Graphical aerosol classification method using aerosol relative optical depth

Chen, Qixiang; Yuan, Yuan; Shuai, Yong; Tan, He-Ping

doi:10.1016/j.atmosenv.2016.03.061

Cited by 37 publications

(19 citation statements)

References 22 publications

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“…The modal refractive index inferring scheme was applied to the AERONET inversion products from the sites listed in Table 1. Considering the regional differences in AROD values, we conclude that aerosols in the NWC region mainly originated from coarse-mode dust particles, while those in other regions were mainly anthropogenic aerosols (AROD < 0.4) [35]. Figure 5c and 5d show variations in the m products from AERONET and estimated modal m values as a function of wavelength.…”

Section: Modal Refractive Indices In Typical Regions Of Chinamentioning

confidence: 91%

“…The AOD 0.44 values in NCP, YRD, and PRD were larger than 1.0, indicating a high aerosol load over these industrialized regions, while the NECP and CTW regions showed annual mean AOD 0.44 values of 0.7-0.8, with NWC showing the lowest value. Considering the regional differences in AROD values, we conclude that aerosols in the NWC region mainly originated from coarse-mode dust particles, while those in other regions were mainly anthropogenic aerosols (AROD < 0.4) [35]. Figure 5c,d show variations in the m products from AERONET and estimated modal m values as a function of wavelength.…”

Section: Modal Refractive Indices In Typical Regions Of Chinamentioning

confidence: 93%

“…Both AROD and AOD 0.44 values can be used to distinguish maritime (MA), continental (CO), desert dust (DD), sub-continental (SC), urban industry (UI), and biomass burning (BB) aerosols [35], which has been applied to the data from the AERONET MAPS-Seoul Campaign to investigate regional variations in aerosol type [36]. Here, only AROD was used to identify pollution and dust aerosols on land.…”

Section: Aerosol Mode Classificationmentioning

confidence: 99%

“…The aerosol models used in the current study were based on the summary of aerosol properties provided by Dubovik et al [12] and those determined in our previous study [35]. We chose four typical aerosol models, including biomass burning (BB), urban industry (UI), sub-continental (SC), and desert dust (DD) aerosol models for performing numerical tests.…”

Section: Aerosol Modelsmentioning

confidence: 99%

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Inferring Fine-Mode and Coarse-Mode Aerosol Complex Refractive Indices from AERONET Inversion Products over China

et al. 2019

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Detailed knowledge of the complex refractive indices (m) of fine- and coarse-mode aerosols is important for enhancing understanding of the effect of atmospheric aerosol on climate. However, studies on obtaining aerosol modal m values are particularly scarce. This study proposes a method for inferring m values of fine- and coarse-mode aerosol using the inversion products from the AERONET ground-based aerosol robotic network. By identifying the aerosol type, modal m values are constrained and then inferred based on a maximum likelihood method. Numerical tests showed that compared with the reference values, our method slightly overestimates the real parts of the refractive indices (n), but underestimates the imaginary parts (k) by 2.11% ± 11.59% and 8.4% ± 26.42% for fine and coarse modes, respectively. We applied this method to 21 AERONET sites around China, which yielded annual mean m values of (1.45 ± 0.04) + (0.0109 ± 0.0046)i and (1.53 ± 0.01) + (0.0039 ± 0.0011)i for fine- and coarse-mode aerosols, respectively. It is observed that the fine mode n decreased from 1.53 to 1.39 with increasing latitude, while fine mode k values were generally larger than 0.008 over most of China. The coarse-mode n and k ranged from 1.52 to 1.56 and from 0.002 to 0.006, respectively.

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Section: Modal Refractive Indices In Typical Regions Of Chinamentioning

confidence: 91%

Section: Modal Refractive Indices In Typical Regions Of Chinamentioning

confidence: 93%

Section: Aerosol Mode Classificationmentioning

confidence: 99%

Section: Aerosol Modelsmentioning

confidence: 99%

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Inferring Fine-Mode and Coarse-Mode Aerosol Complex Refractive Indices from AERONET Inversion Products over China

et al. 2019

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“…Aerosols usually play a pivotal role in determining the properties of the atmosphere, e.g., by reducing visibility or affecting the net radiative fluxes and temperature [1][2][3]. Considerable researches have been carried out on studying the aerosol particle size, optical constant, and concentration in the particle dispersed medium, e.g., Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS), which are the global groundbased aerosol observation networks established to study the properties of the atmospheric aerosols [4,5]. Usually, in the atmosphere, the aerosol affects the radiative transfer, meteorological phenomena, and climate trends.…”

Section: Introductionmentioning

confidence: 99%

Application of Multistep Inversion Method for Online Monitoring Aerosol Particle Size Distribution and Aerosol Concentration

Mao

et al. 2020

Mathematical Problems in Engineering

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Aerosol concentration in the flow is usually time varying, and aerosol particle size distribution (PSD) is considered to be unchanged, which increases the difficulty of the measurement of aerosol PSD and concentration online. To solve these problems, a kind of multistep inversion method based on the angular light-scattering (ALS) signals is proposed. First, the aerosol PSD is estimated using shuffled frog-leaping algorithms (SFLAs) from relative ALS signals. Then, with aerosol PSD as priori information, the aerosol concentration is obtained by the Kalman filter (KF) algorithm, widely used in the real-time control system of industrial facilities for its ability of fast predictions. The result reveals that the performance of the improved SFLA is better than that of the original SFLA in solving the aerosol PSD. Moreover, in studying the aerosol concentration, more accurate results can be obtained with larger standard deviation of process noise or smaller standard deviation of measurement noise, while decreasing sampling time interval can improve the accuracy of retrieval results and reduce time delay to a certain degree. So, to improve retrieval accuracy, the noise should be controlled, and appropriate sampling time interval should be selected. All the numerical simulations confirm that the methodology provides effective and reliable results in real-time estimating.

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Application of multiplatform remote sensing data over East Asia Ocean: aerosol characteristics and aerosol types

Luan,

Cao,

Shen

et al. 2024

Environ Sci Pollut Res

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Graphical aerosol classification method using aerosol relative optical depth

Cited by 37 publications

References 22 publications

Inferring Fine-Mode and Coarse-Mode Aerosol Complex Refractive Indices from AERONET Inversion Products over China

Inferring Fine-Mode and Coarse-Mode Aerosol Complex Refractive Indices from AERONET Inversion Products over China

Application of Multistep Inversion Method for Online Monitoring Aerosol Particle Size Distribution and Aerosol Concentration

Application of multiplatform remote sensing data over East Asia Ocean: aerosol characteristics and aerosol types

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