Analysis of influencing factors of PM2.5 based on regression equation
Year: 2017
Journal of Information and Computing Science, Vol. 12 (2017), Iss. 1 : pp. 14–19
Abstract
According to the AQI data and the meteorological data of Xi’an in the last years, the relationships and the influence principles between PM2.5 and other five monitoring indicators of AQI, weather factors and heating time were analyzed, respectively, by the regression analysis and the ridge regression analysis. The main results include: (1) There were positive correlations between PM2.5 and SO2, NO2 and CO, which shows that SO2, NO2 and CO may be the major gaseous components of forming PM2.5. Therefore, the concentration of PM2.5 can be reduced by considering how to efficiently decrease the concentrations of SO2, NO2, and CO. (2) The relationships between PM2.5 and temperature, sea level press, visibility, wind speed and accumulated precipitation are significantly negatively correlated based on the multiple regression. (3) The concentration of PM2.5 during the heating period was 1.868 times higher than that during non-heating period. Finally, the ridge regression between PM2.5 and all the factors mentioned above shows that SO2, NO2, PM10, CO and heating time were more significant than others.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2024-JICS-22493
Journal of Information and Computing Science, Vol. 12 (2017), Iss. 1 : pp. 14–19
Published online: 2017-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 6