• Journal of Korean Society for Atmospheric Environment
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• v.34 no.2
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• pp.306-320
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• 2018

Emission inventory is the essential component for improving the performance of air quality forecasting system. This study evaluated the simulated daily mean $PM_{2.5}$ concentrations in South Korea and China for 1-year period (Sept. 2016~Aug. 2017) using air quality forecasting system which was applied by the emission inventory of E2015 (predicted CAPSS 2015 for South Korea and KORUS 2015 v1 for the other regions). To identify the impacts of emissions on the simulated $PM_{2.5}$, the emission inventory replaced by E2010 (CAPSS 2010 and MIX 2010) were also applied under the same forecasting conditions. These results showed that simulated daily mean $PM_{2.5}$ concentrations had generally suitable performance with both emission data-sets for China (IOA>0.87, R>0.87) and South Korea (IOA>0.84, R>0.76). The impacts of the changes in emission inventories on simulated daily mean $PM_{2.5}$ concentrations were quantitatively estimated. In China, normalized mean bias (NMB) showed 5.5% and 26.8% under E2010 and E2015, respectively. The tendency of overestimated concentrations was larger in North Central and Southeast China than other regions under both E2010 and E2015. Seasonal differences of NMB were higher in non-winter season (28.3% (E2010)~39.3% (E2015)) than winter season (-0.5% (E2010)~8.0% (E2015)). In South Korea, NMB showed -5.4% and 2.8% for all days, but -15.2% and -11.2% for days below $40{\mu}g/m^3$ to minimize the impacts of long-range transport under E2010 and E2015, respectively. For all days, simulated $PM_{2.5}$ concentrations were overestimated in Seoul, Incheon, Southern part of Gyeonggi and Daejeon, and underestimated in other regions such as Jeonbuk, Ulsan, Busan and Gyeongnam, regardless of what emission inventories were applied. Our results suggest that the updated emission inventory, which reflects current status of emission amounts and spatio-temporal allocations, is needed for improving the performance of air quality forecasting.

• 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 the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.1-7
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• 2020

This study quantified annual uptake and storage of carbon by urban greenspace in institutional lands and suggested improvement of greenspace structures to enhance carbon reduction effects. The study selected a total of five study cities including Seoul, Daejeon, Daegu, Chuncheon, and Suncheon, based on areal size and nationwide distribution. Horizontal and vertical greenspace structures were field-surveyed, after institutional greenspace lots were selected using a systematic random sampling method on aerial photographs of the study cities. Annual uptake and storage of carbon by woody plants were computed applying quantitative models of each species developed for urban landscape trees and shrubs. Tree density and stem diameter (at breast height) in institutional lands averaged 1.4±0.1 trees/100 ㎡ and 14.9±0.2 cm across the study cities, respectively. Of the total planted area, the ratio of single-layered planting only with trees, shrubs, or grass was higher than that of multi-layered structures. Annual uptake and storage of carbon per unit area by woody plants averaged 0.65±0.04 t/ha/yr and 7.37±0.47 t/ha, which were lower than those for other greenspace types at home and abroad. This lower carbon reduction was attributed to lower density and smaller size of trees planted in institutional lands studied. Nevertheless, the greenspace in institutional lands annually offset carbon emissions from institutional electricity use by 0.6 (Seoul)~1.9% (Chuncheon). Tree planting in potential planting spaces was estimated to sequester additionally about 18% of the existing annual carbon uptake. Enhancing carbon reduction effects requires active tree planting in the potential spaces, multi-layered/clustered planting composed of the upper trees, middle trees and lower shrubs, planting of tree species with greater carbon uptake capacity, and avoidance of the topiary tree maintenance. This study was focused on finding out greenspace structures and carbon offset levels in institutional lands on which little had been known.

