# Digitalisation in geosciences for environmental protection

Olaf Kolditz, Diederik Jacques, Francis Claret, Erika Holt, Réka Szöke, David Garcia, Vanessa Montoya, Sergey V. Churakov, István Szöke, Arto Laikari, et al.

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Olaf Kolditz, Diederik Jacques, Francis Claret, Erika Holt, Réka Szöke, et al. Digitalisation in geosciences for environmental protection. Environmental Earth Sciences, 2026, 85 (2), pp.51. 10.1007/s12665-025-12750-y. hal-05493214

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# Digitalisation in geosciences for environmental protection

Olaf Kolditz<fnref n="1" />,<fnref n="8" /> · Diederik Jacques<fnref n="2" /> · Francis Claret<fnref n="3" /> · Erika Holt<fnref n="4" /> · Réka Szöke<fnref n="5" /> · David Garcia<fnref n="6" /> · Vanessa Montoya<fnref n="1" /> · Sergey V. Churakov<fnref n="7" /> · István Szöke<fnref n="5" /> · Arto Laikari<fnref n="4" /> · Min Chen<fnref n="9" /> · Tianyuan Zheng<fnref n="10" />

Received: 25 October 2025 / Accepted: 6 December 2025

© The Author(s) 2026

## Abstract

Data Science (Digitalization and Artificial Intelligence) became more than an important facilitator in various domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly applied in the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilizing of the best experimental and monitoring data as well as model concepts and tools to integrate multi-chemical-physical, coupled processes, multi-scale and probabilistic simulations in Digital Twins (DT) able to mirror or predict the performance of its corresponding existing or future physical implementations including workflows. The call for the Topical Collection was initiated from different actors, including research entities, technical support organizations and nuclear waste management organizations of the European projects EURAD (European Joint Programme on Radioactive Waste Management) and PREDIS (Pre-disposal Management of Radioactive Waste). The Topical Collection attracted a large number of manuscripts, more than eighty of which were published. These articles reveal a strong academic focus on using machine learning to map and assess soil and groundwater resources, hydrology and land use, landslides, and climate protection. They also highlight the core theme of nuclear waste management.

**Keywords** Digitalisation · Environmental protection · Machine learning · Deep geological disposal · Safety assessment · Digital twins · EURAD · HERMES · DITOCO2030 · Model-hub · DECOVALEX · OGSTools

## Introduction

The Topical Collection on the digitisation of geospheres for environmental protection has been very well received. Within two years, a total of more than 240 manuscripts on this topic were submitted, of which more than 80 articles were accepted and published. The most active countries in terms of author numbers were China (560 authors), India (124), Iran (56), Turkey (31), Germany (31), Vietnam (29), Pakistan (28), Nigeria (26), Ethiopia (23) and Indonesia (21) (Fig. 1). A word cloud based on the titles of all published articles can be seen in Fig. 2. Machine learning is the most prominent methodology for AI applications. Most areas of application are studies on mapping and the corresponding assessment of environmental factors for soil, land use and groundwater resources.

<footnote>Olaf Kolditz
olaf.kolditz@ufz.de</footnote>

<footnote n="1">¹ Helmholtz Centre for Environmental Research - UFZ,
Leipzig, Germany</footnote>

<footnote n="2">² Belgium Nuclear Research Centre (SCK-CEN), Mol,
Belgium</footnote>

<footnote n="3">³ BRGM, Orléans, France</footnote>

<footnote n="4">⁴ Technical Research Centre of Finland (VTT), Espoo, Finland</footnote>

<footnote n="5">⁵ Institute for Energy Technology (IFE), Lillestrøm, Norway</footnote>

<footnote n="6">⁶ Amphos 21, Barcelona, Spain</footnote>

<footnote n="7">⁷ Paul Scherrer Institute (PSI), Villigen, Switzerland</footnote>

