United States
 Environmental Protection
 Agency
Environmental Monitoring
Systems Laboratory
Las Vegas NV 89193-3478
 Research and Development
EPA/600/S4-89/037 June 1990
   Project Summary

   Direct/Delayed  Response
   Project: Quality  Assurance
   Report  for  Physical  and
   Chemical Analyses  of  Soils
   from  the  Northeastern  United
   States
  G. E. Byers, R. D. Van Remortel, J. E. Teberg, M. J. Miah, C. J. Palmer, M.
  L. Papp, W. H. Cole, A. D. Tansey, D. L. Cassell, and P. W. Shaffer
  The Northeastern soil survey was
conducted during 1985 as a synoptic
physical and chemical  survey to
characterize a statistical sampling of
watersheds in a region of the United
States believed to be susceptible to
the effects of acidic deposition. This
document    addresses    the
implementation  of  a quality
assurance   program  and  the
verification of the analytical  data
base for the Northeastern  Soil
Survey.  It is focused primarily
towards the users of the data  base
who will be analyzing the data and
making  various assessments and
conclusions relating to the effects of
acidic deposition on the soils of the
Northeastern  region  of the  United
States.  The quality  assurance
program is evaluated  in terms of its
success in  identifying potential
problems that could  have an  effect
on the quality of the data. Verification
procedures  used  to  analyze
laboratory data are described. Quality
is assessed by describing the
detectability, precision, accuracy
(interlaboratory differences), repre-
sentativeness,  completeness, and
comparability of the data for the
quality  assurance samples  used
throughout the soil survey. Detection
limits and two-tiered  precision data
quality objectives were established
for most of the parameters. A step-
function statistical approach  was
used to assess precision.
  This  Project  Summary was
developed by EPA's Environmental
Monitoring Systems Laboratory, Las
Vegas, NV, to  announce key findings
of the research project that is  fully
documented in a  separate report  of
the  same title (see Project  Report
ordering information at back).
Introduction
  The U.S.  Environmental Protection
Agency (EPA), as a participant in the
National Acid Precipitation Assessment
Program, has designed and implemented
a research program to predict the  long-
term response of watersheds and surface
waters in the United States  to acidic
deposition. Based on this research, a
sample of watershed systems will be
classified according to the time scale in
which each system will reach  an acidic
steady state, assuming current levels of
acidic deposition. The Direct/Delayed
Response Project (DORP) was designed
as the terrestrial component of the EPA
Aquatic Effects Research Program.
  The  mapping  for  the  DORP
Northeastern Soil Survey was conducted
during the Spring  and Summer of  1985

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and the sampling took place during  the
Summer  and Fall  of  1985.  These
activities  initiated  the first full-scale
regional  survey of  the  DDRP.  The
physical and  chemical properties that
were measured in the soil samples are
listed below.

Data Quality Objectives and
Assessment
  The quality assurance (QA) program,
soil  sampling  techniques,  sample
preparation, and analytical methods, were
designed to  satisfy  the  data  quality
objectives (DQOs) for field and analytical
data and to  assess the  variability  of
sampling,  preparation, and  analytical
performance. The DQOs for this survey
were  directed  toward  detectability,
precision, and completeness. Accuracy,
representativeness,  and comparability of
the data were also  assessed,  although
specific DQOs  were  not  imposed  on
these attributes.

Detectability
  The instrument detection  limit is the
lowest value that an analytical instrument
can reliably detect  above instrument
background  concentrations and was
calculated for each analyte as three times
the standard  deviation of a low  level
quality  control check  sample, under the
assumption  that its  variability would
approximate the  variability  of  an
analytical  blank  sample.  The  system
detection  limit indicates the variability of
a  blank sample resulting  from  sample
collection, processing, extraction, and
analysis. The system  detection limit was
estimated  for each  parameter as the
variability  in  the  ten percent  of field
duplicates   that   had  the  lowest
concentration  of the analyte of interest.
Contracts with the analytical laboratories
established  maximum  allowable
instrument detection  limits,  defined  as
contract-required  detection  limits;
however,  no  DQOs  were established  for
system detectability.

