# Radiological impact of the Fessenheim Nuclear Power Plant decommissioning through prospective Life-Cycle and Risk Assessment approaches

Paul Robineau, Maria Boltoeva, Nicolas Arbor, Gaetana Quaranta

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Paul Robineau, Maria Boltoeva, Nicolas Arbor, Gaetana Quaranta. Radiological impact of the Fessenheim Nuclear Power Plant decommissioning through prospective Life-Cycle and Risk Assessment approaches. Science of the Total Environment, 2025, 974, pp.179200. 10.1016/j.scitotenv.2025.179200. hal-05010305

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# Radiological impact of the Fessenheim Nuclear Power Plant decommissioning through prospective Life-Cycle and Risk Assessment approaches

Paul Robineau, Maria Boltoeva, Nicolas Arbor, Gaetana Quaranta*

Université de Strasbourg, CNRS, IPHC UMR 7178, 67000 Strasbourg, France

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## HIGHLIGHTS

* Radiological impact assessment of Fessenheim NPP decommissioning is realized.

* Two recently developed LCIA methods (UCrad & CGM) are compared to HERA results.

* ³H, ¹⁴C, and ⁶⁰Co are identified as key radionuclides across assessment methods.

* Worst estimate of public dose is 400 times lower than French average annual dose.

* Future population accounting seems necessary for prospective endpoint assessments.

## GRAPHICAL ABSTRACT

### Radiological impact assessment of the Fessenheim NPP decommissioning

**Discharges (Bq)**

Expected
Annual
Inventory

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atmospheric

<figure><img src="image_5.png" /></figure>

**Midpoint (Sv)**

Methods
UCrad (global)
CGM 1 km
CGM 100 km
CGM 1000 km
CGM 10000 km
HERA (EDF impact study)

<figure><img src="image_6.png" /></figure>

Expected
Annual
Dose

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<figure><img src="image_8.png" /></figure>

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**Prospective Endpoint (DALYs)**

Initial CFs

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Prospective
population
accounting

Distance (km)

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## ARTICLE INFO

Editor: Antonella Petrillo

### Keywords:

Environmental impact
Life-Cycle Assessment
Radiological assessment
Decommissioning
Nuclear Power Plant

## ABSTRACT

The Fessenheim Nuclear Power Plant (NPP) is the first large-scale plant in France scheduled for decommissioning, set to begin in 2026. Few studies have investigated NPP decommissioning's environmental impacts and especially radiological ones, which have been generally understudied within Life-Cycle Assessment (LCA). This work builds on and complements *Électricité de France's* (EDF) regulatory Health & Environmental Risk Assessment (HERA) by conducting an LCA following ISO 14040/44 standards, dynamically accounting for atmospheric and freshwater discharges over 15 years. Radiological impacts on human health are estimated using two recently developed Life-Cycle Impact Assessment (LCIA) methods: UCrad, built as a radiological counterpart to USEtox (designed for global averaged results), and CGM, drawing on the analytical modeling of HERA studies (intended for plant-scale assessments in a screening context). At the midpoint level, ³H, ¹⁴C, and ⁶⁰Co are identified as key radionuclides, with varying contribution to total impacts across methods, the Water Metal cutting and Regular exploitation processes being the primary discharge sources. The worst estimate of public total dose is 400 times lower than the French average annual dose. However, comparisons with EDF's results and other HERA studies show CGM's limited current relevance for bridging HERA and LCA due to divergent results. The creation of adapted characterization factors (CFs) for alternative discharge configurations is recommended. Using QGIS software, population data aligned with shared socio-economic pathways (SSPs) enable creation of

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: paul.robineau@iphc.cnrs.fr (P. Robineau), maria.boltoeva@iphc.cnrs.fr (M. Boltoeva), nicolas.arbor@iphc.cnrs.fr (N. Arbor), quaranta@unistra.fr (G. Quaranta).</footnote>
prospective endpoint-level CFs, allowing comparison between static and prospective UCrad estimates. Neglecting the prospective dimension of NPP decommissioning leads to a 17–21.8 % underestimation of global damages. CGM being site-dependent, population distribution assumptions heavily influence results. Since CGM midpoint impacts seem reasonably interpolated as a function of distance, improvements could involve implementing spatial fits within QGIS to compute representative CGM endpoint impacts, provided an appropriate balance between computation time and spatial resolution is commonly adopted.

## Glossary

**CF** Characterization factor

**CGM** Critical group methodology (in LCA). Radiological impact assessment method which draws on established approaches of Human & Environmental Risk Assessment (HERA) to model RNs' environmental dispersion ("fate") via a Gaussian plume dispersion to assess the exposure of "critical groups" of humans postulated present at given distances to the initial emission. It is best suitable for evaluation at the scale of a plant, and derived from a generic analytical model considered for screening purposes, before a thorough study.

**Critical Group** Representative group of people expected to receive the highest doses, small enough to be relatively homogenous with respect to age, diet and aspects of behavior that affect the doses received

**EDF** Électricité de France

**Endpoint-level impact** In LCA, impact scores equivalent to a valuation of consequences of midpoint impacts on defined areas of protection (human health, ecosystem quality, etc.) are called endpoint impacts or "damages". In the case of radiological impacts on human health, they are expressed in disability-adjusted lost years (DALYs).

**HERA** Health & environmental risk assessment. This approach mobilize an emissions' inventory, fate, exposure, and effect modeling, usually to assess absolute actual risks of a localized specific process/activity for a given population, to guide management for compliance with regulatory thresholds.

**LCA** Life-cycle assessment. This approach mobilize an emissions' inventory, fate, exposure, and effect modeling, usually to assess average impacts of multiple impact categories, relative to a unit of reference, to compare technological alternatives for achieving sustainability.

**Midpoint-level impact** In LCA, impact scores dimensionally homogeneous to a physical quantity (i.e. amount of a reference substance) are called midpoint impacts or "effects". In the case of radiological impacts on human health, the expression of said effect being in Sievert (Sv), it is a measure of risk.

**NPP** Nuclear power plant

**PHWR** Pressurized heavy water reactor

**PWR** Pressurized water reactor

**RN** Radionuclide

**UCrad** Radiological impact assessment method in LCA modeling RN fate through a nested multimedia compartment model describing the environmental dispersion of pollutant emissions. It is designed to produce average results best adapted for technology evaluation and comparison, on the model of USEtox, which is a consensus-based methodology now widely used for the assessment of toxic substances.

## 1. Introduction

A substantial portion of the global nuclear power plant (NPP) fleet is nearing the final stages of its anticipated operational life. As of 2024, approximately 67 % of installed capacity has surpassed the 30-year mark, indicating that a growing number of NPP decommissioning projects can be expected in the near future (IAEA, 2024). However, research has comparatively overlooked the end-of-life phase of large industrial facilities, particularly NPPs, focusing primarily on planning, construction, and operational stages (Invernizzi et al., 2020a, 2020b). This is especially true in the case of academic environmental impacts studies, in particular through the lens of life-cycle assessment (LCA) – a quantitative and multi-criteria environmental impact assessment framework considered the most comprehensive approach available (Joint Research Centre, 2010). Indeed, there are only three academic LCA studies dedicated to investigate NPP decommissioning to date (Iguider et al., 2024; Seier and Zimmermann, 2014; Wallbridge et al., 2013). The earliest study (Wallbridge et al., 2013) examined the decommissioning of the Trawsfynydd NPP in Wales (UK) and was the only study assessing the radiological impact on human health, though it relied on the now-outdated Human Health Damage methodology (Frischknecht et al., 2000). The most recent study (Iguider et al., 2024) was a pilot LCA study, based on the limited public data available in the 2020 version of the decommissioning plan for the Fessenheim NPP in France, which has since been updated (EDF, 2024).

