Atmospheric particulate concentration monitoring method and system based on laser radar
By constructing a photothermal coupled multidimensional detection system and a dynamic radar ratio sequence, the problem of false alarms of high pollution under high humidity layers was solved, and accurate monitoring of particulate matter concentration was achieved, improving the data authenticity and robustness of environmental monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHAANXI LINGFENGZHI NEW ENERGY TECH CO LTD
- Filing Date
- 2026-04-23
- Publication Date
- 2026-05-26
- Estimated Expiration
- Not applicable · inactive patent
Smart Images
Figure CN122084477A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of lidar measurement technology. More specifically, this invention relates to a lidar-based method and system for monitoring atmospheric particulate matter concentration. Background Technology
[0002] In the field of environmental monitoring technology, Mie scattering lidar is the core equipment for detecting the vertical distribution of atmospheric particulate matter. Its physical mechanism is to emit a laser beam toward the atmosphere and receive the backscattered echo signals from particulate matter and atmospheric molecules. Since the intensity of the received echo is controlled by both the backscattering coefficient and the extinction coefficient of the particulate matter, there is a problem of solving a single equation with two unknowns.
[0003] Existing technologies rely on the Fernand inversion algorithm to solve the problem, which forcibly presets a globally constant reference radar ratio to reduce the dimension and improve the cancellation coefficient, and uses a fixed mass conversion factor to map it to particulate matter mass concentration.
[0004] However, this existing technology has significant limitations when facing complex atmospheric environments: in real atmospheric environments, the microscopic physical properties of particulate matter are highly dependent on the dynamic changes of meteorological elements; when the relative humidity is high, particulate matter adsorbs a large amount of water vapor and undergoes hygroscopic deliquescence, resulting in an exponential increase in the equivalent particle size and optical scattering cross section. At this time, the actual radar ratio exhibits a drastic spatial heterogeneity along the vertical height; if a static preset of a constant radar ratio is used, the extinction coefficient iterative integration process accumulates distortion, forcing the system to amplify the scattering signal caused by water vapor condensation and misjudge it as an increase in the dry matter concentration of particulate matter; this detection logic, which separates the meteorological thermodynamic environment from the optical coupling evolution law, makes the system prone to false high pollution warnings when passing through high humidity layers. Summary of the Invention
[0005] To address the technical problem that existing technologies are prone to generating false high-pollution alarms when traversing high-humidity layers, the present invention provides solutions in the following aspects.
[0006] In a first aspect, the present invention provides a method for monitoring atmospheric particulate matter concentration based on lidar, comprising: acquiring a raw backscattered echo signal using a Mie scattering lidar; simultaneously driving a microwave radiometer to acquire atmospheric relative humidity data and atmospheric temperature data; acquiring a range-corrected echo power based on the raw backscattered echo signal; acquiring an absolute temperature gradient based on the atmospheric temperature data; acquiring a corrected relative humidity based on the atmospheric relative humidity data; using the nonlinear compression characteristics of the square root function to nest the absolute temperature gradient and the critical overflow effect of the corrected relative humidity to obtain a thermodynamic deviation index; acquiring a reference lidar ratio; using an algebraic fraction asymptotic structure to convert the thermodynamic deviation index into a dynamic compensation driving force for optical parameters; acquiring a dynamic lidar ratio by combining the reference lidar ratio; using the dynamic lidar ratio and the range-corrected echo power to perform a discretized iterative integration operation layer by layer from the integration boundary point to obtain a corrected extinction coefficient; reconstructing the thermodynamic deviation index into an adaptive attenuation penalty interception network using the quotient of the dynamic lidar ratio and the reference lidar ratio; and acquiring the final mass concentration based on the adaptive attenuation penalty interception network and the corrected extinction coefficient.
