A method for monitoring greenhouse gas emission fluxes from a regional sewer network

Abstract

The present application provides a kind of regional sewer network greenhouse gas emission flux monitoring method, improve the accuracy and efficiency of monitoring.Method includes:S1, draw mobile monitoring route guide chart and construct drainage pipe network node dataset, at least including longitude and latitude coordinates and node number;S2, based on the mobile monitoring route guide chart, monitoring greenhouse gas emission plume data;S3, the greenhouse gas emission plume data is combined with the drainage pipe network node dataset, constructs drainage pipe network node plume dataset, draws emission hotspot distribution diagram;S4, according to the drainage pipe network node plume dataset and emission hotspot distribution diagram, carries out key node static box field monitoring, calculates greenhouse gas emission flux.The method is combined by mobile and static monitoring technology, effectively reduces the uncertainty of sewage pipe network flux measurement, provides a kind of simple operation, economical and efficient and accurate monitoring means.

Description

TECHNICAL FIELD

[0001] The present application relates to greenhouse gas emission monitoring technology, in particular to a regional sewer network greenhouse gas emission flux monitoring method. BACKGROUND

[0002] The urban drainage system (mainly sewer network) is an important part of the entire urban water cycle and an important source of anthropogenic greenhouse gas emissions. The underground sewer network provides a favorable environment for the production of greenhouse gases. For example, methane (CH4) is released into the atmosphere through sewer network nodes (such as inspection wells, pump stations, exhaust control systems, and downstream sewage treatment plant units) that are connected to the atmosphere.

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

[0004] However, the existing monitoring methods have the following problems:

[0005] (1) The mobile measurement method involves using a vehicle equipped with a greenhouse gas analyzer to monitor the concentration of greenhouse gases in the atmosphere in real time. By tracing the source of the emissions and selecting a concentration-flux empirical model, the greenhouse gas flux can be calculated. This method is relatively quick and direct, but the results are uncertain, and it cannot distinguish other potential biological sources of greenhouse gas emissions.

[0006] (2) The static box culvert method involves placing a static box culvert in the target monitoring area to create a relatively stable closed environment. By monitoring the concentration gradient changes, the emission flux can be calculated. This method is relatively accurate but has low monitoring efficiency. The selection of sewer network monitoring nodes is random and relies on experience, making it difficult to accurately cover key emission sources.

[0007] (3) The exhaust gas measurement method involves monitoring the concentration and flux of greenhouse gases at the target point to calculate the emission flux. This method is relatively simple and fast, but it is suitable for closed systems with high greenhouse gas concentrations, stable flow rates, and dry conditions. The sewer network greenhouse gas release flux is disturbed by the flow in the sewer and the ground air, and the water vapor content is high, with a large background interference value, making it difficult to achieve accurate measurement.

[0008] It should be noted that the information disclosed in the above background section is only for understanding the background of the present application, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0009] The main purpose of the present application is to overcome the defects in the background art and provide a regional sewer network greenhouse gas emission flux monitoring method.

[0010] To achieve the above purpose, the present application adopts the following technical solutions:

[0011] A method for monitoring greenhouse gas emission flux of a regional sewer network, comprising the following steps:

[0012] S1, drawing a mobile monitoring route guide map and constructing a sewer network node dataset, the sewer network node dataset at least comprising one of longitude and latitude coordinates or node number;

[0013] S2, monitoring greenhouse gas emission plume data based on the mobile monitoring route guide map;

[0014] S3, combining the greenhouse gas emission plume data with the sewer network node dataset to construct a sewer network node plume dataset, and drawing an emission hotspot distribution map;

[0015] S4, performing field monitoring of key nodes by static box according to the sewer network node plume dataset and the emission hotspot distribution map, and calculating greenhouse gas emission flux.

[0016] Further, step S4 comprises the following sub-steps:

[0017] M1, calculating plume characteristics of the sewer network node;

[0018] M2, determining a typical node based on the emission plume characteristics;

[0019] M3, monitoring greenhouse gas at the typical node;

[0020] M4, calculating total greenhouse gas emission according to the greenhouse gas monitoring data.

