Agricultural field soil greenhouse gas layered collection and analysis method

Abstract

The application discloses a kind of farmland soil greenhouse gas layered collection and analysis method, it is related to agricultural environmental monitoring technical field, comprising the following specific steps: layered monitoring and dynamic data acquisition: multiple layer monitoring nodes covering complete soil profile are laid in the target monitoring area of farmland, the soil porosity and greenhouse gas concentration data of different depths are synchronously collected, and four-dimensional data set containing depth, porosity, concentration and time information is formed after processing;The application realizes the accurate tracing of gas migration path, rate and residence time by marker residual gradient and concentration decay curve, combined with three-dimensional gas migration model, model integrates multi-layered porosity, concentration data and soil moisture content, quantifies the gas exchange flux between different soil layers based on diffusion law and multiphase flow theory, and presents the exchange intensity heat map in visual form, solves the limitation that only single depth concentration data can be obtained by existing method.

Description

Technical Field

[0001] This invention relates to the field of agricultural environmental monitoring technology, specifically a method for stratified collection and analysis of greenhouse gases in farmland soil. Background Technology

[0002] Farmland soils play a crucial role in the global carbon cycle and climate change, serving as a significant source of greenhouse gas emissions. The emission processes of greenhouse gases are closely related to the physicochemical properties of soil profiles. The generation, diffusion, and migration of greenhouse gases at different soil depths exhibit significant stratified heterogeneity. This stratified heterogeneity is influenced by a combination of factors, including soil porosity, soil type, microbial activity, and moisture content. Accurately obtaining the distribution and migration characteristics of greenhouse gases in soil profiles is of great significance for a deeper understanding of the emission mechanisms of greenhouse gases from farmland soils, the development of scientifically sound emission reduction strategies, and the assessment of the impacts of agricultural activities on climate change.

[0003] Existing methods for collecting and analyzing greenhouse gases from farmland soils have several limitations. First, the sampling patterns are relatively fixed, often employing fixed intervals and parameters. This makes it impossible to adjust in real time according to dynamic changes in soil porosity and gas concentration. In areas where soil physicochemical properties change rapidly, this can easily lead to missing or biased data in key areas, thus affecting the accurate understanding of greenhouse gas emission characteristics. Second, effective pollution prevention measures are lacking during sampling. Residual gases along the sampling path can easily cause cross-contamination when sampling at different layers, affecting sample purity and the accuracy of test results. Furthermore, traditional methods typically only obtain gas concentration data at a single depth, making it difficult to trace the source, migration path, and residence time of gases, and also unable to quantify the intensity of gas exchange between different soil layers. This severely limits in-depth analysis of greenhouse gas emission mechanisms from farmland soils and fails to meet the refined needs of modern agricultural environmental monitoring and research. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method for stratified collection and analysis of greenhouse gases in farmland soil. This method achieves stratified monitoring and dynamic data collection by constructing a multi-layered monitoring system, forming a four-dimensional dataset of depth, porosity, concentration, and time. Based on this dataset, sampling parameters are intelligently calculated and dynamically adjusted to ensure precise adaptation of the sampling process to the actual soil conditions. Stratified time-series labeled sampling and pollution prevention treatment technologies are employed to achieve accurate source tracing and pollution control of samples. Multi-dimensional analysis techniques are used to trace the gas migration process and quantify the soil exchange intensity. Finally, comprehensive analysis results are integrated to generate a complete technical support for the entire process of greenhouse gas monitoring in farmland soil, from stratified collection to mechanism analysis. This effectively solves key problems in existing technologies and improves the accuracy and comprehensiveness of greenhouse gas monitoring and analysis in farmland soil.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for stratified collection and analysis of greenhouse gases in farmland soil, the method comprising the following specific steps: Layered monitoring and dynamic data acquisition: Multi-layered monitoring nodes covering the complete soil profile are deployed in the farmland target monitoring area to simultaneously collect soil porosity and greenhouse gas concentration data at different depths. The data are then processed to form a four-dimensional dataset containing depth, porosity, concentration and time information. Intelligent calculation and dynamic adjustment of sampling parameters: Based on the four-dimensional dataset, a correlation model of sampling interval, air permeability parameter and soil properties is constructed, and the optimal parameters are output in real time. When a sudden change in porosity or a concentration peak is detected, the sampling interval of the corresponding area is dynamically reduced and the air permeability parameter is adjusted to match the gas diffusion rate. Stratified time-series labeled sampling and anti-contamination treatment: Stratified sampling is performed according to the adjusted parameters. Unique inert gas markers are added to samples at different depths to establish a depth-time-marker correlation. The sampling path is purged with inert gas at adjacent sampling intervals to remove residual gas. Multidimensional analysis and migration process analysis of gas samples: Gas chromatography-mass spectrometry was used to detect the samples. Based on the residual amount of markers, the gas migration path and residence time were calculated. A three-dimensional migration model was constructed by combining multi-layer data to quantify the gas exchange intensity between different soil layers. Comprehensive Results Generation and Output: By integrating and analyzing data, the system generates stratified greenhouse gas concentration distribution maps, gas migration path tracing reports, and quantitative results of inter-soil exchange intensity, enabling a comprehensive analysis of the greenhouse gas emission process.

