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To analyze the impact of the added value of primary, secondary, and tertiary industry on public attention to air pollution in Handan, Xingtai, and Shijiazhuang, Baidu index is used to build the air pollution attention index. Taking the added value of the primary, secondary, and tertiary industry as the influencing factors, fractional grey multivariable convolution model is used to predict and analyze the public attention to air pollution in these three cities from 2020 to 2024. The results show that the secondary industry has the greatest impact on the public's attention to air pollution compared with the primary industry and the tertiary industry. And the added value of the secondary industry with faster increase will cause a faster increase in the public's air pollution attention from 2020 to 2024, especially in Handan. It is not only helpful to air pollution control, but also helpful in solving the public psychological problems caused by air pollution. [ABSTRACT FROM AUTHOR]

To predict the daily air pollutants, the fractional multivariable model is established. The hybrid model of the grey multivariable regression model with fractional order accumulation model (FGM(0, m)) and support vector regression model (SVR) is used to predict the air pollutants (PM10, PM2.5, and NO2) from December 31, 2018, to January 3, 2019, in Shijiazhuang and Chongqing. The absolute percentage errors (APEs) are used to determine the weights of the FGM(0, m) and SVR. Meanwhile, the Holt–Winters model is used to predict the air quality pollutants for the same location and period. When the mean absolute percent error (MAPE) is 0%–20%, it indicates that the model has good accuracy of fitting and prediction. The MAPE of the hybrid model is less than 20%. It is shown that except for the PM2.5 concentration prediction in Shijiazhuang (13.7%), the MAPE between the forecasting and actual values of the three air pollutants in Shijiazhuang and Chongqing was less than 10%. [ABSTRACT FROM AUTHOR]