ENVIRONMENTAL HEALTH SERIES
Urban and Industrial Health
WATER
CYANIDES NO. 1
STUDY NUMBER 29
ANALYTICAL REFERENCE
SERVICE
MENT OF HEALTH, EDUCATION, AND WELFARE
Public Health Service
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2208
WATER CYANIDES NO. 1
Study Number 29
Report of a Study Conducted by the
ANALYTICAL REFERENCE SERVICE
Raymond J. Lishka, Laurella A. Lederer, and
Earl F. McFarren
TRAINING PROGRAM
National Center for Urban and Industrial Health
U.S. DEPARTMENT OF HEALTH, EDUCATION, AND WELFARE
Public Health Service
Bureau df Disease Prevention and Environmental Control
Cincinnati, Ohio
1967
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The ENVIRONMENTAL HEALTH SERIES of reports was
established to report the results of scientific and engineering
studies of mans environment: the community, whether urban,
suburban, or rural, where he lives, works, and plays; the air,
water, and earth he uses and reuses; and the wastes he produces
and must dispose of in a way that preserves these natural resources.
This SERIES of reports provides for professional users a central
source of information on the intramural research activities of the
Centers in the Bureau of Disease Prevention and Environmental
Control, and on their cooperative activities with State and local
agencies, research institutions, and industrial organizations.
The general subject area of each report is indicated by the
letters that appear in the publication number; the indicators are
AP - Air Pollution
RH - Radiological Health
1 3 TH - Urban and Industrial Health
Triplicate tear-out abstract cards are provided with reports
in the SERIES to facilitate information retrieval. Space is provided
on the cards for the user t s accession number and key words.
Reports in the SERIES will be distributed to requesters, as
supplies permit. Requests should be directed to the Center iden-
tified on the title page.
Public Health Service Publication No. 999-UTH-4
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FOREWORD
The Analytical Reference Service (ARS) is conducted by the Training
Program of the National Center for Urban and Industrial Health to evalu-
ate laboratory methods in the environmental field. Cooperative studies
by member organizations, who analyze identical samples and critically
review methodology, provide the mechanism for:
Evaluation of analytical procedures, including
precision and accuracy, by comparison of the
procedures and results reported by participating
laboratories.
Exchange of information regarding method char-
acteristics.
Improvement or replacement of existing methods
by development of more accurate procedures, and
development of new methodology for determination
of new pollutional compounds.
Samples are designed to contain measured amounts of selected con-
stituents. Decisions as to qualitative makeup are made by the ARS staff,
the membership, and consultants. Notice of each study is sent to the
entire membership. To those who desire to participate, a portion of
the study sample is sent, along with data forms for reporting numerical
values, a criti4ue of the procedures used, comments on modifications,
sources of error, difficulties encountered, or other pertinent factors.
The results and comments received are compiled, and a report of each
study is prepared.
Initially directed toward examination of water, studies now include
air, milk, and food. Some studies are periodically repeated for the
advantage of new members, to evaluate new methods, or to reevaluate
existing methods.
The selection of studies is guided by the responses to questionnaires
periodically circulated among the membership, which now includes 309
Federal, state, and municipal agencies; industries; universities; con-
sulting firms; and foreign agencies.
James P. Sheehy, Chief
Training Program
1 1 - i
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ACKNOWLEDGMENTS
The American Public Health Association granted permission to copy
the distillation procedure, pages 451 and 452, the titration method, pages
453 and 454, and the colorimetric method, pages 455 and 456 from the
12th edition of Standard Methods for the Examination of Water and Waste-
water , copies of which were sent to each participant.
Dwight G. Ballinger, Assistant Chief for Laboratories, Division of
Pollution Surveillance, Federal Water Pollution Control Administration,
and Ferdinand Ludzak, Chemist, Training Program, Federal V.ater Pol-
lution Control Administration, reviewed the final manuscript for technical
accuracy.
Joseph F. Santner, Mathematical Sciences, Office of the Director,
reviewed the statistical treatment of the data.
iv
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STUDIES COMPLETED AND REPORTED
Water-Minerals Calcium, magnesium, hardness, sulfate,
chloride, alkalinity, nitrite, nitrate, sodium,
and potassium. Studies completed in 1956,
1958) and 1961.
Water-Metals Lead, copper, cadmium, aluminum, chro-
mium, iron, manganese, and zinc. Studies
completed in 1957 and 1962. These same
metals plus silver. Study completed in 1965.
Water-Fluoride Fluoride in the presence and absence of
interferences, with and without distillation
by a specified procedure. Studies completed
in 1958 and 1961.
Water-Radioactivity Gross beta activity. Studies completed in
1959 and 1961. Gross beta and strontium-90
activity. Study completed in 1963.
Water-Surfactant Surfactant in various waters. Studies com-
pleted in 1959 and 1963.
Water-Oxygen Demand Biochemical oxygen demand and chemical
oxygen demand. Study completed in 1960.
Chemical oxygen demand. Study completed
in 1965.
Water-Trace Elements Arsenic, boron, selenium, and beryllium.
Study completed in 1962. These same
metals plus vanadium. Study completed
in 1966.
Freshwater Plankton Evaluation of the precision and accuracy
obtainable by the use of various methods
of plankton counting and identification.
Study completed in 1964.
Water-Nutrients Silicate, phosphate, ammonia nitrogen,
organic nitrogen, and nitrate nitrogen.
Study completed in 1966.
Water-Phenols Phenol and 2, 4-dich lorophenol in water by
two specified methods. Study completed in
1966.
v
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Water-Cyanides Potassium cyanide and potassium ferricy-
anide in water by two specified methods.
Study completed in 1967.
Air-Inorganics Chloride, sulfate, fluoride, and nitrate in
aqueous solution and on glass-fiber high-
volume filter mats. Study completed in
1958.
Air-Lead Filter paper tape impregnated with lead.
Study completed in 1961.
Air-Particu lates Microscopic identification of some common
atmospheric particulates. Study completed
in 1964.
Air-Sulfur Dioxide Sulfur dioxide in air by a specified method.
Study completed in 1963.
Water-Pesticides Lindane, heptachior epoxide, DDE, and
die ldrin in water. Study completed in 1965.
Food-Pesticides DOT in milk. Study completed in 1962.
Lindane, heptachior epoxide, DDE, and
die ldrin in food. Study completed in 1965.
vi
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PARTICIPANTS IN THIS STUDY
Atomic Energy Research Establishment, Harwell, Berks, England
British Coke Research Association, Chesterfield, Derby, England
Brown and Caldwell Laboratories, San Francisco, California
Calgon Corporation, Pittsburgh, Pennsylvania
California Department of Public Health, Berkeley
California Department of Public Health, Los Angeles
Central Water Filtration Plant, Chicago, Illinois
City of Cincinnati, Division of Water Pollution Control, Ohio
Connecticut State Department of Health, Hartford
Cyrus Wm. Rice and Company, Pittsburgh, Pennsylvania
DHEW, PHS, NCUIH, Environmental Sanitation Program, Cincinnati,
Ohio
Department of Health Services and Hospital Insurance, Vancouver, B. C.,
Canada
Department of National Health and Welfare, Vancouver, B. C., Canada
Fairbanks Morse Research Center, Beloit, Wisconsin
Ford Motor Company, Detroit, Michigan
Fresno Department of Public Health, California
Indiana State Board of Health, Indianapolis
Institute of Environmental Sanitation, Taipei, Taiwan, China
Iowa State Hygienic Laboratory, Des Moines
Lawrence Experiment Station, Massachusetts
Los Angeles Department of Public Works, Hyperion Treatment Plant,
Playa Del Rey, California
Los Angeles Department of Water and Power, California
Metropolitan Corporation of Greater Winnipeg, Manitoba, Canada
Metropolitan St. Louis Sewer District, Missouri
Metropolitan Sanitary District of Greater Chicago, Illinois
Minneapolis Water Department, Minnesota
Monroe County Health Department, Rochester, New York
Montana State Board of Health, Helena
Nassau County Department of Health, Hempstead, New York
New Jersey State Department of Health, Trenton
New Mexico Department of Public Health, Albuquerque
New York State Conservation Department, Scottsville
New York State Department of Health, Albany
North Carolina Department of Water Resources, Raleigh
North Dakota State Department of Health, Bismarck
North Jersey District Water Supply Commission, Wanaque, New Jersey
Northside Wastewater Treatment Plant, Durham, North Carolina
Ohio Department of Health, Columbus
Ontario Water Resources Commission, Rexdale, Ontario, Canada
Oregon State Board of Health, Portland
Pennsylvania Department of Health, Harrisburg
Ray W. Hawksley Company, Inc., Richmond, California
vii
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Regional Environmental Health Laboratory, Kelly AFB, Texas
Regional Environmental Health Laboratory, McClellan AFB, California
Rhode Island Department of Health, Providence
Roy F. Weston, Incorporated, West Chester, Pennsylvania
Sandia Corporation, Albuquerque, New Mexico
Sixth US Army Medical Laboratory, Fort Baker, California
South Carolina Pollution Control Authority, Columbia
Tennessee Department of Public Health, Nashville
US Army Environmental Hygiene Agency, Edgewood Arsenal, Maryland
USD1, Geological Survey, Columbus . Ohio
USD1, Geological Survey, Little Rock, Arkansas
USD1, FWPCA, Great Lakes-Illinois River Basins Project, Chicago,
U lino is
USD1, FWPCA, Ohio River Basins Project, Wheeling, West Virginia
USD1, FWPCA, Pacific Northwest Water Laboratory, Corvallis, Oregon
University of California, Richmond
University of Leeds, England
Vermont Department of Water Resources, Montpelier
Washington State University, Pullman
viii
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CONTENTS
ABSTRACT
PURPOSE OF THE STUDY
DESIGN OF THE STUDY
TREATMENT OF THE DATA.