[0006] Furthermore, in the layered monitoring and dynamic data acquisition step, a depth range is set according to the monitoring requirements, and multiple monitoring nodes are deployed. The nodes are distributed at a preset vertical spacing to cover a complete profile including the soil tillage layer, plow layer and parent material layer. Each monitoring node synchronously collects soil porosity and greenhouse gas concentration. Soil porosity is obtained by calculating soil bulk density and soil particle density, and greenhouse gas concentration includes concentration data of CO2, CH4 and N2O.

[0007] Furthermore, in the intelligent calculation and dynamic adjustment step of the sampling parameters, based on the generated four-dimensional dataset, a correlation model is constructed through data processing to link the sampling interval, permeability parameters, soil porosity gradient, and gas concentration gradient. The sampling interval follows a low-gradient region using an initial interval, while in high-gradient regions, the interval is reduced inversely proportional to the gradient value, with a minimum interval of no more than 5 cm. The calculation of the permeability parameters is based on a correlation model including the reference diffusion coefficient, target depth porosity, reference porosity, and empirical coefficients. The target depth porosity is obtained by setting up monitoring nodes at the corresponding depth in the target monitoring area of ​​the farmland, collecting soil samples at that depth, and then using standard calculation methods for soil bulk density and soil particle density to obtain porosity data. The reference diffusion coefficient focuses on the greenhouse gases being monitored, prioritizing the use of recognized standard diffusion coefficients under similar soil conditions as the basic reference value. This is then corrected by considering the actual conditions of the monitoring area, such as soil type, real-time temperature, and soil moisture content. The correction is based on experimental data on the correlation between gas diffusion coefficients and soil physicochemical properties. The reference porosity is selected from similar soils in the monitoring area under conventional tillage and self-cultivation conditions. The typical porosity benchmark under certain moisture conditions is determined through two approaches: first, multiple sampling points are conducted on soil profiles within the monitoring area that have not been subject to special disturbances, and the statistical average porosity at different depths is calculated; second, the porosity reference standard for similar farmland soils in the industry is referenced, and the regional soil texture is fine-tuned to ensure that it is comparable to the porosity at the target depth under the same scenario. The empirical coefficient is determined by conducting multiple sampling experiments on plots with soil types and cultivation patterns consistent with those in the monitoring area, and the system records the actual adaptation values ​​of the air permeability parameters under different combinations of reference diffusion coefficient, target depth porosity, and reference porosity. Based on these experimental data, the correspondence between the air permeability parameters and the three factors is established through statistical fitting, thereby determining the empirical coefficient. In subsequent monitoring, the empirical coefficient is iteratively optimized based on the dynamic feedback of the four-dimensional dataset to ensure that the air permeability parameters output by the correlation model can accurately match the gas diffusion rate. When a porosity mutation or gas concentration peak is detected at a certain depth, the adjustment mechanism is triggered to reduce the sampling interval within the preset range above and below that depth to the minimum interval, and the air permeability parameters at the corresponding depth are adjusted simultaneously.

[0008] Furthermore, in the stratified time-series labeling sampling and anti-contamination treatment steps, gas sampling is performed at each depth according to the determined sampling parameters. During the sampling process, inert gas markers are added to gas samples at different depths. Rare gases or stable isotope-labeled inert gases with differentiated isotope abundances are selected. The isotope abundance differences of markers at different depths meet the identifiability requirements. Through the three-dimensional coding algorithm of the markers, a time correlation is established by establishing the gradient change of the marker concentration with the sampling time, thereby forming a three-dimensional correlation system of depth-time-marker. In addition, during the interval between adjacent stratified sampling, the sampling path is directionally purged with high-purity inert gas. The purging direction is from the sampling end to the exhaust end, and the flow rate and time are dynamically set according to the path volume to remove residual gas in the path.

