quality as independent variables and taking the logarithmic values of the chronic toxicity values corresponding to various groups of water quality parameters as dependent variables, and adopting the following formula to perform multi-element linear fitting to obtain K A 、K B 、K C And C A ：
lg(CTV i )＝K A *lg(H i )+K B *lg(pH i )+K C *lg(DOC i )+C A
Wherein:
CTV i : chronic toxicity value before the correction of the water quality parameters of the ith group, mug/L;
K A 、K B 、K C : chronic toxicity: after fitting the water quality parameters, the hardness, the pH and the DOC respectively correspond to coefficients without dimension;
C A : chronic toxicity constant, intercept, dimensionless;
H i : the hardness value of the water body before the correction of the water quality parameters of the ith group is mg/L;
pH i : the water pH value before the i-th group of water quality parameters are corrected is dimensionless;
DOC i : the water solubility organic carbon concentration value of the water body before the i-th group of water quality parameters are corrected is mg/L;
and (5) performing a multi-element linear fitting goodness test.
According to one embodiment of the invention, the watershed water environment parameter correction chronic toxicity value adopts the following formula:
wherein:
CTV i CTV is the chronic toxicity value before and after the correction of the water quality parameter of the ith group, and mu g/L;
H i and H is the water hardness value before and after the correction of the water quality parameter of the ith group and mg/L respectively;
pH i and the pH value is the pH value of the water body before and after the correction of the water quality parameters of the ith group respectively, and the water body is dimensionless;
DOC i and DOC are the water solubility organic carbon concentration values before and after the correction of the ith group of water quality parameters, and mg/L respectively;
K A 、K B 、K C : the coefficient corresponding to the hardness, pH and DOC after the chronic toxicity is fitted with the water quality parameters is dimensionless.
According to one embodiment of the invention, the species average chronicity value SM for each species under a specified water environmental parameter is obtained according to the following formulaCV i ，
In the method, in the process of the invention,
SMCV i represents the species average chronicity value of species i in a specified water environmental parameter, μg/L;
CTV represents chronic toxicity value after water environmental parameter correction, mug/L;
n represents the number of CTVs of species i, one;
i represents the species number, dimensionless.
According to a specific embodiment of the present invention, the sorting of the seed average chronicity values, the obtaining of the cumulative probability comprises the steps of:
arranging and combining water environment parameters of different watercourses to obtain a plurality of water environment parameter combinations;
SMCV under the condition of multiple groups of water quality i The values of the toxicity sequences are respectively sequenced from small to large, and the sequence number R of toxicity sequencing is determined; the cumulative probability F of each species is calculated according to the following formula R ，
In the method, in the process of the invention,
F R representing cumulative probability,%;
r is the serial number of the species order, and is dimensionless;
n-total number of species, dimensionless.
According to a specific embodiment of the invention, the fitting model for determining the average chronic value SMCV of seeds selected under different water quality conditions through model fitting and evaluation comprises the following steps:
Average chronic value SMCV for multiple groups of water quality conditions i Taking the base 10 logarithms, we will get lg (SMCV i ) As an independent variable at the time of model fitting, the value of lg (SMCV i ) Corresponding cumulative probability F R Is a factor changeQuantitatively, a species sensitivity distribution model fit is performed, the species sensitivity distribution model comprising: a normal distribution model, a lognormal distribution model, a logistic distribution model, and a log logistic distribution model; wherein, SMCV i Species average chronicity value for species i;
determining coefficient R according to model fitting 2 Determining a fitting model of the average chronic value SMCV of the seeds under different selected water quality conditions according to the root mean square RMSE and the K-S test result;
respectively taking the seed average chronic value as an independent variable when the model is fitted under the specified water quality condition, and taking the accumulated probability corresponding to the seed average chronic value as a dependent variable to perform the model fitting of the seed sensitivity distribution;
and (3) carrying out model fitting goodness test, and determining a fitting model of the average chronic value of the seeds selected under the given water body environment parameter condition.
According to one embodiment of the invention, environmental parameters including at least three parameters of hardness, pH value and DOC value are selected to correct the toxicity of water ecology, and the hazard concentration of the species is predicted by using a selected fitting model, and HC is used for the treatment of the water ecology 5 Obtaining a final long-term reference value includes: determining the average species chronicity value corresponding to the cumulative probability of 5% as HC of the chronicity 5% species hazard concentration through the species sensitivity distribution curve fitted by the selected fitting model 5 ，HC 5 Divided by the evaluation factor, as a copper long-term water quality benchmark.
Example 1
According to one embodiment of the present invention, the method for predicting chronic toxicity of copper in a watershed water environment and deriving long-term benchmarks according to the present invention is described in detail with reference to the accompanying drawings.
The invention provides a method for predicting 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 comprises a water body environment parameter hardness H value, an acid-base pH value and a soluble organic carbon DOC value;
carrying out chronic toxicity correction through the water quality parameter hardness H value, the pH value of acid and alkali and the DOC value of soluble organic carbon;
obtaining a seed average chronicity value of each species;
sorting the average chronic values of the seeds to obtain the cumulative probability;
determining a fitting model of the SMCV (mean average chronic value) of the seeds selected under different water quality conditions through model fitting and evaluation;
Predicting the hazard concentration of species by using a selected fitting model, and determining the hazard concentration of the species by HC 5 And acquiring a final long-term reference value.
Example 2
According to one embodiment of the present invention, the method for predicting chronic toxicity of copper in a watershed water environment and deriving long-term benchmarks according to the present invention is described in detail with reference to the accompanying drawings.
The invention provides a method for predicting 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 comprises a water body environment parameter hardness H value, an acid-base pH value and a soluble organic carbon DOC value;
data retrieval and data screening are independently completed by a plurality of groups of researchers from a toxicity database and a literature library;
the literature database comprises China knowledge infrastructure engineering, a universal knowledge service platform, a Wipe network and a scientific network;
the toxicity data retrieval comprises the retrieval of compound types, species types, toxicity effect test end point types and water environment parameters;
the compound types include cupric chloride, cupric nitrate, cupric sulfate, cupric acetate, cupric hydroxide, cuprous oxide, cupric oxide and cuprous nitrate;
Species types include at least 1 hard bone carp, 1 hard bone non-carp, 1 zooplankton, 1 benthic animal, 1 other phylum of the above mentioned non-involvement, 1 phytoplankton or hydrovascular plant.
