남북한 중등학교 지구과학 관련 교육과정 및 교과서를 TIMSS 교육과정 분석틀에 의해 비교 분석하였다. 북한의 고등중학교에서는 '지구과학' 과목이 별도로 개설되어 있지 않으며, '지리'에서 부분적으로 지도된다('천문학'은 1996년 고등중학교 교육과정에 처음으로 설정된 과목으로 추정되나 본 연구에서는 구체적 자료를 입수하지 못함). 북한에서 지리는 고등중학교 1∼5학년에서 주당 2시간씩 지도되고 있으며, 1학년 및 5학년용 지리 내용의 대부분이 지구과학과 관련되어 있다. 남한의 경우 지구 과학 영역의 목표는 '자연 현상의 탐구에 흥미와 호기심을 가지고, 기본적인 탐구 방법과 과학 지식을 습득하여 창의적으로 문제를 해결하는 능력을 기르게 한다'는 데 있으며, 북한 지리 교육의 목표는 '김일성 및 김정일 부자에게 충직한 주체형의 공산주의 혁명가 양성'이라고 할 수 있다. 남한과 북한 모두 '지구의 모양과 '지구의 변화 과정'을 많이 지도하고 있는데 비해, 남한에서 많은 비중을 차지하는 '우주 속에서의 지구'가 북한에서 매우 적은 것은 북한에서 '천문학'을 별도로 지도함을 시사한다. 전체적으로 북한의 지구과학 영역의 범위와 수준은 남한의 중학교 수준에 해당한다고 볼 수 있는데, 특히 기상 분야 및 천문분야의 경우는 남한과 수준차가 심하다.

The purpose of this stud was to investigate the degree of recognition as well as the consumption pattern of convenience food products, and related factors among 700 mixed Korean college students using written questionnaire. Students of Seoul region showed the significantly lowest rate of recognition at 36.7%. Results also showed that lifestyles have a significant effect on the degree of recognition of convenience foods. The consumption patterns of convenience foods goes as follows: used frequently-18.2%, once in awhile-73.9%, doesn't use-7.9%. The higher the recognition rate, the higher the consumption rate for convenience foods. When compared in terms of residence, students living at home used more refrigerated foods compared to students living outside of the home. Among the total students living outside of the home, students living on their own scored highest of convenience food consumption. In the case of female students living outside the home, respondents living alone and in dormitories scored the highest. Female students living in dormitories were mainly using refrigerated and canned foods, while those living alone consumed more kimbap and 'sa-bal-myun' in convenience stores. Korean college students mainly consumed frozen food, retort food, and kimbap in convenience stores. The college student that believes that 'You eat to satisfy hunger' significantly used more convenience food while those that marked 'maintain health' consumed the lowest showing a great difference between groups. Results showed that the lower the food habit score, the higher the usage score of convenience foods. The food habit score had a negative correlation with the usage of frozen foods, instant food, and convenience store food. When compared individually, packaged 'ramen' and 'sa-bal-myun' scored the highest points of usage. Frozen fried rice and pre-packaged rice scored the lowest points indicating Korean college students do not consider rice a convenience food. Convenience food consumed in convenience stores ranked the highest among places of consumption, compared to places like home or outdoors; showing that convenience foods were used by people with limited time constraints.