This situation also reflects the limited interest in radiological impact assessment in the field of LCA, where studies almost always treat radiological impacts separately from non-radiological impacts due to the lack of a suitable and consistent life-cycle impact assessment (LCIA) framework (Paulillo et al., 2018). To address this gap, Paulillo et al. recently developed two LCA-compatible impact assessment methodologies – UCrad and CGM – focusing on human health impacts (Paulillo et al., 2020a, 2020b). UCrad aims to serve as a radiological counterpart to USEtox, a widely adopted, consensus-based methodology for assessing toxic substances (Hauschild et al., 2008; Rosenbaum et al., 2008). It employs a similar fate modeling approach – i.e. a nested multimedia compartment model describing the environmental dispersion of pollutant emissions – and is designed to yield average results. Consequently, it is best suited for technology evaluation and comparison. In contrast, CGM draws on established approaches of Human & Environmental Risk Assessment (HERA) to model radionuclides' fate via a generic version of the analytical model used – i.e. Gaussian plume dispersion – to assess exposure of "critical groups" of humans located at specified distances from the initial emission source. This approach is therefore best suitable for initial, plant-level screening, before more comprehensive studies (IAEA, 2001). Beyond their respective fate modeling of radionuclides (RNs), UCrad and CGM align in terms of exposure and effect modeling, theoretically allowing combined application to compare impacts across critical groups and the general population.

However, one key area to evaluate is the actual capacity of CGM to yield results that are comparable to those of actual HERA studies in real-world examples. While HERA and LCA have broad convergence in the methodological tools used, they are generally applied with differing objectives: HERA typically assess the absolute risks associated with a specific, localized process/activity, guiding regulatory compliance, whereas LCA assess the average impacts of multiple impact categories
<figure><img src="image_12.png" /><figcaption>**Fig. 1.** System boundaries for the Radiological impact assessment of Fessenheim NPP decommissioning. Arrows represent emissions of RNs, with A and L indices specifying whether the emission is directed towards the air or liquid environmental compartment.</figcaption></figure>

relative to a functional unit, allowing for comparison of technological alternatives in pursuit of sustainability. The distinction is not absolute, as crossovers between HERA/LCA have occurred and have been studied for their methodological implications and practical applications (Harder et al., 2015; Hauschild et al., 2022; Udo de Haes et al., 2006). To the best of our knowledge, these crossovers have not been applied to the radiological impacts on human health, with the exception of Chapter 5 of Paulillo's PhD thesis, which provides a comparative assessment of the Thermal Oxide Reprocessing Plant at Sellafield (Paulillo, 2018). In the present study, we will focus on a specific life-cycle phase (decommissioning) within a case study of the Fessenheim NPP, to enable a suitable framework for the comparative application of HERA and LCA.

Indeed, the Fessenheim NPP operated by EDF (Électricité de France, a majority state-owned utility) has garnered significant interest as the first large-scale civil nuclear plant to undergo decommissioning in France. As the French historical nuclear program institutionalized a high degree of standardization for its pressurized water reactors (PWRs) (Grubler, 2010), the Fessenheim NPP can be considered a “laboratory” for future decommissioning projects in France. As part of the legal process required for EDF to carry out the Fessenheim NPP decommissioning, the company conducted a regulatory environmental impact study using a HERA approach, which was made publicly available during the dedicated inquiry held in April 2024 (EDF, 2024b). This comprehensive study provides an assessment of impacts directly associated with decommissioning activities, with a focus on on-site operations. Regarding this purpose, it covers air quality and climatic factors, surface water, soil and groundwater, radioecology, biodiversity, population health, human activities, and waste management. These data are significantly more detailed than the limited information available in the 2020 decommissioning plan used in the previous work regarding the Fessenheim NPP decommissioning (Iguider et al., 2024), allowing for the construction of a more accurate life-cycle inventory (LCI) of the decommissioning phase. In particular, the detailed inventory of RNs discharged and the associated effective dose assessment gives a unique opportunity to investigate the expected radiological impacts of the Fessenheim NPP decommissioning through the complementary lens of LCA.

This work then aims to participate in the needed broader dynamic of studies on the end-of-life of industrial facilities and NPPs in particular, by (i) filling a data gap in the literature on the subject, (ii) providing a point of reference for future studies, (iii) assessing the accuracy of the CGM method, and (iv) making methodological recommendations.

## 2. Material and methods

### 2.1. Objective & methodology

In this paper, we thus conduct a prospective LCA, following the ISO 14040/44 standard approach (ISO, 2006a, 2006b), to evaluate the radiological impacts on human health expected to occur during the Fessenheim NPP decommissioning. We employ the state-of-the-art life-cycle impact assessment (LCIA) methodologies, UCrad and CGM, to evaluate impacts at both local and global levels, and we compare the CGM results with those obtained by EDF in their HERA impact study. Moreover, Paulillo and colleagues have recently extended the development of UCrad from the midpoint/effect, in Sievert (Sv), to the endpoint/damage level in Disability-Adjusted Lost Years (DALYs) (Paulillo et al., 2023). Their approach can be readily adapted to CGM by modifying the population under consideration. Furthermore, the extended time scale of an NPP decommissioning (a minimum of 15 years) suggest that population changes could introduce bias in obtained results if not dynamically adjusted. This aspect is therefore also examined to quantify this potential bias and provide methodological recommendations. The characterization factors used are reported in SI.1.

### 2.2. Functional unit

The functional unit (FU) specifies the object of study: “a quantified performance of a product system intended to be used as a unit of reference in a life-cycle assessment”, as defined in ISO 14040/44. Here, the investigated FU corresponds to “Decommissioning of the Fessenheim NPP”, primarily for appropriate application of the CGM method and enable direct comparison with the regulatory HERA study conducted by EDF.

### 2.3. System boundaries

The EDF environmental impact study is used to adapt the system boundaries and elementary processes of the previous pilot LCA regarding the Fessenheim NPP decommissioning – Fig. 1. Included elementary processes are detailed in the next sub-sections. The inventory available in the EDF impact study is focused on the radioactive discharges to the environment, to assess the radiological impact to the public. Therefore, the impact on the workers involved in the NPP decommissioning is out of the scope of our assessment.
“Pre-decommissioning” operations have been ongoing since 2020 and are projected to conclude in 2025. From the regulatory perspective, these operations are not considered part of the decommissioning and are not considered in the EDF impact study. From an LCA perspective, it is important that future work assess this part of the life-cycle: through the evacuation of spent and new fuel, waste and effluents, draining of circuits, and chemical decontamination of certain circuits, this is an essential phase removing 99.9 % of the radioactivity on-site (EDF, 2024). As radioactive wastes (RW) are subject to mandatory conditioning on-site before transportation to their specific site of disposal, potential RNs emissions during transport are here considered negligible. Therefore, we exclude the “Waste transport” process of our present analysis. Furthermore, the EDF impact study only considers and provides detailed data for direct on-site emissions of RNs. Consequently, the “Disposal” elementary process is also excluded from our analysis, even though the UCrad and CGM methods do comprise characterization factors (CFs) for assessing impacts associated with long-term disposal for some of the RW categories produced during decommissioning. That being said, these two last excluded elementary processes are unlikely to contribute significantly to the radiological impact of the NPP decommissioning. By definition, the “Demolition” and “Site rehabilitation” processes occur after “Clean-up for clearance” and only negligible contamination should remain. Discharges can occur in the atmospheric (A) or liquid (L) environmental compartment. Due to the Fessenheim NPP location on the edge of the Grand Canal of Alsace, the word “liquid” must be understood as a synonym for “freshwater” in the present article.

### 2.3.1. Electromechanical dismantling

The “Electromechanical dismantling” process must be divided in two sub-processes, depending on whether the operations are conducted in air or underwater.

