Point source plume influence point identification method based on concentration distribution
By identifying the point source plume influence zone based on concentration distribution, eliminating wind field data errors, and using concentration distribution characteristics and background area judgment thresholds, the problem of accurately identifying the plume influence zone in point source emission monitoring is solved, and the reliability of emission flux estimation is improved.
Patent Information
- Application Number
- CN202511706293.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-20
- Publication Date
- 2026-02-13
AI Technical Summary
Existing technologies for point source emission monitoring rely on wind field data to identify plume impact areas, which results in large errors, high uncertainty in flux calculations, and difficulty in accurately identifying plume impact areas in complex environments.
The gas concentration distribution is obtained through mobile observation. The background area and judgment threshold are determined based on the characteristics of the concentration distribution. The dependence on wind field data is abandoned, and the geometric analysis of the concentration distribution is used to identify the plume influence area, including steps S1-S3.
It enables accurate identification of plume-affected areas without relying on wind field data, reducing the probability of misjudgment and omission, and improving the reliability of emission flux estimation.
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Figure CN121522099A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of environmental monitoring, and in particular to a point source plume influence point identification method based on concentration distribution. BACKGROUND
[0002] Typical industrial point sources such as power plants and waste incineration plants are important sources of greenhouse gas emissions such as carbon dioxide (CO2). Accurate monitoring and quantitative assessment of such point source emissions is a key technical link to implement precise emission reduction policies and achieve carbon neutralization goals. In point source emission monitoring, the cross-sectional flux method is a commonly used emission quantification method. The core of this method is to accurately identify and separate the area directly affected by the point source emission plume (i.e. the plume influence area), and to calculate the flux based on the concentration enhancement data in this area. Therefore, the accurate identification of the plume influence area is the primary prerequisite for ensuring the reliability of the final emission estimation results.
[0003] Traditionally, the identification of the plume influence area relies on wind field information. This method usually obtains wind speed and wind direction through measured or reanalyzed data, and then defines a sector or rectangular area downwind as the potential plume influence range. However, in practical applications, wind field data has significant limitations. Measured wind field is easily affected by factors such as the surrounding terrain, buildings, and thermal disturbances of the monitoring point, and its representativeness is often limited, deviating from the actual transport and diffusion of the plume in the entire observation area. While reanalyzed wind field data is limited by its relatively low temporal and spatial resolution, making it difficult to accurately capture the details of the flow field near the point source, with significant errors in wind speed and direction. Once the wind direction used deviates from the true situation, the boundary of the plume influence area defined will be significantly shifted, resulting in the inclusion of excessive background signals or the omission of part of the plume signals in the flux calculation, ultimately introducing significant estimation uncertainty.
[0004] In recent years, ground-based Fourier transform infrared spectroscopy (FTIR) technology has been widely applied due to its ability to remotely sense atmospheric greenhouse gas column concentrations with high precision and high temporal resolution. Portable FTIR spectrometers are particularly suitable for field monitoring under various environmental conditions, including remote areas, providing a powerful technical means for point source emission monitoring. Despite this, how to robustly identify the plume influence area without relying entirely on wind field data that may have significant errors remains a technical challenge that needs to be addressed in the current point source emission monitoring field. Therefore, the present application proposes a point source plume influence point identification method based on concentration distribution. SUMMARY
[0005] The purpose of this invention is to address the persistent technical challenge in the field of point source emission monitoring: how to robustly identify the plume influence zone without relying entirely on wind field data that may contain significant errors. This challenge is addressed by proposing a point source plume influence point identification method based on concentration distribution.
[0006] The technical solution of this invention: a method for identifying point source plume influence points based on concentration distribution, comprising the following steps:
[0007] S1. Conduct mobile observations around the emission source, simultaneously collecting solar spectrum and location information; obtain the column concentration information of the target gas based on the solar spectrum inversion, and calculate the average mole fraction of the atmospheric dry air column at each monitoring point; combine the location information to generate the spatial distribution of gas concentration along the mobile observation route.
[0008] S2. Based on the spatial distribution characteristics of the gas concentration, determine the concentration background area, determine the background concentration baseline within the background area, and calculate the background concentration variation range;
[0009] S3. Based on the background concentration change range and the observation uncertainty level, determine the plume influence point determination threshold; calculate the enhancement value of the concentration value of each monitoring point relative to the background concentration baseline, compare the enhancement value with the threshold, and if the enhancement value is greater than the threshold, determine that the monitoring point is a plume influence point; based on the spatial distribution of the identified plume influence points, determine the range of the plume influence area.
