Daniel Beene
University of New Mexico
13 Papers
11 Citations
Daniel Beene is an academic researcher from University of New Mexico. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 3, co-authored 6 publications.
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Papers
Environmental risk mapping of potential abandoned uranium mine contamination on the Navajo Nation, USA, using a GIS-based multi-criteria decision analysis approach
TL;DR: This study adopts GIS-based multi-criteria decision analysis (GIS-MCDA), fuzzy logic, and analytic hierarchy process (AHP) to develop a more sophisticated environmental risk map illustrating the potential for AUM contamination across the NN.
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A mass-balance approach to evaluate arsenic intake and excretion in different populations.
Daniel Beene,Philip A. Collender,Andrés Cárdenas,Charles F. Harvey,Linden B. Huhmann,Yan Lin,Johnnye Lewis,Nancy J. LoIacono,Ana Navas-Acien,Anne E. Nigra,Craig Steinmaus,Alexander van Geen +11 more
TL;DR: In this article , the authors apply this idea to assess arsenic exposure misclassification in three previously studied populations in rural Bangladesh, Navajo Nation in the Southwestern United States (n = 619), and northern Chile, under varying assumptions about As sources.
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Individual level spatial-temporal modelling of exposure potential of livestock in the Cove Wash watershed, Arizona
TL;DR: In this article , a novel approach to spatial-temporal modelling that incorporated behaviour classifications taking into account uncertainties, to estimate individual livestock exposure potential was proposed, which was applied in a community-based research project with a Tribal community in the southwest United States to address questions on potential livestock exposure to abandoned uranium mines (AUMs).
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Adjudication and the Adaptive Capacity of Pecan Farmers in the Lower Rio Grande
Daniel Beene
- 01 Jan 2019
TL;DR: In this article, the authors explore what externalities and mechanisms of uncertainty influence agricultural management decisions and use multiple regression models to unpack some of the interplay between physical and institutional factors, spatial relationships, and cropping patterns.
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Workflow for hydrologic modelling with sUAS-acquired aerial imagery
TL;DR: In this paper, a conceptual workflow for the appropriate processing of high-spatial resolution small unmanned aircraft system (sUAS) data for hydrologic modelling of floodplains during flooding is presented.
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