Method for monitoring greenhouse gas emission flux of regional sewage pipe network

Abstract

The invention provides a greenhouse gas emission flux monitoring method for a regional sewage pipe network. The monitoring accuracy and efficiency are improved. The method comprises the following steps: S1, drawing a mobile monitoring route guidance diagram and constructing a drainage pipe network node data set, wherein the drainage pipe network node data set at least comprises latitude and longitude coordinates and node numbers; s2, monitoring greenhouse gas emission plume data based on the mobile monitoring route guidance diagram; s3, combining the greenhouse gas emission plume data with the drainage pipe network node data set, constructing a drainage pipe network node plume data set, and drawing an emission hot spot distribution map; s4, according to the drainage pipe network node plume data set and the emission hot spot distribution diagram, key node static box field monitoring is carried out, and the greenhouse gas emission flux is calculated. According to the method, the uncertainty of sewage pipe network flux measurement is effectively reduced by combining mobile and static monitoring technologies, and a monitoring means which is simple to operate, economical, efficient and accurate in result is provided.

Description

Technical Field

[0001] The invention relates to greenhouse gas emission monitoring technology, and in particular to a method for monitoring greenhouse gas emission flux of a regional sewage pipe network. Background Art

[0002] The urban drainage system (mainly sewage pipe network) is an important part of the entire urban water cycle and an important source of anthropogenic greenhouse gas emissions. The underground drainage pipe network has a favorable environment for the production of greenhouse gases. Taking methane (CH4) as an example, after being produced in the drainage pipe network, it is released into the atmosphere through the pipe network nodes connected to the atmosphere (such as inspection wells, pumping stations, exhaust control systems, and various treatment units of downstream sewage treatment plants).

[0003] At present, the main methods for monitoring greenhouse gas emissions at drainage network nodes include mobile measurement method, static box culvert method and exhaust gas measurement method.

[0004] However, existing monitoring methods have the following problems: (1) The mobile measurement method refers to the use of a greenhouse gas analyzer mounted on a vehicle to monitor the surface atmospheric greenhouse gas concentration in real time, and to calculate the greenhouse gas flux by tracing the emission source and selecting an empirical concentration-flux model. This method is relatively fast and direct, but the results are highly uncertain and cannot distinguish other potential biogenic greenhouse gas emission interferences.

[0005] (2) The static box culvert method refers to a method in which a static box culvert is placed in the target monitoring area to create a relatively stable closed environment and calculate the emission flux by monitoring the changes in concentration gradients. This method is relatively accurate but has a relatively low monitoring efficiency. The selection of pipeline network monitoring nodes is random and relies on experience, making it difficult to accurately cover key emission sources.

[0006] (3) The exhaust gas measurement method refers to a method of calculating the emission flux by monitoring the greenhouse gas concentration and flux at the target point. This method is relatively simple and fast, but it is only suitable for closed systems with high greenhouse gas concentrations, relatively stable flow rates and dryness. The greenhouse gas release flux of the drainage network is greatly disturbed by the airflow in the sewage pipe and on the ground, has a high water vapor content and a large background interference value, making it difficult to achieve accurate measurement.

[0007] It should be noted that the information disclosed in the above background technology section is only used for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0008] The main purpose of the present invention is to overcome the defects existing in the above-mentioned background technology and provide a method for monitoring greenhouse gas emission flux of a regional sewage pipe network.

[0009] To achieve the above object, the present invention adopts the following technical solutions: A method for monitoring greenhouse gas emission fluxes of a regional sewage pipe network comprises the following steps: S1. Draw a mobile monitoring route guidance map and construct a drainage network node data set, wherein the drainage network node data set includes at least one of latitude and longitude coordinates or node numbers; S2. monitoring greenhouse gas emission plume data based on the mobile monitoring route guidance map; S3, combining the greenhouse gas emission plume data with the drainage network node data set, constructing a drainage network node plume data set, and drawing an emission hotspot distribution map; S4. Based on the plume data set of the drainage network nodes and the emission hotspot distribution map, static box field monitoring of key nodes is carried out to calculate the greenhouse gas emission flux.

