WO2023207579A1 - 基于超光谱遥感的痕量气体水平分布探测交通污染源方法 - Google Patents

基于超光谱遥感的痕量气体水平分布探测交通污染源方法 Download PDF

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WO2023207579A1
WO2023207579A1 PCT/CN2023/087614 CN2023087614W WO2023207579A1 WO 2023207579 A1 WO2023207579 A1 WO 2023207579A1 CN 2023087614 W CN2023087614 W CN 2023087614W WO 2023207579 A1 WO2023207579 A1 WO 2023207579A1
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trace
gas
trace gas
horizontal
optical path
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French (fr)
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刘诚
陆川
谈伟
邢成志
林华
林继楠
胡启后
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中国科学技术大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
    • G01N21/274—Calibration, base line adjustment, drift correction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/33—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using ultraviolet light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N2021/1793—Remote sensing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
    • Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters

Definitions

  • the invention belongs to the field of optical measurement technology, and specifically relates to a method for detecting traffic pollution sources based on trace gas horizontal distribution based on hyperspectral remote sensing.
  • Patent document CN212873559U discloses a plant-derived food chemical pollutant detection device. This isopoint sampling instrument cannot observe the pollutant information in the area at one time. It requires manual movement in a short period of time before the sampling vehicle can travel. range to obtain regional pollutant level distribution information, and is not suitable for monitoring the horizontal distribution information of atmospheric pollutants.
  • the purpose of the present invention is to provide a method for detecting traffic pollution sources based on hyperspectral remote sensing trace gas horizontal distribution, which can quickly and accurately obtain the horizontal concentration distribution of atmospheric pollutants in the urban traffic road network through inversion, thereby providing Provide support for traceability of traffic pollution.
  • the embodiment provides a method for detecting traffic pollution sources based on trace gas horizontal distribution based on hyperspectral remote sensing, which includes the following steps:
  • Step 1 Obtain the atmospheric scattering solar spectrum in the visible-ultraviolet spectrum band collected through hyperspectral remote sensing, and simultaneously collect surface environmental parameters, including ambient temperature and pressure data;
  • Step 2 Obtain the total differential slope of oxygen dimers and trace gases based on atmospheric scattering solar spectrum inversion
  • Step 3 Calculate the effective optical path information of the atmospheric scattered solar spectrum in the oxygen dimer based on the total differential slope path of the oxygen dimer.
  • Step 4 Based on the aerosol optical property information and surface environmental parameters, and through the radiative transfer equation, the effective optical path information of the atmospheric scattered solar spectrum in the oxygen dimer is extended to trace gases to obtain the effective optical path information of the trace gases. and photon paths;
  • Step 5 According to the effective optical path information and photon path of the trace gas, convert the total differential slope path of the trace gas into the horizontal concentration information of the trace gas with different effective optical path lengths in the visible-ultraviolet spectrum band;
  • Step 6 Correct the horizontal concentration information of the trace gas to obtain the corrected horizontal concentration information of the trace gas
  • Step 7 Based on the horizontal concentration information and effective optical path information of the trace gas, invert to obtain the horizontal distribution of the trace gas in the observation direction.
  • hyperspectral remote sensing collects and observes atmospheric scattering solar spectra at horizontal and low elevation angles of no more than 1° for traffic pollution source areas.
  • step 2 includes:
  • the collected atmospheric scattered solar spectrum is corrected to subtract the effects of dark current and electronic bias
  • the oxygen dimers and oxygen dimers in different bands can be obtained in real time. Total differential slope of trace gases.
  • step 3 includes:
  • P represents the atmospheric pressure
  • T represents the atmospheric temperature
  • R is the gas specific constant
  • N A is Avogadro's constant
  • C air represents the atmospheric concentration
  • L eff represents the effective optical path information of the gas in the oxygen dimer
  • step 4 includes:
  • the trace gas prior profile, aerosol optical property information, temperature and pressure profile, and geometric position information are used as inputs to the radiation transfer equation.
  • the optical path L y at the target wavelength and the photon path of the trace gas can be obtained by solving the equation.
  • AMF trace_gas the trace gas prior profile is obtained by pre- Obtained by standard wavelength inversion, the corresponding optical path is L x , by establishing the relationship between L x and Ly y : And after fitting, a 0 , a 1 , and a 2 are obtained as three fitting coefficients;
  • one set of fitting coefficients is obtained by fitting in the visible spectrum band, and another set of fitting coefficients is obtained by fitting in the ultraviolet spectrum band;
  • the effective optical path information under oxygen dimer based on O 4 inversion Selection and valid optical path information The fitting coefficient corresponding to the band, and use this set of fitting coefficients to pass The obtained Ly is the effective optical path L trace_gas of the trace gas in the corresponding waveband.
  • step 5 includes:
  • the horizontal concentration information C trace_gas of the trace gas is obtained:
  • SCD trace_gas is the total amount of differential slope of trace gas
  • VCD trace_gas SCD trace_gas /dAMF trace_gas , indicating the total horizontal amount of trace gas
  • the photon path AMF trace_gas of trace gas is obtained by solving the radiation transfer equation.
  • step 6 includes:
  • the horizontal concentration information of the trace gas is corrected based on the correction factor f corr :
  • c trace_gas is the horizontal concentration information of trace gas
  • C corr is the corrected horizontal concentration information of trace gas
  • f corr is the correction factor
  • c retrieved is the average concentration of trace gas inversion
  • c model is the bottom concentration in the prior profile input to the radiative transfer equation
  • dSCD model is the differential slope path of trace gas obtained by inversion of the radiative transfer equation. total amount.
  • step 7 includes:
  • the effective optical path information of the trace gas is divided into n observation lengths, corresponding to n observation directions. According to the observation lengths from long to short, they are L 1 , L 2 , L 3 , ⁇ , L n , corresponding to The horizontal concentration information of trace gases inverted from n spectral bands are C 1 , C 2 , C 3 ,..., C n respectively. Based on the horizontal concentration information of trace gases, the segmentation of trace gases is obtained. Concentration result c n , specifically:
  • the horizontal distribution of trace gases in the observation directions can be obtained.
