Abstract
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.
Highlights
CO is a kind of air pollutant with the largest amount and the widest distribution in the atmosphere
We got the predictive values by the additive calibration models and the ARIMA models
Through the exploratory analysis of CO monitoring data, it was found that the observation variables have certain timing and autocorrelation
Summary
CO is a kind of air pollutant with the largest amount and the widest distribution in the atmosphere. Its existence affects the dissociation of oxygenated haemoglobin, which leads to hypoxia and carbon dioxide retention, and causes symptoms of poisoning [1] It seriously threatens the health and safety of human beings and animal. In the actual application of natural climate environment, the change of temperature, humidity, wind speed, pressure and precipitation and other meteorological factors will have a certain impact on the accuracy of its monitoring data [3]. The data was from the mathematical modeling competition of college students in 2019 It included the monitoring data of CO by NCD and SDD. Part was the exploratory analysis for the monitoring data of SDD and NCD This part included statistical description and hypothesis testing. 4 was the time series calibration model based on ARIMA and multiple linear regression.
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