Estimations of PM2.5 concentrations based on the geographically weighted regression from Himawari-8 AOD

2018 1st International Conference on Environment Prevention and Pollution Control Technology

生态环境科学

Zhu, Wende^1,2 ; Zhang, Qiushuang^2 ; Cai, Kun^2 ; Wang, Lei^2 ; Li, Shenshen^1

State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing

100020, China^1

Computer and Information Engineering College, Henan University, Kaifeng

475004, China^2

关键词: Aerosol optical depths;    Distribution characteristics;    Geographically weighted regression;    Geographically weighted regression models;    High temporal resolution;    Meteorological data;    Planetary boundary layers;    PM2.5 concentration;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/199/2/022009/pdf
DOI  :  10.1088/1755-1315/199/2/022009

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Polar-orbit satellites have the limitation of observed frequency, so the method of PM2.5 concentration estimation based on MODIS AOD is difficult to capture the variation in trend and distribution characteristics of PM2.5 in a day. Himawari-8 geostationary satellite with the advantage of high temporal resolution can obtain the hourly aerosol optical depth(AOD).This paper selected AOD from 2km Himawari-8 Standard Data to estimate the PM2.5 concentration based on Geographically Weighted Regression(GWR)model, and used the ground-level PM2.5 concentrations as training samples of the model. In addition to AOD, the model also took meteorological data such as height of the planetary boundary layer (HPBL) and relative humidity (RH) as parameters to participate in model training to improve the reliability of the results. At the end of the paper, it utilized the model to estimate PM2.5 concentration over eastern China in July 2018 and executed the validation with ground-level PM2.5 concentrations. The result indicated there is a basic consistency in the change trend between them (R=0.74, RMSE=10.07μg/m 3).