• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.11
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• pp.1900-1911
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• 2010

This study focuses on clothing as one of the most seasonal products and investigates consumer behavior related to climate change adaptation. This study addressed four objectives: (1) to identify the clothing behavior of consumers for the adaptation to climate change; (2) to identify the effects of fashion involvement and climate sensitivity on clothing attitude for the adaptation to climate change; (3) to identify the effect of clothing purchase time on climate sensitivity and clothing attitude for the adaptation to climate change; and (4) to identify the effect of consumer demographics on climate sensitivity and clothing attitude for the adaptation to climate change. A survey questionnaire was developed and implemented to collect data for measuring clothing involvement, fashion involvement, and climate sensitivity. In addition, clothing involvement, clothing assortment needs, and clothing worn for the adaptation to climate change were measured. A total of 349 responses were analyzed by t-test, ANOVA and path analysis with SPSS18.0. The results of the analysis are as follows. Changes in temperature were considered more important than changes in weather for the functional needs of clothing, purchase needs, and assortment items needs. The assortment items wearing for the adaptation to climate change varied depending on the temperature and weather. Fashion involvement directly influenced clothing assortment needs and indirectly influenced the clothing worn for the adaptation to climate change. In terms of clothing purchase time, those purchasing clothing before the season begins, tended to have a high fashion involvement and clothing attitude for the adaptation to climate change. Those in their twenties and single, tended to be more sensitive to climate change. This study also discusses the implications for merchandising strategies.

• Journal of the Korean Institute of Landscape Architecture
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• v.17 no.2
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• pp.27-39
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• 1989

The purpose of this study is to prove the factors affecting the fluctuation of daily visitors in five mountainous national park(Kayasan, kyeryongsan, Naejangsan, Soraksan, Songnisan), and to analyze the relationship between these factors and daily visitors in Korea. "Three Factors and Nine Categories"(Aoki, K. & Aoki, Y. : 1974, 1979) has been applied to this study, and statistical analysis method was carried out by computer program SAS and SPSS. The number of daily visitors is calculated based on the data of "Daily entrance ticket sale report" by administration office in each national park. The scope of time period is during the last 5years(1982∼1986: 1825days) and the results were as follows: 1) There were significant differences in the number of daily visitors of each national park among months, days of a week and weather-the same as the previous study of urban park case. But it wold be better for their category classification to be adjusted according to the fluctuation pattern of each national park. 2) The peak of monthly visitors comes in May(Kayasan, Soraksan, Songnisan) or October(Kyeryongsan, Naejangsan). These months are specified as group tour season. On the basis of monthly fluctuation pattern, Each national park were classified into seasonal type, that is, kayasan, Soraksan were proved to be three-season type(Spring, Summer, Autumn), Songnisan to be two-season type(Spring, Autumn), and Naejangsan to be one-season type(Autumn). 3) The weekly pattern differs from three category (weekday, weekend, holiday: Eom, Choi 1986) in the case of urban park study. And there is no significant difference in daily fluctuation pattern by weather (fine, cloudy and rainy day), but significant difference between snowy and the others. This result is due to the characteristics of visitors, which is, the major visits of national park are planned in a advance of the tour, therefore it is difficult to change the plan by the weather. 4) the result of correlation analysis showed that the most influential factor on national park use in Kayasan, Naejangsan, Soraksan and Songnisan is ′Monthly characters (M)′, on the contrary ′Day of week(D)′ in Kyeryongsan only. From the result, The more parks are resource-based, the more ′Monthly characters′-factor is supposed to affect the number of daily visitors rather than ′Day of the week′-factor. This means that kayasan, naejangsan, Sorakson and Songnisan are classified into resource-based type, but on the other hand Kyeryongsan should be classified into intermediate type.

• Korean Journal of Plant Resources
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• v.11 no.1
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• pp.70-79
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• 1998

This study was conducted to understand the effects of weather and tapping date on yield and quality of lactree(Rhus verniciflua) sap yield showed a significant positive correlation with the minimum temperature of one day before sap collection at 1% level and with theminimum humidity of theday of sap collection at 5% level. However, the differences between the maximum and the minimum temperatures and humidities of the day of sap collection were negatively correlated with the sap yield at 5% level. Multiple regression analysis indicated that the minimum temperature of one day bofore sap collection and the minumum humidity of the day sap collection were important factors for increasing sap yield. The high sap yield of lactree by Japanese tapping method was recorded during mid-July and early August. Seasonal variation in lactree sap constituents was observed. The sap collected on 15th of August contained the highest urushiol content (68.3%) and the lowest water content resulting in high quality of lactree sap. By reversed-phase HPLC analysis, fove urushiol components were separated from each other depending on the number of doulbe bonds in the side-chain , and seaxonal variation of urushiol composition was noticed. The 3-C15 triene content fo the sap collected on 5th of July was the highest(77.56%) indicating the major component of urushiol which affects lactree sap quality.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.425-434
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• 2020

