Geochemical Variation in Soils in the 

Piceance Creek Basin, 

Western Colorado 

GEOLOGICAL SURVEY BULLETIN 1479 

Geochemical Variation in Soils in the 

Piceance Creek Basin, 

Western Colorado 

By WALTER E. DEAN, CHARLES D. RINGROSE, 

and RONALD W. KLUSMAN 

GEOLOGICAL SURVEY BULLETIN 1479 

A geochemical baseline study of the 

composition of soils in a 

maJor oil-shale area 

UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON: 1979 
UNITED STATES DEPARTMENT OF THE INTERIOR 

CECIL D. ANDRUS, Secretary 

GEOLOGICAL SURVEY 

H. William Menard, Director 

Library of Congress Cataloging in Publication Data 

Dean, Walter E. 

Geochemical Variation in Soils in the Piceance Creek Basin, Western Colorado 

(Geological Survey Bulletin 1479) 

Includes bibliographical references. 

1. Soils-Colorado-Piceance Creek watershed-Composition. 2. Oil-Shales-ColoradoPiceance Creek watershed. I. Ringrose, Charles D., joint author. II. Klusman, Ronald W., 

joint author. III. Title. IV. Series: United States Geological Survey Bulletin 1479 

QE75.B9 no. 1479 [S599.C6] 557.3'08s [631.4'7'788] 79-15666 

For sale by the Superintendent of Documents, U.S. Government Printing Office 

Washington, D.C. 20402 

Stock No. 024-001-03240-6 
CONTENTS 

Page 

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

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

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

Sampling design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

Chemical analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 

Analysis of variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

Regional geochemical baselines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

Soil groups by Q-mode factor analysis..................................... 15 

Statistical methods................................................... 15 

Results.............................................................. 36 

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 

Comparison of trace elements in oil shale and soils of the Piceance Creek 

basin ............. ,,,................................................... 42 

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 

References cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 

FIGURE 

ILLUSTRATIONS 

1. Sampling design for soils from the Piceance Creek basin, 

Colorado ........................................... . 

2. Generalized geologic map of the Piceance Creek basin showing outcrop patterns of the Uinta, Green River, and 

Wasatch Formations, Cretaceous formations, major 

Page 

3 

streams, and soil sample localities . . . . . . . . . . . . . . . . . . . . . 4 

3. Map of Piceance Creek basin showing locations of samples, 

townships (dotted lines), and supertownships (solid lines) 

used in the analysis-of-variance design, and prototype oilshale lease-tracts C-a and C-b. . . . . . . . . . . . . . . . . . . . . . . . . . 5 

4. Bar graph showing percent of total logarithmic variance at 

each of six analysis-of-variance levels for concentrations 

of 39 elements in soils from the Piceance Creek basin, 

Colorado............................................. 9 

5. Regional distribution of elements having variance-mean 

ratios greater than or equal to 1. 16 

A, Zinc. 

B, Lithium. 

C, Copper. 

D, Magnesium. 

E, Calcium. 

III 
IV 

FIGURE 

TABLE 

CONTENTS 

F, Strontium. 

G, Iron. 

H, Silicon. 

I, Titanium. 

J, Beryllium. 

6. Graphs showing variations in concentrations of A, 

potassium; B, zinc; C, calcium; and D, silicon in soils 

within supertownships in the Piceance Creek basin ..... . 

7. Factor-variance diagrams for 29 oxides in 108 samples and 

32 analytical replicate samples of soils from the Piceance 

Creek basin. . ....................................... . 

8. Maps showing Q-mode factor loadings for A, factor 1; B, 

factor 2; C, factor 3; and D, factor 4 for soils of the 

Piceance Creek basin, Colorado ....................... . 

9. Map showing subdivision of the Piceance Creek basin into 

three elevation zones ................................. . 

10. Graph comparing concentrations of trace elements in oil 

shale from the Green River Formation and soils from the 

Page 

22 

23 

32 

41 

Piceance Creek basin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

TABLES 

1. Summary statistics for concentrations of nine major and 30 

trace elements in 108 samples of soil. .................. . 

2. Analysis-of-variance of soil chemistry ................... . 

3. Comparison of the observed variance of the mean within 

supertownships with the maximum acceptable variance 

of the mean for concentrations of 15 elements in soil 

Page 

6 

8 

samples.............................................. 11 

4. Observed and computed compositions for each of the four 

end-members used in the extended Q-mode factor model 

for soils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

5. Loadings for the four factors used in the extended Q-mode 

model................................................ 25 

6. Summary statistics for observed data and for data computed by the extended Q-mode factor analysis using 

samples as end-members for each of the four factors. . . . . 30 

7. Correlation coefficients among composite compositional 

variables (factor loadings) for each of the four endmember samples used in the extended Q-mode factor 

model and the observed compositional variables (oxides 

of elements). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 

8. Average composition (geometric means) of oil shale from 

the Green River Formation and lower Eocene sandstone 

of the Rocky Mountain region. . . . . . . . . . . . . . . . . . . . . . . . . 38 
GEOCHEMICAL VARIATION IN SOILS 

IN THE PICEANCE CREEK BASIN, 

WESTERN COLORADO 

By WALTER E. DEAN, CHARLES D. RINGROSE1 , 

and RONALD W. KLUSMAN1 

ABSTRACT 

The magnitude and pattern of natural (baseline) variations in chemical composition 

of soils from the Piceance Creek basin of western Colorado, a major oil-shale region, 

were studied using 108 soil samples that were analyzed for 39 elements. These variations also provide a scale by which to measure deviations in soil composition resulting 

from oil-shale production, processing, and subsequent land recovery. Most of the 

variation in elemental abundance in soils occurs between samples separated by 

several kilometers or less. However, 14 of the 39 elements (calcium, iron, magnesium, 

silicon, titanium, boron, beryllium, chromium, copper, gallium, lithium, yttrium, ytterbium, and zinc) show significant variation on a regional scale (samples separated 

by distances greater than 19 kilometers), and baselines can be represented by regional 

maps of element concentration. Most of these elements have regional concentration 

gradients from north to south across the basin. 

Q-mode factor analysis was used to group soils on the basis of similar chemical 

characteristics. Soils in the southern part of the basin contain relatively high concentrations of trace and major elements largely derived from marlstone (oil shale) of the 

Parachute Creek Member of the Eocene Green River Formation. Soils in the central 

part of the basin are mostly developed on sandstone of the Eocene Uinta Formation 

and therefore tend to contain relatively high concentrations of silicon and low concentrations of trace elements. Soils in the northern part of the basin are also developed on 

sandstone but contain relatively high concentrations of calcium, magnesium, strontium, and sodium. 

Oil shale of the Green River Formation tends to be enriched in a number of trace 

elements that can be toxic, especially arsenic, mercury, selenium, and molybdenum, 

relative to soils of the Piceance Creek basin. Land reclamation after the development 

of an oil-shale industry, using untreated or spent oil shale as fill, could result in increases in a number of environmentally sensitive trace elements in the landscape. 

INTRODUCTION 

The oil-shale regions of Colorado hold promise for providing a 

substantial amount of liquid hydrocarbons within the next decade. 

Leasing of the first tracts in the Piceance Creek basin of western Colorado has been completed, and detailed development plans have been 

approved for prototype oil-shale lease-tracts C-a and C-b. As a part 

of the development plans, baselines for concentrations of seven 

elements (arsenic, antimony, mercury, selenium, cadmium, fluorine, 

'Department of Chemistry and Geochemistry, Colorado School of Mines, Golden, Colorado 80401. 1 
2 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

and boron) in soils and plants have been established by the tract 

lessees. These seven elements were chosen because they occur in 

unusually high concentrations in oil-shale and(or) they are easily 

mobilized under high-temperature reducing conditions. However, the 

tracts are relatively small, only seven elements were analyzed, and 

data for these areas do not provide regional baselines for the entire 

Piceance Creek basin. In this report the Piceance Creek basin includes the Wasatch, Green River, and Uinta Formations. 

Concentrations of trace elements and their variances in the soils of 

the basin are unknown. Establishing a regional baseline for surficial 

materials is an essential step in determining which elements may be 

potential environmental hazards during development of an oil-shale 

industry and subsequent land rehabilitation. Knowledge of the 

regional scale of variation of trace-element concentrations, in particular, would allow the determination of the sampling interval required in order to construct a useful geochemical baseline map for the 

critical trace elements in this and adjacent areas. Maps would be required for those elements that vary regionally, but if regional variation is minor or absent, estimates of mean and total variance alone 

would provide sufficient baseline information. 

The main objective of this study was to estimate the magnitude of 

regional variations of major and trace elements in soils within the 

basin. The sampling design was established to test geochemical 

variability at a number of geographic levels (distances between 

samples) as well as the variation between two physiographic types 

(ridgetop vs. valley bottom). Our goal was to provide an approximation of natural (baseline) variations in soil composition within the 

basin and to provide a scale by which to measure deviations from the 

baseline during the course of oil-shale production, processing, and 

land recovery. 

ACKNOWLEDGMENTS 

R. J. Ebens and R. R. Tidball provided valuable assistance with the 

sampling design. A. T. Miesch and R. R. Tidball helped with the 

statistical analyses. J. G. Boerngen did much of the data processing. 

G. J. Saulnier, Jr., provided valuable information on the water 

chemistry of the Piceance Creek basin. 

SAMPLING DESIGN 

The basic sampling plan for the study of soils in the Piceance Creek 

basin is a partially unbalanced nested analysis-of-variance design. 

The design is illustrated diagrammatically in figure 1 and is shown 

with sampling localities identified in figures 2 and 3. The highest 
SAMPLING DESIGN 3 

SAMPLING DESIGN 

LEVEL 2 (Supertownship) 

LEVEL 3 (Township) 

LEVEL 4 (Section) 

LEVEL 5 (Sample) 

FIGURE I.-Sampling design for soils from the Piceance Creek basin. 

geographic sampling level contains nine supertownships, each of 

which consists of four adjacent townships (fig. 3). Within each of the 

36 townships, two sections were chosen at random. Within one of 

these two sections, two samples of soil (A-horizon or the top 10 centimeters (em) where horizons are not developed) were collected at a 

distance of 100 meters (m) from each other. Only one sample was collected from the other section in each township, for a total of three 

samples per township. All three samples within a township were collected either from ridgetops or valley bottoms. Ridgetop and valleybottom sampling alternated according to a checkerboard design (fig. 

3). The decision as to which of the two sections in each township 

would have two samples and which would have only one was made by 

a simple flip of the coin. From the final suite of 108 samples, 32 were 

chosen at random to be replicated in analysis. All 140 samples (108 

samples plus 32 duplicates) were submitted for analysis in a randomized sequence. The analytical design, therefore, consisted of six 

levels, one physiographic (level 1, ridgetop and valley bottom), four 

geographic (level2, supertownship; level3, township; level4, section; 

and level 5, sample), and one analytical (level 6, 32 duplicate 

analyses). Level 1 was used to examine differences between soils on 

ridgetops and those in valley bottoms according to the checkerboard 

design illustrated in figure 3. This level contained 54 samples plus 16 

analytical duplicates from ridgetops and 54 samples plus 16 

analytical duplicates from valley bottoms. Level 2 was used to examine differences between supertownships; that is, differences in 

soils separated by distances greater than about 19 kilometers (km). 

