发表机构
University of Helsinki; University of Eastern Finland（赫尔辛基大学; 芬兰东部大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出AeroMiG框架，用逆伽马混合分布建模气溶胶粒子数尺寸分布，解决偏斜重尾多模态问题，在三个站点数据上相比对数高斯方法拟合更准、误差更低、计算更快。
AI 中文摘要
气溶胶粒子在空气质量、人类健康和气候过程中发挥着关键作用，因此对其准确表征至关重要。表示气溶胶群体的一种常见方式是通过粒子数尺寸分布（PNSDs），该分布描述了不同迁移直径下粒子的浓度。然而，这些分布通常高度偏斜、具有重尾且呈多模态，给传统拟合方法带来了重大挑战。在本研究中，我们提出了一种稳健的参数化框架（AeroMiG），用于使用逆伽马混合分布对PNSDs进行建模。该方法将观测到的尺寸分布表示为多个逆伽马分量的叠加，从而能够灵活建模非对称和重尾结构，而这些结构是广泛使用的基于对数高斯的方未能很好捕获的。所提出方法的性能使用来自三个测量站的真实数据进行评估。结果通过多个标准进行评估，包括预测准确性、计算效率以及总结整体模型质量的综合得分。与广泛使用的气溶胶多模态对数高斯（AeroMG）的对比分析表明，AeroMiG框架始终实现了更高的拟合精度、更低的误差和更快的计算速度。所提出的方法为建模复杂气溶胶尺寸分布提供了一种可靠且计算高效的解决方案，在实时空气质量监测和数据驱动的环境分析中具有潜在应用。
英文摘要
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.