The chronic toxicity value CTV includes the values of each toxicity effect test endpoint type including 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 20% effect concentration EC 20 ；
The hardness H value in the water environment parameter comprises the concentration of calcium ions and the concentration of magnesium ions, and the DOC value of the soluble organic carbon comprises fulvic acid, fulvic acid and humic acid.
Chronic toxicity correction by water quality parameters of hardness H value, pH value of acid-base and DOC value of soluble organic carbon, comprising:
fitting the chronic toxicity with water environmental parameters;
and correcting the chronic toxicity value through the water environment parameters.
The fitting of the chronic toxicity to the water environmental parameters comprises the following steps:
taking the logarithmic values of the hardness H value, the pH value and the DOC value of water quality as independent variables and taking the logarithmic values of the chronic toxicity values corresponding to various groups of water quality parameters as dependent variables, and adopting the following formula to perform multi-element linear fitting to obtain K A 、K B 、K C And C A ：
lg(CTV i )＝K A *lg(H i )+K B *lg(pH i )+K C *lg(DOC i )+C A
Wherein:
CTV i : chronic toxicity value before the correction of the water quality parameters of the ith group, mug/L;
K A 、K B 、K C : chronic toxicity: after fitting the water quality parameters, the hardness, the pH and the DOC respectively correspond to coefficients without dimension;
C A : chronic toxicity constant, intercept, dimensionless;
H i : the hardness value of the water body before the correction of the water quality parameters of the ith group is mg/L;
pH i : the water pH value before the i-th group of water quality parameters are corrected is dimensionless;
DOC i : the water solubility organic carbon concentration value of the water body before the i-th group of water quality parameters are corrected is mg/L;
and (5) performing a multi-element linear fitting goodness test.
The chronic toxicity value of the watershed water environment parameter correction adopts the following formula:
wherein:
CTV i CTV is the chronic toxicity value before and after the correction of the water quality parameter of the ith group, and mu g/L;
H i and H is the water hardness value before and after the correction of the water quality parameter of the ith group and mg/L respectively;
pH i and the pH value is the pH value of the water body before and after the correction of the water quality parameters of the ith group respectively, and the water body is dimensionless;
DOC i and DOC are the water solubility organic carbon concentration values before and after the correction of the ith group of water quality parameters, and mg/L respectively;
K A 、K B 、K C : the coefficient corresponding to the hardness, pH and DOC after the chronic toxicity is fitted with the water quality parameters is dimensionless.
Obtaining a seed average chronicity value of each species; obtaining the average seed chronic value SMCV of each species under the specified water environmental parameters according to the following formula i ，
In the method, in the process of the invention,
SMCV i represents the species average chronicity value of species i in a specified water environmental parameter, μg/L;
CTV represents chronic toxicity value after water environmental parameter correction, mug/L;
n represents the number of CTVs of species i, one;
i represents the species number, dimensionless.
Sorting the average chronic values of the seeds to obtain the cumulative probability;
determining a fitting model of the SMCV (mean average chronic value) of the seeds selected under different water quality conditions through model fitting and evaluation;
predicting the hazard concentration of species by using a selected fitting model, and determining the hazard concentration of the species by HC 5 And acquiring a final long-term reference value.
Example 3
According to one embodiment of the present invention, the method for predicting chronic toxicity of copper in a watershed water environment and deriving long-term benchmarks according to the present invention is described in detail with reference to the accompanying drawings.
According to one embodiment of the present invention, the method for predicting chronic toxicity of copper in a watershed water environment and deriving long-term benchmarks according to the present invention is described in detail with reference to the accompanying drawings.
The invention provides a method for predicting 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 comprises a water body environment parameter hardness H value, an acid-base pH value and a soluble organic carbon DOC value;
Data retrieval and data screening are independently completed by a plurality of groups of researchers from a toxicity database and a literature library;
the literature database comprises China knowledge infrastructure engineering, a universal knowledge service platform, a Wipe network and a scientific network;
the toxicity data retrieval comprises the retrieval of compound types, species types, toxicity effect test end point types and water environment parameters;
the compound types include cupric chloride, cupric nitrate, cupric sulfate, cupric acetate, cupric hydroxide, cuprous oxide, cupric oxide and cuprous nitrate;
species types include at least 1 hard bone carp, 1 hard bone non-carp, 1 zooplankton, 1 benthic animal, 1 other phylum of the above mentioned non-involvement, 1 phytoplankton or hydrovascular plant.
Chronic toxicity values CTV include individual toxic effectsThe test endpoint types are evaluated, and the toxic effect test endpoint types comprise 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 20% effect concentration EC 20 ；
The hardness H value in the water environment parameter comprises the concentration of calcium ions and the concentration of magnesium ions, and the DOC value of the soluble organic carbon comprises fulvic acid, fulvic acid and humic acid.
Chronic toxicity correction by water quality parameters of hardness H value, pH value of acid-base and DOC value of soluble organic carbon, comprising:
fitting the chronic toxicity with water environmental parameters;
and correcting the chronic toxicity value through the water environment parameters.
The fitting of the chronic toxicity to the water environmental parameters comprises the following steps:
taking the logarithmic values of the hardness H value, the pH value and the DOC value of water quality as independent variables and taking the logarithmic values of the chronic toxicity values corresponding to various groups of water quality parameters as dependent variables, and adopting the following formula to perform multi-element linear fitting to obtain K A 、K B 、K C And C A ：
lg(CTV i )＝K A *lg(H i )+K B *lg(pH i )+K C *lg(DOC i )+C A
Wherein:
CTV i : chronic toxicity value before the correction of the water quality parameters of the ith group, mug/L;
K A 、K B 、K C : chronic toxicity: after fitting the water quality parameters, the hardness, the pH and the DOC respectively correspond to coefficients without dimension;
C A : chronic toxicity constant, intercept, dimensionless;
H i : the hardness value of the water body before the correction of the water quality parameters of the ith group is mg/L;
pH i : the water pH value before the i-th group of water quality parameters are corrected is dimensionless;
DOC i : water body dissolution before correction of ith group of water quality parametersOrganic carbon concentration value, mg/L;
and (5) performing a multi-element linear fitting goodness test.
