In this study, we tried to estimate the regional information and contribution of the estimated emission source using modified Hybrid receptor model.
During the period of 2020, the PMF model was proceed using hourly data of components in fine particle (PM2.5) which be formed of mass concentration from the Central Region Atmospheric Environment Research Institute. As a result of ion balance analysis using statistical analysis and equivalent concentration for preprocessing of input data, the concentration of fine particle (PM2.5) was found to be about 22.2 ± 15.3 μg/m³ and the maximum concentration was 104 μg/m³.
From the PMF model, eight factors were selected for estimation of source profile and contribution (soil/dust, secondary sulfate, biomass burning, vehicle, secondary nitrate, industry, coal combustion).
Secondary sulfate source (factor 2) showed the highest contribution (35 %) followed by secondary nitrate source (factor 5, 26 %), vehicle source (factor 4, 16 %), biomass burning source (factor 7, 6 %), industry source (factor 6, 6 %) dust/soil source (factor 1, 6 %), sea salt source (factor 8, 4 %), coal combustion source (factor 3, 1 %). As seasonal contributions, secondary nitrate source and coal combustion source showed a high contribution in the winter period. Secondary sulfate sources and dust/soil sources showed a high contribution in the spring and summer period.
CWT based on HYSPLIT overestimated values owing to the overlapping of several trajectories, especially in the Yellow Sea area (Yellow Sea effect). The ACERWT model significantly improved the limitation of CWT results by decreasing the Yellow Sea effect. The result showed that the northeastern, western, southern area of China, domestic and Japan dominantly affect PM2.5 pollution in the receptor site.
Regions (CS(China sector)1, 2, 3, KS(Korea sector), JS(Japan sector), OS(Other sector)) were divided to confirm the contribution of each region by emission source. OE(Other emission) was emission source that could not be estimated by ACERWT(Secondary aerosol, biomass burning, sea salt, dust/soil etc.). CS2 showed the highest contribution (19 %) followed by CS1 (13 %), CS3 (3 %). JS showed the contribution (3 %) followed by KS (2 %). OS showed the contribution (9 %).
Therefore, we suggest that the ACERWT model will be more useful than the CWT model to estimate the regional influence of the PM2.5 concentration at the receptor site in Korea. In addition, the research approach of this study can be used as a reference tool for studies to improve the limitations of the hybrid receptor model in the future.