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AOD-based PM2.5 inversion model for Hangzhou region

An inversion model and region technology, applied in the field of remote sensing, can solve the problems that the concentration of pollutants is easily affected by meteorological factors such as temperature, humidity, and air pressure, and the limitations of air quality, so as to achieve reliable accuracy of calculation results and convenient calculation process , High industry promotion value and application prospect effect

Inactive Publication Date: 2015-11-04
HANGZHOU NORMAL UNIVERSITY
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Problems solved by technology

[0004] At present, although there are many indicators that can reflect the level of air pollution, and most of them are measured through ground monitoring points with high accuracy, the concentration of pollutants is easily affected by meteorological factors such as temperature, humidity, and air pressure. Therefore, there are certain limitations in the comprehensive evaluation, comparison and true reflection of the air quality in various regions

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  • AOD-based PM2.5 inversion model for Hangzhou region
  • AOD-based PM2.5 inversion model for Hangzhou region
  • AOD-based PM2.5 inversion model for Hangzhou region

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Embodiment Construction

[0055] The present invention will be further described below in conjunction with accompanying drawing.

[0056] Such as figure 1 As shown, an AOD-based PM in Hangzhou area 2.5 inversion model.

[0057] The technical solution adopted by the present invention to solve its technical problems specifically includes the following steps:

[0058] Step (1): For AOD and PM 2.5 The measured data are screened;

[0059] Step (2): normalized preprocessing is carried out to the measured data for model calculation;

[0060] Step (3): set up the BP neural network model based on AOD measured data;

[0061] Step (4): Verify PM with AOD test data 2.5 inversion model.

[0062] As described in step (1) for AOD and PM 2.5 The actual measurement data are screened, as follows: Since AOD describes the absorption and scattering characteristics of atmospheric particles on sunlight and reflects the optical properties of atmospheric aerosols, the data can only be measured from after sunrise to bef...

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Abstract

The invention discloses an AOD (Aerosol Optical Depth)-based PM2.5 inversion model for a Hangzhou region. A related method includes steps of (1) screening test data of AOD and PM2.5; (2) performing normalization pretreatment on test data used for model calculation; (3) constructing a BP (Back Propagation) neural network model based on AOD test data; (4) verifying the PM2.5 inversion model through AOD test data and performing reverse normalization on the simulation result, so that simulation PM2.5 value is obtained. According to the invention, inversion of PM2.5 of Hangzhou region can be realized based on AOD test data and a self-learning process of sample space can be simulated with high precision through the BP neural network. The inversion model provided by the invention is an effective model capable of improving operation precision.

Description

technical field [0001] The invention belongs to the technical field of remote sensing, and in particular relates to atmospheric fine particulate matter (PM) based on the aerosol optical depth (AOD) of the Hangzhou area. 2.5 ) inverse modeling using AOD and PM 2.5 Positive correlation between the establishment of an AOD-based PM in Hangzhou area 2.5 inversion model. Background technique [0002] Atmospheric particulate matter (particulate matter, PM) refers to solid or liquid particulate matter dispersed in the air environment, among which, those with a dynamic diameter of less than or equal to 2.5 microns are fine particulate matter (fine particulate matter, PM 2.5 ). PM 2.5 Because it is easy to carry toxic and harmful substances, and can directly enter the lungs of the human body with breathing, it will cause certain harm to human health, and because of its long transportation distance and long residence time in the air, it can pollute the atmosphere. [0003] Aerosol...

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Application Information

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IPC IPC(8): G06N3/08
Inventor 于之锋周斌郑雪徐曜窦文洁潘玉良齐冰杜荣光
Owner HANGZHOU NORMAL UNIVERSITY
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