Simulated rhizosphere microenvironment system construction method for rhizosphere remediation of organic contaminated soil and application of simulated rhizosphere microenvironment system construction method

Abstract

The invention belongs to the field of environmental treatment of polluted soil, and particularly relates to a construction method of a simulated rhizosphere microenvironment system for rhizosphere remediation of organic polluted soil and application of the simulated rhizosphere microenvironment system. Constructing an actual rhizosphere microenvironment database when the functional plant normally grows, and constructing a simulated rhizosphere microenvironment system of the organic polluted soil without the functional plant through the actual rhizosphere microenvironment database when the functional plant normally grows; the construction of the simulated rhizosphere microenvironment system consists of the establishment of a first simulated rhizosphere microenvironment system and the real-time evaluation and regulation of the simulated rhizosphere microenvironment system. According to the construction method of the simulated rhizosphere microenvironment system, functional plant independence in the rhizosphere repairing process can be achieved, namely limitation of plant growth physiological states is avoided, the duration of a dominant repairing period can be manually regulated and controlled, and the rhizosphere repairing efficiency is effectively controlled.

Description

Technical Field

[0001] The invention belongs to the field of environmental management of contaminated soil, and particularly relates to a method for constructing a simulated rhizosphere microenvironment system for rhizosphere restoration of organic contaminated soil and an application thereof. Background Art

[0002] Soil pollution control is one of the important issues in the field of ecological environment. So far, a variety of remediation technologies including physical, chemical, biological and other means have been developed. Some of these technologies have become the main engineering application processes, while some are still in the research and development, small-scale and pilot stages. At present, the development of economic, efficient, environmentally friendly, low-carbon and green remediation processes has become the mainstream direction of research and application in the field of contaminated soil remediation. Bioremediation is a green, sustainable and most widely used remediation method that uses the metabolic activities of organisms to convert toxic and harmful substances in the environment into harmless substances or reduce their concentrations, thereby improving environmental quality. Generally, bioremediation includes microbial remediation, plant remediation and animal remediation. Among them, microbial remediation has the application advantages of economy and low consumption, but the short-term effectiveness of its effective remediation is a limiting factor that restricts its further development. Animal remediation is greatly affected by soil properties, and its remediation intensity and application volume are relatively weak. Plant remediation integrates the advantages of green, low carbon, economic sustainability, and is suitable for large-scale application, so it has greater development potential.

[0003] Phytoremediation was first used in the remediation of inorganic heavy metal contaminated soils, and has gradually been applied to the treatment of organic contaminated soils in recent years. For the treatment of hydrophilic organic contaminated soils, soil pollutants can be reduced through plant enrichment, but the metabolic mineralization of rhizosphere microorganisms is a more important way to remove pollutants, and this method is more important in the remediation of hydrophobic organic contaminated soils. Based on this, the rhizosphere remediation technology for organic contaminated soils has been derived.

[0004] Plants, as the driving force of rhizosphere remediation technology, are the dominant factors in the remediation of contaminated soil. However, in the process of engineering treatment of contaminated soil on sites and farmlands, how to maintain the growth and physiological activity of plants in the best state is a bottleneck problem in rhizosphere remediation, and it is also the key to determining the continuous and efficient remediation of organic contaminated soil. Limited by natural conditions such as accumulated temperature characteristics and the growth cycle of functional plants, the biomass of plants, the release of rhizosphere secretions, and the accumulation of acidic substances will change significantly, and even produce side effects that are not conducive to rhizosphere remediation. This shows that the effect of rhizosphere remediation of organic contaminated soil is highly dependent on the physiological state of functional plants.

[0005] In view of the above problems, in order to solve the problem that traditional rhizosphere remediation relies on the large-scale planting of functional plants and is limited by the growth and physiological state of plants, and to achieve the independence of rhizosphere remediation on the growth characteristics of functional plants, it is necessary to study a rhizosphere remediation method that is not limited by the growth and physiological state of plants. Summary of the invention

[0006] In order to solve the above problems, the purpose of the present invention is to provide a method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil and its application.

[0007] To achieve the above object, the technical solution adopted by the present invention is:

[0008] A method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil, constructing an actual rhizosphere microenvironment database of functional plants when they grow normally, and constructing a simulated rhizosphere microenvironment system of organic contaminated soil without planting functional plants by benchmarking the actual rhizosphere microenvironment database of functional plants when they grow normally; the method for constructing a simulated rhizosphere microenvironment system consists of the establishment of the first simulated rhizosphere microenvironment system and real-time evaluation and regulation of the simulated rhizosphere microenvironment system.

[0009] The rhizosphere microenvironment data of the contaminated soil without functional plants was detected, and the actual rhizosphere microenvironment database at the benchmark time node (t0) was used to adjust the rhizosphere microenvironment data in the simulated rhizosphere microenvironment system to meet the deviation threshold requirements, thereby completing the establishment of the first simulated rhizosphere microenvironment system.

