• Journal of Environmental Science International
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• v.13 no.4
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• pp.377-387
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• 2004

This study has been performed to clarify the characteristics of temporal and spatial distribution of surface ozone concentration over Jeju Island, one of the cleanest areas in Korea with low emissions of air pollutants. Ozone data are monitored at four sites in Jeju Island. These monitoring sites are located at two urban area(referred to Ido and Donghong), coastal area(Gosan site) and forest site(Chuna site). Ozone data has been routinely collected at these sites for the late four years. The patterns of seasonal cycle of ozone concentrations at all stations show the bimodal with the peaks on spring and autumn and a significant summer minimum. However, the patterns of diurnal variations at rural station, i.e., Gosan and Chuna sites are considerably different to those at urban stations such as Ido and Donghong sites. The patterns of $\DeltaO_3$ variations are very similar with those of monthly mean ozone concentrations and $\DeltaO_3$ values are exceeded 30 ppb, at urban stations. This may be that urban stations are more influenced by local photochemical reactions rather than rural stations. In order to assess the potential roles of meteorological parameters on ozone formation, the meteorological parameters, such as radiation, temperature, and wind are monitored together with ozone concentrations at all stations. The relationships of meteorological parameters to the corresponding ozone concentration are found to be insignificant in Jeju Island. However, at Gosan and Donghong stations, when the sea breeze blew toward the station, the ozone concentration is considerably increased.

• Journal of Environmental Science International
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• v.29 no.2
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• pp.177-189
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• 2020

We analyzed the recent characteristics of Particulate Matter (PM) including PM₁₀ (PM with diameter of less than 10 ㎛) and PM_2.5 (PM with diameter of less than 2.5 ㎛) observed in Busan metropolitan area, and compared them with those measured in Seoul metropolitan area. This analysis includes the monthly, seasonal, and annual variations and differences, in emissions and chemical compositions observed in both Busan and Seoul areas. Synoptic meteorological conditions were investigated at the time when high PM concentrations occurred in each of the two areas. The results showed clearly decreasing trends of annual mean concentrations with strong seasonal variations: lower in summer and higher in winter in both areas. In comparison with Seoul, the seasonal variation in Busan demonstrated relatively lower, but showed greater summer fluctuations than in Seoul metropolitan area. This is implying the importance of secondary generation of PM in summer via active photochemical reaction in Busan area. In high concentration days, Busan's chemical composition of sulfate was higher than that of nitrate in summer, whereas nitrate was higher than sulfate in Seoul. The ratios of NO₃^- to SO₄^2-(N/S ratio) showed lower in Busan approximately by a factor of 1/2(half of N/S ratio) in Busan compared with that in Seoul. Others such as synoptic characteristics and emission differences were also discussed in this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.225-225
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• 2015

Drought events usually evolve slowly in time and their impacts generally span a long period of time. This indicates that the sequence of drought is not completely random. The Hidden Markov Model (HMM) is a probabilistic model used to represent dependences between invisible hidden states which finally result in observations. Drought characteristics are dependent on the underlying generating mechanism, which can be well modelled by the HMM. This study employed a HMM with Gaussian emissions to fit the Standardized Precipitation Index (SPI) series and make multi-step prediction to check the drought characteristics in the future. To estimate the parameters of the HMM, we employed a Bayesian model computed via Markov Chain Monte Carlo (MCMC). Since the true number of hidden states is unknown, we fit the model with varying number of hidden states and used reversible jump to allow for transdimensional moves between models with different numbers of states. We applied the HMM to several stations SPI data in South Korea. The monthly SPI data from January 1973 to December 2012 was divided into two parts, the first 30-year SPI data (January 1973 to December 2002) was used for model calibration and the last 10-year SPI data (January 2003 to December 2012) for model validation. All the SPI data was preprocessed through the wavelet denoising and applied as the visible output in the HMM. Different lead time (T= 1, 3, 6, 12 months) forecasting performances were compared with conventional forecasting techniques (e.g., ANN and ARMA). Based on statistical evaluation performance, the HMM exhibited significant preferable results compared to conventional models with much larger forecasting skill score (about 0.3-0.6) and lower Root Mean Square Error (RMSE) values (about 0.5-0.9).

