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Environmental impact of solution pH on the formation and migration of iron colloids in deep subsurface energy systems

Deep subsurface stimulation processes often promote fluid-rock interactions that can lead to the formation of small colloidal particles that are suspected to migrate through the rock matrix, partially or fully clog pores and microfractures, and promote the mobilization of contaminants. Thus, the goal of this work is to understand the geochemical changes of the host rock in response to reservoir stimulation that promote the formation and migration of colloids. Two different carbonate-rich shales were exposed to different solution pHs (pH = 2 and 7). In this study, iron and other mineral transformations at the shale-fluid interface were first characterized by synchrotron-based XRF mapping. Then, colloids that were able to migrate from the shale into the bulk fluid were characterized by synchrotron-based extended X-ray absorption structure (EXAFS), scanning electron microscopy (SEM), and single-particle inductively coupled plasma time-of-flight mass spectrometry (sp-icpTOF-MS). When exposed to the pH = 2 solution, extensive mineral dissolution and secondary precipitation was observed; iron-(oxyhydr)oxide colloids colocated with silicates were observed by SEM at the fluid-shale interfaces, and the mobilization of chromium and nickel with these iron colloids into the bulk fluid was detected by sp-icpTOF-MS. Iron EXAFS spectra of the solution at the shale-fluid interface suggests the rapid (within minutes) formation of ferrihydrite-like nanoparticles. Thus, we demonstrate that the pH neutralization promotes the mobilization of existing silicate minerals and the rapid formation of new iron colloids. These Fe colloids have the potential to migrate through the shale matrix and mobilize other heavy metals (such as Cr and Ni, in this study) and impacting groundwater quality, as well produced waters from these hydraulic fracturing operations.

54 ENVIRONMENTAL SCIENCES↗

The Pixel Anomaly Detection Tool : a user-friendly GUI for classifying detector frames using machine-learning approaches

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

47 OTHER INSTRUMENTATION↗

Evolution of LIBS technology to mobile instrumentation for expediting firearm-related investigations at the laboratory and the crime scene

Gunshot residue (GSR) is one of the few forensic disciplines that lack accurate screening techniques. This study proposes using a mobile LIBS instrument to detect inorganic GSR and compares performance to a previously validated laboratory instrument. The mobile LIBS is designed with advanced configurations specifically for on-site GSR analysis, including a CMOS detector and a sampling chamber that holds up to six typical GSR collection devices with separate gas flow ports to prevent cross-contamination. A significant novelty of the portable instrument is its image magnification, which allows quick searching and visualization of GSR particle morphology. The single-particle imaging and elemental composition capability is one of a kind and offers superior confirmatory features for GSR. The mobile LIBS performance was evaluated for residues collected from the hands of shooters (100 samples) and non-shooters (200 background samples), analyzed sequentially by the mobile instrument and then the laboratory instrument. Accuracies better than 98.8% were obtained by both instruments, demonstrating their suitability for trace IGSR detection from skin specimens. Implementation of this methodology is anticipated to drastically speed up response times (i.e., from several hours per sample by standard SEM-EDS practice to a few minutes by LIBS). The screening methods can be easily incorporated into workflows to improve decision-making processes at the crime scene and laboratory settings, reduce backlogs, and improve case management.

47 OTHER INSTRUMENTATION↗

Experimental study of the S 38 excited level scheme

Information on the 38 S level scheme was expanded through experimental work utilizing a fusion-evaporation reaction and in-beam γ-ray spectroscopy. Prompt γ-ray transitions were detected by the Gamma-Ray Energy Tracking Array (GRETINA) and recoiling 38 S residues were selected by the Fragment Mass Analyzer (FMA). Tools based on machine-learning techniques were developed and deployed for the first time in order to enhance the unique selection of 38S residues and identify any associated γ-ray transitions. The new level information, including the extension of the even-spin yrast sequence through J π =8( + ), was interpreted in terms of a basic single-particle picture as well shell-model calculations which incorporated the empirically derived FSU interaction. A comparison between the properties of the yrast states in the even-Z, N = 22 isotones from Z = 14 to 20, and for 36 Si– 38 S in particular, was also presented with an emphasis on the role and influence of the neutron 1 p 3/2 orbital on the structure in the region.

