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Investigating multi-temporal PM2.5 response to air pollutants and meteorological factors using ensemble empirical mode decomposition

Authors: Haiyue Fu*, Nanjing Agricultural University, Yiting Zhang, Nanjing Agricultural University, Chuan Liao, Arizona State University
Topics: Human-Environment Geography, Environmental Science, China
Keywords: PM2.5 , Air Pollutants, Meteorological factors, Temporal scale, Ensemble empirical mode decomposition (EEMD)
Session Type: Poster
Presentation File: No File Uploaded

Understanding how PM2.5 is related to various factors at different temporal scales can potentially contribute to mitigating air pollution problem; however, determinants of PM2.5 at different temporal scales remain unclear. In this study, we investigated how PM2.5 was related to different atmospheric and meteorological factors in Nanjing, China during 2014-2018, with explicit consideration of different temporal scales. Five air pollutants and six meteorological factors were selected and ensemble empirical mode decomposition (EEMD) method was applied to obtain their multi-temporal variation in order to quantify the relationship between PM2.5 and air pollutants and meteorological factors at different temporal 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 are similar to PM2.5, while PM2.5 was significantly correlated with air pollutants (CO, O3) and meteorological factors (24-hour cumulative precipitation (PR), atmospheric pressure (AP), daily maximum temperature (MaxT) and minimum temperature (MinT)) at different temporal scales. At daily and monthly scale, PM2.5 was negatively correlated with O3 and AP,MaxT,MinT, but exhibited isotropic correlation with air CO and PR.

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