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Comparison of mixing layer height inversion algorithms using lidar and a pollution case study in Baoding, China

Author:
Ji, Xiangguang, Liu, Cheng, Xie, Zhouqing, Hu, Qihou, Dong, Yunsheng, Fan, Guangqiang, Zhang, Tianshu, Xing, Chengzhi, Wang, Zhuang, Javed, Zeeshan, Liu, Jianguo
Source:
Journal of environmental sciences (China) 2019 v.79 pp. 81-90
ISSN:
1001-0742
Subject:
air pollutants, air pollution, algorithms, case studies, environmental governance, lidar, mixing, particulates, remote sensing, China
Abstract:
Beijing–Tianjin–Hebei area is suffering from atmospheric pollution from a long time. The understanding of the air pollution mechanism is of great importance for officials to design strategies for the environmental governance. Mixing layer height (MLH) is a key factor influencing the diffusion of air pollutants. It plays an important role on the evolution of heavy pollution events. Light detection and ranging (lidar), is an effective remote-sensing tool, which can retrieve high spatial and temporal evolution process within mixing layer (ML), especially the variation of MLH. There are many methods to retrieve MLH, but each method has its own applicable limitations. The Mie-lidar data in Beijing was firstly used to compare three different algorithms which are widely used under different pollution levels. We find that the multi-layer structure near surface may cause errors in the detection of mixing layer. The MLH retrieved based on image edge detection was better than another two methods especially under heavy polluted episode. Then we applied this method to investigate the evolution of the mixing layer height during a pollution episode in December 2016. MLH at Gucheng county showed the positive correlation with the concentration of particulate matters during the start of this pollution episode. The elevated pollution level in Gucheng was not associated with MLH's decrease, and the significantly increased particulate matters raised the boundary layer, which trapped the pollutants near the surface.
Agid:
6230039