• Journal of Korean Society for Atmospheric Environment
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• v.29 no.3
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• pp.275-286
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• 2013

It is well known that exposure to high level of PM (particulate matter) can adversely affect human health. However, little is known about health burden of PM considering the relationship, exposed level of PM, and health level in local communities. And, there is scarcely methodical assessment of uncertainty for application to policies of these assessment results. The scope of this study is divided into two parts: firstly to estimate the death burden of PM10 (particulate matter less then $10{\mu}m$ in diameter) in Seoul metropolitan region, and secondly to evaluate potential uncertainties in these estimates. To estimate the death burden of PM10 in Seoul metropolitan region from 2005~2010, we firstly assessed the relationship between daily mean PM10 and daily death counts in Seoul from 2000~2010, and calculated the death burden of PM10 using BenMAP (Environmental Benefits Mapping and Analysis Program). After that, we identified and characterized uncertainties to substantially influence the results of death burden. The daily mortality risk was increased 1.000227 times (p-value/0.001) associated with $1{\mu}g/m^3$ increase of daily mean PM10 for all ages population, Seoul. And, death burdens of PM10 in Seoul metropolitan region were estimated from 5.51 in 2005 to 5.12 in 2010 per 100,000 people. Finally, we categorized context, model, and input uncertainty and characterized these uncertainties in three dimensions (i.e. location, level, and nature) using uncertainty typology. In our study, we argue that uncertainties need to be identified, assessed, reported and interpreted in order for assessment results to adequately support decision making, such as the establishment of air quality standards based on health burden of air quality.

• Environmental Analysis Health and Toxicology
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• v.30
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• pp.12.1-12.7
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• 2015

Objectives In South Korea, health insurance data are used as material for the health insurance of national whole subject. In general, health insurance data could be useful for estimating prevalence or incidence rate that is representative of the actual value in a population. The purpose of this study was to apply the concept of episode of care (EoC) in the utilization of health insurance data in the field of environmental epidemiology and to propose an improved methodology through an uncertainty assessment of disease course and outcome. Methods In this study, we introduced the concept of EoC as a methodology to utilize health insurance data in the field of environmental epidemiology. The characterization analysis of the course and outcome of applying the EoC concept to health insurance data was performed through an uncertainty assessment. Results The EoC concept in this study was applied to heat stroke (International Classification of Disease, 10th revision, code T67). In the comparison of results between before and after applying the EoC concept, we observed a reduction in the deviation of daily claims after applying the EoC concept. After that, we categorized context, model, and input uncertainty and characterized these uncertainties in three dimensions by using uncertainty typology. Conclusions This study is the first to show the process of constructing episode data for environmental epidemiological studies by using health insurance data. Our results will help in obtaining representative results for the processing of health insurance data in environmental epidemiological research. Furthermore, these results could be used in the processing of health insurance data in the future.

• Atmosphere
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• v.20 no.2
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• pp.111-130
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• 2010

An uncertainty assessment for precipitation datasets simulated by Atmosphere-Ocean Coupled General Circulation Model (AOGCM) is conducted to provide reliable climate scenario over East Asia. Most of results overestimate precipitation compared to the observational data (wet bias) in spring-fall-winter, while they underestimate precipitation (dry bias) in summer in East Asia. Higher spatial resolution model shows better performances in simulation of precipitation. To assess the uncertainty of spatiotemporal precipitation in East Asia, the cyclostationary empirical orthogonal function (CSEOF) analysis is applied. An annual cycle of precipitation obtained from the CSEOF analysis accounts for the biggest variability in its total variability. A comparison between annual cycles of observed and modeled precipitation anomalies shows distinct differences: 1) positive precipitation anomalies of the multi-model ensemble (MME) for 20 models (thereafter MME20) in summer locate toward the north compared to the observational data so that it cannot explain summer monsoon rainfalls across Korea and Japan. 2) The onset of summer monsoon in MME20 in Korean peninsula starts earlier than observed one. These differences show the uncertainty of modeled precipitation. Also the comparison provides the criteria of annual cycle and correlation between modeled and observational data which helps to select best models and generate a new MME, which is better than the MME20. The spatiotemporal deviation of precipitation is significantly associated with lower-level circulations. In particular, lower-level moisture transports from the warm pool of the western Pacific and corresponding moisture convergence significantly are strongly associated with summer rainfalls. These lower-level circulations physically consistent with precipitation give insight into description of the reason in the monsoon of East Asia why behaviors of individually modeled precipitation differ from that of observation.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.95-106
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• 2013