The “Electromechanical dismantling in air” subprocess involves the removal (dismantling or cutting) and packaging as waste the electromechanical equipment present in the various premises (EDF, 2024). At the end of this process, only the civil engineering and structural elements of buildings are remaining, along with the equipment necessary to carry out the Clean-up for clearance process: means of access and support functions such as ventilation and lighting – possibly after reconfiguration. Atmospheric RNs emissions from these operations arise from airborne release of labile surface contamination during component handling elements, contamination at the cutting site, and volatile RNs present on the surface and in the mass bordering the cutting line, which are both thermally affected (EDF, 2024b). Metal cutting being the major source of airborne release, this sub-process is named “Air Metal cutting” in the study.

In each of the two reactor buildings, the “Electromechanical dismantling underwater” sub-process involves filling the reactor building pool with water, dismantling the reactor vessel internals in the pool, followed by the reactor vessel itself, with an intermediary step of pool draining and refilling (EDF, 2024). Liquid RNs emissions from these operations result from suspension in water of surface contamination present on the surface of submerged elements, contamination present at the cutting site and “volatile” RNs present on the surface and in the mass bordering the cutting line, which are both thermally affected. Atmospheric RNs emissions arise from the same previously mentioned pathways, with the additional contribution of the evaporation of water contaminated by the cutting activities (EDF, 2024b). Metal cutting being the major source of RNs release, this sub-process is named “Water Metal cutting” in the study.

Cutting methods can be either mechanical or thermal. According to EDF, mechanical cutting is preferred to reduce RNs release; however, thermal cutting is considered for specific elements where mechanical cutting is not feasible, particularly for thicker components (EDF, 2024b).

### 2.3.2. Clean-up for clearance

The “Clean-up for clearance” process involves the structural

remediation of nuclear buildings, during which radioactivity – resulting from activation, deposition or migration of contamination – likely present in the building structure is primarily removed through mechanical means: sanding, bush-hammering, etc (EDF, 2024). Removing the reinforced concrete of the two activated reactor vessel shafts is accomplished by cutting out concrete blocks. Atmospheric RNs emissions from these operations arise from airborne release of contamination on the surface and in the mass of the concrete during scraping, and contamination present in the mass of the cell shafts concrete blocks during cutting (EDF, 2024b).

### 2.3.3. Regular exploitation

The “Regular exploitation” process accounts for discharges not directly linked to decommissioning activities but to the day-to-day running of the site: laundry, floor washing operations and occasional decontamination – tools, pools, etc. It also includes end-of-life operations not carried out during the pre-decommissioning phase: evaporation and emptying of NPP operating capacities still present during decommissioning, etc (EDF, 2024b).

## 2.4. Inventory analysis

### 2.4.1. Data origin and uncertainties

Our primary data sources are the EDF decommissioning plan and the associated impact study (EDF, 2024a, 2024b). In particular, we use the extensive content of Annex 1 “Radioactive effluents” and Annex 5 “Population and Human health” from EDF, 2024b. Annex 1 provides estimates of expected atmospheric and liquid radioactive discharges resulting from decommissioning operations. It includes a presentation of the methodology used to assess radioactive releases, the scope considered, and the underlying assumptions, the estimated activities per year and the definition of release periods, along with the lists of predominant RNs. This material serves as the basis for conducting the LCIA with UCrad and CGM methods. Annex 5 focuses on assessing the dosimetric impact on human health of radioactive discharges and public exposure to ionizing radiations, following a HERA approach. It serves as the reference point against which our results will be compared, with the representative individual assumed to live in the Fessenheim municipality.

The French Environmental Authority, which evaluated the EDF impact study, has ruled it as “very well constructed and proportionate to the issues at stake”, though it made recommendations for some improvements (Ae, 2023). Given the prospective nature of the study, there are several uncertainties associated with assumptions on the NPP contamination state at the beginning of the decommissioning. Consistently with a HERA approach, the most penalizing hypotheses were selected at each step of the work, resulting in upper-bound estimate. Specifically, a 50 % mark-up was applied to all annual decommissioning discharges to account for potential planning discrepancies, while a 100 % mark-up was applied for activities in the Regular exploitation process due to the absence of experience feedback. This means that every result presented here – from the HERA reference results to our LCA calculations – is likely to represent a worst-case estimate, unless significant problems of radioactive management appears during the decommissioning.

However, the EDF impact study lacks certain elements essential for its complete reproducibility – see Section 2.4.2. Specifically, not all calculations leading to annual flow estimates are detailed in the Annexes, and we observed some internal inconsistencies, such as discrepancies between data tables likely due to rounding errors. The result is a temporal offset in the annual impact scores of the reconstructed HERA dose assessment (Section 2.4.2) and the annual CGM 1 km impact scores directly based on the inventory of discharges (Section 3.1). Another notable example is the identification of predominant RNs. Indeed, Annex 1 of the impact study defines these by excluding RNs from further analysis if their contribution to annual activity is less than 1 %, and their
<figure><img src="image_13.png" /><figcaption>**Fig. 2.** Inventory of the emitted radioactivity for Fessenheim NPP decommissioning. (a) Annual radioactive discharges estimates, by elementary process and RNs. (b) Total discharges per RN. The plain color and white dots pattern in (a) respectively represents ³H and ¹⁴C. Other RNs contribute too little to be visualized. A and L indices specify whether the emission is directed towards the air or liquid environmental compartment.</figcaption></figure>

<caption>**Table 1**</caption>

Summary of total annual effective doses by age group and relevant combination of phase (atmospheric discharges) and typical year (liquid discharges), as defined in Annex 5 of the EDF impact study (EDF, 2024b).

<table><thead><tr><th></th><th>Atmospheric discharges</th><th>Liquid discharges</th></tr></thead><tbody><tr><td>Phase 1 (1–3)<sup>a</sup></td><td>BK<sup>b</sup> pools in water, prior to electromechanical dismantling of vessel and internals (empty BR<sup>b</sup> pools)</td><td>Ordinary year with operating rejects only (i.e. year without pool draining).</td></tr><tr><td>Phase 2 (4–7)</td><td>BR &amp; BK pools in water, during underwater electromechanical dismantling of the reactor vessel and internals</td><td>Year with BK/BR pool draining and operating discharges.</td></tr><tr><td>Phase 3 (8–12)</td><td>BR &amp; BK pools drained (end of draining of the BR &amp; BK pools in the same year)</td><td>BR + BK pool draining year and operating discharges</td></tr></tbody></table><table><thead><tr><th></th><th>Atmospheric</th><th>Liquid</th><th>Adult</th><th>Child 10yo</th><th>Child 1yo</th><th>Identified year<sup>c</sup></th><th>Extrapolated Year<sup>c</sup></th></tr></thead><tbody><tr><td>Effective Dose (Sv)</td><td>Phase 1 (1–3)</td><td>Typical year 0 (Not 3,7,8)</td><td>2.02E-08</td><td>8.45E-09</td><td>1.35E-08</td><td>2</td><td>1</td></tr><tr><td></td><td>Phase 1 (1–3)</td><td>Typical year 1 (3)</td><td>2.38E-08</td><td>1.16E-08</td><td>2.07E-08</td><td>3</td><td>–</td></tr><tr><td></td><td>Phase 2 (4–7)</td><td>Typical year 0 (Not 3,7,8)</td><td>1.29E-07</td><td>1.07E-07</td><td>1.69E-07</td><td>6</td><td>4–5</td></tr><tr><td></td><td>Phase 2 (4–7)</td><td>Typical year 2 (7)</td><td>6.75E-07</td><td>6.72E-07</td><td>7.16E-07</td><td>7</td><td>–</td></tr><tr><td></td><td>Phase 3 (8–12)</td><td>Typical year 0 (Not 3,7,8)</td><td>4.06E-08</td><td>3.49E-08</td><td>5.26E-08</td><td>9</td><td>10–12</td></tr><tr><td></td><td>Phase 3 (8–12)</td><td>Typical year 3 (8)</td><td>6.59E-07</td><td>6.47E-07</td><td>6.76E-07</td><td>8</td><td>–</td></tr></tbody></table>

contribution to the annual effective dose, based on estimated releases (across all categories), is also less than 1 %. However, the effective dose results in Annex 5 include four previously excluded RNs (¹⁰⁸Ag, ¹³⁷mBa, ¹⁵²Gd, and ⁹⁰Y), even though they were excluded from dose assessment in Annex 1 – see SI.2–3. As their total contribution to the dose is less than 1 % in nearly every situation, we decided to exclude them from our analysis for consistency purposes.

