A diagnostic method for nitrogen removal efficiency of ecological sponges based on metagenomic technology

Abstract

The present invention discloses a diagnostic method for nitrogen removal efficiency of an ecological sponge based on metagenomic technology. By high-throughput sequencing of microbial 16SrRNA genes in a bioretention system, the composition and structure of the microbial community in the bioretention system under different plant species, soil matrix ratios, and pollution loads are analyzed, and the influence of different plant species, soil matrix ratios, and pollution loads on the microbial community and the abundance of nitrogen functional genes is studied; the connection between the microbial characteristics of the bioretention system and the nitrogen removal rate is established by correlation analysis, regression analysis, and other methods, so as to predict the nitrogen removal efficiency of the bioretention system by detecting the microbial characteristics; by exploring the influence of typical denitrification factors on the nitrogen removal efficiency of the bioretention system, the plant species, soil matrix ratios, and pollution loads suitable for the construction or operation of the future bioretention system are screened out, and effective, in-depth, and scientific reference and guidance are provided for the promotion of the bioretention system.

Description

Technical Field

[0001] The present invention relates to the field of resource and environmental technology, and in particular to a method for diagnosing nitrogen removal efficiency of an ecological sponge based on metagenomic technology. Background Art

[0002] Nitrogen and phosphorus nutrients, organic matter and heavy metals tend to accumulate on impermeable underlying surfaces during dry periods and are washed away by surface runoff during rainfall, causing serious urban non-point source pollution. Excessive nitrogen load can induce black and smelly urban water bodies. It is also the main cause of eutrophication of downstream rivers and lakes and frequent red tides in coastal waters, which seriously undermines the balance of the ecosystem and threatens the drinking water safety of the people. Therefore, controlling nitrogen pollution in urban rainfall runoff has become a very urgent issue at present, and it is also the focus of urban water environment management and water security in my country.

[0003] The traditional method of dealing with rainwater runoff pollution is to collect it through the municipal pipe network and centrally treat the initial runoff with high pollutant concentrations by sewage treatment plants. This centralized end-of-pipe treatment method faces problems such as high cost, limited treatment capacity, and lack of rainwater resource utilization.

[0004] In recent years, my country has vigorously promoted the construction of sponge cities to solve urban water environment problems such as urban non-point source pollution and waterlogging. As an important low-impact development facility, the bioretention system, also known as the ecological sponge, has achieved remarkable results in rainfall runoff management. It can effectively reduce runoff volume and reduce runoff pollution, and has both ecological and landscape value. The bioretention system can not only reduce runoff volume, reduce runoff pollution, and realize rainwater resource utilization, but also has the advantages of small land occupation, low cost, and good landscape effect. Existing studies have confirmed that the bioretention system has a good removal effect on most pollutants such as suspended particulate matter, heavy metals, and pathogens in rainfall runoff. However, the removal effect of the bioretention system on nitrogen pollution is not ideal. Except for ammonia nitrogen, which can be stably adsorbed by the filling medium (the removal rate is usually maintained at more than 70%), the removal rates of nitrate nitrogen and total nitrogen can fluctuate sharply between 47.7%~100% and 24.1%~99%, respectively. Therefore, in the study of water quality effects of bioretention systems, how to effectively improve the nitrogen removal efficiency has received widespread attention.

[0005] However, existing research mainly focuses on the effects of factors such as plant selection, substrate ratio, structural design, and external meteorological conditions on denitrification in bioretention systems, while research on microorganisms in bioretention systems is not in-depth enough. In the nitrogen removal process of bioretention systems, soil adsorption and plant absorption are temporary, and nitrogen may re-enter the system through desorption and littering. Denitrification, which is dominated by microbial communities involved in the nitrogen cycle, is the main way for permanent denitrification in bioretention systems. Therefore, microorganisms, as the dominant players in the nitrogen removal process in bioretention systems, deserve more attention. Microorganisms play an important role in nitrogen removal in bioretention systems. Understanding the characteristics of microbial populations and nitrogen conversion functions, as well as their dynamic changes in the environment, will help reveal the nitrogen removal mechanism of bioretention systems and provide a scientific basis for evaluating and improving the nitrogen removal function of the system.

