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All issues Volume 11 (2025) EPJ Nuclear Sci. Technol., 11 (2025) 14 Full HTML
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EPJ Nuclear Sci. Technol.
Volume 11, 2025
Euratom Research and Training in 2025: ‘Challenges, achievements and future perspectives’, edited by Roger Garbil, Seif Ben Hadj Hassine, Patrick Blaise, and Christophe Girold
Article Number 14
Number of page(s) 10
DOI https://doi.org/10.1051/epjn/2025017
Published online 14 May 2025

© S. Barbosa et al., Published by EDP Sciences, 2025

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Radon (Rn-222) is a versatile tracer of dynamic processes in the natural environment and has numerous applications, including serving as a proxy for tectonic stress, a tracker of volcanic processes, and a tracer in marine and hydrological settings [1]. Its unique combination of being a ubiquitous, inert gaseous radionuclide with a predominantly terrestrial source and a short half-life (3.8232 (8) d) makes it an exceptional atmospheric tracer [2], ideal for the study of transport and mixing processes [3–5]. The large contrast in radon source strength between land and water enables radon to be used to quantify the relative degree of recent (<21 d) terrestrial influences on marine air masses [6, 7].

Atmospheric baseline observations of greenhouse gases (GHGs), representative of regional-to-hemispheric scale atmospheric composition, constitute GHG measurements in remote locations (e.g., high-altitude observatories, islands, or polar regions) made in well-mixed air masses, well removed from the influence of local pollution sources [7, 8]. Accurate atmospheric baseline observations of GHGs are essential to constrain long-term changes in hemispheric-mean GHG concentrations, differentiate between natural and anthropogenic GHG sources, and improve understanding of the global carbon budget [9–11].

Project NuClim1 (Nuclear observations to improve Climate research and GHG emission estimates) aims to use high-quality measurements of atmospheric radon activity concentration to advance climate science by establishing world-leading high-quality atmospheric measurements of radon [12] and selected GHG concentrations (carbon dioxide (CO2) and methane (CH4)) at the Graciosa Eastern North Atlantic (ENA) site, a remote oceanic location in the middle of the North Atlantic Ocean, belonging to the Azores archipelago. ENA2 was established and supported by the Department of Energy (DOE) of the United States of America with the collaboration of the local government and University of the Azores.

Reliable nuclear techniques, in terms of radon observations with a low enough limit of detection to precisely identify background air masses, are currently available at almost half of the baseline stations of the Global Atmosphere Watch Programme (GAW) of the World Meteorological Organization (WMO), including seven coastal, island or shipborne sites across the North and South Pacific, and two sites in the South Atlantic. The only high-quality radon observations currently made in the Atlantic region are at Cape Point (South Africa) and at King George Island on the Antarctic Peninsula, nothing in the North Atlantic coastal or island regions. NuClim provides this missing puzzle piece by bringing world-leading high-quality atmospheric measurements of radon and GHGs to the Graciosa ENA site, an important location to Europe, “upwind” of the continent. The additions of these new measurements will enable key climate investigations and strengthen EU capabilities in terms of assessment of GHG emissions and aerosol effects on North Atlantic clouds.

Furthermore, project NuClim also plans to enhance the baseline monitoring capability of the renowned Mace Head station (e.g. [13–15]) by upgrading its atmospheric radon monitoring capability from indirect (by progeny) to direct radon measurements. Combined, these efforts at the ENA and Mace Head atmospheric stations will enable better constraints of climate scenarios, and provide valuable information on any latitudinal gradient in baseline atmospheric composition, which in turn will assist the evaluation of GHG mitigation measures.

A further aim of project NuClim is to improve radiation protection and nuclear surveillance capabilities, by complementing (direct) atmospheric radon measurements at the ENA station with indirect (by progeny) measurements enabling improved quantification of the background variability in ambient gamma dose rate. Over Europe the ambient gamma dose is being continuously monitored for nuclear security and safety purposes, including emergency preparedness and response. A common problem of such monitoring networks, impacting their operation, is the difficulty to identify, in an automatic way and in real-time, false positive peaks in the ambient gamma dose rate, typically due to meteorological conditions (heavy rain, snow, dust events and strong atmospheric stability) from other causes (e.g. malfunction of a nuclear facility, or a radioactive event). Therefore alarm thresholds need to be set high enough to be well above the expected natural fluctuations, which can cause low but real anthropogenic releases to be overlooked, and delay intervention. Improved understanding of the background (non-anthropogenic) variability of ambient gamma dose rate and better quantification of the influence of meteorological conditions on ambient gamma dose rate enables to improve the performance of radioactivity environmental monitoring networks [16] like the EUropean Radiological Data Exchange Platform (EURDEP) (https://remon.jrc.ec.europa.eu/About/Rad-Data-Exchange).

