31:T8b7a,**About Me** *Education and Past Experience* I am currently pursuing a Bachelor of Science degree in Pharmaceutical and Industrial Chemistry through the University of Limerick. This involves significant practical experience in analysing chemical processes in a laboratory and writing reports. As part of the 3^rd year of this course, I am required to complete an internship placement for eight months with a suitable organisation either in the public or private sector. At the end of my 2^nd year in 2023, I worked as a summer student for three months (June-August) with the Irish Environmental Protection Agency (EPA). I was a member of the Emergency Preparedness Team in the EPA's Office of Radiation Protection and Environmental Monitoring. During this time, I updated the database for the EPA's computer based ARGOS decision support system, which is used to predict the atmospheric transportation of radioactive plumes over Ireland in the event of nuclear accidents. ARGOS can also estimate the potential repercussions on the Irish agricultural sector as a radioactive contamination. My work involved ensuring the ARGOS database was up to date with information about the Irish population and relevant agricultural data (animals, food, etc.) in order to predict the potential radiological impact on the Irish population. *Current Research Focus* Through my work with the EPA in 2023, I gained a new interest in radioactivity, particularly its effect on the Irish environment. Consequently, for the purpose of completing my internship, I applied to the EPA with the intention of taking part of a suitable project. The EPA responded to say that, as part of their own annual Internship Programme, I could once again work with the Emergency Preparedness team in 2024 (January-August) as they could facilitate a project entitled "Establishing a Baseline of Radioactivity in the Irish Marine Environment". This project would involve working with both the EPA and the International Atomic Energy Agency (IAEA). **Environmental Protection Agency** The Environmental Protection Agency (EPA) was established in July 1993 following increased interest from the Irish public about the protection of the environment in the context of a rapidly industrialising nation^[1]. The EPA has evolved to protect, improve and restore Ireland’s environment through regulation, scientific knowledge and working with others. The work of the EPA is currently spread across many programmes including water management, climate change and environmental monitoring^[2]. As part of its environmental monitoring programme, the EPA is responsible under Irish legislation for continuously monitoring radioactivity in air, food, drinking water and the marine environment. The purpose of the EPA's radioactivity monitoring programme is to assess the radiological impact on the Irish population from exposure to radioactivity in food and the environment. This ongoing (baseline) data can also be used for comparison purposes in the event that a nuclear accident abroad might cause radioactive contamination in Ireland. The EPA’s Radiation Monitoring Laboratory is based in the EPA’s Dublin Campus. The monitoring programme allows for geographical distribution analysis of radionuclides to improve the understanding of the long-term behaviour of artificial radioactivity in the food chain and environment. The monitoring programme also ensures that any increases in radiation levels resulting from an accidental release of radioactivity to the atmospheric or marine environment are detected and assessed rapidly^[3]. The data from the monitoring programme is made available on the websites maintained by the EPA, the OSPAR Commission (who are concerned with the levels of radioactivity in the Northeast Atlantic) and the European Commission. This report will refer to radioactivity measurements from the annual marine monitoring programme conducted by the EPA's radiation monitoring laboratory. The marine programme involves routine sampling of seawater, biota (fish, shellfish and seaweed) and sediment from the Irish coastline, followed by relevant laboratory testing to determine radioactivity concentrations. The EPA analyse around 2000 marine samples every year. Most samples are taken from fixed sample locations throughout Ireland shown in (figure 1). The samples are analysed in the laboratory for a variety of water-soluble radionuclides. For example, seawater is primarily analysed for Caesium, Tritium and Technetium while biota for Caesium, Technetium, Plutonium and Americium. The radioactivity content in the samples is measured by either alpha spectrometry detectors or high-resolution gamma spectrometry detectors. The detector is determined by the nuclide and whether it is an alpha, beta or gamma emitter. The results are presented in units of Bacquerels/litre for seawater and Bacquerels/kilogram for biota and sediment. For the purposes of my project, the EPA's radiation monitoring laboratory compiled radioactivity concentration data between 2010 and 2023 for seawater and biota. There was no new data available for sediment during this period. ![image.png](${await FileAttachment("EPA Marine Sampling Locations.png").url()}) ##### [Fig.1] Environmental Protection Agency marine sampling locations between 2010 and 2023 ^[4]. **International Atomic Energy Agency** The International Atomic Energy Agency (IAEA) was established in July 1957 in response to fears and expectations generated by developments and research into nuclear technologies. As such, the role of the IAEA is to promote the peaceful use of nuclear technologies and advances while ensuring the safety and security of nuclear energy. The IAEA promotes research in nuclear science to advance and improve new technologies and methods to improve the environment^[5]. The IAEA facilitates international collaboration through their emergency preparedness training and exercises, global research projects, nuclear supports and safeguards^[6]. Since 2005, the International Atomic Energy Agency’s Marine Environment Laboratory in Monaco has maintained a Marine Radioactivity Information System (MARIS, https://maris.iaea.org/home) that allows users to search and download the results of measurements of radioactivity in seawater, biota, sediment and suspended matter around the world. The purpose of MARIS is to provide comprehensive data and information on current and historical marine radioactivity levels. This data originates from a range of sources including published scientific papers, technical reports, databases developed within institutes or scientific programmes in IAEA Member States. There are many benefits from using the data in MARIS, including the validation of computational models that simulate the transfer of radionuclides in the marine environment and providing inputs for radiological assessment studies^[7]. Significant work has been carried out by the IAEA to maintain and upload datasets from around the world arising from radiological surveys and research studies in both open ocean and coastal locations. However, the IAEA are aware of datasets (for example from the Irish Sea) that have yet to be uploaded to MARIS (pers comm). **Project Description** *Overview* This project (‘Establishing a Baseline of Radioactivity in the Irish Marine Environment’) was proposed by the EPA as part of their 2024 Internship programme. It was expected the project would benefit both the EPA and the IAEA. For the EPA, the project aimed to quantify activity concentrations for a range of sample types (seawater, fish, seaweed, shellfish, sediment)and radionuclides (Carbon-14, Caesium-137, Plutonium, Polonium-210 and Tecnetium-99) between 2010 to 2023. This data could then be used to establish a 'baseline' of radioactivity in the Irish Sea before before the construction of any new nuclear power plants (NPP) in the UK and France. For the IAEA, the project involved the collation of radioactivity data from the Irish marine environment and submission to the MARIS platform, thereby