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The A-01 wetland treatment system (WTS) is a surface flow wetland planted with giant bulrush [Schoenoplectus californicus (C.A. Mey.) Palla] that is designed to remove Cu and other metals from the A-01 National Pollution Discharge Elimination System (NPDES) effluent at the Savannah River Site near Aiken, SC. Copper, Zn, and Pb concentrations in water were usually reduced 60 to 80% by passage through the treatment system. The Cu concentrations in the wetland sediments increased from about 4 to 205 and 796 mg kg(-1), respectively, in the organic and floc sediment layers in cell 4A over a 5-yr period. Metal concentrations were higher in the two top layers of sediment (i.e., the floc and organic layers) than in the deeper inorganic layers. Sequential extraction was used to evaluate remobilization and retention of Cu, Pb, Zn, Mn, and Fe in the wetland sediment. Metal remobilization was determined by the potentially mobile fraction (PMF) and metal retention by the recalcitrant factor (RF). The PMF values were high in the floc layer but comparatively low in the organic and inorganic layers. High RF values for Cu, Zn, and Pb in the organic and inorganic layers indicated that these metals were strongly bound in the sediment. The RF values for Mn were lower than for the other elements especially in the floc layer, indicating low retention or binding capacity. Retention of contaminants was also evaluated by distribution coefficient (Kd) values. Distribution coefficient (Kd) values were lower for Cu and Zn than for Pb, indicating a smaller exchangeable fraction for Pb.

This study investigated the usability of hyperspectral remote sensing for characterizing vegetation at hazardous waste sites. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. HyMap airborne data (126 bands at 2.3 × 2.3 m spatial resolution), collected over the U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona, were used. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. Regression trees resulted in the best calibration performance of LAI estimation (R 2 > 0.80. The use of REPs failed to accurately predict LAI (R 2 < 0.2). The use of the MTMF-derived metrics (matched OPEN ACCESSRemote Sens. 2012, 4 328 filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (<1 m) found on the sites.

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