A Hybrid Method Using Effluent Discharge and Environmental Monitoring Data for the Retrospective Assessment of Nuclear Facilities
Article information
Abstract
Background
The construction and operation of nuclear facilities require that retrospective environmental impact assessments (REIAs) be conducted to identify the realistic radiologic impact of those facilities, thereby providing useful information for minimizing effects on the public and the environment.
Materials and Methods
A retrospective assessment of nuclear facilities has two main aspects: spatial distribution of radionuclide concentrations and assessment of the dose to the representative individual. In the present study, a comprehensive REIA method was established in combination with effluent discharge and environmental monitoring data obtained for the nuclear facility. Using historical effluent discharge data and local parameters, the spatial distribution of radionuclide concentrations was simulated, and the representative individual was identified. In combination with the monitoring data, the actual radiation doses that representative individuals received were further assessed, ensuring more accurate assessment results.
Results and Discussion
Effluent discharge and environmental monitoring data series collected between 2002 and 2019 for uranium processing facility in China were analyzed. Results indicated a significant declining trend in the radioactive effluent per 100 tons of raw material during that period. Based on the actual data for the discharge of airborne effluent, the maximum effective dose to the representative individual was 0.015 μSv/yr. In comparison, the dose to the representative individual based on environmental monitoring data was close to zero. Those results suggest that the uranium processing facility has had little radiologic impact on the representative individual, despite many years of operation.
Conclusion
The REIA method proposed in this study can use effluent discharge data in conjunction with environmental monitoring data to effectively identify the representative individual and assess the realistic radiologic impact to that individual.
Introduction
As a key component of environmental management, environmental impact assessments (EIAs) examine the environmental effects of projects and play a critical role in promoting sustainable development [1]. Retrospective environmental impact assessments (REIAs) are an essential part of the EIA system, responding to the need to improve the decision-making process. More than 190 countries worldwide have introduced the EIA system, with some countries having used it for more than 50 years [2]. China introduced the EIA system in the 1970s; it subsequently became the country’s basic system of environmental protection. The EIA system includes current situation, predictive, and retrospective assessments. In 2003, China formally promulgated the Law of the People’s Republic of China on Environmental Impact Assessment, which aims to promote sustainable development and prevent environmental pollution at the source by integrating predictive and REIAs [3]. The law stipulates that when the actual situation does not fully comply with the authorized EIA document during project construction and operation, the project implementation company should conduct a post-assessment of the environmental impact and take any necessary improvement measures. To enhance the standardization of REIAs for construction projects, the Ministry of Ecology and Environment of the People’s Republic of China issued the Administrative Measures for Post-environmental Impact Assessment of Construction Projects (Trial Implementation) in 2015. In accordance with the requirements of national legislation, all nuclear facilities and activities must pass an EIA before authorization [4]. The continuous construction and operation of nuclear facilities require that REIAs be conducted to identify the realistic radiologic impact of those facilities, thereby providing useful information for minimizing radiologic impacts on the public and the environment.
The fundamental aim of an REIA is to identify the realistic environmental impact of a project, which is different from the impact anticipated in the predictive EIA. Unlike the predictive EIA, which uses design data, the REIA uses historical data concerning the operation of and discharge from the nuclear facility [5]. For instance, the U.S. Department of Health and Human Services conducted a dose reconstruction project to determine the total cumulative effective radiation dose to the surrounding population at the Savannah River site from 1954 to 1992 [6]. Historical information—such as annual quantities of radioactive materials released from all significant Savannah River sources and people’s living habits and exposure times—was collected to estimate radiation doses. The REIA results revealed that doses for all receptors and scenarios were small relative to doses from background radiation over the 39-year period assessed [7]. Kim and Cheong [8] developed a retrospective assessment framework to adjust the dose constraint for radioactive effluent from nuclear power plants. Based on statistical analyses of discharge data in retrospective assessments, the effective dose constraint for Korean nuclear power plants was proposed to be less than 0.15 mSv/yr, which both exceeded international standards and reflected operational flexibility in practice.