[0010] Optionally, in step S1, the mobile observation is a closed-path mobile observation, and the synchronously collected data also includes meteorological data; after calculating the average mole fraction of the atmospheric dry air column at each monitoring point, the average value of multiple measurements at each monitoring point is further calculated. This serves as the representative concentration value for that monitoring point; the spatial distribution of the generated gas concentration specifically refers to the generated... Spatial distribution.
[0011] Optionally, in step S1, the greenhouse gas column concentration is inverted using the nonlinear least squares method, and the average mole fraction of the target gas in the dry atmospheric air column is obtained. Calculated using the following formula:
[0012]
[0013] in, The column concentration of the target gas, in mol / m³. 2 ; Oxygen column concentration, in mol / m³ 2 0.20924 represents the mole fraction of oxygen in the atmosphere.
[0014] Optionally, in step S2, the method for determining the concentration background region comprises: taking the emission source as the vertex, connecting the adjacent monitoring points on both sides of the monitoring point with the highest concentration respectively, and defining the included angle formed by the two connecting lines as the downwind angle range, the boundaries of which are determined by the two connecting lines; extending in the opposite direction along the two boundaries to obtain two upwind center lines, and taking the two center lines as the boundaries of the concentration background region. The union of the sectors as the spatial range of the background region.
[0015] Optionally, in step S2, the method for determining the background baseline in the background region comprises: in the background region, taking the background measurement points as the vertexes, connecting the adjacent monitoring points on both sides of the monitoring point with the highest concentration respectively, and defining the included angle formed by the two connecting lines as the downwind angle range, the boundaries of which are determined by the two connecting lines; extending in the opposite direction along the two boundaries to obtain two upwind center lines, and taking the two center lines as the boundaries of the concentration background region. Optionally, in step S2, the method for determining the background baseline in the background region comprises: in the background region, taking the background measurement points as the vertexes, connecting the adjacent monitoring points on both sides of the monitoring point with the highest concentration respectively, and defining the included angle formed by the two connecting lines as the downwind angle range, the boundaries of which are determined by the two connecting lines; extending in the opposite direction along the two boundaries to obtain two upwind center lines, and taking the two center lines as the boundaries of the concentration background region. Optionally, in step S2, the method for determining the background baseline in the background region comprises: in the background region, taking the background measurement points as the vertexes, connecting the adjacent monitoring points on both sides of the monitoring point with the highest concentration respectively, and defining the included angle formed by the two connecting lines as the downwind angle range, the boundaries of which are determined by the two connecting lines; extending in the opposite direction along the two boundaries to obtain two upwind center lines, and taking the two center lines as the boundaries of the concentration background region.
[0016]
[0017] wherein, C is the background value, unit: ppm; is the slope of the straight line, unit: ppm / s; is the time, unit: s; is the intercept of the straight line, unit: ppm.
[0018] Optionally, in step S2, the background concentration variation range is defined as the range of the background measurement points in the background region, and the calculation formula is:
[0019]
[0020] wherein, Cmax is the maximum value of the monitoring points in the background region, Cmin is the minimum value of the monitoring points in the background region.
[0021] Optionally, in step S3, the observation uncertainty level is represented by calculating the standard deviation of the multiple measurement results of each monitoring point, and taking the average value of the standard deviations of all monitoring points as the observation uncertainty level; the plume influence point screening threshold is obtained by adding the background concentration variation range to the average value of the standard deviations of all monitoring points , that is, .
[0022] Optionally, in step S3, the enhancement value The calculation formula is:
[0023]
[0024] Optionally, in step S3, the method for determining the extent of the plume influence zone includes: at the left and right boundaries of the continuous plume influence points, selecting the nearest monitoring point on the left and the nearest monitoring point on the right as the boundary points of the plume influence zone, respectively; the continuous plume influence points and the boundary points together constitute the plume influence zone.
[0025] Optionally, the target gas is carbon dioxide, and the solar spectrum is acquired by a Fourier transform infrared spectrometer.
[0026] Compared with the prior art, this application includes at least one of the following beneficial technical effects:
[0027] By calculating the average value of multiple measurements at each monitoring point As a representative value of the concentration at that point, it effectively smooths out the instantaneous fluctuations of a single measurement, improves the stability and representativeness of the spatial distribution data of concentration, and lays the foundation for accurate identification in the future.
[0028] This invention eliminates the reliance on measured or reanalyzed wind field data, and defines the downwind direction and background area by the inherent geometric characteristics of the concentration distribution. This fundamentally avoids misjudgment of the plume range caused by wind field data errors, and enables the method to be reliably applied in scenarios with complex wind fields or missing data.
