science for a changing world

Geochemical Landscapes of the

Conterminous United States—

New Map Presentations for 22 Elements

U.S. Geological Survey Professional Paper 1648

1,000 KILOMETERS

U.S. Department of the Interior 

U.S. Geological Survey
Cover.—Geochemical map showing the distribution of potassium in the conterminous United 

States. The map is based on chemical analyses of 1,323 samples of soils and other surficial 

materials as reported in Boerngen and Shacklette (1981).
Geochemical Landscapes of the

Conterminous United States—

New Map Presentations for 22 Elements

ByH. Gustavsson, B. B0lviken, D.B. Smith, and B.C. Severson

U.S. Geological Survey Professional Paper 1648

U.S. Department of the Interior 

U.S. Geological Survey
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat Director

First printing November 2001

For sale by U.S. Geological Survey, Information Services 

Box 25286, Denver Federal Center 

Denver, CO 80225

This publication is also available online at: 

http://geology.cr.usgs.gov/pub/ppapers/p1648/

Any use of trade, product, or firm names in this publication 

is for descriptive purposes only and does not 

imply endorsement by the U.S. Government
Contents

Abstract.......................................................................................................................................................... 1

Introduction................................................................................................................................................... 1

Sample Collection......................................................................................................................................... 2

Chemical Analysis........................................................................................................................................ 2

Data Treatment and Map Presentation..................................................................................................... 2

Moving Weighted Median.................................................................................................................. 4

Bootstrap Estimate..............................................................................................................................5

Color Scheme....................................................................................................................................... 6

Results and Interpretation........................................................................................................................... 7

Features of the Geochemical Landscape and Correlations with

Known Geology, Climate, and Human Activity.................................................................. 7

Reliability of the Geochemical Maps.............................................................................................. 32

Conclusions................................................................................................................................................. 34

Acknowledgments...................................................................................................................................... 36

References Cited........................................................................................................................................ 36

Figures

1. Map showing geochemical sample stations........................................................................... 3

2. Curve representing the local weight function (Butterworth's) depending

on the distance of the sampling station to the center of a circular window...................... 6

3. Colored surface map of Al distribution in soils and other surficial materials

of the conterminous United States............................................................................................ 8

4. Colored surface map of As distribution in soils and other surficial materials

of the conterminous United States............................................................................................ 9

5. Colored surface map of Ba distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 10

6. Colored surface map of Ca distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 11

7. Colored surface map of Cr distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 12

8. Colored surface map of Cu distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 13

9. Colored surface map of Fe distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 14

10. Colored surface map of Hg distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 15

11. Colored surface map of K distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 16
IV

12. Colored surface map of Li distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 17

13. Colored surface map of Mg distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 18

14. Colored surface map of Mn distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 19

15. Colored surface map of Na distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 20

16. Colored surface map of Ni distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 21

17. Colored surface map of Pb distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 22

18. Colored surface map of Se distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 23

19. Colored surface map of Sr distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 24

20. Colored surface map of Ti distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 25

21. Colored surface map of V distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 26

22. Colored surface map of Y distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 27

23. Colored surface map of Zn distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 28

24. Colored surface map of Zr distribution in soils and other surficial materials

of the conterminous United States.......................................................................................... 29

25. Map of conterminous United States showing approximate location of

selected physiographic and geologic features..................................................................... 30

26. Plot of K in regolith from sample-collection phase 1 versus

sample-collection phase 2........................................................................................................32

27. Potassium map of conterminous United States derived from

aerial gamma-ray surveys......................................................................................................... 33

28. Plot of K in regolith versus radiometric K at the corresponding nearest point................ 34

29. Map of conterminous United States showing the moving correlation 

(Spearman-rank correlation coefficient) between K in regolith samples 

and K determined by airborne gamma-ray surveys............................................................. 35

Tables

1. Methods of chemical analysis for selected elements in soil samples................................ 4

2. Summary information on chemical analysis of the 22 elements plotted

as geochemical maps in figures 3-24....................................................................................... 5
3. Parameter settings for computing regular grid using Bootstrap estimates 

of the moving weighted median...........................................................................

4. Quartiles and relative quartile deviations of interpolated and

smoothed data for each element.........................................................................

Geochemical Landscapes of the

Conterminous United States —

New Map Presentations for 22 Elements

. Gustavsson,1 B. B0lviken,2 D.B. Smith,3 and R.C. Severson3

Abstract

Shacklette and Boerngen (1984) collected soil and other 

regolith samples from 1,323 sites in the conterminous United 

States (7,840,000 km2) and prepared single-element pointsymbol geochemical maps in black and white for 7 major 

and 39 trace elements. We have reprocessed these data, using 

weighted-median and Bootstrap procedures for interpolation 

and smoothing, and produced full-color maps for seven major 

elements (Al, Ca, Fe, K, Mg, Na, and Ti) and 15 trace 

elements (As, Ba, Cr, Cu, Hg, Li, Mn, Ni, Pb, Se, Sr, V, 

Y, Zn, and Zr). Comparison of the K map produced in this 

manner with a corresponding map obtained from airborne 

radiometric measurements of 40K indicates that the reliability 

of the soil maps is good even with this ultra low sample 

density.

Many broad geochemical dispersion patterns for both 

major and trace elements have been delineated. Some of 

these agree with known geologic and physiographic features, 

whereas others seem to reflect variations in natural parameters 

such as soil type and climate. Certain patterns may be due to 

pollution, and others are difficult to interpret in view of the 

present knowledge.

It is concluded that geochemical maps based on ultra 

low density sample distributions, such as those presented in 

this publication, should have potential use in various fields. 

This type of map may be used to (1) establish general 

baselines against which more specific natural geochemical 

variations and human-induced perturbations can be appraised, 

(2) reflect large underlying geologic features and therefore 

delineate geochemical provinces of interest in exploration 

for mineral resources, (3) show how geochemical patterns 

in the regolith are influenced by natural features such as 

soil type, climate, and vegetation, (4) provide a basis for 

research within the field of geomedicine (environmental

1 Geological Survey of Finland.

2 Geological Survey of Norway.

3 U.S. Geological Survey.

geochemistry and health), and (5) show large geochemical 

contrasts between continents.