[0006] Although a lot of work has investigated the effects of denitrification factors on nitrogen removal efficiency and microbial characteristics in bioretention systems, microorganisms are an important link between denitrification factors and nitrogen removal efficiency in bioretention systems. Existing research lacks in-depth exploration of how the nitrogen removal efficiency of bioretention systems responds to changes in denitrification factors through microbial pathways, and lacks exploration of the mechanism of microbial regulation of denitrification in bioretention systems.

[0007] Therefore, it is necessary to develop a diagnostic method for nitrogen removal efficiency of ecological sponges based on metagenomic technology to solve the above problems. Summary of the invention

[0008] The purpose of the present invention is to provide a diagnostic method for nitrogen removal efficiency of ecological sponges based on metagenomic technology, so as to predict the nitrogen removal efficiency of biological retention systems by detecting microbial characteristics.

[0009] In order to achieve the above object, the present invention provides the following technical solutions:

[0010] A diagnostic method for nitrogen removal efficiency of ecological sponges based on metagenomic technology comprises the following steps:

[0011] S1. Build a bioretention system, prepare simulated runoff according to the local rainfall frequency, rainfall intensity and rainfall runoff water quality characteristics for preliminary experiments, and use tap water and the simulated runoff to acclimate the bioretention system until the nitrogen leaching reaches stability;

[0012] S2. Conducting formal experiments in the bioretention system, using the simulated runoff to carry out simulated rainfall experiments and control experiments at at least three influent pollutant concentrations, conducting 5 to 6 simulated rainfall experiments at each concentration level, collecting inlet and outlet water samples for water quality testing, and calculating the pollution removal rate;

[0013] S3. At the beginning and end of the formal experiment, collecting substrate samples of the bioretention system and detecting the physical and chemical properties of the substrate samples;

[0014] S4, extracting DNA of microorganisms in the matrix sample, performing high-throughput sequencing on the 16S rRNA gene of the microorganisms, performing metagenomic sequencing analysis on the microorganisms, and obtaining microbial characteristics of the bioretention system;

[0015] S5. Establish the relationship between the microbial characteristics of the bioretention system and the nitrogen removal rate through correlation analysis and regression analysis to obtain a multiple linear regression model, and use the multiple linear regression model to predict the nitrogen removal efficiency of the bioretention system by detecting the microbial characteristics.

[0016] Furthermore, the metagenomic sequencing analysis includes an analysis of differences in the abundance of nitrogen-related genes of the microorganisms.

[0017] Furthermore, the physical and chemical properties of the matrix sample include: water content, pH value, salinity, NH4 + -N、NO3 - -N, TN, TOC and C / N.

[0018] Furthermore, the calculation formula of the pollutant removal rate is:

[0019]

[0020] in, Removal Rate (%) is the removal rate of a certain pollutant by the bioretention system in a certain rainfall event; the average inflow concentration of a certain pollutant is, EMC i mg L -1 ; The average outflow concentration of a certain pollutant is, EMC e oeLh -1 .

[0021] Furthermore, the main body of the biological retention system is a plexiglass column, and the biological retention system is composed of a gravel layer, a sand layer, a soil matrix layer and a plant layer from bottom to top. The soil matrix layer includes 50% to 80% by mass of local red soil, 10% to 40% of washed sand and 10% to 20% of nutrient soil. The biological retention system is provided with multiple groups according to the different types of plants planted in the plant layer and the different proportions of the soil matrix layer.

