Method and system for evaluating harm of pesticide residue and heavy metal pollution to Chinese wolfberry

Abstract

The invention discloses a method and a system for evaluating harm of pesticide residue and heavy metal pollution to Chinese wolfberry, and relates to the field of pesticide residue and heavy metal pollution evaluation. The method comprises the following steps: sampling lycium barbarum in an ith stage in a whole growth period, obtaining growth evaluation index parameters and hazard factor data of the lycium barbarum, carrying out grey correlation analysis, and obtaining a correlation degree between a pth-type residual pesticide concentration value Cip and a qth-type heavy metal content Miq in the ith stage and the growth evaluation index parameters; taking the normalized correlation degree value as an influence factor of the hazard factor on the growth vigor evaluation index, calculating a first hazard index HIic and a second hazard index HIim of the residual pesticide on the growth vigor of the lycium barbarum in the ith stage according to the obtained influence factor, and carrying out weighting processing on calculation results of the first hazard index HIic and the second hazard index HIim to obtain a weighted result of the first hazard index HIic and the second hazard index HIim; and taking the weighted processing value as a hazard index HIi of the residual pesticide and the heavy metal to the growth vigor of the Chinese wolfberry in the ith stage, and taking the quantized hazard index HIi as a hazard assessment characterization value to the growth vigor of the Chinese wolfberry.

Description

Technical Field

[0001] The invention relates to the field of pesticide residue and heavy metal pollution assessment, and in particular to a method and system for assessing the hazards of pesticide residue and heavy metal pollution to wolfberry. Background Art

[0002] With the continuous progress of society, people are paying more and more attention to the quality and safety of agricultural products, and the problems of pesticide residues and heavy metal pollution in agricultural products have gradually attracted widespread attention. Relevant laws and regulations have also been gradually improved, and the maximum limit of pesticide residues has become lower and lower. Among them, the Ministry of Agriculture has formulated and promulgated relevant industry standards for wolfberry, limiting the maximum limit standard values ​​of 15 pesticides commonly used in wolfberry, such as carbendazim and chlorpyrifos, to regulate farmers' reasonable use of pesticides and eliminate the abuse of pesticides. According to relevant studies, pesticides enter the soil through spraying, irrigation, etc., gradually accumulate and change the physical and chemical properties of the soil. Long-term and large-scale use of pesticides will lead to the destruction of soil structure, the decline of fertility, and the imbalance of microbial communities, which will in turn affect the ecological service function of the soil, lead to water pollution, and reduce biodiversity, directly affecting the taste, color and nutritional value of crops, and reducing the market competitiveness of agricultural products. Heavy metals will change the physical and chemical properties of the soil, destroy the soil structure, and affect the fertility and air permeability of the soil. Heavy metal elements are absorbed by plant roots and then accumulated in the plant body, leading to physiological and metabolic disorders, reducing the plant's disease resistance and yield, causing the plant to wilt or even die. Elements such as mercury and arsenic can weaken and inhibit the activity of nitrifying and ammonifying bacteria in the soil, affecting the supply of nitrogen. And because heavy metal pollutants have low mobility in the soil, they are not easily leached with water and are not degraded by microorganisms. After entering the human body through the food chain, they have great potential harm.

[0003] At present, the evaluation of pesticide residues and heavy metal pollution in wolfberry is based on the harm of contaminated products to human health. Although the qualitative conclusion that pesticide residues and heavy metal pollution will lead to reduced wolfberry production and lower quality has been given, there is a lack of quantitative analysis and evaluation methods for the harm of pesticide residues and heavy metal pollution to wolfberry. To this end, we propose a method and system for evaluating the harm of pesticide residues and heavy metal pollution to wolfberry. Summary of the invention

[0004] The main purpose of the present invention is to provide a method and system for evaluating the hazards of pesticide residues and heavy metal pollution to wolfberry, which can effectively solve the problems in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for assessing the hazards of pesticide residues and heavy metal pollution to wolfberry, comprising:

