CN114565246A - Stream water quality evaluation method based on large zoobenthos comprehensive index - Google Patents

Stream water quality evaluation method based on large zoobenthos comprehensive index Download PDF

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CN114565246A
CN114565246A CN202210145488.1A CN202210145488A CN114565246A CN 114565246 A CN114565246 A CN 114565246A CN 202210145488 A CN202210145488 A CN 202210145488A CN 114565246 A CN114565246 A CN 114565246A
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赵瑞
马千里
姚玲爱
苟婷
梁荣昌
冯雁辉
赵学敏
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South China Institute of Environmental Science of Ministry of Ecology and Environment
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Abstract

The invention discloses a stream water quality evaluation method based on a large zoobenthos comprehensive index, which relates to the technical field of water quality detection and comprises the following steps: s1, sampling benthonic animals; s2, identifying the sample; s3, measuring physical and chemical indexes; s4, dividing functional characters; s5, calculating a functional diversity index; s6, calculating a community diversity index; s7, correlation analysis and screening; and S8, rating. The stream water quality evaluation method disclosed by the invention is used for biologically evaluating the stream water quality in a mode of combining the Shannon-Wiener index H', the functional diversity index and the physicochemical index, and the evaluation method considers the response of community structure and functional change to the environmental influence, can comprehensively represent the structure and function of a stream ecosystem and relatively accurately evaluate the stream water quality condition.

Description

Stream water quality evaluation method based on large zoobenthos comprehensive index
Technical Field
The invention relates to the technical field of water quality detection, in particular to a stream water quality evaluation method based on a large zoobenthos comprehensive index.
Background
Benthonic animals refer to aquatic fauna that live at the bottom of a body of water for all or most of their life history. Besides living in a living environment, most of the habitats are fixed on hard substrates such as rocks and buried in soft substrates such as silt. In addition, there are also benthic species that attach to the surface of plants or other benthic animals, and that inhabit intertidal zones. On the feeding method, feeding in suspension and feeding in sediment are abundant. Most of the invertebrates are huge ecological groups. According to the size, the large-sized benthonic animals and the small-sized benthonic animals are divided.
Large benthonic animals are the most suitable biological indicator group to reflect the current situation of the stream ecosystem. The large benthonic animal community has complex and various structures and rich biological index types, is sensitive to different pollution and interference response, and can comprehensively reflect the disturbance degree of human interference activities on a river ecosystem. As one of the main functional groups in the water ecosystem, compared with fishes, plankton, aquatic plants and the like, the biological monitoring system has the characteristics of wide distribution, multiple types, high water quality sensitivity, weaker mobility, easier collection and identification and the like, and is always an important index organism for water quality biological monitoring. In recent years, biodiversity research has been expanded from population diversity to functional diversity, and characterization of functional composition and functional diversity using functional traits is one of the hotspots in current zoobenthos ecology research. The species functional trait is sensitive to environmental changes and has a plurality of potential indication effects on community and population succession along the environmental gradient. In the past, biological monitoring of the water quality of large benthonic animals mostly adopts biological indexes reflecting the structural attributes of a stream ecosystem, such as: shannon winer index, margriff index and the like, but the evaluation of the functional properties and functional diversity of the benthonic animals is generally neglected by the commonly used index method at present, and the water quality condition of the stream is difficult to be comprehensively, objectively and accurately reflected.
Disclosure of Invention
Aiming at the existing problems, the invention provides a stream water quality evaluation method based on the comprehensive index of large benthonic animals.