improving MARIS as a resource for scientific research. Finally, the project employed an opensource computer application called Observable as an innovative and alternative tool for visualising the data from the Irish marine environment. I was the first person in the EPA to use Observable. The IAEA regularly use Observable and they were keen to share their expertise throughout the project to create the visualisations within this notebook. *Project Necessity* The production of electricity by nuclear fission is prohibited in Ireland by the Electricity Regulation Act, 1999 (Section 18). However, the EPA has a role in monitoring developments in the nuclear sector across Europe with a particular focus on Ireland's nearest neighbouring countries, the UK and France. Both the UK and France have declared their intention to embark on a programme of building new nuclear power plants (the 'new build' programme) to meet increasing demand for electricity and climate change commitments. >United Kingdom: The United Kingdom, currently maintain eight generating Advanced Gas-Cooled Reactors (AGR's) at three locations, (Heysham, Hartlepool and Torness) and a Pressured Water Reactor at Sizewell. The current fleet of reactors generate 6GW of electricity. In the 'race to NetZero' (balancing and reducing carbon emissions), the UK Government have largely invested in their nuclear programme to increase their generation capacity of nuclear energy to 24GW by 2050. As part of this initiative, a large scale nuclear reactor was approved for construction on the southwest coast of England called Hinkley Point C (HPC). HPC is estimated to start generating electricity between 2029-2031. The nuclear power plant at Sizewell (Sizewell B) is scheduled to be decommissioned., However, there are plans to construct a new nuclear power plant (Sizewell C) at this location. If approved, Sizewell C (SZC) will output 1.2GW of nuclear energy up to the year 2055. SZC is considered to be similar to the project HPC. The UK Government have approved a further six locations suitable for nuclear reactors namely, Hartlepool, Bradwell, Mooreside, Heysham, Wylfa and Oldbury-on-Severn. Wylfa, on the west-coast of Wales, is the preferred location for the third new large scale nuclear reactor (after HPC and SZC), whereas the other locations will see mixed developments of Small Modular Reactors (SMR) and Advanced Modular Reactors (AMR)^[8]. Figure 2 shows the nuclear reactor sites. ![image.png](${await FileAttachment("Map UK Nuclear Power Plants.png").url()}) ##### [Fig.2] Nuclear Power Plant locations in the UK^[9] >France: France is a global leader in the generation of nuclear energy. The French government places significant focus on energy security and the country currently operates 56 nuclear reactors. 70% of electricity generated in France comes from nuclear energy. In February 2022, the French government confirmed plans for the construction of six nuclear reactors. The reactors will involve three pairs of European Pressurised Reactors (EPR2) in Penly, Gravelines and Bugey. Construction of the first pair of reactors began in 2024, in Penly, and they are expected to be operational mid-2030s. The French government also stated that the construction of a further eight SMRs was considered. France have also invested in refurbishments to extend the life of existing nuclear reactors. At present, any 900 MWe reactors over 30 years old, undergo a 4-month review process, whereby on completion the reactors can be operational for a further ten years based on continued maintenance. For example, in 2021, the country's 900 MWe reactors (some 32 reactors in total) were granted the ten-year operational extension. The country's 1300 MWe reactors have been under review between 2015-2024. Currently, France is exploring the possibility of increasing the life span of reactors to 60 years. In August 2023, the French Nuclear Safety Authority (ASN) approved the requirements for the extended operation of Tricastin 1, making it the first reactor in the French fleet that can operate beyond 40 years. Figure 3 shows the locations of the Nuclear Power Plants in France^[10]. ##### [Fig.3] Nuclear Power Plant Locations in France^[11]. All Nuclear Power Plants (NPP) are allowed to discharge radioactive waste under license and regulations into the marine environment. In particular, NPPs release large amounts of tritium (H-3) as part of the electricity generation process. Tritium is a Hydrogen isotope that is not easily separated from water. Tritiated water is highly diluted before its release into the marine environment. The levels of radioactivity released are required to be below harmful thresholds. However, releases of nuclear waste into the environment are continuously subject to public scrutiny. Further nuclear developments in the UK and France will increase nuclear discharges into the environment. The proximity of the proposed new reactors to Ireland and the discharges they will release into the Irish Sea and beyond highlights the importance for the EPA to monitor and establish a baseline of radioactivity before these future developments. Once the discharges start to occur then the EPA can then assess any potential radiological impact to the Irish public from the exposure to radioactivity in the marine environment. *Project Scope* This project had several goals: Firstly, to collate radioactivity concentration data in the Irish marine Environment from various scientific resources. The areas of interest were the Irish Sea, Celtic Sea, Bristol Channel, English Channel and the West of Ireland. Figure 4 shows the region of interest (ROI). The data involved measurements in seawater, biota and sediment. The MARIS data base had little information regarding biota post-2019 and sediment post-2010 in the ROI. It was decided between the EPA and the IAEA to limit the radionuclides to Carbon-14 (C-14), Caesium-134 (Cs-134), Caesium-137 (Cs-137), Plutonium isotopes (Pu-238, Pu-239+240, Pu-241), Polonium-210 (Po-210) and Technetium-99 (Tc-99). This was based on what is currently monitored by the EPA. Suspended particles and other radionuclides were out of scope. Secondly, to upload the data to the MARIS database. The IAEA provided an Excel template with various column headings to record sample information such as coordinates, species, unit, etc. Not every column in the template was necessary to complete and this is documented in the data collation section below. The data uploaded to the MARIS database would subsequently appear on the MARIS visual display platform. Thirdly, to become familiar with Observable as it was an integral part of this project. Its unique facilities to could be used to visualise the collated data (in parallel to MARIS). Observable was recommended by Paul McGinnity who works as a Technical Officer in the IAEA's Marine Environment Laboratory in Monaco. The IAEA currently use Observable to experiment with visualisations prior to making them available for MARIS . They also use Observable to create interactive displays of radioactivity measurements from across the world. The IAEA developed an Application Programming Interface (API) for MARIS. An API is a set of rules and protocols that allows various software applications to communicate with each other. An API defines methods and data formats that applications can use to request and exchange information. The MARIS API was subsequently used to link this Observable Notebook to the MARIS database, so that when MARIS is updated with new data in the ROI, the notebook will automatically update to include the new data. The use of Observable and the API would offer new skills and expertise to the EPA and allow the IAEA to assess the performance of their API. ![image.png](${await FileAttachment("Project Areas of Interest.png").url()}) ##### [Fig.4] Map displaying the area of focus of the project^[12]. **Project Method** *Working Routine* Kevin Kelleher (Senior Scientific Officer ) and Robert Ryan (Scientific Officer), who are members