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https://doi.org/10.2139/ssrn.4678872
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/jgrd.50171openalex-extracted
Bounding the role of black carbon in the climate system: A scientific assessment
inferredcites →https://doi.org/10.1007/s00216-003-2133-5openalex-extracted
Development of analytical procedures for determination of total chromium by quadrupole ICP?MS and high-resolution ICP?MS, and hexavalent chromium by HPLC?ICP?MS, in different materials used in the automotive industry
inferredcites →https://doi.org/10.1007/s10098-014-0793-9openalex-extracted
The pollutant emissions from diesel-engine vehicles and exhaust aftertreatment systems
inferredcites →https://doi.org/10.1007/s11356-015-4394-xopenalex-extracted
Elements and polycyclic aromatic hydrocarbons in exhaust particles emitted by light-duty vehicles
inferredcites →https://doi.org/10.1007/s11831-019-09344-wopenalex-extracted
A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning
inferredcites →https://doi.org/10.1007/s12525-021-00475-2openalex-extracted
Machine learning and deep learning
inferredcites →https://doi.org/10.1016/0893-6080(89)90020-8openalex-extracted
Multilayer feedforward networks are universal approximators
inferredcites →https://doi.org/10.1016/j.apr.2019.12.018openalex-extracted
Emission characteristics and chemical composition of particulate matter emitted by typical non-road construction machinery
inferredcites →https://doi.org/10.1016/j.atmosenv.2003.07.007openalex-extracted
Emissions of fuel metals content from a diesel vehicle engine
inferredcites →https://doi.org/10.1016/j.atmosenv.2004.12.044openalex-extracted
Stability of single particle tracers for differentiating between heavy- and light-duty vehicle emissions
inferredcites →https://doi.org/10.1016/j.atmosenv.2006.04.011openalex-extracted
Comparison of oil and fuel particle chemical signatures with particle emissions from heavy and light duty vehicles
inferredcites →https://doi.org/10.1016/j.atmosenv.2007.01.025openalex-extracted
Determination of single particle mass spectral signatures from heavy-duty diesel vehicle emissions for PM2.5 source apportionment
inferredcites →https://doi.org/10.1016/j.atmosenv.2007.08.005openalex-extracted
Using mass spectral source signatures to apportion exhaust particles from gasoline and diesel powered vehicles in a freeway study using UF-ATOFMS
inferredcites →https://doi.org/10.1016/j.atmosenv.2009.10.005openalex-extracted
Compositional characterization of PM2.5 emitted from in-use diesel vehicles
inferredcites →https://doi.org/10.1016/j.atmosenv.2012.05.032openalex-extracted
Seasonal comparisons of single-particle chemical mixing state in Riverside, CA
inferredcites →https://doi.org/10.1016/j.atmosenv.2014.05.039openalex-extracted
An integrated PM2.5 source apportionment study: Positive Matrix Factorisation vs. the chemical transport model CAMx
inferredcites →https://doi.org/10.1016/j.atmosenv.2014.08.009openalex-extracted
Source apportionment of single particles sampled at the industrially polluted town of Port Talbot, United Kingdom by ATOFMS
inferredcites →https://doi.org/10.1016/j.atmosenv.2014.12.060openalex-extracted
Lung burden and deposition distribution of inhaled atmospheric urban ultrafine particles as the first step in their health risk assessment
inferredcites →https://doi.org/10.1016/j.atmosenv.2016.02.032openalex-extracted
Mixing state of ambient aerosols in Nanjing city by single particle mass spectrometry
inferredcites →https://doi.org/10.1016/j.atmosenv.2019.117157openalex-extracted
A refined source apportionment study of atmospheric PM2.5 during winter heating period in Shijiazhuang, China, using a receptor model coupled with a source-oriented model
inferredcites →https://doi.org/10.1016/j.atmosenv.2023.119823openalex-extracted
Chemical speciation of PM emissions from heavy-duty vehicles
inferredcites →https://doi.org/10.1016/j.atmosres.2014.08.016openalex-extracted
Characteristics of individual particles in the atmosphere of Guangzhou by single particle mass spectrometry
inferredcites →https://doi.org/10.1016/j.atmosres.2018.12.021openalex-extracted