Introduction

In their paper "Element concentrations in soils and other 

surficial materials of the conterminous United States," Shacklette and Boerngen (1984) published a number of geochemical 

point-symbol maps (Howarth, 1983) covering the conterminous United States. The maps were based on chemical analyses of 1,323 samples of soils or other regolith materials collected, primarily, along the network of existing roads. At that 

time, such a low sampling density was not considered adequate 

to generate reproducible results. Furthermore, the maps produced by Shacklette and Boerngen were not very illustrative. 

These are probably the two main reasons why the maps 

did not receive proper attention during the 1980's. Recent 

geochemical mapping has, however, shown that significant 

broad geochemical distribution patterns with distinct contrasts 

between subareas exist in many places, even at continental 

scale (Duval, 1990; Xie and Ren, 1993), and that these patterns may be recognized based on one sample per 1/1000 of 

the area studied (e.g., B01viken and others, 1992; Eden and 

Bjorklund, 1994). It was in this context that the importance of 

the Shacklette and Boerngen data set was recognized during 

the early stages of the International Geological Correlation 

Program's Project 259/360 (International Geochemical Mapping) (Darnley and others, 1995). The data set was, at that 

time, and still is at the time of this publication, the only 

national geochemical data set for the conterminous United 

States that was generated according to consistent and standardized sampling and analytical protocols. As such, it presents an 

opportunity to obtain a first approximation of the geochemical 

landscape for this large area of the Earth's surface. We have, 

therefore, drawn new maps of Shacklette and Boerngen's data 

using modern computerized techniques and find the disclosed 

geochemical patterns so interesting that a republication of the 

maps is justified.

1
2 GeocKemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

Sample Collection

This summary of sampling and chemical analysis is based 

on the papers by Shacklette and others (1971), Boerngen and 

Shacklette (1981), and Shacklette and Boerngen (1984).

The sampling was done by U.S. Geological Survey 

(USGS) personnel at sampling stations located along their 

routes of travel to project areas and within project areas in 

various parts of the United States. The location of the stations, 

therefore, depended on both the road network and the destination of the samplers. The sample stations were selected 

approximately every 80 km along the roads, which resulted 

in a total of 1,323 stations, corresponding to an average of 

one sample station per 6,000 km2 for the conterminous United 

States. (It should be noted that there is some discrepancy 

in the number of samples in the above references. Although 

Shacklette and Boerngen (1984) state 1,318 samples were collected, seven of the geochemical maps (Ba, B, Cr, Pb, V, Y, and 

Zr) in that publication show 1,319 samples were plotted. In 

addition, Boerngen and Shacklette (1981), in their tabulation 

of all the data generated in the study, show 1,323 samples. The 

current study used this data set of 1,323 samples as the basis 

for all the new maps shown in figures 3-24.) In most cases 

the stations were located at least 100 m from the road and 

at sites that had natural surficial materials supporting native 

plant growth. Occasionally, the distance to the road had to 

be reduced for practical reasons, and, in some areas, only 

cultivated fields were available for sampling.

The samples were collected in two phases during 

1961-71 and 1971-75 resulting in 962 and 356 samples, 

respectively (fig. 1). (Based on the above-referenced publications, we found it impossible to fully reconcile the samples 

between the two phases of sample collection. Therefore, 

figure 1 distinguishes those samples we could unambiguously 

identify as phase-1 and phase-2 samples from those whose 

placement was uncertain.) One sample was collected at each 

selected station. The materials sampled included that part of 

the regolith that normally is defined as "soil" by soil scientists. 

About 0.25 L of soil was taken at a depth of 20 cm below 

the surface, which is normally in the B horizon of podzols or 

just below the plow zone in cultivated soils. The samples were 

packed in metal-free paper envelopes and shipped to the USGS 

laboratories in Denver, Colo., where they were oven-dried, 

pulverized if necessary, and sieved to a minus-2-mm fraction. 

This fraction was further milled to minus 200 mesh (<75 |im) 

before analysis.

Chemical Analysis

Boerngen and Shacklette (1981) report analytical values 

for 46 elements analyzed by a variety of methods. Table 1 

shows the analytical methods used to analyze the 22 elements 

discussed in this study. For some elements, the methods of

chemical analysis were consistent throughout the duration of 

the project, whereas, for others, the methods were different in 

phase 1 and phase 2 (table 1). The use of different methods 

in phases 1 and 2 may complicate the interpretation of the 

data generated because the accuracy and precision may vary 

between phases. This type of problem will be discussed in 

more detail in the section on reliability of the maps.

Each of the analytical methods described in Boerngen 

and Shacklette (1981) and Shacklette and Boerngen (1984) 

gives the total contents of the element determined. Therefore, 

reported values from the different methods can be directly 

compared for each element. Similar analytical methods were 

used for a reconnaissance geochemical survey of the State of 

Missouri conducted by the USGS during 1969-73 (Conner and 

others, 1972; Tidball, 1974, 1976; Miesch, 1976). After testing 

the sampling and analytical reproducibility in this survey, 

Miesch (1976) concluded that the sampling errors were more 

significant than the analytical errors and that the application of 

more precise analytical methods would have been a waste of 

money. The Missouri geochemical survey included 7,000 samples taken within 180,000 km2 as opposed to 1,300 samples 

from 8,000,000 km2 in the survey reported here. This much 

lower sampling density, with a corresponding greater sampling 

error, indicates that the reproducibility of the analytical results 

reported by Shacklette and Boerngen (1984) is adequate even 

between sample-collection phases.

The data were censored by both lower and upper limits 

of determination (Shacklette and Boerngen, 1984). Fortunately, even though the analytical techniques changed for a 

few elements during the project (table 1), the determination 

limits remained constant. Table 2 summarizes the determination limits, the number of samples with censored values, the 

number of samples not analyzed for a given element, and the 

number of analyses plotted to generate the geochemical maps 

(figs. 3-24).

Data Treatment and 

Map Presentation

The Geological Survey of Finland produced the maps 

from data obtained from the USGS National Geochemical 

Database. The software employed has also been used for generating maps in national and international geochemical atlases 

(Koljonen and others, 1992; Lahermo and others, 1990; Gustavsson and others, 1994, 1995; Lahermo and others, 1996).

