Science Inventory

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

Citation:

Luo, H., M. Astitha, C. Hogrefe, R. Mathur, AND S. Rao. Evaluating Trends and Seasonality in Modeled PM2.5 Concentrations Using Empirical Mode Decomposition. Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir, Turkey, 20(22):13801-13815, (2020). https://doi.org/10.5194/acp-20-13801-2020

Impact/Purpose:

By comparing observed and CMAQ-simulated variability and trends in aerosols, the work in this study addresses the question of how well CMAQ performs when applied to simulate the effects of changing emissions and meteorological variability on ambient particulate matter. To this end the study utilizes the Empirical Mode Decomposition (EMD) technique to determine the time-scale dependency of model performance. Results of this analysis can inform model development efforts by focusing such efforts on time scales most directly connected to the models ability in reproducing observed variability and trends.

Description:

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.