Spectral absorption analysis method of carbon aerosol, electronic device, and storage medium

Abstract

The application provides a spectrum absorption analysis method of carbon aerosol, an electronic device and a storage medium, and relates to the technical field of atmospheric detection. The method comprises the following steps: determining a target tracer in a plurality of emissions corresponding to a main emission source type of a carbon aerosol sample set; measuring and acquiring, by a thermal optical carbon analyzer, a carbon aerosol absorption coefficient of each carbon aerosol sample at a preset wavelength, and fitting and determining an initial absorption coefficient of brown carbon of each carbon aerosol sample at the preset wavelength according to the carbon aerosol absorption coefficient, an absorption wavelength index fluctuation range of black carbon, a preset adjustment step and an absorption wavelength index constraint condition of brown carbon; and iteratively determining target absorption wavelength indexes of brown carbon and black carbon in a preset wavelength range according to the initial absorption coefficient and the concentration of the target tracer in each carbon aerosol sample, so that the accuracy of carbon aerosol analysis can be improved, and the influence of dust on the carbon aerosol absorption coefficient can be excluded.

Description

Technical Field

[0001] This application relates to the field of atmospheric detection technology, and in particular to a spectral absorption analysis method, electronic device and storage medium for carbon aerosols. Background Technology

[0002] With the increasing severity of air pollution, the role of absorbing aerosols in climate effects and air quality has received widespread attention. Black carbon (BC) and brown carbon (BrC) are two major types of light-absorbing carbonaceous aerosols, and their selective absorption of solar radiation can significantly affect atmospheric heating rates, boundary layer stability, and global energy balance. Therefore, accurately quantifying the wavelength-dependent absorption characteristics of black carbon and BrC is of great significance for assessing their direct radiative forcing and formulating emission reduction strategies.

[0003] In existing technologies, the quantitative analysis method for the spectral absorbance of carbon aerosols mainly adopts the traditional separation method based on the absorption wavelength exponent (AAE). This method typically assumes that the absorption wavelength exponent of black carbon is a fixed value of 1.0, and considers that the aerosol absorption in the near-infrared band (such as 880 nm or 980 nm) is entirely contributed by black carbon, while the additional absorption in the short-wave band (such as 405–635 nm) is attributed to BrC and other non-black carbon components. Based on this, the absorption coefficients of black carbon and non-black carbon in the actual atmosphere are estimated. Then, combined with the organic source apportionment results of aerosol mass spectrometry and the multiple linear regression method, the absorption properties of multiple organic aerosol components in the actual atmosphere are quantified.

[0004] It can be seen that existing analytical methods often set the absorption wavelength index of black carbon to a fixed value of 1.0. However, in reality, it is not a fixed value of 1.0 due to the aging process of black carbon particles, changes in mixing state, and morphological evolution. In addition, the absorption coefficient of brown carbon in the near-infrared band cannot be accurately quantified. Therefore, existing analytical methods for carbon aerosols are not accurate enough. Summary of the Invention

[0005] The purpose of this application is to address the shortcomings of the prior art by providing a spectral absorption analysis method, electronic device, and storage medium for carbon aerosols, which can more accurately determine the target absorption wavelength index corresponding to brown carbon within a preset wavelength range and the target absorption wavelength index corresponding to black carbon within a preset wavelength range, thereby improving the accuracy of carbon aerosol analysis.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0007] In a first aspect, the present invention provides a method for spectroscopic absorption analysis of carbon aerosols, the method comprising:

[0008] Based on the main emission source type corresponding to the carbon aerosol sample set, the target tracer with the greatest correlation to the emission source of brown carbon is determined among the various emissions corresponding to the main emission source type. The carbon aerosol sample set includes multiple carbon aerosol samples collected from the same target area at different times.

[0009] The carbon aerosol absorption coefficient of each carbon aerosol sample in the carbon aerosol sample set at a preset wavelength is obtained by measuring the carbon aerosol absorption coefficient of each carbon aerosol sample at a preset wavelength, and the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength is determined by fitting based on the carbon aerosol absorption coefficient of each carbon aerosol sample at a preset wavelength, the fluctuation range of the absorption wavelength index of black carbon, the preset adjustment step size of the absorption wavelength index of black carbon, and the constraint condition of the absorption wavelength index of brown carbon.

[0010] The concentration of the target tracer in each carbon aerosol sample is measured, and the first Pearson correlation coefficient between the initial absorption coefficient of the brown carbon at the preset wavelength and the concentration of the target tracer in each carbon aerosol sample is iteratively calculated based on the initial absorption coefficient of the brown carbon at the preset wavelength and the concentration of the target tracer in each carbon aerosol sample.

[0011] The absorption wavelength index of black carbon at a preset wavelength corresponding to the maximum first Pearson correlation coefficient is determined as the target absorption wavelength index of black carbon within a preset wavelength range. Based on the preset simplified model of carbon aerosol absorption of two components, the target absorption wavelength index of brown carbon within a preset wavelength range is determined by fitting.

[0012] In an optional implementation, determining the absorption wavelength index of black carbon at a preset wavelength corresponding to the maximum Pearson correlation coefficient as the target absorption wavelength index of black carbon within a preset wavelength range, and fitting and determining the target absorption wavelength index of brown carbon within a preset wavelength range based on a preset simplified model for carbon aerosol absorption of two components, includes:

[0013] During each iteration, the initial absorption wavelength index of the black carbon is adjusted according to the fluctuation range of the absorption wavelength index of the black carbon and the preset adjustment step size of the absorption wavelength index of the black carbon.

[0014] Based on the carbon aerosol absorption coefficient of each carbon aerosol sample at a preset wavelength, the initial absorption wavelength index of the adjusted black carbon, and the absorption wavelength index constraint of the brown carbon, the initial absorption coefficient of the brown carbon corresponding to each carbon aerosol sample at a preset wavelength is determined by fitting.

[0015] Based on the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength and the concentration of the target tracer in each carbon aerosol sample, the first Pearson correlation coefficient corresponding to each iteration is calculated.

[0016] Based on multiple iterations of the first Pearson correlation coefficient, the absorption wavelength index of black carbon at the preset wavelength corresponding to the largest first Pearson correlation coefficient is selected as the target absorption wavelength index of black carbon within the preset wavelength range. Then, based on the preset simplified model of carbon aerosol absorption of two components, the target absorption wavelength index of brown carbon within the preset wavelength range is determined by fitting.

[0017] In an optional implementation, after determining the absorption wavelength index of black carbon at a preset wavelength corresponding to the maximum first Pearson correlation coefficient as the target absorption wavelength index of black carbon within a preset wavelength range, and after fitting and determining the target absorption wavelength index of brown carbon within a preset wavelength range according to a preset simplified model for carbon aerosol absorption of two components, the method further includes:

[0018] Based on the nonparametric bootstrapping method, multiple resampling datasets are determined from the carbon aerosol sample set.

[0019] Determine the absorption wavelength index of the black carbon and the absorption wavelength index of the brown carbon within the preset wavelength range for each of the resampled datasets.

[0020] Based on the absorption wavelength index of the brown carbon in the preset wavelength range corresponding to each of the resampled datasets, a first statistical parameter is calculated, the first statistical parameter including: a first confidence interval;

[0021] Based on the absorption wavelength index of the black carbon in the preset wavelength range corresponding to each of the resampled datasets, a second statistical parameter is calculated, which includes a second confidence interval.

[0022] In an optional implementation, the method further includes:

[0023] Based on the first statistical parameter, determine whether the target absorption wavelength index of the brown carbon within the preset wavelength range meets the first preset requirement. If it does not meet the requirement, recalculate the target absorption wavelength index of the brown carbon within the preset wavelength range.

[0024] Based on the second statistical parameter, it is confirmed whether the target absorption wavelength index of the black carbon within the preset wavelength range meets the second preset requirement. If it does not meet the requirement, the target absorption wavelength index of the black carbon within the preset wavelength range is recalculated.

[0025] In an optional implementation, before determining the target tracer with the highest correlation to the emission source of brown carbon among multiple emissions corresponding to the main emission source type based on the main emission source type corresponding to the carbon aerosol sample set, the method further includes:

[0026] Measure the emission concentration of each emitted substance in each carbon aerosol sample in the carbon aerosol sample set;

[0027] Based on the emission concentrations of each emission substance in each carbon aerosol sample, a positive definite matrix factorization model is used to perform source spectrum analysis on the carbon aerosol sample set to identify the main emission source types corresponding to the carbon aerosol sample set.