Level 3 shows differences in soils between townships, within supertownships (samples separated by distances of 3 to 19 km). Level 4 

shows differences between sections, within townships (distances of 

0.1 to 3 km), and level5 shows differences between samples collected 

0.1 km (100 m) apart. Level 6 was used to estimate total analytical 

variance based on 32 pairs of duplicate analyses. The total analytical 

variance includes contributions from sample inhomogeneity and sample preparation, as well as actual analysis. 
4 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

FIGURE 2.-Generalized geologic map of the Piceance Creek basin showing 

outcrop patterns of the Uinta (Tu), Green River (Tgr, stippled), and 

Wasatch (Tw) Formations, Cretaceous formations (K), major streams, and 

soil-sample localities (dots) (from Tweto, 1976). In this report the Piceance 

Creek Basin includes the Wasatch, Green River, and Uinta Formations. 

CHEMICAL ANALYSES 

The 140 samples (108 samples plus 32 duplicates) were analyzed in 

a randomized sequence for 39 elements. The samples were ground in a 

ceramic mill to pass a 100 mesh (less than 149 micrometers or J.A) 

sieve. Details of analytical methods used were described by 

Wahlberg (1976), Huffman and Dinnin (1976), Neiman (1976), and 

U.S. Geological Survey (1976). 

Summary statistics for each of 39 elements in the entire sample 

suite are given in table 1. The results in table 1 and in subsequent 
40° 

00' 

CHEMICAL ANALYSES 

I 

I 1• 

---2--- , •2 

.2 .1 1 

.1 

.1 2 •• 2 1 1 • 

•• 2 

---r--..; 

1 I .2 

2.1 

0 10 20 KILOMETERS 

I J II ! I 

FIGURE 3.-Locations of samples, townships (dashed lines), supertownships 

(solid lines) used in the analysis-of-variance design, and prototype oil-shale 

lease-tracts C-a and C-b, Piceance Creek basin. Numbers by sample 

localities denote whether one or two samples were collected from that locality. Dots indicate ridgetop samples; squares indicate valley-bottom 

samples. 

5 

tables and discussions were derived after log10 transformation of the 

analytical data, although the Q-mode factor analyses were performed 

on the original data (in percent or parts per million). Skewness and 

kurtosis statistics showed that some elements, based on the analyses 

reported here, are approximately normally distributed in soils 

whereas most are approximately log-normally distributed. However, 

all results of statistical analyses obtained from original data were 

closely similar to those obtained from log 10 -transformed data. 
6 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

TABLE I.-Summary statistics for concentrations of nine major and 30 trace elements 

in 108 samples of soil 

[N is the total number of samples, out of 108 that contained measurable element concentrations greater than the 

detection limit for that element. Analytical methods used are: 1, X-ray flourescence by J. S. Whalberg, Michael 

Goff, and J. W. Baker; 2, optical emission spectroscopy by A. L. Sutton and R. M. Lemert; 3, atomic absorbtion 

by V. M. Merritt, Wayne Mountjoy, J. G. Crock, and G. 0. Riddle; 4, specific ion electrode by J_ M. Gardner and 

P. G. Guest; 5, combustion V. E. Shaw; 6, flameless atomic absorbtion by J. A. Thomas and G_ 0. Riddle; 7, 

neutron-activation analysis by H.T. Millard, Jr., R. J. Knite, A. J_ Bartel, J.P. Hemming, R. J. White, R.J. Yinnola, and E. L. Brandt] 

Element 

Al------

Ca------

Fe------

Mg------

K-------

Si------

Na------

Ti------

C, total 

As------

B-------

Ba------

Be------

Co------

Cr------

Cu------

F-------

Ga------

Ge------

Hg------

Li------

Mn------

Mo------

Nb------

Ni------

Pb------

Rb------

Sb------

Sc------

Se------

Sn------

Sr------

Th------

U-------

V-------

Y-------

Yb------

Zn------

Zr------

Analytical 

Method 

1 

3 

1 

5 

1 

2 

2 

2 

2 

2 

2 

4 

2 

1 

6 

3 

2 

2 

2 

2 

2 

3 

1 

2 

1 

1 

2 

7 

7 

2 

2 

2 

3 

2 

Observed 

range 

3.4 -

.46-

1.1 -

.51-

• 77-

15 

.18-

.15-

.87-

Percent 

7.8 

9.8 

2.9 

2.7 

3.2 

34 

3.5 

.38 

12 

Geometric mean 

5.5 

1.9 

1.8 

1.9 

2.3 

26 

.1.4 

.28 

3.0 

Parts per million 

<2.0 - 21 

25 102 

600 -13,000 

.88- 4.4 

l. 2- 1 6 

12 110 

<8.7 - 122 

<400 - 1 ,600 

<2.2 - 39 

<.14- 2. 5 

.01- .22 

16 140 

82 - 1 ,200 

<1. 04- 14 

<2. 2 - 21 

<3.4 -

<3.3 -

40 

<.14-

<2. 1 -

<.11-

<.11-

61 

6.1 -

l. 9 -

7. 5 -

4.9 -

<.28-

45 

54 

42 

56 

190 

4.6 

13 

1.2 

11 

660 

19 

5.4 

120 

36 

6.2 

140 

700 

6.2 

61 

1,400 

2.4 

7.9 

59 

30 

500 

18 

.87 

.041 

34 

480 

5.4 

7.0 

21 

26 

105 

.90 

6.8 

.28 

.82 

280 

12 

3.3 

56 

16 

2.1 

80 

260 

Geometric 

deviation 

1.17 

2.51 

1.22 

1.48 

1.22 

1.15 

1.65 

l. 20 

1.71 

2.01 

1.35 

l. 50 

1.45 

l. 78 

l. 63 

l. 93 

l. 52 

1.82 

2.35 

2.53 

l. 55 

1.65 

2.05 

2.11 

1.83 

2.09 

1.29 

2.70 

l. 66 

2.03 

3.90 

l. 54 

1.23 

1.21 

1. 58 

1. 52 

2.06 

1.23 

l. 55 

N 

108 

108 

108 

108 

108 

108 

108 

108 

108 

107 

108 

108 

108 

108 

108 

108 

85 

107 

105 

108 

108 

108 

102 

97 

107 

107 

108 

103 

99 

97 

92 

108 

108 

108 

108 

108 

107 

108 

108 
ANALYSIS OF VARIANCE 7 

Because there was no clear reason for using original data for some 

elements and transformed data for others, all statistical analyses 

were based on log 10-transformed data except where specifically 

stated otherwise. 

ANALYSIS OF VARIANCE 

Estimates of variance components from the six-level nested 

analysis of variance of concentrations of the 39 elements (table 1) are 

given in table 2 and compared graphically in figure 4. Table 2 and 

figure 4 show that most of the geographic variation for most 

elements occurs between sections (distances of 0.1 to 3 km). This 

result is in accord with analysis-of-variance results for soils from 

other areas, which indicate that most of the variability in soil composition occurs locally (Tidball, 1975; Tidball and Severson, 1975; 

Tidball, 1976; Connor and others, 1976). Of the 39 elements listed in 

table 2, 28 have significant variance components at the section level 

(aluminum, calcium, iron, magnesium, potassium, silicon, sodium, 

titanium, arsenic, boron, barium, beryllium, cobalt, chromium, copper, gallium, mercury, lithium, molybdenum, lead, rubidium, scandium, strontium, vanadium, yttrium, ytterbium, zinc, and uranium). 

Only 11 elements (aluminum, potassium, silicon, sodium, titanium, 

fluorine, lithium, nickel, zinc, total carbon, and uranium) have significant variance components between samples within sections (100m), 

and only seven elements (calcium, sodium, mercury, strontium, total 

carbon, uranium, and thorium) have significant variance components 

between townships within supertownships (distances of 3 to 19 km). 

More than one-third of the elements listed in table 2 (14 of 39) have 

significant variability between supertownships (distances greater 

than 19 km). These 14 elements are calcium, iron, magnesium, silicon, 

titanium, boron, beryllium, chromium, copper, gallium, lithium, yttrium, ytterbium, and zinc. No significant variation was found between samples collected on ridgetops and those collected in valley 

bottoms. This lack of variation means that the ridgetop and valleybottom samples can be regarded as part of the same statistical 

population, and they have been considered as such in all subsequent 

statistical analyses. 

The analysis-of-variance results at the four geographic levels 

(levels 2-5) indicate that most of the geochemical variability in soils 

in the Piceance Creek basin can be described by measuring variability 

between sections within townships and between supertownships. An 

efficient sampling design for future investigations in this area would 

involve sampling at intervals of 3 km within townships that are 

spaced 19 km apart (that is, every other township). 

The significant variance at the supertownship level for 14 elements 
8 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

TABLE 2.-Analysis of varience of soil chemistry 

[Distances (in meters or kilometers) are average distances between randomly selected points within each level] 

Variance components as percentage of total variance 

Between Between 

Total Between Between Townships sections Between 

Element logarithmic ridge tops super within within samples Analytical 

variance and townships super townships (100m) error 

valley bottom (>19 km) townships 

{3-19 km) {0.1-3 km) 

A 1------ 0.0044 0 18 0 158 114 10 

Ca------ .1596 0 128 115 1

48 9 0 

Fe------ .0080 0 127 0 1

38 2 33 

Mg------ .0291 0 128 3 1

57 l ll 

K------- .0075 2 7 0 1

80 6 5 

Si ------ .0038 0 122 0 1

46 122 10 

Na------ .0473 0 4 141 1

46 l 6 3 

Ti------ .0064 l 128 0 1

54 l 9 8 

C, total .0535 l 14 126 19 131 10 

As------ .0914 0 0 0 181 6 13 

B------- .0173 0 118 0 157 5 21 

Ba------ .0313 0 0 0 135 6 58 

Be------ .0264 0 124 0 137 0 39 

Co------ .0624 0 12 0 133 0 55 

Cr------ .0452 0 ll] 0 137 0 46 

Cu------ .0821 0 128 0 118 l 52 

F ------- .0331 3 7 0 15 134 41 

Ga------ .0677 0 110 0 159 0 32 

Ge------ .1378 0 0 ll 0 27 62 

Hg------ .1631 2 10 1

36 131 4 17 

L i ------ .0363 5 123 0 1

58 110 5 

Mn------ .0476 0 0 8 4 0 88 

Mo------ .0976 0 ll 0 1

48 7 33 

Nb------ .1055 0 l 0 17 0 82 

Ni ------ .0694 0 10 0 24 136 30 

Pb------ .1029 0 6 0 1

39 0 55 

Rb------ .0127 0 6 0 177 6 ll 

Sb------ .1867 1 2 0 0 10 86 

Sc------ .0491 0 5 0 137 0 59 

Se------ .0947 l 0 15 0 25 59 

Sn------ .3488 0 2 0 0 33 65 

Sr------ .0350 1 19 1

18 131 5 27 

Th------ .0080 2 0 1

20 20 7 52 

u------- .0071 0 0 1

44 123 127 6 

V------- .0396 0 7 0 152 0 41 

Y------- .0330 0 l 9 0 142 0 49 

Yb------ .0986 0 l 8 0 138 0 54 

Zn------ .0083 l 131 12 132 1]9 5 

Zr------ .0359 2 1 0 28 2 66 

1

Significantly different from zero at 95-percent probability level. 
ANALYSIS OF VARIANCE 

