• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Journal of Environmental Health Sciences
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• v.48 no.4
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• pp.227-235
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• 2022

Background: Abandoned metal mines are classified as vulnerable areas with the highest level of soil contamination among risk regions. People living near abandoned metal mines are at increased risk of exposure to toxic metals. Objectives: This study aimed to evaluate the correlation between soil contamination levels in areas around abandoned metal mine and the blood lead levels of local residents. Moreover, we assess the possibility of using soil contamination levels as a predictive indicator for human exposure level. Methods: Data from the Survey of Residents around Abandoned Metal Mines (2013~2017, n=4,421) and Investigation of Soil Pollution in Abandoned Metal Mines (2000~2011) were used. A random coefficient model was conducted for estimation of the lower level (micro data) of the local resident unit and the upper level (macro data) of the abandoned metal mine unit. Through a fitted model, the variation of blood lead levels among abandoned metal mines was confirmed and the effect of the operationally defined soil contamination level was estimated. Results: Among the total variation in blood lead levels, the variation between abandoned mines was 18.6%, and the variation determined by the upper-level factors such as soil contamination and water contamination was 8.1%, which was statistically significant respectively. There was also a statistically significant difference in the least square mean of blood lead concentration according to the level of soil contamination (p=0.047, low: 2.32 ㎍/dL, middle: 2.38 ㎍/dL, high: 2.59 ㎍/dL). Conclusions: The blood lead concentration of residents living near abandoned metal mines was significantly correlated with the level of soil contamination. Therefore, in biomonitoring for vulnerable areas, operationally defined soil contamination can be used as a predictor for human exposure level to hazardous substances and discrimination of high-risk abandoned metal mines.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.

• Korea Journal of Hospital Management
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• v.9 no.1
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• pp.66-94
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• 2004

The objective of this study is to examine how to maximize the efficiency of hospital management by minimizing the unit cost of hospital operation. For this purpose, this paper proposes to develop a model of the profit maximization based on the cost minimization dictum using the statistical tools of arriving at the maximum likelihood values. The preliminary survey data are collected from the annual statistics and their analyses published by Korea Health Industry Development Institute and Korean Hospital Association. The maximum likelihood value statistical analyses are conducted from the information on the cost (function) of each of 36 hospitals selected by the random stratified sampling method according to the size and location (urban or rural) of hospitals. We believe that, although the size of sample is relatively small, because of the sampling method used and the high response rate, the power of estimation of the results of the statistical analyses of the sample hospitals is acceptable. The conceptual framework of analyses is adopted from the various models of the determinants of hospital costs used by the previous studies. According to this framework, the study postulates that the unit cost of hospital operation is determined by the size, scope of service, technology (production function) as measured by capacity utilization, labor capital ratio and labor input-mix variables, and by exogeneous variables. The variables to represent the above cost determinants are selected by using the step-wise regression so that only the statistically significant variables may be utilized in analyzing how these variables impact on the hospital unit cost. The results of the analyses show that the models of hospital cost determinants adopted are well chosen. The various models analyzed have the (goodness of fit) overall determination (R2) which all turned out to be significant, regardless of the variables put in to represent the cost determinants. Specifically, the size and scope of service, no matter how it is measured, i. e., number of admissions per bed, number of ambulatory visits per bed, adjusted inpatient days and adjusted outpatients, have overall effects of reducing the hospital unit costs as measured by the cost per admission, per inpatient day, or office visit implying the existence of the economy of scale in the hospital operation. Thirdly, the technology used in operating a hospital has turned out to have its ramifications on the hospital unit cost similar to those postulated in the static theory of the firm. For example, the capacity utilization as represented by the inpatient days per employee tuned out to have statistically significant negative impacts on the unit cost of hospital operation, while payroll expenses per inpatient cost has a positive effect. The input-mix of hospital operation, as represented by the ratio of the number of doctor, nurse or medical staff per general employee, supports the known thesis that the specialized manpower costs more than the general employees. The labor/capital ratio as represented by the employees per 100 beds is shown to have a positive effect on the cost as expected. As for the exogeneous variable's impacts on the cost, when this variable is represented by the percent of urban 100 population at the location where the hospital is located, the regression analysis shows that the hospitals located in the urban area have a higher cost than those in the rural area. Finally, the case study of the sample hospitals offers a specific information to hospital administrators about how they share in terms of the cost they are incurring in comparison to other hospitals. For example, if his/her hospital is of small size and located in a city, he/she can compare the various costs of his/her hospital operation with those of other similar hospitals. Therefore, he/she may be able to find the reasons why the cost of his/her hospital operation has a higher or lower cost than other similar hospitals in what factors of the hospital cost determinants.

• Journal of Korea Water Resources Association
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• v.52 no.11
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• pp.917-929
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• 2019

Denitrification in streams is of great importance because it is essential for amelioration of water quality and accurate estimation of $N_2O$ budgets. Denitrification is a major biological source or sink of $N_2O$, an important greenhouse gas, which is a multi-step respiratory process that converts nitrate ($NO_3{^-}$) to gaseous forms of nitrogen ($N_2$ or $N_2O$). In aquatic ecosystems, the complex interactions of water flooding condition, substrate supply, hydrodynamic and biogeochemical properties modulate the extent of multi-step reactions required for $N_2O$ flux. Although water flow in streambed and residence time affect reaction output, effects of a complex interaction of hydrodynamic, geomorphology and biogeochemical controls on the magnitude of denitrification in streams are still illusive. In this work, we built a two-dimensional water flow channel and measured $N_2O$ flux from channel sediment with different bed geomorphology by using static closed chambers. Two independent experiments were conducted with identical flume and geomorphology but sediment with differences in dissolved organic carbon (DOC). The experiment flume was a circulation channel through which the effluent flows back, and the size of it was $37m{\times}1.2m{\times}1m$. Five days before the experiment began, urea fertilizer (46% N) was added to sediment with the rate of $0.5kg\;N/m^2$. A sand dune (1 m length and 0.15 m height) was made at the middle of channel to simulate variations in microtopography. In high- DOC experiment, $N_2O$ flux increases in the direction of flow, while the highest flux ($14.6{\pm}8.40{\mu}g\;N_2O-N/m^2\;hr$) was measured in the slope on the back side of the sand dune. followed by decreases afterward. In contrast, low DOC sediment did not show the geomorphological variations. We found that even though topographic variation influenced $N_2O$ flux and chemical properties, this effect is highly constrained by carbon availability.

• Journal of Life Science
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• v.31 no.9
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• pp.840-848
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• 2021

This study was conducted to estimate annual trends and the environmental effects in the racing records of Jeju horses. The Korean Racing Authority (KRA) collected 48,645 observations for 2,167 Jeju horses from 2002 to 2019. Racing records were preprocessed to eliminate errors that occur during the data collection. Racing times were adjusted for comparison between race distances. A stepwise Akaike information criterion (AIC) variable selection method was applied to select appropriate environment variables affecting racing records. The annual improvement of the race time was -0.242 seconds. The model with the lowest AIC value was established when variables were selected in the following order: year, budam classification, jockey ranking, trainer ranking, track condition, weather, age, and gender. The most suitable model was constructed when the jockey ranking and age variables were considered as random effects. Our findings have potential for application as basic data when building models for evaluating genetic abilities of Jeju horses.