CN110989038B - Near-surface particle concentration estimation method and system - Google Patents
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Abstract
The application discloses a near-surface particle concentration estimation method and system, which comprises the following steps: obtaining an extinction coefficient, wherein the extinction coefficient is obtained from an atmospheric air quality mode; correcting the extinction coefficient by using CALIPO data; training the corrected extinction coefficient, meteorological parameters and model simulation results by adopting a deep neural network method to obtain particle concentration distribution as a first result, wherein the model simulation results are obtained from the atmospheric air quality mode; and calculating the near-surface particle concentration by adopting a weighting method, wherein the adopted index comprises the first result. By using the method and the system, the estimation accuracy of the near-surface particle concentration can be effectively improved.
Description
Technical Field
The application relates to the field of satellite remote sensing, in particular to a method and a system for estimating the concentration of near-ground particles.
Background
Near-surface particulate matter (e.g., PM2.5) can enter the body through the respiratory tract, capillaries, etc., causing various diseases. With the development of remote sensing technology, and the advantage of making up for the insufficient spatial distribution of the ground observation site, a large number of scholars have adopted methods such as simple regression, semi-physical semi-experience, statistical models and the like to estimate the PM2.5 of the region. Most of the areas PM2.5 are estimated based on Aerosol Optical Depth (AOD) and auxiliary data of the earth surface, the weather and the like, but the inversion error of the AOD in a data source increases the estimation error of the PM2.5, and meanwhile, the problem in the aspect of cloud coverage is solved, a foundation space interpolation result is adopted or atmospheric air quality mode data is directly utilized for filling, the effect of a space interpolation method on a far area is poor, the atmospheric air quality mode data is greatly influenced by an original list and weather parameters, and the precision is further improved. The filled cloud coverage area is filled by using the AOD, and the filled AOD is higher than an original value due to the influence of the aerosol moisture absorption growth effect under the cloud condition.
Disclosure of Invention
The application provides a near-ground particle concentration estimation method and system, and solves the problem that the calculation method in the prior art is insufficient in precision.
The embodiment of the application provides a near-surface particle concentration estimation method, which comprises the following steps: obtaining an extinction coefficient, wherein the extinction coefficient is obtained from an atmospheric air quality mode; correcting the extinction coefficient by using CALIPO data; training the corrected extinction coefficient, meteorological parameters and model simulation results by adopting a deep neural network method to obtain particle concentration distribution as a first result, wherein the model simulation results are obtained from the atmospheric air quality mode; and calculating the near-surface particle concentration by adopting a weighting method, wherein the adopted index comprises the first result. Preferably, the estimation method further comprises: verifying the first result and the ground observation data to obtain the corrected first result; calculating the near-surface particle concentration using an indicator comprising the corrected first result. Further preferably, the meteorological parameters include temperature, humidity, wind direction, wind speed. The near-surface particle concentration is near-surface PM2.5 concentration.
The embodiment of the present application further provides a near-surface particle concentration estimation method, where the estimation method further includes: training apparent reflectivity, the meteorological parameters, the surface parameters and population density by adopting the deep neural network method to obtain particle concentration distribution as a second result, wherein the apparent reflectivity is obtained by a high space-time resolution satellite; and calculating the near-surface particle concentration by adopting a weighting method, wherein the adopted indexes comprise the first result and the second result. Preferably, the apparent reflectivity is data of a blue light wave band of 1km, a red light wave band of 0.5km and a near infrared wave band of 2km, and the time resolution of the high space-time resolution satellite is 10min at most; the meteorological parameters comprise temperature, humidity, wind direction and wind speed; the surface parameters include a normalized vegetation index, a digital elevation model, and a land utilization type. Further preferably, the near-surface particle concentration is a near-surface PM2.5 concentration.
The embodiment of the present application further provides a near-surface particle concentration estimation method, where the estimation method further includes: processing the ground observation data by adopting a kriging spatial interpolation method to obtain particle concentration distribution as a third result; and calculating the near-surface particle concentration by adopting the weighting method, wherein the adopted indexes comprise the first result and the third result. Preferably, the near-surface particle concentration is a near-surface PM2.5 concentration.
The embodiment of the present application further provides a near-surface particle concentration estimation method, where the estimation method further includes: training the apparent reflectivity, the meteorological parameters, the surface parameters and the population density by adopting the deep neural network method to obtain a second result, wherein the apparent reflectivity is obtained by the high-space-time-resolution satellite; and calculating the near-surface particle concentration by adopting the weighting method, wherein the adopted indexes comprise the first result, the second result and the third result. Preferably, the apparent reflectivity is data of a blue light wave band of 1km, a red light wave band of 0.5km and a near infrared wave band of 2km, and the time resolution of the high space-time resolution satellite is 10min at most; the meteorological parameters comprise temperature, humidity, wind direction and wind speed; the surface parameters include a normalized vegetation index, a digital elevation model, and a land utilization type. Further preferably, the near-surface particle concentration is a near-surface PM2.5 concentration.