Background: Viral infection outbreaks are emerging public health concerns. They often exhibit seasonal patterns that could be predicted by the application of big data and bioinformatic analyses. Purpose: The purpose of this study was to identify trends in diarrhea-causing viruses such as rotavirus (Gr.A), norovirus G-I, and norovirus G-II in Cheonan, Korea. The identified related factors of diarrhea-causing viruses may be used to predict their trend and prevent their infections. Method: A retrospective analysis of 4,009 fecal samples from June 2010 to December 2019 was carried out at Dankook University Hospital in Cheonan. Reverse transcription-PCR (RT-PCR) was employed to identify virus strains. Information about seasonal patterns of infection was extracted and compared with local weather data. Results: Out of the 4,009 fecal samples tested using multiplex RT-PCR (mRT-PCR), 985 were positive for infection with Gr.A, G-I, and G-II. Out of these 985 cases, 95.3% (n = 939) were under 10 years of age. Gr.A, G-I, and G-II showed high infection rates in patients under 10 years of age. Student's t-test showed a significant correlation between the detection rate of Gr.A and the relative humidity. The detection rate of G-II significantly correlated with wind-chill temperature. Conclusion: Climate factors differentially modulate rotavirus and norovirus infection patterns. These observations provide novel insights into the seasonal impact on the pathogenesis of Gr.A, G-I, and G-II.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.3-12
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• 2007

The purpose of this paper is to analyze the relationship between meteorological factors and agricultural reservoir storage, and predict the reservoir storage by multiple regression equation selected by high correlated meteorological factors. Two agricultural reservoirs (Geumgwang and Gosam) located in the upsteam of Gongdo water level gauging station of Anseong-cheon watershed were selected. Monthly reservoir storage data and meteorological data in Suwon weather station of 21 years (1985-2005) were collected. Three cases of correlation (case 1: yearly mean, case 2: seasonal mean dividing a year into 3 periods, and case 3: lagging the reservoir storage from 1 month to 3 months under the condition of case 2) were examined using 8 meteorological factors (precipitation, mean/maximum/minimum temperature, relative humidity, sunshine hour, wind velocity and evaporation). From the correlation analysis, 4 high correlated meteorological factors were selected, and multiple regression was executed for each case. The determination coefficient ($R^{2}$) of predicted reservoir storage for case 1 showed 0.45 and 0.49 for Geumgwang and Gosam reservoir respectively. The predicted reservoir storage for case 2 showed the highest $R^{2}$ of 0.46 and 0.56 respectively in the period of April to June. The predicted reservoir storage for 1 month lag of case 3 showed the $R^{2}$ of 0.68 and 0.85 respectively for the period of April to June. The results showed that the status of agricultural reservoir storage could be expressed with couple of meteorological factors. The prediction enhanced when the storage data are divided into periods rather than yearly mean and especially from the beginning time of paddy irrigation (April) to high decrease of reservoir storage (June) before Jangma.

• Journal of the Korean association of regional geographers
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• v.2 no.2
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• pp.49-73
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• 1996

This study produces quantitatively weather entropy and information ratio using information theory about frequency in the appearance of precipitation phenomenon and monthly change, and then applies them to observation of the change of their space scale by time. As a result of these, this study defines Pusan, Chongju and Kwangju's weather representativeness and then establishes the range of weather representativeness. Based on weather entropy (statistical parameter)-the amount of average weather information-and information ratio, we can define each area's weather representativeness, which can show us more constant form included topographical, geographical factors and season change. The data used for this study are the daily precipitotion and cloudiness during the recent five years($1990{\sim}1994$) at the 69 stations in Korea. It is divided into class of no precipitation, that of precipitation. The results of this study can be summarized as follows: (1) The four season's mean value of information ratio is the highest value. as 0.641, on the basis of Chongju. It is the lowest as 0.572, on the basis of Pusan. On a seasonal basis, the highest mean value of information rate is April's (spring) in Chongju, and the lowest is October's(fall) in Pusan. Accordingly weather representativeness has the highest in Chongju and the lowest in Pusan. (2) To synthesize information ratio of decaying tendancy and half-decay distance, Chonju's weather representativeness has the highest in April, July and October. And kwangju has the highest value in January and the lowest in April and July. Pusan's weather representativeness is not high, that of Pusan's October is the lowest in the year. (3) If we establish the weather representative character on the basis of Chongju-Pusan, the domain of Chongju area is larger than that of Pusan area in October, July and April in order. But Pusan's is larger than Chongju's in January. In the case of Chongju and Kwangju, the domain of Chongju area is larger than that of Kwangju in October, July and April in order, but it is less than that of Kwangju area in January. In the case of Kwangju-Pusan, the domain of Kwangju is larger than that of Pusan in October, July in order. But in April it is less than Pusan's.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of Environmental Science International
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• v.21 no.2
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• pp.153-163
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• 2012

