• Particle and aerosol research
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• v.12 no.2
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• pp.27-36
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• 2016

The PMF result was compared with the result from the Chemical Mass Balance (CMB) modelling (Lee and Kim, 2007) to estimate major source of PAHs observed at Seoul from August 2002 to December 2003. Five major sources were estimated from PMF and CMB modellings respectively. Among them three major sources (coal combustion for residential, coke oven and biomass burning) were identified at both models.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.3
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• pp.219-236
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• 2009

Particle-phase organic tracers (molecular markers) have been shown to be an effective method to assess and quantify the impact of sources of carbonaceous aerosols. These molecular markers have been used in chemical mass balance (CMB) models to apportion primary sources of organic aerosols in regions where the major organic aerosol source categories have been identified. As in the case of all CMB models, all important sources of the tracer compounds must be included in a Molecular Marker CMB (MM-CMB) model or the MMCMB model can be subject to biases. To this end, the application of the MM-CMB models to locations where reasonably accurate emissions inventory of organic aerosols are not available, should be performed with extreme caution. Of great concern is the potential presence of industrial point sources that emit carbonaceous aerosols and have not been well characterized or inventoried. The current study demonstrates that emissions from industrial point sources in the St. Louis, Missouri area can greatly bias molecular marker CMB models if their emissions are not correctly addressed. At a sampling site in the greater St. Louis Area, carbonaceous aerosols from industrial point sources were found to be important source of carbonaceous aerosols during specific time periods in addition to common urban sources (i.e. mobile sources, wood burning, and road dust). Since source profiles for these industrial sources have not been properly characterized, method to identify time periods when point sources are impacting a sampling site, needs to avoid obtaining biases source apportionment results. The use of real time air pollution measurements, along with molecular marker measurements, as a screening tool to identify when point sources are impacting a receptor site is presented.

• Particle and aerosol research
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• v.10 no.1
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• pp.9-17
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• 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
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• v.19 no.4
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• pp.387-396
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• 2003

A field study was conducted during the summer time of 2002 to determine compositions of volatile organic compounds (VOCs) emitted from vehicles and to develop source emission profiles that is applied to CMB model to estimate the source contribution of certain area. Source emission profile is widely used for the estimation of source contribution by the chemical mass balance model and have to be developed applicable for the target area of estimation. This study was aimed to develop source emission profile and estimation of source contribution of VOCs after application of the chemical mass balance (CMB) receptor model. After considering the emission inventory and other research results for the VOCs in Seoul, Korea, the sources like vehicle emission (tunnel), gas station (gasoline, diesel), solvent usage (painting operation, dry cleaning, graphic art), and gas fuels were selected for the major VOCs sources. Furthermore, ambient air samples were simultaneously collected from 09:00 to 11:00 for four days at eight different official air quality monitoring sites as receptors in Seoul during summer of 2001. Source samples were collected by canisters, and then about seventy volatile organic compounds were analyzed by gas chromatography with flame ionization detector (GC/FID). Based on both the developed source profiles and the database of the receptors, CMB model was intensively applied to estimate mass contribution of VOCs sources. Examining the source profile from the vehicle, the portion of alkanes of VOCs was highest, and then the portion of aromatics such toluene, m/p-xylene were followed. In case of gas fuel. they have their own components; the content of butane, propane, ethane was higher than any other component according to the fuel usage. The average of the source apportionment on VOCs for 8 sites showed that the major sources were vehicle emission and gas fuels. The vehicle emission source was revealed as having the highest contribution with an average of 49.6%, and followed by solvent with 21.3%, gas fuel with 16.1%, gasoline with 13.1%.

• Journal of Korean Society for Atmospheric Environment
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• v.8 no.2
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• pp.112-120
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• 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 Environmental Science International
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• v.12 no.2
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• pp.217-225
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• 2003

Samples of size-fractionated PM10 (airborne particulate matter with aerodynamic diameter less than $10\mu\textrm{m}$) were collected at an urban site in Jeju city from May to September 2002. The mass concentration and chemical composition of the samples were measured. The data sets were then applied to the CMB receptor model to estimate the source contribution of PM10 in Jeju area. The average PM10 mass concentration was 28.80$\mu\textrm{g}/m^3$ ($24.6~33.49\mu\textrm{g}/m^3$), and the FP (fine particle with aerodynamic diameter less than $2.l\mu\textrm{m}$ fraction in PM10 was approximately 8% higher than the CP (coarse particle with aerodynamic diameter greater than $2.l\mu\textrm{m}$ and less than $10\mu\textrm{m}$ fraction in PM10. The CP composition was obviously different from the FP composition, that is, the most abundant water soluble species was nitrate ion in the FP, but sulfate ion in the CP. Also sulfur was the most dominant element in the FP, however, sodium was that in the CP. From CMB receptor model results, it was found that road dust was the largest contributor to the CP mass concentration (45% of the CP) and ammonium nitrate, domestic boiler, and marine aerosol were major sources to the CP mass. However, the secondary aerosol was the most significant contributor to the FP mass concentration (45% of the FP). In this study, it was suggested that the contributions of soil dust and gasoline vehicle became very low due to collinearity with road dust and diesel vehicle, respectively.

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

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.3
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• pp.315-327
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• 2005

The total of 35 target VOCs (volatile organic compounds), which were included in the TO-14, was selected to develop a VOCs' source profile matrix of the Yeosu Petrochemical Complex and to test its collinearity by singular value decomposition(SVD) technique. The VOCs collected in canisters were sampled from 12 different sources such as 8 direct emission sources (refinery, painting, wastewater treatment plant, incinerator, petrochemical processing, oil storage, fertilizer plant, and iron mill) and 4 general area sources (gasoline vapor emission, graphic art activity, vehicle emission, and asphalt paving activity) in this study area, and then those samples were analyzed by GC/MS. Initially the resulting raw data for each profile were scaled and normalized through several data treatment steps, and then specific VOCs showing major weight fractions were intensively reviewed and compared by introducing many other related studies. Next, all of the source profiles were tested in terms of degree of collinearity by SVD technique. The study finally could provide a proper VOCs' source profile in the study area, which can give opportunities to apply various receptor models properly including chemical mass balance (CMB).

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.216-225
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• 2003

Fine particles with aerodynamic diameter less than 2.5 ${\mu}m$ (PM2.5) were collected from three sites in Beijing during April, August, and November 2000 and January 2001. After chemical components in samples are analyzed, a chemical mass balance (CMB) receptor model using PARs as tracers is applied to quantify the source contributions to PM2.5 in Beijing. The results show that the major sources are coal combustion, fugitive dust, vehicle exhaust, secondary sulfate and nitrate, and organic matter while biomass burning and construction dust contribute only a small fraction. In addition, source inventory in Beijing is used to determine the primary source contributions. The two methods result in comparable results. Source apportionment at three sampling sites presents similar contributions to PM2.5 although the sites are far away from each other. However, distinct seasonal pattern is presented for the source contributions from coal combustion, fugitive dust, biomass burning, secondary sulfate and nitrate.