[0028] In an optional implementation, the step of determining the target tracer with the highest correlation to the emission source of brown carbon among multiple emissions corresponding to the main emission source type, based on the main emission source type corresponding to the carbon aerosol sample set, includes:

[0029] Based on the emission concentrations of each emission substance in each carbon aerosol sample in the carbon aerosol sample set and the concentration of organic carbon in each carbon aerosol sample, the target tracer with the highest correlation to the emission source of brown carbon is determined among the various emissions corresponding to the main emission source type.

[0030] In an optional implementation, the step of determining the target tracer with the strongest correlation to the emission source of brown carbon from among multiple emissions corresponding to the main emission source type, based on the emission concentration of each emission substance in each carbon aerosol sample in the carbon aerosol sample set and the concentration of organic carbon in each carbon aerosol sample, includes:

[0031] Based on the emission concentrations of each emission substance in each carbon aerosol sample in the carbon aerosol sample set and the concentration of organic carbon in each carbon aerosol sample, calculate the second Pearson correlation coefficient between the emission concentrations of each emission substance and the concentration of organic carbon.

[0032] Based on the second Pearson correlation coefficients corresponding to each of the emitted substances, the largest second Pearson correlation coefficient among multiple second Pearson correlation coefficients is selected, and the target emitted substance corresponding to the largest second Pearson correlation coefficient is used as the target tracer.

[0033] In an optional embodiment, the absorption wavelength index of the black carbon fluctuates from 0.6 to 1.6, and the absorption wavelength index constraint of the brown carbon indicates that the absorption wavelength index of the brown carbon fluctuates from 2 to 9.

[0034] In a second aspect, the present invention provides a spectroscopic absorption analysis device for carbon aerosols, the spectroscopic absorption analysis device comprising:

[0035] The first determining module is used to determine the target tracer with the greatest correlation to the emission source of brown carbon among the various emissions corresponding to the main emission source type, based on the main emission source type corresponding to the carbon aerosol sample set. The carbon aerosol sample set includes multiple carbon aerosol samples collected from the same target area at different times.

[0036] The acquisition module is used to measure and acquire the carbon aerosol absorption coefficient of each carbon aerosol sample in the carbon aerosol sample set at a preset wavelength using a thermo-optic carbon analyzer, and to determine the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength based on the carbon aerosol absorption coefficient of each carbon aerosol sample at the preset wavelength, the fluctuation range of the absorption wavelength index of black carbon, the preset adjustment step size of the absorption wavelength index of black carbon, and the constraint condition of the absorption wavelength index of brown carbon.

[0037] The calculation module is used to measure the concentration of the target tracer in each carbon aerosol sample, and iteratively calculate the first Pearson correlation coefficient between the initial absorption coefficient of the brown carbon at the preset wavelength and the concentration of the target tracer in each carbon aerosol sample based on the initial absorption coefficient of the brown carbon at the preset wavelength and the concentration of the target tracer in each carbon aerosol sample.

[0038] The second determining module is used to determine the absorption wavelength index of black carbon at a preset wavelength corresponding to the maximum first Pearson correlation coefficient as the target absorption wavelength index of black carbon within a preset wavelength range, and to fit and determine the target absorption wavelength index of brown carbon within a preset wavelength range according to a preset simplified model of carbon aerosol absorption of two components.

[0039] Thirdly, the present invention provides an electronic device, comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the spectral absorption analysis method for carbon aerosols as described in any of the foregoing embodiments.

[0040] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the spectral absorption analysis method for carbon aerosols as described in any of the foregoing embodiments.

[0041] The beneficial effects of this application are:

[0042] The carbon aerosol spectral absorption analysis method, electronic device, and storage medium provided in this application include: determining the target tracer with the highest correlation to brown carbon emission sources among multiple emissions corresponding to the main emission source types corresponding to the main emission source types of the carbon aerosol sample set; the carbon aerosol sample set includes multiple carbon aerosol samples collected from the same target area at different times; measuring the carbon aerosol absorption coefficient of each carbon aerosol sample in the carbon aerosol sample set at a preset wavelength using a thermo-optic carbon analyzer; and fitting and determining the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength based on the carbon aerosol absorption coefficient of each carbon aerosol sample at the preset wavelength, the fluctuation range of the absorption wavelength index of black carbon, the preset adjustment step size of the absorption wavelength index of black carbon, and the constraint condition of the absorption wavelength index of brown carbon; measuring the concentration of the target tracer in each carbon aerosol sample, and based on... The initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength is calculated iteratively. The first Pearson correlation coefficient between the initial absorption coefficient of brown carbon at the preset wavelength and the concentration of the target tracer is calculated. The absorption wavelength index of black carbon at the preset wavelength corresponding to the maximum first Pearson correlation coefficient is determined as the target absorption wavelength index of black carbon within the preset wavelength range. Based on the preset simplified model of carbon aerosol absorption of two components, the target absorption wavelength index of brown carbon within the preset wavelength range is determined by fitting. This realizes the fluctuation range of the absorption wavelength index of black carbon and the preset adjustment step size, which can accurately determine the target absorption wavelength index of brown carbon, the target absorption wavelength index of black carbon within the preset wavelength range, and the target absorption wavelength index of black carbon within the preset wavelength range, thus improving the accuracy of carbon aerosol analysis. In addition, the introduction of a thermo-optic carbon analyzer can eliminate the influence of dust on the absorption coefficient of carbon aerosols. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating the spectral absorption analysis method for carbon aerosols provided in this application embodiment. Figure 1 ;

[0045] Figure 2 A flowchart illustrating the spectral absorption analysis method for carbon aerosols provided in this application embodiment. Figure 2 ;

[0046] Figure 3 A flowchart illustrating the spectral absorption analysis method for carbon aerosols provided in this application embodiment. Figure 3 ;

[0047] Figure 4 A flowchart illustrating the spectral absorption analysis method for carbon aerosols provided in this application embodiment. Figure 4 ;

[0048] Figure 5 A flowchart illustrating the spectral absorption analysis method for carbon aerosols provided in this application embodiment. Figure 5 ;

[0049] Figure 6 A schematic diagram of spectral absorption analysis of a carbon aerosol provided in an embodiment of this application;

[0050] Figure 7 A schematic diagram of the functional modules of a carbon aerosol spectral absorption analysis device provided in an embodiment of this application;

[0051] Figure 8 This is a schematic diagram of an electronic device structure provided in an embodiment of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0053] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0054] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0055] In related technologies, the absorption wavelength exponent (AAE) of aerosols is widely used in the quantitative study of the spectral absorbance of absorbent aerosols to quantitatively allocate the contributions of black carbon (BC) and brown carbon (BrC) to the measured aerosol absorption. Specifically, using multi-band black carbon spectrometer data, assuming a fixed absorption wavelength exponent of 1.0 for black carbon, all aerosol absorption in the near-infrared band (e.g., 880 nm) is attributed to BC, while aerosol absorption in shorter wavelength spectra is attributed to both BC and BrC. Based on this, the absorption coefficients of black carbon and non-black carbon in the actual atmosphere are estimated. Then, combined with the organic source apportionment results from aerosol mass spectrometry and a multiple linear regression method, the absorption properties of multiple organic aerosol components in the actual atmosphere are quantified.

[0056] Therefore, existing analytical methods often set the absorption wavelength index of black carbon to a fixed value of 1.0. However, in reality, due to the aging process of black carbon particles, changes in mixing state, and morphological evolution, the absorption wavelength index of black carbon is not a fixed value of 1.0. In addition, the absorption coefficient of brown carbon in the near-infrared band cannot be accurately quantified. Therefore, existing analytical methods for carbon aerosols are not accurate enough.

[0057] In view of this, the present application provides a spectral absorption analysis method for carbon aerosols. By introducing a thermo-optic carbon analyzer, the influence of dust on the absorption coefficient of carbon aerosols can be eliminated, and the target absorption wavelength index corresponding to brown carbon and black carbon within a preset wavelength range can be determined more accurately, thereby improving the accuracy of carbon aerosol analysis.