Precision
   Precision  is the degree  of scatter of
repeated  measurements.  Measurement
imprecision is distinct from the overall
variability  in  the  population  itself.
Determination  of  measurement
imprecision and its sources  in  the
Northeastern Soil Survey  relied strongly
on the analyses of  the QA samples and
was a function  of  the  intralaboratory
within-batch  precision DQOs defined in
the  QA  Plan.  Overall  variability
(measurement  and  population) was
estimated from the  routine data. No
DQOs were established for the sampling
or preparation phases of the survey.
  The  precision DQOs  were  charac-
terized by use  of a  two-tiered system.
Below a specific concentration, called the
knot, precision is defined  as  a standard
deviation in absolute reporting  units;
above the knot, precision is defined as a
percent relative standard deviation. In
order to  address the  issue  of
concentration-dependent  variance, the
range of  soil analyte concentrations was
divided  into  appropriate  intervals
(windows) within which the error variance
was  relatively constant. A step function
was  fitted  across  the  windows  to
represent the error variance for the entire
concentration range.  Different  step
functions were  used  to assess  the
variability for each QA sample type. The
data  uncertainty in the  routine  samples
due  to  collection  error  was  also
measured.
Accuracy (Interlaboratory
Differences)
  Accuracy  is  the  ability  of  a
measurement system to approximate a
true value.  Accuracy could  not  be
determined because the audit  samples
used in the  survey  were natural  soil
samples with chemical composition  and
physical properties that were  not  known
with any confidence. Due to this lack of
acceptable  values  for  accuracy
estimates,  the data were assessed only
for interlaboratory differences.
  Three types  of comparisons  made
were: (1) the use of a pair-wise statistical
test for significance between laboratories;
(2)  the pooling of audit sample data for
each laboratory for direct interlaboratory
comparisons; and (3)  the  pooling of
laboratory  data for each audit  sample for
comparison of laboratory performance
among audit samples.
Representativeness
  The  representativeness objectives of
the survey were qualitative in nature. The
general objectives were that: (1) the soil
samples collected by the field crews be
representative of the soil sampling class
characteristics, (2)  the samples  be
homogenized and subsampled properly
by the preparation laboratory, and (3) the
QA  duplicate  samples  adequately
represent  the  range  of   analyte
concentrations  found  in  the  routine
samples.
Completeness
  The  100 percent  completen
objectives  of  the  survey  were
determine  whether  (1)  all  ped
designated  for sampling were acti
sampled  by the field crews,  (2)
samples  received  by the  prepara
laboratories were prepared and analy.
and  (3) all  samples  received  by
analytical laboratories were analyzed
that 90   percent  of the  requi
measurements  were made  on
samples.  Enough data were provide<
allow statistically significant  conclus
to be drawn. Data qualifiers, or flags,
completeness were inserted in the <
base to indicate any missing values.

Comparability
  Data comparability  objectives  w
qualitative  in nature.  The goal was
comparability  of data  from the
surveys  within  the DDRP  and  for
DDRP surveys to be comparable  to o
similar  programs. The stated objec
was the uniform use of known, accep
and  documented procedures  for
location, soil sample collection,  sanr
preparation, extraction,  analysis, ,
standard  reporting  units for
Northeastern  Soil Survey. Unif(
QA/QC protocols and on-site  inspect!
assured that these  procedures  w
implemented properly.  The  resull
analytical data  should be comparable
those from other surveys.

Quality Assurance and Qualit
Control Samples
  Quality assurance samples were u;
to independently assess data quality ,
monitor the internal  quality control ((
procedures. The composition and ider
of the QA  samples were unknown to
analyst. Three types of QA samples w
used in the Northeastern Soil Survey:
field duplicates  (soil samples  w
collected  by each  sampling  crew fr
one horizon of  one pedon each day ;
were placed  randomly  in  the  sam
batch with  the  other samples from
same pedon); (2) preparation  duplicc
(one soil  sample per sample batch \
selected and split by  the  preparal
laboratory  and  placed randomly in
sample  batch);  and (3)  natural  at
samples (one duplicate sample pair fr
a  homogenized  bulk  soil  samp
representing one of five soils typical
the eastern  United  States  were  pla<
randomly in the sample batch).
  The composition of QC samples w
known  and the  analytical  results fr
each  laboratory were  required to

