Pollutant concentration contribution identification method and system driven by multipoint networking traceability

Abstract

The invention provides a multipoint networking traceability-driven pollutant concentration contribution identification method and system. The method comprises the following steps: acquiring the position of a site with a high pollutant concentration value and a time period corresponding to the high pollutant concentration value; meteorological data and underlying surface features of the high-value site area are obtained for meteorological simulation; the station carries out backward track tracing according to the meteorological simulation result; performing multi-station networking linkage traceability according to the traceability result to obtain backward tracks and pollution sources of different stations; and combining the backward trajectories of different stations with the air quality space three-dimensional concentration distribution data of the station region with high pollutant concentration value to obtain the time node concentration of different backward trajectories per hour, and quantitatively analyzing the regional pollution and the pollutant concentration change process in the pollution source between different stations. According to the method, the spatial three-dimensional concentration distribution data and the backward trajectory technology are utilized to quantify the exogenous contributions of pollutants in different areas, the uncertainty of calculation area contribution and local contribution results is reduced, and the calculation amount is smaller.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and particularly relates to a method and system for identifying pollutant concentration contributions driven by multi-point networking and traceability. Background Art

[0002] Pollutants not only come from local emissions, but may also be affected by pollution sources in external regions. The impact of such external pollution sources, especially cross-border pollution, has become an issue that cannot be ignored in many cities and regions. Accurately identifying and quantifying the contributions of external pollution sources is crucial for improving air quality, formulating environmental policies, and conducting pollution prevention and control. Currently, the existing methods have the following defects: 1. Existing pollutant source analysis mainly relies on fixed monitoring stations and ground meteorological data. These methods have significant spatial and temporal limitations and are difficult to comprehensively understand the diffusion patterns of pollutants and the contributions of external pollution sources.

[0003] 2. Traditional backward trajectory analysis methods are often based on point data and lack a refined description of pollution distribution in a large-scale area.

[0004] 3. In traditional methods for calculating regional contributions and local contributions, a large amount of calculations or decomposition through statistical models are mainly used to obtain corresponding results, such as methods like the PCA method and the CTM numerical simulation method. However, this calculation method often requires a large amount of historical data for calculation, not only with a large amount of calculation but also a relatively long calculation time. When calculating the regional contribution and local contribution of a city, it is necessary to collect all the station data within the city for calculation, and due to only calculating the numerical value unidirectionally, the uncertainty may be relatively large.

[0005] 4. Since traditional backward trajectories generally only calculate geographical locations, they cannot evaluate the contribution amount, and often regard the trajectory between two points of the backward trajectory as a unified source, without fully considering the differences in pollution sources among different land use types. For example, the emissions in industrial areas mainly come from industrial sources, and the emissions in urban residential areas mainly come from residential sources. This rough zoning treatment method ignores the differences in emission characteristics among different land use types. Summary of the Invention

[0006] In view of the fact that the existing pollutant source tracing methods have certain limitations in determining the proportion of external sources of regional pollution, especially under dynamic meteorological conditions and complex geographical environments, traditional methods often cannot accurately reflect the transport paths and distribution of different pollutants in the air. The main purpose of the present invention is to propose a method and system for identifying pollutant concentration contributions driven by multi-point networking and traceability. By combining spatial three-dimensional concentration distribution data and backward trajectory technology, the proportion of external sources of regional pollution can be estimated more accurately. For , ozone, and long-lived greenhouse gases (such as , methane, etc.), and a tracing method adapted to their different propagation characteristics and life cycles, so as to provide more accurate data support for air quality management.

[0007] The present invention proposes a method for identifying pollutant concentration contributions driven by multi-point networking tracing, and the method includes the following steps: Step 1: Obtain the location of the high-value site of pollutant concentration and the time period corresponding to the high-value of pollutant concentration; Step 2: Obtain the meteorological data of the high-value site area, and perform meteorological simulation on the meteorological data according to the underlying surface characteristics of the high-value site area to obtain a three-dimensional meteorological field; Step 3: The site performs backward trajectory tracing according to the three-dimensional meteorological field to determine the backward trajectory in the first several hours before the high-value time of pollutant concentration; Step 4: Perform multi-site networking linkage tracing according to the backward trajectory in the first several hours before the high-value time of pollutant concentration to determine the backward trajectories and pollution sources of different sites; Step 5: Combine the backward trajectories of different sites with the three-dimensional air quality spatial concentration distribution data of the high-value site area of pollutant concentration, and calculate the concentration at each hourly time node of different backward trajectories; Step 6: Based on the concentration at each hourly time node of different backward trajectories, perform quantitative analysis of regional pollution to determine the regional transmission contribution, the emission flux of pollutants within pollution sources of different land use types, and the change process of pollutant concentration within pollution sources.

