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Southeastern Idaho

G E O L O G I C A L S W R V E Y p R 0 F E, S S ION A L S A P E B 1 (1 S $
Environmental Implications of 

Element Emissions from 

Phosphate-Processing Operations in 

Southeastern Idaho

By R. C. SEVERSON and L. P. GOUGH

GEOLOGICAL SURVEY PROFESSIONAL PAPER 1083

Element concentrations in plants and soils 

near point sources of contamination and 

their possible effects on plant 

and animal health

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

Severson, Ronald Charles, 1945-

Environmental implications of element emissions from phosphate-processing operations in southeastern Idaho.

Geological Survey Professional Paper 1083

Bibliography: p. 19

1. Soil pollution—Idaho. 2. Phosphate industry—Environmental-aspects—Idaho. 3. Plants, Effect of pollution on.

4. Trace elements. I. Gough, L. P., joint author. II. Title. III. Series: United States Geological Survey

Professional Paper 1083. 

TD878.S48 628.5'5 78-8949

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

Washington, D.C. 20402 

Stock Number 024-001-03196-5
CONTENTS

Abstract.....................

Introduction .................

Acknowledgments ............

Collection and analysis of data.

Sampling design........................

Sampling media ........................

Plants .............................

Soils...............................

Sample preparation and chemical analysis 

Statistical analysis .....................

Estimating element burden in soils .......

Page

1

1

1

1

1

2

2

3

3

4

5

Results of analyses ....................................

Significance of emission-related element distributions

Element concentrations in plants ...................

Element concentrations in soils .....................

Correspondence between elements in plants and soils . 

Environmental implications ............................

General impact of element emissions on health .......

Emission-related surface contamination of vegetation

Impact of element emissions on plant health .........

Impact of element emissions on animal health........

Summary .............................................

References cited .......................................

Page

6

6

7

11

12

14

14

16

17

17

18

19

ILLUSTRATIONS

FIGURE 1. Rose diagram showing annual wind-direction distribution at Pocatello and Soda Springs, Idaho.......................

2. Map showing location of sampling sites along transects near Pocatello and Soda Springs, Idaho.......................

3. Photograph of the elemental-phosphorus plant and the vegetation near Pocatello, Idaho..............................

4. Photograph of the diverse rocks and soils north of Soda Springs, Idaho .............................................

5. Graphs illustrating typical relations between element concentrations in a sampling medium and distance of sampling 

site from phosphate-processing operations ....................................................................

6. Diagram of major pathways in the natural migration of trace elements from phosphate-processing emissions ...........

Page 

2

2

3

5

7

16

TABLES

TABLE 1. Common and scientific plant names used in this report............................................................. 4

2. Analytical methods and their detection limits for element analyses in plants and soils ................................ 6

3. Detection ratios for selected elements that have some values below the limits of determination........................ 6

4. Regression statistics for elements and ash in plants sampled near Pocatello, Idaho ................................... 8

5. Regression statistics for elements and ash in plants sampled near Soda Springs, Idaho ............................... 8

6. Regression statistics for elements in soils sampled near Pocatello, Idaho ............................................ 9

7. Regression statistics for elements in soils sampled near Soda Springs, Idaho ........................................ 10

8. Comparison of the estimated concentrations of selected trace elements in big sagebrush, cheatgrass, and bluebunch

wheatgrass at distances of 3 km from phosphate-processing sites at Pocatello and Soda Springs, Idaho............ 11

9. Estimated element concentrations in surface soils northeast (downwind) of phosphate-processing sites near Pocatello,

Idaho, and average concentration of elements in soils as reported in the literature ................................ 13

10. Correlations between element concentrations in plants and in A-horizon soils along transects beginning near phosphateprocessing sites at Pocatello and Soda Springs, Idaho ......................................................... 13

11. Toxicity of selected elements to plants and animals ................................................................ 14

12. Chemistry of washed and unwashed bluebunch wheatgrass samples collected 2 km downwind from an elemental-phosphorus plant, Soda Springs, Idaho ........................................................................... 16

in

ENVIRONMENTAL IMPLICATIONS OF ELEMENT EMISSIONS FROM 

PHOSPHATE-PROCESSING OPERATIONS IN SOUTHEASTERN IDAHO

By R. C. SEVERSON and L. P. GOUGH

ABSTRACT

In order to assess the contribution to plants and soils of certain 

elements emitted by phosphate processing, we sampled sagebrush, 

grasses, and A- and C-horizon soils along upwind and downwind 

transects at Pocatello and Soda Springs, Idaho. Analyses for 70 

elements in plants showed that, statistically, the concentration of 7 

environmentally important elements, cadmium, chromium, fluorine, 

selenium, uranium, vanadium, and zinc, were related to emissions 

from phosphate-processing operations. Two additional elements, 

lithium and nickel, show probable relationships. The literature on 

the effects of these elements on plant and animal health is briefly 

surveyed. Relations between element content in plants and distance 

from the phosphate-processing operations were stronger at Soda 

Springs than at Pocatello and, in general, stronger in sagebrush 

than in the grasses. Analyses for 58 elements in soils showed that, 

statistically, beryllium, fluorine, iron, lead, lithium, potassium, 

rubidium, thorium, and zinc were related to emissions only at 

Pocatello and only in the A horizon. Moreover, six additional 

elements, copper, mercury, nickel, titanium, uranium, and 

vanadium, probably are similarly related along the same transect. 

The approximate amounts of elements added to the soils by the 

emissions are estimated. In C-horizon soils, no statistically significant relations were observed between element concentrations and 

distance from the processing sites. At Soda Springs, the nonuniformity of soils at the sampling locations may have obscured the 

relationship between soil-element content and emissions from 

phosphate processing.

INTRODUCTION

In phosphate processing, the mechanical operations 

of grinding, sorting, and drying, as well as the 

chemical and thermal processes of calcination and 

beneficiation, may release into the atmosphere the 

potentially toxic and therefore environmentally important elements cadmium, chromium, fluorine, lead, 

lithium, mercury, nickel, selenium, silver, uranium, 

vanadium, and zinc. Ore stockpiles, slag, and settling 

ponds can also serve as sources of wind-blown contaminants and, therefore, we consider processing sites 

and not strictly stack emissions as the source area of 

emitted elements. Except for fluorine (University of 

Idaho, 1955-74), no studies have been made to 

evaluate the contribution, if any, of these elements to 

local plants and soils in the southeastern Idaho

phosphate-processing areas. Therefore, we conducted 

this study in May 1975 to assess the impact of element 

emissions on selected plants and soils by comparing 

the element concentrations in samples with distance of 

sampling sites upwind and downwind from these 

operations at Pocatello and Soda Springs. Our second 

goal was to delineate zones of maximum influence 

along selected transects. Our final goal was to apply 

our findings to the analysis of potential, but unproven, 

effects of the emitted elements on the present and 

future health of plants, animals, and humans located 

within the zones of influence.

Interest in the health-related aspects of biologically 

active trace elements has expanded greatly during the 

past 30 years. Comparisons of data from the present 

study with examples of trace-element toxicity to 

plants and animals reported in the literature (for example, Gough and Shacklette, 1976) suggest the potential 

health effects of element emissions from phosphate 

processing.

ACKNOWLEDGMENTS

For preparing samples and performing chemical 

analyses, we thank David Bickford, W. E. Gary, Nancy 

M. Conklin, Johnnie M. Gardner, T. F. Harms, A. W. 

Haubert, R. G. Havens, Claude Huffman, Jr., L. M. 

Lee, R. E. McGregor, V. M. Merritt, H. T. Millard, Jr., 

C. S. E. Papp, Ida Price, L. B. Riley, V. E. Shaw, J. A. 

Thomas, R. E. Van Loenen, and J. S. Wahlberg. We 

also thank George VanTrump, Jr., for his valuable service in computer programing, J. J. Connor, A. T. 

Miesch, and H. T. Shacklette for their critical reviews 

and helpful suggestions throughout the study, and R. 

W. White (deceased) for assistance in mineral identification.

COLLECTION AND ANALYSIS OF DATA 

SAMPLING DESIGN

Sampling was designed to determine whether the element content of selected plants and soils decreased
ELEMENT EMISSIONS FROM PHOSPHATE-PROCESSING OPERATIONS IN SOUTHEASTERN IDAHO

systematically with increasing distance from processing sites at Pocatello and Soda Springs, and if so, 

which decreases could be demonstrated at an acceptable confidence level. Transects originating at and extending in two directions from the processing sites 

were established in relation to the predominant wind 

vectors (fig. 1). At Pocatello, the predominant winds 

are from the southwest; at Soda Springs, the frequency 

of winds from the southeast and northwest is about 

equal. Six points representing ideal sampling locations 

were selected along each transect at increasing 

geometric intervals away from the processing sites, 

the nearest point being 2 km, and the farthest, 64 km. 

The actual locations (fig. 2) deviated from the 

preselected points according to the availability of the 

desired sampling media. At each location, paired 

samples of two plants and two soil horizons were collected within 100 m of each other.

SAMPLING MEDIA

PLANTS

Sagebrush Steppe (Kiichler, 1964) is the dominant 

plant community in the eastern Snake River Plain near 

Pocatello and in the valleys around Soda Springs. The 

phosphate-processing sites are in this community, and 

ranching and dryland farming are also concentrated 

here. At Pocatello, we collected basin big sagebrush,

FIGURE 1.—Annual wind-direction distribution at Pocatello and Soda 

Springs, Idaho. Numbered scale indicates the percent frequency 

for a given wind direction. Modified from Cramer and Bowers, 

1974.

the dominant shrub, and cheatgrass, the dominant annual grass (fig. 3). (See table 1 for scientific names of 

plants cited by common name in this report.) At Soda 

Springs, the sagebrush we collected included some 

mountain big sagebrush and some basin big 

sagebrush. Because of differences in the overall 

climate of the two areas and because samples were collected in early spring, cheatgrass was not found at 

Soda Springs; however, bluebunch wheatgrass was 

abundant and was sampled. When we collected the latter samples, we could not positively identify the grass 

species because there were no diagnostic fertile culms. 

However, a return visit to the exact collecting sites by 

one of us (Gough) in September 1976 enabled us to confirm that the analysis samples were primarily 

bluebunch wheatgrass and some rough fescue.

Paired samples of sagebrush and grass were collected at each sampling location at both Pocatello and 

Soda Springs (fig. 2), except at the sampling location in 

an agricultural-residential area 3 km north of Pocatello 

where sagebrush was absent.

Samples included the terminal 8 to 12 cm of the 

stems (including leaves and flowers) of sagebrush at 

Pocatello and Soda Springs, the leaves, stems, and 

flowers of cheatgrass at Pocatello, and the leaves and 

stems of the bluebunch wheatgrass at Soda Springs. 

Only plants that appeared to be healthy were sampled.