The technical scheme of the invention is as follows:
a stream water quality evaluation method based on a large zoobenthos comprehensive index comprises the following steps:
s1, sampling benthonic animals: selecting a plurality of sampling points at the bottom of the stream, and arranging a sampling net at each sampling point to sample benthonic animals;
s2, sample identification: washing the sample collected in the step S1 with clear water, filtering with a screen, storing the filtered sample in 75% ethanol solution, and identifying the sample to be a species or a genus in a laboratory with a microscope;
s3, physical and chemical index determination: measuring the physical and chemical indexes of the stream water body;
s4, functional character division: selecting a plurality of functional characters and collecting corresponding functional character data based on the stream habitat attributes;
s5, calculating a functional diversity index: calculating a functional diversity index of each sampling point according to the functional trait data obtained in the step S4, wherein the functional diversity index comprises: functional character separation index FD and functional uniformity FE;
s6, calculating a community diversity index: the calculation of the community diversity index Shannon-Wiener index is completed, and the calculation formula of the Shannon-Wiener index H' is shown as the following formula:
Figure BDA0003508725220000021
in the formula: pi represents the ratio of the number of the ith individuals to the total number of the individuals N in the sample, and Pi is Ni/N；
S7, correlation analysis and screening: analyzing the correlation R of the physicochemical indexes measured in the step S3 and the Shannon-Wiener index H' calculated in the step S6 by adopting a linear regression model methodN 2Screening out the physicochemical index with highest correlation, and simultaneously adopting linear regressionAnalyzing the correlation R of the functional diversity index calculated in the step S5 and the Shannon-Wiener index H' calculated in the step S6 by a regression model methodF 2Screening out a functional diversity index with highest correlation;
s8, evaluation grade division: according to the analysis result of the physicochemical indexes and the functional diversity indexes screened in the step S7 on the response of the environmental factors, different weights are given to the community diversity index, the functional diversity index and the physicochemical indexes, and water quality evaluation grade division is performed as shown in the following formula:
Figure BDA0003508725220000031
wherein m is the number of sampling points, A1 Weighting 1/5, A as the diversity index of benthic animal community2Weighting 2/5, A as the index of functional diversity of benthonic animals 32/5 is the weight of the physicochemical index, F is the functional diversity index with the highest correlation, RF 2The correlation coefficient, R, of the highest-correlation functional diversity indexN 2The index is the correlation coefficient of the physicochemical index with the highest correlation, and X is the grade conversion index of the physicochemical index with the highest correlation;
the grade conversion index X is calculated in the following manner: dividing the concentration range of the selected physical and chemical indexes in the stream into 6 parts according to the national water quality standard of the selected physical and chemical indexes, wherein the corresponding numerical value of each part is-1, -0.6, -0.2, 0.6 and 1, and substituting the average value of all sampling points of the physical and chemical indexes with the highest correlation into the range to obtain a grade conversion index X;
the water quality evaluation criteria obtained from the water quality evaluation grade P are: p is more than 1.5, and is very clean; p is more than 1.2 and less than or equal to 1.5, so the cleaning agent is cleaner; p is more than 0.9 and less than or equal to 1.2, and the pollution is light; p is more than 0.6 and less than or equal to 0.9, and the pollution is avoided; p is more than 0.3 and less than or equal to 0.6, and heavy pollution is caused; p is more than 0 and less than or equal to 0.3, and the pollution is serious.
Further, the total number of the sampling points in the step S1 is 30-40, and the distance between every two adjacent sampling points is 5-10km, so that the collected samples can sufficiently reflect the distribution characteristics of the benthic animals.
Furthermore, the sampling net in the step S1 is a D-type net and a sober net with an opening area of 0.3m × 0.3m and a pore size of 60 meshes, so that the sampling efficiency is improved.
Further, in the step S2, the aperture of the screen is 60 meshes, so as to screen out impurities in the sample.
Further, the physicochemical indexes in the step S3 are ammonia nitrogen, BOD5 and COD content in the water body, which are common pollutants or standards in the water body.
Further, the functional traits in step S4 include 10 kinds of characteristics, which are zoobenthos, drift, swimming ability, adsorption ability, shape, size of mature individual, fluid preference, temperature preference, lifestyle and nutrition habit.
Further, the calculation formula of the functional trait separation degree index FD in step S5 is shown as follows:
Figure BDA0003508725220000041
in the formula, Ci is the numerical value of the functional character of the ith item; ai is the relative abundance of the functional trait of item i;
Figure BDA0003508725220000042
the weighted average value of the natural logarithm of the characteristic value of the species is obtained, and x is the number of the species;
the functional uniformity FE is calculated as follows:
Figure BDA0003508725220000043
wherein S is the abundance of species, PEWiThe local weighted uniformity for species i.