[0009] Furthermore, in the hierarchical time-series marker sampling and anti-contamination treatment step, a time correlation is established by using a three-dimensional marker encoding algorithm to establish the gradient change of marker concentration with sampling time. The algorithm formula is as follows: ,in, It is the first A depth, The concentration of markers at time [time]. It is the baseline concentration of the marker. It is the depth coefficient. It is the time gradient coefficient. It is the sampling time. It is the marker type coefficient.

[0010] Furthermore, in the multidimensional analysis and migration process analysis of the gas samples, gas chromatography-mass spectrometry (GC-MS) is used to detect the collected gas samples. Gas chromatography separates greenhouse gas components and determines their concentrations, while mass spectrometry determines the isotopic abundance and residue of the markers. Based on the depth gradient and gas diffusion coefficient of the marker residues, a porosity-corrected migration rate algorithm is used to calculate the gas migration rate, tracing the gas migration path from the generation layer to the sampling layer. An adsorption-coupled residence time algorithm is used to calculate the gas residence time in the soil. Combining multi-layered concentration data, porosity distribution, and marker tracing results, a three-dimensional gas migration model is constructed based on diffusion laws and multiphase flow theory. Specifically, the four-dimensional measured data of depth, porosity, concentration, and time are coupled with simultaneously collected soil moisture content in a multidimensional manner, based on the soil moisture content of paddy fields (…). Experimental data on the correlation between diffusion coefficient and diffusion coefficient were used to construct a coupling formula. (in As a reference diffusion coefficient, For the first Porosity (1-6 represent the proportion of air phase in the soil) is used to dynamically incorporate water content into the diffusion coefficient calculation at each depth. Simultaneously, porosity and concentration data are aligned according to time series to form a dynamic parameter matrix under a depth-time two-dimensional grid, serving as the core basic data for model input. A structured grid is used to spatially discretize the soil profile, with each discrete cell corresponding one-to-one with the depth of a monitoring node. Each discrete cell is a vertical cylinder (its cross-sectional area is consistent with the sampling range of the monitoring node, and its height is the average spacing between adjacent monitoring nodes). To ensure that the physical parameters (porosity, water content, diffusion coefficient) of the discrete units can be directly obtained through interpolation or assignment of measured data, the model and measured data are accurately matched. The upper boundary (soil-atmosphere interface) is set as a concentration boundary, using the background concentration of atmospheric greenhouse gases monitored simultaneously; the lower boundary is set as a zero-flux boundary, ignoring vertical gas exchange due to the dense parent material layer and extremely weak gas migration; the lateral boundary is set as a symmetrical boundary to eliminate the influence of the plot edge effect; the initial conditions use the measured gas concentration and marker concentration data of each discrete unit in the early stage of monitoring, and the gas migration control equation is established based on the diffusion law and multiphase flow theory. (in For the first Layer gas concentration, (The gas generation / consumption source term is obtained through inversion of marker residues); the governing equations are discretized using the finite volume method, with the time step consistent with the data acquisition interval; the discretized algebraic equations are solved iteratively, and the convergence criterion is set as the concentration error between adjacent iteration steps. mol / Finally, the concentration distribution and interlayer flux of each discrete unit at different times are obtained, and the gas exchange intensity between different soil layers is quantified by inverting the soil interface conduction flux algorithm.

[0011] Furthermore, in the multidimensional analysis and migration process analysis step of the gas sample, the gas migration rate is calculated using a porosity-corrected migration rate algorithm, the formula of which is: ,in, , It is the first Gas migration rate at a depth It is the first A marker concentration gradient at a depth of [number], It is a porosity correction function. It is the first The gas diffusion coefficient at a depth, Indicates the first Porosity at a depth of [number] degrees.

[0012] Furthermore, in the stratified time-series labeled sampling and pollution prevention treatment step, the residence time of gas in the soil is calculated using an adsorption-coupled residence time algorithm, and the calculation formula is as follows: ,in, It is the first Gas residence time at a depth, It is the natural decay coefficient of the marker. It is the soil adsorption coefficient.

[0013] Furthermore, in the multidimensional analysis and migration process analysis of the gas samples, the gas exchange intensity between different soil layers is quantified by inverting the soil interface conduction flux algorithm. The algorithm formula is as follows: ,in, It is the first The and the first Gas exchange flux of the layer It is the interfacial conductivity of the soil layer. It is the first The and the first The average sampling interval of the layer, It is the first The and the first Average porosity of the layer.

