A method and system for analyzing emission pollution sources based on lidar

Abstract

The application discloses a kind of based on laser radar's discharge pollution source analysis method and system, the method includes: according to first pollutant concentration information and second pollutant concentration information, respectively to the various pollutant gases directly obtained from ground monitoring station or satellite and the single pollutant gas monitored by differential absorption module, and the various particulates directly obtained from ground monitoring station or satellite and the multiple particulates monitored by differential absorption module are carried out correlation analysis, and correlation analysis result is obtained;Based on correlation analysis result, identify the composition of discharge pollution source in the discharge area to be measured.The application can carry out high spatial resolution and accurate discharge analysis to discharge pollution source.

Description

Technical Field

[0001] This invention belongs to the field of pollution source analysis technology, and in particular relates to a method and system for analyzing emission pollution sources based on lidar. Background Technology

[0002] With rapid industrialization and urbanization, air pollution has become increasingly serious, especially in areas with high pollutant emissions, such as industrial parks, where emissions have a significant impact on air quality and human health. Accurate monitoring and analysis of the emission sources and diffusion pathways of these pollutants are crucial for developing effective pollution control strategies and improving air quality.

[0003] Traditional emission source tracing and prediction primarily rely on ground-based monitoring stations and satellite remote sensing data. Ground-based monitoring stations, due to their limited distribution, struggle to acquire information on the three-dimensional spatial distribution and dynamic changes of pollutants. Satellite remote sensing has limitations in spatiotemporal resolution, cloud cover handling, and nighttime monitoring capabilities. Emission analysis and diffusion prediction based on such data often suffer from incomplete spatial coverage, poor temporal continuity, and missing vertical distribution information. This poses challenges to the precise location of pollution sources and accurate prediction of diffusion paths, making it difficult to meet the reliability and timeliness requirements of precise emission management.

[0004] Lidar technology, as an emerging atmospheric remote sensing method, can acquire three-dimensional concentration distribution and wind field information of pollutants in the atmosphere by emitting and receiving laser pulses of specific wavelengths. However, existing lidar-based monitoring systems mostly adopt fixed scanning modes, which cannot capture the dynamic process of emissions, resulting in low usability of the measured data; a single lidar unit can usually only measure the concentration of a single gas and cannot directly distinguish multiple gas components in the emission source; furthermore, the methods for fusing lidar data with ground station and satellite data are still immature, which restricts the improvement of the ability to accurately locate emission sources.

[0005] In summary, there is an urgent need for a method and system for analyzing emission pollution sources based on lidar, which can accurately analyze, trace, and predict the spread of pollution sources. This is of great significance for building a high-precision emission monitoring and early warning system and implementing precise emission reduction. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for analyzing emission pollution sources based on lidar. This system can accurately analyze, trace, and predict the diffusion of pollution sources, improving the spatiotemporal resolution of emission and pollution monitoring while significantly enhancing the reliability of component analysis and source tracing diffusion analysis.

[0007] To achieve the above objectives, the present invention employs the following technical solution:

[0008] In a first aspect, the present invention provides a method for analyzing emission pollution sources based on lidar, comprising:

[0009] Acquire three-dimensional wind field information, primary pollutant concentration information, and secondary pollutant concentration information of the emission area to be measured;

[0010] The first pollutant concentration information includes concentration data of a single pollutant gas and multiple particulate matter monitored by the differential absorption module; the second pollutant concentration information includes concentration data of multiple pollutant gases and multiple particulate matter directly obtained from ground monitoring stations or satellites; the first pollutant concentration information has high spatial resolution; wherein, the pollutant gas type corresponding to the first pollutant concentration information is included in the pollutant gas type corresponding to the second pollutant concentration information, and the particulate matter type corresponding to the first pollutant concentration information is included in the particulate matter type corresponding to the second pollutant concentration information;

[0011] Based on the concentration information of the first pollutant and the concentration information of the second pollutant, correlation analysis was performed on various pollutant gases directly obtained from ground monitoring stations or satellites and single pollutant gases monitored by differential absorption modules, as well as various particulate matter directly obtained from ground monitoring stations or satellites and multiple particulate matter monitored by differential absorption modules, and the correlation analysis results were obtained.

[0012] Based on the correlation analysis results, the components of the emission sources in the emission area to be measured are identified.

[0013] Optionally, the steps for obtaining the three-dimensional wind field information and the concentration information of the first pollutant in the emission area to be measured include:

[0014] The Doppler wind measurement module of lidar is used to perform VAD scanning of the emission area to be measured to obtain three-dimensional wind field information;

[0015] The differential absorption module of the lidar is used to adaptively scan the emission area to be tested, and the concentration data of a single pollutant gas and multiple particulate matter are obtained from the first pollutant concentration information.

[0016] Optionally, the adaptive scanning of the emission area to be tested includes:

[0017] Based on historical three-dimensional wind field information, the historical patterns of the three-dimensional wind field are obtained;

[0018] The initial scanning method of the differential absorption module is determined based on the historical patterns of the three-dimensional wind field;

[0019] The initial scanning method is adjusted based on the real-time measured three-dimensional wind field information to obtain the adaptive scanning method and the corresponding adaptive scanning parameters;

[0020] Adaptive scanning is performed based on the adaptive scanning method and the corresponding adaptive scanning parameters;

[0021] The adaptive scanning methods include PPI scanning, RHI scanning, and PPI / RHI combined scanning; the adaptive scanning parameters for PPI scanning include azimuth range, distance range, and azimuth step interval; the adaptive scanning parameters for RHI scanning include elevation range, altitude range, and elevation step interval; and the adaptive scanning parameters for PPI / RHI combined scanning include the adaptive scanning parameters for both PPI and RHI scanning.

[0022] Optionally, based on the first pollutant concentration information and the second pollutant concentration information, correlation analysis is performed on various pollutant gases directly obtained from ground monitoring stations or satellites and single pollutant gases monitored by the differential absorption module, as well as various particulate matter directly obtained from ground monitoring stations or satellites and multiple particulate matter monitored by the differential absorption module, to obtain correlation analysis results, including:

[0023] Based on the first pollutant concentration information and the second pollutant concentration information of the emission area to be tested, the first pollutant concentration information and the second pollutant concentration information of the upwind side of the emission area to be tested, as well as the first pollutant concentration information and the second pollutant concentration information of the downwind side of the emission area to be tested are obtained.

[0024] Based on the first and second pollutant concentration information at the upwind end, the correlation coefficients of various pollutant gases observed by ground monitoring stations or satellites at the upwind end and the single pollutant gases monitored by the differential absorption module are calculated to obtain the correlation coefficients of various pollutant gases observed by ground monitoring stations or satellites and the single pollutant gases monitored by the differential absorption module at the upwind end.

[0025] Based on the concentration information of the first pollutant and the second pollutant at the upwind end, the correlation coefficients of various particulate matter observed by ground monitoring stations or satellites at the upwind end and various particulate matter monitored by differential absorption modules are calculated to obtain the correlation coefficients of various particulate matter observed by ground monitoring stations or satellites and various particulate matter monitored by differential absorption modules at the upwind end.

[0026] Based on the first and second pollutant concentration information at the downwind end, the correlation coefficients of various pollutant gases observed by ground monitoring stations or satellites at the downwind end and the single pollutant gases monitored by differential absorption modules are calculated to obtain the correlation coefficients of various pollutant gases observed by ground monitoring stations or satellites and the single pollutant gases monitored by differential absorption modules at the downwind end.

[0027] Based on the concentration information of the first pollutant and the second pollutant at the downwind end, the correlation coefficients of various particulate matter observed by ground monitoring stations or satellites at the downwind end and various particulate matter monitored by differential absorption modules are calculated to obtain the correlation coefficients of various particulate matter observed by ground monitoring stations or satellites and various particulate matter monitored by differential absorption modules at the downwind end.

[0028] The correlation coefficients between various pollutant gases observed by ground monitoring stations or satellites and single pollutant gases monitored by differential absorption modules at upwind and downwind locations, as well as the correlation coefficients between various particulate matter observed by ground monitoring stations or satellites and multiple particulate matter monitored by differential absorption modules at upwind and downwind locations, are used as the results of correlation analysis.

[0029] Optionally, the formula for calculating the correlation coefficient is expressed as follows:

[0030] ;

[0031] ;

[0032] In the formula: r up This represents the correlation coefficient between the m-th pollutant gas or particulate matter observed by a ground monitoring station or satellite and the single pollutant gas or particulate matter monitored by a differential absorption module at the upwind location; r down The correlation coefficient between the m-th pollutant gas or particulate matter observed by the ground monitoring station or satellite and the single pollutant gas or particulate matter monitored by the differential absorption module at the downwind end; This represents the concentration data measured by the differential absorption module at the upwind end for the first type of pollutant gas or particulate matter; j upm This represents the concentration data of the m-th pollutant gas or particulate matter measured by a ground monitoring station or satellite upwind; i up1,k This represents the concentration data of the kth measurement conducted by the differential absorption module at the upwind end for the first type of pollutant gas or particulate matter; n represents the total number of measurements; and k is the sequence number. This represents the average concentration data from n measurements of a single pollutant gas or particulate matter conducted by the differential absorption module at the upwind end; j upm,k This represents the concentration data of the m-th pollutant gas or particulate matter measured by a ground monitoring station or satellite upwind. This represents the average concentration data of n measurements of the m-th pollutant gas or particulate matter conducted by a ground monitoring station or satellite upwind; m represents the total number of pollutant gas types or particulate matter types observed by the ground monitoring station or satellite; i down1,k This represents the concentration data of the kth measurement conducted by the differential absorption module at the downwind end for a single pollutant gas type or particulate matter type; This represents the average concentration data of n measurements taken by the differential absorption module at the downwind end for a single pollutant gas type or particulate matter type. i down1 This represents the concentration data of the first pollutant gas or particulate matter measured by the differential absorption module at the downwind end; j downm This represents the concentration data of the m-th pollutant gas or particulate matter measured downwind by a ground monitoring station or satellite; j downm,k This represents the concentration data of the m-th pollutant gas or particulate matter measured downwind by a ground monitoring station or satellite. This represents the average concentration data of n measurements of the m-th pollutant gas or particulate matter conducted by a ground monitoring station or satellite downwind.

