Quantify the Contribution of Dust and Anthropogenic Sources to Aerosols in North China by Lidar and Validated with CALIPSO
Wang, Zhuang2,3; Liu, Cheng1,2,3,4,5; Hu, Qihou2; Dong, Yunsheng2; Liu, Haoran6; Xing, Chengzhi2; Tan, Wei2
刊名REMOTE SENSING
2021-05-01
卷号13
关键词Raman lidar aerosol classification CALIPSO dust polluted dust anthropogenic aerosol
DOI10.3390/rs13091811
通讯作者Hu, Qihou(qhhu@aiofm.ac.cn)
英文摘要Persistent heavy haze episodes have repeatedly shrouded North China in recent years. Besides anthropogenic emissions, natural dust also contributes to the aerosols in this region. Through continuous observation by a dual-wavelength Raman lidar, the primary aerosol types and their contributions to air pollution in North China were determined. The following three aerosol types can be classified: natural dust, anthropogenic aerosols, and the mixture of anthropogenic aerosols and dust (polluted dust). The classification results are basically consistent with the classification results from the cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) satellite measurements. The relative bias of the lidar ratio between the Raman lidar and CALIPSO is less than 25% over 90% of the cases, indicating that the CALIPSO lidar ratio selection algorithm is reasonable. The classification results show that approximately 45% of aerosols below 1.8 km are contributed by polluted dust during our one year observations. The contribution of dust increased with height, from 6% at 500 m to 28% at 1,800 m, while the contribution of anthropogenic aerosols decreased from 49% to 25%. In addition, polluted dust is the major aerosol subtype below 1.0 km in spring (over 60%) and autumn (over 70%). Anthropogenic aerosols contribute more than 75% of air pollution in summer. In winter, anthropogenic aerosols prevailed (over 80%) in the lower layer, while polluted dust (around 60%) dominated the upper layer. Our results identified the primarily aerosol types to assess the contributions of anthropogenic and natural sources to air pollution in North China, and highlight that natural dust plays a crucial role in lower-layer air pollution in spring and autumn, while controlling anthropogenic aerosols will significantly improve air quality in winter.
资助项目National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory (ARL)
WOS关键词SPECTRAL-RESOLUTION LIDAR ; RAMAN LIDAR ; OPTICAL-PROPERTIES ; SAHARAN DUST ; DEPOLARIZATION RATIO ; PARTICULATE MATTER ; CIRRUS CLOUDS ; AIRBORNE HSRL ; BACKSCATTER ; EXTINCTION
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000650751400001
资助机构National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory (ARL)
内容类型期刊论文
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/122122]  
专题中国科学院合肥物质科学研究院
通讯作者Hu, Qihou
作者单位1.Univ Sci & Technol China, Anhui Prov Key Lab Polar Environm & Global Change, Hefei 230026, Peoples R China
2.Chinese Acad Sci, Key Lab Environm Opt & Technol, Anhui Inst Opt & Fine Mech, Hefei 230031, Peoples R China
3.Univ Sci & Technol China, Dept Precis Machinery & Precis Instrumentat, Hefei 230026, Peoples R China
4.Chinese Acad Sci, Inst Urban Environm, Ctr Excellence Reg Atmospher Environm, Xiamen 361021, Peoples R China
5.Univ Sci & Technol China, Key Lab Precis Sci Instrumentat, Anhui Higher Educ Inst, Hefei 230026, Peoples R China
6.Anhui Univ, Inst Phys Sci & Informat Technol, Hefei 230601, Peoples R China
推荐引用方式
GB/T 7714
Wang, Zhuang,Liu, Cheng,Hu, Qihou,et al. Quantify the Contribution of Dust and Anthropogenic Sources to Aerosols in North China by Lidar and Validated with CALIPSO[J]. REMOTE SENSING,2021,13.
APA Wang, Zhuang.,Liu, Cheng.,Hu, Qihou.,Dong, Yunsheng.,Liu, Haoran.,...&Tan, Wei.(2021).Quantify the Contribution of Dust and Anthropogenic Sources to Aerosols in North China by Lidar and Validated with CALIPSO.REMOTE SENSING,13.
MLA Wang, Zhuang,et al."Quantify the Contribution of Dust and Anthropogenic Sources to Aerosols in North China by Lidar and Validated with CALIPSO".REMOTE SENSING 13(2021).
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