It is now generally known that dynamical climate modeling outputs include systematic biases in reproducing the properties of atmospheric variables such as, preciptation and temerature. There is thus, general consensus among the researchers about the need of bias-correction process prior to using climate model results especially for hydrologic applications. Among the number of bias-correction methods, distribution (e.g., cumulative distribution fuction, CDF) mapping based approach has been evaluated as one of the skillful techniques. This study investigates the uncertainty of using various CDF mapping-based methods for bias-correciton in assessing regional climate change Impacts. Two different dynamicailly-downscaled Global Circulation Model results (CCSM and GFDL under ARES4 A2 scenario) using Regional Spectial Model for retrospective peiod (1969-2000) and future period (2039-2069) were collected over the west central Florida. Total 12 possible methods (i.e., 3 for developing distribution by each of 4 for estimating biases in future projections) were examined and the variations among the results using different methods were evaluated in various ways. The results for daily temperature showed that while mean and standard deviation of Tmax and Tmin has relatively small variation among the bias-correction methods, monthly maximum values showed as significant variation (~2'C) as the mean differences between the retrospective simulations and future projections. The accuracy of raw preciptiation predictions was much worse than temerature and bias-corrected results appreared to be more significantly influenced by the methodologies. Furthermore the uncertainty of bias-correction was found to be relevant to the performance of climate model (i.e., CCSM results which showed relatively worse accuracy showed larger variation among the bias-correction methods). Concludingly bias-correction methodology is an important sourse of uncertainty among other processes that may be required for cliamte change impact assessment. This study underscores the need to carefully select a bias-correction method and that the approach for any given analysis should depend on the research question being asked.

• Journal of the Korean Society of Tobacco Science
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• v.22 no.1
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• pp.91-98
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• 2000

This study was carried out to evaluate the uncertainty in the analysis of menthol content from the mentholated cigarette. Menthol in the sample cigarette was extracted with methanol containing an anethole as an internal standard, and then analyzed by gas chromatography. As the sources of uncertainty associated with the analysis of menthol, were the following points tested, such as the weighing of sample, the preparation of extracting solution, the pipetting of extracting solution into the sample, the preparation of standard solution, the precision of GC injections for standard & sample solution, the GC response factor of standard solution, the reproducibility of menthol analysis, and the determination of water content in tobacco, etc. For calculating the uncertainties, type A of uncertainty was evaluated by the statistical analysis of a series of observation, and type B by the information based on supplier's catalogue and/or certificated of calibration. Sources of uncertainty were subsequently included and mathematically combined with the uncertainty arising from the assessment of accuracy to provide the overall uncertainty. It was shown that the main source of uncertainty came from the errors in the reproducibility of menthol and water determination, the purity of menthol reference material in the preparation of standard solution, and the precision of GC injections for sample solution. The errors in sample weighing and volume measurement contributed relatively little to the overall uncertainty. The expanded uncertainty in the mentholated cigarettes, Korean and American brand, at 0.95 level of statistical confidence was $\pm$0.06 and $\pm$0.07 mg/g for a menthol content of 1.89 and 2.32 mg/g, respectively.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.123-130
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• 2003

The estimation of key soil properties and subsequent quantitative assessment of the associated uncertainties has always been an important issue in geotechnical engineering. It is well recognized that soil properties vary spatially as a result of depositional and post-depositional processes. The stochastic nature of spatially varying soil properties can be treated as a random field. A practical statistical approach that can be used to systematically model various sources of uncertainty is presented in the context of reliability analysis of slope stability Newly developed expressions for probabilistic characterization of soil properties incorporate sampling and measurement errors, as well as spatial variability and its reduced variance due to spatial averaging. Reliability analyses of the probability of slope failure using the different statistical representations of soil properties show that the incorporation of spatial correlation and conditional simulation leads to significantly lower probability of failure than obtained using simple random variable approach.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.138-142
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• 2005

