Acta Optica Sinica, Volume. 41, Issue 9, 0928001(2021)
PM2.5 Concentration Identification Based on Lidar Detection
For the difficulty in measuring the distribution characteristics of PM2.5 concentration in the atmosphere, we used 532 nm lidar to continuously observe the Huainan area from June 1st to December 31st, 2016. A regression prediction model was established concerning the atmospheric boundary layer height, aerosol optical depth, temperature, relative humidity, wind speed, visibility, and measured PM2.5 concentration to identify the PM2.5 concentration. Since the traditional backpropagation neural network (BP) was prone to the local minimum, we adopted a genetic algorithm-based backpropagation neural network (GA-BP) according to the data characteristics and applied the genetic algorithm to finding the optimal weights and thresholds, balancing global and local contradictions. A comparison of the two regression models demonstrates that the GA-BP method is significantly better than the BP method. The correlation index R2of the test set and the mean forecast error are respectively 0.623 and 24.692 μg/m 3 for the BP method, and 0.899 and 7.122 μg/m 3 for the GA-BP method. These results indicate that lidar can effectively monitor the PM2.5 distribution in the atmosphere and provide data support and reference for the monitoring of atmospheric PM2.5 in the Huainan area.
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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
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)