[0021] Further, in step S1, the mobile monitoring route guide map is drawn by a geographic information processing platform, or is drawn by using a programming language through a drawing library; the mobile monitoring route guide map is also imported into a geographic information software system to guide the greenhouse gas monitoring of the regional sewer network based on the mobile measurement method, such as providing route planning, navigation, positioning and progress tracking functions during the monitoring process.

[0022] Further, in step S2, the mobile measurement method is used to monitor the greenhouse gas emission plume of the regional sewer network based on the mobile monitoring route guide map; the mobile window method is used to analyze the monitoring data, identify the greenhouse gas emission plume, and record the space-time label of the greenhouse gas emission plume; in step S3, the space-time label of the monitored greenhouse gas emission plume is added to the sewer network node dataset obtained in step S1, and the monitored greenhouse gas emission plume data is combined with the corresponding sewer network node dataset according to the corresponding space-time label to construct the plume dataset of the sewer network node.

[0023] Further, in step S2, the mobile monitoring method comprises:

[0024] Using a mobile measurement loading platform to carry an online greenhouse gas measurement instrument and a positioning device such as a GPS;

[0025] Real-time monitoring and recording of greenhouse gas emission data of the regional sewer network by the online greenhouse gas measurement instrument;

[0026] Synchronous acquisition of accurate position information of the monitoring data by the positioning device;

[0027] Combining real-time monitoring data and position information, using a moving window method to analyze and identify greenhouse gas emission plumes;

[0028] Recording the space-time label of each emission plume, including position and time, and associating it with the sewer network node data set to form a plume data set.

[0029] Further, in step S2, the plume identification specifically includes:

[0030] Determining the length of the moving window and optimizing the window length according to the movement speed of the monitoring platform;

[0031] Using a percentage interpolation method to process the determined plume data set to exclude the influence of short-term space-time disturbances on the atmospheric background value, thereby obtaining a first smoothed atmospheric background estimation data set;

[0032] Calculating the standard deviation of the background estimation value within each moving window length and presetting a standard deviation limit value, for the standard deviation exceeding the limit value, removing the corresponding data and performing interpolation processing to obtain a second smoothed atmospheric background estimation data set;

[0033] Removing the second smoothed atmospheric background estimation data set from the determined plume data set to obtain the greenhouse gas emission plume data set of the sewer network.

[0034] Further, in step S2, the sewer plume data set with space-time labels includes longitude and latitude coordinates, sampling time, and plume characteristic values; the sewer network node data set corresponding to the space-time labels includes longitude and latitude coordinates, sampling time, and node number; the plume data set of the sewer network node includes longitude and latitude coordinates, sampling time, plume characteristic values, and node number.

[0035] Further, in step M1, based on the monitoring results of multiple mobile measurements of the 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 node; in step M2, plume classification and random sampling are performed to construct a typical node plume data set of the sewer 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.

[0036] Further, the typical node plume data set of the sewer network comprises longitude and latitude coordinates, sampling time, plume characteristic value, and node number.

[0037] Further, the step S4 specifically comprises:

[0038] placing a sealed static box culvert at a typical node of the sewer network;

[0039] connecting a gas sampling or monitoring device at the end of the static box culvert, wherein the gas sampling device samples through a gas bag and is analyzed by an offline gas chromatography to obtain the concentration change of the greenhouse gas and further calculate the emission flux;

[0040] the monitoring device is connected to an online greenhouse gas monitor to monitor and record the concentration of the greenhouse gas in real time and directly obtain the emission flux data;

[0041] calculating the total greenhouse gas emission of the regional sewer network according to the monitoring results of each typical node, wherein the emission of each emission node is converted through a proportional relationship with the emission of the typical node, and the proportional relationship is based on the ratio of the average plume characteristic value of the typical node to each emission node.

