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<p>Analysing aerosol variability in the Indo-Gangetic Plain (IGP) is essential for delineating regional climate influences and air quality effects. The study uses a Gaussian Mixture Model (GMM) to classify columnar aerosol types probabilistically, utilizing quality-controlled AERONET optical properties at urban locations in northern India. Six aerosol categories were identified: mineral dust (MD), carbon-infused dust (MD + C), black carbon combined with coarse particles (BCCP), organic carbon (OC), secondary inorganic aerosols (SIA), and mixed aerosols. Results at the Kanpur site indicate the dominance of fine-mode aerosols, particularly OC and SIA. BCCP represents an absorbing mixed aerosol type, suggesting interactions between carbonaceous aerosols and coarse dust particles. Coarse dust aerosols show enhanced occurrence during pre-monsoon periods associated with regional dust transport and arid meteorological conditions. Seasonal variations demonstrate distinct spatial disparities across the study area, with Jaipur predominantly impacted by dust aerosols, Kanpur characterized by a combination of urban and industrial emissions, and Gandhi College primarily influenced by fine-mode OC and SIA, alongside significant contributions from absorbing mixed BCCP aerosols. Transition probability analysis illustrates the temporal stability of both dust-related and combustion-driven aerosol types, while also indicating frequent interconversions among various aerosol regimes. Assessment of aerosol optical properties suggests that mineral dust may contribute to a warming tendency, while secondary inorganic aerosols are associated with scattering-dominated behaviour, and black carbon-containing coarse particles exhibit enhanced absorption characteristics. The probabilistic GMM-based framework provides a reliable and reproducible approach for aerosol classification, improving understanding of aerosol variability and offering potential applications for regional climate studies and air quality management throughout South Asia.</p>

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Identification of columnar aerosol types and seasonal disparity over urban sites in the Indo-Gangetic Plain using a Gaussian Mixture Model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Nabin Sharma,
  • Sunil Kumar Chaurasiya,
  • Vivek Singh,
  • Narendra Singh,
  • Arti Choudhary

摘要

Analysing aerosol variability in the Indo-Gangetic Plain (IGP) is essential for delineating regional climate influences and air quality effects. The study uses a Gaussian Mixture Model (GMM) to classify columnar aerosol types probabilistically, utilizing quality-controlled AERONET optical properties at urban locations in northern India. Six aerosol categories were identified: mineral dust (MD), carbon-infused dust (MD + C), black carbon combined with coarse particles (BCCP), organic carbon (OC), secondary inorganic aerosols (SIA), and mixed aerosols. Results at the Kanpur site indicate the dominance of fine-mode aerosols, particularly OC and SIA. BCCP represents an absorbing mixed aerosol type, suggesting interactions between carbonaceous aerosols and coarse dust particles. Coarse dust aerosols show enhanced occurrence during pre-monsoon periods associated with regional dust transport and arid meteorological conditions. Seasonal variations demonstrate distinct spatial disparities across the study area, with Jaipur predominantly impacted by dust aerosols, Kanpur characterized by a combination of urban and industrial emissions, and Gandhi College primarily influenced by fine-mode OC and SIA, alongside significant contributions from absorbing mixed BCCP aerosols. Transition probability analysis illustrates the temporal stability of both dust-related and combustion-driven aerosol types, while also indicating frequent interconversions among various aerosol regimes. Assessment of aerosol optical properties suggests that mineral dust may contribute to a warming tendency, while secondary inorganic aerosols are associated with scattering-dominated behaviour, and black carbon-containing coarse particles exhibit enhanced absorption characteristics. The probabilistic GMM-based framework provides a reliable and reproducible approach for aerosol classification, improving understanding of aerosol variability and offering potential applications for regional climate studies and air quality management throughout South Asia.