DISCUSSION OF RESULTS
Sample 1, 1.10 mg of cyanide as
per liter of water
Sample 2, 0.02 mg of cyanide
per liter of water
Sample 3. l. 3 Omg of cyanide
per liter of water.
COMMENTS OF THE PARTICIPANTS
Distillation
Titration
Pyridine-pyrazolone
Miscellaneous
SUMMARY AND CONCLUSIONS
REFERENCES
APPENDICES
A. Tabulation of Results
B. Tests for Normality and
C. Glossary of Statistical Terms
xi
1
1
2
2-11
potassium cyanide
2
as potassium cyanide
S
as potassium ferricyanide
8
11-15
11
12
13
15
15
17
19-44
20
of Outliers . . . . 29
32
34
37
Rejection
D. Comparison of Methods for Significance of .
Difference in Precision and Accuracy . . .
E. Analytical Reference Service Membership .
ix
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ABSTRACT
In this study each participant was shipped three concentrated solutions
in sterile, sealed, glass ampoules. On receipt they were instructed to
dilute 5 ml of each sample to 1 liter with a good quality distilled water
and to analyze each sample by each of two standard methods, copies of
which were provided. The results indicate that neither the titration nor
the colorimetric procedure when used in conjunction with distillation will
measure very precisely either a very low (0.02 mg/l with a titration
standard deviation of ± 0. 035 mg/l and a colorimetric standard deviation
of ±0.020 mgll) or a high (1.10 mg/l with a titration standard deviation
of ±0.333 and a colorimetric standard deviation 01±0.306) cyanide con-
centration. In view of the drinking water limitation of 0. 01 mg per liter
of cyanide, these results indicate the need for an improved method.
xi
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WATER CYANIDES NO. 1
PURPOSE OF THE STUDY
The cyanide ion, CW, is very toxic. The simple alkali cyanides
normally display intense toxicity because CN is formed when they are
dissolved in aqueous solution. Similarly, alkali-metallic cyanides, nor-
mally rather stable in aqueous solution, sometimes decompose to display
varying degrees of toxicity, depending on the complex. According to
Standard Methods for the Examination of Water and Wastewater , the
threshold limit of toxicity at infinite time for fish appears to be 0. 1 mg
CN per liter of water. The Public Health Service Drinking Water
Standards call for less than 0.01 mg CN per liter in a water supply.
The objective of this study was to evaluate the ability of the distil-
lation, titrimetric, and colorimetric procedures (1,2,3) offered in
Standard Methods to quantitatively measure both simple and complex
cyanides in water.
DESIGN OF THE STUDY
To reduce shipping costs and to obtain greater stability, the samples
were prepared as concentrated solutions and shipped in sterile, sealed,
25-ml, glass ampoules. The concentrated solutions were prepared so
that when the participant diluted ml of each sample to I liter, the
sample would contain the amounts of cyanide ion indicated in Table 1.
To ensure stability, the concentrates contained sufficient sodium
hydroxide to raise the pH to ii. 5.
Table 1. Composition of Samples
Sample No. Cyanide ion, mg/l Cyanide compound
1 1.10 KCN
2 0.02 KCN
3 1.30 K 3 Fe(CN) 5
To ensure that each analyst would use the prescribed procedure,
permission was obtained from the American Public Health Association
to reproduce the procedures from Standard Methods , and copies were
sent with the samples to each participant. Each participant was requested
to distill the samples and perform a single analysis on each sample by
each of the two methods provided. They were also instructed to store the
samples in the refrigerator in a closed container if all the samples could
not be analyzed by both methods immediately after dilution of the concen-
trates.
1
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TREATMENT OF THE DATA
On receipt of the results of analysis from each participant, the data
were reviewed and coded for transfer to punch cards. The data were then
key-punched and analyzed by electronic computer for normality of distri-
bution and rejection of outliers.
The data were statistically analyzed by computer for precision and
accuracy. The two methods were also compared by computer by applying
the Students t-test and the F-test to the data to test for a statistically
significant difference in the means and standard deviations obtained by the
two methods.
Bar graphs (Figures 1 through 6) provide a pictorial display of the
data; rejected values are indicated by broken bars.
Analytical results received by the Analytical Reference Service
after statistical analysis was begun (2 months after shipment of the
sample) are not shown in this report.
Some anomalies that appear to exist in the statistical data are due
to rounding off the computer calculations 1 which were carried out to
four decimal places in order to utilize an all-purpose program.
DISCUSSION OF RESULTS
Sample 1 was designed to contain a 1. 10 mg per liter concentration
of cyanide ion, present as free cyanide. Fifty-nine laboratories
(Figure 1) analyzed this sample by method 1, the standard method of
distillation and titrationJ 1 ) Two of the results were rejected as out-
liers. As indicated in Table 2, the mean of the retained results was
0. 97 mg per liter with a relative error of 12 percent and a relative
standard deviation of 34 percent. These results indicate that, at this
concentration, the method produces only fair accuracy and poor pre-
cision.
Forty-seven laboratories (Eigure 2) analyzed this sample by method
2 the standard distillation and colorimetric procedure (2) using pyridine-
pyrazolone. Again, two results were rejected as outliers. By this
method, the mean of the retained results was 0.91 mg per liter with a
relative error of 18 percent and a relative standard deviation of 34
percent. The results indicate that the two methods produce similar
accuracy and precision at this concentration of cyanide. Statistical
tests show no significant difference between the two methods.
2
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Figure 1. Bar graph for sample 1 by method 1.
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LABORATORY NUMBER
Figure 2. Bar graph for sample 1 by method 2.
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Table 2. Summary of Statistical Data on Sample 1
(Amount cyanide added = 1. 10 mg/I)
Method 1 Method 2 Method 3 Method 4
Number of results 57 45 8 4
included
Number of outliers 2 2 0 0
Mean, mg/i 0.97 0.91 1.16 1.10
Mean error, mg/i -0.13 -0.19 +0.06 0.00
Relative error, % 12 18 5 0
Standarddeviation, 0.333 0.306 0.285 0.351
mg / 1
Relative standard 34 34 25 32
deviation, %
95% Tolerance limits ±0.78 ±0.74 ±1.06 ±2.24
Eight analysts used method 3, which is identical to method 2 except
for the addition of a step to extract the final color with n-butyi alcohol.
Excellent accuracy was obtained, as indicated by the relative error of
only 5 percent, and the relative standard deviation of 25 percent shows
fair precision.
The direct benzidine-pyridine procedure, (4 ) method four, was used
by four participants and produced similar accuracy and precision, the
mean being 1. 10 mg per liter and the relative standard deviation, 32
percent.
Sample 2 was designed to contain 0. 02 mg of cyanide per liter.
Fifty-seven laboratories (Figure 3) analyzed this sample by method 1
and eight of the results were rejected as outliers. As indicated in
Table 3, the mean of the retained results was 0. 03 mg per liter, with
a relative error of 56 percent and a relative standard deviation of 1 12
percent. Results indicate that the titration method is not suitable
for measurement of cyanide at this low concentration. This is as
expected, since Standard Methods states that if the concentration is
less than 1. 0 mg per liter the colorimetric procedure should be used.
Forty-seven laboratories (Figure 4) analyzed this sample by
method 2, and seven results were rejected as outliers. The mean of
the retained results was 0.02 mg per liter, with a relative error of
1 percent and a relative standard deviation of 99 percent. The accur-
acy based on the mean appears excellent, but the precision is little
better than with the titration procedure. Statistical tests, however,
5
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LABORATORY NUMBER
Figure 3. Bar graph for sample 2 by method 1.
