The operation of nuclear facilities involves the potential for on-site contamination of soil, primarily resulting from pipe leaks and other operational incidents. Globally, decommissioning process for commercial nuclear power plants have revealed huge-amounts of soil waste contaminated with Cs-137, Sr-90, Co-60, and H-3. For example, Connecticut Yankee in the United States produced approximately 52,800 ton of contaminated soil waste, constituting 10% of the total waste generated during its decommissioning. Environmental remediation costs associated with nuclear decommissioning in the US averaged $60 million per unit, representing a significant 10% of the whole decommissioning expenses. Consequently, this study undertook a preliminary investigation to identify important factors for establishing a site remediation strategy based on radionuclide- and site-specific media- characteristics, focusing the efficiency enhancement for the environmental remediation. The factors considered for this investigation were categorized into physical/environmental, socioeconomic, technical, and management aspects. Physical/environmental factors contained the site characteristics, contamination levels, and environmental sensitivity, while socio-economic factors included the social concerns and economic costs. Technical and management factors included subcategories such as technical considerations, policy aspects, and management factors. Especially, technical factors were further subdivided to consider the site reuse potential, secondary waste generation by site remediation, remediation efficiency, and remediation time. Additionally, our study focused the key factors that facilitate the systematic planning for the site remediation, considering the distribution coefficient (Kd) and hydrogeological characteristics associated with each radionuclide in specific site conditions. Therefore, key factors in this study focus the geochemical characteristics of site media including the particle size distribution, chemical composition, organic and inorganic constituents, and soil moisture content. Moreover, the adsorption properties of site media were examined concerning the distribution coefficient (Kd) of radionuclides and their migration characteristics. Furthermore, this study supported the development of a conceptual framework, containing the remediation strategies that incorporate the mobility of radionuclides, according to the site-specific media. This conceptual framework would necessitate the spatial analysis techniques involving the whole contamination surveys and radionuclide mobility modeling data. By integrating these key factors, the study provides the selection and simulation of optimal remediation methods, ultimately offering the estimated amounts of radioactive waste and its disposal costs. Therefore, these key factors offer foundational insights for designing the site remediation strategies according the sitespecific information such as the distribution coefficient (Kd) and hydrogeological characteristics.

The mobility of radionuclides is largely determined by their radiological properties, geochemical conditions, and adsorption reactions, such as surface adsorption, chemical precipitation, and ion exchange. To evaluate the safety assessments of radionuclides in nuclear sites, it is essential to understand the behavior and mechanism of radionuclides onto soils. Since nuclear power plants are located in coastal areas, the chemical composition of groundwater can vary depending on the intrusion of seawater, altering the adsorption distribution coefficient (Kd) values of radionuclides. This study examines the impact of seawater on the Kd values of clay minerals for cesium (Cs) and strontium (Sr). The results of Cs+ adsorption experiments showed a broad range of Kd values from 36 to 1,820 mL/g at an initial concentration of 1 mg/L and a high sorption coefficient of 15-613 mL/g at an initial concentration of 10 mg/L. Montmorillonite, hydrobiotite, illite, and kaolinite were ranked in terms of their CEC values for Cs+ adsorption, with hydrobiotite having the highest adsorption at 1 mg/L. The results of Sr adsorption experiments showed a wide range of Kd values from 82 to 1,209 mL/g at an initial concentration of 1 mg/L and a lower adsorption coefficient of 6.68-344 mL/g at an initial concentration of 10 mg/L. Both Cs+ and Sr2+ demonstrated lower Kd values at higher initial concentrations. CEC of clays found to have a significant impact on Sr2+ Kd values. Ca2+ ions showed a significant impact on Sr2+ adsorption distribution coefficients, demonstrating the greater impact of seawater on Sr2+ compared to Cs+. These findings can inform future safety assessments of radionuclides in nuclear sites.