[0022] The beneficial effects of the present invention are:

[0023] The present invention analyzes the composition and structure of the microbial community in the bioretention system under different plant species, soil matrix ratios, and pollution loads by high-throughput sequencing of the 16SrRNA gene of microorganisms in the bioretention system, and studies the effects of different plant species, soil matrix ratios, and pollution loads on the microbial community and the abundance of nitrogen functional genes;

[0024] The relationship between the microbial characteristics of the bioretention system and the nitrogen removal rate is established through correlation analysis, regression analysis and other methods, so as to predict the nitrogen removal efficiency of the bioretention system by detecting the microbial characteristics;

[0025] By exploring the impact of typical denitrification factors on the nitrogen removal efficiency of biological retention systems, we can screen out plant species, soil matrix ratios, and pollution loads that are suitable for the construction or operation of future biological retention systems, providing effective, in-depth, and scientific reference and guidance for the promotion of biological retention systems.

[0026] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, some of the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 It is a structural schematic diagram of a bioretention system shown in a preferred embodiment of the present invention;

[0029] Figure 2 A preferred embodiment of the present invention shows a bioretention system for NH4 + -N, NO3 - -N, ON and TN EMC removal rate box plot, where BR_C is Canna, BR_I is Iris, BR_P is Phragmites australis, for each box, the five short lines from top to bottom represent the maximum value, the first quarter value, the median value, the last quarter value and the minimum value. "----" represents the average value;

[0030] Figure 3 A box plot of the absolute abundance of N-related genes in soil samples (after log10 standardization) shown in a preferred embodiment of the present invention;

[0031] Figure 4The absolute abundance heat map of N-related genes in soil samples shown in a preferred embodiment of the present invention (after z-score normalization);

[0032] Figure 5 A heat map showing the correlation between N-related functional genes and water quality indicators in soil samples according to a preferred embodiment of the present invention (where: * indicates p<0.05, ** indicates p<0.01, *** indicates p<0.001);

[0033] Figure 6 A redundancy analysis RDA graph between the absolute abundance of nitrogen functional genes in samples and nitrogen water quality indicators shown in a preferred embodiment of the present invention;

[0034] Figure 7 The redundancy analysis RDA graph of the absolute abundance and physicochemical properties of nitrogen functional genes in the sample shown in a preferred embodiment of the present invention;

[0035] Figure 8 The related independent variables selected for constructing a multiple linear regression model for predicting nitrogen removal efficiency as shown in a preferred embodiment of the present invention;

[0036] Fig. 9 A model summary of a multiple linear regression model for predicting nitrogen removal efficiency shown in a preferred embodiment of the present invention;

[0037] Fig.10 The figure is the test result of the multivariate linear regression model for predicting nitrogen removal rate shown in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0038] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0039] The technical solutions described in the present invention, unless otherwise specified, are all conventional solutions in the art; the reagents or materials described, unless otherwise specified, are all from commercial channels.

[0040] 1.1 Experimental setup

[0041] Build as Figure 1The bioretention system shown. The main body of the bioretention system is a plexiglass (Polymethylmethacrylate, PMMA) column with a diameter of 200 mm and a height of 650 mm. A platform and support feet are designed at the bottom to ensure the stability of the device. From bottom to top, there are gravel layer, sand layer, soil matrix layer and plant layer. The gravel layer is 100 mm thick and filled with gravel with a particle size of 15 to 30 mm. The sand layer is 50 mm thick, filled with washed sand, and separated from the gravel layer by geotextile. 2% (volume ratio) of shredded newspaper is added to the gravel layer and sand layer as a carbon source to promote microbial reproduction. The soil matrix layer is 400 mm thick and is filled with a composite matrix composed of local red soil, washed sand and nutrient soil. Canna ( Canna indica L.), Iris ( Iris tectorum Maxim. ) or reed ( Phragmites australis ). The water outlet of the experimental device is located at the bottom of the column. A layer of tin foil foam paper is wrapped around the outside of the plexiglass column to avoid light, heat insulation and prevent algae growth.

