Articles | Volume 20, issue 22
https://doi.org/10.5194/acp-20-13801-2020
https://doi.org/10.5194/acp-20-13801-2020
Research article
 | 
17 Nov 2020
Research article |  | 17 Nov 2020

Evaluating trends and seasonality in modeled PM2.5 concentrations using empirical mode decomposition

Huiying Luo, Marina Astitha, Christian Hogrefe, Rohit Mathur, and S. Trivikrama Rao

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Latest update: 30 Sep 2023
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Short summary
A new method is introduced to evaluate nonlinear, nonstationary modeled PM2.5 time series by decomposing decadal PM2.5 concentrations and its species onto various timescales. It does not require preselection of temporal scales and assumptions of linearity and stationarity. It provides a unique opportunity to assess the influence of each species on total PM2.5. The results reveal a phase shift in modeled EC/OC concentrations, indicating the need for improved model treatment of organic aerosols.
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