[0058] Figure 1 A flowchart illustrating the spectral absorption analysis method for carbon aerosols provided in this application embodiment. Figure 1 The execution subject of this method can be electronic devices such as computers, servers, and processors. Figure 1 As shown, the method includes:

[0059] S101. Based on the main emission source types corresponding to the carbon aerosol sample set, identify the target tracer with the highest correlation to the emission source of brown carbon among the various emissions corresponding to the main emission source types.

[0060] The carbon aerosol sample set includes multiple carbon aerosol samples collected from the same target area at different times.

[0061] Optionally, the target area can be a coal-fired power plant, a steel plant, a cement plant, or even a transportation station, a suburb, or a residential area, etc., without limitation. Each carbon aerosol sample can be collected using a membrane sampling method. Of course, this application does not limit the sampling time for each carbon aerosol sample; depending on the actual application scenario, it can be a fixed time every day or a random time. Optionally, the number of carbon aerosol samples in the carbon aerosol sample set is at least 30.

[0062] Among them, the emission source type can indicate the main source of carbon aerosols. The emission source type can be pre-classified, and each emission source type can include multiple emission substances.

[0063] For example, emission source types in some scenarios can include: primary crustal sources, secondary formation sources, vehicle exhaust sources, biomass combustion sources, coal combustion sources, and so on. Among these, emissions from primary crustal sources can include: Ca²⁺. + Mg² + Chemical components include Al, Ca, Mg, Fe, Ti, and Si; emissions from secondary sources may include NO3. - SO4² - NH4 + Chemical components; emissions from automobile exhaust sources may include: Pb, Ni, NO2, NO X Chemical components such as EC, Zn, and Br; emissions from biomass combustion sources may include: K + Chemical components such as L-glucan; emissions from coal combustion sources may include: Cl - SO4² - Chemical components such as OC and S.

[0064] In some implementations, a positive matrix factorization (PMF) model can be used to perform source spectrum analysis on the collected carbon aerosol sample set to identify the main emission source types corresponding to the carbon aerosol sample set. Based on the identified main emission source types, the correlation between each emission source type and the emission source of brown carbon can be further analyzed. According to the correlation of each emission source, the target tracer with the strongest correlation to the emission source of brown carbon can be determined. Among them, the target tracer with the strongest correlation to the emission source of brown carbon originates from the same emission source type as brown carbon (BrC), and the two coexist in time and space, with their concentrations changing synchronously.

[0065] S102. The carbon aerosol absorption coefficient of each carbon aerosol sample in the carbon aerosol sample set at a preset wavelength is obtained by measuring the carbon aerosol absorption coefficient of each carbon aerosol sample at the preset wavelength, the fluctuation range of the absorption wavelength index of black carbon, the preset adjustment step size of the absorption wavelength index of black carbon, and the constraint condition of the absorption wavelength index of brown carbon, and the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at the preset wavelength is determined by fitting.

[0066] The absorption wavelength index of black carbon fluctuates from 0.6 to 1.6. Optionally, the preset adjustment step size for the absorption wavelength index of black carbon can be 0.01. The constraint condition for the absorption wavelength index of brown carbon indicates that the absorption wavelength index of brown carbon fluctuates from 2 to 9. Of course, it should be noted that this application does not limit the value of the preset adjustment step size.

[0067] Optionally, for a carbon aerosol sample set, the carbon aerosol absorption coefficient of each carbon aerosol sample at a preset wavelength can be measured using a thermo-optic carbon analyzer; depending on the type of the target tracer, an appropriate method (such as inductively coupled plasma mass spectrometry, chemiluminescence, etc.) can be selected to measure the concentration of the target tracer in each carbon aerosol sample.

[0068] In some embodiments, further, based on the carbon aerosol absorption coefficient of each carbon aerosol sample at a preset wavelength, the fluctuation range of the absorption wavelength index of black carbon, the preset adjustment step size of the absorption wavelength index of black carbon, and the constraint condition of the absorption wavelength index of brown carbon, the initial absorption coefficient of brown carbon at a preset wavelength can be determined by fitting a preset simplified two-component carbon aerosol absorption model using the least squares regression algorithm. The fitting calculation formula corresponding to this preset simplified two-component carbon aerosol absorption model is as follows:

[0069]

[0070] in, K represents the absorption coefficient of the carbon aerosol corresponding to the carbon aerosol sample. BC K represents the black carbon fitting coefficient. BrC AAE represents the brown carbon fitting coefficient. BrC The absorption wavelength index (AAE) of brown carbon is indicated by this index. BC The wavelength index represents the absorption wavelength of black carbon, and λ represents the wavelength of the laser emitted by the thermo-optical carbon analyzer. This represents the absorption coefficient of black carbon corresponding to the carbon aerosol sample. This represents the absorption coefficient of the brown carbon corresponding to the carbon aerosol sample.

[0071] It should be noted that in the above formula, the absorption coefficient of a carbon aerosol corresponding to a certain carbon aerosol sample... The absorption wavelength index (AAE) of black carbon can be measured using a multi-band thermo-optic carbon analyzer. BC The value can be adjusted from 0.6 to 1.6 with a preset adjustment step size of 0.01. During the fitting process, the absorption wavelength index AAE of the brown carbon... BrC The fluctuation range is 2 to 9.

[0072] Understandably, based on the above fitting calculation formula, the least squares method can be used to calculate the absorption wavelength index (AAE) of black carbon corresponding to each carbon aerosol sample at a preset wavelength. BC The absorption wavelength index (AAE) of each type of black carbon was determined by fitting the data at a preset wavelength and based on a preset adjustment step size. BC The corresponding black carbon fitting coefficient K BC Brown carbon fitting coefficient K BrC And the absorption wavelength index (AAE) of brown carbon BrC。 Optionally, in some embodiments, experiments have shown that brown carbon has the highest absorption coefficient at 405 nm. Therefore, for the convenience of subsequent calculations, the preset wavelength can be 405 nm.

[0073] It should be noted that the absorption wavelength index AAE of a black carbon is determined at a preset wavelength based on a preset adjustment step size. BC It can correspond to multiple sets of fitting candidate values, where each set of fitting candidate values ​​can include: a black carbon fitting coefficient K. BC A brown carbon fitting coefficient K BrC And an absorption wavelength index (AAE) of brown carbon. BrC The number of candidate sets for fitting values ​​is the same as the number of carbon aerosol samples in the carbon aerosol sample set.

[0074] Furthermore, the absorption wavelength index AAE of a certain black carbon at a preset wavelength can be used as a basis for further analysis. BC The corresponding multiple brown carbon fitting coefficients K BrC And the absorption wavelength index (AAE) of multiple brown carbons BrC Using formula Calculate multiple initial absorption coefficients of brown carbon at preset wavelengths. .

[0075] S103. Measure the concentration of the target tracer in each carbon aerosol sample, and iteratively calculate the first Pearson correlation coefficient between the initial absorption coefficient of brown carbon at the preset wavelength and the concentration of the target tracer based on the initial absorption coefficient of brown carbon at the preset wavelength for each carbon aerosol sample.

[0076] In some implementations, the concentration of the target tracer in each carbon aerosol sample can be obtained through offline membrane chemistry analysis or online particulate matter monitoring. When using offline membrane chemistry analysis, the same membrane used for sampling as the carbon aerosol sample can be selected to ensure synchronization. When using online particulate matter monitoring, it can be used after time matching with the absorption coefficient data. Optionally, the preset wavelength range can be 405-980 nm. The number of iterations can be determined based on the fluctuation range of the absorption wavelength index of black carbon and the preset adjustment step size of the absorption wavelength index of black carbon. Optionally, if the fluctuation range of the absorption wavelength index of black carbon is 0.6 to 1.6, and the preset adjustment step size is 0.01, then the corresponding number of iterations is 101.

[0077] Understandably, based on the above explanation, for each carbon aerosol sample, the first Pearson correlation coefficient between the two can be iteratively calculated based on the initial absorption coefficient of brown carbon at the preset wavelength corresponding to the absorption wavelength index of each black carbon at the preset wavelength.