Radioactive contaminants, such as 137Cs, are a significant concern for long-term storage of nuclear waste. Migration and retention of these contaminants in various environmental media can pose a risk to the surrounding environment. The distribution coefficient (Kd) is a critical parameter for assessing the behavior of these contaminants and can introduce significant errors in predicting migration and remediation options. Accurate prediction of Kd values is essential to assess the behavior of radioactive contaminants and to ensure environmental safety. In this study, we present machine learning models based on the Japan Atomic Energy Agency Sorption Database (JAEA-SDB) to predict Kd values for Cs in soils. We used three different machine learning models, namely the random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN), to predict Kd values. The models were trained on 14 input variables from the JAEA-SDB, including factors such as Cs concentration, solid phase properties, and solution conditions which are preprocessed by normalization and log transformation. We evaluated the performance of our models using the coefficient of determination (R2) value. The RF, ANN, and CNN models achieved R2 values of over 0.97, 0.86, and 0.88, respectively. Additionally, we analyzed the variable importance of RF using out-of-bag (OOB) and CNN with an attention module. Our results showed that the initial radionuclide concentration and properties of solid phase were important variables for Kd prediction. Our machine learning models provide accurate predictions of Kd values for different soil conditions. The Kd values predicted by our models can be used to assess the behavior of radioactive contaminants in various environmental media. This can help in predicting the potential migration and retention of contaminants in soils and the selection of appropriate site remediation options. Our study provides a reliable and efficient method for predicting Kd values that can be used in environmental risk assessment and waste management.

This study developed prediction models of chlorine bulk decay coefficient by each condition of water quality, measuring chlorine bulk decay coefficients of the water and water quality by water purification processes. The second-reaction order of chlorine were selected as the optimal reaction order of research area because the decay of chlorine was best represented. Chlorine bulk decay coefficients of the water in conventional processes, advanced processes before rechlorination was respectively 5.9072 (mg/L)-1d-1 and 3.3974 (mg/L)-1d-1, and 1.2522 (mg/L)-1d-1 and 1.1998 (mg/L)-1d-1 after rechlorination. As a result, the reduction of organic material concentration during the retention time has greatly changed the chlorine bulk decay coefficient. All the coefficients of determination were higher than 0.8 in the developed models of the chlorine bulk decay coefficient, considering the drawn chlorine bulk decay coefficient and several parameters of water quality and statistically significant. Thus, it was judged that models that could express the actual values, properly were developed. In the meantime, the chlorine bulk decay coefficient was in proportion to the initial residual chlorine concentration and the concentration of rechlorination; however, it may greatly vary depending on rechlorination. Thus, it is judged that it is necessary to set a plan for the management of residual chlorine concentration after experimentally assessing this change, utilizing the methodology proposed in this study in the actual fields. The prediction models in this study would simulate the reduction of residual chlorine concentration according to the conditions of the operation of water purification plants and the introduction of rechlorination facilities, more reasonably considering water purification process and the time of chlorination. In addition, utilizing the prediction models, the reduction of residual chlorine concentration in the supply areas can be predicted, and it is judged that this can be utilized in setting plans for the management of residual chlorine concentration.

아라고나이트는 탄산칼슘(CaCO3)의 동질이상 중 하나이며, 해양 생태계를 포함한 다양한 환경에서 생물학적 및 이화학적 침전 과정을 통해 형성된다. 이러한 아라고나이트의 형성 및 성장뿐만 아니라 아라고나이트 내 스트론튬(Sr)과 같은 미량원소의 치환 특성은 화학종의 농도와 온도와 같은 핵심 인자들에 의해 많은 영향을 받는다. 본 연구에서는 해양 생태계와 유사한 용액 온도와 아라고나이트에 대한 이 용액의 다양한 포화도 조건에서 아라고나이트 내 Sr 병합 특성이 규명되었다. 반응 용액의 주입속도(0.085-17 mL/min), 반응 용액의 이온 농도([Ca]=[CO3] 0.01-1M), 혼합 용액의 온도(5-40 oC)의 다양한 실험 조건에서 컨스턴트-에디션 (constant-addition) 방법을 통해 순수한 아라고나이트가 합성되었다. 또한, 모든 Sr 병합 실험 조건(0.02-0.5 M, 15-40 oC)에서도 순수한 아라고나이트가 형성되었다. 합성된 아라고나이트의 결정도와 결정크기는 포화도 및 온도가 증가함에 따라 상대적으로 더 크게 증가하며 아라고나이트 결정이 더 많이 성장하였음을 지시하였다. 그러나 BET-비표면적을 이용하여 계산된 결정성장속도는 결정 형상 변화에 크게 영향을 받는 것으로 나타나 해석에 주의가 요구된다. 아라고나이트 내 Sr의 분배계수(KSr)는 반응이온의 농도가 0.02에서 0.5 M로 증가할 때 2.37에서 1.57로, 온도가 15에서 40˚C로 증가할 때 1.90에서 1.54로 감소하였으며, 모든 조건에서 KSr 값이 1보다 높게 관찰되었다. 이러한 결과는 KSr가 결정성장속도와 역의 상관관계로서 아라고나이트 내 Sr 병합이 호정성 관계임을 나타낸다.

