United States
Environmental Protection
Agency
Environmental Monitoring and Support
Laboratory
Cincinnati OH 45268
Research and Development
EPA-600/S4-83-036 Oct. 1983
Project Summary
Measurement of Mass Spectra
for the EPA Toxic Substances
Data Base
Lawrence H. Keith
A total of 3024 compounds were
procured to measure high quality mass
spectra for inclusion in the National
Institutes of Health (IMIH)/EPA mass
spectral data base. Compounds were
assayed for purity before mass spectra
were measured and, when necessary,
were purified by thin-layer chromatog-
raphy (TLC), recrystallization, sublima-
tion, or distillation. Compounds that
were sufficiently volatile to be amen-
able to gas chromatography (GC) and
less than 99% pure were introduced by
GC Compounds that were > 99% pure
and sufficiently volatile were intro-
duced by molecular leak. The direct
insertion probe was used for pure but
nonvolatile compounds. Quality con-
trol (QC) procedures included the use
of decafluorotriphenylphosphine (OF
TPP) for the molecular leak and GC
inlets and cholesterol for the direct
insertion probe inlet. Spectra of these
standards were required to meet strin-
gent acceptance criteria before every
four hours of instrument operation.
High quality mass spectra were mea-
sured for 2276 of 3024 compounds
procured.
This Project Summary was developed
by EPA's Environmental Monitoring
and Support Laboratory, Cincinnati,
OH, to announce key findings of the
research project that is fully docu-
mented in a separate report of the same
title. (See Project Report ordering infor-
mation at back).
Introduction
For more than ten years, the U.S. Envi-
ronmental Protection Agency (EPA) has
participated in development and expansion
of the mass spectral data base, a major
resource for pollutant identification in en-
vironmental monitoring. The data base is
disseminated on magnetic tape, in printed
form, and as a component of the NIH/EPA
Chemical Information System, an inter-
active search system available worldwide
via computer networks.
In 1978, when the EPA compiled an
inventory of chemicals manufactured in
the United States, only about 9% of the
43,278 chemicals in the inventory were
represented by mass spectra already entered
in the data base. To facilitate rapid identifi-
cation of environmental pollutants, a proj-
ect was initiated to expand the data base to
include as many as possible of the invento-
ried chemicals. A list of inventoried chemi-
cals absent from the data base was pre-
pared and prioritized according to pro-
duction volume with those chemicals pro-
duced in highest volume assigned highest
priority.
Efforts were made to obtain samples of
high priority chemicals. Each chemical
obtained was assayed to determine purity,
purified if necessary, and analyzed under
controlled conditions to measure a high
quality mass spectrum to represent that
chemical in the data base.
Procedure
A total of 3024 compounds were pro-
cured from the initial list of 13,281
candidate chemicals and were assayed
with TLC or with GC using various detec-
tors (flame ionization, thermal conductivity,
or specific element detector). When nec-
essary, chemicals were purified by TLC,
recrystallization, sublimation, or distillation.
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Chemicals with purity greater than 99%
and sufficient vapor pressure were
introduced into the mass spectrometer
through a molecular leak inlet. When
purity was less than 99% and the
chemicals sufficiently volatile, they were
introduced via GC. Impure chemicals not
amenable to GC introduction were
purified and introduced by direct inlet
probe after purification.
Because the quality of measured mass
spectra was of utmost concern, QC ref-
erence compound spectra were measured
before every four hours of instrument
operation to ensure acceptable mass
spectrometer performance; DFTPP was
used for the molecular leak and GC inlets;
and cholesterol was used for the direct
inlet probe. Spectra of QC compounds
were required to meet specified ion abun-
dance criteria, and the appropriate QC
spectrum was included with sample spec-
tra measured during the appropriate time
period. Sample spectra and accompanying
documentation were provided in both
hard copy form and on magnetic tape
Each spectrum was reviewed visually and
algorithmically to ensure that QC spectra
met acceptance criteria and to estimate a
quality index for sample spectra to be
added to the data base. A spectrum that
did not achieve the required quality index
was rejected, and an attempt was made to
correct the problem. All sample spectra
associated with an unacceptable QC spec-
trum were rejected and remeasured after
an acceptable QC spectrum was obtained.