### 2.4.2. Dynamics of discharges

Annex 1 of the EDF impact study presents two types of data tables

concerning the RNs' discharges estimates in Bq – SI.2. Some tables display the annual emissions for each elementary process across 4 categories: ³H, ¹⁴C, aggregated other β/γ emitters, and α emitters – termed “Aggregated Radionuclides Emissions” (ARE) in SI.2. Other tables provide the disaggregated annual emissions per predominant RN, but elementary processes are aggregated – termed “Predominant Radionuclides Emissions” (PRE) in SI.2. To prevent loss of information, we calculated the contribution to annual total per category for each elementary process, based on ARE tables. The resulting coefficients were subsequently applied to PRE tables to create a fully disaggregated

<footnote><sup>a</sup> Numbers between parentheses indicate the actual decommissioning years associated to the EDF indexation of phases and typical years.</footnote>

<footnote><sup>b</sup> BR and BK refer to reactor and auxiliary buildings, respectively.</footnote>

<footnote><sup>c</sup> Synthesis presenting (i) the identified year to which the combination of atmospheric phase & liquid typical year apply, based on cross-checking of information in EDF study, and (ii) the remaining years that needed to be extrapolated.</footnote>
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>(caption on next column)</figcaption></figure>

**Fig. 3.** Reconstructed effective dose estimates for the Fessenheim NPP decommissioning, by mode of exposure (EDF, 2024b). (a) The annual effective dose for the Adult case. White dots and hatched patterns respectively represent ¹⁴C and other β/γ emitters (⁶⁰Co being predominant). Other RNs contribute too little to be visualized. (b) Effective dose per RN for the Adult case. (c) Normalized deviation from Adult dose estimate for the 10 yo and 1 yo Child cases, extrapolated years excluded.

<figure><img src="image_17.png" /></figure>

inventory of annual discharges – see SI.2. As illustrated in Fig. 2, ³H and ¹⁴C represent most of the discharges, with a predominance of emissions resulting from the Water Metal cutting process during the years [4–7], while the Regular exploitation process is expected to be significant during the years [1–5, 8]. Other processes and RNs contribute minimally to the overall discharges.

### 2.4.3. Dynamics of the EDF dose assessment reference

Annex 5 of the EDF impact study presents several data tables regarding the dose assessment estimates in Sv – SI.3. The stated objective of EDF with their study is to justify the request for specific authorizations concerning radioactive discharges during decommissioning. Consequently, the impacts reported are not detailed annually but represent the worst case for each possible combination of atmospheric discharges phases and typical years for liquid discharges. Table 1 synthesize our approach to derive the annual dose inventory, based on assumption of the linear relationship between discharges and impact. Firstly, we identified the actual years corresponding to these combinations through sequential exclusion and comparison with the annual radioactive discharge data provided in Annex 1 of the EDF impact study (Fig. 3). In contrast to these “identified years”, dose estimates for other years were extrapolated: discharges of missing years were normalized against the discharges of the “identified year”, which serve as a reference for the corresponding phase (atmospheric discharges) and typical year (liquid discharges) combination. Consistently with the discharge-impact linear relationship assumption, the obtained ratios were employed as scaling coefficients to calculate the missing dose estimates for the “extrapolated years” – SI.3.

The results of the dose annual inventory reconstruction are presented in Fig. 3. According to the HERA approach of EDF, the three main RNs responsible for the impacts on the three age groups studied are ³H (0.7–1.2 % of total, around 90 % ingestion, 10 % inhalation), ¹⁴C (91.3–94.5 % of total, virtually 100 % ingestion), and ⁶⁰Co (4.2–7.6 % of total, 5–35 % ingestion, 45–95 % external - plume excluded) – Fig. 3a-b. The latter is associated with the most important variation in the mode of exposure, as ingestion becomes marginal with increasing age categories. The divergence in total impact across age categories reveals varying proportions, all of which within the same order of magnitude – Fig. 3c.

### 2.4.4. Estimation of population dynamics for damage assessment

Consistently with LCA methodology, the development of UCrad to the endpoint level is realized by adding an effect module (comprising dose-response and severity factors) to the previously established fate and exposure modules. Based on the historical approach (Frischknecht et al., 2000), updated data with gaps filled (Global Burden of Disease Collaborative Network, 2021, 2022; ICRP, 2007) lead to determine an aggregated effect factor in DALY/Sv, which scales the midpoint characterization factors in Sv/Bq to yield appropriate DALYs/Bq values for the global population (Paulillo et al., 2023).

Apart from epidemiological models – whose discussion is out of the scope of this study – the resulting characterization factors (CFs) depend on the value of the postulated population, a parameter that varies across time and space. Given that NPP decommissioning activities are spread over time, dynamically adjusting the endpoint level CFs based on population variation seems essential. Since the Fessenheim NPP decommissioning is an upcoming project, population variation primarily
<figure><img src="image_18.png" /><figcaption>**Fig. 4.** Extrapolated population from extraction in QGIS. (a) Map example at world scale. (b) Map example at Europe scale. The pixel color scaling was selected for visualization purposes. (c) Plots represent the resulting interpolated values for the population present in the different surfaces, with plain lines corresponding to the average value across SSP scenarios, and shapes the domain between minimum and maximum values.</figcaption></figure>

involves prospective modeling. Prospective LCA is an emerging sub-field of LCA dedicated to the assessment of systems or products at a future time relative to the one at which the study is conducted (Arvidsson et al., 2024). Such studies typically employs projections from integrated assessment models calibrated to represent the shared socio-economic pathways (SSP) scenarios (Sacchi et al., 2022), narratives widely used in prospective environmental science (Hausfather, 2018).

Following this methodological trend, we use prospective population datasets designed for consistency with the different SSP scenarios, exhibiting a 1 km / 10 years spatiotemporal resolution (Gao, 2017; Jones et al., 2020; Jones and O'Neill, 2016). These datasets are imported into the QGIS software (QGIS Development Team, 2024), and pixel values are extracted for the 2000–2050 period – SI.4. Extending CGM to the endpoint-level requires spatial selection, as illustrated in Fig. 4a-b. Population data within distances of [0–1], [1–100], [100–1000], and] 1000–10,000] km are thus allocated to the damage level CFs calculation of CGM 1, 100, 100 and 10,000 km, respectively. Although CGM is based on the critical group approach rather than a uniform or non-uniform population distribution, we chose to uniformly extract population data inside spatial intervals and assign it to the calculation of the prospective CFs associated to the distance of the further boundary of each interval – yielding a minimum endpoint-level estimate. Section 3.4 provides rationale and control measure for this workflow. Global population data is used for the calculation of prospective CFs for UCrad. Extracted data was fitted with a polynomial interpolation to obtain annually resolved values for the projected decommissioning period (2026–2040) of the Fessenheim NPP – Fig. 4c.

# 3. Results & discussion

## 3.1. LCIA midpoint scores

The characterization factors for the varied spatial scopes of UCrad and CGM were applied to the RNs discharges inventory constructed in Section 2.4.2. Results from the different impact assessments methods are

laid out in Fig. 5, with detailed numerical data available in the supplementary information – SI.5–9.). Since no characterization factors were available for $^{152}$Eu and $^{108m}$Ag, we followed IAEA recommendations by using a close isotope of target RN as a substitute, or the average of isotopes CFs (IAEA, 2001). These substitutions are systematically referenced in parentheses throughout this article. For clarity, results for CGM 1000 and 10,000 km are excluded from Fig. 5, as they show minimal deviation from CGM 100 km, but they are included in the analysis in this section.