[0042] The present application has the following beneficial effects:

[0043] The present application provides a regional sewer network greenhouse gas emission flux monitoring method combining mobile measurement method and static box culvert method, which can realize convenient, rapid and accurate quantitative monitoring of the regional sewer network greenhouse gas emission flux. By drawing a mobile monitoring route guide map and constructing a sewer network node data set, the present application can accurately identify the sewer network greenhouse gas emission points, monitor the greenhouse gas emission plume through the mobile monitoring method, and combine the 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 application 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 typical node plume data set of the sewer network, which further enhances the representativeness and reliability of the monitoring results.

[0044] Other beneficial effects of the embodiments of the present application will be further described below. DETAILED DESCRIPTION

[0045] Figure 1 The present application provides a regional sewer network greenhouse gas emission flux monitoring method combining mobile measurement method and static box culvert method, which can realize convenient, rapid and accurate quantitative monitoring of the regional sewer network greenhouse gas emission flux. By drawing a mobile monitoring route guide map and constructing a sewer network node data set, the present application can accurately identify the sewer network greenhouse gas emission points, monitor the greenhouse gas emission plume through the mobile monitoring method, and combine the 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 application 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 typical node plume data set of the sewer network, which further enhances the representativeness and reliability of the monitoring results.

[0046] Figure 2 The present application provides a regional sewer network greenhouse gas emission flux monitoring method combining mobile measurement method and static box culvert method, which can realize convenient, rapid and accurate quantitative monitoring of the regional sewer network greenhouse gas emission flux. By drawing a mobile monitoring route guide map and constructing a sewer network node data set, the present application can accurately identify the sewer network greenhouse gas emission points, monitor the greenhouse gas emission plume through the mobile monitoring method, and combine the 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 application 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 typical node plume data set of the sewer network, which further enhances the representativeness and reliability of the monitoring results. DETAILED DESCRIPTION

[0047] The embodiments of the present application will be described in detail below. It should be emphasized that the following description is merely exemplary and is not intended to limit the scope of the application or its applications.

[0048] It should be noted that when an element is referred to as being "fixed" or "set" 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, the connection can be for fixing or for coupling or communicating.

[0049] It should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the embodiments of the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0050] In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined with "first", "second", etc. can explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0051] The embodiments of the present application provide a method for monitoring greenhouse gas emission flux of a regional sewer network, comprising the following steps:

[0052] S1, drawing a mobile monitoring route guide map and constructing a sewer network node data set, wherein the sewer network node data set at least includes longitude and latitude coordinates and node numbers;

[0053] S2, monitoring greenhouse gas emission plume data based on the mobile monitoring route guide map;

[0054] S3, combining the greenhouse gas emission plume data with the sewer network node data set to construct a sewer network node plume data set, and drawing an emission hotspot distribution map;

[0055] S4, performing field monitoring of key nodes static chambers according to the sewer network node plume data set and the emission hotspot distribution map, and calculating greenhouse gas emission flux.

[0056] Specifically, refer to Figure 1The preferred embodiment of the method comprises the following steps:

[0057] Drawing a mobile monitoring route guide map:

[0058] Obtaining sewer network geospatial distribution data and road distribution data, drawing a mobile monitoring route guide map based on the sewer network geospatial distribution data and road distribution data, and constructing a sewer network node dataset, which includes latitude and longitude coordinates and node numbers.

[0059] In some embodiments, the mobile monitoring route guide map can be drawn by a geographic information processing platform, or realized by using Python programming language and drawing libraries such as matlotlib. Preferably, the mobile monitoring route guide map is also imported into a geographic information software system to guide regional sewer network greenhouse gas monitoring based on mobile measurement method, such as providing route planning, navigation, positioning and progress tracking functions during monitoring by the geographic information software system.

[0060] Monitoring greenhouse gas emission plume by mobile monitoring method:

[0061] Based on the mobile monitoring route guide map, the mobile measurement method is used to monitor the regional sewer network greenhouse gas emission plume; the mobile window method is used to analyze the monitoring data, identify the greenhouse gas emission plume, and record the spatiotemporal tags (position and time) of the greenhouse gas emission plume; based on the obtained sewer network node dataset, the spatiotemporal tags of the monitored greenhouse gas emission plume are added to construct a sewer network node dataset carrying spatiotemporal tags; according to the corresponding spatiotemporal tags, the monitored greenhouse gas emission plume data is combined with the corresponding sewer network node dataset to construct a sewer network node plume dataset.