[0008] The present invention also proposes a system for identifying pollutant concentration contributions driven by multi-point networking tracing. Among them, the system applies the method for identifying pollutant concentration contributions driven by multi-point networking tracing as described above, and the system includes: Data observation module, used for: Obtain the location of the high-value site of pollutant concentration and the time period corresponding to the high-value of pollutant concentration; Meteorological simulation module, used for: Obtain the meteorological data of the high-value site area, and perform meteorological simulation on the meteorological data according to the underlying surface characteristics of the high-value site area to obtain a three-dimensional meteorological field; Networking tracing module, used for: The site performs backward trajectory tracing according to the three-dimensional meteorological field to determine the backward trajectory in the first several hours before the high-value time of pollutant concentration; Perform multi-site networking linkage tracing according to the backward trajectory in the first several hours before the high-value time of pollutant concentration to determine the backward trajectories and pollution sources of different sites; Quantitative analysis module, used for: By combining the backward trajectories of different stations with the three-dimensional spatial concentration distribution data of air quality in the area of high-value pollutant concentration stations, the hourly time-node concentrations of different backward trajectories are calculated; Based on the hourly time-node concentrations of different backward trajectories, quantitative analysis of regional pollution is carried out to determine the regional transport contribution, the emission fluxes of pollutants within different land-use type pollution sources, and the change process of pollutant concentrations within the pollution sources.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Traditional pollution source analysis methods usually rely on ground monitoring station data, which have limited spatial distribution and are prone to data deviation. To overcome this deficiency, it is necessary to combine three-dimensional spatial concentration distribution data. The three-dimensional spatial concentration distribution data include different types of data such as lidar data, spatial interpolation data of monitoring points, satellite three-dimensional remote sensing data, and numerical simulations. The present invention uses three-dimensional spatial concentration distribution data and backward trajectory technology, which can provide a wider spatial coverage, quantify the exogenous contributions of pollutants in different regions, and enhance the accuracy of quantitative analysis of external regional transport contributions. This is of great significance for the identification and treatment of pollution sources across regions.

[0010] 2. Traditional backward trajectory models may have large errors under complex terrain (such as mountainous areas, coastal areas, etc.) and meteorological conditions (such as inversion layers, poor atmospheric stability). The present invention can more effectively trace the sources of pollutants in a complex environment by combining the backward trajectory model with three-dimensional spatial concentration distribution data, and simultaneously consider the comprehensive effects of local pollution sources and remote pollution sources, especially pollutants with cross-regional transport, overcoming the limitations of traditional methods, which is particularly important when evaluating external regional transport contributions.

[0011] 3. In traditional methods for calculating regional contributions and local contributions, a large amount of calculations are mainly used or the corresponding results are obtained through statistical model decomposition, such as methods like the PCA method and the CTM numerical simulation method. However, this calculation method often requires a large amount of historical data for calculation, not only with a large amount of calculation but also a relatively long calculation time. When calculating the regional contribution and local contribution of a city, all the station data within the city need to be collected for calculation, and due to only calculating the numerical values unidirectionally, the uncertainty may be relatively large. The present invention improves the algorithm by introducing backward trajectory technology, reduces the uncertainty of the calculation results of regional contributions and local contributions, and has a smaller amount of calculation.

[0012] 4. Since traditional backward trajectories generally only calculate geographical locations and cannot evaluate the contribution amount, and often regard the trajectory between two points of the backward trajectory as a unified source, the differences in pollution sources among different land use types are not fully combined. For example, the emissions in industrial areas mainly come from industrial sources, and the emissions in urban residential areas mainly come from residential sources. This rough zoning treatment method ignores the differences in emission characteristics among different land types. The present invention combines backward trajectory technology, three-dimensional spatial concentration distribution data, and land use types to more accurately capture the contributions of different sources in a local area and improve the accuracy of calculation results.

[0013] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flowchart of a method for identifying pollutant concentration contributions driven by multi-point networking and source tracing proposed by the present invention; Figure 2 is a diagram showing the combination of backward trajectories and three-dimensional spatial concentration distribution data; Figure 3 is a framework diagram of a system for identifying pollutant concentration contributions driven by multi-point networking and source tracing proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation of the present invention.

