Method for revealing low-methane emission mechanism of modular anti-clogging constructed wetland based on multi-means coupling

Abstract

The invention relates to the technical field of environmental protection, in particular to a modular anti-clogging constructed wetland low methane emission mechanism revealing method based on multi-means coupling, and the method comprises the following steps: obtaining historical data of a modular anti-clogging constructed wetland, carrying out data analysis, and identifying a plurality of key factors influencing methane emission. According to the method, a plurality of key factors influencing methane emission are identified based on historical data, and the influence of each key factor on methane emission is quantified based on the orthogonal test of the artificial wetland small-scale test comparison system, so that the optimized operation parameters are determined; water quality data, microorganism data, physicochemical index data and greenhouse gas data of the artificial wetland small-scale test comparison system during operation with optimized operation parameters are collected, the collected data are coupled, a low methane emission mechanism is revealed, powerful data support is provided for reducing methane emission, and the method is suitable for popularization and application. The artificial wetland system can be conveniently regulated and controlled, and the environmental pollution is reduced.

Description

Technical Field

[0001] The invention relates to the field of environmental protection technology, and in particular to a method for revealing the low methane emission mechanism of a modular anti-clogging artificial wetland based on multi-means coupling. Background Art

[0002] In the process of sewage treatment in traditional artificial wetland systems, greenhouse gases such as methane are often released, which has adverse effects on the environment. As an innovative artificial wetland technology, modular anti-clogging constructed wetland (MMB-CW) shows the potential to reduce methane emissions due to its unique structure and operation mechanism.

[0003] However, the current methods for reducing methane emissions from MMB-CW systems are still immature and fail to effectively control environmental pollution. Summary of the invention

[0004] In view of this, the object of the present invention is to provide a method for revealing the low methane emission mechanism of a modular anti-clogging artificial wetland based on multi-means coupling.

[0005] In a first aspect, an embodiment of the present invention provides a method for revealing the mechanism of low methane emission of a modular anti-clogging artificial wetland based on multi-means coupling, the method comprising: Obtain historical data of modular anti-clogging constructed wetlands and conduct data analysis to identify multiple key factors affecting methane emissions; Construct a small-scale artificial wetland test comparison system, and conduct an orthogonal test on the small-scale artificial wetland test system with preset orthogonal test parameters for each key factor to determine the optimized operating parameters, wherein the orthogonal test parameters at least include: carbon-nitrogen ratio, hydraulic load, temperature and plant density; Obtain water quality data, microbial data, physical and chemical index data and greenhouse gas data for the artificial wetland pilot comparison system to optimize the operating parameters; The water quality data, microbial data, physical and chemical index data and greenhouse gas data are coupled to construct a hydrodynamic-water quality model, and the microbial data and water quality data are combined to reveal the mechanism of low methane emissions.

[0006] Combined with the first aspect, for each key factor, an orthogonal test is conducted on the artificial wetland pilot system with preset orthogonal test parameters to determine the steps for optimizing the operating parameters, including: For each key factor, an orthogonal test is conducted on the artificial wetland pilot system according to the preset orthogonal test parameters to determine the impact of the key factor on methane emissions; Combined with all impact levels, key factors that are higher than the preset impact level threshold are determined as target key factors; Determine the optimized operating parameters by combining all key factors.

[0007] In combination with the first aspect, the steps of coupling water quality data, microbial data, physical and chemical index data and greenhouse gas data to build a hydrodynamic-water quality model, characterize the extended distribution of microbial communities and combine microbial data and water quality data to reveal the mechanism of low methane emissions include: Based on the hydrodynamic data in water quality data and physical and chemical index data, the particle-scale hydrodynamic model and the continuous-scale hydrodynamic model are used to describe the microscopic motion behavior of matrix particles in porous media; the microscopic motion behavior includes collision, friction, aggregation between particles, and the dynamic influence of fluid shear force on their trajectory; Establish a continuous-scale hydrodynamic-water quality model based on the microscopic motion behavior in porous media; The water quality-hydrodynamic model is coupled with microbial data and greenhouse gas data to reveal the mechanism of low methane emissions.

