Open AccessEnvironmental risk mapping of potential abandoned uranium mine contamination on the Navajo Nation, USA, using a GIS-based multi-criteria decision analysis approach
Author(s) -
Yan Lin,
Joseph Hoover,
Daniel Beene,
Esther Erdei,
Zhuoming Liu
Publication year - 2020
Publication title -
environmental science and pollution research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.845
H-Index - 113
eISSN - 1614-7499
pISSN - 0944-1344
DOI - 10.1007/s11356-020-09257-3
Subject(s) - navajo , geographic information system , environmental science , analytic hierarchy process , geospatial analysis , contamination , multiple criteria decision analysis , normalized difference vegetation index , environmental resource management , geography , cartography , engineering , operations research , geology , ecology , philosophy , biology , oceanography , climate change , linguistics
The Navajo Nation (NN), a sovereign indigenous tribal nation in the Southwestern United States, is home to 523 abandoned uranium mines (AUMs). Previous health studies have articulated numerous human health hazards associated with AUMs and multiple environmental mechanisms/pathways (e.g., air, water, and soil) for contaminant transport. Despite this evidence, the limited modeling of AUM contamination that exists relies solely on proximity to mines and only considers single rather than combined pathways from which the contamination is a product. In order to better understand the spatial dynamics of contaminant exposure across the NN, we adopted the following established geospatial and computational methods to develop a more sophisticated environmental risk map illustrating the potential for AUM contamination: GIS-based multi-criteria decision analysis (GIS-MCDA), fuzzy logic, and analytic hierarchy process (AHP). Eight criteria layers were selected for the GIS-MCDA model: proximity to AUMs, roadway proximity, drainage proximity, topographic landforms, wind index, topographic wind exposure, vegetation index, and groundwater contamination. Model sensitivity was evaluated using the one-at-a-time method, and statistical validation analysis was conducted using two separate environmental datasets. The sensitivity analysis indicated consistency and reliability of the model. Model results were strongly associated with environmental uranium concentrations. The model classifies 20.2% of the NN as high potential for AUM contamination while 65.7% and 14.1% of the region are at medium and low risk, respectively. This study is entirely a novel application and a crucial first step toward informing future epidemiologic studies and ongoing remediation efforts to reduce human exposure to AUM waste.