of the EPA's Emergency Preparedness team, worked closely with me on this project, which commenced in January 2024. Meetings were held regularly through Microsoft Teams with some in-person meetings also. Paul McGinnity from the IAEA liaised through bi-weekly Microsoft Teams meetings as he works closely with the MARIS database. The first stage of the project involved discussions on the sample types and radionuclides of interest to the EPA and IAEA. My daily tasks included, becoming familiar with aspects of marine radioactivity, compiling radioactivity data in the Irish marine environment from published reports and sorting through scientific papers provided by the IAEA to determine their relevancy to the project. Following this, the project progressed to data collation and its submission to the MARIS database. The second stage of the project involved becoming familiar with the Observable application. As this is not routinely used by the EPA, I was involved in regular communication with the EPA's IT department to seek approval for its use and create a user account where I could save the updates to this Notebook. I then devoted time to follow online tutorials to understand the application and its unique features. The final stage of the project involved data visualisation. In June 2024, there was an opportunity for me to visit the IAEA in Monaco to broaden my experience working with Observable. Paul McGinnity and I discussed ideas to graphically represent the marine data that I had collated. Virtual meetings took place with Franck Albinet, a data scientist who has particular interest in applying data science to emergency responses. Franck has worked to expand the data management and analytical capabilities of MARIS. Franck provided advice in using Observable and the planning involved in data visualisation. *Phase 1 - Data Collation* The scope of the project was determined by the data available in MARIS and EPA's interest in establishing a baseline of radioactivity concentrations in the Irish marine environment. As mentioned above, it was decided to limit the radionuclides to C-14, Cs-134, Cs-137, Pu-238, Pu-239+240, Pu-241, Po-210 and Tc-99. As a result, the MARIS database was filtered by these radionuclides and examined. It was discovered for the time period 2010-2023 there was no sediment data available in the region of interest (Figure 4). Therefore, sediment became a priority in terms of collating data. Biota was identified as the second sample type for the collation of data since it is a great bioindicator of radioactivity. This is particularly the case for seaweed. Substantial biota data in the region of interest was already incorporated into the MARIS database between 2010-2019. However, there was no data available for subsequent years. Initially, seawater and suspended particles were included in the project scope. The volume of data was too great for collation considering the amount of time available to the internship. However, the project scope was adjusted to include seawater based on this data being made available by the EPA's radiation monitoring laboratory in July 2024. The IAEA provided an excel template to be used when collating the data for MARIS. The template included headings to be used with each sample type and a list showing how body parts, sampling methods and measurement methods should be recorded (Figure 5). The data headings were colour-coordinated to illustrate the importance of the data for displaying on MARIS. Essential data was to be included in the red columns/cells, data of moderate importance was to be included in orange columns/cells and optional data was to be included in the green columns/cells. The excel template provided a structure to reference the data also. Completed excel spreadsheets of marine radioactivity data were sent to Paul McGinnity for review. Following examination, the data would be uploaded to the MARIS website. | **Biota Reporting Template**|| **Sediment Reporting Template**| | ---- | ---- | ---- | | ![image.png](${await FileAttachment("MARIS Template.PNG").url()}) || ![image@1.png](${await FileAttachment("MARIS Sediment Template.PNG").url()}) | ##### [Fig.5] MARIS template for reporting (1) Biota and (2) Sediment information Marine radioactivity measurements were compiled from four main sources, namely published EPA reports, UK-based Radioactivity in Food and the Environment (RIFE) reports, scientific literature compiled by the IAEA and scientific papers available through the online resource Science Direct, which is the world's leading source for scientific, technical, and medical research. >Environmental Protection Agency Marine Radioactivity Data The Radioactivity Monitoring of the Irish Environment report is published by the EPA's radiation monitoring laboratory. It compiles results of radioactivity monitoring in air, drinking water, the marine environment and a range of foods. The reports contain data from the Irish Sea, the west of Ireland and Northern Ireland (Figure 1). Sediment and biota radioactivity measurements were compiled from the reports published between 2010-2015. The 2010-2011 and 2012-2013 Radioactivity Monitoring of the Irish Environment reports were published by the Radiation Protection Institute Ireland (RPII), however, the RPII merged with the EPA in 2014. The EPA's radiation monitoring laboratory was contacted to provide additional radioactivity data post-2015 for biota and sediment. At the midpoint of the project, Veronica Smith, the Senior Scientific Officer of the EPA's Environmental Monitoring and Surveillance team, provided excel spreadsheets with all EPA marine radioactivity data for biota and seawater between 1995-2023. The scope of the project did not initially include seawater, however this was amended to include the EPA's seawater radioactivity measurements. The EPA's marine radioactivity measurements were published and can be viewed on the EPA's website^[14][15]. As part Ireland's commitment, the EPA must report Irish marine radioactivity measurements in seawater and biota to OSPAR on an annual bases. OSPAR evaluates the data and displays it on the OSPAR Data and Information Management Systems (ODIMS)^[16]. The IAEA regularly upload data to MARIS from OSPAR. As a result, the EPA's and OSPAR's datasets were compared to eliminate duplications. Excel was used to compare the datasets. It should be noted there is some duplication in radioactivity concentrations in biota and seawater. The duplications will be resolved in the future. > Radioactivity in Food and the Environment The annual Radioactivity in Food and the Environment (RIFE) report^[13] is published by environmental regulators and food standards agencies in the UK. It compiles the results of monitoring of radioactivity in food and the environment by the Environment Agency, Food Standards Agency, Food Standards Scotland, Natural Resources Wales, Northern Ireland Environment Agency and the Scottish Environment Protection Agency. The main purpose of the RIFE programme is to monitor the environment and the diet of people living or working near nuclear and selected non-nuclear sites. Marine radioactivity data was collated from RIFE reports published between 2015-2022 (RIFE 21-28). Biota and sediment radioactivity data was extracted from the reports and displayed using the MARIS template. RIFE reports included data mainly from the UK and Northern Ireland. There were two complications with the data from RIFE. Firstly, RIFE reports do include maps displaying their sampling locations, however precise coordinates are omitted. The MARIS template requires longitude and latitude points to be recorded. Google maps was used to manually estimate the longitude and latitude points of the sampling locations. The second complication followed the data review process. The data from the RIFE reports was sent to the IAEA for its examination to be approved for upload to MARIS. The examination highlighted duplicates in the data. It was discovered that RIFE reports repeated data and