[0062] In preferred embodiments, the mobile monitoring method specifically comprises: using a mobile measurement loading platform to carry an online greenhouse gas measuring instrument and a positioning device such as GPS; using the online greenhouse gas measuring instrument to monitor and record the greenhouse gas emission data of the regional sewer network in real time; using the positioning device to synchronously obtain the accurate position information of the monitoring data; combining the real-time monitoring data and the position information, using the mobile window method to analyze and identify the greenhouse gas emission plume; recording the spatiotemporal tags of each emission plume, including position and time, and associating with the sewer network node dataset to form a plume dataset.

[0063] In a preferred embodiment, the plume identification specifically comprises: determining the length of the moving window, and optimizing the window length according to the motion speed of the monitoring platform; processing the determined plume data set by using the percentage interpolation method to exclude the influence of short-term temporal and spatial disturbances on the atmospheric background value, thereby obtaining a first smoothed atmospheric background estimation data set; calculating the standard deviation of the background estimation value within each moving window length, and presetting a standard deviation limit value, for the standard deviation exceeding the limit value, the corresponding data is removed and interpolated to obtain a second smoothed atmospheric background estimation data set; removing the second smoothed atmospheric background estimation data set from the determined plume data set to obtain the greenhouse gas emission plume data set of the drainage pipe network.

[0064] In some embodiments, the drainage pipe network plume data set carrying the spatio-temporal label comprises longitude and latitude coordinates, sampling time, and plume characteristic values; the drainage pipe network node data set corresponding to the spatio-temporal label comprises longitude and latitude coordinates, sampling time, and node number; the plume data set of the drainage pipe network node comprises longitude and latitude coordinates, sampling time, plume characteristic values, and node number.

[0065] Calculate the average emission plume characteristic data:

[0066] Based on the monitoring results of multiple moving measurements of the emission node, calculate the average emission plume characteristic data of each emission node to construct a first emission plume characteristic value data set of the emission node; perform plume classification and random sampling to construct a drainage pipe network typical node plume data set;

[0067] 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 values in the first emission plume characteristic value data set include the average emission plume characteristic value. The drainage pipe network typical node plume data set includes longitude and latitude coordinates, sampling time, plume characteristic values, and node number. Preferably, the proportion of random sampling is not less than 5%.

[0068] In-situ monitoring by static box culvert method:

[0069] In-situ monitoring of greenhouse gases at the typical node by using the static box culvert method to calculate the total amount of greenhouse gas emissions of the regional drainage pipe network.

[0070] In a preferred embodiment, the static chamber method monitoring process specifically comprises: placing a sealed static chamber at a typical node of the drainage network; connecting a gas sampling or monitoring device at the end of the static chamber, wherein the gas sampling device samples through a gas bag and is analyzed by offline gas chromatography to obtain the concentration change of greenhouse gases, and then the emission flux is calculated; the monitoring device is connected to an online greenhouse gas monitor to monitor and record the concentration of greenhouse gases in real time, and directly obtain the emission flux data; according to the monitoring results of each typical node, the total amount of greenhouse gas emissions of the regional drainage network is calculated, wherein the emission amount of each emission node is converted through the proportional relationship with the emission amount of the typical node, and the proportional relationship is based on the ratio of the average plume characteristic value of the typical node to each emission node.

[0071] The regional sewage pipe network greenhouse gas emission flux monitoring method of the present application effectively reduces the measurement uncertainty by combining mobile and static monitoring technologies, and provides a simple, economical and accurate monitoring method.

[0072] The specific embodiments of the present application are further described below.

[0073] As Figure 1 shown, a greenhouse gas monitoring method for a regional sewage pipe network realizes reliable quantitative monitoring, and the specific process comprises the following steps:

[0074] Step 1: Based on the geographical spatial distribution data and road distribution data of the sewage pipe network, a mobile monitoring route guide map is drawn using a geographic information processing platform, and a geographic information software system with GPS positioning function is imported to guide the regional sewage pipe network greenhouse gas monitoring based on the mobile measurement method, and a drainage pipe network node data set is constructed.