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-------
show a significant difference between the methods and indicate that the
colorimetric procedure (2) is superior.
Table 3. Summary of Statistical Data on Sample 2
(Amount cyanide added = 0. 02 mgIl)
Method 1* Method 2* Method 3 Method 4
Number of results 49 40 8 4
included
Number of outliers 8 7 0 0
Mean, mg/l 0,03 0.02 0.04 0.08
Mean error, mg/l +0.01 0.00 +0.02 +0.06
Relative error, % 56 1 112 275
Standard deviation, 0.035 0.020 0.040 0.050
mg/i
Relative standard 112 99 95 68
deviation, O7
+0 .15 +0.32
95% Tolerance limits
* Data are nonnormal.
Eight analysts extracted the pyridine-pyrazolone -cyanogen chloride
color (3) with n-butyl alcohol and obtained a mean value of 0. 04 mg per
liter with a relative error of 112 percent and a relative standard devia-
tion of 95 percent.
Four analysts used the direct benzidine-pyridine method (4) with
even less success, the mean being 0. 08 mg per liter and the standard
deviation, 0. 05 mg per liter.
Sample 3 was designed to contain 1.30 mg of cyanide per liter in
the form of potassium ferricyanide.
Fifty-nine laboratories (Figure 5) analyzed this sample by method 1,
and two results were rejected as outliers. Acceptable accuracy was
obtained with this method, U) as indicated in Table 4 by the relative
error of 5 percent , but rather poor precision was indicated by the
relative standard deviation of 37 percent.
Method 2, used by 46 participants (Figure 6) with only one value
rejected as an outlier, produced even better accuracy as indicated by
the relative error of 2 percent, and similar precision as shown by the
relative snndard deviation of 38 percent.
8
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LABORATORY NUMBER
Figure 6. Bar graph for sample 3 by method 2.
-------
Table 4. Summary of Statistical Data on Sample 3
(Amount cyanide added 1.30 mg/i)
Method 1* Method 2 Method 3 Method 4
Number of results 57 45 8 2
included
Number of outliers 2 1 0 1
Mean, mg/i 1.23 1.27 1.50 0.00
Mean error, mg/i -0.07 0.03 +0.20
Relative error, % 5 2 15
Standard deviation, 0.451 0.480 0 .369
mg/l
Relative standard 37 38 25
deviation, %
95% Tolerance limits ±1.16 ±1.38
* Data are nonnormal.
Method 3, used by eight participants, produced better precision as
shown by the mean of 1.50 mg per liter and the relative standard devia-
tion of 25 percent.
The direct benzidinepyrdine procedure, method 4, was used by
only three participants, and one value was rejected as an outlier. This
left for consideration two results of zero which seemingly indicate good
precision but very poor accuracy. Obviously, the difficulty was that the
cyanide was bound in the ferricyanide complex and required distillation
to free the cyanide to react with the color producing reagent.
There was no statistically significant difference between results
with methods 1 and 2.
COMMENTS OF THE PARTICIPANTS
DISTILLATION
Our general impression is that distillation yields much less than
100 percent extraction of cyanides.
Procedure is very time consuming.
11
-------
In sample 2, using method 2, we found no cyanide; however, using
the same procedure without HgCl and MgCl , a cyanide content of
0.018 mg per liter was obtained.. High chloride content produced low
cyanide results, especially for simple cyanides of low concentration.
It was difficult to control the proper bubble rate at the initial
stage of the distillation. To allow for more variation by increasing
the bubble rate, a more efficient gas washer could be used. An exam-
ple would be a medium- or coarse-grit fritted-glass bubbler fitted in
a 250-ml dispensing buret. This apparatus would produce much smaller
size bubbles that would travel slowly up the column.
Two successive distillations of the original aliquot were required
for maximum detectable recovery on Sample 3 using tartaric acid dis-
tillation. The Serfass Reflux procedure required three successive
distillations of the original aliquot for maximum detectable recovery on
Sample 3.
Agitation during early heating was necessary to prevent boil-over.
A larger flask would have been preferable.
Much difficulty was encountered in trying to obtain a uniform air-
bubble flow with the Milligan gas washer. Samples 1 and3 yielded approx-
imately 20 and 30 percent, respectively, of total cyanide, from the second
hour refluxing as compared to that obtained from the first refluxing.
The use of the word 1 distillation should be deleted from the method
and replaced by a phrase such as refluxing with aeration.
TITRATION
The silver nitrate solution could be made up with the silver nitrate
as a primary standard.
The wrong normality for standard sodium chloride has appeared
from insertion of Sec. 2.4 on p. 86 of SMEWW. (This is as directed
in SMEWW.)
No difficulties were encountered in the titration and the method
seemed to be satisfactory.
The indicator for the titration method does not give a very distinct
endpoint.
Distillation of samples 1 and 2 did not yield enough cyanide to give
a reliable titration volume even with a 1liter sample.
12
-------
With 200 ml of distillate for the titration, the end-point is somewhat
difficult to detect; the end-point is sharper when the volume of titrated
solution is not over 100 ml.
The dilution of an aliquot of the scrubbing solution may result in
variable pH of the titration system. Low dilution may result in a pH
over 13 where, according to Liebig (see Kolthoff and Stenger: Volumetric
Analysis 2, 282, 1947) hydroxyl ion competes with cyanide ion for the
silver. Conversely, Ryan and Culshaw titrate cyanide at a pH of about
13.8. The upper pH limit for titration is controversial. High dilution
may result in a pH below 11, which may release HCN during stirring.
Several participants reported using a silver nitrate titrant of one-
tenth the specified concentration.
PYRIDINE -PYRA ZOLONE
Standard range should be raised to 5 tg per 25 ml when a I-cm cell
is used.
Use of a larger volume like 50 ml for color development would enable
the analyst to use larger aliquots, and it would be easier to manipulate.
Extraction of color with n-butyl alcohol produced a difficult-to-break
emulsion that caused erroneous readings with the spectrophotometer.
The extraction procedure does not specify that the blank should also
be extracted.
The alcohol extraction technique should be reevaluated.
In the butanol extraction, the color faded rapidly.
The colorimetric method, in general, was unsatisfactory.
The pyridine odor is objectionable.
The reagent solutions are unstable.
Used 25-mi graduated cylinders rather than 1- by 8-inch tubes.
In the pyridine-pyrazolone method, the necessity for control of
salt concentrations and for pH adjustments, which can lead to loss of
cyanide, is considered to be unsatisfactory.
It is suggested that a pH meter be used to adjust pH in the colon-
metric procedure. pH paper was found to give false indications, which
lead to off colors.
13
-------
We experienced inconsistent color development. Some samples
and standards would develop the pink color without changing to the blue.
Close pH control with a micro-buret for the acetic acid addition did not
cure this problem.
Some literature states that th Chloramine-T reaction should be
carried out at a reduced temperature (50 to 10°C) by use of an ice-bath.
This would prevent loss of the cyanogen chloride product that is volatile
at 13°C, although the gas is quite soluble in water at 20°C.
The standards should be treated the same as the sample. When
standards are run through the complete procedure, including distillation,
they do not appear to follow Beers Law.
In method 2, fading of the pyridine-pyrazolone-cyanogen chloride
color was encountered. This could be prevented only by use of a satu-
rated pyrazolone solution freshly made on the day of analysis. The
slope of the standard curve varied significantly from day to day.
Filtration,of butyl alcohol extract was necessary to remove last
traces of emulsion for photometric reading accuracy.
Step 4. 1 of the pyridine-pyrazolone method requires each aliquot
of distillate to be diluted to 15 ml with 0. 2 N NaOH before pH adjust-
ment. No results could be obtained by this method, so the sample was
diluted to 15 ml with distilled water and neutralized with 1 + 4 acetic
acid to pH 6-7.
Dilutions of stock }CCN should be made with 0. 2N sodium hydroxide
to prevent loss of HCN.
Black rubber stoppers should be extracted with sodium hydroxide
since I found 0. 5 microgram of positive interference from three No. 4
stoppers.
For the pH adjustment, 1 + 4 acetic acid is too strong. One drop
changes pH from 12 to 8, and another drop puts it close to pH 6. A
dilution to 1 + 9 gives more leeway in pH control.
In subsequent color formation, fading occurred more slowly when
the pH was adjusted to 6. 0 rather than 6 to 7.
The aqueous color should be measured as soon as possible after
the time allowed for color development.
The mixed pyridine-pyrazolone reagent changes color from neutral
to pink much too rapidly.
14
-------
MISCELLANEOUS
Good recovery can be obtained with tartaric acid distillation for
less stable complex cyanides.
