Greenhouse gas infrared spectral fitting method based on instrumental line function iterative inversion

Abstract

This invention discloses a method for fitting infrared spectra of greenhouse gases based on iterative inversion of instrument line function, relating to the field of atmospheric greenhouse gas infrared spectroscopy measurement technology. This method, based on iterative inversion of instrument line function, obtains greenhouse gas concentrations and fits calibrated spectra by iteratively calibrating infrared spectral parameters and instrument line function. By acquiring dynamic conditions such as current ambient temperature and gas pressure, and combining them with absorption cross-section reference values ​​in a database, this invention can calculate the absorption cross-section value under real conditions for each wavenumber point, eliminating errors caused by fixed parameter assumptions. Furthermore, by iteratively optimizing the instrument's center wavenumber and resolution width, the matching degree between the calibrated spectrum and the actual measured spectrum is significantly improved, especially under high noise and complex multi-component interference conditions, enhancing the overall accuracy of spectral fitting and thus ensuring the accuracy and reliability of greenhouse gas concentration inversion data.

Description

Technical Field

[0001] This invention relates to the field of atmospheric greenhouse gas infrared spectroscopy measurement technology, specifically a method for fitting greenhouse gas infrared spectra based on iterative inversion of instrument linear function. Background Technology

[0002] Greenhouse gases (such as carbon dioxide, methane, and nitrous oxide) have unique absorption characteristics in the infrared band. Their absorption spectra are closely related to gas concentration, gas composition, and experimental conditions. Therefore, infrared spectroscopy can be used to perform qualitative and quantitative analysis of greenhouse gases. This method is widely used in climate research, environmental monitoring, and industrial emission control.

[0003] In practical infrared spectroscopy measurements, ideal spectra are often distorted to some extent due to limitations in instrument resolution and optical characteristics. The Instrument Line Shape Function (ILSF) is used to describe the influence of the instrument on the measured spectrum, including resolution width and center wavenumber drift. By introducing the instrument line shape function, the instrument response characteristics can be incorporated into the spectral fitting model, thereby improving the fitting accuracy.

[0004] There is usually a difference between the actual measured spectrum and the theoretical spectrum. This difference may be due to the uncertainty of gas concentration, changes in the experimental environment, and the influence of instrument characteristics.

[0005] By using an iterative inversion method, the actual spectrum is matched with the theoretically calculated ideal spectrum. This not only optimizes instrument parameters (such as center wavenumber and resolution width) but also enables accurate inversion of gas concentration and other physical parameters.

[0006] The limitations of existing technologies include at least the following problems: First, existing infrared spectroscopy methods for measuring greenhouse gas concentrations are limited to using fixed instrument center wavenumber and resolution width parameters, failing to dynamically adjust for actual spectral conditions and noise characteristics. This results in significant errors between the calibrated spectrum and the actual measured spectrum. Furthermore, traditional methods often fail to effectively reflect the distortion effects of instrument characteristics on the spectrum, thus reducing the quantitative accuracy and reliability of greenhouse gas concentration measurements. Second, existing infrared spectroscopy methods for greenhouse gases often rely on idealized assumptions, neglecting the interference effects caused by overlapping absorption peaks of multiple components. This makes it difficult to accurately distinguish and jointly analyze multiple greenhouse gases in complex environments. Such methods are particularly problematic in high-noise, multi-interference environments. In practice, errors can easily accumulate, leading to significant deviations in concentration inversion results. Furthermore, traditional methods typically employ single-calibration or static calibration, lacking an iterative optimization process based on actual measurement data. This makes it difficult to correct instrument parameters in real time. Dynamic changes in external conditions such as ambient temperature and gas pressure cause the absorption spectrum to shift with experimental conditions, further exacerbating concentration measurement errors. Simultaneously, existing methods lack a mechanism to integrate a complete instrument linear function model into infrared spectral fitting, failing to systematically consider the impact of instrument resolution width and center wavenumber drift on spectral shape. This results in insufficient matching between the calibrated and measured spectra, potentially causing mismatches between measurement results and actual concentrations, thus limiting the applicability of spectral analysis methods in complex environmental monitoring. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method for fitting greenhouse gas infrared spectra based on iterative inversion of instrument linear function. This method solves the problem that existing infrared spectral analysis methods for greenhouse gas concentration measurement are limited to fixed instrument center wavenumber and resolution width parameters, failing to dynamically adjust for actual spectral conditions and noise characteristics. This results in significant errors between the calibrated and measured spectra. Furthermore, traditional methods often fail to effectively reflect the distortion effects of instrument characteristics on the spectrum, thus reducing the quantitative accuracy and reliability of greenhouse gas concentration measurements. Secondly, existing greenhouse gas infrared spectral analysis methods often rely on idealized assumptions, neglecting the interference effects of overlapping absorption peaks in multiple components, making it difficult to accurately distinguish and jointly analyze multiple greenhouse gases in complex environments. This method... When dealing with high-noise and multi-interference environments, error accumulation is prone to occur, leading to significant deviations in concentration inversion results. Furthermore, traditional methods typically employ single-calibration or static calibration, lacking an iterative optimization process based on actual measurement data. This makes it difficult to correct instrument parameters in real time. Dynamic changes in external conditions such as ambient temperature and gas pressure cause the absorption spectrum to shift with experimental conditions, further exacerbating concentration measurement errors. Simultaneously, existing methods lack a mechanism to integrate a complete instrument linear function model into infrared spectral fitting, failing to systematically consider the impact of instrument resolution width and center wavenumber drift on spectral shape. This results in insufficient matching between the calibrated and measured spectra, potentially causing inconsistencies between the measured results and actual concentrations, thus limiting the applicability of spectral analysis methods in complex environmental monitoring.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for fitting the infrared spectra of greenhouse gases based on iterative inversion of the instrument's linear function, comprising the following steps: obtaining the infrared spectral wavenumber range, the current ambient temperature, and the current gas pressure, and analyzing the absorption cross-section value of each greenhouse gas at each wavenumber point within the infrared spectral wavenumber range; obtaining the background spectrum and the gas concentration value of each greenhouse gas, and establishing an ideal infrared spectrum of the greenhouse gases by combining the absorption cross-section value of each greenhouse gas at each wavenumber point within the infrared spectral wavenumber range; performing calibration analysis on the ideal infrared spectrum of the greenhouse gases with the instrument's linear function to obtain the initial calibration infrared spectrum of the greenhouse gases; obtaining the measured infrared spectrum of the greenhouse gases, and fitting it with the initial calibration infrared spectrum of the greenhouse gases to obtain the calibration infrared spectrum of the greenhouse gases.