20 ≤ A ≤ 38↗

Argon ions injected parallel and perpendicular to the magnetic field

Preliminary results are reported of measurements obtained with the 0-10-kHz electric-field plasma-wave receivers and single-particle ion detectors of the ARCS-3 sounding-rocket payload over the auroral oval on February 10, 1985, after downward/field-parallel or transverse-spiral injection of Ar ions from the accelerator subpayload (separating itself at 2.3 m/s). The data are presented graphically and briefly characterized. The waves induced by the parallel injections are found to be much weaker than those induced by the perpendicular injections. In addition to the expected response to the Ar beam, an ion flux at pitch angle 90 deg was detected during the second parallel injection.

Erlandson, R. E.↗

Expanding SPoRT RGBs and Machine Learning Techniques to Enhance Air Quality Monitoring in Southern Asia

Air pollution poses significant environmental, public health, and societal concerns in the Hindu Kush Himalaya (HKH) region of south-central Asia, notably during the dry monsoon months (~November to May). Key contributors to poor air quality include dust from the Middle East and western India, persistent nocturnal fog/smog, and biomass burning. To address this issue, we established a robust air quality and chemistry observation and modeling product suite utilizing multi-spectral red-green-blue (RGB) composite satellite products from Korea’s GEO-KOMPSAT-2A satellite, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for dust transport forecasts, and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model to predict aerosols and chemical species concentrations. Our team employed similar RGB recipes transitioned by the NASA Short-term Prediction Research and Transition (SPoRT) Center for the GOES-R era products over the Western Hemisphere, with significant success in depicting dust and nocturnal fog / low clouds, and to a lesser extent smoke and fire hot spots. We will extend these capabilities for the HKH region by applying an artificial intelligence (AI) model that objectively identifies dust from multi-spectral satellite data over the Southwestern United States. The AI model will be calibrated for automated dust detection over HKH from GEO-KOMPSAT-2A satellite data, with a goal of developing a similar AI model for objectively identifying smoke as well. The ultimate goal of this effort is to enhance dust and smoke predictions in the region by establishing improved emission initializations in HYSPLIT and/or WRF-Chem through the automated AI detection of dust and smoke.

Jonathan L. Case↗

Chiral effective field theory calculation of neutrino reactions in warm neutron-rich matter

Neutrino scattering and absorption rates of relevance to supernovae and neutron star mergers are obtained from nuclear matter dynamical structure functions that encode many-body effects from nuclear mean fields and correlations. In this study, we employ nuclear interactions from chiral effective field theory to calculate the density, spin, isospin, and spin-isospin response functions of warm β-equilibrium nuclear matter. We include corrections to the single-particle energies in the mean field approximation as well as vertex corrections resummed in the random phase approximation (RPA), including both direct and exchange diagrams. We find that correlations included through the RPA redistribute the strength of the response to higher energy for neutrino absorption and lower energy for antineutrino absorption. This tends to suppress the absorption rate of electron neutrinos across all relevant energy scales. In contrast, the inclusion of RPA correlations enhances the electron antineutrino absorption rate at low energy and suppresses the rate at high energy. These effects are especially important at high-density and in the vicinity of the neutrino decoupling region. Implications for heavy element nucleosynthesis, electromagnetic signatures of compact object mergers, supernova dynamics, and neutrino detection from galactic supernovae are discussed briefly.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Enhanced Monitoring and Forecasting of South Asian Air Quality Episodes with Multi-Sensor Satellite Products and Dispersion Modeling

