• Acta Optica Sinica
  • Vol. 41, Issue 9, 0928001 (2021)
Songlin Fu1、2、3, Chenbo Xie1、3、*, Lu Li1、2、3, Zhiyuan Fang1、2、3, Hao Yang1、2、3, Bangxin Wang1、3, Dong Liu1、3, and Yingjian Wang1、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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    DOI: 10.3788/AOS202141.0928001 Cite this Article Set citation alerts
    Songlin Fu, Chenbo Xie, Lu Li, Zhiyuan Fang, Hao Yang, Bangxin Wang, Dong Liu, Yingjian Wang. PM2.5 Concentration Identification Based on Lidar Detection[J]. Acta Optica Sinica, 2021, 41(9): 0928001 Copy Citation Text show less
    Schematic diagram of the lidar system
    Fig. 1. Schematic diagram of the lidar system
    Range-corrected signal and extinction coefficient under different weathers obtained by lidar: (a)(b) Fine weather; (c)(d) haze weather
    Fig. 2. Range-corrected signal and extinction coefficient under different weathers obtained by lidar: (a)(b) Fine weather; (c)(d) haze weather
    Quantitative identification of regression model
    Fig. 3. Quantitative identification of regression model
    Results of correlation analysis between aerosol optical depth and PM2.5 mass concentration. (a) Correlation analysis; (b) calculated PM2.5 mass concentration
    Fig. 4. Results of correlation analysis between aerosol optical depth and PM2.5 mass concentration. (a) Correlation analysis; (b) calculated PM2.5 mass concentration
    Identification and prediction based on BP neural network
    Fig. 5. Identification and prediction based on BP neural network
    Identification and prediction based on GA-BP neural network
    Fig. 6. Identification and prediction based on GA-BP neural network
    Technical parameterValue
    Wavelength /mm532
    Single pulse energy /mJ30
    Laser divergence angle /mrad<1
    Telescope diameter /mm200
    Receive field of telescope /mrad2
    Filter bandwidth /nm0.3
    Transmittance of transmitting optical element0.8
    Transmittance of receiving optical element0.3
    Collector sampling frequency /MHz10
    Effective range /km10
    Table 1. Lidar parameter
    ParameterValue
    Population size20
    Maximum genetic algebra40
    Crossover probability0.7
    Mutation probability0.01
    Table 2. Parameters of genetic algorithm
    Correlation analysisR2SlopeIntercept
    Value0.42565.10858.580
    Table 3. Correlation analysis between aerosol optical depth and PM2.5 mass concentration
    FeaturesetTraining setTesting set
    R2RMSER2RMSE
    Value0.73011.7220.62316.437
    Table 4. Comparison of regression model parameters constructed by BP neural network
    FeaturesetTraining setTesting set
    R2RMSER2RMSE
    Value0.9046.0130.8996.176
    Table 5. Comparison of regression model parameters constructed by GA-BP neural network
    MethodMaximum errorMinimum errorMFE
    BP83.4430.18224.692
    GA-BP37.8280.2097.122
    Table 6. [in Chinese]
    Songlin Fu, Chenbo Xie, Lu Li, Zhiyuan Fang, Hao Yang, Bangxin Wang, Dong Liu, Yingjian Wang. PM2.5 Concentration Identification Based on Lidar Detection[J]. Acta Optica Sinica, 2021, 41(9): 0928001
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