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TABLE 4
Predicted Exposures to Respirable Particles for Children
in Six Cities Using a 5-Microenvironment Model
City
NON-SMOKE
EXPOSED:
Portage
Topeka
Watertown
Kingston
St. Louis
Steubenville
SMOKE-
EXPOSED:
Portage
Topeka
Watertown
Kingston
St. Louis
Steubenville
OUTDOOR
Mean* S.D.*
11 1
12 2
19 2
17 2
20 3
41 10
11 1
12 2
19 2
17 2
20 3
41 10
EXPOSURE
Mean* S.D.*
26 15
26 15
30 17
29 16
30 17
40 22
56 41
56 41
60 42
59 42
60 42
70 46
%> 75
/ig/m3**
1
1
2
2
2
7
25
26
28
28
29
38
* in >zg/m3
** Assuming a gamma distribution with the predicted mean
and standard deviation.
204
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FIGURE 1
Figure 1: Estimated distributions of RSP exposure for children liv-
ing in homes with and without smokers in Portage, Wisconsin, and
Steubenville, Ohio.
o
I—
CE
Q_
O
GL
O
UJ
CJ
CE
UJ
0.
•PORTflGE NON-SMOKE
^STEUBENVILLE NON-SMOKE
«PORTflGE SMOKE
TSTEUBENYILLE SMOKE
25 50 75 100 150
RSP CONCENTRRTION UG/M3)
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Empirical Models for Estimating Individual
Exposures to Air Pollutants In a Health Effects Study
C. F. Content, Jr., M.P.H.,
T. H. Stock, Ph.D.,
A. H. Holguln, M.D., M.P.H.,
B. M. Gehan, M.A.,
D. J. Kotchmar, M.D.,
P. A. Buffler, Ph.D., M.P.H.,
B. P. HsI, Ph.D.
Although the research described in this article
has been funded wholly by the United States Environmental
Protection Agency through Grant R808738010 and Contract
2D5526NAEX to the University of Texas at Houston, it
has not been subjected to the Agency's required peer and
policy review and therefore does not necessarily reflect
the views of the Agency and no official endorsement
should be inferred.
206
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In the spring of 1981, Investigators at the University of Texas School of
Public Health at Houston undertook a study of the effect of exposure to air
pollution In asthmatics In the Texas Gulf Coast area. The goal of the Asthma
Study was to examine the relationship between exposure to single pollutants and
combinations of pollutants In the air and the frequency of occurrence of asthmatic
etrack In an asthmatic population. After extensive recruitment and screening, a
panel of 52 medically managed extrinsic asthmatics, ranging In age from 7 to 55
years, was Identified. The study subjects resided In two Houston neighborhoods.
Clear Lake (n=31) and Sunnyslde (n=21).
Twice a day, during the period of May through October, study subjects filled
out log forms on which they reported their dally activities, as well as the
occurrence of general symptoms, changes In medication usage and the results of a
peak expiratory flow maneuver. The asthma specific symptoms, medication and peak
flow data were summarized for each Individual to provide an Indication of whether
an asthma attack had occurred In the twelve hour period.
Previous studies of ozone and asthma had used air monitoring data from a
fixed monitoring station some distance away from residences to describe the
exposure each Individual received. The work of several authors (1,2,3) Indicated
that exposures differed among "micro-environments". As the use of personal
monitors on all the study subjects for the course of the study was not feasible
for both technological and cost reasons, It was deemed desirable to obtain
exposure estimates for each Individual using the micro-environment concept.
The approach taken was to estimate the pollutant concentration In each of
certain broad groups of micro-environments using a regression model which
describes the relationship of the fixed site data to these micro-environments.
Exposure estimates were provided using the micro-environment specific estimates
and the data collected from the study subjects concerning their movement through
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the micro-environments. This regression model Is based on the data obtained from a
subset of the study population. The model for ozone will be described here.
To describe the micro-environment exposures a three-tiered monitoring system
was developed. Details of this monitoring system have been presented by Stock et
a I elsewhere (4). The first level consisted of two fixed monitoring sites, each
located In the center of one of the two study neighborhoods. All study subjects
lived within 2.5 miles of a fixed monitoring site. The second tier consisted of a
mobile monitoring station housed In a large van. Data were collected at each of
twelve residences selected to be representative of the study subjects' residences;
eight were In Clear Lake and four In Sunnyslde. All these houses had some form of
alr-condltlonlng. The areas sampled were the den, kitchen and bedroom of each
house and the area Immediately outdoors. The same pollutants were monitored at
both the fixed site and mobile van; ozone was measured with a chemlluminescence
method Instrument.
Personal monitoring provided the third tier of the sampling network. The
ozone monitors were portable CSI Chemlluminescence samplers. Monitoring of ozone
was labor Intensive, requiring two research assistants to carry the monitors as
they followed each study subject on his or her dally routine. For the younger
study subjects the monitoring Included some time In the classroom. Thirty of the
52 study subjects were Involved In the personal monitoring, each for an average of
sixteen hours on two separate days.
Figure 1 displays the ozone values as measured at the fixed site and Indoors
for one of the residences studied over a nine day period. Note that the Indoor
values are usually near zero. It would appear that little or no relationship
exists. There Is a slight problem Is this Interpretation. It Is known that ozone
follows a strong diurnal cycle outdoors.(Figure 2) A problem arises from this
variation. The deviations around the outdoor diurnal pattern tend to be less than
208
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the range of the pattern Itself. That Is, the variation about the one o'clock
mean Is smaller than the difference between the one o'clock mean and the eight
o'clock mean. There might exist a relationship between the deviations from the
one o'clock mean outdoors and the one o'clock mean Indoors. The obvious
relationship between the fixed site and outdoor mean would tend to Inflate the
amount of variation explained by the model (R-squared) for this regression beyond
what It may actually be. Therefore, the regression model was calculated with
"centered" data. The hour and site specific mean was subtracted from each hour
and site specific observation and the regression was performed using the resulting
values. Symbolically this model Is:
(Yhr-Yhr)-B(FShr-FShr)
where
Yu e hour specific ozone value (Indoors or outdoors)
Yur = hour specific ozone mean (Indoors or outdoors)
FSu = hour specific ozone value at Fixed Site
FSu = hours specific ozone mean at Fixed Site
B = regression coefficient
Because the data are "centered", this model will tend to be less sensitive to
the effects of the diurnal variation and have more ifkellhood of detecting a small
fixed site/Indoor relationship. The coefficient Is an estimate of the slope of the
line relating the fixed site value to the outdoor or Indoor value. Separate
regressions for each residence were performed In which both the Indoor and outdoor
data for each residence were regressed on the fixed site data corresponding to the
time periods during which the van was operating at the residence. The resulting
coefficients were combined over the twelve residences to provide a summary
measure. These residence specific values have been examined for differences which
might relate to characteristics of the residence; no strong differences were found
209
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for ozone.
The fixed site to outdoor summary coefficient Is .78609, (see Table 1) which
Is significant at the 0.001 level. The R-squared value Indicates that about 73 %
of the variation In the outdoors ozone may be explained by the fixed site data.
Surprisingly, the fixed site to Indoor coefficient Is also significant at the
0.001 level. However the coefficient Is extremely small and the R-squared Is only
2.}%. These coefficients were used with the hour specific means to provide an
estimate of the ozone levels In each of several groups of micro-environments. To
Illustrate, this model would predict that a fixed site value of 100 PPB at one
o'clock In the afternoon would Increase the Indoor ozone by only 0.24 PPB above
Its hour specific mean of 1.6 PPB. The outdoor to Indoor coefficient Is also
significant, and approximately twice the fixed site to Indoor coefficient. The
very small R-squared Is due to data from one residence; when this residence Is
deleted from the summary the R-squared Increases to 1.9%. The reason for the
anomalous data at this residence Is not clear.
TABLE 1-RESULTS OF OZONE REGRESSIONS
Summary beta RSQ P
Fixed site-outdoors 0.78609 73.6 <.001
Range (38.1; 90.0)
Fixed site-Indoors 0.005934 2.1 <.001
Range (0.0; 15.4)
Outdoors-Indoors 0.010119 0.4 <.001
Range (0.0; 24.9)
The exposure model also uses data concerning the movement of the study
subjects through micro-environments. Activity patern data were collected using a
section of the twelve hour log shown In Figure 3. For any hour the study subject
could Indicate whether he or she was In any or all of several locations.
210
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Figure 4 presents an estimate of the distribution of time spent outdoors
between 7:00 AM and 7:00 PM by the Clear Lake study subjects. Approximately
of the study subjects spent two hours or less outdoors on an average day.
Patterns In the Sunnyslde nefghborhod were similar.
The exposure model was built using the measured activity data, the ozone
values as measured at the fixed site, hour specific means, and coefficient values
from regressions which provided estimates of the relationship between the fixed
site values and the ozone values outdoors and Indoors. The ozone exposure for
each Individual Is determined by the subject's location and the estimated ozone
concentration In that location. For this model It Is assumed that there Is no
difference In ozone concentration within the various Indoor locations and that the
same holds true for the various outdoor concentrations. Also, It Is assumed that
a closed vehicle had the same concentration as that found Indoors, while an open
vehicle received the outdoors value. Therefore the several locations In the
activity log were treated as two homogeneous groups: Indoors and outdoors.
Individuals who were In more than one group during an hour were assigned the mean
of the estimated ozone level In each of the locations. These simplifications are
required by the limits of the sampling scheme used In the outdoor/Indoor
monItor Ing.
Having generated the ozone exposure estimate for each Individual It Is of
Interest to compare these estimates to the values obtained from the personal
monitoring activities. As the exposure model yielded one hour averages, the
personal monitoring data, which were collected In five minute Intervals, were
reduced to one hour averages. To assess the accuracy of the exposure model two
measures were calculated.
211
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Mean Difference: D = I (P - E)
Mean Squared Difference: MSD = I (P - E)
N
where:
P « personal monitoring concentration.
E * exposure estimate, (Fixed Site or Exposure Model).
N = number of hours monitored.
The mean difference Is simply the average difference between the personal
monitoring value and the exposure estimate value generated for the same time
period. In this case a negative mean difference Indicates the exposure estimate
Is higher than the observed exposure, while a positive difference Indicates that
the exposure estimate underestimates the actual exposure. The mean squared
difference Is a measure of the error In the exposure model. If the differences
center around zero the average difference may be small, but the model may still
have a high degree of error, which the mean squared difference will detect. As It
has been used so often In the past, the fixed site estimates are used as a
comparison for the exposure model.
There were 417 hours of personal monitoring data available for comparison,
for an average of 13.9 hours per subject. The average ozone exposure as
determined by the personal monitoring was 15.96 PPB, with a standard deviation of
19.79 PPB. The average In Sunnyslde was 19.31 PPB and that found In Clear Lake
was 13.02. Loss of exposure estimates due to missing activity data was small,
reducing the number of hours available to 406 from 417.
The results of the comparisons are shown In Table 2. Using the exposure mode!
results In estimates which average 8 PPB below the ozone exposure actually
212
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2
observed. The mean squared difference, 387.67 PPB , represents a reduction of
82.5$ In the error found with the fixed site data. The average reduction In mean
2
squared difference was 2077.5 PPB per person. The difference between fixed
site estimates and personal monitoring data tended to decrease as percent of time
outdoors Increased. The opposite trend was seen with the exposure model; the
error Increased as time outdoors Increased.
TABLE 2-SUMMARY OF DIFFERENCES IN EXPOSURE ESTIMATES
Source of estimate Mean difference Mean squared difference
Fixed site only -31.19 PPB 2218.24 PPB2
Range (-83.6; -2.8) (70.8; 11378.4)
2
Exposure model 7.96 PPB 387.67 PPB
Range (-7.52; 99.06) (1.9; 10821.5)
The large range In mean squared differences Indicates that the exposure model
does not function uniformly across all subjects. The mean squared difference was
smaller for 27 of the 30 Individuals when the exposure model was used. For three
subjects the fixed site actually provided a better estimate of ozone exposure than
the exposure model. Two of these subjects live In the same residence which Is the
only residence In the study completely without alr-condltlonlng. Apparently, a
different fixed site to Indoor relationship exists for this residence than for the
other residences, accounting for the failure of the exposure model. When these
two residences are removed from the analyses, the mean difference between the
personal monitoring and exposure model Is reduced to only 3.67 PPB. The mean
2
squared difference becomes 230 PPB which represents an 89.9$ reduction from the
fixed site estimates. This percentage reduction Indicates that consideration of
alr-condltlonlng might explain 40$ of the remaining error. When examining the
fixed site to Indoor coefficients, a slight alr-condltloning effect was noted; but
this difference was based on only two residences and due to the non-random
213
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sampling scheme used to select the residences to monitor, It was unreasonable to
Include this effect In the model.
In conclusion, a simple ozone exposure model has been obtained based on
activity data, fixed site monitoring data and fixed site to outdoor and Indoor
relationships estimated from the data. The purpose was to provide exposure
estimates for all the health effect study subjects, using relationships obtained
from only a subset of the subjects. Therefore, distinctions among
micro-environments have been avoided except at the gross level and then made only
when the data strongly supported these differences. For ozone, the only
distinction was whether the subject was outdoors or Indoors. It has been assumed
that differences within the outdoor or Indoor environments are small. However,
there Is evidence that this assumption Is not true for some study subjects,
resulting In a few serious errors In their estimated ozone exposure. This rather
simple model resulted In an 82.5$ reduction In the error when compared with the
fixed site estimates. Improvements In this model would result from making more
distinctions among the micro-environments, which would lead to a larger set of
fixed site to micro-environment coefficients.
It Is obvious that future health effects studies will need to Include a
monitoring experiment carefully designed to closely examine the differences among
micro-environments. Choice of micro-environments to examine will depend on the
characteristics of the pollutant and population being examined. It would appear
that the largest determinant of ozone exposure In this study Is whether the
subject was Indoors or not. While the model used In this study represents a
significant Improvement over exposure estimates used In earlier studies, further
refinements to this model can be attained by careful design and Interpretation of
monitoring experiments.
214
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REFERENCES
1. Fugas, M. Assessment of total exposure to an air pollutant. Proceedings of
the International Conference on Environmental Sensing and Assessment, Paper No.
38-5, VI. 2, IEEE 175 - CH 1004-1 ICESA, 1975.
2. Sexton, K., Letz, R. and Spengler, J. D. Human exposure to nitrogen dioxide:
exposure modeling and risk assessment. APCA Paper 82-48.8. Paper presented at the
1982 Air Pollution Control Association Annual Meeting, New Orleans, Louisiana.
1982.
3. Duan, N. Models for human exposure to air pollution. Rand Note N-1884-HHS/RC.
The Rand Corporation, Santa Monica, California. July 1982.
4. Stock, T., Holguln, A., Selwyn, B., Hsl, B., Contant, C., Buffler, P.,
Kotchmar, D. Exposure estimates for the Houston area asthma and runners studies.
Lee, S., Mustafa, M., Mehlman, M. (eds.) In: Advances In Modern Environmental
Toxicology. Vol 2. International Symposium on The BJomedlcal Effects of Ozone and
Related Photochemical Oxldants. 1982.
215
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CQ
8
8
e-
s-
a-
9
8
8
o.
LEGEND
03 FIXED SITE
OZONE VflLUES flS MEflSURED
flT THE FIXED SITE RND INDOORS
5 6
STUDY DflY
10
Figure 1. Plot of ozone values measured at the fixed site and indoors.
-------
OZONE MEflNS flS MERSURED
flT THE FIXED SITE, OUTDOORS RND INDOORS
8-
8-
8-
o-
LEGEND
P 03 FIXED SITE
o 03 OUTDOORS
A ..... A ..... A
...... A ..... A— -A ..... A ..... -A ..... A ..... A ..... A ..... -A ..... A ..... A ..... A ..... A ..... A— — A"'"*" ..... A ..... A
—I 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 I I I I I
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 IS 16 17 18 19 20 21 22 23 24
HOUR OF THE DRY
Figure 2. Plot of mean ozone values measured at the fixed site, outdoors and indoors.
-------
HOURS
PLACE 78
HOflE
INDOORS SCHOOL OR WORK
ELSEWHERE
OUTDOOR:: IN NEIGHBORHOOD
w""™ OUT OF NEIGHBORHOOD
IN OPEN CAR, TRUCK OR BUS
IN CLOSED CAR, TRUCK OR BUS
morn]
ng
If
.
noon
1 12 1
evening
234 567
Figure 3. Activity Data Log.
218
-------
ho
U)
DISTRIBUTION OF TIME OUTDOORS
EXPRESSED fiS PERCENTfiGE OF TOTflL STUDY PERIOD
CLEflR LflKE STUDY SUBJECTS
8-
9-
RCENTRGE
30
I
UJ
°-8-
o.
o-
32
SS
6
V,
6
\
13
% •
7 // 7 yy
f f ' . s* I7" J sS ^
Y//////// * '// 4 <
jf J // /./ * SA _^_^ S _f F T _^
Vx // '/, // // // />/> yv 22,2
^^^^^X^^^^f^Ul-P^aJwJ^ 0 1 0 1 n 0 n n n n
vv // 6< vv xv /x v> /A//r7A/A/s v ^ ^-f^rJ^j-r-^ u u u U u U
0 1 2 3 4 S 6 7 8 9 10 11 12
NUMBER OF HOURS OUTDOORS
Figure 4. Distribution of daytime hours spent outdoors, May-October 1981, by Clear Lake subjects,
-------
CARBON MONOXIDE EXPOSURES IN WASHINGTON, DC AND DENVER,
COLORADO DURING THE WINTER OF 1982-83
by: Gerald G. Akland
Environmental Monitoring Systems Laboratory
Research Triangle Park, NC 27711
ABSTRACT
A study of exposures to carbon monoxide (CO) using Personal Exposure
Monitors (PEMs) was conducted in Washington, DC and Denver, Colorado during the
winter of 1982-83. The primary objective of the study was to validate a
methodology for measuring the distribution of CO exposures in a representative
sample of an urban population so that the risk to the entire population can be
estimated. The methodology for selecting the participants and measurements of
CO will be presented. Preliminary results indicated that for Washington, the
median CO value was 6 ppm, and 1% of the values exceeded 35 ppm. For Denver,
the median value was 8 ppm, and 3.5% of the values exceeded 35 ppm. After
further statistical analysis, the participants' exposure data will be contrasted
to fixed monitor exposure estimates, and an exposure profile for each city will
be determined.
220
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INTRODUCTION
The overall goal of EPA's exposure research program was to develop a
methodology that precisely and accurately determined the public's exposure to
air pollutants. We selected carbon monoxide (CO) for our exposure study because
of available CO instrumentation, known sources, and known CO health effects.
Our study objectives were to (1) develop methodology to estimate urban
population exposure to CO, (2) obtain exposure estimates and relate them to
fixed site measurements, (3) develop methodology for selecting relevant
microenvironments, (4) develop methodology for using Personal Exposure Monitors
(PEM's) to analyze COHb in breath, and (5) develop an activity pattern data
base.
Washington, DC and Denver, Colorado were chosen for the field study because
they differ in elevation, past CO levels based on fixed site data, geographical
area, administrative complexities (single or multiple state), and commuter
patterns. The City of Denver requested a CO exposure study before EPA's
selection. EPA hoped that the selection of Denver as a study area would satisfy
EPA's research objectives and local/state needs for an exposure data base to
assess control strategy alternatives.
Portable CO detectors have been designed for industrial or mining
applications and do not have operational features or CO concentration ranges
suitable for exposure studies. EPA decided to modify an existing industrial CO
monitor, the General Electric PEM, Model CO-3.1 This unit was readily
221
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available, provided real-time data within the expected concentration range, was
previously field tested, generally met the performance specifications, but
lacked data logging capability. To provide the data handling features, the
Magus Group, Inc. of Menlo Park, CA developed a microprocessor system. This
system was integrated with the monitor and repackaged for the study by Rockwell
International.
STUDY DESIGN
Personal CO exposure measurements were taken during November through
February, the period of expected maximum ambient CO concentrations. PEMs were
distributed each evening between 7 pm and 10 pm and collected 24-hr later.
Alveolar CO was collected at the end of each 24-hr sampling period. The
participants took a deep breath and exhaled all air from their lungs. A second
deep breath was taken and held for 20 sec. The first part of the breath was
exhaled into the room and the last portion into a sample bag. The bag was
sealed, returned to the laboratory, and attached to a specially calibrated GE
monitor.
The Research Triangle Institute (RTl) selected the statistical sample of
1Mention of specific trade name does not imply product endorsement.
222
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target population members for EPA as a two-phase sample: 1) data were collected
for all members of a particular household (the screening sample) and 2) personal
exposure data were collected for a stratified random sample of the screening
sample household members. The second-phase sample was stratified to oversample
those population members at high CO exposure risk.
The screening sample was selected as a two-stage area sample. For the
first stage, area segments defined by 1980 Census block groups and enumeration
districts were selected. Census geography variables were used to stratify the
first-stage sample to assure geographic dispersion across the study area. The
Donnelley Marketing Corporation2 provided a list of the households in the
selected area segments. A second-stage sample of 8643 households in Washington
and 4987 households in Denver was selected from this list for the household
screenings. Data on smoking, commuting time, occupation, presence of gas
appliances in the home, and the presence of an attached garage were collected
for all household members.
The second-phase sample of persons selected for personal exposure
monitoring was stratified by the above variables. The sample size was 1773
persons in Washington and 1139 in Denver and was expected to yield 1000 and 500
interviews in Washington and Denver, respectively.
2The Donnellev Marketing Corporation sells lists of names, addresses, and
telephone numbers of residents in many areas of the U.S. The sources of these
lists are based upon telephone directories and vehicle ownership records.
223
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WASHINGTON, DC
Fixed site CO data were collected at nine existing sites throughout urban
Washington, DC. Fifty PEM's were delivered to the contractor for daily
distribution of 20 units with 10 spares. Monitoring began November 8, 1982 and
continued through February 24, 1983. PEM measurements were obtained from
participants in the urban Washington, DC Standard Metropolitan Statistical Area
(SMSA) who were at least 18 years of age at the time of the study. The
estimated population size for this group is 1.12 million. RTI obtained the PEM
and activity information. The Council of Governments, Washington, DC, was the
site coordinator and supplied laboratory space for RTI.
DENVER, COLORADO
The study design for Denver was similar to that for Washington with two
major differences. First, the Mayor of the City and County of Denver requested
that such a study be performed in his city. As a result, local officials from
the city, state, and EPA Regional office established a monitoring committee
chaired by the Mayor's office to coordinate the study and establish new
monitoring sites. Nine additional CO sites3 were added to the existing six
3A major development effort was undertaken by Bill Basbagill of EPA Region 8 to
build an inexpensive temperature-controlled box to house the instrumentation at
the additional sites. For less than $50, Bill was able to construct a box which
maintains temperatures within the required + 5°C.
224
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telemetered sites in the study area. The Air Pollution Control Division of th.