[0033] Optionally, identifying the components of emission sources in the emission area based on correlation analysis results includes:

[0034] Based on the results of correlation analysis and the primary judgment criteria, the pollution source is determined, and the components of the pollution sources in the emission area to be measured are obtained.

[0035] The first criterion includes:

[0036] If the correlation coefficient between a certain pollutant gas observed by a ground monitoring station or satellite and a single pollutant gas monitored by a differential absorption module is greater than the first threshold at the downwind end and less than the second threshold at the upwind end, then the pollutant gas observed by the ground monitoring station or satellite is determined to be a component of the emission source in the emission area to be tested; otherwise, it is not a component of the emission source in the emission area to be tested.

[0037] If the correlation coefficient between a certain particulate matter observed by a ground monitoring station or satellite and a certain particulate matter monitored by a differential absorption module is greater than the third threshold downwind, and less than the fourth threshold upwind, then the particulate matter observed by the ground monitoring station or satellite is determined to be a component of the emission source in the emission area to be tested; otherwise, it is not a component of the emission source in the emission area to be tested. Optionally, the emission source analysis method further includes:

[0038] Based on the three-dimensional wind field information and the concentration information of the first pollutant, a quantitative analysis of the backward trajectory of the emission source components in the emission area to be measured is performed to obtain the potential source area and the concentration weight of the potential source area of ​​the emission source components.

[0039] The quantitative analysis of the backward trajectory of the pollutant source components in the emission area to be tested includes:

[0040] The concentration data of emission pollutant components monitored by the upwind differential absorption module, the three-dimensional wind field information, and the meteorological data of the global data assimilation system are input into the backward trajectory model for simulation to obtain all backward trajectories of emission pollutant components in the emission area to be measured.

[0041] Combining the three-dimensional wind field information and the meteorological data, the backward trajectory of different receptor points corresponding to the concentration of pollutant components emitted at the upwind end of the emission area to be measured is calculated respectively; and cluster analysis is performed based on the backward trajectory of different receptor points to obtain the proportion of each backward trajectory cluster, and the backward trajectory cluster with the largest proportion is taken as the potential source direction of the emission pollutant components.

[0042] Using the PSCF model, combined with three-dimensional wind field information, adaptive scanning method and corresponding adaptive scanning parameters, the region in the potential source direction of the emission area to be measured is divided into backward grids, and the probability that the backward trajectory of different receptor points above the first concentration threshold falls within the backward grid is calculated to obtain the backward contribution probability of all backward grids; the backward grids corresponding to the backward contribution probability exceeding the PSCF threshold are taken as the potential source areas of the emission pollution source components.

[0043] When the components of the emitted pollution source belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the CWT model is used to assign the concentration values ​​of the emitted pollution source components at different receptor points monitored by the differential absorption module to the backward trajectory passing through that point, and the average weighted concentration of each backward grid is calculated to obtain the concentration weight of all backward grids; the concentration weight of the potential source region is obtained by combining the concentration weights of all backward grids.

[0044] When the components of the emitted pollution source do not belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the concentration data of the emitted pollution source components observed by ground monitoring stations or satellites are corrected according to the proportionality coefficient to obtain the corrected concentration data of the emitted pollution source components. Using the CWT model, the corrected concentration values ​​of the emitted pollution source components at different receptor points are assigned to the backward trajectory passing through that point, and the average weighted concentration of each backward grid is calculated to obtain the concentration weight of all backward grids. Combining the concentration weights of all backward grids, the concentration weight of the potential source region is obtained.

[0045] Optionally, the emission pollution source analysis method further includes:

[0046] Based on the three-dimensional wind field information and the concentration information of the first pollutant, a quantitative analysis of the forward trajectory of the emission source components in the emission area to be measured is performed to obtain the source point influence area and the source strength weight of the source point influence area of ​​the emission source components.

[0047] The quantitative analysis of the forward trajectory of the pollutant source components in the emission area to be tested includes:

[0048] The concentration data of emission pollutant components monitored by the downwind differential absorption module, the three-dimensional wind field information, and the meteorological data of the global data assimilation system are input into the forward trajectory model for simulation to obtain all forward trajectories of emission pollutant components in the emission area to be measured.

[0049] Combining the three-dimensional wind field information and the meteorological data, the forward trajectories of different source points corresponding to the concentration of pollutant components emitted downwind of the emission area to be measured are calculated respectively; and cluster analysis is performed based on the forward trajectories of different source points to obtain the proportion of each forward trajectory cluster, and the forward trajectory cluster with the largest proportion is taken as the potential diffusion direction of the emission pollutant components.

[0050] Using the PIF model, combined with three-dimensional wind field information, adaptive scanning method and corresponding adaptive scanning parameters, the region of potential diffusion direction in the emission area to be measured is divided into forward grids, and the probability that the forward trajectory of different source points exceeding the second concentration threshold falls within the forward grid is calculated to obtain the forward contribution probability of all forward grids; the forward grids corresponding to the forward contribution probability exceeding the PIF threshold are taken as the source point influence area of ​​the emission pollution source.

[0051] When the components of the emitted pollution source belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the SWT model is used to assign the concentration values ​​of the emitted pollution source components at different source points monitored by the differential absorption module to the forward trajectory starting from that point, and the average weighted concentration of each forward grid is calculated to obtain the source strength weight of all forward grids; the source strength weight of the affected area of ​​the source point is obtained by combining the source strength weight of all forward grids.

[0052] When the components of the emitted pollution source do not belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the concentration data of the emitted pollution source components observed by ground monitoring stations or satellites are corrected according to the proportional coefficient to obtain the corrected concentration data of the emitted pollution source components. Using the SWT model, the corrected concentration values ​​of the emitted pollution source components at different source points are assigned to the forward trajectory starting from that point, and the average weighted concentration of each forward grid is calculated to obtain the source strength weight of all forward grids. Combining the source strength weights of all forward grids, the source strength weight of the affected area of ​​the source point is obtained.

[0053] Secondly, the present invention provides a lidar-based emission pollution source analysis method for implementing the lidar-based emission pollution source analysis method as described in the first aspect, comprising: a data acquisition module, and an emission analysis module, a source tracing module, and a diffusion prediction module respectively connected to the data acquisition module; the source tracing module and the diffusion prediction module are both connected to the emission analysis module;

[0054] The data acquisition module is used to acquire three-dimensional wind field information, first pollutant concentration information and second pollutant concentration information of the emission area to be measured;

[0055] The emission analysis module is used to perform correlation analysis on various pollutant gases directly obtained from ground monitoring stations or satellites and single pollutant gases monitored by the differential absorption module, as well as various particulate matter directly obtained from ground monitoring stations or satellites and multiple particulate matter monitored by the differential absorption module, based on the first pollutant concentration information and the second pollutant concentration information, to obtain the correlation analysis results; it is also used to identify the components of the emission pollution sources in the emission area to be measured based on the correlation analysis results.

[0056] The source tracing module is used to perform quantitative analysis of the backward trajectory of the emission source components in the emission area to be tested based on the three-dimensional wind field information and the first pollutant concentration information, so as to obtain the potential source area and the concentration weight of the potential source area of ​​the emission source components.

[0057] The diffusion prediction module is used to perform quantitative analysis of the forward trajectory of the emission source components in the emission area to be measured based on three-dimensional wind field information and first pollutant concentration information, so as to obtain the source point influence area and the source strength weight of the emission source components.

[0058] Optionally, the data acquisition module includes a direct acquisition unit and a lidar;

[0059] The direct acquisition unit is used to directly acquire the concentration information of the second pollutant obtained from ground monitoring stations or satellite observations;

[0060] The lidar includes a Doppler wind measurement module and a differential absorption module; the Doppler wind measurement module is used to perform VAD scanning on the emission area to be measured to obtain three-dimensional wind field information; the differential absorption module is used to perform adaptive scanning on the emission area to be measured to obtain single pollutant gas concentration data and multiple particulate matter concentration data in the first pollutant concentration information.

[0061] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0062] This invention provides a method and system for analyzing emission pollution sources based on lidar. The method combines the three-dimensional wind field information of the emission area to be measured obtained by the lidar's Doppler wind measurement module, the first pollutant concentration information obtained by the lidar's differential absorption module, and the second pollutant concentration information directly obtained from ground monitoring stations or satellites. Correlation analysis is then performed to obtain the correlation analysis results. Based on the correlation analysis results, the components of the emission pollution sources in the emission area to be measured are identified. The first pollutant concentration information, obtained by lidar, has high spatial resolution. Combining it with the second pollutant concentration information monitored by ground monitoring stations or satellites for correlation analysis avoids the limitations of existing technologies that rely solely on differential absorption modules or ground monitoring stations for emission analysis, thus improving the accuracy of emission analysis.