Air pollution and particulates represent a serious environmental, public health, and overarching societal concern in the Hindu Kush Himalaya (HKH) region of south-central Asia, especially during the boreal winter and spring months. Frequent contributors to poor air quality of the region include dust and particulate matter transport from the Middle East region and western India, persistent nocturnal fog and smog during the stable dry monsoon months, and biomass burning during the pre-monsoon months of boreal spring. Nocturnal and multi-day persistent stable fog and smog events can occur during the stable conditions of the dry monsoon months that lead to hazardous visibility reductions (e.g., at major airports such as Delhi, India)and poor air quality, particularly in urban corridors.Our project goal is therefore to design and implement a robust air quality and chemistry observation and modeling system using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) models. This system effectively assimilates aerosol and trace gas retrievals from geostationary and polar-orbiting satellites to advance the monitoring and prediction capabilities of air quality and visibility reductions throughout the HKH region. For this presentation, we highlight several[multi-spectral] geostationary satellite products and dispersion modeling capabilities being developed and implemented for improved situational awareness and forecasting of air quality episodes.Launched in December 2018, the Advanced Meteorological Imager (AMI) aboard the Geostationary-Korean Multi-Purpose Satellite-2A (GEO-KOMPSAT-2A, or GK2A) provides 16 channels of multi-spectral information similar to the U.S. GOES-16/17 satellites. Proven community recipes for generating multi-spectral Red-Green-Blue (RGB) composite products to detect specific meteorological phenomena are applied to the AMI data for the HKH region. We implemented four such RGB products consisting of truecolor with Rayleigh correction to aid in smoke detection; dust RGB for monitoring larger particulate matter; nighttime microphysics for fog/smog detection;and natural color fire RGB and the shortwave 3.8 micron channel for fire hot-spot detection.We additionally configured HYSPLIT for dust/sand dispersion and concentration forecasts over HKH. The HYSPLIT dispersion simulations are initially being conducted with two different methodologies: (1) dust/sand lofting based on initializing plume releases from visual inspection of the dust RGB imagery; and (2) dust/sand release and dispersion using the internal HYSPLIT algorithm that determines lofting from input wind speeds and prescribed land use. The goal is to develop a near real-time HYSPLIT modeling solution for routinely simulating the transport and dispersion of dust plumes across the HKH region. This presentation illustrates these products and capabilities for various use cases from the 2019 to 2021 period, highlighting a particularly unhealthy episode during late March 2021 across Nepal.

Remote Sensing↗

Thermally Activated Circularly Polarized Photoluminescence in a 2D Hybrid Perovskite with Giant Spin Splitting

Circularly polarized light generation and detection are critical for future spin-based technologies that inter-convert circularly polarized photons and electron spins. However, detailed mechanisms in such spin-photon interfaces are often either poorly understood or operate at cryogenic temperatures since typically small energies separating spin-split electronic bands facilitate thermally driven spin depolarization. Recently, several 2D hybrid perovskites with polar achiral cations were theoretically demonstrated to exhibit conduction and valence band spin-splitting energies greatly exceeding room-temperature thermal energy, suggesting their utility as spin-photon interfaces with practical operating temperatures. Here, a strong "spin memory" effect is reported in such a polar achiral layered perovskite that enables large room-temperature circularly polarized emission anisotropy following excitation with circularly polarized light. The polarization anisotropy depends strongly on temperature (thermally activated), excitation energy, and crystal orientation with respect to the excitation source. Temperature-dependent photoconductance measurements reveal similar thermally activated carrier generation. These observations suggest a mechanism whereby giant in-plane splitting of single-particle levels protects spin-polarization of photogenerated electrons and holes before recombination. Although polarized light emission is explored in greater detail in chiral perovskites, these results reveal that even without chirality, large spin memory in polar achiral perovskites can enable spin-photon interfaces that operate at elevated temperatures.

14 SOLAR ENERGY↗

Measurement report: The Fifth International Workshop on Ice Nucleation phase 1 (FIN-01): intercomparison of single-particle mass spectrometers

Knowledge of the chemical composition and mixing state of aerosols at a single-particle level is critical for gaining insights into atmospheric processes. One common tool to make these measurements is single-particle mass spectrometry. There remains a need to compare the performance of different single-particle mass spectrometers (SPMSs). An intercomparison of SPMSs was conducted at the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) chamber at the Karlsruhe Institute of Technology (KIT) in November 2014, as part of the first phase of the Fifth International Workshop on Ice Nucleation (FIN-01). In this paper we compare size distributions and mass spectra of atmospherically relevant particle types measured by five SPMSs. These include different minerals, desert and soil dusts, soot, bioaerosol (Snomax; protein granule), secondary organic aerosol (SOA), and SOA-coated mineral particles. Most SPMSs reported similar vacuum aerodynamic diameter (d va ) within typical instrumental ranges from ~100–200 nm (lower limit) to ~2–3 µm (upper limit). In general, all SPMSs exhibited a wide dynamic range (up to ~10 3 ) and high signal-to-noise ratio (up to ~10 4 ) in mass spectra. Common spectral features with small diversities in mass spectra were found with high average Pearson's correlation coefficients, i.e., for average positive spectra r avg-pos =0.74 ± 0.12 and average negative spectra r avg-neg =0.67 ± 0.22. We found that instrument-specific detection efficiency (DE) was more dependent on particle size than particle type, and particle identification favored the use of bipolar, rather than monopolar, instruments. Particle classification from “blind experiments” showed that all instruments differentiated SOA, soot, and soil dust and detected subtle changes in the particle internal mixing but had difficulties differentiating among specific mineral types and dusts. This study helps to further understand the capabilities and limitations of the single-particle mass spectrometry technique in general and the specific performance of the instrument in characterizing atmospheric aerosol particles.