[0078] For example, if a carbon aerosol sample set includes 30 carbon aerosol samples, then in the first iteration, with a preset wavelength of 405 nm and an absorption wavelength index of 0.6 for black carbon, 30 initial absorption coefficients for brown carbon at the preset wavelength can be calculated. Based on these 30 initial absorption coefficients and the concentrations of the target tracers corresponding to the 30 carbon aerosol samples, the first Pearson correlation coefficient between them can be calculated. In the second iteration, with a preset wavelength of 405 nm, a preset adjustment step size of 0.01, and an absorption wavelength index of 0.61 for black carbon, the first Pearson correlation coefficient corresponding to the second iteration can be calculated. This process continues until 101 first Pearson correlation coefficients are obtained. It should be noted that this application does not limit the number of carbon aerosol samples in the carbon aerosol sample set; the number can be flexibly set according to the actual application scenario.

[0079] S104. Determine the absorption wavelength index of black carbon at the preset wavelength corresponding to the maximum first Pearson correlation coefficient as the target absorption wavelength index of black carbon within the preset wavelength range, and determine the target absorption wavelength index of brown carbon within the preset wavelength range by fitting according to the preset simplified model of carbon aerosol absorption of two components.

[0080] Referring to the above explanation, the first Pearson correlation coefficient corresponding to each iteration at the preset wavelength can be calculated. Furthermore, the absorption wavelength index of black carbon corresponding to the largest first Pearson correlation coefficient at the preset wavelength can be selected as the target absorption wavelength index of black carbon within the preset wavelength range. Further, under the constraint of the target black carbon absorption wavelength index, the brown carbon absorption wavelength indices fitted to each carbon aerosol sample can be summarized according to the fitting calculation formula corresponding to the simplified model of carbon aerosol absorption of two components. This determines the target absorption wavelength index of brown carbon within the preset wavelength range. For example, the average of multiple fitted brown carbon absorption wavelength indices can be taken as the target absorption wavelength index of brown carbon within the preset wavelength range. This achieves a more accurate determination of the target absorption wavelength index of brown carbon and black carbon within the preset wavelength range based on the set fluctuation range of the black carbon absorption wavelength index and the preset adjustment step size, thus improving the accuracy of carbon aerosol analysis.

[0081] It should be noted that the target absorption wavelength index of brown carbon and the target absorption wavelength index of black carbon within the preset wavelength range obtained from the above calculations can be used for radiative transfer calculations, thereby quantitatively obtaining the direct radiative forcing and atmospheric heating effects of aerosol absorption, and can be used to assess the contribution of aerosol absorption to atmospheric warming and the climate synergistic benefits of emission reduction measures.

[0082] In summary, this application provides a method for spectral absorption analysis of carbon aerosols. The method includes: determining a target tracer with the highest correlation to brown carbon emissions from multiple emissions corresponding to the main emission source type of the carbon aerosol sample set; the carbon aerosol sample set includes multiple carbon aerosol samples collected from the same target area at different times; measuring the carbon aerosol absorption coefficient of each carbon aerosol sample in the carbon aerosol sample set at a preset wavelength using a thermo-optic carbon analyzer; and fitting and determining the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength based on the carbon aerosol absorption coefficient of each carbon aerosol sample at the preset wavelength, the fluctuation range of the absorption wavelength index of black carbon, the preset adjustment step size of the absorption wavelength index of black carbon, and the constraint condition of the absorption wavelength index of brown carbon; measuring the concentration of the target tracer in each carbon aerosol sample, and determining the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength based on the carbon aerosol absorption coefficient of each carbon aerosol sample at the preset wavelength; and measuring the concentration of the target tracer in each carbon aerosol sample at the preset wavelength. The initial absorption coefficient at a preset wavelength is used to iteratively calculate the first Pearson correlation coefficient between the initial absorption coefficient of brown carbon at a preset wavelength and the concentration of the target tracer. The absorption wavelength index of black carbon at a preset wavelength corresponding to the maximum first Pearson correlation coefficient is determined as the target absorption wavelength index of black carbon within the preset wavelength range. Based on the preset simplified model of carbon aerosol absorption of two components, the target absorption wavelength index of brown carbon within the preset wavelength range is fitted and determined. This allows for the accurate determination of the target absorption wavelength index of brown carbon and black carbon within the preset wavelength range by setting the fluctuation range and preset adjustment step size of the black carbon absorption wavelength index, and combining the first Pearson correlation coefficient between the initial absorption coefficient of brown carbon at the preset wavelength and the concentration of the target tracer obtained in each iteration. This improves the accuracy of the quantitative analysis of carbon aerosol absorption coefficient. In addition, the introduction of a thermo-optic carbon analyzer can eliminate the influence of dust on the carbon aerosol absorption coefficient.

[0083] Figure 2 A flowchart illustrating the spectral absorption analysis method for carbon aerosols provided in this application embodiment. Figure 2 In alternative implementations, such as Figure 2 As shown, the absorption wavelength index of black carbon at the preset wavelength corresponding to the maximum first Pearson correlation coefficient is used as the target absorption wavelength index of black carbon within the preset wavelength range. Based on the preset simplified model of carbon aerosol absorption of two components, the target absorption wavelength index of brown carbon within the preset wavelength range is determined by fitting, including:

[0084] S201. During each iteration, the initial absorption wavelength index of black carbon is adjusted according to the fluctuation range of the absorption wavelength index of black carbon and the preset adjustment step size of the absorption wavelength index of black carbon.

[0085] Optionally, if the absorption wavelength index of black carbon fluctuates within the range of 0.6 to 1.6, and the preset adjustment step size of the absorption wavelength index of black carbon is 0.01, then in each iteration, the initial absorption wavelength index of black carbon can be adjusted within this fluctuation range. For example, in the first iteration, the initial absorption wavelength index of black carbon can be set to 0.6; in the second iteration, it can be set to 0.61; in the third iteration, it can be set to 0.62, and so on.

[0086] S202. Based on the carbon aerosol absorption coefficient of each carbon aerosol sample at the preset wavelength, the initial absorption wavelength index of the adjusted black carbon, and the absorption wavelength index constraint of the brown carbon, the initial absorption coefficient of the corresponding brown carbon at the preset wavelength for each carbon aerosol sample is determined by fitting.

[0087] In some implementations, during each iteration, with the initial absorption wavelength index of black carbon fixed, the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at the preset wavelength can be determined by least squares fitting based on the above fitting calculation formula, according to the carbon aerosol absorption coefficient of each carbon aerosol sample at the preset wavelength, the adjusted initial absorption wavelength index of black carbon, and the absorption wavelength index constraint of brown carbon.

[0088] S203. Based on the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength, calculate the first Pearson correlation coefficient for each iteration.

[0089] Based on the above explanation, the first Pearson correlation coefficient for each iteration can be calculated according to the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength and the concentration of the target tracer in each carbon aerosol sample.

[0090] For example, if a carbon aerosol sample set includes 30 carbon aerosol samples, in the first iteration, with a preset wavelength of 405 nm and an absorption wavelength index of 0.6 for black carbon, 30 sets of fitting candidate values ​​can be calculated. Based on each set of fitting candidate values, the initial absorption coefficient of the corresponding brown carbon at the preset wavelength can be determined. That is, based on the 30 sets of fitting candidate values, 30 initial absorption coefficients of brown carbon at the preset wavelength can be calculated. Based on the 30 initial absorption coefficients and the concentrations of the 30 target tracers corresponding to the 30 carbon aerosol samples, the first Pearson correlation coefficient corresponding to the first iteration can be calculated. Each set of fitting candidate values ​​may include: a black carbon fitting coefficient K. BC A brown carbon fitting coefficient K BrC And an absorption wavelength index (AAE) of brown carbon. BrC .

[0091] During the second iteration, with a preset wavelength of 405 nm, a preset adjustment step size of 0.01, and an absorption wavelength index of 0.61 for black carbon, 30 sets of fitting candidate values ​​can be calculated. Refer to the calculation process of the first iteration. Based on the 30 initial absorption coefficients corresponding to these 30 sets of fitting candidate values ​​and the concentrations of the 30 target tracers corresponding to the 30 carbon aerosol samples, the first Pearson correlation coefficient corresponding to the second iteration can be calculated.

[0092] S204. Based on the multiple first Pearson correlation coefficients corresponding to multiple iterations, the absorption wavelength index of black carbon at the preset wavelength corresponding to the largest first Pearson correlation coefficient is selected as the target absorption wavelength index of black carbon in the preset wavelength range. Based on the preset simplified model of carbon aerosol absorption of two components, the target absorption wavelength index of brown carbon in the preset wavelength range is determined by fitting.