The reagents for the benzidine-pyridine method are stable, and the
only odor is that of n-amyl alcohol.
Apparently weak cyanide solutions deteriorate rapidly, even if in
alkali and refrigerated.
The p-phenylenediamthe hydrochloride procedure gives greater
sensitivity than pyridine-pyrazolone.
The benzidinepyridine method is as satisfactory as the pyridine-
pyrazolone method with respect to sensitivity and accuracy, and the
reagents have good shelf lives. The chief disadvantage of the benzidine-
pyridine method is that maximum color is not reached until after about
alO- tol5 -minutereactionperiod, and the color fades after having reached
maximum intensity. It is necessary to make repeated transmission
measurements until a minimum transmission is obtained. The rate of
change of color intensity is slow enough, however, to permit a set of six
samples to be run together.
The method that depends on the reaction between cyanogen bromide
and a pyridine-barbituric acid reagent gives a color that remains con-
stant for at least 24 hours, although the rate of color development is
slow.
On Sample 3, the modified Aldridge method (color extracted) picked
up no cyanide until the sample was distilled.
SUMMARY AND CONCLUSIONS
The results of this study indicate that free cyanide at a concentra -
tion of 1 mg per liter or greater can be measured with acceptable
accuracy by either of the methods provided for this study; the precision,
however, is poor. The best results were obtained with the pyridine-
pyrazolone method and extraction of the final color, or with the direct
benzidine-pyridine method. The latter method, however, would be
suitable only for relatively clean, colorless samples.
A free cyanide concentration of 0. 02 mg per liter is apparently
near the limit of sensitivity for the titration, which produced poor
accuracy and precision. The pyridine-pyrazolone procedure, method 2,
appears more accurate at this concentration, but the precision still is
poor.
15
-------
Finger, in his investigation, concluded that the Serfass distilla-
tion procedure yields low recovery of simple cyanides in the range below
1.0 mg per liter. The procedure recommended by Williams, (6, 7) using
cuprous chloride, was found to give significantly better recovery of
cyanide at concentrations below 1.0 mg per liter. Adding the butanol
extraction step to the pyridine-pyrazolone procedure produced higher
results but no improvement in precision. It may be that the higher
results are attributable to turbidity caused by entrainment of water in
the butanol extract. This difficulty might be eliminated by passing the
extract through filter paper of a type that will not affect the color of the
extract.
A source of difficulty mentioned by many participants was the adjust-
ment of pH in the colorimetric procedure. The specified 1 + 4 acetic
acid is not dilute enough to permit close control of pH.
Although this is not clearly stated in the procedure, the standards
should each contain the same amount of sodium hydroxide as the blank
and samples.
Sample 3, containing potassium ferricyanide, illustrated the need
for a carefully performed distillation before attempting to measure
complex cyanides. Difficulties encountered in the distillation were
control of the airflow and heat. Electric mantles are more difficult
to control than burners because the heat cannot be reduced as quickly.
Removing the burner from under the boiling flask if the sample starts
to back up the air inlet tube usually prevents loss of sample. Control
of the airflow is facilitated if the inlet tube to the gas scrubber is drawn
to a fine opening, producing very small bubbles.
Several sets of results were submitted by participants who used
procedures other than those discussed. Since only one or two analysts
used each of these procedures, no statistical evaluation was possible
and the data are merely presented in the tabulation of results.
The pink color that developed rather quickly in the mixed pyridine-
pyrazolone reagent was disturbing to some participants but is a normal
occurrence and does not seem to affect the reaction.
Some participants thought an error had been made because the
procedure sent to them specified 0. 0192 N silver nitrate titrant to be
standardized with 0. 0141 N sodium chloride. The instructions were
copied from Standard Methods , which refers to the argentometric chlor-
ide procedure for directions for standardizing the silver nitrate solution.
For chloride measurement, 0. 0141 N is e4uivalent to 0. 5 mg Cl per
1.00 ml, but it is very simple to calculate the e4uivalent silver nitrate.
If the analyst prefers 0. 0192 N sodium chloride, it can be prepared by
dissolving 1. 1222 gram per liter of water.
16
-------
The reason that 1-inch test tubes rather than graduated cylinders
are specified for reaction tubes is that mixing is more easily accom-
plished, and in the St 20, at least, they may be used as
spectrophotometer cells, thereby eliminating a transfer of colored
solution.
A 5-ml microburet is specified for the titration procedure as the
use of a larger buret will result in a loss of accuracy.
In view of the limit of 0.01 mg cyanide per liter of water used for
drinking, this study indicates the need for improved methods that will
provide greater accuracy and precision than that obtained with the
present standard methods.
REFERENCES
1. Distillation and Determination by Titration. Standard Methods for
the Examination of Water and Wastewater. pp. 450-55. 12th
edition. APHA, AWWA, WPCF. New York, 1965.
2. Distillation and Colorimetric Determination. Standard Methods for
the Examination of Water and Wastewater. pp. 450-53 and 455-57.
12th edition. APHA, AWWA, WPCF. New York, 1965.
3. Same as reference 2, except color read after butyl alcohol extraction.
4. Aldridge, W.N. Direct Benzidine-Pyridine Method. Analyst.
70:475. 1945.
5. Finger, J.H. Williams Cuprous Chloride Distillation Followed by
Titration. Laboratory Investigations No. 2. Technical Advisory
and Investigations Section. DHEW, PHS, Cincinnati, Ohio, 1964.
6. Williams, Herbert E. Cyanogen Compounds, 2nd edition. p. 133.
1948.
7. Williams, H.E. A New Method for the Estimation of Ferrocyanides.
Soc. Chem. Ind. J. 31:468-71. 1912.
8. Finger, J.H. Williams Cuprous Chloride Distillation Followed by
Pyridine -Pyrazolone Colorimetric Determination. Laboratory
Investigations No. 2. Technical Advisory and Investigations Section,
DHEW, PHS, Cincinnati, Ohio, 1964.
9. Same as reference 1, but without distillation.
10. Same as reference 2, but without distillation.
17
-------
11. Tartaric Acid Distillation Followed by Titration. Standard Methods
for the Examination of Water, Sewage and Industrial Wastes.
pp. 297-99. 10th edition. APHA, AWWA, FSIWA. New York, 1955.
12. Tartaric Acid Distillation Followed by Colorimetric Determination.
Standard Methods for the Examination of Water, Sewage and Indus -
trialWastes. pp. 297-98and299-301. 10th edition. APHA, AWWA,
FSIWA. New York, 1955.
13. Nusbaum, L., and Skupeko, P. Determination of Cyanide in Sewage
and Polluted Waters. Sewage and Industrial Wastes. 23:875. 1951.
14. Bark, L. S., and Higson. H.G. Pyridine-p-phenylenediamine.
Talanta. 11:621. 1964.
15. Same as reference 2, but modified by using 40 ml of magnesium
chloride and 10 ml of mercuric chloride.
18
-------
APPENDICES
19
-------
APPENDIX A.
TABULATION OF RESULTS
Table A-l. Sample 1, Cyanide (1. 10 mg/l)
Lab. No.
Results
Other
Methods
Method 1
Method 2
Results
References
0003 0.70 0.78
0006 1.08 0.90
0009 1.03 1.13 3
0009 1.08 5
0009 1.08 8
0009 1.17 9
0009 0.91 10
0011 1.07 1.00
1114 1.11 1.50
1123 1.10 0.92 0.73 5
1124 1.25 1.23 1.23 11
1211 1.32 12
1211 1.14 13
1611 1.38
1725 1.09 1.05
1924 0.53
2124 1.12 1.27 1.02 13
2144 0.75 1.25
2222 1.10 1.66 3
2223 1.19 1.28
2326 0.62 0.80
2513 1.00 0.94
2611 0.97 1.18
2714 0.90 0.95
3122 1.80 1.09 1.54 3
20
-------
(Table A-i continued)
Lab. No.
Results
Other
Methods
Method 1
Method 2
Results
References
3126
3211
3221
3226
3311
3322
3416
3535
3716
4112
4211
4311
4421
4511
4511
4523
4611
4711
4911
5111
5111
5221
5311
5611
5711
5811
6112
6226
1. 15
1.05
1. 13
0.50
0. 16
1.00
1.25
0.97
0.84
0.06
1. 25
0.60
0.72
1. 08
3. 93
0.63
0.65
1. 40
1. 05
0.50
0. 87
1.35
0.98
1.12
1. 00
1. 50
0.84
0.71
0.60
0.60
0.48
0.97
0. 64
0. 25
1.24
0. 50
0.80
0.93
3.90
0. 88
0.62
0. 81
0.83
1.28
0. 88
0.90
1, 16
1.00
1.28
0.80
1. 02
1.09
3.37
0.88
1. 00
1.06
1.60
3
11
4
12
8
15
3
3
4
4
21
-------
(Table A-i continued)
Lab. No.