  • the beneficial effects of the present invention include at least:
  • Hyperspectral remote sensing instruments set at different locations are used to collect atmospheric scattering spectra without blocked light paths according to the set azimuth and elevation angle sequences. After inverting to obtain the total differential slope range of trace gases and oxygen dimers, The proportional relationship between oxygen dimers is used to obtain effective optical path information. From this, the detection spectrum length is horizontally segmented to obtain the horizontal concentration distribution of trace gases in different segments. In this way, the air pollution in the urban traffic road network area can be obtained. It can achieve long-term and stable operational observation of the horizontal distribution of air pollutants, which is of practical and reliable significance for monitoring the movement process and transformation process of air pollutants in the urban road network area, as well as the research on the traceability of pollutant generation.
  • Figure 1 is a flow chart of a method for detecting traffic pollution sources based on trace gas level distribution based on hyperspectral remote sensing provided by the embodiment;
  • Figure 2 is a flow chart of the method of inputting environmental parameters to obtain correction factors and photon paths through the radiative transfer equation
  • Figure 3 is a schematic diagram of the NO 2 observation scheme provided by the embodiment.
  • Figure 4 is a diagram of NO 2 level distribution results provided by the embodiment.
  • Figure 1 is a flow chart of a method for detecting traffic pollution sources based on trace gas level distribution based on hyperspectral remote sensing provided by the embodiment.
  • the embodiment provides a method for detecting traffic pollution sources based on the horizontal distribution of trace gases based on hyperspectral remote sensing.
  • the horizontal atmospheric scattering solar spectrum is collected through hyperspectral remote sensing, and based on different effective optical path inversions, the results within the urban traffic road network area are obtained.
  • the horizontal concentration distribution of atmospheric pollutants includes the following steps:
  • Step 1 Obtain the atmospheric scattering solar spectrum in the visible-ultraviolet spectrum band collected through hyperspectral remote sensing, and simultaneously collect surface environmental parameters, including ambient temperature and pressure data.
  • the hyperspectral remote sensing instrument when collecting the atmospheric scattered solar radiation spectrum, is installed in an unobstructed area, which can ignore the spatial obstacles in the lower area and receive light from ultraviolet to visible light.
  • the scattered light in the high-resolution band can observe the atmospheric scattered solar spectrum at a horizontal level several kilometers away and at a low elevation angle of no more than 1° during the day.
  • the selected hyperspectral remote sensing instrument has a sampling spectral resolution of 0.45 to 0.6 nm, and a spectral wavelength range of 300 to 650 nm, including the ultraviolet-visible band.
  • the hyperspectral remote sensing instrument can be rotated to adjust the observation angle by being installed at a high position without obstructions.
  • the observation azimuth range can be adjusted to a 360° panorama with an accuracy of 0.1°.
  • the observation pitch angle range is 0° (horizontal direction) ⁇ 90° (zenith direction), accuracy 0.1°.
  • the effective optical path of hyperspectral remote sensing instruments is generally around 5km, while the near-ground concentration is generally considered to be below 100m.
  • an elevation angle of no more than 1° is selected to collect the atmospheric scattering solar spectrum.
  • an atmospheric temperature and pressure sensor is also equipped, which not only collects the atmospheric scattered solar spectrum, but also records the external environment temperature and pressure data as surface environmental parameters.
  • Step 2 Obtain the total differential slope range of inverted oxygen dimers and other trace gases based on atmospheric scattering solar spectrum inversion.
  • correction processing is required to subtract the effects of dark current and electronic bias, and the corrected atmospheric scattered solar spectrum participates in the inversion.
  • the observed spectrum based on the zenith direction is used as the reference spectrum.
  • the oxygen dioxide in different bands can be inverted in real time.
  • oxygen dimers have multiple significant absorption peaks between spectral collection bands, so that the total differential slope range of multiple oxygen dimers and other trace gases in these multiple ultraviolet-visible light bands can be obtained.
  • the root mean square and relative errors in the least squares inversion process were also analyzed. Filter for more precise results.
  • Step 3 Calculate the effective optical path information of the atmospheric scattered solar spectrum in the oxygen dimer based on the total differential slope path of the oxygen dimer.
  • P represents the atmospheric pressure
  • T represents the atmospheric temperature
  • R is the gas ratio constant, which is generally defaulted to 287.058J/(kg ⁇ K)
  • N A is Avogadro's constant, which is generally defaulted to 6.02 ⁇ 10 23 mol -1
  • C air represents the atmospheric concentration.
  • L eff represents the effective optical path information of the gas in the oxygen dimer
  • Step 4 Based on the aerosol optical property information and surface environmental parameters, and through the radiative transfer equation, the effective optical path information of the atmospheric scattered solar spectrum in the oxygen dimer is extended to other trace gases to obtain the effective light of other trace gases. process information.
  • the aerosol scene type is selected based on the detection area, and the surface environmental parameters of the observation area are determined based on the imaging camera equipped with hyperspectral remote sensing. From this, the effective optical path is extended to other wavelength ranges through the radiation transfer equation, using for analyzing additional trace gases in other wavelength bands.
  • E(z) is the aerosol extinction profile
  • z is the height
  • ⁇ is the total optical thickness of the aerosol
  • H is the height of the atmospheric boundary layer
  • F is the fraction of ⁇ in the boundary layer
  • ⁇ is the free layer aerosol in the troposphere
  • the scale height of , ⁇ is the normalization constant of the exponential factor, which can be calculated using the following formula:
  • d represents the top layer of the extinction profile, which can generally be defaulted to 15km.
  • the trace gas prior profile, aerosol optical property information, temperature and pressure profile, and geometric position information are used as inputs to the radiation transfer equation.
  • the optical path L y at the target wavelength and the photons of the trace gas can be obtained by solving the equation.
  • Path AMF trace_gas The a priori profile of the required trace gas input is obtained through pre-standard wavelength inversion, and its corresponding optical path is L x .