Among available presentation techniques, we chose color 

surface maps (Gustavsson and others, 1997) to show regionalscale trends in element content. Color surface maps are generated from gridded data. Each grid node corresponds to a pixel, 

which is assigned a color depending on the local interpolated 

and smoothed concentration level. A basic problem in producing color surface maps is that the measurements tend to be 

irregularly located in the mapped area, whereas the pixels
1,000 KILOMETERS

Sample-collection 

phase unknown

OaQJ

Figure 1. Map showing geochemical sample stations. Black dots indicate samples from sample-collection phase 1, white dots indicate samples from sample-collection phase 2, gray dots indicate samples whose placement into phase 1 or phase 2 is uncertain.
4 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

Table 1. Methods of chemical analysis for selected elements in soil samples. 

[References are in footnotes]

Element Method of analysis

Sample-collection phase 1 

(962 samples)

Sample-collection phase 2 

(356 samples)

10

11

12

13

14

15

16

17

18

19

20

21

22

Al

As

Ba 

Ca 

Cr 

Cu 

Fe 

Hg 

K 

Li 

Mg 

Mn 

Na 

Ni 

Pb 

Se 

Sr 

Ti 

V 

Y 

Zn 

Zr

Myers and others (1961); Neiman (1976). 

Huffman and Dinnin (1979).

Emission spectrography1 

Arsineevolution-spectrophotometric-isotope

dilution2

Emission spectrography 

EDTA titration 

Emission spectrography 

Emission spectrography 

Emission spectrography 

Flame and flameless atomic absorption4 

Flame photometry5 

Emission spectrography 

Emission spectrography 

Emission spectrography 

Emission spectrography 

Emission spectrography 

Emission spectrography 

X-ray fluorescence spectrometry 

Emission spectrography 

Emission spectrography 

Emission spectrography 

Emission spectrography 

Colorimetry5 

Emission spectrography

Emission spectrography 

Arsineevolution-spectrophotometric-isotope

dilution

Emission spectrography 

X-ray fluorescence spectrometry3 

Emission spectrography 

Emission spectrography 

X-ray fluorescence spectrometry 

Flame and flameless atomic absorption4 

X-ray fluorescence spectrometry 

Flame atomic absorption 

Flame atomic absorption 

Emission spectrography 

Flame atomic absorption 

Emission spectrography 

Emission spectrography 

X-ray fluorescence spectrometry 

Emission spectrography 

X-ray fluorescence spectrometry 

Emission spectrography 

Emission spectrography 

Flame atomic absorption 

Emission spectrography

iviyers ana oiners (lyoiy, nei

2 Huffman and Dinnin (1979).

3 Wahlberg (1976).

4 Huffman and Dinnin (1976).

5 Ward and others (1963).

form a regular grid. For decades, interpolation and smoothing 

techniques have been employed to compute "best" and least 

misleading surfaces from values on an irregular grid. Many of 

these techniques work well for evenly distributed data points, 

but difficulties may arise when the data set is sparse. Stable 

results for sparse data sets typically require more smoothing, 

which leads to fewer details. We chose Bootstrap estimates 

(Efron and Tibshirani, 1991; Stuart and Ord, 1987) of the 

moving weighted median (Bjorklund and Lummaa, 1983) to 

achieve robustness against local outliers.

Moving Weighted Median

Calculation of the moving weighted median to interpolate

a continuous "surface" from scattered point data involves computing, for each grid cell, the median of distance-weighted 

observation values found within a circle of radius R from the 

center of the cell. Assume a circular window centered at a 

pixel and containing n observed sampling points with measured 

values jc;. The values are sorted in ascending order. For odd n, 

the ordinary unweighted moving median is thenX(n+i)/2, and, for 

even «, a value between xn/2 andxn/2+i is computed by linear 

interpolation (Stuart and Ord, 1987). The unweighted moving 

median does not depend on the spatial position of the sampling 

points. Every sampling point carrying a value is at a distance 

d{ from the window center. Given a weight function W, the 

corresponding weight of value xf at distance df is w,- = W(di). 

Now, the frequencies^- (equal to l/ri) ofxf are adjusted by the 

weights yielding new weighted frequencies #/ by
Data Treatment and Map Presentation

Table 2. Summary information on chemical analysis of the 22 elements plotted as geochemical maps in figures 3-24.

Element

Al

As

Ba

Ca

Cr

Cu

Fe

Hg

K

Li

Mg

Mn

Na

Ni

Pb

Se

Sr

Ti

V

Y

Zn

Zr

Concentration 

units

%

ppm

ppm

%

ppm

ppm

%

ppm

%

ppm

%

ppm

%

ppm

ppm

ppm

ppm

%

ppm

ppm

ppm

ppm

Detection 

limit

0.07

0.01

10

0.01

1.0

1.0

0.01

0.01

0.01

5.0

0.005

2.0

0.05

5.0

10

0.1

5.0

0.007

7.0

10

5.0

20

Upper Number of 

determination samples 

limit missing analysis

10 76

66

4

32

4

12

10 6

56

9

65

10 17

6

130

5

4

56

5

6

4

4

75

4

Number of 

samples with 

analysis 

below detection 

limit

0

8

0

0

0

10

0

4

0

48

0

3

86

128

185

228

39

0

25

83

9

3

Number of samples Numberof 

with analysis above samples 

upper determination plotted 

limit

136 1,247

1,257

1,319

1,291

1,319

1,311

2 1,317

1,267

1,314

1,258

1 1,306

1,317

1,193

1,318

1,319

1,267

1,318

1,317

1,319

1,319

1,248

1,319

(1)

where,

the distance weights are rescaled to sum up to unity. 

The corresponding cumulative frequencies, G(xk), are 

expressed by

(2)

/ = 1

The moving weighted median is finally obtained by linear 

interpolation between A> and A>+1 where xk is the greatest 

observed value with G(xk) < 0.5. A bell-shaped function known 

as the Butterworth's function was here used for the distancedependent weights

w =

1

(3)

where,

d (0 < d < R) is the distance to the window center, 

d0 (> 0) is a value indicating the distance at which the

weight is halved, and 

m is the order effecting the steepness of the curve (Gonzales

andWinz, 1987).

Finally w is adjusted by a term depending on R to achieve 0 at 

the window periphery. A large d0 smears out details, whereas 

a small d0 preserves them. A minimum number of samples in 

the window was also used.

