Unveiling the composition and origins of inhalable submicron particles from electric vehicle thermal runaway

Thermal runaway (TR) in lithium-ion batteries (LIBs) presents a major safety risk in electric vehicles (EVs), particularly in enclosed environments, e.g. underground parking and tunnels. These events release large quantities of toxic gases and particulate matter, yet the characteristics of inhalable fine particles remain poorly understood. In this study, we performed a detailed chemical analysis of particles ranging from 0.2 and 2.0 μm collected during a full-scale EV TR experiment using Single-Particle Aerosol Mass Spectrometry (SPAMS). Three source apportionment methods, i.e. K-Means clustering, Adaptive Resonance Theory, and Non-negative Matrix Factorization, were adopted to consistently identify three dominant particle types: Electrolyte-Derived Organic Particles (EDOPs, 78.1 %), Electrode Decomposition Particles (EDPs, 13.2 %), and Electrolyte-Derived Atmospheric Fusion Particles (EDAFPs, 8.7 %). The corrected number concentration reached 1.41 × 10 6 particles·L −1 , with a geometric mean diameter of 0.30 μm and a geometric standard deviation of 1.25. Nearly 80 % of particles originated from electrolytes, enriched in organic fragments, e.g. C 4 H 4 + and PO 3 − , while electrode-related particles contained high levels of toxic ions including Li + , Mn + , F − , and CN − ions. Respiratory tract deposition modeling revealed preferential accumulation in the alveolar and extrathoracic regions, indicating high inhalation exposure potential. This work provides the first full-scale vehicle-level quantification and source-specific inhalation risk characterization of insoluble submicron particles from EV TR events, offering critical insights for toxicological assessment and risk-informed battery safety strategies. Synopsis The study characterized the compositional feature and size distribution of the inhalable particles from the thermal runaway of lithium-ion batteries in electric vehicles, analyzing the risks of the particle emissions to human health based on the classification results.