54 ENVIRONMENTAL SCIENCES↗

Isotopic analysis of Nd nanoparticles using single particle MC-ICP-MS: A comparative study with single particle-ICP-TOF-MS

Single particle - inductively coupled plasma - mass spectrometry (SP-ICP-MS) is a powerful technique for characterization of the elemental and isotopic composition of individual particles. In this work, the capabilities of the newest generation of MC-ICP-MS with acquisition rates down to 50 ms were evaluated for single particle analysis, with a focus on isotopic precision achievable on a single-particle level. Nd (NdVO 4 ) nanoparticles (~120 nm in diameter) were used as case study and were first characterized in terms of mass (respective size) and particle number concentration by SP-ICP-TOF-MS and then by SP-MC-ICP-MS for isotopic precision. For the isotopic ratio measurements, the MC-ICP-MS performance was compared to the ICP-TOF-MS and it was found that the isotope ratio precision was increased (R 2 between 0.98 and 0.99) compared to ICP-TOF-MS (R 2 between 0.88 and 0.97). The accuracy attained on a single particle level, was compared to bulk digestion followed by MC-ICP-MS analysis, and the SP-MC-ICP-MS technique was able to determine the particle population average to be <4 %, percent relative differences for the 142 Nd/ 144 Nd, 143 Nd/ 144 Nd, 145 Nd/ 144 Nd, 146 Nd/ 144 Nd, and 148 Nd/ 144 Nd ratios The detection limit for the SP-MC-ICP-MS approach was also assessed. Here, when utilizing an all Faraday-cup based detection scheme the determined LOD for the measurements was 0.2fg for Nd, per particle. Based on these results, the newest generation of MC-ICP-MS has demonstrated its utility for performing SP measurement, particularly when high precision isotopic determination is warranted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Lead Isotope Fingerprinting of Nanoscale Mineral Particles via Single Particle Inductively Coupled Plasma Mass Spectrometry

Determining lead (Pb) isotopic ratios is of broad interest across chemical disciplines for source tracing and age determinations, but the technique is inherently limited when multiple sources of Pb are present in a single sample. Single particle methods offer a solution to directly resolve multiple distinct isotopic ratios within individual samples. In this study, single particle inductively coupled plasma-mass spectrometry (spICP-MS) was performed using time-of-flight (TOF) and multicollector (MC)-based platforms to measure the Pb isotopic composition of individual nanoscale particles. Distinct Pb isotope ratios were measured in particles from four powdered galena samples of different origins (average single-particle 206Pb/207Pb ratios ranging from 0.91 to 1.28) using both the TOF and the MC-based platforms. Differentiation of galena particles in a mixture (liberated grains and those hosted in a silicate matrix) was possible due to detecting their unique multielemental fingerprints. The differing behavior of the two galena samples during digestion was investigated; undigested galena particles trapped within silicate minerals were detected using spICP-MS, which has implications for Pb recovery in bulk-scale isotopic analysis. The multinuclide detection capability of the ICP-TOF-MS instrument allowed for the simultaneous detection of secondary constituent elements within the nanoparticles and the identification of multiple populations of isotopically distinct Pb-bearing particles in a copper ore sample, whereas the increased sensitivity of the MC instrument enabled quantification of 204Pb, allowing differentiation using multiple isotopic ratios. A single particle isotope ratio analysis module was developed within an open-source spICP-TOF-MS data processing platform, enabling its adoption across chemical disciplines.