The chronic toxicity value of the watershed water environment parameter correction adopts the following formula:
wherein:
CTV i CTV is the chronic toxicity value before and after the correction of the water quality parameter of the ith group, and mu g/L;
H i And H is the water hardness value before and after the correction of the water quality parameter of the ith group and mg/L respectively;
pH i and the pH value is the pH value of the water body before and after the correction of the water quality parameters of the ith group respectively, and the water body is dimensionless;
DOC i and DOC are the water solubility organic carbon concentration values before and after the correction of the ith group of water quality parameters, and mg/L respectively;
K A 、K B 、K C : the coefficient corresponding to the hardness, pH and DOC after the chronic toxicity is fitted with the water quality parameters is dimensionless.
Obtaining a seed average chronicity value of each species; obtaining the average seed chronic value SMCV of each species under the specified water environmental parameters according to the following formula i ，
In the method, in the process of the invention,
SMCV i represents the species average chronicity value of species i in a specified water environmental parameter, μg/L;
CTV represents chronic toxicity value after water environmental parameter correction, mug/L;
n represents the number of CTVs of species i, one;
i represents the species number, dimensionless.
The average chronic value is sequenced to obtain the cumulative probability, which comprises the following steps:
arranging and combining water environment parameters of different watercourses to obtain a plurality of water environment parameter combinations;
SMCV under the condition of multiple groups of water quality i The values of the toxicity sequences are respectively sequenced from small to large, and the sequence number R of toxicity sequencing is determined; the cumulative probability F of each species is calculated according to the following formula R ，
In the method, in the process of the invention,
F R representing cumulative probability,%;
r is the serial number of the species order, and is dimensionless;
n-total number of species, dimensionless.
And determining a fitting model of the SMCV (mean average chronic value) of the seeds selected under different water quality conditions through model fitting and evaluation, wherein the fitting model comprises the following steps of:
average chronic value SMCV for multiple groups of water quality conditions i Taking the base 10 logarithms, we will get lg (SMCV i ) As an independent variable at the time of model fitting, the value of lg (SMCV i ) Corresponding cumulative probability F R For a dependent variable, fitting a species sensitivity distribution model, the species sensitivity distribution model comprising: a normal distribution model, a lognormal distribution model, a logistic distribution model, and a log logistic distribution model; wherein, SMCV i Species average chronicity value for species i;
determining coefficient R according to model fitting 2 Determining a fitting model of the average chronic value SMCV of the seeds under different selected water quality conditions according to the root mean square RMSE and the K-S test result;
respectively taking the seed average chronic value as an independent variable when the model is fitted under the specified water quality condition, and taking the accumulated probability corresponding to the seed average chronic value as a dependent variable to perform the model fitting of the seed sensitivity distribution;
and (3) carrying out model fitting goodness test, and determining a fitting model of the average chronic value of the seeds selected under the given water body environment parameter condition.
The environmental parameters including at least three parameters of hardness, pH value and DOC value are selected to correct the toxicity of water ecology, the hazard concentration of the species is predicted by the selected fitting model, and HC is used for the treatment of the disease 5 Obtaining a final long-term reference value includes: determining the average species chronicity value corresponding to the cumulative probability of 5% as HC of the chronicity 5% species hazard concentration through the species sensitivity distribution curve fitted by the selected fitting model 5 ，HC 5 Divided by the evaluation factor, as a copper long-term water quality benchmark.
Example 4
According to one embodiment of the present invention, the method for predicting chronic toxicity of copper in a watershed water environment and deriving long-term benchmarks according to the present invention is described in detail with reference to the accompanying drawings.
According to one embodiment of the present invention, the method for predicting chronic toxicity of copper in a watershed water environment and deriving long-term benchmarks according to the present invention is described in detail with reference to the accompanying drawings.
The invention provides a method for predicting 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 comprises a water body environment parameter hardness H value, an acid-base pH value and a soluble organic carbon DOC value;
Data retrieval and data screening are independently completed by a plurality of groups of researchers from a toxicity database and a literature library;
the toxicity database comprises an environmental protection agency database of a certain country;
the literature database comprises China knowledge infrastructure engineering, a universal knowledge service platform, a Wipe network and a scientific network;
the toxicity data retrieval comprises the retrieval of compound types, species types, toxicity effect test end point types and water environment parameters;
the compound types include cupric chloride, cupric nitrate, cupric sulfate, cupric acetate, cupric hydroxide, 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 a generation or life sensitivity stage; the reference test species encompasses at least 3 nutritional levels of producer, primary consumer and secondary consumer; the benchmark test species encompasses at least 3 nutritional levels of producer, primary consumer, and secondary consumer;
species types include at least 1 hard bone carp, 1 hard bone non-carp, 1 zooplankton, 1 benthic animal, 1 other phylum of the above mentioned non-involvement, 1 phytoplankton or hydrovascular plant.
The chronic toxicity value CTV includes the values of each toxicity effect test endpoint type including 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 20% effect concentration EC 20 ；
The hardness H value in the water environment parameter comprises the concentration of calcium ions and the concentration of magnesium ions, and the DOC value of the soluble organic carbon comprises fulvic acid, fulvic acid and humic acid.
Chronic toxicity correction by water quality parameters of hardness H value, pH value of acid-base and DOC value of soluble organic carbon, comprising:
fitting the chronic toxicity with water environmental parameters;
and correcting the chronic toxicity value through the water environment parameters.
The fitting of the chronic toxicity to the water environmental parameters comprises the following steps:
taking the logarithmic values of the hardness H value, the pH value and the DOC value of water quality as independent variables and taking the logarithmic values of the chronic toxicity values corresponding to various groups of water quality parameters as dependent variables, and adopting the following formula to perform multi-element linear fitting to obtain K A 、K B 、K C And C A ：
lg(CTV i )＝K A *lg(H i )+K B *lg(pH i )+K C *lg(DOC i )+C A
Wherein:
CTV i : chronic toxicity value before the correction of the water quality parameters of the ith group, mug/L;
K A 、K B 、K C : chronic toxicity: after fitting the water quality parameters, the hardness, the pH and the DOC respectively correspond to coefficients without dimension;
C A : chronic toxicity constant, intercept, dimensionless;
H i : the hardness value of the water body before the correction of the water quality parameters of the ith group is mg/L;
pH i : the water pH value before the i-th group of water quality parameters are corrected is dimensionless;
DOC i : the water solubility organic carbon concentration value of the water body before the i-th group of water quality parameters are corrected is mg/L;
and (5) performing a multi-element linear fitting goodness test.
