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U.S. GEOLOGICAL SURVEY OPEN-FILE REPORT 98-622
National Geochemical Atlas: The Geochemical Landscape of the
Conterminous United States Derived from Stream Sediment and other Solid
Sample Media Analyzed by the National Uranium Resource Evaluation
(NURE) Program (Version 3.01)
by
Jeffrey N. Grossman
Overview of Documentation.
The main documentation of this open-file report takes the form of a standard
Windows help file called ATLASHLP.HLP, found either in the DATA directory of the
CD, or in the directory into which the CD was installed on any computer. This help file
can be opened on any Windows 95, 98, or NT system. If the help file is viewed while the
Geochemical Atlas Project is open under ArcView, then there will be active "hot-links"
between the help file and ArcView. The following document is simply a formatted
version of the Windows help file.
Scope of the CD.
This CD presents maps derived from a subset of the National Uranium Resource
Evaluation (NURE) Hydrogeochemical and Stream Sediment Reconnaissance (HSSR)
data.

Area covered: All samples analyzed in the continental U.S. (~260,000 locations).

Sample media: Solid samples, including stream, lake, pond, spring, and playa
sediments, and soils.

Analyzed elements: Data for eleven elements are included on this release of the
National Geochemical Atlas CD: Na, Ti, Fe, Cu, Zn, As, Ce, Hf, Pb, Th, and U.
Additional elements and sample media, as well as data for Alaska, will appear in
future editions. For all of the details, see the Data Processing section.
Concepts.
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The ArcView project that is run by this CD is intended to let you examine
different aspects of the geochemical landscape of the U.S. in a very flexible manner,
allowing easy analysis of the data. There are two major concepts behind the development
of the CD, and which the user should understand to make best use of this product.
1) The Multiple-View concept: In many GIS products, one can select among
various layers (or “themes”), and view these in a single window. Most images
and polygon layers tend to be opaque, and must be examined one at a time. The
idea here, is that many geochemists want to see variations in different elements
simultaneously. Thus, this CD allows one to look at multiple windows (or
“views”) of the data in a side-by-side fashion. Tools are provided to allow easy
access to the different data views, and to allow one to pan and zoom in all of the
windows at once (see Buttons to Open and Close Views and Buttons to
Synchronize Views for details).
2) The Multiple-Theme concept: There are many ways to look at geochemical
data that are produced from the analysis of a series of well-located samples. On
this CD, one has the option of selecting among three of these methods for each
chemical element.
a) Point (or “spot”) maps of the original data (slow, but shows the original
data).
b) Elemental grid maps derived by gridding the point data (fast, but some
detail is sacrificed by extensive averaging).
c) Maps produced by color-coding polygons in a pre-existing map. The
user can select either a geologic-map or a hydrologic-map base for this
purpose. Colors are assigned according to the median concentration of
the element in samples falling within each polygon. (intermediate
speed; allows extrapolation of the point data).
Background.
The National Uranium Resource Evaluation (NURE) program of the Department
of Energy (DOE) collected a vast amount of chemical data on sediment, soil, and water
samples from the United States in the late 1970’s and early 1980’s. This element of the
NURE program was known as the Hydrogeochemical and Stream Sediment
Reconnaissance (HSSR). The NURE HSSR data have long been available to the public
in a variety of formats, ranging from the original paper reports produced by the DOE (see
Averett, 1984), to comprehensive digital releases on CD-ROM by the U.S. Geological
Survey in the last few years (Hoffman and Buttleman, 1994; 1996), to digital releases on
the Internet of reformatted and cleaned data (Smith, 1998). While these publications
remain the best sources of the complete, primary data, and are accompanied by
documentation of the sampling protocols, sample characteristics, and analytical methods,
they are difficult to use for geochemical research, especially when the study area covers a
wide area of the United States.
This publication is intended to allow the rapid visualization of the geochemical
landscape of the United States using the NURE HSSR data. Here, the user is relieved of
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the responsibility of selecting and processing the raw data; this was done in the
preparation of the CD. A powerful geographic-information system (GIS) tool, the
ArcView program of Environmental Systems Research Institute, Inc. (ESRI), is provided
to allow one to probe and manipulate the processed NURE data. Within the ArcView
environment, multiple presentations of the NURE are provided, ranging from color-coded
point maps, to bitmap-images on a national scale, to interpreted maps based on geologic
and hydrologic units.
Because the NURE HSSR data have been processed by the author for the
production of this CD, the user must use a degree of caution in interpreting the maps
produced here, and in using the data files found on the disc. One must understand the
methods used in deriving the data on this CD in order to judge the significance of any
particular map or data feature. Fortunately, the raw data used in the production of this
CD are available in digital form (Hoffman and Buttleman, 1996), for examination by
sophisticated users.
Data Processing.
Introduction.
The backbone of this CD is a series of DBase (DBF) files, each containing the
point data for a single element in a set of solid (sediment) samples from the NURE HSSR
program. All of the images and map coverages on the CD are derived from these DBF
files. This section outlines the steps used in creating these files.
Underlying data.
The starting point for data processing on this CD is the set of quadrangle-byquadrangle DBF files of NURE HSSR data found in Hoffman and Buttleman (1996).
Note that these files are not the raw NURE data, but are themselves processed from the
original digital files (on tape) produced by DOE. Indeed, the DOE tapes are also not the
true raw data from the program, as there was a manual data-processing step to transfer
data from paper reports.
308 quadrangle files (covering the continental U.S.) from Hoffman and Buttleman
(1996) contained data for stream, lake, or spring sediments, and a subset of 43 of these
files also contained data for soils (Table 1). qqqRecords covering these sample media
were selected for inclusion in this CD.
Initial Data Processing and Clean-up.
Most of the selection of records from the original DBF files, and other primary
data extraction tasks were done with the Paradox database program. The steps in this
procedure were as follows:
1.1
Record selection. Records were extracted from the quadrangle DBF files
for the appropriate sample media using one or more of the field codes
listed in Table 2. (See Hoffman and Buttleman, 1994, for explanation of
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codes.) After surveying each file (through a series of Paradox queries), a
new query was constructed that extracted all records for stream sediments
(wet and dry), lake and pond sediments (including dry lakes), spring
sediments, and soils.
1.2
Field selection. Data fields were chosen from the selected records for
further processing. These included several label fields, the sample-type
fields listed in Table 2, the geographic coordinates, fields for the 54
chemical elements appropriate for solid samples (Ag, Al, As, Au, B, Ba,
Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Eu, Fe, Hf, Ho, K, La, Li, Lu, Mg,
Mn, Mo, Na, Nb, Nd, Ni, P, Pb, Pt, Rb, Ru, Sb, Sc, Se, Si, Sm, Sn, Sr, Ta,
Tb, Th, Ti, U, V, W, Y, Yb, Zn, Zr), and 5 miscellaneous fields that
contain chemical data (CONCN01 through CONCN05). A Paradox query
extracted these fields, and all other data were discarded (including things
like stream characteristics, contamination codes, various labels, and fields
not used for solid sample media).
1.3
Data scaling. Most chemical data in the quadrangle DBF files are stored
in parts-per-billion (ppb). Paradox was used to convert each field into a
more appropriate unit: parts-per-million (ppm) for trace elements, and
wt.% for major elements (Al, Ca, Fe, K, Mg, and Na).
1.4
Record consolidation. Many samples were analyzed by more than one
laboratory, or by more than one method. In these cases, there are multiple
records in the quadrangle DBF files for an individual sample location,
each with analyses for different elements. These records were found and
combined into a single record. Paradox was used to sort the records by
latitude and longitude. A temporary DBF file was generated, and read by
