Human health risk from manganese in groundwater
Machine learning is the key to uncovering where populations across the globe are at risk from manganese in their groundwater-sourced drinking water.
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Machine learning is the key to uncovering where populations across the globe are at risk from manganese in their groundwater-sourced drinking water.
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This report documents the highly variable structure, stratigraphy, and buried topography of the outer rim of the Chesapeake Bay impact crater created by its impact and burial. Lithologies of cores are correlated with borehole geophysical logs to characterize the physical properties of the stratigraphic units and their geophysical signatures. The correlation between cores, well cuttings, and borehole geophysical logs is augmented with seismic-reflection data, and these data are compiled into a lithostratigraphic cross section that illustrates the geological framework of the lower York-James Peninsula and immediate surrounding areas.
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A U.S. Geological Survey program to monitor and assess channel reconfiguration activity is described. A data base available on the world wide web will enable land-management agencies and other interested parties to evaluate the long-term success of various channel reconfiguration projects. A demonstration project on the Lake Fork of the Gunnison River, Colorado, illustrates the program objectives and approach.
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