基于非线性数学方法的PM<sub>2.5</sub>中重金属浓度模拟

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Abstract

Heavy metal concentrations were determined in PM_2.5 samples collected from Gulou and Pukou campus of Nanjing University in 2013. The pollution characteristics of heavy metals in different districts and different seasons were analysed, respectively. The correlations between heavy metal concentrations with meteorological factors or conventional air pollutants were investigated, respectively. The obtained data were pre-processed by principal component analysis, followed by the establishment of rapid evaluation models based on Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM), respectively. Then models obtained from these two nonlinear mathematics methods were compared with those from multiple linear regression model (MLR). The results showed that PM_2.5 concentrations and the average heavy metal concentrations in PM_2.5 were all highest in winner, followed by spring, whereas the lowest in summer and autumn. The significant linear correlations were found between heavy metal concentrations with meteorological factors or air pollutant concentrations. BP-ANN showed the highest correlation coefficients of training models for most tested heavy metals (except for Ba, Cr, and V). SVM showed the highest correlation coefficients of verified models for all the tested heavy metals. All three methods showed good modelling effects on the evaluation of Cd, Cu, Pb, Ni, and Zn, but relatively poor modelling effects on the evaluation of Cr, Fe, Sr, Ti, and V.

Key words： PM_2.5 heavy metals back propagation artificial neural network support vector machine multiple linear regression

Received: 15 July 2016

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X513

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	Articles by authors
	LENG Xiang-zi
	WANG Qin-geng
	QIAN Xin
	LI Hui-ming
	LU Hao

Cite this article:

LENG Xiang-zi,WANG Qin-geng,QIAN Xin等. Simulation of heavy metal concentrations in PM_2.5 based on nonlinear mathematics methods[J]. CHINA ENVIRONMENTAL SCIENCECE, 2017, 37(3): 821-828.

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http://manu36.magtech.com.cn/Jweb_zghjkx/EN/ OR http://manu36.magtech.com.cn/Jweb_zghjkx/EN/Y2017/V37/I3/821

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