• Journal of Environmental Health Sciences
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• v.31 no.4 s.85
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• pp.260-265
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• 2005

Indoor air quality might be affected by source strength of indoor pollutants, ventilation rate, decay rate, outdoor level, and so on. Although technologies measuring these factors exist directly, direct measurements of all factors are not always practical in most field studies. The purpose of this study was to develop an alternative method to estimate the source strength and deposition constant by application of multiple measurements. For the total duration of 60 days, indoor and outdoor $NO_2$ concentrations every 3 days were measured in 30 houses in Seoul, Asan and Daegu. Using a compartment model by mass balance and linear regression analysis, penetration factor (ventilation divided by sum of air exchange rate and deposition constant) and source strength factor (emission rate divided by sum of air exchange rate and deposition constant) were calculated. Subsequently, the source strength and deposition constant were estimated. Natural ventilation was $1.80{\pm}0.42\;ACH,\;1.11{\pm}0.50\;ACH,\;0.92{\pm}0.26\;ACH$ in Seoul, Asan and Daegu, respectively. Calculated deposition constant(K) and source strength of $NO_2,$ in this study were $0.98{\pm}0.28\;hr^{1}\;and\;16.28{\pm}7.47\;ppb/h,$ respectively.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.898-903
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• 2010

This study was carried out to estimate carbon emission using LCA (Life Cycle Assessment) and to establish LCI (Life Cycle Inventory) database of soybean production system. Based on collecting the data for operating LCI, it was shown that input of organic fertilizer was value of 3.10E+00 kg $kg^{-1}$ soybean and it of mineral fertilizer was 4.57E-01 kg $kg^{-1}$ soybean for soybean cultivation. It was the highest value among input for soybean production. And direct field emission was 1.48E-01 kg $kg^{-1}$ soybean during soybean cropping. The result of LCI analysis focussed on greenhouse gas (GHG) was showed that carbon footprint was 3.36E+00 kg $CO_2$-eq $kg^{-1}$ soybean. Especially $CO_2$ for 71% of the GHG emission. Also of the GHG emission $CH_4$, and $N_2O$ were estimated to be 18% and 11%, respectively. It might be due to emit from mainly fertilizer production (92%) and soybean cultivation (7%) for soybean production system. $N_2O$ was emitted from soybean cropping for 67% of the GHG emission. In $CO_2$-eq. value, $CO_2$ and $N_2O$ were 2.36E+00 kg $CO_2$-eq. $kg^{-1}$ soybean and 3.50E-01 kg $CO_2$-eq. $kg^{-1}$ soybean, respectively. With LCIA (Life Cycle Impact Assessment) for soybean production system, it was observed that the process of fertilizer production might be contributed to approximately 90% of GWP (global warming potential). Characterization value of GWP was 3.36E+00 kg $CO_2$-eq $kg^{-1}$.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.36-50
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• 2015

The purpose of this study is to assign emission source profiles of volatile organic compounds (VOCs) and particulate matters (PMs) for chemical speciation, and to correct the temporal allocation factor and the chemical speciation of source profiles according to the source classification code within the sparse matrix operator kernel emission system (SMOKE) in the Seoul metropolitan area. The chemical speciation from the source profiles of VOCs such as gasoline, diesel vapor, coating, dry cleaning and LPG include 12 and 34 species for the carbon bond IV (CBIV) chemical mechanism and the statewide air pollution research center 99 (SAPRC99) chemical mechanism, respectively. Also, the chemical speciation of PM2.5 such as soil, road dust, gasoline and diesel vehicles, industrial source, municipal incinerator, coal fired, power plant, biomass burning and marine was allocated to 5 species of fine PM, organic carbon, elementary carbon, $NO_3{^-}$, and $SO_4{^2-}$. In addition, temporal profiles for point and line sources were obtained by using the stack telemetry system (TMS) and hourly traffic flows in the Seoul metropolitan area for 2007. In particular, the temporal allocation factor for the ozone modeling at point sources was estimated based on $NO_X$ emission inventories of the stack TMS data.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.1
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• pp.49-62
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• 2005