[0009] Furthermore, the specific steps for analyzing the absorption cross-section value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range are as follows: Obtain the ambient temperature reference value, gas pressure reference value, and the absorption cross-section reference value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range; combine the ambient temperature reference value, gas pressure reference value, and the absorption cross-section reference value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range with the current ambient temperature value and the current gas pressure value for comprehensive analysis to obtain the absorption cross-section value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range.

[0010] Furthermore, the specific formula for calculating the absorption cross-section value of each greenhouse gas at wavenumber point V in the infrared spectral wavenumber range is as follows: Where, XsZ(v) i XsC(v) represents the absorption cross-section value of the i-th greenhouse gas at wavenumber point v within the infrared spectral wavenumber range. i α is the reference value of the absorption cross section of the i-th greenhouse gas at wavenumber point v in the infrared spectral wavenumber range, QyL is the current gas pressure value, QyC is the gas pressure reference value, WdC is the ambient temperature reference value, WwD is the current ambient temperature value, α is the temperature correction coefficient stored in the database, i = 1, 2, 3, ..., i0, i0 is the number of greenhouse gases.

[0011] Furthermore, the specific steps for establishing the ideal infrared spectrum of greenhouse gases are as follows: obtain the infrared optical path value of the instrument; input the infrared optical path value of the instrument, the background spectrum, the gas concentration value of each greenhouse gas, and the absorption cross section value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range into the ideal infrared spectrum analysis model of greenhouse gases for comprehensive analysis to obtain the ideal infrared spectrum of greenhouse gases.

[0012] Furthermore, the specific infrared ideal spectral analysis model for greenhouse gases is as follows: Where LxG(v) is the ideal infrared spectrum of greenhouse gases, BjG(v) is the background spectrum, and XsZ(v) is the background spectrum. i Let QnD be the absorption cross-section value of the i-th greenhouse gas at wavenumber point v in the infrared spectral wavenumber range, where v = 1, 2, 3, ..., v0, and v0 is the number of wavenumber points in the infrared spectral wavenumber range. i Let HwG be the concentration value of the i-th greenhouse gas, HwG be the infrared optical path value of the instrument, i = 1, 2, 3, ..., i0, and i0 be the number of greenhouse gases.

[0013] Furthermore, the specific steps for obtaining the initial calibration spectrum of greenhouse gas infrared radiation are as follows: obtain the instrument linear function under the preset instrument center wavenumber and instrument resolution width, and perform convolution analysis with the ideal spectrum of greenhouse gas infrared radiation to obtain the initial calibration spectrum of greenhouse gas infrared radiation.

[0014] Furthermore, the specific formula for calculating the initial infrared calibration spectrum of greenhouse gases is as follows: JzG(v)=∫[H(vv′,v′0,σ)*LxG(v′)]dv′; where JzG(v) is the initial infrared calibration spectrum of greenhouse gases, H(vv′,v′0,σ) is the instrument linear function under the preset instrument center wavenumber and instrument resolution width, v′0 is the preset instrument center wavenumber, σ is the preset instrument resolution width, and LxG(v′) is the ideal infrared spectrum of greenhouse gases.

[0015] Furthermore, the specific expression for the instrument linear function under the preset instrument center wavenumber and instrument resolution width is as follows: Where H(vv′,v′0,σ) is the instrument linear function under the preset instrument center wavenumber and instrument resolution width, v′0 is the preset instrument center wavenumber, σ is the preset instrument resolution width, and π is pi.

[0016] Further, the specific steps for obtaining the greenhouse gas infrared calibration spectrum are as follows: Read the greenhouse gas infrared measurement spectrum and perform error analysis with the initial greenhouse gas infrared calibration spectrum, establishing an error function; adjust the instrument center wavenumber and instrument resolution width with minimizing the error function as the optimization objective; determine whether the adjusted instrument center wavenumber and instrument resolution width meet the preset convergence conditions; if the adjusted instrument center wavenumber and instrument resolution width meet the preset convergence conditions, output the adjusted instrument center wavenumber and instrument resolution width, and mark them as the optimal instrument center wavenumber and optimal instrument resolution width; if the adjusted instrument center wavenumber and instrument resolution width do not meet the preset convergence conditions, perform iterative adjustments until the optimal instrument center wavenumber and optimal instrument resolution width are obtained; perform convolution analysis between the instrument linear function under the optimal instrument center wavenumber and optimal instrument resolution width and the ideal greenhouse gas infrared spectrum to obtain the greenhouse gas infrared calibration spectrum.

