Distinguishing Engineered TiO₂ Nanomaterials from Natural Ti Nanomaterials in Soil Using spICP-TOFMS and Machine...

Identifying engineered nanomaterials (ENMs) made from earth-abundant elements in soils is difficult because soil also contains natural nanomaterials (NNMs) containing similar elements. Here, machine learning models using elemental fingerprints and mass distributions of three TiO₂ ENMs and Ti-based NNMs recovered from three natural soils measured by single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS) was used to identify TiO₂ ENMs in soil. Synthesized TiO₂ ENMs were unassociated with other elements (>98%), while 40% of Ti-based ENM particles recovered from wastewater sludge had distinguishable elemental associations. All Ti-based NNMs extracted from soil had a similar chemical fingerprint despite the soils being from different regions, and >60% of Ti-containing NNMs had no measurable associated elements. A machine learning model best distinguished NNMs and ENMs when differences in Ti-mass distribution existed between them. A trained LR model could classify 100 nm TiO₂ ENMs at concentrations of 150 mg kg^-1 or greater. The presence of TiO₂ ENMs in soil could be confirmed using this approach for most ENM-soil combinations, but the absence of a unique chemical fingerprint in a large fraction of both TiO₂ ENMs and Ti-NNMs increases model uncertainty and hinders accurate quantification.