Factors determining contamination and time trends in cyclic and linear siloxanes in sediments from an industrialized lake in Korea.

Siloxanes, widely used in various consumer and industrial products, are emerging concerns of contaminants. Despite this, limited studies have been conducted on contamination and time trends on siloxanes in coastal environments. In the present study, four cyclic and 15 linear siloxanes were measured in sediments collected from an artificial saltwater lake in Korea during 2001-2016 to investigate contamination, time trends, and ecotoxicological concerns. Cyclic siloxanes were detected in all sediment samples, whereas linear siloxanes were not frequently detected. The highest siloxane concentrations were observed in creeks passing through various industrial complexes, indicating that industrial activities predominantly contributed to siloxane contamination in coastal environments. Decamethylcyclopentasiloxane (D5) and dodecylcyclohexasiloxane (D6) were predominant siloxanes in sediments over the last two decades. Siloxane concentrations significantly increased in creek sediments from 2008 to 2016, whereas those in inshore and offshore regions significantly decreased due to a strong dilution effect by the operation of tidal power plant. This suggests that consumption patterns and coastal development activities are crucial factors determining the contamination and time trends in the sedimentary siloxanes. The sedimentary concentrations of octamethylcyclotetrasiloxane (D4) and D5 exceeded several thresholds, raising the potentials for ecological risks to aquatic organisms.

Ecological risk assessment of heavy metals in desulfurized seawater discharged from a coal-fired power plant in Qingdao.

Despite the prevalence of discharge of large volumes of heavy-metal-bearing seawater from coal-fired power plants into adjacent seas, studies on the associated ecological risks remain limited. This study continuously monitored concentrations of seven heavy metals (i.e. As, Cd, Cr, Cu, Hg, Pb, and Zn) in surface seawater near the outfall of a coal-fired power plant in Qingdao, China over three years. The results showed average concentrations of As, Cd, Cr, Cu, Hg, Pb, and Zn of 2.63, 0.33, 2.97, 4.63, 0.008, 0.85, and 25.00 µg/L, respectively. Given the lack of data on metal toxicity to local species, this study investigated species composition and biomass near discharge outfalls and constructed species sensitivity distribution (SSD) curves with biological flora characteristics. Hazardous concentrations for 5% of species (HC5) for As, Cd, Cr, Cu, Hg, Pb, and Zn derived from SSDs constructed from chronic toxicity data for native species were 3.23, 2.22, 0.06, 2.83, 0.66, 4.70, and 11.07 µg/L, respectively. This study further assessed ecological risk of heavy metals by applying the Hazard Quotient (HQ) and Joint Probability Curve (JPC) based on long-term heavy metal exposure data and chronic toxicity data for local species. The results revealed acceptable levels of ecological risk for As, Cd, Hg, and Pb, but unacceptable levels for Cr, Cu, and Zn. The order of studied heavy metals in terms of ecological risk was Cr > Cu ≈ Zn > As > Cd ≈ Pb > Hg. The results of this study can guide the assessment of ecological risk at heavy metal contaminated sites characterized by relatively low heavy metal concentrations and high discharge volumes, such as receiving waters of coal-fired power plant effluents.

Development and Integration of a Digital Twin Model for a Real Hydroelectric Power Plant.

In this study, a digital twin model of a hydroelectric power plant has been created. Models of the entire power plant have been created and malfunction situations of a sensor located after the inlet valve of the plant have been analyzed using a programmable logic controller (PLC). As a feature of the digital twin (DT), the error prediction and prevention function has been studied specifically for the pressure sensor. The accuracy and reliability of the data obtained from the sensor are compared with the data obtained from the DT model. The comparison results are evaluated and erroneous data are identified. In this way, it is determined whether the malfunction occurring in the system is a real malfunction or a malfunction caused by measurement or connection errors. In the case of sensor failure or measurement-related malfunction, this situation is determined through the digital twin-based control mechanism. In the case of actual failure, the system is stopped, but in the case of measurement or connection errors, since the data are calculated by the DT model, the value in the specified region is known and thus there is no need to stop the system. This prevents production loss in the hydroelectric power plant by ensuring the continuity of the system in case of errors.

Prediction of NOx Emissions in Thermal Power Plants Using a Dynamic Soft Sensor Based on Random Forest and Just-in-Time Learning Methods.

