Literature DB >> 26191968

Estimation of Groundwater Radon in North Carolina Using Land Use Regression and Bayesian Maximum Entropy.

Kyle P Messier1, Ted Campbell2, Philip J Bradley3, Marc L Serre1.   

Abstract

Radon ((222)Rn) is a naturally occurring chemically inert, colorless, and odorless radioactive gas produced from the decay of uranium ((238)U), which is ubiquitous in rocks and soils worldwide. Exposure to (222)Rn is likely the second leading cause of lung cancer after cigarette smoking via inhalation; however, exposure through untreated groundwater is also a contributing factor to both inhalation and ingestion routes. A land use regression (LUR) model for groundwater (222)Rn with anisotropic geological and (238)U based explanatory variables is developed, which helps elucidate the factors contributing to elevated (222)Rn across North Carolina. The LUR is also integrated into the Bayesian Maximum Entropy (BME) geostatistical framework to increase accuracy and produce a point-level LUR-BME model of groundwater (222)Rn across North Carolina including prediction uncertainty. The LUR-BME model of groundwater (222)Rn results in a leave-one out cross-validation r(2) of 0.46 (Pearson correlation coefficient = 0.68), effectively predicting within the spatial covariance range. Modeled results of (222)Rn concentrations show variability among intrusive felsic geological formations likely due to average bedrock (238)U defined on the basis of overlying stream-sediment (238)U concentrations that is a widely distributed consistently analyzed point-source data.

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Year:  2015        PMID: 26191968     DOI: 10.1021/acs.est.5b01503

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  3 in total

1.  Lung and stomach cancer associations with groundwater radon in North Carolina, USA.

Authors:  Kyle P Messier; Marc L Serre
Journal:  Int J Epidemiol       Date:  2017-04-01       Impact factor: 7.196

2.  Bayesian Maximum Entropy space/time estimation of surface water chloride in Maryland using river distances.

Authors:  Prahlad Jat; Marc L Serre
Journal:  Environ Pollut       Date:  2016-09-09       Impact factor: 8.071

3.  Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide.

Authors:  Kyle P Messier; Matthias Katzfuss
Journal:  Ann Appl Stat       Date:  2021-07-12       Impact factor: 2.083

  3 in total

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