基于太赫兹光谱-统计分析的大气PM<sub>2.5</sub>监测研究

doi:10.3964/j.issn.1000-0593(2018)02-0643-07

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Abstract The study presented a quantitative investigation in monitoring the fine particulate matter with the aerodynamic diameters less than 2.5 μm (PM_2.5) in air with terahertz (THz) technique. Absorption bands were observed and linear relations were obtained between PM_2.5 mass and absorbance at selected frequencies with correlation coefficients R of ~0.86. The utilization of principal component analysis (PCA) proved the similar absorption trend in the entire range with increasing PM_2.5 mass. In order to improve the prediction precision, we employed other three statistical methods including partial least square (PLS), support vector machine (SVM) and back propagation artificial neural network (BPANN) for the quantification of PM_2.5. Compared with single linear models, statistical models had larger prediction correlation and smaller errors. For BPANN model, R and root-mean square error (RMSE) achieved 0.999 as well as 0.016 mg in training and 0.912 as well as 0.207 mg. Therefore, the combination of THz technique and statistical methods represents high precision of prediction and really can act as an effective tool to monitor PM_2.5 together with other normal approaches.

Key words：Terahertz；absorbance；PM_2.5；Statistical methods

Received: 2016-10-23 Accepted: 2017-05-06

ZTFLH:

O433

Corresponding Authors: ZHAN Hong-lei, ZHAO Kun E-mail: hlzhan@126.com; zhk@cup.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)02-0643-07 OR https://www.gpxygpfx.com/EN/Y2018/V38/I02/643

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