Hydrogeological fate of radionuclides at decommissioned nuclear power plant sites: Case studies for radionuclides transport model development

1. Introduction

Since the operation of the first nuclear power plant, Kori-1, began in 1978, South Korea now has 28 nuclear power plants. Among them, 23 are pressurized water reactors, 3 are pressurized heavy-water reactors, and 2 have been permanently shut down, playing a crucial role in the country's industrial development and power supply (Korea Hydro and Nuclear Power Co., Ltd., 2024). With the increasing need for carbon neutrality and greenhouse gas reduction, the 10th Basic Plan for Long-Term Electricity Supply and Demand, announced in January 2023, aims to raise the proportion of nuclear energy from 23.4% in 2018 to 34.6% by 2036 (Ministry of Trade, Industry and Energy, 2023). This plan includes the construction of 4 additional nuclear power plants by 2036.

Regardless of the controversy surrounding the expansion or reduction of nuclear power in a given country, ensuring the safe transport and disposal of radioactive waste produced by nuclear power plants remains a significant global issue. Among the various components of radioactive waste, actinides have particularly long half-lives, spanning hundreds of thousands of years, and are highly hazardous to human health (Zhang and Brady, 2002). While the safe storage of nuclear waste is the ultimate responsibility of the nuclear industry, leaked radionuclides can enter the soil, dissolve in pore water, and be transported through advection or diffusion during operation or decommissioning of nuclear power plants (Schick, 2005). As illustrated in Fig. 1, radionuclides can infiltrate groundwater and surface water through advection, dispersion, and adsorption/desorption traveling through unsaturated and saturated zones. Understanding the behavior of radionuclides through various media significantly contributes to site remediation in the event of radionuclide releases (Onishi et al., 1981). Thus, an in-depth review of existing studies, contamination incidents, and assessment tools is crucial for gaining a complete understanding of radionuclide transport.

Fig. 1.

A conceptual diagram for behaviors of radionuclides at decommissioned nuclear power plant sites.

The prediction of radionuclide transport at a given site is typically conducted using a groundwater flow and transport model (Thiessen et al., 1999). Although lessons learned from case studies, particularly involving groundwater modeling, are important to adapt to new sites, such as decommissioning nuclear power plants in South Korea, there has been a lack of thorough review of such historical cases. Identifying appropriate numerical models, determining the required physical and chemical parameters for these models, and managing databases acquired from previous case studies are crucial for the future management of decommissioning nuclear power plants.

The main objective of this review paper is to present case studies related to radionuclide transport at decommissioned nuclear sites, with a particular focus on the development of radionuclide transport models. This paper aims to conduct an in-depth literature review to establish a foundational understanding of radioactive contamination at nuclear power plants, covering contaminant types, extents, effects, and varying site conditions. It offers a concise overview of the objectives of various previous studies and the context of contamination in nuclear power plants. This review paper highlights the collection and processing of data from both literature and field studies, emphasizing the importance of parameter evaluation through quality assurance and quality control (QA/QC). It focuses on the development of a robust database system for organizing field and modeling data, a crucial objective that lays the groundwork for effective machine learning inputs and subsequent analyses. Thus, the paper aspires to contribute to the enhancement of contamination management strategies in decommissioned or decommissioning nuclear power plants.

2. Case studies on contamination in nuclear power plants

There are several studies available in the literature that address various aspects of nuclear power and radiation risk assessment. This review paper examines a few recent examples of such studies. Aly et al. (2020) focused on a nuclear power program in Egypt, assessing radioactivity concentrations in groundwater following a hypothetical reactor accident. Sato et al. (2023) examined the impact of a cooling pond drawdown near the Chornobyl Nuclear Power Plant, observing increased ⁹⁰Sr concentrations in the Pripyat River floodplain. Kim et al. (2020) addressed the challenge of handling soil data in nuclear power plant decommissioning, emphasizing the significant influence of properly managing data at or below detection limits. Byon et al. (2020) evaluated soil contamination at the Kori Unit 1 nuclear power plant decommissioning site, providing a framework for assessing soil contamination and offering a guideline for similar projects based on Kori Unit 1 characterization. These studies collectively contribute to understanding and managing radiation risks in the context of nuclear power programs, accidents, and decommissioning.