Characterization of individual particles and meteorological conditions during the cold season in Zhengzhou using a single particle aerosol mass spectrometer
inferredcites →https://doi.org/10.1016/j.buildenv.2019.106166openalex-extracted
PAHs, elemental and organic carbons in a highway tunnel atmosphere and road dust: Discrimination of diesel and gasoline emissions
inferredcites →https://doi.org/10.1016/j.envint.2018.03.037openalex-extracted
Sources of particulate matter in China: Insights from source apportionment studies published in 1987–2017
inferredcites →https://doi.org/10.1016/j.envpol.2020.114705openalex-extracted
Size-segregated carbonaceous aerosols emission from typical vehicles and potential depositions in the human respiratory system
inferredcites →https://doi.org/10.1016/j.envres.2022.112900openalex-extracted
On-road emissions of fine particles and associated chemical components from motor vehicles in Wuhan, China
inferredcites →https://doi.org/10.1016/j.expthermflusci.2020.110107openalex-extracted
Effect of after-treatment systems on particulate matter emissions in diesel engine exhaust
inferredcites →https://doi.org/10.1016/j.ijms.2011.01.017openalex-extracted
Real time bipolar time-of-flight mass spectrometer for analyzing single aerosol particles
inferredcites →https://doi.org/10.1016/j.jaerosci.2007.08.001openalex-extracted
Chemical characterization of particulate emissions from diesel engines: A review
inferredcites →https://doi.org/10.1016/j.jes.2021.07.002openalex-extracted
Self-feedback LSTM regression model for real-time particle source apportionment
inferredcites →https://doi.org/10.1016/j.jhazmat.2021.126304openalex-extracted
Different characteristics of individual particles from light-duty diesel vehicle at the launching and idling state by AAC-SPAMS
inferredcites →https://doi.org/10.1016/j.molcata.2014.01.026openalex-extracted
High NO oxidation catalytic activity on non-noble metal based cobalt-ceria catalyst for diesel soot oxidation
inferredcites →https://doi.org/10.1016/j.partic.2014.06.008openalex-extracted
Emission profiling of diesel and gasoline cars at a city traffic junction
inferredcites →https://doi.org/10.1016/j.scitotenv.2017.03.099openalex-extracted
Single particle mass spectral signatures from vehicle exhaust particles and the source apportionment of on-line PM 2.5 by single particle aerosol mass spectrometry
inferredcites →https://doi.org/10.1016/j.scitotenv.2018.01.269openalex-extracted
Refined source apportionment of coal combustion sources by using single particle mass spectrometry
inferredcites →https://doi.org/10.1016/j.scitotenv.2019.03.410openalex-extracted
Chemical characterisation of PM2.5 emitted from motor vehicles powered by diesel, gasoline, natural gas and methanol fuel
inferredcites →https://doi.org/10.1016/j.scitotenv.2019.07.367openalex-extracted
Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China
inferredcites →https://doi.org/10.1016/j.scitotenv.2020.139942openalex-extracted
Chemical characterization of PM2.5 emissions and atmospheric metallic element concentrations in PM2.5 emitted from mobile source gasoline-fueled vehicles
inferredcites →https://doi.org/10.1016/j.scitotenv.2020.142456openalex-extracted
RETRACTED: Source appointment of PM2.5 in Qingdao Port, East of China
inferredcites →https://doi.org/10.1016/j.scitotenv.2020.144095openalex-extracted
Review of online source apportionment research based on observation for ambient particulate matter
inferredcites →https://doi.org/10.1016/j.scitotenv.2020.144766openalex-extracted
High-time-resolution PM2.5 source apportionment based on multi-model with organic tracers in Beijing during haze episodes
inferredcites →https://doi.org/10.1016/j.scitotenv.2021.145365openalex-extracted
Real-world emissions of construction mobile machines and comparison to a non-road emission model
inferredcites →https://doi.org/10.1016/j.scitotenv.2021.148003openalex-extracted
Spatiotemporal distribution of atmospheric polycyclic aromatic hydrocarbon emissions during 2013–2017 in mainland China
inferredcites →https://doi.org/10.1016/j.scitotenv.2021.149310openalex-extracted
High contribution of vehicle emissions to fine particulate pollutions in Lanzhou, Northwest China based on high-resolution online data source appointment