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Physical and Chemical Properties Measured in the Direct/Delayed Response Project Northeastern Soil Survey
 Air-dry Moisture Content
 Specific Surface Area
 Total Sand
 Very Coarse Sand
 Coarse Sand
 Medium Sand
 Fine Sand
 Very Fine Sand
 Total Silt
 Coarse Silt
 Fine Silt
 Total Clay
 pH in Deionized Water
 pH in 0.002M CaCI2
 pHin0.01MCaCI2
Ca in 1 .OM Ammonium Chloride
Mg in 1 .OM Ammonium Chloride
K in 1 .OM Ammonium Chloride
Na in 1 .OM Ammonium Chloride
Ca in 1 .OM Ammonium Acetate
Mg in 1 .OM Ammonium Acetate
K in 1 .OM Ammonium Acetate
Na in 1.0M Ammonium Acetate
CEC in 1 .OM Ammonium Chloride
CEC in 1 .OM Ammonium Acetate
Ex. Acidity by f.OM KCl
Ex. Acidity by BaCI2-TEA
Ext. Aluminum in 1 .OM KCl
Ca in 0.002M Calcium Chloride
Mg in 0.002M Calcium Chloride
K in 0.002M Calcium Chloride
Na in 0.002M Calcium Chloride
Fe in 0.002M Calcium Chloride
Al in 0.002M Calcium Chloride
Ext. Fe in Pyrophosphate
Ext. Al in Pyrophosphate
Ext. Fe in Ammonium Oxalate
Ext. Al in Ammonium Oxalate
Ext. Fe in Citrate Dithionite
Ext. Al in Citrate Dithionite
Ext. Sulfate in Deionized Water
Ext. Sulfate in Sodium Phosphate
Sulfate Isotherm 0 mg sulfur/L
Sulfate Isotherm 2 mg sulfur/L
Sulfate Isotherm 4 mg sulfur/L
Sulfate Isotherm 8 mg sulfur/L
Sulfate Isotherm 16 mg sulfur/L
Sulfate Isotherm 32 mg sulfur/L
Total Carbon
Total Nitrogen
Total Sulfur
compared with  the  accepted values  as
the  samples  are analyzed.  This
immediate feedback  on the functioning of
the analytical system allowed  analytical
and sample processing problems  to  be
resolved quickly, with the result that error
from that source was minimized.  Six
types of  QC  samples were used  in  the
 Northeastern  Soil  Survey:  (1) calibration
blanks  were used  as a check for sample
contamination and for baseline drift in the
analytical  instrument immediately after
calibration; (2) reagent blanks underwent
the same treatment  as the  routine
samples  and  served  as  a check  for
reagent contamination; (3)  QC  check
samples contained the analyte of interest
in the  mid-calibration range  and  served
as  a  check  on the  accuracy and
consistency  of the calibration of  the
instrument throughout the  analysis  of the
sample batch;  (4)  detection  limit  QC
check  samples  were low  concentration
samples that  eliminated the necessity of
determining the detection  limit every day
and allowed accuracy to  be  determined
at the low end of the  calibration range;  (5)
matrix  spikes were  sample  aliquots  to
which  known quantities of analyte  are
added  for determining the sample  matrix
effect on  analytical  measurements; and
(6) analytical  duplicates were splits of a
single sample and were used to estimate
analytical within-batch precision.
  In addition  to the  use of QC samples
for quality control, two system audits, or
on-site evaluations were also conducted,
one  immediately after  award of  the
contract  to  the laboratories  and  the
 econd after sample  analysis had begun.
 Internal Consistency Checks
   An  internal consistency  computer
 program provided a meaningful  check of
 routine data by identifying values  that
 differed  from  the  majority of observed
 values  and  that  might have gone
 unnoticed had the check not been made.
 The checks uncovered  errors  in data
 entry and transcription as well as errors
 that occurred on an  analytical batch
 basis.  In  this study,  a correlation
 approach was used to assess  internal
 consistency in which  the  coefficients of
 determination  were  obtained  by
 performing  weighted linear regressions.
 From  the  regressions,  studentized
 residuals and DFFITS statistics  were
 calculated to identify extreme data values
 that could be considered outliers.
   Outliers determined  by the computer
 program and  representing only about 1
 percent  of  the data,  were checked  for
 transcription errors. For a few analyses, a
 significant  number  of  outliers  were
 present. There were  some  parameters
 that did not  correlate well with any of the
 other parameters.