[0016] These and other aspects of the embodiments of the present invention will be clear from the following description and the drawings. In these descriptions and drawings, some specific embodiments of the embodiments of the present invention are specifically disclosed to represent some ways of implementing the principles of the embodiments of the present invention. However, it should be understood that the scope of the embodiments of the present invention is not limited thereto.

[0017] Please refer to Figure 1 , this embodiment provides a method for identifying pollutant concentration contributions driven by multi-point networking and source tracing, and the method includes the following steps: Step 1: Obtain the location of high-value pollutant concentration sites and the time period corresponding to the high-value pollutant concentration; Step 2: Obtain the meteorological data of the high-value site area, and perform meteorological simulation on the meteorological data according to the underlying surface characteristics of the high-value site area to obtain a three-dimensional meteorological field; Step 3: The site performs backward trajectory tracing based on the three-dimensional meteorological field to obtain the backward trajectories of several hours before the time of high pollutant concentration. Step 4: Perform multi-site networked linkage tracing based on the backward trajectories of several hours before the time of high pollutant concentration to obtain the backward trajectories of different sites and the pollution sources. Please refer to Figure 2 , in this step, the air mass flow trajectories of several hours before the high value time of the site are restored through the air pollutant dispersion model and the three-dimensional meteorological field, and the source location information and concentration data of the polluted air mass during this time period at the site are displayed. Combining the hourly three-dimensional concentration distribution data, the concentration at each hourly time node of the backward trajectory is obtained; different sites are networked for linkage tracing; the difference in pollutant concentration between the sites that generate linkage is used to judge the source and generation area of pollutants on the transmission path.

[0018] The specific steps for networking and linking different sites for tracing and using the difference in pollutant concentration between the sites that generate linkage to judge the source and generation area of pollutants on the transmission path are as follows: The first site performs backward trajectory tracing prediction to obtain the air mass source trajectory. According to the air mass source trajectory, obtain the backward trajectory tracing results of the second site adjacent to the air mass source trajectory, and at the same time display the pollutant concentration values of the second site at past times, and so on until the backward trajectory tracing results of the Nth site and the pollutant concentration values of the Nth site at past times are obtained. Calculate the pollutant concentration values at past times in the air mass source trajectory, and judge whether there are new pollution sources added or new pollutants generated in the path of the air mass source trajectory according to the difference in pollutant concentration values between two adjacent sites, and determine the overall movement path and temporal variation process of pollution within the target range. Judge the location of the pollutant source according to the determined overall movement path and temporal variation process of pollution within the target range.

[0019] As Figure 2 shown, taking the triggering of linkage tracing by the observation equipment at point A as an example, the pollutant concentration value at point A with coordinates (x, y, z) at 22h is 50 μg / m 3 , the pollutant concentration value at point B with coordinates (x’, y’, z’) at 21h is 41 μg / m 3 , calculate the concentration difference between point A and point B as 9 μg / m 3 , and judge that there are new pollution sources added or new pollutants generated on the path of the polluted air mass from passing through point B at 21h to passing through point A at 22h.

[0020] Step 5: Combine the backward trajectories from different stations with the three-dimensional spatial concentration distribution data of air quality in the area of the stations with high pollutant concentrations to obtain the hourly time-node concentrations of different backward trajectories. Step 6: Quantitatively analyze the regional pollution based on the hourly time-node concentrations of different backward trajectories to obtain the regional transport contribution, the emission fluxes of pollutants within pollution sources of different land use types, and the variation process of pollutant concentrations within the pollution sources.

[0021] As a preferred embodiment of the present invention, quantitatively analyzing the regional pollution based on the hourly time-node concentrations of different backward trajectories to obtain the regional transport contribution specifically includes the following steps: Determine the stations in the high-value area and the stations linked for traceability with the stations in the high-value area. Obtain the pollutant concentrations of local stations within the current area at the current moment, and obtain the pollutant concentrations of the nearest neighbor stations in the external area of the local boundary at the previous moment. Exclude the influence of rainfall on atmospheric pollutants, and calculate the regional transport contribution based on the pollutant concentrations of local stations within the current area at the current moment and the pollutant concentrations of the nearest neighbor stations in the external area of the local boundary at the previous moment.

