http://rdf.ncbi.nlm.nih.gov/pubchem/reference/36512800

Outgoing Links

Predicate Object
contentType Journal Article
endingPage 104099
issn 1614-7499
issueIdentifier 47
pageRange 104086-104099
publicationName Environmental science and pollution research international
startingPage 104086
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bibliographicCitation Hassan MA, Faheem M, Mehmood T, Yin Y, Liu J. Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach. Environ Sci Pollut Res Int. 2023 Oct;30(47):104086–99. doi: 10.1007/s11356-023-29665-5. PMID: 37698799.
creator http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_97a038063f80e9014d8fef7a53aae661
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date 2023-09-12-04:00^^<http://www.w3.org/2001/XMLSchema#date>
identifier https://pubmed.ncbi.nlm.nih.gov/37698799
https://doi.org/10.1007/s11356-023-29665-5
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http://rdf.ncbi.nlm.nih.gov/pubchem/journal/22074
language English
source https://www.crossref.org/
https://pubmed.ncbi.nlm.nih.gov/
title Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach
discusses http://id.nlm.nih.gov/mesh/M0000599
http://id.nlm.nih.gov/mesh/M0015711
http://id.nlm.nih.gov/mesh/M0488311

Total number of triples: 27.