Retrieval of black carbon aerosol surface concentration using satellite remote sensing observations

Bao, Fangwen1,2,4; Cheng, Tianhai3; Li, Ying1; Gu, Xingfa3; Guo, Hong3; Wu, Yu3; Wang, Ying3; Gao, Jinhui1,4

2019-06

10.1016/j.rse.2019.03.036

REMOTE SENSING OF ENVIRONMENT   影响因子和分区

0034-4257

1879-0704

As an important part of the anthropogenic aerosol, Black Carbon (BC) aerosols in the atmospheric environment have strong impacts on climate change. Recently, most remote sensing studies on aerosol components detection are limited to the inversion of aerosol optical properties, integration of chemistry models or in situ observations. In this paper, an algorithm based on Effective Medium Approximations (EMA) and statistically optimized aerosol inversion algorithm was integrated for retrieving the surface mass concentration of BC aerosols from satellite signals. The sensitivity analyses for the developed forward model proved that the volume fraction of vertical BC is sensitive to the satellite observations and significantly improved especially over bright surface targets or under polluted atmospheric conditions. By updating the forward model and retrieved parameters of the statistically optimized inversion algorithm, three cases of high aerosol loading days were retrieved from Polarization and Anisotropy of Reflectance for Atmospheric Sciences Coupled with Observations from a LiDAR (PARASOL) measurements, which shows a significant ability of BC aerosol detection. Additionally, the validation and closure studies of BC concentration retrievals also indicates an encouraging consistency with correlation (R) of 0.71, mean bias of 3.55, and root-mean-square error (RMSE) of 3.75 when compared against the in-situ observations over South Asia. The accuracy of the retrievals also demonstrates different trends under different levels of aerosol loadings, which shows a higher accuracy in biomass burning seasons (R = 0.75, RMSE = 4.04, Bias = 3.27) while exaggerates the results in the case of clear conditions (R = 0.47, RMSE = 4.83, Bias = 4.00). Finally, the uncertainties of three assumptions, including proposing uniform vertical profile for BC, neglecting light-absorbing aerosols and using spherical EMA models were discussed in our manuscript. The maximum standard deviations caused by these uncertainties on low BC aerosol volume fractions (f(BC) < 1%) are 0.8%, 0.35% and 0.2% while these deviations will change to 0.25%, 0.05% and 1.5% respectively under higher BC fractions (f(BC) > 5%). This conclusion confirmed that the proposed algorithm for BC surface concentration retrieval extends the application of satellite remote sensing in monitoring the extreme biomass burning pollution.

Black carbon concentration Satellite remote sensing Air pollution

SCI ; EI

英语

第一 ; 通讯

Science and Technology Planning Project of Guangdong Province of China[2017A050506003]

Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology

Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:000468256800007

ELSEVIER SCIENCE INC

20191406729388

Aerosols ; Air pollution ; Approximation algorithms ; Atmospheric chemistry ; Carbon ; Climate change ; Fog ; Mean square error ; Optical properties ; Optical radar ; Pollution ; Satellites ; Sensitivity analysis ; Volume fraction

Atmospheric Properties:443.1 ; Air Pollution:451 ; Thermodynamics:641.1 ; Satellites:655.2 ; Light/Optics:741.1 ; Optical Devices and Systems:741.3 ; Chemical Products Generally:804 ; Mathematics:921 ; Mathematical Statistics:922.2

GEOSCIENCES

Web of Science

1.Southern Univ Sci & Technol, Dept Ocean Sci & Engn, Shenzhen, Peoples R China
2.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen, Peoples R China
3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
4.Univ Sci & Technol China, Sch Earth & Space Sci, Hefei, Anhui, Peoples R China

Bao, Fangwen,Cheng, Tianhai,Li, Ying,et al. Retrieval of black carbon aerosol surface concentration using satellite remote sensing observations[J]. REMOTE SENSING OF ENVIRONMENT,2019,226:93-108.

Bao, Fangwen.,Cheng, Tianhai.,Li, Ying.,Gu, Xingfa.,Guo, Hong.,...&Gao, Jinhui.(2019).Retrieval of black carbon aerosol surface concentration using satellite remote sensing observations.REMOTE SENSING OF ENVIRONMENT,226,93-108.

Bao, Fangwen,et al."Retrieval of black carbon aerosol surface concentration using satellite remote sensing observations".REMOTE SENSING OF ENVIRONMENT 226(2019):93-108.