AI 

Ca 

Fe 

Mg 

K 

Si 

Na 

Ti 

c 

As 

B 

Ba 

Be 

Co 

Cr 

Cu 

F 

Ga 

Ge 

Hg 

Li 

Mn 

Mo 

Nb 

Ni 

Pb 

Rb 

Sb 

Sc 

Se 

Sn 

Sr 

v 

y 

Yb 

Zn 

Zr 

Th 

u 

20 40 60 

PERCENT 

EXPLANATION 

1 2 3 4 5 6 

~t~=~=~=~=~=~=~=~=~=~=~= ::::::::::::::::::::::~ ~ 

FIGURE 4.-Percent of total logarithmic variance at each of six analysis-ofvariance levels for concentrations of 39 elements in soils from the Piceance 

Creek basin. 

9 
10 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

suggests that these elements vary regionally across the Piceance 

Creek basin, and that baselines for these elements can be shown best 

by a map based on supertownship means. To explore this possibility 

further, several tests of variance of supertownship means were applied to these 14 elements, and results are presented in table 3. 

Although strontium did not have significant supertownship variance 

at the 95-percent probability level, it was close to being significant. 

For this reason, and because of the significant association of strontium with carbonate-related elements (discussed later), strontium 

was included with the 14 elements having significant variance at the 

supertownship level. 

The variance ratio, u, is an inverse measure of the relative difficulty 

of a geochemical-sampling program to describe the variation between 

map units of a particular size (supertownships in this study). It is 

defined (Miesch, 1976a) as the ratio of the variance among map units 

to the sum of the variances at all levels within the units: 

u= (1) 

where s 2 , s2{3, s 2 , s 2 .. .u and s 2 are the logarithmic variances at the a y u YJ 

supertownship, township, section, sample and analytical levels, 

respectively. Once u has been calculated for each element, the number 

of random samples that must be collected within each supertownship 

to insure that the map pattern is reproducible can be determined, at a 

given probability level, based on mean values for that element. This 

effective number of samples has been defined as nr by Miesch (1976a), 

and he presented curves for the 80- and 95-percent probability levels 

for determining n r knowing the value of u. The n r values listed in 

table 3 indicate that from as few as 2 to as many as 14 samples would 

have to be collected at random from within a supertownship, depending upon the element, to distinguish between supertownships at the 

SO-percent probability level, based on supertownship means. For example, a mean of analyses of only four random samples from within 

each supertownship would be required to map the differences between supertownships based on iron concentrations in soils, but a 

mean of analyses of 14 random samples from each supertownship 

would be required to confidently map the differences in ytterbium 

among supertownships. 
ANALYSIS OF VARIANCE 11 

TABLE 3.-Comparison of the observed varience of the mean within supertownships 

with the maximum acceptable variance of the mean for concentrations of elements 

that show significant variance components at the supertownship level 

[v, observed variance ratio; nr. required minimum number of randomly selected soil samples within each 

supertownship for the 80 percent probability level; Er. maximum acceptable value of the variance of the supertownship means; Es. observed variance of the mean within supertownships; Vm, variance mean ratio. See text for 

equations used to calculate these values] 

Element 

Ca-----

Fe-----

Mg-----

Si-----

Ti-----

8------

Be-----

Cr-----

Cu-----

Ga-----

Li-----

Sr-----

Y------

Yb-----

Zn-----

v 

0.30 

.33 

.33 

.24 

.33 

.22 

.22 

.20 

.40 

.11 

.34 

.18 

• 10 

.08 

• 51 

1 

4 

4 

5 

4 

5 

5 

6 

3 

9 

3 

6 

11 

14 

2 

0.0285 

.0015 

.0053 

.0006 

.0011 

.0028 

.0046 

.0063 

.0197 

.0068 

.0088 

.0047 

.0027 

.0065 

.0029 

0.0325 

1 • 0013 

.0060 

.0008 

.0013 

.0038 

1 • 0044 

.0080 

1 .0104 

.0155 

1 .0076 

.0067 

.0065 

.0188 

1 .0016 

1.38 

1.66 

1.36 

1.05 

1. 38 

.82 

1.44 

.96 

2.21 

.44 

1.10 

1.00 

.46 

.42 

1.63 

1

Es is greater than or equal to Er, and, therefore, the observed 

variance is below the maximum acceptable variance of supertownship 

means. 

Once values of nr have been determined, the maximum acceptable 

error variance (E r) of the means of the mapping units (supertownships) can be calculated according to the following equation 

(Miesch, 1976a): 

(2) 

Because nr was determined at the 80-percent probability level, the 

values of E r listed in table 3 are also at the 80-percent level. The E r 

value is the upper limit of error variance for supertownship means. 

The variance of the mean for a supertownship cannot be estimated 

from equation 2 because the samples from within supertownships 

were nested within townships and sections rather than chosen at ran-
12 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

dom. The variance is estimated as E sand is either less than, equal to, 

or greater than the required E r If the observed error variance for any 

particular element is less than or equal to the E r limit (that is, 

Es~r), then we can assume that the results of our sampling can adequately describe differences between supertownships for that element. If Es>En then some additional sampling is needed to reduce 

the observed variance to a level below the E r limit. For a balanced 

hierarchical sampling design, the observed error variance, E Sf is computed according to the following equation (Miesch, 1976a): 

(3) 

where n13, ny, nd, and nYI are the number of selected townships within 

supertownships, the number of sections within townships, the 

number of samples within sections, and the number of analyses 

within samples. For the sampling design used in this study, a better 

estimate of E8 is obtained using finite population-correction terms 

(Cochran, 1963, p. 23 and 286). These terms are used when a large 

fraction of the sample unit (township, section, and so forth) available 

for sampling is actually sampled. When these correction terms are applied, equation 3 becomes 

Because the actual design was unbalanced, the effective n values at 

each level are somewhat less than if the design were completely 

balanced. The effective n values were calculated according to techniques described by Leone, Nelson, Johnson, and Eisenstat (1968). For 

this particular design, Es was calculated according to the equation, 

s/32 s 2 sl s 2 

E = 3.78 + __ Y- + -- + Yl s 3.15 5.89 7.71 . (4) 

This equation also incorporates finite population-correction terms. E s 

values for each of the 14 elements having significant regional 

variability, plus strontium, are listed in table 3. The conditionEs~r 

is satisfied for five elements-iron, beryllium, copper, lithium, and 

zinc. 
ANALYSIS OF VARIANCE 13 

A final measure of the suitability of supertownship means to 

distinguish between supertownships is the variance-mean ratio, vm, 

defined by Miesch (1976a) as, 

s 2 a v =--· 

m Es (5) 

This equation is another way of examining the variance between map 

units (supertownships in this study) relative to the variance at all 

levels within the map unit. A vm value equal to 1 is approximately 

equivalent to a significant F test of the relationship between v and nr 

at the 80-percent probability level (F=1 +nrv; Miesch, 1976a). This 

statement essentially means that the variance between supertownships should be at least as great as the observed error variance, 

E 81 which itself is a proportioned sum of the variances within the map 

unit (supertownship). The vm value is thus a measure of the stability 

of a geochemical map based on means within a map unit (supertownship). The greater the value of vm, the greater will be the stability (reproducibility) of the map for that particular element. The vm 

values listed in table 3 show that those for 10 elements are greater 

than or equal to 1. In order of increasing vm, these elements are strontium, silicon, lithium, magnesium, calcium, titanium, beryllium, zinc, 

iron, and copper. 

To summarize, we have used several measures of the sufficiency of 

supertownship means for distinguishing between supertownships, 

and, therefore, the adequacy of the supertownship means as map 

values in describing regional geochemical variability in the composition of soils in the Piceance Creek basin. The results of the analysis of 

variance showed that 14 elements have significant variance components at the supertownship level (distances greater than 19 km). 

An additional element, strontium, is close to being significant at the 

level tested and was included with the other 14 elements for further 

testing. Of these 15 elements, 10 (calcium, iron, magnesium, silicon, 

titanium, beryllium, copper, lithium, strontium, and zinc) have 

variance-mean ratios greater than or equal to 1 at the supertownship 

level, indicating that geochemical maps based on supertownship 

means of these elements are sufficiently stable (reproducible) according to the criterion used. Of these 10 elements, 5 (iron, beryllium, 

copper, lithium, and zinc) have observed variances of supertownship 

means that are less than or equal to the maximum acceptable error 

variance, indicating that the sampling was adequate for these 

elements and that no additional sampling is necessary. 
14 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

REGIONAL GEOCHEMICAL BASELINES 

The analysis of variance indicates that geochemical baselines for 

individual elements in soils from the Piceance Creek basin are of four 

different types: (1) that consisting of elements showing no regional 

components of variability; (2) that consisting of elements showing 

significant regional variability, increasing in concentration from 

northeast to southwest; (3) that consisting of elements showing 

significant regional variability with concentrations lowest in the central part of the basin; and (4) that consisting of elements showing 

significant regional variability with highest concentrations in the 

central part of the basin. Baselines for the last three types for the 10 

elements in table 3 that have variance-mean ratios greater than or 

equal to 1 (vm~l.O) are represented as maps based on supertownship 

means in figure 5. 

Another way of illustrating a geochemical baseline is to plot variation in element concentration as a function of geographic distance 

(Tidball and Ebens, 1976). Graphs of element concentrations versus 

distance across the Piceance Creek basin, illustrating the four 

baseline types mentioned above, are shown in figure 6. In figure 6, 

supertownships (fig. 3) were ordered geographically from south to 

north. The original data, geometric mean, and central range (68 percent) are plotted for each supertownship and for each of the four 

elements used as examples. The central range is that in which approximately 68 percent of the sampled population of soils is 

estimated to occur and is within the range of GMIGD and GMxGD, 

where GM and GD are the geometric mean and geometric deviation, 

respectively (Ebens and others, 1973; Tidball and Ebens, 1976). 

