Study on the Influence of Air Pressure and Temperature on PM2.5 by Multivariate Functional Linear Regression Model

Abstract

In recent years, the Internet has developed rapidly, and we have more and more ways to collect data. We find that many data have the characteristics of functions. We can use the important method of functional data analysis to analyze these data. The basic idea of functional data analysis is to treat data with functional properties as a whole for analysis and corresponding processing. In this paper, the daily air pressure, temperature and PM2.5 data of 49 cities with serious PM2.5 pollution in 2017 are sorted out. We use a multivariate functional linear regression model to discuss the influence of pressure and temperature on PM2.5 when the number of basis functions is different.