  • L x and L y By establishing the relationship between L x and L y : And after fitting, three fitting coefficients a 0 , a 1 , and a 2 are obtained; among them, one set of fitting coefficients is obtained by fitting in the visible spectrum band, and another set of fitting coefficients is obtained by fitting in the ultraviolet spectrum band;
  • Step 5 According to the effective optical path information and photon path of the trace gas, convert the total differential slope path of the trace gas into a horizontal total amount, and then convert it into the trace gas concentration of different effective optical path lengths in the visible-ultraviolet spectrum band. information.
  • the horizontal concentration information C trace_gas of the trace gas is obtained:
  • SCD trace_gas is the total differential slope amount of trace gas
  • VCD trace_gas SCD trace_gas /dAMF trace_gas represents the total horizontal amount of trace gas.
  • Step 6 Correct the horizontal concentration information of the trace gas to obtain corrected horizontal concentration information of the trace gas.
  • the horizontal concentration information of the trace gas is corrected based on the correction factor f corr :
  • c trace_gas is the horizontal concentration information of trace gas
  • C corr is the corrected Horizontal concentration information of trace gases
  • f corr is the correction factor, obtained based on radiative transfer simulation assuming different profiles of trace gases and aerosols, the specific formula is:
  • c retrieved is the average concentration of trace gas inversion
  • c model is the bottom concentration in the prior profile input to the radiative transfer equation
  • dSCD model is the differential slope path of trace gas obtained by inversion of the radiative transfer equation. total amount.
  • Step 7 Based on the horizontal concentration information and effective optical path information of the trace gas, invert to obtain the horizontal distribution of the trace gas in the observation direction.
  • the effective optical path information of multiple bands collected in a short period of time is combined with the horizontal concentration information of trace gases in different spectral bands, based on different effective optical path lengths. Length, segment the horizontal concentration information into segments, thereby obtaining the horizontal concentration distribution results in the observation direction. It is assumed that through the inversion results, based on the inversion results of different spectral bands of O 4 , the effective optical path information of the trace gas is divided into n observation lengths. According to the observation lengths from long to short, they are L 1 , L 2 , and L 3 respectively. , ⁇ ,L n .
  • the horizontal concentration information of trace gases inverted corresponding to n spectral bands are C 1 , C 2 , C 3 , ⁇ ,C n respectively. From this, the segmented concentration result c n of the trace gas can be obtained based on the horizontal concentration information of the trace gas, specifically as follows:
  • step 1 when collecting the atmospheric scattering solar spectrum in the visible-ultraviolet spectrum band, the hyperspectral remote sensing instrument is installed on a high building located at longitude 117.2469°E and latitude 31.8632°N. It is the highest point within several kilometers of the surrounding area, and there is no obvious obstruction in the horizontal direction.
  • the set up hyperspectral remote sensing instrument can collect the horizontal solar scattering spectrum by rotating the pitch angle and azimuth angle of its external machine. Specifically, the observation angle is adjusted through rotation, ranging from 0° to 180° in azimuth angles, with intervals of 5°. The accuracy is 0.1°, and the observation pitch angle range is 0° (horizontal) to 90° (zenith). The accuracy is 0.1°.
  • FIG. 3 shows a schematic diagram of the NO2 observation plan.
  • the instrument in Figure 3 is set up on a high building at the location shown, and can be observed in the east direction.
  • the observation direction is set to 60° ⁇ 120° (true north is 0°), and observations can be made.
  • the azimuth angles are spaced every 5°. The reason for this setting is to observe the daily changes in the trace gas concentration distribution of the Hefei City First Ring Road.
  • step 2 when the total differential slope amount of oxygen dimer and other trace gases is obtained based on the atmospheric scattering solar spectrum inversion, the spectrum collected from 10:30 to 14:30 noon on January 11, 2022 is selected. Inversion, the weather was sunny that day, and the ultraviolet band and visible band were selected as the inversion peaks of O 4.
  • the visible band mainly referred to the absorption peak of 470 ⁇ 490nm, and the ultraviolet band mainly referred to the absorption peak of 355 ⁇ 365nm.
  • the influence of dark current and electron bias can be used to obtain O 4 sloping column concentration information in two bands.
  • results with RMS (root mean square error) > 0.005 and r-err (relative error) ⁇ 0.3 are deleted. .
  • step 3 the atmospheric scattering solar spectrum is calculated based on the total differential slope path of the oxygen dimer.
  • hyperspectral remote sensing can record the local current through temperature and pressure sensors while collecting the spectrum. Atmospheric temperature and pressure. The atmospheric temperature and pressure in the local time period from 10:30 to 14:30 on January 11, 2022 are 278.1385K (Kelvin) and 101250P (Pascal) respectively.
  • the near-surface O 4 concentration can be approximately calculated to be 3.0351 ⁇ 10 37 colecules 2 cm -6 . Through the proportional relationship, effective spectral collection at multiple azimuth angles and two bands of observation can be obtained. Optical path, from which the concentration results of trace gases in different wavebands can be calculated.
  • step 4 based on the aerosol optical property information and surface environmental parameters, the effective optical path information of the atmospheric scattering solar spectrum in the oxygen dimer is extended to other trace gases through the radiative transfer equation to obtain the wavelength bands of other trace gases.
  • the radiative transfer equation of the aerosol scenario here we choose to simulate three zenith angles (20°, 40° and 50°) and all azimuth angles within the observation range of 0° to 90°.
  • polynomial fitting is performed for 310nm and 450nm wavelengths, and then based on the wavelength, polynomial fitting can be performed for the visible light band, thus obtaining the inverted trace gas (NO 2 ) in ultraviolet and visible light
  • the effective optical path information of the band and the photon path of the trace gas AMF trace_gas .
  • the optical path lengths L 310 and L 450 at the target wavelength can be obtained by solving the problem.
  • the a priori profile of the required trace gas input is obtained by inversion at 360nm (the strongest absorption peak in the ultraviolet part of O 4 ) and 470nm (the strongest absorption peak in the visible light part of O 4 ).