The first type of baseline, illustrated by potassium in figure 6A, 

shows local variability but no regional components of variation. Summary statistics based on a grand mean and deviation are sufficient to 

describe the variability of an element such as potassium for the entire 

Piceance Creek basin. The other three types of baselines do have 

significant regional components of variance that can be described 

either by maps of element concentration (fig. 5) or by graphs of concentration vs. geographic distribution (figs. 6B-D). 

Three elements (zinc, lithium, and copper) have baselines of thesecond type, showing marked increases in concentration from northeast to southwest across the basin. The concentration patterns of 

these elements are shown in figures 5A-C, and a concentration vs. 

distance graph for zinc is shown in figure 6B. Zinc increases from 68 

parts per million (ppm) in the northeastern part of the basin to almost 

100 ppm in the southwestern part. Concentrations of lithium increase 

from 25 to 57 ppm, and copper concentration increase from 18 to 50 

ppm, in the same direction. 
SOILGROUPSBYQ-MODE FACTORANALYSIS 15 

Calcium, magnesium, and strontium have baselines of the third 

type, with lowest concentrations in an east-west band across the central part of the basin, especially in supertownships 5, 6, and 7 (figs. 

5D-F and 6C). Iron, silicon, beryllium, and titanium show the opposite trend and have baselines of the fourth type; that is, concentrations of these elements are generally higher in the central supertownships (figs. 5G-J and 6D). Of the elements that have significant 

variance components at the supertownship level (table 2) but that do 

not have v m values greater than 1 (table 3), most (boron, chromium, 

gallium, yttrium, and ytterbium) have concentration patterns similar 

to those of silicon, iron, beryllium, and titanium (that is, concentrations are higher in the central townships). 

SOIL GROUPS BY Q-MODE FACTOR ANALYSIS 

STATISTICAL METHODS 

The results of analysis of variance demonstrate that a number of 

elements showed a significant variability on a regional scale 

(distances greater than 19 km). A Q-mode factor analysis was performed to determine if there were any multivariate groupings of individual soil samples. 

The main computer program used for the factor analysis is a 

remote-terminal-oriented interactive version of the extended 

CABFAC program described by Klovan and Miesch (1976) that is applicable when the row-sums of the data matrix are constant. Detailed 

descriptions and applications of the interactive Q-mode programs are 

in Miesch (1976b, 1976c). In this study, the original data make up a 

two-dimensional matrix of element concentrations in weight percent 

or parts per million (columns) for each of 140 samples (rows). A brief 

outline of the steps in the analysis is given below: 

1. Form a two-dimensional matrix of element concentrations in 

weight percent or parts per million (columns) for each of the 

samples (rows). 

2. Convert element concentrations to appropriate oxides in percent. 

3. Adjust the data so that the sum of all oxides for each sample (row) 

is 100 percent. 

4. Transform the data so that the concentration of each oxide is expressed as a proportion of the total range of concentrations for 

that oxide. All data are now on a scale of 0.0 to 1.0. 

5. Construct a factor-variance diagram for each element. Choose the 

smallest number of factors that will account for a satisfactorily 

large proportion of the total variance for the largest number of 

oxides. These factors are used in the Q-mode factor model as 

reference axes. Eliminate those oxides for which the number of 

factors chosen will explain only a small proportion of the total 

variance. 
0 1 0 20 KILOMETERS 0 1 0 20 KILOMETERS 

FIGURE 5.-Regional distribution of elements having variance-mean ratios greater than or equal to 1. Numbers in parentheses 

are mean values for samples in each supertownship. Supertownship numbers are outside parentheses. Dots are sample 

localities. A, Zinc (parts per million). Contour interval 10 parts per million. B, Lithium (parts per million). Contour 

intervallO parts per million. 

...... 

m 

< 

> 

~ 

> t-3 

~ 

0 

z 

z 

r:n 

0 

~ 

t-t 

sn 

"tt 

~ 

n 

t:zj 

> z 

n 

t:zj 

n 

~ 

t:zj 

t:zj 

~ 

ttl 

> r:n 

~ n 

0 

t-t 

0 

~ 

> t:::l 

0 
40° 

00' 

39° 

30' 

3(1.0) 4(1.0) 

• 

.,. 

0 10 20 KILOMETERS 0 1 0 20 KILOMETERS 

FIGURE 5.-Continued. C, Copper (parts per million). Contour interval 10 parts per million. 

D, Magnesium (percent). Contour interval 0.4 percent. 

00 

0 

-t"4 

0 

~ 

0 

c::: 

~ 

00 

t:C 

~ 

~ 

~ 0 

tj 

tz:j 

'""%j 

> 0 

6 

~ 

> z 

> t"4 

~ 

00 

-

00 

1--' 

-.:J 
0 1 0 20 KILOMETERS 0 1 0 20 KILOMETERS 

FIGURE 5.-Continued. E, Calcium (percent). Contour intervall.O percent. F, Strontium (parts per million). Contour interval 

100 parts per million. 

~ 

00 

<: 

> 

~ 

> 

~ 

0 

z 

z 

00 

0 

-t'"'l 

sn 

""0 

-(") 

t_:lj 

> z 

(") 

t_:lj 

(") 

~ 

t_:lj 

t_:lj 

~ 

tc 

> 00 

~z (") 

0 

t'"'l 

0 

~ 

> t1 

0 
0 1 0 20 KILOMETERS 0 1 0 20 KILOMETERS 

FIGURE 5.-Continued. G, Iron (percent). Contour interval 0.5 percent. H, Silicon (percent). Contour interval2 percent. 

r:n 

0 

-t-t 

0 

~ 

0 

c::: 

"'C 

r:n 

to 

~ 

ID 

*: 

0 

tl 

trl 

~ 

;J;> 

() 

t-3 

0 

~ 

;J;> 

z 

;J;> 

t-t 

~ 

r:n 

-

r:n 

~ 

~ 
40° 

00' 

• 3(0.26) 4(0.28) 

. 

0 1 0 20 KILOMETERS 

I""' I I 

.4(2.7) 

• 6(2.6) I 7(2~9) • 

0 1 0 20 KILOMETERS 

FIGURE 5.-Continued. I, Titanium (percent). Contour interval 0.05 percent. J, Beryllium (parts per million). Contour interval 0.5 

parts per million. 

~ 

0 

< 

:> 

~ 

> 1-3 

0 

z 

z 

C/l 

0 

-t-t 

sn 

""C 

n 

-t_:l:j 

:> 

z 

n 

t_:l:j 

n 

~ t_:l:j 

~ 

t:C 

:> 

C/l 

?2 

n 

0 

t-t 

0 

~ 

:> 

tj 

0 
SOIL GROUPS BY Q-MODE FACTOR ANALYSIS 21 

6. Perform the Q-mode factor analysis (computer program EQMAIN, 

Miesch, 1976b) by changing the reference axes from an orthogonal (varimax) position to an oblique position coincident 

with selected extreme sample vectors (the oblique-solution 

method of Imbrie, 1963), which are hereafter called end-member 

samples. The actual compositions of these end-member samples 

are used in the program. Compute composition scores, in weight 

percent, for each end member. Compute composition loadings for 

each sample. These loadings may be interpreted as mixing proportions of each of the end members that are required to approximate each sample composition. 

7. Use computer program EQCHEK to obtain computed and observed data for each sample and summary statistics for each oxide. 

The factor-variance diagram (fig. 7) shows at a glance how much of 

the total variance for each element can be accounted for by any given 

number of factors (end members) in a Q-mode model. A compromise 

must be reached in which as much of the variance as possible can be 

accounted for in as many elements as possible by as few factors as 

possible. For example, in figure 7, obviously a much higher proportion of the variance for most elements is achieved by increasing the 

number of factors from three to four, but little is gained by increasing 

the number of factors from four to five. Also, by increasing the 

number of factors, the Q-mode model becomes more complex, making 

interpretation more difficult. On the basis of the factor-variance 

diagrams in figure 7, the decision was made to derive a four-factor 

model for soils of the Piceance Creek basin. 

Factor-variance diagrams also indicate which elements vary almost 

independently of the others. Factor-variance diagrams of all 39 oxides in the original data matrix showed that very little of the 

variances of 10 elements (barium, fluorine, germanium, mercury, 

niobium, antimony, selenium, tin, thorium, and zirconium) could be 

accounted for by models having fewer than 5 to 10 factors. Because 

the proportions of the total variance that can be attributed to 

analysis are large for most of these elements (table 2; fig. 4), these 10 

elements were eliminated so that the final Q-mode factor model contained 4 factors and accounted for about 40 percent or more of the 

total variance in 29 oxides. 

Each end member in the Q-mode model will have certain chemical 

attributes defined in terms of the compositional variables (29 oxides 

in this study). Each sample can then be regarded as a mixture of the 

four end members in varying proportions. The end members may be 

theoretical samples, or they may represent actual samples from the 

data matrix. For this study, the four reference axes in the Q-mode 

model represent four actual soil samples. After a number of iterative 
22 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

1-

z 

w 

(.) 

a: 

w 

Q. 

~ 

:i 

:J 

0 

~ Q. 

z 

0 

3 

~ 

a: 

w 

Q. 

~ a: 

< Q. 

~ 

c5 

z 

;;::; 

1-

z 

w 

~ w 

Q. 

~ 

:i 

:J 

g 

< (.) 

1-

35 

~ 30 (.) 

a: 

w 

Q. 

~ 25 

z 

0 

(.) 

~ 20 ii5 

15 

5 6 

SUPERTOWNSHIP NUMBER 
1.0 

0.9 

0.8 

(}:: 

0 

u.._ 0. 7 

0 

w 

!'-< 

z 

::::J 

0 

u 0.6 u 

a: 

w 

u 

z 

a: 0.5 

(}:: 

a: 

> 

u.._ 

0 0. 4 z 

0 

!'-< 

(}:: 

0 

(L 0.3 0 

(}:: 

(L 

0.2 

SOIL GROUPS BY Q-MODE FACTOR ANALYSIS 

--o-~ AL2031S -o- CADIS 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

=~=~~§IS 0.4 

-x- K201S -o- SI021S -"7- NA201S -----111- TI021S ~- AS2031S -+-B201S 0.3 ---e- BEOIS -11- COOlS ---1B - CR2031S -----111- CUOIS ----n - QA2031S 

===~: ~~~~s o.2 - - - t> - M002/S --- +- NIOIS ---X- PBOIS - - - o - RB201S - - - "l - SC2031S 

===:: ~~7s o.1 --- +- Y2031S --- EB- TB2031S ---11- ZNOIS ---Ill- C021S ---~- U021S 

5 6 7 10 

NUMBER OF FACTORS 

FIGURE 7.-Factor-variance diagrams for 29 oxides in 108 samples and 32 

analytical replicate samples of soils from the Piceance Creek basin. 

