Regional carbon emission tracing method

Abstract

The invention provides a traceability method for regional carbon emission. The traceability method comprises the following steps: step S1, dividing emission sources in a region; s2, collecting emission data of an emission source; s3, arranging monitoring nodes, and collecting meteorological, geographical and carbon emission data at the nodes; s4, establishing a carbon emission dynamic distribution model, and predicting dynamic distribution of carbon emission under different conditions; and S5, comparing the monitoring data of each emission source in the area with a prediction result of the carbon emission dynamic distribution model, if the monitoring data is within a prediction result range, indicating that the emission activity of each emission source in the area is within a normal range, and if a significant difference exists, indicating that the emission amount of the emission source exceeds a normal level. A wireless sensor network (WSN) system reversely identifies and tracks an abnormal emission source from a monitoring node by analyzing the track and change of the simulated pollutant puff. According to the invention, the monitoring comprehensiveness and accuracy are improved, and efficient environment management and carbon emission control can be realized.

Description

Technical Field

[0001] The invention belongs to the technical field of air pollution prevention and control, and in particular to a method for tracing the source of regional carbon emissions. Background Art

[0002] In order to ensure the realization of carbon trading goals, it is imperative to establish standardized and normalized measurement methods, build a networked regional traceability system based on AI, and realize accurate traceability of regional carbon emissions.

[0003] Current technology can already measure some carbon emissions using monitoring equipment installed at emission sources. However, these devices are often expensive and have limitations in distinguishing carbon-based solid microparticles. In addition, relying solely on a limited number of monitoring points for discrete data collection is far from meeting the needs of comprehensive and accurate emission monitoring, and it is also difficult to achieve regional intelligent and accurate traceability linkage. In order to improve the comprehensiveness and accuracy of monitoring, it is necessary to further optimize technology, reduce costs, and enhance the ability of equipment to identify different types of carbon emissions in order to achieve more efficient environmental management and carbon emission control. Summary of the invention

[0004] In order to solve at least one of the above technical problems, the present invention provides a method for tracing the source of regional carbon emissions, which improves the comprehensiveness and accuracy of monitoring and is conducive to achieving efficient environmental management and carbon emission control.

[0005] Note that the description of these objectives does not prevent the existence of other objectives. One embodiment of the present invention does not need to achieve all of the above objectives. Objectives other than the above objectives can be extracted from the description of the specification, drawings, and claims.

[0006] In the wireless sensor network WSN system, the present invention divides the emission sources in the region; collects emission source data; arranges monitoring nodes, collects meteorological, geographical and carbon emission data at the nodes; and establishes a dynamic distribution model of carbon emissions based on emission source data, node meteorological and geographical data. The present invention adopts a wireless sensor network WSN system to achieve rapid collection, processing and analysis of various types of data. By integrating the meteorological data of each monitoring node, a dynamic distribution model of carbon emissions is constructed, and the chip is distinguished by carbon particles, the analysis of carbon-based solid microparticles is added, covering a more comprehensive range of carbon emission pollutant types, which is conducive to comprehensive real-time monitoring and data analysis of carbon emissions in a specific area, and then timely issuing warnings, accurately tracking various pollutants in carbon emissions, and thus comprehensively and accurately tracing regional carbon emissions.

[0007] The present invention achieves the above technical objectives through the following technical means.

[0008] A method for tracing the source of regional carbon emissions comprises the following steps:

[0009] Step S1, dividing emission sources in the area: spatially locating and classifying different emission sources in the area, and transmitting the divided data information to the wireless sensor network WSN system;

[0010] Step S2, collecting emission data from emission sources: in the wireless sensor network WSN system, according to the different emission sources divided in step S1, the composition, content and emission rate of the generated carbon emission pollutants are calculated;

[0011] Step S3, deploy monitoring nodes and collect meteorological, geographical and carbon emission data at the nodes: deploy monitoring nodes and transmit the collected meteorological, geographical and carbon emission data at the nodes to the wireless sensor network WSN system in real time;

[0012] Step S4, establishing a dynamic distribution model of carbon emissions: in the wireless sensor network WSN system, using the CALPUFF non-steady-state Lagrangian puff model, combined with the meteorological data, geographic data and emission source emission data collected in step S3, simulate the diffusion process of pollutants in the atmosphere and predict the dynamic distribution of carbon emissions under different conditions;

[0013] Step S5, tracing the source of regional carbon emissions: compare the monitoring data of each emission source in the region with the prediction results of the carbon emission dynamic distribution model obtained in step S4. If they are within the prediction results, it means that the emission activities of each emission source in the region are within the normal range. If there are significant differences, it means that the emission of some emission sources exceeds the normal level. The wireless sensor network (WSN) system analyzes the trajectory and changes of the simulated pollutant puffs to reversely identify and track the source of abnormal emissions from the monitoring nodes.