[0029] By selecting the lowest quantile of the background concentration time series for linear fitting to establish a background baseline and calculating the background concentration change amplitude (background gradient), the influence of natural fluctuations and slow drift of background concentration on the recognition results can be effectively removed, significantly improving the accuracy of recognizing the true concentration enhancement signal.
[0030] The overall observation uncertainty level (average standard deviation of each point) is added to the natural variation of background concentration to form the judgment threshold. This threshold comprehensively considers both measurement system error and natural background fluctuations, making the judgment criteria for plume-affected points more scientific and rigorous, and effectively reducing the probability of misjudgment and omission.
[0031] For continuous influence points, the final plume influence area is determined by selecting boundary points to extend outwards. This method fully considers the discrete distribution of the mobile observation points, making the delineated influence area closer to the real, continuous spatial distribution of the plume.
[0032] The present application determines the background area through the concentration distribution geometry analysis without wind field dependence, adopts the background baseline fitting to eliminate the background drift influence, and sets the determination threshold based on the background concentration natural fluctuation and observation uncertainty, realizes the accurate identification of the point source plume influence area, and effectively improves the reliability of the emission flux estimation. BRIEF DESCRIPTION OF DRAWINGS
[0033] Figure 1 The plume influence area identification flowchart provided for the embodiment of the present application is shown in the figure.
[0034] Figure 2 The vehicle-mounted underway measurement route map provided for the embodiment of the present application is shown in the figure.
[0035] Figure 3 The underway route provided for the embodiment of the present application is shown in the figure. distribution map.
[0036] Figure 4 The time series graph provided for the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION
[0037] The embodiments of the present application are described below through specific, concrete examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure in the specification. The present application can also be implemented or applied through other different specific embodiments, and the details in the specification can be modified or changed in various ways based on different views and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.
[0038] EMBODIMENT
[0039] As shown in the figure, the point source plume influence point identification method based on concentration distribution proposed by the present application is described in detail below. Figure 1
[0040] (1) Closed underway observation is carried out around the emission point source, and multiple solar spectra, GPS data and meteorological data are collected; the spectra are subjected to quality screening and pretreatment, and the spectra seriously affected by interference or with poor fitting effect are removed; the greenhouse gas column concentration is inversely calculated by using the nonlinear least square method commonly used in the art in combination with the meteorological data, the atmospheric dry air column average molar fraction of the target gas at each monitoring point is calculated and the average value , the distribution of the underway observation route is obtained; the atmospheric dry air column average molar fraction of the target gas is calculated by using the following formula:
[0041]
[0042] in, This refers to the gas column concentration, in mol / m³. 2 ; Oxygen column concentration, in mol / m³ 2 0.20924 represents the mole fraction of oxygen in the atmosphere.
[0043] The acquisition of the mobile observation route The distribution method is as follows: As monitoring points The representative value is combined with GPS data to form the monitoring points. Spatial distribution.
[0044] (2) Based on The concentration background area is determined based on distribution characteristics as follows: Taking the emission source as the vertex, connect it to the adjacent monitoring points on both sides of the monitoring point with the highest concentration. The angle formed by the two connecting lines is defined as the downwind angle range, and its boundary is determined by these two connecting lines. Extending along the opposite direction of the two boundaries yields two upwind centerlines, and these two centerlines are then used. The union of sectors serves as the spatial extent of the background area.
[0045] The background baseline and background gradient are determined within the background region. The steps for determining the background baseline within the background region are as follows: [The text then describes the process of determining the background baseline at various background measurement points within the background region.] Constructing it as a time series, and determining based on this time series. The lowest 20% quantile is used as the background baseline fitting point. Linear regression fitting is performed on the quantile to obtain the background baseline, which can be represented as follows:
[0046]
[0047] in, Background value, in ppm; The slope of the line is expressed in ppm / s. Time, in seconds; This is the intercept of the line, expressed in ppm.
[0048] In addition, the background gradient is defined as the gradient of each background measurement point within the background region. The range:
[0049]
[0050] in, For each monitoring point in the background area The maximum value, For each monitoring point in the background area The minimum value.
[0051] (3) Calculate the values for each monitoring point Standard deviation and take their average value. As the overall observation uncertainty level, this level is added to the background gradient to obtain the threshold for determining the plume impact point; the threshold for each monitoring point is calculated. The enhancement value relative to the background value is compared with the threshold. If it exceeds the threshold, the monitoring point is determined to be a plume influence point. At the left and right boundaries of continuous plume influence points, the nearest monitoring point on the left and right sides are selected as the boundary points of the plume influence zone, respectively. Thus, continuous plume influence points and boundary points together constitute the plume influence zone. In this embodiment, each monitoring point... Enhancement value The calculation formula is as follows: .