a DOS FORTRAN program, ECLEAN, written by the author
(unpublished). This program searched for consecutive records that had
identical or nearly identical geographic coordinates (within 0.0005
degrees, or ~50 m, of each other). These were assumed to be the same
sample, as round-off errors sometimes affected the 4th decimal place.
ECLEAN then combined these records, element by element, into a single
new record. In the few cases where data for the same element was present
in two or more records, the highest value was arbitrarily chosen. This
process also had the effect of consolidating samples actually collected as
duplicates at a single location into single records. ECLEAN also
eliminated records with no chemical data (and there were many of these).
The program then created a new DBF file with the consolidated data.
Secondary data processing.
At the beginning of this processing stage, the 308 original quadrangle DBF files
have been reduced to 308 new DBF files containing only the geographic and chemicalelement fields of the sediment and soil data, without any duplicate or blank records.
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Major systematic problems, as discussed above, have been corrected. The following
processing steps were used to find and correct additional problems in the datasets, to
search for regional inconsistencies in the data, and to establish the usefulness of data
reported as “upper limits” (e.g., <10 ppm).
2.1
Data surveying. The reduced DBF files were surveyed with a DOS
FORTRAN program, also written by the author, called GRIDPLOT. This
program reads in multiple DBF files, and produces a simple, color,
gridded map of the data for one element on the computer screen. It is
extremely efficient, and allows the rapid visualization of the data (all 308
files can be read, and a plot generated on a 200 MHz PentiumPro PC in
about 1 minute). Systematic errors that were not found during primary
data processing can be seen visually, as discontinuities in the colored map.
In some cases, these could be traced to systematic errors in the quadrangle
DBF files, especially errors in the position of decimal points. These were
corrected by repeating the primary processing for the affected quadrangle.
Other discontinuities are caused by analytical errors, and are handled in
step 2.2.
2.2
Data leveling. In some areas, generally in the western U.S., one or more
quadrangles, or parts of quadrangles, would appear to be discontinuous
with adjacent quadrangles for a given element, when viewed with
GRIDPLOT. In many such instances, a good case can be made that there
is a systematic analytical error (i.e., an accuracy problem, probably due to
different analytical methods or interlaboratory calibration problems)
across the discontinuity. The best argument for the occurrence of this type
of error is that regional chemical trends are seen on both sides of the
discontinuity, and the application of a simple correction factor can make
the data appear continuous. In these cases, a correction factor is supplied
to GRIDPLOT for the affected areas, and the factor is adjusted until the
gridded map appears smooth and continuous. Such corrections can be
displayed graphically in ArcView, by examining the “Data Processing”
themes for each element (see below). In other cases, either no correction
factor can correct the discontinuity, or regional trends are absent in certain
quadrangles and the data appear to be random. Such data were deleted
from this CD, and the “Data Processing” theme will show a correction
factor of zero (see, as a good example, the hafnium data in ArcView).
2.3
Data below detection limits. A negative concentration of an element in the
quadrangle DBF files indicates that the value is an "upper limit" (e.g., “10” implies “<10”). These values present a special problem in creating
map coverages of geochemical data. The philosophy adopted here is a
simple one: steps are taken to ensure that all “upper limits” fall within the
lowest interval in the final map legend, and thus are known to be correctly
categorized. First, two histograms are prepared for each element, one
showing the concentration range of unqualified data, the other showing
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only “upper limits” (Fig. 1). For most elements, the vast majority of the
data fall in the first histogram, and markers are inserted into this plot
showing the values of every 5th percentile (for reference). The second
histogram is displayed below the first, and compared visually. The
strategy is to select a cutoff value below which “upper limits” are to be
retained, such that they do not affect the accuracy of the map. Above this
cutoff, “upper limits” are deleted from the final dataset. In the case shown
in Fig. 1, it would be possible to construct maps using a color legend that
has as its lowest interval the lowest 5th percentile of the data. “Upper
limits” with values of <2 ppm fall unambiguously within this lowest color
interval, and can be merged into the final dataset without affecting the
appearance or accuracy of the map; in practice, the “<” is dropped, and the
value multiplied by 0.5. However, those “upper limits” with values of <6
ppm could have “real values” (had they be measured more precisely) that
fall anywhere within the lowest 30% of the concentration distribution.
Such values cannot be assigned with certainty to the correct color interval
in the map legend, and are simply deleted. The graphical result of
deletions of this type may be small "holes" in the map where grid cells
could not be assigned real values. Table 3 shows the values of these
cutoffs for each of the elements compiled on this CD.
2.4
Data extraction. Once the data are leveled, "upper limit" cutoffs are
established, and areas of “bad data” are identified, the GRIDPLOT
program is run again to utilize its secondary function, which is to extract
values for a single element from all 308 “processed” quadrangle DBF
files. For the special case of uranium, GRIDPLOT was programmed to
make choices about which data field to use for the final value. Uranium is
typically stored in one of five fields in the original quadrangle DBF files:
one labeled as “CONU”, the others as “CONCN01,” “CONCN02,”
“CONCN05,” and “CONUDN.” The CONC05 field was given priority
over the CONU field if both were filled, and data in the CONCN01 and
CONCN02 fields were used in the absence of data in the first two fields.
The CONUDN field (U by delayed neutron) was only coded in few
percent of the samples (in only 9 quadrangles), but these data were not
used here. The output from this data processing step is a series of
“elemental DBF” files of “useable” NURE data.
Major Errors Corrected.
Several major errors in the NURE HSSR data were identified and corrected
during the above data-processing steps. These errors are present in the original DBF files
and composite database of Hoffman and Buttleman (1994; 1996). The errors will be
corrected in the a new database (Smith, 1998), but as of this time only a small part of the
United States is covered by this.
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1.
Miscoded samples: The data survey conducted for each quadrangle DBF
file in step 1.1 uncovered a block of stream-sediment samples miscoded as
stream water in seven quadrangles in the northeastern U.S. (Boston, Glen
Falls, Lake Champlain, Lewiston, Newark, Scranton, and Williamsport).
These records were altered to give them the correct coding prior to any
data processing.
2.
Data in incorrect units-I. In ~30,000 samples collected and analyzed by
Oak Ridge Gaseous Diffusion Plant (ORGDP) and tabulated in the
quadrangle DBF files, major elements (Al, Ca, Fe, K, Mg, and Na) plus
As and Se were all tabulated incorrectly in units other than ppb. Over 70
quadrangles contain data affected by this problem. These records can be
identified from the lack of coding in the “SAMPTYP” field, and a value of
“4” coded in the “SAMPMDC” field. These problems were corrected as a
group in step 1.3.
3.
Data in incorrect units-II. A group of ~15,000 records found in several
dozen quadrangles in the western U.S. (samples analyzed but not collected
by ORGDP) also contain major element data in ppm instead of ppb,
although trace elements are all coded correctly. Most of these are coded
as soils (SAMPTYP=59), talus (SAMPTYP=62), or uncoded in this field
(SAMPTYP=blank), and all have a value of “M” coded in the “LTYPC”
field, which stands for “sediment.” These were also corrected by special
handling in step 1.3.
Description of the ArcView “project”.
Introduction.
The main method of accessing data on this CD is intended to be via the ArcView
project configured by the author. This section describes the components of this project,
and how each one was constructed from the “elemental DBF” files created in the dataprocessing steps outlined above. Advanced users may also want to use the various DBF