sampling area names changed slightly in separate tables. For example, 'North Solway coast' could be 'Solway coast' in another table, but they would have the same radioactivity concentration. The duplicates were removed and the data was approved to be uploaded to MARIS. > IAEA Scientific Literature The IAEA are provided with scientific resources with radioactivity measurements from across the world. They compile the scientific literature into one large file called "ALL PDF's". This file contained over 200 scientific papers with marine radioactivity concentrations from across the world. The file was examined for resources with relevant information regarding the project's region of interest (Figure 4). Initially, there were ten relevant papers, however this was reduced to four when the project scope was adjusted to include biota and sediment between 2010-2023 only. The data was compiled and uploaded to MARIS following the review process. > Science Direct Library ScienceDirect is the world's leading online platform that provides users access to peer-reviewed, full-text scientific, technical and health literature. ScienceDirect is managed by Elsevier, a large academic publishing company. The platform is widely used in industry for academia, research, scientific updates and advancements. There are 800 open access journals and 3.3-million open access articles included in Science direct^[17]. The search tool in ScienceDirect was used to find resources with information relating to radioactivity in the Irish Sea. Three papers were relevant to the project and yet to be uploaded to MARIS. The papers were downloaded and the data collated. Paul McGinnity reviewed and uploaded the data to MARIS. *Phase 2 - Observable* As mentioned in the project scope, Observable is used within the IAEA to experiment with visualisations prior to making them available on MARIS. It was suggested by Paul McGinnity to conduct the project through Observable, as this would improve the IAEA's knowledge on the application and introduce a new skill to the EPA. As Observable was not routinely used by the EPA prior to this project, an extensive review of Observable was required and conducted by the EPA's IT department. A Business Needs Form (BNF) was completed, outlining the need of Observable, its price and what the application would be used for within the project. The BNF was forwarded to the EPA's Enterprise Architecture team, James Woodward, a Contractor working with the EPA's ICT and Communications team, Andy Fanning, EPA's Programme Manager of Environment and Health and Robert Ryan (Scientific Officer) to request approval for the application. Observable is a non-Microsoft application that was in the process of becoming GDPR (General Data Protection Regulation) compliant (May 2024). This was the main concern highlighted during the process of approval. Online meetings were held with James Woodward to discuss the concerns with Observable further. It was explained in detail, all data used within the project was obtained from existing published and public resources and therefore, GDPR was not relevant. Observable was approved for use under the following conditions; Data uploaded to the platform must be public and GDPR compliant, proper care must be taken to manage memory to avoid performance issues and potential crashes and to be aware that support with Observable would be limited as the EPA would not have expertise in the platform. Time was devoted to learning about the Observable application following its approval. Observable has a YouTube channel with tutorials that cover a variety of topics (https://www.youtube.com/@Observablehq/streams). The tutorials offered guidance on the basics such as, creating notebooks, forking (copying) and adding cells to the more advance plotting, mapping and creating dashboards. Observable has an active online forum where other users constantly post and answer questions. Notebooks are published to the Observable Library by users where they can be viewed and forked (copied) by others in the Observable community to their personal account. Forking was a great tool when initially learning about Observable as you could learn from advanced users by experimenting with their code and implementing your own data. Observable was difficult to grasp in the beginning, however, with time and much trial and error it became easier. Knowledge of Observable improved greatly following meetings and collaboration with Paul McGinnity and Franck Albinet at the IAEA's Marine Radioactivity Laboratories in Monaco. *Phase 3 - Data Visualisation* In July 2024, there was an opportunity to visit to Paul McGinnity at the IAEA's Marine Radioactivity Laboratories in Monaco. In the course of the visit, in-person and online meetings were held with Paul McGinnity (https://observablehq.com/@maanchu) and Franck Albinet (https://observablehq.com/@francobollo) to discuss the collated data and the most apt way to display it. Paul McGinnity and Franck Albinet have extensive knowledge and experience in data visualisation and offered several methods to present the radioactivity data. Throughout the week, data displays were created, altered and improved until finalised as they are within the Notebook. It was decided to 1) connect the notebook to the MARIS API, 2) create an interactive user interface, 3) develop an interactive map, 4) use a scatterplot to illustrate the moving average of the data and 5) capture the the data in a boxplot. > MARIS API The IAEA developed an Application Programming Interface (API) for MARIS. An API is a set of rules and protocols that allows various software applications to communicate with each other. An API defines methods and data formats that applications can use to request and exchange information. The MARIS API was designed by the IAEA to allow easier access to the MARIS database. For example, when the MARIS API is incorporated into an Observable notebook it syncs the MARIS website with the notebook. The notebook has access to the data within MARIS and will update as changes occur. The user no longer has to download individual datasets. The MARIS API was imported from the IAEA's Observable Notebook (https://observablehq.com/@maris-iaea/api) and can be viewed under 'Utils and Imports' in the coding section of this notebook. > Interactive User Interface The interactivity of the notebook was a priority. The user interface was created so that users could choose the sample type (biota, seawater and sediment), species type (fish, shellfish and seaweed) in the case biota, nuclide type and aggregation type (mean, max and median) of interest. A slider button was added to the interface to adjust the view of the region of interest by zooming in and out. The user interface is linked to the display map and plots so they will change based on the selections. It should be noted the the user interface was based on the MARIS website. As stated previously the project focused on the radionuclides Carbon-14 (C-14), Caesium-134 (Cs-134), Caesium-137 (Cs-137), Plutonium isotopes (Pu-238, Pu-239+240, Pu-241), Polonium-210 (Po-210) and Technetium-99 (Tc-99), however, some of these nuclides have been excluded from this notebook as there was little data to produce a moving average. Table 1 outlines the data available to view within this notebook. A separate notebook (https://observablehq.com/@radiapro/data-summary) has been created to include all radionuclides. |Sample Type| Nuclide | | ------ | ------ | |Seawater| Cs-137 | |Biota - Fish| Cs-137 | |Biota - Seaweed| Cs-137 | |Biota - Seaweed| Tc-99 | |Biota - Shellfish| Cs-137 | |Biota - Shellfish| Tc-99 | |Sediment | Cs-137 | |Sediment| Pu-239+240 | ##### [Table 1] Summary Table outlining the sample types and radionuclides available within the notebook > Interactive Map of the Irish Marine Environment The map of the Irish Marine Environment illustrates the sampling locations and radioactivity concentrations within the region of interest (Figure 4). When the collation of the data was completed it