CN111220517A - PM based on satellite remote sensing2.5Inversion method for components with different particle sizes - Google Patents
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- 239000000443 aerosol Substances 0.000 claims description 23
- 239000000203 mixture Substances 0.000 claims description 18
- 230000003287 optical effect Effects 0.000 claims description 5
- 239000013618 particulate matter Substances 0.000 claims description 5
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
- G01N15/0205—Investigating particle size or size distribution by optical means, e.g. by light scattering, diffraction, holography or imaging
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Abstract
The invention provides a PM based on satellite remote sensing2.5An inversion method of components with different particle sizes. The invention utilizes the physical property, concentration and other information of MISR AOD data and PM2.5Surface concentration data, extraction of different PM by MISR AOD2.5The proportion of the particle size components, and constructing a PM concentration inversion model with specific particle size to finish different PMs2.5The concentration of the particle size components is predicted, the data of the PM product with the specific particle size generated according to the method has good model precision, and the PM of a certain region can be truly reflected2.5Particle size distribution.
Description
Technical Field
The invention belongs to the technical field of air pollution prevention and control, and particularly relates to PM based on satellite remote sensing2.5An inversion method of components with different particle sizes.
Background
For forty years, along with the rapid increase of the economy of China, the pollution of dust haze in China gradually develops into a prominent environmental problem in China. In recent years, PM has been increasing with the increase of environmental consciousness of our country2.5Pollution has become one of the environmental issues of government and public concern, andPM2.5effective acquisition of concentration information is to develop effective PM2.5The basis of environmental management.
PM2.5The pollution prevention and control of the method needs a fine management mode, and researches show that the suspension residence time of particles with smaller particle sizes in the air is longer, the particles are easier to be absorbed by human respiratory tracts, and the method possibly has stronger harmfulness. However, the current environmental air quality standard (GB3095-2012) in China is about PM2.5Only the standard value of the total mass concentration is considered, and the pollution influence caused by the particles with different particle size components is not considered. Due to PM2.5The health effects of particles with different particle sizes are different, and the existing management mode can cause the deviation of regional atmospheric pollution evaluation to a certain extent, so that effective information support cannot be provided for human health evaluation. On the other hand, the existing atmospheric environment monitoring network in China only has PM2.5Monitoring of total mass concentration, without monitoring of concentrations of components with different particle sizes, PM cannot be supported2.5Fine prevention and control.
Estimation of ground PM by Aerosol Optical Depth (AOD) using satellite remote sensing inversion2.5Is a new technology which is rapidly developed in recent years. However, at present, PM at home and abroad2.5PM (particulate matter) with multiple concerns in satellite remote sensing inversion research2.5For PM2.5The study of different particle size distributions is lacking.
Disclosure of Invention
In order to solve the technical problem, the invention provides a PM based on satellite remote sensing2.5An inversion method of components with different particle sizes. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
The invention adopts the following technical scheme:
in some optional embodiments, a satellite remote sensing based P is providedM2.5The inversion method of different particle size components comprises the following steps:
extracting the original MISR AOD mixture concentration data by using IDL and removing abnormal values;
obtaining the values of the AOD of eight specific components at characteristic time points and space points according to the extracted concentration data of the MISR AOD mixture and the known classification of the MISR AOD mixture which is successfully tested;
calculating the particle diameter relative mass distribution of the MISR eight AOD components;
drawing an AOD component particle size distribution diagram by utilizing the particle diameter relative mass distribution information of the MISR eight AOD components, and comparing the area occupied by the components with the particle size of less than x microns in each component with the area occupied by the components with the particle size of less than 2.5 microns;
calculating the component PM with particle size less than x microns at specific time points and space pointsxWith component PM having particle size of less than 2.5 μm2.5The comparison result of (1);
based on ground PM2.5Concentration data of component PM with particle size less than x micrometer at specific time point and space pointxWith component PM having particle size of less than 2.5 μm2.5According to the specific time point, space point and ground PM2.5The concentration data are matched and multiplied to obtain PM of specific time points and space pointsxConcentration data.
In some optional embodiments, the method previously comprises: obtaining MISR aerosol product data; wherein the MISR aerosol product data comprises: concentration data of the aerosol and physical and photochemical properties data of the aerosol.
In some alternative embodiments, the process of calculating the particle diameter relative mass distribution of each AOD component comprises: and (3) calculating the particle diameter relative mass distribution of each AOD component according to parameter information provided by the obtained MISR aerosol product data by using the aerosol physical and optical property data of eight AODs in the MISR aerosol data and assuming that each AOD component conforms to the log-normal distribution and has constant particle density.
In some alternative embodiments, the values of the eight specific component AODs for the specific time point and spatial point are obtained by the following formula:
wherein, AODi,kIs the AOD value of AOD component i at MISR observation point k, AODjIs the total AOD value of AOD mixture j, Fi,jIndicating the degree of contribution of AOD component i to AOD mixture j, α ═ 1 indicating that mixture j passed the significance test, otherwise indicating failure.