[0010] Further, step S4 includes sub-steps: M1. Calculate the plume characteristics of the drainage network nodes; M2. Determine typical nodes based on the emission plume characteristics; M3. Monitoring greenhouse gases at the typical nodes; M4. Calculate the total amount of greenhouse gas emissions based on greenhouse gas monitoring data.

[0011] Furthermore, in step S1, the mobile monitoring route guidance map is drawn through a geographic information processing platform, or is drawn through a drawing library using a programming language; the mobile monitoring route guidance map is also imported into a geographic information software system to guide greenhouse gas monitoring of regional sewage pipe networks based on mobile measurement methods, such as providing route planning, navigation, positioning and progress tracking functions during the monitoring process.

[0012] Furthermore, in step S2, based on the mobile monitoring route guidance map, a mobile measurement method is used to monitor the greenhouse gas emission plume of the regional drainage network; the moving window method is used to analyze the monitoring data, identify the greenhouse gas emission plume, and record the spatiotemporal labels of the greenhouse gas emission plume; in step S3, based on the drainage network node data set obtained in step S1, the spatiotemporal labels of the monitored greenhouse gas emission plume are added, and according to the corresponding spatiotemporal labels, the monitored greenhouse gas emission plume data are combined with the corresponding drainage network node data set to construct a plume data set of the drainage network node.

[0013] Furthermore, in step S2, the mobile monitoring method includes: Use a mobile measurement platform equipped with online greenhouse gas measuring instruments and positioning equipment such as GPS; Real-time monitoring and recording of greenhouse gas emission data of regional drainage networks through online greenhouse gas measuring instruments; Use positioning equipment to synchronously obtain precise location information of monitoring data; Combining real-time monitoring data with location information, the moving window method is used to analyze and identify greenhouse gas emission plumes; The spatiotemporal labels of each discharge plume, including location and time, are recorded and associated with the drainage network node dataset to form a plume dataset.

[0014] Furthermore, in step S2, plume identification specifically includes: Determine the length of the moving window and optimize the window length according to the movement speed of the monitoring platform; The measured plume data set is processed by percentage interpolation method to eliminate the influence of short-term temporal and spatial disturbances on the atmospheric background value, thus obtaining a smooth atmospheric background estimation data set. The standard deviation of the background estimation value within each moving window length is calculated, and a standard deviation limit is preset. For standard deviations exceeding the limit, the corresponding data are removed and interpolated to obtain a quadratically smoothed atmospheric background estimation data set. The secondary smoothed atmospheric background estimation dataset is removed from the measured plume dataset to obtain the greenhouse gas emission plume dataset of the drainage network.

[0015] Furthermore, in step S2, the drainage network plume dataset carrying spatiotemporal labels includes longitude and latitude coordinates, sampling time, and plume characteristic values; the drainage network node dataset corresponding to the spatiotemporal labels includes longitude and latitude coordinates, sampling time, and node number; the plume dataset of the drainage network node includes longitude and latitude coordinates, sampling time, plume characteristic values, and node number.

[0016] Furthermore, in step M1, based on the monitoring results of multiple mobile measurements of emission nodes, the average emission plume characteristic data of each emission node is calculated to construct a first emission plume characteristic value data set of the emission nodes; in step M2, plume classification and random sampling are performed to construct a plume data set of typical nodes in the drainage network; wherein the average emission plume characteristic value is calculated from the amplitude value or area of ​​the greenhouse gas emission plume obtained by monitoring; and the characteristic values ​​in the first emission plume characteristic value data set include the average emission plume characteristic value.

[0017] Furthermore, the plume data set of typical nodes in the drainage network includes longitude and latitude coordinates, sampling time, plume characteristic values, and node numbers.

[0018] Furthermore, step S4 specifically includes: Place closed static box culverts at typical nodes of the drainage network; A gas sampling or monitoring device is connected to the end of the static box culvert, wherein the gas sampling device takes samples through a gas bag and uses an offline gas chromatography method to analyze the changes in greenhouse gas concentrations, thereby calculating the emission flux; The monitoring device is connected to an online greenhouse gas monitor to monitor and record greenhouse gas concentrations in real time and directly obtain emission flux data; Based on the monitoring results of each typical node, the total greenhouse gas emissions of the regional drainage network are calculated, where the emissions of each emission node are converted into a proportional relationship with the emissions of the typical node, which is based on the ratio of the average plume characteristic values ​​of the typical node to each emission node.