[0008] In combination with the first aspect, the steps to couple the water quality-hydrodynamic model with microbial data and greenhouse gas data to reveal the mechanism of low methane emissions include: The microbial analysis of the samples was carried out using 16S rRNA gene amplicon sequencing and metagenomic sequencing to obtain the temporal succession characteristics of the bacterial community structure along the experimental process; Based on the evolution trajectory of bacterial community structure along the experimental process, combined with the analysis of functional genes and metabolic pathways, the functional characteristics of the community are analyzed; The microbial analysis results were obtained by combining the temporal succession characteristics of the bacterial community structure along the experimental process with the functional characteristics of the community; Establish a correlation between microbial analysis results and water quality data, physical and chemical index data, and greenhouse gas data to reveal the mechanism of low methane emissions.

[0009] In combination with the first aspect, after coupling water quality data, microbial data, physical and chemical index data and greenhouse gas data to build a hydrodynamic-water quality model, and combining microbial data and water quality data to reveal the low methane emission mechanism, it also includes: Isotope tracing verification is used to verify the low methane emission mechanism to determine the contribution of each key factor.

[0010] Combined with the first aspect, isotope tracing verification is used to verify the low methane emission mechanism to determine the contribution of each key factor, including: A 13C-labeled carbon source was added to monitor the concentrations of methane and carbon dioxide and their isotope ratios in the system; By comparing the differences in various parameters before and after the introduction of labeled carbon sources, combined with the methane production rate and methane oxidation rate in different areas of the wetland, the contribution of each key factor was determined.

[0011] Combined with the first aspect, after verifying the low methane emission mechanism using isotope tracing to determine the contribution of each key factor, it also includes: Adjust the operating parameters of the modular anti-clogging constructed wetland based on the low methane emission mechanism to obtain the target operating parameters; Control the modular anti-clogging constructed wetland to operate at target operating parameters.

[0012] In a second aspect, the present application provides a modular anti-clogging artificial wetland low methane emission mechanism revealing device based on multi-means coupling, the device comprising: Identification module, which is used to obtain historical data of modular anti-clogging constructed wetlands and perform data analysis to identify multiple key factors affecting methane emissions; A determination module is used to construct a small-scale artificial wetland test comparison system, and to conduct an orthogonal test on the small-scale artificial wetland test system with preset orthogonal test parameters for each key factor to determine the optimized operating parameters, wherein the orthogonal test parameters at least include: carbon-nitrogen ratio, hydraulic load, temperature and plant density; An acquisition module is used to obtain water quality data, microbial data, physical and chemical index data and greenhouse gas data of the artificial wetland pilot comparison system to optimize the operation parameters; The mechanism revelation module is used to couple water quality data, microbial data, physical and chemical index data and greenhouse gas data to construct a hydrodynamic-water quality model, and combine microbial data and water quality data to reveal the mechanism of low methane emissions.

[0013] In a fourth aspect, the present application provides a readable storage medium, in which computer program instructions are stored. When the computer program instructions are read and executed by a processor, the above method is executed.

[0014] The embodiments of the present invention bring the following beneficial effects: The present application provides a method for revealing the low methane emission mechanism of a modular anti-clogging artificial wetland based on multi-means coupling, the method comprising: obtaining historical data of a modular anti-clogging artificial wetland and performing data analysis to identify multiple key factors affecting methane emissions; constructing an artificial wetland pilot comparison system, and for each key factor, conducting an orthogonal test on the artificial wetland pilot system with preset orthogonal test parameters to determine the optimized operating parameters, wherein the orthogonal test parameters include at least: carbon-nitrogen ratio, hydraulic load, temperature and plant density; obtaining water quality data, microbial data, physical and chemical index data and greenhouse gas data of the artificial wetland pilot comparison system running with optimized operating parameters; coupling water quality data, microbial data, physical and chemical index data and greenhouse gas data to construct a hydrodynamic-water quality model, and combining the microbial data and water quality data to reveal the low methane emission mechanism.

[0015] This application identifies multiple key factors that affect methane emissions based on historical data, and quantifies the impact of each key factor on methane emissions based on orthogonal experiments on artificial wetland pilot comparison systems to determine the optimal operating parameters. Then, by collecting water quality data, microbial data, physical and chemical index data, and greenhouse gas data of the artificial wetland pilot comparison system when operating with optimized operating parameters, and coupling the above collected data, the low methane emission mechanism is revealed, providing strong data support for reducing methane emissions, so as to facilitate the regulation of artificial wetland systems and reduce environmental pollution.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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, the drawings described below are some implementation methods of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A schematic flow chart of a method for revealing the mechanism of low methane emission in a modular anti-clogging constructed wetland based on multi-means coupling provided in an embodiment of the present invention; Figure 2 A schematic diagram of a modular anti-clogging constructed wetland low methane emission mechanism revealing device based on multi-means coupling provided in an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0020] Reference numerals: 10-identification module, 20-determination module, 30-acquisition module, 40-revelation module; 130 - processor, 131 - memory, 132 - bus, 133 - communication interface. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] In order to facilitate the understanding of this embodiment, the technical terms designed in this application are briefly introduced below.