[0022] Rainfall has a certain influence on the concentration of atmospheric pollutants (such as ). For calculating the regional transport contribution, the influence of rainfall needs to be considered to avoid large errors in the results due to rainfall. In the model, it is assumed that when the humidity drops below 80% after rainfall, it is considered that the influence of rainfall on pollutants ends; therefore, excluding the influence of rainfall on atmospheric pollutants specifically includes the following steps: Define that the influence of rainfall on pollutants ends when the humidity drops below 80% after rainfall. Calculate the time period during which rainfall will affect pollutants from the start to the end of rainfall to obtain the rainfall influence time period. The corresponding process has the following relationship: ; where ， represents the time of the influence of rainfall on atmospheric pollutants; represents the time when rainfall may occur; represents the time of the influence of rainfall on the concentration of atmospheric pollutants after rainfall.

[0023] Exclude the stations within the rainfall influence time period from participating in the regional transport contribution to reduce the error of rainfall on the results.

[0024] As a preferred embodiment of the present invention, calculating the regional transport contribution based on the pollutant concentrations of local stations within the current area at the current moment and the pollutant concentrations of the nearest neighbor stations in the external area of the local boundary at the previous moment, the corresponding process has the following relationship: ; Among them, represents T-τ the contribution ratio of regional transport of the local site at time H-Z to the nearest neighbor site in the external area of the local boundary corresponding to the pollutant p to T the regional transport contribution ratio of the local site at time; represents T the concentration of the pollutant H corresponding to the local site at time p at a height of 3 (unit: μg / m ); T-τ represents H-Z the concentration of the pollutant p corresponding to the nearest neighbor site in the external area of the local boundary at a height of 3 (unit: μg / m

[0025] As a preferred embodiment of the present invention, according to the land use type, the pollution sources passed by the backward trajectories between two sites are divided into different categories, such as urban industrial areas (industrial sources), urban residential areas (residential sources), and forest areas (natural sources), etc. For the quantitative analysis of regional pollution of the concentration at each hourly time node of different backward trajectories, the following relationship exists in the process of obtaining the emission fluxes of pollutants in pollution sources of different land use types: ; Among them, represents i the emission flux of the pollutant p in the 2 th pollution source passed by the backward trajectory between two points (unit: μg / (m represents i the emission amount of the pollutant p in the th pollution source passed by the backward trajectory between two points (unit: μg / s); i represents 2 the horizontal area of the

[0026] th pollution source passed by the backward trajectory between two points (unit: m ; Among them, represents T the concentration of the pollutant H corresponding to the local site at time p at a height of 3); express T-τ The altitude at the moment is H-Z The pollutants corresponding to the nearest neighboring stations in the local boundary area outside p The concentration (unit: μg / m 3 ); express T The altitude at the moment is H Local site and T-τ The altitude at the moment is H-Z The backward trajectory between the nearest neighbor sites in the local boundary outside the area passes through a height of h No. i Pollutants in pollution sources p The concentration change (unit: μg / m 3 ); Represents the pollutants in the pollution source that the backward trajectory between two points passes through p The concentration proportional coefficient.

[0027] T The altitude at the moment is H Local site and T-τ The altitude at the moment is H-Z The height of the backward trajectory between the nearest neighbor sites in the local boundary outside the area is h No. i Pollutants in pollution sources p The calculation process of the concentration change has the following relationship: ; in, and They represent the number of points that the backward trajectory between two points passes through. i Pollutants in pollution sources p The emission flux and the pollutants in all pollution sources passed by the backward trajectory between two points p The average emission flux (unit: μg / (m 2 s)); and Represents the pollution sources that the backward trajectory between two points passes through i The distance between the two points and the distance of the backward trajectory between the two points (unit: m); The backward trajectory between two points passes through i The vertical mixing coefficient of each pollution source at height h (unit: m 2 / s); The backward trajectory between two points passes through i The wind speed at the corresponding height h for each pollution source (unit: m / s); The backward trajectory between two points passes through iThe contribution ratio of the pollution amount formed by the direct emission, diffusion effect, and physicochemical processes such as chemical transformation of each pollution source to the concentration change; Indicates the i th pollution source passed by the backward trajectory between two points, and the deviation of the concentration change of the pollution source is expected to be close to 0.

[0028] The concentration proportionality coefficient of the pollutants within the pollution source passed by the backward trajectory between two points p There is the following relationship in the calculation process: .