[0063] This invention provides a method and system for analyzing emission pollution sources based on lidar. The method further includes quantitative analysis (source tracing) of the backward trajectory of emission pollution source components in the emission area under test, based on three-dimensional wind field information and first pollutant concentration information, to obtain the potential source areas and concentration weights of the emission pollution source components. The source tracing method obtains concentration data from multiple different receptor points based on high-resolution concentration data and three-dimensional wind field information, thus determining the potential source direction of the emission pollution source components. It further refines the backward grid based on PSCF and CWT analysis methods, thereby improving the accuracy of emission analysis. This avoids the limitations of current PSCF and CWT analysis methods, which are generally restricted by the resolution of meteorological data and receptor point concentrations, making it impossible to accurately distinguish the concentration levels of emission pollution source components. Therefore, these methods can only be used as receptor points for backward tracing of potential source areas, and the need for coarse grid division during trajectory analysis directly affects the resolution and reliability of the analysis results.

[0064] This invention provides a method and system for analyzing emission pollution sources based on lidar. The method further includes quantitative analysis (diffusion prediction) of the forward trajectory of emission pollution source components in the emission area to be measured based on three-dimensional wind field information and first pollutant concentration information, to obtain the source point influence area and source strength weight of the emission pollution source components. The diffusion prediction method obtains the concentration data of multiple source points based on high-resolution concentration data and three-dimensional wind field information, thereby obtaining the potential diffusion direction of emission pollution source components. On the one hand, it can realize source-oriented contribution analysis, and on the other hand, it can divide the emission area to be measured into a more refined forward grid to improve the accuracy of the analysis results.

[0065] This invention provides a method and system for analyzing emission pollution sources based on lidar. In this method, the concentration information of the first pollutant is acquired using an adaptive scanning method. Based on historical three-dimensional wind field information, the adaptive scanning method and corresponding adaptive scanning parameters of the differential absorption module are determined, further enhancing the accuracy of subsequent emission analysis, source tracing, and diffusion prediction.

[0066] This invention provides a method and system for analyzing emission pollution sources based on lidar. In the correlation analysis process of this method, the concentration data of a single pollutant gas / multiple particulate matter observed by the differential absorption module and the concentration data of multiple air pollutants / multiple particulate matter observed by ground stations or satellites are used to perform correlation analysis of various pollutant gases / particulate matter at the upwind and downwind points of the area to be measured. Combined with a first judgment criterion, the pollution source is determined to obtain the emission pollution source components in the emission area to be measured. This emission analysis module can achieve pollution source analysis specifically for the emission area to be measured, which helps to improve the accuracy of pollution source analysis.

[0067] This invention provides a method and system for analyzing emission pollution sources based on lidar. The system includes a data acquisition module for obtaining three-dimensional wind field information, first pollutant concentration information, and second pollutant concentration information of the emission area to be measured; an emission analysis module for performing correlation analysis based on the data acquired by the data acquisition module to identify the emission pollution source components of the emission area to be measured; and a source tracing module and a diffusion prediction module for performing backward and forward quantitative analysis of the emission pollution sources based on the data acquired by the data acquisition module and the emission pollution source components determined by the emission analysis module, thereby achieving accurate source tracing and diffusion prediction of emission pollution sources. While improving the spatiotemporal resolution of emission and pollution monitoring, this system also significantly improves the reliability of emission component analysis and source tracing diffusion analysis, which is of great significance for building a high-precision emission monitoring and early warning system and implementing precise emission reduction. Attached Figure Description

[0068] Figure 1 The diagram shown is a flowchart of an emission pollution source analysis method based on lidar in one embodiment of the present invention.

[0069] Figure 2 The diagram shown is a schematic representation of the correlation analysis process in one embodiment of the present invention.

[0070] Figure 3 The diagram shown is a schematic diagram of the quantitative analysis process of the backward trajectory of the emission pollution source components in the emission area to be tested, according to one embodiment of the present invention.

[0071] Figure 4 The diagram shown is a schematic flowchart of a quantitative analysis process for the forward trajectory of emission pollution source components in the emission area to be tested, according to one embodiment of the present invention.

[0072] Figure 5 The figure shown is a block diagram of an emission pollution source analysis system based on lidar in one embodiment of the present invention;

[0073] Figure 6 The figure shown is a spatial distribution map of gas concentration at the downwind end of the emission area monitored by the differential absorption module in one embodiment of the present invention;

[0074] Figure 7 The diagram shown illustrates the correlation coefficients between the CO2 concentration observed by the differential absorption module and the concentrations of various gases observed by ground stations / satellites at the downwind end in one embodiment of the present invention.

[0075] Figure 8 The diagram shown is a schematic diagram of multiple forward trajectory clusters at a certain source point in the emission area to be tested, according to one embodiment of the present invention.

[0076] Figure 9 The diagram shown is a schematic representation of the potential influencing factors of a source point in the emission area to be tested, according to one embodiment of the present invention.

[0077] Figure 10 The diagram shown is a source strength weight diagram of a source point in the emission area to be tested, according to one embodiment of the present invention. Detailed Implementation

[0078] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0079] Example 1

[0080] like Figure 1 As shown in the figure, this invention provides a method for analyzing emission pollution sources based on lidar, including...

[0081] S01: Obtain three-dimensional wind field information, first pollutant concentration information, and second pollutant concentration information of the emission area to be measured;

[0082] S02: Based on the first pollutant concentration information and the second pollutant concentration information, correlation analysis is performed on various pollutant gases directly obtained from ground monitoring stations or satellites and single pollutant gases monitored by differential absorption modules, as well as various particulate matter directly obtained from ground monitoring stations or satellites and multiple particulate matter monitored by differential absorption modules, to obtain the correlation analysis results.

[0083] S03: Based on the correlation analysis results, identify the components of the emission sources in the emission area to be measured.

[0084] The first pollutant concentration information includes concentration data of a single pollutant gas and multiple particulate matter monitored by the differential absorption module; the second pollutant concentration information includes concentration data of multiple pollutant gases and multiple particulate matter directly obtained from ground monitoring stations or satellites; the first pollutant concentration information has high spatial resolution; wherein, the pollutant gas type corresponding to the first pollutant concentration information is included in the pollutant gas type corresponding to the second pollutant concentration information, and the particulate matter type corresponding to the first pollutant concentration information is included in the particulate matter type corresponding to the second pollutant concentration information;

[0085] This invention provides a method for analyzing emission pollution sources based on lidar. It combines the three-dimensional wind field information of the emission area to be measured obtained by the lidar's Doppler wind measurement module, the first pollutant concentration information obtained by the lidar's differential absorption module, and the second pollutant concentration information directly obtained from ground monitoring stations or satellites. Correlation analysis is then performed to obtain the correlation analysis results. Based on these results, the components of the emission pollution sources in the emission area to be measured are identified. The first pollutant concentration information, obtained by lidar, has high spatial resolution. Combining it with the second pollutant concentration information monitored by ground monitoring stations or satellites for correlation analysis avoids the limitations of existing technologies that rely solely on differential absorption modules or ground monitoring stations for emission analysis, thus achieving greater accuracy in emission analysis.

[0086] In this embodiment, the steps of obtaining the three-dimensional wind field information and the first pollutant concentration information of the emission area to be measured in step S1 include:

[0087] The Doppler wind measurement module of lidar is used to perform VAD scanning of the emission area to be measured to obtain three-dimensional wind field information;

[0088] The differential absorption module of the lidar is used to adaptively scan the emission area to be tested, and the concentration data of a single pollutant gas and multiple particulate matter are obtained from the first pollutant concentration information.

[0089] Furthermore, adaptive scanning of the emission area to be tested includes:

[0090] Based on historical three-dimensional wind field information, the historical patterns of the three-dimensional wind field are obtained;

[0091] The initial scanning method of the differential absorption module is determined based on the historical patterns of the three-dimensional wind field;

[0092] The initial scanning method is adjusted based on the real-time measured three-dimensional wind field information to obtain the adaptive scanning method and the corresponding adaptive scanning parameters;

[0093] Adaptive scanning is performed based on the adaptive scanning method and the corresponding adaptive scanning parameters;

[0094] The adaptive scanning methods include PPI scanning, RHI scanning, and PPI / RHI combined scanning; the adaptive scanning parameters for PPI scanning include azimuth range, distance range, and azimuth step interval; the adaptive scanning parameters for RHI scanning include elevation range, altitude range, and elevation step interval; and the adaptive scanning parameters for PPI / RHI combined scanning include the adaptive scanning parameters for both PPI and RHI scanning.

[0095] Specifically: First, based on historical three-dimensional wind field information, the historical patterns of the three-dimensional wind field are obtained, and the wind speed and frequency of each wind direction in the emission area to be measured within a certain statistical time period (historical patterns) are obtained. The differential absorption module scanning mode and corresponding scanning parameters suitable for the characteristics of the wind field are initially set (such as PPI scanning and the corresponding azimuth range, distance range and azimuth step interval / RHI scanning and the corresponding elevation range, distance range and elevation step interval).

[0096] When the vertical wind speed is low and the horizontal wind speed is high, PPI scanning is used. The azimuth range of PPI scanning is determined by the proportion of wind direction frequency in the horizontal wind field (directions with a high proportion are within the range of PPI scanning, while those with a low proportion are not). The distance range and the step interval of the azimuth are determined by the magnitude of the horizontal wind speed. When the horizontal wind speed is low and the vertical wind speed is high, RHI scanning is used. The elevation range, distance range, and elevation step interval of RHI scanning are all determined by the magnitude of the vertical wind speed.

[0097] Simultaneously, after each scan by the differential absorption module, a VAD scan of the Doppler wind measurement module is performed to obtain real-time three-dimensional wind field information. During subsequent measurements, the wind field may change. By feeding back the wind speed and direction measured in the real-time wind field using the VAD scan mode to the adaptive scan module, the previously preset differential absorption module scan mode and corresponding scan parameters are adjusted based on the current measurement values. After the next differential absorption module and VAD scan, adjustments are made again based on the current wind field measurement values. This ensures that during the detection of the emission area, the scan mode and scan parameters are suitable for the current wind field characteristics, improving detection efficiency and avoiding unnecessary scans.

