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<p>Hygroscopic growth of aerosols significantly affects radiative forcing and visibility, yet remains challenging due to the interplay among size, composition, and humidity. This study integrates in-situ PM and lidar data to address discrepancies between dry mass concentrations and wet optical measurements. Using machine learning inversion (XGBoost, <i>R</i><sup>2</sup> = 0.98), dry-state size distributions were retrieved from PM data. Mie theory was applied to derive the dry extinction coefficient, and the lidar-based wet extinction coefficient yielded the hygroscopic growth. Aerosol types were classified using Random Forest (accuracy 83.4%), revealing dominance of coarse hygroscopic aerosols in this coastal urban region. Optical response varied with hygroscopicity: wet extinction coefficient increased with RH for hydrophilic types but remained low for hydrophobic aerosols. Notably, clean conditions occasionally showed a sharp increase in wet extinction coefficient despite low PM, highlighting limitations of mass-only assessments. This approach suggests improved aerosol characterization algorithms considering size and hygroscopicity, supporting advanced air quality and climate modeling studies.</p>

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Machine learning-based retrieval of aerosol size and hygroscopicity using horizontal scanning LiDAR and PM data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月2日

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  • Juseon Shin,
  • Juhyeon Sim,
  • Matthias Tesche,
  • Jihyun Yoon,
  • Dukhyeon Kim,
  • Youngmin Noh

摘要

Hygroscopic growth of aerosols significantly affects radiative forcing and visibility, yet remains challenging due to the interplay among size, composition, and humidity. This study integrates in-situ PM and lidar data to address discrepancies between dry mass concentrations and wet optical measurements. Using machine learning inversion (XGBoost, R2 = 0.98), dry-state size distributions were retrieved from PM data. Mie theory was applied to derive the dry extinction coefficient, and the lidar-based wet extinction coefficient yielded the hygroscopic growth. Aerosol types were classified using Random Forest (accuracy 83.4%), revealing dominance of coarse hygroscopic aerosols in this coastal urban region. Optical response varied with hygroscopicity: wet extinction coefficient increased with RH for hydrophilic types but remained low for hydrophobic aerosols. Notably, clean conditions occasionally showed a sharp increase in wet extinction coefficient despite low PM, highlighting limitations of mass-only assessments. This approach suggests improved aerosol characterization algorithms considering size and hygroscopicity, supporting advanced air quality and climate modeling studies.