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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 02 Mar 2020

Submitted as: research article | 02 Mar 2020

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This preprint is currently under review for the journal ACP.

Evaluating Trends and Seasonality in Modeled PM2.5 Concentrations Using Empirical Mode Decomposition

Huiying Luo1, Marina Astitha1, Christian Hogrefe2, Rohit Mathur2, and S. Trivikrama Rao1,3 Huiying Luo et al.
  • 1University of Connecticut, Department of Civil and Environmental Engineering, Storrs-Mansfield, CT, USA
  • 2U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
  • 3North Carolina State University, Raleigh, NC, USA

Abstract. Regional-scale air quality models are being used for studying the sources, composition, transport, transformation, and deposition of fine particulate matter (PM2.5). The availability of decadal air quality simulations provides a unique opportunity to explore sophisticated model evaluation techniques rather than relying solely on traditional operational evaluations. In this study, we propose a new approach for process-based model evaluation of speciated PM2.5 using improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (improved CEEMDAN) to assess how well version 5.0.2 of the coupled Weather Research and Forecasting model – Community Multiscale Air Quality model (WRF-CMAQ) simulates the time-dependent long-term trend and cyclical variations in the daily average PM2.5 and its species, including sulfate (SO4), nitrate (NO3), ammonium (NH4), chloride (Cl) organic carbon (OC) and elemental carbon (EC). The utility of the proposed approach for model evaluation is demonstrated using PM2.5 data at three monitoring locations. At these locations, the model is generally more capable of simulating the rate of change in the long-term trend component than its absolute magnitude. Amplitudes of the sub-seasonal and annual cycles of total PM2.5, SO4 and OC are well reproduced. However, the time-dependent phase difference in the annual cycles for total PM2.5, OC and EC reveal a phase shift of up to half year, indicating the need for proper temporal allocation of emissions and for updating the treatment of organic aerosols compared to the model version used for this set of simulations. Evaluation of sub-seasonal and inter-annual variations indicates that CMAQ is more capable of replicating the sub-seasonal cycles than inter-annual variations in magnitude and phase.

Huiying Luo et al.

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Huiying Luo et al.

Huiying Luo et al.


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Latest update: 05 Jul 2020
Publications Copernicus
Short summary
A new method to evaluate non-linear, non-stationary modeled PM2.5 time series by decomposing decadal PM2.5 concentrations and its species into various time scales. Does not require preselection of temporal scales and assumptions of linearity and stationarity. It provides a unique opportunity to assess influence of each species to the total PM2.5. The results reveal a phase shift in modeled EC/OC concentrations indicating the need for improved model treatment of organic aerosols.
A new method to evaluate non-linear, non-stationary modeled PM2.5 time series by decomposing...