[0014] In the above scheme, the emission sources in the divided area in step S1 are specifically:

[0015] Different emission sources in the area: industrial emissions, traffic emissions, and daily emissions are spatially located and classified, and the divided data information is transmitted to the wireless sensor network (WSN) system.

[0016] In the above scheme, the collection of emission source data in step S2 is specifically as follows:

[0017] In the wireless sensor network WSN system, according to the different emission sources divided in step S1, the fuel usage, fuel consumption and combustion efficiency of each emission source are comprehensively considered to calculate the composition, content and emission rate of the generated carbon emission pollutants.

[0018] In the above scheme, the monitoring nodes are arranged in step S3, and the meteorological, geographical and carbon emission data at the nodes are collected as follows:

[0019] Nodes are arranged in the entire area according to the location of each emission source. The meteorological data and geographic data of each node location are collected using environmental and geographic information detection devices. The gas pollutant data of carbon emissions at each node are collected using environmental gas detection devices. The carbon-based solid microparticle emission data in the carbon emissions at the node are collected using carbon particle differentiation chips. The collected data are transmitted in real time to the wireless sensor network (WSN) system.

[0020] Further, the environment and geographic information detection device includes a wind direction sensor, a wind speed sensor, a temperature sensor, a humidity sensor and a radar, wherein the wind direction sensor is used to collect the wind direction at the node position, the wind speed sensor is used to collect the wind speed at the node position, the temperature sensor is used to collect the temperature at the node position, the humidity sensor is used to collect the humidity at the node position, and the radar is used to collect geographic data at the node position including three-dimensional structural information such as the height and distribution of urban buildings, terrain and vegetation;

[0021] Furthermore, the environmental gas detection device includes an electrochemical sensor, an infrared sensor, and a metal oxide semiconductor sensor. The electrochemical sensor is used to detect the concentration of methane CH4 and sulfur dioxide SO2, the infrared sensor is used to detect the concentration of carbon monoxide CO, and the metal oxide semiconductor sensor is used to detect the concentration of carbon dioxide CO2.

[0022] In the above scheme, the types of monitoring nodes in step S3 include high-altitude fixed nodes, high-altitude mobile nodes, ground fixed nodes and ground mobile nodes.

[0023] In the above scheme, when the monitoring nodes are arranged in step S3, the following conditions need to be met:

[0024] The monitoring network can fully cover the entire area;

[0025] The nodes should be kept at a distance to avoid cross-overlapping of monitoring data;

[0026] Arrange nodes according to the emission concentration gradient of the emission area.

[0027] Furthermore, the arrangement of nodes according to the emission concentration gradient of the emission area is specifically as follows:

[0028] The monitoring area is divided into different concentration gradient blocks, and high pollution areas, medium pollution areas and low pollution areas are identified. In areas with high pollution concentration, more monitoring nodes are arranged to obtain detailed pollution data.

[0029] In the above scheme, the CALPUFF non-steady-state Lagrangian puff model in step S4 includes a CALMET meteorological preprocessing module, a CALPUFF non-steady-state three-dimensional Lagrangian puff transport module and a CALPOST post-processing module;

[0030] The meteorological data of wind speed, wind direction, humidity, temperature and geographic data are input into the CALMET meteorological preprocessing module to simulate the meteorological field and obtain the horizontal and vertical distribution of meteorological data near each node;

[0031] The meteorological field obtained by the CALMET meteorological preprocessing module, as well as the location and emission rate of emission data of different emission sources, are input into the CALPUFF non-steady three-dimensional Lagrangian puff transport module to simulate the carbon emission concentration distribution, the trajectory and change of pollutant puffs;

[0032] In the CALPOST post-processing module, the smoke puff trajectory and emission concentration distribution files are described in 3D to obtain the concentration information of each pollutant in carbon emissions at each node.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention uses a wireless sensor network (WSN) to efficiently process and analyze carbon emission data. This integrated data management method not only significantly improves the efficiency and accuracy of emission monitoring, but also realizes real-time monitoring and dynamic management of emission sources. In addition, due to the reduced wiring requirements and low power consumption operation, the present invention reduces costs while ensuring environmental protection.