[0075] Step 1.1: The geographical spatial distribution data of the sewage pipe network can be obtained from the municipal management department or other relevant competent departments; the road distribution data can be obtained based on OSM and other development information sources.

[0076] Step 1.2: The mobile monitoring route guide map based on the sewage pipe network and road network distribution data can be realized based on the geographic information processing platform such as ArcGIS, or by using Python programming language through the drawing library such as matlotlib.

[0077] Step 1.3: The mobile monitoring route guide map needs to be imported into a geographic information processing software system with GPS positioning function (such as Aovimap, etc.) to guide the regional sewage pipe network greenhouse gas monitoring based on the mobile measurement method.

[0078] Step 1.4: The drainage pipe network node data set is composed of longitude and latitude coordinates and node number.

[0079] Step 2: Based on the mobile monitoring route guide map, the area drainage pipe network greenhouse gas plume is monitored by using the mobile measurement method, the greenhouse gas plume is identified by using the mobile window method, the drainage pipe network plume data set carrying the space-time label is constructed, and the plume data set of the drainage pipe network node is constructed by combining the drainage pipe network node data set carrying the same space-time label.

[0080] 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.

[0081] Step 2.2: The definition of the plume is defined according to the data distribution of the monitored greenhouse gas, and the local space-time fluctuation value (~2ppm) of the atmospheric greenhouse gas mixture ratio (concentration) background value is deducted to define the "amplitude" or "amplitude·distance" (based on GPS coordinates). For example, taking the CH4 plume definition reference as an example, the amplitude is greater than 0.1 ppm, or the area of methane enhancement is greater than 5 ppm.m.

[0082] Step 2.3: The plume identification step is:

[0083] (1) Determine the length of the moving window, and the length is optimized according to the motion speed;

[0084] (2) In order to exclude the influence of short-term disturbance on the atmospheric background value, the determined plume data set is processed based on the percentage interpolation method to obtain a relatively smooth atmospheric background estimation data set; preferably, the number of the fifth position in the sliding window is used as the interpolation number;

[0085] (3) Calculate the standard deviation of the background estimation value in each moving window length, and preset the standard deviation limit value. When the standard deviation is higher than the standard deviation limit value, the value is removed and interpolated to obtain a second relatively smooth atmospheric background estimation data set;

[0086] (4) The drainage pipe network plume data set is obtained by removing the second relatively smooth atmospheric background estimation data set based on the determined plume data set.

[0087] Step 2.4: The drainage pipe network plume data set carrying the space-time label is composed of longitude and latitude coordinates, sampling time and plume characteristic value.

[0088] Step 2.5: The drainage pipe network node data set with the same space-time label is composed of longitude and latitude coordinates, sampling time and node number.

[0089] Step 2.6: The plume data set of the drainage pipe network node is composed of longitude and latitude coordinates, sampling time, plume characteristic value and node number.

[0090] Step 3: Based on the results of mobile measurement monitoring (no less than 2 times), calculate the average emission plume characteristic data, construct the first emission plume characteristic value data set of the research area emission node, and based on the relevant management requirements or scientific standards, classify and randomly sample the plume to construct the typical node plume data set of the drainage pipe network.

[0091] Step 3.1: The average emission plume characteristic value can 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.

[0092] Step 3.2: The plume category can be divided into small plume, medium plume and large plume. Taking greenhouse gas CH4 as an example, the corresponding amplitude value classification can be divided into small plume: 0.1~0.2ppm, medium plume: 0.2~1ppm, large plume:>1ppm; or the corresponding area value classification can be divided into small plume: 50~500ppm.m, medium plume: 500~1000ppm.m, large plume:>1000ppm.m.

[0093] Step 3.3: The random sampling ratio is guided by management requirements, and the recommended ratio is not less than 5%.

