Investigating PM 2.5 responses to other air pollutants and meteorological factors across multiple temporal scales

It remains unclear on how PM 2.5 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 PM 2.5 , 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 PM 2.5 concentration significantly exhibited non-linear downward trend, while the decomposed time series of PM 2.5 concentration by EEMD followed daily and monthly cycles. The temporal pattern of PM 10 , SO 2 and NO 2 is synchronous with that of PM 2.5 . At both daily and monthly scales, PM 2.5 was positively correlated with CO and negatively correlated with 24-h cumulative precipitation. At the daily scale, PM 2.5 was positively correlated with O 3 , daily maximum and minimum temperature, and negatively correlated with atmospheric pressure, while the correlation pattern was opposite at the monthly scale.