was observed that there was not sufficient data to find the moving average of individual sampling locations. A solution was found by splitting the region of interest into three sections, the eastern region of the Irish Sea (EIS), the western region of the Irish Sea (WIS) and the west of Ireland (WOI). The sections can be observed on the map and are represented by black boxes. Included on the map are orange squares showing the locations of the closest operating nuclear power stations to Ireland. The map is interactive and therefore, when the curser is held over a sampling point, a banner appears detailing the radionuclide and aggregation type, radioactivity concentration and the units involved. The colour scheme represents the radioactivity concentrations. The low activity concentrations are represented by the colour yellow and higher concentrations are represented by a dark blue colour. This colour scheme mirrors the one available in MARIS. A summary table was included below the interactive map. The total number of samples (N), arithmetic mean, standard deviation, median, minimum and maximum activity concentration are captured in the table for each region of interest (EIS, WIS and WOI). > Moving Average Plot The moving average plot illustrates the trend in radioactivity concentrations between 2010 to 2023 in each region of interest. The purpose of this chart was to filter out extreme outliers within the data to give a clearer view of the trend in radioactivity concentrations over time. The moving average is calculated by summing up the data points and dividing by the number of data points within the time period of interest and is useful for predicting long-term patterns. The y-axis is in the log form so that the moving averages of each region of interest can be distinguished from each other. This graph will continue to update as more data is included in MARIS. > Boxplot The data was displayed in a boxplot as they are great at summarising large datasets and identifying outliers. Boxplots are also visually easy to interpret and compare. The boxplot provides the following information about datasets, the minimum (the lowest radioactivity concentration excluding outliers), the first quartile (the median of the lower half radioactivity concentrations), the median (the middle radioactivity concentration), the third quartile (the median of the higher radioactivity concentrations) and the maximum (the largest radioactivity concentration excluding outliers). 32:T1183,#### Results and Discussion > *Interactive User Interface* The interactive User interface allows you to view the data you are interested in. Click on the drop down arrows and radio buttons to change the visual displays below. Use the slider labelled "Radius around the center (km)" to alter the zoom of the interactive map. For example, if you select in Caesim-137 (Cs-137) in fish, the interactive graphs will change to match the selection. > *Analysis Example using Cs-137 in Fish* 1) Baseline Radioactivity Concentration The baseline radioactivity concentration for Cs-137 in fish between 2010 and 2023 is captured in the summary table under 'Arithmetic mean'. The baseline radioactivity concentrations are 1.8, 0.3 and 0.1 Bq/kg f.w. for the eastern region of the Irish Sea (EIS), the western region of the Irish Sea (WIS) and the west of Ireland (WOI) respectively. The EIS was found to have the highest baseline radioactivity concentration. EIS encompasses the area of the Irish Sea that is in close proximity to the operating nuclear power station at Heysham. Nuclear power stations are licensed to release controlled radioactive discharges into the marine environment. According to the established baseline for Cs-137 in fish, radioactivity concentrations of samples are dependent on their proximity to nuclear power stations. This can also be observed through the interactive map where samples are more often dark blue signifying higher radioactivity concentrations closer to the UK compared to the eastern region of the Irish Sea and the west of Ireland that tend to have lower concentrations highlighted by the light blue to yellow coloured sampling points. The WIS is effected by the radioactive discharges from nearby nuclear stations in the UK and France. This can be concluded by comparing the radioactivity concentrations in the WIS to the WOI. The WIS continuously has elevated radioactivity concentrations compared to the WOI. The trend observed for Cs-137 in Fish is reproduced in all other sample types and radionuclides. 2) Moving Average Graph The moving average graph illustrates the trend in radioactivity concentrations between 2010-2023. This graph will continuously update alongside the IAEA's MARIS database. The moving average is decreasing with time for the eastern region of the Irish Sea (green). Although the average is decreasing for EIS, the region has consistently higher radioactivity concentrations compared to the WIS and WOI. The moving average for western region of the Irish Sea (purple) has low radioactivity concentrations overall, however, a spike can be observed post 2016, but it begins to decrease and level off after 2021. Variations in fish and their habits may have caused the spike. In this notebook, fish were not separated into categories, for example, salmon, trout, eel. Changes in current could have had an effect on the moving average particularly if more bottom feeders such as eel were tested, as current can change the radioactivity concentrations in sediment affecting bottom feeder's habitats. The west of Ireland (orange) has the lowest radioactivity concentrations and the moving average is relatively consistent over time signifying little change in the area regarding radioactivity. The trend where the eastern region of the Irish Sea has a higher radioactivity concentration than the western region of the Irish and the west of Ireland is repeated in all other sample types and radionuclides. 3) Boxplot The central box representing the EIS (green) and the WIS (purple) are wide, signifying variability in the data. The WOI has a narrow center box indicating less variability in the data. The west of Ireland has more consistent radioactivity concentrations. The 'whiskers' extending from the boxplots extend to the lowest and highest radioactivity concentrations. Points outside of the whiskers are outliers. There are no outliers for the EIS (green) and the 'whiskers' are long. This indicates the data has a long spread and outliers could not be determined. The boxplot is negatively skewed for EIS indicating that the median radioactivity concentration is lower than the mean. Both the WIS (purple) and WOI (orange) have outliers within the data. The boxplot representing the WIS has a slight negative skew whereas the WOI has a positive skew. A positive skew means the median radioactivity concentration is higher than the mean. The information above was correct at the time of writing (Aug 2024)33:Ta12,viewof nuclide = Inputs.select(sampleType == 1 ? new Map([ // select nuclides for seawater ["Cs-137", 33], // ["C-14", 3], ["Tc-99", 15], // ["Po-210", 47], // ["Pu-239,240", 77], // ["Pu-241", 70], // ["Cs-134", 31], // ['Pu-238', 67] ]) : sampleType == 2 ? biotaType == 1 ? new Map([ //select nuclides for fish ["Cs-137", 33]] // ["C-14", 3], //["Tc-99", 15], // ["Po-210", 47], // ["Pu-239,240", 77], // ["Pu-241", 70], //["Cs-134", 31], // ['Pu-238', 67]] ) : biotaType == 2 ? new Map([ // select nuclides for seaweed ["Cs-137", 33], // ["C-14", 3], ["Tc-99", 15]] // ["Po-210", 47], // ["Pu-239,240", 77], // ["Pu-241", 70], // ["Cs-134", 31], //['Pu-238', 67]] ) : new Map([ // select nuclides for shellfish ["Cs-137", 33], // ["C-14", 3], ["Tc-99", 15]] // ["Po-210", 47], // ["Pu-239,240", 77], // ["Pu-241", 70], // ["Cs-134", 31], // ['Pu-238', 67]] ) : new Map([ // select nuclides for sediment ["Cs-137", 33], //["C-14", 3], //["Tc-99", 15], //["Po-210", 47], ["Pu-239,240", 77]]), //["Pu-241", 70], //["Cs-134", 31], // ['Pu-238', 67]]), { {label: html`