Section 2 describes the nuclear and ancillary measurements performed in the project (Sect. 2.1) and outlines the data consolidation strategy (Sect. 2.2). Preliminary results of the project are presented in Section 3 and discussed in Section 4. Concluding remarks are provided in Section 5.

2. Material and methods

Graciosa Island, a small, low-lying island in the Azores archipelago (Fig. 1), is an ideal remote location for atmospheric and marine boundary layer studies. Unlike its counterpart in the North Pacific (Mauna Loa Observatory, 3397 m a.s.l.), and Tenerife's Izaña Station (2373 m a.s.l.), the small size of Graciosa Island (61 km2) and low elevation (402 m a.s.l. at its highest point) reduces the likelihood of local island interference with observations of the remote marine boundary layer. The island hosts the Eastern North Atlantic (ENA) facility, managed by the U.S. Department of Energy's (DOE's) Atmospheric Radiation Measurement (ARM) programme. Detailed observations have been conducted at the Graciosa ENA station since 2013, yielding the largest trove of continuous long-term data on atmospheric conditions in the Earth's marine mid-latitudes. Despite an abundance of atmospheric observations at the ENA station, however, information regarding ambient radioactivity is scarce, with no direct radon measurements. Project NuClim addresses this gap by setting-up a detailed monitoring campaign of radon activity concentration at the ENA site. The nuclear and ancillary measurements included in the NuClim field campaign are presented in Section 2.1, and the corresponding data consolidation approach is summarised in Section 2.2.

thumbnail Fig. 1.

Map showing the location of the stations measuring atmospheric radon activity concentration in the scope of project NuClim (ARM ENA station, Graciosa Island, and Mace Head station, Ireland).

2.1. Nuclear and ancillary measurements

Performing high-quality measurements of atmospheric radon activity concentration is particularly challenging in the marine environment. The Azores are located near the middle of the North Atlantic Ocean, far away from continental sources, and typically experience clean air conditions, thus atmospheric radon activity concentrations are expected to be very low. Specifically, an air mass that has been in long-term equilibrium with just the ocean surface typically has a radon concentration between 0.03 Bq m−3 and 0.08 Bq m−3. This obstacle requires the use of the most sensitive instrument currently available for the continuous measurement of environmental atmospheric radon concentration, the 1500 L two-filter dual-flow-loop radon detector developed by the Australian Nuclear Science and Technology Organisation (ANSTO) [3, 17, 18], with a detection limit below 0.03 Bq m−3, and the least measurement uncertainty of any available radon detector [19, 20].

The direct measurements of atmospheric radon activity concentration will be complemented by indirect (by progeny) measurements performed by sampling air pumped through glass-fibre filters, with the beta emissions of the radon progeny accumulated on the filters recorded with Geiger-Müller counters [21, 22]. The filters are further analysed in the laboratory yielding regular (weekly) observations of cosmogenic beryllium-7 and long-lived lead-210 radionuclides [23]. Spectral gamma radiation measurements will be also performed with a NaI(Tl) scintillator detector and a LaBr2- or CeBr2-spectral-dosemeter enabling to advance understanding of atmospheric wet deposition and natural variability of ambient gamma radiation [24]. Ionising radiation studies are further complemented by measurement of the concentration and average mobility of positive and negative cluster ions using a cluster ion counter [25, 26].

High quality (Integrated Carbon Observations System – ICOS-compliant) measurements of atmospheric carbon dioxide (CO2) and methane (CH4) mixing ratios will be performed in the NuClim campaign using a cavity ring-down spectrometer (CRDS; Picarro Inc., USA) [27]. The Picarro G2301 is an instrument that complies with ICOS requirements for CO2 and CH4 concentration measurements. This instrument has not undergone the full ICOS initial test by the Atmospheric Thematic Centre (ATC) Metrology Lab, but some of those tests, such as water vapour assessment, have been performed by the ICOS Mobile Lab. The sampling manifold is built following ICOS ATC instructions and recommended components, the Valco valve sequencer as an example. Calibration gas cylinders and pressure regulators meet ICOS requirements, but the cylinders are filled and their gas concentrations assigned by the Finnish Meteorological Institute (FMI) instead of ICOS CAL. However, the gas concentrations are on the WMO Central Calibration Laboratories (CCL) scale, as they are assigned against laboratory standards prepared by NOAA. Notably, the laboratory standards ICOS CAL is using the WMO CCL scale and prepared by NOAA. Sampling protocol with target gas measurements and calibrations is adopted from the ICOS protocols [28], likewise the data processing including filtering of anomalous data points and water vapour correction. Since measurements at Graciosa do not belong to the ICOS network, FMI takes care of the data processing.