[0093] After obtaining multiple first Pearson correlation coefficients corresponding to multiple iterations, the largest first Pearson correlation coefficient can be selected from them, and the absorption wavelength index of black carbon corresponding to the largest first Pearson correlation coefficient at a preset wavelength can be used as the target absorption wavelength index of black carbon within the preset wavelength range.

[0094] Continuing with the above example, it can be understood that if the absorption wavelength index of black carbon fluctuates between 0.6 and 1.6, and the preset adjustment step size is 0.01, then it corresponds to 101 iterations, which corresponds to 101 fitting candidate values, and 101 first Pearson correlation coefficients for each of the 101 fitting candidate values. Each fitting candidate value may include 30 sets of fitting candidate values.

[0095] Furthermore, the largest first Pearson correlation coefficient can be selected from the 101 first Pearson correlation coefficients, and the absorption wavelength index of black carbon corresponding to the largest first Pearson correlation coefficient at a preset wavelength can be used as the target absorption wavelength index of black carbon within the preset wavelength range.

[0096] By applying the embodiments of this application, it is possible to obtain a relatively accurate target absorption wavelength index for brown carbon and black carbon within a preset wavelength range through multiple iterative calculations.

[0097] Figure 3 A flowchart illustrating the spectral absorption analysis method for carbon aerosols provided in this application embodiment. Figure 3 In alternative implementations, such as Figure 3As shown, after determining the absorption wavelength index of black carbon at a preset wavelength corresponding to the maximum first Pearson correlation coefficient as the target absorption wavelength index of black carbon within a preset wavelength range, and after fitting and determining the target absorption wavelength index of brown carbon within a preset wavelength range according to a preset simplified model for carbon aerosol absorption of two components, the above method further includes:

[0098] S301. Based on the nonparametric bootstrapping method, multiple resampling datasets are determined from the carbon aerosol sample set.

[0099] The resampled dataset can include multiple resampled carbon aerosol samples, and the number of resampled carbon aerosol samples in the resampled dataset is the same as the number of carbon aerosol samples in the carbon aerosol sample set.

[0100] In some implementations, when specifically determined, multiple resampling datasets can be determined based on the fluctuation range of the absorption wavelength index of black carbon and the preset adjustment step size of the absorption wavelength index of black carbon, according to the nonparametric bootstrap method and the carbon aerosol sample set.

[0101] For example, if the absorption wavelength index of black carbon fluctuates between 0.6 and 1.6, and the preset adjustment step size is 0.01, then it corresponds to 101 iterations. Further, if a carbon aerosol sample set includes 30 carbon aerosol samples, then based on the nonparametric bootstrapping method, a resampled dataset can be obtained by randomly selecting samples with replacement 30 times from the 30 carbon aerosol samples. Since it is sampling with replacement, the same sample is allowed to appear repeatedly in the resampled dataset, and some original samples are also allowed not to be selected. Optionally, repeating the above process, for example, 500 times, yields 500 resampled datasets.

[0102] S302. Determine the absorption wavelength index of black carbon and brown carbon within the preset wavelength range for each resampled dataset.

[0103] For each resampled dataset, the sampling absorption coefficient of brown carbon corresponding to each resampled carbon aerosol sample at the preset wavelength can be determined by using the above fitting calculation formula, based on the carbon aerosol absorption coefficient of each resampled carbon aerosol sample at the preset wavelength and the absorption wavelength index of black carbon corresponding to each resampled carbon aerosol sample at the preset wavelength.

[0104] Furthermore, based on the sampling absorption coefficient of brown carbon corresponding to each resampled carbon aerosol sample at a preset wavelength and the concentration of tracer in each resampled carbon aerosol sample, the sampling Pearson correlation coefficient between the two can be calculated. Thus, within the same resampled dataset, 101 sampling Pearson correlation coefficients corresponding to the absorption wavelength indices of 101 black carbon samples within the preset wavelength range can also be obtained.

[0105] Based on the multiple sampling Pearson correlation coefficients corresponding to each resampled dataset, the absorption wavelength index of black carbon at a preset wavelength corresponding to the largest sampling Pearson correlation coefficient can be selected as the absorption wavelength index of black carbon within the preset wavelength range. Furthermore, under the constraint of the selected black carbon absorption wavelength index, the brown carbon absorption wavelength indices fitted to each resampled dataset can be summarized according to the above fitting calculation formula to determine the target absorption wavelength index of brown carbon within the preset wavelength range. For example, the average value of the brown carbon absorption wavelength indices corresponding to multiple resampled datasets can be taken as the target absorption wavelength index of brown carbon within the preset wavelength range.

[0106] For example, if a resampled dataset includes 500 samples, and each resampled dataset includes 30 resampled carbon aerosol samples, then the method of this application can determine that: the 500 resampled datasets correspond to the absorption wavelength indices of 1500 brown carbon samples and 1500 black carbon samples within a preset wavelength range, wherein each resampled dataset corresponds to the absorption wavelength indices of 30 brown carbon samples and 30 black carbon samples within a preset wavelength range.

[0107] For any resampled dataset, based on a predefined candidate set of 101 black carbon absorption wavelength indices, Pearson correlation coefficients are calculated for each of the 30 samples within the resampled dataset. The candidate black carbon absorption wavelength index corresponding to the highest value among the 101 correlation coefficients is selected as the target black carbon absorption wavelength index for the resampled dataset. Under the constraint of this selected black carbon absorption wavelength index, the brown carbon absorption wavelength indices fitted to each sampled carbon aerosol sample are summarized according to the above fitting formula, and the average value is taken to determine the target absorption wavelength index for brown carbon within the predefined wavelength range. Repeating this process 500 times yields 500 absorption wavelength indices for brown carbon and 500 for black carbon within the predefined wavelength range.

[0108] S303. Calculate the first statistical parameter based on the absorption wavelength index of brown carbon in the preset wavelength range corresponding to each resampled dataset. The first statistical parameter includes: the first confidence interval.

[0109] S304. Calculate the second statistical parameter based on the absorption wavelength index of black carbon within the preset wavelength range corresponding to each resampled dataset. The second statistical parameter includes the second confidence interval.

[0110] In this process, after calculating the absorption wavelength index of brown carbon at a preset wavelength corresponding to each resampled dataset, a related first statistical parameter can be calculated. The first statistical parameter may include a first confidence interval, which can indicate the confidence interval of the absorption wavelength index of brown carbon within the preset wavelength range.

[0111] Of course, it should be noted that in some implementations, the first statistical parameter may also include the first standard deviation, which can indicate the fluctuation error of the absorption wavelength index of brown carbon within a preset wavelength range. For the calculation of the first confidence interval and the first standard deviation, please refer to the relevant calculation principles, which will not be elaborated here.

[0112] Accordingly, the second confidence interval can indicate the confidence interval of the absorption wavelength index of black carbon within a preset wavelength range. In some embodiments, the second statistical parameter may also include the second standard deviation, which can indicate the fluctuation error of the absorption wavelength index of black carbon within a preset wavelength range.

[0113] Based on the above description, in some embodiments, a first confidence interval can be used to evaluate whether the target absorption wavelength index of the brown carbon calculated above within the preset wavelength range meets the first preset requirement, and a second confidence interval can be used to evaluate whether the target absorption wavelength index of the black carbon calculated above within the preset wavelength range meets the second preset requirement.

[0114] In an optional implementation, the method further includes:

[0115] Based on the first statistical parameter, determine whether the target absorption wavelength index of brown carbon within the preset wavelength range meets the first preset requirement. If it does not meet the requirement, recalculate the target absorption wavelength index of brown carbon within the preset wavelength range. Based on the second statistical parameter, confirm whether the target absorption wavelength index of black carbon within the preset wavelength range meets the second preset requirement. If it does not meet the requirement, recalculate the target absorption wavelength index of black carbon within the preset wavelength range.

[0116] Based on the above explanation, after calculating the first confidence interval, it can be determined whether the target absorption wavelength index of brown carbon within the preset wavelength range falls within the first confidence interval. If so, it is determined that the target absorption wavelength index of brown carbon within the preset wavelength range meets the first preset requirement. If not, it is determined that it does not meet the first preset requirement. At this time, refer to the steps S102 to S104 above to recalculate the target absorption wavelength index of brown carbon within the preset wavelength range.