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.278-295
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• 2021

China's shutdown due to COVID-19 in 2020 reduced air pollutant emissions, which is located on the windward side of South Korea. The positive temperature anomaly and negative zonal wind anomaly from northern Mongolia to South Korea through eastern China presented warm and stationary air masses during January and February 2020. Decreased concentrations of PM10, NO₂, and O₃ were measured at Seokmo-ri and Pado-ri, located in the central-western region of South Korea, due to decreased emissions in China from January to March 2020. After China's shutdown from January to March 2020, in Pado-ri, the ratio of monthly average concentrations in that period with those of PM10 and O₃ in the last four years decreased by approximately 0.7-4.7% and 9.2-22.8%, respectively. In January 2020, during the Lunar New Year holidays in China, concentrations of PM10, NO₂, and O₃ at Seokmo-ri and Pado-ri decreased just as much as it did during the same period in the last four years. However, average concentrations in January 2020 decreased before and after the Lunar New Year holidays in China when compared with those in January of the last four years. In Seokmori, ratios of actual and predicted values (${\bar{O}_s$/M) for PM10, NO₂, and O₃ concentrations were calculated as 70.8 to 89.7%, 70.5 to 87.1%, and 72.5 to 97.1%, respectively, during January and March 2020. Moreover, those of Pado-ri were 79.6 to 93.5%, 67.7 to 84.9%, and 83.7 to 94.6%, respectively. In January 2020, the aerosol optical depth (AOD) data showed a higher distribution than that of the last four years due to photochemical reactions in regions from northern Mongolia to eastern China and the Korean Peninsula. However, the decrease in AOD values compared with those of the last four years was attributed to the decrease in emissions of precursors that generate secondary aerosols in China during March 2020.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1653-1667
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• 2020

In this study, we coupled a computation fluid dynamics (CFD) model to the local data assimilation and prediction system (LDAPS), a current operational numerical weather prediction model of the Korea Meteorological Administration. We investigated the characteristics of fine particulate matter (PM2.5) distributions in a building-congested district. To analyze the effects of road emission on the PM2.5 concentrations, we calculated road emissions based on the monthly, daily, and hourly emission factors and the total amount of PM2.5 emissions established from the Clean Air Policy Support System (CAPSS) of the Ministry of Environment. We validated the simulated PM2.5 concentrations against those measured at the PKNU-AQ Sensor stations. In the cases of no road emission, the LDAPS-CFD model underestimated the PM2.5 concentrations measured at the PKNU-AQ Sensor stations. The LDAPS-CFD model improved the PM2.5 concentration predictions by considering road emission. At 07 and 19 LST on 22 June 2020, the southerly wind was dominant at the target area. The PM2.5 distribution at 07 LST were similar to that at 19 LST. The simulated PM2.5 concentrations were significantly affected by the road emissions at the roadside but not significantly at the building roof. In the road-emission case, the PM2.5 concentration was high at the north (wind speeds were weak) and west roads (a long street canyon). The PM2.5 concentration was low in the east road where the building density was relatively low.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.1035-1039
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• 2010

For 65 national forest areas in 1993 to 2008, the ambient sulfur dioxide ($SO_2$) concentrations were measured monthly using passive samplers and compared to the those of urban areas in order to investigate the characteristics of temporal and spatial distribution. In the forest areas, annual average concentration of sulfur dioxide gradually decreased from the beginning year of monitoring to 1997 and then had no significant change, such as the annual trend in urban areas monitored by Ministry of Environment. For the monitoring term, average concentration of sulfur dioxide in the forest areas was 5.6ppb, which was lower than the 10.1ppb in the urban areas and the EC ecological standard level (7.6 ppb). Seasonally, both in forest areas and urban areas the monthly average concentrations were much higher in winter and spring due to much more heating fuel consumption, and lowest in summer. Regional comparison to other regions of Gyeongbuk and Gyeonggi province showed that the concentration of sulfur dioxide was the highest during year. A significant positive correlation between sulfur oxides' emissions and sulfur dioxide concentration by province was observed, reflecting that the size and proximity of sources of atmospheric sulfur oxides could be important factors in determination.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.107-120
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• 2010

The effect of potential future climate change on the inflow of agricultural reservoir and its impact to downstream streamflow by reservoir operation for paddy irrigation water was assessed using the SLURP (semi-distributed land use-based runoff process), a physically based hydrological model. The fundamental input data (elevation, meteorological data, land use, soil, vegetation) was collected to calibrate and validate of the SLURP model for a 366.5 $km^2$ watershed including two agricultural reservoirs (Geumgwang and Gosam) located in Anseongcheon watershed. Then, the CCCma CGCM2 data by SRES (special report on emissions scenarios) A2 and B2 scenarios of the IPCC (intergovernmental panel on climate change) was used to assess the future potential climate change. The future weather data for the year, m ms, m5ms and 2amms was downscaled by Change Factor method through bias-correction using 3m years (1977-2006) weather data of 3 meteorological stations of the watershed. In addition, the future land uses were predicted by modified CA (cellular automata)-Markov technique using the time series land use data fromFactosat images. Also the future vegetation cover information was predicted and considered by the linear regression between monthly NDVI (normalized difference vegetation index) from NOAA AVHRR images and monthly mean temperature using eight years (1998-2006) data.