Further, the national surface water environment quality standard of ammonia nitrogen in the water body in the step S8 is 0-2.0mg/L and is divided into 5 grades, so that NH is more than 03when-N is less than or equal to 0.3, the grade conversion index X is 1, NH is more than 0.33Value of grade conversion index X when-N is less than or equal to 0.60.6, 0.6 < NH3when-N is less than or equal to 0.9, the grade conversion index X is 0.2, NH is more than 0.93when-N is less than or equal to 1.2, the grade conversion index X is-0.2, NH is more than 1.23when-N is less than or equal to 1.5, the grade conversion index X is-0.6, NH is more than 1.53When N is less than or equal to 2, the grade conversion index X is-1;
the national surface water environment quality standard of BOD5 in the water body is 0-10mg/L and is divided into 5 grades, so that the grade conversion index X is 1 when the BOD5 is more than 0 and less than or equal to 1, the grade conversion index X is 0.6 when the BOD5 is more than 1 and less than or equal to 2, the grade conversion index X is 0.2 when the BOD5 is more than 2 and less than or equal to 3, the grade conversion index X is-0.2 when the BOD5 is more than 3 and less than or equal to 4, the grade conversion index X is-0.6 when the BOD5 is more than 4 and less than or equal to 5, and the grade conversion index X is-1 when the BOD5 is more than 5 and less than or equal to 10;
the national surface water environment quality standard of COD in the water body is 0-40mg/L and is divided into 5 grades, so that the grade conversion index X is 1 when the COD is more than 0 and less than or equal to 6, the grade conversion index X is 0.6 when the COD is more than 6 and less than or equal to 12, the grade conversion index X is 0.2 when the COD is more than 12 and less than or equal to 18, the grade conversion index X is-0.2 when the COD is more than 18 and less than or equal to 24, the grade conversion index X is-0.6 when the COD is more than 24 and less than or equal to 30, and the grade conversion index X is-1 when the COD is more than 30 and less than or equal to 40.
The beneficial effects of the invention are:
(1) the stream water quality evaluation method disclosed by the invention is used for biologically evaluating the stream water quality in a mode of combining the Shannon-Wiener index and the functional diversity index, and the evaluation method considers the response of community structure and functional change to the environmental influence, can comprehensively represent the structure and function of a stream ecosystem, and relatively accurately evaluates the stream water quality condition.
(2) The stream water quality evaluation method can judge the pollution degree of water quality more intuitively by establishing the water quality category evaluation parameter P, and judge the relevance of the formula by combining the functional diversity index, the physical and chemical index and the Shannon-Wiener index, so that the water quality can be comprehensively evaluated intuitively, quickly and conveniently.
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FIG. 1 is a process flow diagram of the stream water quality evaluation method of the present invention.
FIG. 2 is a schematic diagram showing the correlation between the Shannon-Wiener index H' and the functional separation index FD in example 1 of the present invention;
FIG. 3 is a graph showing the correlation between the Shannon-Wiener index H' and the uniformity index FE in example 1 of the present invention;
FIG. 4 is a schematic diagram showing the correlation between the Shannon-Wiener index H' and the content of ammonia nitrogen in example 1 of the present invention;
FIG. 5 is a graph showing the correlation between the Shannon-Wiener index H' and the value of BOD5 in example 1 of the present invention;
FIG. 6 is a graph showing the correlation between the Shannon-Wiener index H' and the COD value in example 1 of the present invention.