An embodiment of the present application further provides a near-surface particle concentration estimation system, which is used in any one of the above estimation methods, and the estimation system includes: a first module for obtaining an extinction coefficient, the extinction coefficient being derived from the atmospheric air quality model; a second module, configured to correct the extinction coefficient by using the callpo data; a third module, configured to train the corrected extinction coefficient, the meteorological parameters, and the model simulation result by using the deep neural network method to obtain a particle concentration distribution as a first result, where the model simulation result is obtained from the atmospheric air quality model; and the fourth module is used for calculating the near-surface particle concentration according to the weighting method, and the adopted index comprises the first result.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: the accuracy of estimating the near-surface particulate matter concentration is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram illustrating a method for estimating a particle concentration near the surface according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a method B for estimating a particle concentration near the surface according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a method C for estimating a particle concentration near the surface according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a method D for estimating a particle concentration near the surface according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a near-surface particle concentration estimation system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Example 1
Fig. 1 is a schematic diagram illustrating steps of a method a for estimating a particle concentration near the ground according to an embodiment of the present invention, the method including the following steps:
step 101: and acquiring an extinction coefficient, wherein the extinction coefficient is obtained from an atmospheric air quality mode.
In step 101, the extinction coefficient of the atmosphere is obtained through simulation of a specific atmosphere air quality mode, which may be an NAQ (nested air quality) mode.
Step 102: the extinction coefficient is corrected using the calepo data.
In step 102, the extinction coefficient is corrected by using the extinction coefficient provided by the calmso (Cloud-Aerosol Lidar and interferometric particle Observation), so as to improve the precision of the extinction coefficient.
Step 103: and training the corrected extinction coefficient, meteorological parameters and model simulation results by adopting a deep neural network method to obtain particle concentration distribution as a first result, wherein the model simulation results are obtained from the atmospheric air quality mode.
In step 103, the model simulation result is the particle concentration distribution simulated by the atmospheric air quality model. The meteorological parameters comprise temperature, humidity, wind direction and wind speed.
Wherein, the meteorological parameters can be U wind direction, V wind speed, temperature, visibility and relative humidity.
Step 104: and calculating the near-surface particle concentration by adopting a weighting method, wherein the adopted index comprises the first result.
In step 104, the near-surface particle concentration is calculated using critic (criterion impact high interperitoneation) weighting, using an indicator comprising the first result.
In step 104, the standard deviations δ of the indices are first calculated, respectivelyi(ii) a Secondly, calculating the conflict between the indexes, and based on the correlation between the indexes, if the two indexes have strong positive correlation, indicating that the conflict between the two indexes is low. Therefore, the conflict quantization index between the ith index and other indexes can be expressed as：
rtiIs the correlation coefficient between the two indexes. Therefore, the temperature of the molten metal is controlled,
i is 1, 2, and 3, and indicates the amount of information included in the i-th index, and the larger the value of i, the larger the amount of information included, and the more important it is. The objective weight of the ith index is:
and summing the products of the adopted indexes and the objective weights respectively to obtain the sum of the near-surface particle concentration.
And if the adopted index is only the first result, the first result is the near-surface particle concentration.
Preferably, the method further comprises, before step 104:
step 105: and verifying the first result and the ground observation data to obtain the corrected first result.
In step 105, the ground-based observation data is near-surface particle concentration data acquired by a ground-based observation station, and the first result is corrected by using the ground-based observation data.
For example, the ground-based observation data may be data of PM2.5 concentration acquired by a ground-based observation station.
Example 2
Fig. 2 is a schematic diagram illustrating steps of a near-surface particle concentration estimation method B according to an embodiment of the present invention, the method including the following steps:
step 101: and acquiring an extinction coefficient, wherein the extinction coefficient is obtained from an atmospheric air quality mode.
Step 102: the extinction coefficient is corrected using the calepo data.
Step 103: and training the corrected extinction coefficient, meteorological parameters and model simulation results by adopting a deep neural network method to obtain particle concentration distribution as a first result, wherein the model simulation results are obtained from the atmospheric air quality mode.
Step 106: and training the apparent reflectivity, the meteorological parameters, the surface parameters and the population density by adopting the deep neural network method to obtain particle concentration distribution as a second result, wherein the apparent reflectivity is obtained by a high-space-time resolution satellite.
In step 106, the apparent reflectivity is data of a blue light band of 1km, a red light band of 0.5km and a near infrared band of 2km, and the time resolution of the high spatial-temporal resolution satellite is up to 10 min. The meteorological parameters comprise temperature, humidity, wind direction and wind speed. The surface parameters include a normalized vegetation index, a digital elevation model, and a land utilization type.
