Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea
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 pol...
Saved in:
Main Authors: | Yelim Choi, Bogyeong Kang, Daekeun Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2024-05-01
|
Series: | Aerosol and Air Quality Research |
Subjects: | |
Online Access: | https://doi.org/10.4209/aaqr.230222 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Estimation of Air Pollutant Emissions from Heavy Industry Sector in North Korea
by: Young Won Lee, et al.
Published: (2023-05-01) -
Machine Learning Classification Model to Label Sources Derived from Factor Analysis Receptor Models for Source Apportionment
by: Vikas Kumar, et al.
Published: (2023-04-01) -
A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution
by: Yang Cao, et al.
Published: (2022-12-01) -
Potential Source Density Function: A New Tool for Identifying Air Pollution Sources
by: In Sun Kim, et al.
Published: (2022-01-01) -
Assessment of Metallic Content, Pollution, and Sources of Road Dust in the City of Białystok (Poland)
by: Mirosław Skorbiłowicz, et al.
Published: (2020-06-01)