Near-surface atmospheric fine particulate matter concentration estimation method based on space-time weighted regression model

Abstract

The invention relates to a near-surface atmospheric fine particulate matter concentration estimation method based on a space-time weighted regression model. The method comprises the following steps: S1, acquiring PM2.5 monthly concentration data of a ground monitoring station; S2, obtaining MODIS / Terra 1km AOT data, and eliminating dimensions of the AOT data; S3, obtaining meteorological data andNDVI data, S4, matching the PM2.5 monthly concentration data, the AOT data after dimension elimination of the corresponding month, the meteorological monitoring data of the corresponding month and theNDVI data of the corresponding month, and constructing a monthly AOT-PM2.5 model. The spatial-temporal change characteristics of the atmospheric fine particulate matter concentration can be accurately indicated, the defects of few ground monitoring stations and non-uniform distribution are overcome, and the data provides a scientific basis for atmospheric fine particulate matter exposure health evaluation and influence evaluation of atmospheric fine particulate matters on a land ecosystem.

Description

technical field [0001] The invention relates to the technical field of remote sensing data application, in particular to a method for estimating the concentration of fine particles in the atmosphere near the surface based on a time-space weighted regression model. Background technique [0002] Atmospheric particulate matter (particulate matter, PM) is one of the main urban air pollution. Fine particles refer to particles with an aerodynamic equivalent diameter ≤ 2.5 μm, namely PM 2.5 . PM 2.5 Can penetrate deep into the bronchioles and alveoli. With the development of China's economy in recent years, PM 2.5 Emissions are also increasing, gradually causing people to pay attention to air quality. Although air monitoring stations have been established in 367 cities across the country, the distribution of stations is uneven, and the number of stations is relatively dense in economically developed areas and relatively rare in economically backward areas, which cannot accurat...

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Problems solved by technology

Although air monitoring stations have been established in 367 cities across the country, the distribution of stations is uneven, and the number of stations is relatively dense in economically developed areas and relatively rare in economically backward areas, which cannot accurately reflect PM 2.5 concentration distribution

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