Analysis of influencing factors of PM2.5 based on regression equation
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@Article{JICS-12-014,
author = {JingrongSun},
title = {Analysis of influencing factors of PM2.5 based on regression equation},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {12},
number = {1},
pages = {014--019},
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.
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22493.html}
}
TY - JOUR
T1 - Analysis of influencing factors of PM2.5 based on regression equation
AU - JingrongSun
JO - Journal of Information and Computing Science
VL - 1
SP - 014
EP - 019
PY - 2024
DA - 2024/01
SN - 12
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22493.html
KW - PM2.5, Air Quality Index, meteorological factors, heating period, multiple regression, ridge
regression.
AB - 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.
JingrongSun. (2024). Analysis of influencing factors of PM2.5 based on regression equation.
Journal of Information and Computing Science. 12 (1).
014-019.
doi:
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