Potential Source Density Function: A New Tool for Identifying Air Pollution Sources

In Sun Kim; Yong Pyo Kim; Daehyun Wee

doi:10.4209/aaqr.210236

Potential Source Density Function: A New Tool for Identifying Air Pollution Sources

In Sun Kim, Yong Pyo Kim, Daehyun Wee

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

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.

Original language	English
Article number	210236
Journal	Aerosol and Air Quality Research
Volume	22
Issue number	2
DOIs	https://doi.org/10.4209/aaqr.210236
State	Published - Feb 2022

Bibliographical note

Funding Information:
This work was primarily supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (2019-R1F1A1062571). This research was also supported by Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M1A2A2103953). ISK was partly supported during the study by the Ewha Womans University scholarship of 2016.

Funding Information:
This work was primarily supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (2019-R1F1A1062571). This research was also supported by Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M1A2A2103953). ISK was partly supported during the study by the Ewha Womans University scholarship of 2016. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for providing the HYSPLIT transport and dispersion model and/or READY website (http://www.ready.noaa.gov). The authors also gratefully acknowledge the Emissions of Atmospheric Compounds and Compilation of Ancillary Data (ECCAD, http://eccad.aeris-data.fr/) and the EU Joint Research Centre Emissions Database for Global Atmospheric Research (EDGAR) for providing emission data (http://edgar.jrc.ec.europa.eu/).

Publisher Copyright:
The Author(s).

Keywords

Air pollution
Gaussian process
Regression
Source identification
Trajectory analysis

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10.4209/aaqr.210236

Cite this

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title = "Potential Source Density Function: A New Tool for Identifying Air Pollution Sources",

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 = "Air pollution, Gaussian process, Regression, Source identification, Trajectory analysis",

author = "Kim, {In Sun} and Kim, {Yong Pyo} and Daehyun Wee",

note = "Funding Information: This work was primarily supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (2019-R1F1A1062571). This research was also supported by Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M1A2A2103953). ISK was partly supported during the study by the Ewha Womans University scholarship of 2016. Funding Information: This work was primarily supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (2019-R1F1A1062571). This research was also supported by Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M1A2A2103953). ISK was partly supported during the study by the Ewha Womans University scholarship of 2016. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for providing the HYSPLIT transport and dispersion model and/or READY website (http://www.ready.noaa.gov). The authors also gratefully acknowledge the Emissions of Atmospheric Compounds and Compilation of Ancillary Data (ECCAD, http://eccad.aeris-data.fr/) and the EU Joint Research Centre Emissions Database for Global Atmospheric Research (EDGAR) for providing emission data (http://edgar.jrc.ec.europa.eu/). Publisher Copyright: The Author(s).",

year = "2022",

month = feb,

doi = "10.4209/aaqr.210236",

language = "English",

volume = "22",

journal = "Aerosol and Air Quality Research",

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AU - Kim, Yong Pyo

AU - Wee, Daehyun

N1 - Funding Information: This work was primarily supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (2019-R1F1A1062571). This research was also supported by Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M1A2A2103953). ISK was partly supported during the study by the Ewha Womans University scholarship of 2016. Funding Information: This work was primarily supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (2019-R1F1A1062571). This research was also supported by Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M1A2A2103953). ISK was partly supported during the study by the Ewha Womans University scholarship of 2016. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for providing the HYSPLIT transport and dispersion model and/or READY website (http://www.ready.noaa.gov). The authors also gratefully acknowledge the Emissions of Atmospheric Compounds and Compilation of Ancillary Data (ECCAD, http://eccad.aeris-data.fr/) and the EU Joint Research Centre Emissions Database for Global Atmospheric Research (EDGAR) for providing emission data (http://edgar.jrc.ec.europa.eu/). Publisher Copyright: The Author(s).

PY - 2022/2

Y1 - 2022/2

N2 - 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.

AB - 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.

KW - Air pollution

KW - Gaussian process

KW - Regression

KW - Source identification

KW - Trajectory analysis

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DO - 10.4209/aaqr.210236

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VL - 22

JO - Aerosol and Air Quality Research

JF - Aerosol and Air Quality Research

IS - 2

M1 - 210236

ER -

Potential Source Density Function: A New Tool for Identifying Air Pollution Sources

Abstract

Bibliographical note

Keywords

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