[0098] In this embodiment, step S03 shows that the emission analysis of the emission pollution source includes a correlation analysis step and an identification step;

[0099] like Figure 2As shown, the steps of correlation analysis (based on the concentration information of the first pollutant and the concentration information of the second pollutant, performing correlation analysis between various pollutant gases directly obtained from ground monitoring stations or satellites and single pollutant gases monitored by differential absorption modules, and various particulate matter directly obtained from ground monitoring stations or satellites and multiple particulate matter monitored by differential absorption modules, to obtain the correlation analysis results) include:

[0100] S11: Based on the first pollutant concentration information and the second pollutant concentration information of the emission area to be measured, obtain the first pollutant concentration information and the second pollutant concentration information of the upwind side of the emission area to be measured, as well as the first pollutant concentration information and the second pollutant concentration information of the downwind side.

[0101] Specifically, the locations of the upwind and downwind outlets in the emission area under test are obtained based on three-dimensional wind direction information;

[0102] S12: Based on the first and second pollutant concentration information at the upwind end, calculate the correlation coefficient between various pollutant gases observed by ground monitoring stations or satellites at the upwind end and the single gas monitored by the differential absorption module, and obtain the correlation coefficient between various pollutant gases observed by ground monitoring stations or satellites and the single pollutant gases monitored by the differential absorption module at the upwind end.

[0103] S13: Based on the concentration information of the first pollutant and the second pollutant at the upwind end, the correlation coefficients of various particulate matter observed by the ground monitoring station or satellite at the upwind end and various particulate matter monitored by the differential absorption module are calculated to obtain the correlation coefficients of various particulate matter observed by the ground monitoring station or satellite and various particulate matter monitored by the differential absorption module at the upwind end.

[0104] Specifically, based on steps S12 and S13, the correlation coefficient of the upwind direction can be expressed by the formula:

[0105]

[0106] Where: Where: r up The correlation coefficient between the m-th pollutant gas or particulate matter observed by the ground monitoring station or satellite and the single pollutant gas or particulate matter monitored by the differential absorption module at the upwind end; This represents the concentration data measured by the differential absorption module at the upwind end for the first type of pollutant gas or particulate matter; j upm This represents the concentration data of the m-th pollutant gas or particulate matter measured by a ground monitoring station or satellite upwind; i up1,k This represents the concentration data of the kth measurement conducted by the differential absorption module at the upwind end for the first type of pollutant gas or particulate matter; n represents the total number of measurements; and k is the sequence number. This represents the average concentration data from n measurements of a single pollutant gas or particulate matter conducted by the differential absorption module at the upwind end; j upm,k This represents the concentration data of the m-th pollutant gas or particulate matter measured by a ground monitoring station or satellite upwind. This represents the average concentration data of n measurements of the m-th pollutant gas or particulate matter conducted by a ground monitoring station or satellite upwind; m represents the total number of pollutant gas types or particulate matter types observed by the ground monitoring station or satellite.

[0107] S14: Based on the first pollutant concentration information and the second pollutant concentration information at the downwind end, calculate the correlation coefficient between various pollutant gases observed by the ground monitoring station or satellite at the downwind end and the single gas monitored by the differential absorption module, and obtain the correlation coefficient between various pollutant gases observed by the ground monitoring station or satellite and the single pollutant gas monitored by the differential absorption module at the downwind end.

[0108] S15: Based on the concentration information of the first pollutant and the second pollutant at the downwind end, calculate the correlation coefficient between various particulate matter observed by the ground monitoring station or satellite at the downwind end and various particulate matter monitored by the differential absorption module, and obtain the correlation coefficient between various particulate matter observed by the ground monitoring station or satellite and various particulate matter monitored by the differential absorption module at the downwind end.

[0109] Specifically, based on steps S14 and S15, the correlation coefficient of the downwind outlet can be expressed by the formula:

[0110] ;

[0111] In the formula: r down This represents the correlation coefficient between the m-th pollutant gas or particulate matter observed by the ground monitoring station or satellite and the single pollutant gas or particulate matter monitored by the differential absorption module at the downwind end; n represents the total number of measurements; k is the sequence number; m represents the total number of pollutant gas types or particulate matter species corresponding to the second pollutant concentration information; i down1,k This represents the concentration data of the kth measurement conducted by the differential absorption module at the downwind end for a single pollutant gas type or particulate matter type; This represents the average concentration data of n measurements taken by the differential absorption module at the downwind end for a single pollutant gas type or particulate matter type. i down1 This represents the concentration data of the first pollutant gas or particulate matter measured by the differential absorption module at the downwind end; j downm This represents the concentration data of the m-th pollutant gas or particulate matter measured downwind by a ground monitoring station or satellite; j downm,k This represents the concentration data of the m-th pollutant gas or particulate matter measured downwind by a ground monitoring station or satellite. This represents the average concentration data of n measurements of the m-th pollutant gas or particulate matter conducted by a ground monitoring station or satellite downwind.

[0112] S16: The correlation coefficients between various pollutant gases observed by ground monitoring stations or satellites and single pollutant gases monitored by differential absorption modules at upwind and downwind locations, as well as the correlation coefficients between various particulate matter observed by ground monitoring stations or satellites and multiple particulate matter monitored by differential absorption modules at upwind and downwind locations, are used as the results of correlation analysis.

[0113] The identification steps (based on correlation analysis results, identifying the components of emission sources in the emission area to be measured) include:

[0114] Based on the results of correlation analysis and the primary judgment criteria, the pollution source is determined, and the components of the pollution sources in the emission area to be measured are obtained.

[0115] The first criterion includes:

[0116] If the correlation coefficient between a certain pollutant gas observed by a ground monitoring station or satellite and a single pollutant gas monitored by a differential absorption module is greater than the first threshold at the downwind end and less than the second threshold at the upwind end, then the pollutant gas observed by the ground monitoring station or satellite is determined to be a component of the emission source in the emission area to be tested; otherwise, it is not a component of the emission source in the emission area to be tested.

[0117] If the correlation coefficient between a certain particulate matter observed by a ground monitoring station or satellite and a certain particulate matter monitored by the differential absorption module is greater than the third threshold at the downwind end and less than the fourth threshold at the upwind end, then the particulate matter observed by the ground monitoring station or satellite is determined to be a component of the emission source in the emission area to be tested; otherwise, it is not a component of the emission source in the emission area to be tested. Specifically, in this embodiment, the differential absorption module of the lidar measures a single pollutant gas, including any one of the following: CO2, H2O, NH3, C2H2, HCN, CO, CH4, N2O, etc. The selection of a single pollutant gas can be based on the emission characteristics of the emission area to be tested. For example, the main emission gas of a cement plant is CO2, so CO2 can be selected as the corresponding single pollutant gas. The first threshold, second threshold, third threshold, and fourth threshold in the steps are set according to the type of single pollutant gas and particulate matter.

[0118] Furthermore, based on steps S01-S03, this embodiment of the invention also introduces a method for analyzing emission pollution sources based on lidar, including...

[0119] Based on the three-dimensional wind field information and the concentration information of the first pollutant, a quantitative analysis of the backward trajectory of the emission source components in the emission area to be measured is performed to obtain the potential source area and the concentration weight of the potential source area of ​​the emission source components.

[0120] like Figure 3 As shown, a quantitative analysis of the backward trajectory of pollutant source components in the emission area to be measured includes:

[0121] S21: Input the concentration data of emission pollutant components monitored by the upwind differential absorption module, the three-dimensional wind field information, and the meteorological data of the global data assimilation system into the backward trajectory model for simulation to obtain all backward trajectories of emission pollutant components in the emission area to be measured.

[0122] Specifically, in this embodiment, meteorological data includes assimilated data from the Global Data Assimilation System, reanalysis data, etc.; the backward trajectory model can use trajectory modules such as HYSPLIT, FLEXPART, and WRF-Chem.

[0123] Specifically, before conducting backward trajectory simulation, the following parameters need to be determined: receiver point (setting the geographical location and monitoring height of the upwind location as the endpoint of the backward trajectory), backward simulation time, backward start time, and backward simulation duration (determined based on the study area and pollutant lifetime).

[0124] S22: Combining the three-dimensional wind field information and the meteorological data, calculate the backward trajectory of different receptor points corresponding to the concentration of pollutant source components emitted at the upwind end of the emission area to be measured; and perform cluster analysis based on the backward trajectories of different receptor points to obtain the proportion of each backward trajectory cluster, and take the backward trajectory cluster with the largest proportion as the potential source direction of the emission pollutant source components.

[0125] S23: Using the PSCF model, combined with three-dimensional wind field information, adaptive scanning method and corresponding adaptive scanning parameters, the region in the potential source direction of the emission area to be tested is divided into backward grids, and the probability that the backward trajectory of different receptor points above the first concentration threshold falls within the backward grid is calculated to obtain the backward contribution probability of all backward grids; the backward grids corresponding to the backward contribution probability exceeding the PSCF threshold are taken as the potential source areas of the emission pollution source components.

