Yelim Choi, Bogyeong Kang, Daekeun Kim This email address is being protected from spambots. You need JavaScript enabled to view it.

Department of Environmental Engineering, Seoul National University of Science and Technology, Seoul, Korea


Received: September 22, 2023
Revised: January 26, 2024
Accepted: May 13, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.230222  


Cite this article:

Choi, Y., Kang, B., Kim, D. (2024). Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea. Aerosol Air Qual. Res. 24, 230222. https://doi.org/10.4209/aaqr.230222


HIGHLIGHTS

  • A case study in Korea to classify air pollution sources using machine learning.
  • 91% accuracy achieved by random forest model.
  • Hydrogen chloride and acetaldehyde found as critical variables
  • Effective simplified random forest model enabled by nine variables.
 

ABSTRACT


Urbanization and industrialization pose significant challenges in promptly identifying and managing air pollution sources. The application of machine learning technology offers a promising solution to solve the issue. By analyzing multidimensional datasets containing a wide range of air pollutants, a machine learning approach has the potential to significantly improve air pollution management and facilitate source tracking. This study aims to comprehensively evaluate machine learning-based emission source classification models to provide insights into air pollution source tracking and management. Using 972 datasets consisting of five emission sources and 27 air pollutants, different classification models were implemented and subsequently compared: Random Forest (RF), Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nearest Neighbors (K-NN). The RF model was found to have better predictive performance than the other four models, achieving an accuracy of 0.9691 and a kappa value of 0.9537. Hydrogen chloride and acetaldehyde were the most important variables for classifying emission sources. The findings suggest the potential of machine learning techniques in addressing air pollution challenges, and the classifier model implemented in this study shows great promise for effective emission source identification.


Keywords: Machine learning, Emission sources, Air pollutants, Classification




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