Time Series and Multiple Linear Regression Calibration Model for CO Monitoring Data

CO is a kind of air pollutant with the largest amount and the widest distribution in the atmosphere produced by combustion of carbon containing substances. Real-time monitoring of the concentration of CO 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 CO 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 accuracy of CO was improved. The additive model could effectively calibrate CO monitoring data

ARIMA and Multiple Linear Regression Additive Model for SO2 Monitoring Data’s Calibration

SO2 is one of the main air pollutants produced by industrial waste gas, civil combustion and automobile exhaust. Real-time monitoring of the concentration of SO2 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 SO2 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 accuracy of SO2 was improved. The additive model could effectively calibrate SO2 monitoring data