[0052] Experimental Section
[0053] A closed-loop mobile observation experiment was conducted. The observation system included a commercial vehicle as a mobile platform, carrying a portable FTIR spectrometer, a solar tracker, a GPS receiver, and meteorological instruments. The spectrometer's acquisition wavelength range was 5000–11000 cm⁻¹. -1 The spectral resolution is 0.5 cm⁻¹. -1 When the vehicle stops, the solar tracker is adjusted to track the sun and direct the sunlight beam to the spectrometer. Simultaneously, the GPS receiver records the observation time and latitude / longitude in real time, while the meteorological instrument records parameters such as temperature and air pressure.
[0054] like Figure 2 As shown, the vehicle conducted closed-loop observations in a clockwise direction. It stopped at each pre-set monitoring point and collected 5–10 spectra. After the experiment, the collected spectra were screened using conventional methods, removing spectra that were severely interfered with or had poor fitting results. The column concentration was then retrieved using a nonlinear least squares method, and the results were calculated. Take samples from each monitoring point average As the monitoring point The representative value. And combined with GPS data to generate the mobile observation route. Distribution map, such as Figure 3 As shown, each point represents the location of a monitoring point, and the shade of color indicates the location. The height.
[0055] Because of the spacing between adjacent monitoring points, the horizontal spatial resolution is limited, and the continuous distribution of the real concentration peak value cannot be covered, so the single highest concentration point does not necessarily represent the actual concentration peak position, and the concentration peak position may appear on the sailing route between the adjacent monitoring points on both sides, and the downwind direction cannot be defined by a single direction line, but an angle range. Specifically, taking the emission source as the vertex, connecting the adjacent monitoring points on both sides of the highest concentration monitoring point respectively, the included angle formed by the two connecting lines is defined as the downwind angle range, and the boundary is determined by the two connecting lines. Two upwind center lines can be obtained by extending in the opposite direction along the two boundaries, and the two center lines The union of sectors as the spatial range of the background area, Figure 3 And Figure 4 The triangle point in the middle represents the measurement point of the background area.
[0056] In the background area, the of each background measurement point is constructed as a time series, and the lowest 20% quantile point is taken as the background baseline fitting point, Figure 3 And Figure 4 The large triangle point in the middle is the background baseline fitting point. Linear fitting is performed on the background baseline fitting point to form the background baseline, as shown by the dashed line in Figure 4 , which can be represented as:
[0057]
[0058] Where, is the background value, with units of ppm; is the slope of the straight line, with units of ppm / s; is the time, with units of s; is the intercept of the straight line, with units of ppm.
[0059] At the same time, the range of each monitoring point in the background area is defined as the background gradient, which is used to represent the variation amplitude of the background concentration, and the formula is as follows:
[0060]
[0061] Where, is the maximum value of each monitoring point in the background area, is the minimum value of each monitoring point in the background area.
[0062] For any monitoring point, the concentration enhancement value is defined as:
[0063]
[0064] The standard deviation of each monitoring point is The average standard deviation of all monitoring points is Population impact point screening threshold Defined as:
[0065]
[0066] The criteria for determining the impact point of a plume are:
[0067]
[0068] At the left and right boundaries of the continuous plume influence points, the nearest monitoring point on the left and right sides, respectively, are selected as the boundary points of the plume influence zone. Thus, the continuous plume influence points and the boundary points of the plume influence zone together constitute the plume influence zone. Figure 3 and Figure 4 The diamond-shaped points represent the measurement points within the plume's influence zone.
[0069] The above experiments demonstrate that this invention determines the background region based on the spatial geometric characteristics of concentration distribution. It defines the downwind range by connecting the highest concentration point and its adjacent monitoring points with the emission source as the vertex, and then extending in the opposite direction to form the background region. This method is completely independent of easily disturbed wind field data, fundamentally avoiding systematic biases caused by wind direction and speed errors. Within the background region, a background baseline is established by selecting the lowest 20% quantile in the concentration time series for linear regression fitting, effectively separating the natural fluctuations and slow drifts of the background concentration. Simultaneously, the concentration range within the background region is calculated as the background gradient, quantifying the natural variation amplitude of the background concentration. In the identification stage, the observation uncertainty represented by the average standard deviation of multiple measurements at each monitoring point is added to the background gradient to form a judgment threshold. This threshold comprehensively considers measurement systematic errors and natural background fluctuations. By comparing the concentration enhancement value of each monitoring point with this threshold, accurate identification of plume influence points is achieved. Finally, based on the spatial distribution characteristics of discrete monitoring points, the nearest neighbor point is selected as the boundary point outside the continuous influence point sequence, reasonably defining the complete plume influence area. This method, through a systematic data processing workflow, enables reliable identification of the impact zone of point source plumes under complex meteorological conditions, providing an accurate data foundation for subsequent emission flux calculations.