  • the corresponding optical path of the instrument is L 360 and L 470 , while the absorption peaks of NO 2 are mainly located at 354nm and 440nm, and the corresponding optical path lengths of the instrument are L 354 and L 440.
  • the fitting function can be used to establish the relationship between the O 4 optical path L 360 and the NO 2 optical path L 354 in the ultraviolet part. Similarly, the fitting function is used to establish the relationship between the effective optical path at the absorption peak wavelength.
  • the composite function establishes the connection between the visible light part L 470 and L 440 , and the fitting obtains three fitting coefficients a 0 , a 1 , and a 2 .
  • Steps 5-6 are calculated normally based on the above method to obtain the corrected horizontal concentration information for trace gases, and the corrected horizontal concentration information for trace gases is involved in the calculation of step 7.
  • step 7 under the default that the atmospheric environment will not change in a short period of time, the effective optical path information of multiple bands collected in a short period of time is combined with the trace gas concentration results of different spectral segments, based on different effective optical path lengths, The concentration information is segmented to obtain the horizontal concentration distribution result in the observation direction. It is assumed that through the inversion results, based on the inversion results of different spectral bands of O 4 , the effective optical path is divided into 2 observation lengths. The results of the overall trace gas concentration segment corresponding to the inversion of the two spectral bands are C 1 and C 2 respectively. Thus, based on the trace gas concentration segment results, the trace gas (NO 2 ) segmented concentration results can be obtained, as shown in Figure 4.
  • the method for detecting traffic pollution sources based on the horizontal distribution of trace gases based on hyperspectral remote sensing can fix the hyperspectral remote sensing equipment in an unobstructed area.
  • the elevation angle can be fixed to a horizontal or low elevation angle (not exceeding 1°) direction, by moving the azimuth angle to collect horizontal position atmospheric scattering solar spectral signals in different directions, obtain the effective optical path based on oxygen dimer inversion, and extend to other trace gas wavelengths to obtain several kilometers resolution Trace gas level distribution.

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Abstract