Since radon is an indicator of recent terrestrial influence, and most anthropogenic pollution sources are land based, the simultaneous measurement of atmospheric radon concentration and GHGs in the context of project NuClim enables baseline observations of GHGs from marine air masses least influenced by terrestrial sources, representative of hemispheric background values. Table 1 summarises the measurements currently available at the ENA station and the new measurements undertaken within the scope of project NuClim.

Table 1.

Measurements already available at the ENA station and new measurements, starting in 2025, to be undertaken within the scope of project NuClim.

2.2. Data consolidation

Figure 2 summarises the data consolidation strategy followed in the NuClim project. The different data considered in the project are first prepared for inclusion in the project database by performing ETL (Extract, Transform, Load) activities. The extraction stage aims to retrieve the data from the different data sources, including the field measurements from the various instruments used in the NuClim field campaign, and automatic fetching from ARM of ancillary meteorological data being routinely collected at the ENA station. The transformation step involves all data organisation procedures, including data cleaning, handling of missing values, data merging, conversion to standard formats, data aggregation, and implementation of quality-assurance protocols. In the loading stage the processed data are ingested into a database containing all the consolidated data, including the raw datafiles, the QC/QA quality (controlled/quality assured) data and all relevant metadata.

thumbnail Fig. 2.

Data consolidation workflow for NuClim project's data.

3. Results

Project NuClim started September 1st 2024, and the initial field campaign is currently being set-up. Therefore, only preliminary results are available, based on the analysis of existing data from the station, aimed at supporting the field campaign planning. These analysis focus on the retrieval of information about atmospheric stability at the site (Sect. 3.1) and on the variability of ambient gamma radiation (Sect. 3.2).

3.1. Atmospheric stability

Atmospheric stability determines the contribution of local sources to atmospheric radon concentration. The degree of local influences on the radon observations will be minimised under near neutral conditions. During the day, under strongly unstable conditions, or at night under strongly stable conditions, local influences may dominate.

Atmospheric stability can be characterised by parameters derived from eddy covariance measurements [29], such as friction velocity (u*), reflecting the mechanical turbulence generated by the wind, the Obukhov length L, quantifying stability, and heat fluxes, Q. Figure 3 shows the temporal variability of meteorological parameters over a month (October 2024) from 30-minute ARM data [30, 31]. The wind speed and friction velocity in relation to wind direction over the same period is presented in Figure 4.

thumbnail Fig. 3.

Mean diurnal courses (left) and time series of 30 min data for October 2024 at ARM's Graciosa ENA site. From top to bottom: T – air temperature, RH – relative humidity, ρH2O – water vapor density, v – wind speed, u* – friction velocity, L – Obukhov length, (z−d)/L – stability parameter (z is the measurement height and d the displacement level), Q* – radiation balance, QH – sensible heat flux, QE – latent heat flux, QG – soil heat flux, K↓ – shortwave solar downward radiation, K↑ – shortwave solar reflected radiation, L↓ – longwave downward radiation, L↑ – longwave upward radiation, FCO2 – carbon dioxide flux, albedo.

thumbnail Fig. 4.

Wind speed and friction velocity in relation to wind direction at the Graciosa ENA station in October 2024.

