Magnetic
particles (MPs), with magnetite (Fe3O4) and
maghemite (γ-Fe2O3) as the most
abundant species, are ubiquitously present in the natural environment.
MPs are among the most applied engineered particles and can be produced
incidentally by various human activities. Identification of the sources
of MPs is crucial for their risk assessment and regulation, which,
however, is still an unsolved problem. Here, we report a novel approach,
hierarchical classification-aided stable isotopic fingerprinting,
to address this problem. We found that naturally occurring, incidental,
and engineered MPs have distinct Fe and O isotopic fingerprints due
to significant Fe/O isotope fractionation during their generation
processes, which enables the establishment of an Fe–O isotopic
library covering complex sources. Furthermore, we developed a three-level
machine learning model that not only can distinguish the sources of
MPs with a high precision (94.3%) but also can identify the multiple
species (Fe3O4 or γ-Fe2O3) and synthetic routes of engineered MPs with a precision
of 81.6%. This work represents the first reliable strategy for the
precise source tracing of particles with multiple species and complex
sources.