[0017] Furthermore, the specific expression of the error function is as follows: Where E(v′0,σ) is the error function, v′0 is the preset instrument center wavenumber, σ is the preset instrument resolution width, CgP(v) is the greenhouse gas infrared measurement spectrum, and JzG(v) is the greenhouse gas infrared initial calibration spectrum, where v=1,2,3,…,v0, and v0 is the number of wavenumber points in the infrared spectral wavenumber range.

[0018] The present invention has the following beneficial effects:

[0019] (1) The greenhouse gas infrared spectrum fitting method based on the iterative inversion of the instrument linear function can calculate the absorption cross section value under real conditions for each wavenumber point by acquiring the current ambient temperature, gas pressure and other dynamic conditions, and combining the absorption cross section reference value in the database. This eliminates the error caused by the assumption of fixed parameters. In addition, by iteratively optimizing the instrument center wavenumber and resolution width, the matching degree between the calibration spectrum and the actual measured spectrum is significantly improved. Especially under high noise and complex multi-component interference conditions, the overall accuracy of the spectrum fitting is improved, thereby ensuring the accuracy and reliability of the greenhouse gas concentration inversion data.

[0020] (2) The greenhouse gas infrared spectrum fitting method based on the iterative inversion of the instrument line function compensates for the spectral distortion caused by instrument drift or environmental changes by dynamically adjusting the instrument line function, optimizing the instrument center wavenumber and resolution width. At the same time, the physical characteristics of the instrument are integrated into the calibration spectrum calculation through convolution analysis, which significantly enhances the adaptability and stability of the instrument under different experimental conditions. Especially in long-term monitoring, the automated parameter adjustment reduces the data deviation caused by changes in instrument characteristics, ensuring the consistency and repeatability of experimental data.

[0021] (3) The infrared spectral fitting method for greenhouse gases based on the iterative inversion of the instrument's linear function can effectively distinguish the multi-component spectral characteristics of greenhouse gases by using convolution analysis and error function optimization. Even under conditions of overlapping absorption peaks and complex interference, it can accurately distinguish the contribution of different components to the overall spectrum. At the same time, combined with the optimization and calibration of the instrument's linear function, it reduces the problem of peak broadening caused by insufficient resolution. Thus, the method has wide application value in fields such as complex atmospheric environment monitoring and multi-component industrial emission control, and provides strong support for the joint quantitative analysis of greenhouse gases.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] Figure 1 This is a flowchart of the greenhouse gas infrared spectrum fitting method based on the iterative inversion of the instrument linear function according to the present invention.

[0024] Figure 2 This is a flowchart illustrating the specific steps involved in analyzing the absorption cross-section value of each greenhouse gas at each wavenumber point within the infrared spectral wavenumber range in the greenhouse gas infrared spectral fitting method based on the iterative inversion of the instrument linear function, as described in this invention.

[0025] Figure 3 This is a flowchart illustrating the specific steps involved in obtaining the greenhouse gas infrared calibration spectrum in the greenhouse gas infrared spectrum fitting method based on the iterative inversion of the instrument linear function of this invention. Detailed Implementation

[0026] This application's embodiments address the limitations of existing infrared spectral analysis methods for greenhouse gas concentration measurement. These methods rely on fixed instrument center wavenumber and resolution width parameters, failing to dynamically adjust for actual spectral conditions and noise characteristics. This results in significant errors between calibrated and measured spectra. Furthermore, traditional methods often fail to effectively reflect the distortion caused by instrument characteristics, reducing the quantitative accuracy and reliability of greenhouse gas concentration measurements. Additionally, existing greenhouse gas infrared spectral analysis methods often employ idealized assumptions, neglecting the interference effects of overlapping absorption peaks in multi-component gases. This makes accurate resolution and joint analysis of multiple greenhouse gases in complex environments difficult. Such methods also struggle with handling high-noise environments. In environments with noise and multiple interferences, errors are prone to accumulate, leading to significant deviations in concentration inversion results. Furthermore, traditional methods typically employ single-time or static calibration, lacking an iterative optimization process based on actual measurement data. This makes it difficult to correct instrument parameters in real time. Dynamic changes in external conditions such as ambient temperature and gas pressure cause the absorption spectrum to shift with experimental conditions, further exacerbating concentration measurement errors. Simultaneously, existing methods lack a mechanism to integrate a complete instrument linear function model into infrared spectral fitting, failing to systematically consider the impact of instrument resolution width and center wavenumber drift on spectral shape. This results in insufficient matching between the calibrated and measured spectra, potentially causing mismatches between measurement results and actual concentrations, thus limiting the applicability of spectral analysis methods in complex environmental monitoring.

