• Journal of Korean Society for Atmospheric Environment
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• v.5 no.1
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• pp.1-10
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• 1989

The sensitivity analysis is a method to quantify to what extent the output of a model changes with the values of input parameters. This will lead to increase model accuracy through measurement validation. Three line source air quality models, HIWAY 2, PAL, and CALINE 3 were selected for this study. The input parameters analysed included wind speed, wind direction, stability, emission rate, mixing height, receptor distance, initial dispersion coefficient, surface roughness, and averaging time. It turned out that PAL model generally showed higher concentration than other two models, and that between CALINE 3 and HIWAY 2, CALINE 3 showed higher concentration than HIWAY 2 model near the line sources, but beyond a certain downwind distances HIWAY 2 model showed higher concentration. The modesl were very sensitive to wind speed especially in the range of 0 $\sim$ 1 m/s and to wind direction near the parallel wind to streets. In case of emission rate, the output concentration was directly proportional to these input parameters. And the sensitivity of the input parameters such as stability, mixing height, initial dispersion coefficient, surface roughness, and averaging time were not very significant.

• Journal of Korean Society of Water and Wastewater
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• v.32 no.5
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• pp.411-419
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• 2018

The residual chlorine concentration is an essential factor to secure reliable water quality in the water distribution systems. The chlorine concentration decays along the pipeline system and the main processes of the reaction can be divided into the bulk decay and the wall decay mechanisms. Using EPANET 2.0, it is possible to predict the chlorine decay through bulk decay and wall decay based on the pipeline geometry and the hydraulic analysis of the water distribution system. In this study, we tried to verify the predictability of EPANET 2.0 using data collected from experimental practices. We performed chlorine concentration measurement according to various Reynolds numbers in a pilot-scale water distribution system. The chlorine concentration was predicted using both bulk decay model and wall decay model. As a result of the comparison between experimental data and simulated data, the performance of the limited $1^{st}$-order model was found to the best in the bulk decay model. The wall decay model simulated the initial decay well, but the overall chlorine decay cannot be properly predicted. Simulation also indicated that as the Reynolds number increased, the impact of the wall.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.5
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• pp.604-612
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• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• Journal of Environmental Science International
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• v.18 no.1
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• pp.1-7
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• 2009

In this study a sediment yield is compared by IUSG, IUSG with Kalman filter, tank model and tank model with Kalman filter separately. The IUSG is the distribution of sediment from an instantaneous burst of rainfall producing one unit of runoff. The IUSG, defined as a product of the sediment concentration distribution (SCD) and the instantaneous unit hydrograph (IUH), is known to depend on the characteristics of the effective rainfall. In the IUSG with Kalman filter, the state vector of the watershed sediment yield system is constituted by the IUSG. The initial values of the state vector are assumed as the average of the IUSG values and the initial sediment yield estimated from the average IUSG. A tank model consisting of three tanks was developed for prediction of sediment yield. The sediment yield of each tank was computed by multiplying the total sediment yield by the sediment yield coefficients; the yield was obtained by the product of the runoff of each tank and the sediment concentration in the tank. A tank model with Kalman filter is developed for prediction of sediment yield. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error.

• The Korean Journal of Physiology and Pharmacology
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• v.28 no.2
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• pp.121-127
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• 2024

Vancomycin is a frequently used antibiotic in intensive care units, and the patient's renal clearance affects the pharmacokinetic characteristics of vancomycin. Several advantages have been reported for vancomycin continuous intravenous infusion, but studies on continuous dosing regimens based on patients' renal clearance are insufficient. The aim of this study was to develop a vancomycin serum concentration prediction model by factoring in a patient's renal clearance. Children admitted to our institution between July 1, 2021, and July 31, 2022 with records of continuous infusion of vancomycin were included in the study. Sex, age, height, weight, vancomycin dose by weight, interval from the start of vancomycin administration to the time of therapeutic drug monitoring sampling, and vancomycin serum concentrations were analyzed with the linear regression analysis of the mixed effect model. Univariable regression analysis was performed using the vancomycin serum concentration as a dependent variable. It showed that vancomycin dose (p < 0.001) and serum creatinine (p = 0.007) were factors that had the most impact on vancomycin serum concentration. Vancomycin serum concentration was affected by vancomycin dose (p < 0.001) and serum creatinine (p = 0.001) with statistical significance, and a multivariable regression model was obtained as follows: Vancomycin serum concentration (mg/l) = -1.296 + 0.281 × vancomycin dose (mg/kg) + 20.458 × serum creatinine (mg/dl) (adjusted coefficient of determination, R² = 0.66). This prediction model is expected to contribute to establishing an optimal continuous infusion regimen for vancomycin.