一种基于超光谱遥感的痕量气体水平分布探测交通污染源方法，采用设置在不同位置的超光谱遥感仪器根据设定的方位角和俯仰角序列，采集没有被遮挡光路的大气散射光谱，反演得到痕量气体和氧二聚体的差分斜程总量后，根据氧二聚体的比例关系获得有效光程信息，由此对探测光谱长度进行水平分段，以得到不同分段的痕量气体的水平浓度分布，这样能获得城市交通道路网区域内的大气污染物的水平浓度分布，由此达到长期稳定地大气污染物的水平分布业务化观测，对监测区域内大气污染物的运动过程、转化过程以及污染物生成溯源研究有着切实可靠的意义。

Description

基于超光谱遥感的痕量气体水平分布探测交通污染源方法 技术领域
本发明属于光学测量技术领域，具体涉及一种基于超光谱遥感的痕量气体水平分布探测交通污染源方法。
背景技术
随着经济的高速发展和城市化进程的快速推进，大气环境呈现出区域性、复合性污染特征，来自交通以及生物质燃烧的人为排放源显著增加了污染气体在近地面的浓度，直接影响空气质量和人类的健康，而由于人类生活中对城市的建设过程布局不同，会使得各个气体在不同水平位置产生较大的差异性，比如在道路交通的主干道区域容易产生较多的二氧化氮，而在化工厂附近则会产生较大的化工气体。同时，即使在强排放源附近，边界层的污染气体的水平分布通常是不均匀的，为了获得强排放源附近的污染气体水平分布，对污染物溯源有着关键的意义。
常规的测污染物的方法主要有激光雷达、化学质谱仪等。公开号为CN106199632A的专利申请公开了一种基于激光雷达的大气空间颗粒物垂直分布监测方法，这种方法主要可以获得较高位置垂直区域的污染物浓度分布情况，但是无法对水平近地面的污染物分布获得相关的信息。
专利文献CN212873559U公开了一种植物源性食品化学污染物检测装置，这种等点式采样仪器则无法一次性观测区域范围的污染物信息，需要通过短时间内的人为移动在采样车可以行进到的范围获得区域的污染物水平分布信息，且不适合大气污染物的水平分布信息监测。
发明内容
鉴于上述问题，本发明的目的是提供一种基于超光谱遥感的痕量气体水平分布探测交通污染源方法，能够通过反演快速准确得到城市交通道路网内的大气污染物的水平浓度分布，从而为交通污染溯源提供支持。
为实现上述发明目的，实施例提供了一种基于超光谱遥感的痕量气体水平分布探测交通污染源方法，包括以下步骤：
步骤1，获取通过超光谱遥感采集的可见-紫外光谱波段的大气散射太阳光谱，并同时采集地表环境参数，包括环境温度和压力数据；
步骤2，基于大气散射太阳光谱反演得到氧二聚体和痕量气体的差分斜程总量；
步骤3，根据氧二聚体的差分斜程总量计算大气散射太阳光谱在氧二聚体的有效光程信息。
步骤4，基于气溶胶光学特性信息和地表环境参数，通过辐射传输方程，将大气散射太阳光谱在氧二聚体的有效光程信息拓展到痕量气体，以得到痕量气体的有效光程信息和光子路径；
步骤5，根据痕量气体的有效光程信息和光子路径，将痕量气体的差分斜程总量转化为可见-紫外光谱波段上不同有效光程的痕量气体的水平浓度信息；
步骤6，对痕量气体的水平浓度信息进行修正，以得到修正后的对痕量气体的水平浓度信息；
步骤7，根据痕量气体的水平浓度信息和有效光程信息，反演获得观测方向上的痕量气体水平分布。
在一个实施例的步骤1中，超光谱遥感针对交通污染源区域采集观测仰角为水平及不超过1°的低仰角下的大气散射太阳光谱。
在一个实施例的步骤2，包括：
首先，对采集的大气散射太阳光谱进行校正处理，以扣除暗电流和电子偏置的影响；
然后，以基于天顶方向的观测光谱作为参考光谱，通过将采集的大气散射太阳光谱与参考光谱做差，并基于特征吸收的最小二乘法，可以实时反演得到不同波段的氧二聚体及痕量气体的差分斜程总量。
在一个实施例的步骤3，包括：
首先，根据氧二聚体和氧气含量的平方成比例的关系，通过氧气浓度推断氧二聚体的近似浓度

其中，P表示大气压力，T表示大气温度，R为气体比常数，NA为阿伏伽德罗常数，Cair表示大气浓度。
然后，根据氧二聚体的近似浓度计算不同波段光谱采集的有效光程信息，相关公式为如下：
其中，Leff代表气体在氧二聚体的有效光程信息，代表O4的差分斜程总量，为对应高度上的O4的近似浓度值。
在一个实施例的步骤4，包括：
首先，将痕量气体先验廓线、气溶胶光学特性信息、温度压力廓线以及几何位置信息作为辐射传输方程的输入，求解可以获得目标波长下的光程Ly和痕量气体的光子路径AMFtrace_gas，痕量气体先验廓线是通过预先 标准波长反演获得，其对应的光程为Lx，通过建立Lx和Ly两者的联系：并经过拟合获得a0，a1，a2为三个拟合系数；
其中，在可见光谱波段拟合得到一组拟合系数，在紫外光谱波段拟合得到另一组拟合系数；
然后，在获得每组拟合系数a0，a1，a2后，基于O4反演的在氧二聚体下的有效光程信息选择与有效光程信息所在波段对应的拟合系数，并利用该组拟合系数通过获得的Ly即为痕量气体在对应波段的有效光程Ltrace_gas。
在一个实施例的步骤5，包括：
基于痕量气体的有效光程信息Ltrace_gas和痕量气体的光子路径AMFtrace_gas，进而获得痕量气体的水平浓度信息Ctrace_gas：
其中，SCDtrace_gas为痕量气体的差分斜程总量，VCDtrace_gas＝SCDtrace_gas/dAMFtrace_gas，表示痕量气体的水平总量；
其中，痕量气体的光子路径AMFtrace_gas由辐射传输方程求解得到。
在一个实施例的步骤6，包括：
通过考虑痕量气体的相对剖面与O4相对剖面不同，基于修正因子fcorr对痕量气体的水平浓度信息进行修正：
其中，ctrace_gas是痕量气体的水平浓度信息，而Ccorr是经过修正后的痕量气体的水平浓度信息，fcorr为修正因子，基于假设痕量气体和气溶胶的不同廓线的辐射传输模拟获得，具体公式为：
其中，cretrieved是痕量气体反演出的平均浓度，cmodel是输入到辐射传输方程里的先验廓线中的底层浓度，dSCDmodel为辐射传输方程反演获得的痕量气体的差分斜程总量。
在一个实施例的步骤7，包括：
首先，将痕量气体的有效光程信息分成了n个观测长度，对应n个观测方向，根据观测长度由长至短分别为L1、L2、L3、···、Ln，对应n个光谱波段反演的痕量气体的水平浓度信息分别为C1、C2、C3、···、Cn，由此基于痕量气体的水平浓度信息，获得痕量气体的分段浓度结果cn，具体为：
然后，在获得所有观测方向上的痕量气体的水平浓度信息后，即能够获得观测方向上的痕量气体水平分布。
与现有技术相比，本发明具有的有益效果至少包括：