[0027] The problem addressed in this application's embodiments can be summarized as follows:

[0028] First, the infrared wavenumber range, current ambient temperature, current gas pressure, and absorption cross-section of each greenhouse gas are obtained to establish the ideal infrared spectrum of the greenhouse gases. This step combines the background spectrum and gas concentration values ​​to calculate the ideal spectrum using the absorption cross-section of the greenhouse gases and environmental condition parameters. Second, the ideal infrared spectrum of the greenhouse gases is combined with the instrument's line function, and the initial calibration spectrum of the greenhouse gases is obtained through convolution analysis. Simultaneously, the difference between the actual measured spectrum and the calibration spectrum is analyzed using an error function, combined with the instrument's center wavenumber and resolution width, and the instrument parameters are dynamically adjusted to optimize the line function. Finally, with the goal of minimizing the error function, the instrument's center wavenumber and resolution width are iteratively adjusted until the convergence condition is met to obtain the optimal parameters. Based on the optimal parameters, the instrument's line function is recalibrated and convolved with the ideal spectrum to obtain the infrared calibration spectrum, achieving high-precision fitting.

[0029] Please see Figure 1This invention provides a technical solution: a method for fitting the infrared spectrum of greenhouse gases based on iterative inversion of instrument linear functions, comprising the following steps: obtaining a preset infrared spectral wavenumber range (i.e., the infrared spectral coverage range (e.g., 5400 cm⁻¹)). -1 Up to 9000cm -1 The experiment involves obtaining the current ambient temperature and gas pressure (representing the total pressure of the gas sample under experimental conditions), and analyzing the absorption cross-section of each greenhouse gas at each wavenumber point within the infrared spectral wavenumber range. It also involves acquiring the background spectrum (representing the infrared radiation intensity without gas absorption) and the gas concentration of each greenhouse gas, and establishing an ideal infrared spectrum for the greenhouse gas at each wavenumber point within the infrared spectral wavenumber range. The ideal infrared spectrum of the greenhouse gas is then calibrated against the instrument's linear function to obtain the initial calibration spectrum. Finally, the measured infrared spectrum of the greenhouse gas is acquired and fitted with the initial calibration spectrum to obtain the calibration spectrum. These two calibrated and measured infrared spectra are then used for subsequent quantitative analysis.

[0030] The background spectrum can be obtained using the following method:

[0031] Measurement under gas-free conditions: Evacuate the measuring chamber or sample chamber to ensure that there are no interfering gases inside, or flush the chamber with a gas that is completely transparent to the target wavenumber range (such as dry high-purity nitrogen). Under these conditions, use an infrared spectrometer to measure the intensity of infrared radiation emitted by the light source to obtain the background spectrum.

[0032] Calibration mode measurement: The background spectrum is measured directly using a standard light source with known calibration (such as a blackbody radiation source), which is suitable for high-precision experiments.

[0033] Data processing correction: The background spectrum is indirectly obtained by fitting or correcting the spectral baseline by analyzing the non-absorption region (where there are no absorption peaks of any gas).

[0034] The concentration values ​​of each greenhouse gas can be obtained using the following methods:

[0035] Standard gas calibration method: Use a gas sample (standard gas) of known concentration for measurement, establish a calibration curve between absorption intensity and gas concentration through instrument response, measure the absorption spectrum of the sample gas, and calculate the gas concentration value by fitting it to the calibration curve.

[0036] Theoretical model inversion method: obtained through inversion calculation using the Beer-Lambert Law.

[0037] Chemometrics: Using a known multi-component gas mixture, a characteristic matrix of the gas absorption spectrum is established. By measuring the sample spectrum through multivariate fitting analysis, the gas concentration is deduced.

[0038] Auxiliary sensor method: While performing infrared spectroscopy measurements, the gas concentration of the sample is measured using other gas concentration measuring devices (such as gas chromatographs, mass spectrometers, and electrochemical sensors) for comparison and calibration.

[0039] Database and model matching: Using a known infrared absorption spectrum database (such as HI TRAN), the measured spectrum is inverted and fitted to obtain the gas concentration value.

[0040] Specifically, such as Figure 2 As shown, the specific steps for analyzing the absorption cross-section value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range are as follows: Obtain the ambient temperature reference value, gas pressure reference value, and the absorption cross-section reference value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range (i.e., the gas absorption cross-section value under the ambient temperature reference value and gas pressure reference value); Combine the ambient temperature reference value, gas pressure reference value, and the absorption cross-section reference value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range with the current ambient temperature value and the current gas pressure value for comprehensive analysis to obtain the absorption cross-section value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range.

[0041] The specific formula for calculating the absorption cross-section value of each greenhouse gas at wavenumber point v in the infrared spectral wavenumber range is as follows: Where, XsZ(v) i XsC(v) represents the absorption cross-section value of the i-th greenhouse gas at wavenumber point v within the infrared spectral wavenumber range (this value represents the absorption cross-section value of the greenhouse gas at a set wavenumber, indicating the absorption intensity of the greenhouse gas at the set wavenumber, which affects the overall shape of the spectrum). i α is the reference value of the absorption cross section of the i-th greenhouse gas at wavenumber point V in the infrared spectral wavenumber range, QyL is the current gas pressure value, QyC is the gas pressure reference value, WdC is the ambient temperature reference value, DwD is the current ambient temperature value, α is the temperature correction coefficient stored in the database, i = 1, 2, 3, ..., i0, i0 is the number of greenhouse gases.