[0029] Please refer to Figure 3 , this embodiment also provides a pollutant concentration contribution identification system driven by multi-point networking and tracing. Among them, the system applies the above-mentioned multi-point networking and tracing driven pollutant concentration contribution identification method, and the system includes: Data observation module, used for: Obtain the location of the pollutant concentration high-value site and the time period corresponding to the pollutant concentration high value; Meteorological simulation module, used for: Obtain the meteorological data of the high-value site area, and perform meteorological simulation on the meteorological data according to the underlying surface characteristics of the high-value site area to obtain a three-dimensional meteorological field; Networking and tracing module, used for: The site performs backward trajectory tracing according to the three-dimensional meteorological field to determine the backward trajectory in the first several hours before the time of the pollutant concentration high value; Perform multi-site networking and linkage tracing according to the backward trajectory in the first several hours before the time of the pollutant concentration high value to determine the backward trajectories and pollution sources of different sites; Quantitative analysis module, used for: Combine the backward trajectories of different sites with the three-dimensional concentration distribution data of the air quality in the pollutant concentration high-value site area to calculate the concentration at each hourly time node of different backward trajectories; Based on the concentration at each hourly time node of different backward trajectories, perform quantitative analysis of regional pollution to determine the regional transmission contribution, the emission flux of pollutants within pollution sources of different land use types, and the concentration change process of pollutants within pollution sources.

[0030] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown in sequence according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0031] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following technologies well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logic functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0032] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0033] The above-described embodiments merely represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A method for identifying pollutant concentration contributions driven by multi-point networking traceability, characterized in that, The method includes the following steps: Step 1: Obtain the locations of the high-pollutant-concentration sites and the time periods corresponding to the high values of the pollutant concentrations; Step 2: Obtain the meteorological data of the high-value site area, and perform meteorological simulation on the meteorological data according to the underlying surface characteristics of the high-value site area to obtain a three-dimensional meteorological field; Step 3: The site performs backward trajectory tracing based on the three-dimensional meteorological field to determine the backward trajectories in the several hours before the time of the high pollutant concentration; Step 4: Perform multi-site networking linkage tracing based on the backward trajectories in the several hours before the time of the high pollutant concentration to determine the backward trajectories and pollution sources of different sites; Step 5: Combine the backward trajectories of different sites with the three-dimensional air quality spatial concentration distribution data of the high-pollutant-concentration site area, and calculate the hourly time-node concentrations of different backward trajectories; Step 6: Based on the hourly time-node concentrations of different backward trajectories, perform quantitative analysis of regional pollution to determine the regional transmission contribution, the emission fluxes of pollutants within pollution sources of different land use types, and the change process of pollutant concentrations within pollution sources.

2. The method for identifying pollutant concentration contribution driven by multi-point networking traceability according to claim 1, characterized in that In Step 6, for quantitatively analyzing regional pollution based on the hourly time-node concentrations of different backward trajectories and obtaining the regional transmission contribution, the specific steps are as follows: Determine the high-value area sites and the sites that are linked for tracing with the high-value area sites; Obtain the pollutant concentrations of local sites within the current region at the current moment, and obtain the pollutant concentrations of the nearest neighbor sites in the external area of the local boundary at the previous moment; Exclude the influence of rainfall on atmospheric pollutants, and calculate the regional transmission contribution based on the pollutant concentrations of local sites within the current region at the current moment and the pollutant concentrations of the nearest neighbor sites in the external area of the local boundary at the previous moment.

3. The method for identifying pollutant concentration contribution driven by multi-point networking traceability according to claim 2, characterized in that Excluding the influence of rainfall on atmospheric pollutants specifically includes the following steps: Define the end of the influence of rainfall on pollutants as when the humidity drops below 80% after rainfall; Calculate the time period during which rainfall will have an impact on pollutants from the start to the end of rainfall to obtain the rainfall impact time period; Exclude the sites within the rainfall impact time period from participating in the regional transmission contribution to reduce the error of rainfall on the results.

4. The multi-point network tracing driven pollutant concentration contribution identification method according to claim 3 is characterized in that: In Step 6, when calculating the regional transmission contribution based on the pollutant concentrations of local sites within the current region at the current moment and the pollutant concentrations of the nearest neighbor sites in the external area of the local boundary at the previous moment, the corresponding process has the following relational expressions: ； Among them, represents T - τ the contribution ratio of regional transport of the local site at time H - Z to the nearest neighbor site in the external region of the local boundary with a height of p for T the pollutant corresponding to the nearest neighbor site; represents T the concentration of the pollutant corresponding to the local site at time H with a height of p ; represents T - τ the concentration of the pollutant corresponding to the nearest neighbor site in the external region of the local boundary at time H - Z with a height of p .