[0019] The present invention has the following beneficial effects: The present invention provides a method for monitoring greenhouse gas emission fluxes of a regional sewage network that combines a mobile measurement method and a static box culvert method. The method can realize convenient, rapid and accurate quantitative monitoring of greenhouse gas emission fluxes of a regional sewage network. By drawing a mobile monitoring route guidance map and constructing a drainage network node data set, the present invention can accurately identify greenhouse gas escape points in the sewage network, and monitor greenhouse gas emission plumes through a mobile monitoring method, combined with a static box culvert method for in-situ monitoring, thereby reducing the uncertainty of the mobile measurement method and improving the accuracy and economy of the monitoring results. In addition, the present invention also calculates the average emission plume characteristic data of the emission node through multiple mobile measurement monitoring results, and performs plume classification and random sampling to construct a plume data set of typical nodes in the drainage network, further enhancing the representativeness and reliability of the monitoring results.

[0020] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is an overall flow chart of a method for monitoring greenhouse gas emission fluxes in a regional sewage network according to an embodiment of the present invention.

[0022] Figure 2 It is a schematic diagram of a fixed monitoring device used in the static box culvert method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope and application of the present invention.

[0024] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element. In addition, connection can be used for fixing as well as for coupling or communication.

[0025] It should be understood that the orientation or position relationship indicated by terms such as "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0026] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0027] The embodiment of the present invention provides a method for monitoring greenhouse gas emission flux of a regional sewage pipe network, comprising the following steps: S1. Draw a mobile monitoring route guidance map and construct a drainage network node data set, wherein the drainage network node data set at least includes latitude and longitude coordinates and node numbers; S2. monitoring greenhouse gas emission plume data based on the mobile monitoring route guidance map; S3, combining the greenhouse gas emission plume data with the drainage network node data set, constructing a drainage network node plume data set, and drawing an emission hotspot distribution map; S4. Based on the plume data set of the drainage network nodes and the emission hotspot distribution map, static box field monitoring of key nodes is carried out to calculate the greenhouse gas emission flux.

[0028] Specifically, see Figure 1 , a preferred embodiment of the method comprises the following steps: Draw a mobile monitoring route guidance map: Obtain the geographic spatial distribution data and road distribution data of the sewage pipe network, draw a mobile monitoring route guidance map based on the geographic spatial distribution data and road distribution data of the sewage pipe network, and construct a drainage pipe network node data set, wherein the drainage pipe network node data set includes latitude and longitude coordinates and node numbers.

[0029] In some embodiments, the mobile monitoring route guidance map can be drawn through a geographic information processing platform, or can be implemented using Python programming language through a drawing library such as matlotlib. Preferably, the mobile monitoring route guidance map is also imported into a geographic information software system to guide the greenhouse gas monitoring of the regional sewage pipe network based on the mobile measurement method, such as providing route planning, navigation, positioning and progress tracking functions by the geographic information software system during the monitoring process.

[0030] Mobile monitoring of greenhouse gas emission plumes: Based on the mobile monitoring route guidance map, the mobile measurement method is used to monitor the greenhouse gas emission plume of the regional drainage network; the moving window method is used to analyze the monitoring data, identify the greenhouse gas emission plume, and record the spatiotemporal labels (location and time) of the greenhouse gas emission plume; based on the obtained drainage network node dataset, the spatiotemporal labels of the monitored greenhouse gas emission plume are added to construct a drainage network node dataset with spatiotemporal labels; according to the corresponding spatiotemporal labels, the monitored greenhouse gas emission plume data is combined with the corresponding drainage network node dataset to construct the plume dataset of the drainage network node.

[0031] In a preferred embodiment, the mobile monitoring method specifically includes: using a mobile measurement loading platform to carry an online greenhouse gas measuring instrument and a positioning device such as a GPS; using the online greenhouse gas measuring instrument to monitor and record the greenhouse gas emission data of the regional drainage network in real time; using the positioning device to synchronously obtain the precise location information of the monitoring data; combining the real-time monitoring data and location information, using the moving window method to analyze and identify greenhouse gas emission plumes; recording the spatiotemporal labels of each emission plume, including location and time, and associating it with the drainage network node data set to form a plume data set.