Nuclide Type:

`, sort: true, unique: true })34:T5f3,globalMap = Plot.plot({ width, color: { type: "threshold", scheme: "YlGnBu", domain: [0.25, 0.5, 0.75, 0.95, 0.99].map((d) => d3.quantile(valuesForLegend, d), ) }, projection: { type: "mercator", domain: circle }, marks: [ Plot.geo(land, { fill: "lightgrey", stroke: "Black", strokeWidth: 0.5 }), Plot.graticule(), Plot.geo(circle, { stroke: "#bbb", strokeWidth: 1, strokeDasharray: "5,3" }), Plot.dot( dataWithinRadius, Plot.hexbin( { fill: (g) => d3[aggType](g, (d) => d.activity), title: (g) => `${nuclidesLut[nuclide]} act. conc. (${aggType}): ${d3[aggType](g, (d) => d.activity)} ${whatUnit(sampleType)}` // `${nuclidesLut[nuclide]} activity in ${whatUnit(sampleType)}` }, { x: "longitude", y: "latitude", fillOpacity: 0.9, symbol: "hexagon", stroke: "black", strokeWidth: 0.5, strokeOpacity: 5, binWidth: 6, channels: { activity: ((d) => d.activity) }, tip: { fomart: { activity: ((d) => d.activity) } } } ) ), Plot.geo(EISPolygon), Plot.geo(WISPolygon), Plot.geo(WOIPolygon), Plot.dot(nuclearSitesdata, {x: "Longitude decimal", y: "Latitude decimal", symbol: "square", title: "Name", tip:true, r: 5, stroke: "red", fill: "orange"}), ] })35:T77b,timePlot1 = Plot.plot({ width, height: 500, // color: { scheme: "OrRd" }, // color: { // scheme: "Category10", // type: "categorical" // }, color: { domain: [ "Eastern region of the Irish Sea", "Western region of the Irish Sea", "West of Ireland" ], range: ["#1b9e77", "#7570b3", "#d95f02"] // Colorbind-safe }, y: {type: "log", grid: true, tickFormat: ".2" } , x: {label: "Sampling date"}, marks: [ Plot.dot( dataInBoxes, { x: "begPeriod", y: "activity", fill: "roi", stroke: "roi", r: 3, fillOpacity: 0.4, strokeWidth: 0.5, strokeOpacity: 0.4 } // Plot.hexbin( // { fill: "count" }, // { // x: "begPeriod", // y: "activity", // symbol: "hexagon", // fillOpacity: 0.9, // stroke: "black", // strokeWidth: 0.5, // strokeOpacity: 0.5, // binWidth: 7, // tip: true // } // ) ), Plot.lineY( dataInBoxes, Plot.windowY( { k: dataInBoxes.length > 0 ? dataInBoxes.length / ((dataInBoxes[dataInBoxes.length - 1].begPeriod - dataInBoxes[0].begPeriod) / (3600 * 24 * 365.25 * 1000)) : 1, reduce: "median" }, { x: "begPeriod", y: "activity", z: "roi", stroke: "roi", strokeWidth: 2 } ) ) // Plot.text( // dataInBoxes, // Plot.selectLast({ // x: "begPeriod", // y: "activity", // z: "roi", // fill: "roi", // textAnchor: "end", // text: "roi", // lineAnchor: "top", // lineWidth: 10 // // dx: 3, // // dy: -8 // }) // ) ] })36:T1525,**My Lessons Learned** Throughout this project I learned many lessons about data collation, updating MARIS and using Observable. In terms of data collation, the RIFE reports had much duplication and this involved a diligent review of the data. Regarding MARIS updates, the IAEA were updating the MARIS website in a user acceptance testing environment. This meant the MARIS website was often unavailable and I could not view any relevant data. Finally, I found it initially difficult to grasp the coding language used by Observable. However, I was greatly supported by the IAEA, in particular by Franck Albinet who was very excited about the prospects about the EPA using Observable. **Conclusion** Radioactivity concentration data from the Irish marine environment was compiled for the relevant sample types and radionuclides. The baseline of Radioactivity in the Irish marine environment was established. This is important in preparation for future nuclear developments confirmed by the UK and France. The MARIS website has been improved has a scientific resource. Radioactivity concentrations in samples are dependent on their proximity to nuclear power stations. The eastern region of the Irish Sea saw consistently higher radioactivity concentrations compared to the western region of the Irish Sea and the west of Ireland. Observable is a useful tool for data visualisation and the EPA would benefit from the application. I improved my coding knowledge and I acquired a new skill in using Observable. **Acknowledgements** I would like to express my thanks to the following without whom I could have not completed my project. They are Robert Ryan, who was my line manager through out my internship and offered great support and ideas; Paul McGinnity, who provided great knowledge and expertise in the field of marine radioactivity and data visualisation; Kevin Kelleher, who facilitated the project; Franck Albinet, who provided expertise in data visualisation and Observable; the EPA's IT department who facilitated my use of Observable and the EPA's radioactivity monitoring laboratory who provided their marine radioactivity data to the project. Finally, I would also like to thank the other members of the EPA's Emergency Preparedness team (Anna Grohn, Kilian Smith, Abbie Murphy, Javier Villa, Marc Sturrock and Amy Kelly) as well as EPA senior managers Andy Fanning and Aisling O'Connor. **References** 1. What led to the establishment of Ireland’s EPA 30 years ago? (2024). www.rte.ie. [online] Available at: https://www.rte.ie/brainstorm/2024/0207/1430917-environmental-protection-agency-ireland-history-legislation/. 2. Agency, E.P. (n.d.). Our Purpose, Vision, Values and Behaviours. [online] www.epa.ie. Available at: https://www.epa.ie/who-we-are/roles--responsibilities/mission--vision/. 3. Agency, E.P. (n.d.). Radiation monitoring. [online] www.epa.ie. Available at: https://www.epa.ie/our-services/monitoring--assessment/radiation/radiation-monitoring/. 4. Radioactivity Monitoring of the Irish Environment. Page 26. (2014). Available at: https://www.epa.ie/publications/compliance--enforcement/radiation/RPT_Monitoring_Data_2014_2015.pdf 5. IAEA (2016). History | IAEA. [online] Iaea.org. Available at: https://www.iaea.org/about/overview/history. 6. Iaea.org. (2016). Nuclear technology for development used safely, peacefully, securely | IAEA. [online] Available at: https://www.iaea.org/services/technical-cooperation-programme#:~:text=The%20technical%20cooperation%20programme%20is%20the%20IAEA%E2%80%99s%20primary 7. MARIS (2024). MARIS - Marine Radioactivity Information System. [online] Iaea.org. Available at: https://maris.iaea.org/help/1133 8. GOV.UK. (n.d.). Civil nuclear: roadmap to 2050 (accessible webpage) pages 12-35. [online] Available at: https://www.gov.uk/government/publications/civil-nuclear-roadmap-to-2050/civil-nuclear-roadmap-to-2050-accessible-webpage. 9. GOV.UK. (n.d.). Civil nuclear: roadmap to 2050. Page 31 [online] Available at: https://www.gov.uk/government/publications/civil-nuclear-roadmap-to-2050. 10. world-nuclear.org. (n.d.). Nuclear Power in France - World Nuclear Association. [online] Available at: https://world-nuclear.org/information-library/country-profiles/countries-a-f/france#nuclear-power-plants. 11. Opendata.edf.fr. (2023). Nuclear power stations of EDF SA. [online] Available at: https://opendata.edf.fr/explore/dataset/centrales-de-production-nucleaire-edf/map/?disjunctive.centrale&disjunctive.tranche&disjunctive.sub_sector&sort=-tri&location=6. 12. Worldatlas.com. (2024). Available at: https://www.worldatlas.com/r/w960-q80/upload/c9/62/fe/adobestock-93886824.jpeg. 13. GOV.UK. (n.d.). Radioactivity in food and the environment (RIFE) reports. [online] Available at: https://www.gov.uk/government/publications/radioactivity-in-food-and-the-environment-rife-reports. 14. Environmental Protection Agency (2024). Monitoring & Assessment: Radiation Publications. [online] Www.epa.ie. Available at: https://www.epa.ie/publications/monitoring--assessment/radiation/. 15. OSPAR Commission. (n.d.). About. [online] Available at: https://www.ospar.org/about. 16. odims.ospar.org. (n.d.). ODIMS - Home. [online] Available at: https://odims.ospar.org/en/. 17. ScienceDirect (2023). About ScienceDirect | Peer reviewed literature | Elsevier. [online] www.elsevier.com. Available at: https://www.elsevier.com/products/sciencedirect. 37:T478,summaryTableData = { let dataToSummarise = []; let summaryTableData = []; let average, median, standardDev, maximum, minimum; let regions = [EISPolygon.properties.name,WISPolygon.properties.name,WOIPolygon.properties.name]; for (let i = 0; i < regions.length; i++) { dataToSummarise = dataInBoxes.filter((d) => d.roi == regions[i]) dataToSummarise = dataToSummarise.map((d) => d.activity) average = dataToSummarise.reduce((a, b) => a + b, 0) / dataToSummarise.length median = dataToSummarise.sort((a,b) => a.activity - b.activity)[Math.floor((dataToSummarise.length)/2)] maximum = Math.max(...dataToSummarise) minimum = Math.min(...dataToSummarise) standardDev = Math.sqrt(dataToSummarise.reduce((a, b) => a + (b - average)**2, 0) / dataToSummarise.length) ; summaryTableData[i] = {name:regions[i], N: dataToSummarise.length, average: isNaN(average) ? "-" : f(average), stddev: isNaN(standardDev) ? "-" : f(standardDev), min: dataToSummarise.length == 0 ? "" : f(minimum), max: dataToSummarise.length == 0 ? "" : f(maximum), median: isNaN(median) ? 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Sample Type:

`,\n sort: true, \n unique: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":602,"value":"viewof biotaType = Inputs.select(sampleType != 2 ? new Map([[\"N/A\", -1]]) : new Map([[\"Fish\", 1], [\"Seaweed\", 2] , [\"Shellfish\", 3]]), {\n label: html`

Biota Type:

`,\n sort: true, \n unique: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":124,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":53,"value":"viewof aggType = Inputs.radio([\"max\", \"mean\", \"median\"], {\n value: \"max\",\n label: html`

Aggregation Type:

`,\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":98,"value":" // D3 and Plot expect coordinates to be specified as [longitude, latitude], so if you're setting your center point by grabbing coordinates from Google Maps, you'll need to reverse them.\nviewof kms = Inputs.range([50, 1000], {\n value: 500,\n step: 50,\n label: html`

Radius around the center (km)

`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1340,"value":"### Interactive Map of ${nuclidesLut[nuclide]} Activity Concentrations in ${whatSampleType(sampleType, biotaType)} (${whatUnit(sampleType)}) in the Irish Marine Environment","pinned":false,"hidden":false,"mode":"md"},{"id":57,"value":"globalMap.legend(\"color\", {\n label: `${nuclidesLut[nuclide]} Activity Concentrations in ${whatSampleType(sampleType, biotaType)} (${whatUnit(sampleType)})`, \n width: 320,\n tickFormat: \",.3~s\",\n\n \n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":38,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1360,"value":"### Summary Table: ${nuclidesLut[nuclide]} Activity Concentrations in ${whatSampleType(sampleType, biotaType)} (${whatUnit(sampleType)})","pinned":false,"hidden":false,"mode":"md"},{"id":1663,"value":"\n \n \n \n \n \n \n \n \n \n \n \n ${summaryTableData.map(region => htl.html`\n \n \n \n \n \n \n `)}\n \n

Region of Interest	N	Arithmetic mean	Standard deviation	Median	Minimum - Maximum
${region[\"name\"]}	${region[\"N\"]}	${region[\"average\"]}	${region[\"stddev\"]}	${region[\"median\"]}	${region[\"min\"]} - ${region[\"max\"]}

","pinned":false,"hidden":false,"mode":"html"},{"id":1352,"value":"### Moving Average of ${nuclidesLut[nuclide]} Activity Concentrations in ${whatSampleType(sampleType, biotaType)} (${whatUnit(sampleType)})","pinned":false,"hidden":false,"mode":"md"},{"id":739,"value":"timePlot1.legend(\"color\", {\n label: html`

Regions of Interest

`,\n sort: true,\n unique: true\n // width: 320\n // tickFormat: \",.3~s\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":738,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1356,"value":"### Boxplot of ${nuclidesLut[nuclide]} Activity Concentrations in ${whatSampleType(sampleType, biotaType)} (${whatUnit(sampleType)})","pinned":false,"hidden":false,"mode":"md"},{"id":1640,"value":"boxPlot.legend(\"color\", {\n label: html`