[0007] Preferably, the step of acquiring the original backscattered echo signal using a Mie scattering lidar, simultaneously driving a microwave radiometer to acquire atmospheric relative humidity and atmospheric temperature data, and acquiring the range-corrected echo power based on the original backscattered echo signal includes: emitting a probe laser beam towards the atmosphere using a Mie scattering lidar, and receiving the original backscattered echo signal returning along the altitude; simultaneously driving a microwave radiometer to acquire atmospheric relative humidity and atmospheric temperature data within the vertical probe space; performing cubic spline interpolation based on the spatial resolution of the Mie scattering lidar to obtain atmospheric relative humidity and atmospheric temperature data aligned with the altitude; calculating the average background noise of the original backscattered echo signal at the far end where there is no aerosol layer; subtracting the average background noise from the original backscattered echo signal to extract the effective signal component, and multiplying it by the square of the corresponding altitude term to obtain the range-corrected echo power.
[0008] Preferably, the absolute temperature gradient is equal to the ratio of the absolute value of the difference in atmospheric temperature data between adjacent altitude layers to the altitude difference; the method for obtaining the corrected relative humidity is as follows: when the atmospheric relative humidity data is greater than or equal to the critical humidity threshold, the corrected relative humidity is forcibly set to the critical humidity threshold; otherwise, the corrected relative humidity is determined to be equal to the atmospheric relative humidity data.
[0009] Preferably, the thermodynamic deviation index satisfies the expression: In the formula, Derivation of the layer height for the target Thermodynamic deviation index at the location; Derivation of the layer height for the target Corrected relative humidity at the location; This is the critical humidity threshold. Derivation of the layer height for the target The absolute temperature gradient at that location; This is the standard reference temperature gradient.
[0010] Preferably, the method for obtaining the critical humidity threshold and the standard reference temperature gradient includes: retrieving atmospheric sounding profile datasets from local historical detection cycles and synchronous lidar echo datasets to construct an environmental history sample library; extracting the distribution curve of aerosol hygroscopic growth factor with relative humidity from the environmental history sample library, locking the inflection point of the slope change of the distribution curve in the near-saturation range to calibrate the critical humidity threshold; statistically analyzing the vertical temperature distribution pattern of the altitude layer where inversion weather frequently occurs in the environmental history sample library, calculating the mathematical expectation value of temperature change with altitude within the inversion layer to calibrate the standard reference temperature gradient.
[0011] Preferably, the dynamic radar ratio satisfies the expression: In the formula, Derivation of the layer height for the target Dynamic radar ratio at the location; As a benchmark radar ratio; Derivation of the layer height for the target Thermodynamic deviation index at the location; This serves as the baseline for deviation adjustment.
[0012] Preferably, the downward iteration of the modified extinction coefficient satisfies the expression: In the formula, Derivation of the layer height for the target Corrected extinction coefficient at the location; Derivation of the layer height for the target Distance-corrected echo power at the location; Given the height of the upper floor Corrected extinction coefficient at the location; Given the height of the upper floor Distance-corrected echo power at the location; Derivation of the layer height for the target Dynamic radar ratio at the location; The height step size represents the resolution of space exploration.
[0013] Preferably, the final mass concentration satisfies the expression: In the formula, Derivation of the layer height for the target The final mass concentration at the point; Standard quality conversion factor; Derivation of the layer height for the target Corrected extinction coefficient at the location; Derivation of the layer height for the target Thermodynamic deviation index at the location; Derivation of the layer height for the target Dynamic radar ratio at the location; This is the baseline radar ratio.
[0014] Preferably, the method for obtaining the standard mass conversion factor includes: simultaneously deploying a micro-oscillation balance-based particulate matter monitor with legal metrological qualifications at the lidar detection site to construct a real-time correlation observation network between optical extinction coefficient and physical mass concentration; aligning the corrected extinction coefficient sequence obtained by lidar inversion with the real mass concentration sequence output by the particulate matter monitor using timestamps; introducing a dynamic Kalman filter algorithm to filter out the influence of sudden environmental noise on the observation data; performing linear regression analysis on the smoothed two sets of sequence data; and calibrating the standard mass conversion factor by locking the proportional coefficient when the coefficient of determination in the regression model reaches the highest threshold.