The particular Butterworth function used for the maps 

is shown in figure 2 and the corresponding parameter values 

are given in table 3. The parameter values are chosen by 

experience according to the scale of the map and the sampling 

density. The pixel size was chosen as 5x5 km to yield a sufficiently smooth surface.

Bootstrap Estimate

A procedure termed "Bootstrap" (Efron and Tibshirani, 

1991; Stuart and Ord, 1987) was used to estimate the average 

of repeated moving weighed medians within the window. A 

statistical sample of n items was repeatedly drawn from the 

values within the window one value at a time with replacement 

(i.e., the same geochemical value may appear several times 

in the statistical sample). For the statistical sample drawn, the 

weighted median based on n values was computed. This was 

repeated a number of times (k), and the arithmetic mean of 

the k weighted medians was computed. When drawing the
6 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

1.0

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0.0

Distance halving 

weight

i

50 100 150 200 250 

Distance (km)

300 350 400

Figure 2. Curve representing the local weight function (Butterworth's) depending on the distance of 

the sampling station to the center of a circular window.

k statistical samples at random, every geochemical sampling 

point has the same probability to be included. Because of the 

replacement, the most common values (several geochemical 

sampling points may have the same value) occur very often, 

whereas outliers are rarely included. Bootstrap estimates in 

overlapping interpolation windows yield less uncertainty than 

ordinary methods without repeated sampling. Furthermore, the 

sampling variance (variance between the statistical samples) 

can be estimated and shown on a map.

The sampling variance of the estimate can also be emphasized by "illuminating" the color surface to create the effect of 

shaded relief. Then the variation between neighboring pixels 

is shown as a grainy texture. The more uneven the surface, 

the more local variation is present in the data. The illuminated 

map shows not only the concentration level but also the local 

variation, which may reveal important regional geochemical 

features in the data (Gustavsson, 1995).

Color Scheme

The interpolated grid values (pixel values) are presented 

on a scale with 20 colors ranging from cyan (lowest 10 percent 

of values) to magenta (highest 1 percent of values). The color 

scale is tied at two percentiles of the empirical cumulative frequency curve of gridded values. The color-class intervals were

derived by slicing the interval between these percentiles into 

equal-length slices on a logarithmic scale. When the analytical 

detection limit exceeded the lower percentile, the percentile 

was replaced by the detection limit.

The surface was illuminated by directed and ambient 

light in a lighting model presented by Strauss (1990). The 

resulting shaded-relief maps highlight subtle features, which 

may not be revealed on ordinary color maps. Shaded-relief 

maps are commonly produced by image-processing systems 

and by custom-written programs for geochemistry (Bjb'rklund 

and Gustavsson, 1987; Davenport and others, 1991).

To comprehensively show all possible effects due to relief 

shading of colors in the legend, each class is portrayed on 

an illuminated horizontal rectangle with a bubble (or hemisphere). All possible slopes on the map are represented on the 

surface of the bubble, and the reflection pattern shows where 

the directed light comes from.

The ambient light and a directed light source were located 

in the northeast and 30 degrees from the zenith. The maps 

were plotted in Albers Equal Area projection with standard 

parallels at lat 29°30'N. and 45°30'N. and the origin at lat 

23°N. and long 96°W. (Snyder, 1987). The software for interpolation and plotting was written at the Geological Survey of 

Finland except for basic graphical and statistical procedures, 

which were invoked from the UNIRAS FGL/GRAPHICSTM 

library and the IMSL STAT/LIBRARYTM, respectively.
Results and Interpretation

TableS. Parameter settings for computing regular grid using Bootstrap estimates of the moving weighted median.

Subject Parameter Used value Remarks

Grid Pixel size (km x km)

Number of pixels 

Weighted median R, window radius (km)

Bootstrap

Nmjn , the minimum number of points in

the window 

d0, halving distance of weights (km)

m, order of weight function affecting

steepness 

Sample size at resampling

Number of repeated samples in window

5x5

580 x 920 

400

50 

1

average 63 

30

total pixels 533,600 

Average number of points in 

window is 63

Average number of points in 

window within do is 2

Same as the number of values in

window 

Constant

Table 4. Quartiles and relative quartile deviations of interpolated and smoothed data for each element.

Element (unit)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

Al (%)

As (ppm)

Ba(ppm)

Ca (%)

Cr (ppm)

Cu (ppm)

Fe (%)

Hg (ppm)

K(%)

Li (ppm)

Mg (%)

Mn (ppm)

Na (%)

Ni (ppm)

Pb (ppm)

Se (ppm)

Sr (ppm)

Ti (%)

V (ppm)

Y (ppm)

Zn (ppm)

Zr (ppm)

First quartile Q] 

(25 %)

3.47

4.21

307

0.392

28.6

11.8

1.48

0.0385

1.06

16.4

0.283

257

0.52

11.8

14.5

0.205

77.3

0.192

45.9

18.1

36.8

150

Second quartile Q2 

(50 %)

5.14

5.57

502

0.992

40

18.2

1.95

0.0518

1.51

21.5

0.599

398

0.815

15

16.5

0.293

148

0.253

67.3

22.7

51.7

188.2

Third quartile 03 

(75 %}

6.83

7.06

680

1.93

53.2

23.4

2.59

0.0739

1.86

24.9

0.906

533

1.1

18.7

19.8

0.393

207

0.317

80.3

28.6

65.7

247

Relative quartile 

deviation 

(Q3-Q1 )/(2Q2)

0.33

0.26

0.37

0.78

0.31

0.32

0.28

0.34

0.26

0.2

0.52

0.35

0.36

0.23

0.16

0.32

0.44

0.25

0.26

0.23

0.28

0.26

Results and Interpretation

Features of the Geochemical Landscape 

and Correlations with Known Geology, 

Climate, and Human Activity

Table 4 shows the quartiles and the relative quartile deviations of smoothed data for each element from the whole area. 

The geochemical maps are presented in figures 3-24.

Text continues on page 31
1,000 KILOMETERS

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Figure 3. Colored surface map of Al distribution in soils and other surficial materials of the conterminous United States.
1,000 KILOMETERS

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10 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements
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12 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

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14 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

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16 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

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20 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

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24 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

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26 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

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Results and Interpretation 31

A number of broad geochemical dispersion patterns are 

obtained for both major and trace elements (figs. 3-24). Some 

of these patterns were previously described by Shacklette and 

Boerngen (1984), and further comments are found in the following discussion. The discussion and interpretation of the 

geochemical patterns frequently necessitates reference to physiographic and geologic features of the conterminous United 

States. Figure 25 shows the approximate location of these 

features.