Wherein the high space-time resolution satellite can be a geostationary satellite, Himapari-8/AHI. The meteorological parameters may also include temperature, relative humidity, wind direction, wind speed, boundary layer height, and surface pressure.
Step 107: and calculating the near-surface particle concentration by adopting a weighting method, wherein the adopted indexes comprise the first result and the second result.
In step 107, the near-surface particle concentration is calculated using critic (criterion impact high intersection correlation) weighting, using an indicator comprising the first result and the second result.
In step 107, the standard deviations δ of the indices are first calculated, respectivelyi(ii) a Secondly, calculating the conflict between the indexes, and based on the correlation between the indexes, if the two indexes have strong positive correlation, indicating that the conflict between the two indexes is low. Therefore, the conflict quantization index between the ith index and other indexes can be expressed as:
rtiis the correlation coefficient between the two indexes. Therefore, the temperature of the molten metal is controlled,
i is 1, 2, 3, and denotes the ith fingerThe larger the value of the information amount contained in the target is, the larger the information amount contained in the target is, and the more important the target is. The objective weight of the ith index is:
the near-surface particle concentration is the first result multiplied by θ1And said second result multiplied by theta2The sum of the two.
Example 3
Fig. 3 is a schematic diagram illustrating steps of a near-surface particle concentration estimation method C according to an embodiment of the present invention, the method including the following steps:
step 101: and acquiring an extinction coefficient, wherein the extinction coefficient is obtained from an atmospheric air quality mode.
Step 102: the extinction coefficient is corrected using the calepo data.
Step 103: and training the corrected extinction coefficient, meteorological parameters and model simulation results by adopting a deep neural network method to obtain particle concentration distribution as a first result, wherein the model simulation results are obtained from the atmospheric air quality mode.
Step 108: and processing the ground observation data by adopting a Kriging space interpolation method to obtain particle concentration distribution as a third result.
Step 109: and calculating the near-surface particle concentration by adopting the weighting method, wherein the adopted indexes comprise the first result and the third result.
In step 109, the near-surface particle concentration is calculated using critic (criterion impact high intersection correlation) weighting, using an indicator comprising the first result and the third result.
In step 109, the standard deviation δ of the indices is first calculated separatelyi(ii) a Secondly, calculating the conflict between the indexes, and based on the correlation between the indexes, if the two indexes have strong positive correlation, indicating that the conflict between the two indexes is low. Therefore, the conflict quantization index between the ith index and other indexes can be expressed as:
rtiis the correlation coefficient between the two indexes. Therefore, the temperature of the molten metal is controlled,
i is 1, 2, and 3, and indicates the amount of information included in the i-th index, and the larger the value of i, the larger the amount of information included, and the more important it is. The objective weight of the ith index is:
the near-surface particle concentration is the first result multiplied by θ1And said third result multiplied by θ3The sum of the two.
Example 4
Fig. 4 is a schematic diagram illustrating steps of a near-surface particle concentration estimation method D according to an embodiment of the present invention, the method including the following steps:
step 101: and acquiring an extinction coefficient, wherein the extinction coefficient is obtained from an atmospheric air quality mode.
Step 102: the extinction coefficient is corrected using the calepo data.
Step 103: and training the corrected extinction coefficient, meteorological parameters and model simulation results by adopting a deep neural network method to obtain particle concentration distribution as a first result, wherein the model simulation results are obtained from the atmospheric air quality mode.
Step 106: and training the apparent reflectivity, the meteorological parameters, the surface parameters and the population density by adopting the deep neural network method to obtain particle concentration distribution as a second result, wherein the apparent reflectivity is obtained by a high-space-time resolution satellite.
Step 108: and processing the ground observation data by adopting a Kriging space interpolation method to obtain particle concentration distribution as a third result.
Step 1010: and calculating the near-surface particle concentration by adopting the weighting method, wherein the adopted indexes comprise the first result, the second result and the third result.
In step 1010, the near-surface particle concentration is calculated by a critic (criterion impact high intersection correlation) weighting method, and the adopted index includes the first result, the second result and the third result.
In step 1010, the standard deviation δ of the indices is first calculated separatelyi(ii) a Secondly, calculating the conflict between the indexes, and based on the correlation between the indexes, if the two indexes have strong positive correlation, indicating that the conflict between the two indexes is low. Therefore, the conflict quantization index between the ith index and other indexes can be expressed as:
rtiis the correlation coefficient between the two indexes. Therefore, the temperature of the molten metal is controlled,
i is 1, 2, and 3, and indicates the amount of information included in the i-th index, and the larger the value of i, the larger the amount of information included, and the more important it is. The objective weight of the ith index is:
the near-surface particle concentration is the first result multiplied by θ1Multiplying the second result by θ2And said third result multiplied by θ3The sum of the three.