Results
Other
Methods
Method 1
Method 2
Results
References
6622
6811
7112
7222
7224
7813
7824
8112
8126
9111
9113
9613
9713
9922
0.97
0.93
0.50
0.63
0.86
1.57
0.88
1.20
1.23
1.17
1.00
3.27
0.27
1.20
0.69
0.49
0.76
0.68
0.83
1.70
1.27
1.10
1.00
3.24
0.20
1. 03
1.23
0.93
1.00
3
13
4
3
22
-------
Table A-2. Sample 2, Cyanide (0.02 mg/i)
Lab. No.
Results
Other
Methods
Method 1
Method 2
Results
References
0.00
0.02
0.40
0.01
0.00
0.00
0. 12
0.03
0.02
0.04
0.15
0.02
0.00
0.02
0.08
0.01
0.00
0.05
0.00
0.05
0.00
0.02
0.00
0.02
0.02
0.01
0.04
0.08
0. 10
3
5
8
9
10
5
11
12
13
14
3
3
0003
0006
0009
0009
0009
0009
0009
0011
1114
1123
1124
1211
1211
1611
1725
1924
2124
2144
2222
2223
2326
2513
2611
2714
3122
3126
3211
0.00
0.00
0.00
0.30
0.01
0.00
0.00
0.00
0.00
0.09
0.00
0.00
0.50
0.00
0.01
0.04
0.00
0.01
0.20
0.20
0.10
23
-------
(Table A-2 continued)
Lab. No.
Results
-_______________________
Other
Methods
Method 1
Method 2
Results
References
3221
3226
3311
3322
3416
3535
3716
4112
4211
4311
4421
4511
4511
4523
4611
4711
4911
5111
5111
5221
5311
5611
5711
5811
6112
6226
6622
0.25
0.01
0.05
0.50
0.63
0.02
0.00
0.04
0.09
0.00
0.06
0.05
0.04
0.25
0.00
0.05
0. 11
0.05
0.00
0.05
0.09
0.08
0.00
0. 10
0.04
0.01
0.20
0.02
0.01
0.00
0.02
0.01
0.05
0. 18
0.00
0.04
0.00
0.01
0.07
0.00
0.01
0.03
0.01
0.01
0.08
0.05
0.09
0.02
0.04
0.04
0.01
0.00
0.14
0.04
3
11
4
12
8
15
3
3
4
4
24
-------
(Table A-2 continued)
Lab. No.
Results
Other
Methods
Method 1
Method 2
Results
References
6811
7112
7222
7224
7813
7824
8112
8126
9111
9113
9613
9713
9922
0. 00
0.00
0.00
0.03
0.08
0.08
0.03
0.05
0.00
0.02
0.05
0.02
0. 10
0. 02
0.02
0. 07
0.03
0.04
0.01
0. 15
0. 03
0.02
0.00
0. 03
0.03
0. 02
3
13
4
3
25
-------
Table A-3. Sample 3, Cyanide (1.30 mg/l)
Lab. No.
Results
Other
Methods
Method 1
Method 2
Results
References
0003
0006
0009
0009
0009
0009
0009
0011
1114
1123
1124
1211
1211
1611
1725
1924
2124
2144
2222
2223
2326
2513
2611
2714
3122
3126
3211
1.04
1.33
1.24
3.16
1.25
1.18
1.25
1.38
1.77
0.96
1.23
1.15
1.25
1.38
0.82
1.25
1.12
1.20
0.90
1.35
2.35
1.05
1.22
2.70
1.89
1.06
1.18
1.63
1.48
0.93
2. 19
1.00
1.23
1.04
1.26
0.92
0.98
1.70
1.38
3
1.36
5
1.28
8
0.03
9
0.08
10
1.09
5
1.20
U
1.22
12
1.28
13
1.19
14
1.90
3
1.35
3
26
-------
(Table A-3 continued)
Lab. No.
Results
Other
Methods
Method 1
Method 2
Results
References
3221
3226
3311
3322
3416
3535
3716
4112
4211
4311
4421
4511
4511
4523
4611
4711
4911
5111
5111
5221
5311
5611
5711
5811
6112
6226
6622
1.30
1. 13
0. 16
1.00
1.25
1.35
1. 10
0.05
1. 55
2. 10
0.56
1.16
5.40
0.50
1.40
1.40
1.24
1.00
1.25
1.60
1.10
1.33
0.90
1.40
1.08
1.00
0.80
0.54
1.85
0.84
0.20
1.55
1.94
0.76
1.09
5.40
1.33
1. 24
1.02
1.06
1.45
1.16
1.13
2.46
1.55
11
0.63
4
1.46
12
1.44
8
5.22
15
1.07
3
1.20
3
1.17
13
0.00
4
27
-------
(Table A-3 continued)
Lab. No.
Results
Other
Methods
Method I
Method 2
Results
References
1.16
1.00
0.00
1. 10
1.96
1. 04
1.40
1.82
1.32
1.50
2. 50
1.38
1.68
1.07
1.01
0.78
0.90
1.01
1. 95
1.35
1.31
1. 40
1.39
1.22
0.25
0.00
2.06
3
13
4
3
6811
7112
7222
7224
7813
7824
8112
8126
9111
9113
9613
9713
9922
28
-------
APPENDIX B.
TESTS FOR NORMALITY AND REJECTION OF OUTLIERS
Test for normality
The Kolmogorov-Smirnov goodness of fit test was used to determine
whether the observations reported could reasonably be thought to have
come from a normal distribution.
Briefly, the test involves computing the observed cumulative fre-
quency distribution (the percent of values less than or equal to each
value in the distribution) and comparing it to the theoretical normal
cumulative frequency distribution. The point at which the two distri-
butions, theoretical and observed, show greatest divergence is determined.
Reference of the value of the divergence to a table of critical values for
the Kolmogorov-Smirnov goodness of fit test indicates whether such a
large divergence is likely on the basis of chance. If such a large diver-
gence is not likely, the distribution is designated as nonnormal; otherwise
the distribution is designated as normal.
Tests for rejection of outliers
1. 11 the distribution is designated as nonnormal, the suspected
outlier (the farthest value from the mean) is rejected only if the distance
between it and the mean is greater than three standard deviations; other-
wise the suspected outlier is accepted.
2. If the distribution is designated as normal and the sample size
is less than or equal to 30, the suspected outlier, the farthest value
from the mean, is tested for rejection by a method developed by Dixon.
Briefly, this test involves computing a ratio that compares the distance
of the suspected value from its neighbors to the range of all or most all
of the observations (depending on the exact number in the distribution).
Reference of the ratio to a table of critical values for test ratios for
gross errors indicates whether such a large ratio is likely on the basis
of chance. If the ratio is greater than or equal to the critical value, the
probability that the suspected outlier is from the sample distribution is
small; hence, the outlier is rejected. If the ratio is less than the critical
value, the suspected outlier probably came from the sample distribution;
hence, the suspected outlier is accepted.
* 5 Siegel. Nonparametric Statistics for the Behavioral Sciences.
McGraw-Hill Book Company, Inc. New York, New York. 1956.
pp. 47-51.
W. J. Dixon and F. J. Massey, Jr. Introduction to Statistical Analysis,
Second Editon. McGraw-Hill Book Company, Inc. New York, New
York. 1957. p. 276.
29
-------
3. If the distribution is designated as normal and the sample size
is greater than 30, the suspected outlier is tested for rejection by a
method developed by Santner. + This method employs the statistic
X - X 0 , where X is the sample mean, X 0 is the suspected outlier
S
(the farthest value from the mean), and s is the sample standard devi-
ation. This statistic is compared to a table of critical values to
determine whether its value is larger than would be expected on the
basis of chance. If the statistic is greater than or equal to the critical
value, the suspected outlier is rejected; otherwise, the suspected out-
her is accepted.
Application of tests for normality and for rejection of outliers to ARS
studies
The test for normality and subse4uent test for rejection of outliers
are applied to the observed data in two ways; first, to each method for
a given substance at a given level of concentration, and then to a given
substance at a given level of concentration regardless of method. In
either case, it is first necessary to determine whether the original
distribution is normal or nonnormal. If the original distribution is
designated as nonnormal, method 1 is used to test for rejection of the
suspected outlier farthest from the mean. If the suspected outhier is
not rejected, no further tests for normality or rejection of outliers are
made, and the distribution is designated as nonnormal. On the other
hand, if the suspected outlier is rejected, the new distribution, which
excludes the rejected observation, is then tested for normality. If the
new distribution is nonriormal, the next suspected outlier is tested for
rejection by method 1; and this cycle of testing for normality and testing
for rejection of outliers continues until a suspected value is not rejected
or the test for normality designates the distribution as normal. If the
distribution is designated as normal, subsequent tests for rejection of
outliers made by method 2 or 3 are the same as if the original distri-
bution had been normal. This case is discussed next.