[0094] Step 3.4: The typical node plume data set of the drainage pipe network is composed of longitude and latitude coordinates, sampling time, plume characteristic value and node number.

[0095] 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 pipe network based on the monitoring results.

[0096] Step 4.1: The static box culvert method monitoring process is to place a closed static box culvert at the typical node. Connect a gas sampling or monitoring device at the end of the static box culvert. The gas sampling device is used to obtain concentration changes through bag sampling and offline gas chromatography to obtain greenhouse gas emission flux; the monitoring device is used to connect an online greenhouse gas monitor to measure concentration and obtain greenhouse gas emission flux. As shown in Figure 2 .

[0097] Step 4.2: The total greenhouse gas emissions of the regional drainage pipe network can be obtained by adding up the emissions of each emission node. The emissions of each emission node are based on the emissions of the typical node, which are converted by the ratio of the average plume characteristic value of the typical node to each emission node.

[0098] In summary, the embodiment of the present application innovatively combines mobile measurement method and static monitoring technology, and proposes a new method system for monitoring greenhouse gas emission flux of regional sewer network. The method firstly locates the greenhouse gas emission points in the sewer network by drawing a mobile monitoring route guide map and constructing a sewer network node dataset. Then, the mobile measurement method is used to monitor the greenhouse gas emission plume of the regional sewer network in real time, and the moving window method is used to analyze the monitoring data to identify and record the space-time label of the emission plume. These data are then combined with the sewer network node dataset to form a sewer network node dataset with space-time label, which provides detailed basic data for subsequent analysis. On this basis, the present application further calculates the average emission plume characteristic data of the emission node by multiple mobile measurement monitoring results, constructs the first emission plume characteristic value dataset of the emission node, and performs plume classification and random sampling to construct the typical node plume dataset of the sewer network. Finally, the static box culvert method is used for in-situ monitoring of greenhouse gases at the typical nodes to calculate the total amount of greenhouse gas emissions of the regional sewer network, thereby reducing the uncertainty of the mobile measurement method and improving the accuracy of the monitoring results. The significant technical advantage of the present application lies in the simplicity of operation, the accuracy of the monitoring results and the economy. Through this method system, the present application not only can realize the rapid and accurate quantification of the greenhouse gas emissions of the sewer network, but also can reduce the monitoring cost and improve the monitoring efficiency, thereby providing an efficient and accurate monitoring means for monitoring the greenhouse gas emissions of the regional sewer network in urban water cycle.

[0099] The monitoring method system can effectively reduce the uncertainty of the measurement results of the greenhouse gas emissions of the sewer network based on the mobile measurement method.

