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.

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Fine particulate matter (PM2.5) is a serious global health concern requiring mitigation, but source apportionment is difficult due to the limited variability in bulk aerosol composition between sources. The unique metal fingerprints of individual particles in PM2.5 sources can now be measured and may be used to identify sources. This study is the first to develop a robust machine learning pipeline to apportion PM2.5 sources based on the metal fingerprints of individual particles in air samples collected in Beijing, China. The metal fingerprints of particles in five primary PM2.5 source emitters were measured by single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOF-MS). A novel machine learning pipeline was used to identify unique fingerprints of individual particles from the five sources. The model successfully predicted 63% of the test data set (significantly higher than random guessing at 20%) and had 73% accuracy on a physically mixed sample. This strategy identified metal-containing particles unique to specific PM2.5 sources that confirms their presence and can potentially link PM2.5 toxicity to the metal content of specific particle types in anthropogenic PM2.5 sources.

Connected technologies have engendered a paradigm shift in mobility systems by enabling digital platforms to coordinate large sets of vehicles in real time. Recent research has investigated how a small number of connected vehicles may be coordinated to reduce total system cost. However, platforms may coordinate vehicles to optimize a fleet-wide objective which is neither user nor system optimal. We study the behavior of optimized fleets in mixed traffic and find that, at small penetrations, fleets may worsen system cost relative to user equilibrium, and provide a concrete example of this paradox. Past a critical penetration level, however, optimized fleets reduce system cost in the network, up to achieving system optimal traffic flow, without need for an external subsidy. We introduce two novel notions of fleet-optimal mixed equilibria: critical fleet size for user equilibrium (CFS-UE) and critical fleet size for system optimum (CFS-SO). We demonstrate on the Sioux Falls and Pittsburgh networks that 33% and 83% of vehicles, respectively, must participate in the fleet to achieve system optimum. In Pittsburgh, we find that, although fleets permeate the network, they accumulate on highways and major arterials; the majority of origin-destination pairs are either occupied exclusively by users or by the fleet. Critical fleet size offers regulators greater insight into where fleet and system interests align, transportation planners a novel metric to evaluate road improvements, and fleet coordinators a better understanding of their efforts to optimize their fleet. Funding: This work was supported by the U.S. Department of Transportation [Mobility21] and the National Science Foundation [CMMI-1931827]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1189 .

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