113°00'

10 20 30 40 50 KILOMETERS

i i I I I

EXPLANATION

• Phosphate-processing sites

N-SA Sampling site—Number indicates approximate distance in 

kilometers from phosphate-processing sites

FIGURE 2.—Location of sampling sites along transects near Pocatello 

and Soda Springs, Idaho.
COLLECTION AND ANALYSIS OF DATA

FIGURE 3.—Elemental-phosphorus processing plant at Pocatello,Idaho. Vegetation in the foreground is almost exclusively big sagebrush 

(Artemisia tridentata) and cheatgrass (Bromus tectorum). Photographed May 16, 1975.

SOILS

Paired samples of both A- and C-horizon soils, defined according to traditional usage (U.S. Department of 

Agriculture, 1962, p. 173-188), were collected at each 

sampling location. The paired samples from the C 

horizon were combined at each location in the field to 

yield one sample. The A horizon was sampled by 

removing plant debris from the surface of the ground 

and excavating with a garden spade to a depth of 

about 5 cm. The C horizon was sampled by hand augering to the necessary depth and saving, to the extent 

possible, the centermost part of the auger core. Depth 

of sampling of this horizon was controlled by the 

character of the soil at individual sites—at places 

samples were as shallow as 50 cm where the underlying 

rock was near the surface or where the composition of 

the parent material changed. Most C-horizon samples 

were from depths of 80 to 100 cm.

Soils sampled along the transects near Pocatello are 

remarkably uniform. These soils developed from 

relatively unweathered aeolian sediments derived from

the Snake River flood plain, and they range in 

thickness from less than 50 to more than 200 cm 

(Lewis and others, 1975). Conversely, the soils near 

Soda Springs are highly variable in character (Youngs 

and others, 1925). Those sampled had developed on 

such varied parent materials as limestone residuum, 

aeolian sediments, basalt, valley-fill material of stream 

origin, and colluvium (fig. 4).

SAMPLE PREPARATION AND 

CHEMICAL ANALYSIS

Plant samples were cleaned, pulverized, and dry ashed. Cleaning consisted of agitation in tap water, followed by ultrasonic agitation in deionized distilled water. 

After cleaning, the samples were dried in an oven at 

about 38° C and were then pulverized in a Wiley mill to 

pass through a 1.3-mm-mesh screen. The pulverized 

samples were dry ashed in a muffle furnace at 500° C 

for about 24 hours, and the ash was used for analysis of 

most elements. Pulverized samples were prepared for 

fluorine and selenium analysis by wet digestion.
ELEMENT EMISSIONS FROM PHOSPHATE-PROCESSING OPERATIONS IN SOUTHEASTERN IDAHO

TABLE 1.—Common and scientific plant names used in this report

Common name Scientific name

Alfalfa..... 

Anemone .. 

Barley ..... 

Bean, navy. 

Beet.......

Bluegrass ... 

Bog bilberry 

Cabbage .... 

Cheatgrass . 

Corn........

Citrus .......

Fescue, rough 

Fireweed.....

Gumweed ....

Lettuce ......

Oats................

Pea.................

Poppy ..........

Pnncesplume .......

Sagebrush, basin big.

Sagebrush, mountain big.

Soybean . 

Sweetpea 

Tomato .. 

Turnip...

Violet .................

Wheat .................

Wheatgrass, bluebunch .

Medicago sativa L. 

Pulsatilla patens (L.) Mill. 

Hordeum vulgare L. 

Phaseolus vulgaris L. 

Beta vulgaris L.

Poa sp.

Vaccinium uliginosum L.

Brassica oleracea var. capitata L.

Bromus tectorum L.

Zea mays L.

Citrus sp.

Festuca scabrella Torr. 

Epilobium angustifolium L. 

Grindelia aphanactis Rydb. 

Lactuca sativa L.

Avena sativa L.

Pisum sativum L.

Papaver macrostomum B. et H.

Stanleya pinnata (Pursh) Britt.

Artemisia tridentata Nutt. subsp.

tridentata 

Artemisia tridentata subsp.

vaseyana (Rydb.) Beetle 

Glycine max Merr. 

Lathyrus sp.

Lycopersicum esculentum Mill. 

Brassica napus L.

Viola sp. 

Triticum spp.

Agropyron spicatum (Pursh) Scribn. 

and Smith

Analyses of these two elements are expressed on a dryweight basis, whereas all other analyses are expressed 

on an ash-weight basis.

The mineralogy of the wash residue from samples of 

bluebunch wheatgrass, collected 2 km northwest 

(downwind) of the elemental-phosphorus plant at Soda 

Springs, was examined by X-ray diffraction techniques. The dry residue was ground using a mortar and 

pestle and the resulting powders were X-rayed on a diffractometer using CuKtt radiation. Diffraction patterns 

were recorded for each powder over a 28 range of 2° to 

about 55° at a rate of 2° per minute.

Soil samples were dried under forced air at ambient 

temperature, and the dried material was passed 

through a 2-mm-mesh sieve. The minus-2-mm fraction 

was further ground to pass a 100-mesh sieve (150 /urn 

openings), and this material was used for all analytical 

determinations.

All sample preparations and analytical determinations for both plant and soil materials were done in 

U.S. Geological Survey laboratories at Denver, Colo. 

The analyses of plant materials followed the procedures for specific analytical methods, other than 

emission spectrography, described by Harms and 

Papp (1975). Soil analyses followed the X-ray 

fluorescence procedures described by Wahlberg (1975),

the neutron activation procedures described by 

Millard (1975), and the atomic absorption spectroscopy procedures described by Huffman (1975). 

Meyers, Havens, and Dunton (1961) described the 

emission spectrography analytical technique used for 

both plants and soils. Table 2 lists the analytical 

method and the detection limit for each element in each 

sampling medium.

STATISTICAL ANALYSIS

The error associated with sample preparation and 

analysis was evaluated by examining duplicate 

analytical determinations from splits of samples that 

were randomly selected from the complete sample 

suite. All samples of plants or soils, including the 

splits, were then placed in a randomized sequence and 

submitted for analysis. Analysis of the samples in this 

sequence randomizes any systematic bias that may 

result from the analytical and sample-handling procedures. Analysis of variance was then used to 

estimate the relative error variance due to sample 

preparation and analysis, according to the technique of 

Miesch (1967). Sample preparation and analysis caused excessive error variance in the data for lead and 

molybdenum in plants and for antimony, arsenic, 

barium, boron, germanium, lanthanum, phosphorus, 

scandium, strontium, yttrium, and zirconium in soils 

(Severson and Gough, 1976); these elements were not 

examined further.

Some elements in plant and soil material were not 

detectable by the analytical methods used. If an element was not detectable in one-fourth or more of the 

total number of plant or soil samples, the data were not 

examined further in this study. If an element was not 

detected in only a few samples of either plants or soils 

(table 3), the concentration of the element in these 

samples was considered to be equal to seven-tenths of 

the lower detection limit. (The justification for these 

substitutions is discussed by Severson and Gough, 

1976.)

Variations of element concentrations in plants and 

soils, relative to distance from each phosphateprocessing site, were examined by linear regression. 

The regression model was of the form:

log.oX= 60+WogioD

where X is the concentration of the element, D is the 

distance from the processing site, and 60 and bi are, 

respectively, the regression constant and regression 

coefficient. The statistical significance of each regression was determined by analysis of variance procedures (for example, Davis, 1973, p. 192-204). Coefficients of determination between log element concentration and log distance provide estimates of the propor-
COLLECTION AND ANALYSIS OF DATA

- i

it-*,

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IE 4.—Diverse rocks and soils characteristic of the area north of Soda Springs, Idaho. Loess agricultural soils (foreground) as well as 

>ils derived from the weathering of basalt (middleground) and limestone (background) are common. Photographed May 18, 1975.

r»f hV»p» t-.rtt.fll vnrinnr»o in r'rvnr-cmtrnfirvn that ic r»ronsinor rlist.«nr»fi frrtm a rtrnf*fissinor rtnfiratirtn alnncr tions of the total variance in concentration that is 

associated with distance from the processing sites. The 

covariation between logarithms of element content in 

plant and soil samples among the sample locations 

along each of the transects was estimated by the 

product-moment correlation coefficient.

Figure 5 illustrates the types of relations between 

concentrations and distances that were observed along 

the transects upwind and downwind of the processing 

sites. Figure 5A shows element concentration in a 

single sampling medium decreasing with increasing 

distance from the processing site along both upwind 

and downwind transects. The regressions that describe 

these relations are statistically significant at a probability level of 0.05. This type of relation provides 

strong evidence that the elevated element concentrations in a sampling medium close to the processing site 

are related to emissions. We consider these relations to 

be "significant."

Figures 5B and 5C show element concentrations in a 

sampling medium decreasing significantly with increasing distance from a processing operation along 

only one transect; the other transect shows a nonsignificant increase or decrease in element concentration with distance. This distribution pattern is probably related to element emissions from the processing 

site and is considered "important."

Figures 5D and 5E show element concentrations increasing significantly with increasing distance from 

the processing site along one transect. An increase in 

concentration with distance does not describe accumulation of wind-related emissions from processing 

operations; therefore, these relations are "unimportant" in this study and probably are intrinsic to the 

natural environment.

ESTIMATING ELEMENT BURDEN IN SOILS

We estimated the approximate concentrations of 

elements added to the soil by phosphate-processing 

operations along transects showing statistically 

significant relations between element content in soil
ELEMENT EMISSIONS FROM PHOSPHATE-PROCESSING OPERATIONS IN SOUTHEASTERN IDAHO

TABLE 2.—Analytical methods and their detection limits 

for element analysis in soil plants and soils

[Elements in plants determined on ash, except as indicated. LLD, lower limit 

of determination expressed in parts per million; AA, atomic-absorbtion 

spectroscopy; COL, colorimetric; FLU, fluorimetric; IF, induction furnace; 

NA, neutron activation; SIE, selective ion electrode; SQS, multielement 

semiquantitative spectrography; and XRF, X-ray fluorescence, (...), not 

determined]

Elements

Al ........

B..........

Ba ........

Be .........

C..........

Ca .........

Cd ........

Co .........

Cr .........

Cu ........

F1 .........

Fe .........

Ga ........

Hg ........

K .........

Li .........

Mg ........

Mn ........

Na ........

Nb ........

Ni .........

P..........

Pb ........

Rb ........

Se1 ........

Si .........

Sn .........

Sr .........

Th ........

Ti .........

U .........

V..........

Yb ........

Zn .........

Zr .........

Plant;

Method

SQS ......

SQS ......

SQS ......

AA .......

AA .......

SQS ......

SQS ......

SIE ......

SQS ......

AA .......

SQS ......

SQS ......

AA .......

SQS ......

COL ......

SQS ......

FLU ......

AA .......

SQS ......

SQS ......

FLU ......

SQS ......

SQS ......

AA .......

SQS ......

3

LLD

100

90

2

.2

1

1

1

1

10

4

20

1

25

5

100

10

.01

100

5

2

.4

7

1

20

10

Sc

Method

XRF . . . . .

cn<3

SQS . . . . .

SQS . . . . .

IF .......

XRF

SQS .....

SQS .....

SQS . . . . .

SIE .....

XRF

SQS . . . . .

AA ......

XRF .....

AA ......