<footnote n="8">⁸ Dresden University of Technology, Dresden, Germany</footnote>

<footnote n="9">⁹ Nanjing Normal University, Nanjing, China</footnote>

<footnote n="10">¹⁰ Ocean University of China, Qingdao, China</footnote>
<figure><img src="image_2.png" /><figcaption>Fig. 1 Author location map of the manuscript submission to the Topical Collection</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2 Word cloud generated from the titles of the published articles</figcaption></figure>
From a technical point of view, the articles can be catego-
rised into the following areas, which are represented by the
keywords provided:

- Geotechnics: rock and fracture mechanics, failure, geo-
logical CO2 sequestration and tunnel boring machines
(TBM),

- Hydrology: floods, precipitation, rain gauges, evapo-
ration, water quality, salinity, groundwater levels,
irrigation,

- Landslides and earthquakes,

- Soil: mapping, liquefaction,

- Land use: nexus, remote sensing,

- Urban systems: heat islands, subsidence,

- Climate: snow melt, CO2 concentration Smith et al. (2025),

- MICP: review article on microbial induced carbonate precipitation Wang et al. (2025b).

In addition to the large number of items published in the
topical collection we consider the short-term impact in
terms of the number of citations and downloads. In general,
three theme groups can be distinguished: (i) surface pro-
cesses; (ii) geological/hydrogeological processes; and (iii)
information technology.

The first group, regarding surface processes, covers the
following topics, which have been the most frequently cited
so far: remote sensing from satellite data (surface tempera-
ture) Parvar and Salmanmahiny (2024), flood susceptibility
maps Wahba et al. (2024), subsidence monitoring in urban
areas Zhang et al. (2024), erosion susceptibility mapping
Ahmad et al. (2024); Mokarram et al. (2024); Abiye et al.
(2025), land slide identification Khadka et al. (2025) and
characterization by data-driven methods (machine learning,
celluar automata) Sharma et al. (2025); Guo et al. (2025),
land surface characteristics, eg. automated detection of
sealed areas in urban environments Ahmad et al. (2025).

The second group, which deals with subsurface pro-
cesses, covers the following topics that have been cited
the most so far: soil liquefaction Şehmusoğlu et al. (2025),
rockburst hazard evaluation Liang et al. (2024), Earth
quakes Song et al. (2025); Bai et al. (2025), infiltration into
fractures Wang et al. (2024), failure modes in geotechnical
applications Niu et al. (2025); Fu et al. (2025a). Further-
more, reviews of geochemical and multiphysics numeri-
cal modelling also belong to this group Prasianakis et al.
(2025); Churakov et al. (2024). Several works deal with
groundwater issues, such as the use of various machine
learning approaches to determine groundwater levels or irri-
gation efficiency Adombi and Chesnaux (2025); Gholami
et al. (2025); Wang et al. (2025a); Zhang et al. (2025); Ouali
et al. (2025).

As citation data is not yet representative due to the short
publication times, we are adding the download information.
To date there have been 50,000 downloads of all publica-
tions in the Topical Collection. It is interesting to note that
publications on workflows and software developments in
particular have been downloaded numerous times, such as
the toolbox for basin morphology Topsakal et al. (2025)
and container technology for workflows of code coupling
and high-performance-computing Heinze er al. (2025);
Bilke et al. (2025). In addition, papers on the simulation
of multiphysical and reactive transport processes Samper
et al. (2024); Grigaliuniene et al. (2024); Ahusborde and
Tabrizinejadas (2025) has been downloaded particularly
often Prasianakis et al. (2025); Churakov et al. (2024).

Regarding the third group, the topical collection provides
a particularly good overview of the methods of artificial
intelligence (machine learning) used in environmental earth
sciences. Various machine learning methods are applied,
such as deep learning Şehmusoğlu et al. (2025); Haider et al.
(2025), gradient methods (XGBoost) Ahmad et al. (2024);
Liu et al. (2025), convolutional neuronal networks (CNN)
Fu et al. (2025b), explainable AI Lu et al. (2025); Shetkar
et al. (2025); Dağdeviren et al. (2025), image analysis for
automated landslide detection Khadka et al. (2025); Sharma
et al. (2025); Guo et al. (2025), and physics-informed
machine learning (PINNs) Adombi and Chesnaux (2025);
Tipu et al. (2025). The data basis for the characterization
of surface processes comes mainly from satellites Parvar
and Salmanmahiny (2024); Rossi et al. (2025); Hussein
et al. (2025) and radar interferometry (InSAR) Zhang et al.
(2024); Wang (2025c); Liang et al. (2025).