23 

~FIGURE G.-Variations in concentrations of A, potassium; B, zinc; C, calcium; and D, 

silicon in soils within supertownships from south to north across the Piceance 

Creek basin (see fig. 3 for locations of supertownships). Actual data points within 

each supertownship are plotted as dots. Solid lines in B, C, and D connect geometric 

means for supertownships. The shaded area in A represents the central range 

(68-percent expected range) based on the grand geometric mean and deviation. 

Shaded areas in B, C, and D represent the central range based on geometric means 

and deviations for supertownships. 
24 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

runs through the main interactive Q-mode program (EQMAIN, 

Miesch, 1976b and 1976c), beginning with a varimax rotation and 

then modifying the varimax axes, four vectors representing samples 

of extreme composition were chosen as factor axes (table 4). 

Important advantages of the extended Q-mode factor analysis 

method over conventional methods of factor analysis are that the 

end-member compositions (factor scores) are expressed in units of the 

original data (weight percent of oxides), and the end-member mixing 

proportions (factor loadings) are true mixing proportions that sum to 

1.0 for each sample. The computed compositions for each of the four 

end members listed in table 4 are the factor scores that consist of 

sample compositions adjusted by projection of vectors from 29- to 

4-dimensional space. The factor loadings listed in table 5 are the mixing proportions and indicate how similar each sample is to each of the 

four end members. For example, the recipe for sample 111111KP 

(table 5) is to mix 0.65 parts of end-member 1 (sample 252111KP), 

0.05 parts of end-member 2 (sample 191111KP), 0.29 parts of endmember 3 (sample 151111KP), and 0.01 parts of end-member 4 (sample 142111KP). 

By using the mixing-proportion recipes for each element, compositions based on the Q-mode model can be computed for each sample 

and compared with the observed compositions by goodness-of-fit 

measures. This computation is done simply by multiplying the 

matrix of factor loadings (table 5) by the transpose of the matrix of 

factor scores (computed compositions of end members in table 4). The 

matrix multiplication and computation of summary statistics for the 

entire sample population are accomplished using program EQCHEK 

(Miesch, 1976b). Summary statistics for the four-factor model based 

on 29 elements in 140 samples from the Piceance Creek basin are 

given in table 6. 

The factor loadings (table 5) are essentially chemical variables expressed as mixing proportions of each of the four end members. In 

other words, the 29 observed chemical variables used as input to the 

Q-mode model have been reduced to four composite chemical 

variables (table 5). However, the loadings give no indication of which 

elements had the most influence on the selection of sample 252111KP 

for end-member 1, sample 191111KP for end-member 2, and so forth. 

Some idea of the chemical characteristics of each of the four end 

members can be obtained by scanning the observed compositions of 

the end members given in table 4. For example, from table 4 it is 

readily apparent that end-member 2 is higher in calcium and 

magnesium carbonates and that end-members 3 and 4 are high in 

silica. However, it is difficult to evaluate all29 elements for each end 

member and impossible to determine which elements had the most influence on the model. 
SOIL GROUPS BY Q-MODE FACTOR ANALYSIS 25 

TABLE 4.-0bserved and computed compositions (in weight percent) for each of the four 

end members used in the extended Q-mode factor model for soils 

Factor 1 end member Factor 2 end member Factor 3 end member Factor 4 end member 

Oxide (sample 252111) (sample 191111) (sample 151111) (sample 142111) 

Observed Computed Observed Computed Observed Computed Observed Computed 

Si02--- 57.6 59.2 35.5 39.8 74.7 70.6 67.1 69.2 

A1 203-- 12.5 12.8 6.8 7.2 8.2 11.1 14.4 12.3 

Fe20r- 3.3 3.8 2.1 1.9 1.7 2.4 2.9 2.6 

MgO---- 1.4 2.4 4.8 3.8 .92 1.1 1.9 .92 

CaO---- 1.0 1.1 11.4 13.8 2.4 2.1 2.1 2.2 

Na20--- 1.0 .98 1. 7 .48 .24 2.50 5.2 4.3 

K20---- 3.7 3.8 2.3 2.3 .94 2.8 2.2 2.8 

Ti02--- .61 .66 .27 .32 .55 .50 .55 .49 

Co2---- 18.5 14.8 34.8 30.2 10.5 6.8 3.5 5.0 

As2o3-- .0009 .0015 .0022 .0018 .0006 .0010 .0007 .0002 

8203--- .0030 .0034 .0211 .0208 .0082 .0134 .0142 .0160 

BeO---- .0009 .0011 .0003 .0003 .004 .0005 .0009 .0008 

CoO---- .0017 .0020 .0003 .0002 .0003 .0003 .0018 .0015 

Cr2o3-- .0016 .0152 .0032 .0015 .0018 .0047 .0137 .0137 

CuO---- .0076 .0110 .0034 .0032 .0013 .0000 .0022 .0026 

Ga2o3-- .0037 .0043 .0005 .0005 .0003 .0007 .0050 .0040 

Li20--- .0190 .0158 .0155 .0137 .0037 .0043 .0047 .0012 

MnO---- .14 .14 .063 .057 .0214 .0039 .063 .106 

Mo03--- .0007 .0020 .0001 .0001 .0001 .0000 .0015 .0012 

NiO---- .0049 .0056 .0009 .0007 .0005 .0008 .0040 .0036 

PbO---- .0060 .0061 .0008 .0006 .0004 .0000 .0057 .0045 

Rb20--- .019 .019 .010 .010 .0044 .0103 .0054 .0104 

sc2o3-- .0021 .0020 .0003 .0004 .0003 .0002 .0018 .0015 

SrO---- .023 .031 .053 .053 .0073 .0107 .053 .049 

uo2---- .ooo4 .ooo4 .ooo4 .ooo4 .ooo3 .ooo3 .ooo2 .ooo2 

V203--- .0173 .0164 .0042 .0053 .0044 .0031 .0012 .0089 

v2o3--- .0028 .0034 .0009 .0016 .0012 .0009 .0022 .0021 

Yb 203-- .0005 .0006 .0000 .0001 .0001 .0000 .0002 .0003 

ZnO---- .018 .016 .012 .012 .0057 .0096 .0096 .0069 

Total 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 

TABLE 5.-Loadings for the four factors used in the extended Q-mode model 

Sample 

111111KP 

111121KP 

111122KP 

111211KP 

112111KP 

Factor 1 

Sample 

252111KP 

0.8133 

.5685 

.7086 

-.0036 

-.0374 

Factor 2 

Sample 

191111KP 

-0.0349 

.1482 

.1464 

.6703 

.1157 

Factor 3 

Samp1e 

151111KP 

0.4533 

.5576 

.3536 

.3160 

.6443 

Factor 4 

Sample 

142111KP 

-0.2317 

-.2744 

-.2086 

.0174 

.2774 
26 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

TABLE 5.-Loadings for the four factors used in the extended Q-mode modelContinued 

Factor 1 Factor 2 Factor 3 Factor 4 

Sample Sample Sample Sample 

Sample 252111KP 191111KP 151111KP 142111KP 

112211KP .0997 .3465 .0266 .5272 

112212KP .1551 .3483 -.0406 .5371 

112221KP .2154 .1265 .2743 .3838 

121111KP .1931 .4415 -.1119 .4773 

121211KP .0086 .5606 -.2714 .7022 

121212KP .0013 .5581 -.1532 .5937 

121221KP -.1125 .4393 .0870 .5861 

121222KP -. 1949 .5126 .2986 .3837 

122111Kl' .2476 -.0337 .4946 .2916 

122121KP .2174 .1648 .4546 .1632 

122211KP .2586 -.0322 .5412 .2323 

131111KP .4052 .0266 .8666 -. 2984 

131121KP .1549 .0920 .5161 .2370 

131211KP .0127 .0997 .2248 .6628 

131212KP .0025 .1293 .2411 .6271 

132111KP .4579 .0102 -.0062 .5380 

132211KP .5020 -.1591 .2191 .4380 

132221KP .5134 -.1738 .2783 .3821 

132222KP .4155 -.1533 .5036 .2341 

141111KP .6848 -.0332 .3029 .0455 

141112KP .4600 -.0204 .7628 -.2024 

141121KP .6492 -.0064 .1117 • 2455 

141122KP .4370 .0080 .2788 .2762 

141211KP .4633 -.1465 .2478 .4354 

142111KP .0870 -.0589 .0858 .8860 

142211KP .5606 -.0930 .2940 .2385 

142221KP .7351 -.1708 -. 07 39 .5097 

151111KP .0371 -.0198 1. 0646 -.0819 

151211KP .4750 -.1152 .3482 .2920 

151221KP .6265 -.0210 .5354 -.1408 

151222KP .9324 -.0693 .3599 -.2231 

152111KP .7012 -.1089 .2479 .1598 

152211KP .1339 -.0458 .9660 -.0541 

152212KP .4921 -.1566 .1867 .4778 

152221KP .5045 -.0902 .1619 .4238 

161111KP .5211 .2413 .0587 .17 89 

161112KP .3210 .2678 .3589 .0523 

161211KP .2195 -.0741 .0997 .7550 

161221KP .1319 -.0456 .0739 .8398 

162111KP .6358 -.1235 .5148 -.0271 

162112KP .6848 -.0929 .4589 -.0507 
SOIL GROUPS BY Q-MODE FACTOR ANALYSIS 27 

TABLE 5.-Loadings for the four factors used in the extended Q-mode modelContinued 

Factor 1 Factor 2 Factor 3 Factor 4 

Sample Sample Sample Sample 

Sample 252111KP 191111KP 151111KP 142111KP 

162211KP .6159 -. 15 7 4 .2422 .2993 

162221KP .5111 -.2113 • 3947 .3055 

171111KP .6339 -.1499 .5R75 -.0715 

171112KP .7929 -.0690 .0489 .2272 

171211KP .4414 -.2288 .2569 .5305 

171221KP .2177 -.0683 .8023 .0483 

172111KP .6104 -.0590 .3058 .1428 

172211KP .9251 -.2057 .2732 .0074 

172221KP 1.0020 -. 1821 .1743 .0058 

181111KP .9300 -.0766 .2931 -.1465 

181211KP .4408 .1721 .6516 -.2645 

181221KP .5541 .2235 .2451 -.0227 

182111KP 1.0230 -. 1170 .5290 -.4350 

182211KP .6722 -.1068 .5094 -.0748 

182212KP .6993 -.1169 .3201 .0976 

1R2221KP .7052 -.1350 .4251 .0047 

191111KP .0042 1.0055 -.0125 .0029 

191211KP .4882 .3078 .1013 .1027 

191221KP .3433 .7574 -.2682 .1674 

192111Y..P .5965 -.1042 .3813 .1264 

192211KP .6910 .3888 .0214 -.1012 

192221KP .4895 .5134 .0265 -.0293 

211111KP -.0037 .7783 -.4001 .6255 

211112KP -.0786 .7396 -. 187 7 .5267 

211211KP .2021 .7027 -.2324 .3277 

211221KP .1207 .5043 .4391 -.0641 

211222KP .3643 .5562 -.1464 .2259 

212111KP .2330 .1840 .7164 -.1334 

212121KP .4067 .1914 .2636 .1382 

212211KP -.0845 .6943 .3444 .0459 

221111KP .1397 .4822 .0853 .2929 

221112KP .1882 .3158 .1693 .3267 

221121KP -.0484 .9157 -.2791 .4118 

221211KP .2190 .0241 -.0463 .8032 

222111KP .1354 .3077 -.0471 .6040 

222112KP .0660 .3517 .2048 .3775 

222121KP .2068 .0945 .1482 .5505 

222122KP .1350 .1366 .3307 .3977 

222211KP .0176 .7552 -.4221 .6492 

231111KP -.0107 .6267 .3730 .0110 

231121KP .1457 .5216 -.0225 .3552 
28 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

