Additive Calibration Model for the Monitoring Data of PM2.5 and PM10 Based on ARIMA and Multiple Linear Regression

摘要

Air pollution is harmful to the ecological environment and human health. PM2.5 and PM10 are particularly harmful to human health. Real-time monitoring of the concentration of PM2.5 and PM10 can grasp the air quality in time and take corresponding measures to the pollution sources. Monitoring data may be affected by meteorological factors, so we need to check and correct the monitoring data to improve its accuracy. The difference between the two groups was significant through exploratory analysis. The self-correlation analysis to the data of SDD showed it was high significant. So, ARIMA models were used by time series analysis to the data (A