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Open Access

Retraction

Retraction: Interpretable machine learning framework for predicting Urban air quality

  • The PLOS One Editors

Following the publication of this article [1], concerns were raised about the content of the article and the article’s peer-review. Specifically, this article discusses an interpretable machine learning framework for predicting urban air quality, but several sections of this article appear to discuss methodology and results pertaining to an unrelated study on the topic of Alzheimer’s disease. In reviewing this matter, PLOS identified additional concerns regarding the article’s peer review and compliance with PLOS’ policy on Artificial Intelligence Tools and Technologies.

PLOS regrets that these issues were not addressed prior to the article’s publication.

In light of the above concerns, which call into question the validity and reliability of the published results, the PLOS One Editors retract this article.

All authors did not agree with the retraction.

Reference

  1. 1. Latif RMA, Iqbal T, Abdel Qader I, Ikram A, Alsolai H, Alabdullah B, et al. RETRACTED: Interpretable machine learning framework for predicting Urban air quality. PLoS One. 2025;20(11):e0336241. pmid:41201992

Citation: The PLOS One Editors (2026) Retraction: Interpretable machine learning framework for predicting Urban air quality. PLoS One 21(2): e0342214. https://doi.org/10.1371/journal.pone.0342214

Published: February 4, 2026

Copyright: © 2026 The PLOS One Editors. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.