[0010] After completing the first simulated rhizosphere microenvironment construction, at multiple time points (t n ) Continuously monitor the contaminated soil where no functional plants are planted, benchmark the actual rhizosphere microenvironment database at the same time node, and evaluate the rhizosphere microenvironment data in the simulated rhizosphere microenvironment system; regulate the rhizosphere microenvironment data that exceeds the deviation threshold requirements to meet the deviation threshold requirements, thereby completing the real-time evaluation and regulation of the simulated rhizosphere microenvironment system.

[0011] The actual rhizosphere microenvironment database of the functional plant during normal growth includes actual rhizosphere microenvironment data at different time points;

[0012] The actual rhizosphere microenvironment data include rhizosphere microbial community data, rhizosphere soil environmental factor data and rhizosphere exudate data.

[0013] Microbial community data include species distribution and species diversity; species distribution refers to the relative abundance and number of keystone species and rare species, and species diversity refers to the Shannon index and the community similarity (Com-Similarity) and functional similarity (Fun-Similarity) between the actual rhizosphere microbial community when the functional plants grow normally.

[0014] The rhizosphere soil environmental factor data are soil physical and chemical factors; soil physical and chemical factors are soil pH, inorganic nutrient ions, available nitrogen, available phosphorus and available potassium;

[0015] The data of rhizosphere exudates are one or more of amino acids, sugars, organic acids, fatty acids, sterols, vitamins, nucleotides / purines, and flavonoids.

[0016] Species with a relative abundance ≥1% at the soil bacterial genus level were keystone species, and species with a relative abundance of 0.05%-0.1% at the soil bacterial genus level were rare species.

[0017] The deviation threshold requirement is:

[0018] Thresholds of rhizosphere microbial communities: the number deviation of rare species is ≤50%, the number deviation of keystone species is ≤30%, the relative abundance deviation of each keystone species is ≤30%, the Shannon index deviation is ≤15%, the community similarity (Com-Similarity) is ≥0.7, and the functional similarity (Fun-Similarity) is ≥0.7;

[0019] Thresholds of rhizosphere soil environmental factors: soil pH deviation ≤ 1.5; total amount of soil inorganic nutrient ions is not less than 80% of the content in the database, that is, the difference is ≤ 20%; soil available nitrogen content is not less than 85% of the content in the database, that is, the difference is ≤ 15%; soil available phosphorus content is not less than 85% of the available phosphorus content in the database, that is, the difference is ≤ 15%; soil available potassium content is not less than 80% of the available potassium content in the database, that is, the difference is ≤ 20%;

[0020] Rhizosphere secretion threshold: The content of any rhizosphere secretion is not less than 70% of the content of rhizosphere secretion in the database, that is, the difference is ≤30%.

[0021] Methods for regulating rhizosphere microenvironment data exceeding the deviation threshold requirements include:

[0022] ① Using Nycodenz, a density gradient centrifugation medium with a concentration of 80% to 90% (w / v), to extract the original bacterial cells from the rhizosphere soil of the functional plants, and obtaining cultivable cornerstone species and rare species strains through artificial cultivation, high-throughput separation and identification steps, and then expanding and culturing to obtain culture materials;

[0023] To control the number of rare species, the original bacterial cells are directly added to the contaminated soil in the simulated rhizosphere;

[0024] For species whose relative abundance is lower than that of keystone species in the database, the species culture to be supplemented is added to the simulated rhizosphere contaminated soil;

[0025] For species with relative abundance higher than the keystone species in the database, the propagation culture of isolated and culturable rare species was supplemented into the simulated rhizosphere contaminated soil;

[0026] For the regulation of community structure diversity (Shannon index), community similarity (Com-Similarity) and functional similarity (Fun-Similarity), collaborative monitoring and control are carried out during the above regulation process;

[0027] The above-mentioned regulation contents are carried out in the order of the number of rare species, the number of keystone species, and the relative abundance of each keystone species as required, and the community structure diversity (Shannon index), community similarity (Com-Similarity) and functional similarity (Fun-Similarity) are coordinated in each step of the regulation process;

[0028] ② Use small molecule organic acid or lime to add to simulated rhizosphere contaminated soil to adjust soil pH;

[0029] ③ Use nitrate, phosphate and potassium salt to add into the simulated rhizosphere contaminated soil to complete the regulation of inorganic nutrient ions and available nitrogen, available phosphorus and available potassium;

[0030] ④ Use the solutions of various substances in rhizosphere secretions to replenish the simulated rhizosphere contaminated soil to complete the adjustment of rhizosphere secretion data.

[0031] The method of the present invention can monitor and evaluate the factors of the simulated rhizosphere microenvironment system at multiple time points, and benchmark the corresponding time points (t n ) to complete the simulation of rhizosphere microenvironment system regulation; time node (t n )The denser the setting, the more the simulated rhizosphere microenvironment meets the real rhizosphere microenvironment conditions.

[0032] An application of the method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil, wherein the method is suitable for remediating soil containing organic pollutants without the planting condition of actual functional plants.

[0033] The organic pollutants are hydrophobic organic pollutants or hydrophilic organic pollutants.