files and “shape” (SHP) files of point, vector, and polygon data in their own projects.
These are described and indexed in the file “A_README.1ST” on the CD.
The ArcView project files found on this CD contain a wide variety of “views”
(windows) each incorporating one aspect of the data. A few views contain reference data
(a geologic map, a map of hydrologic units, a shaded-relief map of elevation data, a
sample index map), but the majority comprise data and maps for individual elements.
Element-by-element views.
Introduction. Data and maps for each element are contained in a single view
(titled, for example, “Copper Geochemistry”). Each view encompasses a variety of
methods for examining and analyzing the geochemical data. The data layers, or “themes”
are described below, using copper as an example.
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Point Data. Themes with names of the form “Points: Cu” show the locations of
individual samples that have valid data for that element (Table 3 shows the numbers of
points for each element). Points are color-coded according to the concentration of the
element, with the highest values shown in hot colors, and the lowest values shown in cool
colors and grays. Each color encompasses 5% of the data in the entire set of samples for
the continental U.S. For elements with relatively good analytical detection limits (i.e.,
elements with few "upper limits" in the dataset), the “detection limit cutoff” determined
in step 2.3 of data processing falls in the lowest 5% of the data, and thus 20 color
intervals appear in the legend (from the lowest 5th percentile up to the 95th-100th
percentile). For other elements, the “detection limit cutoff” might fall in a higher
percentile range. In that case, fewer color intervals are present, each still representing 5%
of the data except for the lowest interval. E.g., if 16 color-intervals are given, the lowest
color represents data up to the 25th percentile, and successive colors show the 25th-30th,
30th-35th…95th-100th percentiles. The color-key in this point-data legend is also used for
other themes for the same element, as noted below. In keeping with this, only one copy
of the color-key is visible (by default) in each element-view. The others are hidden, but
can be revealed by clicking on the theme of interest, then using the Theme menu item
called "Hide/Show Legend."
Gridded elemental maps. Themes with names of the form “Grid: Cu” are
elemental concentration maps, produced from a gridded version of the point data. These
bitmap files (Tiff) are based on grids made with the MINC program of Webring (1981),
which employs a minimum curvature interpolation of the point data to create a smooth
surface. The grid-cells used were 2 km on each side. Following the gridding operation,
the program GCLR (unpublished, by R. W. Simpson, USGS, Menlo Park, Calif.) was
used to produce a color-shaded relief map. The color-scheme of these maps is similar to
that used in the point-data themes, as it is based upon the distribution of the underlying
point data. Here, seven intervals are used, corresponding to the lowest 40th, the 40th-80th,
the 80th-90th, the 90th-95th, the 95th-98th, the 98th-99th, and the 99th-100th percentiles. The
legends for all these maps, showing the actual concentration values corresponding to each
color interval, are shown in a special view called Gridded Elemental Map Legends.
Geology-based themes. A visual comparison of the gridded-elemental maps for
most elements in the NURE HSSR sediments/soils dataset with the geologic map of the
U.S. clearly shows that many of the chemical trends roughly follow the geologic trends.
For example, the NE-SW trending highs in many elements in the Appalachian and
Piedmont Provinces in the eastern US exactly parallel the outlines of rock units in the
geologic map of King and Beikman (1974). This is not surprising, as the sediments are
mostly derived from local rocks in the small streams sampled during the NURE HSSR
program. Given this relationship, it is reasonable to create a version of the geologic map
where each polygon is shaded not by lithologic units, but instead by the typical
concentration of an element measured in samples falling within that polygon. This is
what has been done in themes with names of the form “Geology: Cu.”
To accomplish this “chemical shading of the geologic map,” a FORTRAN
program was written to calculate which points fall within each polygon (although various
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GIS packages can perform this operation, they run at least 10x slower than the
FORTRAN program on a single platform). For each polygon that contains at least two
points, the median value plus a few other statistical parameters were calculated, and
written into a new DBF file. In addition, the highest 5% of the values in each polygon
were identified, and written into a second DBF file for later use. Finally, in ArcView, a
legend is constructed for the geologic map based on the median value for the element in
each polygon. The color intervals for the legend are identical to those used in the pointdata theme, and are visible by default.
These geology-based themes provide a way of rapidly identifying the relationship
between rock units, and the sediments and soils associated with them. They also allow
some extrapolation of the geochemical data. For example, even if a geologic polygon
only had sample coverage in its southern half, the resulting theme will show the entire
polygon colored by the median concentration of the element. For this reason, the gridded
elemental maps for elements analyzed in every NURE HSSR sample only cover ~60% of
the land area of the continental U.S., but the geology-based maps of the same elements
cover close to 85% of the area.
WARNINGS: (1) When interpreting geology-based maps, the user should be
aware of which regions have been extrapolated by also examining the underlying point
data. (2) Note also that there are areas in the U.S. where elemental concentrations are not
well-represented by geological polygons. These might include areas where mineralization
creates strong local anomalies within a geological polygon. In these areas, the geologybased maps can give false impressions of the geochemical landscape. Again, the
underlying point data should always be examined before making any geochemical model
or interpretation based upon one of these maps.
Hydrology-based themes. Sometimes it is useful to know the geochemical
characteristics of an entire drainage basin. For this purpose, themes have been
constructed using an identical method to that employed above for making geology-based
themes. But, instead of a using the polygons from a geologic map, the polygons were
taken from the hydrologic unit code (HUC) map of the U.S. Geological Survey. The
resulting themes have names of the type “Hydrology: Cu.” Because drainage basins
often cut across geologic trends, and because the local rocks seem to be the strongest
factor in influencing sediment compositions in much of the U.S., one has to be cautious
in interpreting these hydrology-based geochemical maps. It is especially worth noting
that extrapolations in polygons that are only partially sampled may not be meaningful.
See the warnings under the geology-based themes section
Outlier themes. In constructing the geology- and hydrology-based geochemical
maps for each element, files of data representing the upper 5% of the concentration range
within each polygon were created (see above). These appear as point themes with names
of the form “Geology: Cu outliers” or “Hydrology: Cu outliers.” These data can be
thought of as the “anomalous” points within each polygon. They are plotted as open
symbols, and when viewed simultaneously with the entire point-data theme the symbols
will outline the anomalous points.
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Data Processing themes. In processing the raw data for some elements in step 2.2
(above), data in some quadrangles were multiplied by a “leveling” factor. These factors
are shown graphically as transparent overlays in themes with names of the type “Data
Processing: As.” If no leveling was done in any quadrangle for a given element, then this
theme will be absent.
Leveling factors of 0.0 were applied to quadrangles where data were judged by
the author to be of such poor or uncertain quality that they could not be used for making
maps. The only element for which wholesale deletion of large blocks of data had to be
performed was hafnium in much of the central and western U.S. In these quadrangles, Hf
was analyzed by emission spectroscopy at Oak Ridge Gaseous Diffusion Plant, and has
extremely poor detection limits compared to INAA data for Hf obtained in other
quadrangles.
All point data on this CD in quadrangles in which leveling was done are given as
the original value multiplied by the leveling factor. The original values can be found in
other publications (e.g., Smith, 1998; Hoffman and Buttleman, 1996).