Goodman, Aaron J. [University of Montreal, Quebec,↗

Single-Particle Isotopic Analysis Using the Liquid Sampling-Atmospheric Pressure Glow Discharge Microplasma Coupled to an Orbitrap Mass Spectrometer

Isotope ratio (IR) determinations on individual particles can provide valuable information relative to the sample, including processing and dating, which could be valuable to geological and nuclear material analysis communities. In comparison to “bulk” measurements, in which all particle information is lost and homogenized within a sample, “particle” measurements allow for fingerprinting and can provide unique insights related to the sample’s nano- and micro- compositions. Furthermore, the complex nature of particles, with potential isobaric interferences from either the matrix or other elements within the same particle, poses a significant analytical challenge for conventional mass spectrometry platforms employed for elemental analysis due to their limited mass resolution. Presented here is the first demonstration of an ultrahigh resolution method for single particle (SP) isotopic analysis using the liquid sampling-atmospheric pressure glow discharge (LS-APGD) microplasma ionization source coupled to an Orbitrap mass spectrometer and FTMS Booster X2T acquisition/processing system. For proof of concept, well-characterized CeO 2 microparticles with a nominal diameter of 1.0 μm were analyzed at a mass resolution of ∼330,000. Important instrumental parameters were optimized to enable the detection of single particles. The 142 Ce/ 140 Ce ratio of individual particles and the population average were determined and compared to an SP inductively coupled plasma time-of-flight mass spectrometry (ICP-TOF-MS) analysis. The isotopic accuracy and precision were determined by two methods and found to be in good agreement with the SP-ICP-TOF-MS method, with the linear regression slope method providing a more accurate ratio, showing a ∼1.4% relative difference from the ratio determined from a particle digest. The encouraging performance of the method was further supported by a determined detection limit of 2.9 fg ( 142 Ce). The developed method is anticipated to overcome many of the challenges posed by isobaric interferences encountered in conventional mass spectrometry techniques when analyzing real-world samples for particle isotopic composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Microstructure-dependent particulate populations in ODS steels revealed by single-particle ICP-TOF-MS

In this work, we evaluated the ability of single-particle inductively coupled plasma time-of-flight mass spectrometry (SP-ICP-TOF-MS) to characterize particulates generated from 300 series stainless steels and oxide dispersion–strengthened (ODS) steels. Particulates were produced using two different rotary tools (cutting disk and grinding bit) to assess the influence of particle generation on effluent particulates. Across all materials and preparation techniques, SP-ICP-TOF-MS detected a substantial population of particles containing the expected constituent elements, with average compositions closely matching the nominal alloy values. A secondary population of Y-bearing particles was evident in ODS steels; their formation is strongly favored in particulates generated using the grinding bit. Specifically, in PM2000, Y-bearing particles were predominantly colocated with Al, whereas in 14YWT, Y-bearing particles were less frequently Fe-free but showed stronger colocation with Ti. These trends are consistent with the known microstructural differences between Y–Al–O oxide particles in PM2000 and finer Y–Ti–O nanoclusters in 14YWT. Together, these results demonstrate that SP-ICP-TOF-MS can resolve distinct particulate populations in complex, multicomponent steels at the femtogram mass scale, enabling particle-resolved insight into microstructure-driven phase separation and preparation-dependent particle generation.

Brubaker, Zach E. [Oak Ridge National Laboratory (↗

Beam-induced backgrounds measured in the ATLAS detector during local gas injection into the LHC beam vacuum

Inelastic beam-gas collisions at the Large Hadron Collider (LHC), within a few hundred metres of the ATLAS experiment, are known to give the dominant contribution to beam backgrounds. These are monitored by ATLAS with a dedicated Beam Conditions Monitor (BCM) and with the rate of fake jets in the calorimeters. These two methods are complementary since the BCM probes backgrounds just around the beam pipe while fake jets are observed at radii of up to several metres. In order to quantify the correlation between the residual gas density in the LHC beam vacuum and the experimental backgrounds recorded by ATLAS, several dedicated tests were performed during LHC Run 2. Local pressure bumps, with a gas density several orders of magnitude higher than during normal operation, were introduced at different locations. The changes of beam-related backgrounds, seen in ATLAS, are correlated with the local pressure variation. In addition the rates of beam-gas events are estimated from the pressure measurements and pressure bump profiles obtained from calculations. Using these rates, the efficiency of the ATLAS beam background monitors to detect beam-gas events is derived as a function of distance from the interaction point. These efficiencies and characteristic distributions of fake jets from the beam backgrounds are found to be in good agreement with results of beam-gas simulations performed with theFluka Monte Carlo programme.