Regions of Interest

`,\n sort: true,\n unique: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":330,"value":"boxPlot = Plot.plot({\n // Colorbind-safe\n height: 300 ,\n marginLeft: 200,\n width,\n color: {\n domain: [\n \"Eastern region of the Irish Sea\",\n \"Western region of the Irish Sea\",\n \"West of Ireland\"\n ],\n range: [\"#1b9e77\", \"#7570b3\", \"#d95f02\"],\n },\n // x: { type: logscale ? \"log\": \"linear\"},\n x: {type: \"log\", grid: true, tickFormat: \".2\" } , \n y: {\n label: \"Region Of Interest\",\n domain: [\n \"Eastern region of the Irish Sea\",\n \"Western region of the Irish Sea\",\n \"West of Ireland\"\n ]\n },\n marks: [\n Plot.boxX(\n dataInBoxes,\n { x: \"activity\", y: \"roi\", tip: true, fill: \"roi\" }\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1530,"value":"## Radionuclide Releases from Nearby Nuclear Power Stations ","pinned":false,"hidden":false,"mode":"md"},{"id":1506,"value":"nuclearStationReleases = nuclearReleases","pinned":false,"hidden":false,"mode":"ojs"},{"id":1817,"value":"$36","pinned":false,"hidden":false,"mode":"md"},{"id":1365,"value":"# *Report Concludes Here - Below is Code Only*","pinned":false,"hidden":false,"mode":"md"},{"id":505,"value":"### Required for Map","pinned":false,"hidden":false,"mode":"md"},{"id":44,"value":"land50m = d3.json(\"https://unpkg.com/world-atlas@2.0.2/land-50m.json \")","pinned":false,"hidden":false,"mode":"ojs"},{"id":42,"value":"land = topojson.feature(land50m, land50m.objects.land)","pinned":false,"hidden":false,"mode":"ojs"},{"id":511,"value":"### Get all Activity values for legend","pinned":false,"hidden":false,"mode":"md"},{"id":66,"value":"valuesForLegend = dataWithinRadius.map((d) => d.activity)","pinned":false,"hidden":false,"mode":"ojs"},{"id":521,"value":"### MARIS API","pinned":false,"hidden":false,"mode":"md"},{"id":523,"value":"MARIS API query parameters: \n- Select data by specific IHO (shapeTypeId == 1) area_id (hardcoded) in order to speed up the query. This includes areaId = 9999 (unattributed). \n- sampleTypeId and nuclideId set by input.select control\n- Sampling date >= 1 January 2010\n- unitId Bq/m3 (seawater), Bq/kg, Bq/g C\n \nNotes:\n- Cannot select by detection, use post-processing filter to remove, e.g. \"ND\" values","pinned":false,"hidden":false,"mode":"md"},{"id":4,"value":"params = ({\n areaId: \"1912,2350,2351,2357,2389,3141,4283,9999\",\n sampleTypeId: sampleType,\n nuclideId: nuclide, // 137Cs\n samplingDateFrom: \"2010-01-01\",\n shapeTypeId: 1,\n // bioGroup: sampleType != 2 ? 0 : (biotaType == 1 ? 4 : biotaType == 2 ? 11 : \"14,12,2,13,6\") // not working - accepts integers, not strings (e.g. 4 instead of \"fish\" but query does not see to be correct. In the meantime filter in dataInterim\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":600,"value":"params","pinned":false,"hidden":false,"mode":"ojs"},{"id":6,"value":"dataRaw = getMeasurementData(params)","pinned":false,"hidden":false,"mode":"ojs"},{"id":528,"value":"Additional filtering not supported by the MARIS API: \n- Only include detected values for now (detection == \"=\")","pinned":false,"hidden":false,"mode":"md"},{"id":479,"value":"dataInterim = dataRaw.filter(\n (d) => d.detection != \"ND\" && (sampleType != 2 ? d.bioGroupId == 0 : (biotaType == 1 ? d.bioGroupId == 4 : biotaType == 2 ? d.bioGroupId == 11 : (d.bioGroupId == 14 || d.bioGroupId == 12 || d.bioGroupId == 2 || d.bioGroupId == 13 || d.bioGroupId == 6))))\n\n// ","pinned":false,"hidden":false,"mode":"ojs"},{"id":604,"value":"dataInterim","pinned":false,"hidden":false,"mode":"ojs"},{"id":481,"value":"dataInterim2 =dataInterim.map((d) => ({ ...d, activity: (d[\"detection\"] == \"<\") ? d[\"activity\"]*0.5 : d[\"activity\"]}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":532,"value":"Rules for conversion of units: \n\nSeawater \n- Standardise on Bq/m3\n- \nBiota: \n- Standardise on Bq/kg f.w.\n- Assume that Bq/kg (undefined fresh or dry weight) is **fresh**\n- Convert dry weight values using LaMer average values for each biogroup\n\nSediment: \n- Standardise on Bq/kg d.w.\n- Assume that Bq/kg (undefined fresh or dry weight) is **dry**\n- Convert fresh weight values values using factor of 5\n\nTo do:\n- check if we can access percentwt\n- check if unitId = 13 Bq/kgC is used ","pinned":false,"hidden":false,"mode":"md"},{"id":420,"value":"### Data loading","pinned":false,"hidden":false,"mode":"md"},{"id":410,"value":"data = dataInterim2.map((d) => ({\n ...d,\n begPeriod: new Date(d.begPeriod),\n roi: getROI([d.longitude, d.latitude], [EISPolygon, WOIPolygon, WISPolygon])\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":88,"value":"dataWithinRadius = data.filter(\n (d) => d3.geoDistance([d.longitude, d.latitude], center) < kms / 6371\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":603,"value":"dataWithinRadius","pinned":false,"hidden":false,"mode":"ojs"},{"id":489,"value":"dataInBoxes = dataWithinRadius.filter((d) => d.roi != \"Elsewhere\").sort((a,b) => a.begPeriod - b.begPeriod)","pinned":false,"hidden":false,"mode":"ojs"},{"id":419,"value":"### Filter data using circle","pinned":false,"hidden":false,"mode":"md"},{"id":77,"value":"center = [-5,53.5]","pinned":false,"hidden":false,"mode":"ojs"},{"id":71,"value":"circle = d3\n .geoCircle()\n .center(center)\n .radius((kms / (6371 * Math.PI * 2)) * 360)\n .precision(10)()","pinned":false,"hidden":false,"mode":"ojs"},{"id":415,"value":"### User-defined Regions of Interest (ROI)","pinned":false,"hidden":false,"mode":"md"},{"id":414,"value":"EISPolygon = turf.polygon(\n [\n [\n [-4.5, 52],\n [-2.5, 52],\n [-2.5, 55.5],\n [-4.5, 55.5],\n [-4.5, 52]\n ]\n ],\n { name: \"Eastern region of the Irish Sea\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":412,"value":"WISPolygon = turf.polygon(\n [\n [\n [-6.5, 52],\n [-4.5, 52],\n [-4.5, 55.5],\n [-6.5, 55.5],\n [-6.5, 52]\n ]\n ],\n { name: \"Western region of the Irish Sea\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":413,"value":"WOIPolygon = turf.polygon(\n [\n [\n [-12, 51],\n [-6.5, 51],\n [-6.5, 56],\n [-12, 56],\n [-12, 51]\n ]\n ],\n { name: \"West of Ireland\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":411,"value":"// // Get Region Of Interest\nfunction getROI(point, regions) {\n const region = regions.find((region) => {\n return turf.booleanPointInPolygon(turf.point(point), region);\n });\n return region ? region.properties.name : \"Elsewhere\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":382,"value":"## Utils and Imports","pinned":false,"hidden":false,"mode":"md"},{"id":381,"value":"import { getMeasurementData, luts } from \"@maris-iaea/api\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":380,"value":"turf = require(\"@turf/turf@7\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":435,"value":"function whatUnit(samptypeId) {\n switch(samptypeId) {\n case 1:\n return \"Bq/m3\"\n case 2:\n return \"Bq/kg f.w.\"\n case 3:\n return \"Bq/kg d.w.\"\n default:\n return \"Unknown\"\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1481,"value":"function whatSampleType(samptypeId, biotaTypeID){\n switch(samptypeId) {\n case 1:\n return \"Seawater\"\n case 3: \n return \"Sediment\"\n case 2:\n switch(biotaTypeID) {\n case 1:\n return \"Fish\"\n case 2:\n return \"Seaweed\"\n case 3:\n return \"Shellfish\"\n default:\n return \"Unknown\"\n }\n default:\n return \"Unknown\" \n } \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":437,"value":"function getDryWetFactor(biogroupId) {\n switch(biogroupId) {\n case 11:\n return 0.167278970397589\n case 4:\n return 0.232254394570392\n case 2:\n return 0.226485185185185\n case 14:\n return 0.169374196277088\n case 13:\n return 0.232484162739303\n case 12:\n return 0.181976062091503 \n case 6:\n return 0.194611473702632\n case 5:\n return 0.460238713453779\n default:\n return 1\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":734,"value":"nuclidesLut = _.fromPairs(dataWithinRadius.map((d) => [d.nuclideId, d.nuSymbol]))","pinned":true,"hidden":false,"mode":"ojs"},{"id":733,"value":"## Nuclear sites","pinned":false,"hidden":false,"mode":"md"},{"id":732,"value":"nuclearSites = FileAttachment(\"Operating Nuclear Sites@2.xlsx\").xlsx()","pinned":true,"hidden":false,"mode":"ojs"},{"id":731,"value":"nuclearSitesdata = nuclearSites.sheet(0, { headers: true })","pinned":true,"hidden":false,"mode":"ojs"},{"id":1503,"value":"workbook = FileAttachment(\"Nuclear Power Station Releases.xlsx\").xlsx()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1504,"value":"workbook.sheetNames","pinned":true,"hidden":false,"mode":"ojs"},{"id":1505,"value":"nuclearReleases = workbook.sheet(0, {\n headers: true,\n // range: \"A1:J10\"\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1662,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":1661,"value":"regions = [EISPolygon.properties.name,WISPolygon.properties.name,WOIPolygon.properties.name];","pinned":false,"hidden":false,"mode":"ojs"},{"id":1660,"value":"f = d3.format(\".1f\")","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"1","titling":"auto"}