[0034] The advantages and beneficial effects of the present invention are:

[0035] 1. The method for constructing a simulated rhizosphere microenvironment system of the present invention can achieve functional plant independence in the rhizosphere repair process, that is, it is not limited by the physiological state of plant growth, and can artificially regulate the duration of the dominant repair cycle, effectively controlling the efficiency of rhizosphere repair;

[0036] 2. The method for constructing a simulated rhizosphere microenvironment system of the present invention can accurately maintain the rhizosphere microenvironment parameters of the optimal rhizosphere remediation process, especially maintain a good rhizosphere microbial community structure composed of cornerstone species and rare species, thereby ensuring the structural composition similarity and functional similarity of the rhizosphere microbial community and maintaining the biological metabolic function of organic pollutants;

[0037] 3. The present invention uses the functional plants planted in the target contaminated soil to be repaired under the optimal natural conditions, which is regarded as the optimal growth process of the established functional plants in the polluted ecological environment. Based on the actual rhizosphere soil microenvironment database of the functional plants during normal growth obtained in this process, the constructed simulation method can realize the accurate simulation of the soil rhizosphere microenvironment under the optimal plant growth process state, and obtain the optimal simulated root system repair effect;

[0038] 4. The present invention monitors the actual rhizosphere microenvironment parameters at different time points during the normal growth of functional plants. n =0,1,2,3,……,n), that is, the larger n is, the more realistic the simulation effect of the rhizosphere microenvironment is, and the better the simulation rhizosphere restoration effect is;

[0039] 5. The simulated rhizosphere microenvironment system of the present invention has the versatility of simulating the rhizosphere repair microenvironment of various functional plants, and at the same time has the specificity of simulation parameters for specific functional plants. Therefore, the method for constructing the simulated rhizosphere microenvironment system has both the universality of multifunctional plants and the targeting of the method for constructing the rhizosphere microenvironment of specific functional plants;

[0040] 6. The method for constructing a simulated rhizosphere microenvironment system of the present invention is beneficial to the rhizosphere remediation function treatment of large-scale contaminated soil, avoids the large-scale planting and management of traditional functional plants, saves manpower and material resources, and significantly improves the controllability of rhizosphere remediation management. Therefore, it has considerable application prospects and economic benefits in the large-scale engineering treatment of organic contaminated soil.

[0041] 7. The functional plants of the present invention are not specifically limited and can be plant materials suitable for planting in contaminated soil. At the same time, the method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil is specific to functional plant species. Targeted simulated rhizosphere microenvironment system method parameters are constructed based on each functional plant, and the method can be executed for large-scale simulated rhizosphere remediation of organic contaminated soil. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic flow chart of a method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organically contaminated soil provided in an embodiment of the present invention.

[0043] Figure 2 The difference in the number distribution of cornerstone species and the regulation effect between the rhizosphere soil microenvironment system to be simulated and the ryegrass rhizosphere soil database provided in the embodiment of the present invention, wherein a is the t0 time node, b is the t1 time node, and c is the t2 time node.

[0044] Figure 3 The relative abundance deviation and regulation effect of culturable cornerstone species between the rhizosphere soil microenvironment system to be simulated and the ryegrass rhizosphere soil database provided in the embodiment of the present invention.

[0045] Figure 4 The difference in the number distribution of rare species and the regulation effect between the rhizosphere soil microenvironment system to be simulated and the ryegrass rhizosphere soil database provided in the embodiment of the present invention, wherein a is the t0 time node, b is the t1 time node, and c is the t2 time node.

[0046] Figure 5 The deviation of bacterial community diversity and the regulation effect between the rhizosphere soil microenvironment system to be simulated and the ryegrass rhizosphere soil database provided in the embodiment of the present invention, wherein a is the t0 time node, b is the t1 time node, and c is the t2 time node.

[0047] Figure 6 The pH deviation and regulation effect between the rhizosphere soil microenvironment system to be simulated and the ryegrass rhizosphere soil database provided in the embodiment of the present invention.

[0048] Figure 7 The inorganic ion content deviation and regulation effect between the rhizosphere soil microenvironment system to be simulated and the ryegrass rhizosphere soil database provided in the embodiment of the present invention, wherein a is the t0 time node, b is the t1 time node, and c is the t2 time node. Figure 8 The fertility index deviation and regulation effect between the rhizosphere soil microenvironment system to be simulated and the ryegrass rhizosphere soil database provided in the embodiment of the present invention, wherein a is the t0 time node, b is the t1 time node, and c is the t2 time node. Fig. 9 The community similarity and functional similarity deviation and regulation effect between the rhizosphere soil microenvironment system to be simulated and the ryegrass rhizosphere soil database provided in the embodiment of the present invention, wherein a is the t0 time node, b is the t1 time node, and c is the t2 time node. Fig.10The deviation of the composition content of each rhizosphere secretion and the regulation effect between the rhizosphere soil microenvironment system to be simulated and the ryegrass rhizosphere soil database provided in the embodiment of the present invention, wherein a is the t0 time node, b is the t1 time node, and c is the t2 time node. Fig.11 The present invention provides the degradation characteristics of benzopyrene in the contaminated soil remediation process. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] Example 1: Remediation of benzopyrene-contaminated soil using a simulated rhizosphere microenvironment system

[0051] The soil to be treated in this example is benzopyrene-contaminated soil collected from a coking plant park. The benzopyrene content in the soil is 108.0 mg·Kg -1 The rhizosphere remediation technology of planting functional plant ryegrass and the simulated ryegrass rhizosphere remediation method were used to treat benzopyrene contaminated soil. The complete remediation cycle was 30 days. The application process of the simulated ryegrass rhizosphere remediation method is as follows: Figure 1 The remediation process is carried out in situ within the contaminated area, and the area of ​​the remediation target area for both remediation technologies is 25m 2 , the depth of contaminated soil is 30cm.