[0042] It should be explained that the ambient temperature reference value is a standard temperature value used to calculate and standardize greenhouse gas absorption cross sections. It is usually taken as a common ambient temperature (such as 296K or 25℃), and it can be obtained in the following way:

[0043] Database standard: Read conventionally defined ambient temperature values ​​from international databases (such as HITRAN).

[0044] Experimental equipment calibration: Select the corresponding temperature value as a reference according to the calibration conditions of the experimental equipment.

[0045] The experiment is designed so that the experiment designer can choose the most common or stable temperature value as a reference according to specific needs.

[0046] The gas pressure reference value is standard atmospheric pressure (e.g., 1 atm = 101325 Pa), used for the standardized calculation of the absorption cross section, and can be obtained in the following way:

[0047] Database standard: Use standard atmospheric pressure as a reference value from HITRAN or a similar database.

[0048] Experimental setup: If a specific air pressure (such as low pressure 0.1 atm) is frequently used in the experiment, this air pressure can also be selected as a reference.

[0049] Equipment calibration: Use the default pressure setting provided by the instrument as a reference.

[0050] The absorption cross-section reference value is the absorption cross-section value of the gas under reference ambient temperature and gas pressure, used to further calculate the absorption cross-section under actual experimental conditions. It can be obtained in the following way:

[0051] Database read: HITRAN provides absorption cross-section values ​​under standard environmental conditions. By inputting a specific wavenumber range and target gas, the database is read to obtain reference values ​​of the absorption cross-section at the reference temperature and pressure.

[0052] Experimental measurement: Measure the infrared absorption spectrum of the gas under standard reference conditions (such as 296 K and 1 atm) and invert the reference value of the absorption cross section.

[0053] Theoretical calculation: Based on the molecular properties of the gas, the reference value of the absorption cross section is calculated using the theoretical formula of molecular vibrational energy levels.

[0054] The temperature correction factor stored in the database is a scaling factor describing the change of the absorption cross-section with temperature, reflecting how the shape and intensity of the gas absorption peak are affected by temperature changes. It can be obtained in the following way:

[0055] Database access: Databases such as HITRAN provide correction factor data for the absorption cross section of different greenhouse gases as a function of temperature. The database usually contains absorption cross section values ​​at different temperatures, and temperature correction factors can be fitted using these values.

[0056] Experimental measurement: Measure the absorption spectrum of the gas at different temperatures (e.g., 270K, 296K, 320K), compare the absorption intensity and absorption cross-section at different temperatures, and calculate the temperature correction factor.

[0057] Theoretical calculation: Based on the energy level distribution law of molecular vibration and rotation (such as Boltzmann distribution), the effect of temperature on the absorption cross section is calculated, and the temperature correction coefficient is obtained through formula analysis. The formula is: Temperature correction coefficient = Absorption intensity change rate / Temperature change rate.

[0058] Data fitting: Perform curve fitting on the data points of known absorption cross section values ​​as a function of temperature, and extract the correction coefficient.

[0059] This implementation plan establishes a comprehensive theoretical framework by clearly defining the calculation formula for the absorption cross-section value and its various parameters (such as ambient temperature, gas pressure, absorption cross-section reference value, and temperature correction factor). This framework effectively guides the entire process from experimental conditions to actual calculations. This systematic approach helps to accurately describe the variation patterns of infrared spectra, combines experimental results with theoretical models, and improves the interpretability of experimental data. The use of ambient temperature and gas pressure reference values ​​ensures the standardization and consistency of absorption cross-section calculations, facilitating comparisons between different experiments and models. The use of data from authoritative international databases such as HITRAN increases the accuracy and reliability of the calculations, giving experimental analysis higher credibility. Multiple methods are provided to obtain key parameters (such as temperature correction factor). The data (including absorption cross-section reference values, etc.) encompasses database retrieval, experimental measurement, and theoretical calculation. This diversity allows experimental designers to choose the most suitable method based on their own conditions and resources, enhancing the flexibility of practical operation. For different gas pressures and ambient temperatures, the formula achieves dynamic adjustment of the absorption cross-section value through correction coefficients, demonstrating strong adaptability. With explicit environmental reference values ​​(temperature, pressure), experimental conditions can be optimized, reducing the impact of external interference on measurement results. The introduction of temperature correction coefficients and the description of absorption cross-section changes with temperature facilitate the analysis of the impact of temperature and pressure changes on spectral measurement results, helping experimenters control experimental errors. Since both the absorption cross-section reference values ​​and temperature correction coefficients originate from standard databases or experimental calibration data, the results have strong reusability. Different experimenters or research teams can reuse these data under similar experimental conditions, greatly reducing duplication of work and saving time and resources. The calculation of absorption cross-section values ​​directly affects the subsequent quantitative analysis of gas concentrations. By combining multiple reference values ​​and correction coefficients, the absorption behavior of target gases in infrared spectra can be accurately described, reducing error accumulation and improving the accuracy of quantitative concentration analysis. This provides more accurate data support for greenhouse gas monitoring. Through this calculation framework and method, the infrared absorption characteristics of greenhouse gases can be comprehensively and accurately described, providing a reliable data foundation for climate change and greenhouse gas emission monitoring.

[0060] Specifically, the steps for establishing the ideal infrared spectrum of greenhouse gases are as follows: Obtain the instrument's infrared optical path length (which refers to the total path length of infrared light propagating in the gas sample, usually in centimeters; it reflects the distance of interaction between infrared light and gas molecules and directly affects the absorption intensity of the spectrum); Input the instrument's infrared optical path length, background spectrum, gas concentration value of each greenhouse gas, and absorption cross-section value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range into the ideal infrared spectrum analysis model of greenhouse gases for comprehensive analysis to obtain the ideal infrared spectrum of greenhouse gases.