Lake Paldang is a main resource of drinking water for 20 million people in the greater Seoul area. Dry deposition amounts of nitrogen and sulfur were estimated for three typical days in each season over the watershed of Lake Paldang. Models- 3/CMAQ (Community Multiscale Air Quality) and MM5 (Mesoscale Model) were used to predict air quality and meteorology, respectively. Aerosols as well as gaseous pollutants were considered. Nitrogen was mainly deposited in the form of HNO, while most of sulfur was deposited in the form of SO$_2$. Contribution of secondary pollutants was the largest in fall since they were transported from the greater Seoul area. However, contribution of secondarily-formed particulate pollutants to the nitrogen deposition was the largest in winter because semi-volatile ammonium nitrate favors lower temperature. Annual deposition amounts of nitrogen and sulfur were 37% and 26% of their emission amounts, respectively, over the watershed of Lake Paldang. Higher value of the nitrogen deposition showed a more influence of pollutants emitted in the greater Seoul area.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.892-897
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• 2010

LCA (Life Cycle assessment) was carried out to estimate on carbon footprint and to establish of LCI (Life Cycle Inventory) database of sweetpotato production system. Based on collecting the data for operating LCI, it was shown that input of organic fertilizer was value of 3.26E-01 kg $kg^{-1}$ and it of mineral fertilizer was 1.02E-01 kg $kg^{-1}$ for sweetpotato production. It was the highest value among input for sweetpotato production. And direct field emission was 2.47E-02 kg $kg^{-1}$ during sweetpotato cropping. The result of LCI analysis focussed on greenhouse gas (GHG) was showed that carbon footprint was 4.05E-01 kg $CO_2$-eq. $kg^{-1}$ sweetpotato. Especially $CO_2$ for 71% of the GHG emission and the value was 2.88E-01 kg $CO_2$-eq. $kg^{-1}$ sweetpotato. Of the GHG emission $CH_4$, and $N_2O$ were estimated to be 18% and 11%, respectively. It might be due to emit from mainly fertilizer production (32%) and sweetpotato cultivation (28%) for sweetpotato production system. $N_2O$ emitted from sweetpotato cultivation for 90% of the GHG emission. With LCIA (Life Cycle Impact Assessment) for sweetpotato production system, it was observed that the process of fertilizer production might be contributed to approximately 90% of GWP (global warming potential). Characterization value of GWP and POCP were 4.05E-01 $CO_2$-eq. $kg^{-1}$ and 5.08E-05 kg $C_2H_4$-eq. $kg^{-1}$, respectively.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.5
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• pp.538-555
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• 2012

The purpose of this study was to understand $PM_{2.5}$ chemical characteristics on the Suwon/Yongin area and further to quantitatively estimate $PM_{2.5}$ source contributions. The $PM_{2.5}$ sampling was carried out by a high-volume air sampler at the Kyung Hee University-Global Campus from November, 2010 to October, 2011. The 40 chemical species were then analyzed by using ICP-AES(Ag, Ba, Cr, Cu, Fe, Mn, Ni, Pb, Si, Ti, V and Zn), IC ($Na^+$, $K^+$, $NH_4{^+}$, $Mg^{2+}$, $Ca^{2+}$, $NO_3{^-}$, ${SO_4}^{2-}$ and $Cl^-$), DRI/OGC (OC1, OC2, OC3, OC4, OP, EC1, EC2 and EC3) and GC-FID (acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benzo[a]anthracene, benzo[b]fluoranthene, benzo[a] pyrene, indeno[1,2,3-cd] pyrene, benzo[g,h,i]perylene and dibenzo[a,h,]anthracene). When applying PMF model after performing proper data treatment, a total of 10 sources was identified and their contributions were quantitatively estimated. The average contribution to $PM_{2.5}$ emitted from each source was determined as follows; 26.3% from secondary aerosol source, 15.5% from soil and road dust emission, 15.3% from vehicle emission, 15.3% from illegal biomass burning, 12.2% from incineration, 7.2% from oil combustion source, 4.9% from industrial related source, and finally 3.2% from coal combustion source. In this study we used the ratios of PAHs concentration as markers to double check whether the sources were reasonably classified or not. Finally we provided basic information on the major $PM_{2.5}$ sources in order to improve the air quality in the study area.

