| Predicate |
Object |
| contentType |
Journal Article |
| endingPage |
122905 |
| issn |
1614-7499 |
| issueIdentifier |
58 |
| pageRange |
122886-122905 |
| publicationName |
Environmental science and pollution research international |
| startingPage |
122886 |
| bibliographicCitation |
Mokarram M, Taripanah F, Pham TM. Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic. Environ Sci Pollut Res Int. 2023 Dec;30(58):122886–905. doi: 10.1007/s11356-023-30859-0. PMID: 37979107. |
| creator |
http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_b5a334fbae0674edfb16f4f0b97cd4f7
http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_504b9f6f793f856d91f4f09232420a41
http://rdf.ncbi.nlm.nih.gov/pubchem/author/ORCID_0000-0002-5899-9537 |
| date |
2023-11-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| identifier |
https://doi.org/10.1007/s11356-023-30859-0
https://pubmed.ncbi.nlm.nih.gov/37979107 |
| isPartOf |
https://portal.issn.org/resource/ISSN/1614-7499
http://rdf.ncbi.nlm.nih.gov/pubchem/journal/22074 |
| language |
English |
| source |
https://www.crossref.org/
https://pubmed.ncbi.nlm.nih.gov/ |
| title |
Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic |
| discusses |
http://id.nlm.nih.gov/mesh/M0000599
http://id.nlm.nih.gov/mesh/M0488311 |