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A potential source contribution function (PSCF) can indicate the source areas of high air pollutant concentrations using backward trajectories. However, the conventional two-dimensional PSCF (2D-PSCF) cannot consider the emission and transport height of air pollutants. That missing information might be critical because injection height varies depending on the source type, such as with biomass burning. We developed a simple algorithm to account for the height of trajectories with high concentrations and combined it with the conventional PSCF to devise 3D-PSCF. We demonstrate the applicability of the 3D-PSCF by applying it to particulate PAH data collected from September 2006 to August 2007 in Seoul. We found variation in the results from 3D-PSCF with threshold heights from 3,000 to 1,500 m. Applying 2,000 m as the threshold height in the PSCF calculation most clearly determined the possible source areas of air pollutants from biomass fuel burning that were affecting the air quality in Seoul.

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 machinelearning 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.

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