If the original distribution is designated as normal or a new
distribution that was originally nonnormal is designated as normal after
the rejection of one or more outliers, and if the number of observations
is not greater than 30, then method 2 is used to test for rejection the
suspected outhier farthest from the mean. If the suspected outhier is
not rejected, no further tests are made and the distribution is designated
as normal. If the suspected outlier is rejected, then the suspected outhier
farthest from the mean of the new distribution is tested for rejection and
so on until the suspected value of a new distribution is not rejected; when
this occurs, no further tests are made and the final distribution is desig-
nated as normal. On the other hand, if the number of observations in the
+ J. Santner. Personal communication.
30
-------
original distribution is greater than 30, method 3 is used to test the
suspected outlier for rejection. If the suspected outlier is not rejected,
no further tests are made and the distribution is designated as normal.
If the suspected outlier is rejected, then the suspected outlier farthest
from the mean of the new distribution, which excludes the rejected value 1
is tested for rejection. Testing for outliers continues by this method
until a suspected outlier is not rejected or the number of observations
is no longer greater than 30, in which case method 2 is used for testing
for rejection of any remaining suspected outliers.
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APPENDIX C.
GLOSSARY OF STATISTICAL TERMS
A glossary of statistical terms defined as they are used in this report
is presented to ensure uniform and complete understanding.
Arithmetic mean The sum of a series of test results divided by the
number of tests in the series.
Median Halfway point in the readings when they have been
arranged in order of size (the middle reading of an
odd number of readings, or the average of the middle
two for an even number).
Accuracy The correctness of a measurement, or the degree
of correspondence between the results and the true
value (actual amount added).
Accuracy data Measurements that relate to the difference between
the average test results and the true value when the
latter is known or assumed. The following measures
apply:
Average deviation from true value Average differ-
ence (when always considered as positive) between
the mean determination for each laboratory and the
true value.
Average percent deviation from true value Average
of the differences between a laboratorys replicate
results and the true value expressed as percentages
of the true value.
Mean error The average difference with regard
to sign of the test results from the true results.
Also the difference between the average of a series
of test results and the true results.
Relative error The absolute value of the mean
error of a series of test results as a percentage
of the true result.
Precision A measure of the reproducibility of measurements,
or the degree to which the measurements correspond
to one another.
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Precision data Measurements that relate to the variation among
the test results themselves, i. e., the scatter or
dispersion of a series of test results without
assumption of any prior information. The follow-
ing measures apply:
Variance Sum of squares of deviations of the
average test results from the mean of the series
divided by one less than the total number of aver-
age test results.
Standard deviation Square root of the variance.
Relative standard deviation (coefficient of variation )
Standard deviation expressed asapercentage of the
mean of a series of laboratory results.
Range Difference in magnitude between the highest
and lowest laboratory mean.
Average percent deviation within laboratory Aver-
age of the differences between the replicate results
from a laboratory and the mean of their results,
expressed as percentage of their mean.
Confidence limits Limits within which the true
mean of the population (the theoretically infinite
number of possible replications of the analysis)
will lie with a given probability.
95% Tolerance limits Limits within which one
can state with chosen confidence that 95% of the
individuals of a population will lie.
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APPENDIX H.
COMPARISON OF METHODS FOR SIGNIFICANCE OF
DIFFERENCE IN PRECISION AND ACCURACY
The methods with respect to precision and accuracy are compared
in two ways. In the first case, two methods are compared at a given
level of concentration with respect to precision and with respect to
accuracy. The variances of the two methods are first compared by
the F-test (U to determine whether there is a significant difference
in the precision of the two methods. The means of the two methods
then are compared by the t-test (2) to determine whether there is a
significant difference in the accuracy of the two methods. The t-test
employed is based on the F-test results. These two tests of hypothe-
ses will produce one of the following results.
Outcome 1: s
2 2
Outcome 2: l 2 X 1
Outcome 3: s # 2 l 2
Outcome 4: l x 2
In outcome 1, we conclude that no significant difference in precision
or accuracy exists between the two methods.
In outcome 2, we conclude that there is no significant difference in
precision between the two methods, but there is a significant difference
in the accuracy of the two methods; specifically the method whose mean
is closer to the true value is the more accurate. In outcome 3, we con-
clude there is no significant difference in the accuracy of the two methods,
but the method with the smaller variance is the more precise.
In outcome 4, we conclude that there is a significant difference in the
precision and in the accuracy of the two methods. The method with the
smaller variance is the more precise, and the method whose mean is
closer to the true value is the more accurate.
In the second case, more than two methods are compared at a given
level of concentration with respect to precision and with respect to accur-
acy. Bartletts Test (3) is used first to test the hypothesis of equality of
variances in order to compare the precision of the methods. If the pre-
cision is the same, the Analysis of Variance (4) is then used to test whether
a significant difference exists in the means, in order to compare the
34
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accuracy of the methods. If there is a si&nificant difference in the
means) Duncants Multiple Range Test is used to determine which
method means differ significantly. If the precision is not the same(
then the Kruskal-Wallis One-Way Analysis of Variance by Ranks (7 is
used to determine whether significant difference exists in the means,
in order to compare the accuracy of the methods.
Once again, there are basically four possible outcomes after the
above tests of hypotheses.
Outcome 1: all 2 are equal, all X. are equal
Outcome 2: all s are equal, not all are equal
Outcome 3: not all s are equal, all X. are equal
Outcome 4: not all srare equal, not allX. are equal
In outcome 1, we conclude there is no significant difference in the
precision or the accuracy of the methods.
In outcome 2, we conclude there is no significant difference in the
precision of the methods; however, at least one method does differ
significantly with respect to accuracy, and Duncans Multiple Range
test tells us which methods differ._ For example, in comparinA four
methods, we might conclude X 1 X 9 and X 3 s X 4 but Xuand X2 differ
significantly from X 3 and X 4 ,or we might conlude X 1 = but
X 4 differs significantly from X 1 , X 2 and X 3 .
in outcome 3, we conclude there is no significant difference in the
accuracy of the methods, but the precision is different in at least one
method.
In outcome 4, we conclude that both accuracy and precision of the
methods differ significantly.
REFERENCES
1. Ostle, Bernard. Statistics in Research. Iowa State University
Press. 1963. p. 123.
2. Ostle, Bernard. Statistics in Research. pp. 119-20.
3. Ostle, Bernard. Statistics in Research. pp. 136-38,
35
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4. Hicks, Charles. Fundamental Concepts in the Design of Exper-
iments. Holt, Rinehart, Winston. 1964. pp. 21-28.
5. Hicks, Charles. Fundamental Concepts in the Design of Exper-
iments. Holt, Rinehart, Winston. 1964. pp. 31-33.
6. Kramer, Clyde. Extension of Multiple Range Tests to Group
Means with Unequal Numbers of Replications. Biometrics.
12:307-10. 1956.
7. Siegel, Sidney. Nonparametric Statistics. McGraw-Hill. 1956.
pp. 184-94.
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APPENDIX E.