[0126] Specifically, the 85th percentile of the emission pollution source concentration value detected by the differential absorption module at the receiver point upwind is used as the first concentration threshold;

[0127] Specifically, based on the PSCF (Potential Source Contribution Function) model, the probability that the backward trajectory of different receptor points above the first concentration threshold falls within the backward grid is calculated, and the formula for the backward contribution probability of all backward grids is expressed as:

[0128] When the adaptive scanning mode is PPI scanning:

[0129]

[0130] in, This represents the backward contribution probability of a certain backward grid during PPI scanning; the subscript αr indicates the minimum backward grid determined by the differential absorption module based on the azimuth angle α and distance r corresponding to the PPI scanning mode; the subscript up indicates that the grid is located upwind; m αr It is the number of trajectories passing through grid αr, n αr It is the number of all trajectories passing through grid αr;

[0131] When the adaptive scanning mode is RHI scan:

[0132]

[0133] in, This represents the backward contribution probability of a certain backward grid during RHI scanning; the subscript θz indicates the minimum backward grid determined by the differential absorption module based on the elevation angle θ and height z corresponding to the RHI scanning mode; the subscript up indicates that the grid is located upwind; m θz It is the number of trajectories passing through grid θz, n θz It is the number of all trajectories passing through grid θz;

[0134] When the adaptive scanning mode is a PPI / RHI combined scan: the solution is obtained by combining the two formulas mentioned above;

[0135] S24: Using the CWT model, the concentration weights of potential source areas for emission pollution sources are calculated to obtain the concentration weights of potential source areas;

[0136] Specifically: When the components of the emitted pollution source belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the CWT model is used to assign the concentration values ​​of the emitted pollution source components at different receptor points monitored by the differential absorption module to the backward trajectory passing through that point, and the average weighted concentration of each backward grid is calculated to obtain the concentration weight of all backward grids; the concentration weight of the potential source region is obtained by combining the concentration weights of all backward grids.

[0137] When the components of the emitted pollution source do not belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the concentration data of the emitted pollution source components observed by ground monitoring stations or satellites are corrected according to the proportionality coefficient to obtain the corrected concentration data of the emitted pollution source components. Using the CWT model, the corrected concentration values ​​of the emitted pollution source components at different receptor points are assigned to the backward trajectory passing through that point, and the average weighted concentration of each backward grid is calculated to obtain the concentration weight of all backward grids. Combining the concentration weights of all backward grids, the concentration weight of the potential source region is obtained.

[0138] Specifically, the proportionality coefficient is the ratio of the concentration value of a pollutant measured by lidar to the concentration value obtained by the station / satellite; specifically, based on the CWT (Concentration-Weighted Trajectory) model, the formula for calculating the concentration weight of the feed to the grid is expressed as follows:

[0139] ;

[0140]

[0141] Among them, C αr and C θz These are the average weighted concentrations of the minimum backward grid αr determined by the azimuth angle α and distance r, and the minimum backward grid θz determined by the elevation angle θ and altitude z, respectively, for PPI and RHI scanning modes. up For the backward trajectory of the upwind side, M up This represents the total number of backward trajectories passing through a grid point upwind. It is the backward trajectory of the upwind side. up The concentration value reaching the receiver point is obtained by scanning the PPI or RHI mode of a lidar at the upwind end of the emission area if the emission source composition belongs to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information; otherwise, it is a corrected concentration value. up,αr and τ up,θz These are the trajectories of the upwind side. up The time spent in grids αr and θz.

[0142] Furthermore, based on steps S01-S03, this embodiment of the invention also introduces a method for analyzing emission pollution sources based on lidar, including:

[0143] Based on the three-dimensional wind field information and the concentration information of the first pollutant, a quantitative analysis of the forward trajectory of the emission source components in the emission area to be measured is performed to obtain the source point influence area and the source strength weight of the source point influence area of ​​the emission source components.

[0144] like Figure 4 As shown, a quantitative analysis of the forward trajectory of pollutant source components in the emission area to be measured includes:

[0145] S31: Input the concentration data of emission pollutant components monitored by the downwind differential absorption module, the three-dimensional wind field information, and the meteorological data of the global data assimilation system into the forward trajectory model for simulation to obtain all forward trajectories of emission pollutant components in the emission area to be measured.

[0146] Specifically, in this embodiment, meteorological data includes assimilated data from the Global Data Assimilation System, reanalysis data, etc.; the forward trajectory model can use trajectory modules such as HYSPLIT, FLEXPART, and WRF-Chem.

[0147] Specifically, before conducting forward trajectory simulation, it is necessary to determine the forward simulation parameters: source point (setting the geographical location and monitoring height of the downwind side as the starting point of the forward trajectory), forward simulation time, forward start time, and forward simulation duration;

[0148] S32: Combining the three-dimensional wind field information and the meteorological data, calculate the forward trajectories of different source points corresponding to the concentration of pollutant components emitted downwind of the emission area to be measured; and perform cluster analysis based on the forward trajectories of different source points to obtain the proportion of each forward trajectory cluster, and take the forward trajectory cluster with the largest proportion as the potential diffusion direction of the emission pollutant components.

[0149] S33: Using the PIF model, combined with three-dimensional wind field information, adaptive scanning method and corresponding adaptive scanning parameters, the region of potential diffusion direction in the emission area to be tested is divided into forward grids, and the probability that the forward trajectory of different source points exceeding the second concentration threshold falls within the forward grid is calculated to obtain the forward contribution probability of all forward grids; the forward grids corresponding to the forward contribution probability exceeding the PIF threshold are taken as the source point influence area of ​​the emission pollution source.

[0150] Specifically, based on the PIF (Potential Impact Factor) model, the probability that the forward trajectories of different sources above the second concentration threshold fall within the forward grid is calculated, and the formula for the forward impact factor of all forward grids is expressed as follows:

[0151] When the adaptive scanning mode is PPI scanning:

[0152]

[0153] in, This represents the forward influence factor of a forward grid during PPI scanning; the subscript αr* indicates the minimum forward grid determined by the differential absorption module based on the azimuth angle α and distance r corresponding to the PPI scanning mode; the subscript down indicates that the grid is located downwind; m αr* It is the number of trajectories passing through grid αr*, n αr* It is the number of all trajectories passing through grid αr*;

[0154] When the adaptive scanning mode is RHI scan:

[0155]

[0156] in, This represents the forward influence factor of a forward grid during RHI scanning; the subscript θz* indicates the minimum forward grid determined by the differential absorption module based on the elevation angle θ and height z corresponding to the RHI scanning mode; the subscript down indicates that the grid is located downwind; m θz* It is the number of trajectories passing through grid θz*, n θz* It is the number of all trajectories passing through grid θz*;

[0157] When the adaptive scanning mode is a PPI / RHI combined scan: the solution is obtained by combining the two formulas mentioned above;

[0158] S34: Using the SWT model, calculate the source strength weight of the source point influence area for the emission pollution source to obtain the source strength weight of the source point influence area;

[0159] Specifically, when the components of the emitted pollution source belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the SWT model is used to assign the concentration values ​​of the emitted pollution source components at different source points monitored by the differential absorption module to the forward trajectory starting from that point, and the average weighted concentration of each forward grid is calculated to obtain the source strength weight of all forward grids; combined with the source strength weight of all forward grids, the source strength weight of the influence area of ​​the source point is obtained.

[0160] When the components of the emitted pollution source do not belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the concentration data of the emitted pollution source components observed by ground monitoring stations or satellites are corrected according to the proportional coefficient to obtain the corrected concentration data of the emitted pollution source components. Using the SWT model, the corrected concentration values ​​of the emitted pollution source components at different source points are assigned to the forward trajectory starting from that point, and the average weighted concentration of each forward grid is calculated to obtain the source strength weight of all forward grids. Combining the source strength weights of all forward grids, the source strength weight of the affected area of ​​the source point is obtained.

[0161] Specifically, based on the SWT (Source-Weighted Trajectory) model, the formula for calculating the source strength weight of the forward mesh is expressed as:

[0162] ;

[0163]

[0164] Among them, S αr* and S θz* These are the average weighted source strengths of the minimum forward grid αr determined by the azimuth angle α and distance r, and the minimum forward grid θz* determined by the elevation angle θ and altitude z, respectively, for PPI and RHI scanning modes. down M is the forward trajectory from downwind. downThis represents the total number of forward trajectories passing through a grid point downwind. It is the forward trajectory of the downwind side. down The concentration value originating from the source point, when the pollutant source components belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, can be obtained downwind of the emission area by lidar PPI or RHI scanning mode; otherwise, it is a corrected concentration value. down,αr* and τ down,θz* These are the trajectories of the downwind side. down The time spent in grids αr* and θz*.

[0165] Example 2

[0166] like Figure 5 As shown, this embodiment of the invention provides an emission pollution source analysis system based on lidar, used to implement the emission pollution source analysis method based on lidar described in Embodiment 1, including: a data acquisition module, and an emission analysis module, a source tracing module, and a diffusion prediction module respectively connected to the data acquisition module; the source tracing module and the diffusion prediction module are both connected to the emission analysis module;

[0167] The data acquisition module is used to acquire three-dimensional wind field information, first pollutant concentration information and second pollutant concentration information of the emission area to be measured;

[0168] The emission analysis module is used to perform correlation analysis on various pollutant gases directly obtained from ground monitoring stations or satellites and single pollutant gases monitored by the differential absorption module, as well as various particulate matter directly obtained from ground monitoring stations or satellites and multiple particulate matter monitored by the differential absorption module, based on the first pollutant concentration information and the second pollutant concentration information, to obtain the correlation analysis results; it is also used to identify the components of the emission pollution sources in the emission area to be measured based on the correlation analysis results.

[0169] The source tracing module is used to perform quantitative analysis of the backward trajectory of the emission source components in the emission area to be tested based on the three-dimensional wind field information and the first pollutant concentration information, so as to obtain the potential source area and the concentration weight of the potential source area of ​​the emission source components.

[0170] The diffusion prediction module is used to perform quantitative analysis of the forward trajectory of the emission source components in the emission area to be measured based on three-dimensional wind field information and first pollutant concentration information, so as to obtain the source point influence area and the source strength weight of the emission source components.