采用设置在不同位置的超光谱遥感仪器根据设定的方位角和俯仰角序列，采集没有被遮挡光路的大气散射光谱，反演得到痕量气体和氧二聚体的差分斜程总量后，氧二聚体的比例关系获得有效光程信息，由此对探测光谱长度进行水平分段，以得到不同分段的痕量气体的水平浓度分布，这样能获得城市交通道路网区域内的大气污染物的水平浓度分布，由此达到长期稳定地大气污染物的水平分布业务化观测，对监测城市道路网区域内大气污染物的运动过程、转化过程以及污染物生成溯源研究有着切实可靠的意义。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图做简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动前提下，还可以根据这些附图获得其他附图。
图1是实施例提供的基于超光谱遥感的痕量气体水平分布探测交通污染源方法的流程图；
图2是输入环境参数通过辐射传输方程获得修正因子与光子路径方法的流程图；
图3是实施例提供的NO2观测方案的示意图；
图4是实施例提供的NO2水平分布结果图。
具体实施方式
为使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例对本发明进行进一步的详细说明。应当理解，此处所描述的具体实施方式仅仅用以解释本发明，并不限定本发明的保护范围。
图1是实施例提供的基于超光谱遥感的痕量气体水平分布探测交通污染源方法的流程图。如图1所示，实施例提供的基于超光谱遥感的痕量气体水平分布探测交通污染源方法，通过超光谱遥感采集水平大气散射太阳光谱，基于不同有效光程反演得到城市交通道路网区域内的大气污染物的水平浓度分布。具体包括以下步骤：
步骤1，获取通过超光谱遥感采集的可见-紫外光谱波段的大气散射太阳光谱，并同时采集地表环境参数，包括环境温度和压力数据。
实施例中，在采集大气散太阳射光谱时，超光谱遥感仪器安装在一片无遮挡区域，可以无视较低区域的空间障碍物，通过接收从紫外到可见光 高分辨率波段的散射光，可以在白天观测到数公里外的水平及不超过1°的低仰角下的大气散射太阳光谱。
具体地，选择的超光谱遥感仪器采样光谱分辨率为0.45～0.6nm，而光谱波长范围为300～650nm，包含了紫外-可见波段。同时，超光谱遥感仪器通过安装在周围无遮挡物的高处位置，可以旋转调整观测角度，观测方位角范围可以调整360°全景，精度0.1°，观测俯仰角范围为0°(水平方向)～90°(天顶方向)，精度0.1°。正常情况下，超光谱遥感仪器的有效光程一般主要在5km左右，而近地面浓度一般认为是在100m以下，因此，通过计算可知，当观测仰角不超过1°时，可以观测近地面(100m以下)大气污染物的水平分布情况，因此，本实施例中选择不超过1°的仰角采集大气散射太阳光谱。
实施例中，还配备了大气温度与压力传感器，在采集大气散射太阳光谱的同时还记录外界环境温度与压力数据，作为地表环境参数。
步骤2，基于大气散射太阳光谱反演得到反演氧二聚体和其他痕量气体的差分斜程总量。
实施例中，采集的大气散射太阳光谱参与反演前，还需要进行校正处理，以扣除暗电流和电子偏置的影响，校正后的大气散射太阳光谱参与反演。
在反演时，以基于天顶方向的观测光谱作为参考光谱，通过将采集的大气散射太阳光谱与参考光谱做差，并基于特征吸收的最小二乘法，可以实时反演得到不同波段的氧二聚体(O4)及其他痕量气体的差分斜程总量。其中，氧二聚体分别在光谱采集波段之间具有多个显著吸收峰，由此可以获得这多个紫外-可见光波段的多个氧二聚体以及其他痕量气体的差分斜程总量，同时，还通过对最小二乘法反演过程中的均方根、相对误差进行 筛选以获得更为精确的结果。
步骤3，根据氧二聚体的差分斜程总量计算大气散射太阳光谱在氧二聚体的有效光程信息。
由于大气中的氧二聚体(O4)和氧气含量的平方成比例，通过比例关系，通过氧气浓度推断氧二聚体的近似浓度具体公式计算如下：

其中，P表示大气压力，T表示大气温度，R为气体比常数，一般默认为287.058J/(kg·K)；NA为阿伏伽德罗常数，一般默认为6.02×1023mol-1，Cair表示大气浓度。
在获得氧二聚体的近似浓度后，根据如下公式可以计算不同波段光谱采集的有效光程信息，相关公式为如下：
其中，Leff代表气体在氧二聚体的有效光程信息，代表O4的差分斜程总量，为对应高度上的O4的近似浓度值。
步骤4，基于气溶胶光学特性信息和地表环境参数，通过辐射传输方程，将大气散射太阳光谱在氧二聚体的有效光程信息拓展到其他痕量气体，以得到其他痕量气体的有效光程信息。
实施例中，基于探测区域选择气溶胶的场景类型，同时基于超光谱遥感配备的成像摄像机，判断观测区域的地表环境参数，由此通过辐射传输方程，将有效光程推广到其他波长范围，用于分析其他波段的额外痕量气体。
这里假设气溶胶的消光廓线在对流层一定高度以下存在以下关系：
其中，E(z)是气溶胶消光廓线，z为高度，τ是气溶胶总光学厚度，H为大气边界层的高度，F为边界层中τ的分数，ξ为对流层中自由层气溶胶的标度高度，β是指数因子的归一化常数，可以用下式进行计算：
其中，d代表了消光廓线的顶层，一般可以默认为15km。
通过气溶胶光学特性信息和地表环境参数等参数输入到辐射传输方程中，对多个天顶角和相对方位角进行模拟，基于多项式拟合，可以获得痕量气体的有效光程。
具体地，将痕量气体先验廓线、气溶胶光学特性信息、温度压力廓线以及几何位置信息作为辐射传输方程的输入，求解可以获得目标波长下的光程Ly和痕量气体的光子路径AMFtrace_gas。而输入所需要痕量气体的先验廓线是通过预先标准波长反演获得，其对应的光程为Lx，通过建立Lx和Ly两者的联系：并经过拟合获得a0，a1，a2为三个拟合系数；其中，在可见光谱波段拟合得到一组拟合系数，在紫外光谱波段拟合得到另一组拟合系数；
然后，在获得每组拟合系数a0，a1，a2后，基于O4反演的在氧二聚体下的有效光程信息选择与有效光程信息所在波段对应的拟合系数，并利用该组拟合系数通过获得痕量气体在对应波段的有效光程Ltrace_gas(也就是Ly)。
需要说明的是，当O4反演的在氧二聚体下的有效光程信息是处于可见光谱波段得到的，那么在计算痕量气体的有效光程Ly时，选择可见光谱波段对应的那组拟合系数，并在已知的情况下，利用Ly＝a0+a1·计算得到痕量气体在可见光谱波段的有效光程Ly。当O4反演的在氧二聚体下的有效光程信息是处于紫外光谱波段得到的，那么在计算痕量气体的有效光程Ly时，选择紫外光谱波段对应的另外一组拟合系数，并在已知的情况下，利用计算得到痕量气体在紫外光谱波段的有效光程Ly。