TABLE 5.-Loadings for the four factors used in the extended Q-mode modelContinued 

Factor 1 Factor 2 Factor 3 Factor 4 

Sample Sample Sample Sample 

Sample 252111KP 191111KP 151111KP 142111KP 

231211KP .2175 .1346 .1496 .4983 

232111KP .4231 .1582 -. 1540 .5726 

232112KP .1993 .1926 .3099 .2982 

232211KP .1642 .0716 .3262 .4380 

232212KP .3126 .0319 -.0633 .7188 

232221KP .0850 .0894 .5522 .2734 

232222KP .2404 .0613 .3650 .3333 

241111KP .3470 .1544 .0154 .4833 

241211KP .0714 .0572 .2774 .5940 

241212KP -.0359 .1360 .5006 .3994 

241221KP .0916 .2098 .4068 .2917 

242111KP .0482 .1197 .6490 .1831 

242112KP .2677 .0478 .1120 .5725 

242211KP .2645 .1371 -.1565 .7550 

242212KP .1090 .1842 .2107 .4962 

251111KP .6257 .0013 .1521 .2209 

251112KP .3911 .0008 .4495 .1586 

251211KP .4924 -.1277 .4857 .1496 

251221KP .8146 -.1705 .2985 .0574 

252111KP 1.1566 -.0711 .1589 -. 2444 

252112KP .9532 -.0703 .4515 -.3344 

252121KP 1.1432 -.1098 .2035 -.2369 

252211KP .9949 -.1277 .4454 -.3126 

261111KP .4862 .0885 -.0598 .4850 

261112KP .4741 .1050 .0302 .3907 

261211KP .2157 .0347 .3799 .3697 

261221KP .2352 -.0135 .3879 .3904 

262111KP .6166 -.0565 .3837 .0562 

262121KP -6637 -.0893 .4390 -.0134 

262211KP .3725 .5957 -.0490 .0809 

262212KP .2801 .6004 .1541 -.0346 

271111KP .5986 -.0452 .5031 -.0564 

271211KP .5651 -.0508 .3694 .1163 

271221KP .8130 -.1515 .0315 .3070 

272111KP .2112 .4702 .0485 .2702 

272121KP .2226 • 7838 -.2636 .2572 

272211KP .6931 -.1896 .1670 .3295 

272212KP .3184 -.0346 .7691 -.0528 

281111KP .1828 .7483 -.0866 .1556 

281121KP .2066 .6452 -.1039 .2522 

281122KP .0748 .6835 -.0546 .2963 
SOIL GROUPS BY Q-MODE FACTOR ANALYSIS 29 

TABLE 5.-Loadings for the four factors used in the extended Q-mode modelContinued 

Factor 1 Factor 2 Factor 3 Factor 4 

Sample Sample Sample Sample 

Sample 252111KP 191111KP 151111KP 142111KP 

281211KP .4772 .1497 .5378 -.1647 

282111KP .2522 .5996 .1546 -.0064 

282211KP .4041 .. 5025 -.0300 .1234 

282221KP .5327 -4407 -.0469 .0735 

291111KP .7359 -.1072 .3672 .0041 

291112KP .8146 -. 1478 .1660 .1671 

291121KP .5563 .0026 .5483 -.1073 

291211KP .2237 -.0815 .8944 -. 0366 

292111KP .0820 .6578 .3115 -.0513 

292112KP .2667 .6149 -.0906 .2090 

292121KP .3204 .6799 -.2505 .2502 

292211KP .2070 -7159 -.2264 .3035 
30 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

TABLE 6. -Summary statistics for observed data and for data computed by the extended Q-mode factor analysis using samples as end members for each of the four factors 

[All values are in weight percent] 

Oxide Mean Standard deviation Proportion of 

Observed data Computed data Observed data Computed data variance explained 

Si02--- 60.4 60.4 7.80 7.69 0.89 

A1 20r- 11.3 11.2 1.64 1.44 .71 

Fe2o3-- 2.8 2.8 .60 .47 .56 

MgO---- 2.0 2.0 .87 .66 .58 

CaO---- 4.3 4.2 3.63 3.63 .89 

Na20--- 2.3 2.2 .98 .81 .68 

K20---- 3.0 3.0 .53 .38 .47 

Ti02--- .51 .51 .089 .086 .81 

Co2---- 13.2 13.4 6.7 6.01 .76 

As2o3-- .0010 .0010 .0005 .0003 .35 

B203--- .0220 .0220 .0056 .0043 .47 

BeO---- .0007 .0007 .0002 .0002 • 71 

CoO---- .0012 .0012 .0005 .0004 .81 

cr2o3-- .0102 .0103 .0005 .0005 .77 

CuO---- .0048 .0048 .0031 .0023 .63 

Ga2o3-- .0029 .0029 .0011 .0010 .80 

L i 20--- .0087 .0086 .0044 .0032 .58 

MnO---- .091 .091 .039 .029 .58 

Mo0 3--- .0010 .0010 .0005 .0005 .88 

NiO---- .0032 .0032 .0013 .0012 .83 

PbO---- .0035 .0035 .0015 .0014 .85 

Rb20--- .0127 .0128 .0032 .0023 .45 

sc2o3-- .0012 .0012 .0004 .0004 .83 

SrO---- .0383 .0383 .0149 .0136 .79 

uo3---- .0003 .0003 .0001 .0000 .37 

V203--- .0095 .0096 .0032 .0028 .73 

Y203--- .0022 .0022 .0006 .0005 .45 

Yb203-- .0003 .0003 .0001 .0001 .69 

ZnO---- .0110 .0110 .0024 .0020 .72 

In order to determine which elements contributed to which end 

member, the factor loadings for each sample were treated as composite chemical variables, and correlation coefficients were computed between the loadings and the observed compositional variables (29 element oxides). Results of the correlation analysis are given in table 7. 

To illustrate regional groups of soils within the Piceance Creek 

basin, based on soil chemistry, loadings for each of the four factors 

are plotted on a map of the basin in figure 8. 
SOIL GROUPS BY Q-MODE FACTOR ANALYSIS 31 

TABLE ?.-Correlation coefficients among composite compositional variables (factor 

loadings) for each of the four end-member samples used in the extended Q-mode 

factor model and the observed compositional variables (oxides of elements) 

Factor 1 Factor 2 Factor 3 Factor 4 

Oxide end-member end-member end-member end-member 

sample sample sample sample 

252111KP 191111 KP 151111KP 142111KP 

Si02--- 0~2856 1-0.9106 10.5356 o. 0251 

Al 2o3-- 1 • 5627 1 -.8222 1 .3438 -.0782 

Fe2o3-- 1 .7372 1 -.6067 1 .2510 L.3036 

MgO---- -.1960 1 • 7269 1 .3488 -.1204 

CaO---- 1-.5922 1 .9299 1-.5038 .1648 

Na20--- 1-.3163 1 -.3484 -.0919 1 .6234 

K20---- 1 • 6643 1 -.5363 1 .2819 L.3367 

Ti02--- 1 • 7930 1 -.8026 1 .4527 1-.3769 

C02---- 1-.2116 1 .8209 1-.4209 -.1175 

As20--- • 1911 1 .2658 • 1771 1 -.5396 

820---- 1 • 6183 -.1024 1-.0591 1-.3348 

BeO---- 1 .6477 1 -. 6971 .1453 -.0574 

CoO---- 1 • 5914 1 -.5541 -.1765 .1760 

Cr2o3-- • 5169 1 -.6638 -.0520 .2053 

CuO---- .6710 -.1877 -.1273 1-.2388 

Ga2o3-- 1 .4613 1 -.5238 1-.2571 .3285 

Li 20--- 1 .4274 1 .2839 -.0302 1-.5378 

MnO---- 1 .3774 -.1267 -.4459 .2432 

Mo02--- 1 • 7048 1 -.5595 -.1340 .0521 

NiO---- 1 • 6976 1 -.5646 -.1001 .0292 

PbO---- 1 .5513 1 -.4486 1-.3013 1 .2387 

Rb20--- .6878 1 -.3717 • 1366 1-.3518 

Sc2o3-- .5870 1 -.4258 1-.3055 • 1963 

SrO---- 1 -.5091 1 .6913 1 -. 8001 1 .5848 

vo2---- 1 .4585 .0395 • 1485 L.5296 

V203--- 1 .7197 -.3587 -.1311 -.1299 

Y203-- .5175 -.2238 -.2073 -.0132 

Yb2o3--- .634 1 -.3757 1-.2637 .0630 

ZnO---- 1 • 7454 -.1092 .2038 1-.6800 

1Significant at the 99-percent probability 1 evel ( I r I >O. 21 ) • 
32 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

40° 

00' • 

• 

• 

• 

• 

• 

• • 

0 

• 

• • 

•• 

• 

• 

• • 

• 

• 

• 

• 

• 

• • • • 

• 

• 

• • 

• • 

. • 

10 20 KILOMETERS 

EXPLANATION 

• <0.1 

• 0.1-0.5 

• >0.5 

~ Factor-1 end member 

A 

FIGURE 8.-Maps showing Q-mode factor loadings (table 5) for A, factor 1; B, 

factor 2; C, factor 3; and D, factor 4 for soils of the Piceance Creek basin. 
SOIL GROUPS BY Q-MODE FACTOR ANALYSIS 33 

40° 

oo· • 

• 

• •• • 

• 

-

• • • • • • 

• • • 

• • • • • 

• • 

• • 

• • • 

39° 

30' 

B 

0 10 20 KILOMETERS 

I I I II I I I 

EXPLANATION 

• <0 

• 0-0.3 

• >0.3 

M Factor-2 end member 

FIGURE 8.-Continued. 
34 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

• • 

• 

40° 

00' 

• 

• 

• •• • 

• .. 

• • • • • • 

~ • • • 

• • • • • • 

. • 

39° 

30' 

c 

0 10 20 KILOMETERS 

EXPLANATION 

• <0 

• 0-0.3 

• > 0.3 

M Factor-3 end member 

FIGURE B.-Continued. 
SOIL GROUPS BY Q-MODE FACTOR ANALYSIS 

• 

•• • 

• .. 