Detailed Description
Example 1
A stream water quality evaluation method based on a large zoobenthos comprehensive index comprises the following steps:
s1, sampling benthonic animals: selecting a plurality of sampling points at the bottom of the stream, wherein the total number of the sampling points is 36, the distance between every two adjacent sampling points is 8km, and a sampling net is arranged at each sampling point for sampling the benthonic animals, wherein the sampling net is a D-shaped net and a Sober net with the opening area of 0.3m multiplied by 0.3m and the aperture of 60 meshes;
s2, sample identification: washing the sample collected in the step S1 with clear water, filtering with a 60-mesh screen, storing the filtered sample in 75% ethanol solution, and identifying the species or the genus of the sample in a laboratory by using a microscope;
s3, measuring physical and chemical indexes: measuring the physicochemical indexes of the stream water body, wherein the physicochemical indexes are the contents of ammonia nitrogen, BOD5 and COD in the water body;
s4, functional character division: selecting 10 functional traits and collecting corresponding functional trait data based on stream habitat attributes, wherein the functional traits are benthonic animal character, drift, swimming capacity, adsorption capacity, shape, mature individual size, flow state preference, temperature preference, lifestyle and nutrition habit;
s5, calculating a functional diversity index: calculating a functional diversity index of each sampling point according to the functional trait data obtained in the step S4, wherein the functional diversity index comprises: the functional character separation degree index FD and the functional uniformity FE, and the calculation formula of the functional character separation degree index FD is shown as the following formula:
Figure BDA0003508725220000061
in the formula, Ci is the numerical value of the ith functional character; ai is the relative abundance of the functional trait of item i;
Figure BDA0003508725220000062
the weighted average value of the natural logarithm of the characteristic value of the species is obtained, and x is the number of the species;
the functional uniformity FE is calculated as follows:
Figure BDA0003508725220000071
wherein S is the abundance of species, PEWiLocally weighted uniformity for species i;
s6, calculating a community diversity index: the calculation of the community diversity index Shannon-Wiener index is completed, and the calculation formula of the Shannon-Wiener index H' is shown as the following formula:
Figure BDA0003508725220000072
in the formula: pi represents the ratio of the number of the ith individuals to the total number of the individuals N in the sample, and Pi is Ni/N；
S7, correlation analysis and screening: analyzing the correlation R of the physicochemical indexes measured in the step S3 and the Shannon-Wiener index H' calculated in the step S6 by adopting a linear regression model methodN 2Screening out the physicochemical indexes with the highest correlation, and analyzing the correlation R of the functional diversity index calculated in the step S5 and the Shannon-Wiener index H' calculated in the step S6 by adopting a linear regression model methodF 2And screening out the phasesThe highest functional diversity index of relevance;
s8, evaluation grade division: according to the analysis result of the physicochemical indexes and the functional diversity indexes screened in the step S7 on the response of the environmental factors, different weights are given to the community diversity index, the functional diversity index and the physicochemical indexes, and the water quality evaluation grade division is performed, as shown in the following formula:
Figure BDA0003508725220000073
wherein m is the number of sampling points, A1 Weighting 1/5, A as the diversity index of benthic animal community2Weighting 2/5, A as the index of functional diversity of benthonic animals 32/5 is the weight of the physicochemical index, F is the functional diversity index with the highest correlation, RF 2The correlation coefficient, R, of the highest-correlation functional diversity indexN 2The correlation coefficient of the physicochemical index with the highest correlation is obtained, and X is the grade conversion index of the physicochemical index with the highest correlation;
the grade conversion index X is calculated as: dividing the concentration range of the selected physical and chemical indexes in the stream into 6 parts according to the national water quality standard of the selected physical and chemical indexes, wherein the corresponding numerical value of each part is-1, -0.6, -0.2, 0.6 and 1, and substituting the average value of all sampling points of the physical and chemical indexes with the highest correlation into the range to obtain a grade conversion index X;
the national surface water environment quality standard of ammonia nitrogen in the water body is 0-2.0mg/L and is divided into 5 grades, so that NH is more than 03when-N is less than or equal to 0.3, the grade conversion index X is 1, NH is more than 0.33when-N is less than or equal to 0.6, the grade conversion index X is 0.6, NH is more than 0.63when-N is less than or equal to 0.9, the grade conversion index X is 0.2, NH is more than 0.93when-N is less than or equal to 1.2, the grade conversion index X is-0.2, and NH is more than 1.23when-N is less than or equal to 1.5, the grade conversion index X is-0.6, NH is more than 1.53When N is less than or equal to 2, the grade conversion index X is-1;
the national surface water environment quality standard of BOD5 in the water body is 0-10mg/L and is divided into 5 grades, so that the grade conversion index X is 1 when the BOD5 is more than 0 and less than or equal to 1, the grade conversion index X is 0.6 when the BOD5 is more than 1 and less than or equal to 2, the grade conversion index X is 0.2 when the BOD5 is more than 2 and less than or equal to 3, the grade conversion index X is-0.2 when the BOD5 is more than 3 and less than or equal to 4, the grade conversion index X is-0.6 when the BOD5 is more than 4 and less than or equal to 5, and the grade conversion index X is-1 when the BOD5 is more than 5 and less than or equal to 10;
the national surface water environment quality standard of COD in the water body is 0-40mg/L and is divided into 5 grades, so that the grade conversion index X is 1 when the COD is more than 0 and less than or equal to 6, the grade conversion index X is 0.6 when the COD is more than 6 and less than or equal to 12, the grade conversion index X is 0.2 when the COD is more than 12 and less than or equal to 18, the grade conversion index X is-0.2 when the COD is more than 18 and less than or equal to 24, the grade conversion index X is-0.6 when the COD is more than 24 and less than or equal to 30, and the grade conversion index X is-1 when the COD is more than 30 and less than or equal to 40;
the water quality evaluation criteria obtained from the water quality evaluation grade P are: p is more than 1.5, and is very clean; p is more than 1.2 and less than or equal to 1.5, so the cleaning agent is cleaner; p is more than 0.9 and less than or equal to 1.2, and the pollution is light; p is more than 0.6 and less than or equal to 0.9, and the pollution is avoided; p is more than 0.3 and less than or equal to 0.6, and heavy pollution is caused; p is more than 0 and less than or equal to 0.3, and the pollution is serious.