The chronic toxicity value of the watershed water environment parameter correction adopts the following formula:
wherein:
CTV i CTV is the chronic toxicity value before and after the correction of the water quality parameter of the ith group, and mu g/L;
H i and H is the water hardness value before and after the correction of the water quality parameter of the ith group and mg/L respectively;
pH i and the pH value is the pH value of the water body before and after the correction of the water quality parameters of the ith group respectively, and the water body is dimensionless;
DOC i and DOC are the water solubility organic carbon concentration values before and after the correction of the ith group of water quality parameters, and mg/L respectively;
K A 、K B 、K C : the coefficient corresponding to the hardness, pH and DOC after the chronic toxicity is fitted with the water quality parameters is dimensionless.
Obtaining a seed average chronicity value of each species; obtaining the average seed chronic value SMCV of each species under the specified water environmental parameters according to the following formula i ，
In the method, in the process of the invention,
SMCV i represents the species average chronicity value of species i in a specified water environmental parameter, μg/L;
CTV represents chronic toxicity value after water environmental parameter correction, mug/L;
n represents the number of CTVs of species i, one;
i represents the species number, dimensionless.
The average chronic value is sequenced to obtain the cumulative probability, which comprises the following steps:
arranging and combining water environment parameters of different watercourses to obtain a plurality of water environment parameter combinations;
SMCV under the condition of multiple groups of water quality i The values of the toxicity sequences are respectively sequenced from small to large, and the sequence number R of toxicity sequencing is determined; the minimum toxicity value is ranked as 1, the next ranking as 2, and the ranking is sequentially ranked, if two or more species have the same toxicity value, the two or more species are randomly ranked as continuous ranking, and the number of the species under each ranking is 1;
the cumulative probability F of each species is calculated according to the following formula R ，
In the method, in the process of the invention,
F R representing cumulative probability,%;
r is the serial number of the species order, and is dimensionless;
n-total number of species, dimensionless.
And determining a fitting model of the SMCV (mean average chronic value) of the seeds selected under different water quality conditions through model fitting and evaluation, wherein the fitting model comprises the following steps of:
average chronic value SMCV for multiple groups of water quality conditions i Taking the base 10 logarithms, we will get lg (SMCV i ) As an independent variable at the time of model fitting, the value of lg (SMCV i ) Corresponding cumulative probability F R For a dependent variable, fitting a species sensitivity distribution model, the species sensitivity distribution model comprising: a normal distribution model, a lognormal distribution model, a logistic distribution model, and a log logistic distribution model; wherein, SMCV i Species average chronicity value for species i;
determining coefficient R according to model fitting 2 Determining a fitting model of the average chronic value SMCV of the seeds under different selected water quality conditions according to the root mean square RMSE and the K-S test result;
in addition to the above-mentioned test criteria, red-pool information criteria (Akaike Information Criterion, AIC) and bayesian information criteria (Bayesian Information Criterion, BIC) may be included;
the smaller the ratio of the standard error to the predicted value, the more statistically significant the value; r is R 2 The closer to 1, the greater the goodness of fit of the toxicity data, the more accurate the model fit; the RMSE is the square root of the ratio of the square of the observed value to the true value deviation to the observed number, the statistical parameter is also called the fitting standard deviation of a regression system, the 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; f, the index of the test is the correlation probability p obtained by multi-factor analysis of variance calculation; adopting the P value corresponding to the F statistic to carry out the test; in the multiple regression analysis, AIC and BIC are used as the standard for selecting regression models in order to prevent the problems of overfitting and the like (both strong interpretation of the models and a little tension); in the regression model, the smaller the values are, the better; calculated according to the following formula,
Aic= nln (sum of squares residual) +2 (p+1) -nln (n)
Bic= nln (sum of squares of residuals) + (p+1) In (n) -nln (n)
Where n is the number of samples (number of cases, number of sample parties, etc.) for the logarithmic operator; p is the number of independent variables in the regression equation.
Respectively taking the seed average chronic value as an independent variable when the model is fitted under the specified water quality condition, and taking the accumulated probability corresponding to the seed average chronic value as a dependent variable to perform the model fitting of the seed sensitivity distribution;
and (3) carrying out model fitting goodness test by adopting the test standards, and determining a fitting model of the average chronic value of the seeds selected under the given water body environment parameter condition.
Predicting the hazard concentration of species by using a selected fitting model, and determining the hazard concentration of the species by HC 5 Obtaining a final long-term reference value includes: the cumulative probabilities 5%, 10%, 25%, 50%, 75%, 90% and 95% of the corresponding lg (SMCV) were determined from the species sensitivity distribution curve fitted by the selected fitting model i ) Value, SMCV obtained by taking the inverse logarithm i I.e. chronic 5%, 10%, 25%, 50%, 75%, 90%, 95% species hazard concentration HC 5 、HC 10 、HC 25 、HC 50 、HC 75 、HC 90 、HC 95 The method comprises the steps of carrying out a first treatment on the surface of the The average species chronicity value corresponding to the cumulative probability of 5% is HC with a chronicity of 5% species hazard concentration 5 ，HC 5 Divided by the evaluation factor, as a copper long-term water quality benchmark. The number of species samples is less than 15, and an evaluation factor 3 is taken; the number of species samples was greater than 15 and the evaluation factor was 2.
Example 5
According to one embodiment of the present invention, the method for predicting chronic toxicity of copper in a watershed water environment and deriving long-term benchmarks according to the present invention is described in detail with reference to the accompanying drawings.
The present invention is based on the toxicity endpoint values for growth and reproductive toxicity effects as CTV, performing seed average chronic value (SMCV) calculations and prediction of chronic toxicity. The method is a method for correcting the chronic toxicity-water environment parameters based on different geographical geological element characteristics of a typical river basin or water body and applying the chronic toxicity-water environment parameters to the chronic toxicity prediction and long-term reference deduction of copper.