The seasonal variations of ozone ($O_3$) concentrations were investigated with regard to the relationship between $O_3$ and wind distributions at two different sites (Jung Ang (JA): a semi-closed topography and Seo Chang (SC): a closed topography) within a valley city (Yangsan) and their comparison between these sites (JA and SC) and two non-valley sites (Dae Jeo (DJ) and Sang Nam (SN)) located downwind from coastal cities (Busan and Ulsan). This analysis was performed using the data sets of hourly $O_3$ concentrations, meteorological factors (especially, wind speed and direction), and those on high $O_3$ days exceeding the 8-h standard (60 ppb) during 2008-2009. In summer and fall (especially in June and October), the monthly mean values of the daily maximum $O_3$ concentrations and the number of high $O_3$ days at JA (and SC) were relatively higher than those at DJ (and SN). The increase in daytime $O_3$ concentrations at JA in June was likely to be primarily impacted by the transport of $O_3$ and its precursors from the coastal emission sources in Busan along the dominant southwesterly winds (about 5 m/s) under the penetration of sea breeze condition, compared to other months and sites. Such a phenomenon at SC in October was likely to be mainly caused by the accumulation of $O_3$ and its precursors due to the relatively weak winds under the localized stagnant weather condition rather than the contribution of regional transport from the emission sources in Busan and Ulsan.

• Journal of Animal Science and Technology
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• v.61 no.6
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• pp.324-332
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• 2019

This study aimed to identify the causality between climatic and soil variables affecting the yield of Italian ryegrass (Lolium multiflorum Lam., IRG) in the paddy field by constructing the pathways via structure equation model. The IRG data (n = 133) was collected from the National Agricultural Cooperative Federation (1992-2013). The climatic variables were accumulated temperature, growing days and precipitation amount from the weather information system of Korea Meteorological Administration, and soil variables were effective soil depth, slope, gravel content and drainage class as soil physical properties from the soil information system of Rural Development Administration. In general, IRG cultivation by the rice-rotation system in paddy field is important and unique in East Asia because it contributes to the increase of income by cultivating IRG during agricultural off-season. As a result, the seasonal effects of accumulated temperature and growing days of autumn and next spring were evident, furthermore, autumnal temperature and spring precipitation indirectly influenced yield through spring temperature. The effect of autumnal temperature, spring temperature, spring precipitation and soil physics factors were 0.62, 0.36, 0.23, and 0.16 in order (p < 0.05). Even though the relationship between soil physical and precipitation was not significant, it does not mean there was no association. Because the soil physical variables were categorical, their effects were weakly reflected even with scale adjustment by jitter transformation. We expected that this study could contribute to increasing IRG yield by presenting the causality of climatic and soil factors and could be extended to various factors.

• Journal of Wetlands Research
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• v.21 no.3
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• pp.215-223
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• 2019

In this study, we analyzed the water quality characteristics of major tributaries of Nakdong River through statistical analysis such as correlation analysis, principal component and factor analysis, and cluster analysis. Organic matter and nutrients are highly correlated, and are high in spring and autumn, and seasonal water quality management is required. Principal component and factor analysis showed that 82% of total variance could be explained by 4 principal components such as organic matter, nutrients, nature, and weather. BOD, COD, TOC, and TP items were analyzed as major influencing factors. As a result of the cluster analysis, the four clusters were classified according to seasonal organic matter and nutrient pollution. Kumho River watershed showed high pollution characteristics in all seasons. Therefore, effective management of water quality in tributary streams requires measures in consideration of spatio-temporal characteristics and multivariate statistical techniques may be useful in water quality management and policy formulation.