5. The multi-point network source tracing driven pollutant concentration contribution identification method according to claim 4 is characterized in that: The process of calculating the time period during which rainfall will have an impact on pollutants from the start to the end of rainfall has the following relational expressions: ； wherein ， represents the time when rainfall affects air pollutants; represents the time when rainfall may occur; represents the time when the concentration of air pollutants is affected after rainfall.

6. The method for identifying pollutant concentration contribution driven by multi-point networking traceability according to claim 5, characterized in that, In Step 6, when quantitatively analyzing regional pollution based on the hourly time-node concentrations of different backward trajectories and obtaining the emission fluxes of pollutants within pollution sources of different land use types, the corresponding process has the following relational expressions: ； Among them, represents the pollutant i emission flux within the p th pollution source passed by the backward trajectory between two points; represents the pollutant i emission amount within the p th pollution source passed by the backward trajectory between two points; represents the horizontal area of the i th pollution source passed by the backward trajectory between two points.

7. The method for identifying pollutant concentration contribution driven by multi-point networking traceability according to claim 6, characterized in that In Step 6, when quantitatively analyzing regional pollution based on the hourly time-node concentrations of different backward trajectories and obtaining the change process of pollutant concentrations within pollution sources, the corresponding process has the following relational expressions: ； Among them, represents T the concentration of pollutants corresponding to the local site with height at H ; p The concentration; represents T - τ the concentration of pollutants corresponding to the nearest neighbor site in the external area of the local boundary with height at H - Z ; p The concentration; represents T the change in the concentration of pollutants in the H th pollution source with height passed by the backward trajectory between the local site with height at T - τ and the nearest neighbor site in the external area of the local boundary with height at H - Z ; h The i th; p The concentration change of pollutants in the pollution source; represents the concentration ratio coefficient of pollutants in the pollution source passed by the backward trajectory between two points. p ​ 8. The method for identifying pollutant concentration contribution driven by multi-point networking traceability according to claim 7, characterized in that T The height at a certain moment is H of the local site and T - τ The height at a certain moment is H - Z The height passed by the backward trajectory between the local site and the nearest neighbor site in the external area of the local boundary is h For the i th pollutant in the p pollution source, there is the following relationship in the calculation process of the concentration change: ； in, and They represent the number of points that the backward trajectory between two points passes through. i Pollutants in pollution sources p The emission flux and the pollutants in all pollution sources passed by the backward trajectory between two points p The average emission flux of and Represents the pollution sources that the backward trajectory between two points passes through i The distance between the two points and the distance of the backward trajectory between the two points; The backward trajectory between two points passes through i The corresponding height of pollution sources h The vertical mixing coefficient of The backward trajectory between two points passes through i The corresponding height of pollution sources h wind speed; The backward trajectory between two points passes through i The contribution ratio of direct emissions from each pollution source, diffusion effects, and pollution formed by chemical conversion processes to concentration changes; The backward trajectory between two points passes through i The deviation of the concentration change of each pollution source.

9. The method for identifying pollutant concentration contribution driven by multi-point networking traceability according to claim 8, characterized in that The backward trajectory between two points passes through the pollutants in the pollution source p The calculation process of the concentration proportional coefficient has the following relationship: 。 10. A pollutant concentration contribution identification system driven by multi-point networking traceability, characterized in that, The system applies the method for identifying pollutant concentration contributions driven by multi-point networking tracing as described in any one of claims 1 to 9. The system includes: A data observation module for: Obtain the locations of the high-value pollutant concentration sites and the time periods corresponding to the high-value pollutant concentrations; A meteorological simulation module, for: Obtain the meteorological data of the high-value site area, and perform meteorological simulation on the meteorological data according to the underlying surface characteristics of the high-value site area to obtain a three-dimensional meteorological field; A network tracing module, for: The site performs backward trajectory tracing based on the three-dimensional meteorological field to determine the backward trajectories in the previous several hours of the high-value pollutant concentration time; Perform multi-site networked linkage tracing based on the backward trajectories in the previous several hours of the high-value pollutant concentration time to determine the backward trajectories and pollution sources of different sites; A quantitative analysis module, for: Combine the backward trajectories of different sites with the three-dimensional air quality spatial concentration distribution data of the high-value pollutant concentration site area, and calculate the concentration at each hourly time node of different backward trajectories; Based on the concentration at each hourly time node of different backward trajectories, conduct quantitative analysis of regional pollution to determine the regional transmission contribution, the emission fluxes of pollutants within pollution sources of different land use types, and the change process of pollutant concentrations within pollution sources.

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