XRF .....

XRF .....

AA ......

SQS .....

SQS .....

XRF .....

SQS .....

AA ......

XRF .....

XRF .....

XRF .....

SQS .....

NA ......

XRF .....

NA ......

SQS .....

SQS .....

AA ......

SQS

>ils

LLd

. 10,000

9O

2

1

500

. 1,000

3

1

1

10

. 1,000

5

.01

. 1,000

5

. 300

1

100

10

5

. 2,000

10

1

.1

. 10,000

10

5

1

2

.1

7

10

10

10

'Determined on dried plant material.

TABLE 3.—Detection ratios for selected elements in plants and soils 

at Pocatello and Soda Springs, Idaho, that have some values 

below the limits of determination

[Detection ratio, number of samples in which the element was found in measurable concentrations relative to the number of samples analyzed; leaders (..), element not included in 

the study of a given sampling medium]

Element

Be .....

Cd ....

Co .....

Hg....

Mn . . . .

Mo ....

Nb ....

Ni .....

Pb ....

Zr .....

Big 

sagebrush

48:48

48:48

48:48

48:48

48:48

48:48

48:48

Plants

Cheatgrass

19:24

21:24

24:24

23:24

16:24

23:24

21:24

Bluebunch 

wheatgrass

26:26

21:26

26:26

26:26

21:26

26:26

22:26

So

A horizon

49:50

50:50

50:50

50:50

50:50

49:50

50:50

50:50

ils

C horizon

22:25

25:25

22:25

23:25

24:25

22:25

25:25

25:25

and distance. The estimation method was first used by 

Miesch and Huffman (1972) and was described in 

detail, as applied to this study, by Severson and 

Gough (1976). Using the processing site as the center 

of the circle of influence, the estimation is made by integrating the regression equation over a pie-shaped 

segment of the circle between two distances from the 

processing site.

RESULTS OF ANALYSES

SIGNIFICANCE OF EMISSION-RELATED

ELEMENT DISTRIBUTIONS

Tables 4, 5, 6, and 7 present results for all elements 

included in this study in both plants and soils along 

upwind and downwind transects at Pocatello and Soda 

Springs. The first two columns under a sampling 

medium contain estimates for the intercept (60) and 

slope (61) of the regression line for a specific constituent. A negative slope indicates that the element concentration tends to decrease with increasing distance 

from the processing site. Conversely, a positive slope 

indicates a tendency for the element concentration to 

increase with distance.

The third column of tables 4, 5, 6, and 7 shows the 

probability that the slope of the regression line is actually zero. We consider as significant a probability of 

0.05. The fourth column in the tables presents the probability that the departure of the data from the linear 

regression model could have arisen by chance. Probabilities greater than the critical value of 0.05 indicate that the linear model is not appropriate at this 

level of significance.

The fifth column gives the coefficient of determination between element concentration in each sampling 

medium and distance. This value is a measure of the 

proportion of the total variation in the dependent 

variable, in this case, log element concentration, that is 

accounted for by the regression. For example, a value 

of 0.75 indicates that 75 percent of the observed variation in element concentration is accounted for simply 

by distance from the processing site.

The following classes were established to determine 

important relations between concentration of an element in a sampling medium and emissions from 

phosphate-processing operations: (1) "Significantly 

related," where negative regression slopes were significant at a probability of 0.05 for both downwind and upwind transects and a coefficient of determination equal 

to or greater than 0.50 was observed for one of the two 

transects; (2) "of probable importance," where a 

significant negative regression slope and a coefficient 

of determination equal to or greater than 0.50 were
en 0

o

z 

o 

o

CD

o

Not significant

50 10

RESULTS OF ANALYSES

Upwind Downwind

Significant Significant

50 10 1 10 50

DISTANCE, IN KILOMETERS

A

Significant Significant Not significant

10 50 50 10 10 50

Not significant

50 10

Significant Significant Not significant

D

10 50 50 10 

DISTANCE, IN KILOMETERS

10 50

FIGURE 5.—Typical relations between element concentrations in a sampling medium and distance of sampling site 

from phosphate processing operations. Trend line significance noted in figure. A, Important and related to 

phosphate processing. B, C, Important and probably related to phosphate processing. D, E, Not related to 

phosphate processing.

observed for one of the two transects. Also considered 

to be important in soils were a few elements for which 

the regression was significant at a probability level of 

0.10 or whose coefficients of determination were less 

than 0.50. The reasons for these exceptions will be 

discussed under "Element concentrations in soils."

ELEMENT CONCENTRATIONS IN PLANTS

Of the 23 elements listed in table 4, six in sagebrush 

at Pocatello (cadmium, chromium, fluorine, uranium, 

vanadium, and zinc) possessed significant negative 

regression slopes and acceptable coefficients of determination along both downwind and upwind transects. 

These elements, therefore, meet our criteria as being

significantly related to phosphate-processing operations. Selenium probably should also be added to this 

list because the downwind regression slope was significant and the upwind slope was important at the 0.07 

probability level. Two additional elements, nickel and 

phosphorus, are of probable importance because they 

possess significant negative slopes as well as acceptable coefficients of determination along only one—the 

downwind—transect.

Selenium is the only element in cheatgrass that 

qualified as being significantly related to phosphate 

operations near Pocatello. Chromium and vanadium 

may be considered important as they possessed significant negative slopes for either the downwind or up-
TABLE 4.—Regression statistics for elements and ash in plants sampled near Pocatello, Idaho

significance of the slope is the probability level at which the slope is different from zero; significance of the model is the probability level at which the linear regression mod

the relation; coefficient of determination is an expression of the proportion of the total variation in the dependent variable, element concentration, that is accounted for by 

Northeast (downwind) transect

Big sagebrush

Regression estimates

Constitui

Ash' 2 .. 

Al' .....

B ......

Ba .....

Cd .....

Co .....

Cr .....

Cu .....

F' 2 ...

Fe' .....

Li .....

Mg .....

Mn .....

Na' .....

Ni .....

P .....

Se 2 ...

Si .....

Sr .....

Ti' ...

U .....

V .....

Zn .....

Zr .....

3nt 60

..... 0.822 

.... .369

.... 2.55

.... .526

.... 2.98

.... 1.82

.... 1.55

.... .762

1 91

.... 4.64

.... 4.81

.... 3.32

.... 3.11

.... 1.92

6,

-0.062 

.050 

-.025 

.168 

-.865 

-.075

-.792 

.179 

-1.14 

.011 

-.163 

-.004

.033 

-.096 

-.477 

-.179 

-.536 

.040

-.029 

.086 

-.780 

-.794 

-.310 

.013

Cheatgrass

Significance Coefficient of Regression estimates

slope

0.21 

.56 

.74 

.01

!52

.12

.81 

.10 

.91

.56 

.10 

.01 

.01

!64

.25 

.24

!89

model determination ba

0.82 

.12 

.16 

.12 

.86 

.69

.60 

.28 

.96 

.25 

.28 

.69

.01 

.66 

.39 

.20 

.62 

.64

.12 

.37 

.80 

.66 

.53 

.70

0.18 

.04 

.01 

.58 

.96 

.06

.85 

.27 

.84

.30

.04 

.30 

.81 

.61 

.88 

.03

.16 

.17 

.94 

.85 

.91

0.938 

-.482 

1.92 

2.47 

1.25 

-.105

1.91 

1.73 

-1.89 

-.744 

.506 

.228

2.80 

.189 

1.03 

4.43 

-.462 

5.34

2.76 

-2.13 

.682 

1.96 

2.58 

1.08

6,

-0.053 

.334 

.016 

.104 

-.633 

.322

-.442 

.039 

-.562 

.282 

.114 

-.059

-.060 

-.032 

-.116 

-.202 

-.427 

.124

-.125 

.509 

-.331 

-.375 

-.146 

.323

Southwest (upwind) transect

Big Sagebrush

Significance Coefficient of Regression estimates

slope

0.19 

.06 

.82 

.26 

.04 

.04

.03 

.40

!02 

.32 

.57

.48 

.72 

.53 

.01

ill 

.32

'.05 

.01 

.08 

.15

model determination ba

0.90 

.41 

.03 

.34 

.69 

.14

.12 

.07 

.97 

.91 

.02 

.36

.56 

.70 

.92 

.61 

.32 

.99

.39 

.59 

.94 

.73 

.19 

.26

0.16 

.31

!l2 

.35 

.35

.40 

.07 

.62 

.43 

.10 

.03

.05 

.01 

.04 

.57 

.70 

.24

.10 

.59 

.34 

.29 

.27 

.19

0.698 

.412 

2.46 

2.71 

1.72 

.535

2.08 

1.88 

-2.24 

.106 

1.40 

.423

2.80 

.756 

1.22 

4.32 

-.944 

5.01

3.09 

-.871 

.633 

2.08 

2.81 

2.05

b,

0.053 

-.029 

.065 

.019 

-.748 

-.020

-.397 

.069 

-.273 

-.065 

-.061 

.035

.007 

-.007 

-.025 

.035 

-.154 

-.045

.143 

-.045 

-.332 

-.225 

-.189 

-.081

Significance Coefficient of Regression 

slope

0.20 

.72 

.51 

.80

'.86

'.36

.31 

.60 

.36

.88 

.89 

.69 

.33 

.07 

.39

.14 

.66

!03 

.01 

.46

model determination ba 

0.48 

.07 

.04 

.52 

.78 

.07

.89 

.15 

.21 

.01

.87 

.18

.02 

.60 

.02 

.11 

.59 

.34

.50 

.29 

.37 

.22 

.99 

.16

0.16 

.01 

.05 

.01 

.90 

.02

.76 

.09 

.60 

.08 

.03 

.08

!02 

.10 

.28 

.08

.20 

.02" 

.74 

.39 

.54 

.06

0.798 

.116 

2.21 

2.84 

.954 

.479

1.46 

1.68 

-2.59 

-.298 

.795 

.201

2.88 

.356 

1.16 

4.13 

-.948 

5.52

2.65 

-1.48 

.473 

1.65 

2.47 

1.57

'Percent

determined on the dried material

TABLE 5.—Regression statistics for elements and ash in plants sampled near Soda Springs, Idaho

[Regression equations are based on element concentrations expressed in parts per million of the ashed material, except as indicated; 60 is the intercept and 6, is the slope of 

significance of the slope is the probability level at which the slope is different from zero; significance of the model is the probability level at which the linear regression mod

the relation; coefficient of determination is an expression of the proportion of the total variation in the dependent variable, element concentration, that is accounted for by 

Northwest (downwind) transect

Big sagebrush

Regression estimates

Constituent 60

Ash12 ..

B ......

Cd .....

Co .....

Cr .....

Cu .....

F' 2 ...

Fe' .....

Li .....

Mg .....

Mn .....

Na' .....

Ni .....

P .....

Se 2 ...

Si .....

Sr .....

Ti' ...

U .....

V .....

Zn .....

Zr .....