Combustion optimization is an effective way to improve the efficiency of thermal power generation and reduce carbon and NOx emissions. Real-time and precise NOx emission prediction is the basis for combustion optimization control of thermal power plants. To construct an accurate NOx concentration prediction model, a novel just-in-time learning (JITL) method based on random forest (RF) is proposed in the present work. With this method, first, an improved permutation importance algorithm is proposed to extract important variables. In addition, a similarity index that incorporates temporal and spatial measures is defined to select a local training set representative of the process data. Moreover, considering the influence of model parameters on prediction performance under different working conditions, a process monitoring method based on a moving window (MW) is used to monitor the change in working conditions and guide online updating. The experimental results show that the proposed method has excellent prediction accuracy, with a coefficient of determination of 0.9319, a root-mean-square error of 3.6960 mg/m3, and an average absolute error of 2.7718 mg/m3 on the test set, making it superior to other traditional methods.

Hydroelectric Plant Safety: Real-Time Monitoring Utilizing Fiber-Optic Sensors.

In the context of hydroelectric plants, this article emphasizes the imperative of robust monitoring strategies. The utilization of fiber-optic sensors (FOSs) emerges as a promising approach due to their efficient optical transmission, minimal signal attenuation, and resistance to electromagnetic interference. These optical sensors have demonstrated success in diverse structures, including bridges and nuclear plants, especially in challenging environments. This article culminates with the depiction of the development of an array of sensors featuring Fiber Bragg Gratings (FBGs). This array is designed to measure deformation and temperature in protective grids surrounding the turbines at the Santo Antônio Hydroelectric Plant. Implemented in a real-world scenario, the device identifies deformation peaks, indicative of water flow obstructions, thereby contributing significantly to the safety and operational efficiency of the plant.

A Three-Wavelength Optical Sensor for Measuring the Multi-Particle-Size Channel Mass Concentration of Thermal Power Plant Emissions.

Emissions from thermal power plants have always been the central consideration for environmental protection. Existing optical sensors in thermal power plants usually measure the total mass concentration of the particulate matter (PM) by a single-wavelength laser, bearing intrinsic errors owing to the variation in particle size distribution (PSD). However, the total mass concentration alone cannot characterize all the harmful effects of the air pollution caused by the power plant. Therefore, it is necessary to measure the mass concentration and PSD simultaneously, based on which we can obtain multi-particle-size channel mass concentration. To achieve this, we designed an optical sensor based on the three-wavelength technique and tested its performance in a practical environment. Results showed that the prototype cannot only correctly measure the mass concentration of the emitted PM but also determine the mean diameter and standard deviation of the PSDs. Hence, the mass concentrations of PM10, PM2.5, and PM1 are calculated, and the air pollutants emission by a thermal power plant can be estimated comprehensively.

An Investigation of Efficiency Issues in a Low-Pressure Steam Turbine Using Neural Modelling.

This study utilizes neural networks to detect and locate thermal anomalies in low-pressure steam turbines, some of which experienced a drop in efficiency. Standard approaches relying on expert knowledge or statistical methods struggled to identify the anomalous steam line due to difficulty in capturing nonlinear and weak relations in the presence of linear and strong ones. In this research, some inputs that linearly relate to outputs have been intentionally neglected. The remaining inputs have been used to train shallow feedforward or long short-term memory neural networks using measured data. The resulting models have been analyzed by Shapley additive explanations, which can determine the impact of individual inputs or model features on outputs. This analysis identified unexpected relations between lines that should not be connected. Subsequently, during periodic plant shutdown, a leak was discovered in the indicated line.

Awareness of disaster preparedness between administrative staff and residents in the vicinity of the Genkai and Ikata nuclear power plants following the Fukushima Daiichi nuclear power plant disaster.

