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CORRECTION article

Front. Environ. Sci., 19 December 2025

Sec. Big Data, AI, and the Environment

Volume 13 - 2025 | https://doi.org/10.3389/fenvs.2025.1741667

Correction: Deep learning for 3D reconstruction and trajectory prediction of dust and polluted aerosols in educational environments

This article is a correction to:

  1. Deep learning for 3D reconstruction and trajectory prediction of dust and polluted aerosols in educational environments

    1. Read original article
Zhen Wang Zhen Wang1*Ruijuan HanRuijuan Han2
  • 1Xi’an University of Science and Technology, Art College of XUST, Xi’an, Shanxi, China
  • 2Normal College, Shihezi University, Shihezi, Xinjiang, China

A Correction on
Deep learning for 3D reconstruction and trajectory prediction of dust and polluted aerosols in educational environments

by Wang Z and Han R (2025). Front. Environ. Sci. 13:1582806. doi: 10.3389/fenvs.2025.1582806

Author Zhen Wang was erroneously assigned to affiliation Normal College, Shihezi University, Shihezi, Xinjiang, China. This affiliation has now been removed for Author Zhen Wang and has been replaced with Xi’an University of Science and Technology, Art College of XUST, Xi’an, Shanxi, China.

The original article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: 3D reconstruction, deep learning, aerosol trajectory prediction, hybrid Eulerian-Lagrangian model, machine learning optimization, stochastic corrections, adaptive meshing, indoor air quality monitoring

Citation: Wang Z and Han R (2025) Correction: Deep learning for 3D reconstruction and trajectory prediction of dust and polluted aerosols in educational environments. Front. Environ. Sci. 13:1741667. doi: 10.3389/fenvs.2025.1741667

Received: 26 November 2025; Accepted: 09 December 2025;
Published: 19 December 2025.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2025 Wang and Han. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhen Wang, cmUyODExMUAxNjMuY29t

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.