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Updated: Jun 30, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

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Advances in air quality modeling through artificial intelligence, machine learning, and deep learning: A

Delaney Nelson1, Yunsoo Choi1, Mahsa Payami1

  • 1Department of Earth and Atmospheric Sciences, University of Houston, TX, 77204, USA.

The Science of the Total Environment
|February 27, 2026
PubMed
Summary

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Machine learning (ML) and deep learning (DL) are revolutionizing air quality modeling, improving pollutant forecasts and enhancing traditional methods. Emerging solutions like eXplainable AI (XAI) and Physics-Informed Neural Networks (PINN) promise more transparent and accurate air quality systems.

Area of Science:

  • Environmental Science
  • Computer Science
  • Atmospheric Chemistry

Background:

  • Traditional air quality models (e.g., CTMs) have limitations in accuracy and computational cost.
  • Machine learning (ML) and deep learning (DL) offer novel approaches to overcome these limitations.

Purpose of the Study:

  • To systematically review the evolution of ML/DL in air quality modeling.
  • To categorize current ML/DL approaches and identify challenges and future directions.

Main Methods:

  • Categorization of 112 publications into data-driven and ML-assisted models.
  • Analysis of ML applications in pollutant concentration estimation, CTM refinement, and emulation.

Main Results:

  • Data-driven models significantly improve forecast accuracy for PM2.5 and ozone.
Keywords:
Air pollutionAir quality modelingArtificial intelligenceDeep learningMachine learning

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Last Updated: Jun 30, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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  • ML-assisted methods enhance traditional modeling through bias correction and emulation, reducing computational costs.
  • Challenges include model transparency, uncertainty quantification, data scarcity, and integrating physical laws.

    • Conclusions:

    • ML/DL are crucial for next-generation air quality modeling.
    • eXplainable AI (XAI) and Physics-Informed Neural Networks (PINN) are key to developing faster, reliable, and interpretable systems.
    • These advancements will improve pollution impact assessment and mitigation strategies.