When considering disaster preparedness, one challenge is mitigating the health impacts of evacuations. Nuclear disaster preparedness has evolved based on past experiences from numerous disasters, including the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident. However, there is a lack of comprehensive reporting on the awareness of administrative staff, medical personnel, and residents in the areas surrounding nuclear power plants (NPPs). This study reports on a survey aimed at gaining insights into the understanding and current state of disaster preparedness and elucidating the differences in perceptions of nuclear disaster preparedness among the relevant stakeholders surrounding NPPs. Interview surveys were conducted from 14 to 16 September 2022 in the area surrounding Kyushu Electric Power's Genkai NPP in Saga Prefecture and from 11 to 13 January 2023 in the area around Shikoku Electric Power's Ikata NPP. The surveys targeted administrative, medical, and nursing care facilities and residents. Responses from 57 participants indicated a lack of awareness of natural and nuclear disasters, challenges in evacuation planning, and a gap between nuclear disaster training and residents' understanding of evacuation protocols. This study highlights inadequacies in nuclear disaster preparedness and the need for a better understanding among residents regarding evacuation procedures. This study identified three key issues: (1) a lack of awareness about disasters, including nuclear disasters; (2) concerns about complex disasters and the difficulties in creating evacuation plans; and (3) a discrepancy between nuclear disaster training and residents' understanding of evacuation procedures. To bridge this gap, it is important to deepen residents' understanding of nuclear disasters, continuously convey the lessons learned from the FDNPP accident, and regularly reassess and update nuclear disaster preparedness strategies.

Distribution and bioavailability of mercury in size-fractioned atmospheric particles around an ultra-low emission power plant in Southwest China.

Ultra-low emission (ULE) technology retrofits significantly impact the particulate-bound mercury (Hg) emissions from coal-fired power plants (CFPPs); however, the distribution and bioavailability of Hg in size-fractioned particulate matter (PM) around the ULE-retrofitted CFPPs are less understood. Here, total Hg and its chemical speciation in TSP (total suspended particles), PM10 (aerodynamic particle diameter ≤ 10 µm) and PM2.5 (aerodynamic particle diameter ≤ 2.5 µm) around a ULE-retrofitted CFPP in Guizhou Province were quantified. Atmospheric PM2.5 concentration was higher around this ULE-retrofitted CFPP than that in the intra-regional urban cities, and it had higher mass Hg concentration than other size-fractioned PM. Total Hg concentrations in PM had multifarious sources including CFPP, vehicle exhaust and biomass combustion, while they were significantly higher in autumn and winter than those in other seasons (P < 0.05). Regardless of particulate size, atmospheric PM-bound Hg had lower residual fractions (< 21%) while higher HCl-soluble fractions (> 40%). Mass concentrations of exchangeable, HCl-soluble, elemental, and residual Hg in PM2.5 were higher than those in other size-fractioned PM, and were markedly elevated in autumn and winter (P < 0.05). In PM2.5, HCl-soluble Hg presented a significantly positive relationship with elemental Hg (P < 0.05), while residual Hg showed the significantly positive relationships with HCl-soluble Hg and elemental Hg (P < 0.01). Overall, these results suggested that atmospheric PM-bound Hg around the ULE-retrofitted CFPP tends to accumulate in finer PM, and has higher bioavailable fractions, while has potential transformation between chemical speciation.

Incidence of childhood leukemia before and after shut down of nuclear power plants in Germany in 2011: A population-based register study during 2004 to 2019.

The association between leukemia and proximity to nuclear-power-plants (NPPs) has been assessed in several countries with inconsistent results. A case-control study from Germany had shown an increased risk for childhood leukemia (diagnoses 1980-2003) near NPPs. Germany began shutting down nuclear reactors in 2011, following the Fukushima disaster. We tested whether the previously observed association between leukemia and proximity to NPP persisted despite the shutdown. We used an ecological study design to investigate the incidence of leukemia during 2004 to 2019 in children aged 0 to 14 years living near NPPs where at least one reactor was shut down in 2011. We defined study and control areas as municipalities whose surface area was at least 75% within 10 km or between 10 and 50 km of NPPs, respectively. We calculated age-standardized rates and incidence rate ratios (IRR) using control-areas as the reference. We also computed standardized incidence ratios (SIR) separately for each NPP using incidence rates of the German population as a reference. IRR decreased from 1.20 (95% confidence interval: 0.81-1.77) in 2004 to 2011 to 1.12 (0.75-1.68) in 2012 to 2019. Analyses of single plants showed an excess of childhood leukemia during 2004 to 2019 for the Unterweser-NPP, based only on three cases, and the Krümmel-NPP (n = 14; SIR: 1.98, 1.17-3.35). We found slightly decreasing of leukemia incidence rate ratios after the shutdown of nuclear reactors in 2011. Due to the small number of cases, risk estimates have large uncertainty. Further research including a longer follow-up is warranted. The consistent excess of incidence cases around Krümmel may require analytical epidemiological analysis.