[0042] In this experiment, three kinds of plants, three kinds of substrate ratios, and three kinds of influent pollutant concentrations were set. The plants used were canna, iris, and reed, three common plants in bioretention systems. The substrate ratio was set by changing the mass ratio of local red soil: washed sand: nutrient soil (8:1:1, 7:2:1, 6:3:1). For each substrate ratio and plant, three parallels were set. Therefore, there were a total of 27 bioretention system simulation columns in this experiment. Each bioretention system successively treated rainfall runoff with low, medium, and high influent pollutant concentrations. The concentration settings are shown in Table 1.

[0043] 1.2 Simulated runoff design

[0044] Referring to the typical rainfall frequency, rainfall intensity and runoff water quality characteristics in Shenzhen, the pre-rainfall dry period (ADP) was set to 4 days, the simulated rainfall intensity was 20 mm / h, the rainfall duration was 1 h, the runoff coefficient was 0.9, and the catchment area: the upper surface area of ​​the bioretention system was 15:1. The pollutant concentrations of the simulated runoff and the chemical reagents used are shown in Table 1.

[0045] Table 1 Simulated runoff pollutant concentrations and chemical reagents used

[0046]

[0047] 1.3 Experimental Procedure

[0048] The experimental process is as follows: (1) experimental preparation; (2) acclimatization stage and (3) formal experimental stage. The preliminary experiment includes two parts: (1) experimental device construction; (2) plant planting. The acclimatization stage includes two parts: (1) tap water acclimatization: watering once every 2 days for 2 weeks; (2) simulated runoff acclimatization: acclimatization for about 6 weeks under the simulated runoff conditions designed in Section 1.2. The purpose of the acclimatization stage is to achieve basic stability in the adsorption capacity of the matrix, plant growth status and microbial reproduction status in the bioretention system, and to achieve basic stability in the nitrogen removal rate, so that the subsequent formal experimental results are reliable. During the acclimatization process, the water quality indicators of the effluent are regularly tested to determine whether the bioretention system has reached a stable state.

[0049] When the bioretention system reaches a stable state, the formal experiment begins. Experiments with low, medium, and high influent pollutant concentrations are carried out successively under the simulated runoff conditions designed in Section 1.2, and 5 to 6 simulated rainfalls are carried out at each concentration level. Inlet and outlet water samples are collected for water quality testing and the pollution removal rate is calculated. At the same time, matrix samples are collected for physical and chemical property testing and subsequent molecular biological analysis.

[0050] 2.1 Water sampling and testing

[0051] Influent and effluent samples were collected for some simulated rainfall events in the acclimatization phase and all simulated rainfall events in the formal experimental phase. The samples were immediately filtered through a 0.45 μm filter membrane after collection, and the filtrate was immediately analyzed and tested or stored in a -20°C refrigerator for analysis and testing. The indicators of water quality testing include: NH4 + -N、NO3 - -N, TN, TP and COD. Specific detection methods and instruments are shown in Table 2.

[0052] Table 2 Water quality index detection methods and instruments

[0053]

[0054]

[0055] The removal rate of pollutants in the bioretention system is calculated using the calculation formula based on the average concentration of rainfall events (Event mean concentration, EMC) recommended by the American ASCE-EPA (American society of civil engineers-environmental protection agency):

[0056]

[0057] in, Removal Rate(%) is the removal rate of a certain pollutant by the bioretention system in a certain rainfall event; the average inflow concentration of a certain pollutant is, EMC i mg L -1 ; The average outflow concentration of a certain pollutant is, EMC e mg L -1 .

[0058] 2.2 Soil sample collection and testing

[0059] At the beginning and end of the formal experiment, the matrix samples of each experimental column were collected using the five-point sampling method. For soil sample collection and testing, samples of the soil matrix layer and sand layer of each experimental column after low, medium and high influent pollutant concentration experiments were collected, and a total of 162 mixed soil matrix samples were obtained. After all matrix samples were collected, they were properly handled and stored in accordance with the requirements of the testing standards, including immediate testing, air drying, 4 ℃ refrigeration and -20 ℃ freezing. The physical and chemical properties of the matrix samples include: moisture content, pH value, salinity, NH4 + -N、NO3 - -N, TN (total nitrogen), TC (total carbon), TOC (total organic carbon) and C / N (carbon-nitrogen ratio). See Table 3 for specific detection methods and instruments.