[0052] 1. Construct a database of the actual rhizosphere microenvironment during normal growth of functional plants

[0053] Firstly, a microcosm experiment on ryegrass rhizosphere remediation of benzopyrene-contaminated soil was carried out. By monitoring the rhizosphere microbial community data, rhizosphere soil environmental factor data and rhizosphere secretion data 15 days after normal growth of ryegrass (t0, corresponding to 0d of simulated rhizosphere remediation), 25 days after (t1, corresponding to 10d of simulated rhizosphere remediation) and 35 days after (t2, corresponding to 20d of simulated rhizosphere remediation), a rhizosphere microenvironment database of ryegrass during normal growth was constructed.

[0054] Specifically:

[0055] 1) Data analysis of rhizosphere microbial communities:

[0056] The rhizosphere soil in the ryegrass microcosm experiment was collected at the corresponding time points (15 days after normal growth (t0), 25 days after normal growth (t1), and 35 days after normal growth (t2)). The high-throughput sequencing technology was used to amplify and sequence the V3-V4 region fragment of the 16S rDNA of the rhizosphere soil metagenomic DNA. The original sequence data was obtained for splicing, impurity removal, annotation and other processes to obtain the bacterial abundance distribution data in the rhizosphere soil. Based on the relative abundance value, species with a relative abundance of ≥1% were defined as keystone species, and species with a relative abundance between 0.05% and 0.1% were defined as rare species. The Shannon diversity index (H) was calculated according to formula (I). Formula (I):

[0057] H=-ΣP i ·lnP i

[0058] Where P i is the relative abundance of the ith species.

[0059] The actual rhizosphere microbial community data of ryegrass at three time points (t0, t1 and t2) are shown in Tables 1, 2 and 3, respectively.

[0060] 2) Data analysis of rhizosphere soil environmental factors:

[0061] The rhizosphere soil in the ryegrass microcosm experiment was collected at the corresponding time points (15 days after normal growth (t0), 25 days after normal growth (t1), and 35 days after normal growth (t2)). The pH of the soil was measured using a pH meter, and the PO4 3- 、SO4 2- , Cl - 、NO3 - , Ca 2+ Mg 2+ , Fe 3+ 、Na + , K + and Mn 2+ Ion content monitoring, adding up to get the total amount of soil inorganic ions, using alkaline hydrolysis method to monitor the effective nitrogen content in the rhizosphere soil, using sodium bicarbonate extraction-molybdenum antimony colorimetry to monitor the effective phosphorus content in the rhizosphere soil, using ammonium acetate extraction-flame photometer method to determine the effective potassium content in the rhizosphere soil, using dichloromethane solid-liquid extraction method to extract benzopyrene pollutants in the soil, and using liquid chromatography (HPLC) for quantitative analysis.

[0062] The actual rhizosphere soil environmental factor data of ryegrass at three time points (t0, t1 and t2) are shown in Tables 1, 2 and 3 respectively.

[0063] 3) Analysis of rhizosphere exudates data:

[0064] The ryegrass plants in the ryegrass microcosm experiment were collected at the corresponding time points (15 days after normal growth (t0), 25 days after normal growth (t1) and 35 days after normal growth (t2)). After cleaning the roots, every 5 plants were collected in a 50mL centrifuge tube, and 25mL of sterile water was added to each tube for rhizosphere secretion collection and culture. After 48 hours of culture, the collected liquid was filtered through a 0.45μm filter membrane and measured by liquid chromatography-mass spectrometry (LC-MS). Finally, the detected secretions were relatively quantified and classified according to the chromatographic peak area, and the relative peak area values ​​of amino acids, sterols, sugars, vitamins, organic acids, nucleotides / purines, fatty acids and flavonoids were obtained. Among them, amino acids include α-alanine, glycine, histidine, lysine, and proline; sugars include fructose, galactose, glucose, maltose, and raffinose; organic acids include acetic acid, lactic acid, syringic acid, succinic acid, and tartaric acid; fatty acids include palmitic acid and stearic acid; sterols include campesterol and sitosterol; vitamins include nicotinic acid and p-aminobenzoic acid; nucleotides / purines include adenine and guanine; flavonoids include flavonoids and isoflavones. The sum of the peak areas of the compounds in each category was calculated, and the content characterization of each type of rhizosphere secretions was finally obtained through logarithmic transformation (lg()).

[0065] The actual rhizosphere secretion data of ryegrass at three time points (t0, t1 and t2) are shown in Tables 1, 2 and 3 respectively.

[0066] In summary, the actual rhizosphere microenvironment survey database of ryegrass at three time nodes (t0, t1 and t2) was completed.