[0023] Modular anti-clogging constructed wetland (MMB-CW) is a wetland system composed of multiple independent modules, each of which contains specific substrates, plants and microbial communities. These modules can be flexibly combined as needed to meet the needs of different scales and types of wastewater treatment.

[0024] After introducing the technical terms involved in this application, the application scenarios and design concepts of the embodiments of this application are briefly introduced.

[0025] In the sewage treatment process, the production and emission of methane is an important environmental issue. Methane is mainly generated through anaerobic digestion, and the amount of methane produced varies depending on temperature, pH value, sludge retention time, etc.

[0026] Although the existing MMB-CW system has demonstrated the potential for methane emission reduction by optimizing water flow paths and using efficient matrix materials, it still lacks data support and lacks stability and reliability, resulting in the failure to effectively control environmental pollution caused by methane emissions.

[0027] Based on this, an embodiment of the present application provides a method for revealing the low methane emission mechanism of a modular anti-clogging artificial wetland based on multi-means coupling.

[0028] Example 1 This application provides a method for revealing the mechanism of low methane emission in modular anti-clogging artificial wetlands based on multi-means coupling, combined with Figure 1 As shown, the method includes: S110, obtain historical data of modular anti-clogging constructed wetlands and perform data analysis to identify multiple key factors affecting methane emissions.

[0029] S120, constructing a small-scale artificial wetland comparison system, and conducting an orthogonal test on the small-scale artificial wetland system with preset orthogonal test parameters for each key factor to determine the optimized operating parameters, wherein the orthogonal test parameters include at least: carbon-nitrogen ratio, hydraulic load, temperature and plant density.

[0030] S130, obtaining water quality data, microbial data, physical and chemical index data and greenhouse gas data of the artificial wetland pilot comparison system to optimize the operation parameters.

[0031] S140, coupling water quality data, microbial data, physical and chemical index data and greenhouse gas data to construct a hydrodynamic-water quality model, and combining microbial data and water quality data to reveal the mechanism of low methane emissions.

[0032] This application collects and analyzes historical data to identify multiple key factors that affect methane emissions, constructs and executes orthogonal experiments to quantify the impact of each key factor on methane emissions to obtain optimized operating parameters, and couples the artificial wetland pilot comparison system to optimize the operating parameters. Under the operating conditions, water quality data, microbial data, physical and chemical index data, and greenhouse gas data characterize the extended distribution of microbial communities and reveal the low methane emission mechanism. In this application, a variety of technical means are combined to comprehensively and reliably reveal the low methane emission mechanism, which is conducive to improving sewage treatment efficiency.

[0033] In step S110, the data sources of historical data can be domestic and foreign literature on artificial wetland methane emissions, laboratory records, historical water quality data (such as COD, ammonia nitrogen, total phosphorus, etc.), hydrodynamic data (such as flow rate, residence time) and methane emission flux obtained by online monitoring. Preferably, a representative longer period (such as one year) is selected to cover the changes in different seasons. After preprocessing the historical data, data analysis and processing are performed (such as first performing correlation analysis to preliminarily screen possible influencing factors, then principal component analysis to perform dimensionality reduction feature extraction to narrow the scope, and finally using multiple linear regression or random forest, support vector machine and other algorithms to establish a prediction model to evaluate the contribution of each factor to methane emissions) to determine multiple key factors that affect methane emissions.

[0034] In combination with the first aspect, step S120 includes: S121, construct a small-scale artificial wetland comparison system, and conduct an orthogonal test on the small-scale artificial wetland system with preset orthogonal test parameters for each key factor to determine the impact of the key factors on methane emissions.