[0117] In some implementations, when recalculating, various parameters such as the preset adjustment step size of the absorption wavelength index of black carbon, the fluctuation range of the absorption wavelength index of black carbon, and the constraint conditions of the absorption wavelength index of brown carbon can be adjusted. These parameters are not limited here. The adjustment is made until the target absorption wavelength index of brown carbon within the preset wavelength range meets the first preset requirement.

[0118] For the recalculation process of the target absorption wavelength index corresponding to black carbon within the preset wavelength range, please refer to the relevant content on brown carbon, which will not be repeated here.

[0119] Figure 4 A flowchart illustrating the spectral absorption analysis method for carbon aerosols provided in this application embodiment. Figure 4 In alternative implementations, such as Figure 4 As shown, before determining the target tracer with the strongest correlation to brown carbon emission sources among various emissions corresponding to the main emission source types based on the main emission source types corresponding to the carbon aerosol sample set, the process also includes:

[0120] S401. Measure the emission concentration of each emission substance in each carbon aerosol sample in the carbon aerosol sample set.

[0121] Optionally, depending on the object being measured, different measurement methods can be used to measure the emission concentration of each emitted substance in each carbon aerosol sample in the carbon aerosol sample set. In some embodiments, the measurement methods adopted may include various methods such as optical methods and electrochemical methods, which are not limited here.

[0122] S402. Based on the emission concentration of each emission substance in each carbon aerosol sample, a positive definite matrix factorization model is used to perform source spectrum analysis on the carbon aerosol sample set to identify the main emission source types corresponding to the carbon aerosol sample set.

[0123] After obtaining the emission concentrations of each emission substance in each carbon aerosol sample, the positive matrix factorization (PMF) model can be used to perform source spectrum analysis on the carbon aerosol sample set, thereby identifying the main emission source types corresponding to the carbon aerosol sample set.

[0124] In some implementations, the main emission source type corresponding to the carbon aerosol sample set can be a primary crustal source, a secondary formation source, a vehicle exhaust source, a biomass combustion source, a coal combustion source, etc., and is not limited here.

[0125] In an optional implementation, the above-mentioned method of identifying the target tracer with the strongest correlation to the emission source of brown carbon among multiple emissions corresponding to the main emission source types corresponding to the carbon aerosol sample set includes:

[0126] Based on the emission concentrations of each emission substance and the concentration of organic carbon in each carbon aerosol sample in the carbon aerosol sample set, the target tracer with the strongest correlation to the emission source of brown carbon is identified among multiple emissions corresponding to the main emission source types.

[0127] After determining the main emission source types corresponding to the carbon aerosol sample set, the correlation between the emission concentration of each emission substance and the concentration of organic carbon in each carbon aerosol sample can be calculated. Based on the correlation between the two, the target tracer with the highest correlation to the emission source of brown carbon can be determined among the various emissions corresponding to the main emission source types. This allows the influence of dust on the carbon aerosol absorption coefficient to be eliminated by introducing the target tracer.

[0128] Figure 5 A flowchart illustrating the spectral absorption analysis method for carbon aerosols provided in this application embodiment. Figure 5 In alternative implementations, such as Figure 5 As shown, based on the emission concentrations of various emitting substances in each carbon aerosol sample and the concentration of organic carbon in each carbon aerosol sample in the carbon aerosol sample set, the target tracer with the strongest correlation to the emission source of brown carbon was determined from multiple emissions corresponding to the main emission source types, including:

[0129] S501. Based on the emission concentrations of each emission substance in each carbon aerosol sample in the carbon aerosol sample set and the concentration of organic carbon in each carbon aerosol sample, calculate the second Pearson correlation coefficient between the emission concentration of each emission substance and the concentration of organic carbon.

[0130] S502. Based on the second Pearson correlation coefficients corresponding to each emission substance, select the largest second Pearson correlation coefficient among multiple second Pearson correlation coefficients, and use the target emission substance corresponding to the largest second Pearson correlation coefficient as the target tracer.

[0131] In some implementations, for each emission substance, a second Pearson correlation coefficient can be calculated based on the emission concentration of the emission substance in each carbon aerosol sample and the concentration of organic carbon in each carbon aerosol sample. It can be understood that if there are K substances, K second Pearson correlation coefficients will be calculated accordingly. Then, the target emission substance corresponding to the largest second Pearson correlation coefficient can be selected as the target tracer.

[0132] By applying the embodiments of this application, the target tracer can be dynamically adjusted for different target areas. This not only accurately eliminates the influence of sand and dust on the carbon aerosol absorption coefficient, but also calculates a relatively accurate target absorption wavelength index for brown carbon within a preset wavelength range and the target absorption wavelength index for black carbon within a preset wavelength range.

[0133] In an optional implementation, the absorption wavelength index of black carbon fluctuates from 0.6 to 1.6, and the absorption wavelength index constraint of brown carbon indicates that the absorption wavelength index of brown carbon fluctuates from 2 to 9.

[0134] Of course, it should be noted that, depending on the actual application scenario, the specific content of the absorption wavelength index fluctuation range of black carbon and the absorption wavelength index constraint conditions of brown carbon are not limited to this embodiment.

[0135] Figure 6 This is a schematic diagram of the spectral absorption analysis of a carbon aerosol provided in an embodiment of this application. Figure 6 In (a), curve L1 is a schematic diagram of the relationship between the black carbon absorption coefficient and wavelength obtained using existing technology, and curve L2 is a schematic diagram of the relationship between the black carbon absorption coefficient and wavelength obtained using this application. Wherein, the AAE corresponding to curve L1... BC =1.00, the AAE corresponding to curve L2 BC =0.81. The comparison shows that the absorption coefficient of black carbon at all wavelengths decreases monotonically with increasing wavelength, exhibiting a typical power-law spectral morphology. Furthermore, the results of the two schemes tend to be consistent in the long-wavelength range (e.g., 445-980 nm), indicating that the spectral morphology of BC absorption is stable and relatively less sensitive to parameter perturbations. The differences between the two schemes are more pronounced in the short-wavelength range (e.g., 405–445 nm), indicating that AAE... BC The value of ...

[0136] Figure 6 In (b), curve L3 is a schematic diagram of the relationship between the absorption coefficient of brown carbon and wavelength obtained using existing technology, and curve L4 is a schematic diagram of the relationship between the absorption coefficient of brown carbon and wavelength obtained using this application. The AAE corresponding to curve L3 is... BrC =5.52, the AAE corresponding to curve L4 BrC =4.89. The comparison shows that the absorption coefficient of brown carbon is mainly concentrated in the short-wavelength band. The amplitude of the absorption coefficient is relatively high in the short-wavelength band (e.g., 405–445 nm), and it rapidly decays with increasing wavelength to near zero in the long-wavelength band, consistent with the typical spectral characteristics of BrC absorption: "strong in short wavelengths, weak in long wavelengths." It can be seen that this method can effectively distinguish the absorption contributions of BC and BrC in the spectral dimension and lock the absorption of BrC into the short-wavelength band.

[0137] Figure 6 (c) The light blue bar chart is a schematic diagram of the relationship between the black carbon absorption contribution rate and wavelength obtained using the prior art, and the dark blue bar chart is a schematic diagram of the relationship between the black carbon absorption contribution rate and wavelength obtained using the present application. Figure 6(d) shows a green bar graph illustrating the relationship between the absorption contribution rate of brown carbon and wavelength obtained using existing techniques, and an orange-green bar graph illustrating the relationship between the absorption contribution rate of brown carbon and wavelength obtained using the present application. The comparison shows that BC dominates the entire wavelength band, and its contribution ratio increases further with increasing wavelength; the contribution ratio of BrC is only significant in the short wavelength band and decreases rapidly with increasing wavelength. This result further verifies the physical consistency of the decomposition results from the perspective of "relative contribution": long-wavelength absorption is mainly determined by BC, while BrC has a considerable contribution in short-wavelength absorption.

[0138] Figure 6 (e) represents the relative change parameter of black carbon absorption in the prior art compared to that of this application. Figure 6 (f) represents the relative change parameter of brown carbon absorption in the prior art compared to this application. From Figure 6 (e) It can be seen that the black carbon absorption coefficient of the prior art shows a positive difference compared with that of this application, and the difference is more significant in the short-wavelength band, indicating that improving AAE BC This will cause shortwave absorption to be more predominantly allocated to BC. From Figure 6 (f) It can be seen that the difference in brown carbon absorption coefficient between the prior art and this application is negative, indicating that the BrC absorption of the prior art is weakened. This is because, when the total absorption is constant, an increase in the BC absorption distribution will directly compress the BrC absorption, and it also reflects the characteristic that BrC absorption is more sensitive to parameter settings. Therefore, the optimal AAE obtained by this application is adopted. BC With AAE BrC This will significantly improve the reliability of BrC absorption calculations, thereby enabling robust characterization of the separation and quantification of BC and BrC absorptions.