Results
Of the 3024 chemicals procured for this
project high quality mass spectra were
measured for 2276 compounds and added
to the data base. For the remaining 748
chemicals, acceptable mass spectra could
not be measured. Approximately 380
chemicals could not be purified adequately;
87 were not amenable to analysis with
mass spectrometry; 278 did not produce
spectra that could be correlated with
chemical structure
Conclusions And
Recommendations
High quality mass spectra can be pro-
duced in large numbers if a carefully
controlled and documented program is
maintained. Much more time was required
to locate sources of many compounds
than was estimated. Often the chemical
obtained had no indication of purity, and
many contained significant amounts (5 to
25%) of impurities. Handling and storage
of large numbers of chemicals required
establishing safe procedures for chem-
icals that may be toxic, flammable, volatile,
noxious, or sensitive to light, air, moisture,
or heat Adding to the data base 2276
high quality spectra measured through
this project has increased the probability
of rapid and valid identification of environ-
mental pollutants in environmental moni-
toring activities.
Lawrence H. Keith is with Radian Corporation, Austin. TX 78766.
Ann Alford-Stevens is the EPA Project Officer (see below).
The complete report, entitled "Measurement of Mass Spectra for the EPA Toxic
Substances Data Base," (Order No. PB 83-255 844; Cost: $8.50. subject to
change) will be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA 22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Environmental Monitoring and Support Laboratory
U.S. Environmental Protection Agency
Cincinnati, OH 45268
*U.S. GOVERNMENT PRINTING OFFICE 1983-659-017/7203
United States
Environmental Protection
Agency
Center for Environmental Research
Information
Cincinnati OH 45268
Official Business
Penalty for Private Use $300
-------
United States
Environmental Protection
Agency
Environmental Monitoring Systems
Laboratory
Las Vegas NV 89114
Research and Development
EPA-600/S4-83-035 Oct. 1983
SEPA Project Summary
Statistical Correlations of Surface
Wind Data: A Comparison
Between a National Weather
Service Station and a Nearby
Aerometric Monitoring Network
John E. Langstaff, Anthony D. Thrall, and Mei-Kao Liu
This report presents a statistical anal-
ysis of wind data collected at a net-
work of stations in the Southeast Ohio
River Valley. The study determines the
extent to which wind measurements
made by the National Weather Service
(NWS) station at the Tri-State Airport
can be used to estimate the wind mea-
surements at network stations. A com-
bined stratification/regression analysis
was conducted. The analysis shows
that NWS station measurements can
be used to gain insight into the wind
measurements at network stations, and
a methodology is identified for carrying
this out With this methodology, we
demonstrate that the wind data collect-
ed at the airport can be used to provide
input to a complex-terrain wind model
for estimating the surface wind in the
study area for periods prior to the
establishment of the monitoring net-
work
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
Monitoring of human exposure to toxic
and hazardous chemicals released to the
environment can be expensive and time
consuming, particularly if extensive mete-
orological data must be acquired to support
modelling of pollutant distributions. Cost
effectiveness could be improved if data
from existing networks such as National
Air Surveillance Network (NASN) and/or
National Weather Service (NWS) stations
could be used to estimate surface wind
measurements in a given study area.
Recently, the Environmental Monitoring
Systems Laboratory, Las Vegas, of the
U.S. Environmental Protection Agency
(EPA), conducted a field measurement
program in the Southeast Ohio River Valley
in support of the design and development
of an exposure assessment monitoring
network As part of this study, surface
wind data were collected from a network
of stations temporarily established in the
Southeast Ohio River Valley and compared
with wind measurements made by the
National Weather Service (NWS) station
at the TrnState Airport
Weather observations recorded from
the NWS stations at the airport provide an
extended and continuous history at a single
location. Therefore, a primary objective of
this study was to examine the corrections,
if any, between the NWS wind measure-
ments and those made at other locations.