As shown in Fig. 5a and d, there is a global coherence between the EDF impact study (Fig. 3) and CGM 1 km. Indeed, both highlight $^{3}$H, $^{14}$C, and $^{60}$Co as primary contributors to the impact, with similar relative proportions – Table 2. This suggests that CGM 1 km could be a practical LCIA alternative to a classical HERA study. That being said, a temporal offset in impacts is noticeable between the 2 methods, potentially due to the limited transparency of certain elements of the EDF impact study, as discussed in Section 2.4.1. Additionally, CGM 1 km consistently yields higher midpoint scores than the EDF impact study, by a factor of 6.5. One explanation for this discrepancy lies in the differing distance underlying assumptions: the EDF study considers the actual distance of 1.3–3.3 km between the Fessenheim municipality and the NPP, while CGM at 1 km uses a fixed, generic distance. CGM characterization factors for the 2.5 km distance (atmospheric, but not freshwater discharges) can be found in Paulillo’s PhD thesis, Appendix A (Paulillo, 2018). Based on these data, around 40 % of the difference between CGM 1 km and the EDF impact study can be explained by this distance parameter. Furthermore, the EDF impact study uses a more accurate RN fate modeling that takes into account the proper geographic situation of the Fessenheim NPP – unlike the generic model of CGM. The remaining 60 % discrepancy suggests that the EDF study results should be viewed as a benchmark for evaluating CGM’s accuracy. Nonetheless, the total midpoint impact scores obtained of 1.77 or 11.61 µSv should be contextualized in relation to the annual dose of the statistical average French person, around 4,5 mSv, two-thirds of which comes from naturally occurring radioactivity (IRSN, 2021).
<figure><img src="image_19.png" /></figure>

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**Fig. 5.** Radiological midpoint impact assessment. (a-c) Midpoint impacts resulting from annual RNs discharges for (a) CGM 1 km (b) CGM 100 km (c) UCrad. (d-f) Integrated midpoint impacts per RN, for (d) CGM 1 km, (e) CGM 100 km and (f) UCrad. Entries in parentheses report the CF substitutes used where necessary. Plain color, white dots and hatched pattern respectively represent ³H, ¹⁴C, and other β/γ emitters (⁶⁰Co being predominant).
<table><caption>**Table 2**
Summary of major RNs relative contributions (%) to total midpoint impact scores, according to the different assessment methods.</caption><thead><tr><th rowspan="2">Method</th><th colspan="3">Relative contributions (%)</th><th rowspan="2">Total Midpoint impact (Sv)</th></tr><tr><th>Tritium-3 (³H)</th><th>Carbon-14 (¹⁴C)</th><th>Cobalt-60 (⁶⁰Co)</th></tr></thead><tbody><tr><td>EDF impact study (HERA)</td><td>0.66-1.23</td><td>91.23-94.53</td><td>4.22-7.63</td><td>1.77·10<sup>-6</sup></td></tr><tr><td>CGM 1 km</td><td>2.04</td><td>87.36</td><td>9.98</td><td>1.16·10<sup>-5</sup></td></tr><tr><td>CGM 100 km</td><td>1.44</td><td>1.22</td><td>97.31</td><td>4.64·10<sup>-7</sup></td></tr><tr><td>CGM 1000 km</td><td>1.42</td><td>0.02</td><td>98.54</td><td>4.56·10<sup>-7</sup></td></tr><tr><td>CGM 10000 km</td><td>0.17</td><td>5·10<sup>-4</sup></td><td>99.82</td><td>4.22·10<sup>-7</sup></td></tr><tr><td>UCrad</td><td>18.94</td><td>48.04</td><td>32.86</td><td>1.66·10<sup>-10</sup></td></tr></tbody></table>

The impact profile shifts when examining midpoint scores at further distances from the Fessenheim NPP – Fig. 5b and e. According to CGM 100, 1000, and 10,000 km, ⁶⁰Co emerges as the major cause of impacts, with a contribution increasing with the distance considered – Table 2. This originates from modeling of the different modes of emission in CGM. Indeed, ⁶⁰Co is primarily emitted through liquid discharges, whereas discharges of ¹⁴C are atmospheric, and ³H is released in a mix of the two pathways. CGM models the freshwater compartments as rivers, with the general assumption of perfect horizontal and vertical mixing, and only estimates the total concentration of RNs in the water body. Consequently, there is nearly no dilution effect due to constraints from riverbanks (Paulillo et al., 2020a). As illustrated by Figs. 6a-b, this results in very close characterization factors at all distances, whereas dilution following atmospheric discharges leads to CF value decreases of orders of magnitude – see SI.10. Therefore, midpoint impacts from ¹⁴C strongly decline with distance (full atmospheric discharges), the ones from ³H show an intermediary decline (mixed atmospheric and liquid discharges), and the ones from ⁶⁰Co stay quite similar (full liquid discharges), hence the increasing dominant contribution of this radionuclide with distance – Fig. 6c.

On the other side, UCrad output does not depend on distance from the emission (Fig. 5c and f): CFs are representative of average impacts for the global population. Therefore, the total midpoint score from this method amount to 0.17 nSv, significantly lower than the other impact assessments discussed in this work – Table 2. Even though the UCrad's characterization factors are mostly in line with those of CGM 1000 km (Fig. 6a), the ratios between the CFs of ³H, ¹⁴C and ⁶⁰Co differ by a factor from 3 to 13 – see SI.10. As this difference does not occur between variants of CGM, it can be attributed to the different fate modeling of those two methods, which give different weight to parameters such as radioactive decay (Paulillo et al., 2020a). This leads to more balanced contributions of the major RNs in UCrad (which is less sensible to radioactive decay), with ³H no longer marginal – Table 2.

Overall, CGM and UCrad converge in regard to the elementary processes contributions, both following the discharges dynamic of ¹⁴C and ⁶⁰Co in the Water Metal cutting and Regular exploitation processes, despite ³H being emitted twice as much as the two combined – Fig. 2. Based on the tendency of our results, it seems that one interesting use of UCrad/CGM combination could be to derive an interval of minimum/maximum impact, as they respectively underestimate/overestimate the EDF reference results at 1 km – Fig. 6c. However, the derived interval spans several orders of magnitude, and the midpoint variation of CGM in relation to distance depends on the radionuclide and the emission pathway (as CGM is more sensible to radioactive decay). It is also clear that, by construction, any CGM result at 100, 1000, 10,000 km will provide a lower impact estimate than CGM 1 km at the midpoint level. It does not necessarily translates in lower impact estimates at the endpoint level. For example, the Section 3.3 of this work demonstrate a case where UCrad yields an impact estimate between CGM 100 and 1000 km at the endpoint level. Furthermore, the results are also influenced by the

population distribution assumptions, which we discuss through a sensitivity analysis in Section 3.4. This combined application should thus be used with caution, with results and interpretation carefully conducted to prevent misleading of the public.

## 3.2. CGM method accuracy

Evaluating the actual usefulness of the CGM methodology as a bridge between HERA and LCA frameworks hinges on how its results align with those derived through classical risk assessment methods. This relationship may reveal either accordance, systematic overestimate/underestimate, or variable alignment. This can affect absolute scores but also estimates of the RNs' contributions. The previous section shows that despite convergence in the estimated contributions of RNs, the total score obtained from CGM itself can be notably higher than that produced by more established and precise HERA approach.

An evident source of discrepancy would be that the dispersion from a Gaussian plume model depends on local environmental variations that generic models fail to capture accurately. Even within the HERA approach, generic assessments used for preliminary screening have some parameters that must be as representative as possible of the specific case studied (IAEA, 2001). Key uncertainty categories include meteorological (wind speed and direction, boundary layer height and precipitation), source terms (timing and duration of release, isotopic composition, emitted amount per radionuclide, physiochemical form and height of the release), and modeling (parametrization of turbulent diffusion schemes and wet and dry deposition). A review ranking the weight of these uncertainties for atmospheric discharges identified the release rate of RNs and wind direction as most influential; plume rise / release height, wind speed, release timing, precipitation and linked parameters as contextually influential; atmospheric stability, temperature and other modeling parameters as moderately or even not particularly influential (Leadbetter et al., 2020).