[0100] The above further describes the present application in conjunction with specific / preferred embodiments, and cannot be deemed to limit the specific implementation of the present application to these descriptions. For those skilled in the art to which the present application belongs, without departing from the concept of the present application, they can make several substitutions or variations to the described embodiments, and these substitutions or variations shall be deemed to fall within the protection scope of the present application. In the description of the present application, the description of the terms "an embodiment", "some embodiments", "a preferred embodiment", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are contained in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to 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 case of no mutual contradiction, those skilled in the art can combine and combine the different embodiments or examples described in the present application and the features of the different embodiments or examples. Although the embodiments of the present application 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 fluxes from regional sewage pipe networks in urban water cycles, characterized in that, Includes the following steps: S1. Draw a mobile monitoring route guidance map for the regional sewage pipe network and construct a corresponding drainage pipe network node dataset; wherein, acquire the geospatial distribution data of the sewage pipe network and road distribution data, draw the mobile monitoring route guidance map based on the geospatial distribution data of the sewage pipe network and road distribution data, and construct the drainage pipe network node dataset, the drainage pipe network node dataset including latitude and longitude coordinates and node numbers; by drawing the mobile monitoring route guidance map and constructing the drainage pipe network node dataset, the greenhouse gas emission points in the sewage pipe network can be accurately located; S2. Based on the mobile monitoring route guidance map, use the mobile measurement method to monitor greenhouse gas emission plume data; S3. Combine the greenhouse gas emission plume data with the drainage network node dataset to construct a drainage network node plume dataset and draw an emission hotspot distribution map. S4. Based on the plume dataset and emission hotspot distribution map of the drainage network nodes, conduct on-site monitoring of static boxes at key nodes and calculate greenhouse gas emission fluxes. In step S2, the moving window algorithm is used to analyze the monitoring data, identify greenhouse gas emission plumes, and record the spatiotemporal labels of the greenhouse gas emission plumes. In step S3, based on the drainage network node dataset obtained in step S1, the spatiotemporal labels of the monitored greenhouse gas emission plumes are added. According to the corresponding spatiotemporal labels, the monitored greenhouse gas emission plume data are combined with the corresponding drainage network node dataset to construct the plume dataset of the drainage network nodes. The mobile monitoring method includes: using a mobile measurement loading platform to carry an online greenhouse gas measuring instrument and a positioning device; monitoring and recording greenhouse gas emission data of the regional drainage network in real time through the online greenhouse gas measuring instrument; synchronously acquiring the precise location information of the monitoring data using the positioning device; combining the real-time monitoring data and location information, analyzing and identifying greenhouse gas emission plumes using the moving window method; recording the spatiotemporal labels of each emission plume, including location and time, and associating them with the drainage network node dataset to form a plume dataset; The drainage network plume dataset with spatiotemporal labels includes latitude and longitude coordinates, sampling time, and plume feature values; the drainage network node dataset with corresponding spatiotemporal labels includes latitude and longitude coordinates, sampling time, and node number; the drainage network node plume dataset includes latitude and longitude coordinates, sampling time, plume feature values, and node number. Step S4 includes the following sub-steps: M1, calculating the plume characteristics of the drainage network nodes; M2, determining typical nodes based on the plume characteristics; M3, monitoring greenhouse gases at the typical nodes; M4, calculating the total greenhouse gas emissions based on the greenhouse gas monitoring data. In step M1, based on the monitoring results of multiple moving 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 dataset for the emission nodes. In step M2, plume classification and random sampling are performed to construct the typical node plume dataset of the drainage network. The average emission plume characteristic value is calculated from the amplitude or area of ​​the monitored greenhouse gas emission plumes. The characteristic values ​​in the first emission plume characteristic value dataset include the average emission plume characteristic value. This enables accurate quantitative monitoring of greenhouse gas emission fluxes from urban sewage pipe networks.

2. The method for monitoring greenhouse gas emission fluxes from regional sewage pipe networks as described in claim 1, characterized in that, In step S1, the mobile monitoring route guidance map is drawn using a geographic information processing platform or by 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 the monitoring of greenhouse gases in regional sewage pipe networks based on the mobile measurement method, including providing route planning, navigation, positioning and progress tracking functions during the monitoring process.

3. The method for monitoring greenhouse gas emission fluxes from regional sewage pipe networks as described in claim 1, characterized in that, In step S2, plume identification specifically includes: Determine the length of the moving window and optimize it based on the movement speed of the monitoring platform; The measured plume dataset was processed using percentage interpolation to eliminate the influence of short-term spatiotemporal disturbances on atmospheric background values, thereby obtaining a smoothed atmospheric background estimation dataset. Calculate the standard deviation of the background estimation values ​​within the length of each moving window, and preset the standard deviation limit. For standard deviations exceeding the limit, the corresponding data are removed and interpolated to obtain a second-smooth atmospheric background estimation dataset. The greenhouse gas emission plume dataset of the drainage network is obtained by removing the quadratically smoothed atmospheric background estimation dataset from the measured plume dataset.

4. The method for monitoring greenhouse gas emission fluxes from regional sewage pipe networks as described in any one of claims 1 to 3, characterized in that, Step S4 specifically includes: Enclosed static box culverts are placed at typical nodes of the drainage network. A gas sampling or monitoring device is connected to the end of the static box culvert. The gas sampling device takes samples through a gas bag and analyzes them using offline gas chromatography 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 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. The emissions of each emission node are converted by the ratio of the emissions of the typical node to the average plume characteristic value of each emission node.

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