Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data.
- DOI
- 10.1007/s11869-021-01126-3
- Published
- 2022
- Container
- Air quality, atmosphere, & health
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1007/s11869-021-01126-3,
title = {Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data.},
author = {Muthukumar P and Cocom E and Nagrecha K and Comer D and Burga I and Taub J and Calvert CF and Holm J and Pourhomayoun M},
year = {2022},
journal = {Air quality, atmosphere, \& health},
doi = {10.1007/s11869-021-01126-3},
url = {https://doi.org/10.1007/s11869-021-01126-3}
}RIS
TY - JOUR TI - Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data. AU - Muthukumar P AU - Cocom E AU - Nagrecha K AU - Comer D AU - Burga I AU - Taub J AU - Calvert CF AU - Holm J AU - Pourhomayoun M PY - 2022 JO - Air quality, atmosphere, & health DO - 10.1007/s11869-021-01126-3 UR - https://doi.org/10.1007/s11869-021-01126-3 ER -
APA
P, M., E, C., K, N., D, C., I, B., J, T., CF, C., J, H., & M, P. (2022). Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data.. Air quality, atmosphere, & health. https://doi.org/10.1007/s11869-021-01126-3
Source records
- pubmed · retrieved 2026-09-25T19:34:34.750Z