Articles | Volume 17, issue 4
https://doi.org/10.5194/essd-17-1529-2025
https://doi.org/10.5194/essd-17-1529-2025
Data description paper
 | 
14 Apr 2025
Data description paper |  | 14 Apr 2025

Distribution and sources of fallout 137Cs and 239+240Pu in equatorial and Southern Hemisphere reference soils

Gerald Dicen, Floriane Guillevic, Surya Gupta, Pierre-Alexis Chaboche, Katrin Meusburger, Pierre Sabatier, Olivier Evrard, and Christine Alewell

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Cited articles

Alewell, C., Meusburger, K., Juretzko, G., Mabit, L., and Ketterer, M. E.: Suitability of 239+240Pu and 137Cs as tracers for soil erosion assessment in mountain grasslands, Chemosphere, 103, 274–280, https://doi.org/10.1016/j.chemosphere.2013.12.016, 2014. 
Alewell, C., Pitois, A., Meusburger, K., Ketterer, M., and Mabit, L.: 239+240Pu from “contaminant” to soil erosion tracer: Where do we stand?, Earth-Sci. Rev., 172, 107–123, https://doi.org/10.1016/j.earscirev.2017.07.009, 2017. 
Aoyama, M., Hirose, K., and Igarashi, Y:. Re-construction and updating our understanding on the global weapons tests 137Cs fallout, J. Environ. Monit., 8, 431–438, https://doi.org/10.1039/B512601K, 2006. 
Arata, L., Meusburger, K., Bürge, A., Zehringer, M., Ketterer, M. E., Mabit, L., and Alewell, C.: Decision support for the selection of reference sites using 137Cs as a soil erosion tracer, Soil, 3, 113–122, https://doi.org/10.5194/soil-3-113-2017, 2017. 
Bennett, B. G.: Worldwide dispersion and deposition of radionuclides produced in atmospheric tests, Health Phys., 82, 644–655, https://doi.org/10.1097/00004032-200205000-00011, 2002. 
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Short summary
Fallout radionuclides (FRNs) such as 137Cs and 239+240Pu are considered to be critical tools in various environmental research. Here, we compiled reference soil data on these FRNs from the literature to build a comprehensive database. Using this database, we determined the distribution and sources of 137Cs and 239+240Pu. We also demonstrated how the database can be used to identify the environmental factors that influence their distribution using a machine learning algorithm.
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