• Journal of the Korean earth science society
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• v.43 no.1
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• pp.60-76
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• 2022

This study presents a software full setup and the following test execution times in a Linux cluster for the United Kingdom Earth System Model (UKESM) and then compares the model results from control and experimental simulations of the UKESM relative to various observations. Despite its low resolution, the latest version of the UKESM can simulate tropospheric chemistry-aerosol processes and the stratospheric ozone chemistry using the United Kingdom Chemistry and Aerosol (UKCA) module. The UKESM with UKCA (UKESM-UKCA) can treat atmospheric chemistryaerosol-cloud-radiation interactions throughout the whole atmosphere. In addition to the control UKESM run with the default CMIP5 SO₂ emission dataset, an experimental run was conducted to evaluate the aerosol effects on meteorology by changing atmospheric SO₂ loading with the newest REAS data over East Asia. The simulation period of the two model runs was 28 years, from January 1, 1982 to December 31, 2009. Spatial distributions of monthly mean aerosol optical depth, 2-m temperature, and precipitation intensity from model simulations and observations over East Asia were compared. The spatial patterns of surface temperature and precipitation from the two model simulations were generally in reasonable agreement with the observations. The simulated ozone concentration and total column ozone also agreed reasonably with the ERA5 reanalyzed one. Comparisons of spatial patterns and linear trends led to the conclusion that the model simulation with the newest SO₂ emission dataset over East Asia showed better temporal changes in temperature and precipitation over the western Pacific and inland China. Our results are in line with previous finding that SO₂ emissions over East Asia are an important factor for the atmospheric environment and climate change. This study confirms that the UKESM can be installed and operated in a Linux cluster-computing environment. Thus, researchers in various fields would have better access to the UKESM, which can handle the carbon cycle and atmospheric environment on Earth with interactions between the atmosphere, ocean, sea ice, and land.

• Journal of Energy Engineering
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• v.23 no.3
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• pp.72-81
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• 2014

Intergovernmental Panel on Climate Change(IPCC) suggested the three methodology, Tier 1/2/3, considering with the accuracy and difficulty of greenhouse gas emission statistics according to the report determined as the international criterion. In Korea, the existing inventory building was made by the Top-down approach applying with the emission factors for transportation in the entire energy consumption, the emission factors were investigated under the domestic traffic situation which did not reflect by the continuing increase of vehicle and the change of road section. From the suggestion of IPCC, which it is estimated that the emission estimation of $CO_2$ in greenhouse gas emission could be calculated more accurate by the carbon content according to the fuel, the establishment of measures to respond to climate change from the latest greenhouse gas emissions statistics will be able to improve the accuracy of national statistics using monthly or seasonally the analysis of carbon content about the transportation fuels.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.3
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• pp.313-325
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• 2011

From October 2009 to June 2010, major greenhouse gases (GHG: $N_2O$, $CH_4$, $CO_2$) soil emission were measured from upland cabbage field at Kunsan ($35^{\circ}$56'23"N, $126^{\circ}$43'14"E), Korea by using closed static chamber method. The measurements were conducted mostly from 10:00 to 18:00LST during field experiment days (total 28 days). After analyzing GHG concentrations inside of flux chamber by using a GC equipped with a methanizer (Varian CP3800), the GHG fluxes were calculated from a linear regression of the changes in the concentrations with time. Soil parameters (e.g. soil moisture, temperature, pH, organic C, soil N) were also measured at the sampling site. The average soil pH and soil moisture were ~pH $5.42{\pm}0.03$ and $70.0{\pm}1.8$ %WFPS (water filled pore space), respectively. The ranges of GHG flux during the experimental period were $0.08\sim8.40\;mg/m^2{\cdot}hr$ for $N_2O$, $-92.96\sim139.38mg/m^2{\cdot}hr$ for $CO_2$, and $-0.09\sim0.05mg/m^2{\cdot}hr$ for $CH_4$, respectively. It revealed that monthly means of $CO_2$ and $CH_4$ flux during October (fall) were positive and significantly higher than those (negative value) during January (winter) when subsoil have low temperature and relatively high moisture due to snow during the winter measurement period. Soil mean temperature and moisture during these months were $17.5{\pm}1.2^{\circ}C$, $45.7{\pm}8.2$%WFPS for October; and $1.4{\pm}1.3^{\circ}C$, $89.9{\pm}8.8$ %WFPS for January. It may indicate that soil temperature and moisture have significant role in determining whether the $CO_2$ and $CH_4$ emission or uptake take place. Low temperature and high moisture above a certain optimum level during winter could weaken microbial activity and the gas diffusion in soil matrix, and then make soil GHG emission to the atmosphere decrease. Other soil parameters were also discussed with respect to GHG emissions. Both positive and negative gas fluxes in $CH_4$ and $CO_2$ were observed during these measurements, but not for $N_2O$. It is likely that $CH_4$ and $CO_2$ gases emanated from soil surface or up taken by the soil depending on other factors such as background concentrations and physicochemical soil conditions.