[0067] Table 1

[0068]

[0069]

[0070] Table 2

[0071]

[0072]

[0073]

[0074] Table 3

[0075]

[0076]

[0077] Furthermore, the cornerstone species and rare species microbial materials for regulating the rhizosphere microenvironmental processes at each time point were prepared: high-throughput strain separation was performed on the rhizosphere soil samples at the three time points of t0, t1 and t2. The original bacterial flora in the rhizosphere soil at the three time points was separated by density gradient centrifugation. The density gradient centrifugation medium Nycodenz with a concentration of 80% to 90% (w / v) was added to the water-soil mixture of the rhizosphere soil (1:10, w / v), and after being fully shaken and mixed at 180rpm for 5 minutes, it was centrifuged at 14000rpm, the bacterial layer was aspirated, and the original bacterial flora was obtained. The original flora was cultured on beef extract peptone solid medium (beef extract 3.0 g·L -1 , Peptone 10.0 g·L -1 、NaCl 5.0g·L -1 , agar powder 15.0g·L -1 ) plates for spreading and culture. Each soil sample was spread on 30 plates and cultured at constant temperature at 25°C (10 plates), 30°C (10 plates) and 35°C (10 plates) to grow colonies. A high-throughput fully automatic microbial colony picking workstation was used to pick bacteria for each plate. The picked strains were automatically inoculated into 1.5 mL of beef extract peptone liquid medium (beef extract 3.0 g·L -1 , Peptone 10.0 g·L -1 、NaCl 5.0g·L -1 ), and shake culture is carried out at the corresponding culture temperature when the plate is coated to obtain a bacterial liquid. Then, three-level streak purification and separation are carried out on a beef extract peptone solid culture medium plate, and finally a purified strain is obtained, which is stored by slant streak culture. After sequencing and analysis of the amplified fragments of the isolated and purified strains using the universal primers (8F / 1492R) of 16SrDNA, the genus level classification to which each picked colony belongs is detected. The strains after classification and identification are compared with the keystone species and rare species obtained by annotating the high-throughput sequencing data, and finally the keystone species and rare species microbial materials that can be used for the regulation of rhizosphere microenvironment processes at various time points are obtained.

[0078] 2. Detect the rhizosphere microbial community data, rhizosphere soil environmental factor data and rhizosphere secretion data of contaminated soil without functional plants, compare with the actual rhizosphere microenvironment database at the benchmark time node (t0), adjust the rhizosphere microenvironment data in the simulated rhizosphere microenvironment system to meet the deviation threshold requirements, and thus complete the establishment of the first simulated rhizosphere microenvironment system.

[0079] Specifically:

[0080] 1) Detection and adjustment of rhizosphere microbial community data for benzopyrene-contaminated original soil without functional plants planted initially; rhizosphere microbial community data include relative abundance and number of keystone species and rare species, Shannon index, community similarity (Com-Similarity) and functional similarity (Fun-Similarity);

[0081] Compared with the above-mentioned database of the actual rhizosphere microenvironment of ryegrass, the number of species with differences among rare species reached 18, accounting for 58% of the rare species (31) in the database at 0d, exceeding the threshold of no more than 50% deviation. Therefore, the original bacterial cells isolated by Nycodenz were added to the original soil until the number of rare species with differences after replenishment regulation was 8, and the difference rate reached 26%, meeting the threshold requirement ( Figure 2 ); The number of keystone species in the original soil is 14, which is consistent with the number of keystone species in the database and meets the deviation threshold requirement ( Figure 3 ).

[0082] In the comparison of the relative abundance of each keystone species, the relative abundance of Acinetobacter (41% deviation), Geobacillus (39% deviation), Ochrobactrum (72% deviation) and Sediminibacterium (37% deviation) in the original soil were all lower than the corresponding values ​​in the database, and the deviation ratio was higher than 30%, exceeding the threshold requirement and requiring regulation; therefore, the culture of the corresponding keystone species was used to replenish the original soil until the relative abundance deviation after replenishment was lower than 30%, meeting the threshold requirement ( Figure 4 ); For the keystone species Bacillus (-51% deviation) and Nocardioides (-53% deviation) whose relative abundance in the original soil is higher than the corresponding value in the database, the deviation ratio is higher than 30%, which exceeds the threshold requirement. Any rare species is used. In this example, cultures of Bacteroides, Pullulanibacillus, Rubrobacter, Alicyclobacillus, Ammoniphilus, Desulfitobacterium, Lutispora, Brevibacillus, Mobilitalea, Delftia, and Virgibacillus are used to replenish the original soil until the deviation of the relative abundance of the keystone species after replenishment is lower than 30%, and the relative abundance of each rare species still maintains the threshold requirement of rare species (0.05% to 0.1%) ( Figure 4 ).