In line with the original intention of the Topical Collec-
tion Kolditz et al. (2023), numerous articles are devoted
to the topic of radioactive waste management; a detailed
discussion follows in the next section on Deep Geological
Disposal).

Digitalisation for nuclear waste
management: Deep geological repositories
(DGR)

The starting point for the Topical Collection had been the
current efforts of digitalisation for geotechnical applications
such as geological CO2 storage (Dutta et al. 2025; Gasan-
zade and Bauer 2025; Li et al. 2025) and deep geological
disposal of radioactive waste (Table 1).

The development of digitisation methods in DGR is
currently gaining in importance, as the first repository for
radioactive waste in Finland is already under construction
and further sites for deep geological disposal have been pro-
posed and are undergoing safety assessments. Digitalisation
<table><caption>**Table 1** Overview studies related to “Deep Geological Disposal”</caption><thead><tr><th colspan="2">Coupled processes:</th></tr></thead><tbody><tr><td colspan="2"><strong>Rock Mechanics and Reactive Transport</strong></td></tr><tr><td>Large deformation numerical analysis of cone penetration test considering sand state effect</td><td>Yang et al. (2025)</td></tr><tr><td>A rock joint roughness coefficient determination method incorporating the se-net attention mechanism and CNN</td><td>Zhou et al. (2025)</td></tr><tr><td>Study on hydraulic fracture propagation under the influence of natural fractures in fractured reservoirs</td><td>Gao et al. (2025)</td></tr><tr><td>High fidelity simulations for coupled processes, multi-physics and chemistry in geological disposal of nuclear waste</td><td>Churakov et al. (2024)</td></tr><tr><td>Multiphase flow and reactive transport benchmark for radioactive waste disposal</td><td>Samper et al. (2024)</td></tr><tr><td>Assessment of intermediate level radioactive waste disposal cell evolution in granitic host rock applying abstracted reactive transport models</td><td>Grigaliuniene et al. (2024)</td></tr><tr><td>Geochemistry and machine learning: methods and benchmarking</td><td>Prasianakis et al. (2025)</td></tr><tr><td colspan="2"><strong>Software development:</strong></td></tr><tr><td colspan="2"><strong>Workflows, Virtual Reality and Digital Twins</strong></td></tr><tr><td>Reproducible HPC software deployments, simulations, and workflows - a case study for far-field deep geological repository assessment</td><td>Bilke et al. (2025)</td></tr><tr><td>Combining FEFLOW and OpenGeoSys for interoperable workflows in environmental geotechnics</td><td>Heinze et al. (2025)</td></tr><tr><td>VR-EX - an immersive virtual reality serious game for science communication about the electrical resistivity tomography measurements in the Mont Terri rock laboratory</td><td>Graebling et al. (2024)</td></tr><tr><td>Immersive virtual reality fieldwork investigation of a landslide in Hokkaido</td><td>Zulhilmi and Hayakawa (2025)</td></tr><tr><td>Digital twin of underground research laboratory as a valuable instrument at early stages of a geological disposal programme</td><td>Svitelman et al. (2025)</td></tr></tbody></table>

plays an important role in these assessment processes, as the relevant data must be available in digitalised and publicly accessible form. In addition, safety analyses (e.g. geotechnical feasibility and assessment of long-term safety) will be performed using models. This requires a high level of reliability of the data basis and underlying modelling methods as well as tools.

Related publications in the present Topical Collection can be sorted into two groups regarding process simulation and software development in the field of deep geological repositories (Table 1).

The first section deals with the simulation of rock mechanics and reactive transport processes. A particular challenge for deep geological deposition is that of coupled processes, i.e. the interaction between physics and geochemistry within the repository. The state-of-the art challenges of modelling coupled processes in deep geological repositories are described in Churakov et al. (2024).