If the existence of a statistically signifi-
cant correlation between data measure-
ments made atTrt-State Airport and those
made at other locations could be establish-
ed, the NWS data could be utilized to
derive wind patterns in prior years. These
synthesized wind patterns could then be
used in a complex terrain wind field model
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to reproduce the detailed spatial distribu-
tion of the wind field.
Study Area
The study area encompasses the tri-state
junction of West Virginia, Kentucky, and
Ohio along the Ohio River. The area covers
over 250 square miles and contains ap-
proximately 160,000 people. The major
population centers are Huntington, Ashland,
and Ironton. The study area was divided
into a 44 x 33 grid of 1 km x 1 km cells.
The origin of the grid (La, the lower left
hand corner) was placed at 347000 E and
4243000 N in Zone 17 of the UTM
coordinate Wind monitoring stations were
installed at 12 sites in the study area The
names and UTM coordinates of the net-
work stations (as well as the NWS stations)
are given in Table 1.
Data Analysis
For each of these 12 sites, hourly average
wind speeds and directions were calculated
along with their standard deviations, the
latter being computed from instantaneous
observations every two minutes. The data
used in this analysis cover the period
February 1, 1980 through February 28,
1981, except for periods where data were
missing. The distribution of wind speeds
and directions for these 12 sites was
plotted as wind roses (monthly).
Two types of analyses were conducted
to examine the relationship among wind
measurements collected at different moni-
toring sites. First an analysis of the correla-
tion between stations was conducted using
the full data set Second, after the data
were stratified into bins based on wind
direction, a regression analysis was per-
formed to 1) determine the correlation of
the NWS station measurements with those
taken at the other 12 stations, and 2)
provide a procedure for calculating the
wind and speed directions at these 12
stations.
The results of a linear regression analysis
of the NWS station on each of the 12
stations of the network are given in Table
2.
The degree of correlation evident in
Table 2 indicates that prediction errors can
be significantly reduced by regressing the
station wind field on NWS measurements
rather than by estimating the wind field
solely on the basis of station averages.
Further improvements are possible by
stratification of the data.
First, the data were stratified into bins
on the basis of the NWS wind speed and
direction. Then, for each bin and each of
the 12 network stations, a linear regression
was performed using NWS wind speed
Table 1. Names and Coordinates of Meteorological Stations
UTM Coordinates
Site
East
North
Ashland Business College (ABC)
Ashland Synthetic Fuels (ASF)
Ashland City Building (ASH)
Bamer Residence (BAM)
Condit Elementary School (CON)
Fire Station No. 2 (FIR)
Flatwoods (FLA)
Huntington Water Corporation (HUN)
KEN Department of Human Resources (KEN)
Ohio Department of Transportation (ODT)
Sunrise Hill (SUN)
Worthington (WOR)
Tri-State Airport (NWS)
353,841
360,049
357,195
355,458
356,9 15
359,756
352,012
376,500
354,524
355,049
365,500
348,670
363,750
4,260,707
4,249,390
4.259,951
4,273,024
4,257,829
4,255,098
4,266,646
4,254,412
4,257, 122
4,263,500
4,260,085
4,268, 183
4,247,500
Table 2. Regression Results for Each Network Station, Based on NWS Station Data
(a) Wind Speed
STATION
ABC
ASF
ASH
BAM
CON
FIR
FLA
HUN
KEN
ODT
SUN
WOR
CORRELATION
0.70
0.66
0.70
0.79
0.76
0.55
0.77
0.78
0.77
0.76
0.63
0.75
SLOPE