The variability of meteorological variables during the period under consideration is typical of an oceanic climate. The absolute water vapor content in the air ρH2O varied from 10 g m−3 to 27 g m−3. During most days in October 2024, high values of incoming solar radiation were observed, the maximum values exceeding 600 W m−2. The surface radiation balance also reached high values exceeding 400 W m−2. Average wind speed was about 4 m s−1–5 m s−1, but wind speed values exceeding 8 m s−1 were not uncommon. The dominant winds were from the NW and SW sector. Meteorological conditions in October 2024 favoured the development of turbulence near the measuring station. This is indicated by the values of turbulent sensible heat fluxes QH and latent heat fluxes QE, whose maximum 30-minute values exceeded 200 W m−2. The latent heat flux was mainly positive, indicating that evaporation dominated the station environment regardless of the time of day. On the other hand, the sensible heat flux at night was characterized by negative values, which indicates that the ground in the station's surroundings cooled more intensively in relation to the overlying air. Negative values of CO2 flux during the day indicate intensive uptake of CO2 by plants and release of this gas into the atmosphere at night. The intensive development of turbulence is evidenced by both the distinct diurnal course of QH and QE with a maximum during the day, as well as the Obukhov length L or the stability parameter (negative values during the day, positive values at night). The heat flux into the ground QG played a marginal role in the energy balance. Another indicator of turbulence intensity, the friction velocity u*, also reached high values (an average of 0.6 m s−1 during the day and 0.4 m s−1 at night). The source area of turbulent fluxes (the area on the surface that predominantly contributes to the fluxes measured by the eddy covariance instrument) is computed for different atmospheric stability conditions [32]. Figure 5 shows that regardless of stability, the source area of the heat and CO2 fluxes covers the land area. In the case of unstable conditions, this area had a diameter of about 300 m−400 m. In the case of stable conditions, it was larger and had a diameter of about 500 m−600 m.

thumbnail Fig. 5.

Source area of turbulent fluxes calculated for stable (left) and unstable (right) conditions in October 2024. White solid lines indicate source area with contribution threshold p=25%, 50%, 75% and 90%; red dashed lines indicate 100 m, 200 m, 300 m and 400 m distance from the measurement site. Photo by Google.

3.2. Ambient gamma radiation

Ambient gamma radiation is being monitored at the ENA station in the scope of ARM's Gamma Radiation Monitoring campaign since 2015. Measurements are performed using an NaI(Tl) scintillation of 3″×3″ (Scionix, Holland) equipped with an electronic total count Single Channel Analyzer (SCA) measuring gamma radiation in the range 475 keV to 3000 keV in order to reduce the Compton background in the 50 keV–475 keV low-energy range [33]. The total count of gamma rays registered by the scintillator is recorded every minute.

A subset from January to November 2024 of the 1 min and 1 h-averaged time series of total gamma counts at the ENA site is shown in Figure 6. The time series of gamma radiation observations displays a rich temporal pattern which includes long-term, diurnal, and short-term (sub-daily) variability. A zoom for two different weeks is presented in Figure 7, showing clearly the association between sharp peaks in ambient gamma radiation and the occurrence of precipitation [16, 34]. In some (rarer) cases a precipitation event does not originate enhancements in gamma radiation, as shown here in the case of May 1st 2024, which could result from a distinct growth process of the raindrops under specific weather conditions [34], but further investigation is required.

thumbnail Fig. 6.

Time series of total gamma counts every 1 min (grey points) and corresponding 1 h-averaged observations (solid line).

thumbnail Fig. 7.

Weekly time series of total gamma counts recorded every 1 min (grey points), 1 h-averaged (black line), and 1 min precipitation observations (blue points): week from 2024-01-29 to 2024-02-04 (top) and from 2024-04-29 to 2024-05-05 (bottom).

Enhancements in gamma radiation are also visible in the absence of precipitation, here apparent in the first days of February and the last days of April (Fig. 7 and detail in Fig. 8). These diurnal cycles of enhanced gamma radiation counts result from conditions of atmospheric stability and weak winds enabling the build-up of near surface radon progeny during the night (Fig. 8).

thumbnail Fig. 8.

Zoom of total gamma counts recorded every minute (top), and 1 min-averaged wind speed (bottom).

4. Discussion

The unique geographical setting of the Azores, near the middle of the North Atlantic, and the detailed meteorological and atmospheric information available from ARM's ENA facility, which provides the largest trove of continuous long-term data on atmospheric conditions in the Earth's marine mid-latitudes, are key for climate and environmental radioactivity studies. Observations of total gamma radiation counts exhibit a complex temporal variability reflecting a diverse range of both atmospheric processes and surface conditions [35]. The interpretation of these data, available from ENA since 2015, will be improved by the spectral gamma radiation measurements that will be performed in the scope of project NuClim. The quality assured gamma radiation spectra, together with filter-based measurements of Be-7 and Pb-210, will enable to progress beyond the state of the art on the understanding of ambient radioactivity in the marine environment, allowing the identification of gamma-emitting sources and characterisation of the relative contributions to surface observations of ambient radioactivity in the oceanic environment [36].