Example 2
The present embodiment is different from embodiment 1 in that: the number of sampling points in step S1 is different.
S1, sampling benthonic animals: selecting 30 sampling points at the bottom of the stream, arranging a sampling net at each sampling point for sampling the benthonic animals, wherein the distance between every two adjacent sampling points is 5km, and the sampling net is a D-shaped net and a Sober net with the opening area of 0.3m multiplied by 0.3m and the aperture of 60 meshes.
Example 3
The present embodiment is different from embodiment 1 in that: the number of sampling points in step S1 is different.
S1, sampling benthonic animals: selecting 40 sampling points at the bottom of the stream, arranging a sampling net at each sampling point for sampling the benthonic animals, wherein the distance between every two adjacent sampling points is 10km, and the sampling net is a D-shaped net and a Sober net with the opening area of 0.3m multiplied by 0.3m and the aperture of 60 meshes.
Example 4
The present embodiment is different from embodiment 1 in that: the functional properties selected in step S4 are different.
S4, functional character division: based on the stream habitat attributes, 8 functional characters are selected and corresponding functional character data are collected, wherein the functional characters are benthonic creativity, drift, swimming capacity, adsorption capacity, shape, mature individual size, flow state preference and temperature preference functional characters respectively, and the corresponding functional character data are collected.
Example 5
The present embodiment is different from embodiment 1 in that: the functional properties selected in step S4 are different.
S4, functional character division: based on the stream habitat attributes, 8 functional traits are selected and corresponding functional trait data are collected, wherein the functional traits are swimming capacity, adsorption capacity, shape, size of mature individual, flow state preference, temperature preference, life style and nutrition habit functional traits of the benthonic animals and the corresponding functional trait data are collected.
Example 6
The present embodiment is different from embodiment 1 in that: the physicochemical indexes selected in step S7 are different.
S7, correlation analysis and screening: analyzing the correlation R of the physicochemical indexes measured in the step S3 and the Shannon-Wiener index H' calculated in the step S6 by adopting a linear regression model methodN 2Screening out the physicochemical indexes with the highest correlation, and analyzing the correlation R of the functional diversity index calculated in the step S5 and the Shannon-Wiener index H' calculated in the step S6 by adopting a linear regression model methodF 2And screening out the functional diversity index with the highest correlation, wherein the selected physical and chemical index is the BOD5 value in the water body.
Example 7
The present embodiment is different from embodiment 1 in that: the physicochemical indexes selected in step S7 are different.
S7, correlation analysis and screening: analyzing the correlation R of the physicochemical indexes measured in the step S3 and the Shannon-Wiener index H' calculated in the step S6 by adopting a linear regression model methodN 2Screening out the physicochemical indexes with the highest correlation, the same asAnalyzing the correlation R of the functional diversity index calculated in the step S5 and the Shannon-Wiener index H' calculated in the step S6 by using a linear regression model methodF 2And screening out the functional diversity index with the highest correlation, wherein the selected physical and chemical index is the COD content in the water body.
Examples of the experiments
Carrying out simulation experiments on the methods in five groups of embodiments 1 and 4-7 of the invention, selecting a plurality of positions of the same stream for sampling, wherein 36 sampling points in embodiment 1 are selected as representatives because the embodiments 2 and 3 are different from embodiment 1 only in the number of sampling groups; examples 4 and 5 show that the selected functional traits are different, examples 6 and 7 show that the selected physicochemical indexes are different, and the species of the benthonic animals are identified as follows:
example 1: 4035 benthic animal samples are 83 in total, wherein 71 kinds of Insecta, 3 kinds of Crustacea, 3 kinds of Bispood, 4 kinds of gastropoda, 2 kinds of Oligochaeta and the rest are annelid animals.