The three water quality parameters, hardness, DOC and pH, which most significantly affect copper toxicity are described below as examples. As shown in fig. 1, the method for predicting chronic toxicity of copper and deducing long-term reference in water environment of river basin according to the present invention comprises the following steps:
step a, toxicity data acquisition, screening and summarizing;
step a1, a data acquisition process;
step a2, a data screening process;
the conditions met by data screening are:
1) Chronic toxicity data was reported from literature in recent 10 years and from the national environmental protection agency database;
2) Each toxicity data must include scientific, complete test condition parameters, preferably hardness, DOC, and pH.
3) Chronic toxicity values include NOEC, LOEC, NOEL, LOEL, MATC and EC 20 MATC is the geometric mean of NOEC and LOEC (or NOEL and LOEL);
4) Toxicity tests must be performed under a range of environmental conditions in standard operating procedures;
step a3, data summarizing process:
the resulting data set includes the name of the species latin, the type of biological test, the type of toxic effect, the endpoint indicator, the test conditions, the exposure time, and the data source.
The detailed toxicity data acquisition procedure is as follows:
the modeled chronic toxicity data is preferentially collected from the environmental protection agency toxicity database of a country. If the toxicity data is insufficient, the effective data (ISI Web of Knowledge) of the SCI scientific quotation index query is supplemented by the last 10 years. And (3) inputting keywords such as metal names, names of species to be tested, chronic toxicity and the like through a database and a literature retrieval engine, and deriving a toxicity data set meeting the conditions. And (3) screening qualified toxicity data on the premise of meeting the conditions of the step a 2. In the data compiling process, information such as compound types, tested biological types, toxic effect types, end point indexes, test conditions, exposure time, data sources and the like is recorded and is arranged into a modeling basis set.
Data screening and summarization were performed using the chronic toxicity endpoint of the flea diminuta (Ceriodaphnia dubia) as shown in table 1.
Table 1 fuzzy anilox flea toxicity data screening, calculation and summarization paradigms
Step b, setting a baseline water quality condition during data correction, and setting the water hardness, the pH and the DOC baseline water quality condition as the hardness of 50mg/L, pH and the DOC of 5mg/L according to the water quality condition of surface water and the proper condition for aquatic organism survival;
step b1, constructing different multiple regression equations, and fitting chronic toxicity and water environmental parameters;
c, taking the logarithm of the CTV of each piece of data obtained after screening and integrating in the step a and the corresponding water pH, hardness and DOC value based on 10, and performing linear fitting by using a formula (1) and a formula (2) to obtain a formula (3) and a formula (4) respectively:
lg(CTV)＝K 1 lg(H)+K 2 lg(pH)+K 3 lg(DOC)+C 1 (1)
lg(CTV)＝K 4 lg(H)+K 5 lg(pH)+K 6 lg(DOC)+K 7 lg(H)×lg(pH)×lg(DOC)+C 2 (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 method, in the process of the invention,
K 1 、K 2 、K 3 : linear combination formula (1) is provided with coefficients corresponding to lg (H), lg (pH) and lg (DOC) after chronic toxicity-water quality parameter fitting, and is dimensionless;
K 4 、K 5 、K 6 、K 7 linear combination (2) is that after chronic toxicity-water quality parameter fitting, the corresponding coefficients of lg (H), lg (pH), lg (DOC) and lg (H) and lg (DOC) and lg (pH) are respectively, and dimensionless;
C 1 、C 2 the linear combination (1) and (2) are respectively the chronic toxicity constants, which are intercept and dimensionless;
H represents the water hardness value (CaCO) 3 Calculating) with the value of 50mg/L respectively; the pH value represents the pH value of the water body, and the values are 7 respectively, and are dimensionless; DOC represents the concentration value of water-soluble organic carbon, and the value is 5mg/L respectively;
step b2, performing a multi-element linear fitting goodness test and a significance test of a regression equation, selecting an optimal linear fitting equation, and adopting an F test:
the toxicity-water environment parameter fitting goodness-of-fit test indexes are as follows: square R of correlation coefficient 2 Root mean square RMSE, red pool information criteria (Akaike Information Criterion, AIC) and bayesian information criteria (Bayesian Information Criterion, BIC);
the smaller the ratio of the standard error to the predicted value, the more statistically significant the value; r is R 2 The closer to 1, the greater the goodness of fit of the toxicity data, the more accurate the model fit; the RMSE is the square root of the ratio of the square of the observed value to the true value deviation to the observed number, the statistical parameter is also called the fitting standard deviation of a regression system, the 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; f, the index of the test is the correlation probability p obtained by multi-factor analysis of variance calculation; adopting the P value corresponding to the F statistic to carry out the test; in the multiple regression analysis, AIC and BIC are used as the standard for selecting regression models in order to prevent the problems of overfitting and the like (both strong interpretation of the models and a little tension); in the regression model, the smaller the 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 squares of residuals) + (p+1) ln (n) -nln (n) (9)
Where n is the number of logarithmic operators as the sample size (number of cases, number of sample sides, etc.), and p is the number of independent variables in the regression equation.
After calculation, the ratio 273.0 of standard error to predicted value of equation (2) is much higher than 2.392 of equation (1), the statistics of the second group are considered poor, and the coefficient R is determined 2 0.5531 and 0.5525, respectively, with p values less than 0.001, comparing to select the first set of linear combinations as the best solution, with RMSE of 0.3787, f value of 61.23, aic of 5.996, bic of 10.54, and the residuals after fitting shown in fig. 2a, 2b and 2c;
step b3, correcting each toxicity data according to the water hardness, the pH and the DOC according to a formula (10), and obtaining CTV under the condition of baseline water quality, wherein the CTV after correction is:
wherein:
CTV i CTV is the chronic toxicity value before and after the correction of the water quality parameter of the ith group, and mu g/L;
H i and H is the water hardness value before and after the correction of the water quality parameter of the ith group and mg/L respectively;
pH i and the pH value is the pH value of the water body before and after the correction of the water quality parameters of the ith group respectively, and the water body is dimensionless;
DOC i and DOC are the water solubility organic carbon concentration values before and after the correction of the ith group of water quality parameters respectively, mg/L；
K A 、K B 、K C : the coefficient respectively corresponding to the hardness, the pH and the DOC after the chronic toxicity is fitted with the water quality parameters is dimensionless;
taking the fuzzy anilox flea (Ceriodaphnia dubia) as an example, the CTV under the condition of baseline water quality is obtained according to the hardness, pH and DOC correction of the water body.
