A comprehensive study on the validation and application of multi-lognormal distribution models for atmospheric particles

Abstract

The particle number size distribution (PNSD) is crucial for evaluating air quality and mitigating environmental pollution, as particles of different sizes have diverse effects on human health and climate. However, obtaining a comprehensive understanding of PNSD is challenging due to its inherent complexities and variability. Multi-lognormal distribution models are employed to fit PNSD, as seen in climate models, but discrepancies between model fits and observed PNSD persist. This study adopts hourly data from 2017 to 2020 across eight monitoring sites in diverse environments—rural, urban, mountainous, and polar, and compares the observed PNDS with those simulated by multi-lognormal distribution models. The results demonstrated that the model generally achieved a high correlation with observed PNSD data (r2 > 0.75), effectively capturing key characteristics of nucleation and Aitken mode particles. However, the model had a tendency to overestimate the total number concentration by approximately 1.09 times, particularly noticeable under conditions of high concentrations of smaller particles. The model successfully represented prevalent bimodal size distribution patterns in urban areas with high ultrafine particle concentrations, though its performance was slightly less accurate in scenarios involving trimodal distributions. Despite these strong correlations and the model's ability to reflect diurnal and seasonal variations, which suggests its broad applicability and utility, there were notable limitations on smaller time scales and in specific particle size ranges. These limitations were particularly evident in capturing detailed phenomena relevant to new particle formation events, indicating areas where model refinement is necessary. The results highlighted the importance of investigating discrepancies between model predictions and actual observations, which is crucial for refining climate models that utilize PNDS. The uniform comparison facilitated a detailed exploration of particle properties from model results, offering deeper insights into aerosol behavior and its environmental impacts.