inferredcites →https://doi.org/10.1016/j.scitotenv.2022.153104openalex-extracted
Source apportionment of particle number concentrations: A global review
inferredcites →https://doi.org/10.1016/j.scitotenv.2022.153117openalex-extracted
Real-time chemical characterization of single ambient particles at a port city in Chinese domestic emission control area — Impacts of ship emissions on urban air quality
inferredcites →https://doi.org/10.1016/j.scitotenv.2022.155056openalex-extracted
Source apportionment of PM2.5 in Seoul, South Korea and Beijing, China using dispersion normalized PMF
inferredcites →https://doi.org/10.1016/j.scitotenv.2022.158601openalex-extracted
Multi-pollutant emission characteristics of non-road construction equipment based on real-world measurement
inferredcites →https://doi.org/10.1016/j.scitotenv.2023.161451openalex-extracted
Assessing the effects of short-term traffic restriction policies on traffic-related air pollutants
inferredcites →https://doi.org/10.1016/j.scitotenv.2023.162371openalex-extracted
Quantitative source apportionment and driver identification of soil heavy metals using advanced machine learning techniques
inferredcites →https://doi.org/10.1016/j.scitotenv.2023.162914openalex-extracted
Multi-component emission characteristics and high-resolution emission inventory of non-road construction equipment (NRCE) in China
inferredcites →https://doi.org/10.1016/j.surfcoat.2004.05.006openalex-extracted
Nanocoatings for engine application
inferredcites →https://doi.org/10.1016/s0003-2670(97)00135-9openalex-extracted
Classification of single particles by neural networks based on the computer-controlled scanning electron microscopy data
inferredcites →https://doi.org/10.1016/s0021-8502(97)10001-5openalex-extracted
Characterization of particles in combustion engine exhaust
inferredcites →https://doi.org/10.1016/s1044-0305(97)00078-0openalex-extracted
Negative ion laser desorption ionization time-of-flight mass spectrometry of nitrated polycyclic aromatic hydrocarbons
inferredcites →https://doi.org/10.1016/s1352-2310(03)00122-5openalex-extracted
A study of trace metals and polycyclic aromatic hydrocarbons in the roadside environment
inferredcites →https://doi.org/10.1021/ac9809682openalex-extracted
Classification of Single Particles Analyzed by ATOFMS Using an Artificial Neural Network, ART-2A
inferredcites →https://doi.org/10.1021/ac990434gopenalex-extracted
Relative Sensitivity Factors for Alkali Metal and Ammonium Cations in Single-Particle Aerosol Time-of-Flight Mass Spectra
inferredcites →https://doi.org/10.1021/acs.est.1c01339openalex-extracted
Machine Learning: New Ideas and Tools in Environmental Science and Engineering
inferredcites →https://doi.org/10.1021/acs.est.1c04706openalex-extracted
Air Pollution, Ultrafine Particles, and Your Brain: Are Combustion Nanoparticle Emissions and Engineered Nanoparticles Causing Preventable Fatal Neurodegenerative Diseases and Common Neuropsychiatric Outcomes?
inferredcites →https://doi.org/10.1021/acs.est.3c00026openalex-extracted
Machine Learning in Environmental Research: Common Pitfalls and Best Practices
inferredcites →https://doi.org/10.1021/acs.estlett.2c00835openalex-extracted
Single-Particle Metal Fingerprint Analysis and Machine Learning Pipeline for Source Apportionment of Metal-Containing Fine Particles in Air
inferredcites →https://doi.org/10.1021/acsearthspacechem.1c00344openalex-extracted
Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets
inferredcites →https://doi.org/10.1021/es0017413openalex-extracted
Source Apportionment of Fine Particulate Matter by Clustering Single-Particle Data: Tests of Receptor Model Accuracy
inferredcites →https://doi.org/10.1021/es0108077openalex-extracted
Measurement of Emissions from Air Pollution Sources. 5. C1−C32Organic Compounds from Gasoline-Powered Motor Vehicles
inferredcites →https://doi.org/10.1021/es048715fopenalex-extracted
Emissions of Metals Associated with Motor Vehicle Roadways
inferredcites →https://doi.org/10.1021/es0489947openalex-extracted
Determination of Single Particle Mass Spectral Signatures from Light-Duty Vehicle Emissions
inferredcites →https://doi.org/10.1021/es0607281openalex-extracted
