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Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Recent advances in understanding secondary organic aerosol: Implications for global climate forcing
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Chemical and microphysical characterization of ambient aerosols with the aerodyne aerosol mass spectrometer
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Understanding atmospheric organic aerosols via factor analysis of aerosol mass spectrometry: a review
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Characterization of urban and rural organic particulate in the Lower Fraser Valley using two Aerodyne Aerosol Mass Spectrometers
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Mixing order of sulfate aerosols and isoprene epoxydiols affects secondary organic aerosol formation in chamber experiments
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Impact of Urban Pollution on Organic-Mediated New-Particle Formation and Particle Number Concentration in the Amazon Rainforest
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Photochemical Aging of α-pinene and β-pinene Secondary Organic Aerosol formed from Nitrate Radical Oxidation
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Deconvolution and Quantification of Hydrocarbon-like and Oxygenated Organic Aerosols Based on Aerosol Mass Spectrometry
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Particle-Phase Chemistry of Secondary Organic Material: Modeled Compared to Measured O:C and H:C Elemental Ratios Provide Constraints
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Ambient aerosol sampling using the Aerodyne Aerosol Mass Spectrometer
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Model for Simulating Aerosol Interactions and Chemistry (MOSAIC)
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Aging of biomass burning aerosols over West Africa: Aircraft measurements of chemical composition, microphysical properties, and emission ratios
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Urban pollution greatly enhances formation of natural aerosols over the Amazon rainforest
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High concentration of ultrafine particles in the Amazon free troposphere produced by organic new particle formation
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Overview of the HI-SCALE Field Campaign: A New Perspective on Shallow Convective Clouds
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Greedy function approximation: A gradient boosting machine.
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Submodel Selection and Evaluation in Regression. The X-Random Case
inferredcites →https://doi.org/10.5194/acp-11-1039-2011openalex-snapshot
Evidence for a significant proportion of Secondary Organic Aerosol from isoprene above a maritime tropical forest
inferredcites →https://doi.org/10.5194/acp-11-12049-2011openalex-snapshot
Effects of aging on organic aerosol from open biomass burning smoke in aircraft and laboratory studies
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Characterization of a real-time tracer for isoprene epoxydiols-derived secondary organic aerosol (IEPOX-SOA) from aerosol mass spectrometer measurements
inferredcites →https://doi.org/10.5194/acp-15-3687-2015openalex-snapshot
Submicron particle mass concentrations and sources in the Amazonian wet season (AMAZE-08)
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Aerosol characterization over the southeastern United States using high-resolution aerosol mass spectrometry: spatial and seasonal variation of aerosol composition and sources with a focus on organic nitrates
inferredcites →https://doi.org/10.5194/acp-15-7497-2015openalex-snapshot
Secondary organic aerosol formation from the β-pinene+NO 3 system: effect of humidity and peroxy radical fate
inferredcites →https://doi.org/10.5194/acp-15-8871-2015openalex-snapshot
Examining the effects of anthropogenic emissions on isoprene-derived secondary organic aerosol formation during the 2013 Southern Oxidant and Aerosol Study (SOAS) at the Look Rock, Tennessee ground site
inferredcites →https://doi.org/10.5194/acp-16-11563-2016openalex-snapshot
Volatility and lifetime against OH heterogeneous reaction of ambient isoprene-epoxydiols-derived secondary organic aerosol (IEPOX-SOA)
inferredcites →https://doi.org/10.5194/acp-17-2477-2017openalex-snapshot
Regional influence of wildfires on aerosol chemistry in the western US and insights into atmospheric aging of biomass burning organic aerosol
inferredcites →https://doi.org/10.5194/acp-17-839-2017openalex-snapshot
Chemical oxidative potential of secondary organic aerosol (SOA) generated from the photooxidation of biogenic and anthropogenic volatile organic compounds