For atmospheric studies, indirect radon observations via its airborne particle-bound short-lived progeny have several disadvantages, as the observations can be influenced by distance from the surface (below 80 m a.g.l.), rainfall or high humidity, and the concentrations can be underestimated in low aerosol conditions. In the scope of project NuClim, direct, high-sensitive measurements of atmospheric radon activity concentration can be used not only to contribute to climate change monitoring, but also, together with gamma spectrometry, to improve the objective quantification of air pollution events and identification of their source regions.

Atmospheric stability information supports the interpretation of radon and ambient gamma radiation observations. Atmospheric stability will be analysed continuously during the NuClim campaign at the ENA station from eddy covariance observations performed every 30-minutes. However, it has to be noted that the eddy covariance measurements at the ENA station are performed at an height of about 3 m, while radon and GHG observations will be performed at 10 m height.

5. Conclusion

Project NuClim contributes directly to the climate change adaptation topic of the Euratom Work Programme 2023–2025 for nuclear research and training, supporting EU's climate policies. Monitoring and understanding fluxes and variability of GHGs is crucial for reducing anthropogenic emissions of GHGs and stabilising their impacts on climate. Bottom-up approaches relying on inventories from emissions reported at the national level need to be verified by top-down approaches based on GHG observations. NuClim will collect new data on atmospheric radon concentration enabling more accurate determination of baseline GHG concentrations supporting top-down verification efforts of European GHG emissions.

The NuClim project benefits in particular from the metrology research of the European Metrology Programme for Innovation and Research (EMPIR). As part of this programme, a new traceability to the SI system has been developed for radon and radon flux measurements: 19ENV01 traceRadon, thus providing the basis for worldwide comparability of radon and radon flux measurements in outdoor air (http://traceradon-empir.eu/). This was in response to a traceability gap identified by the European Metrology Network for Radiation Protection (EMN RP) (https://www.euramet.org/european-metrology-networks/radiation-protection). Traceability to the SI system means ensuring that the measured values are correct within the assigned uncertainty. This not only ensures the quality assurance of the measured values in NuClim, but also provides reliable calibration data, which for the first time allows the calculation of the full uncertainty budget of the measured values at the measurement site: instead of relative values with statistical uncertainty, absolute values with uncertainty from calibration and statistics are available. This corresponds to a requirement of the EMN RP for Euratom research and training programme for 2026–2027, where it was advised to establish a mechanism that implements quality assurance and traceability to the SI system by ensuring metrology participation as a matter of course.

NuClim also advances the EU's expertise in radiation protection. The real time monitoring of ambient gamma dose rate plays a key role in the EU strategy regarding nuclear security and safety. By improving the characterisation and discrimination of natural influences on gamma dose levels, NuClim contributes to the enhancement of the sensitivity and reliability of the European detection network. On the radiation protection side, the possibility of determining an exact measured value of the radon activity concentration in the outdoor air has attracted attention. This will support the discussion on dose determination, e.g. in EURADOS WG3, which is a harmonization approach due to the heterogeneous application of dose conversion in the member states.

Acknowledgments

The NuClim field campaign is supported by the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) user facility, a U.S. Department of Energy (DOE) Office of Science user facility managed by the Biological and Environmental Research Program. Support from the ARM staff at ENA, Bruno Cunha and Tercio Silva, is gratefully acknowledged. The project 19ENV01 traceRadon has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. 19ENV01 traceRadon denotes the EMPIR project reference.

Funding

Project NuClim received funding from the EURATOM research and training program 2023–2025 under Grant Agreement No 101166515.

Conflicts of interest

The authors have nothing to disclose.

Data availability statement

Meteorological and eddy covariance data are available from Atmospheric Radiation Measurement (ARM) data [31, 37]. Ambient gamma radiation data are available at [38].

Author contribution statement

Conceptualization, S.B. and S.C.; Writing – Original Draft Preparation, S.B.; K.F.; W.P.; A.R.; S.R.; Writing – Review & Editing, S.C., X.C., A.M., D.M., D.K., A.W., J.B.R., J.H., T.A., H.A., N.D., M.E.S., J.C., H.K.L., E.A., M.K.