[0060] Table 3 Matrix physical and chemical properties detection methods and instruments

[0061]

[0062]

[0063] 3.1 DNA extraction

[0064] The DNA of microorganisms in the matrix samples was extracted using the DNeasy® PowerSoil® Kit (QIAGEN, Germany). The integrity of the extracted DNA was tested by 1% agarose gel electrophoresis. The DNA concentration was tested using a NanoDrop 2000 ultra-micro spectrophotometer (ThermoFisher, USA), and its purity was determined based on the OD260 / OD280 value.

[0065] 3.2 High-throughput sequencing

[0066] High-throughput sequencing of microbial 16SrRNA genes. The 16SrRNA gene contains 10 conservative regions and 9 hypervariable regions, of which the conservative regions have little difference among bacteria, while the hypervariable regions have certain specificity, and this specificity changes with different kinship. Therefore, the 16SrRNA gene can be used as a characteristic nucleic acid sequence to reveal biological species and is considered to be the most suitable indicator for bacterial phylogeny and classification identification.

[0067] The main steps of high-throughput sequencing analysis of microorganisms are: (1) using universal primers to amplify the 16SrRNA gene and metagenome in the extracted DNA; (2) identification, purification and quantification of PCR products; (3) construction of gene libraries and paired-end sequencing using the MiseqPE300 sequencing platform (Illumina, USA). Specifically, the primers corresponding to the V3-V4 region were selected in this example:

[0068] Upstream primer 338F: ACTCCTACGGGAGGCAGCAG;

[0069] Downstream primer 806R: GGACTACHVGGGTWTCTAAT.

[0070] The reaction conditions of PCR were as follows: 95 °C pre-denaturation for 3 min; 27 cycles: 95 °C denaturation for 30 s, 55 °C annealing for 30 s, 72 °C extension for 45 s; 72 °C incubation for 10 min, and finally to 10 °C. Three PCR replicates were performed for each sample, and the PCR products of the three replicates were mixed. The products were detected by 2% agarose gel electrophoresis. If the background of the amplified bands was clean and the band positions were clear, the extracted DNA met the amplification requirements and could be used for subsequent analysis.

[0071] The original sequencing sequence was quality controlled using fastp (version 0.20.0) software, and spliced ​​using FLASH (version 1.2.7) software:

[0072] (1) Filter the bases with a quality value of less than 20 at the tail of the reads, set a 50 bp window, and if the average quality value in the window is less than 20, cut off the back end bases from the window, filter the reads below 50 bp after quality control, and remove the reads containing N bases;

[0073] (2) Based on the overlap relationship between PE reads, the paired reads are spliced ​​(merged) into a sequence with a minimum overlap length of 10 bp;

[0074] (3) The maximum mismatch ratio allowed in the overlap region of the spliced ​​sequence is 0.2, and non-compliant sequences are screened out;

[0075] (4) Differentiate samples based on the barcodes and primers at both ends of the sequence and adjust the sequence direction. The allowed number of mismatches for the barcode is 0, and the maximum number of mismatches for the primers is 2.

[0076] 4.1 Metagenomic data analysis

[0077] (1) Quality control

[0078] The first step is to filter the raw data and remove low-quality bases in the sequence. The specific quality control values ​​are controlled using FastQC and trimmomtic software to remove sequences with base quality lower than Q20. The data retained after filtering is clean data for subsequent analysis.

[0079] (2) Data assembly

[0080] To improve the reliability of subsequent analysis, the clean data was then denovo assembled. Sequence assembly was performed using megahit (parameters set to -k 21, 33, 55, 77). The assembled sequence data was directly used for the prediction of overall function. The assembled contigs were evaluated using Quast software.

[0081] (3) ORF prediction

[0082] Prodigal was used to predict protein.

