Spatio-Temporal Variation Characteristics of PM2.5 and O3 in the Yellow River Great Bend Urban Agglomeration from 2020 to 2023

The Yellow River Great Bend Urban Agglomeration is a key area in the ecological protection and high-quality development strategy of the Yellow River Basin. In the process of coordinated regional development, the contradiction between economic development and environmental protection has become increasingly prominent, and the pollution problems of PM2.5 and O3 have become prominent. Based on the observation data of air pollutants and meteorological data of 15 cities from 2020 to 2023, this study explored the spatio-temporal variation characteristics of PM2.5 and O3 concentrations in this region and the influence of meteorological factors (temperature, relative humidity, wind speed, and precipitation). The results showed that the proportion of days with good air quality in the Yellow River Great Bend Urban Agglomeration metropolitan area increased first and then decreased from 2020 to 2023. PM2.5 concentrations were highest in winter and lowest in summer, with moderate levels in spring and autumn. In contrast, O3 concentrations peaked in summer and reached their lowest levels in winter. In terms of spatial variation, the spatial distribution of the number of PM2.5 polluted days roughly decreases from northwest to southeast, with Taiyuan City having the largest number of polluted days. The number of days with O3 pollution roughly shows a pattern of more in the middle and less around the periphery. Spatial autocorrelation analysis indicates that the PM2.5 concentration and O3 concentration in the Yellow River Great Bend Urban Agglomeration have obvious high-value and low-value spatial agglomeration characteristics. Meteorological elements have a significant influence on the concentrations of PM2.5 and O3. The occurrence frequencies of PM2.5 pollution and O3 pollution were significantly higher respectively within the temperature ranges of −10 to 15 °C and 20 to 30 °C, as well as under the condition of RH > 50% and in the range of 30% to 70% of the relative humidity. Statistical analysis revealed a universally significant negative correlation between wind speed and PM2.5 concentrations across all cities (mean R = −0.09, binomial test p < 0.001), confirming the critical role of stagnant conditions in local pollutant accumulation. The results of this study can provide important references for regional precise pollution control and environmental quality improvement and are of great significance for promoting regional sustainable development.