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...
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| Format: | Article |
| Language: | English |
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Springer
2022-01-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.210236 |
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| _version_ | 1825197544209121280 |
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| author | In Sun Kim Yong Pyo Kim Daehyun Wee |
| author_facet | In Sun Kim Yong Pyo Kim Daehyun Wee |
| author_sort | In Sun Kim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e0c7c49f2b49415c96aae676e9dea0e0 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-e0c7c49f2b49415c96aae676e9dea0e02025-02-09T12:18:40ZengSpringerAerosol and Air Quality Research1680-85842071-14092022-01-0122211810.4209/aaqr.210236Potential Source Density Function: A New Tool for Identifying Air Pollution SourcesIn Sun Kim0Yong Pyo Kim1Daehyun Wee2Department of Environmental Science and Engineering, Ewha Womans UniversityDepartment of Chemical Engineering and Materials Science, System Health & Engineering, Ewha Womans UniversityDepartment of Environmental Science and Engineering, Ewha Womans UniversityAbstract 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.https://doi.org/10.4209/aaqr.210236Gaussian processRegressionTrajectory analysisAir pollutionSource identification |
| spellingShingle | In Sun Kim Yong Pyo Kim Daehyun Wee Potential Source Density Function: A New Tool for Identifying Air Pollution Sources Aerosol and Air Quality Research Gaussian process Regression Trajectory analysis Air pollution Source identification |
| title | Potential Source Density Function: A New Tool for Identifying Air Pollution Sources |
| title_full | Potential Source Density Function: A New Tool for Identifying Air Pollution Sources |
| title_fullStr | Potential Source Density Function: A New Tool for Identifying Air Pollution Sources |
| title_full_unstemmed | Potential Source Density Function: A New Tool for Identifying Air Pollution Sources |
| title_short | Potential Source Density Function: A New Tool for Identifying Air Pollution Sources |
| title_sort | potential source density function a new tool for identifying air pollution sources |
| topic | Gaussian process Regression Trajectory analysis Air pollution Source identification |
| url | https://doi.org/10.4209/aaqr.210236 |
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