[0007] According to the growth characteristics of wolfberry in the whole growth period, the whole growth period of wolfberry is divided into the full flowering stage, the full fruit stage, the vegetative growth stage, the autumn fruit growth stage, and the autumn fruit harvest stage, and the growth evaluation index parameters of wolfberry in each stage are obtained;

[0008] Wherein, the growth evaluation index in the flowering stage includes any one of plant height, number or size of leaves per plant, average circumference of leaves, flowering amount, and stem color value;

[0009] The growth evaluation index at the peak fruiting stage includes any one of plant height, crown width, trunk diameter or circumference, leaf area index, single plant yield, and yield per unit area;

[0010] The growth evaluation index in the vegetative growth stage includes any one of root density, root growth per unit time, diameter, and stem growth per unit time;

[0011] The growth evaluation index in the autumn fruit growth stage includes any one of yield per unit area, fruit color uniformity, and fruit size uniformity;

[0012] The growth evaluation index in the autumn fruit harvesting stage includes any one of the yield per unit area, the uniformity of the fruit appearance color, and the fruit appearance color value;

[0013] Sampling of wolfberry in the i-th stage of the whole growth period, measuring the hazard factor data of wolfberry samples, the hazard factor data including residual pesticide concentration value and heavy metal content, and performing grey correlation analysis on the obtained hazard factor data and growth evaluation index parameters, to obtain the p-th type of residual pesticide concentration value C in the i-th stage ip The correlation between the growth evaluation index parameters r ip And the content of heavy metals of category q M iq The correlation between the growth evaluation index and iq ;

[0014] The correlation degree r ip and r iq Normalization is performed to obtain the normalized correlation value. and As the influencing factor of the hazard factor on the growth evaluation index, the influencing factor k of the p-th type of residual pesticide and the q-th type of heavy metal on the growth evaluation index in the i-th stage is obtained. ip and k iq ,in, Correlation r p and r q The normalization formula is:

[0015]

[0016] In the formula, is the correlation degree r ip Normalized value; is the correlation degree r iq Normalized value; P i is the type of pesticide residues in wolfberry samples in stage i; Q i are the types of heavy metals contained in wolfberry samples in phase i;

[0017] According to the obtained impact factor k ip Calculate the first harm index HI of residual pesticides to the growth of wolfberry in the i-th stage ic , according to the obtained impact factor k iq Calculate the second harm index HI of heavy metals to the growth of wolfberry in stage i im , the calculation formulas of the first hazard index and the second hazard index are respectively:

[0018]

[0019] In the formula, rf ip is the concentration C of the p-type residual pesticide in the i-th stage ip Harmful value at level; rf iq is the content M of the qth type of heavy metal in the i-th stage iq Harmful value at the level;

[0020] The concentration of the p-type residual pesticide in the i-th stage is C ip The harmful value rf ip The calculation formula is:

[0021]

[0022] In the formula, a p 、b p 、n p are all toxicity constants of the p-type residual pesticides, t ip Maintain the concentration C of the p-type residual pesticide in the i-th stage ip The duration of the level; λ is a constant coefficient, and λ∈(3.5,8);

[0023] The content of heavy metals of type q in stage i is M iq The harmful value rf iq The calculation formula is:

[0024]

[0025] In the formula, M is the content of heavy metal of type q in stage i iq The mean of the sampled values; where M is the content of heavy metal of type q in stage i iq The maximum value among the sample values;

[0026] The first hazard index HI ic and the second hazard index HI im The calculated results are weighted, and the weighted value is used as the harm index HI of residual pesticides and heavy metals to the growth of wolfberry in the i-th stage i , to quantify the hazard index HI i As a characterization value for the damage assessment of wolfberry growth; damage index HI i The calculation formula is: HI i =αHI ip +βHI iq , α and β are the first hazard index HI ic and the second hazard index HI im The weight coefficient is , and α, β∈(0,1); α+β=1.