Yang et al. (2025) are developing new methods for modelling large deformations. Geomechanical processes in geotechnical systems are usually mapped with small deformations (fixed reference system for deformation processes). The large pressure and temperature changes induced by the storage of solid and gas-developing substances can also induce large deformations, so that appropriate calculation methods are required. To characterise rock mechanical properties (e.g. strength), Zhou et al. (2025) are developing machine learning methods using convolutional neural networks (CNN), a method of deep learning algorithms. Since experimental methods for determining rock strength are very costly, machine learning methods can help to extract essential features. When the rock strength is exceeded, fracture formation and crack propagation occur. Gao et al. (2025) are working on modelling crack propagation through hydro-mechanical processes. The modelling of coupled processes and, in particular, the validation of models based on experimental data for geological deep storage is also the subject of the DECOVALEX project (Birkholzer et al. 2025).

In addition to geomechanical processes, reactive transport processes play an important role in DGRs. The transport of radionuclides from the final repository is particularly important for safety analysis considerations. Very long time periods must be taken into account, which means that machine learning methods can play a major role in accelerating complex simulations. To this end, AI methods must be carefully tested with benchmarks (Prasianakis et al. 2025). Reactive transport processes are particularly complicated when transported in multiple fluid phases. The storage of low- and intermediate-level radioactive waste produces large quantities of gases that significantly influence not only the hydraulic conditions in a storage system, but also the reactive transport processes (Samper et al. 2024). Final repositories are constructed in various host rocks (clay, salt and crystalline). The geochemical processes depend heavily on the pore fluids and the fluid-rock interactions in these host rocks (Grigaliuniene et al. 2024).

The second topic area related to deep geological disposal concerns software development to support comprehensive model-based safety analyses. This essentially involves the development of efficient workflows for system analyses and the use of virtual reality methods for data and model integration. The complex safety analyses not only require the coupling of different calculation tools, but also the reproducibility of the simulation results must be guaranteed at all times.

Heinze et al. (2025) are developing an interoperable workflow for coupling two software platforms. This allows, for example, groundwater models with FEFLOW to be used as a basis for geotechnical simulations with OpenGeoSys. This interoperable software coupling combines the
advantages of both platforms and avoids parallel developments. Bilke et al. (2025) are developing so-called container technologies that can be used to port complex workflows to parallel computers. The use of high-performance-computing (HPC) achieves a considerable acceleration in calculations. In addition, container technology ensures the reproducibility (bit by bit) of the calculation results in the long term.

Virtual reality (VR) methods are developing increasingly great potential for complex geotechnical projects such as underground research laboratories and deep geological repositories. Large amounts of heterogeneous information can be visually processed for data and model integration, making them a prerequisite for the development of digital twins for real systems. A prototype for a digital twin of the Mont Terri underground research laboratory that can be explored virtually was developed by Graebling et al. (2024). In addition, VR methods can also be used to develop so-called serious games, which allow part of the laboratory to be explored in a playful way. These serious games for exploring a geophysical experiment were developed for the visitor center of the Mont Terri laboratory. VR methods are also increasingly being used for geomorphological processes, e.g., landslides (Zulhilmi and Hayakawa 2025). Software systems for data and model integration, including virtual reality methods, are important prerequisites for the development of digital twins for final storage systems. Various institutions are working intensively on this, and synergies in the complex software development process can be exploited (Svitelman et al. 2025).

## Lessons learned and outlook

With more than 200 submissions and 80 published articles on the topic of Digitalization of Geospheres for Environmental Protection, the Topical Collection impressively demonstrates the relevance and immense activity in the field of digitalization in geosciences.

However, it is also necessary to critically examine in which areas and with which data bases the use of AI methods is sensible and efficient. The availability of many AI methods as open source sometimes tempts people to simply use the methods and produce corresponding data without questioning the added value of using AI. For example, different machine learning methods are often used to find out a posteriori which method was the most efficient and accurate for certain data sets, without going into the reasons in detail. These types of method comparisons are of limited value, as the results are often not transferable to other data bases and areas of application.

A promising direction is the combination of machine learning methods with physics-based approaches, e.g.,

physics-inspired neural networks (PINNs). This allows the advantages of data- and process-based concepts to be combined Adombi and Chesnaux (2025); Tipu et al. (2025).