References

  1. S. Barbosa, R. Donner, G. Steinitz, Radon applications in geosciences – Progress & perspectives, Eur. Phys. J. Spec. Topics 224, 597 (2015) [CrossRef] [Google Scholar]
  2. W. Zahorowski, S. Chambers, A. Henderson-Sellers, Ground based radon-222 observations and their application to atmospheric studies, J. Environ. Radioact. 76, 3 (2004) [CrossRef] [Google Scholar]
  3. S. Chambers, A. Williams, W. Zahorowski, A. Griffiths, J. Crawford, Separating remote fetch and local mixing influences on vertical radon measurements in the lower atmosphere, Tellus B: Chem. Phys. Meteorol. 63, 843 (2011) [CrossRef] [Google Scholar]
  4. A.G. Williams, W. Zahorowski, S. Chambers, A. Griffiths, J.M. Hacker, A. Element, S. Werczynski, The vertical distribution of radon in clear and cloudy daytime terrestrial boundary layers, J. Atmos. Sci. 68, 155 (2011) [CrossRef] [Google Scholar]
  5. X. Chen, J. Paatero, V.-M. Kerminen, L. Riuttanen, J. Hatakka, V. Hiltunen, P. Paasonen, A. Hirsikko, A. Franchin, H.E. Manninen, et al., Responses of the atmospheric concentration of radon-222 to the vertical mixing and spatial transportation, Boreal Environ. Res. 21, 299 (2016) [Google Scholar]
  6. S.D. Chambers, S.-B. Hong, A.G. Williams, J. Crawford, A.D. Griffiths, S.-J. Park, Characterising terrestrial influences on Antarctic air masses using radon-222 measurements at King George Island, Atmos. Chem. Phys. 14, 9903 (2014) [CrossRef] [Google Scholar]
  7. S.D. Chambers, A.G. Williams, F. Conen, A.D. Griffiths, S. Reimann, M. Steinbacher, P.B. Krummel, L.P. Steele, M.V. van derSchoot, I.E. Galbally, et al., Towards a universal “baseline” characterisation of air masses for high-and low-altitude observing stations using Radon-222, Aerosol Air Qual. Res. 16, 885 (2016) [CrossRef] [Google Scholar]
  8. S.D. Chambers, W. Zahorowski, A.G. Williams, J. Crawford, A.D. Griffiths, Identifying tropospheric baseline air masses at Mauna Loa Observatory between 2004 and 2010 using radon-222 and back trajectories, J. Geophys. Res.: Atmos. 118, 992 (2013) [CrossRef] [Google Scholar]
  9. M.F. Lunt, M. Rigby, A.L. Ganesan, A.J. Manning, R.G. Prinn, S. O’Doherty, J. Mühle, C.M. Harth, P.K. Salameh, T. Arnold, et al., Reconciling reported and unreported HFC emissions with atmospheric observations, Proc. Natl. Acad. Sci. 112, 5927 (2015) [CrossRef] [Google Scholar]
  10. P.G. Canadell, et al., Global carbon and other biogeochemical cycles and feed backs, in The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2021), pp. 673–816 [Google Scholar]
  11. H.D. Matthews, S. Wynes, Current global efforts are insufficient to limit warming to 1.5 C, Science 376, 1404 (2022) [CrossRef] [Google Scholar]
  12. WMO-GAW, WMO/TD-No. 1201, 1st International Expert Meeting on Sources and Measurements of Natural Radionuclides Applied to Climate and Air Quality Studies (2004) [Google Scholar]
  13. S. Biraud, P. Ciais, M. Ramonet, P. Simmonds, V. Kazan, P. Monfray, S. O’Doherty, T.G. Spain, S.G. Jennings, European greenhouse gas emissions estimated from continuous atmosphericmeasurements and radon 222 at Mace Head, Ireland, J. Geophys. Res.: Atmos. 105, 1351 (2000) [CrossRef] [Google Scholar]
  14. C. O’Dowd, D. Ceburnis, J. Ovadnevaite, A. Vaishya, M. Rinaldi, M.C. Facchini, Do anthropogenic, continental or coastal aerosol sources impact on a marine aerosol signature at Mace Head?, Atmos. Chem. Phys. 14, 10687 (2014) [CrossRef] [Google Scholar]
  15. W. Xu, J. Ovadnevaite, K.N. Fossum, C. Lin, R.-J. Huang, C. O’Dowd, D. Ceburnis, Aerosol hygroscopicity and its link to chemical composition in the coastal atmosphere of Mace Head: Marine and continental air masses, Atmos. Chem. Phys. 20, 3777 (2020) [CrossRef] [Google Scholar]
  16. A. Melintescu, S. Chambers, J. Crawford, A. Williams, B. Zorila, D. Galeriu, Radon-222 related influence on ambient gamma dose, J. Environ. Radioact. 189, 67 (2018) [CrossRef] [Google Scholar]
  17. S. Whittlestone, W. Zahorowski, Baseline radon detectors for shipboard use: Development and deployment in the First Aerosol Characterization Experiment (ACE 1), J. Geophys. Res.: Atmos. 103, 16743 (1998) [CrossRef] [Google Scholar]