Special handling of zinc data. The data for zinc was handled a bit differently from
that of other elements. This is because very different analytical methods were employed
in the eastern and western U.S., having drastically different limits of detection (see, e.g.,
Smith, 1998). In the east, data are reported down to 5 ppm, while in the west, the
detection limit was generally ~30 ppm, and far more data are reported as "upper limits."
This made it impossible to combine the data on a national scale, as it would be necessary
to suppress the very precise eastern data on maps in order to display all of the western
data in the lowest map-legend colors. Thus, there are two copies of the geology-based
and hydrology-based themes for Zn, one that is optimized for the eastern data, and the
other for the western data. The outlier point-themes are given as combined east and west
coverages. A national Zn point-data theme is provided in addition to the separate east
and west point themes. The gridded Zn map is a composite of gridded elemental maps
prepared separately for the eastern and western point data.
Other themes. Element-views contain several themes as a convenience for users
wishing to explore the data further. To use these, the user must make independent
decisions of what themes in the view he/she wishes to display, and turn the themes on or
off manually (rather than by using the handy shortcut buttons provided on the toolbar).
Themes may be chosen by clicking on the square boxes on the left side of the elementview window. Note that in order to read the names of themes, it may be necessary to
widen the left part of the window by sliding the vertical bar separating it from the right
half of the window.
Each element-view contains, by default, a shaded-relief topographic map named
"Shaded Relief" and a map of streams and rivers named "Streams." These are described,
along with other themes one might wish to import into an element-view, below under
Other Coverages.
Gridded elemental map legends.
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This view contains an image showing the concentrations of each element
corresponding to each color interval in the gridded elemental maps. Taking just the left
side of this legend as an example:
the gridded elemental map shown in the "Arsenic Geochemistry" view has its
dark blue color corresponding to <2.4 ppm As, light blues representing 2.4 to
5.2 ppm, greens representing 5.2 to 7.6 ppm As, etc., up to magenta
representing >22 ppm As. Note that because the grid is actually a shadedrelief rendition of the data, each color grades somewhat from high saturation
(left side of color bar) to low saturation (right side of color bar).
Geologic Map Views.
Two views contain the digital version of the King and Beikman (1974) geologic
map of the U.S. developed by Schruben et al. (1997). The “Geologic Map” view
contains the colored map, and the “Geology Key” view shows an annotated legend of the
map. The polygons from this geologic map were used in making the geology-based
geochemical maps described above.
To display these views, open the main project window (titled "Gc_atlas.apr" if
you followed the installation procedure for users who own ArcView 2 or 3, or "Atlas.apr"
if you installed the "lite" version of ArcView). Then, click on the "Views" button on the
left side of the project window, and double-click on the "Geologic Map" or "Geology
Key" entries on the right side of the project window.
Hydrology Index View.
The “Hydrology index” view shows the Hydrologic Unit Code polygons from the
“1:2,000,000 Hydrologic Unit map of the Conterminous United States” by G.J. Allord
(available on-line: see http://nsdi.usgs.gov/nsdi/wais/water/huc2m.HTML). The
polygons from this map were used in making the hydrology-based geochemical maps
described above.
To display this view, open the main project window (titled "Gc_atlas.apr"
if you followed the installation procedure for users who own ArcView 2 or 3, or
"Atlas.apr" if you installed the "lite" version of ArcView). Then, click on the "Views"
button on the left side of the project window, and double-click on the " Hydrology index"
entry on the right side of the project window.
NURE Sample Index View.
This view shows the distribution of samples used in the data files on this CD.
Polygons delimit areas that are dominated by samples of the same type, as listed in the
original quadrangle DBF files. Shown are only those areas where samples of a given
type have significant areal coverage. Note that there are overlapping polygons in many
regions, indicating that more than one type of sample was analyzed in that area. Most of
the sample-types displayed in this map derive from the SAMPTYP field of the
quadrangle DBF files. In areas where the SAMPTYP filed was left blank, the sample
type reflects coding in the SAMPMDC plus SAMPSRC fields (ORGDP samples), or the
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LTYPC field. The polygons shown in this map are schematic, and whether a given area
had sufficient sample density for inclusion in a polygon was decided by “eyeball.”
Other Coverages View.
The “Other Coverages” view contains miscellaneous data themes that a user
might wish to use in an analysis of the NURE geochemical data.
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
Streams. This theme contains a fairly detailed vector coverage of streams and rivers
in the continental U.S., derived from the version of the U.S. Environmental Protection
Agency’s “Reach File 1,” (1:500,000) originally converted to Arc/INFO by K.J.
Lanfear of the U.S. Geological Survey. For information, see
http://nsdi.epa.gov/nsdi/projects/rf1_meta.html.
Lakes. This theme is derived from the same "Reach File 1" as the Streams theme
(above). It contains polygons showing lakes in the continental U.S.
Ecoregions. This theme shows a map of Bailey’s Ecoregions (Bailey et al., 1994.
Documentation is available on-line at http://www.epa.gov/grd/bailey/).
NURE quads. This is a polygon theme outlining the 1 by 2 degree quadrangles in
which NURE samples used on this CD were analyzed.
Shaded Relief. This theme contains a large image file depicting a color shaded relief
map of elevation data in the continental U.S. The map was created from 15 arcsecond digital elevation model (DEM) data, itself derived from 3 arc-second DEM
data (Michael Webring, USGS Denver, personal communication). An Albers
projection (base latitude = 23oN, central meridian = 96 oW, standard parallels at 29.5
and 45.5 oN, Clarke 1866 spheroid) was applied to the 15 arc-second DEM data, and
the resulting data was sampled with a grid cell size of 468 m. The image was created
by multi-directional hillshading from azimuths 225, 270, 315, 360o with a sun
elevation of 30o, and a vertical exaggeration of 5. Colors are assigned as follows:
Min. elevation (m)
Below sea level
30
75
150
300
500
1000
1750
>2500
Max. elevation (m)
29
74
149
299
499
999
1749
2499
Color
dark green
med. dark green
med. lt. green
light green
tan
beige
light brown
dark brown
white
Special ArcView Controls.
Window display tools.
The following buttons on the ArcView tool bar open and close single-element views:
13
The following buttons open and close other views:
…Geologic Map view
…NURE Sample Type Index view
…Other Coverages view
…Legends for gridded elemental maps
If the view is open, it closes, and if it is closed, it opens. The visible area and placement
on the screen shown in a newly opened view will be the same as they were when last
closed.
Theme selection tools.
Four buttons appear on the ArcView toolbar that allow one to select which themes
are displayed in open single-element views:
makes only the geology-based geochemical map visible for each element currently
displayed.
makes only the hydrology-based geochemical map visible for each element
currently displayed.
makes only the elemental-grid map visible for each element currently displayed.
makes only the topographic color shaded-relief map visible for each element
currently displayed.
Tools for synchronizing views.
Two special buttons appear on the ArcView toolbar that help to arrange views on the
screen, and make it so that every view has the same areal extent:
Tile visible views.
This button will force all of the open views to have the same window-size. It also adjusts
the vertical slider in each window (the one separating the theme names on the left from
the data window on the right) so that when the view-window gets small, the legend part
of the window doesn’t occupy too much space. The user should use this button after
changing the number of visible views.
Adjust view extents.
14
This button forces every open view to have the same areal extent. After using this tool,
all windows will have the same extent as the active (highlighted) window. Typically, this
tool would be used after panning or zooming was done in one of the views. For example,
if one decides to zoom in on a feature in the Arsenic Geochemistry view, then by