[0061] The specific model for ideal infrared spectral analysis of greenhouse gases is as follows: Where LxG(v) is the ideal infrared spectrum of greenhouse gases, BjG(v) is the background spectrum, and XsZ(v) is the background spectrum. i Let QnD be the absorption cross-section value of the i-th greenhouse gas at wavenumber point v in the infrared spectral wavenumber range, where v = 1, 2, 3, ..., v0, and v0 is the number of wavenumber points in the infrared spectral wavenumber range. i Let HwG be the concentration value of the i-th greenhouse gas, HwG be the infrared optical path value of the instrument, i = 1, 2, 3, ..., i0, and i0 be the number of greenhouse gases.

[0062] In this implementation scheme, by comprehensively analyzing the instrument's infrared optical path length, background spectrum, gas concentration, and absorption cross-section, the ideal infrared spectrum of greenhouse gases can be accurately simulated. The infrared optical path length directly affects the interaction between infrared light and gas molecules, thus affecting the absorption intensity. Considering these factors can significantly improve the measurement accuracy of greenhouse gas concentration, thereby obtaining more reliable spectral data. By establishing an ideal infrared spectral analysis model for greenhouse gases, the absorption characteristics of each greenhouse gas can be comprehensively considered, and optimization analysis can be performed based on the instrument's optical path length. In this way, the model can not only more accurately predict the absorption of each gas in the infrared spectrum, but also further refine the influence of each wavenumber point within the wavenumber range, improving the adaptability and accuracy of spectral analysis. This method can perform comprehensive spectral analysis by inputting multiple parameters (such as gas concentration, absorption cross-section, infrared optical path length, etc.). This comprehensive analysis can maximize the consideration of various variables that may occur in the actual measurement process, thereby improving the reliability of gas concentration measurement and avoiding errors that may occur in single-parameter analysis.

[0063] Specifically, the steps to obtain the initial calibration spectrum of greenhouse gas infrared radiation are as follows: obtain the instrument linear function under the preset instrument center wavenumber and instrument resolution width, and perform convolution analysis with the ideal spectrum of greenhouse gas infrared radiation to obtain the initial calibration spectrum of greenhouse gas infrared radiation.

[0064] The specific formula for calculating the initial calibration spectrum of greenhouse gases in infrared is as follows: JzG(v)=∫[H(vv′,v′0,σ)*LxG(v′)]dv′; where JzG(v) is the initial calibration spectrum of greenhouse gases in infrared, H(vv′,v′0,σ) is the instrument linear function under the preset instrument center wavenumber and instrument resolution width, which is a distribution function of wavenumber offset ν-ν′. It represents the contribution of the ideal infrared spectrum of greenhouse gases LxG(v′) to the calibration spectrum of greenhouse gases in infrared when the instrument observation wavenumber is ν, v′0 is the preset instrument center wavenumber, σ is the preset instrument resolution width, and these two parameters determine the shape of the instrument linear function. LxG(v′) is the ideal infrared spectrum of greenhouse gases.

[0065] The instrument linear function, under the preset instrument center wavenumber and instrument resolution width, adopts the Lamohr function, the specific expression of which is as follows: Where H(vv′,v′0,σ) is the instrument linear function under the preset instrument center wavenumber and instrument resolution width, v′0 is the preset instrument center wavenumber, σ is the preset instrument resolution width, and π is pi.

[0066] In this implementation scheme, the introduction of the instrument linear function (using the Lamortz function) fully considers the influence of instrument resolution and center wavenumber on the measurement results. Through convolution processing, the instrument characteristics are combined with the ideal spectrum, which helps to more accurately simulate and calibrate the measured spectrum. The use of the instrument linear function effectively reflects the instrument's processing characteristics for infrared spectral signals, including the influence of wavenumber shift and resolution bandwidth. Convolution analysis can correct instrument-induced errors into theoretical calculations, enhancing the reliability of the results. Presetting the instrument resolution bandwidth and center wavenumber can not only optimize experimental design but also provide a unified benchmark for data comparison between different instruments in subsequent analyses. The convolution method combining the ideal spectrum and the instrument linear function can deduce the actual value of the instrument resolution, thereby calibrating the instrument performance. The convolution process refines the distribution shape of the spectral peaks, allowing the spectral signals of greenhouse gases to more clearly reflect their true physical characteristics. Using the Lamortz function can accurately describe the instrument's processing characteristics for spectral signals. The attenuation and dispersion characteristics of the gas reduce errors caused by insufficient instrument resolution. The initial calibration spectrum integrates instrument characteristics and gas feature information, providing higher-precision basic data for subsequent quantitative analysis. The calibrated spectrum can better match the actual measured values, facilitating the acquisition of gas concentrations through fitting and inversion. After parameterization of the instrument linear function, it can be adapted to different types of instruments or measurement conditions, such as resolution changes or band center shifts. It is suitable for calibration and analysis of various gases and under different experimental environments. The establishment of the calibration spectrum provides a standardized basis for data comparison between different experiments, reducing data deviations caused by instrument differences. The calibration spectrum can be applied to multiple experiments or measurements of different instruments, improving the reusability of experimental data. The calibrated spectrum can more accurately reflect the absorption characteristics of greenhouse gases, providing reliable theoretical support for atmospheric environmental monitoring and greenhouse gas emission measurement. Especially in the joint analysis of large-scale, multi-type greenhouse gases, the calibration spectrum is an indispensable part.

