长三角对流层甲醛柱浓度时空变化及驱动因素

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Abstract This study uses OMI satellite data to analyse the temporal and spatial changes of the tropospheric formaldehyde column concentration in the Yangtze River Delta from 2005 to 2016. The BP and RBFN neural network models are used to perform regression simulation on the tropospheric formaldehyde column concentration at the county scale and analysis of the proportion of emissions from various departments using non-methane volatile organic compounds (NMVOC) data in 2008 and 2010. The results show that the tropospheric formaldehyde column concentration in the Yangtze River Delta urban agglomeration has an increasing trend from 2005 to 2010 and a downward trend from 2011 to 2016. The concentrations are higher in northern Anhui, northern Jiangsu, Shanghai and nearby areas, while those in southwestern Zhejiang are lower. In addition, NMVOC have significantly increased the concentration of formaldehyde in economically developed areas. The industrial sector's emissions are widely distributed in the Yangtze River Delta, and the VOC emissions from the power sector are much smaller than those from the industrial sector, and the distribution is also very sparse. The amount of VOC emissions generated by residents' lives is between the above two, with a clear North-South differentiation. Those from the transportation sector are mainly concentrated in southern Jiangsu, northern Zhejiang and Shanghai, and are distributed in strips along the transportation lines. What's more, the fitting accuracy of the neural network can reach 0.6~0.8, which is 0.3~0.4 higher than that of the linear regression, which proves that machine learning algorithms can better simulate the concentration of the formaldehyde column with NMVOC. The VOC emissions generated by residents' lives contribute most to the tropospheric formaldehyde column concentration. Studying the long-term temporal and spatial changes of the tropospheric formaldehyde column concentration and its influencing factors is conducive to in-depth study of ozone pollution, and it also provides a scientific basis for atmospheric governance and policy making.

Key words： formaldehyde (HCHO) spatio-temporal variation BP neutral network RBF neural network Yangtze River Delta area

Received: 12 March 2021

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X515

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	QIAN Yun
	WU Jian-sheng
	TAN Xi
	LUO Yu-hang
	LU Tian-hua

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QIAN Yun,WU Jian-sheng,TAN Xi等. Spatiotemporal variation of tropospheric formaldehyde concentration and its driving factors in Yangtze River[J]. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(11): 4973-4981.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2021/V41/I11/4973

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