The change of precipitation and temperature due to the global. warming eventually caused the variation of water availability in terms of potential evapotranspiration, soil moisture, and runoff. In this reason national long-term water resource planning should be considered the effect of climate change. Study of AOGCM-based scenario to proposed the plausible future states of the climate system has become increasingly important for hydrological impact assessment. Future climate changes over East Asia are projected from the coupled atmosphere-ocean general circulation model (AOGCM) simulations based on Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 and B2 scenarios using multi-model ensembles (MMEs) method (Min et al. 2004). MME method is used to reduce the uncertainty of individual models. However, the uncertainty increases are larger over the small area than the large area. It is demonstrated that the temperature increases is larger over continental area than oceanic area in the 21st century.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.127-127
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• 2021

In order to estimate parameter uncertainty of hydrological models, the consideration of the likelihood functions which provide reliable parameters of model is necessary. In this study, the Bayesian Markov Chain Monte Carlo (MCMC) method with informal likelihood functions is used to analyze the uncertainty of parameters of the SURR model for estimating the hourly streamflow of Gunnam station of Imjin basin, Korea. Three events were used to calibrate and one event was used to validate the posterior distributions of parameters. Moreover, the performance of four informal likelihood functions (Nash-Sutcliffe efficiency, Normalized absolute error, Index of agreement, and Chiew-McMahon efficiency) on uncertainty of parameter is assessed. The indicators used to assess the uncertainty of the streamflow simulation were P-factor (percentage of observed streamflow included in the uncertainty interval) and R-factor (the average width of the uncertainty interval). The results showed that the sensitivities of parameters strongly depend on the likelihood functions and vary for different likelihood functions. The uncertainty bounds illustrated the slight differences from various likelihood functions. This study confirms the importance of the likelihood function selection in the application of Bayesian MCMC to the uncertainty assessment of the SURR model.

• Structural Engineering and Mechanics
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• v.63 no.1
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• pp.47-53
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• 2017

The fundamental goal of this study is to minimize the uncertainty of the median fragility curve and to assess the structural vulnerability under earthquake excitation. Bayesian Inference with Markov Chain Monte Carlo (MCMC) simulation has been presented for efficient collapse response assessment of the independent intake water tower. The intake tower is significantly used as a diversion type of the hydropower station for maintaining power plant, reservoir and spillway tunnel. Therefore, the seismic fragility assessment of the intake tower is a pivotal component for estimating total system risk of the reservoir. In this investigation, an asymmetrical independent slender reinforced concrete structure is considered. The Bayesian Inference method provides the flexibility to integrate the prior information of collapse response data with the numerical analysis results. The preliminary information of risk data can be obtained from various sources like experiments, existing studies, and simplified linear dynamic analysis or nonlinear static analysis. The conventional lognormal model is used for plotting the fragility curve using the data from time history simulation and nonlinear static pushover analysis respectively. The Bayesian Inference approach is applied for integrating the data from both analyses with the help of MCMC simulation. The method achieves meaningful improvement of uncertainty associated with the fragility curve, and provides significant statistical and computational efficiency.

• Nuclear Engineering and Technology
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• v.19 no.4
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• pp.249-265
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• 1987

For the probabilistic risk assessment of the high level radioactive waste repository, some methods have been proposed up to now. Since the system has highly uncertain input parameters, the evaluated risk for some input parameter values has high uncertainty. In this paper, methods of uncertainty and sensitivity analysis are devised to analyse systematically these factors and applied to a probabilistic risk assessment model of the high level waste repository, The statistical package SPUSA developed through this study can be used for any other fields, e.g., statistical thermal margin analysis, source term uncertainty analysis, etc.