• Particle and aerosol research
• /
• v.10 no.1
• /
• pp.9-17
• /
• 2014

Several studies have been carried out on the source contribution of the particulate Polycyclic Aromatic Hydrocarbons (PAHs) over Seoul by using the Chemical Mass Balance Model (CMB)(Lee and Kim, 2007; Kim et al., 2013). To confirm the validity of the modeling results, the modified model employing a photochemical loss rate along with varying residence times and the standard model that considers no loss were compared. It was found that by considering the photochemical loss rate, a better performance was obtained as compared to those obtained from the standard model in the CMB calculation. The modified model estimated higher contributions from coke oven, transportation, and biomass burning by 4 to 8%. However, the order of the relative importance of major sources was not changed, coke oven followed by transportation and biomass burning. Thus, it was concluded that the standard CMB model results are reliable for identifying the relative importance of major sources.

• Journal of Korean Society for Atmospheric Environment
• /
• v.8 no.4
• /
• pp.229-239
• /
• 1992

A receptor model application was performed by using a chemical mass balance (CMB) model to identify and apportion the specific source of airborne organic pollutants, particularly polynuclear aromatic hydrocarbons (PAHs). Source profiles of PAHs produced from the combustion of fossil fuels for CMB modeling were prepared by measuring them in emission gases. The emission sources which were examineed for the development of PAH source profiles are a coal-fired furnace using Yontan, a bunker-C iol heating boiler, and gasoline-and diesel engine automobiles. The ambient concentrations of PAHs were determined at four sites in Daejon city in 1991 with a seasonal variation. Wintertime air samples contained more extractable organic matter than summertime samples. The results of CMB modeling were various depending on the sampling sites and seasons, but the emission from bunker-C oil heating boliers was the predominant factor to affect local air quality throughout the year.

• Journal of Korean Society for Atmospheric Environment
• /
• v.16 no.5
• /
• pp.477-485
• /
• 2000

The data set was collected on fifty-eight different days with a 24-h sampling period from October 27, 1995 through August 25, 1996. From the chemical mass balance (CMB) analysis of $PM_{2.5}$ in the Chongju area, the contributions from soil, gasoline, diesel, light and heavy oil combustion were 2.6%, 15.4%, 9.0%, 28.8% and 1.5%, respectively. Residual $NO_{3}^{-}$), residual $SO_{4}^{2-}$ and residual OC, possibly formed in the atmosphere. represented additional 8.0, 10.2, and 1.6% of the $PM_{2.5}$, respectively. Other unidentified sources constituted the remaining 22.9%. From the CMB analysis, the $PM_{2.5}$ source contribution for fall, winter, spring and summer were 92, 76.8, 77.5 and 59.2%, respectively.

• Journal of Korean Society for Atmospheric Environment
• /
• v.8 no.2
• /
• pp.112-120
• /
• 1992

Twenty four metal, nonmetal elements and 4 major anions in total suspended particulates (TSP) collected at two sites in Daejon city from october to december in 1991 by a Hi-vol sampler were thoroughly analyzed by Inductively Coupled Plasma/ Atomic Emission Spectrometry (ICP/AES) and Ion Chromatography (IC). These analyzed data were used to perform a receptor modeling using the Chemical Mass Balance (CMB) for the source apportionment of TSP sample. Approximately 60% TSP weight in industrial complex area was influenced by potential industrial sources and 25% was by heating fuels and automobile emissions, whereas a half of TSP in residential area was influenced by surrounding environment and more than 35% of TSP was influenced by heating fuels. The CMB model provided source apportionment results reasonably and scientifically with a minor limitation.

• Journal of Korean Society for Atmospheric Environment
• /
• v.19 no.6
• /
• pp.789-803
• /
• 2003

The purpose of this study is to compare the usefulness between Gaussian dispersion model and receptor model with the experimental result of the dispersion tracing of the particulate pollutants from Taean coal-fired power plants. For this purpose, the component analysis of the collected PM 10 samples was performed. In order to trace the pollution sources, factor analysis was done with the result of the component analysis. As a result of the correlativity analysis of the fifteen power plants' profiles offered by US EPA, the correlativity of No.11202 source profile showed highest rate up to 84.5%. Thus it was adopted as proper one and the contribution rate by each pollution source was calculated by Chemical Mass Balance (CMB)-8 model. The contribution rate, which was the effect rate of the power plants on each measuring point, were calculated with a range of 24∼52％ and the standard error was below 0.9 $\mu\textrm{g}$/㎥. This indicates the selection of the source profile was appropriate. Also, the concentrations of each point were calculated by the ISCST3 which is suggested by US EPA as one of the regulatory Gaussian dispersion model. The calculation result showed that the predicted concentration was 50∼58 $\mu\textrm{g}$/㎥, comparing with the measured result of 9∼65 $\mu\textrm{g}$/㎥. It was found that the concentration calculated by ISCST3 was underpredicted. It was thought that the receptor model was more favorable than the Gaussian dispersion model in estimating the effect of the particulate matter on a certain receptive point.

• Journal of Korean Society of Environmental Engineers
• /
• v.30 no.3
• /
• pp.302-313
• /
• 2008

The purposes of this study are to investigate the concentration levels of fine particles, so called PM$_{2.5}$, to identify the affecting sources, and to estimate quantitatively the source contributions of PM$_{2.5}$. Ambient air sampling was seasonally carried out at two sites in Pohang(a residential and an industrial area) during the period of March to December 2003. PM$_{2.5}$ samples were collected by high volume air samplers with a PM$_{10}$ Inlet and an impactor for particle size segregation, and then determined by gravimetric method. The chemical species associated with PM$_{2.5}$ were analyzed by inductively coupled plasma spectrophotometery(ICP) and ion chromatography(IC). The results showed that the most significant season for PM$_{2.5}$ mass concentrations appeared to be spring, followed by winter, fall, and summer. The annual mean concentrations of PM$_{2.5}$ were 36.6 $\mu$g/m$^3$ in the industrial and 30.6 $\mu$g/m$^3$ in the residential area, respectively. The major components associated with PM$_{2.5}$ were the secondary aerosols such as nitrates and sulfates, which were respectively 4.2 and 8.6 $\mu$g/m$^3$ in the industrial area and 3.7 and 6.9 $\mu$g/m$^3$ in the residential area. The concentrations of chemical component in relation to natural emission sources such as Al, Ca, Mg, K were generally higher at both sampling sites than other sources. However, the concentrations of Fe, Mn, Cr in the industrial area were higher than those in the residential area. Based on the principal component analysis and stepwise multiple linear regression analysis for both areas, it was found that soil/road dust and secondary aerosols are the most significant factors affecting the variations of PM$_{2.5}$ in the ambient air of Pohang. The source apportionments of PM$_{2.5}$ were conducted by chemical mass balance(CMB) modeling. The contributions of PM$_{2.5}$ emission sources were estimated using the CMB8.0 receptor model, resulting that soil/road dust was the major contributor to PM$_{2.5}$, followed by secondary aerosols, vehicle emissions, marine aerosols, metallurgy industry. Finally, the application and its limitations of chemical mass balance modeling for PM$_{2.5}$ was discussed.