In addition to historical discharge data, environmental monitoring data are also used in REIAs. For instance, the United Kingdom’s annual Radioactivity in Food and the Environment reports demonstrated that the level of human-produced radioactivity to which United Kingdom residents were exposed remained below the legal limit. Radiologic environmental monitoring data indicated that natural sources accounted for approximately 84% of all radiologic exposures, and fabricated radioactive material accounted for less than 0.2% of an individual’s exposure. In 2020, representative individuals in Capenhurst, Cheshire, received a dose of 0.17 mSv [9]. Vives I Batlle et al. [10] used a comprehensive set of monitoring data representative of the first year after the Fukushima nuclear accident to retrospectively assess the impact on marine biota.
Retrospective assessment of nuclear facilities has two main aspects: spatial distribution of the radionuclide concentrations and assessment of dose to the representative individual. The International Commission on Radiological Protection defined the representative person as an individual receiving a dose representative of the doses to the more highly exposed individuals in a population [11]. The effluent-based model used in REIAs has generally been identical to the model in predictive EIAs, and that model can easily identify the location of the representative person. The parameters used in an REIA should be realistic and local. However, they are difficult to obtain, and thus the assessment results are relatively conservative. Assessment results from an environmental monitoring-based model reflect exposures more realistically, but make it difficult to identify the representative person. To overcome the limitations of traditional evaluation methods, methods to reduce conservativeness and facilitate objectivity must be comprehensively applied in an REIA.
To that end, we developed a comprehensive REIA model that estimates the actual radiation doses that individuals and populations could have received from nuclear operations. Historical discharge and monitoring data series obtained between 2002 and 2019 for a uranium processing facility in China were analyzed in the present study. The objectives of the analysis were to identify the main characteristics of retrospective assessment, to establish a comprehensive REIA model combining the effluent and monitoring data from the nuclear facility, and to assess the actual radiation doses associated with a uranium processing facility in China as a case study. The results could be helpful in supporting the accuracy of retrospective assessment and in the decision-making necessary for minimizing radiologic effects to the public and the environment.
Materials and Methods
1. Main Characteristics of a Retrospective Assessment
In a predictive EIA, the designed discharge data and conservative operating parameters are used to anticipate the environmental impacts. In contrast, an REIA uses historical discharge data and actual operating parameters to quantify the actual radiologic effect on the representative person. Those are the basic characteristic of an REIA.
In a retrospective assessment of nuclear facilities, records such as the facility operation status, production capacity, waste treatment efficiency, and other operating parameters related to emissions are first obtained for analysis. Sources of all radioactive gaseous and liquid effluents are obtained by online monitoring and chemical analysis. Those effluent discharge data include the physical state and chemical composition of radionuclides and their concentrations and isotope proportions. Retrospective analyses can assess the effects occurring within the time scale at issue. Nuclear safety authorities can request safety assessments every 10 years based on the life cycle of the nuclear facility, which is also the appropriate time for an REIA.
All relevant environmental parameters obtained for the REIA, such as meteorologic parameters, water diffusion parameters, and dose conversion factors, should be realistic and local. Those parameters are useful for revealing the radionuclide concentration fields formed by the operation of the nuclear facility.
The quantitative assessment of the radiation impact is based mainly on a comparison between the effective dose to the representative person and the dose constraint set before the operation of the facility. The concept of representative individuals, as opposed to critical groups, has been clarified in publication no. 103 from the International Commission on Radiological Protection as being a more authentic group of people [12]. Use of the appropriate assessment models and the environmental monitoring results from the nuclear facility’s surroundings can identify the representative individuals who are likely to be most affected by the operation of the facility. Retrospective assessments conducted over the years will clarify whether the dose constraint set at the beginning of the facility’s operation was reasonable and whether the dose to the representative individuals falls below the constraint set by the nuclear safety authority.
Fig. 1 shows the main characteristics of a retrospective assessment for nuclear facilities, including the actual discharge data, refined radionuclide concentration field, and representative individuals that can be realistically identified.
Main characteristics of a retrospective assessment of nuclear facilities.