[0067] Specifically, such as Figure 3As shown, the specific steps for obtaining the greenhouse gas infrared calibration spectrum are as follows: Read the greenhouse gas infrared measurement spectrum and perform error analysis with the initial greenhouse gas infrared calibration spectrum, establishing an error function; Adjust the instrument center wavenumber and instrument resolution width with minimizing the error function as the optimization objective; Determine whether the adjusted instrument center wavenumber and instrument resolution width meet the preset convergence conditions (i.e., the mean square error of the error spectrum decreases to within the preset measurement spectrum noise level); If the adjusted instrument center wavenumber and instrument resolution width meet the preset convergence conditions, output the adjusted instrument center wavenumber and instrument resolution width, and mark them as the optimal instrument center wavenumber and optimal instrument resolution width; If the adjusted instrument center wavenumber and instrument resolution width do not meet the preset convergence conditions, perform iterative adjustments until the optimal instrument center wavenumber and optimal instrument resolution width are obtained; Perform convolution analysis between the instrument linear function under the optimal instrument center wavenumber and optimal instrument resolution width and the ideal greenhouse gas infrared spectrum to obtain the greenhouse gas infrared calibration spectrum.

[0068] The specific expression for the error function is as follows: Where E(v′0,σ) is the error function, v′0 is the preset instrument center wavenumber, σ is the preset instrument resolution width, CgP(v) is the greenhouse gas infrared measurement spectrum, and JzG(v) is the greenhouse gas infrared initial calibration spectrum, where v=1,2,3,…,v0, and v0 is the number of wavenumber points in the infrared spectral wavenumber range.

[0069] In this implementation scheme, during measurement experiments, the instrument's center wavenumber may drift, and the resolution width may be affected by environmental factors or instrument aging, leading to error accumulation. Through iterative optimization of the error function, the center wavenumber and resolution width are dynamically adjusted to achieve optimal performance, significantly improving the instrument's measurement accuracy and stability. The preset center wavenumber and resolution width may deviate from actual experimental conditions; the optimization algorithm, by adjusting based on actual measurement data, can compensate for this deviation. By minimizing the error function, the calibrated spectrum is matched as closely as possible to the actual measured spectrum, reducing the deviation between them and improving the applicability of the calibrated spectrum to the actual measured spectrum. The calibrated spectrum can more accurately reflect the actual infrared absorption characteristics of greenhouse gases, facilitating subsequent quantitative analysis. The optimization algorithm effectively filters noise by using a convergence condition where the mean square error decreases to within the noise level of the measured spectrum. The interference of sound on spectral calibration makes the final calibration spectrum more accurate. The definition of the error function E(v′0, σ) directly quantifies the difference between the measured spectrum and the initial calibration spectrum, providing a clear optimization target for instrument optimization and avoiding the subjectivity of qualitative analysis. Its automated error function optimization and instrument parameter adjustment avoid the tediousness and inefficiency of manual adjustment, improving the speed and efficiency of spectral calibration. The optimization process follows a fixed algorithm logic, which has good repeatability in different experiments and facilitates the standardization of calibration procedures. Under different greenhouse gas types, different wavenumber ranges, and different experimental environments, the model can flexibly adapt to different experimental needs by dynamically adjusting the center wavenumber and resolution width. Through dynamic optimization of instrument parameters, this method can be applied to a variety of infrared spectrometers, enhancing its versatility. The quantitative measurement of greenhouse gas concentrations requires extremely high spectral calibration accuracy. By optimizing instrument parameters and generating calibration spectra that are closer to reality, the accuracy of concentration inversion results can be significantly improved. The calibrated spectra provide high-quality input data for concentration calculation, reducing the concentration calculation deviation caused by spectral errors. The error function E(v′0,σ) clearly quantifies the difference between the two sets of spectral data through a simple quadratic form. The calculation method is clear and intuitive, easy to implement and extend. Its optimization process can be flexibly implemented by combining various algorithms (such as gradient descent, genetic algorithm, etc.), further improving the applicability of the model.

[0070] In summary, this application has at least the following effects:

[0071] By acquiring dynamic conditions such as current ambient temperature and gas pressure, and combining them with absorption cross-section reference values ​​in the database, the absorption cross-section value under real conditions can be calculated for each wavenumber point, eliminating errors caused by fixed parameter assumptions. In addition, by iteratively optimizing the instrument's center wavenumber and resolution width, the matching degree between the calibrated spectrum and the actual measured spectrum is significantly improved. Especially under high noise and complex multi-component interference conditions, the overall accuracy of spectral fitting is improved, thereby ensuring the accuracy and reliability of greenhouse gas concentration inversion data.

[0072] By dynamically adjusting the instrument's line function, optimizing the instrument's center wavenumber and resolution width, spectral distortion caused by instrument drift or environmental changes is compensated. At the same time, by incorporating the instrument's physical characteristics into the calibration spectrum calculation through convolution analysis, the instrument's adaptability and stability under different experimental conditions are significantly enhanced. Especially in long-term monitoring, automated parameter adjustment reduces data deviation caused by changes in instrument characteristics, ensuring the consistency and repeatability of experimental data.