• The Journal of Engineering Geology
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• v.28 no.1
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• pp.69-79
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• 2018

The Korean government attempts to reduce $CO_2$ emissions by 37% to 314.7 Mt $CO_2$, down from the estimated 850.6 Mt $CO_2$ until 2030 in order to confront green house effect. In this context, in 2014, Korean government launched $CO_2$ Storage Environmental Management Research (K-COSEM) Center for carrying out pilot-scale research on $CO_2$ leakage from underground $CO_2$ storage facilities. For the detection of $CO_2$ leakage, it is necessary to identify hydrologeological and geophysical characteristics of the subject area. In the study site of Naesan-ri, Daeso-myeon, Eumseong-gun, Chungbuk Province, two times injection tests (June 28-July 24, 2017 and August 07-September 11, 2017) of $CO_2$ and $SF_6$ dissolved waters, respectively, was conducted to understand the leakage behavior of $CO_2$ from underground. The injection well was drilled to a depth of 24 m with a 21-m casing and screen interval of 21~24 m depth. Two times resistivity surveys on August 18, 2017 and September 1, 2017, were conducted for revealing the flow of the injected water as well as the electrical properties of the study site. The study results have shown that the high-resistivity zone and the low-resistivity zone are clearly contrasted with each other and the flow direction of the injected water is similar to natural groundwater flow. Besides, the low resistivity zone is widely formed from the depth of injection to the shallow topsoil, indicating that the weathered zone of high permeability has high $CO_2$ leakage potential.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.919-932
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• 2019

Fine particulate matter (PM2.5) is not only affected by anthropogenic emissions, but also intensifies, migrates, decreases by hydrometeorological factors. Therefore, it is essential to understand relationships between the hydrometeorological factors and PM2.5 concentration. In Korea, PM2.5 concentration is measured at the ground observatories and estimated data are given to locations where observatories are not present. In this way, the data is not suitable to represent an area, hence it is impossible to know accurate concentration at such locations. In addition, it is hard to trace migration, intensification, reduction of PM2.5. In this study, we analyzed the relationships between hydrometeorological factors, acquired from Global Land Data Assimilation System (GLDAS), and PM2.5 by means of Bayesian Model Averaging (BMA). By BMA, we also selected factors that have meaningful relationship with the variation of PM2.5 concentration. 4 PM2.5 concentration models for different seasons were developed using those selected factors, with Aerosol Optical Depth (AOD) from MODerate resolution Imaging Spectroradiometer (MODIS). Finally, we mapped the result of the model, to show spatial distribution of PM2.5. The model correlated well with the observed PM2.5 concentration (R ~0.7; IOA ~0.78; RMSE ~7.66 ㎍/㎥). When the models were compared with the observed PM2.5 concentrations at different locations, the correlation coefficients differed (R: 0.32-0.82), although there were similarities in data distribution. The developed concentration map using the models showed its capability in representing temporal, spatial variation of PM2.5 concentration. The result of this study is expected to be able to facilitate researches that aim to analyze sources and movements of PM2.5, if the study area is extended to East Asia.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.5
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• pp.97-104
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• 2017

Greenspace enlargement through proper landscape planting is essential to creating a low carbon society. This study analyzed changes in stem diameter growth rates(DGR), ratios of below ground/above ground biomass(B/A), and carbon sequestration by age of major landscape tree species. Landscape trees for study were 11 species and 112 individuals planted in middle region of Korea. The DGR and B/A were analyzed based on data measured through a direct harvesting method including root digging. The carbon sequestration by tree age was estimated applying the derived regression models. The annual DGR at breast height of trees over 30 years averaged 0.72 cm/yr for deciduous species and 0.83 cm/yr for evergreen species. The B/A of the trees over 30 years averaged 0.23 for evergreen species and 0.40 for deciduous species, about 1.7 times higher than evergreen species. The B/A by age in this study did not correspond to the existing result that it decreased as tree ages became older. Of the study tree species, cumulative carbon sequestration over 25 years was greatest with Zelkova serrata(198.3 kg), followed by Prunus yedoensis(121.7 kg), Pinus koraiensis(117.5 kg), and Pinus densiflora (77.4 kg) in that order. The cumulative carbon sequestration by Z. serrata offset about 5% of carbon emissions per capita from household electricity use for the same period. The growth rates and carbon sequestration for landscape trees were much greater than those for forest trees even for the same species. Based on these results, landscape planting and management strategies were explored to improve carbon sequestration, including tree species selection, planting density, and growth ground improvement. This study breaks new ground in discovering changes in growth and carbon sequestration by age of landscape trees and is expected to be useful in establishing urban greenspaces towards a low carbon society.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.4
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• pp.268-273
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• 2023