[0083] During the above regulation process, the community structure diversity (Shannon index), community similarity (Com-Similarity) and functional similarity (Fun-Similarity) are monitored and calculated respectively. The Shannon index is calculated by the above formula (I), and the community similarity (Com-Similarity) and functional similarity (Fun-Similarity) are calculated by the following formula (II):

[0084] Similarity=1-(∑|X[i]-Y[i]|) / (∑|X[i]+Y[i]|)

[0085] Where X[i] is the relative abundance or functional parameter of the original soil species, and Y[i] is the relative abundance or functional parameter of the species in the database. Functional similarity is calculated using Microresp TM Technology (CN201611123263.7) was used for detection, and the carbon source substrates used included (unit: mg·g -1 ): citric acid (30), galactose (30), glucose (30), malic acid (30), alanine (30), cysteine ​​(7.5), arginine (30), protocatechuol (7.5), fructose (30), lysine (30), trehalose (30), oxalic acid (30), arabinose (30), aspartic acid (7.5), aminobutyric acid (30), acetylglucosamine (7.5) and tyrosine (7.5); the functional metabolic activity was characterized by the respiration intensity of soil microorganisms, and cresol red (12.5 mg·L -1 ), potassium chloride (150mmol·L -1 )、Sodium bicarbonate (2.5mmol·L -1 ) and agar (1%) were equipped with indicators, and the signal was detected at a wavelength of 570nm under the microplate reader. The average Shannon index in the original soil was determined to be 3.38, which was 19% different from the value in the database (4.17), exceeding the threshold requirement of 15%. After the above-mentioned rare species and keystone species parameters were adjusted, the deviation of the Shannon index was reduced to 6%, meeting the threshold requirement ( Figure 5 );

[0086] At the same time, after calculation, the community similarity (Com-Similarity) between the original soil and the database parameters is 0.79, which has met the threshold (≥0.7) requirement. After the above-mentioned regulation, the community similarity (Com-Similarity) still reaches 0.82, which also meets the threshold requirement; the functional similarity (Fun-Similarity) between the original soil and the database parameters is 0.89, which has met the threshold (≥0.7) requirement. After the above-mentioned regulation, the functional similarity (Fun-Simi larity) reaches 0.95, which still meets the threshold (≥0.7) requirement ( Figure 6 ).

[0087] 2) For the original soil contaminated by benzopyrene without the initial planting of functional plants, the rhizosphere soil environmental factor data were tested and adjusted. The rhizosphere soil environmental factor data included soil pH, inorganic nutrient ions, available nitrogen, available phosphorus and available potassium;

[0088] The pH of the original soil is 7.13, and the deviation from the parameter in the database (7.26) meets the threshold (≤1.5) requirement, so no regulation is required ( Figure 7 );Ion chromatography was used to detect inorganic nutrient ions in the original soil, including PO4 3- 、SO4 2- , Cl - 、NO3 - , Ca 2+ Mg 2+ , Fe 3+ 、Na + , K + and Mn 2+ , the concentrations of each ion are: PO4 3- (15.23mg·Kg -1 )、SO4 2- (78.51mg·Kg -1 ), Cl - (55.37mg·Kg -1 )、NO3 - (113.25mg·Kg -1 ), Ca 2+ (163.23mg·Kg -1 ),Mg 2+ (56.79mg·Kg -1 ), Fe 3+ (51.02mg·Kg -1 ), Na + (302.14mg·Kg -1 ), K + (137.86mg·Kg -1 ) and Mn2+ (15.88mg·Kg -1 ), the total amount reaches 989.28mg·Kg -1 , the deviation is -3.8%, which meets the threshold requirement of difference ≤ 20% ( Figure 8 ); In the soil fertility index, the contents of available nitrogen, available phosphorus and available potassium in the original soil are all higher than the corresponding parameters in the database, meeting the assessment threshold requirements ( Fig. 9 ).

[0089] 3) Detect and adjust the rhizosphere secretion data for the original soil contaminated by benzopyrene without the initial planting of functional plants;

[0090] Glycine, histidine, lysine, proline, galactose, glucose, maltose, lactic acid, syringic acid, succinic acid, stearic acid, campesterol, p-aminobenzoic acid, guanine and isoflavone compounds were detected respectively. The peak areas were summed and logarithmically transformed (lg()) to finally obtain the content characterization of various types of rhizosphere secretions. By comparing the parameter contents in the database, it was found that the contents of amino acids, sugars, vitamins, organic acids and nucleotides / purines in the original soil were lower than the parameters in the database by more than 30%, exceeding the threshold requirements. Therefore, α-alanine was used for amino acid supplementation, fructose and raffinose were used for sugar supplementation, acetic acid and tartaric acid were used for organic acid supplementation, palmitic acid was used for fatty acid supplementation, sitosterol was used for sterol supplementation, niacin was used for vitamin supplementation, adenine was used for nucleotide / purine supplementation, and flavonoids were used for flavonoid supplementation. Each compound was prepared into 100 mol·mL -1 The mother solution was diluted 10 times to prepare a standard solution for LC-MS analysis. Based on the peak area of ​​the known concentration standard substance, the relative quantification of the rhizosphere secretion content in the original soil sample and the database was performed, and then the relative quantitative difference was calculated. The original soil was replenished according to this value until the content of each rhizosphere secretion met the regulation threshold requirement (≤30%). After replenishment and regulation, the above low-content rhizosphere secretion categories have met the threshold requirements ( Fig.10 ).

