Environmental governance based on multiple linear regression fitting - Yu Zou; Zhao, Jinrun Xu (International Scientific Technical and Economic Research, 2025)
Regarding the construction of a mathematical model between air quality index (AQI) and different pollutant concentrations, we constructed pollutants respectively: PM2.5, PM10, CO, NO2, O3, SO2. firstly, we pre-processed the data in Annex 1, and analysed the data by visualising its missing values and boxplots to get no obvious outliers and missing values between the data. Secondly, we established multiple linear regression to fit the linearity between AQI and pollutant concentration, and fitted the expression: y=15.431 + 0.71*X_1  + 0.077*X_2  - 0.24*X_3  + 11.867* X_4+ 0.386*X_5  + 0.273*X_6, with the goodness-of-fit of  as good, and T-test and F-test were conducted to prove the significant difference. prove that there are significant differences. Secondly, considering the influence of multicollinearity between the constructed indicators, we introduced the variance inflation factor VIF to test the indicators, and concluded that the construction of indicators is reasonable. At the same time, we take the pollutants as the sub-sequence and the AQI index as the parent sequence to construct the grey correlation analysis, to derive the grey correlation between each of its pollutants and the AQI, and to rank them, see Table 3.In this paper, based on the constructed regression equations, the collected air quality data of the national cities are fitted, and it is concluded that the ten cities with the best air quality are Dazhou City, Hegang City, Heihe City, Jiamusi City, Shuangyashan, Yichun, Nanchong, Qiqihar, and Suining.