[0171] Specifically, the data acquisition module includes a direct acquisition unit and a lidar;

[0172] The direct acquisition unit is used to directly acquire the concentration information of the second pollutant obtained from ground monitoring stations or satellite observations.

[0173] Specifically, in this embodiment, VAD scanning involves performing at least three radar scans on the target measurement area at a preset elevation angle and azimuth angle; wherein the elevation angle A is fixed at a preset angle, and the azimuth angle B consists of at least three non-overlapping angles; 0 < A < 90°;

[0174] The lidar includes a Doppler wind measurement module and a differential absorption module; the Doppler wind measurement module is used to perform VAD scanning of the emission area to be measured to obtain three-dimensional wind field information; the differential absorption module is used to perform adaptive scanning of the emission area to be measured to obtain single pollutant gas concentration data and multiple particulate matter concentration data in the first pollutant concentration information.

[0175] In this embodiment, the emission analysis module includes:

[0176] The upwind and downwind data acquisition unit is used to obtain the first pollutant concentration information and the second pollutant concentration information at the upwind end of the emission area to be measured, as well as the first pollutant concentration information and the second pollutant concentration information at the downwind end, based on the first pollutant concentration information and the second pollutant concentration information of the emission area to be measured.

[0177] The upwind data calculation unit, connected to the upwind and downwind data acquisition units, is used to calculate the correlation coefficient between various pollutant gases observed by the upwind ground monitoring station or satellite and a single gas monitored by the differential absorption module, based on the first pollutant concentration information and the second pollutant concentration information at the upwind location. This yields the correlation coefficient between the various pollutant gases observed by the ground monitoring station or satellite and the single pollutant gas monitored by the differential absorption module at the upwind location. It is also used to calculate the correlation coefficient between various particulate matter observed by the upwind ground monitoring station or satellite and multiple particulate matter monitored by the differential absorption module, based on the first pollutant concentration information and the second pollutant concentration information at the upwind location.

[0178] The downwind data calculation unit, connected to the upwind and downwind data acquisition units, is used to calculate the correlation coefficient between various pollutant gases observed by the downwind ground monitoring station or satellite and a single gas monitored by the differential absorption module, based on the first pollutant concentration information and the second pollutant concentration information at the downwind end, to obtain the correlation coefficient between the various pollutant gases observed by the ground monitoring station or satellite and the single pollutant gas monitored by the differential absorption module at the downwind end; it is also used to calculate the correlation coefficient between various particulate matter observed by the downwind ground monitoring station or satellite and multiple particulate matter monitored by the differential absorption module, based on the first pollutant concentration information and the second pollutant concentration information at the downwind end, to obtain the correlation coefficient between the various particulate matter observed by the ground monitoring station or satellite and the multiple particulate matter monitored by the differential absorption module at the downwind end;

[0179] The identification unit, which is connected to both the upwind and downwind data calculation units, is used to determine the pollution source composition in the emission area to be measured. This is based on the correlation coefficients between various pollutant gases observed by ground monitoring stations or satellites and single pollutant gases monitored by the differential absorption module at the upwind and downwind points, as well as the correlation coefficients between various particulate matter observed by ground monitoring stations or satellites and multiple particulate matter monitored by the differential absorption module at the upwind and downwind points.

[0180] The first criterion includes:

[0181] If the correlation coefficient between a certain pollutant gas observed by a ground monitoring station or satellite and a single pollutant gas monitored by a differential absorption module is greater than the first threshold at the downwind end and less than the second threshold at the upwind end, then the pollutant gas observed by the ground monitoring station or satellite is determined to be a component of the emission source in the emission area to be tested; otherwise, it is not a component of the emission source in the emission area to be tested.

[0182] If the correlation coefficient between a certain particulate matter observed by a ground monitoring station or satellite and a certain particulate matter monitored by a differential absorption module is greater than the third threshold downwind, and less than the fourth threshold upwind, then the particulate matter observed by the ground monitoring station or satellite is determined to be a component of the emission source in the emission area to be tested; otherwise, it is not a component of the emission source in the emission area to be tested.

[0183] In this embodiment, the tracing module includes:

[0184] The backward trajectory simulation unit is used to input the concentration data of emission pollutant components monitored by the upwind differential absorption module, the three-dimensional wind field information, and the meteorological data of the global data assimilation system into the backward trajectory model for simulation, so as to obtain all backward trajectories of emission pollutant components in the emission area to be measured.

[0185] The backward trajectory analysis unit, connected to the backward trajectory simulation unit, is used to combine three-dimensional wind field information and the meteorological data to calculate the backward trajectories of different receptor points corresponding to the concentration of pollutant source components emitted at the upwind end of the emission area to be measured; and to perform cluster analysis based on the backward trajectories of different receptor points to obtain the proportion of each backward trajectory cluster, and to take the backward trajectory cluster with the largest proportion as the potential source direction of the emission pollutant source components.

[0186] The backward contribution probability unit, connected to the backward trajectory analysis unit, is used to divide the region of potential source direction in the emission area to be tested into backward grids using the PSCF model, combined with three-dimensional wind field information, adaptive scanning mode and corresponding adaptive scanning parameters, and to calculate the probability that the backward trajectory of different receptor points above the first concentration threshold falls within the backward grid, thus obtaining the backward contribution probability of all backward grids; the backward grids corresponding to the backward contribution probability exceeding the PSCF threshold are taken as the potential source areas of the emission pollution source components;

[0187] The backward concentration weighting unit, connected to the backward contribution probability unit, is used to assign the concentration values ​​of the emitted pollution source components at different receptor points monitored by the differential absorption module to the backward trajectory passing through that point when the emitted pollution source components belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, using the CWT model. It then calculates the average weighted concentration of each backward grid to obtain the concentration weight of all backward grids. Combining the concentration weights of all backward grids, the concentration weight of the potential source region is obtained. When the emitted pollution source components do not belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the concentration data of the emitted pollution source components observed by ground monitoring stations or satellites are corrected according to a proportionality coefficient to obtain corrected concentration data of the emitted pollution source components. Using the CWT model, the corrected concentration values ​​of the emitted pollution source components at different receptor points are assigned to the backward trajectory passing through that point, and the average weighted concentration of each backward grid is calculated to obtain the concentration weight of all backward grids. Combining the concentration weights of all backward grids, the concentration weight of the potential source region is obtained.

[0188] In this embodiment, the diffusion prediction module includes:

[0189] The forward trajectory simulation unit is used to input the concentration data of emission pollutant components monitored by the downwind differential absorption module, the three-dimensional wind field information, and the meteorological data of the global data assimilation system into the forward trajectory model for simulation, so as to obtain all forward trajectories of emission pollutant components in the emission area to be measured.

[0190] The forward trajectory analysis unit, connected to the forward trajectory simulation unit, combines the three-dimensional wind field information and the meteorological data to calculate the forward trajectories of different source points corresponding to the concentration of pollutant components emitted downwind of the emission area to be measured; and performs cluster analysis based on the forward trajectories of different source points to obtain the proportion of each forward trajectory cluster, and takes the forward trajectory cluster with the largest proportion as the potential diffusion direction of the emission pollutant components.

[0191] The forward contribution probability unit, connected to the forward trajectory analysis unit, is used to divide the region of potential diffusion direction in the emission area under test into forward grids using the PIF model, combined with three-dimensional wind field information, adaptive scanning mode and corresponding adaptive scanning parameters, and to calculate the probability that the forward trajectory of different source points exceeding the second concentration threshold falls within the forward grid, thus obtaining the forward contribution probability of all forward grids; the forward grids corresponding to the forward contribution probability exceeding the PIF threshold are taken as the source point influence area of ​​the emission pollution source;

[0192] The forward source strength weighting unit, connected to the forward contribution probability unit, is used to assign the concentration values ​​of the emitted pollutant source components monitored by the differential absorption module from different source points to the forward trajectory starting from that point when the emitted pollutant source components belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, using the SWT model. It then calculates the average weighted concentration of each forward grid to obtain the source strength weight of all forward grids. Combining the source strength weights of all forward grids, the source strength weight of the affected area of ​​the source point is obtained. When the emitted pollutant source components do not belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the concentration data of the emitted pollutant source components observed by ground monitoring stations or satellites are corrected according to a proportionality coefficient to obtain corrected concentration data of the emitted pollutant source components. Using the SWT model, the corrected concentration values ​​of the emitted pollutant source components from different source points are assigned to the forward trajectory starting from that point, and the average weighted concentration of each forward grid is calculated to obtain the source strength weight of all forward grids. Combining the source strength weights of all forward grids, the source strength weight of the affected area of ​​the source point is obtained.

[0193] In summary, the emission pollution source analysis system described in this embodiment includes a data acquisition module, an emission analysis module, a source tracing module, and a diffusion prediction module. The data acquisition module can acquire three-dimensional wind field information measured by lidar, as well as first pollutant concentration information with high spatial resolution; it can also directly acquire second pollutant concentration information monitored by ground monitoring stations or satellites. The emission analysis module combines the two types of data acquired by the data acquisition module to perform correlation analysis on single pollutant gases or multiple particulate matter (correlation analysis between the concentrations of single pollutant gases or multiple particulate matter monitored by lidar and ground monitoring stations), thereby accurately analyzing the emission area to be measured. The system identifies the components of emission pollution sources. Based on the source tracing and diffusion prediction modules, backward and forward analyses are performed on the emission pollution source components obtained from the emission analysis module. The results of these analyses include the potential source region and concentration weights of the emission pollution source components, as well as the source point influence region and source strength weights of the emission pollution source components. This emission pollution source analysis system enables accurate emission analysis, source tracing, and diffusion prediction of emission pollution sources. While improving the spatiotemporal resolution of emission and pollution monitoring, it also significantly enhances the reliability of emission component analysis and source tracing diffusion analysis, which is of great significance for constructing a high-precision emission monitoring and early warning system and implementing precise emission reduction.

