Graphic modeling of radioecological data using Violinplot
DOI:
https://doi.org/10.55905/oelv21n9-197Keywords:
computational mathematics, applied statistics, radioactive elements, outliers, environmental planning, environmental managementAbstract
Data collection of environmental radioactive chemical contaminants is information of fundamental importance for decision-making in environmental planning and management. In this case, data analyses should be performed with a high level of significance within robust statistical planning. The objective of this study was to utilize the Violinplot as a graphical modeling tool for statistical analysis of radioecology data. To achieve this, a computational algorithm was developed in the Python language. The findings clearly demonstrate that the Violinplot is an outstanding data analysis tool for robust statistical planning in environmental management.
References
ACEVEDO, M. F. Data Analysis and Statistics for Geography, Environmental Science, and Engineering. Florida: CRC Press, 2012.
AMARAL, Romilton dos Santos. Determinação de urânio na fosforita por meio de medidas radiométricas e análise por ativação. Tese de Mestrado-PRONUCLEAR, n° 41, Dissertação apresentada ao Departamento de Energia Nuclear da Universidade Federal de Pernambuco, 1987.
APT, K. E. Applicability of the Weibull distribution function to atmospheric radioactivi-ty data. Atmospheric Environment, v. 10, n. 9, p. 777-781, 1976. doi: 10.1016/0004-6981(76)90079-2.
BARNET, V.; LEWIS, T. Outliers in Statistical Data. London: John Wiley & Sons, 1996.
BERTHOUEX, P. M.; HUNTER, W. G. Simple Statistics for Interpreting Environmental Data. Journal (Water Pollution Control Federation), v. 53, n. 2, p. 167-175, 1981.
BULUBASA, G.; ALINA, D. C.; MIU, F.; ENE, M. R. Activity concentrations of 238U, 232Th, 226Ra 137Cs and 40K radionuclides in honey samples from Romania. Lifetime cancer risk estimated. Journal of Environmental Radioactivity, v. 234, n. 106626, 2021. doi: 10.1016/j.jenvrad.2021.106626.
DOWNING, D.; CLARK, J. Estatística Aplicada. São Paulo: Saraiva, 1998.
FRIGGE, M.; HOAGLIN, D.C.; IGLEWICZ, B. Some implementations of the boxplot. The American Statistician, v. 43, n. 1, p. 50-54, 1989. doi: 10.1080/00031305.1989.10475612.
HAMERMESH, D. Plus change: the annual report on the economic status of the profession, 1993-1994. Academe, v. 80, n. 2, p. 5-89, 1994.
MIKOLAJ, P. G. 1972. Environmental Applications of the Weibull Distribution Function: Oil Pollution. Science, v. 176, n. 4038, p. 1019-1021, 1972. doi: 10.1126/science.176.4038.1019.
MURTEIRA, B. J. F. Probabilidades e Estatística. Lisboa: McGraw-Hill, 1990.
OFUNGWU, J. 2014. Statistical Applications for Environmental Analysis and Risk Assessment (Statistics in Practice). Nova Jersey: Wiley.
PARZEN, E. On estimation of a probability density function and mode. The Annals of Mathematical Statistics, v. 33, n. 3, p. 1065-1076, 1962. doi: 10.1214/aoms/1177704472.
PENTREATH, R. J. 2009. Radioecology, radiobiology, and radiological protection: frameworks and fractures. Journal of Environmental Radioactivity, v. 100, n. 12, p. 1019-1026, 2009. doi: 10.1016/j.jenvrad.2009.06.004.
RAMALHO, L. Fluent Python: Clear, Concise, and Effective Programming. Florida: O'Reilly Media, 2022.
SINGH, A. K.; SINGH, A.; ENGELHARDT, M. The Lognormal Distribution in Environmental Applications.” (Washington, D.C.: Technology Support Center Issue, EPA/ 600/R-97/006). 1997.
SCOTT, D. Multivariate density estimation: theory, practice, and visualization. New Jersey: Wiley Online Library, 2015.
SHEPPARD, S. C.; SHEPPARD, M.I.; TAIT, J. C.; SANIPELLI, B. L. Revision and meta-analysis of selected biosphere parameter values for chlorine, iodine, neptunium, radium, radon and uranium. Journal of Environmental Radioactivity, v. 89, n. 2, p. 115-137, 2006. doi: 10.1016/j.jenvrad.2006.03.003.
SILVA, Cleomacio Miguel da. 2000. Urânio, Rn-222 e Po-210 em águas de abastecimento público da Região Metropolitana do Recife. Dissertação de Mestrado não publicada. Programa de Pós-Graduação em Tecnologias Energéticas e Nucleares, Universidade Federal de Pernambuco (UFPE), 2000.
SILVA, Cleomacio Miguel da. Radionuclídeos naturais em regiões anômalas do Nordeste do Brasil. Tese de Livre-Docência. Universidade de Pernambuco (UPE), 2019.
SILVERMAN, B. W. Density estimation for statistics and data analysis. Monographs on statistics and applied probability. London: Chapman and Hall, 1986.
STEIGER, B. Statistical Analysis for Environmental Data with R. Saarbrücken: VDM Verlag Dr. Mueller E.K, 2008.
Vandecasteele, C. M. Environmental monitoring and radioecology: a necessary synergy. Journal of Environmental Radioactivity, v. 72, n. 1-2, p. 17-23, 2004. doi: 10.1016/S0265-931X(03)00181-4.
UNITED NATIONS. Department of Economic and Social Affairs Sustainable Development. Available in: https://sdgs.un.org/goals. Accessed on 6/2/2023.
VOGELTANZ-HOLM, N.; SCHWARTZ, G. G. Radon and lung cancer: What does the public really know? Journal of Environmental Radioactivity, v. 192, p. 26-31, 2018. doi: 10.1016/j.jenvrad.2018.05.017.
WIERSMA, G. B. Environmental Monitoring. Florida: CRC Press, 2007.
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