..... 0.690

.... 2.56

.... 2.41

.... 2.78

.... .039

.... .780

.... 2.60

.... 1.56

4 gg

.... 4.98

.... 1.78

b,

-0.117 

-022 

.123 

.176 

-.643 

-.009

-.736 

.285 

-1.25 

-.117 

-.834 

-.198

.173 

-.325 

-.220 

-.191 

-1.04 

-.327

-.075 

-.115 

-.496 

-1.11 

-.559 

-.132

Significance Coefficient of

slope

0.01 

.70 

.43 

.24 

.01 

.91

!os

!l3 

!28

.25 

.02

!02 

'.28

.48 

.38

.38

Bluebunch Wheatgrass

Regression estimates

model determination b0 6,

0.61 

.28 

.76 

.22 

.93 

.12

.67 

.11 

.99 

.59 

.46 

.26

.65 

.32 

.01 

.14 

.91 

.16

.20 

.43 

.96 

.77 

.96 

.39

0.47 

.02 

.06 

.14 

.50

.63 

.28 

.86 

.21 

.79 

.11

.13 

.45 

.61 

.45 

.79 

.12

.05 

.08 

.73 

.85 

.56 

.08

0.880 

-.365 

1.69 

2.56 

2.34 

.113

2.00 

1.88 

-2.14 

-.491 

.524 

-.146

2.27 

.017 

1.35 

3.85 

.340 

5.66 

1.99

-1.70 

.874 

2.58 

3.16 

1.16

-0.092 

.276 

.203 

.101 

-.937 

.101

-.439 

.024 

-.431 

.161 

-.099 

.127

.298 

.151 

-.199 

.189 

-.911 

-.063 

.372

.174 

-.354 

-.725 

-.442 

.279

Southeast (upwind) transect

Big Sagebrush

Significance Coefficient of Regression estimates

slope model d(

0.22 

.18 

.08 

.50

.11

.02 

.81

'.30 

.48 

.22

.03 

.35 

.10 

.19

'.08 

.05

.39

!oi

.15

0.40 

.99 

.05 

.52 

.42 

.10

.68 

.37 

.83 

.67 

.25 

.02

.59 

.19 

.31 

.31 

.97 

.44 

.12

.78 

.32 

.19 

.60 

.74

stermination ba 6,

0.19 

.17 

.26 

.05 

.60 

.01

.45 

.01 

.60 

.11 

.05 

.15

.39 

.09 

.25 

.16 

.84 

.27 

.32

.08 

.70 

.70 

.56 

.19

0.623 

.141 

2.51 

2.46 

1.78 

.357

2.37 

1.92 

-2.36 

-.108 

.807 

.448

2.66 

.366 

1.38 

4.45 

-.660 

4.54

3.00 

-1.30 

.671 

2.41 

2.95 

1.48

-0.008 

.007 

.084 

.139 

-.428 

-.099

-.634 

.100 

-.396 

-.118 

.193 

.024

.087 

.004 

-.119 

.007 

-.291 

-.031

.206 

-.054 

-.334 

-.486 

-.146 

-.010

Significance Coefficient of Regression 

slope model determination ba 

0.84 

.86 

.31 

.11

^25

^28

!07 

.17 

.72

.23 

.92 

.15 

.87 

.01 

.69

.07 

.42

C01 

.02 

.86

0.78 

.23 

.75 

.67 

.97 

.18

.66 

.14 

.42 

.30 

.25 

.07

.09 

.96 

.12 

.20 

.93 

.98

.88 

.60 

.93 

.96 

.92 

.32

<0.01

'.09 

.19 

.54 

.11

.82 

.10 

.55 

.24 

.15 

.01

.12 

.16

.48 

.01

.25 

.06 

.66 

.63 

.36

0.892 

-.165 

1.83 

2.57 

1.55 

.271

1.69 

1.79 

-2.37 

-.435 

.413 

-.100

2.61 

-.011 

.833 

4.12 

-.299 

5.58

2.48 

-1.69 

.546 

2.19 

2.70 

1.23

'Percent

2Determined on dried material
TABLE 6. — Regression statistics for elements in soils sampled near Pocatello, Idaho

e regression equat

del adequately desc

r by the linear reg

Northeast (downwind) transect

A horizon

Regression estimates

Element 60 b,

Al'.. . 

Be .....

Ca' ...

Co ....

Cr ....

Cu ....

F' ....

Fe' ....

Ga ....

Hg ....

K' ....

Li ....

Mg' ...

Mn 1 ..,

Nb ....

Ni ....

Pb ....

Rb ...

Si' ...

Sn ....

Th ....

U ....

V ....

Yb ....

Zn ....

...... 0.726

..... .158

..... .876

..... -.936

..... 1.18

..... 1.42

......-1.15

..... 1.20

..... 1.48

..... 1.51

..... 1.17

— 489

..... 2.07

..... 2.12

-0.011 

.-132 

.029 

.097 

-.022

-.116 

-.070 

-.248 

-.072 

0 

-.228

-.045 

-.103 

.038 

-.093 

-.010 

-.017

-.143 

-.172 

-.110 

.010 

.052 

-.155

-.063 

-.100 

-.167 

-.034 

-.236

Significance Coefficient of Regression estimates

slope model determination &„

0.56 

.01 

.73 

.44 

.22

.21 

.10 

.01 

.01 

.99 

.05

.01 

.01 

.47 

.11 

.19 

.22

.07 

.01 

.01 

.59 

.64 

.01

.02 

.04 

.07 

.71 

.01

0.07 

.02 

.92 

.93 

.08

.07 

.27 

.08 

.11 

.99 

.01

.44 

.85 

.91 

.28 

.20 

.08

.54 

.29 

.50 

.84 

.03 

.02

.89 

.31 

.88 

.42 

.99

0.04 

.58 

.01 

.06 

.22

.23 

.24 

.60 

.51

'.33

.76 

.64 

.06 

.24 

.11 

.22

.27 

.75 

.58 

.03 

.02 

.71

.49 

.35 

.28 

.01 

.76

0.626 

.186 

.382 

.804 

.845

1.71 

1.18 

-1.19 

.204 

1.18 

-1.78

.179 

1.31 

.304 

-1.57 

.019 

1.18

1.15 

1.26 

1.77 

1.46 

.071 

.965

-.591 

.505 

1.82 

.433 

1.75

6,

0.045 

.008 

-.049 

-.049 

0

.085 

0 

-.035 

.008 

0 

-.058

.023 

-.019 

-.086 

.146 

-.052 

0

.031 

-.016 

.016 

.023 

.135 

-.011

.004 

-.033 

.039 

.014 

-.005

C horizon

Significance

Southwest (upwind) trans

A horizon

Coefficient of Regression estimates

slope model determination &0 b t

0.35 

.87 

.32 

.41 

.99

.05 

.99 

.70 

.80 

.99 

.74

.39 

.72 

.14 

.17 

.14 

.99

.18 

.75 

.44 

.13 

.36 

.81

.85 

.51 

.18 

.82 

.86

0.22 

.01 

.24 

.18

.65

'. '.04 

.02

! .'03

.19 

.04 

.45 

.40 

.44

.39 

.03 

.10 

.46 

.21 

.01

.01 

.18 

.39 

.01 

.01

0.763 

.260 

.121 

.232 

.845

1.82 

1.23 

-1.28 

.340 

1.20 

-1.65

.300 

1.35 

-.019 

-1.06 

-.015 

1.19

1.26 

1.31 

1.88 

1.52 

.186 

1.14

-.464 

.496 

1.87 

.613 

1.92

-0.018 

-.055 

.061 

.110 

0

.001 

.008 

.033 

-.048 

-.011 

-.137

-.029 

-.023 

.046 

-.074 

-.005 

.006

-.016 

.020 

-.032 

.007 

-.022 

-.063

-.045 

-.029 

-.013 

-.075 

-.058

Significance Coefficient of Regression 

slope

0.34 

.51 

.36 

.28 

.99

.92 

.85 

.52 

.01 

.69 

.29

.02 

.20 

.22 

.08 

.69 

.84

.73 

.59 

.20 

.55 

.74 

.13

.05 

.43 

.69 

.40 

.01

model determination &„

0.12 

.07 

.79 

.79 

.99

.82 

.11 

.64 

.09 

.23 

.30

.68 

.58 

.72 

.19 

.66 

.18

.13 

.17 

.08 

.06 

.01 

.01

.17 

.13 

.23 

.14 

.14

0.09 

.05 

.08 

.12

.04 

.53 

.02 

.11

.42 

.16 

.14 

.26 

.02

.02 

.15 

.04 

.01 

.21

.33 

.06 

.02 

.08 

.50

0.695 

.136 

.115 

.561 

.835

1.75 

1.25 

-1.18 

.274 

1.18 

-1.83

.242 

1.30 

.164 

-1.21 

.029 

1.16

1.18 

1.14 

1.82 

1.48 

.028 

1.16

-.542 

.427 

1.84 

.466 

1.79

'Percent
TABLE 1 .—Regression statistics for elements in soils sampled near Soda Springs, Idaho

[Regression equations are based on element concentrations expressed in parts per million, except as indicated; ba is the intercept and 6, is the slope of the regression equati

slope is the probability level at which the slope is different from zero; significance of the modelis the probability level at which the linear regression model adequately descr

ficient of determination is an expression of the proportion of the total variation in the dependent variable, element concentration, that is accounted for by the linear reg

(.....), no data available]

Northwest (downwind) transect

A horizon

Regression estimates

Element ba

Al 1 .. .. 

Be .....

C' ....

Ca 1 ...

Co ....

Cr ....

Cu ....

F 1 ....

Fe' ....

Ga ....

Hg ....

K 1 ....

Li ....

Mg' ...

Na' ...

Nb ....

Ni ....

Pb ....

Rb ...

Si 1 ...

Sn ....

Th ....

Ti' ..

U ....

V ....

Yb ....

Zn ....