[0035] With reference to the modular anti-clogging artificial wetland, appropriate dimensions (such as length, width and height) and durable materials (such as PVC board, fiberglass, etc.) are set to ensure the durability and stability of the system. Based on the layout and pipeline connection of the modular anti-clogging artificial wetland, the wetland is divided into several modules and filled with corresponding fillers. Each module can independently control the hydraulic load, carbon-nitrogen ratio, temperature and plant density and conduct real-time data monitoring. After gradually introducing sewage and running stably for a period of time, data collection work for the artificial wetland pilot comparison system is carried out.

[0036] Afterwards, the orthogonal test parameters are obtained. In this embodiment, the orthogonal test parameters include carbon-nitrogen ratio, hydraulic load, temperature and plant density. Among them, the carbon-nitrogen ratio will affect microbial metabolism and nitrogen cycle process; hydraulic load will affect pollutant removal efficiency and methane production; temperature will affect microbial activity and chemical reaction rate; plant density refers to the number and distribution of plants in the wetland, and plant density will change the oxygen supply and the effect of root secretions on microorganisms. After that, determine the factor level, select the orthogonal table, and adjust the corresponding parameters in each module according to the combination setting of the orthogonal table, and record the water quality changes, microbial community structure, methane emissions and other data of each group of experiments, and then perform data analysis to find the optimal level of each key factor.

[0037] As an example, the four experimental parameters above are set horizontally: C / N ratio (carbon-nitrogen ratio): 3:1, 5:1, 7:1; Hydraulic load (HLR): 0.5 m³ / (m²·d), 1.0 m³ / (m²·d), 1.5 m³ / (m²·d) Temperature: 15°C, 20°C, 25°C; Plant density: low density (5 plants per square meter), medium density (10 plants per square meter), high density (15 plants per square meter).

[0038] Combined with the above example, there are currently four factors, each with three levels. At this time, you can choose the L9 (34) orthogonal table and arrange nine groups of experiments to fully cover the combination of each factor, as shown in Table 1: Table 1 is the experimental plan table developed based on the L9 (34) orthogonal table.

[0039]

[0040] According to Table 1, multiple groups of experiments were conducted, and the methane emission flux, water quality data, and physical and chemical data under different conditions were recorded. Variance analysis was performed on the recorded data to calculate the degree of influence of each key factor on the methane emission flux.

[0041] S112, combining all the impact levels, determining the key factors that are higher than the preset impact level threshold as target key factors.

[0042] For each key factor, the influence degree of the key factor is compared with the preset influence degree threshold, and when it is greater than the preset influence degree threshold, that is, the influence capacity of the key factor is large enough, it is used as the target key factor. For example, among the above four key factors, the influence degree of carbon-nitrogen ratio, temperature and plant density is greater than the preset influence degree threshold, and the target key factors at this time are carbon-nitrogen ratio, temperature and plant density.

[0043] S113, determining the optimized operating parameters in combination with all key factors.

[0044] The target key factors determined in step S112 and their corresponding parameter values ​​are combined to obtain the optimized operating parameters. In this way, through the design and implementation of the above orthogonal test scheme, the influence of factors such as C / N ratio, hydraulic load, temperature and plant density on the methane emission flux and water quality and physical and chemical indicators in the artificial wetland system can be systematically studied, providing a scientific basis for optimizing system operation.

[0045] In combination with the first aspect, step S130 includes: The artificial wetland pilot comparison system operates with the optimized operating parameters determined in step S120. When it reaches a relatively stable state after running for a period of time, the various sampling points of the artificial wetland pilot comparison system are regularly collected to obtain water quality data, microbial data, physical and chemical index data and greenhouse gas data.

[0046] Among them, water quality indicators include at least one of pH value, chemical oxygen demand (COD), biochemical oxygen demand (BOD), total nitrogen, and total phosphorus; microbial data include at least one of bacteria, archaea and other microbial species, dominant bacterial flora, and enzyme activity; physical and chemical indicators include at least one of temperature, humidity, wetland water depth, suspended solids (SS), and heavy metal ions; greenhouse gas data include at least greenhouse gas concentrations such as methane, carbon dioxide, and nitrous oxide. All monitoring data are transmitted to the database in real time, and preliminary sorting and cleaning are carried out to ensure the accuracy and completeness of the data.

[0047] In combination with the first aspect, step S140 includes: S141, based on the hydrodynamic data in water quality data and physical and chemical index data, uses particle-scale hydrodynamic models and continuous-scale hydrodynamic models to describe the microscopic motion behavior of matrix particles in porous media; the microscopic motion behavior includes collision, friction, aggregation between particles, and the dynamic influence of fluid shear force on their trajectory.

