Response surface optimization method for reducing heavy metal content in crops under biogas slurry return mode

Abstract

The present invention relates to a response surface optimization method for reducing the heavy metal content of crops in a biogas slurry return mode, comprising the following steps: selecting multiple test factors, and setting the gradient value of each test factor in each test based on the Box-Behnken principle of the response surface method; based on the set gradient value, conducting potted plant tests of multiple biogas slurry return crops, collecting the heavy metal element content of the plants after the end of the crop growth period; taking the heavy metal element content as the dependent variable and the gradient of each test factor as the independent variable, performing polynomial fitting based on the test data to obtain a multiple regression model equation; obtaining a response surface diagram based on the multiple regression model equation, and obtaining the optimal conditions for minimizing the heavy metal content of biogas slurry cultured crops. Compared with the prior art, the present invention has the advantages of flexibility, speed, convenience, low cost, etc., and can provide an optimal solution to the problem of heavy metal accumulation in crops when biogas slurry is returned to the field.

Description

Technical Field

[0001] The invention belongs to the technical field of heavy metal pollution control, and relates to a crop growth optimization control method, in particular to a response surface optimization method for reducing the heavy metal content of crops in a biogas slurry returning mode. Background Art

[0002] Biogas slurry is the wastewater produced by treating agricultural and animal husbandry waste such as livestock manure through biogas projects. It is rich in essential macronutrients for plants such as nitrogen, phosphorus, and potassium, and essential micronutrients for plants such as iron, zinc, and copper. Using it for crop production is beneficial to maximize the resource utilization of biogas slurry, reduce the use of chemical fertilizers and pesticides, improve soil, and increase crop yield and quality. However, unreasonable application of biogas slurry will also lead to excessive deposition of heavy metals, threatening crop yield and food security.

[0003] At present, the treatment of heavy metal pollution is mainly divided into biological control methods and physical adsorption methods. Biological control methods are low-cost and environmentally friendly, but slow to take effect. For example, Chinese patent application CN110283845A discloses a composite biological repair method for heavy metals in contaminated soil by planting transgenic Solanum nigrum, supplemented by earthworms and microbial agents; physical adsorption methods are fast, but costly and prone to secondary pollution. For example, Chinese patent application CN112960725A discloses a method for using nanoparticles to physically adsorb heavy metal impurities in biogas slurry. The organic combination of the two methods can form a synergistic effect to enhance the removal effect of heavy metals. Chinese patent application CN113058984A discloses a cadmium and lead repair method that introduces herbaceous fiber crops after adding coconut shell biochar passivator to farmland soil, but this method fixes the weights of the two methods and does not have operability in different application environments. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a flexible, fast and convenient response surface optimization method for reducing the heavy metal content of crops in the biogas slurry return mode.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A response surface optimization method for reducing the heavy metal content of crops in a biogas slurry return mode comprises the following steps:

[0007] Select multiple experimental factors and set the gradient value of each experimental factor in each experiment based on the Box-Behnken principle of the response surface method;

[0008] Based on the set gradient value, multiple potted experiments of returning biogas slurry to fields were conducted to collect the heavy metal element content of the plants after the crop growth period.

[0009] Taking the content of heavy metal elements as the dependent variable and the gradient of each experimental factor as the independent variable, polynomial fitting was performed based on the experimental data to obtain the multivariate regression model equation;

[0010] A response surface diagram is obtained based on the multivariate regression model equation, and the optimal conditions for minimizing the heavy metal content of crops grown in biogas slurry are obtained.

[0011] Furthermore, the crop refers to various plants cultivated in agriculture for profit or food, preferably rice.

[0012] Furthermore, the test factors include biological control factors, physical adsorption factors and biogas slurry application rate.

[0013] Furthermore, the biological control factor is the introduction of a heavy metal element regulator, and the corresponding gradient value is the coverage percentage of the heavy metal element regulator.

[0014] Furthermore, the heavy metal element regulator is a plant, animal or microorganism with strong ability to adsorb or degrade heavy metal elements, preferably duckweed.

[0015] Furthermore, the physical adsorption factor is the addition of a heavy metal element physical adsorbent, and the corresponding gradient value is the application amount of the heavy metal element physical adsorbent.

[0016] Furthermore, the heavy metal element physical adsorbent includes biochar, zeolite, humic acid or coal.

[0017] Preferably, the heavy metal element physical adsorbent is biochar.

[0018] Furthermore, the heavy metal elements include one or more of Zn, Cd, Cr, Pb, As, and Cu.