..... 0.660

..... .784

..... 1.25

— 969

..... .236

.....-1.68

..... .237

...... .299

..... 1.15

..... 1.19

97/1

..... .256

..... 1.96

6,

0.117 

.155 

-.277 

-.670 

.022

.116 

.183 

-.243 

.105 

.033 

.026

.060 

.014 

-.421 

.198 

.154 

.098

.180 

.172 

.167 

.105 

-.142 

.123

.100 

.102 

.032 

.215 

.031

Significance Coefficient of Regression estimates

slope model determination ba

0.04 

.18 

.08 

.01 

.73

.24 

.02 

.02 

.08 

.68 

.75

.55 

.02 

.02 

.01 

.20

.10 

.08 

.03 

.01 

.21 

.03

.03 

.05 

.47 

.01 

.52

0.51 

.14 

.81 

.96 

.32

.07 

.82 

.93 

.70 

.58 

.01

.35 

.42 

.89 

.59 

.92 

.41

.49 

.64 

.81 

.60 

.04 

.83

.72 

.04 

.15 

.26 

.12

0.35 

.17 

.27 

.50 

.01

.13 

.46 

.45 

.27 

.02 

.01

.50 

.04 

.44 

.41 

.46 

.16

.25 

.27 

.39 

.48 

.15 

.40

.37 

.33 

.04 

.59 

.04

0.397 

-.224 

1.06 

1.48 

.714

1.44 

1.10 

-1.17 

.014 

1.10 

-2.25

.046 

1.36 

.432 

-1.82 

-.407 

.848

.980 

1.19 

1.64 

1.22 

-.226 

.806

-.732 

.157 

1.52 

.208 

1.64

6,

0.320 

.394 

-.806 

-.846 

.109

.324 

.095 

-.028 

.269 

.131 

.441

.165 

.047 

-.384 

.513 

.282 

.300

.315 

.093 

.172 

.233 

.334 

.222

.208 

.261 

.273 

.224 

.158

C horizon

Significance

Southeast (upwind) trans

A horizon

Coefficient of

slope model determination

0.12 

.02 

.06 

.08 

.14

.05 

.54 

.73 

.10 

.14 

.06

.03 

.40 

.16 

.09 

.04 

.10

.17 

.14 

.33 

.06 

.05 

.13

.11 

.03 

.14 

.14 

.19

0.49 

.76 

.63 

.57 

.46

.65 

.10 

.03 

.53 

.46 

.63

.72 

.18 

.43 

.54 

.68 

.52

.41 

.46 

.23 

.63 

.67 

.47

.50 

.75 

.46 

.46 

.34

Regression estimates

60

0.750 

.293 

.763 

.747 

.923

1.68 

1.34 

-1.10 

.356 

1.25 

-1.56

.235 

1.48 

-.059 

-.950

I.'l2

1.34 

1.39 

1.93 

1.35 

.199 

1.10

-.540 

.346 

1.80 

.422 

2.00

6,

-0.021 

-.011 

-.268 

-.352 

-.055

-.014 

.021 

-.078 

-.041 

-.015 

-.106

.023 

-.087 

-.061 

-.093 

.006 

.091

-.095 

-.023 

-.007 

.104 

-.041 

.019

.018 

.030 

.012 

.046 

-.085

Significance Coefficient of Regression 

slope model determination ba

0.65 

.84 

.06 

.14 

.36

.84 

.71 

.13 

.46 

.78 

.03

.63 

.03 

.05 

.10 

.87 

.24

.23 

.70 

.87 

.07 

.67 

.74

.70 

.61 

.71 

.67 

.04

0.96 

.68 

.97 

.97 

.22

.97 

.47 

.77 

.90 

.99 

.01

.99 

.35 

.48 

.08 

.99 

.71

.91 

.63 

.94 

.98 

.39 

.85

.94 

.95 

.10 

.94 

.56

0.02

^27 

.17 

.07

'.01

.18 

.05 

.01 

.34

.02 

.34 

.27 

.21

.11

.12 

.01

!25 

.02 

.01

.01 

.02 

.01 

.02 

.30

0.498 

-.151 

.872 

1.20 

.529

1.45 

1.11 

-1.12 

.115 

1.11 

-1.94

-.039 

1.30 

-.060 

-1.55 

-.640 

.951

.906 

1.09 

1.53 

1.16 

.275 

1.07

-.727 

.094 

1.39 

.129 

1.72

'Percent
RESULTS OF ANALYSES 11

wind transect; they have, however, lower than acceptable coefficients of determination.

At Soda Springs (table 5), seven elements in 

sagebrush (cadmium, chromium, fluorine, selenium, 

uranium, vanadium, and zinc) are significantly related 

to phosphate-processing operations. Lithium and 

nickel show significant negative regression slopes and 

acceptable coefficients of determination along the 

downwind transect but not along the upwind transect; 

therefore, they are considered to be importantly 

associated with the processing operations. Phosphorus 

and sodium may also be important for they too show 

significance only along the downwind transect; 

however, they lack sufficiently high coefficients of 

determination.

In the bluebunch wheatgrass samples, cadmium, 

fluorine, selenium, uranium, vanadium, and zinc are 

significantly related to the phosphate-processing 

operations. The only other element of possible importance is chromium; however, along the upwind 

transect its negative regression slope is not significant, whereas along the downwind transect its 

negative slope is significant but its coefficient of determination is unacceptably low.

Overall, seven elements in plant tissue (cadmium, 

chromium, fluorine, selenium, uranium, vanadium, and 

zinc) are significantly associated with phosphateprocessing operations. Two additional elements, 

lithium and nickel, show frequent importance. 

Phosphorus and sodium may also reflect the impact of 

the phosphate operations, but unlike the first nine 

elements, which demonstrate importance along several 

transects and in a variety of plants sampled, these two 

show only questionable infrequent importance.

Although basically the same elements are involved 

in important relations at Pocatello and Soda Springs, 

there appear to be, in general, higher concentrations of 

these elements in the plants sampled at Soda Springs. 

This condition may be due either to differences in 

climate, phosphate-processing activity, element 

availability, and plant species or varieties, or to combinations of all these factors. Table 8 compares the 

relative concentrations of these elements to be expected in the plants at Pocatello and Soda Springs 

3 km from the processing sites, according to the regression equations.

At Pocatello, where the downwind vector is greater 

than the upwind vector (fig. 1), many more downwind 

samples than upwind samples showed associations 

between elements in plants and the phosphate operations. At Soda Springs, however, the two main wind 

patterns, which are opposite but of about equal frequency, suggest that the effect of emissions on plants 

should reflect the somewhat stronger influence of the

TABLE 8.— Comparison of the estimated concentrations of selected 

trace elements in big sagebrush, cheatgrass, and bluebunch 

wheatgrass at distances of 3 km from phosphate-processing sites 

at Pocatello and Soda Springs, Idaho.

[Estimated concentration values (parts per million, in ash, except as indicated) were 

calculated using regression equations from tables 4 and 5. Leaders).....), relation between 

element concentration in plant material and distance was not significant. Values in 

parentheses indicate that the relation is of questionable significance]

Estimated concentrations Estimated concentrations 

in big sagebrush ____in grass_____ 

Soda Springs 

Pocatello (bluebunch 

Element Transect Pocatello Soda Springs (cheatgrass) wheatgrass)

Cd . . . . .

Cr .....

F1 .....

Li ......

Ni .....

Se1 . . . . .

U ......

V ......

Zn .....

..... downwind . .

..... downwind . .

78

23

. 400

77

. 360

43

48

12

3

. 460

. (95)

. 920

. 520

130 (9)

38 ......

270 (50)

117 ......

100 70

28 ......

. . 16 ......

28 ......

.35 .64 .22

(.16) (.08)

7 ......

3 ......

510 ......

150 (33)

1,870 ......

(760) ......

78

18

(62)

45

29

.80

.30

5

2

170

77

890

390

'Concentrations determined on dry weight of material analyzed.

wind from the southeast. Table 5 shows that this is 

generally true, particularly for the chemical data on 

grasses. Still, concentrations of emission-related 

elements were generally higher downwind than upwind 

at both sites (table 8). Furthermore, the concentrations 

of cadmium, chromium, fluorine, uranium, vanadium, 

and zinc calculated for plants from the regression equations were unusually high 3 km from the processing 

operations, as compared with similar plant materials 

sampled in the Powder River Basin of Wyoming and 

Montana (Gough and Severson, 1976). Emissions from 

the phosphate operations, therefore, contributed 

substantial quantitites of certain trace elements to the 

plants that were sampled.

ELEMENT CONCENTRATIONS IN SOILS

Tables 6 and 7 present regression statistics for 28 

elements in soils from the transects near Pocatello and 

Soda Springs, respectively. As discussed earlier, important relations between the concentrations of an element in soil and distance from processing sites are 

recognized as those that (1) show a statistically significant decrease in element concentration with increasing 

distance along at least one of the two transects, and (2) 

have a coefficient of determination of at least 0.50.

Nine elements (beryllium, fluorine, iron, lead, 

lithium, potassium, rubidium, thorium, and zinc) in
12 ELEMENT EMISSIONS FROM PHOSPHATE-PROCESSING OPERATIONS IN SOUTHEASTERN IDAHO

A-horizon soils downwind of the Pocatello operations 

(table 6) meet our criteria for being importantly related 

to processing-plant emissions. Six additional elements 

(copper, mercury, nickel, titanium, uranium, and 

vanadium) have regression lines whose slopes are 

significant at the 0.10 probability level and have coefficients of determination less than 0.50. Of these 15 relations, departures from the linear model are nonsignificant at the 0.10 probability level for 4 elements 

(beryllium, fluorine, mercury, and thorium). Therefore, 

for 11 of the 15 relations, a more complicated model 

might better describe the relation between element 

concentration and distance. In A-horizon soils upwind 

of the Pocatello operations (table 6), only two elements 

(iron and zinc) meet our criteria for being importantly 

related to processing-plant emissions.

In A-horizon soils downwind of the Soda Springs 

operations, only calcium meets our criteria; upwind, 

there are no elements that we consider importantly 

related to phosphate-processing emissions. Most relations in A-horizon soils attributable to Pocatello 

processing-plant emissions occur in the transect 

which, according to the wind patterns shown in figure 

1, are downwind of the emission sources and hence 

reflect the typical primary path of airborne emissions 

in this area. The wind patterns at Soda Springs suggest that processing-plant emissions should appear in 

A-horizon soils along both transects. However, 

because of the soil variability at Soda Springs, element 

concentrations were not interpreted as being 

significantly related to distance from the emission 

sources.

Along all transects, only five elements in C-horizon 

soils meet our criteria for being important. We believe, 

however, that these element concentrations are not 

associated with processing operations because it is improbable that the precipitation in this area (about 50 

cm annually) could have transported them through the 

soil profile to the C horizon in sufficient quantities 

since the processing factories began operations (about 

50 years ago) to account for the statistically significant relations. Therefore, these relations are probably 

intrinsic to the natural environment (fig. 5).

Table 9 lists the estimated accumulations of those 

elements that presumably reflect processing-plant 

emissions in the soils near Pocatello, as well as the 

estimated average concentrations of selected elements 

in surface soils reported in the literature. The accumulation estimates are based on the linear regression model that describes the relationship between the 

logarithm of element concentration in soil and the 

logarithm of distance from the emission source. These 

element concentrations are total concentrations and 

may not represent in direct proportions the amounts of

elements available to plants or subject to movement 

by percolating water. Table 9 includes nickel, uranium, 

and vanadium even though they do not meet our 

criteria for determining important relations. These 

elements are included because the slopes of their 

regression lines are significant between 0.05 and 0.10 

probability levels and because they are demonsrated to 

be important in both sagebrush and cheatgrass along 

the same transect. Of the 12 elements in table 9, we 

consider fluorine, vanadium, and zinc to be present in 

unusually large amounts in the upper 5 cm of soil close 

to the processing plants.

CORESPONDENCE BETWEEN ELEMENTS 

IN PLANTS AND SOILS

Table 10 indicates for each transect the correlations 

between element concentrations in plants and total element concentrations in A-horizon soils and their levels 

of significance. Eleven elements (chromium, copper, 

fluorine, iron, lead, lithium, magnesium, nickel, 

uranium, vanadium, and zinc) showed correlations that 

are significant at the 0.10 probability level between 

soil and at least one plant species along at least one 

sample transect. A significant correlation may reflect a 

sensitive plant response to soil variation or direct introduction of airborne emissions to both plant and soil, 

or both.

