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.