Abstract
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.
Highlights
IntroductionThe 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
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
Few studies have investigated the effect of air pressure on PM2.5, and this study found that the response of PM2.5 to air pressure is inversely correlated at different temporal scales and is more strongly correlated on the large time scale
Summary
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. Human activities, such as vehicle exhaust emission and industrial p roduction[7,8], are the dominant factors of P M2.5 pollution[9] Natural environment, such as meteorological conditions (e.g., precipitation and wind speed)[10], facilitate the transportation and diffusion of P M2.5, while atmospheric chemical reactions stimulate the secondary formation of P M2.511. All of these factors interact with P M2.5 at different spatial and temporal scales, and can have varying effects on PM2.5 distribution. It is a heavily air-polluted area by PM2.5, attributed to the combined effects of large amount of population and on-road vehicles, valley basin landform, numerous polluting enterprises in the suburbs (e.g., petrochemical factories), and the prevailing wind direction of east-south-east (Fig. 1)
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