步骤5，根据痕量气体的有效光程信息和光子路径，将痕量气体的差分斜程总量转化为水平总量，再转化为可见-紫外光谱波段上不同有效光程的痕量气体浓度信息。
实施例中，基于痕量气体的有效光程信息Ltrace_gas和痕量气体的光子路径AMFtrace_gas，进而获得痕量气体的水平浓度信息Ctrace_gas：
其中，SCDtrace_gas为痕量气体的差分斜程总量，VCDtrace_gas＝SCDtrace_gas/dAMFtrace_gas，表示痕量气体的水平总量。
步骤6，对痕量气体的水平浓度信息进行修正，以得到修正后的对痕量气体的水平浓度信息。
实施例中，通过考虑痕量气体的相对剖面与O4相对剖面不同，基于修正因子fcorr对痕量气体的水平浓度信息进行修正：
其中，ctrace_gas是痕量气体的水平浓度信息，而Ccorr是经过修正后的 痕量气体的水平浓度信息，fcorr为修正因子，基于假设痕量气体和气溶胶的不同廓线的辐射传输模拟获得，具体公式为：
其中，cretrieved是痕量气体反演出的平均浓度，cmodel是输入到辐射传输方程里的先验廓线中的底层浓度，dSCDmodel为辐射传输方程反演获得的痕量气体的差分斜程总量。
步骤7，根据痕量气体的水平浓度信息和有效光程信息，反演获得观测方向上的痕量气体水平分布。
实施例中，默认短时间内大气环境不会改变的情况下，将短时间内采集的多个波段的有效光程信息结合不同光谱段的痕量气体的水平浓度信息，基于不同的有效光程长度，将水平浓度信息进行分段，由此获得观测方向上的水平浓度分布结果。假设通过反演结果，基于O4不同光谱波段的反演结果，将痕量气体的有效光程信息分成了n个观测长度，根据观测长度由长至短分别为L1、L2、L3、···、Ln。对应n个光谱波段反演的痕量气体的水平浓度信息分别为C1、C2、C3、···、Cn。由此可以基于痕量气体的水平浓度信息，获得痕量气体的分段浓度结果cn，具体为：
由此可以获得水平区域内的痕量气体浓度数公里分辨率的各观测方向上的水平分布情况。
实验例
步骤1中，在采集可见-紫外光谱波段的大气散射太阳光谱时，将超光谱遥感仪器安装在位于经度117.2469°E、纬度31.8632°N的高楼上，该 处是周边数公里范围内的最高处，在水平方向观测均无明显遮挡。设置的超光谱遥感仪器可以通过旋转其外机的俯仰角和方位角采集水平太阳散射光谱，具体地，通过旋转调整观测角度，范围包括方位角为0°～180°，每5°一个间隔，精度0.1°，观测俯仰角范围为0°(水平)～90°(天顶)，精度0.1°，在白天通过控制观测角度可以采集到没有遮挡的数公里外的水平以及低仰角(不超过1°)大气散射太阳光谱，同时配备有大气温度和压力传感器，可以在采集光谱的同时记录外界环境温度与压力数据。图3给出了NO2观测方案示意图，图3仪器设置在图示位置的高楼上，可以对东方向进行观测，这里设定观测方向为60°～120°(正北为0°)，可以观测整个城市内多个环线道路网区域内的情况。方位角每5°一个间隔，之所以这样设置是为了观测合肥市一环线的痕量气体浓度分布情况日变化情况。
步骤2中，基于大气散射太阳光谱反演得到反演氧二聚体和其他痕量气体的差分斜程总量时，选择2022年1月11日中午10:30～14:30采集的光谱进行反演，当天天气为晴天，选择紫外波段和可见波段两处作为O4的反演波峰，其中可见光波段主要参考470～490nm的吸收峰，紫外光波段主要参考355～365nm的吸收峰，基于扣除暗电流和电子偏正的影响，可以获得两个波段下的O4斜柱浓度信息，同时，基于筛选，删除RMS(均方根误差)>0.005以及r-err(相对误差)<0.3的结果。
步骤3中，根据氧二聚体的差分斜程总量计算大气散射太阳光谱在氧二聚体的有效光程信息时，超光谱遥感可以在采集光谱的同时通过温度与压力传感器记录当地当时的大气温度和压力，2022.01.11当地10:30～14：30时间段内的大气温度和压力分别为278.1385K(开尔文)和101250P(帕斯卡)，可以近似计算出近地表O4浓度为3.0351×1037colecules2cm-6。通过比例关系，可以获得在观测的多个方位角和两个波段光谱采集的有效 光程，由此可以计算出痕量气体在不同波段的浓度结果。
步骤4中，基于气溶胶光学特性信息和地表环境参数，通过辐射传输方程，将大气散射太阳光谱在氧二聚体的有效光程信息拓展到其他痕量气体，以得到其他痕量气体波段的有效光程信息的过程中，通过气溶胶情景的辐射传输方程，这里选择对三个天顶角(20°、40°和50°)和观测0°～90°范围内全部方位角进行模拟，基于360nm和470nm波长的O4，对310nm和450nm波长进行多项式拟合，再基于波长，可以对可见光波段进行多项式拟合，由此可以获得反演的痕量气体(NO2)在紫外和可见光波段的有效光程信息和痕量气体的光子路径AMFtrace_gas。
具体地，通过将痕量气体先验廓线、温度压力廓线以及几何位置信息，作为辐射传输方程的输入，求解可以获得目标波长下的光程L310和L450。而输入所需要痕量气体的先验廓线是通过在360nm(O4紫外部分最强吸收峰)和470nm(O4可见光部分最强吸收峰)反演获得，其仪器对应的光程为L360和L470，而NO2的吸收峰主要位于354nm和440nm，其仪器对应的光程为L354和L440，由于之前计算获得的是O4吸收峰波长(360nm和470nm)下的有效光程，要想推广到痕量气体吸收峰所对应的波长(如NO2是354nm和440nm)下的有效光程，需要建立O4吸收峰波长下的有效光程和NO2及其他痕量气体吸收峰波长下的有效光程之间的联系，这里通过大量数据机器学习，可以用拟合函数建立紫外部分O4光程L360和NO2光程L354两者的联系，同样的用拟合函数建立可见光部分L470和L440两者的联系，拟合获得a0，a1，a2为三个拟合系数。
具体如下式：

由此获得痕量气体的有效光程Ltrace_gas。
步骤5-步骤6基于上述的方式正常计算，得到修正后的对痕量气体的水平浓度信息，该修正后的对痕量气体的水平浓度信息参与步骤7的计算。