• • A; • • • • • 

• • • 

• • • 

• • 

0 10 

I II II I I 

20 KILOMETERS 

I 

EXPLANATION 

• < 0.2 

• 0.2-0.5 

• >0.5 

M Factor-4 end member 

FIGURE B.-Continued. 

35 

• 
36 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

RESULTS 

Table 7 indicates that the elements having the greatest influence in 

grouping samples having high loadings for factor 1 (those samples 

most similar to sample 252111KP) are titanium, zinc, iron, vanadium, 

molybdenum, nickel, rubidium, copper, potassium, ytterbium, 

beryllium, cobalt, scandium, aluminum, lead, yttrium, and chromium 

in approximately that order of importance. A glance at table 4 shows 

that the factor-1 end member does indeed contain higher concentrations of these elements than the other three end members. Samples 

that load high on factor 1 tend to be the most trace-element rich of all 

samples in this study. Samples having highest loadings for factor 1 

are from the southern part of the basin (fig. SA). 

Soil samples that load high on factor 2 tend to contain high concentrations of the carbonate-related elements calcium, total carbon, 

magnesium, and strontium, and lower concentrations of the resistate 

elements silicon, aluminum, titanium, and beryllium (tables 4 and 7). 

Samples having high loadings for factor 2 are from both the northern 

and southern parts of the basin (fig. 8B); samples having lowest 

loadings for factor 2 occur over most of the central part of the basin. 

The inverse relationship between the carbonate and resistate 

variables can also be seen by comparing the map of factor-2 loadings 

(fig. 8B) and maps of element concentrations based on supertownship 

means (fig. 5). 

Soil samples having high loadings for factor 3 are from scattered 

areas throughout the basin (fig. 8C), have relatively high concentrations of silicon and titanium, and tend to have low concentrations of 

strontium, calcium, total carbon, and manganese (tables 4 and 7). 

Soil samples having high loadings for factor 4 tend to be from the 

northern and central parts of the basin (fig. 8D). These samples tend 

to have relatively high concentrations of sodium and strontium and 

relatively low concentrations of zinc, arsenic, lithium, and vanadium 

(tables 4 and 7). 

DISCUSSION 

The map patterns of factor loadings shown in figure 8 and the map 

patterns of element concentration shown in figure 5 can be explained 

in general terms of regional geology, physiography, and hydrology of 

the Piceance Creek basin. Parent material for most of the soils from 

the northern and central parts of the basin is sandstone of the Uinta 

Formation that overlies the Green River Formation (fig. 2). However, 

in the southern part of the basin, most soils are derived from finer 

grained dolomitic marlstone of the Green River Formation. A few 

samples used in this investigation are from soils derived from sandstone of the Wasatch Formation that underlies the Green River For-
DISCUSSION 37 

mation (fig. 2). In general, the Green River Formation contains 

higher concentrations of most trace and minor elements than does 

sandstone of early Eocene age in the same area (table 8). Therefore, 

soils derived from the Green River Formation would be expected to 

contain higher concentrations of most trace elements than do soils 

derived from sandstone of either the underlying Wasatch Formation 

or the overlying Uinta Formation. 

Additional evidence for contributions of higher trace-element concentrations from the Green River Formation was provided by Candito (1977) and McNeal, Feder, Ringrose, and Klusman (1976). Candito 

examined concentrations of boron, molybdenum, zinc, lithium, 

arsenic, mercury, and organic carbon in soils developed on the Uinta 

Formation and on the Parachute Creek Member of the Green River 

Formation on lease site C-a. He found significant differences in concentrations of lithium, boron, molybdenum and organic carbon between soils developed on the two formations, the concentrations of all 

four elements being higher in the soils developed on the Parachute 

Creek. McNeal, Feder, Ringrose, and Klusman (1976) measured concentrations of 32 elements in sediments from four streams, two each 

from the Piceance Creek and Uinta basins. They found significant 

between-stream variability for 19 of these elements, including the 

carbonate-related elements calcium, magnesium, strontium, lithium, 

carbonate carbon,· sodium, boron, and barium. They interpreted these 

differences as being due to variable contributions from the carbonate 

strata of the Green River Formation. 

In a general way, the map-distribution pattern of factor-1loadings 

(fig. SA) is the inverse of the map-distribution pattern of factor-4 

loadings (fig. SD); samples having highest loadings for factor 1 are in 

the southern part of the basin, and those having highest loadings for 

factor 4 are in the central and northern parts of the basin. Analysisof-variance results have shown that several trace elements having 

regional components of variance have concentration gradients in 

soils across the basin that increase from northeast to southwest. The 

northeast-to-southwest gradient is particularly evident in maps of 

concentrations of zinc, lithium, and copper (figs. 5A-C). It is not surprising, therefore, that samples having high loadings for factor 1 

(that is, samples high in trace elements) should come from the 

southern part of the basin, because soils there are derived more from 

the Green River Formation than are soils in the north. The distribution of samples having high loadings for factor 4 reflects the inverse 

relationship between factor-4 loadings and zinc and lithium; factor-4 

loadings are highest in the central and northern parts of the basin 

where concentrations of these two elements are lowest. Factor-4 

samples also tend to be rich in silicon (table 4), reflecting the fact that 

most of these soils are derived from sandstone. 
38 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

TABLE B.-Average composition (geometric means) of Green River Formation oil shale 

and lower Eocene sandstones of the Rocky Mountain region 

Oxide 

Al 2o3 

CaO 

Fe2o3 

MgO 

K20 

Si02 

Na2o 

Ti02 

C02 

As2o3 

8203 

BeO 

CoO 

Cr2o3 

CuO 

Ga2o3 

Li 203 

MnO 

Mo03 

NiO 

PbO 

Rb20 

sc2o3 

SrO 

Oil shale, 

Green River 

Formation I 

5

7 .o 

51.0 

2.9 

3.7 

2.6 

.0046 

.0230 

.0013 

.0051 

.0050 

.0013 

.0210 

.0320 

.0015 

.0032 

.0022 

.0012 

.0350 

.0150 

.0093 

[Leaders(·--), not determined or not reported] 

Mahogany zone, 

Green River 

Formation2 

0.0038 

.0240 

.0005 

.0012 

.0700 

.0061 

.0013 

.0030 

.0320 

.0026 

.0420 

.0003 

.0013 

.0011 

.1100 

.0160 

.0034 

.0022 

.0005 

Wasatch Formation, 

Piceance Creek 

basin 3 

6.0 

2.0 

.77 

.53 

1.3 

81 

.68 

.27 

1.3 

.0052 

.0004 

.0012 

.0008 

.0010 

.0150 

.0008 

.0008 

.0110 

.0034 

.0010 

.0002 

Lower Eocene 

sandstones, Rocky 

Mountain region4 

6.6 

2.4 

1. 2 

.55 

1.4 

73 

0 51 

.20 

0 71 

.0042 

.0006 

.0019 

.0010 

.0012 

.0190 

.0011 

.0008 

.0160 

.0035 

.0012 

.0003 

1

From u.s. Geological Survey (1976); includes Mahogany zone. 

212 composite samples from the Mahogany zone, Poulson and others (1977). 312 samples of the Wasatch Formation, Piceance Creek basin, Vine and 

Tourtelot (1973). 

4216 samples of lower Eocene (including the 12 Wasatch samples) from the 

Rocky Mountain region, Vine and Tourtelot (1973). 5

USGS Green River oil-shale standard, SGR-1, Flanagan (1976). 
DISCUSSION 39 

The highest loadings for factor 2 are for soils in the northern and 

southern parts of the basin (fig. 8B). Samples having high loadings 

for factor 2 are characterized by relatively high concentrations of the 

carbonate-related elements calcium, magnesium, strontium, and 

lithium (tables 4 and 7), and low concentrations of the resistate 

elements silicon, aluminum, titanium, and beryllium. This pattern of 

high-carbonate soils in the northern and southern parts of the basin 

and low-carbonate soils in the central part of the basin is also 

reflected in the distributions of magnesium, calcium, and strontium 

(figs. 5D-F), which have highest concentrations in the northern and 

southern parts of the basin. Three of the resistate elements (iron, 

titanium, and beryllium) have significant regional components of 

variance and have map distributions opposite those for calcium, 

magnesium, and strontium, with a ridge of higher concentrations 

running east-west across the central part of the basin (figs. 5G, 51, 

and 5J). 

The occurrence of more carbonate-rich soils in the southern part of 

the basin is probably related to the fact that most soils there are 

derived from carbonate-rich strata of the Green River Formation. 

The carbonate-rich soils in the northern part of the basin (fig. 8B) cannot be accounted for by the occurrence of the Green River Formation 

because most soils in this area are derived from sandstone of the 

Uinta Formation (fig. 2). Relatively high concentrations of the carbonate-related elements calcium, magnesium, strontium, and sodium 

in soils in the northern part of the basin may be related to climate, 

ground-water conditions, compositional variability within the Uinta 

Formation, or a combination of the three. At least some of the carbonate is derived from ground water. Springs are common in the 

northern part of the basin and form white coatings, travertine 

mounds, and flowstone of calcium carbonate. Many of the springs are 

associated with a fracture system that trends approximately N. 70° 

W. across the northern part of the basin (George J. Saulnier, Jr., U.S. 

Geological Survey, oral commun., 1977). This fracture system apparently acts as a conduit for ground water from a lower aquifer consisting of fractured marlstone below the Mahogany zone in the 

Parachute Creek Member of the Green River Formation. The water in 

the lower aquifer in the northern part of the basin is characterized by 

very high concentrations of sodium and bicarbonate (several 

thousands of milligrams per liter, or mg/L, each) resulting from the 

dissolution of nahcolite from the lower part of the Parachute Creek 

Member (Weeks and others, 1974). 

Above the Mahogany zone is a second aquifer in fractured 

marlstone of the Parachute Creek Member and fractured sandstone 

of the lower part of the overlying Uinta Formation. Water in the upper aquifer also contains high concentrations of sodium and bicar-
40 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

bonate, but concentrations are about an order of magnitude less than 

concentrations in the lower aquifer (hundreds rather than thousands 

of milligrams per liter). The upper aquifer also contains about 50 

mg/L each of magnesium and calcium, about an order of magnitude 

higher than concentrations of these two ions in the lower aquifer 

(Weeks and others, 1974). Water from either or both of these two 

aquifers, introduced into soils by springs, would result in precipitation of CaC03 and possibly N a2C03• Strontium and magnesium would 

be coprecipitated with calcium, resulting in high concentrations of 

calcium, magnesium, strontium, and sodium in soils in the northern 

part of the basin, as reflected in the distributions of high loadings for 

factors 2 and 4 (tables 4 and 7; figs. 8B and 8D). 