  18. Y. Xia, H. Sartorius, C. Schlosser, U. Stöhlker, F. Conen, W. Zahorowski, Comparison of one- and two-filter detectors for atmospheric 222Rn measurements under various meteorological conditions, Atmos. Meas. Tech. 3, 723 (2010) [CrossRef] [Google Scholar]
  19. A. Röttger, S. Röttger, C. Grossi, A. Vargas, R. Curcoll, P. Otahal, M.A. Hernandez-Ceballos, G. Cinelli, S. Chambers, S.A. Barbosa, et al., New metrology for radon at the environmental level, Meas. Sci. Technol. 32, 124008 (2021) [CrossRef] [Google Scholar]
  20. S. Röttger, A. Röttger, F. Mertes, V. Morosch, T. Ballé, S. Chambers, Evolution of traceable radon emanation sources from MBq to few Bq, Appl. Radiat. Isotopes 196, 110726 (2023) [CrossRef] [Google Scholar]
  21. J. Paatero, J. Hatakka, R. Mattsson, I. Lehtinen, A comprehensive station for monitoring atmospheric radioactivity, Radiat. Prot. Dosim. 54, 33 (1994) [CrossRef] [Google Scholar]
  22. J. Paatero, J. Hatakka, T.H. Virtanen, Outdoor radon-222 in Arctic Finland, Environ. Sci.: Atmos. 3, 1453 (2023) [CrossRef] [Google Scholar]
  23. J. Paatero, J. Hatakka, K. Holmén, K. Eneroth, Y. Viisanen, Lead-210 concentration in the air at Mt. Zeppelin, Ny-Å lesund, Svalbard, Phys. Chem. Earth Parts A/B/C 28, 1175 (2003) [CrossRef] [Google Scholar]
  24. J. Paatero, Wet deposition of radon-222 progeny in Northern Finland Mea sured with an automatic precipitation gamma analyser, Radiat. Prot. Dosim. 87, 273 (2000) [CrossRef] [Google Scholar]
  25. S. Mirme, A. Mirme, A. Minikin, A. Petzold, U. Hõrrak, V.-M. Kerminen, M. Kulmala, Atmospheric sub-3 nm particles at high altitudes, Atmos. Chem. Phys. 10, 437 (2010) [CrossRef] [Google Scholar]
  26. M. Kulmala, S. Tuovinen, S. Mirme, P. Koemets, L. Ahonen, Y. Liu, H. Junninen, T. Petäjä, V.-M. Kerminen, On the potential of the Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles, Aerosol Res. 2, 291 (2024) [CrossRef] [Google Scholar]
  27. C.W. Rella, H. Chen, A.E. Andrews, A. Filges, C. Gerbig, J. Hatakka, A. Karion, N.L. Miles, S.J. Richardson, M. Steinbacher, C. Sweeney, B. Wastine, C. Zellweger, High accuracy measurements of dry mole fractions of carbon dioxide and methane in humid air, Atmos. Meas. Tech. 6, 837 (2013) [CrossRef] [Google Scholar]
  28. ICOS RI, ICOS Atmosphere Station Specifications V2.0, edited by O. Laurent (2020) [Google Scholar]
  29. D. Cook, Surface Energy Balance System (SEBS) Instrument Handbook (2024) [CrossRef] [Google Scholar]
  30. R. Sullivan, D. Billesbach, E. Keeler, B. Ermold, S. Pal, Eddy Correlation Flux Measurement System (1997) [Google Scholar]
  31. J. Kyrouac, Y. Shi, M. Tuftedal, Atmospheric Radiation Mea surement (ARM) user facility Surface Meteorological Instrumentation (MET) 2024-01-01 to 2024-11-26, Eastern North Atlantic (ENA) Graciosa Island, Azores, Portugal (C1), Data set accessed 2024-11-28 at https://doi.org/10.5439/1786358 (2013) [Google Scholar]
  32. H. Schmid, Source areas for scalars and scalar fluxes, Bound. Layer Meteorol. 67, 293 (1994) [CrossRef] [Google Scholar]
  33. H. Zafrir, G. Haquin, U. Malik, S. Barbosa, O. Piatibratova, G. Steinitz, Gamma versus alpha sensors for Rn-222 long-term monitoring in geological environments, Radiat. Meas. 46, 611 (2011) [CrossRef] [Google Scholar]
  34. S. Barbosa, P. Miranda, E. Azevedo, Short-term variability of gamma radiation at the {ARM} Eastern North Atlantic facility (Azores), J. Environ. Radioact. 172, 218 (2017) [CrossRef] [Google Scholar]
  35. S. Barbosa, J.A. Huisman, E.B. Azevedo, Meteorological and soil surface effects in gamma radiation time series – Implications for assessment of earthquake precursors, J. Environ. Radioact. 195, 72 (2018) [CrossRef] [Google Scholar]
  36. S. Barbosa, N. Dias, C. Almeida, G. Silva, A. Ferreira, A. Camilo, E. Silva, Precipitation-driven gamma radiation enhancement over the Atlantic Ocean, J. Geophys. Res.: Atmos. 128, e2022JD037570 (2023) [CrossRef] [Google Scholar]
  37. R. Sullivan, D. Cook, E. Keeler, S. Pal, J. Kyrouac, Atmospheric Radiation Measurement (ARM) user facility Surface Energy Balance System (SEBS) (2014) [Google Scholar]
  38. S. Barbosa, Pre-processed Gamma Radiation Measurements at ENA (Graciosa Island, Azores) (2018), https://rdm.inesctec.pt/dataset/cs-2017-001 [Google Scholar]

