• Water for future
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• v.24 no.3
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• pp.36-41
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• 1991

1960년대 초부터 미 국립항공우주국(NASA)에서 기상위성을 지구궤도에 올리면서 시작되고 우주개발 선도국들에 의해 수 없이 발사되어 지구상공을 선회하고 있는 각종 실험위성, 자원탐사위성들로부터 이전까지만해도 지엽적이고 단편적인으로 알려지던 지구환경현황들이 이제는 지구전체에 대한 시시각각의 정보로 확대되고 있다. 기상위성들에 장착된 Sensor들로는 구름과 기상현상의 분포는 물론이고 각 대양의 해수면 온도 분포들이 파악되고 있으며 식물지수에 의한 지상의 식물분포의 계절적 변화양상에서 열대림의 사막화 추세들까지도 분석된다. 특히 위성탐사에 의한 남극 오존홀 (Ozone Hole)의 확인은 최근악화 되고 있느 swlrnchs 환경문제에 대한 커다란 주의를 환기시켜 주었다. 대양의 Phytoplankton 분포가 계절에 따라 위성자료에 의해 분석되므로서 해양의 생산능력(Productivity)의 변화도 알게되고 있다. 해양수면의 높이를 측정했던 초단파(microwave)영역의 SAR 자료는 구름을 투과하여 지구표면을 전천후 Monitoring할 수 있는 다음 세대의 Sensor로 각광을 받고 있으며 앞으로 유럽과 일본, 카나다, 소련 등에서 이들 새로운 Sensor들이 탑재 될 자원탐사 위성(ERS)과 RADASAT 등의 위성이 계속해서 개발되고 있어 이들에 의한 지구환경상태 진단은 크게 각광받게 될 것이다. 그외에도 해면풍 운량, 총강우량 분포, 대기 투명도, 대기의 열수지등의 계절적 변화에 대한 인공위성자료 해석을 통하여 지구의 온난화nas제가 본격적으로 ud가되고 있다. 또한 자원탐사위성인 Landsat 과 SPOT 등의 위성에 의해서는 각대륙의 토지 이용도 변화, 토사의 이도, 지질도 작성, 입체도 제착등과 농산물수확량의 예측있어서 괄목할 만한 발전이 계속되고 있다. 더욱이 NASA와 일본, 유럽등에서 지구관측을 위해서 준비하고 있는 각종 지구관측위성(EOS)들이 실용화 될 2000년 대에는 일반 지구환경감시는 물론 수계환경 감시 체계구축에 획기적인 진전이 있을 것으로 기대된다.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.503-516
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• 2019

We have estimated the vertical column density (VCD) of formaldehyde (HCHO) on a global scale using a multiple linear regression method (MRM) with Ozone Monitoring Instrument (OMI) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. HCHO VCDs were estimated in regions of biogenic, pyrogenic, and anthropogenic emissions using independent variables, including $NO_2$ VCD, land surface temperature (LST), an enhanced vegetation index (EVI), and the mean fire radiative power (MFRP), which are strongly correlated with HCHO. To evaluate the HCHO estimates obtained using the MRM, we compared estimates of HCHO VCD data measured by OMI ($HCHO_{OMI}$) with those estimated by multiple linear regression equations (MRE) ($HCHO_{MRE}$). Good MRM performances were found, having the average statistical values (R = 0.91, slope = 1.03, mean bias = $-0.12{\times}10^{15}molecules\;cm^{-2}$, percent difference = 11.27%) between $HCHO_{MRE}$ and $HCHO_{OMI}$ in our study regions where high HCHO levels are present. Our results demonstrate that the MRM can be a useful tool for estimating atmospheric HCHO levels.

