A simplified potential source density function based on predefined discretization

Jeong Eun Kim, In Sun Kim, Soo Ran Won, Daehyun Wee

Research output: Contribution to journalArticlepeer-review

Abstract

The potential source contribution function (PSCF) method is widely used in the analysis of air pollutant source areas, but it also faces several limitations. To address such limitations, the potential source density function (PSDF) method was developed based on Gaussian process regression (GPR). However, the PSDF model requires more computational resources than the PSCF model. Here, we present an enhanced model with improved speed. We discretized the PSDF method by assigning a predetermined spatial correlation between cells through a priori known correlation length scale. The time taken was reduced by 25–30% from that of the original PSDF method, while the values representing the air pollution sources exhibited only a slight difference from the original ones. Our new method reduces the time required for computational calculations, measures potential sources with comparable precision, and ensures the reliability and source intensity of the results.

Original languageEnglish
JournalJournal of Engineering Research (Kuwait)
DOIs
StateAccepted/In press - 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Keywords

  • Discretization
  • Gaussian process
  • Regression
  • Source identification
  • Trajectory analysis

Fingerprint

Dive into the research topics of 'A simplified potential source density function based on predefined discretization'. Together they form a unique fingerprint.

Cite this