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December 11, 2025AlgorithmsOpen Access

Statistical and Machine Learning Models for Air Quality: A Systematic Review of Methods and Challenges

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Authors

LPLuzneyda Ballesteros PeinadoTGTeresa GuardaGVGermán Herrera Vidal

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Overview

Systematic review highlights machine learning's rise in air quality forecasting, suggesting a new framework for predictive models.

Key Points

  • This study reviews various statistical and machine learning methods for predicting air quality.
  • Conducted a systematic literature review of 412 articles from 2016 to 2025
  • Analyzed using PRISMA 2020 protocol with thematic filters and bibliometric tools
  • Categorized statistical models like MLR and ARIMA, alongside ML methods such as Random Forest and LSTM
  • Found a significant shift toward machine learning methods, particularly in Asia (73.2%)
  • Identified 1177 predictor variables and 307 performance metrics related to air quality
  • Only 12% of studies provided direct comparisons of methods, highlighting limited insights

Cite This Study

Peinado et al. (2025) studied this question.

synapsesocial.com/papers/69401b172d562116f28f73dfhttps://doi.org/10.3390/a18120783
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