• Atmosphere
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• v.21 no.4
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• pp.429-438
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• 2011

The climatology of surface UV radiation for Seoul, presented in Cho et al. (1998; 2001), has been updated using measurement of surface erythemal ultraviolet (EUV) and total ultraviolet (TUV) irradiance (wavelength 286.5~363.0 nm) by a Brewer Spectrophotometer (MK-IV) for the period 2004~2010. The analysis was also carried out together with the broadband total (global) solar irradiance (TR ; 305~2800 nm) and cloud amount to compare with the UV variations, measured by Seoul meteorological station of Korean Meteorological Agency located near the present study site. Under all-sky conditions, the day-to-day variability of EUV exhibits annual mean of 98% in increase and 31% in decrease. It has been also shown that the EUV variability is 17 times as high as the total ozone in positive change, whereas this is 6 times higher in negative change. Thus, the day to day variability is dominantly caused rather by the daily synoptic situations than by the ozone variability. Annual mean value of daily EUV and TUV shows $1.62kJm^{-2}$ and $0.63MJm^{-2}$ respectively, whereas mean value of TR is $12.4MJm^{-2}$ ($143.1Wm^{-2}$). The yearly maximum in noon-time UV Index (UVI) varies between 9 and 11 depending on time of year. The highest UVI shows 11 on 20 July, 2008 during the period 2004~2010, but for the period 1994~2000, the index of 12 was recorded on 13 July, 1994 (Cho et al., 2001). A 40% of daily maximum UVI belongs to "low (UVI < 2)", whereas the UVI less than 5% of the maximum show "very high (8 < UVI < 10)". On average, the maximum UVI exceeded 8 on 9 days per year. The values of Tropospheric Emission Monitoring Internet Service (TEMIS) EUV and UVI under cloud-free conditions are 1.8 times and 1.5 times, respectively, higher than the all-sky measurements by the Brewer. The trend analysis in fractional deviation of monthly UV from the reference value shows a decrease of -0.83% and -0.90% $decade^{-1}$ in the EUV and TUV, respectively, whereas the TR trend is near zero (+0.11% $decade^{-1}$). The trend is statistically significant except for TR trend (p = 0.279). It is possible that the recent UV decrease is mainly associated with increase in total ozone, but the trend in TR can be attributed to the other parameters such as clouds except the ozone. Certainly, the cloud effects suggest that the reason for the differences between UV and TR trends can be explained. In order to estimate cloud effects, the EUV, TUV and TR irradiances have been also evaluated for clear skies (cloud cover < 25%) and cloudy skies (cloud cover ${\geq}$ 75%). Annual mean values show that EUV, TUV and TR are $2.15kJm^{-2}$, $0.83MJm^{-2}$, and $17.9MJm^{-2}$ for clear skies, and $1.24kJm^{-2}$, $0.46MJm^{-2}$, and $7.2MJm^{-2}$ for cloudy skies, respectively. As results, the transmission of radiation through clouds under cloudy-sky conditions is observed to be 58%, 55% and 40% for EUV, TUV and TR, respectively. Consequently, it is clear that the cloud effects on EUV and TUV are 18% and 15%, respectively lower than the effects on TR under cloudy-sky conditions. Clouds under all-sky conditions (average of cloud cover is 5 tenths) reduced the EUV and TUV to about 25% of the clear-sky (cloud cover < 25%) values, whereas for TR, this was 31%. As a result, it is noted that the UV radiation is attenuated less than TR by clouds under all weather conditions.

• Korean Journal of Environmental Agriculture
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• v.40 no.3
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• pp.211-218
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• 2021

BACKGROUND: Fine particulate matter (PM_2.5) is produced by chemical reactions between various precursors. PM_2.5 has been found to create greater human risk than particulate matter (PM₁₀), with diameters that are generally 10 micrometers and smaller. Ammonia (NH₃) and nitrogen oxides (NOx) are the sources of secondary generation of PM_2.5. These substances generate PM_2.5 through some chemical reactions in the atmosphere. Through chemical reactions in the atmosphere, NH₃ generates PM_2.5. It is the causative agent of PM_2.5. In 2017 the annual ammonia emission recorded from the agricultural sector was 244,335 tons, which accounted for about 79.3% of the total ammonia emission in Korea in that year. To address this issue, the agricultural sector announced the inclusion of reducing fine particulate matter and ammonia emissions by 30% in its targets for the year 2022. This may be achieved through analyses of its emission characteristics by monitoring the PM_2.5 and NH₃. METHODS AND RESULTS: In this study, the PM_2.5 concentration was measured real-time (every 1 hour) by using beta radiation from the particle dust measuring device (Spirant BAM). NH₃ concentration was analyzed real-time by Cavity Ring-Down Spectroscopy (CRDS). The concentrations of ozone (O₃) and nitrogen dioxide (NO₂) were continuously measured and analyzed for the masses collected on filter papers by ultraviolet photometry and chemiluminescence. CONCLUSION: This study established air pollutant monitoring system in agricultural areas to analyze the NH₃ emission characteristics. The amount of PM_2.5 and NH₃ emission in agriculture was measured. Scientific evidence in agricultural areas was obtained by identifying the emission concentration and characteristics per season (monthly) and per hour.