0.71
0.57
0.85
0.49
0.89
0.37
0.78
0.65
0.70
0.62
0.65
0.81
INTERCEPT
-0.12
0.34
-0.53
-0.11
-0.38
-0.06
-0.56
-0.27
0.54
0.15
1.09
-0.37
MEAN
2.1
2.4
2.5
1.5
2.4
1.0
1.8
1.6
2.7
2.1
3.2
2.3
STANDARD
DEVIATION
1.60
1.39
1.91
1.00
1.84
1.02
1.60
1.20
1.43
1.28
1.63
1.76
(b) Wind Direction
STATION
SLOPE
INTERCEPT
ABC
ASF
ASH
BAM
CON
FIR
FLA
HUN
KEN
ODT
SUN
WOR
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
-11.00
1.74
-13.05
0.22
-16.55
-13.50
-7.00
-6.37
-5.09
-8.66
14.94
-22.59
c u'
STATION
ABC
ASF
ASH
BAM
CON
FIR
FLA
HUN
KEN
ODT
SUN
WOR
CORRELATION
0.75
0.74
0.72
0.80
0.79
0.81
0.74
0.70
0.84
0.78
0.75
0.61
SLOPE
0.59
0.61
0.52
0.37
0.69
0.51
0.49
0.40
0.74
0.55
0.71
0.45
INTERCEPT
-0.34
0.09
0.01
0.06
-0.12
0.07
0.21
0.11
-0.22
0.20
-0.26
-0.01
MEAN
-0.5
-0.1
-0.1
0.1
-0.2
-0.0
0.1
-0.0
-0.4
0.1
-0.4
-0.1
STANDARD
DEVIATION
1.80
1.90
1.73
1.09
1.98
1.47
1.52
1.25
2.01
1.60
2.25
1.71
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Table 2. (continued)
STATION
ABC
ASF
ASH
BAM
CON
FIR
FLA
HUN
KEN
ODT
SUN
WOR
CORRELATION
0.80
0.61
0.67
0.87
0.75
0.52
0.76
0.85
0.87
0.81
0.81
0.78
SLOPE
0.55
0.35
0.58
0.44
0.66
0.13
0.57
0.56
0.74
0.59
0.80
0.65
INTERCEPT
-0.36
-0.64
-0.22
-0.11
-0.07
0.21
-0.10
-0.07
-0.24
0.09
-0.24
-0.02
MEAN
-0.8
-1.1
-0.8
-0.5
-0.7
0.1
-0.5
-0.4
-0.8
-0.4
-1.1
-0.6
STANDARD
DEVIATION
1.73
1.65
2.54
1.40
2.25
0.63
1.87
1.57
2.15
1.81
2.65
2.28
and direction as independent variables.
This approach was adopted in order to
improve the performance of the linear
model.
The predicted values were obtained using
the regression equation for each bin. The
RMSE and the interquartile range provide
measures of the degree of spread of ob-
served values about the predicted values.
For the prediction to be reliable, there
must be a sufficient number of measure-
ments in each bin. This constrains the
number of bins that can be used. Figure 1
illustrates the flow of data in processing
and analysis.
1 u and v are orthogonal wind velocity components.
NWS Station Data
(Hourly Data)
Aerometric Network Data
12 Stations
(Hourly A verages)
Average Adjacent Hours
NWS Station Data
(Hourly A verages)
Stratification According to
Speeding and Direction
Frequency of Occurrences
in Each Bin
Statistical
Distribution Analysis
Sorting of Data from Each
Station Based on the
NWS Stratification
NWS Station Data
in Each Bin
Network Station Data
in Each Bin
Display of Wind Hoses
for Each Bin
Statistical and Regression
Analysis for Each Bin
Display of Predicted Wind
Vectors for Each Bin
Figure 1. Flow chart of data processing and data analysis.
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John E. Langstaff, Anthony D. Thrall, and Mei-Kao Liu are with Systems
Applications, Inc., San Rafael, CA 94903.
Joseph V. Behar is the EPA Project Officer (see below).
The complete report, entitled "Statistical Correlations of Surface Wind Data: A
Comparison Between a National Weather Service Station and i Nearby
Aerometric Monitoring Network," (Order No. PB 83-237 263; Cost: $16.0O,
subject to change} will be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA 22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Environmental Monitoring Systems Laboratory
U.S. Environmental Protection Agency
P.O. Box 15027
Las Vegas, NV 89114
ft U.S. GOVERNMENT PRINTING OFFICE: 19(3-659-017/7209
United States
Environmental Protection
Agency
Center for Environmental Research
Information
Cincinnati OH 45268
Official Business
Penalty for Private Use $300
Pi> 0000329 _ .
U S ENV1H PHOTtCUUN AGENCY
HtGiON 5 UtfRAKY ^T
230 S DEAKttORN STREET
CHICAGO IL 00604
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