[0015] In a second aspect, the present invention provides an atmospheric particulate matter concentration monitoring system based on lidar, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-described atmospheric particulate matter concentration monitoring method based on lidar is implemented.
[0016] By adopting the above technical solution, the above-mentioned method for monitoring atmospheric particulate matter concentration based on lidar is generated into a computer program and stored in a memory so that it can be loaded and executed by a processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.
[0017] The beneficial effects of this invention are as follows:
[0018] 1. This invention breaks through the technical bottleneck of traditional single optical monitoring being unable to detect external thermodynamic environmental disturbances. By simultaneously introducing atmospheric relative humidity data and atmospheric temperature data, a multi-dimensional detection system coupled with light and heat is constructed. By further extracting the absolute temperature gradient reflecting the resistance to water vapor enrichment, and using the nonlinear compression characteristics of the square root function to nest it with the critical overflow effect of humidity to calculate the thermodynamic deviation index, it reflects the entire physical process of particulate matter in the real atmosphere adsorbing water vapor and undergoing deliquescence under temperature inversion and high humidity weather conditions. It completes the decisive parameters of aerosol physical morphology evolution from the source dimension, and improves the system's ability to perceive and quantify complex meteorological conditions.
[0019] 2. This invention utilizes an algebraic fractional asymptotic structure with asymptotic upper and lower limits to generate a dynamic radar ratio sequence, transforming the thermodynamic deviation index into a dynamic compensation driving force for optical parameters under the original dry state. This can achieve smooth stretching under slight disturbances and prevent mathematical divergence under extreme water vapor condensation, restoring the optical limit law of the transition from moist aerosol to stable water droplets. Combined with backward differential iterative integral calculation to solve the corrected extinction coefficient, it effectively breaks the chain of extinction signal distortion and error accumulation caused by the spatial heterogeneity distribution of water vapor, enhancing the robustness of beam attenuation calculation in vertical space.
[0020] 3. In the final concentration mapping stage, this invention uses the corrected optical parameters as a basis and the linear quotient of the deviation of the dynamic radar ratio from the reference radar ratio to reconstruct the thermodynamic deviation index into an adaptive attenuation penalty interception network. This operation can compress the equivalent value of the extinction signal that is abnormally expanded due to water vapor adhesion at the end of the data stream, filter out the false incremental share contributed by water vapor components in the total optical volume, achieve decoupling between the real dry matter physical properties of particulate matter and the optical detection signal, eliminate the false high pollution false alarm phenomenon that is very easy to occur when the system passes through the high humidity layer, and improve the authenticity and reliability of the output data of the environmental monitoring platform. Attached Figure Description
[0021] Figure 1 This is a flowchart illustrating the atmospheric particulate matter concentration monitoring method based on lidar in this invention. Detailed Implementation
[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0023] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0024] This invention discloses a method for monitoring atmospheric particulate matter concentration based on lidar, referring to... Figure 1 This includes steps S1-S4:
[0025] S1. Simultaneously acquire photothermal multidimensional detection parameters and perform spatial alignment and background noise reduction to calculate the distance-corrected echo power.
[0026] First, a Mie scattering lidar is used to emit a detection laser beam toward the atmosphere and receive the original backscattered echo signal returning along the altitude. Because the equivalent particle size and complex refractive index of particulate matter in the natural atmosphere change drastically with thermodynamic conditions, single optical monitoring lacks the ability to perceive disturbances in the external thermodynamic environment. This leads to aerosols with a water vapor coating being easily mistaken for dry particles, resulting in a lack of a source dimension for physical information acquisition. Therefore, this invention introduces atmospheric relative humidity and atmospheric temperature data in the same spatial dimension simultaneously with the acquisition of optical scattering signals, constructing a photothermal coupled multidimensional detection system. Specifically, a microwave radiometer is simultaneously driven to acquire atmospheric relative humidity and atmospheric temperature data in the vertical detection space. This operation completes the external thermodynamic parameters that determine the evolution of the aerosol's physical morphology, establishing the data input boundary for subsequent removal of the optical influence of water vapor.