The present study estimated rumen fermentation characteristics and greenhouse gas emissions of different forages. Alfalfa, timothy, tall fescue, Italian ryegrass, and rice straw as the main forage sources for Hanwoo were used in the present study. Crude protein was highest in alfalfa but lowest in rice straw (p<0.05). Ether extract was higher in alfalfa and Italian ryegrass than in the other forages (p<0.05). Crude ash was highest in rice straw but lowest in tall fescue (p<0.05). Neutral detergent fiber was highest in tall fescue but lowest in alfalfa (p<0.05). Acid detergent fiber was highest in Italian ryegrass and rice straw but lowest in alfalfa (p<0.05). In vitro digestibilities of dry matter (DMD) and neutral detergent fiber (NDFD) were highest in timothy but lowest in rice straw (p<0.05). Rumen pH was highest (p<0.05) in alfalfa, while ammonia-N was higher (p<0.05) in alfalfa and Italian ryegrass than in the other forages. Total volatile fatty acid was highest (p<0.05) in timothy, while acetate and propionate were highest (p<0.05) in alfalfa and rice straw, respectively. Acetate to propionate ratio was higher (p<0.05) in alfalfa, timothy, and Italian ryegrass than in rice straw. Rice straw had lowest total gas (mL) (p<0.05) but highest its per DMD and NDFD. Rice straw had higher (p<0.05) CO₂ (per DMD and NDFD) compared to alfalfa (per DMD and NDFD), timothy (per DMD and NDFD), tall fescue (per NDFD), and Italian ryegrass (per DMD). Again, rice straw had higher (p<0.05) CH₄ (per DMD and NDFD) compared to timothy (per DMD and NDFD) and tall fescue (per NDFD). Therefore, this study indicates that timothy has a higher nutrient digestibility and volatile fatty acid in the rumen leading to a reduction of greenhouse gas emission.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.258-266
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• 2023

This study analyzed methodologies for estimating carbon stocks of perennial woody crops and the research cases in overseas countries. As a result, we found that Australia, Bulgaria, Canada, and Japan are using the stock-difference method, while Austria, Denmark, and Germany are estimating the change in the carbon stock based on the gain-loss method. In some overseas countries, the researches were conducted on estimating the carbon stock change using image data as tier 3 phase beyond the research developing country-specific factors as tier 2 phase. In South Korea, convergence studies as the third stage were conducted in forestry field, but advanced research in the agricultural field is at the beginning stage. Based on these results, we suggest directions for the following four future researches: 1) securing national-specific factors related to emissions and removals in the agricultural field through the development of allometric equation and carbon conversion factors for perennial woody crops to improve the completeness of emission and removals statistics, 2) implementing policy studies on the cultivation area calculation refinement with fruit tree-biomass-based maturity, 3) developing a more advanced estimation technique for perennial woody crops in the agricultural sector using allometric equation and remote sensing techniques based on the agricultural and forestry satellite scheduled to be launched in 2025, and to establish a matrix and monitoring system for perennial woody crop cultivation areas in the agricultural sector, Lastly, 4) estimating soil carbon stocks change, which is currently estimated by treating all agricultural areas as one, by sub-land classification to implement a dynamic carbon cycle model. This study suggests a detailed guideline and advanced methods of carbon stock change calculation for perennial woody crops, which supports 2050 Carbon Neutral Strategy of Ministry of Agriculture, Food, and Rural Affairs and activate related research in agricultural sector.