[0014] Compared with existing technologies, this method for stratified collection and analysis of greenhouse gases in farmland soil has the following advantages: I. This invention achieves precise tracking of gas migration paths, rates, and residence times by combining marker residue gradients and concentration decay curves with a three-dimensional gas migration model. The model integrates multi-layer porosity, concentration data, and soil moisture content, and quantifies the gas exchange flux between different soil layers based on diffusion laws and multiphase flow theory. It also presents a heat map of exchange intensity in a visual form, overcoming the limitation of existing methods that can only obtain concentration data at a single depth. This invention can systematically reveal the complete migration process of greenhouse gases from the generation layer to the sampling layer, clarify the contribution and mechanism of each soil layer in emissions, and has important practical value for promoting the green and low-carbon transformation of agriculture.

[0015] Second, this invention constructs a multi-layer monitoring system to continuously collect a four-dimensional dataset of depth, porosity, concentration, and time. Based on this, it intelligently calculates and dynamically adjusts sampling parameters. When a sudden change in porosity or a peak in gas concentration is detected, the sampling interval is automatically reduced and the aeration parameters are adjusted to ensure high-density data collection in key areas. This allows for flexible response to dynamic changes in soil physicochemical properties and effectively avoids data loss or bias. At the same time, the stratified time-series labeling and pollution prevention treatment technology, by adding inert gas markers and combining them with directional purging paths, completely eliminates the risk of cross-contamination, ensures sample purity, and significantly improves the scientific rigor of greenhouse gas emission mechanism research.

[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a flowchart of a method for stratified collection and analysis of greenhouse gases in farmland soil; Figure 2 This is a flowchart illustrating the steps of a method for stratified collection and analysis of greenhouse gases in farmland soil, including multidimensional analysis and migration process interpretation of gas samples. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0020] Example 1: As Figure 1 As shown, representative plots were delineated within the target monitoring area of ​​the paddy field. Based on the characteristics of the paddy field soil profile (loose topsoil, dense plow pan, and stable parent material layer), the depth range for the three-layer coverage structure was determined. An integrated monitoring node was adopted, comprising a soil porosity sensor, a greenhouse gas concentration sensor, a soil moisture sensor, a built-in gas sampling pipeline, and a data transmission module. The sampling pipeline is made of corrosion-resistant polytetrafluoroethylene (PTFE), and the sensor probe has a waterproof sealing design (IP68 protection rating), suitable for the high humidity environment of the paddy field. The overall dimensions of the node are 3cm in diameter and 2cm in length. To ensure minimal disturbance to the soil profile after insertion, a portable soil drilling machine was used to drill vertically with a diameter of 4cm, at a depth consistent with the set monitoring depth. After drilling, a PVC protective sleeve with permeable holes was fitted onto the hole wall, and a 2cm thick bentonite sealing layer was wrapped around the outside of the sleeve. The bottom was sealed with a silicone plug to prevent vertical leakage of paddy field water. Then, the monitoring nodes were inserted into the sleeve sequentially at the preset depth, with the node spacing precisely controlled by the sleeve scale. After insertion, fine soil particles were filled into the gap between the sleeve and the node, compacted, and fixed to ensure close contact between the node and the soil.

[0021] Each monitoring node is equipped with a LoRa wireless communication module and a GPS timing unit, and the time of all nodes is synchronized through GPS timing. Data acquisition commands are sent uniformly by the host computer. After receiving the command, the nodes synchronously start sensor data acquisition and gas sample pre-storage. The acquisition cycle can be set remotely by the host computer. After the acquired data is cached by the node's built-in storage module, it is uploaded to the host computer in real time through the wireless communication module to avoid data loss. It also supports offline storage and is suitable for field environments without network access.

[0022] Based on the generated four-dimensional dataset and considering the rapid gas diffusion characteristics of dryland soil, a correlation model adapted to the dryland environment is constructed through data processing. This model establishes the correlation between sampling interval, aeration parameters, soil porosity gradient, and gas concentration gradient. The optimal sampling parameters for each depth are output in real time. Specifically, the aeration parameter refers to the ventilation rate of the sampling pipeline. Its calculation is based on a correlation model incorporating the reference diffusion coefficient, target depth porosity, reference porosity, and empirical coefficients. An electronic flow controller is used for adjustment. The control logic is as follows: the correlation model outputs the optimal ventilation rate value in real time; upon receiving this signal, the electronic flow controller... The internal valve opening is adjusted using pulse width modulation technology to precisely control the gas flow rate in the pipeline, ensuring that it matches the gas diffusion rate at the current depth. When a peak N2O concentration (the main gas emitted in dry land) is detected at the boundary between the cultivated layer and the plow pan (where porosity changes abruptly due to cultivation) or when the peak N2O concentration is detected, the adjustment mechanism is triggered to reduce the sampling interval within a preset range above and below that depth to a preset minimum interval. The electronic flow controller synchronously responds to the adjusted ventilation rate parameters output by the correlation model. The real-time adaptation of the ventilation parameters is completed through dynamic fine-tuning of the valve opening, ensuring that the sampling efficiency and the gas diffusion state are precisely matched.

