Korean Journal of Agricultural and Forest Meteorology
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v.19
no.3
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pp.93-101
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2017
The distribution of inter-annual variation in temperature would help evaluate the likelihood of a climatic risk and assess suitable zones of crops under climate change. In this study, we evaluated two methods to estimate the standard deviation of temperature in the areas where weather information is limited. We calculated the monthly standard deviation of temperature by collecting temperature at 0600 and 1500 local standard time from 10 automated weather stations (AWS). These weather stations were installed in the range of 8 to 1,073m above sea level within a mountainous catchment for 2011-2015. The observed values were compared with estimates, which were calculated using a geospatial correction scheme to derive the site-specific temperature. Those estimates explained 88 and 86% of the temperature variations at 0600 and 1500 LST, respectively. However, it often underestimated the temperatures. In the spring and fall, it tended to had different variance (e.g., increasing or decreasing pattern) from lower to higher elevation with the observed values. A regression analysis was also conducted to quantify the relationship between the standard deviation in temperature and the topography. The regression equation explained a relatively large variation of the monthly standard deviation when lapse-rate corrected temperature, basic topographical variables (e.g., slope, and aspect) and topographical variables related to temperature (e.g., thermal belt, cold air drainage, and brightness index) were used. The coefficient of determination for the regression analysis ranged between 0.46 and 0.98. It was expected that the regression model could account for 70% of the spatial variation of the standard deviation when the monthly standard deviation was predicted by using the minimum-maximum effective range of topographical variables for the area.
This study investigated the association between $PM_{2.5}$ concentrations obtained with portable real-time monitors and those obtained with gravimetric methods in national urban air-quality monitoring sites in Seoul, South Korea. We used the SidePak AM510 Personal Aerosol Monitor (TSI Inc., 500 Cardigan Road Shoreview, MN) and DustTrak DRX 8533 (TSI Inc., 500 Cardigan Road Shoreview, MN) as portable real-time monitors for measuring $PM_{2.5}$ concentrations and compared these values with those measured with the PMS-103 or SEQ 47/50 models operated by Federal Reference Method (FRM) or the European Committee for Standardization(ECS), respectively, in national urban air-quality monitoring sites in Seoul. Measurements were conducted every other day in the winter and spring seasons of 2014. The estimated daily mean concentrations of $PM_{2.5}$ ranged between 13.4 and $161.9{\mu}g/m^3$ using AM 510 and between 22.0 and $156.0{\mu}g/m^3$ using DustTrak. The Spearman correlation coefficient for $PM_{2.5}$ concentrations between AM 510 and gravimetric results was 0.99, and the correlation between DustTrak and gravimetric results was 0.87. The correction factor suggested was 0.42 and 0.29 for AM 510 and DustTrak, respectively. We found that $PM_{2.5}$ concentrations measured with real-time monitors could overestimate true $PM_{2.5}$ concentrations and therefore the application of a correction factor (0.43) is strongly suggested for quantification when Real-time monitors were operated of $PM_{2.5}$ levels at urban atmospheric environment of South Korea.
Hsu, Hua;Zuidhof, Martin J.;Recinos-Diaz, Guillermo;Wang, Zhiquan
Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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2001.06a
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pp.1510-1510
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2001
NIRS uses reflectance signals resulting from bending and stretching vibrations in chemical bonds between carbon, nitrogen, hydrogen, sulfur and oxygen. These reflectance signals are used to measure the concentration of major chemical composition and other descriptors of homogenized and freeze-dried whole broiler carcasses. Six strains of chicken were analyzed and the NIRS model predictions compared to reference data. The results of this comparison indicate that NIRS is a rapid tool for predicting dry matter (DM), fat, crude protein (CP) and ash content in the broiler carcass. Males and females of six commercial strain crosses of broiler chicken (Gallus domesticus) were used in this study (6$\times$2 factorial design). Each strain was grown to 16 weeks of age, and duplicate serial samples were taken for body composition analysis. Each whole carcass was pressure-cooked, homogenized, and a representative sample was freeze-dried. Body composition determined as follows: DM by oven dried method at 105$^{\circ}C$ for 3 hours, fat by Mojonnier diethyl ether extraction, CP by measuring nitrogen content using an auto-analyzer with Kjeldhal digest and ash by combustion in a muffle furnace for 24 hour at 55$0^{\circ}C$. These homogenized and freeze-dried carcass samples were then scanned with a Foss NIR Systems 6500 visible-NIR spectrophotometer (400-2500nm) (Foss NIR Systems, Silver Spring, MD., US) using Infra-Soft-International, ISI, WinISl software (ISI, Port Matilda, US). The NIRS spectra were analyzed using principal component (PC) analysis. This data was corrected for scatter using standard normal “Variate” and “Detrend” technique. The accuracy of the NIRS calibration equations developed using Partial Least Squares (PLS) for predicting major chemical composition and carcass descriptors- such as body mass (BM), bird dry matter and moisture content was tested using cross validation. Discrimination analysis was also used for sex and strain identification. According to Dr John Shenk, the creator of the ISI software, the calibration equations with the correlation coefficient, $R^2$, between reference data and NIRS predicted results of above 0.90 is excellent and between 0.70 to 0.89 is a good quantifying guideline. The excellent calibration equations for DM ($R^2$= 0.99), fat (0.98) and CP (0.92) and a good quantifying guideline equation for ash (0.80) were developed in this study. The results of cross validation statistics for carcass descriptors, body composition using reference methods, inter-correlation between carcass descriptors and NIRS calibration, and the results of discrimination analysis for sex and strain identification will also be presented in the poster. The NIRS predicted daily gain and calculated daily gain from this experiment, and true daily gain (using data from another experiment with closely related broiler chicken from each of the six strains) will also be discussed in the paper.