Iron, lithium, magnesium, and lead showed significant correlations between soil and sagebrush but not 

between soil and grasses at both Pocatello and Soda 

Springs. However, the correlations were observed for 

the upwind transects only (table 9), and it is unlikely, 

therefore, that these relations reflect emissions from 

processing operations. Also, it is not possible to 

generalize that the concentrations of any of these 

elements in plants are due entirely to extraction of the 

elements from the soils because there are no consistent 

correlations for any single element among all 

transects.

We have discussed elsewhere the prediction of element concentrations in either plants or soils by using 

correlations between the fluorine and zinc concentrations in plants and the total concentrations of elements 

in soil along the downwind transect at Pocatello 

(Severson and Gough, 1976). We also discussed the difficulty of using such relations for prediction purposes 

at Soda Springs (Gough and Severson, 1976).

Other investigators have also reported the general 

lack of relation between element concentrations in 

plant ash and the total element concentration in soil. 

Shacklette, Sauer, and Miesch (1970) noted that the 

correlations between element concentrations in the ash 

of parts of trees or garden vegetables and the total ele-
RESULTS OF ANALYSES

TABLE 9.—Estimated element concentrations in surface soils northeast (downwind) of phosphateprocessing sites near Pocatello, Idaho, and average concentration of elements in soils as 

reported in the literature

[Element concentrations (in ppm) between selected distances (in km) are calculated by intergration of prediction equations using 

the appropriate regression estimates for slope and intercept in table 6; estimates for background concentration represent the 

arithmetic means of element concentrations in surface soils sampled in this study 32 and 64 km northeast of Pocatello; percent 

variation explained is an estimate of the proportion of the total variation in element concentration along a transect that is accounted for by the regression; averages from the literature are arithmetic means and represent soil and surficial material from 

the conterminous United States; leaders (.....), no data availiable]

13

Present Study

Estimated concentration between 

selected distances

Element

Be ...............

F................

Fe ...............

K ...............

Li ...............

Ni ...............

Pb ..............

Rb ..............

Th ..............

U ...............

V................

Zn ...............

1-2 km

...... 1.8

...... 19,300

...... 25

...... 26

...... 28

...... 86

...... 3.4

...... 120

2-4 km

1.6 

880 

20,200 

18,600

23 

24 

25 

80

12.5 

3.2 

98 

102

4-16 km

1.4 

650 

18,500 

17,700

20 

20 

20 

69

10.3 

2.8 

82 

76

16-64 km

1.1 

460 

16,800 

16,600

18 

16 

16 

60

8.3 

2.4 

63 

55

Estimated Percent variation 

background explained

1.1 

480 

16,800 

16,700

18 

19 

16 

61

8.3 

2.8 

78 

58

58.1 

59.9 

51.0 

76.3

64.4 

27.4 

75.2 

57.9

70.9 

35.4 

28.1 

76.3

Other studies' 

(average 

concentration)

1 

400 

25,000 

23,000

25 

20 

20

'76 

54

'Shacklette, Boerngen, Cahill, and Rahill (1973); Shacklette, Boerngen, and Keith (1974); Shacklette, Boerngen, and Turner 

(1971); Shacklette, Hamilton, Boerngen, and Bowles (1971).

TABLE 10.—Correlations between element concentrations in plants and in A-horizon soils along transects beginning near phosphateprocessing sites at Pocatello and Soda Springs, Idaho

[r, product-moment correlation coefficient between logarithms of concentration; N, number of sample pairs; **, *, indicate significance of the correlation at the 0.01 and 0.05 probability

levels respectively; (...), no data availiable]

Element

Pocatello Soda Springs

Northeast (downwind) transect Southwest (upwind) transect Northwest (downwind) transect Southeast (upwind) transect

Cheatgrass Big sagebrush Cheatgrass Big sagebrush

Bluebunch 

wheatgrass Big sagebrush

Bluebunch 

wheatgrass

N N

Al .....

Co .....

Cr .....

Cu ....

F......

Fe

Li .....

Mg . . . . Mn . . . .

Na ....

Ni .....

Pb .... 

Si .....

Ti .....

U .....

V......

Zn .....

. 0.03

.85

. -.46

.95*

. -.24

.47

. -.09

. -.03

.03

.78

.13 

. -.39

. -.19

.93*

.80

.96**

5

5

5

5

5

5

5

5

5

5

5 

5

5

5

5

5

-0.37

.76

.32

99**

-.15

-.11

.61

.41

.46

.59

-.48 

.48 -.39

.86*

.76

.87*

6

6

6

6

6

6

6

6

6

6

6 

6

6

6

6

6

0.39

.13

0

-.29

.67

89*

.24

— 1Q

-.87* 

.60

.75

4Q

.40

.77

6

6

6

6

6

6

6

6

6

6 

6

6

6

6

6

0.10

— 53

0

_ 1 Q

— 49

-.16

-.12

.52

-.62 — 99

0

.14 — OQ

.80

6

6

6

6

6

6

6

6

6

6 

6

6

6

6

6

0.48 -.34

-.85*

89*

-.84*

-.13

-.51

.23

.52

-.72

-.70

-.03 

.07

.22 -.79

-.57

-.80

6

6

6

6

6

6

6

6

6

6

6

6 

6

6

6

6

6

-0.25 -.25

— 8^*

-.84*

.10

00

-.41

OQ

-.01

.03

-.23 

-.25

.15 -.58

-.18

-.59

6

6

6

6

6

6

6

6

6

6

6

6 

6

6

6

6

6

0.74

.30 -.53

-.05

.10

- 7Q*

.12

.83*

.18

.67

.29

-.14 

.17 -.38

-.30

.03 .82*

7

7

7

7

7

7

7

7

7

7

7

7 

7

7

7

7

7

0.73

.64 -.04

.16

.17

.64

.58 -.12

.14

.21

.34

.17 -.18

.67 -.29

.06

.34

7

7

7

7

7

7

7

7

7

7

7

7 

7

7

7

7

7

ment concentration in soil in a study in Georgia were 

inconsistent, were not reproducible, and probably 

resulted largely from chance. J. A. Erdman (botanist, 

U.S. Geol. Survey, oral commun., 1976) reported no 

good correspondence between chemistry of plant ash 

and total element concentration in soil for 29 elements 

in sagebrush and soil in the Powder River Basin of 

Wyoming and Montana.

This lack of correspondence between soil and plant 

chemistry is not surprising. Mitchell (1964, p. 342)

reported that no two plant species growing in the same 

soil will necessarily extract the same quantity of an 

element. Furthermore, he reported that the element 

uptake by a single plant species varies if the plant is 

grown in different soils. Soil properties, such as pH, 

organic-matter content, microbial population, 

oxidation-reduction potential, and water regime, certainly affect the element availability in soil and influence the absorption of elements by plants. In addition, the gross composition of the soil, the total concen-
14 ELEMENT EMISSIONS FROM PHOSPHATE-PROCESSING OPERATIONS IN SOUTHEASTERN IDAHO

tration of the element of interest, its concentration 

relative to that of other elements, and its compositional form or forms also affect the amount of the element available to, and absorbed by, a plant species.

ENVIRONMENTAL IMPLICATIONS

GENERAL IMPACT OF ELEMENT EMISSIONS

ON HEALTH

Having assessed the elements contributed to 

selected plants and soils by emissions from phosphateprocessing operations and having delineated zones of 

maximum influence along selected transects, we now 

apply the results of this study to an analysis of the 

potential, but unproven, effects of the emitted 

elements on the present and future health of plants and 

animals within the zones of influence. Such an application must be approached with caution because little is 

known about the biologic responses to chronic 

chemical insult at the molecular level. In the following 

discussion, we speculate on a few potential health effects, keeping in mind the constraints originally imposed on the study by its design and major purpose. Table 

11 summarizes information from the literature on the

toxicity of elements, determined to be important in 

this study, on plant and animal health. We did not attempt to review the literature relative to human 

health.

A very important aspect of element toxicity is the interaction between the elements. Bowen (1966) listed 

six basic mechanisms of toxic action of elements 

within the cell; these are their ability to (1) inactivate 

enzymes, particularly by the more electronegative 

metals such as copper, mercury, and silver (these 

metals are highly reactive with the amino, imino, and 

sulphydryl groups in proteins and thereby render them 

inactive); (2) function as antimetabolites (arsenate and 

chlorate substitute for phosphate and nitrate, respectively); (3) form stable precipitates or chelates with 

essential metabolites (aluminum, beryllium, scandium, 

titanium, yttrium, and zirconium react with 

phosphate); (4) catalyze the decomposition of ATP 

(adenosine triphosphate); (5) combine with the cell 

membrane and thereby affect its permeability (gold, 

cadmium, copper, mercury, lead, and free halogens affect the transport of sodium, potassium, and organic 

molecules across membranes); and (6) replace structurally or electrochemically important elements

TABLE 11.—Suspected and known toxic effects of selected elements in plants and animals

On plants On animals

Element Under natural conditions Under man-induced conditions Under natural dietary conditions Under man-induced dietary conditions

Cadmium Not demonstrated (Fleischer and 

others, 1974).

Chromium . Probable. Growth of only certain 

species on serpentine soils 

(Brooks, 1972); excessive Ni, 

unfavorable Mg:Ca ratios, and 

deficiency of Mo may be limiting factors (Vanselow, 1966). 

Soil from "poison spots" in Oregon contain as much as 2-3 percent chromic oxide (McMurtrey 

and Robinson, 1938).

Fluorine ... Unusual. Plants growing in acid 

soils have accumulated toxic

levels of F (Allaway, 1968).

Moderate (Brooks, 1972). 3 ppm Cd in plant 

tissue caused growth depression (no species mentioned) 1 (Allaway, 1968). Growth 

reduction when nutrient solution con 

tained 0.2 ppm—beets, beans, turnips 

1 ppm—corn, lettuce; 5 ppm—tomato 

barley; 9 ppm—cabbage (Page and others 

1972).

Severe (Brooks, 1972), moderate (Allaway, 

1968). Cr in the form of chromates particularly toxic (McMurtrey and Robinson, 

1938). 1,370-2,740 ppm Cr in soil caused 

chlorosis in citrus; 10-15 ppm Cr (as 

K2Cr207 ) in nutrient solution toxic to barley (Mertz, 1974a). 4-8 ppm Cr in leaves 

of corn was toxic2 (Soane and Saunder, 

1959).

Moderate when absorbed through the roots 

(Bowen, 1966). 30-300 ppm F in plant 

tissue generally reduced growth (depending on species and conditions)4; 200 ppm 

F in mature leaves of citrus associated 

with reductions in yield and growth"; 

<200 ppm F in tops of alfalfa caused no 

toxic effects4 (National Research Council, 

1971).

Not demonstrated (Fleischer and 

others, 1974).

Not demonstrated (Underwood, 1971).