Impacts of Thermal Drainage on Bacterial Diversity and Community Construction in Tianwan Nuclear Power Plant.

As one of the low-carbon and high-efficient energy sources, nuclear power is developing vigorously to alleviate the crisis of global climate warming and realize carbon neutrality goals. Meanwhile, the ecological effect of thermal drainage in the nuclear power plant is significantly remarkable, which environmental assessment system has not yet referred to microorganisms. The rapid response of microbial diversity and community structure to environmental changes is crucial for ecosystem stability. This study investigated the bacterial diversity, community construction, and the co-occurrence patterns by 16S rRNA gene amplicon sequencing among gradient warming regions in Tianwan Nuclear Power Plant. The alpha diversity of the high warming region was the lowest in summer, which was dominated by Proteobacteria, whereas the highest bacterial diversity presented in high warming regions in winter, which harbored higher proportions of Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes. The spatial distribution of bacterial communities showed clear separation especially in summer. Strong correlations were between community compositions and environmental factors, such as salinity, DO, TN, and temperature in summer. Furthermore, remarkable seasonality in bacterial co-occurrence patterns was discovered: the robustness of the bacterial co-occurrence network was promoted in winter, while the complexity and robustness were decreased in summer due to the warming of thermal drainage. These findings reveal the potential factors underpinning the influence of thermal drainage on bacterial community structure, which make it possible to predict the ecological effect of the nuclear power plants by exploring how the microbial assembly is likely to respond to the temperature and other environmental changes.

Blood data trends of children in Fukushima after the Great East Japan Earthquake: Fukushima health management survey.

BACKGROUND: After the Great East Japan Earthquake on March 11, 2011 and the subsequent accident at the Tokyo Electric Power Company-operated Daiichi Nuclear Power Plant, the Fukushima Prefecture government initiated the Fukushima Health Management Survey (FHMS) to assess the long-term health effects of the disaster on Fukushima residents. The blood tests of children aged ≤15 years between 2011 and 2012 did not reveal any changes regarding peripheral blood data; however, long-term monitoring is still necessary. Therefore, this study aimed to investigate the long-term health status of children aged ≤15 years who had evacuated the Fukushima Prefecture. METHODS: From 2011 to 2018, 71,250 evacuees aged 15 years or younger participated in the FMHS and were subjected to blood tests. By analyzing the data of the comprehensive health check survey managed by the FHMS, we examined the changes in hemoglobin (Hb) levels, white blood cell (WBC) counts, including fractions, and platelet (PLT) counts among children from 2011 to 2018. RESULTS: Minor fluctuations in Hb levels, PLT counts, and WBC counts were observed during the study period, but the central 95% intervals of distribution of the laboratory values were generally within previously reported reference intervals. In particular, there was no increase in the proportions of patients with anemia, polycythemia, or deviating WBC counts. CONCLUSION: From 2011 to 2018, there was no increase in the percentages of children with anemia, polycythemia, or deviating WBC counts among the Fukushima Prefecture evacuees.

Influence of post-disaster evacuation on childhood obesity and liver dysfunction: The Fukushima Health Management Survey.

BACKGROUND: After the Great East Japan Earthquake and subsequent Fukushima Daiichi Nuclear Power Plant accident in 2011, the Fukushima Prefectural Government launched a long-term health management survey for the population of Fukushima. Results of the Comprehensive Health Check (CHC) showed that some children aged 6-15 years, who resided in the evacuation area at the time of the disaster, had obesity, hyperlipidemia, liver dysfunction, and/or renal dysfunction from as early as 2011. The aim of the present study was to determine the long-term trend of obesity and hepatic enzyme abnormalities in Fukushima children. METHODS: We evaluated the changes in body mass index standard deviation score (BMI-SDS), aspartate aminotransferase, alanine aminotransferase, and gamma-glutamyl transpeptidase from 2011 to 2018. RESULTS: Obesity (BMI-SDS ≥ 2) was significantly associated with hepatobiliary enzyme abnormalities. The mean BMI-SDS was significantly higher in 2011 after the disaster, but then soon showed a gradual decrease. The frequency of obesity did not increase significantly after the disaster. There were no significant differences in the prevalence of hepatobiliary enzyme abnormalities in the children aged 6-15 years of either sex from 2011 to 2018. CONCLUSIONS: In the present study, we found that the increase in the mean BMI-SDS after the disaster was temporary, suggesting that the frequency of obesity and liver dysfunction might not have been significantly influenced by the disaster.

