气候变化领域集成服务门户(CCPortal): Evaluating trends and seasonality in modeled PM2.5 concentrations using empirical mode decomposition

CCPortal

DOI	10.5194/acp-20-13801-2020
	Evaluating trends and seasonality in modeled PM2.5 concentrations using empirical mode decomposition
	Luo H.; Astitha M.; Hogrefe C.; Mathur R.; Trivikrama Rao S.
发表日期	2020
ISSN	16807316
起始页码	13801
结束页码	13815
卷号	20 期号:22
英文摘要	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 coupledWeather Research and Forecasting model-Community Multiscale Air Quality model (WRF-CMAQ) simulates the time-dependent longterm trend and cyclical variations in 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 reveals a phase shift of up to half a 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 interannual variations indicates that CMAQ is more capable of replicating the subseasonal cycles than interannual variations in magnitude and phase. © Author(s) 2020.
语种	英语
scopus关键词	aerosol formation; air quality; annual variation; concentration (composition); decomposition analysis; particulate matter; seasonal variation; seasonality; trend analysis
来源期刊	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/152817
作者单位	University of Connecticut, Department of Civil and Environmental Engineering, Storrs-Mansfield, CT, United States; U.S. Environmental Protection Agency, Research Triangle Park, NC, United States; North Carolina State University, Raleigh, NC, United States
推荐引用方式 GB/T 7714	Luo H.,Astitha M.,Hogrefe C.,et al. Evaluating trends and seasonality in modeled PM2.5 concentrations using empirical mode decomposition[J],2020,20(22).
APA	Luo H.,Astitha M.,Hogrefe C.,Mathur R.,&Trivikrama Rao S..(2020).Evaluating trends and seasonality in modeled PM2.5 concentrations using empirical mode decomposition.Atmospheric Chemistry and Physics,20(22).
MLA	Luo H.,et al."Evaluating trends and seasonality in modeled PM2.5 concentrations using empirical mode decomposition".Atmospheric Chemistry and Physics 20.22(2020).