2.3. Colorado School of Mines Research Institute

Kim et al. (2020) investigated a challenge in nuclear power plant decommissioning, particularly regarding the handling of soil data, where most samples were near the detection limit (BDL). Traditional methods of managing BDL data, like ignoring it or replacing it with zero or the detection limit, are statistically biased and not very useful. Drawing from statistical techniques in environmental science, such as the Kaplan-Meier (KM) approach, Robust Regression on Order Statistics (ROS), and maximum likelihood (MLE), they explored ways to handle BDL data in the context of nuclear decommissioning. The results emphasized that properly using BDL data can significantly influence dose/risk, waste volume, and disposal costs in decommissioning nuclear power plant projects.

This study was based on soil data from the decommissioning project of the Colorado School of Mines Research Institute (CSMRI). The institute site, located in Jefferson County, Colorado, was used for various mining-related research activities from 1912 to 1985. These activities involved establishing extraction processes for natural resources, causing contamination of nearby buildings and soils with natural radioactive elements (e.g., uranium, thorium, and radium). The S.M. Stoller Corporation conducted a soil survey at the site, collecting data on various radionuclide concentration levels (²²⁶Ra, ²²⁸Ra, ²²⁸Th, ²³⁰Th, ²³²Th, ²³⁴U, ²³⁵U, and ²³⁸U). The data consisted of 30 data points with a 30% censoring percentage, and different detection limits (DLs) were used for different radionuclides.

For the radionuclides modeling, the key information used for analysis included contaminated zones, soil concentrations, saturated and unsaturated zones, and values related to occupancy and ingestion. Initially, the study estimated residual radioactive concentrations using various methods, such as ignoring BDL data, using the DL in place of BDL data, and employing statistical approaches. The study used the Environmental Protection Agency's ProUCL statistical software for analyzing BDL data. The RESRAD code (Ramsdell et al., 2012) was utilized to calculate the overall dose value and the associated uncertainty, considering individual input parameters. To determine the input parameter value for RESRAD, they used both site-specific and default values. Specifically, radionuclide concentrations and the characteristics of the contaminated zone were among the site-specific parameters.

Additionally, the study analyzed the uncertainty of the model using Latin hypercube sampling (LHS) with 1000 samples. LHS divided the sample distribution into equally likely intervals, and one random sample was chosen from each interval to represent the whole range of the distribution, as noted in Table 1 of Kim et al. (2020). Subsequently, they employed a regression approach to determine the correlation between resultant dose and parameter values applied to the model, assessing their relative importance in affecting the dose. They also used partial rank correlation coefficients to calculate the correlation between input and output parameters and to estimate the contributions of individual parameters to uncertainties in the dose results.

Table 1.

Commonly used modeling tools.

The study compared residual radioactivity concentrations using statistical methods such as KM, ROS, and MLE, against traditional approaches like disregarding BDL data or replacing it with DL. The findings suggest that ignoring BDL data leads to narrow distributions with high peak values for residual concentrations of various radionuclides. However, incorporating BDL leads to increased variability while reducing peak values. Furthermore, they suggested that the MLE method generally provides the highest average concentration estimates and the lowest standard deviations for the majority of radionuclides (²²⁶Ra, ²²⁸Ra, ²²⁸Th, ²³⁰Th, and ²³²Th). This may be because it forces the data into parametric distributions. The KM method, a nonparametric approach, results in the lowest means and the largest standard deviations for these radionuclides, including ²³⁴U. In contrast, the ROS method yields estimates falling between the KM and MLE methods regarding both mean and standard deviation. The results from traditional methods can occasionally resemble those obtained from statistically robust methods (KM, ROS, and MLE).

In summary, the choice of method has a significant impact on the estimated residual concentrations, with MLE generally providing higher mean estimates, KM lower means with higher variability, and ROS falling in between. The findings showed that substituting BDL data with DL yields the highest mean concentrations of soil radioactivity for the majority of the radionuclides examined. Ignoring BDL values gives the next highest concentrations for these radionuclides, which implies that treating BDL data by either ignoring it or substituting it with DL data represents a conservative approach in analyzing dose values, though the differences in the resulting mean concentrations were relatively insignificant.