[0027] Furthermore, due to the different sampling frequencies of the detection devices, this invention performs cubic spline interpolation based on the spatial resolution of the Mie scattering lidar to obtain atmospheric relative humidity data and atmospheric temperature data aligned with altitude. Further, the average background noise of the original backscattered echo signal at the far end without an aerosol layer is calculated, and the average background noise is subtracted from the original backscattered echo signal to extract the effective signal component. This component is then multiplied by the square of the corresponding altitude term to obtain the range-corrected echo power used to characterize the attenuation relationship of the actual atmospheric scattering intensity.
[0028] S2. Extract the absolute temperature gradient and correct the relative humidity, and use nonlinear mathematical nesting to construct a thermodynamic deviation index for quantifying aerosol phase change.
[0029] It should be noted that, due to the extremely nonlinear characteristics of aerosol particles adsorbing water vapor, the scattering cross section of particulate matter will undergo nonlinear expansion when the relative humidity of the environment approaches the saturation point. Furthermore, the temperature inversion structure in the atmosphere, where the temperature rises anomalously with altitude, will block the vertical convection of water vapor, leading to the high enrichment of water vapor at a specific altitude layer and causing abrupt changes in the optical properties of particulate matter. Therefore, this invention extracts the absolute temperature gradient that reflects the resistance to water vapor enrichment and uses the nonlinear compressibility of the square root function to nest it with the critical overflow effect of relative humidity, thereby constructing a thermodynamic deviation index, which reflects the severity of particulate matter deviating from its original dry state.
[0030] Specifically, this invention calculates the ratio of the absolute value of the difference in atmospheric temperature data between adjacent height layers to the height difference along the vertical detection path to obtain the absolute temperature gradient at the corresponding height. When the atmospheric relative humidity data is greater than or equal to the critical humidity threshold, the corrected relative humidity is forcibly set to the critical humidity threshold; otherwise, the corrected relative humidity is determined to be equal to the atmospheric relative humidity data. Further, based on the corrected relative humidity and the absolute temperature gradient, the thermodynamic deviation index at the target derivation layer height is calculated; the thermodynamic deviation index satisfies the expression:
[0031]
[0032] In the formula, Derivation of the layer height for the target The thermodynamic deviation index at that point is dimensionless. Derivation of the layer height for the target Corrected relative humidity at [location], dimensionless, unit: percentage; Critical humidity threshold, dimensionless, unit is percentage; Derivation of the layer height for the target The absolute temperature gradient at a point is measured in degrees Celsius per hundred meters, with the dimension being temperature divided by altitude. The standard reference temperature gradient is quantified by temperature divided by altitude, and its unit is degrees Celsius per 100 meters.
[0033] The formula contains two combined terms with clear physical representations. The first term is a humidity overflow control term constructed using a fractional asymptote mechanism. As the corrected relative humidity increases and approaches the critical humidity threshold, the value of this combined term rises sharply, reproducing the nonlinear volume expansion law caused by aerosols absorbing moisture in a near-saturated state. The second term is an inversion retardation attenuation term constructed using the nonlinear compression characteristics of the square root function. Since the absolute temperature gradient is positive, this core structure generates a product factor that is always greater than 1, thereby effectively amplifying the exacerbating effect of the inversion phenomenon on the abnormal retention of water vapor. At the same time, based on the mathematical characteristic of the decreasing growth slope of the square root function, this term also smoothly suppresses the numerical divergence caused by the surge in absolute temperature gradient under extreme weather conditions, so that this term, as a product factor, can smoothly capture the abnormal water vapor retention characteristics caused by the physical inversion layer. The product of the two terms allows the thermodynamic deviation index to quickly reach its maximum peak when high humidity and inversion weather occur simultaneously.