TABLE 2 CTV paradigm before and after correction of fuzzy anilox fleas
| Species name | CTV i (μg/L) | CTV under baseline Water quality conditions (μg/L) |
| Fuzzy reticulate fleas | 7.897 | 8.170 |
| Fuzzy reticulate fleas | 19.36 | 27.18 |
| Fuzzy reticulate fleas | 24.50 | 110.67 |
| Fuzzy reticulate fleas | 34.60 | 156.29 |
| Fuzzy reticulate fleas | 9.170 | 25.98 |
| Fuzzy reticulate fleas | 12.00 | 52.42 |
| Fuzzy reticulate fleas | 34.00 | 33.83 |
| Fuzzy reticulate fleas | 40.00 | 24.10 |
| Fuzzy reticulate fleas | 59.00 | 35.83 |
| Fuzzy reticulate fleas | 41.00 | 16.46 |
| Fuzzy reticulate fleas | 1.800 | 8.080 |
Step c, calculating the seed average chronic value SMCV under the baseline condition according to the formula (11) i ：
In the method, in the process of the invention,
SMCV i represents the species average chronicity value of species i in a specified water environmental parameter, μg/L; CTV represents chronic toxicity after correction of environmental parameters of water bodyValues, μg/L;
n represents the number of CTVs of species i, one;
i represents a species, dimensionless;
SMCV for each species at baseline water quality conditions was obtained as shown in table 3.
TABLE 3 SMCV at baseline Water quality conditions
Step d, calculating the cumulative probability of different species;
step d1, selecting a water body environment parameter combination with the hardness of 50mg/L, pH 7 and DOC of 5mg/L under the baseline condition;
Step d2, SMCV under baseline conditions i The values of the toxicity are respectively sequenced from small to large, the toxicity rank R (the rank of the minimum toxicity value is 1, the rank of the secondary is 2, the toxicity values of two or more species are sequentially sequenced, if the toxicity values of the two or more species are the same, the toxicity values of the two or more species are arbitrarily sequenced to be continuous ranks, and the number of the species under each rank is 1);
step d3, calculating the cumulative probability F of the species according to the formula (12) R ，
In the method, in the process of the invention,
F R representing cumulative probability,%;
r is the serial number of the species order, and is dimensionless;
n-total number of species, dimensionless.
The cumulative probability of each species under baseline conditions was obtained from steps d 1-d 3, arranged in order of decreasing size, see table 4.
TABLE 4 cumulative probability under baseline water quality conditions
/>
Step e, model fitting and evaluation; SMCV (surface mounted device) i Taking the base 10 logarithms, we will get lg (SMCV H,i ) As an independent variable at the time of model fitting, the value of lg (SMCV i ) Corresponding F R SSD model fitting (including: normal distribution model, lognormal distribution model, logistic distribution model, log-logistic distribution model) was performed for the dependent variables, and the coefficient of determination (R) was determined by model fitting according to equation (6), equation (7) and equation (8) 2 ) The Root Mean Square (RMSE) and K-S test (p-value) results are shown in table 5, and the lognormal distribution model is the best fit model of SMCV under baseline conditions in the four models by comparison, and the SSD fitting curve is shown in fig. 3.
And (3) checking a model fitting goodness basis: for parametric models, p-value>In the case of 0.05 (reflecting that the model corresponds to the theoretical distribution), the coefficient R is determined 2 The larger the root mean square RMSE, the smaller its distribution is, the more consistent it is with a certain theoretical distribution.
Table 5 long term Water quality benchmark model fitting results under baseline conditions
| Fitting model | R 2 | RMSE | p(K-S) |
| Normal distribution model | 0.9394 | 0.0681 | >0.05 |
| Lognormal distribution model | 0.9573 | 0.0571 | >0.05 |
| Logical Style model | 0.9417 | 0.0667 | >0.05 |
| Logarithmic logic cliff model | 0.9542 | 0.0591 | >0.05 |
Step f, determining the lg (SMCV) corresponding to the cumulative probabilities of 5%, 10%, 25%, 50%, 75%, 90% and 95% according to the SSD curve fitted by the best-fit model under the baseline water quality condition determined by the method described in the steps a-e i ) Value, SMCV obtained by taking the inverse logarithm i I.e. chronic 5%, 10%, 25%, 50%, 75%, 90%, 95% species hazard concentration HC 5 、HC 10 、HC 25 、HC 50 、HC 75 、HC 90 、HC 95 The method comprises the steps of carrying out a first treatment on the surface of the See table 6;
TABLE 6 Long term species hazard concentration
HC 5 After dividing by the evaluation factor (according to national standard HJ 831-2017, the effective toxicity data includes species number f greater than 15 and covers enough nutrition level, the evaluation factor takes a value of 2), the chronic toxicity of copper can be predicted, and the copper long-term reference value LWQC based on the water environment is calculated. HC under baseline in table 7 5 Dividing by the evaluation factor, namely, the long-term water quality standard under the water quality conditions of 50mg/L, pH of hardness and 5mg/L of DOC, and representing the maximum concentration of copper (calculated by any 1-hour arithmetic average concentration) in water bodies which do not produce chronic harmful effects on 95% of Chinese freshwater aquatic organisms and ecological functions thereof.
