Analysis of the Trend Characteristics of Air Pollutants in the Fenwei Plain Based on the KZ Filter

In order to improve air quality, China has implemented a series of the most stringent control measures ever in recent years. Quantitatively analyzing the contribution of emissions to the trend change in air pollutants is an essential scientific basis for verifying the effectiveness of air pollution control. We based our study on the air quality online monitoring data and meteorological element data of 11 cities in the Fenwei Plain from 2018 to 2022. We quantitatively investigated the changing patterns of NO2, O3, PM10, and PM2.5 and their influencing factors in the major cities of the Fenwei Plain by using the KZ filtering and MLR modeling analysis methods. The results show the following: (1) The long-term fractions of NO2, PM10, and PM2.5 in the Fenwei Plain decreased by 10.5, 33.1, and 17.1 mu g center dot m-3, with decreases of 25.8%, 29%, and 28.8%, respectively, from 2018 to 2022. The long-term fractions of O3 showed the characteristics of decreasing and then increasing, with 2020 as the dividing line. (2) The short-term components of NO2, PM10, and PM2.5 contributed the most to the total variance, with the proportion of short-term components ranging from 34.7% to 69.8%, 53% to 73%, and 43% to 58%, respectively. The seasonal components of O3 contributed the most to the total variance, with the proportion of short-term components ranging from 54% to 70.7%. (3) Most cities in the Fenwei Plain had unfavorable meteorological conditions with regard to NO2, PM10, and PM2.5 in 2018-2020 and favorable meteorological conditions in terms of NO2, PM10, and PM2.5 in 2020-2022. O3 showed different characteristics from the other three pollutants. Most cities in the Fenwei Plain had meteorological conditions in 2018-2019 that were unfavorable for improving O3 levels. In 2019-2021, meteorological conditions were favorable for improving O3 levels, while in 2021-2022, meteorological conditions were unfavorable for improving O3 levels.