ANALYTICAL REFERENCE SERVICE MEMBERSHIP
STATE AGENCiES
Alabama State Department of Public Health, Montgomery
Alabama Water Improvement Commission, Montgomery
Arizona State Department of Public Health, Phoenix
Arkansas State Department of Health, Little Rock
California Department of Water Resources, Los Angeles
California Department of Water Resources, Sacramento
California State Department of Public Health, Los Angeles
California State Department of Public Health, Air and Industrial Hygiene
Laboratory, Berkeley
California State Department of Public Health, Sanitation and Radiation
Laboratory, Berkeley
Colorado Department of Public Health, Denver
Connecticut State Department of Heal th, Hartford
Delaware Water Pollution Commission, Dover
Florida State Board of Health, Jacksonville
Florida State Board of Health, Miami
Florida State Board of Health, Pensacola
Florida State Board of Health, Winter Haven
Georgia Department of Public Health, Atlanta
Hawaii Department of Health, Laboratories Branch, Honolulu
Hawaii Department of Health, Occupational and Radiological Health
Section, Honolulu
Idaho Department of Health, Boise
Ill inois Department of Air Pollution Control, Chicago
Illinois Department of Public Health, Springfield
Illinois Department of Public Health, State Sanitary Water Board,
Springfield
Illinois State Water Survey Division, Champaign
Illinois State Water Survey Division, Peoria
Indiana State Board of Health, Indianapolis
Iowa State Hygienic Laboratory, Des Moines
Iowa State Hygienic Laboratory, Iowa City
Kansas State Department of Health, Topeka
Kentucky State Department of Health, Division of Laboratory Services,
Frankfort
Kentucky State Department of Health, Public Health Laboratories
Division, Frankfort
Kentucky State Department of Health, Radiological Health Program,
Frankfort
Lawrence Experiment Station, Massachusetts
Louisiana State Board of Health, New Orleans
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Maryland State Department of Health, Baltimore
Massachusetts Department of Public Health, Amherst
Massachusetts Department of Public Health, Boston
Michigan State Department of Health, Lansing
Michigan Water Resources Commission, Lansing
Minnesota Department of Health, Engineering Laboratories Section,
Minneapolis
Missouri Department of Public Health and Welfare, Jefferson City
Montana Bureau of Mines and Geology, Butte
Montana State Board of Health, Helena
Nebraska Department of Agriculture and Economic Development, Lincoln
Nebraska State Department of Health, Lincoln
Nevada State Department of Health, Las Vegas
Nevada State Department of Health, Reno
New Hampshire Department of Health, Concord
New Hampshire Water Pollution Commission, Concord
New Jersey State Department of Health, Trenton
New Jersey State Department of Health, Environmental Health Labora-
tories, Trenton
New Mexico Department of Public Health, Santa Fe
New York State Air Pollution Control Board, Albany
New York State Conservation Department, Oakdale, L. I.
New York State Conservation Department, Scottsville
New York State Department of Air Pollution Control, New York City
New York State Department of Health, Albany
North Carolina Department of Water Resources, Raleigh
North Dakota State Department of Health, Bismarck
Ohio Department of Agriculture, Reynoldshurg
Ohio State Department of Health, Columbus
Oklahoma State Health Department, Oklahoma City
Oregon State Board of Health, Division of Sanitation and Engineering.
P 0 rtland
Pennsylvania Department of Agriculture, Harrisburg
Pennsylvania Department of Health, Bureau of Air Pollution Control,
Pittsburgh
Pennsylvania Department of Health, Division of Air Pollution Control,
Harrisburg
Pennsylvania Department of Health, Division of Sanitary Engineering,
Harrisburg
Puerto Rico Aqueduct and Sewer Authority
Puerto Rico Institute of Health Laboratories, Hato Rey
Rhode Island State Department of Health, Providence
South Carolina Department of Agriculture, Columbia
South Carolina Pollution Control Authority, Columbia
South Dakota Department of Health, Pierre
Tennessee Department of Public Health, Divison of Industrial Hygiene
Service, Nashville
Tennessee Department of Public Health, Radiological Health Service,
Nashville
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Tennessee Stream Pollution Control Board, Nashville
Texas State Department of Health, Austin
Utah State Department of Health, Salt Lake City
Vermont Department of Water Resources) Montpelier
Vermont State Department of Health, Barre
Vermont State Public Health Laboratory, Burlington
Virginia State Department of Health, Bureau of Industrial Hygiene,
Richmond
Virginia State Department of Health, Bureau of Laboratories, Richmond
Virginia State Water Control Board, Richmond
Washington State Department of Health, Seattle
Washington State Food and Drug Laboratory) Seattle
West Virginia Department of Natural Resources, Charleston
Wisconsin Department of Agriculture) Madison
Wisconsin State Laboratory of Hygiene, Madison
MUNICIPAL AGENCIES
Baltimore Health Department, Maryland
Beaumont Health Department, Texas
Bureau of Air Pollution Control and Heating Inspection, Cincinnati, Ohio
Central District Filtration Plant, Chicago, Illinois
Charlotte Water Department, North Carolina
City of Allentown, Bureau of Water, Pennsylvania
City of Amarillo, Water Reclamation Sewage Treatment Plant, Texas
City of Durham, Department of Water Resources, North Carolina
City of Erie, Bureau of Water, Pennsylvania
City of Los Angeles, Department of Water and Power, California
City of Miami, Alexander Orr, Jr. Water Treatment Plant, Florida
City of New York, Department of Health) New York
City of New York, Department of Health, Food and Drug Laboratory
New York
City of Niagara Falls, Division of Water Laboratories, New York
City of Seattle, Water Department, Washington
City Water Department of Chattanooga, Tennessee
City of Yonkers, Bureau of Water, New York
Department of Health and Hospitals, St. Louis, Missouri
Department of Public Works and Utilities, Flint, Michigan
Department of Service and Buildings, Dayton, Ohio
Easterly Pollution Control Center, Cleveland, Ohio
Erie County Laboratory, Buffalo, New York
Fresno Department of Public Health, California
Houston Health Department, Texas
Long Beach Water Department, California
Los Angeles Air Pollution Control District, California
Los Angeles County Air Pollution Control District, California
Los Angeles County Flood Control District, California
Los Angeles County Sanitation District, Pomona, California (In coop-
eration with FWPCA)
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Los Angeles Department of Public Works, California
Louisville Water Company, Kentucky
Metropolitan St. Louis Sewer District, Missouri
Metropolitan Sanitary District of Greater Chicago, Illinois
Metropolitan Utilities District, Omaha, Nebraska
Metropolitan Water District of Southern California, La Verne, California
Minneapolis Water Department, Minnesota
Monroe County Department of Health, Rochester, New York
Nassau County Department of Health, Hempstead, New York
Nassau County Department of Health, Mineola, New York
Newburgh Water Department, New York
North Jersey District Water Supply Commission, Wanaque
Orange County Air Pollution Control District, Anaheim, California
Pasadena Department of Public Health, California
Philadelphia Department of Public Health, Environmental Health
Laboratory, Pennsylvania
Philadelphia Department of Public Health, Public Health Laboratory,
Pennsylvania
Philadelphia Suburban Water Company, Bryn Mawr, Pennsylvania
Philadelphia Water Department, Pennsylvania
Philadelphia Water Department, Belmont Laboratory, Pennsylvania
Philadelphia Water Department, Torres dale Laboratory, Pennsylvania
St. Louis County Water Company, Missouri
Springwells Filtration Plant, Dearborn, Michigan
Topeka Water Department, Kansas
Water Pollution Control Board, Cincinnati, Ohio
FEDERAL AGENCIES
Associated Universities Incorporated, Brookhaven National Laboratory,
Upton, Long Island, New York
DHEW, Food and Drug Administration, Chicago, Illinois
DHEW, Food and Drug Administration, Division of Pharmacology,
Washington, D.C.
DREW, PHS, Air Pollution Technical Information Center, Division of
Air Pollution, Washington, D. C.
DHEW, PHS, Communicable Disease Center, Savannah, Georgia
DHEW, PHS, Division of Dental Public Health, Bethesda, Maryland
DREW, PHS, Information and Education Office, Division of Air Pollution,
Washington, D.C.
DHEW, PHS, Interstate Carrier Branch, Division of Environmental
Engineering and Food Protection, Washington, D.C.
DHEW, PHS, Northeast Shellfish Sanitation Research Center, Narragan-
sett, Rhode Island
DHEW, PUS, Northeastern Radiological Health Laboratory, Winchester,
Massachusetts
DHEW, PHS, Radiological Health Laboratory, Rockville, Maryland
DHEW, PHS, Regional Office IV, Atlanta, Georgia
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DHEW, PHS, Southeastern Radiological Health Laboratory, Montgomery,
Alabama
DHEW, PBS, Southwestern Radiological Health Laboratory, Las Vegas,
Nevada
District of Columbia Department of Public Health, Washington, D. C.
Fourth U. S. Army Medical Laboratory, Fort Sam Houston, Texas
Oak Ridge Institute of Nuclear Studies, Oak Ridge, Tennessee
Pearl Harbor Naval Shipyard, Honolulu , Hawaii
Regional Environmental Health Laboratory, McClellan AFB, California
San Francisco Bay Naval Shipyard, Vallejo, California
Sixth U.S. Army Medical Laboratory, Fort Baker, California
Tennessee Valley Authority, Chattanooga, Tennessee
Tennessee Valley Authority, Wilson Dam, Alabama
2794th USAF Dispensary, Kelly AFB, Texas
USAF Hospital (HWEBB), Wright Patterson AFB, Ohio
U. S. Army Environmental Hygiene Agency, Edgewood Arsenal, Maryland
U.S. Bureau of Sports, Fisheries, and Wildlife, Fish-Pesticide Research
Laboratory, Denver, Colorado
USD1, Bureau of Reclamation, Denver, Colorado
USD1, FWPCA, Great Lakes-Illinois River Basins Project, Chicago,
l l finois
USD1, FWPCA, Ohio River Basin Project, Wheeling, West Virginia
USD1, FWPCA, Pacific Northwest Water Laboratory, Corvallis, Oregon
USD1, FWPCA, Technical Services Division, Washington, D. C.
