Multi-scale analysis based on spatial Markov chain model provides insights into long-term and short-term SO2 control in China

Sulfur dioxide (SO2) is a major atmospheric pollutant in China, and understanding its spatiotemporal dynamics is essential for effective air quality management. Using hourly SO2 data from more than 2000 urban monitoring stations during 2015–2022, this study examines SO2 variation at daily, monthly, and annual scales. Spatial patterns and temporal trends are analyzed using the Global Moran's I Index and both traditional and spatial Markov chain models, with spatial proximity defined by inverse distance. Model predictions are validated using independent data from 2023.The results show clear seasonal variability at daily and monthly scales, with higher SO2 concentrations in winter and lower levels in summer, alongside a sustained national decline at the annual scale. Significant spatial autocorrelation is observed across all time scales, particularly in coal-dependent and industrial regions. Incorporating spatial proximity substantially changes transition probabilities, demonstrating strong spatial spillover effects, especially at shorter time scales. Validation results confirm the robustness of the spatial Markov model. Overall, the findings underscore the importance of spatial dependence and temporal scale in analysing SO2 dynamics and provide scientific support for coordinated regional air pollution control strategies in China.