• Clean Technology
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• v.19 no.3
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• pp.306-312
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• 2013

We investigated the effects of the concentration of carbon dioxide on the char-$CO_2$ gasification reaction under isothermal conditions of $850^{\circ}C$ using the Drayton coal. Potassium carbonate was used to improve the low-temperature gasification reactivity. The enhancement of carbon dioxide concentration increased the gasification rate of char, while gasification rate reached a saturated value at the concentration of 70%. The best $CO_2$ concentration for gasification is determined to be 70%. We compared the shrinking core model (SCM), volumetric reaction model (VRM) and modified volumetric reaction model (MVRM) of the gas-solid reaction models. The correlation coefficient values, by linear regression, of SCM are higher than that of VRM at low concentration. While the correlation coefficients values of VRM are higher than that of SCM at high concentration. The correlation coefficient values of MVRM are the highest than other models at all concentration.

• The Korean Journal of Pharmacology
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• v.31 no.2
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• pp.251-259
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• 1995

Pharmacokinetics and pharmacodynamics of metoprolol, a selective beta-l blocker, were examined for 360 minutes after intravenous bolus administration of metoprolol to 6 dogs. Plasma concentration and excreted amount in the urine metoprolol were measured by liquid chromatography with fluorescence detection. PR interval and heart rate were measured by ECG monitoring. Blood pressure was monitored through intraarterial catheter in femoral artery and cardiac output by thermodilution method using Swan-Ganz catheter. To analyze the effect site concentration-response relationship, plasma concentration and pharmacological effects were simultaneously fitted to a two pharmacokinetic compartment linked to pharmacodynamic model with NONLIN program. Results are as follows. 1) The plasma concentration of metoprolol after intrvenous injection decreased biexponentially. The terminal half-life estimated was $1.33{\pm}0.40$ hours and the volume of distribution at steady state (Vdss) and the total body clearance were $1.04{\pm}0.4\;L/kg,\;6.55{\pm}2.21\;L/hr$, respectively. The central compartment volume of distribution and peripheral compartment volume of distribution were $0.35{\pm}0.14L/kg\;and\;0.69{\pm}0.34L/kg$. The renal clearance and intercompartment clearance were $0.53{\pm}0.25\;L/min\;and\;0.35{\pm}0.19\;L/min$. 2) Simulated biophase concentration-response curve shows hyperbolic relationship and the estimated concentration-effect relationship was best explained by Emax model when the prolongation of PR interval and the reduction of the heart rate were used as pharmacodynamic parameters. Emax and EC50 were estimated to be $26.3{\pm}4.7\;msec\;and\;88.8{\pm}82.3\;g/ml$ for PR interval, and $48.7{\pm}18.8\;beats/min\;and\;113.5{\pm}78.7\;ng/ml$ for heart rate, respectively. 3) The changes of cardiac output-effect site concentration relationship was best fitted by a linear model and the slope of the relationship was $0.005{\pm}0.003$. Diastolic blood pressure-effect site concentration relationship was also explained by the linear model and the slope of the relationship was $0.038{\pm}0.034$.

• Journal of Environmental Science International
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• v.6 no.4
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• pp.351-357
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• 1997

In the Atmosphere under the various physical and chemical condition different chemical reactions occur and there are a number of air pollutants which are generated by photochemical reaction by absorbing solar energy. Therefor various testing simulation was done as foundation work to develop the numerical model for the prediction of concentration of air pollutants. It was shown change of msjor air pollutants concentration In according to variation of photodissociation speed constant, Kl and Initial condition of air pollutants concentration which plays major role In photochemical reaction. The photochemical reaction model which was used In this study Is found to be useful for understanding relationship among the concentration of reacting air pollutants and the prediction of concentration of air pollutants in urban atmosphere.

• Journal of Korean Society for Atmospheric Environment
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• v.16 no.5
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• pp.539-544
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• 2000

A simple analytic model is proposed here to analyze the concentration deficit field caused by a large area of vegetated area. With non-dimensional deposition velocity chosen as small parameter, the regular perturbation method is exploited to derive the mass balance equation and the dynamic equations for the concentration deficit field, Analytic solutions to those equations are obtained in a closed form for several cases of interest, assuming that the concentration field is stationary and the plume can be nicely approximated as Gaussian for a point source. The results suggest that quite a negligible fraction (less than 1%) of the gaseous air pollutants emitted into the air is removed by the vegetated area of which width is 4 km in wind-wise direction, the typical dimension of the Restricted Development Zones around the metropolitan regions in South Korea.