Erratum to: Toward a Causal PM2.5 Concentration Forecasting by Inferencing from Local Vehicle Tracking with a Low-Cost End-to-End Sensor System

Authors

  • Chuong Dinh Le Institute of Computer Science, Universität Bonn, Germany https://orcid.org/0009-0000-5935-3714
  • Hoang Viet Pham Institute of Computer Science, Universität Bonn, Germany
  • Thinh Gia Tran Electrical and Computer Engineering Department, Vietnamese-German University, Vietnam
  • An Dinh Le Department of Electrical and Computer Engineering, University of California, USA https://orcid.org/0009-0000-4684-715X
  • Anh-Duy Pham Joint Lab Artificial Intelligence & Data Science, Osnabrück University, Germany
  • Dat Thanh Vo Department of Mechanical, Automotive and Materials Engineering, University of Windsor, Canada https://orcid.org/0009-0005-8560-9687
  • Hien Bich Vo Electrical and Computer Engineering Department, Vietnamese-German University, Vietnam
  • Huy-Dung Han School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Vietnam

DOI:

https://doi.org/10.47852/bonviewAIA620211027

Abstract

During the production process, incorrect text was inadvertently introduced into the Conclusion section of the published article [Toward a Causal PM2.5 Concentration Forecasting by Inferencing from Local Vehicle Tracking with a Low-Cost End-to-End Sensor System].

The correct Conclusion should read as follows:

This study has successfully demonstrated the feasibility of our novel approach to estimate PM2.5 concentrations using traffic density and readily available hardware, hinting at the possibility of PM2.5 measurement based solely on the amounts of motorbikes and cars. The implementation of a streamlined vehicle counting algorithm based on Yolov7 ensures robust performance in various conditions. For estimation, it is recommended to use the Cubist mode over the mathematical method due to Cubist’s superior performance. By synergizing hardware and algorithmic advancements, we have opened avenues for further research into the determinants of urban air quality. Future work can focus on refining the system design, incorporating additional environmental factors, and exploring more sophisticated machine learning models to further enhance prediction accuracy.

The Publisher apologizes to the authors and readers for this error.

 

Reference

[1] Le, C. D., Pham, H. V., Tran, T. G., Le, A. D., Pham, A.-D., Vo, D. T., . . . , & Han, H.-D. (2026). Toward a causal PM2.5 concentration forecasting by inferencing from local vehicle tracking with a low-cost end-to-end sensor system. Artificial Intelligence and Applications, 4(2), 187–196. https://doi.org/10.47852/bonviewAIA62024212


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Published

2026-07-22

Issue

Vol. 4 No. 3 (2026)

Section

Corrections

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Copyright (c) 2026 Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Le, C. D., Pham, H. V., Tran, T. G., Le, A. D., Pham, A.-D., Vo, D. T., Vo, H. B., & Han, H.-D. (2026). Erratum to: Toward a Causal PM2.5 Concentration Forecasting by Inferencing from Local Vehicle Tracking with a Low-Cost End-to-End Sensor System. Artificial Intelligence and Applications, 4(3), 443. https://doi.org/10.47852/bonviewAIA620211027