• Journal of the Korean earth science society
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• v.38 no.2
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• pp.129-140
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• 2017

Global solar radiation was calculated in this research using ground-base measurement data, meteorological satellite data, and GWNU (Gangneung-Wonju National University) solar radiation model. We also analyzed the accuracy of the GWNU model by comparing the observed solar radiation according to the total cloud cover. Our research was based on the global solar radiation of the GWNU radiation site in 2012, observation data such as temperature and pressure, humidity, aerosol, total ozone amount data from the Ozone Monitoring Instrument (OMI) sensor, and Skyview data used for evaluation of cloud mask and total cloud cover. On a clear day when the total cloud cover was 0 tenth, the calculated global solar radiations using the GWNU model had a high correlation coefficient of 0.98 compared with the observed solar radiation, but root mean square error (RMSE) was relatively high, i.e., $36.62Wm^{-2}$. The Skyview equipment was unable to determine the meteorological condition such as thin clouds, mist, and haze. On a cloudy day, regression equations were used for the radiation model to correct the effect of clouds. The correlation coefficient was 0.92, but the RMSE was high, i.e., $99.50Wm^{-2}$. For more accurate analysis, additional analysis of various elements including shielding of the direct radiation component and cloud optical thickness is required. The results of this study can be useful in the area where the global solar radiation is not observed by calculating the global solar radiation per minute or time.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1409-1416
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• 2015

The change of temperature influences on the various aspect, especially human health, plant and animal's growth, economics, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly temperature data at the Suwon monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). Among five meteorological variables, radiation, amount of cloud, and wind speed are more influence on the temperature. The radiation influences during spring, summer and fall, whereas wind speed influences for the winter time. Also, among four greenhouse gas variables and five pollution variables, chlorofluorocarbon, methane, and ozone are more influence on the temperature. The monthly ARE model explained about 43-69% for describing the temperature.

• Journal of Climate Change Research
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• v.1 no.2
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• pp.147-161
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• 2010

The temporal and spatial distributions of surface solar radiation were calculated by the one layer solar radiative transfer model(GWNU) which was corrected by multi layer Line-by-Line(LBL) model during 2009 in South Korea. The aerosol optical thickness, ozone amount, cloud fraction and total precipitable water were used as the input data for GWNU model run and they were retrieved from Moderate Resolution Imaging Spectrometer(MODIS), Ozone Monitoring Instrument(OMI), MTSAT-1R satellite data and the Regional Data Assimilation Prediction System(RDAPS) model result, respectively. The surface solar radiation was calculated with 4 km spatial resolution in South Korea region using the GWNU model and the results were compared with surface measurement(by pyranometer) data of 22 KMA solar sites. The maximum values(more than $5,400MJ/m^2$) of model calculated annual solar radiation were found in Andong, Daegu and Jinju regions and these results were corresponded with the MTSAT-1R cloud amount data. However, the spatial distribution of surface measurement data was comparatively different from the model calculation because of the insufficient correction and management problems for the sites instruments(pyranometer).