步骤7中，默认短时间内大气环境不会改变的情况下，将短时间内采集的多个波段的有效光程信息结合不同光谱段的痕量气体浓度结果，基于不同的有效光程长度，将浓度信息进行分段，由此获得观测方向上的水平浓度分布结果。假设通过反演结果，基于O4不同光谱波段的反演结果，将有效光程分成了2个观测长度。对应2个光谱波段反演的痕量气体整体浓度段结果分别为C1和C2。由此可以基于痕量气体浓度段结果，获得痕量气体(NO2)分段浓度结果具体如图4所示。
总之，上述实施例提供的基于超光谱遥感的痕量气体水平分布探测交通污染源方法，通过将超光谱遥感设备在固定在一片无遮挡区域，可以在白天将仰角固定为水平或者低仰角(不超过1°)方向，通过移动方位角采集不同方向上的水平位置大气散射太阳光谱信号，基于氧二聚体反演获得有效光程，并延伸到其他痕量气体波长下，获得数公里分辨率的痕量气体水平分布情况。
以上所述的具体实施方式对本发明的技术方案和有益效果进行了详细说明，应理解的是以上所述仅为本发明的最优选实施例，并不用于限制本发明，凡在本发明的原则范围内所做的任何修改、补充和等同替换等，均应包含在本发明的保护范围之内。

Claims (8)

  1. 一种基于超光谱遥感的痕量气体水平分布探测交通污染源方法，其特征在于，包括以下步骤：
    步骤1，获取通过超光谱遥感采集的可见-紫外光谱波段的大气散射太阳光谱，并同时采集地表环境参数，包括环境温度和压力数据；
    步骤2，基于大气散射太阳光谱反演得到氧二聚体和痕量气体的差分斜程总量；
    步骤3，根据氧二聚体的差分斜程总量计算大气散射太阳光谱在氧二聚体的有效光程信息。
    步骤4，基于气溶胶光学特性信息和地表环境参数，通过辐射传输方程，将大气散射太阳光谱在氧二聚体的有效光程信息拓展到痕量气体，以得到痕量气体的有效光程信息和光子路径；
    步骤5，根据痕量气体的有效光程信息和光子路径，将痕量气体的差分斜程总量转化为可见-紫外光谱波段上不同有效光程的痕量气体的水平浓度信息；
    步骤6，对痕量气体的水平浓度信息进行修正，以得到修正后的对痕量气体的水平浓度信息；
    步骤7，根据痕量气体的水平浓度信息和有效光程信息，反演获得观测方向上的痕量气体水平分布。
  2. 根据权利要求1所述的基于超光谱遥感的痕量气体水平分布探测交通污染源方法，其特征在于，步骤1中，超光谱遥感针对交通污染源区域采集观测仰角为水平及不超过1°的低仰角下的大气散射太阳光谱。
  3. 根据权利要求1所述的基于超光谱遥感的痕量气体水平分布探测 交通污染源方法，其特征在于，步骤2，包括：
    首先，对采集的大气散射太阳光谱进行校正处理，以扣除暗电流和电子偏置的影响；
    然后，以基于天顶方向的观测光谱作为参考光谱，通过将采集的大气散射太阳光谱与参考光谱做差，并基于特征吸收的最小二乘法，可以实时反演得到不同波段的氧二聚体及痕量气体的差分斜程总量。
  4. 根据权利要求1所述的基于超光谱遥感的痕量气体水平分布探测交通污染源方法，其特征在于，步骤3，包括：
    首先，根据氧二聚体和氧气含量的平方成比例的关系，通过氧气浓度推断氧二聚体的近似浓度

    其中，P表示大气压力，T表示大气温度，R为气体比常数，NA为阿伏伽德罗常数，Cair表示大气浓度。
    然后，根据氧二聚体的近似浓度计算不同波段光谱采集的有效光程信息，相关公式为如下：
    其中，Leff代表气体在氧二聚体的有效光程信息，代表O4的差分斜程总量，为对应高度上的O4的近似浓度值。
  5. 根据权利要求1所述的基于超光谱遥感的痕量气体水平分布探测交通污染源方法，其特征在于，步骤4，包括：
    首先，将痕量气体先验廓线、气溶胶光学特性信息、温度压力廓线以 及几何位置信息作为辐射传输方程的输入，求解可以获得目标波长下的光程Ly与痕量气体的光子路径AMFtrace_gas，痕量气体先验廓线是通过预先标准波长反演获得，其对应的光程为Lx，通过建立Lx和Ly两者的联系：并经过拟合获得a0，a1，a2为三个拟合系数；
    其中，在可见光谱波段拟合得到一组拟合系数，在紫外光谱波段拟合得到另一组拟合系数；
    然后，在获得每组拟合系数a0，a1，a2后，基于O4反演的在氧二聚体下的有效光程信息选择与有效光程信息所在波段对应的拟合系数，并利用该组拟合系数通过获得的Ly即痕量气体在对应波段的有效光程Ltrace_gas。
  6. 根据权利要求1所述的基于超光谱遥感的痕量气体水平分布探测交通污染源方法，其特征在于，步骤5，包括：
    基于痕量气体的有效光程信息Ltrace_gas和痕量气体的光子路径AMFtrace_gas，进而获得痕量气体的水平浓度信息Ctrace_gas：
    其中，SCDtrace_gas为痕量气体的差分斜程总量，VCDtrace_gas＝SCDtrace_gas/dAMFtrace_gas，表示痕量气体的水平总量；
    其中，痕量气体的光子路径AMFtrace_gas由辐射传输方程求解得到。
  7. 根据权利要求1所述的基于超光谱遥感的痕量气体水平分布探测交通污染源方法，其特征在于，步骤6，包括：
    通过考虑痕量气体的相对剖面与O4相对剖面不同，基于修正因子fcorr对痕量气体的水平浓度信息进行修正：
    其中，ctrace_gas是痕量气体的水平浓度信息，而Ccorr是经过修正后的痕量气体的水平浓度信息，fcorr为修正因子，基于假设痕量气体和气溶胶的不同廓线的辐射传输模拟获得，具体公式为：
    其中，cretrieved是痕量气体反演出的平均浓度，cmodel是输入到辐射传输方程里的先验廓线中的底层浓度，dSCDmodel为辐射传输方程反演获得的痕量气体的差分斜程总量。
  8. 根据权利要求1所述的基于超光谱遥感的痕量气体水平分布探测交通污染源方法，其特征在于，步骤7，包括：
    首先，将痕量气体的有效光程信息分成了n个观测长度，对应n个观测方向，根据观测长度由长至短分别为L1、L2、L3、···、Ln，对应n个光谱波段反演的痕量气体的水平浓度信息分别为C1、C2、C3、···、Cn，由此基于痕量气体的水平浓度信息，获得痕量气体的分段浓度结果cn，具体为：
    然后，在获得所有观测方向上的痕量气体的水平浓度信息后，即能够获得观测方向上的痕量气体水平分布，从而对城市交通道路网区域内的交通污染状况进行溯源。
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