1. The largest scale regional pattern observed on the maps 

is formed by elements such as Ba, Ca, K, Mg, Na, 

and Sr. All these elements show significantly higher 

concentrations in the western part of United States than 

in the eastern part. This main pattern likely represents 

a complex interaction of factors such as bedrock composition, topography, climate, soil development, and 

vegetation. It is difficult to judge the contribution for 

each factor.

Shacklette and Boerngen (1984) point out that the 

abundances of Ba, Ca, Mg, K, Na, and Sr are 

markedly different on either side of a line extending 

from western Minnesota southward through east-central Texas, which marks the approximate boundary 

between classes of moist-to-wet soils in the Eastern 

United States and dry soils of the West as mapped by 

the U.S. Soil Conservation Service (1969). Thus, the 

effect of climate on soil formation is probably a major 

factor in the development of these regional geochemical patterns.

2. Another striking feature is the low concentration of 

many elements in portions of the Gulf-Atlantic Coastal 

Flats and the Gulf-Atlantic Rolling Plain as defined 

by Hammond (1964). This area includes the State of 

Florida and portions of North Carolina, South Carolina, 

Georgia, Alabama, Mississippi, and Texas. This feature 

is likely due to a combination of the abundance of 

quartz sand in surficial material and the wet climate, 

which causes leaching of many elements from the 

upper soil horizons. The only exceptions among the 

mapped elements to this trend of low concentration 

are Zr and Y. These elements show relatively high 

concentrations along the Gulf-Atlantic Coastal Flats of 

northern Florida, southern Alabama and Mississippi, 

and eastern South and North Carolina. This trend is 

believed to reflect the placer accumulation of heavy 

minerals, such as zircon and xenotime, within sandy 

soils and their resistance to weathering.

3. The feature described in no. 2 above is interrupted by 

the alluvial plain of the Mississippi River. This is best 

seen in the distribution of Al, Ba, K, Mg, Na, Sr, and 

Zn. It appears that the flood plains of the Mississippi 

River system contain long-transported sediments with 

a composition more typical for the West (no. 1 above) 

than for the Southeast (no. 2 above).

4. Northern California and southern Oregon show high 

levels of Cu, Cr, and Ni. This is consistent with the

presence of ultramafic rocks in this area (Jennings, 

1977; Walker and MacLeod, 1991).

5. The area in north-central Nebraska with low concentrations of As, Cu, Cr, Fe, Li, Mg, Mn, Ni, Pb, Ti, V, 

Y, and Zn corresponds to the Nebraska Sand Hills, 

the largest dune field in the Western Hemisphere (Ahlbrandt and Fryberger, 1980).

6. The area of central Colorado containing elevated concentrations of Pb and Zn corresponds to the Colorado 

mineral belt, a region of historic precious- and basemetal mining (Tweto and Sims, 1963). This zone of 

increased Pb and Zn concentration also shows up on 

a geochemical map of Colorado based on data from 

stream sediments collected during the National Uranium Resource Evaluation (NURE) program (Plumlee 

and others, 1993; Grossman, 1998).

7. High concentrations of As, Cu, Hg, Pb, and Zn in 

northern Idaho corresponds to the Coeur d'Alene 

mining district, a region of historic base- and preciousmetal mining (Ransome, 1908).

8. The southern Arizona porphyry copper province

(Titley, 1982) is shown by increased abundance of Cu 

andPb.

9. Immediately to the north of the Arizona copper province is a region of increased abundance of Cr, Ni, 

and V. This area seems to correspond to a belt of Precambrian rocks that include diabase, diorite, gabbro, 

pyroxenite, and basalt as shown by Wilson and others 

(1969).

10. The area of eastern Oregon and Washington showing 

high Fe, Mg, V, and Ti coincides with exposures 

of the Columbia River Basalt Group (Huntting and 

others, 1961; Swanson and others, 1979; Walker and 

MacLeod, 1991).

11. The Ca map shows two prominent highs—one extending from southern Texas, just east of the Big Bend area, 

into southeastern New Mexico and the other in western 

Utah and eastern Nevada. The area of high Ca in southern Texas and southeastern New Mexico shows close 

correspondence to outcrops of Lower Cretaceous and 

Permian limestones and Quaternary deposits derived 

from these limestones (Geologic Atlas of Texas, 1977, 

1981, 1982; Dane and Bachman, 1965). The area in 

western Utah and eastern Nevada contains numerous 

exposures of Paleozoic limestones exposed by basinand-range faulting (Stewart and Carlson, 1978; Hintze, 

1980).

12. A slight increase in Pb abundance is noted in the 

vicinity of the cities of Cleveland, Ohio; Miami, 

Fla.; Buffalo, N.Y; Pittsburgh, Pa.; Chicago, 111.; San 

Francisco, Calif.; and Los Angeles, Calif. This may 

reflect an anthropogenic component of the geochemical landscape from industrial pollution and automobile 

exhaust.

13. The distribution of Hg shows generally higher concentrations in the Eastern than in the Western United
32 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

States. Specific areas with relatively high Hg concentrations are seen in the South along the coast of Louisiana, Mississippi, Alabama, and the Florida panhandle, 

and in the North along the coasts of Lake Michigan, 

Lake Superior, and Lake Huron. These features may be 

attributed to high contents of organic matter in samples 

from these coastal areas.

14. The pattern of elevated As concentration from western 

Pennsylvania through West Virginia, Kentucky, and 

Tennessee coincides, at least in part, with the Appalachian Basin, which produces high-arsenic coal, and 

with the distribution of power plants that burn the coal 

(Goldhaber and others, 2000).

These 14 examples are only a selection of geochemical 

patterns that can be related to known natural features or associated with anthropogenic pollution. Other observed patterns 

have no obvious explanation; for example, the higher abundance of Se in the Eastern United States as compared to the 

Western United States.

Reliability of the Geochemical Maps

A visual comparison of the sample-locality map (fig. 1) 

with each of the geochemical maps (figs. 3-24) shows no obvious geochemical patterns unique to either of the two sampling 

phases. For K, we have also estimated the correlation between 

pairs of neighboring samples, one originating in phase 1 and

the other in phase 2 (fig. 26). The correlation coefficient for 31 

such pairs is 0.65, which is significant at p<0.01. These results 

and the discussion in the Chemical Analysis section indicate 

that differences in analytical methods in the two phases of 

the project do not cause errors that overshadow the sampling 

errors.