USD1, FWPCA, Water Quality Activities, Cincinnati, Ohio
USD1, Geological Survey, Columbus, Ohio
USD1, Geological Survey, Denver, Colorado
USD1, Geological Survey, Little Rock, Arkansas
USD1, Geological Survey, Philadelphia, Pennsylvania
USD1, Geological Survey, Sacramento, California
Walter Reed Army Medical Center, Washington, D. C.
Water PoHution Control Federation, Washington, D. C.
FOREIGN AGENCIES
British Coke Research Association, Chesterfield, Derbyshire, England
Central Electricity Research Laboratories, Leatherhead, Surrey, England
Citys Institute for Health Protection, Belgrade, Yugoslavia
Comissao Inter-Municipal de Controle da Poluicao das Aguas E Do Ar,
Sao Paulo, Brazil
Department of Energy, Mines and Resources, Ottawa, Ontario, Canada
Department of Health Services and Hospital Insurance, Vancouver, Canada
Department of Municipal Laboratories, Hamilton, Ontario, Canada
Department of National Health and Welfare, Vancouver, Canada
Department of National Health and Welfare, Occupational Health Division,
Ottawa, Ontario, Canada
Department of National Health and Welfare, Public Health Engineering
Division, Ottawa, Ontario, Canada
41
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Department of Public Health, Sydney, Australia
Instituto Nacional de Obras Sanitarias, Caracas, Venezuela
Israel Institute of Technology, Haifa
Metropolitan Corporation of Greater Winnipeg, Manitoba, Canada
Metropolitan Water, Sewage, and Drainage Board, Sydney, Australia
Minestere de la Sante, Montreal, Quebec, Canada
National Institute of Hygienic Sciences, Tokyo, Japan
National University of Colombia, Bogota
Newcastle and Gateshead Water Company, Newcastle Upon Tyne, England
Obras Sanitarias de la Nacion, Buenos Aires, Argentina
Ontario Water Resources Commission, Toronto, Ontario, Canada
Osaka City Institute of Hygiene, Japan
Permutit Company, Limited, London, England
Public Health Laboratory Service, London, England
Scientific Research Council, Kingston, Jamaica, West Indies
Taiwan Institute of Environmental Sanitation, Air Pollution and Radiolog-
ical Health Section, Taipei, Taiwan, China
Taiwan Institute of Environmental Sanitation, Division of Water Quality
and Pollution Control, Taipei, Taiwan, China
Taiwan Institute of Environmental Sanitation, PHA, Taiwan, China
Taiwan Institute of Environmental Sanitation, Taichung Water Laboratory,
Taichung, Taiwan, China
Taiwan Institute of Environmental Sanitation, Tainan Water Laboratory,
Tainan, Taiwan, China
Taiwan Institute of Environmental Sanitation, Taitung Water Laboratory,
Taitung, Taiwan, China
Taiwan Institute of Environmental Sanitation, Training Section, Taipei,
Taiwan, China
United Kingdom Atomic Energy Authority, Didcot, Berks, England
University of Beograd, Yugoslavia
University of Leeds, England
Waste Water Research Laboratory of the Swedish Forest Industries,
Stockholm, Sweden
Water Research Association, Marlow, Buckinghamshire, England
UNIVERSITIES
University of California, Richmond
University of California, Riverside
University of California, Engineering Material Laboratory, Berkeley
University of California, Industrial Hygiene Engineering, Berkeley
Clemson University, South Carolina
Columbia University, Plainsboro, New Jersey
Cornell University, Ithaca, New York
University of Dayton, Ohio
Drexel Institute of Technology, Philadelphia, Pennsylvania
Fairleigh Dickinson University, Madison, New Jersey
University of Florida, College of Engineering, Gainesville
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Iowa State College, Ames
University of Kansas, Lawrence
Louisiana State University, Baton Rouge
University of Minnesota, Minneapolis
New York University Medical Center, New York
University of North Carolina, Chapel Hill
University of Pittsburgh, Pennsylvania
Purdue University, Lafayette, Indiana
Rennselaer Polytechnic Institute, Troy, New York
Rutgers The State University, Department of Agricultural Chemistry,
New Brunswick, New Jersey
Rutgers The State University, Department of Environmental Science,
New Brunswick, New Jersey
St. Marys College, Winon.a, Minnesota
University of Vermont, Burlington
Washington State University, Pullman
INDUSTRIES
American Bio - Chemical Laboratory, Incorporated, Baltimore, Maryland
American Cyanamid Company, Wayne, New Jersey
Anaconda Company, Grants, New Mexico
Battelle Memorial Institute, Columbus, Ohio
Bethlehem Steel Company, Bethlehem, Pennsylvania
Bio-Technics Laboratories, Incorporated, Los Angeles, California
Borg-Warner Corporation, Des Plaines, Illinois
Brown and Caldwell Laboratories, San Francisco, California
Calgon Corporation, Pittsburgh, Pennsylvania
California Water Service Company, San Jose, California
Carnation Research Laboratories, Van Nuys, California
Chrysler Corporation, Detroit, Michigan
Controls for Radiation Incorporated, Sandusky, Ohio
Coyne Chemical Company, Los Angeles, California
Culiigan Incorporated, Northbrook, Illinois
Cyrus Wm. Rice and Company, Pittsburgh, Pennsylvania
Dow Chemical, Midland, Michigan
E.I. du Pont de Nemours and Company, Aiken, South Carolina
Ekroth Laboratories, Incorporated, Brooklyn, New York
Emery Industries, Incorporated, Cincinnati, Ohio
FMC Corporation, Pocatello, Idaho
Fairbanks, Morse and Company Research Center, Beloit, Wisconsin
General Electric Company, Louisville, Kentucky
Good Year Atomic Corporation, Piketon, Ohio
H. C. Nutting Company, Cincinnati, Ohio
Hammond -Montel, Incorporated, Elmhurst, New York
Havens-Emerson, Oradell, New Jersey
Hazleton Nuclear Science Corporation, Palo Alto, California
Holzmacher, McLendon and Murrell, Melville, New York
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Industrial Chemicals, Incorporated, South Bend, Indiana
INFILCO, General American Transportation Corporation, Tucson, Arizona
Jonics, Incorporated, Watertown, Massachusetts
Johns -Manville Research and Engineering Center, Manville, New Jersey
Kern-Tech Laboratories, Incorporated, Baton Rouge, Louisiana
Kennecott Copper Corporation, Salt Lake City, Utah
Magnus Chemical, Garwood, New Jersey
Mailinckrodt Chemical Works, St. Louis, Missouri
Maryland Cooperative Milk Producers, Incorporated, Baltimore, Maryland
McCrone Associate, Incorporated, Chicago, Illinois
Midwest Research Institute, Kansas City, Missouri
Minute Maid Company, Anaheim, California
Monsanto Company, St. Louis, Missouri
Moutrey and Associates, Incorporated, Oklahoma City, Oklahoma
NALCO Chemical Company, Chicago, illinois
New Mexico Institute of Mining and Technology, Socorro
Nuclear-Chicago Corporation, Des Plaines, Illinois
Pacific Gas and Electric Company, Emeryville, California
Pacific Gas and Electric Company, San Francisco, California
Pan American World Airways, Patrick Air Force Base 1 Florida
Pomeroy, Johnston and Bailey, Pasadena, California
Radiation Applications, Incorporated, Long Island, New York
Radiation Detection Company, Mountain View, California
Ray W. Hawksley Company, Incorporated, Richmond, California
Reynolds Electrical and Engineering Company, Incorporated, Industrial
Hygiene, Las Vegas, Nevada
Reynolds Electrical and Engineering Company, Incorporated, Radiological
Sciences Department, Las Vegas, Nevada
Roy F. Weston, Incorporated, West Chester, Pennsylvania
Sandia Corporation, Albuquerque, New Mexico
Shell Chemical Company, Princeton, New Jersey
Southern Testing and Research Laboratories, Incorporated, Wilson
North Carolina
Tracerlab, Incorporated, Richmond, California
U. S. Industrial Chemicals Company, Tuscula, Illinois
W. R. Grace and Company, Dearborn Chemical Division, Lake Zurich,
Illinois
Wastewater Analysis Corporation, Lincoln Park, Michigan
Water Service Laboratories, Incorporated, New York, New York
York Research Corporation, Stamford, Connecticut
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