3. Contaminant investigation tools and result analysis

General modeling tools for radionuclide transport encompass various software and codes used to simulate the movement of radioactive materials in the environment. These tools are crucial for assessing the dispersion, fate, and potential risks associated with radionuclides. Some commonly used modeling tools are included in Table 1.

These tools are essential for assessing and estimating how radionuclides behave in different environmental settings, including soil, groundwater, surface water, or the atmosphere. The selection of a specific tool depends on the modeling objectives, the characteristics of the system under study, and the level of detail required for the analysis.

4. Data collection and processing

Radionuclide transport modeling involves a variety of parameters that describe the behavior of radioactive materials in the environment. These parameters can be specific to the particular radionuclides being modeled and the characteristics of the site. The advection-dispersion governing equation incorporating multi-species reactions can be written as (Hwang et al., 2019; Aquant Inc., 2023):

where C_i (M L^-3) represents a species concentration, denotes time (T), D (L² T^-1) indicates the hydrodynamic dispersion, Ωc (M L^-2 T^-1) represents the source/sink term, λ_i (T^-1) is the first-order decay rate, and is water flux in the variably-saturated zone (L T^-1). In the scenario of a straightforward decay chain, there is a single parent species, similar to what occurs in radioactive decay. However, for organic compounds undergoing degradation, a specific species may originate from multiple parent sources due to the complexity of the degradation pathways. The retardation factor, R, is given by:

where ρ_b represents the bulk density (M L^-3) and K_d indicates the equilibrium partition coefficient that characterizes a linear Freundlich adsorption isotherm refers to the relationship between the concentration of a substance adsorbed onto a surface and its concentration in the surrounding medium, where the adsorption process follows a linear form of the Freundlich model (M^-1 L³). It is important to note that under variably-saturated conditions, the water saturation level is included in the definition of the retardation factor (R). The parameters required for modeling radionuclide transport are collected using the variables specified in the corresponding equation.

Table 2 lists the simulation conditions applied to nuclear power plant sites and their corresponding radionuclide species. Bugai et al. (2019) performed ⁹⁰Sr simulations in groundwater system with a velocity ranging from 0.47 to 0.56 m/d. The distribution coefficient and retardation factor applied are 0.8 and 3.8 d^-1, respectively. Aly et al. (2020) also simulated ¹³⁷Cs and ⁹⁰Sr to perform a risk assessment of a nuclear power plant site in El-Negila, Egypt. The retardation and distribution parameters applied for the ⁹⁰Sr simulations were 5.5 and 0.8, respectively.

Table 2.

Parameter values used in literature for radionuclide modeling.

Table 3 provides an overview of several radionuclides commonly discharged from nuclear power plants, outlining key characteristics that influence their environmental behavior and potential impacts. ¹³¹I, with a short half-life of 8 days, is soluble in water and can be absorbed by vegetation, posing concerns about thyroid exposure in humans. ¹³⁷Cs, a fission byproduct with a half-life of 30 years, is soluble and binds to soil particles, making it persistent in the environment and capable of uptake by plants. ⁹⁰Sr, another fission byproduct with a half-life of 28.8 years, behaves chemically like calcium and is known for bioaccumulation in the food chain, particularly in dairy products. ²³⁹Pu, a product of nuclear reactors and certain weapons, is an alpha emitter and poses hazards if inhaled or ingested. ⁹⁹Tc, with a half-life of 211,000 years, is highly soluble and mobile in the environment, raising concerns about its longterm impact. Understanding these characteristics is crucial for assessing the environmental fate and potential risks associated with radionuclide discharge from nuclear facilities.

Table 3.

Characteristics of selected radionuclides discharged from nuclear power plants (IAEA, 2019).