[0070] The above specific embodiments are merely several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant teachings of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.
Claims
1. A method for identifying point source plume influence points based on concentration distribution, characterized in that, Includes the following steps: S1. Conduct mobile observations around the emission source, simultaneously collecting solar spectrum and location information; obtain the column concentration information of the target gas based on the solar spectrum inversion, and calculate the average mole fraction of the atmospheric dry air column at each monitoring point; combine the location information to generate the spatial distribution of gas concentration along the mobile observation route. S2. Based on the spatial distribution characteristics of the gas concentration, determine the concentration background area, determine the background concentration baseline within the background area, and calculate the background concentration variation range; S3. Based on the background concentration change range and the observation uncertainty level, determine the threshold for identifying the plume influence point; Calculate the enhancement value of the concentration value at each monitoring point relative to the background concentration baseline, compare the enhancement value with the threshold, and if the enhancement value is greater than the threshold, the monitoring point is determined to be a plume influence point; based on the spatial distribution of the identified plume influence points, determine the range of the plume influence area.
2. The method for identifying point source plume influence points based on concentration distribution according to claim 1, characterized in that, In step S1, the mobile observation is a closed-path mobile observation, and the data collected simultaneously also includes meteorological data; after calculating the average mole fraction of the dry air column at each monitoring point, the average value of multiple measurements at each monitoring point is further calculated. This serves as the representative concentration value for that monitoring point; the spatial distribution of the generated gas concentration specifically refers to the generated... Spatial distribution.
3. The method for identifying point source plume influence points based on concentration distribution according to claim 1, characterized in that, In step S1, the column concentration of greenhouse gases is inverted using the nonlinear least squares method, and the average mole fraction of the target gas in the dry atmospheric air column is obtained. Calculated using the following formula: ， in, The column concentration of the target gas, in mol / m³. 2 ; Oxygen column concentration, in mol / m³ 2 0.20924 represents the mole fraction of oxygen in the atmosphere.
4. The method for identifying point source plume influence points based on concentration distribution according to claim 1, characterized in that, In step S2, the method for determining the concentration background area includes: taking the emission point source as the vertex, connecting it to adjacent monitoring points on both sides of the monitoring point with the highest concentration, defining the included angle formed by the two connecting lines as the downwind angle range, the boundary of which is determined by the two connecting lines; extending along the opposite direction of the two boundaries to obtain two upwind center lines, and taking the two center lines as the reference points. The union of sectors serves as the spatial extent of the background area.
5. The method for identifying point source plume influence points based on concentration distribution according to claim 1, characterized in that, In step S2, the method for determining the background baseline within the background area includes: within the background area, determining the background measurement points... Constructing it as a time series, and determining based on this time series. The lowest 20% quantile is used as the background baseline fitting point, and linear regression is performed to obtain the background baseline, which is expressed as: ， in, Background value, in ppm; The slope of the line is expressed in ppm / s. Time, in seconds; This is the intercept of the line, expressed in ppm.
6. The method for identifying point source plume influence points based on concentration distribution according to claim 1, characterized in that, In step S2, the change in background concentration Defined as each background measurement point within the background area The range is calculated using the following formula: ， in, For each monitoring point in the background area The maximum value, For each monitoring point in the background area The minimum value.
7. The method for identifying point source plume influence points based on concentration distribution according to claim 1, characterized in that, In step S3, the observation uncertainty level is calculated by determining the standard deviation of multiple measurements at each monitoring point. And take the standard deviation of all monitoring points. average To characterize; the plume influence point screening threshold By the change in the background concentration Standard deviation of all monitoring points average Adding them together, we get: .
8. The method for identifying point source plume influence points based on concentration distribution according to claim 1, characterized in that, In step S3, the enhancement value The calculation formula is: 。 9. The method for identifying point source plume influence points based on concentration distribution according to claim 1, characterized in that, In step S3, the method for determining the range of the plume influence area includes: at the left and right boundaries of the continuous plume influence points, selecting the nearest monitoring point on the left and the nearest monitoring point on the right as the boundary points of the plume influence area, respectively, and the continuous plume influence points and the boundary points together constitute the plume influence area.
10. The method for identifying point source plume influence points based on concentration distribution according to claim 1, characterized in that, The target gas is carbon dioxide, and the solar spectrum is acquired using a Fourier transform infrared spectrometer.