Effect of soil organic matter on the fate of 137Cs vertical distribution in forest soils.

Understanding the fate of the vertical distribution of radiocesium (137Cs) in Japanese forest soils is key to assessing the radioecological consequences of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident. The 137Cs behavior in mineral soil is known to be primarily governed by interaction with clay minerals; however, some observations suggest the role of soil organic matter (SOM) in enhancing the mobility of 137Cs. Here we hypothesized that soil organic carbon (SOC) concentration profile determines the ultimate vertical pattern of 137Cs distribution in Japanese forest soils. In testing this hypothesis, we obtained soil samples that were collected before the FDNPP accident at four forest sites with varying SOC concentration profiles and quantified the detailed vertical profile of 137Cs inventory in the soils roughly half a century after global fallout in the early 1960 s. Results showed that the higher the SOC concentration in the soil profile, the deeper the 137Cs downward penetration. On the basis of the data for surface soils (0-10 cm), the 137Cs retention ratio for each of the 2-cm thick layers was evaluated as the ratio of 137Cs inventory in the target soil layer to the total 137Cs inventory in and below the soil layer. A negative correlation was found between the ratio and SOC concentration of the layer across all soils and depths. This indicates that the ultimate fate of 137Cs vertical distribution can be predicted as a function of SOC concentration for Japanese forest soils, and provides further evidence for SOM effects on the mobility and bioavailability of 137Cs in soils.

An integrated environmental risk assessment framework for coal-fired power plants: A fuzzy logic approach.

This study quantifies the environmental risk of a coal-fired thermal power plant during operation by using environmental monitoring data, site surveys, and documented evidence. The following criteria are assessed: emissions (CO, SO2 , NOx , PM10 ), impact on aquatic ecosystem (fish protection at cooling water intake and cooling water discharge temperature), and waste management (fly ash and bottom ash). Fuzzy sets were defined for each criterion, taking environmental regulatory context as an expert judgment. A survey was conducted with multiple stakeholders to determine the relative importance of risk factors. The survey results showed that the most concerned risks are SO2 and NOx emissions. The proposed method estimates the risk of each environmental criterion separately and then accumulates them into an environmental risk index (ERI). Accordingly, we assessed the Catalagzi coal-fired power plant, which has been in operation on the Black Sea coast in northwestern Turkey. For this case study, the ERI resulted in a value of 0.78 (on a scale of 0-1), showing high environmental risk to the facility. Moreover, the applicability of the proposed framework was tested in several existing coal-fired power plants using simultaneous measurements. All studied coal-fired power plants in Turkey have unacceptable pollutants (PM10 , SO2 , and NOx ) concentration levels indicating high health risk potential. The application of the integrated environmental risk assessment framework showed that new environmental regulations are needed in Turkey to specify more strict emission limits and to monitor CO2 , fine particulate matter emissions, cooling water discharge, and fish protection at cooling water intake.

A Compact IIoT System for Remote Monitoring and Control of a Micro Hydropower Plant.

Remote monitoring and operation evaluation applications for industrial environments are modern and easy means of exploiting the provided resources of specific systems. Targeted micro hydropower plant functionalities (such as tracking and adjusting the values of functional parameters, real-time fault and cause signalizing, condition monitoring assurance, and assessments of the need for maintenance activities) require the design of reliable and efficient devices or systems. The present work describes the design and implementation procedure of an Industrial Internet of Things (IIoT) system configured for a basic micro hydropower plant architecture and assuring simple means of customization for plant differences in structure and operation. The designed system features a set of commonly used functions specific to micro hydropower exploitation, providing maximum performance and efficiency.

Design of Fire Risk Estimation Method Based on Facility Data for Thermal Power Plants.

