Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona

Marsac, Kara E.; Burnley, Pamela C.; Adcock, Christopher T.; Haber, Daniel A.; Malchow, Russell L.; Hausrath, Elisabeth M.

doi:10.1016/j.jenvrad.2016.07.012

Title: Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona

Abstract

Here, this study compares high-resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (National Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likelymore »« less

Authors:

Marsac, Kara E.;

; Adcock, Christopher T.; Haber, Daniel A.; Malchow, Russell L.; Hausrath, Elisabeth M.

Publication Date:: Thu Dec 01 00:00:00 EST 2016

Research Org.:: Nevada Test Site/National Security Technologies, LLC, Las Vegas, NV (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1325309

Alternate Identifier(s):: OSTI ID: 1302512

Report Number(s):: DOE/NV/25946-2556
Journal ID: ISSN 0265-931X; S0265931X16302429; PII: S0265931X16302429

Grant/Contract Number:: AC52-06NA25946; NA0001982

Resource Type:: Published Article

Journal Name:: Journal of Environmental Radioactivity

Additional Journal Information:: Journal Name: Journal of Environmental Radioactivity Journal Volume: 165 Journal Issue: C; Journal ID: ISSN 0265-931X

Publisher:: Elsevier

Country of Publication:: United Kingdom

Language:: English

Subject:: 58 GEOSCIENCES; 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; airborne; Gamma-ray; radioactivity; geology; predictive model

Citation Formats


                    Marsac, Kara E., Burnley, Pamela C., Adcock, Christopher T., Haber, Daniel A., Malchow, Russell L., and Hausrath, Elisabeth M. Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona.  United Kingdom: N. p., 2016. 
Web.  doi:10.1016/j.jenvrad.2016.07.012.

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                    Marsac, Kara E., Burnley, Pamela C., Adcock, Christopher T., Haber, Daniel A., Malchow, Russell L., & Hausrath, Elisabeth M. Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona.  United Kingdom.  https://doi.org/10.1016/j.jenvrad.2016.07.012

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                    Marsac, Kara E., Burnley, Pamela C., Adcock, Christopher T., Haber, Daniel A., Malchow, Russell L., and Hausrath, Elisabeth M. Thu .  
"Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona".  United Kingdom.  https://doi.org/10.1016/j.jenvrad.2016.07.012.

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@article{osti_1325309,

  title        = {Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona},

  author       = {Marsac, Kara E. and Burnley, Pamela C. and Adcock, Christopher T. and Haber, Daniel A. and Malchow, Russell L. and Hausrath, Elisabeth M.},

  abstractNote = {Here, this study compares high-resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (National Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likely due to a number of factors including the need to take into account the evolution of soil geochemistry during chemical weathering and the influence of aeolian addition. Refinements to the forward models were made using ASTER visualizations to create subunits of similar exposure rate within the Chinle Formation, which contains multiple lithologies and by grouping alluvial units by drainage basin rather than age.},

  doi          = {10.1016/j.jenvrad.2016.07.012},

  journal      = {Journal of Environmental Radioactivity},

  number       = C,

  volume       = 165,

  place        = {United Kingdom},

  year         = {Thu Dec 01 00:00:00 EST 2016},

  month        = {Thu Dec 01 00:00:00 EST 2016}

}

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