[0139] In addition, it should be noted that, Figure 6 (c)- Figure 6 (f) The line at the top of the bar chart is used to indicate the fluctuation range corresponding to the bar chart.

[0140] The relative change parameters of black carbon absorption of the prior art relative to this application and the relative change parameters of brown carbon absorption of the prior art relative to this application can be calculated using the following formulas:

[0141]

[0142]

[0143] in, This indicates the relative change in black carbon absorption parameters between the prior art and this application. This indicates the relative change in brown carbon absorption parameters between the prior art and the present application; AAE BCThe absorption coefficient of black carbon is 1.00. AAE BC The absorption coefficient of black carbon when it is 0.81; AAE BrC The brown carbon absorption coefficient is 5.52. AAE BrC The brown carbon absorption coefficient is 4.89.

[0144] In summary, this application provides a spectral absorption analysis method for carbon aerosols. By setting the fluctuation range of the absorption wavelength index of black carbon, the optimal absorption wavelength index of black carbon is screened, thus solving the problem of AAE caused by changes in the BC mixing state, aging, etc. BC Errors introduce problems; it can accurately calculate the absorption of brown carbon in the infrared band that is easily overlooked, avoiding underestimation of the contribution of BrC absorption; it can dynamically identify tracers of carbon aerosol absorption coefficients in different regions, accurately eliminating the influence of dust on carbon aerosol absorption coefficients.

[0145] Figure 7 This is a functional module diagram of a carbon aerosol spectral absorption analysis device provided in this embodiment. The basic principle and technical effects of this device are the same as those in the corresponding method embodiment described above. For the sake of brevity, parts not mentioned in this embodiment can be referred to the corresponding content in the method embodiment. Figure 7 As shown, the spectral absorption analysis device 100 includes:

[0146] The first determining module 110 is used to determine the target tracer with the greatest correlation to the emission source of brown carbon among a variety of emissions corresponding to the main emission source type, based on the main emission source type corresponding to the carbon aerosol sample set. The carbon aerosol sample set includes multiple carbon aerosol samples collected from the same target area at different times.

[0147] The acquisition module 120 is used to measure and acquire the carbon aerosol absorption coefficient of each carbon aerosol sample in the carbon aerosol sample set at a preset wavelength using a thermo-optic carbon analyzer, and to fit and determine the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength based on the carbon aerosol absorption coefficient of each carbon aerosol sample at the preset wavelength, the fluctuation range of the absorption wavelength index of black carbon, the preset adjustment step size of the absorption wavelength index of black carbon, and the constraint condition of the absorption wavelength index of brown carbon.

[0148] The calculation module 130 is used to measure the concentration of the target tracer in each carbon aerosol sample, and iteratively calculate the first Pearson correlation coefficient between the initial absorption coefficient of the brown carbon at the preset wavelength and the concentration of the target tracer in each carbon aerosol sample based on the initial absorption coefficient of the brown carbon at the preset wavelength and the concentration of the target tracer in each carbon aerosol sample.

[0149] The second determining module 140 is used to determine the absorption wavelength index of black carbon at a preset wavelength corresponding to the maximum first Pearson correlation coefficient as the target absorption wavelength index of black carbon within a preset wavelength range, and to fit and determine the target absorption wavelength index of brown carbon within a preset wavelength range according to a preset simplified model of carbon aerosol absorption of two components.

[0150] In an optional implementation, the second determining module 140 is specifically used to adjust the initial absorption wavelength index of black carbon according to the fluctuation range of the absorption wavelength index of black carbon and the preset adjustment step size of the absorption wavelength index of black carbon during each iteration.

[0151] Based on the carbon aerosol absorption coefficient of each carbon aerosol sample at a preset wavelength, the initial absorption wavelength index of the adjusted black carbon, and the absorption wavelength index constraint of the brown carbon, the initial absorption coefficient of the brown carbon corresponding to each carbon aerosol sample at a preset wavelength is determined by fitting.

[0152] Based on the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength and the concentration of the target tracer in each carbon aerosol sample, the first Pearson correlation coefficient corresponding to each iteration is calculated.

[0153] Based on multiple iterations of the first Pearson correlation coefficient, the absorption wavelength index of black carbon at the preset wavelength corresponding to the largest first Pearson correlation coefficient is selected as the target absorption wavelength index of black carbon within the preset wavelength range. Then, based on the preset simplified model of carbon aerosol absorption of two components, the target absorption wavelength index of brown carbon within the preset wavelength range is determined by fitting.

[0154] In an optional implementation, the second determining module 140 is further configured to determine multiple resampling datasets based on the carbon aerosol sample set using a nonparametric bootstrapping method.

[0155] Determine the absorption wavelength index of the black carbon and the absorption wavelength index of the brown carbon within the preset wavelength range for each of the resampled datasets.

[0156] Based on the absorption wavelength index of the brown carbon in the preset wavelength range corresponding to each of the resampled datasets, a first statistical parameter is calculated, the first statistical parameter including: a first confidence interval;

[0157] Based on the absorption wavelength index of the black carbon in the preset wavelength range corresponding to each of the resampled datasets, a second statistical parameter is calculated, which includes a second confidence interval.

[0158] In an optional implementation, the second determining module 140 is further configured to determine, based on the first statistical parameter, whether the target absorption wavelength index of the brown carbon within the preset wavelength range meets the first preset requirement; if not, to recalculate the target absorption wavelength index of the brown carbon within the preset wavelength range.

[0159] Based on the second statistical parameter, it is confirmed whether the target absorption wavelength index of the black carbon within the preset wavelength range meets the second preset requirement. If it does not meet the requirement, the target absorption wavelength index of the black carbon within the preset wavelength range is recalculated.

[0160] In an optional implementation, the first determining module is further configured to measure the emission concentration of each emission substance in each carbon aerosol sample in the carbon aerosol sample set;

[0161] Based on the emission concentrations of each emission substance in each carbon aerosol sample, a positive definite matrix factorization model is used to perform source spectrum analysis on the carbon aerosol sample set to identify the main emission source types corresponding to the carbon aerosol sample set.

[0162] In an optional implementation, the first determining module is further configured to determine, based on the emission concentration of each emission substance in each carbon aerosol sample in the carbon aerosol sample set and the concentration of organic carbon in each carbon aerosol sample, the target tracer most correlated with the emission source of brown carbon among the various emissions corresponding to the main emission source type.

[0163] In an optional implementation, the first determining module is further configured to calculate a second Pearson correlation coefficient between the emission concentration of each emission substance and the concentration of organic carbon in each carbon aerosol sample in the carbon aerosol sample set.

[0164] Based on the second Pearson correlation coefficients corresponding to each of the emitted substances, the largest second Pearson correlation coefficient among multiple second Pearson correlation coefficients is selected, and the target emitted substance corresponding to the largest second Pearson correlation coefficient is used as the target tracer.

[0165] In an optional embodiment, the absorption wavelength index of the black carbon fluctuates from 0.6 to 1.6, and the absorption wavelength index constraint of the brown carbon indicates that the absorption wavelength index of the brown carbon fluctuates from 2 to 9.

[0166] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0167] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors, or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0168] Figure 8 This is a schematic diagram of an electronic device provided in an embodiment of this application. This electronic device can be integrated into the aforementioned spectral absorption analysis device. Figure 8 As shown, the electronic device may include a processor 210, a memory 220, and a bus 230. The memory 220 stores machine-readable instructions executable by the processor 210. When the electronic device is running, the processor 210 communicates with the memory 220 via the bus 230, and the processor 210 executes the machine-readable instructions to perform the steps of the above method embodiment. The specific implementation and technical effects are similar and will not be described in detail here.

[0169] Optionally, this application also provides a storage medium storing a computer program, which, when run by a processor, executes the steps of the above-described method embodiments. The specific implementation and technical effects are similar and will not be repeated here.

