Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data

Luo, Nana; Zhang, Yue; Jiang, Yize; Zuo, Chen; Chen, Jiayi; Zhao, Wenji; Shi, Wenzhong; Yan, Xing

doi:10.1016/j.envpol.2024.123838

Cited by 3 publications

(1 citation statement)

References 68 publications

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“…Aerosols are liquid and solid particles suspended in the atmosphere, and are one of the least known components of the climate system [1][2][3]. The spatiotemporal characteristics of atmospheric aerosols are very important for weather forecasting [4,5], plant growth, and air quality evaluation [6,7].…”

Section: Introductionmentioning

confidence: 99%

Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China

Luo,

Zou,

Zang

et al. 2024

Atmosphere

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Machine learning methods have been recognized as rapid methods for satellite-based aerosol retrievals but have not been widely applied in geostationary satellites. In this study, we developed a wide and deep learning model to retrieve the aerosol optical depth (AOD) using Himawari-8. Compared to traditional deep learning methods, we embedded a “wide” modeling component and tested the proposed model across China using independent training (2016–2018) and test (2019) datasets. The results showed that the “wide” model improves the accuracy and enhances model interpretability. The estimates exhibited better accuracy (R2 = 0.81, root-mean-square errors (RMSEs) = 0.19, and within the estimated error (EE) = 63%) than those of the deep-only models (R2 = 0.78, RMSE = 0.21, within the EE = 58%). In comparison with extreme gradient boosting (XGBoost) and Himawari-8 V2.1 AOD products, there were also significant improvements. In addition to higher accuracy, the interpretability of the proposed model was superior to that of the deep-only model. Compared with other seasons, higher contributions of spring to the AOD concentrations were interpreted. Based on the application of the wide and deep learning model, the near-real-time variation of the AOD over China could be captured with an ultrafine temporal resolution.

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Section: Introductionmentioning

confidence: 99%

Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China

Luo,

Zou,

Zang

et al. 2024

Atmosphere

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Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China

Xu,

Ma,

Luo

et al. 2024

Atmospheric Environment

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Analyses of the 2016–2023 Dust Storms in China Using Himawari-8 Remote Sensing Observations

Luo,

Hu,

Piao

et al. 2024

Remote Sensing

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The March 2021 dust storm in China degraded air quality across a wide area of Asia. Atmospheric circulation and meteorological factors play an important role in the occurrence of dust storms. To understand whether decreasing or increasing these factors can mitigate dust storms, this study utilizes remote sensing imagery data from the Himawari-8/-9 satellites to understand spatial and temporal variations in China’s 2016–2023 dust storms. Our findings are as follows: (1) in 2016–2023, dust storms covered northern China, with Xinjiang, Inner Mongolia, Gansu, and Ningxia being high-frequency areas; (2) the origins of the dust storms are northwest of Mongolia and Xinjiang, with upper air masses originating from Siberia and concentrating in central-west Inner Mongolia and northern Gansu; (3) dew point temperature, wind speed, cloud cover, and atmospheric circulation are important determinants of the occurrences of dust storms. Analyzing trends and influential factors of dust storms is important as this provides a scientific basis for decision-making in dust storm management.

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Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data

Cited by 3 publications

References 68 publications

Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China

Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China

Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China

Analyses of the 2016–2023 Dust Storms in China Using Himawari-8 Remote Sensing Observations

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