2. Retrospective Assessment Methodology
The methodology of retrospective assessment for nuclear facilities has two parts. First, the impact of the facility’s operation is assessed using credible models and the facility’s gaseous and liquid radioactive effluent discharge data to verify the validity of the model. The location of representative individuals is determined by the model. Second, the surrounding environment is monitored, and radionuclide concentrations in the environmental media are determined to judge the increase of those concentrations attributable to the operation of the nuclear facility. If concentrations are found to be at the same level as the background data before the nuclear facility began operation, then the nuclear facility’s operations can be considered to have had no obvious effect on the environment. If artificial radionuclides can be detected and are significantly higher in concentration than the concentrations caused by nuclear explosions, an assessment model based on environmental monitoring data is used to evaluate the actual dose to the representative individuals.
The predictive EIA of a nuclear facility includes only the first part of the foregoing analysis. In past REIAs, special investigations were also conducted after unique accidents or abnormal emissions. Fig. 2 shows the retrospective evaluation methodology as described.
Flow chart of a retrospective assessment (RA) for nuclear facilities.
3. Evaluation Model Based on Effluent Discharge Data
The models used for atmospheric and surface water dispersion in predictive EIAs can generally also be used in REIAs based on discharge data. However, atmospheric dispersion and hydrogeologic parameters should be measured on site; default or conservative parameters should not be used. Conformance verification of the retrospective with the predictive model will reveal the validity of the predictive model and its results.
An important aspect of the retrospective assessment is the identification of the representative individuals and calculation of their external and internal exposures. The representative groups can be determined based on the assessment model of effluent data and the population distribution around the nuclear facility. An on-site investigation of life habits can quickly determine the representative individuals.
Fig. 3 shows the effluent exposure pathways for the public, allowing the model to initially identify representative groups. The calculations of the doses to those groups are indicative and preliminarily identify the populations affected by the nuclear facility’s operations. The exposures to gaseous effluents that permit the calculation of dose to the representative individuals include external exposures to air immersion and surface sedimentation, and internal exposures to inhalation and ingestion. The exposures to liquid effluents that permit the calculation of dose to the representative individuals include mainly internal exposures by drinking polluted water and ingesting crops irrigated with polluted water and fish living in polluted water, and external exposures to shore sedimentation caused by participation in activities on the shore of polluted water bodies.
Exposure pathways for gaseous and liquid effluents from nuclear facilities.
4. Assessment Model Based on Environmental Monitoring Data
The calculation results of the model in section 3 in Materials and Methods can identify representative groups. However, where within these groups is the representative individual who is receiving the largest effective dose? Generally speaking, a facility’s operation and effluents will distribute radionuclides to the facility’s surroundings, and those radionuclides will enter into the environmental media over time. Environmental monitoring methods of various types can identify changes in nuclide concentrations in the environmental media.
The International Organization for Standardization has published two standards in this area: Measurement of Radioactivity in the Environment—Guidelines for Effective Dose Assessment Using Environmental Monitoring Data: Part 1: Planned and Existing Exposure Situation (ISO 20043-1:2021) and Measurement of Radioactivity in the Environment—Guidelines for Effective Dose Assessment Using Environmental Monitoring Data: Part 2: Emergency Exposure Situation (ISO/CD 20043-2:2023). Those standards identify the need for effective dose assessment based on environmental monitoring data, and recommend effective dose calculation as a routine method in retrospective assessments.
However, the actual effective dose to the representative individual based on the actual environmental monitoring data has not really been applied, mainly because the life habits of representative individuals are directly related to exposure pathways. Components ingested, residence factors, and dose conversion factors all require long-term follow-up investigations. In addition, determining how to deduct the local environmental background such that the data obtained reflect the actual impact of the facility’s operations on the surrounding environment constitutes a vital aspect of a dose assessment based on environmental monitoring. Selection of the environmental background data also attracts controversy, given the development of monitoring capabilities and trace measurement technologies.
Fig. 4 presents a model of internal and external exposures for assessing the dose to representative individuals based on environmental monitoring.
Dose assessment method for representative individuals living near nuclear facilities. DCFimm, radionuclide air immersion dose conversion factor; DCFinh, radionuclide inhalation dose conversion factor; DCFdep, radionuclide ground deposition dose conversion factor; DCFing, dose conversion factor of ingestion and exposure to radionuclides.