[0083] (4) Sequence redundancy removal

[0084] The redundancy of these proteins was identified using CD-HIT v6.266. All data were plotted using R and the omic cloud platform (https: / / www.omicshare.com).

[0085] (5) Calculate coverage and depth based on contigs

[0086] Bwa was used to map reads back to the filtered raw data, and the resulting SAM files were converted to BAM format using Samtools. The coverage and depth were calculated using the calc.coverage.in.bam.depth.pl script.

[0087] (6) Sequence alignment based on Diamond

[0088] Then use Diamond software --more sensitive mode to compare with NCycDB database. Functional annotation is analyzed based on NCycDB database. Use Diamond software to set e-value to 1e-5 to compare with NCycDB database and output the comparison results.

[0089] Through the above steps, we know the influence of the factors affecting the denitrification effect of the bioretention system on the nitrogen removal efficiency of the bioretention system, and analyze it from the microbial level. The details are as follows:

[0090] 4.2 Effects of typical denitrification factors on nitrogen removal efficiency in bioretention system construction

[0091] Please refer to Table 4. Through the permanova test, it was found that there was no significant difference in the nitrogen removal efficiency of the biological retention system with different substrate ratios. Therefore, we will not discuss the removal efficiency of soil substrate ratio for water quality indicators in the following section.

[0092] Table 4 Effect of soil matrix ratio on water quality

[0093]

[0094] See also Figure 2 The results of the formal experimental stage show the removal efficiency of nitrogen from different influent runoffs by the bioretention system constructed by canna, iris and reed.

[0095] (1) For NH4 + -N removal: Canna, iris and reed to NH4 + The average removal rates of -N were 65.1%, 68.7% and 60.7% respectively. + -N removal capacity was the strongest, significantly higher than that of reed ( P <0.01, while Canna indica was also significantly higher than Phragmites australis ( P <0.001).

[0096] (2) For NO3 - -N removal: Canna, iris and reed to NO3 - The average removal rates of -N were 28.7%, 15.1% and 17.2% respectively. - -N removal capacity is basically the same, while Canna indica has a higher removal capacity for NO3 - -N removal ability was significantly higher than that of iris and reed ( P <0.001).

[0097] (3) For ON removal: The average removal rates of canna, iris and reed for ON were 63.9%, 45.2% and 47.7%, respectively. The removal capacity of iris and reed for ON was basically the same, while canna was significantly higher than iris and reed ( P <0.01).

[0098] (4) Removal of TN: The average removal rates of TN by canna, iris and reed were 55.4%, 43.3% and 40.3%, respectively. The removal capacity of TN by iris and reed was basically the same, while that of canna was significantly higher than that of iris and reed ( P <0.01).

[0099] In general, the nitrogen removal performance of iris and reed is basically the same; among them, the removal rates of ammonia nitrogen by canna and iris are higher than that of reed, while canna is better at removing nitrate nitrogen. The removal rates of organic nitrogen and total nitrogen are the same. The nitrogen removal performance of reed is quite different from that of canna: the removal rates of ammonia nitrogen, organic nitrogen and total nitrogen are significantly lower than that of canna.

[0100] 4.3 Effect of nitrogen-related gene abundance in bioretention systems

[0101] See also Figure 3 The abundance of nitrogen-related functional genes in canna, iris and reed under medium pollution load was significantly higher than that under low and high pollution loads (P < 0.001), and the abundance of nitrogen-related functional genes under high pollution load was slightly lower than that under medium pollution load (P less than 0.05). In general, when the pollution load increased from low to medium, the stress resistance of each plant increased. When the pollution load increased from medium to high, the stress resistance of canna was significantly higher than that of the other two plants.