[0035] The present invention adopts the CALPUFF non-steady-state Lagrangian puff model, which comprehensively considers meteorological conditions and geographical environmental factors to simulate the dynamic distribution of carbon emissions in the entire region. At the same time, by using a carbon particle differentiation chip, it is possible to distinguish carbon-based solid microparticles of different sizes, detect their concentrations, and accurately collect the composition information of carbon-based solid microparticles. Compared with traditional tracing methods that only target gaseous pollutants, the method of the present invention enhances the equipment's ability to identify different types of carbon emissions, provides more accurate analysis results, and covers a more comprehensive range of pollutant types.

[0036] In view of the lack of accurate regional carbon emission tracing technology at present, the present invention proposes a new regional carbon emission tracing technology based on wireless sensor network (WSN), innovates the layout of regional monitoring nodes, arranges the monitoring nodes in a complex regional environment where multiple pollution sources emit pollutants at the same time, collects data from fixed monitoring points and mobile monitoring points, and effectively processes the carbon emission data through the wireless sensor network (WSN). Breakthrough the key technology of intelligent fusion analysis of fixed monitoring points and mobile monitoring points in the complex gas source multi-point emission mode, to achieve accurate tracing of regional carbon emissions.

[0037] Note that the description of these effects does not prevent the existence of other effects. One embodiment of the present invention does not necessarily have all of the above effects. Effects other than the above can be clearly seen and extracted from the description of the specification, drawings, claims, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the process of the method for tracing the regional carbon emissions of the present invention.

[0039] Figure 2 Schematic diagram of the distribution of monitoring nodes and emission sources in the present invention.

[0040] Figure 3 The figure is a schematic diagram of the process of establishing the CALPUFF non-steady-state Lagrangian puff model in the present invention. DETAILED DESCRIPTION

[0041] The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0042] Figure 1 As shown, a method for tracing the source of regional carbon emissions includes the following steps:

[0043] Step S1, dividing emission sources in the area: spatially locating and classifying different emission sources in the area, and transmitting the divided data information to the wireless sensor network WSN system;

[0044] Step S2, collecting emission data from emission sources: in the wireless sensor network WSN system, according to the different emission sources divided in step S1, the composition, content and emission rate of the generated carbon emission pollutants are calculated;

[0045] Step S3, deploy monitoring nodes and collect meteorological, geographical and carbon emission data at the nodes: deploy monitoring nodes and transmit the collected meteorological, geographical and carbon emission data at the nodes to the wireless sensor network WSN system in real time;

[0046] Step S4, establishing a dynamic distribution model of carbon emissions: in the wireless sensor network WSN system, using the CALPUFF non-steady-state Lagrangian puff model, combined with the meteorological data, geographic data and emission source emission data collected in step S3, simulate the diffusion process of pollutants in the atmosphere and predict the dynamic distribution of carbon emissions under different conditions;

[0047] Step S5, tracing the source of regional carbon emissions: compare the monitoring data of each emission source in the region with the prediction results of the carbon emission dynamic distribution model obtained in step S4. If they are within the prediction results, it means that the emission activities of each emission source in the region are within the normal range. If there are significant differences, it means that the emission of some emission sources exceeds the normal level. The wireless sensor network (WSN) system analyzes the trajectory and changes of the simulated pollutant puffs to reversely identify and track the source of abnormal emissions from the monitoring nodes.

[0048] The emission sources in the divided area in step S1 are specifically:

[0049] Different emission sources in the area: industrial emissions, traffic emissions, and daily emissions are spatially located and classified, and the divided data information is transmitted to the wireless sensor network (WSN) system.

[0050] The specific steps of collecting emission source data in step S2 are:

[0051] In the wireless sensor network WSN system, according to the different emission sources divided in step S1, the fuel usage, fuel consumption and combustion efficiency of each emission source are comprehensively considered to calculate the composition, content and emission rate of the generated carbon emission pollutants.

[0052] In step S3, monitoring nodes are arranged, and the meteorological, geographical and carbon emission data at the nodes are collected as follows:

[0053] Appropriate nodes are arranged in the entire area according to the location of each emission source. The meteorological data and geographic data of each node location are collected using environmental and geographic information detection devices. The gas pollutant data related to carbon emissions at each node are collected using environmental gas detection devices. The carbon-based solid microparticle emission data in the carbon emissions at the node are collected using carbon particle differentiation chips. The collected data are transmitted in real time to the wireless sensor network (WSN) system.

[0054] The environment and geographic information detection device includes a wind direction sensor, a wind speed sensor, a temperature sensor, a humidity sensor and a radar. The wind direction sensor is used to collect the wind direction at the node position, the wind speed sensor is used to collect the wind speed at the node position, the temperature sensor is used to collect the temperature at the node position, the humidity sensor is used to collect the humidity at the node position, and the radar is used to collect geographic data at the node position, including three-dimensional structural information such as the height and distribution of urban buildings, terrain and vegetation;

[0055] The environmental gas detection device includes an electrochemical sensor, an infrared sensor, and a metal oxide semiconductor sensor. The electrochemical sensor is used to detect the concentration of methane CH4 and sulfur dioxide SO2, the infrared sensor is used to detect the concentration of carbon monoxide CO, and the metal oxide semiconductor sensor is used to detect the concentration of carbon dioxide CO2.