 Data Management
   The field  sampling and  analytical data
 were  entered into the  Northeastern
 survey data bases at Oak Ridge National
 Laboratory  in Tennessee. Both data
 bases progressed through three  phases:
 raw, verified, and validated. The QA staff
 at  the EPA  Environmental  Monitoring
 Systems Laboratory  at Las   Vegas,
 Nevada, verified the two data bases. The
 field  sampling data  were entered  into
 data sets from specialized forms, visually
  checked, and frozen  as the  official raw
  data base. The analytical  data  were
  entered into data  sets and visually
  checked,  thereby allowing  errors  in
  transcription  to  be identified  corrected.
  The verification  stage was accomplished
  by  a   systematic  evaluation   of
  completeness and coding accuracy, and
  flags were used in the data base to note
  discrepancies.  The  verified  data base
  was used to  assess data for this  QA
  report.  The validation stage  identified,
  confirmed, and  flagged data  values that
  warrant special attention or caution when
  used for data analysis.

  Results and  Discussion

  Detectability
    The calculated  instrument detection
  limits were less  than the  contract-
  required detection limits in the majority of
  the cases of  parameters  for  which
  detection limits were  established. The
  DQOs established for  detectability were
  not met  for the cation exchange capacity
  parameters in  ammonium  chloride and
  ammonium  acetate,  aluminum  in
  potassium chloride, total carbon, and total
  nitrogen.  The  calculated  instrument
  detection limits  were  an  order  of
  magnitude above the DQOs for the cation
  exchange parameters.

  Precision
    The analytical within-batch precision
  objectives were satisfied for most of the
  parameters. These included clay, the pH
  parameters, exchangeable cations, cation
  exchange capacity  and  acidity,

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extractable  iron  and  aluminum,  the
extractable sulfates and sulfate isotherm
parameters, and  total  carbon,  nitrogen,
and sulfur. Occasionally, an objective was
not met for an upper or lower tier  of a
parameter. There  were some instances
where the DQOs were slightly exceeded
for  these parameters either  above or
below the knot. The DQOs were not met
for total sand and silt, extractable cations
in  calcium  chloride, and sulfate  zero
isotherm parameters. When the two  tiers
were pooled over the total concentration
range  for each  of the parameters, a
precision index showed that the particle
size and extractable cations  in calcium
chloride did not meet the overall DQOs.
  Although  no DQOs were  set for the
preparation  laboratory phase,  the
preparation  laboratory  within-batch
precision also met the DQOs set for
analytical within-batch  precision  for all
parameters except sand and silt, calcium
and  magnesium  in  ammonium acetate,
the two  cation exchange  capacities,
acidity in barium  chloride,  cations in
calcium chloride, extractable sulfate in
water,  sulfate zero  isotherm,  and  total
nitrogen. This  indicates   that  the
preparation  laboratories  performed
relatively well in subsampling  the  bulk
samples.
  Within-batch  imprecision  estimates
increased, as expected, from analytical to
sample preparation  to field sampling. The
between-batch precision estimates were
generally low.