[0019] Furthermore, after obtaining the multiple regression model equation, variance analysis and statistical analysis are performed on the multiple regression model equation to carry out subsequent steps based on the multiple regression model equation that meets the set fitting effect requirements.

[0020] Furthermore, the indicators of the variance analysis include p-value and p-value of lack-of-fit term;

[0021] The statistical analysis indicators include R 2 , R 2 Calibration Value and Adeq Precision.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] (1) The present invention takes into account a variety of factors, including biological control methods, physical adsorption methods and the amount of biogas slurry applied, and obtains the optimal control conditions for reducing the heavy metal content of crops in the biogas slurry return mode based on the response surface methodology. It can effectively improve the green recycling and reuse mode of biogas slurry, ensure that the adverse effects on crop yield and quality are minimized, and help promote the application of ecological biogas slurry fertilization in agriculture.

[0024] (2) The present invention uses the Box-Behnken response surface optimization method to screen the weights and slurry application amounts of various methods for reducing the heavy metal content in crops. The number of experiments required is small, which is more cost-effective than single-factor experiments. At the same time, it is flexible and can quickly, easily and accurately obtain the optimal model in different application environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the process of the present invention;

[0026] Figure 2 It is a three-dimensional curve diagram of the interactive effect of duckweed and biogas slurry on the As content in the embodiment;

[0027] Figure 3 It is a three-dimensional curve diagram of the interactive effect of biochar and biogas slurry on the As content in the embodiment;

[0028] Figure 4 It is a three-dimensional curve diagram of the interactive effect of biochar and duckweed on the As content in the embodiment;

[0029] Figure 5 The effect of biogas slurry on Cd content in the examples;

[0030] Figure 6 The influence of duckweed on the Cd content in the examples;

[0031] Figure 7 This is the effect of biochar on the Cd content in the example. DETAILED DESCRIPTION

[0032] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0033] The response surface optimization method is a statistical method that uses graphical technology to display the functional relationship and determine the optimal conditions by taking the response of the system as a function of multiple factors. It can determine the optimal values ​​of different factors in different application environments with the help of a limited number of experiments. Based on the above theory, the inventor of this application creatively proposed a response surface optimization method for reducing the heavy metal content of crops in the mode of returning biogas slurry to the field. Combining a variety of heavy metal content control methods and using potted plant experiments, it provides an optimal solution to the problem of heavy metal accumulation in crops in the mode of returning biogas slurry to the field in a flexible, fast, convenient and low-cost manner.

[0034] refer to Figure 1 As shown, the specific steps of the response surface optimization method include:

[0035] S1. Select multiple experimental factors and set the gradient value of each experimental factor in each experiment based on the Box-Behnken principle of the response surface method.

[0036] The experimental factors include biological control factors, physical adsorption factors and the amount of biogas slurry applied. The biological control factors are based on the biological control method, specifically the introduction of heavy metal element regulators, including plants, animals and microorganisms with strong adsorption or degradation capabilities for heavy metal elements, preferably duckweed, and the corresponding gradient value is the coverage percentage of the heavy metal element regulator. The physical adsorption factors are based on the physical adsorption method, specifically the addition of heavy metal element physical adsorbents, including biochar, zeolite, humic acid and coal, preferably biochar, and the corresponding gradient value is the application amount of heavy metal element physical adsorbents. The gradient value of the biogas slurry application amount is the amount of biogas slurry replacing chemical fertilizers.

[0037] Under the Box-Behnken principle, each experimental factor only needs to be designed with three gradients: high level (+1), middle value (0), and low level (-1). The gradient value is determined according to the actual application situation. The number of experiments corresponding to the three experimental factors is 17.

[0038] S2. Based on the set gradient value, a potted experiment of returning multiple biogas slurry to the field is carried out to collect the heavy metal element content of the plants after the crop growth period ends.

[0039] The crop targeted by the method can be various plants cultivated in agriculture for profit or food, preferably rice.

[0040] In a specific embodiment, the preliminary preparation work for measuring the heavy metal content of plants after the end of the crop growth period is: select 4 rice plants with average growth, dry them in an oven and grind them into powder, weigh about 0.25g each, record the actual weighed amount, put them into a 50ml centrifuge tube, add 6ml nitric acid and 2ml hydrogen peroxide, cool them at room temperature overnight and then heat them at 120℃ for 2h, open the tube cap and stop heating when 1ml of liquid remains in the tube, and add deionized water to make the volume 25ml.

[0041] Heavy metal elements include Zn, Cd, Cr, Pb, As, Cu, etc., among which Cd and Pb are determined by atomic absorption spectrometry, As is determined by atomic fluorescence, and the remaining elements are determined by ICP-MS.