State of Colorado operated all 15 sites (following uniform standard operating
procedures except for the manual data reduction from the nine new sites). All
fixed data, including meteorological data, flowed from the State to the
Environmental Monitoring Systems Laboratory through the Regional Office.
The other change to the design was that 500 instead of 1000 participants
were selected, but each participant carried the monitor for two consecutive
days. Multiple measurements per participant were used to estimate the influenc
of activity pattern on the overall exposure profile.
The PEM measurements were obtained from a sample of the non-
institutionalized, non-smoking residents of the urban Denver SMSA who were at
least 18 years of age at the time of the study. The estimated population size
for this group was 245,000.
PEDCo conducted the exposure monitoring program. EPA provided 25 PEM's f
a daily distribution of 10 units, with 5 instruments for replacements.
Monitoring began on November 1, 1982, and continued through February 1983.
PRELIMINARY RESULTS
Data available for each participant include (1) data from the various
questionnaires; (2) time the CO monitor button was pressed, the corresponding '
reading, and the corresponding activity diary information; (3) the CO readings
on the hour for each of the 24 hours, and (4) the CO concentration in the breat
225
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samples. In addition, fixed site monitoring data, meteorological data, and land
use data were also collected.
Monitoring data, once validated, will be statistically analyzed. The
statistical analysis will estimate the distribution of personal CO exposure,
average time spent in various activities and at various locations, the
significance of selected exposure factors, and the relationship between CO
values measured by PEM's and simultaneous values reported by fixed-site
monitors.
SAMPLING RESULTS AND RESPONSE RATES - WASHINGTON, DC
Between November 8, 1982 and February 24, 1983, 1455 households were
interviewed. Of these, 944 (64.9%) agreed to participate, 184 (12.6%) refused
to participate, and 198 (13.6%) were not interviewed for a variety of reasons.
Of the 100 sampling periods originally planned. 98 were carried out. Two
sampling periods (November 11 and 12) were cancelled due to equipment problems.
A total of 814 subject days were completed. Twelve percent of the 944
participant days (130 days) were lost due to monitor malfunction.
Activity diaries had not been entered into the computer by the middle of
April. A total of 870 breath samples were obtained and successfully analyzed
for CO content. Twenty-two were lost because of leaks in the sample bag. The
arithmetic mean of the 870 samples is 5.4 ppm with a standard deviation of 5.2
ppm.
226
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Figure 1 presents an unweighted cumulative frequency distribution of the
638 maximum 1-hr PEM results (one value/subject/day) collected through the
middle of January. This median is about 6 ppm, and 1% of the values exceed 35
ppm (fixed site data were not available for comparison). Interpretation of
these data will be possible after further analyses of the completed data set.
For this time period, 86% of all completed samples were valid within ± 10% and
% 1.5 ppm based on pre- and post-sample calibration slopes and intercepts,
respectively.
SAMPLING RESULTS AND RESPONSES RATES - DENVER, COLORADO
Of the 1000 eligible individuals in the primary and three supplementary
samples, 424 agreed to be interviewed. In addition, 60 of the 139 eligible
individuals identified through field screening agreed to be interviewed. Of
these 485 eligible individuals, 454 were interviewed successfully and
participated in the study.
A total of 1094 subject-days of participation were scheduled. The 454
individuals participating in the study yielded 900 subject-days; 446 subjects
participated in two sampling periods, while eight subjects participated in only
one sampling period.
22?
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Of the 900 activity diaries, 778 were accurate and complete. Significant
omissions occurred in 66 diaries, primarily because of PEM failures. Diaries
were filled out incorrectly in 52 cases.
A total of 895 breath samples were obtained and successfully analyzed for
CO content. Thirty samples were lost because of leaks in the sample bag. The
arithmetic mean of the 895 samples is 7.0 ppm with a standard deviation of 5.6
ppm.
Figure 2 is a preliminary unweighted cumulative frequency distribution of
the maximum 1-hr PEM data (shown by solid line). The median is about 8 ppm, and
3.5% of the values exceed 35 ppm. The dashed line represents approximately 1000
daily maximum 1-hr per fixed site. The plot suggests that the distribution of
maximum 1-hr PEM levels is slightly, but consistently higher than the
corresponding distribution of daily fixed-site maxima, except at the upper
decile where the difference is much greater.
Figure 3 is a quantile/quantile comparison of the permanent fixed CO
monitors with the nine additional CO monitors. The plot is a simple cumulative
distribution of 1-hr maximum fixed data from the permanent site (x-axis) and the
corresponding distribution from the temporary sites (y-axis). The new sites
have a larger percentage of lower values (< 6 ppm) than the permanent sites.
Also, 80% of the permanent site values are less than 14 ppm (arrows on Figure
3). In contrast, 80% of the temporary site values are less than 12 ppm. This
difference might be attributed to the downtown location of the permanent sites
where traffic density is higher.
228
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8-
7-
6-
5-
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Note: N = 638
x = 8.3
s= 15.5
%>35ppm=
2-
10 15 20
30
40 50 60
PERCENTAGE
70 80 85 90
95
98
Figure 1. Cumulative Frequency Distribution of
Maximum PEM Levels in Washington, DC.
229
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MAXIMUM DAILY PEM CO CONCENTRATION
PPM
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Percent of Daily Maximum 1-hr Values
Less Than Value Shown on Right Scale
PERMANENT SITES
Figure 3. Quantile/Quantile Plot of Permanent vs,
Temporary Sites in Denver, Colorado.
7 8:
1.00
231
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The following Washington and Denver data will be further analyzed: 1)
questionnaire and diary responses, 2) analysis of personal CO exposure
distribution, 3) relationships between breath, personal CO exposure, and
activity patterns, 4) significance of selected exposure factors, and 5) fixed
vs. PEM data.
SUMMARY AND CONCLUSIONS
Although interpretation of the study results is not possible at this time,
we have shown that measuring exposures in an urban area is possible. The
willingness of the public to carry monitors and prepare activity diaries has
been demonstrated. Although the present monitoring system works, we propose the
following improvements: 1) automatic data dumping, 2) keystroke entry of
activity information, 3) adding programming capabilities, 4) repackaging to
reduce size and weight, 5) improving battery life, size, and recharging time,
and 6) modifying pump connections for easier replacement and cleaning.
We also believe a new city should be studied in the winter of 1983-84 to
permit a more realistic test of normal cold temperatures, which were not
experienced in the winter of 1982-83.
232
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ACKNOWLEDGEMENTS
The complexities involved in the design and execution of this study
necessarily mean that a number of individuals contributed to its success. The
author thanks the individuals and organizations listed below:
City and County of Denver
Region 8 - Denver
State of Colorado
Washington, DC
Council of Governments
PEDCo
RTI
Rockwell International
EMSL/RTP
Cooper Wayman and Jack Green
Bill Basbagill, Jim Lehr, Marshall Payne
and Charles Stevens
Steve Arnold, Rick Kramer, and Gordon MacRae
Allen Delman and Trevis Markle
Ted Johnson and Tom Wey
Ty Hartwell, Ray Michie, Roy Whiteraore,
and Harvey Zelon
Frank Burmann
Ron Drago, Charles Rodes, and Robert Jungers
233
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Personal Exposure to Nitrogen Dioxide
1 213
Jamesj^J. Quackenboss, John D. Spengler, Marty S. Kanarek, Richard
Letz, Colin P. Duffy, and Mark D. Lindsay
Department of Preventive Medicine and
The Institute for Environmental Studies
University of Wisconsin
504 North Walnut Street
Madison, WI 53705
2
Department of Environmental Health Sciences
Harvard School of Public Health, Boston, MA 02115
Department of Physiology,
Harvard School of Public Health, Boston, MA 02115
Abstract
As part of a longitudinal air pollution/health study (Harvard Six Cities
Study), personal exposure to NO , time spent in various locations and
household concentrations were measured for nearly 350 individuals in the
Portage, Wisconsin area for one week during both the summer and winter of
1981-1982. Average levels of NO measured outside these homes were 13.55
yg/rn (S.D. 5.64) during the summer, and 15.35 yg/m (S.D. 6.15) during
the winter. Indoor concentrations in homes with gas stoves averaged about 20
yg/m higher in the summer (mean I/O ratio 2.85) and 36 yg/m (mean I/O
ratio 3.93) in the winter than levels measured outside of the home. Personal
exposures were closely related to indoor levels for households with gas stoves
(r=0.80, p<0.001), and electric stoves (r=0.65, p<0.001). This may reflect
the influence of spending nearly 65% of the average day at home in the summer
compared with about 15% spent outdoors. An average of less that 5% of the
their time was spent outdoors during the winter: the association between
outdoor and personal levels of NO was weakest during this season for both
gas (r=0.13, p<0.22) and electric (r=0.16, p<0.14) stove groups. These
measures of exposure and time allocation suggest that there is a wide range of
variability in personal exposures to NO that may not be adequately
accounted for by simple stratifications based on cooking fuel type.
The work described in this paper was not funded by the U.S. Environmental
Protection Agency and therefore the contents do not necessarily reflect the
views of the Agency and no official endorsement should be inferred.
23k
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Personal Exposures to Nitrogen Dioxide
In the course of daily activities, individuals move about from location
to location, breathing samples of the air from each. The amount sampled is
determined, in part, by the duration of time spent there, while the magnitude
of pollutant present in each location during the specific time periods is a
complex function of the ambient background level, of the proximity to sources
of pollutant generation and release, and of various mechanisms for removal or
dilution. Nitrogen dioxide (NO ) is formed as a byproduct of high tempera-
ture combustion.
Outdoor concentrations are closely related to the proximity of the
sampled location to major sources, such as motor vehicle traffic and fossil
fuel power plants, to meteorological factors influencing the transfer and
dilution of N0?, and to atmospheric conversion reactions (1). Indoor
concentrations are driven, in part, by these outdoor levels as well as by
usage patterns for sources of unvented or improperly vented indoor combustion
(e.g. gas stoves and unvented kerosene space heaters), by air infiltration and
ventilation rates, and by chemical reactions and adsorption or absorption of
the gas on indoor surfaces (2,3).
Until recently, epidemiological studies of the health effects of air
pollution have relied on measurements of "ambient" air quality that are
obtained from fixed location monitoring stations, rather than on the actual
exposures of the individuals whose health status is being tested, to draw
inferences regarding possible exposure-response relationships (4-7) . Several
recent reports have indicated that such exposure measurements fail to
adequately assess individual exposure (2,5,7,8-12). This inadequacy is
acutely apparent for NO , since several reports have shown that NO
235
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concentrations consistently exhibit a declining gradient as one moves from
kitchens with gas stoves to non-kitchen areas in these homes, to outdoor
locations nearby, and then to kitchen and non-kitchen locations inside
households that use electricity for cooking (13-18). The importance of these
departures from fixed station ambient air monitoring results is underscored by
summaries of human activity pattern studies (19) indicating that an average of
90% of each day is spent indoors by employed men, while homemakers spent
nearly 95% of their time indoors (2,20-23).
Physically, exposure may be defined as the pollutant concentration
present at the exchange boundaries of a receptor during specified times. Ott
(20) has incorporated both space and time dimensions by conceptualizing
exposure as the event that "a person comes into contact with a pollutant."
Contact is then specified as the intersection of an individual and a pollutant
in the same location at the same time. Location may be narrowly defined in
terms of a three-dimensional coordinate system (X, Y, Z) , aggregated by
existing environmental zones such as rooms or floors within a household, or
more broadly classified as inside the home itself. With each level of
averaging, some within zone variability may be lost, although these
differences are likely to be smaller than those between zones (eg. between
homes, or between indoor and outdoor locations) and are probably of minimal
health significance (24). At a more general level, Duan (25,26) aggregates
locations with homogeneous pollutant concentrations at specified times into
microenvironments with concentrations y-> and then groups these to form a
reduced number of microenvironment types for sampling and modelling purposes.
Exposure may then represented as a linear combination of the individual's
"sample" average for each microenvironment type (C ), weighted by his or her
K.
time allocation to microenvironments of that type (t, ):
236
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E = EC, t,, (k = 1,,..,K microenvironment types).
k. K.
This model is a generalization of that proposed by Fugas (4) for deriving
a "weighted weekly exposure" (WWE) estimate of total exposure by using the
concentration measured at one location (y.) to represent the levels sampled
by individuals in all microenvironments of the same type (C ). This
K.
approach does not incorporate variability among samples collected by different
individuals at distinct times, and is sensitive to a potential lack of inde-
pendence between these individual sample concentrations and individual time
allocations. This potential may be realized when pollutants are generated as
a result of individual activities, such as cooking or smoking, that are likely
to increase in frequency as more individuals are present at a given location.
Personal exposure monitoring implicitly incorporates both the time spent in
various microenvironments of each type (t, ), and the variability in air
samples obtained by different individuals from each microenvironment type
(C ). This allows for direct evaluation of both variability and central
K.
tendency for human exposures, and for estimation of average sample
concentrations (C, ) for each microenvironment type (26).
K.
The development of a small, inexpensive, and reliable passive monitor for
NO by Palmes, et al. (27) has made a large-scale general population study
of personal exposures to NO practical. This potential, combined with
concerns regarding the adequacy of current exposure assessment practices and
with several recent reports advocating personal exposure studies as part of
epidemiological investigations into the health effects of air pollution
(2,5,8-11,28), have prompted our investigations. The objectives for this
study were:
1. To examine the relationship between personal exposure and indoor and
outdoor NO concentrations.
237
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2. To explore the association between household members in personal
exposures.
3. To determine those household and individual characteristics that are
most important in explaining variability in personal exposure.
4. To determine what types of measurements are needed to adequately
assess personal NO exposures.
Previous personal exposure studies have demonstrated differences between
indoor, outdoor and individual exposures for N0r using smaller study popu-
lations (29), especially for gas homes (30,31) or those using unvented space
heaters (32). Mean personal exposures for families with gas stoves have been
shown to be closer to indoor than to outdoor levels, while those using
elective stoves were indistinguishable from ambient outdoor measures. In
addition, personal exposures to NO- were found to be closely related to
other family members (30).
Methods
Individual exposure to N0_, time spent in various locations, and house-
hold concentrations were measured for nearly 350 volunteers residing in 82
homes in the Portage, Wisconsin area for one week during both the summer and
winter of 1981-1982 as a component of the exposure assessment activities for a
longitudinal air pollution/health study (33) . Households were separated into
groups of approximately 10 homes each that were sampled during the same
week-long period to allow for travel time and initial visits to each home.
The protocol for these visits included a description of study objectives, and
instructions for using the passive diffusion NO monitors and for completing
an activity diary. An example diary page was filled out for that day; this
was checked for completeness and corrected with each subject to reduce
recording errors. All household residents were asked to participate, although
238
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this was not always possible. During the winter phase, short visits were made
to each home to return their summer results and to encourage continued
participation.
Sample Selection
The target population selected was that of families whose school-aged
children were participating in the Harvard Six Cities Study from the Portage
area. The basic sampling design was a stratified cluster sample (34) in which
the primary sampling unit (cluster) was the family, and the secondary sampling
units (elements within cluster) were individual family members. Households
were stratified by type of cooking fuel used (gas or electricity). Letters
describing the study and requesting volunteers were sent out to obtain
approximately equal numbers of gas- and electric-cooking fuel homes. A
postage-paid response postcard was provided, and non-respondents were
contacted by telephone. A more detailed description of the study was sent to
those indicating a willingness to consider participating in the study. From
this pool of household units, approximately equal numbers of each cooking fuel
type were randomly allocated (without replacement) into a group of homes that
were then visited and sampled during the same week.
NO Monitoring
Passive diffusion NO dosimeters developed by Palmes et al. (27) were
used to determine week-long average N0_ concentrations for fixed household
locations, and average exposures of individuals. These monitors are simple to
use, have a good shelf life before and after exposure, and give results that
are both accurate and reproducible (35). Extensive use has been made of these
devices for monitoring indoor and outdoor levels of NO (14-16,18,36,37).
Household monitors were placed by project staff outside near the home, in
at least one bedroom on each floor, and in the kitchen at 4 to 6 ft. (1.2 to
239
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1.8 meters) in height. Kitchen monitors were placed between 8 and 10 ft. (2.4
to 3.05 meters) from the stove. Indoor monitors were placed to avoid windows,
corners, and heating vents; outdoor monitors were located on the shady side of
the house, away from driveways, roads, and exhaust vents. Each participant
was also assigned a monitor, his or her name was written on tape affixed to
the top end, and the monitor identification number was recorded on the
activity diary. Volunteers were instructed to wear their monitor at
approximately breathing height by clipping it onto their shirt collar or
lapel, to keep it outside of coats or jackets, and to keep their monitor
nearby when not wearing it.
Integrated average NO concentrations measured by the kitchen monitors
and by bedroom monitors on the same floor were grouped together to give one
compartment average. When appropriate, NO measurements from non-kitchen
floors were aggregated to give a second compartmental average. These two
within home averages were then combined to yield a single household average
for indoor NO concentrations.
During the winter phase, integrated average NO concentrations were
measured inside of 17 schools that were attended by participating children.
Monitors were placed in one classroom on each floor, and a school-wide average
was estimated from these for the time period sampled. To allow for direct
comparison with student's personal monitors, a time-weighted average for each
week-long time period during which a group of households was concurrently
sampled was calculated. This was done by weighting the school-wide average
concentration by the proportion of the time these monitors were exposed that
corresponded to each week-long personal monitoring sample period.
Monitors were prepared and analyzed at the Wisconsin State Laboratory of
Hygiene using methods based on Palmes et al. (27) with modification by Wolfson
-------
(38). Analyses of replicate pairs of these monitors, used for household
3
measurements, has given a precision estimate of 1.68 yg/m (18). Their
3
sensitivity has been estimated as approximately 1128 (ug/m )h, with an
accuracy of within 10% for these preparation and analysis procedures (39,40).
Survey Materials
For each day of the week-long sampling period, participants recorded the
time periods they spent in each of 5 general location categories: (1) Inside
of their home, (2) outside (any where), (3) inside of a motor vehicle, (4)
inside at work or school, and (5) inside at other indoor locations. The
reporting format was developed to derive week-long totals for the proportion
of time spent in each category, and was field tested during a pilot study
(31). In addition, time spent "cooking or helping to cook" and "near people
who are smoking" was also recorded to obtain information about time spent near
potential sources of N02. However, the lack of a clear relationship between
time spent near stoves or smokers and the possible N0? emission rate for
each (per unit of fuel or per cigarette) makes interpretation of these values
difficult. During the winter phase, time periods near smokers at home were
separated from those away from home, since the possible effect of smoking
within the home on indoor NO concentrations should be included by the
monitors located there.
Participants were also instructed to record the amount of time each day
that they did not wear their monitor and the location where it was left during
this time period. These time periods were tabulated for several locations and
the individuals monitored exposure was corrected using the measured concen-
trations for that location, weighted by the time the monitor was left there
(31). For each day, a marginal total for the amount of time spent in each
location category was estimated; week-long totals and proportions were derived
2k]
-------
from these. Errors in recording leading to potential uncertainties in
interpretation were also recorded, as a total number of hours in question.
In order to assess the validity (41) of this reporting procedure for
determining average time allocation, a separate format 24-hour recall
questionnaire derived from Chapin (42) was administered to a random sample of
12 subjects during the pilot study. For all 5 location categories, the
correlation coefficients between the two responses for a one day period
exceeded 94%. All 5 correlations were statistically significant with an
overall error rate of a=0.025, using the Bonferroni method for non-orthogonal
tests (43), indicating that these two methods give approximately the same
results. Most discrepancies were due to the limitation of the diary format to
1/2-hour intervals. For longer term, week-long averages this reporting format
should provide valid reports of time allocation.
A subject questionnaire was administered during the initial visit to
ascertain individual demographic characteristics, smoking habits, and commut-
ing patterns. Ventilation, heating and cooking systems for the workplace as
well as potential sources of occupational NO- exposure (such as welding,
gasoline or diesel engines, gas ovens or flames) were also requested. Charac-
teristics of the schools were determined by interviewing the maintenance and
engineering staff for each monitored school.
Household characteristics were detailed using an additional questionnaire
that was completed by a parent and returned by mail to our office. This
included questions regarding (1) sources, such as the fuels used for cooking,
heating water and space heating, the presence of a stove or oven pilot light,
and the household cooking patterns; (2) factors potentially influencing
ventilation rates such as the kitchen exhaust fan's venting and usage, and the
use of plastic on windows to reduce air infiltration, and neighborhood
242
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characteristics. This questionnaire was developed for the study conducted by
Spengler et al. (18).
Statistical Analyses
Personal exposures to NO were compared with indoor and outdoor mea-
surements using the sample means for each cooking fuel group, and Pearson
product-moment correlation coefficients. As a result of the natural cluster-
ing of household members, the variance in individual exposures can be de-
scribed by a standard Model II (random effects) ANOVA layout (with varying
22 2
numbers of observations in each family): a. + a where a. is
2
the variance component for random differences between households; a
represents individual variability within households (44). From this
formulation, an expression for the intra-family (intraclass) correlation
between any two members of the same household was used to indicate the
proportion of total variance that is associated with household factors that
are shared by its members:
p = S 2/(S 2 + S2), where
A A
S 2 = (S 2 - S 2)/{[En. - (£n.2)/En.]/(N-l)}
A b w j j J
22 2
estimates o. , S, and S are the between and within class mean
A b w
squares, n. is the number of observations from the j household
(j=l,...,a; a is the number of homes), and N is the total sample size (45).
2
Variance of the sample mean is estimated by var(x)=S, /N; the standard
error is s.e. = \/var(x) (45).
The relationship between personal NO exposure and individual and
household factors was evaluated using stepwise multiple linear regression
analysis (46). Logarithms of the N0« concentration measurements were taken
to compensate for heterogeneity of their variances (47). A pattern of increas-
ing spread with increasing NO,, concentrations has been previously reported
243
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for indoor NO concentrations (18). Missing values for quantitative
independent variables were filled to conform to a linear model (47,48) using
the BMDP program PAM (49) , and the error degrees of freedom appropriately
reduced. However, the frequency of missing values was very small, both for
individual-level variables (<1%) and for household-level variables (<6%; these
cases were primarily missing outdoor monitors), so that these substitutions
should have a minor influence on our results. As a consequence of the
two-stage nature of this sample (individuals nested within household) , a
two-stage regression analysis was employed to evaluate household-level
variables separately from those specific to the individual.
First, individual characteristics were fit, together with (a-1) indicator
variables for household, to the log-transformed personal exposure data.
Although this assumes a common slope across households relating these vari-
ables to exposure, it was impractical to fit independent slopes to each home
given the limited number of observations in each. Averages for personal
exposures in each home were adjusted to the same (mean) value for the
individual-level variables selected in the stepwise procedure.