본 연구는 농촌 급식 초등학교 5, 6학년 아동을 대상으로 학교급식의 영양섭취실태를 조사하였으며, 조사한 결과는 다음과 같다. 남녀학생의 평균신장은 각각 141.9±5.82㎝, 142.5±5.82㎝로 한국 소아 발육 표준치의 99.5%, 98.9%였으며 평균체중은 남학생이 36.3±7.39㎏, 여학생이 35.7±8.35㎏으로 한국 소아 발육 표준치의 98.1%, 96.4%를 나타내 여학생의 체중은 표준치보다 미달되었다. 부모의 교육수준은 고졸 이상이 가장 많았으며, 직업은 부의 경우 남학생은 농업, 여학생은 상업이, 모의 경우는 남녀 학생의 39.0%가 농업에 종사한다고 응답하였다. 부모의 월수입 정도는 101만원 이상∼200만원 미만이 56.4%로 가장 많았다. 에너지 섭취량은 남학생이 489 ㎉, 여학생이 443㎉로 권장량의 67∼70%를 나타내어서 매우 부족한 상태였고, 단백질 섭취량 역시 남학생이 16.5g, 여학생이 15.7g으로 권장량(78∼83%)에는 미달이었다. 칼슘 섭취량을 살펴보면 남학생은 180㎎, 여학생이 169㎎으로 남녀 모두 권장량에 미달이었다. 탄수화물은 총 열량 섭취량의 68∼69%, 단백질은 총 단백질 섭취량의 61∼63%를 섭취하고 있었다. 지질은 총 지질 섭취량의 15∼16%였다. 그러나 학교급식에서 제공되는 우유로부터의 칼슘 섭취량이 늘어나 학교급식에서 우유급식의 효과를 볼 수 있었다. 철분 및 인의 섭취량도 권장량에 매우 미달이었다. 비타민은 niacin(114∼119%)과 ascorbic acid(114.4∼114.5%)를 제외한 비타민 A, 비타민 B_1, 비타민 B_2의 섭취량 모두 권장량에 미달이었다. 식습관 점수에 따른 분류를 보면 식습관이 `나쁘다(poor)`로 평가한 그룹이 전체의 43.6%(72명)에 해당하였으며, 여학생의 식습관 점수(9.3±3.6)가 남학생(8.4±2.5)보다 유의적으로 높았다. 이상의 결과를 종합하여 보면, 농촌 초등학교에 학교급식이 이루어지고 있지만 영양소 섭취량은 권장량보다 훨씬 미달이었고, 식습관 점수도 매우 낮아서 학교급식의 양적 확대와 더불어 질적 개선 및 지속적인 효과를 얻기 위한 체계적인 운영이 필요하리라 생각된다. 또한 아동들의 올바른 식품 선택 및 식습관 형성을 위한 실질적인 영양교육 지도가 필요하며 영양에 관한 지식, 태도 및 행동의 변화를 유도할 수 있는 영양교육 program의 개발, 보급이 시급하다고 사료된다.

This study was designed to investigate clinical features, allergy-inducing substances as well as foods, and nutritional behavior of allergic patients. Sixty-eight adult male and 78 adult female patients were surveyed by using the self-developed questionnairs. The results were as follows: 1. The common target organs of allergic symptoms included bronchus, skin and nose. 2. 37.7% of the subjects did not recognize their allergy inducing substances. Among the known substances, house-dust mite, food, temperature and weather change, pollens, smoke, air pollution materials, drugs and even stress provoked symptoms. 3. As for the food allergy, a number of foods were found to be implicated in allergic reactions. These foods included pork, chicken, seafoods such as mackerel, crab & shrimp, milk, egg, peanut, apple and pupa. Symptoms and signs of food reactions included urticaria, asthma, and rhinitis. 4. The nutrition adequacy ratio (NAR) of energy was below 0.75 for male patients and about 0.80 for female patients. And 24.0% of the subjects were regarded as excellent, 31.5% as fair, and 44.5% as poor in food related behavior evaluation.

The propriety of the numerical model application was examined on Paldang resevoir and its inflow tributaries located in the center of the Korean peninsula and the long term water quality forecast of the oxygen profile was carried out in this syduy. The input data of the model was the capacity of the reservoir, catchment area, percolation, diffusion rate, vertical mixing rate, dissolution rate from the bottom of the reservoir, outflow of the resevoir, water quality measurement and meteorology data of the drainage basin, and the output result was the annual estimation value of the dissolved oxygen concentration and the biochemical oxygen demand. The modeling method is based on the measured or calculated boundary condition dividing the water area into several blocks from the macorscopic aspect and considering the mass balance in these blocks. As the result of the water quality forecast, it was expected that the water quality in Northern Han River and Paldang reservoir would maintain the recent level, but that the water quality in the Southern Han River and its inflow tributary would worsen below the grade 4 of the life environmental standard from around 2000 owing to the decrease of DO concentration and the increase of BOD concentration.

Heatwaves can affect human health and vegetation growth and bring about energy problems and socioeconomic damages, so the analysis and prediction of the heatwave is a crucial issue under a warming climate. This paper examines the production of STCI (Standard Temperature Condition Index) using ASOS (Automated Synoptic Observing System) in-situ observation data for the period of 1979-2020, and an STCI predictability assessment with an RF (Random Forest) model using ERA5 (ECMWF Reanalysis 5) meteorological variables. The accuracy was quite high with the MAE (Mean Absolute Error) of 0.365 and the CC (Correlation Coefficient) of 0.873, which corresponded to 7% to 10% difference for the range of STCI<1.5, and to 1% to 3% difference for the range of STCI>1.5, in terms of the probability density function. Also, we produced gridded maps for the summer STCI from 1979 to 2020 by utilizing the ERA5 raster data for the RF prediction model, which enables the spatial expansion of the ASOS point-based STCI to a continuous grid nationwide. The proposed method can be applied to forecasting of STCI by adopting future meteorological or climatic datasets.