Accuracy (Interlaboratory
Differences)
  The  Scheffe's  pair-wise   multiple
comparison test  showed that  about 5
percent of the interlaboratory differences
were significantly different  and  0.8
percent were highly significantly different.
The latter could be considered to be of
concern to  the  data  user.  Highly
significant differences were shown  mostly
for the cations in calcium chloride. About
half  the  cases  were  in  the  Bw audit
sample.
  The  lowest interlaboratory differences
were shown  for pH and the highest
differences  were shown for  the  cation
exchange capacity parameters, calcium
in calcium  chloride,  and  cations in
ammonium  chloride.  The  mean
interlaboratory differences for laboratories
1,2,3, and 4 were 9.5,  9.3, 11.6, and 6.9
percent, respectively.
  Among  the  audit  samples,  the
laboratories showed  the   highest
differences overall for the C horizon audit
sample and the lowest differences for the
A audit sample. The mean interlaboratory
differences for the Oa, A, Bs, Bw, and C
audit samples were 13.8, 9.3, 14.5, 10.7,
and 15.8 percent, respectively.
  No single  laboratory was consistently
superior to the others for all parameters
or parameter groups regarding  low
differences. Each laboratory appears  to
have individual  strengths for specific
analytical methods. This is probably a
reflection  of  the  combination   of
experience,  instrumentation, and
laboratory  management practices within
each  laboratory.  This  resulted in  a
patchwork of differences on a  parameter
group basis.
Representativeness
  The  field  duplicates  were repre-
sentative of the range of concentration in
the range of the routine samples for most
parameters. The  natural audit  samples
and  the preparation duplicates  were
rarely  representative  of the  routine
samples. The preparation duplicates
consistently  represented  only  the
extreme lower range of routine sample
concentrations for a given parameter. The
preparation   laboratory  personnel
apparently  selected  bulk samples  for
duplication  based  not  on  random
selection but  on the quantity  of  each
sample  available.  The  sampling crews
were able to collect the largest amount of
sample  from the thicker  horizons
normally found in the lower portion of the
soil  pedon.  Horizon  type  selection
appears to be highly skewed (73 percent)
toward  the transitional B and  C horizons
which typically have very low  analyte
concentrations in their extractions.
Completeness
  Ninety-six percent  of the designated
pedons were  sampled. Although  this
does not fully satisfy the  DQO of 100
percent for sampling completeness, a
sufficient  number  of  pedons  were
sampled  to  enable  estimates  and
conclusions to be drawn from the data.
The requested soil sampling and sample
processing tasks were performed on 100
percent of the samples received by the
preparation  laboratory which satisfies the
DQO  of 100  percent. The analytical
completeness level exceeded 99 percent
for all  parameters. Sufficient data were
generated to make conclusions for each
parameter in  the data bases,  with  the
possible exception of iron in the calcium
chloride extract.
Comparability
  The verified data bases were used
the assessment of data  quality for t
the Northeastern and the Southern E
Ridge Province soil surveys. The c
bases from each survey therefore can
compared  to each other.  Flags w
applied consistently.
  Sufficient audit sample  data w
available  from the  DDRP contr
laboratory analyses  to provide
estimate of the audit sample composi
for  use  in the assessment of precis
interlaboratory  differences,  £
comparability.  Data from  each ai
sample  can be compared between
two surveys for any given  parame
Significant differences  can thus
attributed to  differing  amounts
measurement  error. Reanalyses h,
corrected all data significantly affected
methods amendments which occurred
the survey progressed.
  Identical soil preparation methods w
used in  preparing  soil  samples  for
two surveys. The procedure for selecl
the preparation  duplicate was refined
the Southern Blue  Ridge survey, result
in  better  representativeness   of
preparation duplicate.
  Due to  an  inconsistent application
the sampling of  the field duplicates in
Northeastern survey, the variances
the field  duplicates tend to fluctu
among the pedons. Overall within-ba
variability  was greater  in the South
Blue  Ridge   survey  than   in 1
Northeastern survey. This suggests t
the  measurement   error  in  t
Northeastern  survey  may  have be
somewhat  underestimated.  However
does not  mean that  the routine d
between regions are not comparable. 1
same methodology was used  in i
routine soil sampling  for each  surv
There  were  no  deviations from I
sampling  protocols  that   woi
compromise the integrity of  the  rout
data. These field sampling discrepanc
that could  affect data comparability w>
documented  during the Northeast*
survey  and were resolved. The  fii
sampling audit  team did  not report <
deviations  from the sampling protoc
that would compromise the  integrity
the routine data for the  Southern B
Ridge Province  Soil Survey.
  As part of the DDRP, an interlaborat
methods   comparison  study  w
conducted which compared the  analy
of soils for two laboratories using '
DDRP methods to 16 statistically chos
external laboratories. These  laborator
used  their own  methods which w<

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similar,  but  not identical, to the DDRP
methods. The results of  the  study  will
show the comparability of the DDRP data
with  other similar surveys  using  other
laboratories.