[0194] Example 3

[0195] Based on the emission pollution source analysis method described in Example 1 and the emission pollution source analysis system described in Example 2, this example provides a specific demonstrative embodiment:

[0196] This embodiment uses the CO2 and particulate matter concentration data observed by lidar in Fujian Province as an example, and combines the method in Example 1 to analyze the pollution source of a synthetic leather factory's emission sources, and obtains the following experimental results;

[0197] like Figure 6 The image shows the spatial distribution of CO2 concentration at the downwind end of the emission area monitored by the differential absorption module PPI scan. It shows the PPI scanning range determined by the azimuth angle range (α1~α2) and the distance r, as well as the CO2 concentration distribution at different source points within the range. As can be seen from the figure, the color representing CO2 concentration presents different depths throughout the fan-shaped area, corresponding to different concentration values. This demonstrates the high spatiotemporal resolution of the lidar within the observed range, which is beneficial for subsequent pollutant analysis, source tracing, and diffusion prediction.

[0198] like Figure 7The diagram shows the correlation coefficients between the CO2 concentration monitored by the differential absorption module and the concentrations of various gases observed by ground stations / satellites at the downwind end. In the diagram, the single gas concentration of the first pollutant observed by the lidar is CO2, and the second pollutants observed by the ground stations / satellites are CO, O3, NO2, and SO2. The correlations between CO2 and CO, NO2, and SO2 are all greater than 0.3, therefore these three gases can be considered as pollutant components of the emission source. The correlation between CO2 and O3 is less than 0, therefore it can be ruled out as a pollutant component of the emission source.

[0199] like Figure 8 The diagram shows a cluster of forward trajectories from a source point in the emission area to be measured. This diagram was obtained by inputting the CO2 concentration observed by lidar as the source strength into HYSPLIT along with three-dimensional wind field meteorological data. The different numbers on the trajectory represent the proportion of each trajectory. The sixth trajectory shown in the diagram has a proportion of 2.44%, indicating a less likely potential diffusion direction. The first trajectory has a proportion of 28.05%, which is the most likely potential diffusion direction. This trajectory will be used for subsequent analysis of potential impact factors and source strength weighted trajectories.

[0200] like Figure 9 The diagram shows the potential impact factors of a source point in the emission area to be measured. This diagram is obtained by inputting the CO2 concentration observed by lidar as the source intensity and three-dimensional wind field meteorological data into the PIF formula. Different letters correspond to different grid areas and also to different impact probability values. The grid area with the lowest forward impact probability shown in the diagram is the area represented by the letter f, with a probability of 0.3, while the grid area with the highest forward impact probability is the area represented by the letter a, with a probability of 0.8.

[0201] like Figure 10 The diagram shows the source strength weight of a source point in the emission area to be measured. The diagram uses the CO2 concentration observed by lidar as the source strength, and combines it with three-dimensional wind field and meteorological data to input into the SWT formula. Different letters correspond to different grid areas, and also to different source strength contribution values. The grid area with the smallest forward source strength weight shown in the diagram is the area represented by the letter f, with a corresponding source strength contribution of 200 ppm. The grid area with the largest forward source strength weight is the area represented by the letter a, with a corresponding concentration contribution of 450 ppm.

[0202] Based on the above analysis, it can be seen that the high spatial resolution of single CO2 gas concentration observed by lidar enables the analysis of other gas components in the pollution source, and the diffusion prediction of the emission source is realized. It also quantifies the probability and source strength weight of the emission source as the source point on other areas.

[0203] In summary, the present invention provides a method and system for analyzing emission pollution sources based on lidar, which can accurately analyze, trace, and predict the diffusion of pollution sources. While improving the spatiotemporal resolution of emission and pollution monitoring, it also significantly improves the reliability of component analysis and source tracing diffusion analysis.

[0204] Example 4

[0205] This embodiment provides a computer-readable storage medium storing a computer program that, when executed, implements the lidar-based emission pollution source analysis system described in Embodiment 1.

[0206] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0207] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0208] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0209] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for analyzing emission pollution sources based on lidar, characterized in that, include: Acquire three-dimensional wind field information, primary pollutant concentration information, and secondary pollutant concentration information of the emission area to be measured; The first pollutant concentration information includes concentration data of a single pollutant gas and multiple particulate matter monitored by the differential absorption module; the second pollutant concentration information includes concentration data of multiple pollutant gases and multiple particulate matter directly obtained from ground monitoring stations or satellites; the first pollutant concentration information has high spatial resolution; wherein, the pollutant gas type corresponding to the first pollutant concentration information is included in the pollutant gas type corresponding to the second pollutant concentration information, and the particulate matter type corresponding to the first pollutant concentration information is included in the particulate matter type corresponding to the second pollutant concentration information; Based on the first pollutant concentration information and the second pollutant concentration information, the first pollutant concentration information and the second pollutant concentration information corresponding to the upwind end of the emission area to be tested, and the first pollutant concentration information and the second pollutant concentration information corresponding to the downwind end of the emission area to be tested are obtained. Based on the first pollutant concentration information and the second pollutant concentration information corresponding to the upwind area of ​​the emission area to be measured, calculate the correlation coefficient between various pollutant gases acquired by ground monitoring stations or satellites and single pollutant gases monitored by differential absorption modules at the upwind area, as well as the correlation coefficient between various particulate matter acquired by ground monitoring stations or satellites and multiple particulate matter monitored by differential absorption modules at the upwind area. Based on the first pollutant concentration information and the second pollutant concentration information corresponding to the downwind end of the emission area to be tested, calculate the correlation coefficients between various pollutant gases acquired by ground monitoring stations or satellites and single pollutant gases monitored by differential absorption modules at the downwind end, as well as the correlation coefficients between various particulate matter acquired by ground monitoring stations or satellites and multiple particulate matter monitored by differential absorption modules at the downwind end. The correlation coefficients at the upwind and downwind points are used as the results of the correlation analysis. Based on the results of correlation analysis and the primary judgment criteria, the components of the emission sources in the emission area to be tested were identified. The first criterion includes: If the correlation coefficient between a certain pollutant gas observed by a ground monitoring station or satellite and a single pollutant gas monitored by a differential absorption module is greater than the first threshold at the downwind end and less than the second threshold at the upwind end, then the pollutant gas observed by the ground monitoring station or satellite is determined to be a component of the emission source in the emission area to be tested; otherwise, it is not a component of the emission source in the emission area to be tested. If the correlation coefficient between a certain particulate matter observed by a ground monitoring station or satellite and a certain particulate matter monitored by a differential absorption module is greater than the third threshold at the downwind end and less than the fourth threshold at the upwind end, then the particulate matter observed by the ground monitoring station or satellite is determined to be a component of the emission source in the emission area to be measured; otherwise, it is not a component of the emission source in the emission area to be measured.

2. The method for analyzing emission pollution sources based on lidar according to claim 1, characterized in that, The steps for obtaining the three-dimensional wind field information and the concentration information of the first pollutant in the emission area to be tested include: The Doppler wind measurement module of lidar is used to perform VAD scanning of the emission area to be measured to obtain three-dimensional wind field information; The differential absorption module of the lidar is used to adaptively scan the emission area to be tested, and the concentration data of a single pollutant gas and multiple particulate matter are obtained from the first pollutant concentration information.

3. The method for analyzing emission pollution sources based on lidar according to claim 2, characterized in that, The adaptive scanning of the emission area to be tested includes: Based on historical three-dimensional wind field information, the historical patterns of the three-dimensional wind field are obtained; The initial scanning method of the differential absorption module is determined based on the historical patterns of the three-dimensional wind field; The initial scanning method is adjusted based on the real-time measured three-dimensional wind field information to obtain the adaptive scanning method and the corresponding adaptive scanning parameters; Adaptive scanning is performed based on the adaptive scanning method and the corresponding adaptive scanning parameters; The adaptive scanning methods include PPI scanning, RHI scanning, and PPI / RHI combined scanning; the adaptive scanning parameters for PPI scanning include azimuth range, distance range, and azimuth step interval; the adaptive scanning parameters for RHI scanning include elevation range, altitude range, and elevation step interval; and the adaptive scanning parameters for PPI / RHI combined scanning include the adaptive scanning parameters for both PPI and RHI scanning.

4. The method for analyzing emission pollution sources based on lidar according to claim 1, characterized in that, The formula for calculating the correlation coefficient is expressed as follows: ； ； In the formula: r up This represents the correlation coefficient between the m-th pollutant gas or particulate matter observed by a ground monitoring station or satellite and the single pollutant gas or particulate matter monitored by a differential absorption module at the upwind location; r down The correlation coefficient between the m-th pollutant gas or particulate matter observed by the ground monitoring station or satellite and the single pollutant gas or particulate matter monitored by the differential absorption module at the downwind end; This represents the concentration data measured by the differential absorption module at the upwind end for the first type of pollutant gas or particulate matter; j upm This represents the concentration data of the m-th pollutant gas or particulate matter measured by a ground monitoring station or satellite upwind; i up1,k This represents the concentration data of the kth measurement conducted by the differential absorption module at the upwind end for the first type of pollutant gas or particulate matter; n represents the total number of measurements; and k is the sequence number. This represents the average concentration data from n measurements of a single pollutant gas or particulate matter conducted by the differential absorption module at the upwind end; j upm,k This represents the concentration data of the m-th pollutant gas or particulate matter measured by a ground monitoring station or satellite upwind. This represents the average concentration data of n measurements of the m-th pollutant gas or particulate matter conducted by a ground monitoring station or satellite upwind; m represents the total number of pollutant gas types or particulate matter types observed by the ground monitoring station or satellite; i down1,k This represents the concentration data of the kth measurement conducted by the differential absorption module at the downwind end for a single pollutant gas type or particulate matter type; This represents the average concentration data of n measurements taken by the differential absorption module at the downwind end for a single pollutant gas type or particulate matter type. i down1 This represents the concentration data of the first pollutant gas or particulate matter measured by the differential absorption module at the downwind end; j downm This represents the concentration data of the m-th pollutant gas or particulate matter measured downwind by a ground monitoring station or satellite; j downm,k This represents the concentration data of the m-th pollutant gas or particulate matter measured downwind by a ground monitoring station or satellite. This represents the average concentration data of n measurements of the m-th pollutant gas or particulate matter conducted by a ground monitoring station or satellite downwind.

