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<p>Aerosol type classification is very of importance to calculate aerosol radiative forcing, which further influences future climate change. In the present study, the spatiotemporal variations of aerosol optical property (AOP) were investigated during 2007–2021 by simultaneously passive and active satellite remote sensors, including aerosol optical depth at 550&#xa0;nm (AOD<sub>550</sub>) and Ångström exponent (AE<sub>470–660</sub>) retrieved from MODerate resolution Imaging Spectroradiometer (MODIS), absorbing aerosol optical depth (AAOD<sub>355−500</sub>) from Ozone Monitoring Instrument (OMI). Further, key aerosol types were distinguished by two clustering methods using pre-defined threshold of AOP in Xinjiang (high aerosol loadings), West China. The magnitude of AOD and AAOD in all seasons follow the order by Spring&gt; Summer&gt; Autumn&gt; Winter, but AE for Spring&lt; Summer&lt; Autumn&lt; Winter, indicating the complexity of aerosol sources in the study region. Using the AOD–AE method (Technique-Ⅰ), five major aerosol types were identified in clean continental (CC), clean marine (CM), biomass burning/urban–industrial (BB/UI), desert dust (DD), and mixed (MIX). In all the seasons, DD was the dominant aerosol type over Southern Xinjiang. BB/UI type was obviously increased in winter owing to enhanced anthropogenic activities and industrial emissions. In addition, sub-classification of AAOD-AE method (Technique-Ⅱ) found the BB/UI type in winter were mainly composed of absorbing (carbonaceous) and non-absorbing (sulphate) aerosol over Northern Xinjiang. Meantime, the obtained results from two clustering methods showed reasonable consistency with the aerosol types from spaceborne lidar of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). These results can improve the aerosol type (absorbing and non-absorbing) characterization in climate models to decrease the uncertainty in the aerosol radiative, climatic effect and air quality monitoring for areas where aerosol measurements are lacking.</p>

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Classification of key aerosol types from multiple satellite products in Xinjiang, West China

  • 期刊论文

  • 发表日期：

    2026年3月5日

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  • Honglin Pan,
  • Xia Li,
  • Shuting Li,
  • Maoling Ayitikan,
  • Yuting Zhong

摘要

Aerosol type classification is very of importance to calculate aerosol radiative forcing, which further influences future climate change. In the present study, the spatiotemporal variations of aerosol optical property (AOP) were investigated during 2007–2021 by simultaneously passive and active satellite remote sensors, including aerosol optical depth at 550 nm (AOD550) and Ångström exponent (AE470–660) retrieved from MODerate resolution Imaging Spectroradiometer (MODIS), absorbing aerosol optical depth (AAOD355−500) from Ozone Monitoring Instrument (OMI). Further, key aerosol types were distinguished by two clustering methods using pre-defined threshold of AOP in Xinjiang (high aerosol loadings), West China. The magnitude of AOD and AAOD in all seasons follow the order by Spring> Summer> Autumn> Winter, but AE for Spring< Summer< Autumn< Winter, indicating the complexity of aerosol sources in the study region. Using the AOD–AE method (Technique-Ⅰ), five major aerosol types were identified in clean continental (CC), clean marine (CM), biomass burning/urban–industrial (BB/UI), desert dust (DD), and mixed (MIX). In all the seasons, DD was the dominant aerosol type over Southern Xinjiang. BB/UI type was obviously increased in winter owing to enhanced anthropogenic activities and industrial emissions. In addition, sub-classification of AAOD-AE method (Technique-Ⅱ) found the BB/UI type in winter were mainly composed of absorbing (carbonaceous) and non-absorbing (sulphate) aerosol over Northern Xinjiang. Meantime, the obtained results from two clustering methods showed reasonable consistency with the aerosol types from spaceborne lidar of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). These results can improve the aerosol type (absorbing and non-absorbing) characterization in climate models to decrease the uncertainty in the aerosol radiative, climatic effect and air quality monitoring for areas where aerosol measurements are lacking.