pressing this button, all other visible views would be zoomed to the same area. To make
all views exactly the same, use the tile button (above) first to assure that all windows
have the same size.
Map Ratio tools.
Two special buttons allow one to calculate and plot the ratio of any two elements
in the ArcView project. One does the calculation point-by-point, the other polygon-bypolygon.
The
button appears on the ArcView toolbar right next to the Window Display and
Theme Selection buttons. This allows one to calculate the ratio between two GeologyBased element maps, polygon by polygon over the entire continental U.S. First, select
the numerator and denominator element from the menus that appear after pressing the
button. After a short delay, select a file name (and path) for a new ArcView Shape file
which will be created on your hard disk. Heed the warning that is displayed before this
step… if you try to overwrite an existing file, this tool will not work correctly. ~16
megabytes of disk space are needed for the output file. Next, select a scaling factor for
the calculated ratios. This would usually be 1.0, but if you are taking the ratio of an
element in % to one in ppm, you might wish to enter 10000 here. Likewise, you might
have other reasons for scaling the data. The resulting map of the ratios of the two
elements appears in a view called “Ratio View.” The legend colors will be based on
percentiles of the values (ratios) assigned to each polygon.
The
button appears on the lower ArcView toolbar, next to the “identify” tool. It
works in a similar fashion to the polygon-ratio tool, except that now the ratios are
calculated point by point. Because it will take a long time to do this calculation on the
entire dataset, you will first need to select a region (with the mouse) where you want the
calculations performed. The rest of the procedure is the same as above. The size of the
output data file will depend on the area chosen, and will be >20 megabytes for some
element-pairs done over the entire nation. Note that point-by-point ratios will NOT be
calculated for any data that were present in the original NURE dataset as "upper limits"
(“<” or negative values).
Tutorial: Working with Element views.
Example 1. Examine NURE data for 4 elements in Colorado.
I) Get an overview of the geochemistry of Colorado
15
a) Assuming that the CD has just been installed, you have opened the Geochemical
Atlas Project and now see a
gridded geochemical
map of uranium in the
entire
U.S.
Use the colored
buttons on the toolbar to add views of Th, Ce, and Na to the ArcView display:
click on then then .
b) You now see three small windows for the other elements on top of the original
uranium map. You can now "tile" the maps in the ArcView window, making
them all the same size by clicking the
button. Note that large monitors
operating in high resolution display modes are desirable if you want to look at
many elements at once. If for some reason any of the element-views does not
display a gridded elemental map, click the
button.
c) The four element-views now cover the ArcView window. Next, zoom in to
Colorado by selecting the
button, and then using the mouse to drag a
rectangle approximately covering Colorado in any element view.
d) Now, one map shows Colorado, and the other three still show other areas, possibly
the entire U.S. You can zoom the remaining maps to Colorado by clicking the
button.
e) You can examine the legends for these maps by clicking the
button. Click
the button a second time to close this window when you are done with the
legends.
II) Get more detail.
a) Select a small region of the state for further examination. Repeat steps I.c and I.d
above to display the northwest corner of Colorado in all the views. Zoom in on
~1/8 of the area of the state.
b) Now, look at the data a different way. Click on the
button, and each
element view changes to show you geology-based maps. The polygons that you
see come from the King and Beikman (1974) geologic map. Colors represent the
median value of each element in samples collected from within each polygon.
The legend showing concentrations corresponding to each color are in the lefthand pane of each window.
c) You can see a similar representation of the same data by clicking on the
button, with the polygons now representing hydrologic units instead of geologic
units. The legends for the geology-based maps also apply to the hydrology-based
maps.
d) If you wish to get information about any polygon shown on the geology- or
hydrology-based maps, click on the
button. You also have to select the
correct theme in the legend of the map you wish to query: if you are interested in
16
the geology-based map for uranium, select the theme labeled "Geology: U" by
clicking on the legend with this label in the left-hand pane of the Uranium
Geochemistry window (don't change the check-box, just click anywhere else on
the legend). Now you can click on any polygon in the uranium map, and get
information about it. When you do this, an "Identify Results" window pops up
showing all of the data behind the polygon. Included are the median (or 50th
percentile), 90th and 95th percentile values for samples in the polygon, as well as
the number of samples plus the high and low values. Also displayed is
information about the polygon itself.
III) Examine the actual point data.
a) When looking at point data, it is best to limit both the region displayed and the
number of maps to small values. Close the Th, Ce, and Na windows by again
clicking on
and . Maximize the remaining uranium view by again
clicking on the
button.
b) Perhaps you want to look at the points on a topographic base, showing streams.
Click the
button to bring up the shaded-relief topographic map. Then go to
the left-hand pane of the uranium view, and find the theme labeled "Streams."
Click on its check box to display the stream reach data (there is no button on the
toolbar to do this). Finally, bring up the point data by finding the theme labeled
"Points: U" and clicking on its check box. The color-coding for the points is the
same as for the "Geology: U" theme. If you want to verify this fact, select the
"Points: U" theme by clicking on it in the left-hand pane, then go to the Theme
menu and select "Hide/Show Legend."
c) Another feature that you can now take advantage of is the ability to identify
"anomalous" data. Find the theme labeled "Geology: U outliers" in the left-hand
pane of the uranium view. Click on its check box, and you will see new symbols
outlining some of the colored data points. These represent samples with U
concentrations that were in the top 5% of all samples collected in the same
geologic-map polygon. You could re-display those polygons by un-checking the
box for the "Shaded Relief" theme, and re-checking the box for the "Geology: U"
theme (note that you can't display these two themes at the same time because both
are opaque).
d) You can query the names of streams or the values of points in the same way
described in II.d above. Be sure to select the theme that you want to query using
its legend before clicking on the map. Note that you have to hit lines and points
with great precision in ArcView to successfully query them. If you try to query
point themes at small map-scales, you may find that you get many answers at
once in the "Identify Results" pop-up window.
Example 2. Adding themes to an element view.
17
Suppose that you want to add themes from the "Other Coverages" view onto one of the
elemental map views. For example, you want to put an overlay of the labeled 1-by-2
degree NURE quadrangles on top of the gridded elemental map of titanium. This is
easily accomplished using cut-and-paste operations.
a) Open the "Titanium Geochemistry" view by clicking on the
button. (Note that
if NO views are open, this and other buttons that apply to ArcView views will not be
visible. If this happens, you must open the main project window named either
"gc_atlas.apr" or "atlas.apr", depending on the version of the program running, select
"Views" in the left-hand pane of this window, click on the desired view in the righthand pane, and then click the "Open" button on the window's toolbar.)
b) If the gridded Ti map is not visible in the "Titanium Geochemistry" view, click
the
button to display it.
c) Now open the "Other Coverages" view by clicking the
button. Select the
theme labeled "Quadrangles" by clicking anywhere on its legend in the left-hand pane
of the "Other Coverages" view. Then copy this theme by pulling down the "Edit"
menu, and selecting "Copy Themes." (The standard windows copy-shortcut, ControlC, won't work.)
d) Go back to the "Titanium Geochemistry" view, and paste the new theme by using
pulling down the "Edit" menu and selecting "Paste." (The standard windows pasteshortcut, Control-V, will also work.)
e) Order matters in a view-legend. Drawing is done starting with the bottom-most
theme and proceeding to the top-most one. You can change the legend's order by