Because of the population growth and industrialization in recent decades, the air quality over the world has been worsened with the increase of PM10 concentration. Korea is located near the eastern part of China which has many industrial complexes, so the consideration of China’s air quality is necessary for the PM10 prediction in Korea. This paper examines a machine learning-based modeling of the prediction of tomorrow’s PM10 concentration in the form of a gridded map using the AirKorea observations, Chinese cities’ air quality index, and NWP (numerical weather prediction) model data. A blind test using 23,048 cases in 2019 produced a correlation coefficient of 0.973 and an MAE (mean absolute error) of 4.097㎍/㎥, which is high accuracy due to the appropriate selection of input variables and the optimization of the machine learning model. Also, the prediction model showed stable predictability irrespective of the season and the level of PM10. It is expected that the proposed model can be applied to an operative system if a fine-tuning process using a larger database is accomplished.

The increased frequency and intensity of wildfires can cause damages to the ecosystem and the atmospheric environment. Rapid identification of the wildfire damages is also important for establishing forest restoration, budget planning, and human resources allocation. Because the wildfires need to be examined for vast areas, satellite remote sensing has been adopted as an effective method. Many studies for the detection of wildfires and the analysis of burn severity have been conducted using mid- and high-resolution images. However, they had difficulties in the sensitivity problem of NBR (Normalized Burn Ratio) for multi-temporal images. This paper describes the feasibility of the detection and classification of wildfire burn severity using Sentinel-2 images with K-means and ISODATA (Iterative Self-Organizing Data Analysis Techniques Algorithm) methods for a case of the Andong fire in April 2020. The result can be a reference to the appropriate classification of large-scale wildfire severity and decision-making for forest restoration planning.

The purpose of this study was to examine the intention of consumer acceptance of technology in agricultural production by applying the unified theory of acceptance and use of technology (UTAUT) to smart farm. In particular, this study analyzed the intention to accept the technology of agricultural students, farmers, start-up farmers, returning farmers, and returnees in the general manufacturing industry and high-tech industries, and in agricultural sectors corresponding to primary industries. The results showed that performance expectancy, social influence, facilitating conditions, IT development level, and reliability had a significant influence on the intention to use smart farm technology. However, effort expectancy and price value were rejected because no significant impact on use intention was tested. In addition, the influences of the variables showing their influence were reliability (β=.569) > IT development level (β=.252) > social influence (β=.235) > performance expectancy (β=.182) > facilitating conditions (β=.134).

Global warming due to the increase of greenhouse gases may significantly affect various aspects of the Earth’s environment and human life. In particular, the impacts of climate change on agriculture would be severe, leading to damages to crop yields. This paper examines the experimental prediction of rice yield in China using DNN (deep neural network) and climate model data for the period between 1979 and 2009. The DNN model built through the process of hyperparameter optimization can mitigate an overfitting problem and cope with outlier cases. Our model showed approximately 38.7% improved accuracy than the MLR (multiple linear regression) model, in terms of correlation coefficient with the yield statistics. We found that the diurnal temperature range and potential evapotranspiration were the critical factors for rice yield prediction. Our DNN model was also robust to extreme conditions such as drought in 2006 and 2007 in China, which showed its applicability to the future simulation of crop yields under climate change.

Drought is one of the natural disasters that slowly begin to accumulate over a long period. Although there are many kinds of drought indices, one single universally accepted definition does not currently exist, which makes it difficult to evaluate drought severity comprehensively and objectively. This paper describes the comparisons of satellite-based drought indices such as SPI (Standardized Precipitation Index), NDDI (Normalized Difference Drought Index), NMDI (Normalized Multi-band Drought Index), VHI (Vegetation Health Index) and SDCI (Scaled Drought Condition Index) to analyze agricultural drought in Korea. Through an experiment using the five drought indices, we found that VHI and the SPI2 calculated from 2-month accumulated precipitation were highly correlated and appropriate to express agricultural drought in South and North Korea. Also, the SPI2 and VHI showed close relationships with hydro-meteorological factors and vegetation production variables. For future work, it is necessary to develop a comprehensive drought index which can cover various aspects of drought including precipitation, evapotranspiration, soil moisture, and vegetation state.