In some optional embodiments, the area occupied by the component with the particle size less than x microns is compared with the area occupied by the component with the particle size less than 2.5 microns in each component to obtain the comparison result
This result reflects the ratio of the concentrations of particulate matter of the respective particle sizes in AOD component i.
In some alternative embodiments, the component PM having a particle size less than x microns at a particular point in time and space is calculated from the following equationxWith component PM having particle size of less than 2.5 μm2.5The comparison result of (1):
wherein, AODiIs the AOD value of AOD component i;
the comparison results were obtained for the area of the components with a particle size of less than x microns compared to the area of the components with a particle size of less than 2.5 microns.
The invention has the following beneficial effects: the invention utilizes the physical property, concentration and other information of MISR AOD data and PM2.5Prediction of concentration by MISR AOD with different PMs2.5The proportion of the particle size components, and constructing a PM concentration inversion model with specific particle size to finish different PMs2.5Prediction of the concentration of particle size Components particular particles produced according to the inventionThe PM product data has good model precision and can truly reflect the PM of a certain area2.5Particle size distribution.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
Detailed Description
The following description sufficiently presents specific embodiments of the invention to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others.
As shown in FIG. 1, in some illustrative embodiments, a PM based on satellite remote sensing is provided2.5Different particle size component inversion method, physical property and concentration information of MISR AOD data and PM2.5Prediction of concentration by MISR AOD and PM2.5Constructing an inversion model by using the scale factors of different particle size components, which specifically comprises the following steps:
s1: MISR aerosol product data were acquired.
Wherein the MISR aerosol product data comprises: concentration data of the aerosol and physical and photochemical properties data of the aerosol.
The MISR aerosol product data can be downloaded by the atmospheric science data center of the NASA Lanli research center.
S2: the raw MISR AOD mixture concentration data was extracted using IDL and outliers were rejected.
S3: from the extracted MISR AOD mixture concentration data and the known successful inspection MISR AOD mixture classes, values for eight specific component AODs at specific time and space points were obtained:
wherein, AODi,kIs the AOD value of AOD component i at MISR observation point k, AODjIs the total AOD value of AOD mixture j, Fi,jIndicating the degree of contribution of AOD component i to AOD mixture j, α ═ 1 indicating that mixture j passed the significance test, otherwise indicating failure.
S4: the particle diameter relative mass distributions of the eight MISR AOD components were calculated based on the physical and optical properties of the MISRAOD components.
Specifically, using the aerosol physical and optical property data of eight AODs in the MISR aerosol data, assuming that each AOD component conforms to a log-normal distribution and has a constant particle density, the particle diameter relative mass distribution of each AOD component is calculated based on the parametric information provided by the obtained MISR aerosol product data.
S5: utilizing the particle diameter relative mass distribution information of the MISR eight AOD components to draw a particle size distribution diagram of the AOD components, comparing the area occupied by the components with the particle size of less than x microns in each component with the area occupied by the components with the particle size of less than 2.5 microns, and recording the comparison result obtained by calculation as
This result reflects the ratio of the concentrations of particulate matter of the respective particle sizes in AOD component i.
S6: calculating the PM of the component having a particle diameter of less than x μm at a specific time point and space point based on the data calculated in step S5xWith component PM having particle size of less than 2.5 μm2.5The comparison result of (1):
wherein, AODiIs the AOD value of AOD component i;
the results are obtained by comparing the area occupied by the component with the particle size smaller than x with the area occupied by the component with the particle size smaller than 2.5 in each component.
S7: based on ground PM2.5Concentration data ofThe component PM with the particle size less than x microns at the specific time point and space point calculated in the step S6xWith component PM having particle size of less than 2.5 μm2.5According to the specific time point, space point and ground PM2.5The concentration data are matched and multiplied to obtain PM of specific time points and space pointsxConcentration data.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
Claims (6)
1. PM based on satellite remote sensing2.5The inversion method of different particle size components comprises the following steps:
extracting the original MISR AOD mixture concentration data by using IDL and removing abnormal values;
obtaining the values of the AOD of eight specific components at characteristic time points and space points according to the extracted concentration data of the MISR AOD mixture and the known classification of the MISR AOD mixture which is successfully tested;
calculating the particle diameter relative mass distribution of the MISR eight AOD components;
drawing an AOD component particle size distribution diagram by utilizing the particle diameter relative mass distribution information of the MISR eight AOD components, and comparing the area occupied by the components with the particle size of less than x microns in each component with the area occupied by the components with the particle size of less than 2.5 microns;
calculating the component PM with particle size less than x microns at specific time points and space pointsxWith component PM having particle size of less than 2.5 μm2.5The comparison result of (1);
based on ground PM2.5Concentration data of component PM with particle size less than x micrometer at specific time point and space pointxWith component PM having particle size of less than 2.5 μm2.5According to the specific time point, space point and ground PM2.5The concentration data are matched and multiplied to obtain PM of specific time points and space pointsxConcentration data.