[0073] By utilizing convolution analysis and error function optimization, the multi-component spectral characteristics of greenhouse gases can be effectively distinguished. Even under conditions of overlapping absorption peaks and complex interference, the contribution of different components to the overall spectrum can be accurately identified. At the same time, by combining the optimization and calibration of the instrument's line function, the problem of peak broadening caused by insufficient resolution is reduced. Thus, the method has broad application value in fields such as complex atmospheric environment monitoring and multi-component industrial emission control, and provides strong support for the joint quantitative analysis of greenhouse gases.

[0074] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0075] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for fitting infrared spectra of greenhouse gases based on iterative inversion of instrument linear functions, characterized in that, Includes the following steps: Obtain the infrared spectral wavenumber range, current ambient temperature, and current gas pressure, and analyze the absorption cross-section of each greenhouse gas at each wavenumber point within the infrared spectral wavenumber range. Specifically: Obtain ambient temperature reference values, gas pressure reference values, and absorption cross-section reference values ​​for each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range; By combining the ambient temperature reference value, gas pressure reference value, and the absorption cross-section reference value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range with the current ambient temperature value and current gas pressure value, the absorption cross-section value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range is obtained. The specific formula is as follows: ； in, , In order, the wavenumber points in the infrared spectral wavenumber range are as follows: The next Absorption cross-section values ​​and reference values ​​for absorption cross-sections of various greenhouse gases. , , , The values ​​are, in order: current gas pressure, reference gas pressure, reference ambient temperature, and current ambient temperature. The temperature correction factor is stored in the database. , The number of greenhouse gases; The background spectrum and the gas concentration values ​​of each greenhouse gas were obtained, and the ideal infrared spectrum of the greenhouse gases was established by combining the absorption cross-section values ​​of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range. Specifically: Obtain the infrared optical path value of the instrument; The instrument's infrared optical path value, background spectrum, gas concentration value of each greenhouse gas, and absorption cross section value of each greenhouse gas at each wavenumber point in the infrared spectral wavenumber range are respectively input into the greenhouse gas infrared ideal spectral analysis model for comprehensive analysis to obtain the greenhouse gas infrared ideal spectrum. The specific model for ideal infrared spectral analysis of greenhouse gases is as follows: ； in, , In order, the ideal infrared spectrum of greenhouse gases and the background spectrum are shown. In the wavenumber range of the infrared spectrum at the wavenumber point The next The absorption cross-sectional values ​​of various greenhouse gases, among which, , This represents the number of wavenumber points in the infrared spectral wavenumber range. For the first The concentration values ​​of various greenhouse gases, This is the infrared optical path value of the instrument; The ideal infrared spectrum of greenhouse gases is calibrated against the instrument's line function to obtain the initial calibration infrared spectrum of greenhouse gases, which is as follows: The instrument linear function at the preset instrument center wavenumber and instrument resolution width is obtained, and then convolved with the ideal infrared spectrum of greenhouse gases to obtain the initial calibration spectrum of greenhouse gases. The specific formula is as follows: ； in, For the initial infrared calibration spectrum of greenhouse gases, This is the instrument linear function under preset instrument center wavenumber and instrument resolution width. The preset instrument center wavenumber, The preset instrument resolution width, This represents the ideal infrared spectrum of greenhouse gases. The infrared measurement spectrum of greenhouse gases is obtained and fitted with the initial infrared calibration spectrum of greenhouse gases to obtain the infrared calibration spectrum of greenhouse gases, which is as follows: Read the infrared measurement spectrum of greenhouse gases, perform error analysis with the initial calibration infrared spectrum of greenhouse gases, and establish an error function; With minimizing the error function as the optimization objective, the instrument's center wavenumber and resolution width are adjusted. Determine whether the adjusted instrument center wavenumber and instrument resolution width meet the preset convergence conditions; If the adjusted instrument center wavenumber and instrument resolution width meet the preset convergence conditions, then the adjusted instrument center wavenumber and instrument resolution width are output and marked as the optimal instrument center wavenumber and optimal instrument resolution width. If the adjusted instrument center wavenumber and instrument resolution width do not meet the preset convergence conditions, iterative adjustments will be made until the optimal instrument center wavenumber and optimal instrument resolution width are obtained. The instrument linear function at the instrument's optimal center wavenumber and optimal resolution width is convolved with the ideal infrared spectrum of greenhouse gases to obtain the greenhouse gas infrared calibration spectrum.

2. The method for fitting greenhouse gas infrared spectra based on iterative inversion of instrument linear function according to claim 1, characterized in that, The specific expression for the instrument linear function under the preset instrument center wavenumber and instrument resolution width is as follows: ； in, This is the instrument linear function under preset instrument center wavenumber and instrument resolution width. The preset instrument center wavenumber, The preset instrument resolution width, Pi is the mathematical constant of a circle.

3. The method for fitting greenhouse gas infrared spectra based on iterative inversion of instrument linear function according to claim 1, characterized in that, The specific expression for the error function is as follows: ； in, Let be the error function. The preset instrument center wavenumber, The preset instrument resolution width, For greenhouse gas infrared measurement spectra, The initial infrared calibration spectrum of greenhouse gases, where, , This represents the number of wavenumber points in the infrared spectral wavenumber range.

Images