[0034] It should be added that the above critical humidity threshold Compared with standard reference temperature gradient The calibration needs to be obtained based on statistical regression analysis of long-term historical meteorological observation big data. Specifically, this invention retrieves atmospheric sounding profile datasets and synchronous lidar echo datasets from local historical detection cycles to construct an environmental historical sample library covering various typical meteorological characteristics. Furthermore, this invention extracts the distribution curve of aerosol hygroscopic growth factor with relative humidity from the historical sample library, identifies the inflection point of the slope change of the curve in the near-saturation range where nonlinear expansion occurs, and thus calibrates the critical humidity threshold. Furthermore, this invention calculates the expected value of temperature variation with altitude within the inversion layer by statistically analyzing the vertical temperature distribution patterns of the altitude layer where inversion weather frequently occurs in a historical sample database, thereby calibrating the standard reference temperature gradient. In this embodiment, the critical humidity threshold It is 0.94, the standard reference temperature gradient. The temperature is 0.60 degrees Celsius per 100 meters.
[0035] S3. Based on the algebraic fractional asymptotic structure, the thermodynamic deviation index is transformed into a dynamic compensation driving force, generating a dynamic radar ratio sequence that evolves with space.
[0036] It should be noted that, since the hygroscopic swelling of particulate matter has an optical limit that evolves into pure water droplets, the conventional technique of setting a globally fixed radar ratio will deviate from the actual medium properties, resulting in severe model distortion. Therefore, this invention utilizes an algebraic fractional asymptotic structure with asymptotic upper and lower limits to transform the thermodynamic deviation index into a dynamic compensation driving force for optical parameters under the original dry state. The generated dynamic radar ratio sequence enables the beam attenuation calculation to conform to the natural gradual change process of the physical properties of real particulate matter layer by layer along the vertical space.
[0037] Specifically, the baseline radar ratio obtained through long-term calibration of a solar photometer under typical local dry climate conditions is extracted; further, the dynamic radar ratio at the target derivation layer height is calculated by combining the thermodynamic deviation index at the target derivation layer height with the baseline radar ratio; the dynamic radar ratio satisfies the expression:
[0038]
[0039] In the formula, Derivation of the layer height for the target The dynamic radar ratio at a given location is expressed in solid angle derivatives and is measured in steradian degrees (sr). The reference radar ratio is a solid angle derived quantity, and its unit is steradian (sr). Derivation of the layer height for the target The thermodynamic deviation index at that point is dimensionless. The deviation adjustment base is dimensionless and ranges from 0.5 to 2.0. In this embodiment, it is set to 1.0.
[0040] In this formula, a bounded optical compensation structure is constructed through an algebraic fraction asymptotic structure: when the environmental disturbance is slight and the thermodynamic deviation index is small, the fraction structure exhibits quasi-linear characteristics to achieve smooth numerical stretching; when the water vapor condenses violently and the thermodynamic deviation index surges, the denominator of the algebraic fraction is forced to approach the numerator, so that the product result gradually approaches the set upper limit of the value, effectively preventing the infinite mathematical divergence caused by extreme weather, and truly restoring the physical limit law that the optical parameters tend to saturate when the wet aerosol transitions to stable water droplets. This core mechanism enables the dynamic radar ratio to sensitively track high humidity anomalies and always maintain extremely high numerical stability.
[0041] S4. Perform backward differential iteration to extract the corrected extinction coefficient, and use the adaptive attenuation penalty interception network to decouple and purify the final mass concentration.