The reliability of the obtained geochemical patterns can 

be judged by other comparisons. As pointed out by Darnley 

(1993), surface K abundance obtained by airborne gamma-ray 

spectrometry can provide an independent reference against 

which K distribution derived from sampling of surficial materials may be evaluated. Figure 27 shows the K map of the 

conterminous United States derived from aerial gamma-ray 

surveys (Duval and others, 1990; Duval and Riggle, 1999). A 

visual comparison of this map with the K map based on the 

ultra low density data of Shacklette and Boerngen (1984) (fig. 

11) shows many similarities.

On both maps there is higher K abundance in the Western 

than in the Eastern United States, prominent K lows in the 

Gulf-Atlantic Coastal Rats, and prominent highs in the western United States from the Big Bend area of Texas across 

southern New Mexico and Arizona through southeastern California and Nevada. Both maps show what is evidently a redistribution of K from the upper regions of the Mississippi River 

system, where the source material is relatively K-rich, to the 

flood plains of the lower Mississippi, where the local surficial 

materials are relatively K-poor. In addition, features such as 

relative K-low areas are seen on both maps in (1) the Pacific

3.5 

3.0 

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2.0

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separated by less than 25 km were included.
Results and Interpretation 33

5500 5000

40°

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30° 

3500 3000

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34 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

3.5

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0.5

0.0

Corr. coeff. = 0.72

N = 1303

Point distance < 3 krr

012345

K (%) in regolith

Figure 28. Plot of K in regolith versus radiometric K at the corresponding nearest point.

northwest, (2) the area through southern Missouri, eastern 

Oklahoma, western Arkansas, eastern Texas, and western Louisiana, and (3) the area through southern Kentucky, central 

Tennessee, eastern Mississippi, and Alabama.

The two data sets of K (regolith chemical analysis and 

airborne radiometry) were also compared statistically by (1) 

plotting a scattergram and estimating the correlation coefficient (Pearson's) for the overall covariation (fig. 28) and (2) by 

mapping a spatially moving correlation coefficient (Spearmanrank) for the survey area (fig. 29). The data pairs needed 

for these computations were generated by using the raw geochemical data and the radiometric values at the nearest grid 

point (Phillips and others, 1993) for each regolith sample. 

The scattergram and the overall coefficient of correlation (N 

= 1,323) were computed using standard methods. The scattergram (fig. 28) and the significant overall correlation coefficient (0.72) indicate that the two independent data sets reflect 

the same phenomena.

A moving correlation coefficient was estimated using a 

method introduced by Bolviken and others (1997). A circle 

was drawn around each sample site on the geochemical map 

in such a way that it included the 29 closest neighboring 

samples. The Spearman-rank correlation coefficient between 

the chemical and radiometric K values within the circle was 

calculated (29+1 = 30 pairs of values, 28 degrees of freedom) 

and assigned to the site. This procedure was repeated for 

all 1,323 sample sites. The resulting map shows the regional

variation of the correlation coefficient between the two K data 

sets for mutually overlapping sets of 30 neighboring samples 

(fig. 29).

There may be effects of biases in the data. Shacklette 

and Boerngen (1984) point out apparent differences in values 

between certain sampling routes. Specifically, they mention 

high values for Ce, Co, Ga, and Pb predominate on the routes 

across the Great Plains and the North-Central States, suggesting the possibility of systematic errors in sampling or laboratory analysis. It seems, however, that the smoothing technique 

to some extent compensates for such errors because they are 

not easily detected on the colored maps.

Conclusions

The feasibility of ultra low density geochemical maps 

may be appraised by their ability to show regional geochemical patterns that (1) indicate large underlying geologic features, (2) reflect the influence of human activity, (3) agree with 

major features of the geochemical landscape obtained with 

higher sample density, and (4) correlate with other features 

such as soil types, climate, vegetation, etc. The examples given 

in the previous section indicate that many of the variations 

observed on the accompanying maps meet one or more of 

these criteria.
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1981) and K determined by airborne gamma-ray surveys (Phillips and others, 1993).

to

01
36 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

It is concluded that ultra low density geochemical maps 

such as those presented in this publication should have potential use in various fields since they may (1) establish general 

baselines against which more specific natural geochemical 

variations and human-induced perturbations can be recognized, (2) reflect large underlying geologic features and can 

therefore be used to delineate geochemical provinces of interest in exploration for mineral resources, (3) show how geochemical patterns in the regolith are influenced by natural 

features such as soil type, climate, and vegetation, (4) provide 

a basis for research within the field of geomedicine (environmental geochemistry and health), and (5) show large geochemical contrasts between continents, perhaps indicating that 

even sparser sampling than that used here could be adequate 

for global geochemical mapping.

Acknowledgments

We acknowledge the support of the Geological Surveys 

of Finland, Norway and the United States. We thank Arthur 

G. Darnley and Robert G. Garrett of the Geological Survey 

of Canada and the International Geological Correlation Program Projects 259/360 for constructive comments during the 

planning and preparation of maps and manuscript, and Rolf 

Nilsen of the Geological Survey of Norway for performing the 

moving correlation analysis of the two potassium data sets. 

Thoughtful reviews by G.K. Lee and S.M. Smith of the U.S. 

Geological Survey greatly improved the manuscript.

References Cited

Ahlbrandt, IS., and Fryberger, S.G., 1980, Eolian deposits in the 

Nebraska Sand Hills: U.S. Geological Survey Professional Paper 

1120-A,24p.

Bjorklund, A., and Gustavsson, N., 1987, Visualization of geochemical 

data on maps: New options. Journal of Geochemical Exploration, 

v. 29, p. 89-103.

Bjorklund, A., and Lummaa, M., 1983, Representation of regional, local 

and residual variability of geochemical data by means of filtering 

techniques: Irkutsk, USSR, Proceedings of the 2nd International 

Symposium on Methods of Prospecting Geochemistry, p. 25-34, 

[translated into Russian].

Boerngen J.G., and Shacklette. H.T., 1981, Chemical analyses of soils 

and other surficial materials of the conterminous Unites States: 

U.S. Geological Survey Open-File Report 81-197,143 p.