Unusual. Lethal fluorosis of sheep and 

other livestock reported following 

volcanic eruption on Iceland (Thorarinsson, 1970). Endemic fluorosis 

reported from areas where drinking 

water contained unusually high F 

(Underwood, 1971).

Moderate to high (Allaway, 1968). Suspected of killing a horse—80 ppm Cd in liver, 

410 ppm Cd in Kidney (Lewis, 1972). 

30-60 ppm Cd in diet of sheep for 191 

days reduced growth and feed intake 

(Doyle and others, 1972). 45 ppm Cd in 

diet of rats for 6 months caused slight 

toxic symptoms (Underwood, 1971).

Of little significance (Mertz, 1974a). Toxicity low (Allaway, 1968). 30-40 mg/kg 

(ppm) zinc chromate lethal to calves within 1 month, about 20 times that amount 

lethal to cows; 30 ppm Cr in liver diagnostic of Cr toxicity3 . Potentially carcinogenic (Bowen, 1966). 50 ppm in diet associated with growth depression in experimental animals (Underwood, 1971).

Moderate (Allaway, 1968). Ambient air primary source of F in forage as F not readily absorbed through roots (National Research Council, 1971). Concentration of F 

in dietary dry matter above which normal performance may be affected: beef 

or dairy heifers, 40 ppm; horses, 60 ppm; 

finishing cattle, 100 ppm; broiler chickens, 300 ppm; breeding hens, 400 ppm; 

turkeys, 400 ppm (National Research 

Council, 1974).
ENVIRONMENTAL IMPLICATIONS 

TABLE II.—Suspected and known toxic effects of selected elements in plants and animals—Continued

15

On plants

Element Under natural conditions Under man-induced conditions

On animals

Under natural dietary conditions Under man-induced dietary conditions

Lithium .... Unusual. Citrus appears sensitive 

to an Li concentration in soil of 

about 12 ppm (Mertz, 1974b).

Nickel ..... Probable. Growth of only certain 

species on serpentine soils 

(Brooks, 1972); excessive Cr, 

unfavorable Mg:Ca ratios, and 

deficiency of Mo may be limiting 

factors (Vanselow, 1966). Apetalous forms of anemone growing over nickel silicate deposit 

(Malyuga, 1964).

Selenium... Probable. Toxic to most plants 

(McMurtrey and Robinson, 

1938). Only certain species 

grow on seleniferous soils (Cannon, 1971).

Uranium ... Probable. Long-term low-level natural radiation from pitchblende 

outcrops suspected as cause of 

deformities in fruit of bog bilberry, and of flower color variation in fireweed (Shacklette, 

1962, 1964). Variation in flowers of princesplume growing 

over carnotite (Cannon, 1960).

Vanadium . Unknown.

Zinc ....... Probably none (Allaway, 1968).

Flower modifications in poppy 

growing in soils high in Zn 

(Malyuga, 1964). Chlorosis of 

plants growing in Zn peat underlain with sphalerite and 

dolomite (Cannon, 1955).

Slight (Brooks, 1972). Many crops susceptible to "injury" when Li is applied to soil 

in form of soluble salts (McMurtrey and 

Robinson, 1938). Many plants tolerant of 

"high" Li levels (Mertz, 1974b). Toxic to 

citrus when Li concentration in soil (as 

Li2SOJ was 2-5 ppm and in leaves 140- 

220 ppm2 (Aldrich and others, 1951).

Severe (Brooks, 1972). Toxic to plant growth 

(McMurtrey and Robinson, 1938). Poisonous to plants even at relatively "low" concentrations; 40 ppm in tomato toxic, 150 

ppm stopped growth1 (Sauchelli, 1969). 

12-246 ppm Ni in leaves of corn toxic, 14- 

34 ppm Ni in leaves of tobacco toxic2 

(Soane and Saunder, 1959).

Moderate (Brooks, 1972). 700 ppm Se in 

wheat caused no effects when sulfur content of plant high, <250 ppm Se caused 

chlorosis of leaves when sulfur content 

was low4 (Trelease and Beath, 1949). 

Chlorosis and dwarfing of gumweed when 

grown in seleniferous test plots (Cannon, 

1964). Toxic at levels of 50-100 ppm4 

(Allaway, 1968). Ability to absorb Se 

highly variable among species (Ganje, 

1966).

Moderate (Brooks, 1972). Abnormal flowers 

noted on princesplume grown in test 

plots having a radiation source (Cannon, 

1960).

Unknown. Of little significance (Mertz, 1974b).

Moderate (Brooks, 1972). Stunting of plants 

grown in test plots treated with sodium 

vanadate (Cannon, 1960). With "very 

low" levels of V in nutrient solutions 

plant growth was depressed (Allaway, 

1968). >2 ppm V in tops probably toxic 

to peas and soybeans2 (Pratt, 1966).

Moderate (Brooks, 1972). "Large" quantities in soil toxic to plants (McMurtrey 

and Robinson, 1938). Chlorosis of leaves 

of sweetpea, tomato, bluegrass, and violet 

when grown in nutrient solutions containing zinc acetate (Cannon, 1955). 

About 150 ppm Zn in leaves of corn, soybeans, wheat, barley, and oats appeared 

to be toxic; about 200 ppm in citrus leaves 

is toxic' (Sauchelli, 1969). >50 ppm Zn 

in navy bean decreased yields' (Melton 

and others, 1970). 2,000 ppm Zn in certain bryophytes produced no apparent 

toxic effects5 (Shacklette, 1965).

Probably none. Relatively nontoxic 

(Underwood, 1971), like Zn, Mn, and 

Cr.

Toxic effects by 3-5 ppm Se in forage 

produced in "animals"4 (Oldfield, 

1974). 4 ppm Se is tolerance limit 

in forage plants used for cattle and 

sheep" (Sullivan and Garber, 1947, 

cited by Sauchelli, 1969). Water containing 0.5 ppm Se potentially dangerous (Ganje, 1966).

Unknown, either from its chemical or 

radiation properties.

Unknown.

Probably none (Allaway, 1968).

Moderate to low (Allaway, 1968). 700 ppm 

Ni in diet depressed growth of chicks; 

1,600 ppm Ni depressed growth of young 

mice; 1,000 ppm Ni had no effect on rats 

or monkeys (Underwood, 1971). Potentially carcinogenic (Bowen, 1966).

High. >4-5 ppm Se in animal diets generally depressed growth rates (Allaway, 

1968). Soils having X).5 ppm Se regarded as potentially hazardous to livestock; 10-15 ppm Se in diet of swine 

produced selenosis within 2-3 weeks 

(Underwood, 1971).

Unknown, either from its chemical or radiation properties.

Moderate (Allaway, 1968). >20-25 ppm V 

in diet caused growth depression in chicks; 

25 ppm V in diet of rats was toxic, 50 

ppm caused diarrhea and mortality; 

relative toxicity of five elements to rats 

when fed 25 ppm diets: As<Mo<Te<Se 

(Underwood, 1971).

Low (Allaway, 1968). >1,000 ppm Zn in diet 

(as ZnCO4 ) of weanling pigs depressed 

growth and caused arthritis and internal hemorrhage; 4,000 ppm, mortality 

high; 1,000 ppm Zn in diet of lambs reduced gains, 900 ppm Zn in diet of feeder cattle reduced gains (Underwood, 

1971).

'Concentrations assumed to be determined on the ashed material. 

Concentrations determined in the dried material.

3 A. A. Case, clinical veterinarian, University of Missouri, written commun., 1977. 

"Concentrations assumed to be determined on the dried material. 

'Concentrations determined on the ashed material.
16 ELEMENT EMISSIONS FROM PHOSPHATE-PROCESSING OPERATIONS IN SOUTHEASTERN IDAHO

(lithium replaces sodium, cesium replaces potassium). 

Figure 6 illustrates the major paths traveled by 

elements emitted into the air from phosphateprocessing operations. Soils and ground water are 

more stable reservoirs for these elements; however, the 

elements may be incorporated into animals and 

humans either directly through the air or by first passing into vegetation that is then consumed. In an 

agronomic system, the food chain terminates at the 

herbivore level (if humans as omnivores are not considered) and can result in the concentration of elements 

in animal tissue. If there is no physiological 

mechanism to eliminate these elements, acute or 

chronic pathological symptoms may develop. Relative 

toxicity, however, depends on many interrelated factors, which we will discuss shortly. Monitoring the 

concentration of these elements in forage plants is an 

important method of predicting the quality of animal 

health.

HUMANS

TRACE ELEMENTS IN AIR

FIGURE 6.—Major pathways in the natural migration of trace elements from phosphate-processing emissions.

EMISSION-RELATED SURFACE CONTAMINATION 

OF VEGETATION

The influence of washing on the element concentration in vegetation was determined from a suite of 

washed and unwashed grasses. We collected 24 

samples of bluebunch wheatgrass within a 100-m 

radius 2 km downwind of the elemental-phosphorus 

plant at Soda Springs. We washed one-half of these 

samples, using the methods discussed earlier, and then 

submitted for analysis these samples, along with the 

transect study samples, in a randomized sequence.

Of the 24 elements reported from washed and unwashed grass in table 12, 13 occurred in significantly 

greater amounts (probability level <0.05) in the unwashed samples than in the washed samples. Washing 

removed aluminum, boron, cadmium, chromium, 

fluorine, iron, lead, nickel, selenium, silver, titanium, 

uranium, and zirconium. Of these 13 elements, 

aluminum, iron, titanium, and zirconium may be more 

closely linked to local dust contamination than to the 

processing-site emissions. The remaining nine 

elements are known constituents of the local 

phosphate rock and probably reflect, in one form or 

another, the elemental-phosphorus operation; of these 

elements, the concentrations of cadmium, chromium, 

fluorine, nickel, selenium, and uranium were identified 

in this study as increasing in sagebrush and grasses 

toward the phosphate-processing operations. Fluorine 

and silver are of particular interest because the unwashed grass samples contained more than three times 

more fluorine and almost three times more silver than 

did the washed samples.

TABLE 12.—Chemistry of 24 washed and unwashed bluebunch 

wheatgrass samples collected 2 km downwind from an elementalphosphorus plant, Soda Springs, Idaho

[Values determined on plant ash, except as indicated. Values are in parts per million (ppm), 

except as indicated. Detection ratio is the number of samples having measurable property relative to the number of samples analyzed for that property; significance of F values 

were determined using 1 and 11 degrees of freedom of the numerator and denominator 

mean square, respectively; *, significant F value at the 0.05 probability level (critical 

value of 4.84);**, significant F value at the 0.01 probability level (critical value of 9.65). 

The number of samples having a measurable amount of each constituent is the same as 

the number of samples analyzed for that constituent, except for Ag, for which 22 samples

had measurable amounts and Zr, for which 23 samples had measurable amounts]

Geometric mean

Constituent

Ash1 .......

Ag ........

Al 1 ........

B..........

Ba ........

Ca1 ........

Cd ........

Cr .........

Cu ........

F1 - 2 ........

Fe1 ........

Mg1 .......

Mn ........

Mo ........

Ni .........

P..........