Example 5
Fig. 5 is a schematic diagram of a near-surface particle concentration estimation system according to an embodiment of the present invention, which includes a first module 201, a second module 202, a third module 203, and a fourth module 204.
The first module 201: for obtaining an extinction coefficient, the extinction coefficient being derived from the atmospheric air quality model.
The extinction coefficient of the atmosphere is obtained through simulation of a specific atmosphere air quality mode, which may be an NAQ (nested air quality) mode.
The second module 202: for correcting the extinction coefficient using the CALIPO data.
The extinction coefficient is corrected by using the extinction coefficient provided by CALIPO (Cloud-Aerosol Lidar and interferometric Path finder Satellite Observation), so that the precision of the extinction coefficient is improved.
The third module 203: and the model simulation system is used for training the corrected extinction coefficient, the meteorological parameters and the model simulation result by adopting the deep neural network method to obtain particle concentration distribution as a first result, and the model simulation result is obtained by the atmospheric air quality mode.
And the model simulation result is the particle concentration distribution simulated by the atmospheric air quality mode. The meteorological parameters comprise temperature, humidity, wind direction and wind speed.
Wherein, the meteorological parameters can be U wind direction, V wind speed, temperature, visibility and relative humidity.
The fourth module 204: for calculating the near-surface particle concentration according to the weighting method, the adopted index comprises the first result.
Calculating the near-surface particle concentration by using a CRITIC (criterion impact high Intercriterion correlation) weighting method, wherein the adopted index comprises the first result.
First, the standard deviation delta of the indexes is calculated respectivelyi(ii) a Secondly, calculating the conflict between the indexes, and based on the correlation between the indexes, if the two indexes have strong positive correlation, indicating that the conflict between the two indexes is low. Therefore, the conflict quantization index between the ith index and other indexes can be expressed as:
rtiis the correlation coefficient between the two indexes. Therefore, the temperature of the molten metal is controlled,
i is 1, 2, 3, which indicates the amount of information contained in the i-th index, and the larger the value thereof,the greater the amount of information contained, the greater the importance. The objective weight of the ith index is:
and summing the products of the adopted indexes and the objective weights respectively to obtain the sum of the near-surface particle concentration.
And if the adopted index is only the first result, the first result is the near-surface particle concentration.
The system may be used in any of the above embodiments.
It is noted that the near-surface particulate concentration described in all of the above embodiments may be a PM2.5 concentration.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (4)
1. A near-surface particle concentration estimation method, comprising the steps of:
obtaining an extinction coefficient, wherein the extinction coefficient is obtained from an atmospheric air quality mode;
correcting the extinction coefficient by using CALIPO data;
training the corrected extinction coefficient, meteorological parameters and model simulation results by adopting a deep neural network method to obtain particle concentration distribution as a first result, obtaining the model simulation results by the atmospheric air quality mode, verifying the first result and ground observation data to obtain the corrected first result, wherein the meteorological parameters comprise temperature, humidity, wind direction and wind speed;
training apparent reflectivity, the meteorological parameters, surface parameters and population density by adopting the deep neural network method to obtain particle concentration distribution as a second result, wherein the apparent reflectivity is obtained by a high-time-space resolution satellite, the apparent reflectivity is data of a blue light wave band of 1km, a red light wave band of 0.5km and a near infrared wave band of 2km, and the surface parameters comprise a normalized vegetation index, a digital elevation model and a land utilization type;
processing the ground observation data by adopting a kriging spatial interpolation method to obtain particle concentration distribution as a third result;
and calculating the near-surface particle concentration by using a CRITIC weighting method, wherein the adopted indexes comprise the corrected first result, the second result and the third result.
2. The estimation method according to claim 1, characterized in that the time resolution of the high space-time resolution satellite is up to 10 min.
3. The estimation method according to claim 1 or 2, characterized in that the near-surface particle concentration is a near-surface PM2.5 concentration.
4. A near-surface particle concentration estimation system for use in the method of any of claims 1-3, comprising:
a first module for obtaining an extinction coefficient, the extinction coefficient being derived from the atmospheric air quality model;
a second module, configured to correct the extinction coefficient by using the callpo data;
a third module, configured to train the corrected extinction coefficient, the meteorological parameters, and the model simulation result by using the deep neural network method to obtain a particle concentration distribution as a first result, where the model simulation result is obtained from the atmospheric air quality model;
and the fourth module is used for calculating the near-surface particle concentration according to the weighting method, and the adopted index comprises the first result.
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| CN113156395B (en) * | 2021-04-07 | 2022-08-12 | 中国科学院大气物理研究所 | Aerosol laser radar data fusion method and system |
| CN113064220A (en) * | 2021-06-03 | 2021-07-02 | 四川九通智路科技有限公司 | Visibility measuring system and measuring method based on nonlinear autoregressive neural network |
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