B0lviken, B., Nilsen, R., and Ukkelberg, A., 1997, A new method 

for spatially moving correlation analysis in geomedicine:. 

Environmental Geochemistry and Health, v. 19, p. 143-153.

B0lviken, B., Stokke, PR., Feder, J., and J0ssang, I,1992, The 

fractal nature of geochemical landscapes: Journal of Geochemical 

Exploration, v. 43, p. 91-109.

Conner, J.J., Feder, G.L, Erdman, J.A., and Tidball, R.R., 1972, 

Environmental geochemistry studies in Missouri—A 

multidisciplinary study: Montreal, Canada, 24th International 

Geological Congress, August 1972, Symposium 1, p. 7-14.

Dane, C.H., and Bachman, G.O., 1965, Geologic Map of New Mexico: 

U.S. Geological Survey in cooperation with New Mexico Institute 

of Mining and Technology, State Bureau of Mines and Mineral 

Resources Division, and the University of New Mexico, Department 

of Geology, scale 1:500,000.

Darnley, A.G., 1993, Some notes on the importance of airborne 

gamma-ray spectrometry in International geochemical mapping: 

Journal of Geochemical Exploration, v. 49, p. 201-212.

Darnley, A.G., Bjorklund, A.J., B0lviken, B., Gustavsson, N., Plant, J.A., 

Steenfelt, A., Tauchid, M., Xie, Xuejing 1995, A global geochemical 

database for environmental and resource management, 

recommendations for international geochemical mapping: Final 

Report for IGCP 259: UNESCO Earth Sciences Report No. 19,122 p.

Davenport, PH., Nolan, W.L, and Kilfoil, G.J., 1991, Image-processing 

of geochemical data to map geological structures and alteration 

zones, in Mrna, F., ed., Exploration Geochemistry 1990: Prague, 

Czechoslovakia, Proceedings of the Third International Joint 

Symposium of the IAGC and the AEG, Geological Survey, Prague, 

and Czech Academy of Sciences, p 57-91.

Duval, J.S., 1990, Modern aerial gamma-ray spectrometry and

regional potassium map of the conterminous United States: Journal 

of Geochemical Exploration, v. 39, p. 249-253.

Duval, J.S., Jones, W.J., Riggle, F.R., and Pitkin, J.A., 1990, Potassium 

and thorium maps of the conterminous United States: U.S. 

Geological Survey Open-File Report 90-338,17 p., scale 1:2,500,000.

Duval, J.S., and Riggle, F.R., 1999, Profiles of gamma-ray and magnetic 

data from aerial surveys over the conterminous United States: U.S. 

Geological Survey Digital Data Series DDS-0031,3 discs.

Eden, P, and Bjorklund, A., 1994, Ultra-low density sampling of 

overbank sediment in Fennoscandia: Journal of Geochemical 

Exploration v. 51, p. 265-289.

Efron, B., and Tibshirani, R., 1991, Statistical data analysis in the 

computer age: Science, v. 253, p. 390-395.

Geologic Atlas of Texas, 1977, Del Rio sheet: University of Texas at 

Austin, Bureau of Economic Geology, scale 1:250,000.

Geologic Atlas of Texas, 1981, Sonora sheet: University of Texas at 

Austin, Bureau of Economic Geology, scale 1:250,000.

Geologic Atlas of Texas, 1982, FortStockton sheet: University of Texas 

at Austin, Bureau of Economic Geology, scale 1:250,000.

Goldhaber, M.B., Irwin, E.R., Atkins, J.B., Lee, Rob, Zappia, Humbert., 

Black, D.D., and Finkelmen, R.B., 2000, Environmental impact 

of elevated arsenic in southern Appalachian Basin coals, in 

Centeno, J.A., Collery, Philippe, Vernet, Guy, Finkelman, R.B., Gibb, 

Herman, and Etienne, Jean-Claude, eds., Metal Ions in Biology 

and Medicine: Volume 6, Proceedings of the Sixth International 

Symposium on Metal Ions in Biology and Medicine [held in San 

Juan, Puerto Rico, May 7-10,2000], p. 38-40.

Gonzales, R.G., and Winz, P., 1987, Digital image processing (2nd ed.): 

Reading, Pa., Addison-Wesley, 503 p.
References Cited 37

Grossman, J.N., 1998, 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 Evaluation (NURE) Program: U.S. Geological Survey OpenFile Report 98-622, version 3.01, CD-ROM.

Gustavsson, N., 1995, Geochemical maps showing Bootstrap 

estimates of the moving weighted median [abs.], in Halleraker, 

J., and Misund, A., eds., The 5th Seminar on Hydrogeology 

and Environmental Geochemistry, B. B0lviken Seminar 1995: NGU 

Report 95.138,1 p.

Gustavsson, N., Lampio, E., Nilsson, B., Norblad, G., Ros, F, and 

Salminen, R. 1994, Geochemical maps of Finland and Sweden: 

Journal of Geochemical Exploration, v. 51, p. 143-160.

Gustavsson, N., Lampio, E., and Tarvainen, T. 1997, Visualization of 

geochemical data on maps at the Geological Survey of Finland: 

Journal of Geochemical Exploration, v. 59, p. 197-207.

Hammond, E.H., 1964, Analysis of properties in land form mapping: 

Annals of the Association of American Geographers, v. 54, p. 11-23.

Hintze, L.F., 1980, Geologic map of Utah: Utah Geological and Mineral 

Survey, scale 1:500,000.

Howarth, R.J., 1983, Mapping, in Howarth, R.J., ed., Statistics 

and Data Analysis in Geochemical Prospecting: Amsterdam, 

Elsevier Scientific Publishing Company, Handbook of Exploration 

Geochemistry, Volume 2, p. 111-205.

Huffman, C., Jr., and Dinnin, J.I., 1976, Analysis of rocks and soil 

by atomic absorption spectrometry and other methods, in Miesch, 

A.T., Geochemical Survey of Missouri—Methods of Sampling, 

Laboratory Analysis and Statistical Reduction of Data: U.S. 

Geological Survey Professional Paper 954-A, p. 12-14.

Huntting, M.T., Bennett, W.A.G., Livingston, V.E., Jr., and Moen, W.S., 

1961, Geologic map of Washington: Washington Department of 

Conservation, Division of Mines and Geology, scale 1:500,000.