• Journal of Environmental Health Sciences
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• v.39 no.5
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• pp.391-407
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• 2013

Micropollutants emerge in surface water through untreated discharge from sewage and wastewater treatment plants (STPs and WWTPs). Most micropollutants resist the conventional systems in place at water treatment plants (WTPs) and survive the production of tap water. In particular, pharmaceuticals and endocrine disruptors (ECDs) are micropollutants frequently detected in drinking water. In this review, we summarized the distribution of micropollutants at WTPs and also scrutinized the effectiveness and mechanisms for their removal at each stage of drinking water production. Micropollutants demonstrated clear concentrations in the final effluents of WTPs. Although chronic exposure to micropollutants in drinking water has unclear adverse effects on humans, peer reviews have argued that continuous accumulation in water environments and inappropriate removal at WTPs has the potential to eventually affect human health. Among the available removal mechanisms for micropollutants at WTPs, coagulation alone is unlikely to eliminate the pollutants, but ionized compounds can be adsorbed to natural particles (e.g. clay and colloidal particles) and metal salts in coagulants. Hydrophobicities of micropollutants are a critical factor in adsorption removal using activated carbon. Disinfection can reduce contaminants through oxidation by disinfectants (e.g. ozone, chlorine and ultraviolet light), but unidentified toxic byproducts may result from such treatments. Overall, the persistence of micropollutants in a treatment system is based on the physico-chemical properties of chemicals and the operating conditions of the processes involved. Therefore, monitoring of WTPs and effective elimination process studies for pharmaceuticals and ECDs are required to control micropollutant contamination of drinking water.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.6
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• pp.631-637
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• 2005

A UV-MFRSR instrument was used to measure the global and diffuse irradiances in 7 narrowband channels in the UV range 299.4, 304.4, 310.9, 317.3. 324.5, 331.3 and 367.4 nm at Gwangju ($35^{circ}\;13'N\;126^{circ}\;50'E$), Korea. Spectral UV-AOD was retrieved using the Langley plot method for data collected from April 2002 to July 2004. Temporal variation of AOD at 367.4 nm ($AOD_{367nm}$) showed a maximum in June ($0.95\pm0.43$) and a minimum in February ($0.31\pm0.14$). Clear seasonal variation of $AOD_{367nm}$ was observed with average values of $0.68\pm0.29,\;0.82\pm0.41,\;0.48\pm0.22\;and\;0.42\pm0.21$ in spring, summer, fall and winter, respectively, Average Angstrom exponent for the entire monitoring period was $2.03\pm0.75$ in the UV-A ($324.5\∼367.4$ nm) range. Seasonal variation of the Angstrom exponent showed a maximum in spring and a minimum in summer. The lowest Angstrom exponent in summer might be due to hygroscopic growth of particles under conditions of high relative humidity. UV-AOD changes under different atmospheric conditions were also analyzed. Uncertainty in retrieving spectral UV-AOD was also estimated to range between $\pm0.218\;at\;304.4\;nm\;and\;\pm0.135\;at\;367.4\;nm$. Major causes of uncertainty were total column ozone retrieval and extraterrestrial irradiance retrieval at shorter and longer wavelengths, respectively.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.5
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• pp.569-579
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• 2011

This study evaluated the indoor air quality of 26 government offices located in Seoul. The pollutant samples were taken from Jan. 13th to Jan. 29th and Feb. 20th to Feb. 23rd, 2010 in the offices. The target indoor pollutants for this study were $PM_{10}$, formaldehyde, carbon monoxide, carbon dioxide, total bacteria counts, total volatile organic compounds, radon, nitrogen dioxide, ozone, and asbestos which were controlled by the indoor air quality law for the multiple-use facilities management. The results of this study showed that some pollutants of the 38.5% offices exceeded the standards of the air quality guideline. The correlation analysis of the same pollutants between indoor and outdoor represented that $NO_2$ (r=0.629, p<0.05) and $O_3$ (r=0.459, p<0.01) were significant, however, $PM_{10}$ and CO were not. The correlation analysis between different pollutants showed that CO and TVOC (total volatile organic compounds: r=0.724; p<0.01), CO and $NO_2$ (r=0.674; p<0.01), HCHO and humidity (r=0.605; p<0.01), $CO_2$ and TVOC (r=0.534; p<0.01), TBC (total bacteria counts) and Asbestos (r=0.520; p<0.01) were significant. The energy-saving system of government buildings in winter caused under-ventilated and poor air quality. This study suggests that the concentrations of radon and $CO_2$ should be used as an indicator for monitoring indoor air quality and maintaining effective ventilations.