Articles | Volume 20, issue 22
Atmos. Chem. Phys., 20, 13801–13815, 2020
https://doi.org/10.5194/acp-20-13801-2020
Atmos. Chem. Phys., 20, 13801–13815, 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 et al.

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Cited articles

Astitha, M., Luo, H., Rao, S. T., Hogrefe, C., Mathur, R., and Kumar, N.: Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the contiguous United States, Atmos. Environ., 164, 102–116, 2017. 
Banzhaf, S., Schaap, M., Kranenburg, R., Manders, A. M. M., Segers, A. J., Visschedijk, A. J. H., Denier van der Gon, H. A. C., Kuenen, J. J. P., van Meijgaard, E., van Ulft, L. H., Cofala, J., and Builtjes, P. J. H.: Dynamic model evaluation for secondary inorganic aerosol and its precursors over Europe between 1990 and 2009, Geosci. Model Dev., 8, 1047–1070, https://doi.org/10.5194/gmd-8-1047-2015, 2015 
Chang, P. C., Flatau, A., and Liu, S. C.: Review Paper: Health Monitoring of Civil Infrastructure, Struct. Health Monit., 2, 257–267, 2003. 
Chen, X., Wu, Z., and Huang, N. E.: The time-dependent intrinsic correlation based on the empirical mode decomposition, Adv. Adapt. Data Anal., 02, 233–265, 2010. 
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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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