Influence of regional biomass burning on the highly elevated organic carbon concentrations observed at Gosan, South Korea during a strong Asian dust period

Development and application of three-dimensional potential source contribution function (3D-PSCF)

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.

A multivariate receptor modeling study of air-borne particulate PAHs: Regional contributions in a roadside environment

Understanding the geographic source contributions by particulate polycyclic aromatic hydrocarbons (PAHs) is important for the Korean peninsula due to its downwind location from source areas. Regional influence of particulate PAHs was previously identified using diagnostic ratios applied to mobile source dominated roadside sampling data (Kim et al., 2012b). However, no study has yet been conducted to quantify the regional source contributions. We applied a multivariate receptor modeling tool to identify and quantify the regional source contributions to particulate PAHs in Seoul. Sampling of roadside particulate PAHs was conducted in Seoul, Korea for four years between May 2005 and April 2009, and data analysis was performed with a new multivariate receptor model, Solver for Mixture Problem (SMP). The SMP model identified two sources, local mobile source and transported regional source, and quantified their source contributions. Analysis of the particulate PAHs data reveals three types of episodic periods: a high regional source contribution period with one case, a high mobile source contribution period with three cases, and a normal contribution period with eight cases. Four-year average particulate PAHs source contributions from the two sources are 4.6 ng m(-3) and 10.7 ng m(-3) for regional and mobile sources, respectively and equivalent to 30% and 70% of the total estimated contribution from each of these sources.