Time Series Calibration Model for NO2 Based on Multiple Linear Regression

Abstract

NO2 is one of the main air pollutants, and is the precursor of PM2.5, PM10, and O3 pollutions. Real-time monitoring of the concentration of NO2 can grasp the air quality in time and take corresponding measures to the pollution sources. Monitoring data may be affected by the internal factors and the external factors. ARIMA was used for the internal factor as A. Meteorological factors were taken as external factors, and the difference of NO2 between the standard data and monitoring data was taken as dependent variable. Multivariate linear regression was modeled as B. Time series calibration model was obtained Y=A+B. The error analysis showed that the accuracies of NO2 was improved. Therefore, the model based on ARIMA and multiple linear regression could effectively calibrate NO2 monitoring data.