In the second stage of analysis, these adjusted household averages were
used as response variable to fit a model for the home-level variables. The
residual mean square (RMS) from this stage incorporates both the within
household variability, and the random factors varying from home to home:
22 2
RMS = SA + Sw /(N/a), where S is the estimated among
2
household component of the residual variance; S estimates the
w
within-household variance component and is given by the RMS from the first
stage regression (49).
In order to compare the ability of various types of measurements to
represent personal exposure a series of regression models were fit separately
-------
for the summer and winter data sets: (a) Outdoor NO,., concentrations only;
(b) Outdoor NO- measurements together with household characteristics; (c)
Outdoor and indoor NO measurements. Comparisons among these models were
made using three summary indices:
(1) The Residual Mean Square (RMS) from the second stage regression; and
(2) The coefficient of variation (CV), using the geometric standard error
from the RMS of the regression equation and the geometric mean;
2
(3) The multiple correlation coefficient, R for the proportion of
variance in the second stage data set that was explaned.
Quality Control
Replicate monitors were used for most homes. Allocation of these pairs
was rotated between kitchen, bedroom, outdoor, and personal monitor locations.
Inter-laboratory analyses of unknowns were compared between the Wisconsin and
Harvard laboratories for both phases. All coding, data entry, and correction
procedures were verified. For each variable, records having values exceeding
two standard deviations from the mean were identified. These cases were then
checked for transcription or calculation errors, or for possible explanations
of their values.
245
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Results
A total of 357 volunteers from 88 homes participated in the summer phase:
150 were from 38 gas-cooking fuel homes; 207 resided in 50 homes with electric
stoves. Of this study population, 142 were classified as workers, 33 as
non-workers, and 182 as students. During the winter phase there were 324
participants from 82 homes: 127 were from 34 gas-stove households; 197 were
from 48 electric-stove households. There were 127 workers, 26 non-workers,
and 171 students during this phase. Relative proportions for each of these
three general population classifications were similar across both stove type
classification and study phase.
Mean N0? concentrations are summarized in Table 1 for these homes and
for the participants living there. Average levels of N0_ measured outside
these homes were generally quite low during both phases, with combined means
3 3
of 13.43 pg/m and 16.46 yg/m NO for summer and winter, respectively.
The lower outdoor averages for gas stove homes may be attributable to 29 of
these homes being located in rural areas, while only 9 were in Portage. In
comparison, 38 of the electricstove homes were located in Portage and 12 were
rural. The small numbers of households in the Gas-Urban and Electric-Rural
cells did not allow for meaningful analysis of possible interactions between
community and fuel type. Indoor NO concentrations in homes with gas-stoves
were consistantly higher during both the summer and winter phases than those
measured outside or inside homes with electric-stoves. To permit comparison
of indoor and outdoor levels, difference scores and ratios were calculated;
their means are given in Table 1. Mean differences and ratios were largest
for gas stove homes. For both gas- and electric-cooking fuel homes, absolute
differences were greater in the winter than in the summer phase. Similar
patterns are evident for the variability of these measurements: standard
deviations were greater for the indoor measurements in the gas stove group,
246
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and increased in the winter, along with the higher concentrations. Mean
personal NO exposures for families from homes with gas stoves were closest
to, but somewhat below, average indoor concentrations in both their magnitude
and variability. For families with electric stoves, the mean for personal
exposure was somewhat closer to the outdoor mean in the summer, and between
indoor and outdoor means for the winter. The mean of indoor measurements
3
obtained at 17 schools during the winter phase was 15.87 vig/m , which is
close to the outdoor mean NO- concentration.
Average personal exposures for each household are compared with levels
measured outside the home in Figure 1. For the summer phase, there was
considerable overlap in average personal exposure between families from homes
with gas and electric stoves. In addition, this figure shows the large
variability in these averages for personal exposure, especially in the winter
phase for families with gas stoves. Linear correlation coefficients between
average personal exposure and outdoor levels are shown in Table 2. These were
r=0.40 (p<0.001) for the gas- and r=0.35 (p<0.001) for the electric-cooking
fuel homes in summer, while in the winter the corresponding correlatins were
only r=0.13 (p>0.22) and r=0.16 (p>0.14). These graphs and their associated
correlation coefficients indicate that only a small portion of the variability
in average personal NO exposures between families can be accounted for by
differences in measurements made outside their homes. In contrast, average
indoor home NO concentrations demonstrated a much closer association with
the average personal exposures those living there, as is shown in Figure 2 and
by the correlation coefficients in Table 2. This association was strongest in
gas homes, r=0.80 (p<0.001) for both summer and winter.
Intra-class correlation coefficients are shown in Table 3 to indicate the
proportion of variability in individual personal exposures that may be ex-
plained by common characteristics of those micro-environments shared by
-------
individuals from the same home. Given the overall increase in variability in
the winter phase relative to the summer phase, it is not surprising that these
shared characteristics explained less of this variation in the winter than in
the summer phase. However, for gas stove homes, the degree of association
between members of the same household for personal exposure was too great to
assume that individual measurements were independent—an assumption necessary
for least squares regression analysis.
Part of this association may be attributable to time spent at home
relative to other locations. Mean percentages for time spent in each of five
locations is summarized in Table 4 for workers, non-workers and students
during the summer and winter phases. Time spent inside at home was the
largest mean proportion for all three groups, with overall mean percentages of
65.41% in the summer and 67.47% in the winter. This implies that the home is
the principle location of exposure to air pollutants, although time spent in
other locations can not be neglected when considering total integrated expo-
sure. Specifically, time spent at work or school accounted for nearly 20% of
the average day during the winter for students and workers, making this an
important microenvironment contributing to NO exposures. Working with or
nearby welding, gas space heaters, or gasoline engines may be responsible for
driving the winter phase personal N0_ expsoures of 3 individuals from
electric stove homes to levels exceeding many of the cases from gas stove
homes. In contrast, time spent away from gas-cooking fuel homes may account
for some of the overlapping in personal exposures between the two cooking fuel
groups.
This activity data and N0_ measurements were combined to form a partial
time-weighted average (TWA) model. Home average indoor and outdoor levels
were weighed by the proportion of time spent there, their total was compared
with individual exposure measurements rather than the household averages.
2^8
-------
2
The Pearson correlation coefficients were r=0.65 (R =0.42) for the gas
2
cooking fuel group, r=0.37 (R =0.14) for the electric group in the summer.
School exposures were estimated by weighing the school average indoor level by
the time spent there for elementary, middle and high school students in the
winter phase; this was added to the estimates for home and outdoor exposures.
2
For students, correlations of these TWA estimates were r=0.83 (R =0.68) and
2
r=0.68 (R =0.46), from the gas-and electric-fuel groups, respectively.
2
Non-student correlations were 0.54 (R =0.29) for individuals from gas stove
2
homes, and 0.32 (R =0.10) for those having electric stoves.
Individual and household characteristics were evaluated separately using
a two stage, stepwise multiple regression analysis. Significant personal
characteristics during the summer phase were: (a) full-time worker (vs.
student or part-time); (b) commuting distance to and from work each day; (c)
sex is female; and (d) working with or near gas furnaces, boilers, ovens, or
flames. In the winter phase, significant predictors included (a) working with
or near welding or cutting torches (arc or flame), and (b) the individual's
age. Household characteristics were evaluated using the adjusted average
personal exposures for each home as the response variable. The presence of a
stove pilot light, outdoor NO concentration, and having a gas clothes dryer
were significant predictors for the summer data set, explaining 45% of the
variation in the adjusted household averages for log transformed personal
exposures (CV=6.7%, RMS=0.07). During the winter, the use of gas as the
2
cooking fuel was the significance home-level predictor, with R =67.1
(CV=7.4, RMS=0.13).
For comparing the ability of distinct types of measurements to represent
personal exposures, two additional models were fit. The first assumes that
only outdoor NO concentrations and a simple geographic classification
(urban vs. rural) are available; the second assumes that indoor averages for
-------
kitchen and non-kitchen zones, and an overall indoor average NCL
concentrations are available. For the summer data, the first model had an
R2=11.7% (CV=7.16, RMS-0.101), compared with R2=67.3% (CV=6.3, RMS=0.041)
2
for the second model and with R =44.5% for the model based on home
2
characteristics. Measurements of N0? outside the home gave an R =9.0%
(CV=9.33, RMS=0.36) during the winter, compared with R2=82.9 for the model
2
using indoor NO measurements, and with R =67.1 for the home characteris-
tics model.
The coefficient of variation (CV) for 93 replicate pairs of Palmes' tubes
was 4.52% for the summer phase. Absolute differences were less than 5 yg/m
in 98% of these pairs, with a precision estimate (square root of \ the
3
variance of the difference scores) of 1.0 yg/m . For the winter monitoring
phase the CV was 4.99%, and the precision estimate was 1.32 yg/m for 81
3
replicate pairs. Absolute differences were less than 5 yg/m in 95% of
these. Inter-laboratory comparisons gave a difference between means for the
3
Wisconsin and Harvard laboratories of 1.16 yg/m or 3.3% (S.D. of difference
3 3
scores, 1.6 yg/m ) for summer, and 3.29 yg/m or 6.51% (S.D. of difference
scores, 3.56 yg/m ) for the winter phase, indicating generally good
agreement between these labs.
250
-------
Discussion
The relationships between mean indoor and outdoor NO. concentrations
observed in this study compare favorably with several previous investigations
in demonstrating the close association between the use of gas for cooking and
elevated levels of NO inside of homes, with the magnitude of departure from
outdoor concentrations being greatest during the winter when both intentional
ventilation and unintentional infiltration rates may be reduced, and stove use
is likely to be increased (13-16,18,50). Conversely, average indoor NO
concentrations in electric stove homes were below those measured outdoors,
possibly explained by the reactive nature of NO (3). This difference was
also increased in during the winter, suggesting that for NO being inside
these homes may be protective against exposure to outdoor levels. Given the
proportion of time spent at home by our study population, the levels of N0_
they are exposed to there contribute a major time concentration component to
their total personal exposures. This impact is most apparent in both the mean
comparisons and correlations between indoor and personal N0~ exposures for
the gas stove households. By comparison outdoor measurements are a poor
estimator of personal exposures, especially during the winter when more time
is spent indoors and the levels of NO found there differ most from those
outdoors.
These results have several consequences for epidemiological studies of
the health effects of air pollutants, from both outdoor and indoor sources.
By using only the outdoor component of exposure, several key confounding
variables are omitted from consideration. These have been identified in our
regression analyses for personal characteristics such as occupation and
occupational exposures (welding, gas fire equipment), and as age and sex. For
household characteristics, indoor NO concentrations were closely associated
251
-------
with individual exposure. The variability in these concentration measurements
could only be partially represented by source (gas stove, pilot light, gas
dryer) and ventilation factors of the home as related to personal exposures.
Errors in the determination of the independent variable, exposure, may have
especially serious consequences for the ability of health studies to derive an
exposure-response relationship, since these errors may bias parameter
estimates to falsely imply the presence or absence of an effect.
Were all cases to conform to their group means both for indoor N0~
levels and for activity patterns, adjustment of ambient air pollution
monitoring data to estimate personal exposures would be simple. However,
measures of central tendency fail to convey information regarding the consid-
erable variability observed here and by other investigators for indoor NO,.,
concentrations. Yet this is precisely what is implied by statistically
controlling for the presence of indoor sources in covariate-type analyses of
the health effects of air pollution.
A similar problem is presented to those studies attempting to demonstrate
health effects associated with the use of gas as opposed to electric stoves.
Although the group means for personal NO exposure diverge, this study
demonstrates that there is a considerable overlap between these two groups.
This overlap might be attributable to variability in exposures experienced in
other locations, which in turn is related to the individual's own pattern of
activities. Such misclassification of individual exposures will reduce the
sensitivity of these studies. It also may explain some of the inconsistancies
in their results, and of suggested modification of health impacts over time,.
Biological changes in children as they grown older may make them less
susceptible to environmental challenges, but social changes may modify their
exposures by altering their mobility patterns. As less time is spent at home
252
-------
and more is allocated to school and other locations, this will alter the
individual's total integrated exposure even though he or she remains
classified as having a gas or electric stove.
Acknowledgements
The interest, cooperation, and hospitality of the participating families
are greatly appreciated, as is the assistance given by the staff of the
Pardeeville, Portage, and Rio school systems. We acknowledge the efforts of
William Knight for his assistance with graphics and programming, of Dr. Mari
Palta for her suggestions for data analysis, and Gary Hoffman at the Wisconsin
State Laboratory of Hygiene for his assistance in the preparation and analysis
of the monitors. This study was supported primarily by a grant from Wisconsin
Power Companies, and through a cooperative agreement between the Environmental
Monitoring Surveillance Laboratory (EMSL) of the U.S. EPA and the Harvard
School of Public Health.
253
-------
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257
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TABLE 1. INDOOR, OUTDOOR AND PERSONAL MONITOR NO CONCENTRATIONS (yg/m3)
FOR BOTH PHASES AND STOVE TYPES
Phase
Stove
Location
Mean
S.D.
Mean Mean
1-0 I/O
Diff Ratio
Summer Gas Indoor
Outdoor
Ave . Personal
Elect. Indoor
Outdoor
Ave. Personal
Winter Gas Indoor
Outdoor
Ave. Personal
Elect. Indoor
Outdoor
Ave. Personal
29.60
11.54
25.33
12.33
15.17
17.93
50.61
15.35
44.02
9.63
17.24
15.57
11.67
5.06
7.67
4.89
5.62
4.91
23.48
6.15
16.65
5.15
5.53
5.94
(38)
17.90 2.85
(38)
(38)
(50)
-2.65 0.86
(47)
(50)
(34)
36.43 3.93
(33)
(34)
(48)
-7.71 0.60
(47)
(48)
t
Number of households (a).
Average personal exposure for each household.
258
-------
TABLE 2. PEARSON PRODUCT-MOMENT CORRELATION OF HOUSEHOLD AVERAGE PERSONAL
NO,, EXPOSURE WITH INDOOR AND OUTDOOR CONCENTRATIONS*
Phase
Summer
Winter
Stove
Gas
Elect.
Gas
Elect.
Indoor
0.80
0.66
0.80
0.65
Location
Outdoor
0.40
0.35
0.13?
0.161"
Kitchen
0.78
0.69
0.75
0.65
a
(38)
(50)
(34)
(48)
.All p-values (that r=0) <0.001 except as noted.
p>0.22.
p>0.14.
TABLE 3. INTRACLASS CORRELATION (p) BETWEEN MEMBERS OF THE SAME HOUSEHOLD
FOR PERSONAL N02 EXPOSURE
Phase
Stove
Summer
Winter
Gas
Elect.
Gas
Elect.
0.63
0.24
0.50
0.19
45.67
12.82
194.51
16.88
26.62
40.48
194.29
71.73
38
50
34
48
150
206
127
197
t
Number of households.
Number of valid personal exposures.
259
-------
TABLE 4. MEAN PERCENT TIME SPENT IN VARIOUS LOCATIONS FOR 3 POPULATION GROUPS
Population Group
Phase Location
Summer Home
(S.D.)
Outside
(S.D.)
Motor Vehicle
(S.D.)
Work/School
(S.D.)
Other Indoors
(S.D.)
N
Winter Home
(S.D.)
Outside
(S.D.)
Motor Vehicle
(S.D.)
Work/School
(S.D.)
Other Indoors
(S.D.)
Workers
59.32
(11.92)
12.33
(9.13)
5.79
(4.22)
15.52
(10.87)
7.04
(6.38)
137
66.07
(11.41)
3.31
(5.35)
5.62
(5.56)
18.64
(10.36)
6.36
(6.00)
Non-
Workers
75.22
(12.11)
12.91
(9.87)
4.43
(2.72)
0.19
(0.76)
7.24
(6.38)
32
83.25
(8.39)
1.87
(2.01)
4.25
(2.50)
3.04
(7.05)
7.59
(5.34)
Students
68.34
(12.46)
14.96
(9.31)
3.30
(4.26)
4.40
(7.81)
9.00
(9.57)
177
66.14
(10.12)
3.86
(3.29)
3.25
(2.57)
19.47
(7.49)
7.27
(6.19)
Combined
Totals
65.41
(13.28)
13.73
(9.35)
4.39
(4.28)
8.41
(10.60)
8.06
(8.2)
346
67.47
(11.47)
3.49
(4.17)
4.24
(4.14)
17.85
(9.70)
6.95
(6.05)
127
26
176
329
260
-------
HOME RVERRGE PERSONRL N02 EXPOSURES
VS. OUTDOOR LEVELS (SUMMER)
_J
cr
o
CO
CK
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CC
QC.
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20-
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OUTDOOR CONCENTRflTION (>uG/m3)
HOME RVERRGE PERSONRL N02 EXPOSURES
VS. OUTDOOR LEVELS (WINTER)
80.
70.
60-
50.
40.
30-
20-
10.
0-
* X IS C-RS
x 0 IS ELECTRIC
-
* X *
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0- 10. 20. 30- 40. 50. 60. 70- 80- 9C
OUTDOOR CONCENTRflTION
Figure 1. Average personal NO exposure for each household compared
with outdoor concentrations for summer and winter.
261
-------
HOME nVERflGE PERSONflL N02 EXPOSURES
VS.RVERRGE HOME LEVEL (SUMMER)
1
ex
o
CO
o;
LU
CL
LU
CD
cr
a:
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* afl* „
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5. 10. 15. 20. 25. 30. 35. 40. 45. 50. 55. 60. 65-
RVERflGE HOME LEVEL (.uG/m3)
HOME RVERRGE PERSONRL N02 EXPOSURES
VS.RVERRGE HOME LEVEL (WINTER)
X IS GRS
0 IS ELECTRIC 3K
5(€ ^
*
* X
^
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HVERRGE HOME LEVEL (juG/m3!
80-
90.
Figure 2. Average personal NO exposure for each home compared with
average indoor concentrations for summer and winter.
262
-------
COMPARISON OF PERMEATION AND DIFFUSION-TYPE PASSIVE SAMPLERS
VERSUS
CHARCOAL TUBE COLLECTION OF SELECTED GASES
BY
PHILIP W. WEST, Ph.D. and A.S. Lorica, Ph.D.
WEST-PAINE LABORATORIES, INC.
7979 GSRI AVENUE
BATON ROUGE, LOUISIANA 70820
AND
JOHN W. STORMENT, C.I.H.
WESTERN ELECTRIC COMPANY
9595 MANSFIELD ROAD
SHREVEPORT, LOUISIANA 71130
ABSTRACT
A study has been made of the performance characteristics of
permeation-type samplers (REAL, .Inc.), diffusion-type devices (3M and
DuPont) and charocal tube collection of four gases of interest. The gases
studied were Freon 113, trichloroethylene, 1,1,1-trichloroethane, and
perchloroethylene. Concentrations of the gases were ten, fifty, one
hundred, and one hundred fifty per cent of the respective TLV's with face
velocities of 50 and 500 ft. per minute. Temperatures of 20°C and 30°C, and
relative humidities of 30% and 80% were included.
Passive monitors are recommended. The permeation-type monitors were
essentially free of problems arising from environmental variables and gave
consistently good results. They can be considered the most reliable,
accurate, and generally satisfactory devices for personal monitoring as
determined for the gases studied. The diffusion-type samplers were found to
show errors of as much as 99% when used in humid atmospheres, but were
generally satisfactory for use in atmospheres having humidities below 80
percent. 263
-------
INTRODUCTION
Previous studies of the performance characteristics of passive
monitors have been limited to diffusion-type samplers such as the DuPont
Pro-Tek and 3M 3500 badges (1,2). The permeation-type passive monitor
developed at Louisiana State University, by West and his co-workers
(3,4,5,6,7,8) has yet to be studied alongside the diffusion-type monitors
and the charocal tube. It was the purpose of this investigation to compare
the performances of the diffusion-type monitors, represented by 3M and
DuPont Pro-Tek, the permeation-type monitors, represently by REAL
Minimonitor (Reizner Environmental Analytical Laboratory, P.O. Box 3341,
Baton Rouge, Louisiana 70821), and the charocal tube in an atmosphere
containing four organic chlorine compounds encountered in the dry-cleaning
industry and in metal degreasing, namely, Freon 113, (1,1,2-trichloro-
1,2,2-trifluoroethane), 1,1,1-trichloroethane, trichloroethylene, and
tetrachloroethylene (a.k.a. perchloroethylene).
EXPERIMENTAL
Organic Vapors and their Concentrations
Test mixtures of the following compounds representing approximately
10%, 50%, 100%, and 150% of their respective TLV's were made by injecting
appropriate amounts of the pure liquids which were either AR grade or were
spectroscopic quality.
Freon 113 - For spectroscopic use.
1,1,1-Trichloroethane - MCB Reagents, 98% by GC.
Trichloroethylene - Certified ACS Reagent; Fisher Scientific Co.,
Chemical Mfg. Division, Fairlawn, N.J. 074010
Perchloroethylene - For spectroscopic use.
Standard Gas
The standard gas that was used to calibrate the GC gas results was
supplied by Gas Analytical Services, Inc. 10172 Mammoth Ave. Baton Rouge,
Louisiana 70814, according to the following specifications: Freon 113, 109
- 121 ppm; 1,1,1-trichloroethane, 46.0 - 48.1 ppm; trichloroethylene, 18.0
26k
-------
- 18.3 ppm; and perchloroethylene, 18.6 - 20.8 ppm.
The Exposure Chamber
The exposure chamber was an elongated toroid, closed loop type
designed by West-Paine Laboratories, Inc. The chamber main body, with a
volume of 80 L, was made of 1/8" and 3/8" Teflon-coated "Lucite" SAR, E.I.
DuPont de Nemours Co., Inc. The Teflon-coated inside surface of the chamber
served to minimize adsorption of the test vapor molecules. The chamber was
essentially an assembly of two parrallel long arms of rectangular cross-
section, joined together at each end by a U-joint, one of which housed the
blower used to mix and circulate the mixture of gaseous test species.
The monitors were placed in the two long arms of the chamber which
were accessible through removeable but tight-fitting covers.
Face velocity was measured with Datametrics Dresser 100 VJ_ Air
Velocity and Temperature Meter with the probe inside the chamber operative
on the hot-wire anemometer principle.
A "Linseis" US 6£ Linear Strip Chart Recorder used in connection with
a Type "K" thermocouple gave a continuous print-out of the temperature
inside the chamber.
Two "Neptune" Dyna Pumps, Model 2, Universal Electric Co. Michigan,
equipped with Teflon diaphragms were located outside the chamber. One pump
was used in the network to sample automatically the gas mixture inside the
chamber; the other was used to draw the gaseous mixture inside the chamber
through the charocal tubes and back to the chamber through Cole-Parmer
flowmeter, Model FM 042-15 with 150 mm flowtube and stainless steel float,
Cole-Parmer Instrument Co.,Chicago, IL 60648.
A Perkin-Elmer Gas Chromatograph Sigma 115 System continuously
monitored the concentrations of the organic pollutants in the chamber. A
15' x 1/8" x 0.012" GC column was used packed with 20% SP-2100 - 0.1%
carbowax 1500 on Chromosorb W, HP 80/100 mesh.