In this paper, we propose a data classification and analysis method to estimate fire risk using facility data of thermal power plants. To estimate fire risk based on facility data, we divided facilities into three states-Steady, Transient, and Anomaly-categorized by their purposes and operational conditions. This method is designed to satisfy three requirements of fire protection systems for thermal power plants. For example, areas with fire risk must be identified, and fire risks should be classified and integrated into existing systems. We classified thermal power plants into turbine, boiler, and indoor coal shed zones. Each zone was subdivided into small pieces of equipment. The turbine, generator, oil-related equipment, hydrogen (H2), and boiler feed pump (BFP) were selected for the turbine zone, while the pulverizer and ignition oil were chosen for the boiler zone. We selected fire-related tags from Supervisory Control and Data Acquisition (SCADA) data and acquired sample data during a specific period for two thermal power plants based on inspection of fire and explosion scenarios in thermal power plants over many years. We focused on crucial fire cases such as pool fires, 3D fires, and jet fires and organized three fire hazard levels for each zone. Experimental analysis was conducted with these data set by the proposed method for 500 MW and 100 MW thermal power plants. The data classification and analysis methods presented in this paper can provide indirect experience for data analysts who do not have domain knowledge about power plant fires and can also offer good inspiration for data analysts who need to understand power plant facilities.

Assessment of hazardous radionuclide emission due to fly ash from fossil fuel combustion in industrial activities in India and its impact on public.

Traditionally coal has been extensively used as a dominating fossil fuel in a wide range of industries due to its abundance. In India, industries like thermal power plants, cement industries, iron, and steel industries along with many captive power plants consume a huge quantity of coal each year to meet energy demand. Coal combustion releases blackish-grey colored fly ash waste is one of the most imperative sources of radionuclides like Radium (226Ra), Thorium (232Th), Potassium (40K) and Uranium (238U). The estimated industrial fly ash is ∼308.416 Million Tonnes (MT) in 2019, considered as an emerging environmental problem. This study represents the first-ever radionuclide emission from Indian fly ash generated across various major industries. The results reveal that the estimated 226Ra, 232Th, 238U, and 40K radionuclides were estimated to be ∼27.473 TBq, ∼44.351 TBq, ∼41.089 TBq, and ∼111.091 TBq respectively. The potential radionuclide hotspot regions across the nation are identified, which could be used as an important tool to assess its impact on the chronic exposure of millions of residents living near these sources. Cleaner or green energy could be the best alternative to combat the unseen health disaster. More effective and safe utilization of fly ash can minimize the hazardous effect of radionuclides emission.

Low-carbon development pathways for provincial-level thermal power plants in China by mid-century.

Phasing out thermal power plants is vital to combatting climate change. Less attention has been given to provincial-level thermal power plants, which are implementers of the policy of phasing out backward production capacity. To improve energy efficiency and reduce negative environmental impacts, this study proposes a bottom-up cost-optimal model to explore technology-oriented low-carbon development pathways for China's provincial-level thermal power plants. Taking 16 types of thermal power technologies into consideration, this study investigates the impacts of power demand, policy implementation, and technology maturity on energy consumption, pollutant emissions, and carbon emissions of power plants. The results show that an enhanced policy combined with a reduced thermal power demand would peak carbon emissions of the power industry at approximately 4.1 GtCO2 in 2023. Meanwhile, most of the inefficient coal-fired power technologies should be eliminated by 2030. Carbon capture and storage technology should be gradually promoted in Xinjiang, Inner Mongolia, Ningxia, and Jilin after 2025. Energy-saving upgrades on 600 MW and 1000 MW ultra-supercritical technologies should be emphatically carried out in Anhui, Guangdong, and Zhejiang. By 2050, all thermal power will come from ultra-supercritical and other advanced technologies.

Factors influencing acceptability of final disposal of incinerated ash and decontaminated soil from TEPCO's Fukushima Daiichi nuclear power plant accident.

TEPCO's Fukushima Daiichi nuclear power plant accident prompted extensive decontamination work. The decontaminated soil and incinerated ash generated by the process are scheduled for final disposal by March 2045 outside Fukushima Prefecture. The final disposal is unprecedented worldwide. Clarifying their acceptability will contribute to the final disposal of decontaminated soil and incinerated ash, as well as add knowledge about the perceived risk of low-concentration radioactive waste. A questionnaire survey was conducted to assess the psychological factors influencing final disposal acceptability. The results of the structural equation modeling demonstrated stable results, with risk perception decreasing acceptability, social benefits increasing acceptability, and personal benefits having limited impact. The initiative for the final disposal of decontaminated soil and incinerated ash can facilitate the reconstruction of Fukushima Prefecture after the disaster. Trust and intergenerational expectations are critical factors influencing the acceptability of this disposal. The responses were classified based on the relevance of moral norms using cluster analysis and moral foundations. The influence of each element on acceptability varied depending on the cluster. Trust was identified as the most influential factor in acceptability, regardless of the level of importance placed on moral norms.