The diurnal variation in pollen vertical distributions in the atmosphere was observed by a surface-based lidar remote sensing technique. Aerosol extinction coefficient and depolarization ratio at 532 nm were obtained from lidar measurements in spring ($4^{th}$ May - $2^{nd}$ June) 2009 at Gwangju Institute of Science & Technology (GIST) located in Gwangju, Korea ($35.15^{\circ}E$, $126.53^{\circ}N$). Unusual variations of depolarization ratio were observed for six days from $4^{th}$ to $9^{th}$ May. Depolarization ratios varied from 0.08 to 0.14 were detected at the low altitude in the morning. The altitude with those high depolarization ratios was increased up to 1.5 - 2.0 km at the time interval between 12:00 and 14:00 LT and then decreased. The temporal variations in high values of depolarization ratios from lidar measurements show good agreement in patterns with the sampled pollen concentrations measured using the Burkard trap sampler. This study demonstrates that the pollen distribution data obtained by lidar measurements can be a useful tool for investigating spatial and temporal characteristic of pollen particles.
Precipitation is closely related to various hydrometeorological phenomena, such as runoff and evapotranspiration. In Korean Peninsula, observing rainfall intensity using weather radar and rain gauge network is dominating due to their accurate, intuitive and precise detecting power. However,since these methods are not suitable at ungauged regions, rainfall detection using satellite is required. Satellite-based rainfall data has coarse spatial resolution (10 km, 25 km), and has a limited range of usage due to its reliability of data. The aim of this study is to obtain finer scale precipitation. Especially, to make the applicability of satellite higher at ungauged regions, 10 km satellite-based rainfall data was downscaled to 1 km data using MODerate Resolution Imaging Spectroradiometer (MODIS) based cloud property. Downscaled precipitation was verified in urban region, which has complex topographical and environmental characteristics. Correlation coefficient was similar in summer (+0), decreased in spring (-0.08) and autumn (-0.01), and increased in winter (+0.04) season compared to Global Precipitation Measurement (GPM) based precipitation. Downscaling without calibration using in situ data could be useful in areas where rain gauge system is not sufficient or ground observations are rarely available.
Kim, Jong Soo;Hong, Soon Mo;Kim, Myoung Sook;Kim, Yo Yong;Shin, Eun Sang
Journal of Korean Society for Atmospheric Environment
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v.30
no.3
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pp.281-290
/
2014
This study was conducted to evaluate the distribution characteristics of $PM_{10}$ and heavy metals concentrations in the ambient air of Gyeonggi-do area by region and season from February, 2013 to March, 2014. The regression model for the prediction of formation characteristics and contamination degree of $PM_{10}$ and heavy metals by correlation analysis and regression analysis for using the multivariate statistical analysis was also established. The main wind direction during the investigation period was South East (SE) and West South West (WSW) winds, and the concentration of $SO_2$ at Ansan with industrial region showed 1.6 times higher than Suwon, Euiwang with residential region. The concentrations (median) of Pb, Cu and Ni at Ansan showed 3.2~4.5, 1.9~2.2 and 1.7~2.6 times respectively higher than those at Suwon. By the seasonal concentration variation, the concentrations of $PM_{10}$, Pb, Fe and As in winter and spring (December to May) showed 1.7, 1.9, 1.9 and 2.7 times respectively higher than those in summer and fall (June to November). As, Fe and $PM_{10}$ had a big difference by the seasonal factors, and Cu and Ni were evaluated to be influenced by the regional factors. From the results of correlation analysis among the target items, the correlation coefficient of PM and Mn had 0.82 (p/0.01) and that of Fe and Mn had 0.82 (p/0.01), which showed high correlation. And the correlation coefficients for $SO_2$ and Pb, CO and $PM_{10}$ were 0.66 (p/0.01) and 0.62 (p/0.01) respectively. The multiple linear regression models for $PM_{10}$, Pb, Cu, Cr, As, Ni, Fe and Mn were established by independent variables of CO, $SO_2$ and meteorological factors (wind speed, relative humidity). In the regression models, independent variable $SO_2$ was in cause-and-effect relationship with all dependent variables, and $PM_{10}$, Fe and Mn were influenced by CO and wind speed, and Pb, Cu, Ni and As had a main factor of $SO_2$.
