Satellite-based identification of aerosol particle species using a 2D-space aerosol classification model

Mao, Qianjun; Huang, Chuanjun; Chen, Qixiang; Zhang, Hengxing; Yuan, Yuan

doi:10.1016/j.atmosenv.2019.117057

Cited by 19 publications

(10 citation statements)

References 39 publications

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“…Previous studies generally used a threshold-based approach for identifying aerosol types with satellite input variables [9,[13][14][15][16], with aerosol optical properties and trace-gas information (with internal uncertainties) being obtained from various satellite sensors. Although uncertainties in satellite input parameters may lead to misclassification of aerosols, the early methods were rarely evaluated as reported in [25].…”

Section: Discussionmentioning

confidence: 99%

“…Various satellite-based variables have been used to classify aerosol types. Aerosol optical properties such as aerosol optical depth (AOD), Ångström exponent (AE), fine-mode fraction (FMF), and aerosol index were used as inputs in earlier satellite aerosol classification methods [12][13][14][15][16]. Column densities of the trace gases NO 2 , HCHO, SO 2 , and CO were also utilized in accounting for the abundances of several aerosol types [12,17].…”

Section: Introductionmentioning

confidence: 99%

“…The vertical aerosol mask data were evaluated by comparison with ground-based lidar measurements [23]. However, Mao et al [16] investigated the performance of satellite aerosol classification models using measurement data from the ground-based AErosol RObotic NETwork (AERONET) [24], with the agreement between satellite-and ground-based results ranging from 36% to 91%. Choi et al [25] recently proposed a new satellite classification method for identifying aerosol types based on a 'random forest' (RF) model, which is a machine-learning technique.…”

Section: Introductionmentioning

confidence: 99%

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Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model

et al. 2021

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The spatial coverage of satellite aerosol classification was improved using a random forest (RF) model trained with observational data including target (aerosol type) and input (satellite measurement) variables. The AErosol RObotic NETwork (AERONET) aerosol-type dataset was used for the target variables. Satellite input variables with many missing data or low mean-decrease accuracy were excluded from the final input variable set, and good performance in aerosol-type classification was achieved. The performance of the RF-based model was evaluated on the basis of the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical SSA wavelength dependence for individual aerosol types was consistent with that obtained for aerosol types by the RF-based model. The spatial coverage of the RF-based model was also compared with that of previously developed models in a global-scale case study. The study demonstrates that the RF-based model allows satellite aerosol classification with improved spatial coverage, with a performance similar to that of previously developed models.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model

et al. 2021

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“…In recent years, satellite datasets have been gradually used in aerosol recognition, including multi-angle observations of atmospheric aerosol provided by MISR to extract aerosol type information (e.g., size, absorbing versus non-absorbing and shape) [19,40]. Some reliable satellite-based identification models (e.g., AOD vs. aerosol relative optical depth [AROD]) have been developed to obtain aerosol types information with adequate spatial resolution and high coverage [41,42].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets

Huang

Sun

et al. 2021

Remote Sensing

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Long-term (2000–2019) assessment of aerosol loads and dominant aerosol types at spatiotemporal scales using multi-source datasets can provide a strong impetus to the investigation of aerosol loads and to the targeted prevention control of atmospheric pollution in densely populated regions with frequent anthropogenic activities and heavy aerosol emissions. This study uses multi-source aerosol datasets, including Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), Moderate Resolution Imaging Spectroradiometer (MODIS), and Aerosol Robotic Network (AERONET), to conduct a long-term variation assessment of aerosol load, high aerosol load frequency, and dominant aerosol types over Asia. The results indicate that regional aerosol type information with adequate spatial resolution can be combined with aerosol optical depth (AOD) values and heavy aerosol load frequency characterization results to explore the key contributors to air pollution. During the study period, the aerosol load over the North China Plain, Central China, Yangtze River Delta, Red River Delta, Sichuan Basin, and Pearl River Delta exhibited an increasing trend from 2000–2009 due to a sharp rise in aerosol emissions with economic development and a declining trend from 2010–2019 under stricter energy conservation controls and emissions reductions. The growth of urban/industrial (UI) type and biomass burning (BB) type aerosol emissions hindered the improvement of the atmospheric environment. Therefore, in future pollution mitigation efforts, focus should be on the control of UI-type and BB-type aerosol emissions. The Indus–Ganges River Plain, Deccan Plateau, and Eastern Ghats show a continuously increasing trend; however, the aerosol load growth rate of the last decade was lower than that of the first decade, which was mainly due to the decrease in the proportion of the mixed type aerosols.

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“…As a solution, satellite-based classification models have been proposed that use threshold values of satellite input variables such as aerosol optical properties and column densities of trace gases [23][24][25][26][27][28][29]. However, misclassification of aerosols can be caused by internal uncertainties in satellite input variables.…”

Section: Introductionmentioning

confidence: 99%

Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model

et al. 2021

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Aerosol types in Asian capital cities were classified using a random forest (RF) satellite-based aerosol classification model during 2018–2020 in an investigation of the contributions of aerosol types, with or without Aerosol Robotic Network (AERONET) observations. In this study, we used the recently developed RF aerosol classification model to detect and classify aerosols into four types: pure dust, dust-dominated aerosols, strongly absorbing aerosols, and non-absorbing aerosols. Aerosol optical and microphysical properties for each aerosol type detected by the RF model were found to be reasonably consistent with those for typical aerosol types. In Asian capital cities, pollution-sourced aerosols, especially non-absorbing aerosols, were found to predominate, although Asian cities also tend to be seasonally affected by natural dust aerosols, particularly in East Asia (March–May) and South Asia (March–August). No specific seasonal effects on aerosol type were detected in Southeast Asia, where there was a predominance of non-absorbing aerosols. The aerosol types detected by the RF model were compared with those identified by other aerosol classification models. This study indicates that the satellite-based RF model may be used as an alternative in the absence of AERONET sites or observations.

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Satellite-based identification of aerosol particle species using a 2D-space aerosol classification model

Cited by 19 publications

References 39 publications

Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model

Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model

Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets

Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model

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