Arctic sea ice as an indicator of climate change plays an important role in controlling global climate system. Thus, accurate observation and prediction of Sea Ice Concentration (SIC) is essential for understanding global climate change. In this study, we aim to improve the prediction accuracy of SIC by using machine learning and Regional Climate Model (RCM) data for a more robust method and a higher spatial resolution. Using the CORDEX RCM and NASA SIC data between January 1981 and December 2015, we developed three statistical models using Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Deep Neural Network (DNN) which can deal with the non-linearity problem, respectively. The DNN model showed the best performance among the three models with the significant correlation between the predictive and observed SIC (r=0.811, p-value < 0.01)and the Root Mean Square Error (RMSE) of 0.258. With deeper considerations of the polar fronts and the characteristics of ocean current and tide, the DNN model can be applied for near future prediction of Arctic sea ice changes.

Intergovernmental Panel on Climate Change (IPCC) provides various prospects of future climate change under the Representative Concentration Pathways (RCP) scenarios using General Circulation Models (GCMs) of Coupled Model Intercomparison Project (CMIP). This paper describes a modified application of Ensemble Bayesian Model Averaging (EBMA) to produce daily mean temperature ensembles using 19 GCMs provided by CMIP. We proposed two types of approach: (1) monthly weighting scheme for a whole area (EBMA.v1) and (2) monthly weighting for each grid point (EBMA.v2), which can take into account the spatially heterogeneous pattern of GCM. For the training period of 1979- 2005 for East Asia, 9,855 sets of daily temperature ensembles (27 years × 365 days) were produced and compared to the ERA-Interim reanalysis data of European Centre for Medium-Range Weather Forecasts (ECMWF), which showed better validation statistics than the general mean and median ensembles. In particular, EBMA.v2 outperformed EBMA.v1 by diminishing the large errors of inland areas where the surface heterogeneity is larger than the ocean. The EBMA.v2 was able to handle the problem of spatial variability by employing monthly and spatially varying weighting scheme. We finally produced daily mean temperature ensembles for the period of 2006-2100 by using the EBMA.v2 under the RCP 6.0 scenario, which are going to be provided on the web.

The COMS (Communication, Ocean and Meteorological Satellite) has been used in numerical weather prediction and meteorological monitoring over East Asia and Oceania since it has been launched in 2010. For more active utilization in climate research, the COMS level 3 products should be available in appropriate spatial and temporal resolutions. We compared different methods to generate monthly sea surface temperature (SST) products from the COMS time-series data. We employed three techniques for aggregating the time series, which are arithmetic mean, timeslot average, and moving average, and also used mean ensemble of them. Each level 3 dataset around South Korea was compared with monthly SST product from the Moderate Resolution Imaging Spectroradiometer (MODIS) of Aqua satellite during April 2011-March 2014. The timeslot average showed better root mean squared difference (RMSD) during the initial operational period of the COMS, when the retrieved values could be somewhat unstable. Daytime aggregations were derived more accurately by using the arithmetic mean or moving average, and the accuracy of nighttime aggregation was improved by the mean ensemble. Also, the timeslot average presented reasonable results particularly for coastlines where the standard deviation and missing value ratio were greater than normal. Because an optimal aggregation technique was variable depending on spatial and temporal conditions, we should be careful in selecting appropriate method for generation of the COMS level 3 products according to research objectives.

This paper described the estimation of corn and soybeans yields of four states in the US Midwest using time-series satellite imagery and climate dataset between 2001 and 2012. We first constructed a database for (1) satellite imagery acquired from Terra MODIS (Moderate Resolution Imaging Spectroradiometer) including NDVI (Normalized Di°erence Vegetation Index), EVI (Enhanced Vegetation Index), LAI (Leaf Area Index), FPAR (Fraction of Photosynthetically Active Radiation), and GPP (Gross Primary Productivity), (2) climate dataset created by PRISM (Parameter-Elevation Regressions on Independent Slopes Model) such as precipitation and mean temperature, and (3) US yield statistics of corn and soybeans. ˜en we built OLS (Ordinary Least Squares) regression models for corn and soybeans yields between 2001 and 2010 after correlation analyses and multicollinearity tests. These regression models were used in estimating the yields of 2011 and 2012. Comparisons with the US yield statistics showed the RMSEs (Root Mean Squared Errors) of 0.892 ton/ha and 1.095 ton/ha for corn yields in 2011 and 2012 respectively, and those of 0.320 ton/ha and 0.391 ton/ha for soybeans yields. ˜is result can be thought of as a good agreement with the in-situ statistics because the RMSEs were approximately 10% of the usual yields: 9 ton/ha for corn and 3 ton/ha for soybeans. Our approach presented a possibility for extending to more advanced statistical modeling of crop yields using satellite imagery and climate dataset.