[0048] The discrete element method (DEM) or smoothed particle hydrodynamics (SPH) method is used to simulate the microscopic motion behavior of matrix particles in porous media and calculate the dynamic influence of fluid on the particle motion trajectory based on the fluid dynamics equation.

[0049] The model input parameters are set according to the physical properties of the actual matrix material (such as particle shape, size, and density).

[0050] Specifically, the simulation content includes collision (such as simulating the frequency and intensity of collisions between particles, considering the influence of particle surface roughness), friction (such as calculating the friction coefficient between particles, evaluating the influence of friction on particle motion trajectory), aggregation (such as analyzing the aggregation behavior of particles under the action of water flow, studying its influence on water flow resistance and methane emissions), fluid shear force (such as simulating the dynamic influence of fluid shear force on particle trajectory, and exploring its performance under different flow rates), etc.

[0051] The Navier-Stokes equation is used to describe the flow behavior of fluids in porous media; Darcy's law is used to describe the seepage phenomenon, which is applicable to fluid flow under low Reynolds number conditions. Among them, the key parameters include: porous media properties (such as porosity, permeability, effective diffusion coefficient, etc.), fluid properties (such as viscosity, density, flow rate, etc.), boundary conditions (such as pressure and flow conditions at the inlet and outlet), and simulation content (such as flow field distribution: calculating the velocity field and pressure field of the fluid in the porous medium). Mass transfer is also used to describe the transport process of pollutants or dissolved substances in porous media.

[0052] S142, Establish a continuous-scale hydrodynamic-water quality model based on the microscopic motion behavior in porous media.

[0053] The particle motion behavior (such as velocity, position, and interaction force) obtained from the particle-scale hydrodynamic model is used as input parameters, and the parameters in the continuous-scale model (such as effective diffusion coefficient and resistance coefficient) are adjusted according to the particle-scale results. The particle-scale hydrodynamic model and the continuous-scale hydrodynamic model are coupled to construct a continuous-scale hydrodynamic-water quality model. This will help to more accurately predict the migration and transformation of pollutants and methane emissions in artificial wetland systems.

[0054] In this application, a particle-scale hydrodynamic model and a continuous-scale hydrodynamic model are used to describe the movement of matrix particles and the flow of pore water, and a continuity model of porous media is established. A particle-scale water quality model and a continuous-scale water quality model are used to describe the migration and transformation process of pollutants in the pore space, and a complete continuous-scale hydrodynamic-water quality model is established to improve the accuracy and reliability of the simulation.

[0055] S143, couple the water quality-hydrodynamic model with microbial data and greenhouse gas data to reveal the mechanism of low methane emissions.

[0056] In this embodiment, various data of a small-scale artificial wetland comparison system operating with optimized parameters are collected, and the various data are coupled to construct a hydrodynamic-water quality model to reveal the mechanism of low methane emission by dripping.

[0057] In combination with the first aspect, step S143 includes: S1431, 16S rRNA gene amplicon sequencing and metagenomic sequencing were used to conduct microbial analysis on the samples, and the temporal succession characteristics of the bacterial community structure along the experimental process were obtained.

[0058] Water and sediment samples were collected at different time points and locations (such as water inlet, water outlet, and matrix layers at different depths). Sterile operations should be used during this process to avoid external contamination. Afterwards, total DNA was extracted and the quality and concentration of DNA were evaluated by gel electrophoresis and quantitative PCR to ensure the quality of subsequent sequencing.

[0059] The V3-V4 region of the 16S rRNA gene of bacteria and archaea was amplified by PCR and sequenced using high-throughput sequencing platforms such as Illumina MiSeq to generate high-quality sequence data.

[0060] Perform whole genome sequencing on the same sample to capture the complete microbial community structure and its functional gene information, and use platforms such as Illumina HiSeq or NovaSeq for large-scale sequencing to obtain more comprehensive microbiome data.

[0061] S1432, based on the evolution trajectory of bacterial community structure along the experimental process, combined with the analysis of functional genes and metabolic pathways, analyze the functional characteristics of the community.

[0062] First, data analysis was performed. Specifically, the sequencing data were clustered into Operational Taxonomic Unit (OTU), classified and annotated using databases such as Greengenes and Silva, and Alpha diversity and Beta diversity analysis was performed (to evaluate the changes in microbial diversity between different samples, and to draw PCoA or NMDS diagrams to show differences in community structure).

