Correlation analysis and application investigation of multi-angle simultaneous polarization measurement data and concentration of suspended particulate matter in the atmosphere

被引:4
|
作者
Yuan, Xuan [1 ]
Song, Jiawei [1 ,2 ]
Zeng, Nan [1 ]
Guo, Jun [1 ]
Ma, Hui [1 ,3 ]
机构
[1] Tsinghua Univ, Shenzhen Key Lab Minimal Invas Med Technol, Tsinghua Shenzhen Int Grad Sch, Inst Opt Imaging & Sensing,Guangdong Res Ctr Polar, Shenzhen, Peoples R China
[2] Tsinghua Univ, Dept Phys, Beijing, Peoples R China
[3] Tsinghua Berkeley Shenzhen Inst, Ctr Precis Med & Healthcare, Shenzhen, Peoples R China
关键词
suspended particulate matter; polarization; periodical canonical correlation analysis (PCCA); locally weighted linear regression (LWLR); prediction; MASS CONCENTRATION; AIR-POLLUTION; SCATTERING; PARTICLES; AEROSOLS; TIME;
D O I
10.3389/fenvs.2022.1031863
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Determining the composition, particle size distribution and concentration changes of suspended particulate matter in the atmosphere is important for evaluating the quality of air and its impact on public health. The scattering and absorption of light by suspended particulate matter can change the polarization state of light, which can be used to extract characteristic information of measured particles. Firstly, we use our previously developed multi-angle simultaneous polarization measurement device to monitor the particulate matter around Dianshan Lake, Shanghai, and obtain high-throughput, high-dimensional Stokes data for nearly 1 month. The correlation between the Stokes data measured and the reference concentrations of five suspended particulate matter (Si, K, Fe, Ca, and Zn) was analyzed using the Periodical canonical correlation analysis (PCCA) method. The study shows a strong correlation between the three Stokes vectors and the concentrations of two types of suspended particulate matter in the atmosphere. Moreover, a prediction model for the concentration change of suspended particles was proposed by combining the locally weighted linear regression (LWLR) and the auto regressive moving average (ARMA) model. The prediction results on the concentration change of K and Fe in the atmosphere verified the validity of our method. The research in this work offers the possibility of continuous analysis and prediction of atmospheric suspended particulate matter in real environments.
引用
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页数:13
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