[0042] Specifically, this invention extracts the pure atmospheric layer at the top of the troposphere as the integration boundary point, and calculates the initial extinction coefficient at this boundary point using Rayleigh scattering theory, serving as the initial reference value for iterative calculations. Since forward integration from bottom to top causes an exponential amplification of errors, this invention employs backward differential iterative logic, utilizing the dynamic radar ratio and range-corrected echo power at the corresponding altitude to perform discretized iterative integration layer by layer from the integration boundary point downwards, solving for the corrected extinction coefficient that can suppress abnormal water vapor propagation paths. In the field of lidar, the discretized iterative integration operation uses the Fernald algorithm or the Klett algorithm; both Fernald and Klett algorithms are well-known technologies and will not be elaborated upon here. The downward iteration of the corrected extinction coefficient satisfies the expression:
[0043]
[0044] In the formula, Derivation of the layer height for the target The corrected extinction coefficient at a certain point has the dimension of the reciprocal of the height and the unit is per meter; Derivation of the layer height for the target Distance-corrected echo power at the location; Given the height of the upper floor The corrected extinction coefficient at a certain point has the dimension of the reciprocal of the height and the unit is per meter; Given the height of the upper floor Distance-corrected echo power at the location; Derivation of the layer height for the target The dynamic radar ratio at the location is dimensionless. The height step size is the resolution of space exploration, in meters.
[0045] In this formula, the operational logic of the iterative calculation lies in constructing the energy attenuation constraint relationship between adjacent height layers; with the known upper layer height... The state parameters are prior inputs, and the layer height is derived by combining them with the target. The echo observation value and the dynamic radar ratio are compared, and the true extinction state of the lower atmosphere is deduced in reverse through the differential compensation mechanism in the denominator. This backward integration mechanism effectively compresses the mathematical divergence error in the system calculation process, and by relying on the layer-by-layer substitution of the dynamic radar ratio sequence, the optical attenuation anomaly induced by the uneven distribution of water vapor in the vertical propagation path is eliminated.
[0046] It should be noted that since the obtained corrected extinction coefficient characterizes the total optical cross-section of the dry matter core and the surrounding water vapor layer, directly mapping it to a conventional scale would cause the physical volume of water vapor to be incorrectly included in the weight of dry matter, resulting in a false increment. Therefore, this invention uses the corrected optical parameters as a basis and reconstructs the thermodynamic deviation index into an adaptive attenuation penalty interception network at the end of the mass mapping data stream using the linear quotient of the radar ratio deviation. This operation filters out the incremental share of the total optical volume contributed by the water vapor component, realizing the decoupling and purification between the physical properties of dry matter and the optical detection signal.
[0047] Therefore, based on the corrected extinction coefficient, thermodynamic deviation index, and dynamic radar ratio at the target derivation layer height, the final mass concentration at the target derivation layer height is calculated; the final mass concentration satisfies the expression:
[0048]
[0049] In the formula, Derivation of the layer height for the target The final mass concentration at the point is expressed as mass divided by volume, with units of micrograms per cubic meter. The standard quality conversion factor has the dimension of mass divided by area and the unit is micrograms per square meter. Derivation of the layer height for the target The corrected extinction coefficient at a certain point has the dimension of the reciprocal of the height and the unit is per meter; Derivation of the layer height for the target The thermodynamic deviation index at that point is dimensionless. Derivation of the layer height for the target The dynamic radar ratio at a given location is expressed in solid angle derivatives and is measured in steradian degrees (sr). The reference radar ratio is denoted by solid angle and its unit is steradian (sr).
[0050] The formula contains two core physical combination terms. The first term is the total equivalent quantity term, which is obtained by multiplying the standard mass conversion factor and the corrected extinction coefficient, representing the apparent concentration of the overall matter including dry matter and surrounding water vapor. The second term is a water vapor weight penalty attenuation term constructed using a linear proportional quotient. Its operating mechanism is to directly extract the quotient of the dynamic radar ratio and the reference radar ratio as an adaptive adjustment gain, and amplify the thermodynamic deviation index accordingly. When the detector passes through a high-humidity zone, the thermodynamic deviation index increases and drives the numerical basis of the penalty attenuation term to increase, causing the calculation denominator to increase significantly. This division operation compresses the equivalent value of the extinction signal that is abnormally expanded due to water vapor adhesion downward, filters out the optical interference data caused by water vapor, and ensures that the final output mass concentration locks the true dry matter content of particulate matter. Moreover, the greater the positive deviation of the dynamic radar ratio from the reference radar ratio, the more direct the linear amplification effect of the quotient is, thereby intensifying the suppression effect of the penalty attenuation term and realizing the closed-loop adaptive balance adjustment of data stream characteristics.