Introduction of CaC03 from ground water might be expected to be 

more prevalent in soils in valley walls and bottoms, producing a 

significant difference in composition between ridgetop and valleybottom soils. That carbonate precipitation in ridgetop soils does occur is indicated by the common occurrence of CaC03 coatings on rock 

fragments in soils on the ridges. Precipitation of carbonate minerals 

in soils in the northern part of the basin, even on ridgetops, may be 

aided by the fact that this part of the basin is lower in elevation, 

receives less rain and snow than most of the rest of the basin, and has 

a higher rate of evapotranspiration. Figure 9 shows that most of the 

northern part of the basin is at elevations less than 2,286 m, and 

much of the area is less than 1,981 m. Wymore (1974) has shown that 

there are marked differences in precipitation-evapotranspiration balances related to elevation differences within the Piceance Creek and 

Yellow Creek watershed. At elevations between 2,134 and 2,430 m, 

precipitation is about 4.6 em per year, and evapotranspiration is 

about 10 em per year. Below 1,829 m, precipitation is less than 3 em 

per year, and evapotranspiration is greater than 12 em per year. 

Temperatures also show marked differences with elevation, decreasing by almost 1 oc on an annual basis for each 100-m rise in 

elevation. These marked climatic differences suggest that the higher 

carbonate content of soils in the northern part of the basin (relative to 

the central part of the basin) may be related to lower precipitation, 

lower elevation, and higher temperature. 

Evidence for climatic control on soil composition was also reported 

by Candito (1977). He found that concentrations of several trace 

elements in soils developed on the Uinta Formation on lease tract 

C-a are strongly controlled by organic carbon and pH, which, in turn, 

are controlled by climatic differences manifested in differences in 

available moisture and vegetation cover. 
DISCUSSION 41 

0 1 0 20 KILOMETERS 

Lu.J....LL _ __J__ __ __, 

FIGURE 9.-Subdivision of the Piceance Creek basin into three elevation zones. 

Samples having high loadings for factor 3 are scattered throughout 

the basin and show no regional trends (fig. 8C). The end-member 

sample for factor 3 is a soil developed on sandstone of the Wasatch 

Formation underlying the Green River Formation. Tables 4 and 7 indicate that factor-3 soils tend to be among the most silica rich in the 

basin. The scattered map distribution of these samples (fig. 8C) suggests that they represent a residuum of high-silicon soils that differ 

from factor-4 soils probably because of low concentrations of sodium 

and strontium. As discussed above, the high concentrations of 

sodium and strontium in factor-4 soils are probably the result of additions from ground water. 
42 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

COMPARISON OF TRACE ELEMENTS IN OIL SHALE 

AND SOILS OF THE PICEANCE CREEK BASIN 

As indicated in table 8, dolomitic marlstone of the Green River Formation contains higher concentrations of most trace elements than 

sandstone of the underlying Wasatch Formation. Although no 

analyses are available, we would expect the sandstone of the overlying Uinta Formation also to contain lower concentrations of most 

trace elements relative to the Green River Formation. Therefore, oilshale development resulting in land fill containing either untreated or 

spent oil shale has the potential of producing higher trace-element 

concentrations in surficial materials. The volumes of material would 

be considerably less in an in situ or modified in situ mining process 

than in either surface or subsurface mining processes. A comparison 

of trace-element concentration in soils from the Piceance Creek basin 

(table 1) with concentration in oil shale is shown in figure 10. Oil shale 

tends to contain substantially (5 to 10 times) higher concentrations of 

arsenic, mercury, and selenium, and slightly higher concentrations of 

fluorine, lithium, vanadium, and molybdenum. 

Arsenic, antimony, mercury, selenium, cadmium, fluorine, and 

boron have been specified by the U.S. Geological Survey as being of 

particular environmental concern because they occur in relatively 

high concentrations in oil shale. The lessees of the prototype oil-shale 

tracts are required to establish baselines for these seven elements in 

oil shale, soil, stream sediment, and water on the lease sites. 

In view of the risk of molybdenosis in cattle grazing on forage 

grown on coal-mine spoils (Erdman and others, 1978), we also include 

molybdenum in the list of trace elements that should be of particular 

environmental concern. Molybdenosis is a nutritional disease caused 

when high concentrations of molybdenum depress the physiological 

availability of copper. Molybdenosis in cattle is of particular concern 

when the ratio of copper to molybdenum in forage is less than 2.0 

(Erdman and others, 1978; it is usually due to high levels of 

molybdenum rather than low levels of copper). Results of analyses of 

barley grown on spent oil shale and on soils from the Piceance Creek 

basin, reported by Harbert and Berg (1974), indicate that plants 

grown on spent shale have ratios of copper to molybdenum below the 

critical limit of 2.0 (1.2 to 1.8). However, plants grown on the soils 

have ratios of copper to molybdenum considerably above the critical 

limit, increasing from about 5.5 in soils from the northern part of the 

basin to about 6.6 in soils from the southern part of the basin. The 

north-to-south regional difference in the ratio of copper to 

molybdenum in plants reflects the regional north-to-south increase in 

copper in soils in the Piceance Creek basin (fig. 5C). These data suggest that any land reclamation in the Piceance Creek basin using oil 

shale could induce an increase in several of the better known noxious 

trace elements. 
COMPARISON OF TRACE ELEMENTS IN OIL SHALE AND SOILS 

0 

0 

0 

ci 

C\1 

0 

0 

r- -q 

~ 

t'll 0 al - g r It) 

u. 

c::c/5 0 

r- - g ~ C\1 

0 

r- :.J - g 

0 

r- al - g 0 <:-· () 

N > 0 

~ - ~ C/) ::J c:: <( () N .0 

a.. 0 

r -~ 

z 0 

r- t'll 0 - g (!)~ z 

0 t'll ::J al - ~ ....J 

~ u ~ en <:-· a: Q) 

I 

w en - ~ a.. r- c/5 en .0 ::JN r- en <:-- () a: 

-c 

I 

<( 

~ () - It) a.. 

Ol <:-· ::J I c-.<:-· 

~ .0 - C\1 

<:-· <:-· ~~o t'll c:: r (!) N -~ 

<:--

r- - ~ 0 

I Q) 

c-;· en - ;; r-c-. .0 c-;· en 

It) 

r - 0 

c:i 

~------------------------------~~ 

FIGURE 10.-Comparison of concentrations of trace elements in oil 

shale from the Green River Formation (Heavy lines) and soils from 

the Piceance Creek basin (light lines). Data for mercury are from 

Donnell and Shaw (1977). Data for all other elements in oil shale are 

compiled in U.S. Geological Survey (1976). Data for soils are from 

table 1. 

43 
44 VARIATION IN SOILS, PICEANCE CREEK BASIN, COLORADO 

CONCLUSIONS 

1. Most of the variation in elemental abundance in soils from the 

Piceance Creek basin occurs between sections within townships; 

that is, over distances of about 3 to 10 km. Of the 39 elements 

discussed in this report, 28 show significant variance between 

sections. Only seven elements (calcium, sodium, mercury, strontium, total carbon, uranium, and thorium) have significant 

variance between townships within supertownships (distances of 

10 to 19 km), and only 11 elements (aluminum, potassium, 

silicon, sodium, titanium, total carbon, fluorine, lithium, nickel, 

zinc, and uranium) have significant variance between samples 

(100 m). None of the elements show significant variability between samples collected on ridgetops and those collected in valley 

bottoms. 

2. Fourteen elements (calcium, iron, magnesium, silicon, titanium, 

boron, beryllium, chromium, copper, gallium, lithium, yttrium, 

ytterbium, and zinc) show significant variation between supertownships (distances greater than 19 km) at the 95-percent probability level. The distribution of strontium is close to significant 

at this level and it has been included with these 14 elements 

because of its close association with carbonate-related variables. 

Of these 15 elements, 10 (calcium, iron, magnesium, silicon, 

titanium, beryllium, copper, lithium, zinc, and strontium) have 

variance-mean ratios (vm) greater than or equal to 1.0 at the 

supertownship level, indicating that supertownship means are 

sufficiently stable (reproducible) to use in constructing 

geochemical maps. The geochemical maps for these 10 elements 

show three different types of regional trends. Concentrations of 

zinc, lithium, and copper have a regional gradient across the Piceance Creek basin, increasing from northeast to southwest. 

Iron, beryllium, silicon, and titanium are most concentrated in 

soils in the central part of the basin, and least concentrated in 

soils in the northern and southern parts of the basin. Concentrations of calcium, magnesium, and strontium show the opposite 

map pattern; that is, their concentrations are greatest in the 

north and south and least in the center of the basin. Of theremaining five elements that have significant variance components at the supertownship level (namely boron, chromium, 

gallium, yttrium, and ytterbium) but do not have supertownship 

mean values sufficiently stable to construct geochemical maps, 

most show regional trends similar to those of iron, beryllium, 

silicon, and titanium, with highest values in the central part of 

the basin. However, the observed trends for these five elements 

are not judged to be reliable in themselves because the supertownship means are not sufficiently reproducible. 
REFERENCES CITED 45 

3. Soil samples having similar chemical composition were examined 

by Q-mode factor analysis. Based on the Q-mode analysis, 

several regional trends are apparent that substantiate the 

regional trends determined by examination of maps of supertownship means for individual elements. Soils in the southern 

part of the basin are mostly derived from fine-grained dolomitic 

marlstone of the Green River Formation and therefore contain 

relatively high concentrations of the carbonate-related elements 

calcium, magnesium, strontium, lithium, and total carbon, and 

low concentrations of the resistate elements silicon, aluminum, 

titanium, and beryllium. These soils also tend to be enriched in 

most trace elements because Green River marlstone contains 

higher trace-element concentrations than do underlying and 

overlying sandstones of the Wasatch and Uinta Formations. 

Soils in the central part of the basin are mostly derived from 

sandstone of the Uinta Formation and therefore tend to contain 

relatively high concentrations of silicon and low concentrations 

of carbonate-related elements and most trace elements. Soils in 

the northern part of the basin are also derived from sandstone of 

the Uinta Formation but are distinguished from soils in the central part of the basin by higher concentrations of calcium, 

magnesium, strontium, and sodium. The higher concentrations 

of these four carbonate-related elements in the northern part of 

the basin may be due to additions of dissolved salts from ground 

water and(or) greater aridity as indicated by lower precipitation, 

greater evapotranspiration, and higher temperature. 

4. Relative to soils in the Piceance Creek basin, oil shale of the Green 

River Formation is enriched in four of seven trace elements 

(arsenic, mercury, selenium, and fluorine) that are considered to 

be environmentally sensitive in areas being considered for oilshale development. In addition, oil shale contains elevated levels 

of molybdenum that could result in molybdenosis in certain 

ruminants grazing on forage grown on oil-shale spoils. This 

enrichment suggests that land reclamation in the Piceance Creek 

basin using oil shale as fill could produce an increase in several 

trace elements of environmental concern. 

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tr U.S. GOVERNMENT PRINTING OFFICE: 1979-677-129/10