[0027] A system for evaluating the hazards of pesticide residues and heavy metal pollution to wolfberry, comprising: a hazard factor data acquisition module, a data analysis module, a data processing module, a first hazard index calculation module, a second hazard index calculation module, and a hazard assessment characterization value calculation module;

[0028] The hazard factor data collection module is used to sample wolfberries at the i-th stage during the whole growth period, obtain the growth evaluation index parameters of wolfberries at each stage, and measure the hazard factor data of the wolfberry samples, wherein the hazard factor data includes the residual pesticide concentration value and the heavy metal content;

[0029] The data analysis module is used to perform grey correlation analysis on the acquired hazard factor data and growth evaluation index parameters to obtain the p-th category residual pesticide concentration value C in the i-th stage. ip Correlation between growth evaluation index parameters ip And the content of heavy metals of category q M iq Correlation between growth evaluation index and iq ;

[0030] The data processing module is used to obtain the correlation degree r ip and r iq Normalization is performed to obtain the normalized correlation value. and As the influencing factor of the hazard factor on the growth evaluation index, the influencing factor k of the p-th type of residual pesticide and the q-th type of heavy metal on the growth evaluation index in the i-th stage is obtained. ip and k iq ;

[0031] The first hazard index calculation module is used to obtain the influence factor k ip Calculate the first harm index HI of residual pesticides to the growth of wolfberry in the i-th stage ic ;

[0032] The second hazard index calculation module is used to calculate the impact factor k according to the obtained iq Calculate the second harm index HI of heavy metals to the growth of wolfberry in stage i im ;

[0033] The hazard assessment characterization value calculation module is used to calculate the first hazard index HI ic and the second hazard index HI im The calculated results are weighted, and the weighted value is used as the harm index HI of residual pesticides and heavy metals to the growth of wolfberry in the i-th stage i , to quantify the hazard index HI i As a characterization value for assessing the harm to the growth of wolfberry.

[0034] The system further includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor.

[0035] The present invention has the following beneficial effects:

[0036] Compared with the existing technology, the growth evaluation index parameters of wolfberry at each stage in the whole growth period are obtained, wolfberry at the i-th stage in the whole growth period is sampled, the hazard factor data of the wolfberry samples are measured, and the gray correlation analysis of the obtained hazard factor data and the growth evaluation index parameters is performed to obtain the p-th type of residual pesticide concentration value C in the i-th stage. ip , Class q heavy metal content M iq Correlation between growth evaluation index parameters ip and r iq , with the normalized correlation value and As the influencing factor of the hazard factor on the growth evaluation index, the influencing factor k of the p-th type of residual pesticide and the q-th type of heavy metal on the growth evaluation index in the i-th stage is obtained. ip and k iq , according to the obtained impact factors, calculate the first harm index HI of residual pesticides on the growth of wolfberry in the i-th stage ic and the second hazard index HI im , for the first hazard index HI ic and the second hazard index HI im The calculated results are weighted, and the weighted value is used as the harm index HI of residual pesticides and heavy metals to the growth of wolfberry in the i-th stage i , to quantify the hazard index HIi As a characterization value for hazard assessment of wolfberry growth, it can realize quantitative analysis and evaluation of the hazards of pesticide residues and heavy metal pollution to wolfberry. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a method for evaluating the hazards of pesticide residues and heavy metal pollution to wolfberry of the present invention;

[0038] Figure 2 The present invention is a structural block diagram of a system for evaluating the hazards of pesticide residues and heavy metal pollution to wolfberries. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with specific implementation methods, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limiting the present invention. In order to better illustrate the specific implementation methods of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0040] The specific implementation process of the technical solution of the present invention includes the following steps:

[0041] Step 1: According to the growth characteristics of wolfberry in the whole growth period, the whole growth period of wolfberry is divided into the blooming stage, the fruiting stage, the vegetative growth stage, the autumn fruit growth stage, and the autumn fruit harvesting stage, and the growth evaluation index parameters of wolfberry in each stage are obtained;

