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Modelling Particulate Matter (PM10) Variations During Transboundary Haze Events Using a Modified Quantile Regression

Nur Alis Addiena A Rahim1,2, Norazian Mohamed Noor1,2, Izzati Amani Mohd Jafri1,2

  • 1Faculty of Civil Engineering Technology Universiti Malaysia Perlis Arau Perlis Malaysia.

Analytical Science Advances
|July 17, 2025
PubMed
Summary

This study introduces new air quality models to forecast particulate matter (PM10) pollution 1-3 days ahead during Southeast Asian haze events. Advanced feature selection improved model accuracy, aiding early warning systems.

Keywords:
hybrid modelling techniquesparticulate matterpredictive analyticsquantile regressiontransboundary haze

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Data Science

Background:

  • Transboundary haze in Southeast Asia causes significant environmental and health issues.
  • Accurate forecasting of particulate matter (PM10) is crucial for managing haze events.

Purpose of the Study:

  • To develop and evaluate novel air quality forecasting models for PM10 concentrations during transboundary haze in Malaysia.
  • To enhance predictive accuracy by integrating advanced feature selection techniques with quantile regression.

Main Methods:

  • Development of hybrid models: Quantile Regression with Relief-based ranking (QR-Relief), correlation-based selection (QR-correlation), and Principal Component Analysis (QR-PCA).
  • Evaluation of model performance using metrics like Mean Absolute Error (MAE), Normalized Absolute Error (NAE), and Root Mean Square Error (RMSE).
  • Validation using an independent dataset from 2019 to confirm real-world applicability.

Main Results:

  • The hybrid models (QR-Relief, QR-correlation, QR-PCA) significantly outperformed traditional Quantile Regression and Multiple Linear Regression models.
  • Feature selection techniques demonstrably improved the predictive reliability of the air quality models.
  • Validated models showed strong performance, confirming their utility for real-world haze event prediction.

Conclusions:

  • Advanced feature selection integrated with quantile regression offers a robust framework for accurate PM10 forecasting.
  • The developed models provide valuable decision-support tools for environmental and public health management during haze events.
  • This methodological advancement supports the creation of effective early warning systems for transboundary haze.