[0051] It should be added that the above standard quality conversion factor This invention relies on a high-temporal-resolution online dynamic measurement cross-comparison mechanism. Specifically, it synchronously deploys a micro-oscillation balance-based particulate matter monitor with legal metrological qualifications at the lidar detection site to construct a real-time correlation observation network between optical extinction coefficient and physical mass concentration. The corrected extinction coefficient sequence obtained from lidar inversion is timestamped with the actual mass concentration sequence output by the particulate matter monitor, and a dynamic Kalman filter algorithm is introduced to filter out the influence of sudden environmental noise on the observation data. Linear regression analysis is performed on the two smoothed sequences, and the proportional coefficient at which the coefficient of determination in the regression model reaches its highest threshold is locked as the calibration result, thereby establishing the standard mass conversion factor. The absolute value.
[0052] This invention also discloses an atmospheric particulate matter concentration monitoring system based on lidar, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the atmospheric particulate matter concentration monitoring method based on lidar according to this invention.
[0053] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.
Claims
1. A method for monitoring atmospheric particulate matter concentration based on lidar, characterized in that, include: The original backscattered echo signal is obtained using a Mie scattering lidar, and a microwave radiometer is simultaneously driven to obtain atmospheric relative humidity data and atmospheric temperature data. The distance-corrected echo power is obtained based on the original backscattered echo signal. The absolute temperature gradient is obtained based on the atmospheric temperature data, and the corrected relative humidity is obtained based on the atmospheric relative humidity data. The thermodynamic deviation index is obtained by nesting the absolute temperature gradient and the critical spillover effect of the corrected relative humidity using the nonlinear compression characteristics of the square root function. A baseline radar ratio is obtained, and the thermodynamic deviation index is converted into a dynamic compensation driving force for optical parameters using an algebraic fraction asymptotic structure. The dynamic radar ratio is then obtained by combining the baseline radar ratio. Using the dynamic radar ratio and range-corrected echo power, a discretized iterative integration operation is performed layer by layer from the integration boundary point to obtain the corrected extinction coefficient. The thermodynamic deviation index is reconstructed into an adaptive attenuation penalty interception network using the quotient of the dynamic radar ratio and the reference radar ratio. The final mass concentration is obtained based on the adaptive attenuation penalty interception network and the corrected extinction coefficient.
2. The method for monitoring atmospheric particulate matter concentration based on lidar according to claim 1, characterized in that, The process of acquiring the raw backscattered echo signal using a Mie scattering lidar, simultaneously driving a microwave radiometer to acquire atmospheric relative humidity and atmospheric temperature data, and obtaining the range-corrected echo power based on the raw backscattered echo signal includes: The Mie scattering lidar emits a probe laser beam toward the atmosphere and receives the original backscattered echo signal returning along the altitude. Synchronously drive the microwave radiometer to acquire atmospheric relative humidity and atmospheric temperature data in the vertical detection space; Based on the spatial resolution of the Mie scattering lidar, cubic spline interpolation was performed to obtain atmospheric relative humidity and atmospheric temperature data aligned with altitude. Calculate the average background noise of the original backscattered echo signal at the far end where there is no aerosol layer; The effective signal component is extracted by subtracting the average background noise from the original backscattered echo signal and multiplying it with the square of the corresponding height to obtain the range-corrected echo power.