1) External radiation includes direct external radiation, immersion radiation, and ground sedimentation external radiation
Direct external exposure is calculated primarily by direct measurement of the X- and gamma-ray D1 (background deducted) in the environment, as Equation (1):
where T1, T2, and T3 are, respectively, the times of direct external exposure, submersion exposure, and surface sedimentary exposure to the public (H); 0.7 is the conversion factor between the air absorbed dose and the effective dose; DCFimm is the radionuclide air immersion dose conversion factor, expressed in (Sv/s)/(Bq/m3); DCFdep is the radionuclide ground deposition dose conversion factor, expressed in (Sv/s)/(Bq/m3).
2) Internal exposure from ingestion of agricultural and animal products
Internal exposure to ingested agricultural and animal products is calculated mainly by sampling and measuring the concentration of radionuclides in commonly used foods or drinking water, or by monitoring the concentration of radionuclides in cultivated soil, calculating the concentration in agricultural products and feed crops using a concentration transfer factor, and then calculating the concentration of radionuclides in animal products.
If the concentration of radionuclides in animals, agricultural products, and other foods can be directly measured, the dose from exposures to public food intake is calculated using the formula, as Equation (2):
where Cfood is the concentration of radionuclides in animals, agricultural products, and other foods, expressed in Bq/kg; DCFing is the dose conversion factor of ingestion and exposure to radionuclides, expressed in Sv/Bq; and Ufood is the annual intake by residents of animals, agricultural products, and other foods, expressed in kg/yr.
To directly measure Cfood in drinking water, the dose from the internal exposure caused by drinking water is calculated using the formula, as Equation (3):
where Cdrink is the concentration of radionuclides in drinking water, expressed in Bq/L; DCFing is the dose conversion factor of ingestion and exposure to radionuclides, expressed in Sv/Bq; and Uwater is the annual water intake for residents, expressed in L/yr.
If the concentration of radionuclides in cultivated soil or animal feed can be measured, the concentration of radionuclides in animals and agricultural products caused by plant growth or feed eaten by animals is calculated using the two formulas, as Equation (4):
where Csoil is the radionuclide concentration in cultivated soil, expressed in Bq/kg; Bv is the concentration factor of nuclides ingested from soil for crops, expressed in (Bq·kg−1)(fresh crop)/Bq·kg−1(dry soil); and th is the time from harvest to consumption of agricultural products, expressed in year, as Equation (5):
where Cfeed is the radionuclide concentration in feed crops, expressed in Bq/kg; Cwater is the radionuclide concentration in drinking water for animals, expressed in Bq/L; Bm is the concentration factor for animal products (meat and milk) from feed crops, expressed in day/kg; and th is the time from slaughter to consumption of the animal, expressed in year.
3) Internal exposure from air inhalation
The concentration of radionuclides in air is analyzed by airborne aerosol sampling and calculated, as Equation (6):
where Cair is the radionuclide concentration in air, Bq/m3; DCFinh is the radionuclide inhalation dose conversion factor, expressed in Sv/Bq; and Ra is annual air intake for the public, expressed in m3/yr.
Results and Discussion
1. Statistical Analysis of Actual Discharge Data from a Nuclear Facility
For this retrospective assessment, historical discharge data between 2002 and 2019 of airborne effluent from a uranium processing facility in China were collected and analyzed. Emission intensity for the uranium processing facility was identified based on the normalized emission proposed in China to represent radioactive release into the environment per unit of output. Fig. 5 presents the radioactive effluent discharge into the environment per 100 tons raw material from 2002 to 2019, which demonstrates a significant decreasing trend in radioactive effluent per 100 tons raw material during that period. The peak value of 9.01×105 Bq per 100 tons raw material was reached in 2005, whereas the nadir value of 1.00×103 Bq was reached in 2012 and 2016. A slight increase in discharge intensity close to 1.00×103 Bq per 100 tons raw material was observed between 2016 and 2019. The declining emission intensity demonstrates cleaner operation of the uranium processing facility and a reduced radiologic impact.
Normalized emissions of radioactive discharge from a uranium processing facility during 2002–2019.