In our study of the Fessenheim NPP, several influential environmental parameters deviate significantly from the generic values considered in the CGM fate module – see Table 3. This discrepancy may explain the divergence, raising the question of how much CGM results can differ from local risk assessment studies and whether this deviation would be CGM consistently overestimating impacts. Notably, the only prior work comparing HERA and CGM also found CGM impact scores were significantly higher than those from HERA assessment, even though CGM's non-generic environmental parameters (currently defining its CFs) were tailored to match their case study (Paulillo, 2018). While a systematic exploration of this problematic is out of the scope of the present work, we aim to make a preliminary step towards its consideration. For this purpose, we searched academic literature for risk assessment studies that could serve as a basis for our analysis. Necessary requirements were the reporting of the disaggregated emitted discharges (Bq) and resulting midpoint impact (Sv) per radionuclide, along with details on the environmental parameters used. Preference was given to fairly recent studies, to minimize bias due to variations in the exposure modeling, and focus on routine discharges, especially during decommissioning. Very few studies could be found, as academic publications addressing radioactive effluents from NPPs after the operation phase are rare, most of the effort being directed towards simulation of anticipated accidents during operation (Kang and Cheong, 2020). Two studies were selected for our purposes. Subramanian et al. (2020) investigated the public dose of adults and infants at 1 km due to atmospheric discharges during a hypothetical Indian Pressurized Heavy Water Reactors (PHWRs) NPP decommissioning. This study is notable not only for its disaggregated results per radionuclide, but also for using the same release height as CGM in their simulation. Ayoub et al. (2024) estimated the dose resulting from freshwater discharges under normal operation conditions for an adult individual living in the nearest residential area of the hypothetical river modeled. Furthermore, the parameters of this hypothetical river align with the Fessenheim case study.
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(caption on next column)

**Fig. 6.** CFs sensibility to distance and the resulting effect on midpoint impacts. (a) CFs for emission to the air compartment. (b) CFs for emission to the freshwater compartment. (c) Midpoint total impacts for major RNs of interest during the Fessenheim NPP decommissioning, according to the different CGM versions (plain markers), EDF impact study (hollow markers at 1 km), and UCrad (lines, no consideration of distance). Entries in parentheses report the CF substitutes used where necessary.

Relevant environmental parameters are reported in Table 3.

Using data from these two publications, we calculated impact/discharges ratios (Sv/Bq) to construct coefficients dimensionally homogeneous to the characterization factors of CGM 1 km – see SI.11. This enables us to gauge the potential deviation in CGM results if applied to the same discharges considered. As shown in Fig. 7, CGM can indeed significantly diverge from classical HERA studies in the case of atmospheric releases (up to 2 orders of magnitude, Fig. 7a), even when the same height release is considered. The divergence often trends towards a CGM overestimate, though some exceptions occur. Characterization factors for alternative environmental conditions, like varied wind speeds and directions, would help tailor CGM CFs to specific cases, reducing discrepancies while retaining CGM’s generic applicability. The divergence becomes extreme for liquid releases (ten orders of magnitude, Fig. 7b), likely due to stark differences in river size modeling. The so-called “medium size river” parameters in CGM originate from a focus on the coastal Thermal Oxide Reprocessing Plant (THORP) at Sellafield (UK), in completion of recommended parameters from IAEA, 2001. However, there are a significant number of inland nuclear facilities: given their important water needs, a larger than medium size river might be more appropriate to ensure the generic nature of CGM. Updating the CGM characterization factors for freshwater discharges, to reflect the average size of rivers actually used by nuclear facilities, would thus enhance relevance. One step further would be the implementation of a regionalization approach, which is recognized as necessary to account for spatial variability in general, especially in the case of human health impacts (Joint Research Centre, 2010). This would be especially appropriate given that CGM has by design a site-dependent aspect.

### 3.3. LCIA endpoint scores

Independently of the discussion relating to the representativeness of the CGM methodology at midpoint level, we extended the analysis to the endpoint level for the two LCIA methods – see Fig. 8. The initial impact score, designated as “Static UCrad”, is calculated using the original characterization factors from UCrad (Paulillo et al., 2023). In contrast, alternative computations, labeled as “Prospective UCrad”, employ newly derived CFs based on the population dynamics constructed in Section 2.4.4 – see SI.1 for CFs construction, SI.9 for UCrad detailed numeric results, and SI.12 for synthesis underlying the results of this section. Fig. 8a illustrates that neglecting population dynamics leads to a clear underestimation of the damage to human health. According to this work, the missed impact value could represent something between [17–21.8] % of the actual impact – Table 4. Since DALYs is the endpoint level recommended metric and is meaningful at the collective level, other LCIA methods (e.g. USEtox) focused on human health impact share a similar framework for the midpoint-to-endpoint transition. Thus, our results have the general implication that all these LCIA methods would significantly benefit from dynamic population accounting. Implementation of this approach in all human health related LCIA methods would also be necessary to allow LCA practitioners to conduct proper prospective comparisons between different human health impact categories.

The same prospective approach was subsequently applied to CGM midpoint characterization factors to derive endpoint CFs – see SI.1 for CFs, SI.5–8 for CGM detailed numeric results, and SI.12 for synthesis
<caption>**Table 3**</caption>

Comparison of environmental parameters used for fate modeling in CGM in the different studies considered in this work. Due to the absence of details from the EDF impact study, approximate values were defined for illustration purposes.

<table><thead><tr><th rowspan="2">Emission</th><th rowspan="2">Parameter</th><th colspan="2">CGM Fate module (Paulillo et al., 2020b)</th><th colspan="3">Fessenheim NPP case study (EDF, 2024b)</th><th colspan="2">Atmospheric case study (Subramanian et al., 2020)</th><th colspan="2">Liquid case study (Ayoub et al., 2024)</th></tr><tr><th>Value</th><th>Comments</th><th>Years 1–8</th><th>Years 9–12</th><th>Comments</th><th>Value</th><th>Comments</th><th>Value</th><th>Comments</th></tr></thead><tbody><tr><td rowspan="2">Air</td><td>Height of discharge (m)</td><td>100</td><td>Based on Sellafield THORP stack height (IAEA, 2001) recommended value</td><td>56</td><td>[10–30]</td><td>EDF value for Fessenheim NPP</td><td>100</td><td>Typical stack height for Indian PHWRs NPP</td><td>–</td><td>–</td></tr><tr><td>Fraction of time wind blows towards the receptor</td><td>0.25</td><td></td><td>0.50</td><td>0.50</td><td>Approximation from EDF reported <i>Météo-France</i> wind compass 2018 (North and South dominant)</td><td>–</td><td>In-house program used, based on hourly meteorological data for 3 years. Only the computed dilution factor of 1.22 • 10<sup>-7</sup> s/m<sup>3</sup> is reported</td><td>–</td><td>–</td></tr><tr><td rowspan="3">Freshwater</td><td>Wind speed, geometric mean (m/s)</td><td>2</td><td>(IAEA, 2001) recommended value</td><td>4</td><td>3</td><td>Approximation from EDF reported <i>Météo-France</i> 2018 measures</td><td>–</td><td>–</td><td>241</td><td>Hypothetical river parameters used for simulation</td></tr><tr><td>Width (m)</td><td>21</td><td>Defined as a “medium-sized river”.</td><td>150–300</td><td></td><td>Approximation from own Open Street Map measures</td><td>–</td><td>–</td><td>4.4</td><td></td></tr><tr><td>Depth (m)</td><td>0.34</td><td>Calculated from (IAEA, 2001)</td><td>2.25–4.60</td><td></td><td>Calculated from (IAEA, 2001) Table III</td><td>–</td><td>–</td><td></td><td></td></tr><tr><td></td><td>Net flow rate (m<sup>3</sup>/s)</td><td>0.70</td><td>Table III</td><td>–</td><td></td><td></td><td>–</td><td>–</td><td></td><td></td></tr><tr><td></td><td>Flow rate (m<sup>3</sup>/s)</td><td>5</td><td></td><td>400–2000</td><td></td><td>Calculated from (IAEA, 2001) Table III</td><td>–</td><td>–</td><td>1000</td><td></td></tr></tbody></table>