본 연구의 목적은 하천에서 흐름방향 유속의 횡분포식에 기반하여 1차원 종분산계수를 이론적으로 유도하고 이들의 타당성을 검증하는 것이다. 이를 위해 본 논문의 전편 “I. 흐름방향 유속의 횡포식”에서는 SKM을 도입하여 삼각형 단면수로에서 횡분포식을 해석적으로 유도하였다. 본 논문의 후편 “II. 종분산계수”에서는 전편에서 유도된 유속의 횡분포식을 기반으로 1차원 종분산계수 이론식을 새롭게 개발하였다. 개발된 종분산계수 이론식을 검증하기 위해 전편과 동일한 하천에서 수행된 추적자 농도 실험 결과를 이용한 관측 종분산계수와 비교 분석하였다. 또한 개발된 종분산 계수식을 기존의 식들과 비교하여 본 연구에서 개발된 식의 차별점 및 우수성을 검토하였다. 결과적으로 무차원 종분산계수는 무차원 횡확산계수에 반비례하고, 하폭 대 수심비의 제곱에 비례하였다. 그리고 Manning의 조도계수의 제곱에 반비례함을 확인할 수 있었다.

본 연구의 목적은 하천에서 흐름방향 유속의 횡분포식에 기반하여 1차원 종분산계수를 이론적으로 유도하고 이들의 타당성을 검증하는 것이다. 이를 위해 본 논문의 전편 “I. 흐름방향 유속의 횡포식”에서는 Shiono-Knight Model (일명 SKM)을 도입하여 삼각형 단면수로에서 횡분포식을 해석적으로 유도하였다. 본 논문의 후편 “II. 종분산계수”에서는 전편에서 유도된 유속의 횡분포식을 Fischer (1968)의 삼중 적분식에 대입하여 1차원 종분산계수 이론식을 새롭게 개발하였다. 본래 SKM은 Navier-Stokes 방정식을 근간으로 개발되어 주로 직선수로이면서 사다리꼴 단면이나 복단면 수로에 적용되어 왔지만, 본 연구에서는 사행으로 인한 최심선의 변동을 고려할 수 있는 삼각형을 단면형상으로 가정하였다. 유도된 해석해를 검증하기 위해 자연하천에서 실측된 유속자료와 비교 분석하였다. 또한 유도된 횡분포식을 이용하여 단면평균유속을 산정하고, 이를 Manning의 유속식의 결과와 비교 검증하였다. 본 연구에서 개발한 이론식은 비록 유속의 횡분포를 경우에 따라서 섬세하게 재현하지는 못하더라도 조도계수를 포함한 몇 가지 기본적인 수리 및 지형자료만 측량한다면 유속의 관측없이 비교적 정확한 유속분포를 산출해 낼 수 있는 장점이 있었다.

수공구조물의 설계 시 적절한 확률수문량을 추정하는 것은 매우 중요하며, 이러한 확률수문량을 추정하기 위해서는 표본으로서의 수문자료를 잘 표현할 수 있는 확률분포형을 찾아야 한다. 이와 같이 수문자료에 통계적 특성을 잘 표현할 수 있는 확률분포형을 찾기 위해서 적합도 검정을 실시하며, 적합도 검정 중 하나인 확률도시 상관계수 검정은 비교적 최근에 개발되어 그 사용법이 간단하며 높은 기각능력을 갖는다고 알려져 있다. 본 연구에서는 왜곡도 계수의 영향을 고려할 수 있는 도시위치공식을 이용하여 확률도시 상관계수 검정통계량을 유도하고 그 기각능력을 검토하였으며, 그 결과를 기존에 왜곡도 계수를 고려하지 않은 확률도시 상관계수 검정 방법과 비교해보았다. 그 결과 본 연구에서 유도된 확률도시 상관계수 검정에 의한 기각능력이 기존의 검정 방법들 보다 뛰어났으며, 특히 표본 크기가 작을수록, 발생 분포형이 형상 매개변수를 가질 경우 기각능력이 높게 나타나는 것으로 나타났다.

When the story lateral forces of multi-story frame were not distributed properly during a seismic design, sufficient seismic performances should not secured. Thus, studies on the distribution of story lateral forces that make the uniform distribution of story damage are positively necessary. In this study, a lot of dynamic analyses of multi-story frames were conducted using various seismic waves to find the optimum distributions of story damage and the distributions of story shear in that circumstance were calculated.