[0023] Gas samples were taken at various depths according to the adjusted sampling parameters. During the sampling process, unique inert gas markers were added to the gas samples at different depths. The markers were rare gases with differentiated isotopic abundances, adapted to the humid environment of paddy fields, and protected from dilution or dissolution by water. A three-dimensional encoding algorithm was used to determine the marker concentration. The concentrations of markers at different depths and time points were determined. A three-dimensional correlation system of depth, time, and markers was established by utilizing the time variations in marker type, isotopic abundance, and concentration gradient. The amount added was strictly controlled to avoid interfering with greenhouse gas detection. During the intervals between adjacent stratified sampling, to address the issue of residual mud and moisture along the paddy field sampling path, high-purity inert gas was used for directional purging from the sampling end to the exhaust end. The purging parameters were calculated based on the actual volume of the sampling path and a replacement factor was set. ≥3, purge flow rate The purging rate is set to 0.5-2 L / min based on the pipe's inner diameter, length, and anti-clogging requirements, with a purging time of [missing information]. Through formula Calculations show that for high-humidity environments containing mud, a low-pressure airflow of 0.3-0.5 L / min should be used for pre-purging for 30-60 seconds to remove trace amounts of mud adhering to the inner wall of the pipeline. Then, routine purging should be performed according to the parameters calculated in the above formula. After purging, residual gas samples should be collected immediately in the path, and the residual amount of the marker should be detected using gas chromatography-mass spectrometry. The residual amount threshold is set to ≤0.01 ppm. If the detection result meets the threshold requirement, it is determined that there is no residue, and the next stratified sampling can be carried out. If it does not meet the requirement, the purging time should be extended by 50% or the flow rate increased by 30% and purging should be repeated until the residual amount meets the standard.

[0024] like Figure 2 As shown, gas chromatography-mass spectrometry (GC-MS) was used to detect the collected gas samples, focusing on separating and determining the concentration of CH4, the main emission gas from paddy fields. Simultaneously, the isotopic abundance and residue of the markers were accurately determined. Based on the depth gradient of the marker residues and the gas diffusion coefficient of the paddy field, a porosity-corrected migration rate algorithm was applied. The migration rate of CH4 from the generation layer (mostly the boundary between the topsoil and plow pan) to the sampling layer was calculated to clearly trace its migration path using an adsorption-coupled residence time algorithm. By fitting the kinetic equations and obtaining the residence time of gas in moist soil, and combining multi-layered data and porosity distribution, a three-dimensional gas migration model adapted to the paddy field environment is constructed based on diffusion laws and multiphase flow theory. The four-dimensional measured data of depth, porosity, concentration, and time are coupled with synchronously collected soil moisture content in a multi-dimensional manner. Based on the soil moisture content of paddy fields... Experimental data relating to the diffusion coefficient were used to construct a coupling formula. (in As a reference diffusion coefficient, For the first Porosity (1-6 represent the proportion of air phase in the soil) is used to dynamically incorporate water content into the diffusion coefficient calculation at each depth. Simultaneously, porosity and concentration data are aligned according to time series to form a dynamic parameter matrix under a depth-time two-dimensional grid, serving as the core basic data for model input. A structured grid is used to spatially discretize the soil profile, with each discrete cell corresponding one-to-one with the depth of a monitoring node. Each discrete cell is a vertical cylinder (its cross-sectional area is consistent with the sampling range of the monitoring node, and its height is the average spacing between adjacent monitoring nodes). To ensure that the physical parameters (porosity, water content, diffusion coefficient) of the discrete units can be directly obtained through interpolation or assignment of measured data, the model and measured data are accurately matched. The upper boundary (soil-atmosphere interface) is set as a concentration boundary, using the background concentration of atmospheric greenhouse gases monitored simultaneously; the lower boundary is set as a zero-flux boundary, ignoring vertical gas exchange due to the dense parent material layer and extremely weak gas migration; the lateral boundary is set as a symmetrical boundary to eliminate the influence of the plot edge effect; the initial conditions use the measured gas concentration and marker concentration data of each discrete unit in the early stage of monitoring, and the gas migration control equation is established based on the diffusion law and multiphase flow theory. (in Let be the gas concentration in the i-th layer. (The gas generation / consumption source term is obtained through inversion of marker residues); the governing equations are discretized using the finite volume method, with the time step consistent with the data acquisition interval; the discretized algebraic equations are solved iteratively, and the convergence criterion is set as the concentration error between adjacent iteration steps. mol / Finally, the concentration distribution and interlayer flux of each discrete element at different times were obtained, and the flux transfer algorithm at the soil interface was used. Quantify the intensity of gas exchange between different soil layers, and fully consider the influence of soil moisture on gas exchange.