[0032] In a preferred embodiment, the plume identification specifically includes: determining the length of the moving window and optimizing the window length according to the movement speed of the monitoring platform; using the percentage interpolation method to process the measured plume data set to eliminate the influence of short-term temporal and spatial disturbances on the atmospheric background value, thereby obtaining a first-time smoothed atmospheric background estimation data set; calculating the standard deviation of the background estimation value within each moving window length, and presetting the standard deviation limit. For standard deviations exceeding the limit, the corresponding data are removed and interpolated to obtain a second-time smoothed atmospheric background estimation data set; the second-time smoothed atmospheric background estimation data set is removed from the measured plume data set to obtain a greenhouse gas emission plume data set of the drainage network.

[0033] In some embodiments, a drainage network plume dataset carrying spatiotemporal labels includes longitude and latitude coordinates, sampling time, and plume characteristic values; a drainage network node dataset corresponding to the spatiotemporal labels includes longitude and latitude coordinates, sampling time, and node number; and a plume dataset of a drainage network node includes longitude and latitude coordinates, sampling time, plume characteristic values, and node number.

[0034] Calculate the average emission plume characteristics data: Based on the results of multiple mobile measurements of the emission nodes, the average emission plume characteristic data of each emission node is calculated to construct the first emission plume characteristic value data set of the emission node; plume classification and random sampling are performed to construct the plume data set of typical nodes in the drainage network; In some embodiments, the average emission plume characteristic value is calculated from the amplitude value or area of ​​the greenhouse gas emission plume obtained by monitoring; the characteristic value in the first emission plume characteristic value data set includes the average emission plume characteristic value. The typical node plume data set of the drainage network includes longitude and latitude coordinates, sampling time, plume characteristic value, and node number. Preferably, the proportion of random sampling is not less than 5%.

[0035] Static box culvert method for in-situ monitoring: The static box culvert method is used to carry out in-situ monitoring of greenhouse gases at typical nodes, and the total amount of greenhouse gas emissions from the regional drainage network is calculated.

[0036] In a preferred embodiment, the static box culvert monitoring process specifically includes: placing a closed static box culvert at a typical node of the drainage network; connecting a gas sampling or monitoring device at the end of the static box culvert, wherein the gas sampling device samples through an air bag and uses offline gas chromatography analysis to obtain changes in greenhouse gas concentration, and then calculates the emission flux; the monitoring device is connected to an online greenhouse gas monitor to monitor and record greenhouse gas concentrations in real time to directly obtain emission flux data; based on the monitoring results of each typical node, the total greenhouse gas emissions of the regional drainage network are calculated, wherein the emissions of each emission node are converted through a proportional relationship with the emissions of the typical node, and the proportional relationship is based on the ratio of the average plume characteristic value of the typical node to that of each emission node.

[0037] The method for monitoring greenhouse gas emission flux of a regional sewage pipe network of the present invention effectively reduces measurement uncertainty by combining mobile and static monitoring technologies, and provides a monitoring means that is simple to operate, economical and efficient, and has accurate results.

[0038] The specific embodiments of the present invention are further described below.

[0039] like Figure 1 As shown, a greenhouse gas monitoring method for a regional sewage network is provided to achieve reliable quantitative monitoring, and the specific process includes the following steps: Step 1: Based on the geographic spatial distribution data of the sewage network and the road distribution data, use the geographic information processing platform to draw a mobile monitoring route guidance map, and import the geographic information software system with GPS positioning function to guide the regional sewage network greenhouse gas monitoring based on mobile measurement method, and at the same time build a drainage network node data set.

[0040] Step 1.1: The geographic spatial distribution data of the sewage pipe network can be obtained through the municipal management department or other relevant competent departments; the road distribution data can be obtained based on development information sources such as OSM.

[0041] Step 1.2: Drawing a mobile monitoring route guidance map based on the sewage pipe network and road network distribution data can be based on a geographic information processing platform such as ArcGIS, or using the Python programming language and a drawing library such as matlotlib.

[0042] Step 1.3: The mobile monitoring route guidance map needs to be imported into a geographic information processing software system with GPS positioning function (such as Aowei Map, etc.) to guide the greenhouse gas monitoring of the regional sewage network based on the mobile measurement method.