Table 4 lists the field estimation values for total activity of key radiologically significant radionuclides (¹³⁷Cs, ⁹⁰Sr, ^238-241Pu, and ²⁴¹Am) at different sites in the Chernobyl exclusion zone (CEZ). According to Bugai et al. (2022), The cooling pond represents another important ‘point source,’ contaminated as a result of the contaminated water discharged from the ChNPP Unit 4 emergency cooling system and radioactive fallout on the surface of the water. Key contamination sources and their inventories are outlined in Table 4. ChNPP Unit 4 ‘Sarcophagus’ is a wellknown ‘hot spot’ for groundwater contamination, with potential contamination sources including a buried soil layer related to the accident, containing nuclear fuel materials.

Table 4.

Characteristics of radionuclides contaminated in Chernobyl nuclear power plant (unit: Bq) (Bugai et al., 2022).

The case studies revealed that accurate prediction of radionuclide transport heavily depends on the uncertainties associated with the physical and chemical parameters used in the numerical models. In the context of radionuclide transport modeling, the evaluation of parameter values, commonly known as QA/QC for model inputs, is vital in guaranteeing the reliability and precision of simulation results. This phase involves a thorough examination and validation of the input parameters used in the transport models, aiming to assess their quality, consistency, and appropriateness. Parameter values such as initial concentrations, dispersion coefficients, decay constants, and other relevant factors are scrutinized to meet established quality standards. Rigorous QA/QC procedures are implemented to verify the accuracy of the input data, drawing on calibration exercises, field measurements, and laboratory analyses. The evaluation of parameter values is an integral step in the modeling process, contributing to the overall robustness and credibility of the simulations, and providing a foundation for making informed decisions regarding radionuclide transport in the studied environments. This ensures that the modeling outcomes align with real- world conditions, enhancing the reliability of predictions and supporting the effective management of radiological risks.

5. Database system development

The case studies revealed that investigating a particular site requires extensive data collection, and managing a reliable database system is crucial for achieving successful model outcomes and, consequently, effective site management. The development of a robust database system is essential for efficiently managing and organizing large volumes of data in diverse applications. In the context of radionuclide transport modeling, a comprehensive database system serves as the backbone for storing, retrieving, and managing various parameters, observational data, and simulation results. Table 5 explores the key aspects of developing a database system necessary for general radionuclide transport modeling.

Table 5.

Essential considerations of developing a database system for radionuclide transport modeling.

In summary, the development of a database system for radionuclide transport modeling requires meticulous attention to architecture, data quality, user interface, integration capabilities, security, and ethical considerations. This is essential to create a reliable and user-friendly tool that advances scientific understanding and facilitates informed decision-making in radiological environmental assessments. A well-organized dataset serves as the foundation for constructing machine learning models that can effectively decipher complex relationships within radionuclide transport systems, contributing to more accurate predictions and informed decision-making in radiological environmental assessments.

6. Conclusions

As the need for decommissioning aging nuclear power plants increases, the technology for efficient site remediation has become crucial. For effective remediation, a systematic assessment of subsurface contamination and prediction of its spread are necessary. Additionally, an integrated system that evaluates and predicts contamination and selects optimal remediation technologies is required.

To establish a fundamental understanding of radioactive contamination at nuclear power plants, a literature review was conducted on the status of radioactive contamination and radionuclide transport modeling at these sites. This review expanded into case studies that analyze the types, extents, effects, and site conditions of contamination. Additionally, the review covered essential aspects for developing a data system for radionuclide transport modeling, including database architecture, data quality, user interface, integration capabilities, security, and ethical considerations. The review suggests that the appropriate selection of numerical models for specific site conditions, thorough data collection and processing, and the development of reliable database systems are key factors for the effective management of decommissioned nuclear power plants.

Through this comprehensive review, a foundation for modeling contaminant transport in heterogeneous unsaturated zones, which has not been adequately addressed previously, can be established. Furthermore, by providing detailed literature information on the distribution and transport of contaminants at contaminated sites, this review paper lays the groundwork for more accurate assessments and efficient site remediation.

Acknowledgments

This study was supported by a National Research Foundation of Korea (NRF) grant (RS-2023-00243600) funded by the Korean government, Ministry of Science, and ICT.

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