265
-------
The gas sampling network consisted on one 10-port Valco Air-
Activated gas sampling valve. Two 10-mL sampling loops were connected to
this valve such that one loop was continuously being flushed with fresh
samples as the other was loading sample onto the analytic column. A 3-way
ASCO 832061 stainless steel body, stainless steel seat ASCO solenoid.
controlled through the GC data processor was used to sample the pollutant
mixture inside the chamber.
The relative humidity inside the chamber was continuously monitored
with a Lab-Line Electro-hygrometer, Lab-Line Instruments, Inc. Melrose
Park, IL.
Experimental Procedure
Exposure p_f Monitors, Desorption and GC_ Analysis
Five to ten monitors were positioned inside the chamber facing the
gas flow and at sufficient distance from the blower motor to avoid
turbulent flow. A measured amount of each liquid organic pollutant was
injected into the injection port with the appropriate Hamilton GC syringe.
After the last liquid had been injected, the blower motor and GC sampling
network were immediately activated and the starting time recorded. Only
mixtures of the four organic pollutants representing either 10%, 50%, 100%,
or 150% TLV of each pollutant were used. By means of an "MSA Ventilation
Smoke Tube Kit" Part No. 458481, Mine Safety Appliances Co., Pittsburgh, PA
15208, instantaneous mixing inside the chamber was observed when the blower
was run at a speed that would give an air flow of 500 ft/min.
The monitors were exposed to the pollutant mixture for more than five
hours. After exposure, the monitors were taken out of the chamber for
immediate analysis or set aside in the freezer for later study.
The analyses involved desorption with either one mL (for charcoal
tubes), three mL (for diffusion-type monitors) or five mL (for REAL
monitors) of pre-cooled carbon disulfide, GC grade, J.T. Baker Chemical
Co., Phillipsburg, N.J. 08865, followed by the GC separation and
266
-------
determination of the pollutants in the carbon disulfide solution after at
least an hour of contact between the CS^ and the adsorbent taken from each
monitor. A second chromatographic run of this solution was made and the
results were averaged for each pollutant. The order of elution was: 1)
Freon 113; 2) 1,1,1-trichloroethane; 3) trichloroethylene; and 4)
perchloroethylene.
In addition to the predetermined amount of pollutants introduced
into the chamber, the face velocity, temperature, and relative humidity
were also controlled. Two face velocities were used: 1) 50 ft/min, to
correspond to the air flow in a laboratory where there is little movement
of air or personnel; and 2) 500 ft/min, to simulate air motion in a
laboratory where a great deal of relevant movement takes place. The
temperature either at 30°+ 2°C or 20° +_ 2°C. The relative humidity was
kept either in the 20 - 40% or 70 - 90% range except that at low temperature
the relative humidity was maintained in the 60 - 70% range.
In experimental runs where the chamber leaked as shown by an
uncharacteristically rapid decrease in the concentrations of the
pollutants measured,volumes of the pure liquids were subsequently injected
into the chamber to restore as closely as possible the initial
concentrations.
For the calibration of the REAL Monitors, each monitor was filled
with 1.35 g of activated charcoal (20 - 40 mesh, Lot 382, supplied by REAL,
Inc.). The permeation constant, k_, of each monitor for each pollutant was
determined by exposing the monitor to a mixture of all four pollutants for
the same length of time used in the actual experimental run.
The sampling rates for the charocal tubes ranged from 2.09 mL/min to
22 mL/min, depending upon the concentration of the pollutant mixture. The
sampling rate used was the maximum possible without breakthrough of the
pollutants. 267
-------
RESULTS
The k_ values used in the calculations for the permeation-type REAL
monitor are given in Table 1.
TABLE I - Permeation Constants, k*
Monitor
No.
1
2
3
4
5
6
7
8
9
10
Freon 113
504
492
468
491
638
432
479
485
493
447
532
500
511
509
683
446
538
537
594
491
1,1,1-Trichloro-
ethane
181
175
174
178
225
153
173
178
179
162
194
182
187
177
236
150
183
184
198
167
Trichloro-
ethylene
116
114
117
115
137
96.1
108
112
116
109
87.6
82.9
87.3
80.6
105
66.1
82.8
84.1
89.5
77.9
Perch loro-
ethylene
64.4 90.6
65.5
67.2
66.3
76.6
56.1
61.1
65.1
64.6
62.7
89.2
95.9
86.9
110
72.8
93.7
95.9
101
95.2
.H.',
second, for
* The first column is for
and 60 - 70% R.H.
The temperature effect on the permeation constant noted by West and co-
workers (3,4,8) is evident for Freon 113, perchloroethylene, and 1,1,1-
trichloroethane. The l£ values for trichloroethylene, on the other hand,
decreased with temperature which could mean a decrease in its mass transfer
rate through the permeable membrane with increase in temperature. The
following summary of the range of values of the coefficient of variation of
the permeation constants gives an indication of the precision involved.
Freon 113
1,1,1-trichloro-
ethane
Trichloro-
ethylene
Perchloro-
ethylene
Coefficient of
Variation
5.0 - 11 9.5 - 15 2.2 - 8.4 15 - 23
The percentage relative error, which by definition, is the absolute
error divided by the true value, expressed in percentage, is given in Table
268
-------
2. It might be pointed out that the true value was taken to be the value
given by the GC sampling network. By absolute error is meant the difference
between the value shown by a particular monitor and the true value.
At 30°C and 26 - 28% relative humidity and 500 ft/min face velocity,
the diffusion-type 3M 3500 and the REAL minimonitor showed good accuracy.
The other diffusion-type monitor, DuPont Pro-Tek, did not perform as well
as 3M and REAL. However, at 50 ft/min face velocity, the DuPont Pro-tek had
improved accuracy. The 3M and REAL devices also performed well at 50 ft/min
face velocity but the latter seemed less able to hold perchloroethylene
(9). At 20°C the REAL monitor performed excellently for all pollutants
studied. This behavior towards perchloroethylene, also shown by the
charcoal tube, might be characteristic of activated charcoal, the
absorbent used in these monitors. This view is supported by a series of
experiments involving the use of different kinds of activated charcoal in
the REAL minimonitor, which gave consistently low values for
perchloroethylene.
At high relative humidity, which often attends the actual use of
these personnel monitors, only the REAL monitor and the charcoal tube
continued to function well; the DuPont and 3M monitors apparently could not
retain the pollutant molecules in the presence of a large number of highly
polar water molecules.
The coefficient of variation of the TWA values had the following
range of values:
Charcoal tubes, 3.3 - 16;
3 M, 1.3 - 18;
DuPont, 1.7 - 26;
REAL, 2.4 - 18
It might be pointed out that the frequency of occurrence of the
higher values was relatively low. The confidence in passive monitors
inspired by impressive evidence presented in recent publications (1-8)
269
-------
seems to be justified.
Using an error ceiling of 35% and the data in Table 2 as basis for
comparison of the overall performance of the three passive monitors
studied, it can be seen that only 16% of the REAL data go beyond the error
ceiling as compared with 27% and 41%, respectively, for the 3M and DuPont
data. Of the three passive monitors included in this investigation, the
permeation-type REAL minimonitor showed, on the whole, dependable
performance. Indeed, it might be convenient and timely to adopt the
permeation-type REAL monitor as standard in place of or at least equivalent
to the charcoal tube.
CONCLUSIONS
1. Passive monitors, whether diffusion - or permeation-type, are as
reliable as the charocal tube.
2. Of the three passive monitors included in this work, only the
permeation-type REAL minimonitor continued to perform well at high
relative humidity.
3. On the whole, therefore, the permeation-type REAL monitor came out
with a better performance and it might now be time to adopt it as
standard in place of the charcoal tube.
ACKNOWLEDGMENTS
Financial support from the following organizations is gratefully
acknowledged:
Council for International Exchange of Scholors, Eleven Dupont Circle, N.W.
Suite 300, Washington, D.C. 20036, which administers the Fulbright-Hays
Fellowships, supported Dr. Lorica for 11 months;
Philippine-American Educational Foundation, 12th Floor, Magsaysay Center,
1680 Roxas Blvd., Manila, Philippines, underwrite the travel expenses of
Dr. Lorica to and from the United States.
University of the Philippines, Quezon City 3004, Philippines, granted
sabbatical and research leaves consecutively to Dr. Lorica.
270
-------
Louisiana State University, Baton Rouge, LA 70803-1804, U.S.A., where
Dr. Lorica was visiting scholar.
West-Paine Laboratories, Inc. 7979 GSRI Avenue, Baton Rouge, LA 70820,
U.S.A., supported Dr. Lorica upon termination of CIES funding.
The work described in this paper was not funded by the U.S.
Environmental Protection Agency and therefore the contents do not
necessarily reflect the views of the Agency and no offical endorsement
should be inferred.
REFERENCES
1. Feigley, Charles E. and Chastain, James B. An experimental
comparison of three diffusion samplers exposed to concentration
profiles of organic vapors Am. Ind. Hyg. Assoc. J. 43(4): 227
2. Voelte, D. R. and Weir, F. W. A dynamic-flow chamber comparison of
three passive organic vapor monitors with charcoal tubes under single
and multiple solvent exposure conditions Am. Ind. Hyg. Assoc. J.
42(12): 845 (1981).
3. Reiszner, K. D. and West, P. W. The collection and determination of
sulfur dioxide incorporating permeation and the West-Gaeke procedure
Environ. Sci. Techno!. 7: 526-32 (1973).
4. McDermott, D. L., Reiszner, K. D. and West, P. W. Development of
long-term sulfur dioxide monitor using permeation sampling Environ.
Sci. Techno!. 13: 1087 (1979).
5. Bell, D. R., Reiszner, K. D. and West, P. W. A permeation method for
the determination of average concentrations of carbon monoxide in the
atomsphere Anal. Chim. Act 77: 245-254 (1975).
6. Nelms, L. H., Reiszner, K. D. and West, P. W. A personal vinyl
chloride monitoring device with permeation technique for sampling
Anal. Chem. 49: 994-998 (1977).
271
-------
7. Hardy, J. K., Dasgupta, P. K., Reiszner, K. D. and West, P. W.
Personal chlorine monitor utilizing permeation sampling Environ.
Sci. Technol. 13: 1090-1093 (1979).
8. Ryan, Roland L. and West, Philip W. A hydrogen fluoride personal
monitor using permeation sampling Am. Ind. Hyg. Assoc. J. 43: 640-
44 (1982).
9. Gergory, E.D. and Elia, V.J. Sample retentivity properties of passive
organic vapor samplers and charocal tubes under various conditions of
sample loading, relative humidity, zero exposure level periods and a
competitive solvent. Am. Ind. Hyg. Assoc, J. 44: (2) 88 - 96 (1983).
272
-------
TABLE 2
PERCENTAGE RELATIVE ERROR
50 Ft/MIN FACE VELOCITY
FREON 113
1,1,1
TRICHLOROETHANE
TRICHLORO-
ETHYLENE
PERCHLORO-
ETHYLENE
N>
FREON 113
1,1,1
TRICHLOROETHANE
TRICHLORO-
ETHYLENE
PERCHLORO-
ETHYLENE
C. Tube
3M
DuPont
REAL
C. Tube
3M
DuPont
REAL
C. Tube
3M
DuPont
REAL
C. Tube
3M
DuPont
REAL
C. Tube
3M
DuPont
REAL
C. Tube
3M
DuPont
REAL
C. Tube
3M
DiiPoNt
REAL
C. Tube
3M
DuPont
REAL
TLV
-2.2
-9.7
-4.9
+3.6
+3.3
-13
-11
-19
+18
-28
-23
-13
-24
-19
+1.7
-46
ro*-
TLV
" +776
+5.3
-7.5
-1.2
+13
+8.7
+9.0
-2.6
+30
-8.7
+7.1
0.0
-15
-18
+36
-3.2
+13
-76
-89
+3.2
+16
-26
-48
-14
+27
+14
+28
+11
-14
-7.3
+37
-37
""+5.7 "
-80
-91
-1.0
+12
-25
-56
+0.8
+16
+36
+106
+20
-17
0
+99
+20
" 50%""
TLV
-18
-1.8
-25
-8.0
-17
-14
-24
-18
-8.0
-28
-26
-25
-41
-24
-8.4
-38
500 Ft/MIN
""' ~ 50* "
TLV
-24
+1.3
-18
-9.7
-22
-2.0
-2.4
-12
-13
-16
-6.0
-3.8
-47
-12
+45
-13
+11
-88
-95
+5.4
+16
-46
-74
-17
+33
-10
+13
+12
-18
-12
+8
-33
FACE
-12
-85
-93
+17
-9.4
-46
-72
-2.4
+2.7
-6.9
+22
+42
-37
-27
+28
-10
-3.8
-16
-47
-0.5
-17
-30
-25
+14
-20
-13
-18
-26
-19
+4.8
-50
VELOCITY
-5.5
-11
-53
-13
+0.5
-3.9
-18
+0.7
+13
-25
+12
-1.1
-28
-31
+58
-1.5
TOO*
TLV
-2.2
-85
-92
+1.6
-44
-68
-21
+11
+5.1
+14
+4.2
-24
-16
+4.5
-40
TOO*
TLV
+3.4
-88
-94
+3.2
+28
-49
-76
-3.4
+39
0
+23
+36
-11
-24
+23
-7,8
"" 150%"
TLV
+22
-38
-69
-14
-5.6
-35
-14
+24
+19
+5.1
-46
-6.8
+12
-39
" rs'oY
TLV
-1.7
-25
-57
-12
+4.6
-8.6
-21
-2.4 _„
+16
-8.0
-8.2
-25
-9.3
+35
+1,5 „„
+3.3
-87
-91
+9.0
-53
-64
-7.2
+24
-4.6
+24
+6.9
-23
-25
+18
-39
+4.2
-84
-94
-10
+9.2
-45
-77
-0,3
+18
+0.8
+24
+41
-24
-18
+68
+1.?
NOTE: At each TLV the left column gives values at 30% R.H., right column values at 80% R.H.
-------
SESSION V
ACID DEPOSITION
275
-------
AUTOMATED ACQUISITION, FILTERING AND
REDUCTION OF
ION CHROMATOGRAPHIC DATA
RICHARD C. GRAHAM, JOHN K. ROBERTSON, AND JEFFREY S. POULIN
SCIENCE RESEARCH LABORATORY
US MILITARY ACADEMY
WEST POINT, NEW YORK 10996
In the past several years, few problems have received the national and
international attention that acid precipitation has received (1,2). For
informed decisions about pollution control strategies to be reached by
policymakers, large amounts of data on the chemical composition of
precipitation must be amassed. This data must address and be able to define
trends in chemistry and hopefully to delineate sources of pollution from which
acid is derived. Trends in the spatial and temporal variability of
precipitation are being addressed by national networks such as the National
Atmospheric Deposition Program, the MultiState Atmospheric Precipitation
Sampling Program, and the Canadian Sampling Program (3). In addition to these
deposition oriented networks, a need exists to sample a storm from beginning
to end on a systematic basis such as reviewed by Robertson et. al. (4). An
intensity weighted sampler used by the Science Research Laboratory at the US
Military Academy collects one sample of 7 ml volume every 0.01" of
precipitation. For each sample collected a rather complete ionic chemistry
workup is desired so as to obtain pH, concentration of major cations
(monovalent and divalent) and major anions. With average precipitation of 47"
per year at West Point, it became obvious early in our effort that we must
276
-------
automate as much as possible every aspect of our data collection, storage, and
processing effort. We chose to begin our automation effort with the ionic
chemistry analysis since this would represent the largest savings of time.
INSTRUMENTATION
Since the originally designed DIONEX Model 14 was initially only capable
of determining either anions or cations, a second detector and associated
circuitry were obtained from DIONEX and the system replumbed to allow for use
of both sets of columns at the same time. Anion analyses are performed using
the 500 mm x 3 mm or two 250 mm x 3 mm separator column(s) followed by a 6 x
250 mm suppressor column. The eluent is the 0.0024 M NaHC03/0 .003^1 Na2C03
prepared by diluting 2 gms of each anhydrous salt to 8 liters using > 15
megohm-cm deionized water. Monovalent cation (Na+, Mty"1", 1C1") analysis was
performed using a 6 x 250 mm separator column followed by a 9 x 250 mm
suppressor column. A 0.007 _M HN03 eluent is prepared by dilution of 55 ml 1 _N
HN03 solution to 8 liter using > 15megohm-cm deionized water. Deionized water
is obtained from a Millipore water treatment system consisting of a reverse
osmosis column, carbon adsorption column, two mixed bed strong ion exchange
resins and a 0.2 micron particulate filter.
A Varian 8055 liquid chromatographic autosampler was added to enhance the
DIONEX capabilities. A Valco ACV-10-UHPa-N60 10 port sampling valve with two
100 microliter sampling loops was used to allow the simultaneous injection of
a single sample onto each of the sets of columns on the ion chromatograph.
The sampling valve is plumbed as in Figure 1.
277
-------
-L/V3n -73
-------
The original sampling loop as supplied by DIONEX was left in the eluent
flow line as the ion chromatograph is used both for research and for teaching.
OBJECTIVES
The objectives defined at the outset of the project were as follows:
1. To provide a physical link between the DIONEX and a
microcomputer.
2. To provide a link between the Varian Autosarapler and a
microcomputer.
3. To convert the analog signal from the DIONEX to a digital
signal for input to the microcomputer.
4. To have the autosampler signal the microcomputer when a sample
is injected.
5. To read the rack and vial number of the sample being analyzed.
6. To provide a sample control system.
7. To store the digital information for later processing.
8. To smooth the digital data by an accepted technique.
9. To process the smoothed data to obtain peak height, peak area
and retention time of each peak.
10. To sort the output file from objective 9 to include sample
identification data from the sample control system.
11. To transfer the sample data from the microcomputer to a
mainframe computer for archival, statistical analysis, etc.
To date all but objectives 6, 10 and 11 have been fully implemented.
Programming efforts on the remainder are continuing. We are also pursuing a
means to accomplish objective 9 simultaneously with the acquisition of the
analog data which will require a foreground/background programming effort.
279
-------
The overall system is shown as block flow charts in Figure 2 and 3. The
program operation can be divided into four sections — Administrative, Data
Acquisition, Data Storage, and Data Processing. Each of the subsystems will
be discussed in subsequent paragraphs.
Administrative
The administrative portion of the code is an interactive session allowing
the analyst to enter run conditions, date, number of samples to be analyzed,
number of wash vials, source of sample, etc. The answers to the questions
asked during the session control the naming of two types of data storage files
— a file containing the sample identification, run conditions, etc. and a
second which contains the digital chromatographic data. It is anticipated
that as the software continues to develop that the header file will be
accessed to determine if the sample being analyzed is a standard or not, and
then using the standards for construction of calibration charts. If the
sample is an unknown, then the peak height will be converted to concentration.
At this time, the conversion of peak heights to concentration still requires
some "manual" manipulations of the data files. Work is ongoing to fully
automate this process. The data files are dynamically created during the run
as the rack and vial number are read at the autosampler.
Data Acquisition
To write the computer code for the acquisition of data from the ion
chromatograph and from the autosampler, the internal workings of the
280
-------
fl
REQUIRE n
STORE
HERDER INFO
RNRLOG TO
DIGITRL
CONVERSION
WRITE DIGITRL
DRTR TO DISC
REQUIRE RND
STORE
RUN CONDITIONS
\/
R.ERD RRCK RND
VIRL NUMBER
\/
NRME RND OPEN
FIGURE 2 FLON EHRRT FOR RDMINI5TR.R \ IVE, DRTR REQUISITION.
RND DRTR 5TORRGE
281
-------
NJ
CO
(SJ
p
OPEN DRTR
FILE
SORT BY
RNRLTTE
V
CLOSE FILES
SMOOTH DRTR
CRLCULRTE PERK
PRRRMETERS
TRn.N5.MIT DRTR
FO MRINFRRME
V
NRiTE OUTPU
FILE
CRLCULRTE.
CONCENTRRTION5
END
-------
autosampler had to be completely understood. The timing of actions in the
program had to be closely linked to the sequencing of events occurring in the
autosampler and to the signals from the ion chromatograph. The autosampler is
placed in operation by depressing the multiple/single run button. As the rack
rotates under the injection needle, the program delays for several seconds.
When the needle has pierced the septum, the rack and vial number information
is available as 5 bit binary coded decimal (BCD) output on the interface
cable. This information is read by a parallel digital board on the
microcomputer and is immediately used to dynamically name and open a data file
for the chromatographic data. Since the inject cycle on the autosampler
allows for flushing of the tubing carrying sample to the injection loops, the
program waits for a signal from the autosampler that the Valco valve has
turned to the inject position (See Figure 1).
The signal is quite weak and required that an amplifier circuit be built
(Figure 4) which could be detected by the Schmitt Trigger on the Mine. The
0-1 volt signal from the DIONEX is fed via 22 gauge copper wire to the
preamplifier on the MING which allows programmable "autogaining" of the
preamplifier. The signal is then fed through the internal bus structure of
the MING to the analog to digital converter. The Schmitt Trigger on the MING
initiates the acquisition and conversion of the analog signal to digital form.
Simultaneously a realtime clock is triggered to maintain the time of a run and
also to control the timed acquisition of data. The clock module generates
interrupts to the processor to signal that an Analog-Digital conversion must
be made. Data acquisition requires that buffers be initialized and
subsequently filled with the converted data. Data acquisition continues until
283
-------
6 VOLTS
MNCKN 512
100 MICRO.FRRRD
28k
-------
the run time as specified by the operator is exceeded. The data as acquired
is in integer form, and must, therefore, be converted to floating point. All
of the buffer management and integer to floating point conversion is handled
by system subroutines. The preceding is continued until the rack and vial
number that are read indicate that a stop pin has been encountered, at which
i'ne data acquisition ceases and data processing begins.
Data Storage
After the data is acquired from the DIONEX and is resident in the memory
of the microcomputer, the data is transferred to either a floppy disc or the
hard disc of the microcomputer system. The files as explained earlier are
named and opened as the rack and vial number of the sample are read from the
autosampler.
Data Processing
Data is smoothed using a modified Savitzky-Golay smoothing technique
(5,6). The particular technique is a seven point moving box car average.
Hacker, et. al. (7) and Walraven (8) indicate that significant shift in the
position of a peak may be caused by a single pass of such a smoothing routine.
They also showed that insignificant loss of peak height and no resultant peak
shift would occur if the algorithm smoothing the peaks were to include four
passes—the first and third from time equal zero to end of run, and two and
four from time equal to the end of run to zero.
285
-------
The expression for the filter function is:
y(n+3)=(x(n-3)+x(n-2)+...+x(n+2)+x(n+3)) (1)
7
where y(n+3) is the value of the filtered data point and the x(n-3), etc. are
the values of the unfiltered data points.