[0042] S3. Taking the heavy metal element content as the dependent variable and the gradient of each experimental factor as the independent variable, polynomial fitting was performed based on the experimental data to obtain the multivariate regression model equation.

[0043] S4. In order to improve the model effect, in a preferred embodiment, after obtaining the multiple regression model equation, variance analysis and statistical analysis are performed on the multiple regression model equation to perform subsequent steps based on the multiple regression model equation that meets the set fitting effect requirements.

[0044] The main measurement indicators of variance analysis are p-value and lack-of-fit p-value. The closer the p-value is to 0 and the closer the p-value is to 1, the higher the explanatory power of the model. The main measurement indicator of statistical analysis is R 2 , R 2 Correction value and Adeq Precision. The closer the former two are to 1, the better the model fitting effect. The latter is the model signal-to-noise ratio. If it is greater than 4, it means that the model has a strong signal response design.

[0045] S5. Based on the final multiple regression model equation, the response surface diagram was obtained to obtain the optimal conditions for minimizing the heavy metal content in crops grown in biogas slurry.

[0046] In a specific implementation manner, the above steps S1, S3, S4 and S5 are all performed by Design-Expert10 software.

[0047] Example

[0048] The response surface optimization method for reducing the heavy metal content of crops in the biogas slurry return mode provided in this embodiment adopts the following steps:

[0049] (1) The introduction of duckweed was used as a biological control method, and the application of biochar was used as a physical adsorption method. The response surface methodology and Box-Behnken principle were used to design the experiment in Design-Expert 10 software.

[0050] The duckweed coverage percentage, biochar application amount, and slurry replacement of chemical fertilizers were used as indicators for duckweed control, biochar adsorption, and slurry application. The slurry application amount for full slurry replacement of chemical fertilizers (+1) was 1200 ml, the slurry application amount for half slurry replacement of chemical fertilizers (0) was 600 ml, and no slurry was applied for full chemical fertilizers (-1). The high level value of duckweed coverage percentage (+1) was 80% water surface coverage, the middle value (0) was 50% water surface coverage, and the low level value (-1) was 20% water surface coverage. The high level value of biochar application (+1) was 50 t·hm -2 , the middle value (0) is 30t·hm -2 , low level value (-1) is 10t·hm -2 .

[0051] The specific settings of the experimental factor gradient levels are shown in Table 1. The number of experiments corresponding to the three experimental factors is 17, and the specific experimental design scheme is shown in Table 2.

[0052] Table 1 Box-Behnken test factor levels

[0053]

[0054] Table 2 Box-Behnken experimental design

[0055]

[0056]

[0057] (2) Potted experiments were conducted according to the experimental design. The rice variety “Zaoyou Xiangjing” was used, and the duckweed species was mainly Spirodela oligorrhiza. After collection, it was cultured in clean water. The biochar was mainly composed of rice husk charcoal. The physical and chemical properties of biogas slurry and potted soil are shown in Table 3.

[0058] The rice cultivation barrel is 30cm high and has an upper surface area of ​​about 900cm 2 Put the soil that has passed through a 4 mm sieve into a bucket with a thickness of 20 cm. Mix the soil from 17 buckets No. 1 to 17 with biochar and fertilizer and pour them into the bucket. Add biogas slurry and water until it is 6 cm higher than the soil. Transplant the rice seedlings with the same growth into the bucket, with 6 plants per bucket as the plant replication.

[0059] After the rice growth period is over, take 4 rice plants with average growth from each barrel, dry them in an oven and measure the dry weight. Grind the rice samples after measuring the dry weight into powder. Weigh about 0.25g of each portion, record the actual weighed amount, and then place it in a 50ml plastic centrifuge tube, add 6ml nitric acid and 2ml hydrogen peroxide, loosen the tube cover, cool it at room temperature overnight, heat it at 120℃ for 2h, then open the tube cover and continue heating. Stop heating when there is 1ml of liquid left in the tube, add deionized water, and make up to 25ml.

[0060] The Cd and As contents of the digested samples were measured, where Cd was determined by atomic absorption spectroscopy (graphite furnace) and As was determined by atomic fluorescence.