[0102] See also Figure 4 The absolute abundance of nitrogen transformation functional genes is shown in Figure 2. In general, the N-related genes of all plants are more suitable for denitrifying bacteria under medium influent pollutant concentration, that is, the environment with medium influent pollutant concentration is more suitable for denitrifying bacteria, because the rich organic matter in medium-concentration water is conducive to the reproduction of bacteria. For the denitrification process, nG , nirK , nI and sZ The absolute abundance of genes was 1.13×10 6 ~ 5.93×10 6 , 1.40×10 6 ~ 7.17×10 6 , 5.06×10 5 ~ 1.4×10 7 and 2.47×10 5 ~ 6.71×10 6 copies·g -1 In general, the abundance of denitrification genes in the bioretention system with medium influent pollutant concentration was higher than that in the bioretention system with low and high influent pollutant concentration. However, the abundance of denitrification functional genes in the Canna bioretention system was higher than that in the Iris bioretention system and the Phragmites australis bioretention system, indicating that the denitrification potential increased under appropriately high influent pollutant concentrations.

[0103] See also Figure 5 For ON removal: amoB_B, hao, nifK, and ansB genes were significantly correlated (P less than 0.001). + -N removal is mostly absorption, for NO3 --N removal: amoC_B and amoB_B genes were significantly correlated (P less than 0.001). For TN removal: amoB_B gene was significantly correlated (P less than 0.001). In summary, amoB_B gene has a significant correlation with nitrogen removal of various water quality indicators.

[0104] See also Figure 6 and Figure 7 The RDA graph was used to show the relationship between the absolute abundance of nitrogen functional genes, water quality indicators and environmental factors in each group of samples. The explanatory power of the water quality index coordinate axes RDA1 and RDA2 were 57.1% and 25.15%, respectively. The explanatory power of the physical and chemical property coordinate axes RDA1 and RDA2 were 52.66% and 28.31%, respectively. The results showed that:

[0105] (1) The total nitrogen removal rate was positively correlated with the ammonia nitrogen removal rate and the organic nitrogen removal rate.

[0106] (2) Total nitrogen, pH, moisture content, nitrate nitrogen and ammonia nitrogen content are environmental factors that affect microbial communities. Moisture content, nitrate nitrogen and ammonia nitrogen content are negatively correlated with pH, ​​total nitrogen content is positively correlated with pH, ​​and moisture content is positively correlated with nitrate nitrogen content.

[0107] See also Figure 8 , Fig. 9 and Fig.10 , using the above method to predict the total nitrogen removal rate

[0108] The stepwise entry method selected amoB_B x1 gdh_K00261 x2 nirB x3 ureC x4 amoC_Bx5 as independent variables.

[0109] According to the above data, a multiple linear regression model is constructed: y(EMC_TN) = 0.07 * x1 + 0.02 * x2 -0.01*x3+0.01*x4+0.11*x5 +0.317 (R 2 =0.680).

[0110] T test can evaluate whether the influence of each variable on the dependent variable is significant. In the figure, the t test statistics are 0.002, 0.025, 0.000, 0.002, and 0.018, respectively, and the significance is less than 0.05. This shows that the five independent variables have a significant influence on the dependent variable.

[0111] Multicollinearity diagnosis can determine the linear correlation between independent variables. The tolerance of the model in the figure is less than 1, and the VIF is less than 10. This shows that the correlation between the five independent variables is low.

[0112] The DW test can be used to determine whether there is autocorrelation in the residuals. Based on the significance level of 0.05, the sample size of 54, and the number of independent variables of 5, check the DW test table and determine the critical value d l and d u They are 1.34 and 1.77 respectively. The DW of the regression model is 1.370, 0<DW<=d l , indicating that the residuals are positively autocorrelated to a certain extent.

[0113] The estimated standard deviation can be used to test the prediction accuracy of the regression model. The standard estimated error of the regression model S = 0.05517), the predicted mean value y = 0.4903. Then S / y = 11.25% < 15%, indicating that the prediction accuracy is high.

[0114] The above-mentioned multiple linear regression model can be used to predict the nitrogen removal efficiency of the bioretention system by detecting microbial characteristics. Specifically, it includes: 1) extracting microbial DNA from the soil matrix layer of a bioretention system; 2) high-throughput sequencing of the microbial 16SrRNA gene; 3) performing metagenomic data analysis to obtain the relative abundance of each nitrogen functional gene; 4) substituting the relative abundance values ​​of each nitrogen functional gene into the above-mentioned multiple linear regression model to obtain the total nitrogen removal rate of the bioretention system.