Pb ........

Se2 ........

Si .........

Sr .........

Ti1 ........

U .........

V..........

Zn .........

Zr .........

Washed 

grass

6.7 

2.7 

.54 

70 

340

4.5 

57 

78 

72 

.0050

.35 

1.4 

320 

22 

16

11,300 

79 

1.3 

390,100 

250

.020 

4.1 

230 

920 

29

Unwashed 

grass

8.6

6.1 

1.4 

100 

400

3.9 

82 

200 

66 

.0167

.74 

1.0 

320 

21 

28

12,400 

110 

1.6 

380,400 

190

.054 

6.1 

280 

920 

54

F value

34 ** 

92 ** 

117 ** 

20 ** 

1.5

0.8 13 ** 

55 ** 

2 155 **

41 ** 

11 **

0 

0 22 **

2 21 **

7.4 * 

0 

8 *

41 ** 

14 ** 

3 

0 

8.3 *

'Values in percent 

'Value determined on dry materal.
ENVIRONMENTAL IMPLICATIONS 17

The presence of apatite, shown by X-ray diffractograms of the dried plant-wash residue, suggests that 

the concentrations of calcium and phosphorus should, 

like fluorine, have differed significantly in washed and 

unwashed samples. The fact that they did not is 

possibly because these two elements are major constituents of the plants that were studied, and any additional amount present on the unwashed samples probably is relatively small compared to the total content of 

these elements in the plants.

Although the washed samples in this comparison 

study were more rigorously cleaned than they would be 

in nature, animals grazing near the processing operations following a heavy rain may be less likely to consume high levels of certain potentially toxic trace 

elements such as cadmium, chromium, fluorine, nickel, 

and silver. This comparison also demonstrates that 

precipitation may account for the highly variable 

fluorine values obtained on different sampling dates in 

the University of Idaho studies (University of Idaho, 

1955-74). The great differences observed in our study 

between washed and unwashed plant samples exemplify the need to consider carefully the objectives of 

any sampling program conducted near a point source 

of emissions. The analysis of washed samples reveals 

the presence of elements largely incorporated by the 

plants—the type of information necessary to determine the concentrations of elements, suspected to be 

related to some emission source, that are potentially 

toxic to the plants. The analysis of unwashed forage 

plants, however, is more important in veterinary toxicology, but such analysis should consider the frequency of natural cleansing mechanisms (relative to sampling times).

IMPACT OF ELEMENT EMISSIONS ON 

PLANT HEALTH

In this report we correlate elevated levels of seven 

elements in vegetation as being significantly 

associated with phosphate-processing operations. 

Table 11 lists cases described in the literature where 

these and two additional elements, considered to be important in this study, have been demonstrated to be 

toxic to plants under both natural and man-induced 

conditions. (Table 1 lists the scientific names of the 

plants mentioned by their common names in table 11.)

Of the nine elements considered in table 11, 

chromium, nickel, and selenium generally are most toxic if absorbed from the soil. Fluorine, which is 

moderately toxic to plants if absorbed through the 

roots, is highly toxic if absorbed through foliage. The 

remaining five elements (cadmium, lithium, uranium, 

vanadium, and zinc) have little toxic effect under 

natural conditions. Radiation from uranium ore,

however, was thought to have caused mutations in 

plants (Shacklette, 1962, 1964). Even under maninduced conditions, the relative toxicity of these lastmentioned five elements is considered to be low.

In this study, we did not attempt to identify specific 

plants having symptoms of element toxicity. The concentrations of toxic elements in plants presented in 

table 8 may be compared to levels of toxicity given in 

table 11. Several interdependent extrinsic and intrinsic 

factors influence relative element toxicity: (1) the 

genetics of the plant involved (differences being found 

even between varieties), (2) the availability of the 

emission-related elements to the plant, (3) the distance 

from the processing operations, and (4) the climate of 

the area. The values in table 8 indicate that, of the nine 

elements suspected as being contributed to vegetation 

3 km from phosphate-processing operations, only 

chromium and zinc have concentration ranges known 

to be toxic to some plants. Fluorine may also affect 

plants but probably only along downwind transects. 

As discussed earlier, relatively high values for these 

elements occur only within about 4 km of the processing sites. Outside this zone, element concentrations in 

tissue are probably not sufficiently high to produce 

toxicity symptoms.

Even if conditions favor the development of toxicity 

symptoms in plants near the processing operations, 

the exact cause of the symptoms would remain uncertain. For example, Brewer (1966, p. 181) stated: 

"Although fluoride toxicity symptoms are relatively 

characteristic, a number of other factors, such as excessive salts, extreme moisture stress, and certain 

mineral deficiencies, will produce similar symptoms. 

For this reason, visual diagnosis must usually be confirmed by chemical analysis of the leaves or other plant 

tissues."

IMPACT OF ELEMENT EMISSIONS ON ANIMAL 

HEALTH

Since the early 1950's, farmers and ranchers have 

charged that the health of the livestock grazing near 

phosphate-processing operations at Soda Springs was 

being affected (Ben Gomm, U.S. Bureau of Land 

Management, written commun., 1976). Losses from 

fluorosis have been documented, and financial settlements for individual animals and, in some cases, 

whole herds have been made. All occurrences of 

fluorosis were reported from locations relatively near 

the processing plant and presumably were the result of 

the ingestion of forage having high concentrations of 

fluorine. Studies by scientists of the University of 

Idaho College of Agriculture (University of Idaho, 

1955-74), have confirmed the existence of high levels 

of fluorine in alfalfa and grasses and certain cultivated
18 ELEMENT EMISSIONS FROM PHOSPHATE-PROCESSING OPERATIONS IN SOUTHEASTERN IDAHO

plants near the processing sites at Pocatello and Soda 

Springs.

Comparison of tables 8 and 11 shows that fluorine 

may have concentrations in sagebrush and cheatgrass 

that are high enough to produce fluorosis in horses and 

cattle. Of the other elements, cadmium may be sufficiently high to affect the growth of sheep. The concentrations of chromium, vanadium, and zinc are high and 

under certain circumstances could be toxic. The concentrations of lithium, nickel, selenium, and uranium 

are relatively low in the plants sampled and probably 

pose an insignificant health hazard.

As with plant toxicity, the potential toxicity of these 

elements to grazing animals in the area depends on 

several factors: (1) the species and feeding habit of the 

animal (horses and sheep are more likely than cattle to 

consume contaminated soil as well as plants); (2) the 

animal's diet (a diet of predominantly perennial, as opposed to annual, plants likely would contain higher 

concentrations of the contaminants; supplemental 

feeds imported from outside the area would lessen the 

impact of locally grown forage); (3) the distance between animal grazing and emission source (an animal 

grazing more than 8 km from a source is unlikely to 

consume highly contaminated forage); (4) the season of 

the year (contaminants are more likely to be concentrated in forage plants at the end of the growing season 

than at the beginning), (5) the general activity of the 

phosphate-processing operations through time; (6) the 

winds and their influence on the distribution of emissions, which tend to settle out more downwind than upwind; and (7) the amount, form, and frequence of 

precipitation, which may wash and therefore dilute the 

surface contamination of forage.

SUMMARY

1. The concentrations of several elements in plants 

and soils tend to increase nearer the phosphateprocessing operations. The southwest wind vector at 

Pocatello is the predominant vector; therefore, we 

found most of the significant relations between concentrations and processing emissions along the northeast 

(downwind) transect. At Soda Springs, both transect 

directions had strong wind vectors, although the wind 

is more frequent from the southeast. In the plants 

sampled near Soda springs, we found significant relations along both transect directions, but the more 

significant ones were along the northwest transect; in 

the soils, relations may have been obscured by the 

variability of the soil materials sampled.

2. The concentrations of cadmium, chromium, 

fluorine, selenium, uranium, vanadium, and zinc in 

plants were significantly associated with phosphateprocessing operations. Lithium and nickel had frequent importance, and phosphorus and sodium had infrequent and questionable importance. Cadmium, 

chromium, fluorine, uranium, vanadium, and zinc had 

unusually high concentrations within 4 km of the processing sites. Concentrations of fluorine and uranium 

were highest at Pocatello; those of cadmium and zinc 

were highest at Soda Springs. Element content in 

plants decreased most precipitously within about 4 km 

of the processing sites and continued to decrease 

steadily but less dramatically as far as 16 to 32 km 

from the sites.

3. In general, sagebrush reflected higher concentrations of element emissions from the processing operations than did grasses.

4. In the A-horizon soils along the northeast (downwind) transect at Pocatello, beryllium, fluorine, iron, 

lead, lithium, potassium, rubidium, thorium, and zinc 

are interpreted as being significantly related to 

phosphate processing. Southwest (upwind) of 

Pocatello, the concentrations of iron and zinc in 

A-horizon soil decreased with increasing distance from 

the emission sources, but we believe that only zinc is 

possibly associated with the emissions.

5. Both transects at Soda Springs showed many conflicting relations in A-horizon soils, the element concentrations in some cases increasing with increasing 

distance from the emission sources; in others, decreasing. Because of these conflicts, we could not determine 

with confidence which relations, if any, were caused by 

the processing emissions.

6. We interpret all relations in C-horizon soils along 

all transects as not being caused by processing emissions.

7. We could discern no clear patterns for relations 

between the content of any element in the ash of plants 

and the total concentration of that element in 

A-horizon soils for any transect at either study area. 

Therefore, we could make no generalizations about 

plant uptake of emission-related elements from the soil 

or about the availability of these elements to plants.

8. Of the nine elements (cadmium, chromium, 

fluorine, lithium, nickel, selenium, uranium, vanadium, 

and zinc) commonly added to vegetation near 

phosphate-processing operations, the concentrations 

of only chromium, zinc, and possibly fluorine are 

within the ranges documented as being toxic to some 

plants. These high levels generally occur only downwind and within 8 km of the processing operations. In 

this study, however, we did not try to identify instances of toxicity of these or any other elements in the 

plants studied.
REFERENCES CITED 19

9. Of the same nine elements commonly added to 

vegetation near the processing sites, we speculate that 

only cadmium and fluorine may be present in sufficiently high concentrations, at some times and under 

certain circumstances, to be toxic to grazing animals. 

However, chromium, vanadium, and zinc concentrations are also high, and these elements could potentially be toxic. Of these five elements, only fluorine has 

been positively implicated with adverse effects on 

animal health. As in the soils, the high concentrations 

of these five elements are generally downwind and 

within 8 km of the processing operations. Additional 

studies are needed to assess adequately the effects of 

element emissions from phosphate processing on 

animal and human health.

10. By comparing the chemistry of washed and unwashed grass samples collected 2 km downwind of an 

elemental-phosphorus plant at Soda Springs, we 

discovered that the unwashed samples had significantly higher concentrations of 13 elements, most of which 

are considered to be environmentally important. 

Periodic cleansing by rainfall may alter appreciably 

the concentration of elements consumed by grazing 

animals. Washed plants contained high concentrations 

of the potentially toxic elements fluorine and silver, 

and unwashed samples contained as much as three 

times these concentrations.

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