[0042] Among them, the growth evaluation index at the flowering stage includes any one of plant height, number or size of leaves per plant, average circumference of leaves, flowering amount, and stem color value;

[0043] The growth evaluation index at the peak fruiting stage includes any one of plant height, crown width, trunk diameter or circumference, leaf area index, single plant yield, and yield per unit area;

[0044] The growth evaluation index in the vegetative growth stage includes any one of the following: root density, root growth per unit time, diameter, and stem growth per unit time;

[0045] The growth evaluation index during the autumn fruit growth stage includes any one of the yield per unit area, fruit color uniformity, and fruit size uniformity;

[0046] The growth evaluation index during the autumn fruit harvest period includes any one of the yield per unit area, the uniformity of the fruit appearance color, and the fruit appearance color value;

[0047] For the growth evaluation index parameters of the flowering period:

[0048] Plant height: The taller the plant, the more vigorous its growth;

[0049] Number or size of leaves per plant: The more leaves there are and the larger the size, the more vigorous the growth;

[0050] Flowering amount: The more flowers there are, the more vigorous the growth is;

[0051] Stem color value: the darker the color, the more vigorous the growth;

[0052] For the growth evaluation index parameters at the peak fruiting stage:

[0053] Tree height and crown width: By measuring tree height and crown width, the growth status and luxuriance of branches and leaves of fruit trees can be evaluated;

[0054] Trunk diameter or circumference: It can be used to assess the growth potential of fruit trees. Generally speaking, the thicker the trunk, the stronger the growth potential.

[0055] Leaf Area Index: Leaf Area Index refers to the size of the leaf area of ​​a fruit tree per unit area. By measuring the area of ​​the fruit tree leaves, the growth potential of the fruit tree can be evaluated. Generally speaking, the larger the leaf area index, the stronger the growth potential.

[0056] Yield: The yield evaluation indicators of fruit trees in the peak fruit-bearing period include single-tree yield and unit area yield. By counting the number or weight of fruits on the fruit tree, the single-tree yield of the fruit tree can be evaluated; by counting the yield of fruit trees within a certain area, the unit area yield of the fruit tree can be evaluated.

[0057] Step 2: Sample wolfberries at the i-th stage of the entire growth period and measure the hazard factor data of the wolfberry samples, which include residual pesticide concentration values ​​and heavy metal contents.

[0058] Step 3: Perform grey correlation analysis on the acquired hazard factor data and growth evaluation index parameters to obtain the p-th type of residual pesticide concentration value C in the i-th stage. ip Correlation between growth evaluation index parameters ip And the content of heavy metals of category q M iq Correlation between growth evaluation index and iq ;

[0059] Step 4: Obtain the correlation r ip and r iq Normalization is performed to obtain the normalized correlation value. and As the influencing factor of the hazard factor on the growth evaluation index, the influencing factor k of the p-th type of residual pesticide and the q-th type of heavy metal on the growth evaluation index in the i-th stage is obtained. ip and k iq ,in, Correlation r p and rq The normalization formula is:

[0060]

[0061] In the formula, is the correlation degree r ip Normalized value; is the correlation degree r iq Normalized value; P i is the type of pesticide residues in wolfberry samples in stage i; Q i are the types of heavy metals contained in wolfberry samples in phase i;

[0062] Step 5: According to the obtained impact factor k ip Calculate the first harm index HI of residual pesticides to the growth of wolfberry in the i-th stage ic ; The calculation formula is: In the formula, rf ip is the concentration C of the p-type residual pesticide in the i-th stage ip The harmful value under the level; the calculation formula is: In the formula, a p 、b p 、n p are all toxicity constants of the p-type residual pesticides, t ip Maintain the concentration C of the p-type residual pesticide in the i-th stage ip The duration of the level; λ is a constant coefficient, and λ∈(3.5,8);