[0043] Step 1.4: The drainage network node data set consists of latitude and longitude coordinates and node numbers.

[0044] Step 2: Based on the mobile monitoring route guidance map, the mobile measurement method is used to monitor the greenhouse gas emission plume of the regional drainage network, and the moving window method is used to identify the greenhouse gas emission plume. A drainage network plume dataset with spatiotemporal labels is constructed, and the plume dataset of the drainage network nodes is constructed by combining the drainage network node dataset with the same spatiotemporal labels.

[0045] Step 2.1: The mobile monitoring method is composed of a mobile measurement loading platform (such as an electric vehicle), an online greenhouse gas measuring instrument and a GPS.

[0046] Step 2.2: The plume is defined as “amplitude” or “amplitude·distance” (based on GPS coordinates) based on the local spatiotemporal fluctuation value (~2ppm) of the atmospheric greenhouse gas mixing ratio (concentration) background value after deducting the data distribution of the monitored greenhouse gases. For example, taking the CH4 plume definition reference as an example, the amplitude is greater than 0.1ppm, or the area is greater than 5ppm.m.

[0047] Step 2.3: The plume identification step is: (1) Determine the length of the moving window, which is optimized based on the movement speed; (2) In order to eliminate the influence of short-term spatial and temporal disturbances on the atmospheric background value, the measured plume data set is processed based on the percentage interpolation method to obtain a relatively smooth atmospheric background estimation data set; preferably, the number at the 5% position in the sliding window is used as the interpolation number; (3) Calculate the standard deviation of the background estimation value within each moving window length, preset the standard deviation limit, and when the standard deviation is higher than the standard deviation limit, remove the value and perform interpolation to obtain a second smoother atmospheric background estimation data set; (4) The plume dataset of the drainage network is obtained by eliminating the secondary smoother atmospheric background estimation dataset based on the measured plume dataset.

[0048] Step 2.4: The drainage network plume dataset carrying spatiotemporal labels is composed of latitude and longitude coordinates, sampling time, and plume characteristic values.

[0049] Step 2.5: The drainage network node data set with the same spatiotemporal label is composed of latitude and longitude coordinates, sampling time, and node number.

[0050] Step 2.6: The plume data set of the drainage network node is composed of latitude and longitude coordinates, sampling time, plume characteristic value, and node number.

[0051] Step 3: Based on the mobile measurement monitoring results (no less than 2 times), calculate the average emission plume characteristic data, construct the first emission plume characteristic value dataset of the emission nodes in the study area, classify and randomly sample the plumes based on relevant management needs or scientific standards, and construct a plume dataset of typical nodes in the drainage network.

[0052] Step 3.1: The average emission plume characteristic value may be calculated by amplitude value or area. The characteristic value in the first emission plume characteristic value data set is composed of the average plume characteristic value.

[0053] Step 3.2: The plume categories can be divided into small plumes, medium plumes and large plumes. Taking the greenhouse gas CH4 as an example, the corresponding amplitude values ​​of the classification can be divided into small plumes: 0.1~0.2ppm, medium plumes: 0.2~1ppm, large plumes: >1ppm; or the corresponding area value classification can be divided into small plumes: 50~500ppm.m, medium plumes: 500~1000ppm.m, large plumes: >1000ppm.m.

[0054] Step 3.3: The random sampling ratio is guided by management needs, and the recommended ratio is not less than 5%.

[0055] Step 3.4: The typical node plume dataset of the drainage network consists of latitude and longitude coordinates, sampling time, plume characteristic values, and node numbers.

[0056] Step 4: Use the static box culvert method to conduct in-situ monitoring of greenhouse gases at typical nodes, and calculate the total greenhouse gas emissions of the regional drainage network based on the monitoring results.

[0057] Step 4.1: The static box culvert monitoring process is to place a closed static box culvert at a typical node. A gas sampling or monitoring device is connected to the end of the static box culvert. The gas sampling device is used to obtain concentration changes through air bag sampling and offline gas chromatography to obtain greenhouse gas emission flux; the monitoring device is used to connect to an online greenhouse gas monitor to measure concentration and obtain greenhouse gas emission flux. Figure 2 shown.

