Machine learning-fugacity framework reveals the fate and environmental drivers of per- and polyfluoroalkyl substances in a mountainous river.
- DOI
- 10.1016/j.envpol.2026.128464
- Published
- 2026 Sep 15
- Container
- Environmental pollution (Barking, Essex : 1987)
- Publisher
- Not recorded
- Open access
- unknown
Credibility signals
limited evidence Score 43/100 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.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- 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:10.1016/j.envpol.2026.128464,
title = {Machine learning-fugacity framework reveals the fate and environmental drivers of per- and polyfluoroalkyl substances in a mountainous river.},
author = {Li Y and Lv W and Ren J and Tang J and Dai J and He Q and Cui Y and Huo L and Zhang K and Pan Y},
year = {2026},
journal = {Environmental pollution (Barking, Essex : 1987)},
doi = {10.1016/j.envpol.2026.128464},
url = {https://doi.org/10.1016/j.envpol.2026.128464}
}RIS
TY - JOUR TI - Machine learning-fugacity framework reveals the fate and environmental drivers of per- and polyfluoroalkyl substances in a mountainous river. AU - Li Y AU - Lv W AU - Ren J AU - Tang J AU - Dai J AU - He Q AU - Cui Y AU - Huo L AU - Zhang K AU - Pan Y PY - 2026 JO - Environmental pollution (Barking, Essex : 1987) DO - 10.1016/j.envpol.2026.128464 UR - https://doi.org/10.1016/j.envpol.2026.128464 ER -
APA
Y, L., W, L., J, R., J, T., J, D., Q, H., Y, C., L, H., K, Z., & Y, P. (2026). Machine learning-fugacity framework reveals the fate and environmental drivers of per- and polyfluoroalkyl substances in a mountainous river.. Environmental pollution (Barking, Essex : 1987). https://doi.org/10.1016/j.envpol.2026.128464
Source records
- pubmed · retrieved 2026-09-27T06:53:46.590Z