본 연구는 식품, 화장품 및 의약품 소재로 활용 가능성에 대해 알아보고자 우산나물 뿌리와 지상부에 대한 항산화 활성을 측정하였다. 우산나물 뿌리 열수 추출물(RHW)은 4.58 mg/g의 플라보노이드와 59.11 mg/g의 폴리페놀 화합물을 함유하였으며, 지상부 열수 추출물(APHW)은 각각 2.79 mg/g과 48.01 mg/g을 함유하였다. SOD 유사활성능은 RHW에서 23.74%, APHW는 21.61%를 나타내었다. 아질산염 소거능은 2,000 μg/mL에서 RHW는 pH 1.2에서 63.06%였으며, pH 3.0에서는 47.16%로 아질산염을 50% 소거하는 IC50은 ascorbic acid 99.93 ug/mL, RHW 1,150.85 μg/mL, 그리고 APHW에서는 1,610.25 μg/mL이었다. DPPH free radical 소거능에 대한 IC50은 RHW 99.87 μg/mL, APHW 118.29 μg/mL를 나타내었다. Xanthine oxidase에 대한 IC50은 RHW에서 139.62 μg/mL였으며, APHW는 111.11 μg/mL으로 xanthine oxidase 저해능은 우산나물 지상부의 열수 추출물인 APHW가 뿌리 열수 추출물인 RHW보다 좀더 낮은 농도에서 50%의 활성을 나타내었으나, 플라보노이드와 폴리페놀 화합물 함량, SOD 유사활성, 아질산염 소거 및 DPPH free radical 소거능은 RHW가 APHW보다 높은 활성을 나타내었다. 이상의 결과 우산나물은 기능성 식품과 화장품 그리고 의약품 개발을 위한 항산화 소재로 활용 할 수 있을 것으로 판단된다.

In this study, we produced satellite-based drought indices such as NDVI(normalized difference vegetation index), NDWI(normalized difference water index), and NDDI(normalized difference drought index) and extracted time-series principal components from the drought indices using EOF(empirical orthogonal function) method to examine their relationships with climatic variables. We found that the first principal components of the drought indices for Sonid Right Banner, Inner Mongolia were dominant in cold seasons and were closely related to low temperature, little rainfall, and high surface albedo. The satellite-based drought indices and their EOF analyses can be utilized for the studies of cold-season drought and warm-season drought as well.

Wildfires are recently increasing in frequencies and intensities worldwide. Hence, reliable and continuous monitoring of sudden occurrences of wildfire is demanded, and geostationary meteorological satellites are an alternative to detection of wildfire in large areas. We currently have two geostationary meteorological satellites for the Korean Peninsula: the Korean COMS(Communication, Ocean and Meteorological Satellite) and the Japanese MTSAT(Multifunctional Transport Satellite). However, neither of them provides satellite products for wildfire detection although the MODIS(Moderate Resolution Imaging Spectroradiometer) on polar-orbiting satellites has been operated for wildfire detection for a decade. In this study, we applied the MODIS algorithm for wildfire detection to the COMS and the MTSAT in order to evaluate the detection performances for South Korea. Both satellites were successful in detection of big fires, but the COMS was better in detecting small fires because of its higher saturation temperature of 350 K approx. at 4-μm band. The comparison results will be informative for an emergency plan of COMS and for the preparation of next-generation geostationary meteorological satellite.

The CMDPS(COMS Meteorological Data Processing System) provides 16 meteorological products by processing satellite data obtained from 1 visible and 4 infrared channels. It is expected that the COMS(Communication, Ocean and Meteorological Satellite) products will contribute to numerical weather prediction and climate change monitoring. For more active utilization of the products, we first need a scheme for the quality management based on outlier detection. This paper describes a method for detecting spatial and temporal outliers from the COMS products using Moran scatterplot and wavelet transform. The applicability of our method was made sure through the tests for seven products such as sea surface temperature, land surface temperature, aerosol optical depth, total precipitable water, upper tropospheric humidity, cloud top height, and cloud top temperature. Their quality was estimated very good in terms of our spatio-temporal statistical approach. For future work, we need more comprehensive study on the outlier detection, particularity focusing on the threshold setting in accordance with physical characteristics of each product.