AeroMig: Multi-modal Inverse-Gamma modeling for aerosol particle number size distributions

2026-10-05

AI Summary

This study addresses the challenge of accurately characterizing aerosol size distributions, which frequently exhibit skewed, heavy-tailed, and multimodal features that conventional log-normal fitting methods fail to capture. We propose a parametric framework based on inverse gamma mixture distributions to overcome the limitations of existing models. By superimposing multiple components, this approach precisely captures asymmetric and heavy-tailed distribution structures, complemented by an efficient parameter estimation technique. Empirical evaluations demonstrate that the proposed method significantly outperforms prevailing approaches in fitting accuracy, error control, and computational efficiency. Ultimately, this work provides a more flexible and precise solution for modeling complex aerosol size distributions.

Abstract

Aerosol particles play a critical role in air quality, human health, and climate processes, making their accurate characterization essential. One common way to represent aerosol populations is through particle number size distributions (PNSDs), which describe the concentration of particles across different mobility diameters. However, these distributions are often highly skewed, heavy-tailed, and multi-modal, posing significant challenges for conventional fitting approaches. In this study, we propose a robust parametric framework (AeroMiG) for modeling PNSDs using Inverse-Gamma mixture distributions. The method represents observed size distributions as a superposition of multiple Inverse-Gamma components, enabling flexible modeling of asymmetric and heavy-tailed structures that are not well captured by widely-used Log-Gaussian based approaches. The performance of the proposed method is evaluated using real-world data from three measurement stations. Results are assessed using multiple criteria, including predictive accuracies, computational efficiency, and a composite score summarizing overall model quality. Comparative analysis against a widely used Aerosol Multi-mode Log-Gaussian (AeroMG) demonstrates that the AeroMiG framework consistently achieves improved fitting accuracy, lower error, and faster computation. The proposed approach provides a reliable and computationally efficient solution for modeling complex aerosol size distributions, with potential applications in real-time air quality monitoring and data-driven environmental analysis.

Problem

• Aerosol particle number size distributions
• Inverse-Gamma mixture
• Multi-modal modeling
• Heavy-tailed distributions
• Parametric fitting

Innovation

• Inverse-Gamma mixture
• Particle number size distributions
• Multi-modal modeling
• Parametric framework
• Computational efficiency