Other overarching topics for AI research in the geosciences for environmental protection, which affect broad areas of the topics covered by the Topical Collection, such as water resources, land use and climate protection, include the standardisation of data for interdisciplinary research, IT methods and software tools in accordance with FAIR principles, efficient data processing and computing algorithms on high-performance computers, and ethical issues in the use of AI methods.

In terms of digitalisation in radioactive waste management, the European Partnership on Radioactive Waste Management (EURAD) has become a key player. From the outset, great importance was attached to professional knowledge management Beattie et al. (2022); Knuuti et al. (2022). In the first phase of the EU project, the MODATS work package dealt with various aspects of digitization Altmairer et al. (2022)<fnref n="1" />.

Complete repository modelling requires detailed studies of in-situ experiments in underground research laboratories. In collaboration with Swiss colleagues from the Federal Office of Topography (swisstopo), and as part of the EURAD-MODATS work package, Graebling et al. (2022) have developed a digital twin of the Mont Terri underground research laboratory with various functionalities Graebling et al. (2022). For example, it can be used as a visual database of the laboratory via virtual reality methods (Fig. 3). The digital twin is also already being actively used for planning scientific experiments (e.g. the cyclic deformation CD-A experiment (right part in Fig. 3, Ziefle et al. (2022, 2024))). The full-scale emplacement (FE) experiment was used as an example of big data integration and uncertainty analysis (see left part in Fig. 3). Furthermore, the digital twin serves as a modern communication tool for science and public relations at the laboratory’s visitor centre, using serious game concepts (see lower left part in Fig. 3, Graebling et al. (2024)).

The EURAD-HERMES Model-Hub provides an online platform for collaboration in data integration and process modelling<fnref n="2" />. The basic idea relies on the thermo-hydro-mechanical-chemical (THMC) benchmark gallery of OpenGeoSys (OGS). Figure 4 illustrates the concept and structure of the EURAD-HERMES Model-Hub, which consists of three layers. The top layer (shown in blue), which is the user’s starting point, shows how the examples and benchmarks are organised. The first row of this layer refers to the two international projects, EURAD and DECOVALEX, as

<footnote n="1">¹ https://www.ejp-eurad.eu/implementation/monitoring-equipment-and-data-treatment-safe-repository-operation-and-staged-closure</footnote>

<footnote n="2">² https://www.opengeosys.org/hub/</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 3** EURAD-MODATS: Visualization as platform for data and model integration using virtual reality and serious games (Underground Research Laboratory (URL) Mont Terri)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 4** EURAD-HERMES: Model-Hub as a collaboration platform for benchmarking THMC models</figcaption></figure>

well as to classic benchmarks that also rely on analytical solutions. The lower row is organised by scale, i.e. laboratory, URL and field scale examples. The middle row focuses on the material behaviour (constitutive equations) of potential host rocks (clay, salt and crystalline) and refers to the THMC benchmarking collection of OGS. The centre field explains the concept of the Model-Hub itself. The second

layer (shown in green) refers to specific examples that have been implemented as Jupyter notebooks in the area of the thematic layer above. These notebooks can be downloaded and executed online without the need for any additional software installation. The notebooks serve as a code interface for collaboration. GitLab is used for version management in the software management system. The third layer (shown in
orange) provides an overview of the software modules and numerical codes for THMC process simulation. The Model-Hub is designed as an open system, which can be extended in terms of software use and examples. Please note that the developed model hub is supported by two projects: DigBen, which is funded by the Federal Ministry of Research, Technology and Space and provides the technical framework, and EURAD-HERMES, which provides content for the benchmarks and examples from the field of radioactive waste management. The Model-Hub generally has a wider scope, including other geoscientific application fields.

Furthermore, considerable effort is being invested in the development of concepts for digital twins. The basic idea is to develop a digital counterpart to the real system so that the evolution of the real system can be modelled for predictions and optimization of the system in operational use, including real-time data. There will be no digital twins per se. On the one hand, digital twins must be developed specifically for certain purposes and functions. On the other hand, generic concepts are important in order to be able to link the functionalities later on. It is becoming apparent that the development of digital twins involves complex software projects that can only be managed by large teams in a structured and interdisciplinary manner.