[0063] Step 6: According to the obtained impact factor k iq Calculate the second harm index HI of heavy metals to the growth of wolfberry in stage i im , the calculation formula is: In the formula, rf iq is the content M of the qth type of heavy metal in the i-th stage iq The harmful value under the level; the calculation formula is: In the formula, M is the content of heavy metal of type q in stage i iq The mean of the sampled values; where M is the content of heavy metal of type q in stage i iq The maximum value among the sample values;

[0064] Step 7: First hazard index HI ic and the second hazard index HI im The calculated results are weighted, and the weighted value is used as the harm index HI of residual pesticides and heavy metals to the growth of wolfberry in the i-th stage i , using the quantitative hazard index HIi As a characterization value for the damage assessment of wolfberry growth; damage index HI i The calculation formula is: HI i =αHI ip +βHI iq , α and β are the first hazard index HI ic and the second hazard index HI im The weight coefficient is , and α, β∈(0,1); α+β=1.

[0065] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for evaluating the hazard of pesticide residues and heavy metal pollution to wolfberry, characterized in that: include: According to the growth characteristics of wolfberry in the whole growth period, the whole growth period of wolfberry is divided into the full flowering stage, the full fruit stage, the vegetative growth stage, the autumn fruit growth stage, and the autumn fruit harvest stage, and the growth evaluation index parameters of wolfberry in each stage are obtained; Sampling of wolfberry in the i-th stage of the whole growth period, measuring the hazard factor data of wolfberry samples, the hazard factor data including residual pesticide concentration value and heavy metal content, and performing grey correlation analysis on the obtained hazard factor data and growth evaluation index parameters, to obtain the p-th type of residual pesticide concentration value C in the i-th stage ip The correlation between the growth evaluation index parameters r ip And the content of heavy metals of category q M iq The correlation between the growth evaluation index and iq ; The correlation degree r ip and r iq Normalization is performed to obtain the normalized correlation value. and As the influencing factor of the hazard factor on the growth evaluation index, the influencing factor k of the p-th type of residual pesticide and the q-th type of heavy metal on the growth evaluation index in the i-th stage is obtained. ip and k iq ,in, According to the obtained impact factor k ip Calculate the first harm index HI of residual pesticides to the growth of wolfberry in the i-th stage ic , according to the obtained impact factor k iq Calculate the second harm index HI of heavy metals to the growth of wolfberry in stage i im , the calculation formulas of the first hazard index and the second hazard index are respectively: In the formula, rf ip is the concentration C of the p-type residual pesticide in the i-th stage ip Harmful value at level; rf iq is the content M of the qth type of heavy metal in the i-th stage iq Harmful value at the level; The first hazard index HI ic and the second hazard index HI im The calculated results are weighted, and the weighted value is used as the harm index HI of residual pesticides and heavy metals to the growth of wolfberry in the i-th stage i , to quantify the hazard index HI i As a characterization value for assessing the harm to the growth of wolfberry.

2. The method for evaluating the hazard of pesticide residues and heavy metal pollution to wolfberry according to claim 1, characterized in that: The growth evaluation index at the flowering stage includes any one of plant height, number or size of leaves per plant, average circumference of leaves, flowering amount, and stem color value; The growth evaluation index at the peak fruiting stage includes any one of plant height, crown width, trunk diameter or circumference, leaf area index, single plant yield, and yield per unit area; The growth evaluation index in the vegetative growth stage includes any one of root density, root growth per unit time, diameter, and stem growth per unit time; The growth evaluation index in the autumn fruit growth stage includes any one of yield per unit area, fruit color uniformity, and fruit size uniformity; The growth evaluation index during the autumn fruit harvesting period includes any one of yield per unit area, fruit appearance color uniformity, and fruit appearance color value.

3. The method for evaluating the hazard of pesticide residues and heavy metal pollution to wolfberry according to claim 1, characterized in that: Correlation r p and r q The normalization formula is: In the formula, is the correlation degree r ip Normalized value; is the correlation degree r iq Normalized value; P i is the type of pesticide residues in wolfberry samples in stage i; Q i are the types of heavy metals contained in wolfberry samples in phase i.

