Estimation of PM10 concentration changes in Shanxi province using MODIS satellite data

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

生态环境科学

Li, Hui^1 ; Liu, Bing^1 ; Liu, Huiyuan^2 ; Wang, Yacheng^3 ; Jia, Wei^1

College of Mapping Science and Engineering, Shandong University of Science and Technology, Qingdao

266510, China^1

College of Information Science and Engineering, Shandong University of Agricultural, Tai'an

271018, China^2

Aerospace Star Technology Co. Ltd., Beijing

100086, China^3

关键词: Aerosol optical thickness;    Correlation coefficient;    Deep blue algorithms;    Distribution characteristics;    Environmental Monitoring;    Environmental pollution control;    Pm10 mass concentrations;    Regression analysis models;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/199/3/032078/pdf
DOI  :  10.1088/1755-1315/199/3/032078

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

In view of the difficulty in monitoring the concentration and distribution of large-scale regional indicators, firstly, this paper uses the deep blue algorithm to invert the aerosol optical thickness of Shanxi province in 2016. Secondly, this paper uses the results of the inversion to establish the correlation regression analysis with the PM10 mass concentration data obtained from the monitoring station obtained by the China National Environmental Monitoring Center. Then, by analyzing this model, this paper inverts the concentration and distribution characteristics of PM10 in a wide range of regions, and finally obtains the distribution characteristics of PM10 concentration in the survey area. The results show that the correlation coefficient of the regression analysis model established in this paper reaches 0.804, and the data shows a good consistency in the trend of change. This model is used to inverse large area PM10 concentration. It helps to quickly and efficiently obtain the distribution characteristics of PM10 mass concentration in this area. This will provide effective help for related research such as environmental pollution control.