A Novel Framework for Electron Microscopy‐Based Atmospheric Particulate Matter Analysis: Ensuring Representativeness and Quantifying Uncertainty

Abstract Scanning electron microscopy (SEM) is a critical tool for characterizing the morphology, elemental composition, and size characteristics of atmospheric single particles. Although advancements in computer‐controlled technologies have significantly improved analytical throughput (>1,000 particles/hour), the analysis of particulate matter (PM) samples still faces two fundamental challenges: first, determining how many particles need to be analyzed (i.e., the analysis threshold) to ensure statistical representativeness and second, quantifying the data uncertainty caused by the limited number of particles analyzed. Herein, we established an innovative framework addressing both challenges: a multicriteria analysis threshold evaluation system was developed to determine analysis thresholds, and a cyclic overlapping block bootstrap (COBB) method was proposed to quantify data uncertainty arising from finite particle counts. Analysis of 38 PM samples (479,200 particles) encompassing diverse emission sources, urban environments, and seasonal variations revealed that sample complexity dictated analysis thresholds. Environmental samples required higher thresholds (approximately 4,300 particles for active sampling and 5,000 for passive sampling) than source samples (approximately 3,600 particles) primarily due to their more complex composition. COBB analysis demonstrated an inverse correlation between component abundance and relative uncertainty. Notably, trace components (abundance <1.0%) exhibited persistently high uncertainty even with 2,000‐particle analyses. This framework establishes systematic methodologies spanning standardized SEM data acquisition to uncertainty quantification, substantially enhancing the scientific rigor, and cross‐study comparability of SEM‐based atmospheric PM research.