Journal of the Korea Academia-Industrial cooperation Society
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v.18
no.11
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pp.697-704
/
2017
This study was carried out to investigate and alleviate the vibration problem of commercial high-speed trains. First, the measurement of the carbody vibration was performed, in order to determine the vibration level of the high-speed train. The measurement result showed that the vibration level of the driver cab was higher than that of the passenger car and that the vibration became bigger toward the trailing end of the train. The vertical vibration of the driver cab and passenger car was larger than the transverse vibration, and the maximum value of the vibration in the ballast section was larger than that in the concrete section. A dynamic analysis was carried out to improve the vibration of the KTX-Sancheon train. The results of the analysis showed that it is necessary to reduce the vibration of the driver cab and both ends of the passenger cars. To reduce the vibration of the driver cab, it was recommended that the stiffness of the secondary coil spring be reduced and the damping coefficient of the secondary vertical damper be increased. It was found that the failure of the suspension system could be the origin of the vibration problem of the high-speed train. The proper management of wheel wear plays an important role in the improvement of the operation efficiency and reduction of the carbody vibration of high-speed trains, and research is underway to change the present wheel profile to increase the mileage between wheel turning.
Thisstudy presentsthe method for deriving surface visibility from satellite retrieved AOD. To do thisthe height of aerosol distribution isrequired. This distribution would be in thisstudy represented by the two heights; if there is a discrete atmospheric layer, which is physically separated from the above layer, the upper height of the layer is assumed as Aerosol Layer Height(ALH). In this case there is clear minimum in the Relative Humanity vertical distribution. Otherwise PBLH(Planetary Boundary Layer Height) is used. These heights are obtained from the forecast data of Regional Data Assimilation and Prediction System(RDAPS). The surface visibility is estimated from MODIS AOD and ALH/PBLH, using Koschmieder's Law for ALH and the empirical relations for PBLH. The estimated visibility are evaluated from the visibility measurements of 9 eve-measurement stations and 17 PWD22 stations for the spring of 2015 and 2016. Verification of the estimated visibility shows that there are considerable differencesin statistical verification value depending on stations, years, morning(Terra)/afternoon(Aqua). The better results are shown in the midwest part of korean peninsula for Terra of 2016. The results are summarized as; correlation coefficients of higher than 0.65, for low visibility RMSE of 3.62 km and ME of 2.29 km or less, POD of higher than 0.65 and FAR of 0.5 or less. Verification results were better with increase in the number of low-visibility data.
This study was performed to investigate tree injury with air pollution and acid precipitation in industrial area and rural area. This study analyzed the ion properties of pollutant precipitated in the forest of Ulsan & Onsan area and correlation between S $O_2$concentration in air and the degree of forest decline. pH of industrial area was lower than that of rural area and electrical conductivity and pH had a negative correlation(r=-.7861$^{**}$). Correlation of cation and anion(especially S $O_{4}$$^{2-}$, N $O_{3}$$^{[-10]}$ ) in precipitation and S $O_2$in air was higher in industrial area. In seasonal change, winter and spring were higher. In the analysis of correlation between forest decline and variables of precipitation properties, correlation coefficient was higher by following order: S $O_{4}$$^{2-}$>pH>EC>N $O_{3}$$^{[-10]}$ >S $O_{2}$$^{2-}$>C $l^{[-10]}$ . Regression formula by computation was Y = 5.1007-0.7811 $X_2$(pH) +0.0253 $X_{5}$ (S $O_{4}$$^{2-}$) +0.0275 $X_{6}$ (N $O_{3}$$^{[-10]}$ ). In considering the result of this study, it was predicted that air pollution and acid rain would affect soil acidification and forest decline continuously.y.
The objective of this study was to estimate air quality trends in the study area by surveying monthly and seasonal concentration trends. To do this, the mass concentration of $PM_{10}$ samples and the metals, ions, and total carbon in the $PM_{10}$ were analyzed. The mean concentration of $PM_{10}$ was $33.9{\mu}g/m^3$. The composition of $PM_{10}$ was 39.2% ionic species, 5.1% metallic species, and 26.6% carbonic species (EC and OC). Ionic species, especially sulfate, ammonium, and nitrate, were the most abundant in the $PM_{10}$ and had a high correlation coefficient with $PM_{10}$. Seasonal variation of $PM_{10}$ showed a similar pattern to those of ionic and metallic species. with high concentration during the winter and spring seasons. $PM_{10}$ showed high correlation with the ionic species $NO_3{^-}$ and $NH_4{^+}$. In addition, $NH_4{^+}$ was highly correlated with $SO{_4}^{2-}$ and $NO_3{^-}$. We obtained four factors through factor analysis and determined the pollution sources using the United States Environmental Protection Agency(U.S. EPA) pollution profile. The first factor accounted for 51.1% of $PM_{10}$ from complex sources, that is, soil, motor vehicles, and secondary particles: the second factor indicated marine sources; the third factor, industry-related sources; and the last factor, heating-related sources. However, the pollution profile used in this study may be somewhat different from the actual situation in Korea because it was from US EPA. Therefore, to more accurately estimate the pollutants present, it is necessary to create a pollution profile for Korea.
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