Example 4: 3948 benthic animal samples, 81 species in total, wherein 69 species of Insecta, 4 species of Crustacea, 3 species of Bisporules, 3 species of gastropoda, 2 species of Oligochaeta, and the rest are annelids.
Example 5: 4078 benthic animal samples, 79 species in total, 65 species of Insecta, 6 species of Crustacea, 4 species of Bispood, 1 species of gastropoda, 3 species of Oligochaeta, and the rest are annelid animals.
Example 6: the benthic animal samples have 3875 species and 80 species in total, wherein 71 species of Insecta, 4 species of Crustacea, 2 species of Bispongiopsis and 3 species of gastropoda, and the rest are annelid animals.
Example 7: 4107 benthonic animal samples, which are 82 in total, wherein 68 insects, 7 crustaceans, 6 bivalves, 1 gastropoda and the balance annelid animals are selected.
It can be seen that the species of the large benthonic animals in the above 5 examples were similar and could be used as a control group for experiments.
The functional traits in 5 groups of examples are divided, and the functional diversity index with the highest correlation is selected by taking example 1 as an example, and as can be seen from fig. 2 and 3, the functional trait separation degree index FDThe functional character separation degree index FD is selected as the functional diversity index, the data analysis software is used for calculating the functional character separation degree indexes FD of all 36 groups of sampling points, and the 36 groups of data are substituted into the formula in the step S8
Figure BDA0003508725220000111
In the formula, m is 36. The results are shown in table 1:
table 15 mean functional trait separation index FD in the examples of group
Examples FD
Example 1 0.73
Example 4 0.82
Example 5 0.64
Example 6 0.55
Example 7 0.68
The Shannon-Wiener index H' in the 5 sets of examples was calculated, the software used for the analysis was IBM SPSS19.0, and the 36 sets of data were substituted into the formula in step S8
Figure BDA0003508725220000112
In the formula, m is 36.
The results are shown in table 2:
TABLE 25 set of examples Shannon-Wiener index H'
Examples H’
Example 1 2.7
Example 4 2.5
Example 5 1.8
Example 6 3.2
Example 7 2.9
Counting the physicochemical indexes of 36 sampling points in 5 groups of embodiments, and correlating the 36 groups of physicochemical indexes in each group of embodiments with a correlation coefficient R of a Shannon-Wiener index HN 2Calculating, analyzing the software used by IBM SPSS19.0 and OringPro8.0, and screening out the physicochemical indexes with highest correlation, wherein the physicochemical indexes with highest ammonia nitrogen content are selected in examples 1, 4 and 5, and as shown in FIGS. 4-6, the correlation coefficient R of the 3 physicochemical indexes of example 1 and the Shannon-Wiener index H' is shownN 2Examples of the invention6, screening out a physicochemical index with the highest BOD5 index and example 7, screening out a physicochemical index with the highest COD content and converting the physicochemical index into a grade conversion index X, wherein the correlation coefficient result and the grade conversion index X are shown in Table 3:
table 35 sets of examples in the physicochemical index of grade transformation index X and its associated index R with Shannon-Wiener index HN 2
Figure BDA0003508725220000121
The above results show that when R isN 2When the index is more than 0.8, the correlation coefficient R of the physicochemical index and the Shannon-Wiener index HN 2The influence is obvious; when 0.8 > RN 2When the index is more than 0.6, the correlation coefficient R of the physicochemical index and the Shannon-Wiener index HN 2The influence is obvious; when 0.6 > RN 2When the index is more than 0.4, the correlation coefficient R of the physicochemical index and the Shannon-Wiener index H2The influence is general; when R isN 2When the ratio is less than 0.4, the correlation coefficient R of the physicochemical index and the Shannon-Wiener index HN 2The effect is less significant. Correlation coefficient R of physicochemical indexes of ammonia nitrogen content and Shannon-Wiener index H' in examples 1, 4 and 5N 2The influence is obvious, and the correlation coefficient R of the physicochemical index BOD5 in the example 6 and the Shannon-Wiener index HN 2The influence is obvious, and the correlation coefficient R of the physicochemical index COD and the Shannon-Wiener index H' in the example 7N 2The influence is more remarkable.