TABLE 7 Long term Water quality benchmark
| HC 5 | Assessment factor | Long term baseline (. Mu.g/L) (hardness 50mg/L, pH 7 and DOC 5 mg/L) |
| 10.84 | 2 | 5.4 |
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (7)
1. A method for predicting chronic toxicity and deriving long-term benchmarks of copper in a watershed water environment, comprising the steps of:
acquiring and processing long-term water ecological toxicity data through data retrieval and data screening, wherein the water ecological toxicity data at least comprises a water body environment parameter hardness H value, an acid-base pH value and a soluble organic carbon DOC value;
carrying out chronic toxicity correction through the water quality parameter hardness H value, the pH value of acid and alkali and the DOC value of soluble organic carbon;
obtaining a seed average chronicity value of each species;
sorting the average chronic values of the seeds to obtain the cumulative probability;
determining a fitting model of the SMCV (mean average chronic value) of the seeds selected under different water quality conditions through model fitting and evaluation;
predicting the hazard concentration of species by using a selected fitting model, and determining the hazard concentration of the species by HC 5 Acquiring a final long-term reference value;
wherein the chronic toxicity correction comprises:
fitting the chronic toxicity with water environmental parameters;
correcting the chronic toxicity value through the water environmental parameters;
wherein, the fitting of the chronic toxicity and the water environmental parameters comprises the following steps:
taking the logarithmic values of the hardness H value, the pH value and the DOC value of water quality as independent variables and taking the logarithmic values of the chronic toxicity values corresponding to various groups of water quality parameters as dependent variables, and adopting the following formula to perform multi-element linear fitting to obtain K A 、K B 、K C And C A ：
lg(CTV i )＝K A* lg(H i )+K B* lg(pH i )+K C* lg(DOC i )+C A
Wherein:
CTV i : chronic toxicity value before the correction of the water quality parameters of the ith group, mug/L;
K A 、K B 、K C : chronic toxicity: after fitting the water quality parameters, the corresponding coefficients of the hardness H, the pH and the DOC are dimensionless;
C A : chronic toxicity constant, intercept, dimensionless;
H i : the hardness value of the water body before the correction of the water quality parameters of the ith group is mg/L;
pH i : the water pH value before the i-th group of water quality parameters are corrected is dimensionless;
DOC i : the water solubility organic carbon concentration value of the water body before the i-th group of water quality parameters are corrected is mg/L;
performing multi-element linear fitting goodness test;
the chronic toxicity value of the watershed water environment parameter correction adopts the following formula:
wherein:
CTV i CTV is the chronic toxicity value before and after the correction of the water quality parameter of the ith group, and mu g/L;
H i And H is the water hardness value before and after the correction of the water quality parameter of the ith group and mg/L respectively;
pH i and the pH value is the pH value of the water body before and after the correction of the water quality parameters of the ith group respectively, and the water body is dimensionless;
DOC i and DOC are the water solubility organic carbon concentration values before and after the correction of the ith group of water quality parameters, and mg/L respectively;
K A 、K B 、K C : the coefficient corresponding to the hardness, pH and DOC after the chronic toxicity is fitted with the water quality parameters is dimensionless.
2. The method for chronic toxicity prediction and long term benchmark development of copper in a watershed water environment of claim 1, wherein the data retrieval and data screening are independently accomplished by groups of researchers from toxicity databases and literature libraries;
the toxicity data retrieval comprises the retrieval of compound types, species types, toxicity effect test end point types and water environment parameters;
the literature database comprises China knowledge infrastructure engineering, a universal knowledge service platform, a Wipe network and a scientific network.
3. The method of predicting and deriving chronic toxicity of copper in a watershed water environment according to claim 2, wherein the compound types include cupric chloride, cupric nitrate, cupric sulfate, cupric acetate, cupric chloride hydroxide, cupric hydroxide, cuprous oxide, cupric oxide and cuprous nitrate;
Species types include at least 1 hard bone carp, 1 hard bone non-carp, 1 zooplankton, 1 benthic animal, 1 animal of other phylum not mentioned above, 1 phytoplankton or hydrovascular plant;
the chronic toxicity value CTV includes the values of each toxicity effect test endpoint type including 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 20% effect concentration EC 20 ；
The hardness H value in the water environment parameter comprises the concentration of calcium ions and the concentration of magnesium ions, and the DOC value of the soluble organic carbon comprises fulvic acid, fulvic acid and humic acid.
4. The method for predicting the chronic toxicity of copper in a watershed water environment and deriving a long-term baseline according to claim 1, wherein the average seed chronic value SMCV for each species under the specified water environment parameters is obtained according to the formula i ，
In the method, in the process of the invention,
SMCV i represents the species average chronicity value of species i in a specified water environmental parameter, μg/L;
CTV represents chronic toxicity value after water environmental parameter correction, mug/L;
n represents the number of CTVs of species i, one;
i represents the species number, dimensionless.
5. The method of predicting chronic toxicity and long term baseline derivation of copper in a watershed aqueous environment of claim 1, wherein ranking the seed average chronic values to obtain the cumulative probability comprises the steps of:
arranging and combining water environment parameters of different watercourses to obtain a plurality of water environment parameter combinations;
SMCV under the condition of multiple groups of water quality i The values of the toxicity sequences are respectively sequenced from small to large, and the sequence number R of toxicity sequencing is determined;
the cumulative probability F of each species is calculated according to the following formula R ，
In the method, in the process of the invention,
F R representing cumulative probability,%;
r is the serial number of the species order, and is dimensionless;
n-total number of species, dimensionless.
6. The method for predicting the chronic toxicity and deriving a long term benchmark for copper in a watershed water environment according to claim 1, wherein determining a fitted model of the average chronic value SMCV for the species selected under different water quality conditions by model fitting and evaluation comprises the steps of:
average chronic value SMCV for multiple groups of water quality conditions i Taking the base 10 logarithms, we will get lg (SMCV i ) As an independent variable at the time of model fitting, the value of lg (SMCV i ) Corresponding cumulative probability F R For a dependent variable, fitting a species sensitivity distribution model, the species sensitivity distribution model comprising: a normal distribution model, a lognormal distribution model, a logistic distribution model, and a log logistic distribution model; wherein, SMCV i Species average chronicity value for species i;
determining coefficient R according to model fitting 2 Determining average chronic value SMCV of seeds under different selected water quality conditions according to root mean square RMSE and K-S test resultsFitting a model;
respectively taking the seed average chronic value as an independent variable when the model is fitted under the specified water quality condition, and taking the accumulated probability corresponding to the seed average chronic value as a dependent variable to perform the model fitting of the seed sensitivity distribution;
and (3) carrying out model fitting goodness test, and determining a fitting model of the average chronic value of the seeds selected under the given water body environment parameter condition.
