Research on PM2.5 time series characteristics based on data mining technology

International Conference on Energy Engineering and Environmental Protection 2017

能源学;生态环境科学

Zhao, Lifang^1 ; Jia, Jin^1

Department of Environmental Science, Zhejiang University, Hangzhou

310058, China^1

关键词: Data mining technology;    Government regulation;    Pattern association;    PM2.5 concentration;    Quality information;    Real time monitoring;    Sequential-pattern mining;    Time series characteristic;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/121/3/032007/pdf
DOI  :  10.1088/1755-1315/121/3/032007

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

With the development of data mining technology and the establishment of environmental air quality database, it is necessary to discover the potential correlations and rules by digging the massive environmental air quality information and analyzing the air pollution process. In this paper, we have presented a sequential pattern mining method based on the air quality data and pattern association technology to analyze the PM2.5time series characteristics. Utilizing the real-time monitoring data of urban air quality in China, the time series rule and variation properties of PM2.5under different pollution levels are extracted and analyzed. The analysis results show that the time sequence features of the PM2.5concentration is directly affected by the alteration of the pollution degree. The longest time that PM2.5remained stable is about 24 hours. As the pollution degree gets severer, the instability time and step ascending time gradually changes from 12-24 hours to 3 hours. The presented method is helpful for the controlling and forecasting of the air quality while saving the measuring costs, which is of great significance for the government regulation and public prevention of the air pollution.