Conclusions and
Recommendations
  Analysis of data from the  Northeastern
Soil  Survey  indicate  areas where
improvement is  needed  in  the  QA
program. The quality  assurance data are
presented in a manner considered to be
the most appropriate  for  use by  the
primary data users. The development of
this  approach  resulted  from  regular
interactions  with the  data  users. In
addition, the statistical approaches taken
and the formats used were assessed in
depth by several  external  reviewers to
ensure agreement  in  the  final
presentations. A great deal of information
is  included  in  the   many  figures  and
tables.  Each  user  has  a subjective
conception for data quality  as well as a
need for a specific level of data quality
desired  for his/her own  use. The user is
therefore encouraged to become familiar
in detail with the text, figures, and tables
in  order to  best  assess  the  data for
his/her specific needs.
  A  computerized  data  entry  and
verification system should be developed
that will calculate the final data values
and produce a list of flags and data entry
errors. A computer link  between  the
laboratories  and the quality  assurance
staff  should be  established  that  will
enable the transfer of preliminary  and
final  data.  The  verification  program
should be  designed to  evaluate  the
quality  control  checks and  other
contractual  requirements,  thereby
inducing  the  laboratories to  assume
much of  the  responsibility  for
identification and correction  of  errant
data.  Evaluation  of the  blind  audit
samples  should be  made part of  the
computer verification system and a better
procedure  for  checking  these values
should be  developed.  The  internal
consistency  checks should become an
integral part of the verification process.
  Attention should be given to  improving
detectability  in  future  surveys. Both
instrument   detection  and  system
detection limits should be addressed in
the  data  quality  objectives  for  future
surveys.
  The DQOs for total  sand and total silt
should be increased from  1.0 percent to
3.0  percent.  A  two-tiered  precision
objective  should  be defined for  the
extractable cations in calcium  chloride.
Specific data quality objectives  should be
defined for system wide measurement.
  Low concentration  audit   samples,
entered  into  the  system during  the
sampling phase,  should be utilized as
substitutes  for soil  blank samples. A
quality control soil audit sample should
be  incorporated  into  the  quality
assurance program to better monitor the
analytical results of the laboratories. The
laboratories should be required to report
the analytical results of the analyses on a
batch-by-batch basis to  the  QA staff
immediately after the analysis of  each
batch.  The laboratory protocols  should
specify  a statistically  valid  method for
selecting the preparation duplicate.
  An effort should be  made to locate an
uncontaminated filter material for the
determination  of  the  basic cations or
modify the pretreatment  procedure for
the  filter  material  used.  Additional
methods details should be reviewed and
provided where appropriate  in order to
reduce  within-laboratory  analytical
variability.
  The  DDRP staff should consider the
possibility of choosing  laboratories to
perform analyses  on a parameter  basis
for  those  parameters  or  parameter
groups  that revealed inherently  high
differences  or  where  specialized
instrumentation is used, e.g., total carbon.
nitrogen, and sulfur. A  more  stringent
laboratory selection procedure should be
adopted in the  pre-evaluation process for
the selection of contract laboratories.
  The  issue  of accuracy  should be
addressed because the current approach
using interlaboratory differences  has
limited utility. Data quality objectives for
accuracy should be established.

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G. £ flyers, R. D. Van Remortel, J. E. Teberg, M. J, Miah, M. L Papp, W. H. Cole
   and A. D. Tansey are with Lockheed Engineering and Sciences Company, Las
   Vegas,  NV 89119; C.  J. Palmer is  with  Environmental Research Center,
   University of Nevada, Las Vegas, NV 89114; and D. L. Cassell and P. W. Shaffer
   are with NSI Technology Services Corporation, Corvallis, OR 97333.
L J. Blume is the EPA Project Officer (see below)
The complete report, entitled "Direct/Delayed Response Project: Quality Assurance
   Report  for Physical and Chemical Analyses of Soils from  the Northeastern
   United States," (Order No. PB90-219 395IAS; Cost: $31.00, subject to change)
   will be available only from:
       National Technical Information Service
       5285 Port Royal Road
       Springfield, VA22161
       Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
       Environmental Monitoring Systems Laboratory
       U.S. Environmental Protection Agency
       Las Vegas,  NV 89193-3478
United States
Environmental Protection
Agency
Center for Environmental Research
Information
Cincinnati OH 45268
 Official Business
 Penalty for Private Use $300
US. OFFICIAL MAiL"
                                                                                    '^A'ENALTY

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