The correlation coefficient R of the functional diversity index with the highest correlation with the Shannon-Wiener index H' in the 5 groups of examplesF 2Statistics were performed and the results are shown in table 4:
TABLE 45 set of examples relating functional diversity index to Shannon-Wiener index H' correlation coefficient RF 2
Figure BDA0003508725220000122
Figure BDA0003508725220000131
Finally, the evaluation grades of 5 groups of examples are divided, and the data are substituted into the formula of the step S8
Figure BDA0003508725220000132
The results are shown in table 5:
water quality evaluation grade P in Table 55 group of examples
Examples P
Example 1 0.8938
Example 4 0.8749
Example 5 0.4709
Example 6 1.0742
Example 7 0.8308
The data in table 4 are substituted into the evaluation criteria of the water quality evaluation rating P: p is more than 1.5, and is very clean; p is more than 1.2 and less than or equal to 1.5, so the cleaning agent is cleaner; p is more than 0.9 and less than or equal to 1.2, and the pollution is light; p is more than 0.6 and less than or equal to 0.9, and pollution is avoided; p is more than 0.3 and less than or equal to 0.6, and heavy pollution is caused; p is more than 0 and less than or equal to 0.3, and the pollution is serious, and the following can be seen:
the stream section water quality in example 1 was medium pollution, the stream section water quality in example 4 was medium pollution, the stream section water quality in example 5 was heavy pollution, the stream section water quality in example 6 was light pollution, and the stream section water quality in example 7 was medium pollution.

Claims (8)

1. A stream water quality evaluation method based on a large zoobenthos comprehensive index is characterized by comprising the following steps:
s1, sampling benthonic animals: selecting a plurality of sampling points at the bottom of the stream, and arranging a sampling net at each sampling point to sample benthonic animals;
s2, sample identification: washing the sample collected in the step S1 with clear water, filtering with a screen, storing the filtered sample in 75% ethanol solution, and identifying the sample to be a species or a genus in a laboratory with a microscope;
s3, physical and chemical index determination: measuring the physical and chemical indexes of the stream water body;
s4, functional character division: selecting a plurality of functional characters and collecting corresponding functional character data based on the stream habitat attributes;
s5, calculating a functional diversity index: calculating a functional diversity index of each sampling point according to the functional trait data obtained in the step S4, wherein the functional diversity index comprises: functional character separation index FD and functional uniformity FE;
s6, calculating a community diversity index: the calculation of the community diversity index Shannon-Wiener index is completed, and the calculation formula of the Shannon-Wiener index H' is shown as the following formula:
Figure FDA0003508725210000011
in the formula: pi represents the ratio of the number of the ith individual to the total number of the individual N in the sample, and Pi is Ni/N；
S7, correlation analysis and screening: analyzing the correlation R of the physicochemical indexes measured in the step S3 and the Shannon-Wiener index H' calculated in the step S6 by adopting a linear regression model methodN 2Screening out the physicochemical indexes with the highest correlation, and analyzing the correlation R of the functional diversity index calculated in the step S5 and the Shannon-Wiener index H' calculated in the step S6 by adopting a linear regression model methodF 2Screening out a functional diversity index with highest correlation;
s8, evaluation grade division: according to the analysis result of the physicochemical indexes and the functional diversity indexes screened in the step S7 on the response of the environmental factors, different weights are given to the community diversity index, the functional diversity index and the physicochemical indexes, and the water quality evaluation grade division is performed, as shown in the following formula:
Figure FDA0003508725210000021
wherein m is the number of sampling points, A1Weighting 1/5, A as the diversity index of benthic animal community2Weighting 2/5, A as the index of functional diversity of benthonic animals32/5 is the weight of the physicochemical index, F is the functional diversity index with the highest correlation, RF 2The correlation coefficient, R, of the highest-correlation functional diversity indexN 2The correlation coefficient of the physicochemical index with the highest correlation is obtained, and X is the grade conversion index of the physicochemical index with the highest correlation;
the grade conversion index X is calculated in the following manner: dividing the concentration range of the selected physical and chemical indexes in the stream into 6 parts according to the national water quality standard of the selected physical and chemical indexes, wherein the corresponding numerical value of each part is-1, -0.6, -0.2, 0.6 and 1, and substituting the average value of all sampling points of the physical and chemical indexes with the highest correlation into the range to obtain a grade conversion index X;
the water quality evaluation criteria obtained from the water quality evaluation grade P are: p is more than 1.5, and is very clean; p is more than 1.2 and less than or equal to 1.5, so the cleaning agent is cleaner; p is more than 0.9 and less than or equal to 1.2, and light pollution is caused; p is more than 0.6 and less than or equal to 0.9, and the pollution is avoided; p is more than 0.3 and less than or equal to 0.6, and heavy pollution is caused; p is more than 0 and less than or equal to 0.3, and the pollution is serious.