inferredcites →https://doi.org/10.5194/acp-19-12749-2019openalex-snapshot
Chemical composition and hydrolysis of organic nitrate aerosol formed from hydroxyl and nitrate radical oxidation of α -pinene and β -pinene
inferredcites →https://doi.org/10.5194/acp-19-1571-2019openalex-snapshot
Free tropospheric aerosols at the Mt. Bachelor Observatory: more oxidized and higher sulfate content compared to boundary layer aerosols
inferredcites →https://doi.org/10.5194/acp-21-10081-2021openalex-snapshot
Eight years of sub-micrometre organic aerosol composition data from the boreal forest characterized using a machine-learning approach
inferredcites →https://doi.org/10.5194/acp-21-5101-2021openalex-snapshot
Aerosol characteristics at the Southern Great Plains site during the HI-SCALE campaign
inferredcites →https://doi.org/10.5194/acp-7-3385-2007openalex-snapshot
Including the sub-grid scale plume rise of vegetation fires in low resolution atmospheric transport models
inferredcites →https://doi.org/10.5194/acp-9-2891-2009openalex-snapshot
Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data
inferredcites →https://doi.org/10.5194/acp-9-5155-2009openalex-snapshot
The formation, properties and impact of secondary organic aerosol: current and emerging issues
inferredcites →https://doi.org/10.5194/amt-11-5687-2018openalex-snapshot
A machine learning approach to aerosol classification for single-particle mass spectrometry
inferredcites →https://doi.org/10.5194/amt-13-2457-2020openalex-snapshot
Characterization of anthropogenic organic aerosols by TOF-ACSM with the new capture vaporizer
inferredcites →https://doi.org/10.5194/gmd-5-1471-2012openalex-snapshot
The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Data‐Driven Compound Identification in Atmospheric Mass Spectrometry
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Receptor modeling techniques for particulate matter source apportionment: progress and perspectives
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Indoor and urban air quality: control and improvements
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From receptor models to machine learning: Advancing source apportionment of carbonaceous aerosol
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Exploring AI potential in optical analysis of organic and elemental carbons: A review of emerging applications in airborne particulate characterization
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Integrating sustainability in atmospheric chemistry analysis
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Modeling of secondary organic aerosols (SOA) based on two commonly used air quality models in China: Consistent S/IVOCs contribution but large differences in SOA aging
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The impacts of regional transport on anthropogenic source contributions of PM2.5 in a basin city, China
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Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning
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Molecular characterization of atmospheric organic aerosols: Contemporary applications of high-resolution mass spectrometry
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Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters
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Linking Precursors and Volatility of Ambient Oxygenated Organic Aerosols Using Thermal Desorption Measurement and Machine Learning
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An Ensemble Machine Learning Approach for Predicting Sources of Organic Aerosols Measured by Aerosol Mass Spectrometry
inferred← citeshttps://doi.org/10.1021/acsestair.4c00331openalex-snapshot
Enhancing Differentiation of Oxygenated Organic Aerosol: A Machine Learning Approach to Distinguish Local and Transboundary Pollution
inferred← citeshttps://doi.org/10.1021/acsestair.5c00001openalex-snapshot
Advancing Aerosol Chemistry with Machine Learning: A Short Review
inferred← citeshttps://doi.org/10.1021/acsestair.5c00134openalex-snapshot
A Review on the Development of Airborne Particulate Metals Detection Technologies
inferred← citeshttps://doi.org/10.1021/acsmeasuresciau.3c00060openalex-snapshot
Recent Developments in Machine Learning for Mass Spectrometry
inferred← citeshttps://doi.org/10.1080/02786826.2023.2193237openalex-snapshot
Factors influencing ambient particulate matter in Delhi, India: Insights from machine learning
inferred← citeshttps://doi.org/10.5194/acp-25-9601-2025openalex-snapshot
Influencing factors of the gas–particle distribution of oxygenated organic molecules in the urban atmosphere and deviation from equilibrium partitioning: a random forest model study
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