4. The method for evaluating the hazard of pesticide residues and heavy metal pollution to wolfberry according to claim 1, characterized in that: The concentration of the p-type residual pesticide in the i-th stage is C ip The harmful value rf ip The calculation formula is: In the formula, a p , b p 、n p are all toxicity constants of the p-type residual pesticides, t ip Maintain the concentration C of the p-type residual pesticide in the i-th stage ip The duration of the level; λ is a constant coefficient, and λ∈(3.5,8); erf() is the error function of the residual pesticide hazard value.

5. The method for evaluating the hazard of pesticide residues and heavy metal pollution to wolfberry according to claim 1, characterized in that: The content of heavy metals of type q in stage i is M iq The harmful value rf iq The calculation formula is: In the formula, M is the content of heavy metal of type q in stage i iq The mean of the sampled values; where M is the content of heavy metal of type q in stage i iq The maximum value among the sample values.

6. The method for evaluating the hazard of pesticide residues and heavy metal pollution to wolfberry according to claim 1, characterized in that: Hazard Index HI i The calculation formula is: HI i =αHI ip +βHI iq , α and β are the first hazard index HI ic and the second hazard index HI im ’s weight coefficient, and α, β∈(0,1); α+β=1.

7. A system for assessing the hazards of pesticide residues and heavy metal pollution to wolfberry, characterized in that: The system is used to implement the steps of a method for evaluating the hazard of pesticide residues and heavy metal pollution to wolfberry as described in any one of claims 1 to 6, comprising: a hazard factor data acquisition module, a data analysis module, a data processing module, a first hazard index calculation module, a second hazard index calculation module, and a hazard assessment characterization value calculation module; The hazard factor data acquisition module is used to sample wolfberries in the i-th stage during the whole growth period, obtain the growth evaluation index parameters of wolfberries in each stage, and measure the hazard factor data of the wolfberry samples, wherein the hazard factor data include the residual pesticide concentration value and the heavy metal content; wherein, according to the growth characteristics of wolfberries during the whole growth period, the whole growth period of wolfberries is divided into the full flowering stage, the full fruit stage, the vegetative growth stage, the autumn fruit growth stage, and the autumn fruit harvesting stage; The data analysis module is used to perform grey correlation analysis on the acquired hazard factor data and growth evaluation index parameters to obtain the p-th category residual pesticide concentration value C in the i-th stage. ip Correlation between growth evaluation index parameters ip And the content of heavy metals of category q M iq Correlation between growth evaluation index and iq ; The data processing module is used to obtain the correlation degree r ip and r iq Normalization is performed to obtain the normalized correlation value. and As the influencing factor of the hazard factor on the growth evaluation index, the influencing factor k of the p-th type of residual pesticide and the q-th type of heavy metal on the growth evaluation index in the i-th stage is obtained. ip and k iq ,in, The first hazard index calculation module is used to obtain the influence factor k ip Calculate the first harm index HI of residual pesticides to the growth of wolfberry in the i-th stage ic ; The calculation formula is: The second hazard index calculation module is used to calculate the impact factor k according to the obtained iq Calculate the second harm index HI of heavy metals to the growth of wolfberry in stage i im , the calculation formula is: The hazard assessment characterization value calculation module is used to calculate the first hazard index HI ic and the second hazard index HI im The calculated results are weighted, and the weighted value is used as the harm index HI of residual pesticides and heavy metals to the growth of wolfberry in the i-th stage i , to quantify the hazard index HI i As a characterization value for assessing the harm to the growth of wolfberry.

8. The pesticide residue and heavy metal pollution hazard assessment system for wolfberry according to claim 7, characterized in that: The system also includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor, wherein the processor can implement the steps of a method for assessing the hazard of pesticide residues and heavy metal pollution to wolfberry as described in any one of claims 1 to 6 when running the electronic program.

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