In EURAD the strategic study DITOCO2030 is dedicated to develop digital twin concepts for radioactive waste management<fnref n="3" />. The DITOCO2030 Work Package aims to accelerate the development and integration of digital twins (DTs) throughout the entire radioactive waste management (RWM) lifecycle. In general, DTs offer transformative benefits, including cost and efficiency gains, enhanced safety and risk mitigation, improved regulatory compliance and optimised lifecycle management. Combining continuous data flows with physics-based and data-driven models enables DTs to support predictive maintenance, design optimisation, long-term safety case management and transparent stakeholder engagement. This ultimately fosters greater confidence and transparency in decision-making. The DITOCO2030 project aims to establish how to bridge the research and development (R&D) gap between fragmented digital twins in different disciplines, common data environments and decision-making platforms. This will enhance our understanding of the potential and constraints of digital twins (DTs) in waste management throughout their entire lifecycle. As a strategic study, DITOCO2030 currently focuses on mapping the requirements of RWM disciplines and identifying key R&D gaps and challenges related to interoperability, data governance and model credibility. It also proposes measurable indicators for performance assessment, design optimisation, cost estimation and stakeholder communication. By

promoting open standards and a unified markup language for nuclear waste management, the work package aims to close the existing R&D gap and establish the foundation for interoperable, reliable, and scalable DT ecosystems. Recommendations will also be developed regarding international standards, methodologies and markup languages to guide the implementation of DT. Within the EURAD-2 framework, this coordinated approach will support the transition towards safer, more efficient and transparent radioactive waste management, thereby strengthening the nuclear sector's innovation capacity and long-term societal value.

## Editorial note: Tribute to Professor Peter Dietrich

Professor Peter Dietrich has been Associate Editor for Environmental Earth Sciences for over ten years and is responsible for the subject areas of Environmental Geophysics and the new focus topic Research Tools. Peter is Professor of Environmental and Engineering Geophysics at the Eberhard Karls University of Tübingen and has headed the Department of Monitoring and Exploration Technologies at the UFZ for 20 years. Peter studied geophysics at the TU Bergakademie Freiberg and obtained his doctorate at the University of Tübingen. We would like to take this opportunity to thank Peter for his many years of valuable work as Associate Editor for Environmental Earth Sciences. He has been a great inspiration to the journal and we look forward to continuing to work together for EES. His scientific work is not only closely linked to geophysics; he also recognised and promoted the potential of digitalisation in the environmental sciences at an early stage. We would therefore like to dedicate the Topical Collection on digitalisation to him on his 60th birthday. We congratulate Peter warmly and wish him continued creativity and scientific success in the future.

**Acknowledgements** The Topical Collection on Digitalisation of Geospheres for Environmental Protection is part of a series of papers focusing on a variety of digitalisation aspects in environmental earth sciences (see introduction above). Some of the papers presented are part of large international projects in radioactive waste management such as DECOVALEX Birkholzer et al. (2025) and EURAD Zuidema (2023). This work is also contributing to the European Joint Programme on Radioactive Waste Management EURAD "European Partnership on Radioactive Waste Management" (Grant Agreement No 101166718). Environmental Earth Sciences publishes Topical Collections to promote emerging topics in environmental sciences in a structured way Zheng et al. (2023).

**Author Contributions** OK: concept, writing, editing; DJ: concept, writing, editing; FC: concept, editing; EH: concept, editing; RZ: concept, writing, editing; DG: concept, editing; VM: concept, editing; SC: concept, writing, IZ: concept; AL: concept, writing; MC: concept, review; TZ: concept, review

<footnote n="3">³ https://www.ejp-eurad.eu/implementation/next-generation-digital-twins-support-optimisation-construction-and-operation</footnote>
**Funding** Open Access funding enabled and organized by Projekt DEAL.

**Data Availability** No datasets were generated or analysed during the current study.

## Declarations

**Competing interests** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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