[0056] The types of monitoring nodes in step S3 include high-altitude fixed nodes, high-altitude mobile nodes, ground fixed nodes and ground mobile nodes.

[0057] Monitoring nodes and emission source distribution, such as Figure 2 There are four main types of monitoring nodes: high-altitude fixed nodes, high-altitude mobile nodes, ground fixed nodes, and ground mobile nodes. When arranging monitoring nodes in step S3, the following conditions need to be met:

[0058] Ensure that the monitoring network has comprehensive coverage of the entire region;

[0059] The nodes should maintain an appropriate distance to avoid overlapping of monitoring data;

[0060] Appropriate nodes are arranged according to the emission concentration gradient of the emission area.

[0061] The arrangement of appropriate nodes according to the emission concentration gradient of the emission area is specifically as follows:

[0062] The monitoring area is divided into different concentration gradient blocks, and high-pollution areas, medium-pollution areas and low-pollution areas are identified. More monitoring nodes are deployed in areas with high pollution concentrations, such as industrial areas and near busy roads, to obtain detailed pollution data.

[0063] In the wireless sensor network (WSN) system, the CALPUFF non-steady-state Lagrangian puff model is used to simulate the diffusion process of pollutants in the atmosphere and predict the dynamic distribution of carbon emissions under different conditions by combining meteorological data, geographical data and emission data of emission sources.

[0064] The CALPUFF non-steady Lagrangian puff model in step S4 includes a CALMET meteorological preprocessing module, a CALPUFF non-steady three-dimensional Lagrangian puff transport module and a CALPOST post-processing module; a CALPUFF non-steady Lagrangian puff model is established through a wireless sensor network (WSN) to simulate the carbon emission process, such as Figure 3 As shown:

[0065] The meteorological data of wind speed, wind direction, humidity, temperature and geographic data are input into the CALMET meteorological preprocessing module to simulate the meteorological field and obtain the horizontal and vertical distribution of meteorological data near each node;

[0066] The meteorological field obtained by the CALMET meteorological preprocessing module, as well as the location and emission rate of emission data of different emission sources, are input into the CALPUFF non-steady three-dimensional Lagrangian puff transport module to simulate the carbon emission concentration distribution, the trajectory and change of pollutant puffs;

[0067] In the CALPOST post-processing module, the smoke puff trajectory and emission concentration distribution files are described in 3D to obtain the concentration information of each pollutant in carbon emissions at each node.

[0068] The present invention adopts advanced statistical analysis technology in a wireless sensor network (WSN) system to evaluate the carbon emission data simulated by the CALPUFF non-steady-state Lagrangian puff model.

[0069] Compare the monitoring data of each node with the prediction results of the model. If it is within the range of the prediction results, it means that the emission activities of each emission source in the area are within the normal range. If there is a significant difference, it means that the emission of the emission source exceeds the normal level. At this time, the wireless sensor network WSN system will analyze the trajectory and changes of the simulated pollutant smoke puffs, and reversely identify and track the source of abnormal emissions from the monitoring node. It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that those skilled in the art can understand.

[0070] The series of detailed descriptions listed above are only specific descriptions of feasible embodiments of the present invention. They are not intended to limit the scope of protection of the present invention. All equivalent embodiments or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for tracing the source of regional carbon emissions, characterized in that: The following steps are involved: Step S1, dividing emission sources in the area: spatially locating and classifying different emission sources in the area, and transmitting the divided data information to the wireless sensor network WSN system; Step S2, collecting emission data from emission sources: in the wireless sensor network WSN system, according to the different emission sources divided in step S1, the composition, content and emission rate of the generated carbon emission pollutants are calculated; Step S3, deploy monitoring nodes and collect meteorological, geographical and carbon emission data at the nodes: deploy monitoring nodes and transmit the collected meteorological, geographical and carbon emission data at the nodes to the wireless sensor network WSN system in real time; Step S4, establishing a dynamic distribution model of carbon emissions: in the wireless sensor network WSN system, using the CALPUFF non-steady-state Lagrangian puff model, combined with the meteorological data, geographic data and emission source emission data collected in step S3, simulate the diffusion process of pollutants in the atmosphere and predict the dynamic distribution of carbon emissions under different conditions; Step S5, tracing the source of regional carbon emissions: compare the node monitoring data with the prediction results of the carbon emission dynamic distribution model obtained in step S4. If it is within the prediction result range, it means that the emission activities of various emission sources in the region are within the normal range. If there is a significant difference, it means that the emission of some emission sources exceeds the normal level. The wireless sensor network (WSN) system analyzes the trajectory and changes of the simulated pollutant puffs to reversely identify and track the source of abnormal emissions from the monitoring node.