using the mouse to drag items up or down in the left-hand pane of any view window.
You can also create new views from scratch, borrowing themes from any other views and
copying them into the new view. Go to the main project window, select "Views", and
then click the "New" button. A new view window appears. Cut an paste whatever
themes you want into this view window. You have complete control over the mapprojection of the new view through the "Properties" item under the "View" menu.
Note: If you create a new view and you want to use themes containing images, you must
select the correct projection for the view BEFORE copying the image-theme. Examples
of such themes are the Shaded Relief topographic map and any gridded elemental map.
Using the "Projection"
button under View-Properties, select "Projections of the United States" from the
"Category" submenu. Then click the radio-button marked "Custom." Make sure the
table is filled in as follows:
Projection:
Albers Equal-Area Conic
Spheroid:
Clarke 1866
Central Meridian:
-96
Reference Latitude:
23
18
Standard Parallel 1:
Standard Parallel 2:
False Easting:
False Northing
29.5
45.5
0
0
Example 3. Printing maps.
There are two basic ways to print things that you see on your screen in ArcView. One is
to print a view directly using the "File" menu, and selecting the "Print" command as you
would in any windows program. This will print the map that is displayed in the active
view window, but you will not get any legends, scalebars, north arrows, etc., on the
printout. The other way is to create a "Layout" in ArcView, and then print that.
A layout can be thought of as a page of output, and it can contain any number of views,
legends, north arrows, scalebars, labels, lines, and other information. You can access
your layouts from the main project window named either "gc_atlas.apr" or "atlas.apr",
depending on the version of the program running, and selecting "Layouts" in the lefthand pane of this window. Once created, layouts can be printed from the "File" menu,
and will come out just as they appear on the screen.
Layouts can be created manually, or you can have ArcView create some basic layouts for
you.
a) Have ArcView create a layout for you. Say you want to print out the contents of an
Arsenic map you create. Go to the "Arsenic Geochemistry" view, and manipulate the
themes to show what you want.
The legend that will appear in your layout will contain only themes that are "checked"
in the left-hand pane of the view window, and which are not "hidden." For example,
the legend of the theme "Points: As" is hidden by default; if you want it to be printed,
go to the "Theme" menu and select "Hide/Show Legend."
Go to the "View" menu and select "Layout." Follow the brief instructions, and
ArcView will create a layout showing your map, its legend, a scale bar, a north arrow,
and a title. You can edit these, or rearrange them using various ArcView tools
available when a layout window is open (you'll have to explore these yourself, or use
the "Help" menu for ArcView). When the appearance is satisfactory, use the standard
print command.
b) Create a layout manually. Say you want a layout to include an arsenic map and a
lead map side-by-side. Go to the "Arsenic Geochemistry" view, and manipulate the
themes to show what you want. Then do the same in the "Lead geochemistry" view.
Next, go to the main project window, select "Layouts" and click on the "New" button.
Set up the page format with the "Page setup" option of the "Layout" menu. Then
click on the
tool, and drag a box for the arsenic view to appear in. The program
19
will ask you which view to put in the box, and you select "Arsenic Geochemistry."
Drag another box on the layout, and put the Lead map in it. You can add other items
like scale bars, legends, etc., by clicking the
button and holding it down until a
drop-down menu appears as follows:
The second item on this lets you make legends, the third is for scale bars, and the
fourth makes north arrows, all in ways analogous to the way you created the viewframes for As and Pb. Titles can then be added with the "T" button. See ArcView's
internal help for more information.
Notes on layouts.
North arrows always point straight up when ArcView creates them in a layout. This will
almost certainly not point north, however, on a projected map. You can rotate the arrow
by double-clicking on it in the layout (first make sure the selection tool, depicted by the
arrow-button on the toolbar, is engaged). Then, enter a rotation angle manually in the
dialog box that appears (yes, that's right, ArcView is not capable of helping you out…
you must guess!).
You may have trouble placing items exactly where you want them in a layout if
"snapping" is turned on. To turn it off, go to the "Layout" menu, select the "Properties"
item, and uncheck the "Snap to Grid" box.
Legends in layouts are difficult to change. They can be re-sized using standard Windows
"click-and-stretch" methods, but not always to the desired size. Their contents can be
changed in two ways: (1) go to the ArcView view that the legend represents, and change
the legend there; or, (2) convert the legend into individual graphics elements and edit
these as a group or one at a time. The latter is accomplished by selecting the legend, and
using the "Simplify" command under the "Graphics" menu. Then, select one or more
elements with the mouse for moving, resizing, editing, deleting, etc. Note that after you
simplify a legend, it is no longer linked in any way to the view that created it.
References.
Averett, Walter R., 1984, Guide to the data reports of the Hydrogeochemical and Stream
Sediment Reconnaissance; Bendix Field Engineering Corporation, Prepared for the
U.S. Department of Energy under contract No. DE-AC07-76GJ01664: U.S.
Department of Energy Open-File Report GJBX-5(84), 27 p.
Bailey, R.G., Avers, P.E., King, T., McNab, W.H., 1994, Ecoregions and subregions of
the United States (map). U.S. Geological Survey, Washington, DC., Scale
1:7,500,000; colored. Accompanied by a supplementary table of map unit
descriptions compiled and edited by McNab, W.H. and Bailey, R.G. Prepared for the
U.S. Department of Agriculture, Forest Service.
20
Hoffman, J.D. and Buttleman, Kim, 1994, National Geochemical Data Base: National
Uranium Resource Evaluation Data for the Conterminous United States: U.S.
Geological Survey Publication, DDS-18-A, one CD-ROM.
Hoffman, J.D. and Buttleman, K., 1996, National Geochemical Data Base: 1. National
Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment
Reconnaissance (HSSR) data for Alaska, formatted for GSSEARCH data base
software; 2. NURE HSSR Data formatted as dBASE files for Alaska and the
conterminous United States; 3. NURE HSSR Data for Alaska and the conterminous
United States as originally compiled by the Department of Energy: U.S. Geological
Survey Publication, DDS-18-B, one CD-ROM.
King, P.B. and Beikman, H.M., 1974, Geologic map of the United States (exclusive of
Alaska and Hawaii) on a scale of 1:2,500,000: U.S. Geological Survey, 3 color plates.
Schruben, P.G., Arndt, R.E., and Bawiec, W.J., 1997, Geology of the Conterminous
United States at 1:2,500,000 Scale--A Digital Representation of the 1974 P.B. King
and H.M. Beikman Map: U.S. Geological Survey Publication, DDS-11, release 2,
one CD-ROM.
Smith, S.M., 1998, National Geochemical Database: Reformatted data from the National
Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment
Reconnaissance (HSSR) Program, v.1.00. U.S. Geological Survey Open File Report
97-492,WWW release only, URL: http://greenwood.cr.usgs.gov/pub/open-filereports/ofr-97-0492/index.htm
Webring, M.W., 1981, MINC—A gridding program based on minimum curvature: U.S.
Geological Survey Open-File Report 81-1224, 41 p.
21
Table 1. Quadrangles included in this CD. Shown is the southwest corner (north latitude, west longitude)
of the 1 by 2 degree quadrangle area, and filled circles to indicate whether sediments (stream, lake, and
spring) or soils were present.
Name
Abilene
Adel
Albany
Albuquerque
Alexandria
Alliance
Amarillo
Andalusia
Ardmore
Arminto
Ashland
Ashton
Athens
Atlanta
Augusta
Austin
Aztec
Baker
Bakersfield
Baltimore
Bangor
Bath
Baton Rouge
Bay City
Beaufort
Beaumont
Beeville
Belleville
Big Spring
Billings
Binghamton
Birmingham
Bluefield
Blytheville
Boise
Boston
Bozeman
Brigham City
Broken Bow
Brownfield
Brownwood
Brunswick
Butte
Caliente
Canton
Carlsbad
Casper
Cedar City
Challis
Charleston
Charlotte
Charlottesville
Chattanooga
Cheyenne
Choteau
Clarksburg
Cleveland
Clifton
Clinton
Clovis
Corner
32 100
42 120
42 74
35 108
31 94
42 104
35 102
31 88
34 98
43 108
46 92
44 112
33 84
33 86
33 82
30 98
36 108
44 118
35 120
39 78
44 70
43 70
30 92
28 96
34 78
30 96
28 98
38 90
32 102
45 110
42 76
33 88
37 82
35 90
43 118
42 72
45 112
41 114
41 100
33 104
31 100
31 82
46 114
37 116
40 82
32 106
42 108
37 114
44 116
38 82
35 82
38 80
35 86
41 106
47 114
39 82
41 82
33 110
35 100
34 104
Seds




























































