| Literature DB >> 26969052 |
Shicheng Long1, Yun Zhu2, Carey Jang3, Che-Jen Lin4, Shuxiao Wang5, Bin Zhao5, Jian Gao6, Shuang Deng6, Junping Xie1, Xuezhen Qiu1.
Abstract
This article describes the development and application of a streamlined air control and response modeling system with a novel response surface modeling-linear coupled fitting method and a new module to provide streamlined model data for PM2.5 attainment assessment in China. This method is capable of significantly reducing the dimensions required to establish a response surface model, as well as capturing more realistic response of PM2.5 to emission changes with a limited number of model simulations. The newly developed module establishes a data link between the system and the Software for Model Attainment Test-Community Edition (SMAT-CE), and has the ability to rapidly provide model responses to emission control scenarios for SMAT-CE using a simple interface. The performance of this streamlined system is demonstrated through a case study of the Yangtze River Delta (YRD) in China. Our results show that this system is capable of reproducing the Community Multi-Scale Air Quality (CMAQ) model simulation results with maximum mean normalized error<3.5%. It is also demonstrated that primary emissions make a major contribution to ambient levels of PM2.5 in January and August (e.g., more than 50% contributed by primary emissions in Shanghai), and Shanghai needs to have regional emission control both locally and in its neighboring provinces to meet China's annual PM2.5 National Ambient Air Quality Standard. The streamlined system provides a real-time control/response assessment to identify the contributions of major emission sources to ambient PM2.5 (and potentially O3 as well) and streamline air quality data for SMAT-CE to perform attainment assessments.Keywords: Air quality modeling; Attainment assessment; PM(2.5); RSM-Linear coupled fitting; Response surface model
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Year: 2015 PMID: 26969052 DOI: 10.1016/j.jes.2015.05.019
Source DB: PubMed Journal: J Environ Sci (China) ISSN: 1001-0742 Impact factor: 5.565