Advances in Methodological Evolution and Integration of Single-Particle Analysis for Nanoparticles in Environmental Matrices

Chen Yao; Chen Miao; Jiang Xu

doi:10.53941/ges.2026.100017

Highlights

Systematic summary of separation and extraction of nanoparticles from environmental samples
Identification and quantification of engineered and natural nanoparticles across atmospheric, aquatic, and terrestrial systems
Future application of single-particle mass spectrometry coupled with optical and spectroscopic techniques
Enhanced data interpretation for nanoparticle classification, toxicity mechanism elucidation and risk prediction with machine learning

Abstract

Environmental heterogeneity significantly influences the transport, transformation, and ecological risks of nanoparticles (NPs). Traditional bulk-scale analytical approaches often overlook inter-particle heterogeneity, thereby limiting a nuanced understanding of nanoparticle behavior and associated risks. Since single-particle analysis can enable high-throughput and highly sensitive detection of NPs in complex environmental matrices, this review emphasizes that characterizing NPs at the single-particle level is therefore crucial for accurately assessing their environmental fate and biological impacts. This review traces the evolution of single-particle Inductively Coupled Plasma Mass Spectrometry (spICP-MS) from its quadrupole foundations to time-of-flight configurations, and summarizes their applications across environmental matrix. The optimization and integration of strategies such as physical separation, chemical extraction, and functionalized material pretreatment laid the methodological foundation for single-particle analysis. The applications of spICP-MS are demonstrated in the identification, quantification, and source tracing of metallic NPs in atmospheric, aquatic, soil, and biological samples. Special attention is given to its multidimensional coupling with optical and spectroscopic techniques, as well as emerging machine learning-enabled frameworks for intelligent data interpretation and risk prediction. Ultimately, this work highlights the pivotal role of multidisciplinary technological integration and data-driven strategies in advancing environmental nanoscience toward an era focused on mechanistic insight and predictive modeling.

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