Soils




























































Name
Cody
Columbia
Columbus
Corbin
Corpus Christi
Cortez
Craig
Cumberland
Cutbank
Dalhart
Dallas
Death Valley
Del Rio
Delta
Denver
Dickinson
Dillon
Dodge City
Dothan
Douglas
Driggs
Dubois
Durango
Dyersburg
Eastport
Eau Claire
Ekalaka
El Dorado
El Paso
Elk City
Elko
Elmira
Ely
Emory Peak
Enid
Escalante
Escanaba
Evansville
Fargo
Flagstaff
Florence
Forsyth
Fort Dodge
Fort Smith
Fort Sumner
Fremont
Gadsden
Gallup
Georgetown
Gillette
Glasgow
Glendive
Glens Falls
Goldfield
Grand Canyon
Grand Forks
Grand Island
Grand Junction
Great Falls
Greeley
Corner
44 110
35 88
39 84
36 86
27 98
37 110
40 108
39 80
48 114
36 104
32 98
36 118
29 102
39 114
39 106
46 104
45 114
37 102
31 86
31 110
43 112
44 114
37 108
36 90
44 68
44 92
45 106
33 94
31 108
45 116
40 116
42 78
39 116
29 104
36 98
37 112
45 88
37 88
46 98
35 112
34 80
46 108
42 96
35 96
34 106
41 98
34 88
35 110
33 80
44 106
48 108
47 106
43 74
37 118
36 114
47 98
40 100
39 110
47 112
40 106
Seds




























