[0058] Step 4.2: The total amount of greenhouse gas emissions from the regional drainage network can be obtained by adding up the emissions from each emission node. The emissions from each emission node are based on the emissions from the typical node and are calculated by the ratio of the average plume characteristic value of the typical node to that of each emission node.

[0059] In summary, the embodiment of the present invention proposes a new method system for monitoring greenhouse gas emission flux in a regional sewage network by innovatively integrating mobile measurement method and static monitoring technology. The method first accurately locates the greenhouse gas emission point in the sewage network by drawing a mobile monitoring route guidance map and constructing a drainage network node data set. Then, the greenhouse gas emission plume of the regional drainage network is monitored in real time by the mobile measurement method, and the monitoring data is analyzed by the moving window method to identify and record the spatiotemporal labels of the emission plume. These data are then merged with the drainage network node data set to form a drainage network node data set with spatiotemporal labels, providing detailed basic data for subsequent analysis. On this basis, the present invention further calculates the average emission plume characteristic data of the emission node through multiple mobile measurement monitoring results, constructs the first emission plume characteristic value data set of the emission node, and performs plume classification and random sampling to construct a plume data set of typical nodes in the drainage network. Finally, the static box culvert method is used at the typical node to monitor greenhouse gases in situ and calculate the total amount of greenhouse gas emissions in the regional drainage network, thereby reducing the uncertainty of the mobile measurement method and improving the accuracy of the monitoring results. The significant technical advantages of the present invention are its simplicity of operation, accuracy of monitoring results and economy. Through this method system, the present invention can not only realize the rapid and accurate quantification of greenhouse gas emissions from sewage pipe networks, but also reduce monitoring costs and improve monitoring efficiency, providing an efficient and accurate monitoring means for monitoring greenhouse gas emissions from regional sewage pipe networks in urban water cycles.

[0060] This monitoring method system can effectively reduce the uncertainty of the measurement results of greenhouse gas emissions from sewage networks based on mobile measurement methods.

[0061] The above content is a further detailed description of the present invention in combination with specific / preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, without departing from the concept of the present invention, it can also make several substitutions or modifications to these described embodiments, and these substitutions or modifications should be regarded as belonging to the protection scope of the present invention. In the description of this specification, the description of the reference terms "an embodiment", "some embodiments", "preferred embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in combination with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In the absence of mutual contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples. Although the embodiments of the present invention and their advantages have been described in detail, it should be understood that various changes, substitutions and modifications can be made herein without departing from the scope of protection of the patent application.

Claims

1. A method for monitoring greenhouse gas emission flux in a regional sewage network, characterized in that: The following steps are involved: S1. Draw a mobile monitoring route guidance map and construct a drainage network node data set, wherein the drainage network node data set at least includes latitude and longitude coordinates and node numbers; S2. monitoring greenhouse gas emission plume data based on the mobile monitoring route guidance map; S3, combining the greenhouse gas emission plume data with the drainage network node data set, constructing a drainage network node plume data set, and drawing an emission hotspot distribution map; S4. Based on the plume data set of the drainage network nodes and the emission hotspot distribution map, static box field monitoring of key nodes is carried out to calculate the greenhouse gas emission flux.

2. The method for monitoring greenhouse gas emission flux of a regional sewage pipe network according to claim 1, characterized in that: Step S4 includes the sub-steps of: M1. Calculate the plume characteristics of the drainage network nodes; M2. Determine typical nodes based on the emission plume characteristics; M3. Monitoring greenhouse gases at the typical nodes; M4. Calculate the total amount of greenhouse gas emissions based on greenhouse gas monitoring data.

3. The method for monitoring greenhouse gas emission flux of a regional sewage pipe network according to claim 1, characterized in that: In step S1, the mobile monitoring route guidance map is drawn through a geographic information processing platform, or is drawn using a programming language through a drawing library; the mobile monitoring route guidance map is also imported into a geographic information software system to guide greenhouse gas monitoring of regional sewage pipe networks based on mobile measurement methods, such as providing route planning, navigation, positioning and progress tracking functions during the monitoring process.