Jennings, C.W., 1977, Geologic map of California: California Division of 

Mines and Geology, California Geologic Data Map Series, Map No. 

2, scale 1:750,000.

Koljonen, I, ed., 1992, The geochemical atlas of Finland, Part 2: Till:, 

Espoo, Geological Survey of Finland, 218 p.

Lahermo, P., llmasti, M., Juntunen, R., andTaka, M, 1990, The 

geochemical atlas of Finland, Part 1: The hydrogeochemical 

mapping of Finnish groundwater: Espoo, Geological Survey of 

Finland, 66 p.

Lahermo, P., Vaananen, P., Tarvainen, T, and Salminen, R., 

1996, The geochemical atlas of Finland, Part 3: Environmental 

geochemistry—Stream waters and sediments: Espoo, Geological 

Survey of Finland, 149 p.

Miesch, A.T., 1976, Geochemical survey of Missouri—Methods of 

sampling, laboratory analysis, and statistical reduction of data: U.S. 

Geological Survey Professional Paper 954-A, 39 p.

Myers, A.T., Havens, R.G., and Dunton, P.J, 1961, A spectrochemical 

method for the semiquantitative analysis of rocks, minerals and 

ores: U.S. Geological Survey Bulletin 1084-1, p. 207-229.

Neiman, H., 1976, Analysis of rocks, soils, and plant ashes by

emission spectroscopy, in Miesch, A.T., Geochemical Survey 

of Missouri—Methods of Sampling, Laboratory Analysis, and 

Statistical Reduction of Data: U.S. Geological Survey Professional 

Paper 954-A, p. 14-15.

Phillips, J.D., Duval, J.S., and Ambroziak, R.A., 1993, National 

geophysical data grids; gamma-ray, gravity, magnetic and 

topographic data for the conterminous United States: U.S. 

Geological Survey Digital Data Series DDS-0009, CD-ROM.

Plumlee, G.S., Smith, S.M., Toth, M.I., and Marsh, S.P., 1993, Integrated 

mineral-resource and mineral-environmental assessments of 

public lands: Applications for land management and resource 

planning: U.S. Geological Survey Open-File Report 93-571,18 p.

Ransome, F.L, 1908, The geology and ore deposits of the Coeur 

d'Alene district, Idaho: U.S. Geological Survey Professional Paper 

62,203 p.

Shacklette, H.T., and Boerngen, J.G., 1984, Element concentrations 

in soils and other surficial materials of the conterminous United 

States: U.S. Geological Survey Professional Paper 1270,105 p.

Shacklette, H.T., Hamilton, J.C., Boerngen, J.G., and Bowles, 

J.M., 1971, Elemental composition of surficial materials in the 

conterminous United States: U.S. Geological Survey Professional 

Paper574-D,69p.

Snyder, J.P., 1987, Map projections—A working manual: U.S. 

Geological Survey Professional Paper 1395,383 p.

Stewart, J.H., and Carlson, J.E., 1978, Geologic map of Nevada: U.S. 

Geological Survey and Nevada Bureau of Mines and Geology, 

scale 1:500,000.

Strauss, P.S., 1990, A realistic lighting model for computer animators: 

Computer Graphics and Applications, v. 10, p. 56-64.

Stuart A., and Ord, J.K., 1987, Kendall's advanced theory of statistics, 

volume 1: Distribution theory (5th ed.): London, Charles Griffin and 

Company Limited, 604 p.

Swanson, D.A., Wright,T.L, Hooper, PR., and Bentley, R.D., 1979, 

Revisions in strati graphic nomenclature of the Columbia River 

Basalt Group: U.S. Geological Survey Bulletin 1457-G, 59 p.

Tidball, R.R., 1974, Average composition of agricultural soils in 

Missouri counties: U.S. Geological Survey Open-File Report 74-66, 

25 p.

Tidball, R.R., 1976, Geochemical survey of Missouri—Chemical 

variation of soils in Missouri associated with selected levels of 

the soil classification system: U.S. Geological Survey Professional 

Paper 954-6,16 p.

Titley, S.R., 1982, Geologic setting of porphyry copper deposits, 

southeastern Arizona, in Titley, S.R., ed., Advances in Geology of 

the Porphyry Copper Deposits, Southwestern Northern America: 

Tucson, University of Arizona Press, p. 37-58.

Tweto, Ogden, and Sims, P.K., 1963, Precambrian ancestry of the 

Colorado mineral belt: Geological Society of America Bulletin, v. 74, 

p. 991-1014.

U.S. Soil Conservation Service, 1969, Distribution of principal kinds of 

soils—Orders, Suborders, and Great Groups, in National Atlas of 

the United States of America: U.S. Geological Survey, sheet 86, 2 p.
38 Geochemical Landscapes of the Conterminous United States—New Map Presentations for 22 Elements

Wahlberg, J.S., 1976, Analysis of rocks and soils by X-ray

fluorescence spectrometry, in Miesch, AT., Geochemical Survey 

of Missouri—Methods of Sampling, Laboratory Analysis and 

Statistical Reduction of Data: U.S. Geological Survey Professional 

Paper 954-A, p. 11-12.

Walker, G.W., and MacLeod, N.S., 1991, Geologic map of Oregon: U.S. 

Geological Survey, scale 1:500,000.

Ward, F.N., Lakin, H.W., Canney, F.C., and others, 1963, Analytical 

methods used in geochemical exploration bythe U.S. Geological 

Survey: U.S. Geological Survey Bulletin 1152,100 p.

Wilson, E.D., Moore, R.T., and Cooper, J.R., 1969, Geologic map of 

Arizona: Arizona Bureau of Mines and U.S. Geological Survey, 

scale 1:500,000.

Xie Xuejing, and Ren Tianxiang, 1993, National geochemical 

mapping and environmental geochemistry—Progress in China, 

in Davenport, P.M., ed., Geochemical Mapping: Journal of 

Geochemical Exploration, v. 49, p. 15-34.

Manuscript approved for publication July 31,2001 

Published in the Central Region, Denver, Colorado 

Editing, page layout, photocomposition—Richard W. Scott, Jr. 

Cover design—Carol A. Quesenberry 

Graphics bythe authors

tV U.S. GOVERNMENT PRINTING OFFICE: 2001 — 773-049 / 30065 Region No. 8