The transfer function, H(f), of this filter formula exhibits unity gain
for low frequency output. Higher frequencies are greatly attenuated so that
almost no high frequency "noise" appears in the output data. For the moving
average filter, the transfer function is:
M
H(f)=A +2 A cos 2 K ft (2)
K=l
where f = the frequency of the input data and t = the sampling rate. For a
seven point filter, K=3 and M=2. This gives the transfer function:
H(f)=l+2 cos 2 ft + 2 cos 4 ft + 2 cos 6 ft) (3)
7
A graph of H(f) versus 2 ft enables visualization of the filtering action
286
-------
o
XcD
6
O'
0
+ o £
cvl
0
a
O
28?
-------
The dashed graph represents the transfer function of the smoothing filter
employed in the program. It represents the effect of four passes of the
moving average formula on the data. The solid graph represents the effect of
one pass. As can be seen, the effect of four successive applications of the
filter is that noise above a frequency component (2 ft) of 0.20 is for all
practical purposes eliminated.
Whereas for the single pass, only those frequency components of 2 ft
equal to 0.28, 0.56 and 0.85 are eliminated. In the region between these
values, the frequency components are passed and for the region 0.28 _< 2 ft<^
0.56, the amplitude is reversed, thus subtracting from the net signal.
The peak height, peak width, retention time, and several other parameters
were determined using chromatography peak processing routines on the MINC.
After all of the peaks have been identified and the peak areas tabulated, the
data is transferred via a Network communications package to a larger mainframe
system for conversion of the peak heights to concentrations using a linear
least squares fit of peak height vs. concentration calibration curve. The
resulting concentrations and other sample identification data are stored on
the mainframe using the RIM-5 data base system. Data retrieval and subsequent
statistical analysis of data are accomplished on the mainframe.
Future work
In the future, we plan to streamline the working of the program to allow
more efficient use of the computer resources and to allow for simultaneous
processing and acquisition of data. Several other routines for the sorting
and merging of data are currently being written.
288
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REFERENCES
1. Carter, J., Environmental Priorities and Programs, Message to the
Congress, 2 August 1979.
2. Memorandum of Intent Between the Government of Canada and the Government
of the United States of America Concerning Transboundary Air Pollution, signed
5 August 1980.
3. Wisneiski
4. Robertson, J.K., R.C. Graham and T.W. Dolzine, Chemistry of Precipitation
from Sequentially Sampled Storms, EPA 600/4-80-004, US Environmental
Protection Agency, Research Triangle Park, NC, 1980, 117 pages.
5. Savitzky, A., and M.J.E. Golay, Smoothing and Differentiation of Data by
Simplified Least Squares Procedures, Anal. Chera. 36:1627,1964.
6. Steiner, J., Tremonia and DeHour, Comments of the Smoothing and
Differentiation of Data by Simplified Least Squares Procedure, Anal. Chem.
44:1909,1972.
7. Hacker, H., R.C. Graham, and J.K. Robertson, Interfacing a Digital MINC-11
Microcomputer with a Polarograph, Proceedings of the Digital Equipment
Computer Users Society Symposium, Los Angeles, 1981, p.467-473.
8. Walraven, R., Digital Filters, Proceedings of the Digital Equipment
Computer Users Society, San Diego, California, 1980, p.827-834.
289
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DESIGN AND TESTING OF A PROTOTYPE
RAINWATER SAMPLER/ANALYZER
Richard J. Thompson
School of Public Health
University of Alabama in Birmingham
Birmingham, AL 35294
A device for the collection and analysis of rainwater has been conceptualized;
component parts are undergoing testing. The sampler will record the
temperature, pH, conductivity and volume of collected rain samples and note
the time of capture/analysis. Ions from samples of known volume will be
trapped for subsequent laboratory analysis.
Performance characteristics will be presented which are designed to meet the
short-term needs of meteorological modeling and atmospheric chemists as well
as the long-term needs of soil scientists.
290
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The collection and analysis of precipitation matter is vital to the
definition and understanding of the "acid rain" problems. It has been shown
that there are grave problems in the monitoring of rainwater (1,2), and that
samples decay on standing (2-10). Even in laboratories which should be among
the best, the measurement of all constituents in all samples varies by over a
factor of 2 and some constituents measurements in some samples varies by over a
factor of 10 in both rain (5) and synthetic deposition samples (11). This
could mean that there is potentially an error of at least ± 0*3 and as much as
+ 1-0 pH units in the monitoring data being collected today. Even in data from
the National Atmospheric Deposition (NADP) program one may note a difference of
one pH unit between field and laboratory measurements for some samples.
The errors existent in monitoring the chemical constituents of rainwater,
pH, and conductivity are largely ignored, although this problem has been
addressed in the literature (1,8,9). It has been shown that phosphate, nitrate
and ammonium are unstable, in the order given (3,4,5,10). Cooling (5,6) or
freezing (4) collected samples is said to promote stability. Storage in glass
or polyethylene is claimed not to affect stability (5).
One solution to the vexing problem of identifying change sample stability
is to follow the NADP procedure (contributed by the author) of measuring pH and
conductivity in the field and in the laboratory. Another advance would be to
isolate chemical constituents in a manner such that decay is avoided. Another
need is for sequential, sampling rather than the bulk samples collected in the
NADP, the NOAA-WMO-EPA network (the oldest national network) and other networks
(2,8).
A number of sequential samplers have been devised (6,12,13,14) one of
which measures pH (14). None of the samplers, however, measures conductivity,
nor provides for the immobilization of chemical contents, although one design
291
-------
provides for refrigeration to reduce temperature in order to minimize
biological activity and chemical decay (6).
In table 1, characteristics of the Rainwater Sampler/Analyzer (RWSA-1) are
shown. The RWSA-1 designed is intended to avoid most of the problems given
above. Measurements will be made of pH, conductivity, ionic composition, and
acidity. Stored samples will be analyzed to determine decay.
The capabilities of the RWSA-1 are given in table 2. Note that the
sampler capabilities address the needs of both meteorologists for short-term
data and biologists and ecologists for seasonal data. Also addressed is the
need for the determination of sample stability.
In table 3, the operational features include operator control to allow for
varying intensities of rain. The storage of cations and ions (on ion exchange
resins) is a novel feature of this device. The designed operating range of
-50°F to +50°F is of course subject to proof in the field. The micro-processor
control of measurements and the operator processor control of sampling options
is also a novel feature.
The engineering of the electronics for this device and its construction
are from venture capital. The objectives of the testing of the prototype is
given in table 4. As noted, modification of the RWSA-1 is a possibility which
field test results may dictate. This project will commence when prototypes are
availab!e.
In table 5, a portion of the planned future studies is given. The last
item given, "show applicability of RWSA-1 to generating data for quality
assurance program development is direly needed and may prove to be a notable
advance in "acid-rain" work.
Due to the demonstration of storage effects from some samples from some
sites, it would obviously be of interest to monitor rain samples at a site for
292
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a year before tailoring a quality assurance program to that site. Because of
the unique nature of sites and of rain samples, only from a year's data can a
meaningful site specific quality assurance program be developed.
The sequential collection of samples and concommittant measurement of pH
and conductivity is shown in diagram 1. In diagram 2, the interfacing of the
various components around the micro-processor is depicted.
Although the National Acid Deposition Plan does not make mention of, nor
allude to, any monitoring difficulties (15), those of us who have had to deal
with "real-work" precipitation monitoring programs are aware of the problems
mentioned above. The RWSA-1 may prove to be of significant help in making
precise the data for the chemical monitoring of precipitation. Without more
precise monitoring data, defensible relationships between cause and effect of
"acid-rain" will not be possible. If non-metal oxides go up, "acid-rain" must
come down, but the relationship between emissions and deposition will not be
convincingly demonstrable without monitoring data more precise than that
currently obtainable.
The work described in this paper was not funded by the U.S. Environmental
Protection Agency and therefore, the contents do not necessarily reflect the
views of the agency and no official endorsement should be inferred.
Acknowledgment: The purchase of prototypes described and the bench and
field testing are to be performed under NOAA contract No. NAOLRA00125, Design
and Testing of a Prototype Rainwater Sampler/Analyzer.
293
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TABLE 1 - RAINWATER SAMPLER ANALYZER-1
CHARACTERISTICS
SENSOR-OPENS COLLECTOR COVER TO RAIN/SNOW
TWO COLLECTORS AND TWO CHANNELS
CHANNEL I - WILL MEASURE pH, CONDUCTIVITY AND TEMPERATURE
CHANNEL II - WILL MEASURE VOLUME AND PASS SAMPLES THROUGH COLUMNS
COLLECTOR SIZE VARIABILITY WILL PERMIT RWSA-1 USE IN AREAS OF
DIFFERENT RAINFALLS
ONE TO TEN UNITS OF RAIN CAN BE LOADED ON EACH COLUMN
SIX COLUMNS ARE AVAILABLE ON THE RWSA-1 PER SEOUENCE
AN AUXILIARY MODULE (AM) CAN BE COUPLED TO THE RWSA-1 TO EXTEND THE
COLLECTION CAPABILITY TO 12 COLUMNS
THE RWSA-1 (AND AM) CAN BE POWERED BY 110 VAC OR BY AN OPTIONAL
BATTERY PACK
OPTIONAL HEATED VERSIONS FOR RAIN/SNOW COLLECTION
DESORBER OPTION TO INTERFACE WITH ION CHROMATOGRAPH
CLOSED COLLECTION SURFACES OF TEFLON
IONS LOADED ON RESINS FROM FIXED VOLUMES
MICRO-PROCESSOR CONTROLLED BY OPERATOR
ALL DATA RECORDED AND PRINTED
23k
-------
TABLE 2 - RWSA-1 CAPABILITIES
DETERMINE RAINWATER COMPONENTS IN "REAL-TIME"
"REAL-TIME" MEASUREMENT OF CONDUCTIVITY, pH, TIME, DATE &
TEMPERATURE
DETERMINE RAINWATER COMPONENT PROFILE OF RAIN EPISODES
DETERMINE DEPOSITION - EPISODE AND ANNUAL
DETERMINE SEASONAL COMPOSITION VARIATION
STORE' BULK SAMPLE TO ALLOW THE DETERMINATION OF SAMPLE STABILITY
FOR EACH SAMPLE
TABLE 3 - RWSA-1 OPERATIONAL DESIGN FEATURES
COLLECTOR CONTROL TO ACCOMMODATE RAINS OF VARYING INTENSITY OR
AMOUNT
BOTH ACTIONS AND ANIONS STORED
"AS IS" RAINWATER SAMPLED FOR OTHER ANALYSES
OPERATES BETWEEN +50°F AND -50°F
MEASUREMENTS MICRO-PROCESSOR CONTROLLED
COLLECTION OPTIONS PROGRAMMABLE BY OPERATOR
295
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TABLE 4 - PROTOTYPE TEST OBJECTIVES
COMPLETE RWSA "BENCH-MODEL"
DEVELOP "BENCH-MODEL" TEST PROTOCOL
TEST "BENCH-MODEL" FOR PERFORMANCE AND RELIABILITY
MODIFY AS NEEDED TO RWSA-1
DEVELOP A FIELD-TEST PROTOCOL FOR THE RWSA-1
FIELD-TEST TWO RWSA-1 COPIES FOR PERFORMANCE PRECISION AND
RELIABILITY
MODIFY RWSA-1 AS NEEDED
RETEST (IF MODIFIED) AND COMPARE WITH OTHER SAMPLING SYSTEMS
DEMONSTRATE VIABILITY OF STORED RESINS
DEMONSTRATE DEVICE CAPABILITY TO PROFILE EPISODES
TABLE 5 - FUTURE STUDIES
DETERMINE EPISODE PROFILES AT SELECTED HIGH EMISSION LOCALES
DETERMINE EPISODE PROFILES AT SELECTED INTERMEDIATE EMISSION
LOCALES
DETERMINE EPISODE PROFILES AT SELECTED BACKGROUND EMISSION LOCALES
DETERMINE CAUSE OF SAMPLE DECAY
BENCH TESTING BY OTHER LABORATORIES
FIELD TEST AT OTHER LABORATORIES
COMPARE WITH OTHER SYSTEMS
SHOW APPLICABILITY OF RWSA-1 TO GENERATING DATA FOR QUALITY
ASSURANCE PROGRAM DEVELOPMENT
296
-------
VOLUME
DETECTOR
0.1 CM
pH METER
0-14 0.05 pH
CONDUCTIVITY
METER
0-300 ^MICROMHO
TEMPERATURE
(FLUID)
± 0.10 C
TEMPERATURE
(OUTSIDE AIR)
±0.10 C -40-+70 C
PRECIP SENSOR
-FIRST DROP--
NONVOLATILE
MEMORY
BACK-UP
PWR SOURCE
MICROPROCESSOR
BASED
SYSTEM CONTROLLER
REAL-TIME CLOCK
(YEAR-DAY-TIME)
LINE PRINTER
PUMP& VALVE
ACTIVATORS
RAIN DOOR
OPEN & CLOSE
OPERATOR
TEST PANEL &
DISPLAY
-------
RAIN DOOR
NJ
U>
OO
DATA TO
LIQUID STORAGE
CALIBRATED VESSELS
-------
REFERENCES
1. Tyree, S.Y., Jr. Rainwater Acidity Measurement Problems. Atmos. Environ.
15:57, 1981.
2. Thompson, Richard J. The Sampling and Analysis Techniques in Current Use in
the EPA/NOAA/WMO Precipitation Network. WMO Special Environmental Report No.
10. Air Pollution Measurement Techniques, WMO No. 460, Geneva, 1967. p. 40.
3. Feeley, Herbert W. et. al. The Chemical Analysis of Deposition Samples.
Environ. Meas. Lab. Environ 0 1980, EML 391. I-43-I-74.
4. Mueller, K.P. et. a±., Deposition Atmos. Poll. Proc. Colloq. 1981. via C.A.
97:?03034q.
5. Linkaityte, E. et. al. Change in the Composition of [atmospheric] Precipitate
Samples in Relation to the Conditions of Time & Storage. Zashch. Atmos.
Zagryaz. 3:38,1976. via C.A. 87:28105h
6. Coscio, M.R., Pratt, G.C. and Krupa, S.V., An Automatic, Refrigerated,
Sequential Precipitation Sampler. Atmos. Environ. 16:1939, 1982.
7. Lewin, Ella E. and Torp, Ulrik. Influence of Contamination on the Analysis of
Precipitation Samples. Atmos. Environ. 16:795, 1982.
8. Hansen, D.A. and Hidy, G.M. Review of Questions Regarding Rain Acidity Data.
Atmos. Environ. 16, 2107 (1982).
9. Jervis, T.R., Rainfall Acidity: Natural Variance and Subsequent Time
Dependence of pH. Atmos. Environ. 13:1601, 1979.
10. Peterson, D.L. et. al. For. Res. Rep. (Univ. 111. Urbana-Champaign. Agri.
Exp. Sta. Dept. For.Tl979, 79.
11. Feeley, Herbert W. and Bogen, Donald C. The Chemical Analysis of Deposition
Samples. An Inter!aboratory Comparison. Environ Q (US Dept Energy Environ.
Meas Lab) 1978. 1,3-43.
12. Asman, Willem A.H., Draft Construction and Operation of a sequential rain
sampler. Water, Air, Soil Pollut., 13:235, 1980.
13. Raynor, Gilbert S. and McNeil, John P., An Automobile Sequential Precipitation
Sampler. Atmos. Environ. 13:149, 1979.
14. Lopez-Gonzalez, Jose Alberto, Determination of raviations in the Chemical
Composition of rainfall in several regions of Texas through the use of an
automated sequential sampler and analyzer. Dissertation, University of Texas,
Austin, 1982. via Diss. Abstr. Int. B. 43:809, 1982.
15. National Acid Deposition Plan, Prepared by the Interagency Task Force on Acid
precipitationDraft January, 1981, Dr. Cris Bernabo, Executive Secretary,
Council on Environmental Quality, Washington, D.C.
299
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ORGANIC ACIDITY IN PRECIPITATION FROM
REMOTE AREAS OF THE WORLD
I'.' i I I i a rn C . K e e n e
James '!. Ga I I owey
J . Dav id Hoi den
Department of Environmental Sciences
University of Virginia
CharlottesviI Ie, Virginia 22903
300
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INTRODUCTION
The enthropogenic emissions of SOo and NO into the atmopshere
has caused widespread acidification of precipitation in Northern
EL rope and eastern North America (1,2). The study of precipita-
tion chemistry developed largely in response to this perturbation,
and consequently most research has focused primarily on major
inorganic chemical constituents. The organic constituents, and
particularly carboxylic acids, have received relatively little
attention. Carboxyl ic acids are a common constituent of aerosols
and precipitation (3). In industrialized regions of the Northern
Hemipshere, these acids make a negligible contribution to the free
acidity of precipitation (4,5,6,7,8). However, evidence suggests
that formic and acetic acids are a major source of free acidity in
precipitation fror.i remote regions of the world (9,10). This paper
reports a summary of current findings regarding organic acidity in
precipitation collected by 1he Global Precipitation Chemistry
Project (GPCP).
Study Site, Collection Procedures and Analytical Protocol
The (GPCP) was established in 197? to determine the composi-
tion of precipitation, and processes that control the composition,
in remote regions of the world. To date, 5 stations have: been
established. They are Katherine, Australia; San Carlos do Rio
Negro, Venezuela; Poker Flat Research Range, Alaska; ST.George's,
0 e r ID u d a ; and Amsterdam Island in the Indian Ocean. G a I I o w a y e. t
31. (5) gave detailed descriptions of the sites, sample collection
procedure arid analytical protocol.
301
-------
Samples were collected by event in scrupulously washed
polyethylene containers. Immediately after a sample was
collected, the pH's of two aliquots were measured at the field
site. If sufficient volume remained, the sample was divided into
two 250 ml aliquots. One aliquot was treated with chloroform to
prevent biological activity during sample storage and shipment to
the University of Virginia. The other aliquot was left untreated.
During the first two years of operation, samples were
analyzed for the following major inorganic species: H + , C a 2 + ,
Mg2 + , Ma+, K+, NH4 + , S042~, H03-, Cl~ and Si04~4. P043" was
measured only in samples from San Carlos de Rio Negro.
Development of Hypothesis
Ion balances calculated from inorganic data suggested that
unmeasured proton doners contributed significantly to the free
acidity of precipitation at certain sites (9,10). Comparisons
between pH measured in the field and in the laboratory suggested
that these acids were present when samples were collected and
that, in the absence of chloroform, they were consumed by biolog-
ical activity prior to analysis. It was hypothesized that weak
organic acids were the unmeasured proton doners and the substrate
for biological activity.
METHODS
Keene £± si. (10) developed accurate and precise techniques
to measure organic anions and total acidity in the samples. Ion
Exclusion Chromotography (11) was used to identify and quantify
organic acid anions. Formate and acetate were found in all
aliquots which had been treated with a biocide. Citrate,
302
-------
propionate, glycolate and/or lactate were observed infrequently
and always at very low concentrations. Succinate, butyrate and
valarete were not found. Therefore, the technique was optimized
for the quantification of formate and acetate and was verified by
standard additions, replicates, comparisons with free acidity and
comparisons with total acidity.
A technique incorporating Johansson's (12) modifications of
Gran's (13) functions was developed to measure total acidity.
Because organic acids dissociated throughout the titration below
pH 7.00, it was impossible to differentiate strong from weak
acidity using Gran's technique. f-''easurements of total acidity
were verified with replicates and comparisfons with calculated
tota I acidity.
DISCUSSION
Kceneet^l. (10) presented a small set of direct measure-
ments on precipitation samples collected at Katherine, Australia
between 9 December 1981 and 26 January 1982. Results showed that
the loss of free acidity in untreated aliquots was directly
proportional to the concentrations of dissociated organic anions
in treated aliquots (Figure 1). On a volume weighted basis, formic
and acetic acids contributed 64? of the free acidity and 63 '!• of
the total acidity in the 12 samples. Unmeasured proton donors
contributed 21 r1 of the measured total acidity.
The volume weighted averages and ranges of H+ and the sum of
dissociated organic anions are compared for 3 sites in Figure 2.
The large range in both H+ and Z R C 0 2 for precipitation from
Amsterdam; Island resulted from a single low volume event which
303
-------
KAlriERINE, AUSTRALIA
O
-c-
32
30
28
26
24
22
20
18
X
O 12.
8
6
4
2
N =
slope =
Y intercept =
R =
12
O.9O
O.3
O.98
.OO05
1 — p-
2
— 1—
4
T-
6
— 1—
8
— 1 T~
10 12
— T"
14
T~
16
— 1—
18
— r~
20
-T T~
22 24
— I"
26
-I 11 1
28 3O 32
DISSOCIATED (peg//)
Figure 1. Plot of the sum of dissociated organic
versus the difference between H+ (AH+)
precipitation from Katherine,
anions (ZRC02~) in treated aliquots
in treated and untreated aliquots of
Australia.
-------
cr
o>
a.
o
vn
50
40
30
20
10
Figure 2
Range
Volume
Weighted
Average
12Sept81-11July 82
9 Dec 81-28 Mar 82
3May82-15Sept82
H + XRCO2
IDissoc.]
KATHERINE
AUSTRALIA
N = 38
H+ IRCO2
IDissoc.l
POKER FLAT
ALASKA
N=14
IDissoc.i
AMSTERDAM
ISLAND
N=22
A Comparison of the volume weighted averages and ranges of H+
organic anions (ZRC02) in treated aliquots of precipitation f
the GPCP.
and dissociated
from 3 sites in
-------
contained high concentrations of formic and acetic acids.
Although the sample may have been contaminated, we had no
justifiable reason for excluding if from the analysis. On a
volume weighted basis, organic acids contributed 59^ of the free
acidity in precipitation at Katherine, 34? at Poker Flat and 25£
at Amsterdam Island. Low volume events tended to have higher
absolute and relative concentrations of organic acidity.
The regression of AH + vs IRC02~ (Figure I) indicated that AH
could be used to estimate the organic acidity of treated aliquots
in which formate and acetate had not been measured. Two assump-
tions were implied when using this estimate: (I) Organic acids
were the only constituents, contributing to free acidity, which
disappeared from untreated aliquots; and (2) these acids were
completely consumed in untreated aliquots. Keene e.± sJ_. (10)
demonstrated that these assumptions were satisfied for samples
from Katherine. A similar comparison of AH+ and £RC02~ for
samples from Amsterdam Island indicated that the assumptions were
also satisfied for that site. However, results from Poker Flat
suggested that the second assumption was not satisfied, resulting
in an underestimte of £ R C 0 2 ~ • ^e speculated that because of the
relatively short time interval between collection and analysis of
samples from the site, all organic acids had not been consumed in
untreated aliquots. Data was not available to evaluate the valid-
ity of the assumptions for samples from San Carlos and Bermuda.
H+ is compared with AH+, for the 5 GPCP sites, in Figure 3.
