• Journal of Korean Society of Environmental Engineers
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• v.30 no.3
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• pp.302-313
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• 2008

The purposes of this study are to investigate the concentration levels of fine particles, so called PM$_{2.5}$, to identify the affecting sources, and to estimate quantitatively the source contributions of PM$_{2.5}$. Ambient air sampling was seasonally carried out at two sites in Pohang(a residential and an industrial area) during the period of March to December 2003. PM$_{2.5}$ samples were collected by high volume air samplers with a PM$_{10}$ Inlet and an impactor for particle size segregation, and then determined by gravimetric method. The chemical species associated with PM$_{2.5}$ were analyzed by inductively coupled plasma spectrophotometery(ICP) and ion chromatography(IC). The results showed that the most significant season for PM$_{2.5}$ mass concentrations appeared to be spring, followed by winter, fall, and summer. The annual mean concentrations of PM$_{2.5}$ were 36.6 $\mu$g/m$^3$ in the industrial and 30.6 $\mu$g/m$^3$ in the residential area, respectively. The major components associated with PM$_{2.5}$ were the secondary aerosols such as nitrates and sulfates, which were respectively 4.2 and 8.6 $\mu$g/m$^3$ in the industrial area and 3.7 and 6.9 $\mu$g/m$^3$ in the residential area. The concentrations of chemical component in relation to natural emission sources such as Al, Ca, Mg, K were generally higher at both sampling sites than other sources. However, the concentrations of Fe, Mn, Cr in the industrial area were higher than those in the residential area. Based on the principal component analysis and stepwise multiple linear regression analysis for both areas, it was found that soil/road dust and secondary aerosols are the most significant factors affecting the variations of PM$_{2.5}$ in the ambient air of Pohang. The source apportionments of PM$_{2.5}$ were conducted by chemical mass balance(CMB) modeling. The contributions of PM$_{2.5}$ emission sources were estimated using the CMB8.0 receptor model, resulting that soil/road dust was the major contributor to PM$_{2.5}$, followed by secondary aerosols, vehicle emissions, marine aerosols, metallurgy industry. Finally, the application and its limitations of chemical mass balance modeling for PM$_{2.5}$ was discussed.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.883-893
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• 2019

In this study, crop coefficients were calculated in two different methods and the results were evaluated. In the first method, appropriateness of GLDAS-based evapotranspiration was evaluated by comparing it with observed data of Cheongmi-cheon (CMC) Flux tower. Then, crop coefficient was calculated by dividing actual evapotranspiration with potential evapotranspiration that derived from GLDAS. In the second method, crop coefficient was determined by using MLR (Multiple Linear Regression) analysis with vegetation index (NDVI, EVI, LAI and SAVI) derived from MODIS and in-situ soil moisture data observed in CMC, In comparison of two crop coefficients over the entire period, for each crop coefficient GLDAS K_c and SM&VI K_c, shows the mean value of 0.412 and 0.378, the bias of 0.031 and -0.004, the RMSE of 0.092 and 0.069, and the Index of Agree (IOA) of 0.944 and 0.958. Overall, both methods showed similar patterns with observed evapotranspiration, but the SM&VI-based method showed better results. One step further, the statistical evaluation of GLDAS K_c and SM&VI K_c in specific period was performed according to the growth phase of the crop. The result shows that GLDAS K_c was better in the early and mid-phase of the crop growth, and SM&VI K_c was better in the latter phase. This result seems to be because of reduced accuracy of MODIS sensors due to yellow dust in spring and rain clouds in summer. If the observational accuracy of the MODIS sensor is improved in subsequent study, the accuracy of the SM&VI-based method will also be improved and this method will be applicable in determining the crop coefficient of unmeasured basin or predicting the crop coefficient of a certain area.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.37-42
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• 2014

Fine root distribution was investigated in Pinus densiflora stands using soil core sampling and minirhizotrons, and conversion factors and regression equations were developed for converting minirhizotron data into fine root biomass. Fine root biomass was measured by soil core sampling from October, 2012 to September, 2013 once a month except for the winter, and surface area of fine roots was estimated by minirhizotrons from May to August, 2013 once a month. Fine root biomass and surface area were significantly higher in the upper soil layers than in the lower soil layers. Fine root biomass showed seasonal patterns; the mean fine root biomass ($kg{\cdot}ha^{-1}$) in summer (3,762.4) and spring (3,398.0) was significantly higher than that in autumn (2,551.6). Vertical and seasonal patterns of fine root biomass might be related to the soil bulk density, nutrient content and temperature with soil depth, and seasonal changes of soil and air temperature. Conversion factors (CF) between fine root surface area from minirhizotron data and fine root biomass from soil core sampling were developed for the three soil depths. Then a linear regression equation was developed between the predicted fine root biomass using CF and the measured fine root biomass (y = 79.7 + 0.93x, $R^2=0.81$). We expect to estimate the long-term dynamics of fine roots using CF and regression equation for P. densiflora forests in Korea.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.4
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• pp.100-108
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• 2016