[0091] In summary, the establishment of the initial (t0) simulated rhizosphere microenvironment system of benzopyrene contaminated soil was completed.

[0092] 3. In the process of simulating rhizosphere remediation of benzopyrene-contaminated soil, at t1 and t2, respectively, the actual rhizosphere microenvironment database at the same time node is benchmarked to evaluate the rhizosphere microenvironment data in the simulated rhizosphere microenvironment system; the rhizosphere microenvironment data that exceeds the deviation threshold requirements are adjusted to meet the deviation threshold requirements, thereby completing the real-time evaluation and regulation of the simulated rhizosphere microenvironment system.

[0093] 1) Evaluation and regulation of rhizosphere microbial community data:

[0094] The regulation process showed that the number of rare species in the simulated rhizosphere soil met the deviation requirement within 50% at t1, but the proportion of differential species reached 52% at t2. According to the regulation method for the initial construction of the simulated rhizosphere microenvironment, by adding the original bacterial cells isolated by Nycodenz, the proportion of differential species was reduced to 19% after supplementation, meeting the threshold requirement ( Figure 2 ). In the analysis of the number of keystone species, the number of keystone species at the two time points is consistent with the number of keystone species at the corresponding time points in the database, meeting the deviation threshold requirement ( Figure 3 ). In the comparison of the relative abundance of each keystone species at t1 and t2, for the keystone species whose relative abundance in the simulated rhizosphere soil is lower than the relative abundance in the database and exceeds the deviation threshold, the culture of the corresponding keystone species is used to replenish the original soil; for the keystone species whose relative abundance is higher than the corresponding value in the database and exceeds the deviation threshold, the same rare species culture used in the initial construction of the simulated rhizosphere soil microenvironment is used to replenish the original soil until the deviation of the relative abundance of the keystone species after replenishment is less than 30%, and the relative abundance of each rare species still maintains the rare species threshold requirement (0.05% to 0.1%) ( Figure 4 ). During the regulation process of the above two time nodes, the community structure diversity (Shannon index), community similarity (Com-Similarity) and functional similarity (Fun-Similarity) were monitored and calculated respectively. It was determined that the mean Shannon index in the rhizosphere simulated soil at the two time nodes ( Figure 5 ), community similarity (Com-Similarity) and functional similarity (Fun-Similarity) ( Figure 6 ) and the corresponding values ​​in the database meet the threshold requirements and no further adjustment is required.

[0095] 2) Evaluation and regulation of rhizosphere soil environmental factor data:

[0096] The soil environmental factors in the simulated rhizosphere soil remediation process were monitored. At time t1, the soil pH value exceeded 1.64 compared with the pH value in the database, which exceeded the deviation threshold (≤1.5). Therefore, small molecule organic acids were applied to the soil to reduce the soil pH. In this case, gulong acid solution was used for pH adjustment until the pH difference reached 1.29 after adjustment, thus meeting the deviation threshold requirement ( Figure 7), the pH value does not need to be adjusted at the t2 time node. The above-mentioned various inorganic nutrient ion contents were detected for the rhizosphere simulated soil using ion chromatography. The deviations of the total amount of inorganic ions at both time nodes and the total amount of inorganic ions in the corresponding database met the threshold requirement of the difference ≤ 20% ( Figure 8 ). In the soil fertility index, the effective nitrogen content in the simulated rhizosphere soil decreased by 16% compared with the organic nitrogen in the database at time t2, exceeding the deviation threshold of 15%. Therefore, ammonium nitrate solution was added to the simulated rhizosphere soil until the reduction ratio increased to 10%, thus meeting the threshold requirement; similarly, the effective phosphorus content in the simulated rhizosphere soil decreased by 16% compared with the organic phosphorus in the database at time t2, exceeding the deviation threshold of 15%. Therefore, potassium phosphate solution was added to the simulated rhizosphere soil until the reduction ratio increased to 6%, thus meeting the threshold requirement; the effective potassium content at both time nodes was higher than the corresponding parameters in the database, meeting the threshold requirement ( Fig. 9 ).

[0097] 3) Evaluation and regulation of rhizosphere exudate data:

[0098] The composition and content of rhizosphere secretions in the simulated rhizosphere remediation soil were tested with reference to the above method. At the t1 time node, the difference between the relative content of sterols and sugar compounds and the corresponding content in the database reached 32% and 42%, respectively. The compounds used in the initial rhizosphere soil microenvironment construction were used for replenishment until the deviation of the relative content of sterols and sugar compounds reached 1% and 2%, respectively; at the t2 time node, the difference between the content of amino acids, vitamins, nucleotides / purines and fatty acids and the database reached 38%, 38%, 39% and 47%, respectively. The same compounds as above were used for replenishment until the difference between the relative content and the database content met the threshold requirement of less than 30% ( Fig.10 ).

[0099] In summary, the evaluation and regulation of soil microenvironment system during the simulated rhizosphere remediation of benzopyrene contaminated soil was completed.

