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Machine Learning Classification Model to Label Sources Derived from Factor Analysis Receptor Models for Source Apportionment
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-0-387-30164-8openalex-snapshot
Encyclopedia of Machine Learning
inferredcites →https://doi.org/10.1007/978-0-387-84858-7openalex-snapshot
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
inferredcites →https://doi.org/10.1007/978-1-4614-6849-3openalex-snapshot
Applied Predictive Modeling
inferredcites →https://doi.org/10.1016/b978-0-12-411643-6.00015-6openalex-snapshot
Prediction in Medicine – The Data Mining Algorithms of Predictive Analytics
inferredcites →https://doi.org/10.1016/c2018-0-02034-4openalex-snapshot
Introduction to Algorithms for Data Mining and Machine Learning
inferredcites →https://doi.org/10.1016/j.apr.2021.101206openalex-snapshot
Real-time source apportionment of fine particle inorganic and organic constituents at an urban site in Delhi city: An IoT-based approach
inferredcites →https://doi.org/10.1016/j.apr.2022.101423openalex-snapshot
Development and evaluation of an integrated method using distance- and probability-based profile matching approaches in receptor modeling
inferredcites →https://doi.org/10.1016/j.atmosenv.2007.12.056openalex-snapshot
Inter-comparison of receptor models for PM source apportionment: Case study in an industrial area
inferredcites →https://doi.org/10.1016/j.atmosenv.2014.05.039openalex-snapshot
An integrated PM2.5 source apportionment study: Positive Matrix Factorisation vs. the chemical transport model CAMx
inferredcites →https://doi.org/10.1016/j.atmosenv.2015.08.087openalex-snapshot
Contributions to cities' ambient particulate matter (PM): A systematic review of local source contributions at global level
inferredcites →https://doi.org/10.1016/j.atmosenv.2019.03.013openalex-snapshot
An assessment of important SPECIATE profiles in the EPA emissions modeling platform and current data gaps
inferredcites →https://doi.org/10.1016/j.scitotenv.2012.06.107openalex-snapshot
Physiological responses of the European cockle Cerastoderma edule (Bivalvia: Cardidae) as indicators of coastal lagoon pollution
inferredcites →https://doi.org/10.1016/j.scitotenv.2020.140091openalex-snapshot
Global review of recent source apportionments for airborne particulate matter
inferredcites →https://doi.org/10.1016/j.scitotenv.2020.140332openalex-snapshot
Real-time measurement and source apportionment of elements in Delhi's atmosphere
inferredcites →https://doi.org/10.1016/j.scitotenv.2021.145324openalex-snapshot
Real-time characterization and source apportionment of fine particulate matter in the Delhi megacity area during late winter
inferredcites →https://doi.org/10.1016/j.scitotenv.2021.151954openalex-snapshot
Exploring sources and health risks of metals in Beijing PM2.5: Insights from long-term online measurements
inferredcites →https://doi.org/10.1080/10473289.2007.10465319openalex-snapshot
Receptor Modeling of Ambient Particulate Matter Data Using Positive Matrix Factorization: Review of Existing Methods
inferredcites →https://doi.org/10.1080/10962247.2016.1140693openalex-snapshot
Review of receptor modeling methods for source apportionment
inferredcites →https://doi.org/10.1504/ijep.2012.051192openalex-snapshot
Enhancing source apportionment with receptor models to foster the air quality directive implementation
inferredcites →https://doi.org/10.4209/aaqr.220386openalex-snapshot
Machine Learning Classification Model to Label Sources Derived from Factor Analysis Receptor Models for Source Apportionment
inferredcites →https://doi.org/10.5094/apr.2010.026openalex-snapshot
The development and uses of EPA’s SPECIATE database
inferredcites →https://doi.org/10.5094/apr.2011.016openalex-snapshot
Application of receptor modeling methods
inferredcites →https://doi.org/10.5860/choice.33-1577openalex-snapshot
Artificial intelligence: a modern approach
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1016/j.apr.2025.102839openalex-snapshot
Exploring AI potential in optical analysis of organic and elemental carbons: A review of emerging applications in airborne particulate characterization
inferred← citeshttps://doi.org/10.1016/j.envres.2025.122998openalex-snapshot
Towards holistic river pollution source apportionment: Multi-Faceted framework across scenarios, media, pollutants, and sources
inferred← citeshttps://doi.org/10.1016/j.jenvman.2025.127506openalex-snapshot
Spatiotemporal evolution characteristics, driving factors, and risk assessment of water quality in long-distance inter-basin water transfer projects during 2015–2023
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2024.172822openalex-snapshot
Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning
inferred← citeshttps://doi.org/10.1021/acsestair.5c00134openalex-snapshot
A Review on the Development of Airborne Particulate Metals Detection Technologies
inferred← citeshttps://doi.org/10.36371/port.2024.3.7openalex-snapshot
Classification Of Biomaterials and Their Applications
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