It remains unclear on how PM2.5 interacts with other air pollutants and meteorological factors at different temporal scales, while such knowledge is crucial to address the air pollution issue more effectively. In this study, we explored such interaction at various temporal scales, taking the city of Nanjing, China as a case study. The ensemble empirical mode decomposition (EEMD) method was applied to decompose time series data of PM2.5, five other air pollutants, and six meteorological factors, as well as their correlations were examined at the daily and monthly scales. The study results show that the original PM2.5 concentration significantly exhibited non-linear downward trend, while the decomposed time series of PM2.5 concentration by EEMD followed daily and monthly cycles. The temporal pattern of PM10, SO2 and NO2 is synchronous with that of PM2.5. At both daily and monthly scales, PM2.5 was positively correlated with CO and negatively correlated with 24-h cumulative precipitation. At the daily scale, PM2.5 was positively correlated with O3, daily maximum and minimum temperature, and negatively correlated with atmospheric pressure, while the correlation pattern was opposite at the monthly scale.

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It remains unclear on how transportation network interacts with economic network in an urban-rural agglomeration, while such knowledge is crucial for urban-rural system governance and sustainability. We explored such spatial interactions in the Zhongyuan Urban-Rural Agglomeration (ZURA) from 1995 to 2015. The structure of transportation network was measured by spatial syntax model, and that of economic network was gauged by improved gravity model. The associations between transportation and economic networks were investigated by conducting bivariate spatial autocorrelation analysis. The global Moran's I showed that the two networks were positively correlated from 1995 to 2015. The local Moran's I identified "high-high" associations between transportation and economic networks around core cities in 1995, which further extended to surrounding cities. Our results reveal that peripheral cities with highly developed transportation system have little spatial economic impact on neighboring rural areas, while cities with low transportation accessibility restrained its external economic influence of neighboring cities and rural areas. Our findings shed light on future urban-rural system governance, where the "multi-center, unbalanced" growth patterns of economic network and the "multi-core, multi-directional" structure of transportation network are more likely to contribute to the sustainability of urban-rural systems.