利用ME-2模型提升PM<sub>2.5</sub>源解析效果

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Abstract

In order to test the improvement of source apportionment of PM_2.5 using the ME-2 model on unconstrained PMF analyses, a total of 153 daily PM_2.5 samples were collected continuously from September 10, 2017 to August 29, 2018 at an industrial site in northern Shenzhen. The concentrations of 31 chemical compositions were determined, 17 of which were selected for model iteration. The annual mean concentration of PM_2.5 at the industrial site was 32.3μg/m³, and nine sources of PM_2.5 were identified by a prior PMF run, which were secondary sulfate, secondary nitrate, aged sea salt, soil dust, industrial emissions, coal combustion, biomass burning, ship emissions and vehicle emissions. However, the apportionment with PMF yielded mixed factors. Based on the PMF results and priori information obtained, four constrained factor profiles were input into the ME-2 model as a rotational control technique for model simulation. Compared to the PMF solution, tracers were more concentrated in source profiles of ME-2 results, and the ME-2 iteration provided a more significant fitting for time series of tracer concentrations and corresponding source contributions. Secondary nitrate, aged sea salt and industrial emissions were overestimated by 9% to 51% in the PMF results, while secondary sulfate, coal combustion and biomass burning were underestimated by 19% to 40%. The ME-2 result was found to be more environmentally and statistically significant than that of PMF and it could provide a more accurate scientific basis for pollution prevention and control at the same time.

Key words： PM_2.5 positive matrix factorization (PMF) multilinear engine (ME-2) source apportionment mixed factor

Received: 06 March 2019

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X513

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	Articles by authors
	SHEN Hang-yin
	HE Ling-yan
	LIN Li-liang
	JIANG Jia-hao
	GAO Mao-shang
	HUANG Xiao-feng

Cite this article:

SHEN Hang-yin,HE Ling-yan,LIN Li-liang等. Improved PM_2.5 source apportionment results using the multilinear engine (ME-2) model[J]. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(9): 3682-3690.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2019/V39/I9/3682

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