On a volume weighted basis, organic acids accounted for most of
the variability in free acidity from site to site. Strong mineral
acidity, as estimated from the difference between H+ and AH+, was
306
-------
20
15
10
18Nov80-28Mar82
H+
4Sept80-9Mar81
3May80-11July82
0)
a.
0
19%
49%
53%
AH+
25May80-15July80
16 Sept 82-15 Feb83
20Oct80-11July82
31%
30%
POKER FLAT
ALASKA
N=55
SAN CARLOS
VENEZUELA
N = 11
KATHERINE
AUSTRALIA
N = 93
AMSTERDAM
ISLAND
N = 35
BERMUDA
N=36
Figure 3. A comparison between the volume weighted average of H+ in treated aliquots
and that of the difference between H+ (AH+) in treated and untreated
aliquots of precipitation from the 5 sites in the GPCP.
-------
relatively constant at all sites. When interpreting these
results, three factors were considered.
1. Approximations of ZRC02~ based on AH"1" underestimated
organic acidity in precipitation at Poker Flat.
2. At Amsterdam Island, 15£ of the original free acidity in
precipitation was neutralized by seasalt alkalinity
(14) .
3. Precipitation on Bermuda contained a significant
anthropogenic component of strong mineral acidity (15).
The free acidity of precipitation at the site was also
neutralized by seasalt alkalinity.
Considering these factors, as well as actual measurements and
estimates based on AH+, we arrived at 4 conclusions regarding
precipitation acidities at GPCP sites. All conclusions were based
on volume weighted averages.
1) Free acidities at the five sites ranged from a minimum
of 9 yeq/& at Amsterdam Island to a maximum of 19 yeq/£
at Kather i ne.
2) Organic acidities at the 2 oceanic sites ranged from 2
to 3 yeq/£ and contributed 25 to 305? of the free
acidities.
3) Organic acidities at terrestrial sites ranged from 5 to
10 yeq/£ and contributed 35 to 605!. of t-h e free
acidities.
4) Strong mineral acidities ranged from 7 to 9yeq/£ at al I
sites.
ACKNOWLEDGEMENTS
We thank Howard Clark in San Carlos de Rio Megro, Mike Cogan
and Charles Lasater at Poker Flat Research Range, Andre Gaudry on
Amsterdam Island, Tim Jickells on Bermuda and Judy Locke in
Katherine for the excellent jobs they have done operating field
stations for the GPCP. We also thank Carl 0. Moses and M.
Robbins Church for valuable comments during the investigation and
Linda Zieler and Klaus Scott for assistance with sample analysis
308
-------
and data reduction. Funding was provided by the National Oceanic
and Atmospheric Administration, the Environmental Protection
Agency, and the U . S. Department of Energy. The Academic
Computing Center at the University of Virginia, Char I ottes v i I I e,
provided computer facilities for data reduction.
REFERENCES
1. Likens, G. E. and Butler, T. J. Recent acidification of
precipitation in North America. Atmo_.s_j. £jiljvr.o_n.. , 15: 1103,
1 981 .
2. Glass, N. R., Arnold, D. E., Galloway, J. N., Hendrey, G. R.,
Lee, J . J . , M c F e e , W . W . , N o r t o n , S . A . , Powers, C . F . ,
Rambo, D. L. and Schofield, C. L. Effects of acid
precipitation. EnYj. ££_LL !e_cJi., 16: 162A, 1982.
3 . Graedel, T. E. and h'eschler, C. J. Chemistry within aqueous
atmospheric aerosols and raindrops. Re.Vj. fieo.^^.^ '.S_£a.c.e.
PhZS.-, 1 9: 505, 1 981 .
4. Galloway, J. N., Likens, G. E. and Edgerton, E . S. Acid
precipitation in the Northeastern United States: pH and
acidity. .S c. JLe.n £6 , 194: 722, 1976.
5 . Lunde, G., G ether, J., Gjos, N. and Lande, M . - D . S. Organic
pollutants in precipitation in Norway. Almo.Sj. Environ. f 11:
1007, 1977.
6. Hoffman, I','. A., Jr., Lindberg, S. E. and Turner, R. R. Some
observations of organic constituents in rain above and below
a forest canopy. EjLY_l.rfiil_i. .Sil-i. lQ£.h • , 14: 991, 1980.
7 . Barcelona, M . J . , Liljestrand, M . M . and Morgan, J . J .
Determination of low molecular weight volatile fatty acids in
aqueous samples. AJxaJ-j. CJie_nLi.> 52: 321, 1980.
8. Likens, G. E . , Edgerton, E. S. and Galloway, J. N . The
composition of organic carbon in precipitation. J_£j._Lu.£. (in
press) , 1 983.
9. Galloway, J. N., Likens, G. E., Keene, ',=' . C. and Miller, J. M
Composition of precipitation in remote ereas of the world. A
BfiS. 18: 8771, 1982.
10. Keene, W. C., Gal Iowa y, J. N. and H olden, J. D. Measurement of
weak organic acidity in precipitatin from remote areas of the
world. J_L Geo£Ji;y_.s_i- Re^., (in press), 1983.
309
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11. Dionex Corporation. Analysis of inorganic anions and organic
acicis in carbonated beverages. Application Mo. #25,
Sunnyvale, Ca., 1 980.
12. Johansson, A. Automatic titration by stepwise addition of
equal volumes of titrant. Ajial.y..£±, 95: 535, 1970.
13. Gran, G. Determination of the equivalence point in potentio-
metric titration. Part II. Analysl, 77: 661, 1952.
14. Galloway, J. N., Gaudry, A., Likens, G. E., Lambert, G. and
t'iller, J. M. Composition of rain on Amsterdam Island,
Indian Ocean. (in preparation), 1983.
15. Jickells, T. C., Knap, A. H., Church, T. M . , Galloway, J. N.
and Hitler, J. M. Acid precipitation on Bermuda.
297: 55, 1 982.
310
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A FIELD INTERCOM?ARISON OF PARTICLE AND GAS DRY DEPOSITION MEASUREMENT
AND MONITORING METHODS.
Donald A. Dolske and Donald F. Gatz
State Water Survey Division
Illinois Department of Energy and Natural Resources
Champaign, Illinois 61820-9050
Abstract
From 3 through 30 June, 1982, concurrent dry deposition flux
measurements were performed using various currently available
techniques. Researchers from more than a dozen U.S. and Canadian
institutions gathered at a site near Champaign, Illinois. The Illinois
State Water Survey (SWS) coordinated the field program and collected
ambient meteorolgical and pollutant concentration data.
Micrometeorological methods used included eddy correlation, eddy
accumulation, particle concentration variance, and concentration
profiles / modified Bowen ratio computations. Deposition flux
collection methods using surrogate surfaces such as polyethylene
buckets, funnels, and plastic or Teflon disks were also used. The
principal species for which deposition was measured were particles,
particulate sulfur, sulfate, and nitrate, as well as gaseous nitric
acid, sulfur dioxide, and ozone. An overview of the experiment is
presented, with a preliminary examination of some early results.
Introduction
Refinement in the ability to accurately measure dry deposition of
airborne pollutants has been the object of much recent research.
Detailed model descriptions of the transport, transformation, and fate
of airborne materials must include reliable estimates of dry deposition
rates. To date no consensus has been reached regarding a routine and
practical method by which dry deposition can be monitored [1]. This
intercomparison of monitoring and measurement methods was an approach to
the verification of deposition determinations from several of the
methods now available.
-------
The Intel-comparison experiments were conducted from 3 through 30
June, 1982, (Julian Dates 154 through 181) at a 32 hectare (800 m X 400
m) field site, 14 km southwest of Champaign, Illinois. Figure 1 shows
plan views of the site and the location of the participant experiments.
The methods included in the field program and the participating research
organizations are listed in Table 1. Vegetation at the site consisted
of grass, 25 to 30 cm high. The surrounding farm fields were planted in
soybeans and corn, and the local topography is essentially flat.
Prevailing winds in the region during June are south to southwest, so
the linear arrangement and spacing of instruments provided the best
potential to minimize interferences between experiments.
Table 1. EXPERIMENTS IN THE JUNE, 1982 DRY DEPOSITION INTERCOM?ARISON
Method Organization Acronym
Eddy-Correlation Atmospheric Environment Service (Canada) AES
Battelle Pacific Northwest Laboratory PNL
Oregon State University OSU
Argonne National Laboratory ANL
Governors State University GSU-V
USEPA/Environmental Science Research Lab. EPA
Atmospheric Environment Service (Canada) AES
Concord Scientific Corporation (Canada) CSC
The Colorado College TCC
Battelle Pacific Northwest Laboratory PNL
University of Illinois, Medical Center UIMC
Ontario Ministry of the Environment (Canada) MOE
Carnegie-Mellon University C-MU
USDOE/Environmental Measurements Laboratory EML
Governors State University GSU-B
University of Illinois, Medical Center UIMC
Oak Ridge National Laboratory ORNL
Variance
Eddy-Accumulation
Gradient
Filtration/Model
Surrogate Surfaces
Vegetation Washing
312
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Figure 1A. Plan view of intercomparison field site.
Poles
Gravel Road *
Building
Paved Road
Paved Pad
o
V
Experimental Array
(see Figure 1B)
-100m
Figure IB. Plan view of instrumentation array.
sws
CMU
• • • •
CMU
UIMC EPA
A MOE
AES
TCC
. c.,.c
A .SWS
C V A
PNL
AIMLA ,-0,,
CSC A Ggy,
ANL/OSU •
EML
.10m ^ Tower-mounted gear
' • • Ground - level samplers
313
-------
Each of the deposition measurement experiments was set up at the
field site as normally used by the participating researchers. A
sampling protocol was agreed upon which maximized the comparability of
the results. Most ambient pollutant concentration measurements and
deposition collection on surrogate surfaces were done at a height of 1.5
m above the ground. Eddy correlation and eddy accumulation measurements
were done at 6.0 m or 3.0 m, while concentration gradients were
monitored at the 0.75, 1.5, 3.0, 6.0, and 9.0 m heights. For
experiments with collection periods 12 h or longer, primary sample
change times of 0700 and 1900 CDT were specified. Micrometeorological
experiments, with much shorter continuous collection periods, generally
operated whenever conditions would allow. The month of June 1982
featured frequent periods of light rain and winds from unfavorable
directions. Sampling was impossible for about 25 percent of the month,
when northerly winds created improper fetch conditions because of site
buildings and the instrument array orientation. Figure 2 is a plot of
the times during the month when each experiment was actively collecting
data. The times indicated probably overestimate the actual amount of
valid samples, but are an indication of the temporal density with which
flux measurements were made during the intercomparison field program.
314
-------
FIGURE 2. PERIODS OF DATA COLLECTION DURING JUNE, 1982.
JULIAN DATE, 1982
154 155 156 157 158 159 160 161 162 163 164 165 166 167 168
ISWS-Q , ' ' ' ' '
ISWS-F
MOE L-
CMU
D AES
O
5 PNL
K GSU-B
5: GSU-V
« EML
157 158 159 160 161 162 163 164 165 166 167 16
—i 1 1 1 1
—i 1 1 ' 1 1 1 1 i i i i_
—i—i 1—i 1 1 1—I 1—I 1 i i i i i i i i i i i i
L,
(r
< TCC
a.
CSC <—> ' '
QSy <-> I 1 U LJ l_l I 1 I 1 I , LJ I I L
UIMC ' ' ' ' ' ' ' ' '
ANL 1 1 LJ LJi_, I 1 l_J LJ I , L
£p^ LJ LJUI II—I I—II lUl—l I I 1 t L_
168 169 170 171 172 173 174 175 176 177 178 179 180 181 182
<- 1 1 1 1 i
'
CMU —
3 AES ~
O
5 PNL
t- GSU-8 —
5: GSU-V i_
p EML ' ' ' ' •-
< TCC -1 ' ' >-, uu u
°~ CSC ' ' <->
OSU —' '—' ' ' U
UIMC ' ' '
ANL —
_ju i i ut 11 II II II I U I Lit il It II i i i i | i ,
Results
The discussion presented here is based on a partial set of results.
Data from some of the experiments has yet to be fully analyzed; however,
some points of general interest are apparent in the data so far reported
to SWS. It must be noted that dry deposition rates are strongly
dependent on prevailing meteorological conditions and characteristics of
the receiving surface. These experiments were conducted over a grassy
field during a one-month period. Applying these results to other
surfaces and other seasons calls for awareness of the limitations of
315
-------
such an extension. Table 2 shows the range and overall mean deposition
velocities, v^ , found for several chemical species by some of the
methods used in the intercomparison. These results do not necessarily
reflect one-to-one temporal correspondence in the data, as indicated in
Figure 2. A sample by sample comparative analysis must be completed
before direct and quantitative relationships between methods can be
defined.
The values of v, shown in Table 2 are comparable to measurements
made during September, 1981 at the same site [2], Good agreement among
most of the methods is apparent for sulfate and particulate sulfur, with
v^ of about 0.2 to 0.3 cm/s. Some short time scale variations, i.e.
on the order of minutes, were observed in the magnitude and direction of
size resolved total particle fluxes. Diurnal trends were evident in
many of the measurements. Deposition of gaseous pollutants showed very
strong diurnal variations, with v, near zero at night and up to several
cm/s during daylight hours. For both sulfur dioxide and nitric acid
vapor, the daytime deposition rate was not limited by vegetative canopy
resistance to mass transfer. Particulate sulfur and sulfate v^ near
zero were also observed at night, while v^ up to 1.0 cm/s were observed
during daylight hours. Size fractionated aerosol samples and calculated
deposition velocities suggest that large (diameter > 2.0 ym) particle
associated sulfate may account for 20 to 50 percent of the total sulfate
flux.
316
-------
It is very likely that the overall averaged v^ results depend on
the response of each method to a number of highly variable factors.
There remains much detail to be carefully screened and analyzed before
firm conclusions and comparisons can be drawn. The temporal resolution
and particle size sensitivity of each experiment must be considered
before the good agreement between the various measurement methods can be
termed significant.
TABLE 2. OVERALL RESULTS FROM SEVERAL DEPOSITION MEASUREMENTS.
P.I.
CMU
GSU
EML
PNL
ANL
TCC
CSU
METHOD
Teflon disk
Teflon disk
Petri dish
Petri dish
GLAD bulk
Aerochem 301
Aerochem 301
Profile
Eddy-corr elat ion
Profile
Profile
PARAMETER
S0=
N03
SO;
NOj
so;;
305
so;
Particulate S
Particulate S
HN03
S02
.22
1.0
.35
2.5
.14
.17
.40
.3
.25
3.0
1.8
cm-s
+ .1
+ .4
+ .2
± -8
+ .1
± -1
± -2
+ .2
± -2
± 1-1
+ 1.8
RANGE
( .17 -
( -5 -
( .18 -
(1.4 -
( .01 -
( .03 -
(0.1 -
-
(0.1 -
(1.1 -
(0.0 -
.42)
1.8 )
.61)
3.8 )
.44)
.31)
1.0 )
0.8 )
4.9 )
7.0 )
317
-------
Acknowledgements
Coordination of the intercomparison study and partial support for
the participants was provided by the Illinois State Water Survey, under
a cooperative agreement with the U.S. Environmental Protection Agency,
CR808863. In addition, a number of U.S. and Canadian government
agencies and other institutions funded the efforts of several
particpants.
References
1. Hicks, B.B., M.L. Wesely, and J.L. Durham. "Critique of methods to
measure dry deposition." Workshop Summary Report,
EPA-600/9-80-050. USEPA / ESRL, Research Triangle Park, NC, 1980.
83pp.
2. Dolske, D.A., and D.F. Gatz. A field intercomparison of sulfate dry
deposition monitoring and measurement methods: preliminary results.
In Proceedings of ACS Acid Rain Symposium, Las Vegas, NV, March
1982. Ann Arbor Science Publishers, in press.
318
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A COMPARISON OF AMBIENT AIRBORNE SULFATE CONCENTRATIONS DETERMINED BY
SEVERAL DIFFERENT FILTRATION TECHNIQUES.
Donald A. Dolske and Gary J. Stensland
Water Survey Division
Illinois Department of Energy and Natural Resources
P.O. Box 5050, Station A
Champaign, Illinois 61820-9050
Abstract
Ambient airborne concentrations of sulfate, total particulate
sulfur, and sulfur dioxide gas were measured at a rural Champaign
County, Illinois, site from 3 through 30 June, 1982. Several different
filtration techniques were used concurrently to collect pollutants 1.5
meters above ground in a grass - covered 32 hectare field. Filter
changes were sychronized at 12 or 24 hour intervals. Single stage
filters and multiple stage series filter pack methods with Nuclepore,
Teflon, Nylon, and cellulose fiber materials were used. Comparisons are
made between the results of methods employing different filter
materials, exposure schedules, and extraction and analysis techniques.
Preliminary results show large differences in concentrations determined
by Teflon and Nuclepore filter methods.
Objectives
During June, 1982, a field intercomparison of dry deposition
measurement methods was held at Champaign, Illinois [11. The Illinois
State Water Survey (SWS) collected ambient meteorological and air
pollutant concentration data in support of the flux measurements made by
research groups participating in the intercomparison. Several different
filtration methods were used concurrently to measure concentrations of
chemical species of interest. Most of the methods provided sulfur
concentration results, in addition to various other species. Because
all ambient air sampling was synchronized with the flux measurement
319
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schedule, the sulfate, particulate sulfur, and sulfur dioxide
concentration data from commonly used filtration methods can
conveniently be compared. Earlier comparisons of the field performance
of cellulose fiber (Whatman 41) and polycarbonate membrane (Nuclepore)
[2] indicated rather small differences in sulfate collection efficiency.
The present study extends that result by comparisons to Teflon and
Nuclepore filters.
Methods
Eight filtration methods were used continously from 3 through 30
June, 1982. Flame photometric sulfur monitors were also operated at the
rural site, 14 km southwest of Champaign, Illinois. Results from
measurements made with dichotomous samplers using Teflon filters, a
high-volume sampler using 20 X 25 cm type A glass fiber filters, and
cascade impactors using Teflon substrates have not yet become available.
The five methods for which data are available are summarized in Table 1.
Exposure conditions for the first two filter types listed in Table
1 were carefully matched. The 1.0 ym Teflon (Membrana Corp., Zefluor),
and 0.8 ym pore diameter Nuclepore filters were mounted in 37 mm
diameter open face holders and exposed facing down under inverted 30 cm
diameter funnels 1.5 m above ground. The volume of air sampled was
calculated from initial and final flow rates for every daily sample.
Every fourth day, a dry test meter was inserted into the vacuum line and
the actual air volume sampled was recorded. The sample volumes
320
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determined by these two methods agreed within about two percent. To
minimize the possibility of overloading or clogging the filters, the 37
mm pairs were exposed on a half-hour on, half-hour off cycle. Filters
were changed daily at 0700 CDT, resulting in 720 minutes of active
sampling time per 24 h period. Field and laboratory blank filters were
collected every fifth day. The filters were extracted in 30 ml of pH
3.0 hydrochloric acid and agitated for 12 h. Sulfate analysis was
performed by automated colorimetry [3]. The sulfate results from these
filters are referred to here as SWSTS04 (Teflon) and SWSNS04
(Nuclepore).
Table 1. FILTER TYPES USED AT CHAMPAIGN, JUNE, 1982.
Typical
Filter Filter Flow Rate
Type Size (L / min)
1.
2.
3.
4.
5.
1.0 yin Teflon
0.8 ym Nuclepore
0.4 ym Nuclepore 2
Filter pack:
1.0 ym Teflon
Treated cellulose
Filter pack:
2.0 ym Teflon
Nylon
Treated cellulose
37 mm
37 mm
X 4 mm
47 mm
47 mm
24.
24.
0.8
28.
18.
Sample
Vo lume
(cu.m)
17.5
17.5
1.15
38.
26.
Reference
Label
SWSTS04
SWSNS04
SWSPS
SWSFPS04
SWSFPS02
MOEFPS04
MOEPFS02
MOEFPS02
Two circular streaker samplers (PIXE International Corp.) were
operated continuously during the month. 0.4 ym Nuclepore filters
mounted in a plastic frame rotated past a 2 X 4 mm Teflon vacuum orifice
at 1 mm/h. The filters were changed weekly. Elements were determined
by proton induced x-ray emission (PIXE) at 2 mm intervals, so that a
321
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total sulfur concentration moving-average value was determined for each
2 h period. The flow rate was limited to 0.8 L/min by the pores of the
filter, which act as critical orifices. The 2 h data were averaged over
24 h periods from 0700 CDT, to correspond with the other results
presented here. The streakers were sometimes co-located at 1.5 m above
ground level, and at other times one sampler was moved to 6.0 m above
ground. All of the data were averaged together, ignoring the
differences in sampler height, to arrive at the ambient 24 h values used
in this comparison. The 24 h averaged particulate sulfur results are
referred to as SWSPS.
A two-stage series filter pack, consisting of a 1.0 ym Teflon
particle filter and a second stage sulfur dioxide absorbing filter was
mounted in a 47 mm open face plastic holder, and exposed under an
inverted funnel at 1.5 m above the ground. The sulfur dioxide absorbing
filter was a double layer of cellulose fiber material (Whatman 41) which
had been treated with a saturated solution of potassium carbonate in 25
percent (v/v) glycerol and water. Mean flow rate and dry test meter
determinations of sample volumes, and filter blank collection were
performed as with the SWSTS04 and SWSNS04 filters. The filter packs
were run continuously, with daily filter changes at 0700 CDT. The
Teflon filters were extracted in 35 ml of dilute sodium carbonate /
sodium bicarbonate buffer for 60 minutes with ultrasound. The cellulose
filters were extracted in 30 ml of the same buffer, but with the
addition of 0.05 percent hydrogen peroxide. After 30 min of ultrasonic
agitation, 10 ml of additional buffer plus peroxide was added, followed
322
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by 30 min of agitation. These solutions were then analyzed for sulfate
by ion chromatography [4]. The Teflon filter sulfate result is referred
to as SWSFPS04j and the treated cellulose filter sulfur dioxide result
as SWSFPS02.
The Ontario Ministry of the Environment provided three-stage filter
packs consisting of a 2.0 ym Teflon particle filter, 1.0 ym Nylon
(Membrana Corp., Nylasorb) nitric acid absorbing filter, and a double
layer of potassium carbonate / glycerol treated cellulose (Whatman 41)
sulfur dioxide absorbing filter. Eight filter holders were mounted
under a shelter 1.5 m above ground. Seven 12 h samples, and one field
blank were collected for each 3.5 day period. A sequential air sampler
(Metrex Instruments, Ltd.) recorded air volume sampled for each filter
pack, and sequenced filters at 0700 and 1900 CDT each day. For
comparison with the 24 h samples from other methods, means of the two
daily 12 h concentrations were calculated. The Teflon filters were
extracted in 25 ml of distilled water for 15 minutes in an ultrasonic
bath. The Nylon filters were also ultrasonically extracted, but 25 ml
of 0.003 N sodium hydroxide was used. The cellulose filters were
ultrasonically extracted 30 min in 0.05 percent hydrogen peroxide, then
an additional 25 ml of fresh peroxide solution was added prior to the
final 15 min of agitation. The liquid samples were analyzed for sulfate
by ion chromatography. The Teflon filter sulfate result is referred to
as MOEFPS04, and the total of sulfur detected on the Nylon and cellulose
filters, attributed to sulfur dioxide, as MOEFPS02.