[0061] Table 3 Physicochemical properties of biogas slurry and soil

[0062]

[0063] (3) Use Design-Expert 10 software to perform quadratic polynomial fitting on the experimental data of As element and obtain the regression equation:

[0064] As element content (mg·kg -1 )

[0065] =0.3930-0.0023A-0.0003B+0.0231C+0.0245AB+0.0135AC

[0066] +0.0141BC-0.0639A 2 -0.1128B 2 -0.0275C 2

[0067] The Design-Expert 10 software was used to perform a polynomial fit on the experimental data of the Cd element and the regression equation was obtained:

[0068] Cd content (mg·kg -1 )

[0069] =0.0137-1.25×10 -4 A+5.125×10 -3 B-2.5×10 -4 C

[0070] (4) The experimental results and analysis of As are shown in Tables 4, 5 and 6. The p-value of the model is 0.1053, and the p-value of the lack-of-fit term is 0.3374, indicating that the model has a moderate fit. 2 is 0.774, R 2 The correction value is 0.483, indicating that the correlation between the dependent variable and the independent variable is average. The Adeq Precision value is 4.363>4, indicating that the signal-to-noise ratio is normal.

[0071] Table 4 Box-Behnken test results (As content)

[0072]

[0073] Table 5 Box-Behnken experimental model variance analysis (As element)

[0074]

[0075]

[0076] Table 6 Statistical analysis of Box-Behnken experimental model (As element)

[0077]

[0078] The experimental results and analysis of Cd element are shown in Table 7, Table 8 and Table 9. The quadratic term of the model is not significant, but if only the linear term is analyzed, the p-value of the model is 0.0113, and the p-value of the lack-of-fit term of the model is 0.9168, indicating that the model has a high degree of fit. 2 is 0.561, R 2 The correction value is 0.460, indicating that the correlation between the dependent variable and the independent variable is average. The Adeq Precision value is 6.225>4, indicating that the signal-to-noise ratio is normal.

[0079] Table 7 Box-Behnken test results (Cd element content)

[0080]

[0081] Table 8 Box-Benhken experimental model variance analysis (Cd element)

[0082]

[0083] Table 9 Statistical analysis of Box-Behnken experimental model (Cd element)

[0084]

[0085]

[0086] (5) The three-dimensional curve diagram of the interactive effect of biogas slurry, duckweed and biochar on As content is shown in Figure 2 , Figure 3 , Figure 4 The optimal conditions for minimizing the content of As in rice were obtained by Design-Expert 10 software: all biogas slurry replaced chemical fertilizers, 80% duckweed covered, and biochar application of 10 t·hm -2 Under this condition, the theoretical minimum As content is 0.160 mg kg -1 .

[0087] The effect of biogas slurry, duckweed and biochar on Cd element is shown in the figure Figure 5 , Figure 6 , Figure 7The optimal conditions for minimizing the Cd content in rice obtained by Design-Expert10 software are: all biogas slurry replaces chemical fertilizer, 20% duckweed cover, and biochar application of 50 t·hm -2 Under this condition, the theoretical minimum Cd content is 0.008 mg kg -1 .

[0088] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A response surface optimization method for reducing the heavy metal content of crops in the biogas slurry return mode, characterized in that: The following steps are involved: Select multiple experimental factors and set the gradient value of each experimental factor in each experiment based on the Box-Behnken principle of the response surface method; Based on the set gradient value, multiple potted experiments of returning biogas slurry to fields were conducted to collect the heavy metal element content of the plants after the crop growth period. Taking the content of heavy metal elements as the dependent variable and the gradient of each experimental factor as the independent variable, polynomial fitting was performed based on the experimental data to obtain the multivariate regression model equation; Based on the multivariate regression model equation, a response surface diagram is obtained to obtain the optimal conditions for minimizing the heavy metal content of crops grown in biogas slurry; The test factors include biological control factors, physical adsorption factors and biogas slurry application rate; The biological control factor is the introduction of a heavy metal element regulator, the corresponding gradient value is the coverage percentage of the heavy metal element regulator, and the heavy metal element regulator is duckweed; The physical adsorption factor is the addition of a heavy metal element physical adsorbent, the corresponding gradient value is the application amount of the heavy metal element physical adsorbent, and the heavy metal element physical adsorbent is biochar; The heavy metal elements include one or more of Zn, Cd, Cr, Pb, As, and Cu.

2. The response surface optimization method for reducing heavy metal content in crops under the biogas slurry return mode according to claim 1, characterized in that: After the multiple regression model equation is obtained, variance analysis and statistical analysis are performed on the multiple regression model equation to carry out subsequent steps based on the multiple regression model equation that meets the set fitting effect requirements.

3. The response surface optimization method for reducing heavy metal content in crops under the biogas slurry return mode according to claim 2, characterized in that: The indicators of the variance analysis include p-value and p-value of lack-of-fit item; The statistical analysis indicators include R 2 , R 2 Calibration Value and Adeq Precision.

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