Soils




























































22
Name
Green Bay
Greensboro
Greenville
Greenwood
Hailey
Hamilton
Hardin
Harrisburg
Harrison
Hartford
Hattiesburg
Havre
Helena
Hobbs
Holbrook
Hot Springs
Houston
Huntington
Hutchinson
Idaho Falls
Iron Mountain
Iron River
Jackson
James Island
Jenkins
Johnson City
Joplin
Jordan
Jordan Valley
Kalispell
Kingman
Kingston
Klamath Falls
Knoxville
La Junta
Lake Champlain
Lake Charles
Lamar
Lander
Laredo
Las Cruces
Las Vegas
Lawton
Leadville
Lemmon
Lewiston
Lewistown
Limon
Lincoln
Little Rock
Llano
Lovelock
Lubbock
Lund
Macon
Manhattan
Manteo
Marble Canyon
Marfa
Marion
Mariposa
Marquette
McAlester
Corner
44 90
36 80
34 84
33 92
43 116
46 116
45 108
40 78
36 94
41 74
31 90
48 110
34 92
32 104
34 112
43 104
29 96
38 84
38 98
43 114
45 90
46 90
32 92
32 80
37 84
36 84
37 96
47 108
42 118
48 116
35 116
44 78
42 122
35 84
37 104
44 74
30 94
38 104
42 110
27 100
32 108
36 116
34 100
39 108
45 104
44 72
47 110
39 104
40 98
34 94
30 100
40 120
33 102
38 116
32 84
39 98
35 76
36 112
30 106
40 84
37 120
46 88
34 96
Seds































































Soils































































Name
McAllen
McDermitt
Memphis
Meridian
Mesa
Milbank
Miles City
Millett
Moab
Monterey
Montgomery
Montreal
Montrose
Nashville
Natchez
Needles
New Ulm
Newark
Newcastle
Nogales
Norfolk
Ogden
Ogdensburg
Okanogan
Oklahoma City
O'Neill
Paducah
Palestine
Phenix City
Phoenix
Pittsburgh
Plainview
Pocatello
Poplar Bluff
Port Isabel
Portland
Pratt
Prescott
Presidio
Preston
Price
Pueblo
Raleigh
Rapid City
Raton
Rawlins
Reno
Rice Lake
Richfield
Richmond
Ritzville
Roanoke
Rochester
Rock Springs
Rocky Mount
Rolla
Rome
Roswell
Roundup
Russellville
St. Cloud
St. Johns
St. Louis
Corner
26 100
41 118
35 92
32 90
33 112
45 98
46 106
39 118
38 110
36 122
32 88
45 74
38 108
36 88
31 92
34 116
44 96
40 76
43 106
31 112
36 78
41 112
44 76
48 120
35 98
42 100
37 90
31 96
32 86
33 114
40 80
34 102
42 114
36 92
26 98
43 72
37 100
34 114
29 106
42 112
39 112
38 106
35 80
44 104
36 106
41 108
39 120
45 92
38 114
37 78
47 120
37 80
43 78
41 110
35 78
37 92
34 86
33 106
46 110
35 94
45 96
34 110
38 92
Seds































































Soils



















































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
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

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

23
Name
Salina
Salisbury
Salton Sea
San Angelo
San Antonio
San Bernardino
Sandpoint
Santa Fe
Savannah
Scottsbluff
Scranton
Seguin
Shelby
Sherbrooke
Sheridan
Sherman
Shiprock
Shreveport
Silver City
Sioux City
Socorro
Spartanburg
Spokane
Springfield
Sterling
Texarkana
Thermopolis
Thief River Falls
Tooele
Torrington
Trinidad
Corner
38 112
38 76
33 116
31 102
29 100
34 118
48 118
35 106
32 82
41 104
41 76
29 98
48 112
45 72
44 108
33 98
36 110
32 94
32 110
42 98
34 108
34 82
47 118
37 94
40 104
33 96
43 110
48 98
40 114
42 106
37 106
Seds































Soils































Name
Trona
Tucson
Tucumcari
Tularosa
Tulsa
Tupelo
Twin Falls
Tyler
Utica
Vernal
Vincennes
Vya
Waco
Walker Lake
Wallace
Warren
Washington
Watertown
Waycross
Wells
West Point
White Sulphur
Wichita
Springs
Wichita Falls
Williams
Williamsport
Wilmington
Winchester
Winnemucca
Winston-Salem
Wolf Point
Corner
35 118
32 112
35 104
33 108
36 96
34 90
42 116
32 96
43 76
40 110
38 88
41 120
31 98
38 120
47 116
41 80
38 78
44 98
31 84
41 116
33 90
46 112
37 98
33 100
35 114
41 78
39 76
37 86
40 118
36 82
48 106
Seds































Soils































24
Table 2. Field codes in the DBF files of Hoffman and Buttleman
(1996) used for sample selection. Not all fields were coded in each
sample.
Field name
Sediment codes
Soil Codes
SAMPLE
2.x, 2.x.x
3.x
SAMPMDC
4

LTYPC
M

SAMPTYP
11, 12, 13, 14, 15, 37, 58, 59
50, 55, 60, 61, 63, 64,
70, 71, 72, 73, 96, 97,
99
25
Table 3. Parameters relevant to the processing of each element in the NURE
HSSR data for soils and sediments. The “detection limit cutoff” is the value
above which data reported as "upper limits" was deleted; below this cutoff,
"upper limits" were retained in the final dataset. The “number of points”
column shows the total number of unique records containing valid data for
each element. Zinc was processed separately for samples in the eastern and
western parts of the U.S., as the analytical methods used in the two regions
resulted in very different lower limits of detection.
Element
Detection limit cutoff Number of points
Na
0.051 %
253,600
Ti
1300 ppm
250,633
Fe
0.51 %
256,475
Cu
11 ppm
204,193
Zn (eastern US)
6 ppm
66,676
Zn (western US)
30 ppm
115,597
As
4 ppm
79,527
Ce
21 ppm
248,167
Hf
3 ppm
163,294
Pb
11 ppm
180,756
Th
6 ppm
255,361
U
0.5 ppm
259,418
26
Figure 1. Illustration of the method used to select "upper limits" (values
preceded by a “<”) for an arbitrary element for inclusion on this CD. The
“real values” histogram, showing unqualified data, is typically a log-normal
distribution like this one. Small horizontal tick marks show the locations of
every 5th percentile in the distribution of measured concentrations. For this
element, most of the data below the limit of detection were assigned a value of
<2 ppm. In a small number of cases, the limit of detection was placed at <6
ppm; these analyses were done by a different method . The “real data” are a
mixture of analyses by these two methods. In this example, the "upper limits"
given as <6 ppm were discarded because the actual concentrations for these
samples could be as high as the 30th percentile of the population’s distribution.
Those "upper limits" reported as <2 ppm were retained, as they all fall in the
lowest 5th percentile of the population. In making maps, these were converted
to real values of 1 ppm.