space-time continuous PM2.5 inversion method

An inversion, space-time technology, applied in the direction of instruments, electrical digital data processing, measuring devices, etc., can solve the problems of increasing the satellite AOD missing rate and low AOD inversion accuracy, so as to overcome the influence, improve the anti-noise ability and ensure stability sexual effect

Pending Publication Date: 2019-04-19
天津珞雍空间信息研究院有限公司
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Problems solved by technology

In addition, the inversion of AOD is related to the albedo of the surface. When the albedo of the surface is high, the surface is highlighted. The inversion accuracy of AOD is low, and the missing rate of satellite AOD will be increased when the quality control and screening of data are carried out.

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Embodiment 1

[0030] Embodiment one: if figure 1 As shown, a kind of space-time continuous PM2.5 inversion method that the application provides comprises the following steps:

[0031] s100. Establish a random forest regression model, input the meteorological dynamic indicators and satellite AOD in the inversion area as explanatory variables into the random forest network, then train the random forest regression model to obtain the optimal model, and invert the PM2.5 under the optimal model Concentration Satellite Estimates The dynamic indicators include the surface temperature, surface pressure, wind speed, relative humidity and boundary layer height of the monitoring area; AOD is the aerosol optical thickness, which is the integral of the aerosol extinction coefficient in the vertical direction, and quantitatively describes the reduction of the aerosol to light. The physical quantity of action can be used to characterize the degree of turbidity of the atmosphere.

[0032] The random fores...

Embodiment 2

[0042] Embodiment two, on the basis of the above-mentioned embodiment one, also includes the following steps before step s500:

[0043] s600. Create satellite estimates of PM2.5 concentration and spatial interpolation The fitting function of , which compensates for missing data from satellite observations.

[0044] Satellite observations have missing data, while spatial interpolation Due to the sparse and uneven distribution of stations, the interpolation accuracy also varies in space. Combining the results of the two can not only reduce the error to a certain extent, but also increase the spatial coverage of the inversion results. The specific execution process is divided into two steps. Firstly, the fitting function between the two is established, and there is a spatial interpolation function for no satellite observation. pixels to reconstruct the satellite retrieval results and fill in the missing data.

Embodiment 3

[0046] On the basis of the first embodiment, the monitoring area is divided into several sub-areas, and then PM2.5 inversion is performed on each sub-area using the method described in the first embodiment.

[0047] The specific segmentation method includes the following steps:

[0048] s710. Take the geographical static index and the measured concentration of PM2.5 as samples, and calculate the correlation coefficient between each static index and the measured concentration of PM2.5;

[0049] The static index refers to a parameter that is related to the PM2.5 concentration and does not change in a short period of time. In this embodiment, the static index is adopted as the artificial emission density of particulate matter AE, population density Pop and elevation DEM; for example, with the same At the time point, the measured concentration of PM2.5 at the monitoring station and the average emission density AE, population density Pop, and elevation DEM near the station are used...

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Abstract

The invention discloses a space-time continuous PM2.5 inversion method. The method comprises the steps of establishing a random forest regression model , wherein meteorological dynamic indexes of an inversion area and a satellite AOD serve as interpretation variables to be input into a random forest network, then training the random forest regression model to obtain an optimal model, and conducting inversion under the optimal model to obtain a PM2.5 concentration satellite estimation value; Determining a root-mean-square error of the PM2.5 concentration satellite estimation value; Calculatinga spatial interpolation observed by each station by using a common Kriging interpolation algorithm; Determining a root-mean-square error of the spatial interpolation; And using an inverse variance weighting method to fuse the PM2.5 concentration satellite estimation value and the spatial interpolation to obtain a final PM2.5 concentration inversion value. According to the method, the influence ofmultiple collinearity on an inversion result is overcome, the good noise resistance is achieved, seamless estimation of the near-surface PM2.5 concentration is achieved, and data support is provided for real-time continuous air quality monitoring of an area.

Description

technical field [0001] The present disclosure generally relates to the technical field of environmental monitoring, in particular to the monitoring technology of particulate matter in the air, and in particular to a space-time continuous PM2.5 inversion method. Background technique [0002] In the past few decades, due to rapid urban expansion and industrialization, a large amount of particulate matter (PM, that is, particles with a diameter between 1 nanometer and 100 microns) has been emitted into the air, resulting in frequent occurrence of haze events, especially in economically developed countries. and densely populated metropolitan areas. [0003] Atmospheric particulate matter is the most important component of aerosols and affects weather and climate systems through direct or indirect effects. Specifically, on the one hand, aerosols can directly absorb and scatter solar radiation and disturb the energy budget of the earth-atmosphere system; secondly, they can act as...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G01N15/06
CPCG01N15/06G06F30/20
Inventor 史硕李治平马昕宋沙磊徐宝莫云龙
Owner 天津珞雍空间信息研究院有限公司
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