| Issue |
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|
|
|---|---|---|
| Article Number | 03041 | |
| Number of page(s) | 4 | |
| Section | Digital Development and Environmental Management of Energy Supply Chain | |
| DOI | https://doi.org/10.1051/e3sconf/202021403041 | |
| Published online | 07 December 2020 | |
ARIMA and Multiple Linear Regression Additive Model for SO2 Monitoring Data’s Calibration
1 School of Humanities and Social Sciences, Guangzhou Civil Aviation College, Guangzhou, Guangdong
2 School of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong
a 10000583@caac.net
b 28270031@qq.com
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.
© The Authors, published by EDP Sciences, 2020
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