Aerosol type classification and its temporal distribution in Kanpur using ground-based remote sensing

被引:0
|
作者
Sharma, Nabin [1 ]
Kumar, Sarvan [2 ]
Patel, Kalpana [1 ]
机构
[1] SRM Inst Sci & Technol, Dept Phys, Delhi NCR Campus, Ghaziabad 201204, India
[2] VBS Purvanchal Univ, Dept Earth & Planetary Sci, Jaunpur 222002, Uttar Pradesh, India
关键词
Dust ratio; Pollution aerosol; AERONET; Kanpur; Fine mode fraction; GANGETIC BASIN; AERONET; DUST; INDO; DEPOLARIZATION; RATIO; VARIABILITY;
D O I
10.1016/j.jastp.2024.106366
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Based on AERONET version 3 level 2 inversion products, we classify aerosol types and investigate their temporal distribution in the atmosphere using particle linear depolarization ratio (PLDR) and single scattering albedo (SSA) at the wavelength of 1020 nm over Kanpur. It is for the first time over the North Indian region the work has been emphasized. The remarkable findings over Kanpur station indicate that SSA and coarse-mode particles in the atmosphere increased with increasing PLDR at 440, 675, 870, and 1020 nm wavelengths. It is observed in the 2-dimensional histogram that the rate of occurrence of aerosols is high when the fine mode fraction (FMF) is high and the dust ratio (Rd) d ) is low. The atmosphere of Kanpur is partially influenced by dust-dominated mixture (DDM), pollution-dominated mixture (PDM), and pure dust (PD) with 53% whereas, the rest of the dust-free pollution aerosols are 47%. The annual mean occurrence rate for different aerosol types is 5% for Strongly Absorbing (SA), 20% for Moderately Absorbing (MA), 19% for Weakly Absorbing (WA), 3% for Non-Absorbing (NA), 27% for DDM, 22% for PDM, and 4% for PD, ranging from January 2001 to January 2022. There is a variation in the distribution of various types of pollution particles, which is influenced by the changing seasons. The rate of occurrence of dust-free pollution aerosols is 47%, mostly observed throughout the post-monsoon and winter seasons. The PLDR values in the atmosphere of Kanpur are almost balanced equally because it is affected by both (dust and dust-free) pollution aerosols and the changes can be seen due to the frequent occurrence of dust storms and anthropogenic activities.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] Ground-based remote sensing provides alternative to satellites for monitoring cyanobacteria in small lakes
    Cook, Katherine, V
    Beyer, Jessica E.
    Xiao, Xiangming
    Hambright, David
    WATER RESEARCH, 2023, 242
  • [32] Variation of the aerosol optical properties and validation of MODIS AOD products over the eastern edge of the Tibetan Plateau based on ground-based remote sensing in 2017
    You, Yingchang
    Zhao, Tianliang
    Xie, Yong
    Zheng, Yu
    Zhu, Jun
    Xia, Junrong
    Cao, Le
    Wang, Chenggang
    Che, Huizheng
    Liao, Yao
    Duan, Jingxin
    Zhou, Jiashu
    Zhou, Xiaoou
    ATMOSPHERIC ENVIRONMENT, 2020, 223 (223)
  • [33] Measurement of the precipitable water by remote sensing and ground-based methods over the black sea region
    Papkova, A. S.
    Kalinskaya, D., V
    Shukalo, D. M.
    Papkova, Yu, I
    27TH INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS, ATMOSPHERIC PHYSICS, 2021, 11916
  • [34] Vegetation predicts soil shear strength in Arctic soils: Ground-based and remote sensing techniques
    Wall, Wade A.
    Busby, Ryan
    Bosche, Lauren
    ANNALS OF FOREST RESEARCH, 2024, 67 (01) : 155 - 166
  • [35] Assessment of aerosol types on improving the estimation of surface PM2.5 concentrations by using ground-based aerosol optical depth dataset
    Chen, Qi-Xiang
    Huang, Chun-Lin
    Yuan, Yuan
    Mao, Qian-Jun
    Tan, He-Ping
    ATMOSPHERIC POLLUTION RESEARCH, 2019, 10 (06) : 1843 - 1851
  • [36] Verification of aerosol classification methods through satellite and ground-based measurements over Harbin, Northeast China
    Chen, Qi-Xiang
    Shen, Wen-Xiang
    Yuan, Yuan
    Tan, He-Ping
    ATMOSPHERIC RESEARCH, 2019, 216 (167-175) : 167 - 175
  • [37] Climatological aspects of aerosol optical properties in North China Plain based on ground and satellite remote-sensing data
    Xia, Xiangao
    Chen, Hongbin
    Goloub, Philippe
    Zong, Xuemei
    Zhang, Wenxing
    Wang, Pucai
    JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER, 2013, 127 : 12 - 23
  • [38] Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data
    Chen, Lijuan
    Fei, Ying
    Wang, Ren
    Fang, Peng
    Han, Jiamei
    Zha, Yong
    REMOTE SENSING, 2021, 13 (12)
  • [39] Measurements of aerosol properties from aircraft, satellite and ground-based remote sensing: A case-study from the Dust and Biomass-burning Experiment (DABEX)
    Johnson, B. T.
    Christopher, S.
    Haywood, J. M.
    Osborne, S. R.
    McFarlane, S.
    Hsu, C.
    Salustro, C.
    Kahn, R.
    QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2009, 135 (641) : 922 - 934
  • [40] Evaluating statistical cloud schemes: What can we gain from ground-based remote sensing?
    Gruetzun, V.
    Quaas, J.
    Morcrette, C. J.
    Ament, F.
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2013, 118 (18) : 10507 - 10517