7. The method for predicting the chronic toxicity and deriving long-term benchmarks of copper in a watershed water environment according to any one of claims 1-6, wherein environmental parameters including at least three parameters of hardness, pH and DOC are selected for correcting the toxicity of water ecology, the hazard concentration of species is predicted by a selected fitting model, and the method comprises the steps of 5 Obtaining a final long-term reference value includes: determining the average species chronicity value corresponding to the cumulative probability of 5% as HC of the chronicity 5% species hazard concentration through the species sensitivity distribution curve fitted by the selected fitting model 5 ，HC 5 Divided by the evaluation factor, as a copper long-term water quality benchmark.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102999709A (en) * | 2012-12-20 | 2013-03-27 | 中国环境科学研究院 | Underground water grading and zoning evaluation method in agricultural activity area |
| WO2015159711A1 (en) * | 2014-04-18 | 2015-10-22 | 栗田工業株式会社 | Water-quality management device, water-quality management system, and water-quality management method |
| CN108876044A (en) * | 2018-06-25 | 2018-11-23 | 中国人民大学 | Content popularit prediction technique on a kind of line of knowledge based strength neural network |
| CN109580897A (en) * | 2018-10-26 | 2019-04-05 | 浙江工业大学 | It is a kind of difference fresh water environment in copper ion to the determination method of scenedesmus obliquus poisonous effect and application |
| CN111554358A (en) * | 2020-04-22 | 2020-08-18 | 中国人民大学 | Prediction method of heavy metal toxicity end point and ocean water quality reference threshold |
| CN112216342A (en) * | 2020-09-29 | 2021-01-12 | 天津大学 | Seawater aquatic organism water quality reference value derivation method for polybrominated diphenyl ether organic pollutants |
| CN113392510A (en) * | 2021-05-27 | 2021-09-14 | 中国科学院水生生物研究所 | Zinc-protected aquatic organism toxicity value hardness correction and water quality reference derivation method |
| CN113406287A (en) * | 2021-05-27 | 2021-09-17 | 中国科学院水生生物研究所 | Regional protection aquatic organism water quality benchmark derivation method for optimally controlling heavy metal pollutant chromium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8911630B2 (en) * | 2009-01-14 | 2014-12-16 | Savannah River Nuclear Solutions, Llc | Process for treating waste water having low concentrations of metallic contaminants |
| CN105608324B (en) * | 2015-12-30 | 2017-03-01 | 中国环境科学研究院 | Basin Heavy Metal In Sediments urban eco landscape forest method based on poisonous effect |
| GB2560790B (en) * | 2017-12-20 | 2019-05-15 | Intellitect Water Ltd | A water network monitor, monitoring system and method |
| CN111259519B (en) * | 2020-01-09 | 2021-03-26 | 大连理工大学 | Method for checking local water quality reference of drainage basin |
-
2021
- 2021-10-09 CN CN202111174632.6A patent/CN113917101B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102999709A (en) * | 2012-12-20 | 2013-03-27 | 中国环境科学研究院 | Underground water grading and zoning evaluation method in agricultural activity area |
| WO2015159711A1 (en) * | 2014-04-18 | 2015-10-22 | 栗田工業株式会社 | Water-quality management device, water-quality management system, and water-quality management method |
| CN108876044A (en) * | 2018-06-25 | 2018-11-23 | 中国人民大学 | Content popularit prediction technique on a kind of line of knowledge based strength neural network |
| CN109580897A (en) * | 2018-10-26 | 2019-04-05 | 浙江工业大学 | It is a kind of difference fresh water environment in copper ion to the determination method of scenedesmus obliquus poisonous effect and application |
| CN111554358A (en) * | 2020-04-22 | 2020-08-18 | 中国人民大学 | Prediction method of heavy metal toxicity end point and ocean water quality reference threshold |
| CN112216342A (en) * | 2020-09-29 | 2021-01-12 | 天津大学 | Seawater aquatic organism water quality reference value derivation method for polybrominated diphenyl ether organic pollutants |
| CN113392510A (en) * | 2021-05-27 | 2021-09-14 | 中国科学院水生生物研究所 | Zinc-protected aquatic organism toxicity value hardness correction and water quality reference derivation method |
| CN113406287A (en) * | 2021-05-27 | 2021-09-17 | 中国科学院水生生物研究所 | Regional protection aquatic organism water quality benchmark derivation method for optimally controlling heavy metal pollutant chromium |
Non-Patent Citations (10)
| Title |
|---|
| 《铝对水生生物的毒性与硬度的相关关系探讨》;石慧等;《生态毒理学报》;第11卷(第01期);141-152 * |
| Assessing effects of dissolved organic carbon and water hardness on metal toxicity to Ceriodaphnia dubia using diffusive gradients in thin films (DGT);Michael H. Paller等;《Science of The Total Environment》;第697卷(第10期);178-197 * |
| BLM预测水中重金属生物有效性研究进展;王春艳;陈浩;安立会;秦延文;吴坤炳;郑丙辉;;环境科学与技术(第08期);81-86 * |
| 不同生长阶段斑马鱼对Cu~(2+)的毒性响应差异;廖伟;刘大庆;冯承莲;金小伟;刘娜;白英臣;吴代赦;;《环境科学研究》;第33卷(第03期);626-633 * |
| 基于SSD方法比较中美物种对铜的敏感性差异;赵天慧, 周北海, 方怡向等;《环境科学研究》;第27卷(第08期);873-880 * |
| 基于累积概率分布法评估典型水环境因子对铜毒性的影响;廖伟, 冯承莲, 刘大庆等;《环境工程》;第37卷(第11期);25-29 * |
| 常闻捷，王遵尧，刘红玲主编.《基于太湖流域水生态区的水质基准研究与标准管理示范》.南京：河海大学出版社,2019,(第一版),198. * |
| 我国铜的淡水生物水质基准研究;吴丰昌, 冯承莲, 曹宇静等;《生态毒理学报》;第06卷(第06期);617-628 * |
| 王春艳 ; 陈浩 ; 安立会 ; 秦延文 ; 吴坤炳 ; 郑丙辉 ; .BLM预测水中重金属生物有效性研究进展.环境科学与技术.2011,(第08期),81-86. * |
| 生物配体模型预测太湖水体中Cu的形态分布和生物有效性;余海静, 张深, 邹国防;《环境化学》;第33卷(第07期);1107-1114 * |
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