323
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Results and Discussion
The overall arithmetic mean concentrations for 23 days in June,
1982 when all five methods had valid samples are listed in Table 2.
Note that the units for all species are yg of sulfur / cu.m. The range
in sulfate sulfur observed was 0.20 to 7.67 yg/cu.m; for sulfur
dioxide, the range was 0.27 to 5.49 yg/cu.m.
Table 2. MEAN AND (MEDIAN) CONCENTRATIONS ( yg/cu.m) FOR 23 DAYS.
SULFATE PARTICULATE SULFUR SULFUR DIOXIDE
SWSFPS04 2.45 (2,05) SWSPS 1.82 (l.5l) SWSFPS02 2.42 (2,20)
SWSTS04 2.33 (2.09) MOEFPS02 2.56 (2,59)
MOEFPS04 2.19 (1.82)
SWSNS04 1.28 (1.19)
SWSFPS04 and MOEFPS04 are plotted in Figure 1 [5], showing the
consistent good agreement between the filter pack methods. A Bartlett
method [6] regression line, MOEFPS04 = SWSFPS04 * .906 + .005, suggests
that the 2.0 Urn Teflon filter slightly undercollects sulfate relative
to the 1.0 ym filter. The possibility of some "missed" sulfate is
further supported by the sulfur dioxide results, discussed below. These
two sets of filters were extracted and analyzed independently by
separate laboratories. An interlaboratory exchange of a blind sample
series for sulfate gave results that agreed within three percent. Thus,
the analytical procedures should not contribute to the differences in
the final concentration determination. Figure 2, a plot of SWSTS04 and
SWSFPS04, compares two sets of 1.0 ym Teflon filters. The exposure
32k
-------
schedules, flow rates, and extraction methods, and analytical techniques
were different, yet there is good agreement bbtween these two sets of
concentration data. The regression line is SWSTS04 = 1.09 * SWSFPS04 -
.335.
Figure 1. RESULTS OF TWO FILTER PACK METHODS FOR SULFATE.
7.50
...+....+....+....+....+....+....+....+....+.•..+....+....+..•.
6.25
5.00
M
0
E
F
P 3.75
S
0
4
2.50
1.25
0.00
1 1
1 1
1 1
1
1
1 1
1
1
1
1
1 11
1
12
1
+
.60 1.8 3.0 4.2 5.4 6.6
SWSFPS04
Figure 3 compares SWSTS04 with SWSNS04, the 37 mm diameter filters
exposed, extracted, and analyzed under carefully matched conditions.
The regression line, SWSNS04 = SWSTS04 * .437 + .277, does not show the
good agreement found in Figures 1 and 2. The 0.8 ym Nuclepore filter
seems to undercollect sulfate relative to all the Teflon methods by
about 50 percent. Confidence region calculations show the data to be
less tightly grouped than in the Teflon method comparisons.
325
-------
Figure 2. RESULTS OF TWO 1.0 ym TEFLON FILTER SAMPLING METHODS.
! i "".
7.50 + +
6.25
S 5.00
W
S
T
S
0 3.75
4
2.50
1.25
1 1
2 2
1 11
221
".60
.. ..
1.8
.
3.0 4.2
SWSFPS04
.. ..
5.4
.. ..
6.6
Figure 3. RESULTS OF MATCHED - EXPOSURE TEFLON AND NDCLEPORE FILTERS.
3.6 +
3.0 +
S 2.4 +
W
S
N
S
0 1.8 +
4
1.2
.60
1 1
2
2
11
1 1 11
1
+....+....+....+....+....+....+....+....+....+...
.750 2.25 3.75 5.25 6.75
SWSTS04
.....
8.25
The relatively low sulfate determinations by the 0.8 ym Nuclepore
filters are probably due to poor small particle collection, rather than
extraction and analysis problems. Figure 4 compares the total
particulate sulfur collected on a 0.4 ym Nuclepore filter, SWSPS, with
326
-------
the daily mean sulfate sulfur from the three Teflon filter methods,
S04BAR. The regression line is SWSPS = S04BAR * .687 + .243, again
demonstrating an apparent undercollection. The PIXE elemental analysis
does not involve an extraction step. In both Figure 3 and 4, the
greatest percentage differences between the Teflon filter methods and
the Nuclepore filters occur at higher concentrations. Dichotomous
sampler data from this site in September, 1981, show that the percentage
of sulfur in the small aerosol fraction increases during episodes of
high total sulfate concentration. This also supports the idea that
differences between the Teflon and Nuclepore filters are due to
undercollection. Further interpretation of these comparisons will be
greatly aided by the completion of the data set. The additional methods
include size fractionated samples of several kinds, and other filter
media.
Figure 4. PARTICULATE SULFUR AND MEAN SULFATE RESULTS.
4.50 .
3.75
S 3.00
W
S
P
S
2.25
1.50
.750
h +
1
1
1
1
1
1
1
1
1 1
1
1
1
11 1
1 1
1
1
1 1
12
11
.60 1.8 3.0 4.2 5.4
S04BAR
6.6
327
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Acknowledgements
This work was supported by the US Environmental Protection Agency
through Cooperative Agreement 808863, and by the US Department of Energy
through contract number DEAS0276EV01:199. Walter Chan of the Ontario
Ministry of the Environment generously provided the filter pack data
which were collected under the auspices of the Acidic Precipitation in
Ontario Study.
References
1. Dolske, D.A. and D.F. Gatz. A field intercomparison of particle and
gas dry deposition measurement and monitoring methods. This
Proceedings. 1983.
2. Stensland, G.J. and J.D. Bartlett. Measurements of atmospheric
nitrate, sulfate, ammonium, and calcium using various filter setups.
Proc. 72nd APCA meeting. Cincinnati, OH. 1979.
3. Peden, M.E., L.M. Skowron and P.M. McGurk. "Precipitation sample
handling, analysis, and storage procedures." USDOE Research Rept.
4, COO-1199-57. Illinois State Water Survey, Urbana, IL. 1979.
4. Dionex Corporation. "System 12 analyzer operation and maintenance."
Sunnyvale, CA. 1979.
5. All plots in this text were generated by BMDP statistical software.
Univ. California, Berkeley, CA. 1983.
6. Smith, M.V., R.W. Shaw, Jr. and R.J. Paur. An alternative to least
squares statistics for comparison of duplicate measurements. Atmos.
Env. 17(1): 65-71. 1983.
328
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Comparison of Surrogate Surface Techniques for Estimation of
Sulfate Dry Deposition
John J. Vandenberg and Kenneth R. Knoerr, Duke University School of Forestry and
Environmental Studies, Durham, N.C., 27706.
Surrogate surfaces, often used to estimate the dry deposition of sulfate to
vegetation, were simultaneously exposed within and above a hardwood forest
canopy. Surfaces representing both rough and smooth textural types included
deposition buckets, petri dishes, filter paper, Teflon configurations and
polycarbonate membranes. The dry deposition rate of sulfate was correlated
across the surface types and the magnitude of the deposition rates were
compared. The petri dish and filter plate surfaces were found to represent the
best devices for the estimation of dry deposition to smooth and rough artificial
surfaces, respectively. Further work is now needed to relate the dry deposition
to surrogate surfaces to that on natural surfaces.
This study was a cooperative effort of the Duke University School of
Forestry and Environmental Studies, the Aerosol Research Branch of the U.S.
Environmental Protection Agency, and the Research Triangle Institute. This
paper has been reviewed in accordance with the U.S. Environmental Protection
Agency"s peer and administrative review policies and approved for presentation
and publication.
329
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Comparison of Surrogate Surface Techniques for Estimation of
Sulfate Dry Deposition
Natural vegetation is thought to be effective in removing atmospheric
pollutants through dry deposition as well as wet deposition processes (1,2).
However, difficulties related to the leaching of internal plant sulfate have
limited the accurate assessment of the dry deposition component of pollutant
removal by natural surfaces. Although a number of researchers have relied upon
surrogate deposition surfaces to estimate the flux rate of many materials (3-5),
little work has been done to intercompare the surfaces. In our study, the dry
deposition rates of sulfate particles to artificial surfaces within and above a
mature hardwood forest were measured over an annual range of synoptic weather
conditions. Sampling levels were at heights of 1, 12, 25, and 36 m above the
forest floor, corresponding with the forest floor region, the region between
overstory and understory canopies, the area of canopy closure, and above the
forest foliage, respectively.
Surrogate surfaces representing both rough and smooth textural types
included 28.6 cm diameter polyethylene deposition buckets, 9 cm diameter
polystyrene petri dishes with and without Whatman 41 cellulose filters taped
inside, 9 cm wide by 27 cm long Teflon sheets in both solid bar form and as
Teflon sheets affixed to a stainless steel core, 4.7 cm diameter Nuclepore
#111105 polycarbonate membranes held in circular filter holders, and a sheet of
Pallflex E70-2075W cellulose-glass filter paper held within a stainless steel
frame to expose a 13.2 cm by 18.2 cm rectangular portion of filter paper.
Ambient concentrations of sulfate and sulfur dioxide were monitored at the top
and bottom of the tower with U.S. EPA high-volume samplers and Huey sulfation
plates, respectively, with all chemical analysis performed by USEPA and Research
330
-------
Triangle Institute laboratories.
For periods when on-site high-volume samples were not available, ambient
sulfate concentrations were estimated from measurements made by the Research
Triangle Institute (RTI), Research Triangle Park, N.C., some 14 miles away.
Comparison of simultaneous high-volume measurements made at the Duke Forest and
RTI sites demonstrated a strong relationship in the ambient sulfate
concentration at these locations.
The dry deposition rates of sulfate to the artificial surfaces were
evaluated on the basis of their magnitude and precision. Correlation
coefficients calculated for the dry deposition rate of sulfate to the various
surrogate surfaces were generally positive, with a number of significant
coefficients as shown by asterisks in Table 1. Blank positions in Table 1
indicate insufficient sample size or interference from outlier points which
biased the interpretation of the coefficients and were therefore excluded from
further consideration. Those correlations which were shown by plots of the data
values to indicate strong trends between the sampling surfaces are underscored.
A grouping of the surrogate surfaces was made based on the correlation
coefficients. One group, characterized by generally smooth, low profile surface
types, was composed of the Teflon configurations, the petri dish surfaces and
the polycarbonate membrane. A second group, having smooth surfaces with a
greater profile in each dimension included the inner and outer surfaces of the
deposition buckets. One other surface with a low profile and a relatively rough
texture, i.e. the filter plate, comprised a separate type. In the first two
groups, strong and significant correlations between the surfaces tended to
tie each group together, while the filter plate type was seen to respond in a
unique manner. Ambient concentration of the sulfur oxides and sulfate
deposition rates to the surrogate surfaces were not well correlated.
331
-------
For diverse reasons, several of the techniques (i.e. deposition buckets,
Teflon surfaces and polycarbonate membranes) were found to have limited
reliability. In general, these surrogate surface types demonstrated a high
degree of data variability and low levels of net accumulation of sulfate dry
deposition relative to background amounts. In contrast, the petri dish and
filter plate surrogate surfaces demonstrated a high degreee of precision and
adequate surface accumulations. Thus the petri dish and filter plate surfaces
were found to represent the best devices for the estimation of dry deposition to
smooth and rough artificial surfaces, respectively. Average dry deposition
rates to these samplers during the season of the year with foliage on the trees
were 13.6 and 53.6 ug SO42~/m2/hr, respectively. Average deposition
rates for all the surrogate surfaces during the foliated season of the year are
shown in Table 2.
The wide range of surface deposition rates estimated from the variety of
deposition surfaces emphasizes the uncertainty of the individual measurement
t ^chniques as well as the dependency of sulfate dry deposition on surface
characteristics. In spite of these limitations, the use of surrogate surfaces
provides at least an approximate estimate of sulfate flux rates not currently
obtainable from natural surfaces. A critical research need is studies
emphasizing surface deposition on natural vegetation. Such studies should
provide a relationship between the deposition to surrogate surfaces in this and
other studies to that on natural surfaces.
332
-------
4-1
(U
O
CQ
(0
C
0 Q)
o C
0)-H (U W tiC T3 <0 HE H ID -rH 4-> -H.C O ,Q
,* W ^4-1 -H S -H M-l -H 4-1 -H HO;
-- -v - -^ •- •• — -' (DO OS
ft PH
Filter Plate .35 .31 .14 .36 .29 -.27 .22
Polycarbonate .50 .25 .61*+ ^52 .33
Membrane
Petri Dish .05 .07 .47* .61* .90*
Petri Dish -.14 -.27 .34 .02 .55 .77
Filter
Steel Teflon .42* .24 .57 .28 .69
Upfacing
Steel Teflon .13 -.02 .65* .48
Downfacing
Solid Teflon .33 .50 .82*
Upfacing
Solid Teflon .82*
Downfacing
*
Bucket Inside .88
Asterisks indicate significant coefficients at the p£ 0.05 level.
+Correlations which were shown by plots of the data points to indicate
strong trends between sampling surfaces are underscored.
333
-------
TABLE 2. MEAN DEPOSITION RATES OF SULFATE TO SURROGATE SURFACES EXPOSED WITHIN
THE FOREST FOLIAGE
Mean Deposition Rate Sample
Surface ug SO4=/n>2/hr Size
Filter Plate 53.6 21
Petri Dish 13.6 15
Petri Dish Filter 45.7 3
Polycarbonate Membrane 34.8 10
Steel Core Teflon Upfacing 7.0 21
Steel Core Teflon Downfacing 1.6 21
Solid Core Teflon Upfacing 6.4 8
Solid Core Teflon Downfacing 3.0 8
Bucket Inside 32.3 13
Bucket Outside 5.4 13
33^
-------
REFERENCES
1. Smith, W.H. Air pollutants and forests: Interactions between air
contaminants and forest ecosystems. Springer-Verlag, New York, 1981.
379 pp.
2. Hill, A.C. Vegetation: a sink for atmospheric pollutants. J. Air Poll.
Control Assoc. 21: 342-346, 1971.
3. Hicks, B.B., M. L. Wesely, and J. L. Durham. Critique of methods to measure
dry deposition: Workshop summary. U.S. Environmental Sciences Research
Laboratory, Research Triangle Park, N. C., 1980. 71 pp.
4. Nihlgard, B. Precipitation, its chemical composition and effect on soil
water in a beech and spruce forest in south Sweden. Oikos 21: 208-217, 1970.
5. Schlesinger, W., and W. Reiners. Deposition of water and cations on
artificial foliar collectors in fir-krummholz of New England Mountains.
Ecology 55: 378-386, 1974.
335
-------
Dry Deposition of Sulfate Within a Hardwood Forest Canopy
Vegetation provides a major surface area available for the deposition of
atmospheric gaseous and particulate pollutants and may therefore provide an
important sink for sulfur oxides. As particles and gases such as sulfate and
sulfur dioxide flow through a forest environment the deposition processes of
sedimentation, impaction, and diffusion take place. These deposition processes
result in a decrease in the ambient concentration of these pollutants within and
beneath the forest canopy. Previous studies have found most of the deposition
of particles to be on the canopy foliage rather than to the ground surface (1).
Ideally, such deposition to the foliage should be observed as a decreasing
pollutant concentration gradient from above the forest canopy to the forest
floor (2). Vertical differences in ambient pollutant concentrations and
deposition rates may therefore reveal the sink or source role of a forest.
Direct measurements of sulfate dry deposition to natural surfaces have been
hindered by technique difficulties and the leaching of foliar sulfate, leading
to the use of surrogate deposition surfaces. In our study, canopy flux
estimates were made based on petri dish and filter plate surfaces which
represent oposite ends of the surface texture spectra. Natural leaf surfaces
may have collection properties somewhere between them. These surrogate surfaces
were also chosen as adequate surface representatives because they provide
deposition estimates in which more confidence can be placed than with a number
of other techniques. The choice of these surrogate surfaces is examined in
another paper by the authors in this volume.
The total dry sulfate flux to the canopy was estimated as the summation of
the flux to the top and bottom of the collectors multiplied by the mean leaf
area per unit ground surface, plus the deposition to the unit ground surface.
336
-------
The forested research site has a leaf area index (defined as the plan
surface area of leaves per unit ground surface area) between 4 and 7 (3).
Taking an average value of 5.5, this figure was multiplied by a typical
deposition rate for each surface type to estimate a flux rate for the upwards
facing leaf surface area. Our data were not detailed enough to define a
vertical distribution of deposition rates associated with the vertical
distribution of leaf area. Thus an average deposition rate was used for
calculations with the total surface area to estimate the total sulfate dry
depo sit ion rate.
In previous studies the deposition of aerosols to upwards facing surfaces
has been found to range from 0.17 to 19 times greater than the deposition to
similar downfacing surfaces (4, 5). In the present study the sulfate deposition
to upwards facing Teflon surfaces averaged 3.2 times the deposition to downwards
facing Teflon surfaces, a value similar to that found for lead deposition to
Teflon disks (up/down = 4.0) (6). For our purposes, on the basis of these
Teflon surface measurements, we used a factor of 0.31 to estimate sulfate
deposition to the downwards facing surface area of leaves. The deposition to
the ground was estimated on the basis of the rate to the upfacing surfaces and
unit ground surface.
A review of climatological data revealed that during the seven months when
the trees were foliated (April through October) rainfall occurred approximately
7% of the time (7). The flux rate calculations were therefore adjusted by this
amount through a multiplication by 0.93 to calculate the flux of sulfate
aerosols during only dry events. The calculations of the dry flux of sulfate to
the foliated oak-hickory-tulip poplar canopy during 1981 based on the petri dish
flux rates is shown in Table 1 and that based on the filter plate flux rates in
Table 2.
337
-------
The sulfate flux rate totaled 5.3 and 21.0 kg SO^ /ha/(foliated canopy
during 1981) for the petri dish and filter plate surfaces, respectively. These
values may be modified to represent the elemental sulfur present in the sulfate
particles (SO4~-S) through a division by three to compensate for the
molecular weight of sulfur and oxygen in the sulfate molecules. This conversion
yields flux rates of 1.8 and 7.0 kg SO4~-S/ha/(foliated canopy) for the
petri dish and filter plate surfaces, respectively. These values compare
favorable with other sulfate dry deposition estimates based in part on petri
dish deposition rate measurements of 4.8 kg SO4~-S/ha/(foliage of a chestnut
oak canopy) (8).
338
-------
TABLE 1. CALCULATION OF THE MEAN TOTAL DRY SULFATE FLUX PATE TO A FOLIATED
FOREST CANOPY ON THE BASIS OF PETRI DISH SURROGATE SURFACE FLUX RATES
Total flux rate = (upfacing flux + downfacing flux + ground flux)
Mean flux rate to petri dish surfaces = 13.6 ug SO4~/m /hr
during foliated periods
Mean leaf area index = 5.5
Upfacing flux to downfacing flux ratio = 1 : 0.31
Upfacing leaf flux =5.5x13.6 =74.8ug S04=/m2/hr
Downfacing leaf flux =5.5x13.6x0.31 =23.2ug S04=/m2/hr
Ground surface flux = 1 x 13.6 =13.6 ug S04=/m2/hr
Dry flux rate to canopy during foliated period = 112 ug SO4=/m2/hr
Total dry flux to canopy
during the foliated period = 5.3 kg SO4=/ha/(foliated canopy
during 1981)
339
-------
TABLE 2. CALCULATION OF THE MEAN TOTAL DRY SULFATE FLUX RATE TO A FOLIATED
FOREST CANOPY ON THE BASIS OF FILTER PLATE SURROGATE SURFACE FLUX RATES
Total flux rate = (upfacing flux + downfacing flux + ground flux)
Mean flux rate to filter plate surfaces = 53.6 ug SO^~/m /hr
during foliated periods
Mean leaf area index = 5.5
Upfacing flux to downfacing flux ratio = 1 : 0.31
Upfacing leaf flux = 5.5 x 53.6 = 298.4 ug SO4=/«2/hr
Downfacing leaf flux = 5.5 x 53.6 x 0.31 = 91.4 ug SO4=/m2/hr
Ground surface flux = 1 x 53.6 = 53.6 ug SO4=/m2/hr
Dry flux rate to canopy during foliated period = 440 ug SO4=/m2/hr
Total dry flux to canopy
during the foliated period = 21.0 kg SO4=/ha/(foliated canopy
during 1981)
-------
REFERENCES
1. Raynor, G.S., J.V. Hayes, and E.G. Ogden. Particulate dispersion into and
within a forest. Boundary-Layer Meteorology 7: 429-456, 1974.
2. Sehmel, G.A. Particle and gas dry depostiion: a review. Atmos. Environ.
14: 983-1011, 1980.
3. Christensen, Norman. Duke University Department of Botany, personal
communication, 1982.
4. Little, P. Deposition of 2.75, 5.0 and 8.5 um particles to plant and soil
surfaces. Environ. Pollut. 12: 293-305, 1977.
5. Aylor, D.E. Deposition of particles in a plant canopy. J. App. Meteor.
14: 52-57, 1975.
6. Elias, R.W., and C. Davidson. Mechanisms of trace element deposition from
the free atmosphere to surfaces in a remote high sierra canyon. Atmos.
Environ. 14: 1427-1432, 1980.
7. NOAA. National Oceanic and Atmospheric Administration, Environmental Data
and Information Service, National Climatic Center, Asheville, North
Carolina. Local Climatological Data, Monthly Summary for Raleigh-Durham
Airport, North Carolina, 1981.
8. Lindberg, S.E., R.C. Harriss, R.R. Turner, D.S. Shriner, and D.D. Huff.
Mechanisms and rates of atmospheric deposition of selected trace elements
and sulfate to a deciduous forest watershed. ORNL/TM-6674, Oak Ridge
National Laboratory, Oak Ridge, TN., 1979.
-------
SESSION VI
ORGANIC POLLUTANTS
3^3
-------
EVALUATION OF SOLID SORBENTS FOR
COLLECTION OF VOLATILE ORGANICS
IN AMBIENT AIR
by
L. J. Hillenbrand and R. M. Riggin
BATTELLE
Columbus Laboratories
Columbus, Ohio 43201
ABSTRACT
The procedure is based on the equilibrium adsorption model of
Dubinin-Radushkevich and the adsorption kinetics model of Wheeler and
Robell. The method has had some success in correlating and predicting
the relative adsorbability of various organic species on charcoal and
has been used to describe the adsorption of hazardous vapors at low
concentrations in air.