5. The method for analyzing emission pollution sources based on lidar according to claim 1, characterized in that, The method for analyzing pollution sources also includes: Based on the three-dimensional wind field information and the concentration information of the first pollutant, a quantitative analysis of the backward trajectory of the emission source components in the emission area to be measured is performed to obtain the potential source area and the concentration weight of the potential source area of ​​the emission source components. The quantitative analysis of the backward trajectory of the pollutant source components in the emission area to be tested includes: The concentration data of emission pollutant components monitored by the upwind differential absorption module, the three-dimensional wind field information, and the meteorological data of the global data assimilation system are input into the backward trajectory model for simulation to obtain all backward trajectories of emission pollutant components in the emission area to be measured. Combining the three-dimensional wind field information and the meteorological data, the backward trajectory of different receptor points corresponding to the concentration of pollutant components emitted at the upwind end of the emission area to be measured is calculated respectively; and cluster analysis is performed based on the backward trajectory of different receptor points to obtain the proportion of each backward trajectory cluster, and the backward trajectory cluster with the largest proportion is taken as the potential source direction of the emission pollutant components. Using the PSCF model, combined with three-dimensional wind field information, adaptive scanning method and corresponding adaptive scanning parameters, the region in the potential source direction of the emission area to be measured is divided into backward grids, and the probability that the backward trajectory of different receptor points above the first concentration threshold falls within the backward grid is calculated to obtain the backward contribution probability of all backward grids; the backward grids corresponding to the backward contribution probability exceeding the PSCF threshold are taken as the potential source areas of the emission pollution source components. When the components of the emitted pollution source belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the CWT model is used to assign the concentration values ​​of the emitted pollution source components at different receptor points monitored by the differential absorption module to the backward trajectory passing through that point, and the average weighted concentration of each backward grid is calculated to obtain the concentration weight of all backward grids; the concentration weight of the potential source region is obtained by combining the concentration weights of all backward grids. When the components of the emitted pollution source do not belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the concentration data of the emitted pollution source components observed by ground monitoring stations or satellites are corrected according to the proportionality coefficient to obtain the corrected concentration data of the emitted pollution source components. Using the CWT model, the corrected concentration values ​​of the emitted pollution source components at different receptor points are assigned to the backward trajectory passing through that point, and the average weighted concentration of each backward grid is calculated to obtain the concentration weight of all backward grids. Combining the concentration weights of all backward grids, the concentration weight of the potential source region is obtained.

6. The method for analyzing emission pollution sources based on lidar according to claim 1, characterized in that, The method for analyzing pollution sources also includes: Based on the three-dimensional wind field information and the concentration information of the first pollutant, a quantitative analysis of the forward trajectory of the emission source components in the emission area to be measured is performed to obtain the source point influence area and the source strength weight of the source point influence area of ​​the emission source components. The quantitative analysis of the forward trajectory of the pollutant source components in the emission area to be tested includes: The concentration data of emission pollutant components monitored by the downwind differential absorption module, the three-dimensional wind field information, and the meteorological data of the global data assimilation system are input into the forward trajectory model for simulation to obtain all forward trajectories of emission pollutant components in the emission area to be measured. Combining the three-dimensional wind field information and the meteorological data, the forward trajectories of different source points corresponding to the concentration of pollutant components emitted downwind of the emission area to be measured are calculated respectively; and cluster analysis is performed based on the forward trajectories of different source points to obtain the proportion of each forward trajectory cluster, and the forward trajectory cluster with the largest proportion is taken as the potential diffusion direction of the emission pollutant components. Using the PIF model, combined with three-dimensional wind field information, adaptive scanning method and corresponding adaptive scanning parameters, the region of potential diffusion direction in the emission area to be measured is divided into forward grids, and the probability that the forward trajectory of different source points exceeding the second concentration threshold falls within the forward grid is calculated to obtain the forward contribution probability of all forward grids; the forward grids corresponding to the forward contribution probability exceeding the PIF threshold are taken as the source point influence area of ​​the emission pollution source. When the components of the emitted pollution source belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the SWT model is used to assign the concentration values ​​of the emitted pollution source components at different source points monitored by the differential absorption module to the forward trajectory starting from the corresponding source point, and the average weighted concentration of each forward grid is calculated to obtain the source strength weight of all forward grids; the source strength weight of the affected area of ​​the source point is obtained by combining the source strength weight of all forward grids. When the components of the emitted pollution source do not belong to the pollutant gas type or particulate matter type corresponding to the first pollutant concentration information, the concentration data of the emitted pollution source components observed by ground monitoring stations or satellites are corrected according to the proportional coefficient to obtain the corrected concentration data of the emitted pollution source components. Using the SWT model, the corrected concentration values ​​of the emitted pollution source components at different source points are assigned to the forward trajectory starting from the corresponding source point, and the average weighted concentration of each forward grid is calculated to obtain the source strength weight of all forward grids. Combining the source strength weights of all forward grids, the source strength weight of the influence area of ​​the source point is obtained.

7. A lidar-based emission pollution source analysis system, used to implement the lidar-based emission pollution source analysis method as described in any one of claims 1-6, characterized in that, include: The system includes a data acquisition module, and an emission analysis module, a source tracing module, and a diffusion prediction module, all connected to the data acquisition module. The source tracing module and the diffusion prediction module are both connected to the emission analysis module. The data acquisition module is used to acquire three-dimensional wind field information, first pollutant concentration information and second pollutant concentration information of the emission area to be measured; The emission analysis module is used to obtain, based on the first pollutant concentration information and the second pollutant concentration information, the first pollutant concentration information and the second pollutant concentration information corresponding to the upwind end of the emission area to be tested, and the first pollutant concentration information and the second pollutant concentration information corresponding to the downwind end of the emission area to be tested. The emission analysis module is also used to calculate the correlation coefficient between various pollutant gases acquired by ground monitoring stations or satellites and single pollutant gases monitored by differential absorption modules at the upwind end of the emission area to be measured, based on the first pollutant concentration information and the second pollutant concentration information corresponding to the upwind end of the emission area to be measured; and the correlation coefficient between various particulate matter acquired by ground monitoring stations or satellites and multiple particulate matter monitored by differential absorption modules at the upwind end. The emission analysis module is also used to calculate the correlation coefficients between various pollutant gases acquired by ground monitoring stations or satellites and single pollutant gases monitored by differential absorption modules at the downwind end, and the correlation coefficients between various particulate matter acquired by ground monitoring stations or satellites and multiple particulate matter monitored by differential absorption modules at the downwind end, based on the first pollutant concentration information and the second pollutant concentration information corresponding to the downwind end of the emission area to be tested. The emission analysis module is also used to take the correlation coefficients at the upwind and downwind points as the results of correlation analysis. The emission analysis module is also used to identify the components of emission pollution sources in the emission area to be tested based on the correlation analysis results and the first judgment criterion. The first criterion includes: If the correlation coefficient between a certain pollutant gas observed by a ground monitoring station or satellite and a single pollutant gas monitored by a differential absorption module is greater than the first threshold at the downwind end and less than the second threshold at the upwind end, then the pollutant gas observed by the ground monitoring station or satellite is determined to be a component of the emission source in the emission area to be tested; otherwise, it is not a component of the emission source in the emission area to be tested. If the correlation coefficient between a certain particulate matter observed by a ground monitoring station or satellite and a certain particulate matter monitored by a differential absorption module is greater than the third threshold at the downwind end and less than the fourth threshold at the upwind end, then the particulate matter observed by the ground monitoring station or satellite is determined to be a component of the emission source in the emission area to be tested; otherwise, it is not a component of the emission source in the emission area to be tested. The source tracing module is used to perform quantitative analysis of the backward trajectory of the emission source components in the emission area to be tested based on the three-dimensional wind field information and the first pollutant concentration information, so as to obtain the potential source area and the concentration weight of the potential source area of ​​the emission source components. The diffusion prediction module is used to perform quantitative analysis of the forward trajectory of the emission source components in the emission area to be measured based on three-dimensional wind field information and first pollutant concentration information, so as to obtain the source point influence area and the source strength weight of the emission source components.

8. The emission pollution source analysis system based on lidar according to claim 7, characterized in that, The data acquisition module includes a direct acquisition unit and a lidar; The direct acquisition unit is used to directly acquire the concentration information of the second pollutant obtained from ground monitoring stations or satellite observations; The lidar includes a Doppler wind measurement module and a differential absorption module; the Doppler wind measurement module is used to perform VAD scanning on the emission area to be measured to obtain three-dimensional wind field information; the differential absorption module is used to perform adaptive scanning on the emission area to be measured to obtain single pollutant gas concentration data and multiple particulate matter concentration data in the first pollutant concentration information.

Images