underlying the results of this section. Results for the 3 major RNs ($^3$H, $^{14}$C and $^{60}$Co) are displayed in Fig. 8b. The interplay between population increase with the distance considered and the respective evolution of midpoint impacts for these three RNs accounts for the variation of endpoint impact scores across the different versions of CGM. As demonstrated in Section 3.1, these RNs exhibits distinct dynamics in midpoint scores relative to the distance from the discharge source, attributable to their respective emission pathways. The $^{14}$C radionuclide discharges occurs predominantly through the atmospheric pathway, leading to a sharp decline of midpoint impacts as distance increases. Starting from the 100 km distance, the parallel increase in the affected population results in a stabilization of endpoint impacts. The $^3$H and $^{60}$Co radionuclides discharges occurs through mixed or purely liquid pathway, respectively. The increase of affected population offsets the midpoint impact decrease, resulting in an overall increase in the total endpoint impact score as the distance increases – Table 4.

### 3.4. CGM endpoint approach limitations

A crucial assumption in CGM – and (IAEA, 2001) upon which it is based – is considering the population of the critical group as concentrated in a specific location. This contrasts with the uniformly distributed population approach adopted by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR, 2017). However, merely selecting the population present at the spatial boundaries of CGM is highly susceptible to account improperly for the increase in affected population with distance. Given that real population distributions are actually very inhomogeneous, this can result in scenarios where the affected population at 1 km is identical to that at 1000 km in one case study, while being very different in another. Such inconsistencies can obscure the interpretation of results and diminish the utility of endpoint-level CGM.

In the context of extending CGM to the endpoint level, we uniformly extracted population dynamics within the spatial ranges delimited by the CGM specific distances. Since the endpoint results are computed based on the CGM midpoint CFs associated with these distances, three main approaches to population attribution within the spatial intervals can be considered. Attributing the population to the boundary closest or farthest to the emission respectively corresponds to the worst and

minimal case estimates. A third option would involve defining spatial intervals for population extraction around the boundaries and attribute the population based on proximity to the nearest boundary. However, the first option is not applicable to populations within the area under 1 km, as CGM 1 km represents the closest-to-emission assessment available. Additionally, the third option would result in a differing population mix for each version of CGM, leading to an endpoint impact score positioned between the minimal and worst case estimate, with its actual position varying from case study to case study. We thus opted to aggregate the population within the spatial interval at the farthest boundary. Despite its limitations, we argue that this approach facilitates endpoint assessments across all CGM distance categories, ensuring consistency in population attribution and clarity in results interpretation. These considerations constitute the minimal requirements for appropriate comparisons with future studies that investigate endpoint-level impacts using CGM.

Nevertheless, we investigated potential biases associated with different possible treatment of the population distribution with a sensitivity analysis. For this purpose, a straightforward comparison was conducted based on the previously fitted power-law decrease of midpoint impacts from $^3$H, $^{14}$C and $^{60}$Co radionuclides (see Fig. 6c), across different population spatial distributions in the [1–1000] km range – see SI.13. The specific CGM distances (1, 100, 1000 km) were used as boundaries to delimit 2 spatial subsets representing short and long distances: [1–100] and [100–1000] km. We selected the SSP average of population in year 2026 as an example to define the affected population in these two spatial subsets (7.53·10⁶ and 3.83·10⁸ people, respectively). Four typical spatial repartitions of this affected population were examined, as illustrated by Fig. 9:

* the two main assumptions used by the CGM/IAEA and UNSCEAR approaches: line position (population concentration at the boundaries) and pseudo-uniform distribution (population uniformly distributed inside the set boundaries)

* two slightly less unrealistic cases: population clusters (denoted A and B) were positioned at defined locations within the spatial interval, modeled using Gaussian distributions of varying maximum size to simulate the differing population density of urban and rural areas.
<figure><img src="image_28.png" /><figcaption>**Fig. 7.** Comparison of impact/discharges ratios examples with CGM 1 km characterization factors. (a) Atmospheric (Subramanian et al., 2020). (b) Liquid / Freshwater (Ayoub et al., 2024). Entries in parentheses report the CF substitute used where necessary.</figcaption></figure>

As shown in Fig. 9, the variation in endpoint impact score across different population distributions depends on both the radionuclide and the spatial scale examined. Impact scores ratios, compared to the “line position” baseline, range from 1.06 (only a 6% difference) to nearly one order of magnitude, depending on the major radionuclide considered. The [100–1000] km interval exhibits the more pronounced ratio variations, largely due to the larger space interval considered which accentuates difference in population distribution. However, this spatial interval contribution to the total collective impact varies depending on the radionuclide – see Table 5 – thus modulating the influence of the

population assumption on the impact scores. The three distinct emission pathways of our major RNs allow for a comprehensive description of the possible scenarios. For instance, $ ^{60}Co $ is emitted through the liquid pathway, which is associated to minimal impact attenuation over distance. This results in a dominant contribution of the [100–1000] km interval to the impacts in every case (around 97.6 %) and a small variation between population distribution assumptions by up to 13 %. Conversely, $ ^{14}C $ is mostly emitted through the atmospheric pathway, which is associated to a steep impact decline with distance. This makes the [100–1000] km interval contributes systematically less to the total impact (48–75 %), while the population distribution assumption has a pronounced effect, increasing scores by a factor of 4 to 8. Meanwhile, $ ^{3}H $ represents an intermediate situation due to the mixed atmospheric and liquid emission pathways.

In summary, the influence of CGM's fate modeling propagates to the endpoint-level assessment. Our analysis indicates that assumptions regarding the population distribution will exert minimal influence on endpoint assessments for liquid discharges and substantial influence in the case of atmospheric discharges. This suggests that for CGM to be effectively applied at the endpoint level, moving beyond the limitations of our approach essentially requires a more realistic modeling of population distribution. Specifically, when midpoint impacts of key radionuclides can be reliably fitted as a function of distance, implementing these fitted impact-distance relationships in QGIS could enable direct allocation to actual population data. However, from a practical standpoint, this approach is very computationally intensive, highlighting the need for a balanced and widely accepted trade-off between computation time and spatial resolution. Future research should aim to establish such a trade-off in order to support methodological harmonization, especially as CGM is still a very recent and in-development method.

## 4. Conclusions

In this paper, we applied the recently developed UCrad and CGM LCIA methods to investigate the radiological human health impacts anticipated during the Fessenheim NPP decommissioning, drawing on data from the EDF impact study. This approach allowed for identification of the key elementary processes and RNs associated with both releases (Bq) and midpoint impacts (Sv). These elements are related in a complicated manner, as exemplified by the way specific combination of RNs and emission pathways predominate in driving the midpoint impacts depending on the spatial scale of the assessment. Across impact assessment methods, $ ^{3}H $, $ ^{14}C $, and $ ^{60}Co $ consistently emerge as the major RNs of interest, though their contribution to the total impact score vary. Similarly, atmospheric discharges during Water Metal cutting and liquid discharges from Regular exploitation are consistently identified as the major elementary processes leading to the midpoint impacts.