[0063] Functional gene analysis was then performed. Specifically, the metagenomic data were functionally annotated using databases such as KEGG and CAZy to identify functional genes related to methane metabolism (such as pmoA, mcrA, etc.); the abundance changes of these functional genes in different samples were analyzed to determine their potential role in methane emissions.

[0064] Then, the metabolic pathways are reconstructed. Specifically, the metabolic network of the microbial community is reconstructed through databases such as MetaCyc and KEGG, especially key metabolic pathways such as carbon cycle and nitrogen cycle. The focus is on studying metabolic processes related to methane production and oxidation, such as the metabolic activities of methanogens and denitrifiers.

[0065] S1433, combining the temporal succession characteristics of the bacterial community structure along the experimental process with the functional characteristics of the community, the microbial analysis results were obtained.

[0066] The results of 16S rRNA gene amplicon sequencing and metagenomic sequencing were combined to establish a time series model of the microbial community, analyze the dynamic changes of the microbial community at different time points and under different treatment conditions (i.e., temporal succession characteristics), and identify key microbial groups and their role in methane emissions.

[0067] S1434, Establish correlation between microbial analysis results and water quality data, physical and chemical indicators and greenhouse gas data to reveal the mechanism of low methane emissions.

[0068] The results of microbial analysis were correlated with the methane emission flux in the collected water quality data, physical and chemical index data, and greenhouse gas data to explore the potential connection between microbial composition and environmental factors. The results were interpreted and the main microbial groups affecting methane emissions (such as methanogens, denitrifying bacteria, etc.) were determined based on the results of community structure and functional gene analysis. The ecological functions of these microbial groups in artificial wetland systems and their impact mechanisms on water quality and methane emissions were explained.

[0069] Through the above steps, the structural and functional characteristics of the microbial community in the modular anti-clogging constructed wetland system can be systematically revealed, thus providing a scientific basis for revealing the mechanism of low methane emissions.

[0070] In combination with the first aspect, step S140 includes: S150, isotope tracer verification is used to verify the low methane emission mechanism to determine the contribution of each key factor.

[0071] Specifically, step S150 includes: S151, add a 13C-labeled carbon source to monitor the concentrations and isotope ratios of methane (CH4) and carbon dioxide (CO2) in the system.

[0072] S152, by comparing the differences in various parameters before and after the introduction of labeled carbon sources, combined with the methane production rate and methane oxidation rate in different areas of the wetland, the contribution of each key factor was determined.

[0073] After coupling multiple methods to reveal the low methane emission mechanism in step S140, the contribution of each key factor is determined by the isotope tracing verification method to accurately reveal the low methane emission mechanism. Specifically: Step S161: Inject a 13C-labeled carbon source (such as 13C-acetate, 13C-glucose) into the substrate layer through the water inlet or directly to ensure uniform distribution, and set up a control group without adding the labeled carbon source for comparative analysis. Use a portable gas analyzer (such as Picarro G2201-i) to monitor and record the concentrations of methane (CH4) and carbon dioxide (CO2) and their isotope ratios.

[0074] Afterwards, step S162 compares the changes in CH4 and CO2 concentrations before and after the introduction of the labeled carbon source, as well as the changes in their isotope ratios, analyzes data at different time points and locations, and identifies areas and periods of significant changes. Subsequently, combined with the microbial analysis results in S140, specific microbial groups are associated with changes in CH4 and CO2 concentrations to determine which microbial groups show significant changes in functional activity after the introduction of the labeled carbon source, especially those microorganisms involved in methane production and oxidation (such as methanogens and methanotrophs).

[0075] Based on the change of CH4 concentration and the isotope labeling information, mathematical models (such as the first-order kinetic model) are used to calculate the methane production rate in each region; based on the change of CH4 and CO2 concentrations and their isotope ratios, similar models are used to calculate the methane oxidation rate in each region. The reliability of the results is verified through repeated experiments and multi-point sampling, and the rate differences under different experimental conditions are compared to evaluate the impact of environmental factors on methane emissions.