[0025] By integrating and analyzing data, a stratified greenhouse gas concentration distribution map is generated, which intuitively presents the peak concentration characteristics of CH4 near the plow pan; a gas migration path tracing report is generated, which clarifies the main generation depth and migration direction of CH4; and the quantitative results of inter-soil exchange intensity are output. These results can be directly used to guide field measures such as paddy field water management and fertilization timing optimization, providing precise data support for the research and development of greenhouse gas emission reduction technologies for paddy fields.

[0026] Example 2: In the target monitoring area of ​​dryland cornfields, considering the impact of corn growth stages on the soil environment, a monitoring range covering the corn root activity layer was delineated, and multiple monitoring nodes were set up. The nodes were distributed at a preset vertical spacing to adapt to the alternating dry and wet environmental characteristics of dryland soil, ensuring stable data collection under both dry and wet soil conditions. The monitoring range of a single node met the spatial representativeness requirements of the plot. Each monitoring node simultaneously collected soil porosity and CO2, CH4, and N2O concentration data at different depths. Porosity was obtained through the standard calculation method of soil bulk density and particle density. Data was continuously collected at set time intervals. To address the detection interference caused by the easy loosening of dryland soil particles, outliers were filtered out and smoothed to form a four-dimensional dataset of depth-porosity-concentration-time, covering the full-cycle monitoring needs from corn sowing to harvest.

[0027] Based on the generated four-dimensional dataset and considering the rapid gas diffusion characteristics of dryland soil, a correlation model adapted to the dryland environment is constructed through data processing. This model establishes the correlation between sampling interval, aeration parameters, soil porosity gradient, and gas concentration gradient. The optimal sampling parameters for each depth are output in real time. Specifically, the aeration parameter refers to the ventilation rate of the sampling pipeline. Its calculation is based on a correlation model incorporating the reference diffusion coefficient, target depth porosity, reference porosity, and empirical coefficients. An electronic flow controller is used for adjustment. The control logic is as follows: the correlation model outputs the optimal ventilation rate value in real time; upon receiving this signal, the electronic flow controller... The internal valve opening is adjusted using pulse width modulation technology to precisely control the gas flow rate in the pipeline, ensuring that it matches the gas diffusion rate at the current depth. When a peak N2O concentration (the main gas emitted in dry land) is detected at the boundary between the cultivated layer and the plow pan (where porosity changes abruptly due to cultivation) or when the peak N2O concentration is detected, the adjustment mechanism is triggered to reduce the sampling interval within a preset range above and below that depth to a preset minimum interval. The electronic flow controller synchronously responds to the adjusted ventilation rate parameters output by the correlation model. The real-time adaptation of the ventilation parameters is completed through dynamic fine-tuning of the valve opening, ensuring that the sampling efficiency and the gas diffusion state are precisely matched.

[0028] Gas sampling was conducted at various depths according to the adjusted sampling parameters. During the sampling process, unique inert gas markers were added to gas samples from different depths. Stable isotope-labeled inert gases were selected as markers, and the isotope abundance of the markers at different depths showed clear and identifiable differences. These markers were also well-suited to the environment of dryland soils with a high particle content and were not easily adsorbed by soil particles. A three-dimensional correlation between depth, time, and markers was achieved by analyzing the time-varying changes in marker type, isotope abundance, and concentration gradient. The amount added was strictly controlled to avoid interfering with greenhouse gas detection. During the intervals between adjacent stratified sampling, to address the issue of residual dryland soil particles, high-purity inert gas was used for directional purging from the sampling end to the exhaust end. The purging parameters were calculated based on the actual volume of the sampling path and a replacement factor was set. ≥3, purge flow rate The purging rate is set to 0.5-2 L / min based on the pipe's inner diameter, length, and anti-clogging requirements, with a purging time of [missing information]. Through formula Calculations show that for high-humidity environments containing mud, a low-pressure airflow of 0.3-0.5 L / min should be used for pre-purging for 30-60 seconds to remove trace amounts of mud adhering to the inner wall of the pipeline. Then, routine purging should be performed according to the parameters calculated in the above formula. After purging, residual gas samples should be collected immediately in the path, and the residual amount of the marker should be detected using gas chromatography-mass spectrometry. The residual amount threshold is set to ≤0.01 ppm. If the detection result meets the threshold requirement, it is determined that there is no residue, and the next stratified sampling can be carried out. If it does not meet the requirement, the purging time should be extended by 50% or the flow rate increased by 30% and purging should be repeated until the residual amount meets the standard.