[0115] In summary, the present invention analyzes the composition and structure of the microbial community in the bioretention system under different plant species, soil matrix ratios, and pollution loads by high-throughput sequencing of the 16SrRNA gene of the microorganisms in the bioretention system, and studies the effects of different plant species, soil matrix ratios, and pollution loads on the microbial community and the abundance of nitrogen functional genes; establishes the relationship between the microbial characteristics of the bioretention system and the nitrogen removal rate through correlation analysis, regression analysis and other methods, so as to predict the nitrogen removal efficiency of the bioretention system by detecting the microbial characteristics; by exploring the effects of typical denitrification factors on the nitrogen removal efficiency of the bioretention system, screens out plant species, soil matrix ratios, and pollution loads suitable for the construction or operation of future bioretention systems, and provides effective, in-depth, and scientific reference and guidance for the promotion of bioretention systems.

[0116] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A diagnostic method for nitrogen removal efficiency of ecological sponges based on metagenomic technology, characterized in that: The method comprises the following steps: S1, constructing a bioretention system, preparing simulated runoff for preliminary experiments according to the local rainfall frequency, rainfall intensity and rainfall runoff water quality characteristics, and acclimating the bioretention system with tap water and the simulated runoff successively until the nitrogen leaching reaches stability; S2, conducting formal experiments in the bioretention system, using the simulated runoff to successively carry out simulated rainfall experiments and control experiments with at least three influent pollutant concentrations, conducting 5 to 6 simulated rainfall experiments at each concentration level, collecting inlet and outlet water samples for water quality testing, and calculating the pollutant removal rate; S3, at the beginning and end of the formal experiment, collecting matrix samples of the bioretention system, and testing the physical and chemical properties of the matrix samples; S4, extracting DNA of microorganisms in the matrix samples, performing high-throughput sequencing on the 16S rRNA gene of the microorganisms, performing metagenomic sequencing analysis on the microorganisms, and obtaining the microbial characteristics of the bioretention system; S5. The relationship between the microbial characteristics of the bioretention system and the nitrogen removal rate is established through correlation analysis and regression analysis to obtain a multiple linear regression model, and the multiple linear regression model is used to predict the nitrogen removal efficiency of the bioretention system by detecting the microbial characteristics; the metagenomic sequencing analysis includes an analysis of the differences in the abundance of nitrogen-related genes of the microorganisms; the multiple linear regression model uses the relative abundance of five genes amoB_B, gdh_K00261, nirB, ureC and amoC_B as independent variables.

2. The diagnostic method for nitrogen removal efficiency of ecological sponges based on metagenomic technology according to claim 1, characterized in that: The physical and chemical properties of the matrix sample include: water content, pH value, salinity, NH4 + -N、NO3 - -N, TN, TOC and C / N.

3. The diagnostic method for nitrogen removal efficiency of ecological sponges based on metagenomic technology according to claim 1, characterized in that: The calculation formula of the pollutant removal rate is: Wherein, Removal Rate is the removal rate of a certain pollutant by the bioretention system in a certain rainfall event, in units of %; EMC i is the average inflow concentration of a pollutant, in mg·L -1 ;EMC e is the average outflow concentration of a pollutant, in mg·L -1 .

4. The diagnostic method for nitrogen removal efficiency of ecological sponges based on metagenomic technology according to claim 1, characterized in that: The main body of the bioretention system is an organic glass column. The bioretention system is composed of a gravel layer, a sand layer, a soil matrix layer and a plant layer from bottom to top. The soil matrix layer includes 50% to 80% by mass of local red soil, 10% to 40% of washed sand and 10% to 20% of nutrient soil. The bioretention system is provided with multiple groups according to different types of plants planted in the plant layer and different proportions of the soil matrix layer.

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