2. The PM based on satellite remote sensing according to claim 12.5The inversion method of components with different particle sizes is characterized in that data are required to be acquired before the method, and the acquired data comprise: MISR aerosol product data; wherein the MISR aerosol product data comprises: concentration data of the aerosol and physical and photochemical properties data of the aerosol.
3. PM based on satellite remote sensing according to claim 22.5The inversion method of components with different particle diameters is characterized in that the process of calculating the particle diameter relative mass distribution of each AOD component comprises the following steps: and (3) calculating the particle diameter relative mass distribution of each AOD component according to parameter information provided by the obtained MISR aerosol product data by using the aerosol physical and optical property data of eight AODs in the MISR aerosol data and assuming that each AOD component conforms to the log-normal distribution and has constant particle density.
4. PM based on satellite remote sensing according to claim 32.5The inversion method of components with different particle sizes is characterized in that the values of the AOD of the eight specific components at the specific time points and the specific space points are obtained by the following formula:
wherein, AODi,kIs the AOD value of AOD component i at MISR observation point k, AODjIs the total AOD value of AOD mixture j, Fi,jDenotes the degree of contribution of AOD component i to AOD mixture j, α ═1 indicates that mixture j passed the significance test, otherwise it indicates a failure.
5. PM based on satellite remote sensing according to claim 42.5The inversion method of components with different particle sizes is characterized in that the comparison result obtained by comparing the area occupied by the components with the particle sizes of less than x microns in each component with the area occupied by the components with the particle sizes of less than 2.5 microns is
This result reflects the ratio of the concentrations of particulate matter of the respective particle sizes in AOD component i.
6. PM based on satellite remote sensing according to claim 52.5The method for inverting components with different particle diameters is characterized in that the component PM with the particle diameter less than x microns at a specific time point and a specific space point is calculated by the following formulaxWith component PM having particle size of less than 2.5 μm2.5The comparison result of (1):
wherein, AODiIs the AOD value of AOD component i;
the comparison results were obtained comparing the area of the components with a particle size of less than x microns with the area of the components with a particle size of less than 2.5 microns.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107202750A (en) * | 2017-05-17 | 2017-09-26 | 河北中科遥感信息技术有限公司 | A kind of satellite-ground integrated monitoring quantitative remote sensing method for amalgamation processing of Atmospheric particulates |
| CN108426815A (en) * | 2018-04-20 | 2018-08-21 | 中国科学院遥感与数字地球研究所 | A kind of fine particle concentration of component evaluation method near the ground |
| CN109030301A (en) * | 2018-06-05 | 2018-12-18 | 中南林业科技大学 | Aerosol optical depth inversion method based on remotely-sensed data |
| WO2019065069A1 (en) * | 2017-09-27 | 2019-04-04 | 京セラ株式会社 | Measurement device component |
| CN110160924A (en) * | 2019-06-27 | 2019-08-23 | 中国科学院遥感与数字地球研究所 | A kind of particle concentration detection method |
| CN110411918A (en) * | 2019-08-02 | 2019-11-05 | 中国科学院遥感与数字地球研究所 | A kind of PM2.5 concentration remote-sensing evaluation method based on satellite polarization technology |
-
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- 2019-12-06 CN CN201911240283.6A patent/CN111220517B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107202750A (en) * | 2017-05-17 | 2017-09-26 | 河北中科遥感信息技术有限公司 | A kind of satellite-ground integrated monitoring quantitative remote sensing method for amalgamation processing of Atmospheric particulates |
| WO2019065069A1 (en) * | 2017-09-27 | 2019-04-04 | 京セラ株式会社 | Measurement device component |
| CN108426815A (en) * | 2018-04-20 | 2018-08-21 | 中国科学院遥感与数字地球研究所 | A kind of fine particle concentration of component evaluation method near the ground |
| CN109030301A (en) * | 2018-06-05 | 2018-12-18 | 中南林业科技大学 | Aerosol optical depth inversion method based on remotely-sensed data |
| CN110160924A (en) * | 2019-06-27 | 2019-08-23 | 中国科学院遥感与数字地球研究所 | A kind of particle concentration detection method |
| CN110411918A (en) * | 2019-08-02 | 2019-11-05 | 中国科学院遥感与数字地球研究所 | A kind of PM2.5 concentration remote-sensing evaluation method based on satellite polarization technology |
Non-Patent Citations (2)
| Title |
|---|
| YANG LIU: "Estimating fine particulate matter component concentrations and size distributions using statellite retrieved fractional aerosol optical depth:part1—method development", 《JOURNAL OF THE AIR AND WASTE MANAGEMENT ASSOCIATION》 * |
| 马宗伟: "基于卫星遥感的我国PM2.5时空分布研究", 《中国博士学位沦为全文数据库 工程科技Ⅰ辑》 * |
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