[0029] Gas chromatography-mass spectrometry (GC-MS) was used to analyze the collected gas samples, focusing on separating and determining the components and concentrations of CO2 and N2O, the main emission gases from dryland. Simultaneously, the residual amount and isotopic abundance of markers were accurately measured. Based on the depth gradient of marker residues and the gas diffusion coefficient in dryland, the migration rates of the two gases were calculated, tracing their migration paths from the generation layer to the sampling layer. The residence time of gases in dryland soil was obtained by fitting the kinetic equations to the marker concentration decay curves. Combining multi-layered data and porosity distribution, a three-dimensional gas migration model adapted to the dryland environment was constructed based on diffusion laws and multiphase flow theory. The model incorporated the influence of soil texture and moisture content fluctuations on gas exchange. The model's spatial resolution matched the sampling interval, and its temporal resolution matched the data acquisition frequency, accurately quantifying the gas exchange intensity between different soil layers.

[0030] By integrating and analyzing data, a stratified greenhouse gas concentration distribution map is generated, clearly showing the concentration distribution characteristics of CO2 and N2O in the topsoil. A gas migration path tracing report is generated, clarifying the main source depth, migration direction and residence time of the two gases. The quantitative results of inter-soil exchange intensity are output. This set of results can be used to optimize fertilization methods, farming systems and mulching measures in dryland maize fields, providing a scientific basis for improving the carbon sequestration capacity of dryland and reducing greenhouse gas emissions.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for stratified collection and analysis of greenhouse gases in farmland soil, characterized in that, The method includes the following specific steps: Layered monitoring and dynamic data acquisition: Multi-layered monitoring nodes covering the complete soil profile are deployed in the farmland target monitoring area to simultaneously collect soil porosity and greenhouse gas concentration data at different depths. The data are then processed to form a four-dimensional dataset containing depth, porosity, concentration and time information. Intelligent calculation and dynamic adjustment of sampling parameters: Based on the four-dimensional dataset, a correlation model of sampling interval, air permeability parameter and soil properties is constructed, and the optimal parameters are output in real time. When a sudden change in porosity or a concentration peak is detected, the sampling interval of the corresponding area is dynamically reduced and the air permeability parameter is adjusted to match the gas diffusion rate. Stratified time-series labeled sampling and anti-contamination treatment: Stratified sampling is performed according to the adjusted parameters. Unique inert gas markers are added to samples at different depths to establish a depth-time-marker correlation. The sampling path is purged with inert gas at adjacent sampling intervals to remove residual gas. Multidimensional analysis and migration process analysis of gas samples: Gas chromatography-mass spectrometry was used to detect the samples. Based on the residual amount of markers, the gas migration path and residence time were calculated. A three-dimensional migration model was constructed by combining multi-layer data to quantify the gas exchange intensity between different soil layers. Comprehensive Results Generation and Output: By integrating and analyzing data, the system generates stratified greenhouse gas concentration distribution maps, gas migration path tracing reports, and quantitative results of inter-soil exchange intensity, enabling a comprehensive analysis of the greenhouse gas emission process.

2. The method for stratified collection and analysis of greenhouse gases in farmland soil according to claim 1, characterized in that, In the layered monitoring and dynamic data acquisition steps, a depth range is set according to the monitoring requirements, and multiple monitoring nodes are set up. The nodes are distributed at a preset vertical spacing to cover a complete profile including the soil tillage layer, plow layer and parent material layer. Each monitoring node simultaneously collects soil porosity and greenhouse gas concentration. Soil porosity is obtained by calculating soil bulk density and soil particle density, and greenhouse gas concentration includes concentration data of CO2, CH4 and N2O.