2. The method for tracing the source of regional carbon emissions according to claim 1, characterized in that: The emission sources in the divided area in step S1 are specifically: Different emission sources in the area: industrial emissions, traffic emissions, and daily emissions are spatially located and classified, and the divided data information is transmitted to the wireless sensor network (WSN) system.

3. The method for tracing the source of regional carbon emissions according to claim 1, characterized in that: The specific steps of collecting emission source data in step S2 are: In the wireless sensor network WSN system, according to the different emission sources divided in step S1, the fuel usage, fuel consumption and combustion efficiency of each emission source are comprehensively considered to calculate the composition, content and emission rate of the generated carbon emission pollutants.

4. The method for tracing the source of regional carbon emissions according to claim 1, characterized in that: In step S3, monitoring nodes are arranged, and the meteorological, geographical and carbon emission data at the nodes are collected as follows: Nodes are arranged in the entire area according to the location of each emission source. The meteorological data and geographic data of each node location are collected using environmental and geographic information detection devices. The gas pollutant data of carbon emissions at each node are collected using environmental gas detection devices. The carbon-based solid microparticle emission data in the carbon emissions at the node are collected using carbon particle differentiation chips. The collected data are transmitted in real time to the wireless sensor network (WSN) system.

5. The method for tracing the source of regional carbon emissions according to claim 4, characterized in that: The environment and geographic information detection device includes a wind direction sensor, a wind speed sensor, a temperature sensor, a humidity sensor and a radar. The wind direction sensor is used to collect the wind direction at the node position, the wind speed sensor is used to collect the wind speed at the node position, the temperature sensor is used to collect the temperature at the node position, the humidity sensor is used to collect the humidity at the node position, and the radar is used to collect geographic data at the node position, including three-dimensional structural information such as the height and distribution of urban buildings, terrain, and vegetation.

6. The method for tracing the source of regional carbon emissions according to claim 4, characterized in that: The environmental gas detection device includes an electrochemical sensor, an infrared sensor, and a metal oxide semiconductor sensor. The electrochemical sensor is used to detect the concentration of methane CH4 and sulfur dioxide SO2, the infrared sensor is used to detect the concentration of carbon monoxide CO, and the metal oxide semiconductor sensor is used to detect the concentration of carbon dioxide CO2.

7. The method for tracing the source of regional carbon emissions according to claim 1, characterized in that: The types of monitoring nodes in step S3 include high-altitude fixed nodes, high-altitude mobile nodes, ground fixed nodes and ground mobile nodes.

8. The method for tracing the source of regional carbon emissions according to claim 1, characterized in that: When the monitoring nodes are arranged in step S3, the following conditions need to be met: The monitoring network can fully cover the entire area; The nodes should be kept at a distance to avoid cross-overlapping of monitoring data; Arrange nodes according to the emission concentration gradient of the emission area.

9. The method for tracing the source of regional carbon emissions according to claim 8, characterized in that: The arrangement of nodes according to the emission concentration gradient of the emission area is specifically as follows: The monitoring area is divided into different concentration gradient blocks, high pollution areas, medium pollution areas and low pollution areas are identified, and more monitoring nodes are deployed in areas with high pollution concentrations.

10. The method for tracing the source of regional carbon emissions according to claim 1, characterized in that: The CALPUFF non-steady-state Lagrangian puff model in step S4 includes a CALMET meteorological preprocessing module, a CALPUFF non-steady-state three-dimensional Lagrangian puff transport module and a CALPOST post-processing module; The meteorological data of wind speed, wind direction, humidity, temperature and geographic data are input into the CALMET meteorological preprocessing module to simulate the meteorological field and obtain the horizontal and vertical distribution of meteorological data near each node; The meteorological field obtained by the CALMET meteorological preprocessing module, as well as the location and emission rate of emission data of different emission sources, are input into the CALPUFF non-steady three-dimensional Lagrangian puff transport module to simulate the carbon emission concentration distribution, the trajectory and change of pollutant puffs; In the CALPOST post-processing module, the smoke puff trajectory and emission concentration distribution files are described in 3D to obtain the concentration information of each pollutant in carbon emissions at each node.

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