2. Assessment Results Based on Actual Discharge Data
In a traditional radiologic impact assessment, the designated assessment region was a circle with the nuclear facility at the center and a radius of 80 km. The region was further divided into 192 subregions based on 12 concentric circles and 16 orientations. The radii of the 12 concentric circles were 1, 2, 3, 5, 10, 20, 30, 40, 50, 60, 70, and 80 km. In combination with the actual discharge data for airborne effluents and the environmental parameters of the surroundings, the spatial distribution of radionuclide concentrations around the nuclear facility was obtained (Fig. 6). Considering all the subregions, the maximum effective dose to the representative individual was 0.015 μSv/yr.
Radionuclide concentrations surrounding the nuclear facility. UTM, Universal Transverse Mercator.
3. Assessment Results Based on Environmental Monitoring Data
Based on the assessment results in Fig. 6, the REIA investigated the life habits of the representative groups. The radiologic environments in which the representative groups were located were also monitored periodically. Table 1 presents the environmental monitoring results. The radiologic environment surrounding the nuclear facility had been investigated before the facility began operations. The monitoring data were found to be consistent with the original regional background radiologic environment. Of all the exposure pathways, the ingestion of locally produced biota was a significant source of dose for the representative groups.
Comparison between Monitoring and Background Data
As Table 1 demonstrates, most monitoring data approximated the environmental background. The uranium concentration in grain was lower than the background, indicating that the dose to the representative individual was close to zero based on the evaluation model established in Fig. 4. The uranium processing facility was thus understood to have had little radiologic impact on the representative individual, despite its many years of operation. In comparison, the dose to the representative individual based on environmental monitoring data was lower than that based on effluent data. In this study, the former was close to zero, whereas the latter was 0.015 μSv/yr. The main reason for the discrepancy was the relatively conservative spatial simulation of nuclide concentrations in the assessment model applied to the effluent data. In future, the computational fluid dynamics model with the mesh refinement process should be used to simulate the spatial distribution of nuclide concentrations. Compared with the results based on effluent data, results from an assessment using environmental monitoring data can truthfully reflect the dose to the representative individual. A background investigation of the radiologic impact is essential to the accuracy of a retrospective assessment. However, given limitations in sampling or analysis methods, the background investigation is sometimes not representative. In such cases, the location of the control point can be determined based on the results of an assessment using effluent data. The location with a dose equal to 1/1,000 of the annual effective dose was defined as the point completely unaffected by the operation of the nuclear facility. With the control point taken as the environmental background, the radiologic environmental monitoring data, deducting the background, can be more effective in assessing actual impact in an REIA.
Conclusion
Using the effluent and monitoring data obtained for a nuclear facility, we developed a comprehensive REIA model to estimate the actual radiation doses that individuals and populations could have received from nuclear operations. Historical effluent and monitoring data series between 2002 and 2019 were collected for a uranium processing facility in China and analyzed in this study. Results demonstrated a significant declining trend in radioactive effluent per 100 tons raw material during the period 2002–2019. Based on the actual discharge of airborne effluent, the maximum effective dose to the representative individual among all subregions was found to be 0.015 μSv/yr. In comparison, the dose to the representative individual based on the environmental monitoring data was close to zero. Those observations suggest that the uranium processing facility had little radiologic impact on the representative individual, despite its many years of operation. As an essential part of the EIA system, the comprehensive REIA model proposed here can identify the realistic radiologic impact of a nuclear facility, thereby providing useful information for minimizing such impacts on the public and the environment. To attain even more accurate assessment results, future studies should focus on combining a more refined assessment model with effluent discharge and environmental monitoring data.
Notes
Conflict of Interest
No potential conflict of interest relevant to this article was reported.
Acknowledgements
This work was supported by the Youth Foundation of China Institute for Radiation Protection. The authors sincerely thank the editors and anonymous reviewers for their constructive comments and feedback to improve this paper.
Ethical Statement
This article does not contain any studies with human participants or animals performed by any of the authors.
Author Contribution
Conceptualization: Kang J, Lian B, Wu F, Chen H. Methodology: Kang J, Lian B. Writing - original draft: Kang J, Wu F. Writing - review & editing: Kang J, Chen H. Approval of final manuscript: Kang J, Lian B, Wu F, Chen H.