[0076] Afterwards, the microbial community structure and functional gene analysis results in S140 were combined with the methane production rate and oxidation rate in order to identify key microbial groups and their role in low methane emissions, and to reveal the mechanism, listing which microorganisms or metabolic pathways inhibit methane production (for example, some denitrifying bacteria may reduce methane production by competing for substrates or producing inhibitory substances), which microorganisms (such as methanotrophic bacteria) efficiently consume the produced methane to reduce its emissions, and explaining how the interactions between different microbial groups can achieve low methane emissions (for example, the dynamic balance between methanogens and methanotrophic bacteria), thereby revealing the mechanism of low methane emissions.

[0077] In combination with the first aspect, after step S150, the method further includes: S160, adjusting the operating parameters of the modular anti-clogging constructed wetland based on the low methane emission mechanism to obtain target operating parameters.

[0078] S170, controlling the modular anti-clogging artificial wetland to operate with target operating parameters.

[0079] It is understandable that after revealing the low methane emission mechanism, the low methane emission mechanism revealed by various technical means can be coupled to adjust the operating parameters to adapt to the low methane emission mechanism, reduce the methane emissions of the modular anti-clogging artificial wetland, and benefit environmental protection.

[0080] In the second aspect, the present application also provides a modular anti-clogging artificial wetland low methane emission mechanism revealing device based on multi-means coupling, combined with Figure 2 As shown, the device includes: an identification module 10, a determination module 20, an acquisition module 30, and a disclosure module 40.

[0081] The identification module 10 is used to obtain historical data of the modular anti-clogging artificial wetland and perform data analysis to identify multiple key factors affecting methane emissions.

[0082] The determination module 20 is used to construct a small-scale artificial wetland comparison system, and for each key factor, an orthogonal test is performed on the small-scale artificial wetland system with preset orthogonal test parameters to determine the optimized operating parameters, wherein the orthogonal test parameters include at least: carbon-nitrogen ratio, hydraulic load, temperature and plant density.

[0083] The acquisition module 30 is used to obtain water quality data, microbial data, physical and chemical index data and greenhouse gas data of the artificial wetland pilot comparison system to optimize the operation parameters.

[0084] The disclosure module 40 is used to couple water quality data, microbial data, physical and chemical index data and greenhouse gas data to construct a hydrodynamic-water quality model, and to reveal the low methane emission mechanism in combination with the microbial data and water quality data.

[0085] In a third aspect, the present application provides an electronic device, Figure 3 As shown, the electronic device includes a memory 131 and a processor 130. The memory 131 is used to store computer programs, and the processor 130 runs the computer programs to enable the electronic device to perform the above method.

[0086] Furthermore, combined with Figure 3 The electronic device shown further includes a bus 132 and a communication interface 133 , and the processor 130 , the communication interface 133 and the memory 131 are connected via the bus 132 .

[0087] The memory 131 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 133 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 132 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0088] The processor 130 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 130. The above processor 130 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 131, and the processor 130 reads the information in the memory 131 and completes the steps of the method of the above embodiment in combination with its hardware.

[0089] In a fourth aspect, an embodiment of the present application provides a readable storage medium, in which computer program instructions are stored. When the computer program instructions are read and executed by a processor, the above method is executed.

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0091] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0092] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0093] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0094] Finally, it should be noted that the above embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above embodiments, those skilled in the art should understand that any person skilled in the art can still modify the technical solutions recorded in the above embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for revealing the mechanism of low methane emission in modular anti-clogging artificial wetlands based on multi-means coupling, characterized in that: The method comprises: Obtain historical data of modular anti-clogging constructed wetlands and conduct data analysis to identify multiple key factors affecting methane emissions; Constructing a small-scale artificial wetland test comparison system, and conducting an orthogonal test on the small-scale artificial wetland test system with preset orthogonal test parameters for each key factor to determine the optimized operating parameters, wherein the orthogonal test parameters at least include: carbon-nitrogen ratio, hydraulic load, temperature and plant density; Obtaining water quality data, microbial data, physical and chemical index data, and greenhouse gas data of the artificial wetland pilot comparison system operating with the optimized operating parameters; The water quality data, the microbial data, the physical and chemical index data and the greenhouse gas data are coupled to construct a hydrodynamic-water quality model, and the low methane emission mechanism is revealed by combining the microbial data and the water quality data.