4. The method for monitoring greenhouse gas emission flux of a regional sewage pipe network according to claim 2, characterized in that: In step S2, based on the mobile monitoring route guidance map, the mobile measurement method is used to monitor the greenhouse gas emission plume of the regional drainage network; the moving window algorithm is used to analyze the monitoring data, identify the greenhouse gas emission plume, and record the spatiotemporal labels of the greenhouse gas emission plume; in step S3, based on the drainage network node data set obtained in step S1, the spatiotemporal labels of the monitored greenhouse gas emission plume are added, and according to the corresponding spatiotemporal labels, the monitored greenhouse gas emission plume data are combined with the corresponding drainage network node data set to construct the plume data set of the drainage network node.

5. The method for monitoring greenhouse gas emission flux of a regional sewage pipe network according to claim 4, characterized in that: In step S2, the mobile monitoring method includes: Use a mobile measurement platform equipped with online greenhouse gas measuring instruments and positioning equipment such as GPS; Real-time monitoring and recording of greenhouse gas emission data of regional drainage networks through online greenhouse gas measuring instruments; Use positioning equipment to synchronously obtain precise location information of monitoring data; Combining real-time monitoring data with location information, the moving window method is used to analyze and identify greenhouse gas emission plumes; The spatiotemporal labels of each discharge plume, including location and time, are recorded and associated with the drainage network node dataset to form a plume dataset.

6. The method for monitoring greenhouse gas emission flux of a regional sewage pipe network according to claim 4, characterized in that: In step S2, plume identification specifically includes: Determine the length of the moving window and optimize the window length according to the movement speed of the monitoring platform; The measured plume data set is processed by percentage interpolation method to eliminate the influence of short-term temporal and spatial disturbances on the atmospheric background value, thus obtaining a smooth atmospheric background estimation data set. The standard deviation of the background estimation value within each moving window length is calculated, and a standard deviation limit is preset. For standard deviations exceeding the limit, the corresponding data are removed and interpolated to obtain a quadratically smoothed atmospheric background estimation data set. The secondary smoothed atmospheric background estimation dataset is removed from the measured plume dataset to obtain the greenhouse gas emission plume dataset of the drainage network.

7. The method for monitoring greenhouse gas emission flux of a regional sewage pipe network according to claim 4, characterized in that: In step S2, the drainage network plume dataset carrying spatiotemporal labels includes longitude and latitude coordinates, sampling time, and plume characteristic values; the drainage network node dataset corresponding to the spatiotemporal labels includes longitude and latitude coordinates, sampling time, and node number; the plume dataset of the drainage network node includes longitude and latitude coordinates, sampling time, plume characteristic values, and node number.

8. The method for monitoring greenhouse gas emission flux of a regional sewage pipe network according to claim 2, characterized in that: In step M1, based on the results of multiple mobile measurements of emission nodes, the average emission plume characteristic data of each emission node is calculated to construct a first emission plume characteristic value data set of the emission nodes; in step M2, plume classification and random sampling are performed to construct a plume data set of typical nodes in the drainage network; wherein the average emission plume characteristic value is calculated from the amplitude value or area of ​​the greenhouse gas emission plume obtained by monitoring; the characteristic values ​​in the first emission plume characteristic value data set include the average emission plume characteristic value.

9. The method for monitoring greenhouse gas emission flux of a regional sewage pipe network according to claim 8, characterized in that: The typical node plume data set of the drainage network includes longitude and latitude coordinates, sampling time, plume characteristic values, and node numbers.

10. The method for monitoring greenhouse gas emission flux of a regional sewage pipe network according to any one of claims 1 to 9, characterized in that: Step S4 specifically includes: Place closed static box culverts at typical nodes of the drainage network; A gas sampling or monitoring device is connected to the end of the static box culvert, wherein the gas sampling device takes samples through a gas bag and uses an offline gas chromatography method to analyze the changes in greenhouse gas concentrations, thereby calculating the emission flux; The monitoring device is connected to an online greenhouse gas monitor to monitor and record greenhouse gas concentrations in real time and directly obtain emission flux data; Based on the monitoring results of each typical node, the total greenhouse gas emissions of the regional drainage network are calculated, where the emissions of each emission node are converted into a proportional relationship with the emissions of the typical node, and the proportional relationship is based on the ratio of the average plume characteristic value of the typical node to that of each emission node.

Images