• Journal of environmental and Sanitary engineering
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• v.16 no.4
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• pp.21-30
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• 2001

Vehicles, especially diesel-using, are a major source of airborne particulate matter(PM), nitrogen dioxide($NO_2$) and so on in metropolitan cities such as Seoul. Therefore workers, who are mainly merchants, near roadside may be highly exposed to air pollutants from exhausted emissions of vehicles. This means that occupational type and location can affect the workers＇health by exposure to outdoor pollutions of ambient as well as indoor pollutions of working condition, respectively. In this study, we simultaneously measured the PM3.5 and $NO_2$concentrations in indoor and outdoor of shoes repair shops in Seoul, which were generally located at roadside in Korea. Shoes repairmen were highly exposed to PM3.5 and $NO_2$ both indoor and outdoor of repair shops comparing with other sub-population groups. High exposure to air pollutants for shoes repairmen was considered to be outdoor source from exhausted emission of vehicles and indoor source from working condition. The $PM3.5/NO_2$ concentration ratio was $1.17{\pm}$0.59 in roadside, of which ratio was higher 7han ratios of other studies. This result suggested that major air pollutant in Seoul was fine particle. Also, this PM3.5 to $NO_2$ ratio will be used in environmental exposure and risk assessment by estimation of PM3.5 concentration as measuring the only $NO_2$ concentration with small and accurate $NO_2$ passive sampler.

• Journal of Environmental Science International
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• v.13 no.7
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• pp.645-653
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• 2004

Indoor air quality is affected by source strength of pollutants, ventilation rate, decay rate, outdoor level, and so on. Although technologies measuring these factors exist directly, direct measurements of all factors are not always practical in most field studies. The purpose of this study was to develop an alternative method to estimate these factors by application of multiple measurements. For the total duration of 30 days, daily indoor and outdoor $NO_2$ concentrations were measured in 30 houses in Brisbane, Australia, and for 21 days in 40 houses in Seoul, Korea, respectively. Using a box model by mass balance and linear regression analysis, penetration factor (ventilation divided by sum of air exchange rate and deposition constant) and source strength factor (emission rate divided by sum of air exchange rate and deposition constant) were calculated, Sub-sequently, the ventilation and source strength were estimated. In Brisbane, the penetration factors were $0.59\pm0.14$ and they were unaffected by the presence of a gas range. During sampling period, geometric mean of natural ventilation was estimated to be $l.l0\pm1.5l$ ACH, assuming a residential $NO_2$ decay rate of 0.8 hr^{-1}$ in Brisbane. In Seoul, natural ventilation was $1.15\pm1.73$ ACH with residential $NO_2$ decay rate of 0.94 hr^{-1}$ Source strength of $NO_2$ in the houses with gas range $(12.7\pm9.8$ ppb/hr) were significantly higher than those in houses with an electric range $(2.8\pm2,6$ ppb/hr) in Brisbane. In Seoul, source strength in the houses with gas range were $l6.8\pm8.2$ ppb/hr. Conclusively, indoor air quality using box model by mass balance was effectively characterized.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.904-910
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• 2010

LCA (Life Cycle Assessment) carried out to estimate carbon footprint and to establish of LCI (Life Cycle Inventory) database of pepper production system. Pepper production system was categorized the field cropping (redpepper) and the greenhouse cropping (greenpepper) according to pepper cropping type. The results of collecting data for establishing LCI D/B showed that input of fertilizer for redpepper production was more than that for greenpepper production system. The value of fertilizer input was 2.55E+00 kg $kg^{-1}$ redpepper and 7.74E-01 kg $kg^{-1}$ greenpepper. Amount of pesticide input were 5.38E-03 kg $kg^{-1}$ redpepper and 2.98E-04 kg $kg^{-1}$ greenpepper. The value of field direct emission ($CO_2$, $CH_4$, $N_2O$) were 5.84E-01 kg $kg^{-1}$ redpepper and 2.81E+00 greenpepper, respectively. The result of LCI analysis focussed on the greenhouse gas (GHG), it was observed that the values of carbon footprint were 4.13E+00 kg $CO_2$-eq. $kg^{-1}$ for redpepper and 4.70E+00 kg $CO_2$-eq. $kg^{-1}$ for greenpepper; especially for 90% and 6% of $CO_2$ emission from fertilizer and pepper production, respectively. $N_2O$ was emitted from the process of N fertilizer production (76%) and pepper production (23%). The emission value of $CO_2$ from greenhouse production was more higher than it of field production system. The result of LCIA (Life Cycle Impact Assessment) was showed that characterization of values of GWP (Global Warming Potential) were 4.13E+00 kg $CO_2$-eq. $kg^{-1}$ for field production system and 4.70E+00 kg $CO_2$-eq. $kg^{-1}$ for greenhouse production system. It was observed that the process of fertilizer production might be contributed to approximately 52% for redpepper production system and 48% for greenpepper production system of GWP.