In Sun Kim1, Yong Pyo Kim2, Daehyun Wee This email address is being protected from spambots. You need JavaScript enabled to view it.1 

1 Department of Environmental Science and Engineering, Ewha Womans University, Seoul 03760, Korea
2 Department of Chemical Engineering and Materials Science, System Health & Engineering, Ewha Womans University, Seoul 03760, Korea


Received: September 7, 2021
Revised: December 22, 2021
Accepted: January 11, 2022

 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.210236  

  • Download: PDF


Cite this article:

Kim, I.S., Kim, Y.P., Wee, D. (2022). Potential Source Density Function: A New Tool for Identifying Air Pollution Sources. Aerosol Air Qual. Res. https://doi.org/10.4209/aaqr.210236


HIGHLIGHTS

  • A new method is proposed to locate and quantify source areas of air pollutants.
  • The method requires only backward trajectories and sampling data at a receptor site.
  • The method is based on Gaussian process regression, a machine-learning technique.
 

ABSTRACT


Potential source density function (PSDF) is developed to identify, that is, locate and quantify, source areas of ambient trace species based on Gaussian process regression (GPR), a machine-learning technique. The PSDF model requires backward trajectories and sampling data at a receptor site in the calculation as in the conventional model to locate source areas of ambient trace species, such as the potential source contribution function (PSCF). The PSDF model can identify source areas quantitatively and provide information on the reliability of the estimation, while the PSCF model cannot. To verify and evaluate the capability of the PSDF model, tests are carried out using three scenarios based on ambient trajectory analysis data and simulated source distributions. The test results demonstrate that the PSDF model can identify the sources of ambient trace species more accurately than the PSCF model. The PSDF model can quantify the size of the source contaminating the air parcels passing through it, and the model can detect the variation of source intensity. Also, in the test, we evaluate reliability of the information provided by the PSDF model. In addition, future works are recommended to improve the model and increase its applicability.


Keywords: Gaussian process, Regression, Trajectory analysis, Air pollution, Source identification




Share this article with your colleagues 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.