2. The method according to claim 1, characterized in that For each key factor, an orthogonal test is conducted on the artificial wetland pilot system with preset orthogonal test parameters to determine the steps of optimizing the operating parameters, including: For each key factor, an orthogonal test is conducted on the artificial wetland pilot system according to preset orthogonal test parameters to determine the influence of the key factor on methane emissions; Combined with all impact levels, key factors that are higher than the preset impact level threshold are determined as target key factors; Determine the optimized operating parameters by combining all key factors.

3. The method according to claim 1, characterized in that The steps of coupling the water quality data, the microbial data, the physical and chemical index data and the greenhouse gas data to construct a hydrodynamic-water quality model, characterizing the extended distribution of the microbial community and combining the microbial data and the water quality data to reveal the low methane emission mechanism include: Based on the water quality data and the hydrodynamic data in the physical and chemical index data, a particle-scale hydrodynamic model and a continuous-scale hydrodynamic model are used to describe the microscopic motion behavior of matrix particles in the porous medium; wherein the microscopic motion behavior includes collision, friction, aggregation between particles and the dynamic influence of fluid shear force on their trajectory; Establishing the continuous-scale hydrodynamic-water quality model based on the microscopic motion behavior in the porous medium; The water quality-hydrodynamic model is coupled with the microbial data and the greenhouse gas data to reveal the mechanism of low methane emissions.

4. The method according to claim 1, characterized in that: The step of coupling the water quality-hydrodynamic model with the microbial data and the greenhouse gas data to reveal the low methane emission mechanism includes: The microbial analysis of the samples was carried out using 16S rRNA gene amplicon sequencing and metagenomic sequencing to obtain the temporal succession characteristics of the bacterial community structure along the experimental process; Based on the evolution trajectory of the bacterial community structure along the experimental process, combined with the analysis of functional genes and metabolic pathways, the functional characteristics of the community are analyzed; Combining the temporal succession characteristics of the bacterial community structure along the experimental process with the functional characteristics of the community, a microbial analysis result is obtained; A correlation relationship between the microbial analysis results and the water quality data, the physical and chemical index data and the greenhouse gas data is established to reveal the low methane emission mechanism.

5. The method according to claim 1, characterized in that After the steps of coupling the water quality data, the microbial data, the physical and chemical index data and the greenhouse gas data to construct a hydrodynamic-water quality model, and combining the microbial data and the water quality data to reveal the low methane emission mechanism, the method further includes: Isotope tracing verification is used to verify the low methane emission mechanism to determine the contribution of each of the key factors.

6. The method according to claim 5, characterized in that The steps of verifying the low methane emission mechanism by isotope tracing verification to determine the contribution of each of the key factors include: A 13C-labeled carbon source was added to monitor the concentrations of methane and carbon dioxide and their isotope ratios in the system; By comparing the differences in various parameters before and after the introduction of labeled carbon sources, combined with the methane production rate and methane oxidation rate in different areas of the wetland, the contribution of each of the key factors was determined.

7. The method according to claim 5, characterized in that After verifying the low methane emission mechanism by isotope tracing verification to determine the contribution of each of the key factors, the method further includes: Adjusting the operating parameters of the modular anti-clogging constructed wetland based on the low methane emission mechanism to obtain target operating parameters; The modular anti-clogging artificial wetland is controlled to operate at target operating parameters.

8. A modular anti-clogging artificial wetland low methane emission mechanism revealing device based on multi-means coupling, characterized in that: The device comprises: Identification module, which is used to obtain historical data of modular anti-clogging constructed wetlands and perform data analysis to identify multiple key factors affecting methane emissions; A determination module is used to construct a small-scale artificial wetland test comparison system, and to conduct an orthogonal test on the small-scale artificial wetland test system with preset orthogonal test parameters for each key factor to determine the optimized operation parameters, wherein the orthogonal test parameters at least include: carbon-nitrogen ratio, hydraulic load, temperature and plant density; An acquisition module, used for acquiring water quality data, microbial data, physical and chemical index data and greenhouse gas data of the artificial wetland pilot comparison system operating with the optimized operating parameters; The mechanism revealing module is used to couple the water quality data, the microbial data, the physical and chemical index data and the greenhouse gas data to construct a hydrodynamic-water quality model, and to reveal the low methane emission mechanism in combination with the microbial data and the water quality data.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the method according to any one of claims 1 to 6.

10. A storage medium, characterized in that: The storage medium stores computer program instructions, and when the computer program instructions are read and executed by a processor, the method according to any one of claims 1 to 6 is executed.

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