The climatic index for tourism(CIT) has recently been advanced, which includes complete human energy balance models such as physiological equivalent temperature(PET) and universal thermal climate index(UTCI). This study investigated human thermal sensation and comfort at Woljung-ri Beach, Jeju, Republic of Korea, in spring and summer 2015 for landscape planning and design in beach areas. Microclimatic data measurements and human thermal sensation/comfort surveys from ISO 10551 were conducted together. There were 869 adults that participated. As a result, perceptual and thermal preference that consider only physiological aspects had high coefficients of determination($r^2$) with PET in linear regression analyses: 92.8% and 87.6%, respectively. However, affective evaluation, personal acceptability and personal tolerance, which consider both physiological and psychological aspects, had low $r^2s$: 60.0%, 21.1% and 46.4%, respectively. However, the correlations between them and PET were all significant at the 0.01 level. The neutral PET range in perceptual for human thermal sensation was $25{\sim}27^{\circ}C$, but a PET range less or equal to 20% dissatisfaction, which was recommended by ASHRAE Standard 55, could not be achieved in perceptual. Only PET ranges in affective evaluation and personal tolerance affected by both aspects were qualified for the recommendation as $21{\sim}32^{\circ}C$ and $17{\sim}37^{\circ}C$, respectively. Therefore, the PET range of $21{\sim}32^{\circ}C$ is recommended to be used for the human thermal comfort zone of beach areas in landscape planning and design as well as tourism and recreational planning. PET heat stress level ranges on the beach were $2{\sim}5^{\circ}C$ higher than those in inland urban areas of the Republic of Korea. Also, they were similar to high results of tropical areas such as Taiwan and Nigeria, and higher than those of western and middle Europe and Tel Aviv, Israel.

• Journal of Wetlands Research
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• v.21 no.2
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• pp.102-113
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• 2019

Slack-tide sampling was carried out at 6 stations at high and low tide for a tidal cycle during spring tide of the early summer (June) and summer (July, August) of 2016 to determine the difference of water quality according to tide in Masan Bay, Korea. The mixing regime of all the water quality components investigated was well explained through the correlation with SAL. In the early summer and summer, TURB, DSi and NNN which mainly flow into the bay from the streams and SS, COD, AMN and $H_2S$ which mainly indicate the internal sink and source materials have a property of conservative mixing and non-conservative mixing, respectively. The conservative mixing showed a good linear relationship of the water quality between high and low tide, and the non-conservative mixing showed a variation of different pattern each other. Factor analysis performed on the concentration difference data sets between high and low tide helped in identifying the principal latent variables for them. In early summer, multiple effects (tidal action, natural influx and internal sinks and sources etc.) acted in combination for the differences to be distributed evenly in four factors (VF1~4), since there were few allochthonous inputs as a low-water season. On the contrary, in summer, the parameters showing large concentration difference at ST-1 affected by stream water were concentrated in one factor (VF1) and clearly distinguished from the parameters affected by the internal sinks and sources. In fact, there is no estuary (bay) that always maintains steady state flow conditions. The mixing regime of an estuary might be changed at any time due to the change of flushing time, and furthermore the change of end-member conditions due to the internal sinks and sources makes the occurrence of concentration difference inevitable. Therefore, when investigating the water quality of the estuary, it is necessary to take a sampling method considering the tide to obtain average water quality data.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Journal of agriculture & life science
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• v.50 no.5
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• pp.173-182
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• 2016

This study was aimed to identify the phenotypic relationships between calving interval and lactation curve parameters in Korean Holstein cow. The data of 36,505 lactation records was obtained from the Dairy Herd Improvement program run by Dairy Cattle Improvemnet Center of National Agricultural Federation of Korea. All lactation records were collectied from the multiparous cows calving between 2011 to 2013. The estimated lactation curves were drawn using Wood model based on actual milk yield records, and NLIN Procedure of SAS program (ver. 9.2). General linear multivariate models for calving interval, 305-d milk yield, lactation parameters(A, b, c), persistency, peak day, and peak yield included fixed effects of calving year-season (spring, summer, fall and winter) and parity(2, 3 and 4). For calving interval, 305-d milk yield, lactation parameters(A, b, c), persistency, peak day and peak yield, all two fixed effect(calving year-season, parity) were significant(p<0.05). The estimated lactation functions using Wood model for 2, 3, and 4 parity were yt=24.66t0.175e-0.00302t, yt=24.69t0.192e-0.00334t, and yt=24.22t0.200e-0.00341t, respectively. Phenotypic correlation (partial residual correlation) between calving interval and 305-d milk yield, A, b, c, persistency, peak day, and peak yield were 0.093, -0.014, 0.028, -0.046, 0.099, 0.085, and 0.052, respectively. To conclude, if calving interval increase then ascent to peak, persistency, peak day and peak yield are increase, and descent after peak is decrease. So, total 305-d milk yield is increase.