3. The method for stratified collection and analysis of greenhouse gases in farmland soil according to claim 1, characterized in that, In the intelligent calculation and dynamic adjustment step of the sampling parameters, based on the generated four-dimensional dataset, a correlation model is constructed through data processing to link the sampling interval, air permeability parameters, soil porosity gradient, and gas concentration gradient. The sampling interval follows the principle of using an initial interval in low gradient regions and reducing the interval inversely proportional to the gradient value in high gradient regions, with the minimum interval not exceeding 5 cm. The calculation of air permeability parameters is based on a correlation model that includes the reference diffusion coefficient, target depth porosity, reference porosity, and empirical coefficients. When a porosity mutation or gas concentration peak is detected at a certain depth, an adjustment mechanism is triggered to reduce the sampling interval within a preset range above and below that depth to the minimum interval and simultaneously adjust the air permeability parameters at the corresponding depth.

4. The method for stratified collection and analysis of greenhouse gases in farmland soil according to claim 1, characterized in that, In the stratified time-series labeling sampling and anti-contamination treatment steps, gas sampling is performed at each depth according to the determined sampling parameters. During the sampling process, inert gas markers are added to gas samples at different depths. Rare gases or stable isotope-labeled inert gases with differentiated isotope abundances are selected. The isotope abundance differences of the markers at different depths meet the identifiability requirements. Through the three-dimensional coding algorithm of the markers, the gradient change of the marker concentration with the sampling time is established to establish a time correlation, forming a three-dimensional correlation system of depth-time-marker. In addition, during the interval between adjacent stratified sampling, the sampling path is directionally purged with high-purity inert gas. The purging direction is from the sampling end to the exhaust end, and the flow rate and time are dynamically set according to the path volume to remove residual gas in the path.

5. The method for stratified collection and analysis of greenhouse gases in farmland soil according to claim 4, characterized in that, In the hierarchical time-series marker sampling and anti-contamination treatment step, a time correlation is established by using a three-dimensional marker encoding algorithm to establish the gradient change of marker concentration with sampling time. The algorithm formula is as follows: ,in, It is the first A depth, The concentration of markers at time [time]. It is the baseline concentration of the marker. It is the depth coefficient. It is the time gradient coefficient. It is the sampling time. It is the marker type coefficient.

6. The method for stratified collection and analysis of greenhouse gases in farmland soil according to claim 1, characterized in that, In the gas sample multidimensional analysis and migration process analysis step, gas chromatography-mass spectrometry is used to detect the collected gas sample. The greenhouse gas components are separated and their concentrations are determined by gas chromatography, and the isotopic abundance and residual amount of the marker are determined by mass spectrometry. Based on the depth gradient of the residual amount of the marker and the gas diffusion coefficient, the gas migration rate is calculated by the porosity-corrected migration rate algorithm to trace the migration path of the gas from the generation layer to the sampling layer. The residence time of gas in soil was calculated by adsorption-coupled residence time algorithm. Combined with multi-layer concentration data, porosity distribution and marker traceability results, a three-dimensional gas migration model was constructed based on diffusion law and multiphase flow theory. The gas exchange intensity between different soil layers was inverted and quantified by soil interface conduction flux algorithm.

7. The method for stratified collection and analysis of greenhouse gases in farmland soil according to claim 6, characterized in that, In the multidimensional analysis and migration process analysis step of the gas sample, the gas migration rate is calculated using a porosity-corrected migration rate algorithm, the formula of which is: ,in, , It is the first Gas migration rate at a depth It is the first A marker concentration gradient at a depth of [number], It is a porosity correction function. It is the first The gas diffusion coefficient at a depth, Indicates the first Porosity at a depth of [number] degrees.

8. The method for stratified collection and analysis of greenhouse gases in farmland soil according to claim 6, characterized in that, In the stratified time-series labeled sampling and pollution prevention treatment steps, the residence time of gas in the soil is calculated using an adsorption-coupled residence time algorithm. The calculation formula is as follows: ,in, It is the first Gas residence time at a depth, It is the natural decay coefficient of the marker. It is the soil adsorption coefficient.

9. The method for stratified collection and analysis of greenhouse gases in farmland soil according to claim 6, characterized in that, In the multidimensional analysis and migration process analysis of the gas samples, the gas exchange intensity between different soil layers is quantified by inverting the soil interface conduction flux algorithm. The algorithm formula is as follows: ,in, It is the first The and the first Gas exchange flux of the layer It is the interfacial conductivity of the soil layer. It is the first The and the first The average sampling interval of the layer, It is the first The and the first Average porosity of the layer For the first Layer gas concentration, It is the first Layer gas concentration.

10. A soil greenhouse gas stratification collection and analysis device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, characterized in that, When the processor executes the computer program, it implements a method for stratified collection and analysis of greenhouse gases in farmland soil as described in any one of claims 1-9.

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