This work stands as complementary to the EDF impact study following a classical HERA approach, by extending consideration to spatial scales beyond the vicinity of the Fessenheim NPP. Despite divergences in total midpoint scores, the findings corroborate that the radiological human health burden associated with planned operations for the upcoming decommissioning, in the worst-case estimate, is 400 times lower than the French average annual dose, which is of general interest for the public and decision-making bodies. Workers' exposure was not assessed as it was out of the scope of the study. We point out again that the legally defined decommissioning phase, per French regulation, does not encompass pre-decommissioning activities, which result in the removal of 99.9 % of the radioactivity present on site after the operation phase (EDF, 2024). Emissions from this period should be assessed post-completion to check the relevance of results for the formal decommissioning phase, moving towards a comprehensive assessment of the NPP life-cycle.

From a methodological perspective, our comparative analysis shows that the CGM method can yield results that range from moderate to extremely divergent midpoint impacts when compared to the
<figure><img src="image_29.png" /><figcaption>**Fig. 8.** Radiological endpoint impact assessment. (a) Integrated endpoint impacts linked to major RNs for static – based on the existing CFs (Paulillo et al., 2023) – and prospective versions of UCrad – taking into account the population dynamics. (b) Integrated endpoint impacts linked to major RNs for the different versions of CGM, with prospective accounting of the population. Bars represent the average value, and error bars are the minimum and maximum values.</figcaption></figure>

<caption>**Table 4**</caption>

Summary of total endpoint impact scores, according to the different assessment methods. As all CGM scores displayed were computed using population dynamics, the mention of “prospective” is implied but not systematically repeated, for reading comfort. Endpoint score for the EDF impact study was extrapolated from the ratio of EDF impact study on CGM 1 km at the midpoint level.

<table><thead><tr><td>Method</td><td>Total Endpoint impact (DALYs)</td></tr></thead><tbody><tr><td>EDF impact study (HERA)</td><td>0.20 [+0.02;-0.01] (10<sup>-3</sup>)</td></tr><tr><td>CGM 1 km</td><td>1.33 [+0.09;-0.06] (10<sup>-3</sup>)</td></tr><tr><td>CGM 100 km</td><td>2.16 [+0.16;-0.14]</td></tr><tr><td>CGM 1000 km</td><td>107.8 [+7.2;-6.8]</td></tr><tr><td>CGM 10000 km</td><td>2091 [+99;-64]</td></tr><tr><td>Prospective UCrad</td><td>0.84 [+0.03;-0.02]</td></tr><tr><td>Static UCrad</td><td>0.68</td></tr></tbody></table>

established HERA approach. This inconsistency limits its current relevance as a practical bridge between HERA and LCA. On one side, CGM seems consistently biased towards overestimated absolute results, which can help produce worst estimates for the population critically exposed. On the other side, the overestimation is not consistent across all RNs, which could prevent identification of the actual RNs' contribution to the impacts. Adaptation of the environmental parameters of the CGM fate modeling is probably necessary, to allow practitioners selecting alternative CFs that are more representative of their local case study. This first step towards regionalization would align with the site-dependent nature of the method while preserving its generic framework, which is key for a consistent HERA/LCA combined approach. We also performed the assessment at the endpoint level, following the very recent development of UCrad. As decommissioning operations are spread over time, we show that population dynamics must be accounted for to prevent unrepresentative assessments. For retrospective assessments, historical

<figure><img src="image_30.png" /><figcaption>**Fig. 9.** Assessment of the effect of the assumption on population distribution on endpoint impact score, for the major RNs, using fits from the CGM method at midpoint level.</figcaption></figure>
<table><caption>**Table 5**

Contributions to the collective impact in the ranges considered, depending on the typical population spatial distribution, for the major RNs.</caption><thead><tr><th rowspan="2">Population distribution</th><th rowspan="2">Range (km)</th><th colspan="3">Relative contributions (%)</th></tr><tr><th>Tritium-3 (³H)</th><th>Carbon-14 (¹⁴C)</th><th>Cobalt-60 (⁶⁰Co)</th></tr></thead><tbody><tr><td rowspan="2">Line position</td><td>[1–100]</td><td>7.18</td><td>50.26</td><td>2.47</td></tr><tr><td>[100–1000]</td><td>92.82</td><td>49.74</td><td>97.53</td></tr><tr><td rowspan="2">Pseudo uniform</td><td>[1–100]</td><td>6.93</td><td>51.99</td><td>2.44</td></tr><tr><td>[100–1000]</td><td>93.07</td><td>48.01</td><td>97.54</td></tr><tr><td rowspan="2">Clusters (A)</td><td>[1–100]</td><td>6.30</td><td>36.73</td><td>2.42</td></tr><tr><td>[100–1000]</td><td>93.70</td><td>63.27</td><td>97.58</td></tr><tr><td rowspan="2">Clusters (B)</td><td>[1–100]</td><td>5.12</td><td>25.27</td><td>2.32</td></tr><tr><td>[100–1000]</td><td>94.88</td><td>74.73</td><td>97.68</td></tr></tbody></table>

population data should be used, whereas for prospective ones, we recommend the approach exemplified in our work, i.e. using SSP scenarios consistent datasets, as this framework is nearly consensual in the field of prospective LCA. Moreover, as human health LCIA methods share the same midpoint-to-endpoint framework, this recommendation apply beyond the ionizing radiations impact category. Implementing this approach to all human health LCIA methods would be necessary to allow proper comparisons between human health impact categories when the activity or system assessed is spread over time. By adapting the endpoint-level framework of UCrad to CGM, we show the importance of relevant assumptions for population distribution and suggest ways to go forward. Finally, although the number of RNs with a characterization factor in these two LCIA methods is important and covers most of the releases occurring in routine operations, decommissioning is expected to give rise to specific discharges. As shown by our reliance to use substitute CFs for specific RNs in this work, another possible development for these methods would be to establish the CFs of the RNs likely to be released during NPP end-of-life operations.

A potentially important source of divergence we did not analyze here, due to its complexity, pertains to the exposure model. Indeed, population exposure can fluctuate depending on cultural habits. It is plausible that the generic assessment parameters recommended (IAEA, 2001), which are used in CGM, may be more representative of the Western population. This could partially account for the less pronounced divergence observed between CGM and classic HERA approach in our French case study compared to the Indian case study of Subramanian et al., 2020. Given that the exposure modeling is shared between CGM and UCrad, this consideration is relevant to both LCIA methods. It underscores our recommendation for regionalization as a critical target of development for these methodologies, which would also favor their implementation in advanced global LCIA methods like Impact World + (Bulle et al., 2019). Moreover, this could be incorporated from the start into future iterations of UCrad/CGM aimed at assessing impacts on ecosystems – a natural progression towards establishing an LCA framework suitable for comprehensive comparison with conventional toxic substances.

Despite the limitations identified and analyzed, this study encourages the comparative use of LCA and HERA approaches in radiological assessments, as the divergence of numerical results has a meaning that can be interpreted, which enables a more comprehensive assessment of the radiological impact of activities such as nuclear power plant decommissioning. Current regulations, currently limited to the HERA approach, could evolve to require this type of comparative assessment from operators.

### CRediT authorship contribution statement

**Paul Robineau:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Maria Boltoeva:** Writing – review & editing, Validation, Supervision, Software, Resources, Investigation,

Funding acquisition, Conceptualization. **Nicolas Arbor:** Writing – review & editing, Validation, Supervision, Software, Resources, Project administration, Methodology, Funding acquisition, Conceptualization. **Gaetana Quaranta:** Writing – review & editing, Validation, Supervision, Software, Resources, Methodology, Funding acquisition, Conceptualization.

### Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work, following acceptance with minor revisions, the authors occasionally used ChatGPT (OpenAI) to help improve readability of some sections – without altering the factual content. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

### Funding

This work is part of the CO2InnO project, which is funded by the European Union (Interreg Upper Rhine 2021–2027, A1.3).

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgement

The authors acknowledge the CO2InnO project for the financial support, and the OHM Fessenheim. They also acknowledge the help of A. Paulillo regarding discussions on the UCrad and CGM methodologies.

### Data availability

All data supporting the findings of this study are available within the paper or in the supplementary information corresponding to the original data file.

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