气候变化领域集成服务门户(CCPortal): Analysis and accurate prediction of ambient PM2.5 in China using Multi-layer Perceptron

CCPortal

DOI	10.1016/j.atmosenv.2020.117534
	Analysis and accurate prediction of ambient PM2.5 in China using Multi-layer Perceptron
	Feng R.; Gao H.; Luo K.; Fan J.-R.
发表日期	2020
ISSN	1352-2310
卷号	232
英文摘要	Whether PM2.5 can be long-range transported and what role secondary inorganic aerosols play in the episodes of haze have aroused numerous debates. In this work, Multi-layer Perceptron (MLP) is used to analyze and predict ambient PM2.5 in eight regional core cities in China to resolve the clashes, and the conclusions are listed as follows. Gaseous air pollutants (SO2, NO2, O3 and CO) are way more momentous than meteorological conditions in shaping PM2.5. PM2.5 level is dominated by the ups and downs of gaseous air pollutants, indicating the predominance of secondary inorganic aerosols and the existence of thermodynamic equilibrium between PM2.5 and gaseous air pollutants. The secondary PM2.5 tends to be generated within one hour. We quantitatively demonstrate that the primary emissions change and long-range transport are ubiquitously and conspicuously insignificant throughout the main cities of China and reductions of the gaseous air pollutants are most essential for regulating PM2.5. Furthermore, the phenology of local flora as the minor cause and lopsided thermodynamic equilibrium shift triggered by temperature change as the major cause elicit the severity of PM2.5 in wintertime--for every Celsius degree of temperature drop, PM2.5 increases by 5.9 μg/m3. © 2020 Elsevier Ltd
关键词	Insignificance of long-range transport Machine learning Secondary inorganic aerosols Thermodynamic equilibrium
语种	英语
scopus关键词	Aerosols; Atmospheric movements; Accurate prediction; Long range transport; Meteorological condition; Multi layer perceptron; Primary emissions; Secondary inorganic aerosol; Temperature changes; Thermodynamic equilibria; Air pollution; carbon monoxide; nitrogen dioxide; ozone; sulfur dioxide; aerosol; ambient air; atmospheric pollution; climate conditions; long range transport; particulate matter; prediction; aerosol; air pollutant; air pollution; air temperature; ambient air; Article; atmosphere; atmospheric transport; China; city; flora; geographic distribution; measurement accuracy; meteorology; multilayer perceptron; particulate matter; phenology; prediction; priority journal; quantitative analysis; secondary inorganic aerosol; thermodynamics; winter; China
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/249147
作者单位	State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China; Hangzhou Feng's Culture Creation Co., Ltd, Hangzhou, 310027, China; Zhejiang Construction Investment Environment Engineering Co, Ltd., Hangzhou, 310013, China
推荐引用方式 GB/T 7714	Feng R.,Gao H.,Luo K.,et al. Analysis and accurate prediction of ambient PM2.5 in China using Multi-layer Perceptron[J],2020,232.
APA	Feng R.,Gao H.,Luo K.,&Fan J.-R..(2020).Analysis and accurate prediction of ambient PM2.5 in China using Multi-layer Perceptron.ATMOSPHERIC ENVIRONMENT,232.
MLA	Feng R.,et al."Analysis and accurate prediction of ambient PM2.5 in China using Multi-layer Perceptron".ATMOSPHERIC ENVIRONMENT 232(2020).