Cite this article as: Susana Barbosa, Scott Chambers, Wlodzimierz Pawlak, Krzysztof Fortuniak, Jussi Paatero, Annette Röttger, Stefan Röttger, Xuemeng Chen, Anca Melintescu, Damien Martin, Dafina Kikaj, Angelina Wenger, Kieran Stanley, Joana Barcelos Ramos, Juha Hatakka, Timo Anttila, Hermanni Aaltonen, Nuno Dias, Maria Eduarda Silva, João Castro, Hanna K. Lappalainen, Eduardo Azevedo, Markku Kulmala. Using nuclear observations to improve climate research and GHG emission estimates – the NuClim project, EPJ Nuclear Sci. Technol. 11, 14 (2025). https://doi.org/10.1051/epjn/2025017.

All Tables

Table 1.

Measurements already available at the ENA station and new measurements, starting in 2025, to be undertaken within the scope of project NuClim.

In the text

All Figures

thumbnail Fig. 1.

Map showing the location of the stations measuring atmospheric radon activity concentration in the scope of project NuClim (ARM ENA station, Graciosa Island, and Mace Head station, Ireland).
In the text
thumbnail Fig. 2.

Data consolidation workflow for NuClim project's data.
In the text
thumbnail Fig. 3.

Mean diurnal courses (left) and time series of 30 min data for October 2024 at ARM's Graciosa ENA site. From top to bottom: T – air temperature, RH – relative humidity, ρH2O – water vapor density, v – wind speed, u* – friction velocity, L – Obukhov length, (z−d)/L – stability parameter (z is the measurement height and d the displacement level), Q* – radiation balance, QH – sensible heat flux, QE – latent heat flux, QG – soil heat flux, K↓ – shortwave solar downward radiation, K↑ – shortwave solar reflected radiation, L↓ – longwave downward radiation, L↑ – longwave upward radiation, FCO2 – carbon dioxide flux, albedo.
In the text
thumbnail Fig. 4.

Wind speed and friction velocity in relation to wind direction at the Graciosa ENA station in October 2024.
In the text
thumbnail Fig. 5.

Source area of turbulent fluxes calculated for stable (left) and unstable (right) conditions in October 2024. White solid lines indicate source area with contribution threshold p=25%, 50%, 75% and 90%; red dashed lines indicate 100 m, 200 m, 300 m and 400 m distance from the measurement site. Photo by Google.
In the text
thumbnail Fig. 6.

Time series of total gamma counts every 1 min (grey points) and corresponding 1 h-averaged observations (solid line).
In the text
thumbnail Fig. 7.

Weekly time series of total gamma counts recorded every 1 min (grey points), 1 h-averaged (black line), and 1 min precipitation observations (blue points): week from 2024-01-29 to 2024-02-04 (top) and from 2024-04-29 to 2024-05-05 (bottom).
In the text
thumbnail Fig. 8.

Zoom of total gamma counts recorded every minute (top), and 1 min-averaged wind speed (bottom).
In the text

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