43 PARTICLE ACCELERATORS↗

Marine aerosol generation experiments in the High Arctic during summertime

The rapidly warming Arctic has transitioned to thinner sea ice which fractures, producing leads. Few studies have investigated Arctic sea spray aerosol (SSA) produced from open ocean, leads, and melt ponds, which vary in salinity and organic and microbial community composition. A marine aerosol reference tank was deployed aboard an icebreaker to the Arctic Ocean during August–September 2018 to study SSA generated from locally collected surface waters. Aerosol generation experiments were carried out using water collected from the marginal ice zone, a human-made hole in sea ice near the North Pole, and both lead and melt pond water during an ice floe drift period. Salinity, chlorophyll a, organic carbon, nitrogen, and microbial community composition were measured. Eukaryotic plankton and bacterial abundance were elevated in experimental water from the marginal ice zone, but the relative contributions from major eukaryotic taxonomic groups varied little across the experiments. The chemical composition of individual SSA particles was analyzed using Raman microspectroscopy and computer-controlled scanning electron microscopy with energy-dispersive X-ray spectroscopy. Individual sea salt aerosol, primary organic aerosol, and mineral dust particles were observed. Sea salt aerosol constituted 44–95% of individual submicrometer and 68–100% of supermicrometer particles, by number, generated during each experiment. Carbon was detected in 85%, by number, of the individual sea salt particles, with visible organic coatings. Carbohydrates were detected in 72% of particles, by number, with smaller contributions from long-chain fatty acids (13%) and siliceous material (15%). SSA generated from melt pond water contained only long-chain fatty acids and siliceous material. Quantification of the ice-nucleating activity showed that locally produced SSA may define the High Arctic background ice-nucleating particle population, but cannot account for the peak atmospheric concentrations observed. As the Arctic warms, the increasing SSA emissions have a complex dependence on changing biological and physical processes.

54 ENVIRONMENTAL SCIENCES↗

Utilizing HYSPLIT for Emergency Response Modeling at SRS

At SRS, emergency responders use a variety of tools to detect, track, and mitigate hazardous material releases into the atmosphere. Two models currently used at SRS are Puff-Plume and the Lagrangian Particle Dispersion Model (LPDM), a Gaussian and Lagrangian model, respectively. A decision has been made to replace LPDM with the more widely-supported Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for evaluating inhalation and ingestion doses following a release. HYSPLIT is designed to compute complex dispersion and deposition simulation To achieve the implementation of HYSPLIT, we have developed a preliminary UI framework that will allow HYSPLIT to be run on ATG computers without the need for active network connections, thus avoiding the loss of capabilities in the event of a network outage during an emergency.

Earley, Ian↗

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Advancing automated classification of atmospheric aerosols from Single-Particle Mass Spectrometry (SPMS) data remains challenging due to overlapping ion signatures, compositional diversity, and limited labeled data. This study evaluates supervised and semi-supervised learning frameworks to enhance aerosol identification by jointly leveraging labeled and unlabeled spectra. Four models were compared: a supervised Support Vector Machine (SVM), a self-training SVM, a stacked autoencoder classifier, and a stacked autoencoder trained using a temporal-ensembling Mean Teacher approach. All models achieved high and stable accuracies (90.0 %–91.1 %), surpassing previous results on the same dataset (87 %) and matching the performance of state-of-the-art deep learning methods. Despite small global metric differences (≤ 1 %), semi-supervised variants yielded up to 5 %–10 % improvements for compositionally rare particle types – such as soot (0.77 % of spectra, F1-score: 0.93–0.97) and hazelnut pollen (0.98 % of spectra, F1-score: 0.97–1.00) – equating to roughly ∼ 187 additional correctly classified spectra. These gains are scientifically significant, as such rare particles exert disproportionate influence on radiative absorption and ice nucleation processes; their improved detection reduces modeled uncertainties in aerosol absorption optical depth and mixed-phase cloud ice nucleation rates. The models' residual misclassifications (≈ 9 %) largely arise from true spectral overlap among chemically adjacent species (e.g., Na- vs. K-feldspar, coated vs. uncoated feldspars), reflecting physical compositional continuity rather than algorithmic error. Collectively, these findings demonstrate that leveraging unlabeled data to learn robust spectral representations and refine classification enhances both fidelity and interpretability, bridging data-driven analysis with aerosol–climate process understanding.

54 ENVIRONMENTAL SCIENCES↗