3. The method for monitoring atmospheric particulate matter concentration based on lidar according to claim 1, characterized in that, The absolute temperature gradient is equal to the ratio of the absolute value of the difference in atmospheric temperature data between adjacent altitude layers to the altitude difference; the method for obtaining the corrected relative humidity is as follows: When the atmospheric relative humidity data is greater than or equal to the critical humidity threshold, the corrected relative humidity will be forcibly set to the critical humidity threshold; otherwise, the corrected relative humidity will be determined to be equal to the atmospheric relative humidity data.
4. The method for monitoring atmospheric particulate matter concentration based on lidar according to claim 1, characterized in that, The thermodynamic deviation index satisfies the expression: ； In the formula, Derivation of the layer height for the target Thermodynamic deviation index at the location; Derivation of the layer height for the target Corrected relative humidity at the location; This is the critical humidity threshold. Derivation of the layer height for the target The absolute temperature gradient at that location; This is the standard reference temperature gradient.
5. The method for monitoring atmospheric particulate matter concentration based on lidar according to claim 4, characterized in that, The methods for obtaining the critical humidity threshold and the standard reference temperature gradient include: Retrieve local historical atmospheric sounding profile datasets and synchronous lidar echo datasets from the historical sounding period to construct an environmental history sample library; The distribution curves of aerosol hygroscopic growth factor with relative humidity were extracted from the environmental history sample library. The inflection point of the slope change of the distribution curve in the near-saturation range was identified, and the critical humidity threshold was calibrated. By analyzing the vertical temperature distribution patterns of the altitude layer where temperature inversions frequently occur in the historical environmental sample database, the expected value of temperature variation with altitude within the inversion layer was calculated, and the standard reference temperature gradient was calibrated.
6. The method for monitoring atmospheric particulate matter concentration based on lidar according to claim 1, characterized in that, The dynamic radar ratio satisfies the expression: ； In the formula, Derivation of the layer height for the target Dynamic radar ratio at the location; As a benchmark radar ratio; Derivation of the layer height for the target Thermodynamic deviation index at the location; This serves as the baseline for deviation adjustment.
7. The method for monitoring atmospheric particulate matter concentration based on lidar according to claim 1, characterized in that, The downward iteration of the corrected extinction coefficient satisfies the expression: ； In the formula, Derivation of the layer height for the target Corrected extinction coefficient at the location; Derivation of the layer height for the target Distance-corrected echo power at the location; Given the height of the upper floor Corrected extinction coefficient at the location; Given the height of the upper floor Distance-corrected echo power at the location; Derivation of the layer height for the target Dynamic radar ratio at the location; The height step size represents the resolution of space exploration.
8. The method for monitoring atmospheric particulate matter concentration based on lidar according to claim 7, characterized in that, The final mass concentration satisfies the expression: ； In the formula, Derivation of the layer height for the target The final mass concentration at the point; Standard quality conversion factor; Derivation of the layer height for the target Corrected extinction coefficient at the location; Derivation of the layer height for the target Thermodynamic deviation index at the location; Derivation of the layer height for the target Dynamic radar ratio at the location; This is the baseline radar ratio.
9. A method for monitoring atmospheric particulate matter concentration based on lidar according to claim 8, characterized in that, The methods for obtaining the standard quality conversion factor include: Simultaneously deploy a micro-oscillation balance method particulate matter monitoring instrument with legal metrological qualifications at the lidar detection site to construct a real-time correlation observation network between optical extinction coefficient and physical mass concentration; The corrected extinction coefficient sequence obtained by lidar inversion is timestamped with the actual mass concentration sequence output by the particulate matter monitor. A dynamic Kalman filter algorithm is introduced to filter out the impact of sudden environmental noise on the observation data. Linear regression analysis is performed on the two smoothed series data, and the standard quality conversion factor is calibrated by locking the proportional coefficient when the coefficient of determination in the regression model reaches the highest threshold.
10. An atmospheric particulate matter concentration monitoring system based on lidar, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the method for monitoring atmospheric particulate matter concentration based on lidar according to any one of claims 1-9.