2. The method as claimed in claim 1, wherein the total number of the sampling points in the step S1 is 30-40, and the distance between every two adjacent sampling points is 5-10 km.
3. The method for evaluating the quality of a stream based on the comprehensive index of large benthonic animals according to claim 1, wherein the sampling net in step S1 is a D-type net and a sober net with an opening area of 0.3m x 0.3m and a pore size of 60 meshes.
4. The method for evaluating the quality of the stream water based on the comprehensive index of the large benthonic animals as claimed in claim 1, wherein the mesh size of the screen in the step S2 is 60 meshes.
5. The method for evaluating the stream water quality based on the comprehensive index of the large benthonic animals according to claim 1, wherein the physicochemical indexes in the step S3 are ammonia nitrogen, BOD5 and COD content in the water body.
6. The method as claimed in claim 1, wherein the functional traits of step S4 include 10 kinds of zoobenthos, drift, swimming ability, adsorption ability, shape, size of mature individual, fluid state preference, temperature preference, life style and nutrition habit.
7. The method for evaluating the quality of a stream water based on the comprehensive index of the large benthonic animals according to claim 1, wherein the functional trait separation degree index FD in the step S5 is calculated according to the following formula:
Figure FDA0003508725210000031
in the formula, Ci is the numerical value of the ith functional character; ai is the relative abundance of the functional trait of item i;
Figure FDA0003508725210000032
the weighted average value of the natural logarithm of the characteristic value of the species is obtained, and x is the number of the species;
the functional uniformity FE is calculated as follows:
Figure FDA0003508725210000033
wherein S is the abundance of species, PEWiThe local weighted uniformity for species i.
8. The method as claimed in claim 1, wherein the national surface water environment quality standard of ammonia nitrogen in water in step S8 is 0-2.0mg/L divided into 5 grades, so that 0 < NH3when-N is less than or equal to 0.3, the grade conversion index X is 1, NH is more than 0.33when-N is less than or equal to 0.6, the grade conversion index X is 0.6, NH is more than 0.63when-N is less than or equal to 0.9, the grade conversion index X is 0.2, NH is more than 0.93when-N is less than or equal to 1.2, the grade conversion index X is-0.2, and NH is more than 1.23when-N is less than or equal to 1.5, the grade conversion index X is-0.6, NH is more than 1.53When N is less than or equal to 2, the grade conversion index X is-1;
the national surface water environment quality standard of BOD5 in the water body is 0-10mg/L and is divided into 5 grades, so that the grade conversion index X is 1 when the BOD5 is more than 0 and less than or equal to 1, the grade conversion index X is 0.6 when the BOD5 is more than 1 and less than or equal to 2, the grade conversion index X is 0.2 when the BOD5 is more than 2 and less than or equal to 3, the grade conversion index X is-0.2 when the BOD5 is more than 3 and less than or equal to 4, the grade conversion index X is-0.6 when the BOD5 is more than 4 and less than or equal to 5, and the grade conversion index X is-1 when the BOD5 is more than 5 and less than or equal to 10;
the national surface water environment quality standard of COD in the water body is 0-40mg/L and is divided into 5 grades, so that the grade conversion index X is 1 when the COD is more than 0 and less than or equal to 6, the grade conversion index X is 0.6 when the COD is more than 6 and less than or equal to 12, the grade conversion index X is 0.2 when the COD is more than 12 and less than or equal to 18, the grade conversion index X is-0.2 when the COD is more than 18 and less than or equal to 24, the grade conversion index X is-0.6 when the COD is more than 24 and less than or equal to 30, and the grade conversion index X is-1 when the COD is more than 30 and less than or equal to 40.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116660486A (en) * 2023-05-24 2023-08-29 重庆交通大学 Water quality evaluation standard determining method based on large benthonic animal BI index

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