[0100] Finally, after completing the 30-day restoration process of rhizosphere simulation, the benzopyrene residue characteristics at four time points, t0, t1, t2 and the restoration end point (30 days), were compared. After 30 days of restoration, the benzopyrene residue in the soil restored by the simulated rhizosphere microenvironment system was the lowest, from the initial 112 mg·Kg -1 Reduced to 49.78mg·Kg -1 However, the residual amount of benzopyrene in the soil repaired by ryegrass rhizosphere was only 108 mg·Kg -1 Reduced to 56.67 mg·Kg -1, indicating that the simulated rhizosphere repair microenvironment system constructed by the present invention can avoid the change of rhizosphere microenvironment caused by the weakening of the growth physiological state of the functional plant ryegrass during the natural repair application process, thereby interfering with the drawbacks of rhizosphere repair efficiency, and also reflects the maintenance effect of the simulated rhizosphere microenvironment construction method on the stability of the soil rhizosphere repair function. At the same time, the method of the present invention can guide the repair of actual large-scale contaminated soil through the data of the microcosm experiment, avoiding the large-scale planting and management of traditional functional plants, and saving manpower and material resources.

Claims

1. A method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil, characterized in that: Construct a database of the actual rhizosphere microenvironment during normal growth of functional plants. By benchmarking the actual rhizosphere microenvironment database during normal growth of functional plants, construct a simulated rhizosphere microenvironment system of organic contaminated soil without planting functional plants. The construction of the simulated rhizosphere microenvironment system consists of the establishment of the first simulated rhizosphere microenvironment system and the real-time evaluation and regulation of the simulated rhizosphere microenvironment system.

2. The method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil according to claim 1, characterized in that: The rhizosphere microenvironment data of the contaminated soil without functional plants was detected, and the actual rhizosphere microenvironment database at the benchmark time node (t0) was used to adjust the rhizosphere microenvironment data in the simulated rhizosphere microenvironment system to meet the deviation threshold requirements, thereby completing the establishment of the first simulated rhizosphere microenvironment system.

3. The method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil according to claim 1, characterized in that: After completing the first simulated rhizosphere microenvironment construction, at multiple time points (t n ) Continuously monitor the contaminated soil without functional plants, benchmark the actual rhizosphere microenvironment database at the same time point, and evaluate the rhizosphere microenvironment data in the simulated rhizosphere microenvironment system; The rhizosphere microenvironment data that exceeds the deviation threshold requirements are regulated to meet the deviation threshold requirements, thereby completing the real-time evaluation and regulation of the simulated rhizosphere microenvironment system.

4. The method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil according to claim 2 or 3, characterized in that: The actual rhizosphere microenvironment database of functional plants during normal growth includes the actual rhizosphere microenvironment data at different time points; The actual rhizosphere microenvironment data include rhizosphere microbial community data, rhizosphere soil environmental factor data and rhizosphere exudate data.

5. The method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil according to claim 4, characterized in that: The microbial community data are species distribution and species diversity parameters; species distribution is the relative abundance and number of keystone species and rare species; species diversity parameters are the Shannon index and the community similarity (Com-Similarity) and functional similarity (Fun-Similarity) between the actual rhizosphere microbial community when the functional plants grow normally; The rhizosphere soil environmental factor data are soil physical and chemical factors; soil physical and chemical factors are soil pH, inorganic nutrient ions, available nitrogen, available phosphorus and available potassium; The data of rhizosphere exudates are one or more of amino acids, sugars, organic acids, fatty acids, sterols, vitamins, nucleotides / purines, and flavonoids.

6. The method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil according to claim 5, characterized in that: Species with a relative abundance ≥1% at the soil bacterial genus level were keystone species, and species with a relative abundance of 0.05%-0.1% at the soil bacterial genus level were rare species.

7. The method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil according to claim 2 or 3, characterized in that: The deviation threshold requirement is: Thresholds of rhizosphere microbial communities: the number deviation of rare species is ≤50%, the number deviation of keystone species is ≤30%, the relative abundance deviation of each keystone species is ≤30%, the Shannon index deviation is ≤15%, the community similarity (Com-Similarity) is ≥0.7, and the functional similarity (Fun-Similarity) is ≥0.7; Thresholds of rhizosphere soil environmental factors: soil pH deviation ≤ 1.5; total amount of soil inorganic nutrient ions is not less than 80% of the content in the database, that is, the difference is ≤ 20%; soil available nitrogen content is not less than 85% of the content in the database, that is, the difference is ≤ 15%; soil available phosphorus content is not less than 85% of the available phosphorus content in the database, that is, the difference is ≤ 15%; soil available potassium content is not less than 80% of the available potassium content in the database, that is, the difference is ≤ 20%; Rhizosphere secretion threshold: The content of any rhizosphere secretion is not less than 70% of the content of rhizosphere secretion in the database, that is, the difference is ≤30%.

8. An application of the method for constructing a simulated rhizosphere microenvironment system for rhizosphere remediation of organic contaminated soil according to claim 1, characterized in that: The method is suitable for repairing soil with organic pollutants under the condition that no actual functional plants are planted.

9. The use according to claim 8, characterized in that: The organic pollutants are hydrophobic organic pollutants or hydrophilic organic pollutants.

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