[0170] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0171] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0172] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0173] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0174] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0175] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need further definition and explanation in subsequent figures. The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for spectroscopic absorption analysis of carbon aerosols, characterized in that, The method includes: Based on the main emission source type corresponding to the carbon aerosol sample set, the target tracer with the greatest correlation to the emission source of brown carbon is determined among the various emissions corresponding to the main emission source type. The carbon aerosol sample set includes multiple carbon aerosol samples collected from the same target area at different times. The carbon aerosol absorption coefficient of each carbon aerosol sample in the carbon aerosol sample set at a preset wavelength is obtained by measuring the carbon aerosol absorption coefficient of each carbon aerosol sample at a preset wavelength, and the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength is determined by fitting based on the carbon aerosol absorption coefficient of each carbon aerosol sample at a preset wavelength, the fluctuation range of the absorption wavelength index of black carbon, the preset adjustment step size of the absorption wavelength index of black carbon, and the constraint condition of the absorption wavelength index of brown carbon. The concentration of the target tracer in each carbon aerosol sample is measured, and the first Pearson correlation coefficient between the initial absorption coefficient of the brown carbon at the preset wavelength and the concentration of the target tracer in each carbon aerosol sample is iteratively calculated based on the initial absorption coefficient of the brown carbon at the preset wavelength and the concentration of the target tracer in each carbon aerosol sample. The absorption wavelength index of black carbon at a preset wavelength corresponding to the maximum first Pearson correlation coefficient is determined as the target absorption wavelength index of black carbon within a preset wavelength range. Based on the preset simplified model of carbon aerosol absorption of two components, the target absorption wavelength index of brown carbon within a preset wavelength range is determined by fitting. The step of fitting and determining the initial absorption coefficient of brown carbon at the preset wavelength for each carbon aerosol sample based on the carbon aerosol absorption coefficient at the preset wavelength, the fluctuation range of the absorption wavelength index of black carbon, the preset adjustment step size of the absorption wavelength index of black carbon, and the constraint condition of the absorption wavelength index of brown carbon includes: Based on the carbon aerosol absorption coefficient of each carbon aerosol sample at a preset wavelength, the fluctuation range of the absorption wavelength index of black carbon, the preset adjustment step size of the absorption wavelength index of black carbon, and the constraint condition of the absorption wavelength index of brown carbon, the initial absorption coefficient of brown carbon at a preset wavelength for each carbon aerosol sample is determined by fitting a preset simplified two-component carbon aerosol absorption model using the least squares regression algorithm. The fitting calculation formula for the preset simplified two-component carbon aerosol absorption model is as follows: in, K represents the absorption coefficient of the carbon aerosol corresponding to the carbon aerosol sample. BC K represents the black carbon fitting coefficient. BrC AAE represents the brown carbon fitting coefficient. BrC The absorption wavelength index (AAE) of brown carbon is indicated by this index. BC The wavelength index represents the absorption wavelength of black carbon, and λ represents the wavelength of the laser emitted by the thermo-optical carbon analyzer. This represents the absorption coefficient of black carbon corresponding to the carbon aerosol sample. This represents the absorption coefficient of the brown carbon corresponding to the carbon aerosol sample.

2. The method according to claim 1, characterized in that, The step of determining the absorption wavelength index of black carbon at a preset wavelength corresponding to the maximum first Pearson correlation coefficient as the target absorption wavelength index of black carbon within a preset wavelength range, and fitting and determining the target absorption wavelength index of brown carbon within a preset wavelength range according to a preset simplified model for carbon aerosol absorption of two components, includes: During each iteration, the initial absorption wavelength index of the black carbon is adjusted according to the fluctuation range of the absorption wavelength index of the black carbon and the preset adjustment step size of the absorption wavelength index of the black carbon. Based on the carbon aerosol absorption coefficient of each carbon aerosol sample at a preset wavelength, the initial absorption wavelength index of the adjusted black carbon, and the absorption wavelength index constraint of the brown carbon, the initial absorption coefficient of the brown carbon corresponding to each carbon aerosol sample at a preset wavelength is determined by fitting. Based on the initial absorption coefficient of brown carbon corresponding to each carbon aerosol sample at a preset wavelength and the concentration of the target tracer in each carbon aerosol sample, the first Pearson correlation coefficient corresponding to each iteration is calculated. Based on multiple iterations of the first Pearson correlation coefficient, the absorption wavelength index of black carbon at the preset wavelength corresponding to the largest first Pearson correlation coefficient is selected as the target absorption wavelength index of black carbon within the preset wavelength range. Then, based on the preset simplified model of carbon aerosol absorption of two components, the target absorption wavelength index of brown carbon within the preset wavelength range is determined by fitting.

3. The method according to claim 1, characterized in that, After determining the absorption wavelength index of black carbon at a preset wavelength corresponding to the maximum first Pearson correlation coefficient as the target absorption wavelength index of black carbon within a preset wavelength range, and fitting and determining the target absorption wavelength index of brown carbon within a preset wavelength range according to a preset simplified model for carbon aerosol absorption of two components, the method further includes: Based on the nonparametric bootstrapping method, multiple resampling datasets are determined from the carbon aerosol sample set. Determine the absorption wavelength index of the black carbon and the absorption wavelength index of the brown carbon within the preset wavelength range for each of the resampled datasets. Based on the absorption wavelength index of the brown carbon in the preset wavelength range corresponding to each of the resampled datasets, a first statistical parameter is calculated, the first statistical parameter including: a first confidence interval; Based on the absorption wavelength index of the black carbon in the preset wavelength range corresponding to each of the resampled datasets, a second statistical parameter is calculated, which includes a second confidence interval.

4. The method according to claim 3, characterized in that, The method further includes: Based on the first statistical parameter, determine whether the target absorption wavelength index of the brown carbon within the preset wavelength range meets the first preset requirement. If it does not meet the requirement, recalculate the target absorption wavelength index of the brown carbon within the preset wavelength range. Based on the second statistical parameter, it is confirmed whether the target absorption wavelength index of the black carbon within the preset wavelength range meets the second preset requirement. If it does not meet the requirement, the target absorption wavelength index of the black carbon within the preset wavelength range is recalculated.

5. The method according to claim 1, characterized in that, Before determining the target tracer with the strongest correlation to the emission source of brown carbon among multiple emissions corresponding to the main emission source types based on the main emission source types corresponding to the carbon aerosol sample set, the method further includes: Measure the emission concentration of each emitted substance in each carbon aerosol sample in the carbon aerosol sample set; Based on the emission concentrations of each emission substance in each carbon aerosol sample, a positive definite matrix factorization model is used to perform source spectrum analysis on the carbon aerosol sample set to identify the main emission source types corresponding to the carbon aerosol sample set.

6. The method according to claim 1, characterized in that, The step of identifying the target tracer with the strongest correlation to the emission source of brown carbon from among multiple emissions corresponding to the main emission source types corresponding to the carbon aerosol sample set includes: Based on the emission concentrations of each emission substance in each carbon aerosol sample in the carbon aerosol sample set and the concentration of organic carbon in each carbon aerosol sample, the target tracer with the highest correlation to the emission source of brown carbon is determined among the various emissions corresponding to the main emission source type.

7. The method according to claim 6, characterized in that, The step of determining the target tracer with the strongest correlation to brown carbon emission sources from among multiple emissions corresponding to the main emission source types, based on the emission concentrations of each emission substance in each carbon aerosol sample in the carbon aerosol sample set and the concentration of organic carbon in each carbon aerosol sample, includes: Based on the emission concentrations of each emission substance in each carbon aerosol sample in the carbon aerosol sample set and the concentration of organic carbon in each carbon aerosol sample, calculate the second Pearson correlation coefficient between the emission concentrations of each emission substance and the concentration of organic carbon. Based on the second Pearson correlation coefficients corresponding to each of the emitted substances, the largest second Pearson correlation coefficient among multiple second Pearson correlation coefficients is selected, and the target emitted substance corresponding to the largest second Pearson correlation coefficient is used as the target tracer.

8. The method according to any one of claims 1-7, characterized in that, The absorption wavelength index of the black carbon fluctuates from 0.6 to 1.6, and the absorption wavelength index constraint of the brown carbon indicates that the absorption wavelength index of the brown carbon fluctuates from 2 to 9.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the spectral absorption analysis method for carbon aerosols as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the spectral absorption analysis method for carbon aerosols as described in any one of claims 1-8.

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