Size Distribution Characteristics of Elemental Carbon Emitted from Chinese Vehicles: Results of a Tunnel Study and Atmospheric Implications
inferredcites →https://doi.org/10.1021/es981227lopenalex-extracted
Characterization of Polycyclic Aromatic Hydrocarbons in Motor Vehicle Fuels and Exhaust Emissions
inferredcites →https://doi.org/10.1023/b:toca.0000029725.88068.97openalex-extracted
Control of NO x emissions from diesel engine by selective catalytic reduction (SCR) with urea
inferredcites →https://doi.org/10.1023/b:tril.0000044495.26882.b5openalex-extracted
The History and Mechanisms of ZDDP
inferredcites →https://doi.org/10.1029/2001jd001562openalex-extracted
Aerosol time‐of‐flight mass spectrometry during the Atlanta Supersite Experiment: 1. Measurements
inferredcites →https://doi.org/10.1038/s41592-018-0260-3openalex-extracted
Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry
inferredcites →https://doi.org/10.1080/10473289.2004.10470973openalex-extracted
Emission Rates and Comparative Chemical Composition from Selected In-Use Diesel and Gasoline-Fueled Vehicles
inferredcites →https://doi.org/10.1080/10473289.2007.10465319openalex-extracted
Receptor Modeling of Ambient Particulate Matter Data Using Positive Matrix Factorization: Review of Existing Methods
inferredcites →https://doi.org/10.1080/19338244.2019.1644277openalex-extracted
A historical job-exposure matrix for occupational exposure to diesel exhaust using elemental carbon as an indicator of exposure
inferredcites →https://doi.org/10.1109/5.726791openalex-extracted
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/cnna.1990.207512openalex-extracted
Several image processing examples by CNN
inferredcites →https://doi.org/10.1109/tbme.2015.2468589openalex-extracted
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
inferredcites →https://doi.org/10.1126/science.1127647openalex-extracted
Reducing the Dimensionality of Data with Neural Networks
inferredcites →https://doi.org/10.3155/1047-3289.57.10.1190openalex-extracted
Development of Molecular Marker Source Profiles for Emissions from On-Road Gasoline and Diesel Vehicle Fleets
inferredcites →https://doi.org/10.3390/app10072458openalex-extracted
Seasonal and Long-Term Trend of on-Road Gasoline and Diesel Vehicle Emission Factors Measured in Traffic Tunnels
inferredcites →https://doi.org/10.3390/app12073623openalex-extracted
Emissions of Carbonaceous Particulate Matter and Ultrafine Particles from Vehicles—A Scientific Review in a Cross-Cutting Context of Air Pollution and Climate Change
inferredcites →https://doi.org/10.3724/sp.j.1096.2013.30545openalex-extracted
Analysis of Diesel Exhaust Particles Using Single Particle Aerosol Mass Spectrometry
inferredcites →https://doi.org/10.4209/aaqr.2019.04.0191openalex-extracted
Chemical Characterization of Fine Particulate Matter in Gasoline and Diesel Vehicle Exhaust
inferredcites →https://doi.org/10.4209/aaqr.220386openalex-extracted
Machine Learning Classification Model to Label Sources Derived from Factor Analysis Receptor Models for Source Apportionment
inferredcites →https://doi.org/10.5194/acp-14-7585-2014openalex-extracted
Characterization of particulate matter emissions from on-road gasoline and diesel vehicles using a soot particle aerosol mass spectrometer
inferredcites →https://doi.org/10.5194/acp-17-6779-2017openalex-extracted
Measurement of PM and its chemical composition in real-world emissions from non-road and on-road diesel vehicles
inferredcites →https://doi.org/10.5194/acp-19-3223-2019openalex-extracted
Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017
inferredcites →https://doi.org/10.5194/acp-20-9871-2020openalex-extracted
Tracking separate contributions of diesel and gasoline vehicles to roadside PM 2.5 through online monitoring of volatile organic compounds and PM 2.5 organic and elemental carbon: a 6-year study in Hong Kong
inferredcites →https://doi.org/10.5194/amt-11-5687-2018openalex-extracted
A machine learning approach to aerosol classification for single-particle mass spectrometry
inferredcites →https://doi.org/10.5194/amt-17-299-2024openalex-extracted
Machine learning approaches for automatic classification of single-particle mass spectrometry data
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