PM<sub>2.5</sub> Concentration Identification Based on Lidar Detection

Songlin Fu; Chenbo Xie; Lu Li; Zhiyuan Fang; Hao Yang; Bangxin Wang; Dong Liu; Yingjian Wang

doi:10.3788/AOS202141.0928001

Acta Optica Sinica, Volume. 41, Issue 9, 0928001(2021)

PM_2.5 Concentration Identification Based on Lidar Detection

Songlin Fu^1,2,3, Chenbo Xie^1,3、*, Lu Li^1,2,3, Zhiyuan Fang^1,2,3, Hao Yang^1,2,3, Bangxin Wang^1,3, Dong Liu^1,3, and Yingjian Wang^1,3

Author Affiliations

¹Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China

²Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, Anhui 230026, China

³Advanced Laser Technology Laboratory of Anhui Province, Hefei, Anhui 230037, China

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Abstract Get PDF(in Chinese)

Figures & Tables(12)

Fig. 1. Schematic diagram of the lidar system

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Fig. 2. Range-corrected signal and extinction coefficient under different weathers obtained by lidar: (a)(b) Fine weather; (c)(d) haze weather

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Fig. 3. Quantitative identification of regression model

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Fig. 4. Results of correlation analysis between aerosol optical depth and PM_2.5 mass concentration. (a) Correlation analysis; (b) calculated PM_2.5 mass concentration

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Fig. 5. Identification and prediction based on BP neural network

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Fig. 6. Identification and prediction based on GA-BP neural network

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Table 1. Lidar parameter

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Table 1. Lidar parameter

Technical parameter	Value
Wavelength /mm	532
Single pulse energy /mJ	30
Laser divergence angle /mrad	<1
Telescope diameter /mm	200
Receive field of telescope /mrad	2
Filter bandwidth /nm	0.3
Transmittance of transmitting optical element	0.8
Transmittance of receiving optical element	0.3
Collector sampling frequency /MHz	10
Effective range /km	10

Table 2. Parameters of genetic algorithm
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Table 2. Parameters of genetic algorithm
Parameter Value
Population size 20
Maximum genetic algebra 40
Crossover probability 0.7
Mutation probability 0.01

Table 3. Correlation analysis between aerosol optical depth and PM_2.5 mass concentration
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Table 3. Correlation analysis between aerosol optical depth and PM_2.5 mass concentration
Correlation analysis R² Slope Intercept
Value 0.425 65.108 58.580

Table 4. Comparison of regression model parameters constructed by BP neural network
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Table 4. Comparison of regression model parameters constructed by BP neural network
Featureset Training set Testing set
R² RMSE R² RMSE
Value 0.730 11.722 0.623 16.437

Table 5. Comparison of regression model parameters constructed by GA-BP neural network
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Table 5. Comparison of regression model parameters constructed by GA-BP neural network
Featureset Training set Testing set
R² RMSE R² RMSE
Value 0.904 6.013 0.899 6.176

Table 6. [in Chinese]
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Table 6. [in Chinese]
Method Maximum error Minimum error MFE
BP 83.443 0.182 24.692
GA-BP 37.828 0.209 7.122

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Songlin Fu, Chenbo Xie, Lu Li, Zhiyuan Fang, Hao Yang, Bangxin Wang, Dong Liu, Yingjian Wang. PM_2.5 Concentration Identification Based on Lidar Detection[J]. Acta Optica Sinica, 2021, 41(9): 0928001

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Paper Information

Category: Remote Sensing and Sensors

Received: Sep. 14, 2020

Accepted: Dec. 1, 2020

Published Online: May. 8, 2021

The Author Email: Xie Chenbo (cbxie@aiofm.ac.cn)

DOI:10.3788/AOS202141.0928001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Lidar parameter

Table 1. Lidar parameter

Table 2. Parameters of genetic algorithm

Table 2. Parameters of genetic algorithm

Table 3. Correlation analysis between aerosol optical depth and PM2.5 mass concentration

Table 3. Correlation analysis between aerosol optical depth and PM2.5 mass concentration

Table 4. Comparison of regression model parameters constructed by BP neural network

Table 4. Comparison of regression model parameters constructed by BP neural network

Table 5. Comparison of regression model parameters constructed by GA-BP neural network

Table 5. Comparison of regression model parameters constructed by GA-BP neural network

Table 6. [in Chinese]

Table 6. [in Chinese]

Table 3. Correlation analysis between aerosol optical depth and PM_2.5 mass concentration

Table 3. Correlation analysis between aerosol optical depth and PM_2.5 mass concentration