The very low vapor concentrations being employed, i.e., parts
per billion, extend the application of the model to very low sorbent
loadings and also tend to emphasize calibration and reproducibility
uncertainties arising from the high-gain detector sensitivities that
must be employed. From the plots of breakthrough time-versus-sorbent
weight, the sorbent capacity and sorption kinetics are obtained.
When adsorption is completed, a programmed temperature rise is
used to desorb the vapor. A complete and facile desorption of the vapor
almost certainly implies that the vapor adsorption is limited to physical
processes and the prediction procedure for the adsorption of a new vapor
is based on this fact. Once a few reference vapors have been tried, the
adsorption of the others may be predicted.
The results indicate that a detailed evaluation of a candidate
sorbent can be obtained through the method investigated in this program.
This paper has been reviewed in accordance with the U.S.
Environmental Protection Agency's peer and administrative review
policies and approved for presentation and publication.
-------
ZVALUATION OF SOLID SORBENTS FOR COLLECTION OF ORGANIC VAPORS IN AIR, L. J.
Hillenbrand and R. M. Riggin, Battelle, Columbus Laboratories, 505 King Avenue,
Columbus, Ohio 43201.
The problem of sampling atmospheric organic vapors by adsorption at parts
per billion concentration imposes several special requirements on the sorbent
to be used. Tenax GC resin has been widely employed; nevertheless, some field
problems have arisen implying difficulties with complete desorption of some
vapors and candidate replacement resins have been suggested. This Work
Assignment is concerned with the demonstration of a protocol suitable for
qualifying such resins. Since the resin is to be used in a permeation bed
configuration, we prefer a model that uses that configuration to describe
vapor breakthrough characteristics.
The procedure used is based on the equilibrium adsorption model of
Dubinin-Radushkevich(l) and the adsorption kinetics model of Wheeler and
Robell(2). The method has had some success in correlating and predicting the
relative adsorbability of various organic species on charcoal and has been
used to describe the adsorption of hazardous vapors at low concentrations in
air. The plan for our program is intended to
« Demonstrate that the Model is Applicable
« Describe the Critical Parameters
a Obtain Numerical Values for Adsorption Capacity and
Kinetics of Selected Reference Vapors
• Demonstrate Use of the Model for Predicting Performance
of Untested Vapors.
This presentation will concentrate on the first two of these needs; work
is continuing on a variety of systems for demonstration of the full scope of
the applications possible.
-------
Whatever model is used, several special circumstances must be recognized.
First, since the volatile organic vapors to be sampled typically are in parts
per billion concentration, the partial pressure of the vapor may be 10 to
10 that of its saturation vapor pressure (P/P < 10 ) and such low partial
pressures place restrictions on the adsorption models that are applicable.
Second, in most cases the sorption is to be accomplished in the presence of
relatively overwhelming amounts of atmospheric moisture and the need for HO
rejection has been one of the factors that led to the use of the low surface
energy organic resin sorbents. Further, since the sorbed vapors must be
readily and quantitatively desorbed by moderate increased in temperature, we
infer that chemisorption mechanisms generally will be unacceptable. Acceler-
ation of the desorption by temperature rise can be tolerated only to the ex-
tent that no appreciable degradation occurs at the same time.
(3)
For the model as applied by Jonas , the time of breakthrough (t, ) of a
b
vapor passing through a given bed of absorbent is expressed by
W
In
(1)
where
- the ratio of the inlet concentration of adsorbate
vapor to the breakthrough concentration, in con-
sistent units. CQ alone is in g/cm3.
Q - gas volume flow rate, cm /min
W « weight of adsorbent, g
we * the kinetic level of bed saturation achievable
at chosen C /C, ; g/g
kv • pseudo first order rate constant for the adsorp-
tion process in that bed, min'1
p - bulk density of the packed bed, g/cra3.
346
-------
This can be arranged to form
where
and
= a + bW
W p
cV *
O V
b = W /C Q
e o
(2)
(3)
(4)
Experimental values of t are obtained as a function of bed weight, W,
for a fixed value of C /C, . The value of W is derived from Equations (2) and
o b e
(4) using known values of C and Q. Equation (3) then can be used to obtain
the value of the adsorption rate constant since all the other quantities are
known. The volume flow rate, Q, and the values of C and C, are used as inde-
pendent variables with values that are set by the adsorption conditions of
interest.
The method of procedure is a simple one. Using the apparatus shown
schematically in Figure 1, the procedure is as follows:
Temperature Programmed Oven Detector
N£ and
Test
Gas
Supply
and
Control
-
D—
Pressure i
Gauged— ^
Gsuge
Vent
Bypass Loop
] —
Preheat Resin
Lo°P Cartridge
U R
| 1 v
Pre.
Ga
otameter
3
> sure
uge
Figure 1. Arrangement of Apparatus.
3^7
-------
The method of procedure is a simple one:
(1) A reference resin (Tenax GC) is characterized using a few
vapors that also will serve as references in later work.
The characterization involves adsorption of the vapor on
resin columns of different length (weight) so that a corre-
lation of breakthrough time and bed weight can be produced
for a specified breakthrough concentration.
(2) Following each adsorption trial, the vapor is desorbed by a
programmed temperature rise and the completeness of desorp-
tion is tested following various periods of storage for the
sorbed vapor.
(3) The full adsorption capacity of the reference resin is tested
by several experiments in which the resin bed weight is held
constant and vapor concentration is varied.
The kinetic sorption capacity for the given inlet vapor concentration is
determined by plotting variations of t, with W for some chosen value of C,/C
D bo
and we note that under these high sensitivity recording conditions the mid-
points of the breakthrough curves, see for example Figure 2, are more pre-
cisely defined than points near the start of the breakthrough profile. We
tested the constancy of slope for several values of C, /C in order to deter-
b o
mine the validity of using the more precise portion of the curve for this
purpose.
The kinetic model identifies the rate constant, k , as a pseudo first
order constant that pertains to the initial portion of the breakthrough curve
where sorbent site occupancy is low so that the available concentration of
sorbent "sites" is constant and nearly equal to the initial sorbent capacity.
At later portions of the curve both vapor and sorbent site concentrations are
varying so that the pseudo first order constant will be found to vary as the
level of occupancy of the resin is allowed to vary according to the point,
348
-------
800
1600
Benzene
Desorption
in N2 Flow
0 1C 20 30 40 50
Minutes
10 20 30
Minutes
Figure 2. Comparison of Breakthrough and Desorption
Curves for Two Different Sorbents.
C, /C , chosen for the analysis. In this report the points for C,/C = 0.05,
bo bo
0.10, 0.20, and 0.50 have been tried for evaluation of k . The C, /C value of
v bo
0.05 represents the lowest concentration that it appears possible to analyze
with reasonable accuracy under these high sensitivity conditions.
When adsorption is completed, a programmed temperature rise is used to
desorb the vapor. A complete and facile desorption of the vapor almost cer-
tainly implies that the vapor adsorption is limited to physical processes and
the prediction procedure for the adsorption of a new vapor is based on this
fact. The same basis for prediction applies to the several reference vapors
employed so that once one reference vapor is tried the adsorption of the other
two might be predicted. However, the matter is not quite this simple since
the presence or absence of dipolar character in the vapor complicates the
basis by which the physical adsorption is predicted. In prior work with
-------
carbon sorbents, this feature has been handled by keeping a simple model for
prediction of the adsorption and by using reference vapors that characterize
nonpolar, moderately polar, and highly polar adsorbates. For the present
study, these are being represented by benzene, 1,2-dichloroethane, and acetone
vapors.
For the study of benzene vapor sorption by Tenax GC, resin bed depths of
1.0, 3.0, 5.0, and 6.5 inches were tried. The 1.0-inch bed depth proved to be
too close to the critical value, for which breakthrough begins immediately,
and was abandoned. For the remaining three bed depths, initial benzene vapor
concentrations of 120 and 580 ppb were employed and the breakthrough times
were measued at C /C equal to 20, 10, 5, and 2, corresponding to 5, 10, 20,
and 50 percent vapor concentration breakthrough. This multiplicity of inlet
and breakthrough concentrations permitted several analyses of the adsorption
kinetics for improved precision of the kinetic constants. In Figure 2, the
results for Chromosorb 101 illustrate a sorbent with good sorption capacity
tut with poor sorption kinetics so that appreciable breakthrough concentra-
tions appear very quickly.
Only the averages of individual determinations are plotted in Figure 3
for the adsorption of benzene on Tenax GC. The lines represent the least
square slopes determined from the complete set of data and the expected
straight line relationship between t and W is illustrated. Similar data
were obtained for 1,2-dichloroethane vapor. The slope b, for the data at
C, /C = 0.5, 0.2, and 0.05, was found to be independent of the choice of
C,/C for both benzene and 1,2-dichloroethane. These vapors showed complete
b o
desorption and no abnormalities were experienced either during adsorption or
desorption of the vapors from the resin. The data for acetone demonstrated
350
-------
Benzene, 580 ppb
on
Tenax GC
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Weight of Resin, W, grams
Figure 3. Variation in Breakthrough Time
With Sorbent Bed Weight.
otherwise and are discussed separately.
For acetone, we note that whereas the vapor is rapidly adsorbed, the
adsorption capacity of the resin for this vapor is comparatively small, see
Figure 4.
It further was noted that the desorption peaks for this highly dipolar
vapor, acetone, average only 78.0 percent of the size of the adsorption peaks
and extraneous high molecular weight species appear in the desorption curve,
see Figure 5. It is striking that the extra peaks, representing high molecu-
lar weight but unknown species, on the average were found to be equal in size
to the adsorption peak for acetone in this FID system.
351
-------
8.0
— Acetone. 720
on
Tenax
0 0.1 0.2 0.3 0.4 05 0.6 0.7 0.8
Weight of Resin. W. grams
Figure 4. Variations in Breakthrough Time for Acetone
on Tenax GC
l^ — Start Uf- Start Desorption | 3
I Adsorption I Heat 1 C/Minute
C/Minute I
Hold
200 C
Start
Cooling
Figure 5. Sample Tracing of Recorder Plot for Acetone
Adsorption and Desorption.
The spectrographic grade acetone used in this work, and its mixture with
zero N9 in the charge cylinder, were analyzed and no obvious explanation for
these peaks based on pre-existing impurities was found. We currently favor
352
-------
the conclusion that the artifact peaks represent species desorbed from the
resin by acetone and roughly in proportion to the amount of acetone taken up.
The data presented illustrate that the Wheeler and Robell model is a
good representation of sorbent-sorbate systems that behave as required for the
present application. The data for Chromosorb 101, and for acetone, illustrate
some of the deviations in performance that are unacceptable and which become
readily recognizable once the features of the model are understood. Table 1
is a summary of the slope (b), and intercept (a), values obtained. The nearly
5-fold change in benzene concentration had no effect on capacity estimate, and
the pseudo first order nature of the rate constant is confirmed by the inter-
cept values. Table 2 shows relative values of the rate constant as obtained
and as anticipated from a simple kinetic model.
TABLE 1. SLOPE AND INTERCEPT VALUES OBTAINED
FOR VAPORS ON TENAX GC
Benzene 1, 2-Dichloroethane
V
a
b
(Ppb)
(minutes)
at Cb/co =
=
-
(minutes/g)
at C,/C0 =
=
=
0
0
0
0,
0.
0.
.05
.20
.50
.05
.20
50
580
-'i9
-12
.3
.0
-1.46
121
123
.0
.9
124.4
120
-18
-1t
-1
121,
127.
134.
.6
.6
.46
.5
5
6
640
-13
-8
-?.
103
103
102
,7
.95
.SO
.0
.3
.8
kv J^sJ. Y'" (Co/Ob!"!
v \ c_ yi_ s J
353
-------
TABLE 2. RELATIVE VALUES OF THE RATE CONSTANT (k )
OBTAINED FOR VAPORS ON TENAX GC. V
cb/co For Benzene at 1. 2-Dichloroethane
580 ppb 120 ppb at C40
0.05 |1.00| 1.04 1.41
0.20 0.86 0.90 1.16
Predict |l .00| 1.00 0.89
kv iCv] = k[Cs][Cv]
Under physical adsorption conditions, monolayer adsorption of vapor on
relatively open surfaces will be extremely small at the low values of P/P
o
employed here (< 10~5). The Dubinin-Radushkevich isotherm indicates that the
major part of the adsorption should occur in the micropores, especially those
o
with diameters < 30 A. According to that isotherm, see Figure 6, a plot of
2
e versus the equilibrium amount of vapor sorption (W = liquid cc of sorbate
(g sorbent) is a straight line with slope K/g 2 and the intercept W is the
apparent total adsorption capacity available. If the system is well behaved,
W£ (from the earlier kinetic treatment) should equal W d where d is the
liquid density of the adsorbate. The series of experiments described with
W constant and various partial pressures of vapor were made for the purpose
of testing this isotherm and obtaining the values of the constants. It is
satisfying to note that, for benzene on Tenax GC, W does equal W d.. . Figure
7 shows the anticipated straight line relationship.
For reference vapors, we can set 3 =1.00 and so evaluate K. The process
of prediction for untested vapors, or for arranging the vapors in sequence of
sorbability, is based on estimating values of 8 for those vapors. This aspect
354
-------
In W.. = In W0 - Kt-2/£2
-In W,
c - RT In (P0/P)
W0 = Wv de
Intercept = In Wo
Slope = K/jS2
ire f
11.0
10.0
8.0
a.o
4
j. The Equilibrium Adsorption Isothe
/
Benzene
on
Tenax GC
/
Qf^-
JP
/
.2 5.0 5.0 7.0 7
t2 x 1C'7
Figure 7. The Isotherm Data for Benzene on Tenax GC at 31 C.
is currently under study in our continuing program.
The features that give Tenax GC the good kinetics for adsorption and
desorption of vapor are revealed by further examination of its structure.
Table 3 lists bulk densities, and He- and Hg-displacement densities for the
two resins, and gives comparative pore volumes estimated from these. The
355
-------
TABLE 3. DENSITY AND PORE VOLUME ESTIMATES
FOR TENAX GC AND CHROMOSORB 101.
Densities, y/cc
^Hfl
Sorbent dK du <70u
D riG
Tenax GC 0.1 J>8 1.10- 0.295 0.335
Chromosorb 101 0.346 1-05g 0.400 0.596
Pore Volumes, cc/g
Total <70/j 70
microns in diameter) for which Tenax GC has almost 3.0 cc/g whereas Chromo-
sorb 101 has about 0.4 cc/g. These are the pores that give ready access to
the micropores in Tenax GC. We note that, for benzene on Tenax GC, the
apparent total pore volume available, W , is 0.0183 cc/g which is about one-
o
half of the micropores < 300 A. The cumulative pore volume distribution for
Tenax GC shown in Figure 8 indicates that W corresponds to the pore volume
o
available in Tenax GC below about 110 A. This comparison is clearly an over-
simplification but is qualitatively consistent with the expectations of the
Dubinin-Radushkevich isotherm.
Electron microscopy photographs of Tenax GC show it to have a worm-like
structure with diameters below 0.5 microns. These structural elements appar-
ently are nonporous since their peripheral surfaces roughly account for the
2
total surface area available, 25.5 m /g. Thus, the pores are interstices
between irregular cylindrical elements and these are easily available through
the very low density arrangement provided. Presumably, a competitive adsor-
bent must provide an open structure with similar accessibility.
356
-------
0.03
1CO 200
o
Pore Diameter, A
300
Figure 8. Cumulative Surface Area and Pore Volume
Distributions for Tenax GC.
This work has been funded under a Work Assignment from the Environmental
Monitoring Systems Laboratory of the Environmental Protection Agency.
REFERENCES
(1) Dubinin, M. M., Chem. Rev. 60_, 235-41 (1960).
(2) Wheeler, A., and Robell, A. J., J. Catalysis 13_, 299-305 (1969)
(3) Jonas, L. A., and Rehrmann, J. A., Carbon 11, 59-64 (1973).
357
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DETECTION OF ENVIRONMENTAL POLLUTANTS USING
PIEZOELECTRIC CRYSTAL SENSORS
Mat H. Ho
Department of Chemistry
University of Alabama in Birmingham
Birmingham, Alabama 35294
ABSTRACT
In recent years, coated piezoelectric crystal sensors have become of
increasing interest for detection of trace amount of pollutants from ambient
air. Not only are they highly sensitive detectors, but they are also simple,
inexpensive, low power consumption, light-weight and portable devices.
The principle of the detector is that the frequency of vibration of an
oscillating crystal is decreased by the adsorption of a gaseous sample onto its
coated surface. The decrease in frequency is a measure of the amount of gas
adsorbed. This linear relationship between frequency change and added mass
enables a piezoelectric crystal to be used as a sorption analytical detector
with a detection limit of about 10~10g. The selectivity of detector can be
achieved by coating the crystal with a substance which selectively adsorbs the
pollutant one want to detect.
In this paper, we report new methods and coatings for the specific
detection of mercury, organophosphorus pesticides, and formaldehyde. The effect
of flow rate, amount of coating, cell configuration, and practical instrument
suitable for use as personal monitor will be presented. The use of immobilized
enzyme as coating for sensitive and specific detection of formaldehyde will also
be described.
358
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I. PRINCIPLE
It has been known for a long time that the depostion of a small mass of
material on the surface of a piezoelectric crystal oscillator lowers its reso-
nant frequency. In the early days of communication, it was common practice to
lower the frequency of a piezoelectric quartz crystal by applying graphite or
ink onto its surfaces. When more graphite or ink applied, the frequency of
vibration is lowered. This observation has also been used by crystal manufac-
turers to adjust the frequency of the crystals. However, no quantitative rela-
tionship between the deposited mass and the change of frequency had been
investigated until the late 1950's. This relationship and the theoretical basis
for piezoelectric mass measurement were derived by Sauerbrey (1,2) and
Stockbridge (3). The mass sensitivity of AT-cut, quartz crystals vibrating in
the thickness-shear mode can be obtained from the following equation:
F2
AF - - 2.24 x 1(T6 q
AM " A
q
where AF is the change in the resonant frequency, in Hz, caused by a change in
the mass AMq of the electrically-driven portion of the crystal, AM- is the
change in mass of the electrically-driven portion of the crystal in g, Fq is
the basic resonant frequency of the crystal before any foreign mass is added
in Hz, and A is the area of the electrically-driven portion of the crystal in
f\
cm . Sauerbrey (1,2) made the fundamental assumption that deposition of any
foreign mass, which behaves in the same way as a quartz increment of equal mass,
AMq, will cause the frequency shift Af. Sauerbrey also successfully tested this
equation by coating the quartz crystal with a thin, evenly distributed film of
metal, weighing the crystal with film on a microbalance and monitoring the fre-
quency change. For commercially available 9 MHz, AT-cut, quartz crystals having
r\
an electrode area of 0.20 cm , the mass sensitivity is 907 Hz/pg. For 15 MHz
359
-------
crystal having an electrode area of 0.10 cm2, the mass sensitivity is 5040 Hz/yg.
The quantitative relationship between the deposited mass and the change of
frequency of piezoelectric quartz crystals give them wide-spread application as
transducers for mass detection (4). The use of coated piezoelectric quartz
crystals as sorption detectors for toxic gases in air has been reported (5-7).
The principle of the detector is that the frequency of an oscillating crystal is
decreased by adsorption of a foreign material on its coated surface. A sampled
gas is adsorbed by a coating on the crystal's surface which is specific for that
gas, thereby increasing the mass on the crystal and decreasing the frequency.
The frequency shift is proportional to the concentration of sampled gas. The
interest in coated piezoelectric crystals for detection of environmental pollu-
tants has been increased recently due to their properties of high sensitivity,
rapid response, low cost, low power consumption (which can be operated on
rechargeable batteries), light weight and portability for field use that are
inherent in the piezoelectric detection method, in spite of some limitations.
II. APPARATUS
A schematic diagram of the experimental setup with the piezoelectric
quartz crystal sensor is shown in Figure 1. The coated crystal was housed in
the detector cell and was driven in its fundamental frequency by an oscillator
(International Crystal Mfg. Co., Oklahoma City, Oklahoma). The oscillator was
powered by a Heathkit variable power supply (Model IP-28). The frequency output
from the oscillator was monitored by using a digital readout frequency meter
(Heath Schlumberger, Benton Harbor, Michigan). The frequency change could be
read on either the frequency meter or recorded by adapting a digitalto-analog
converter (DAC) circuit to the output of the frequency meter. Standard con-
centrations of the test atmosphere were prepared by the saturation, diffusion
and permeation methods for Hg, organophosphorous compounds and formaldehyde,
360
-------
1
Coi
2-*-
D
ent
-«—
Sc
I
it
"low
:rolle
: rubber
? 3
r Co
Piezoelec
^ Crv
• fa
4
ated
trie v
stal \
v vV '
\JlXS amp ling
| Valve.
v
Flow
Meter
Detector
Cell
5
r+Tc
IVent
*
Figure 1. Schematic Diagram of the Exprimental Setup.
(1) Recorder; (2) Digital-to-Analog Converter; (3) Frequency Meter;
(4) Oscillator; (5) Power Supply
respectively (8). Samples from the generation system were introduced into the
detector cell by a 4-port sampling valve (Varian Instrument Div., Palo Alto,
California). Diffusion cell and permeation tube were obtained from Analytical
Instrument Development, Inc., Avondale, Pennsylvania.
A typical piezoelectric crystal consists of a quartz plate, electrodes,
and holder as shown in Figure 2. In order to be successfully used as sensor
in the detection of gases, the crystal must possess several important characte-
ristics such as stable frequency output, low temperature coefftrient, high mass
sensitivity and inert to its environment. An AT-cut, quartz crystal is a
material that best meets these requirements. The crystals used in these studies
were 9 MHz and 15 MHz with vacuum- deposited gold electrodes located on the
center of each side of the quartz plate. Figure 3 shows the design of the
detector cell. This design provides the best contact between the coating and
the gas stream since the sample is split into two equal streams which directly
361
-------
Quartz Plate
Electrode
Holder
u—o
Outlet
Figure 2. Piezoelectric Quartz
Crystal
Quartz Crystal
Gold Electrode
Holder
Socket
24/40 Ground
Joint
Figure 3. Detailed Design of
Dectector Cell
and simultaneously impinge on both sides of the coated crystal. The cell was
made from Teflon or Pyrex ground joint glass and the crystal is connected to the
oscillator through a socket. This enabled not only the rapid and easy removal
of crystals for exchanging or recoating, but also provided a sealed detector
cell as well as optimum electrical contact. Several techniques such as vacuum
deposition, dipping, painting with a small brush or cotton swab, or dropping
with a microsyringe can be used to apply the coating onto the surface of the
piezoelectric crystal.
Figure 4 shows the schematic diagram of the portable detector which is
capable of field use. The device contains a coated sensing crystal, a
reference crystal, an air sampling pump and solid-state electronics with
digital display of the frequency differences. Since the sampled gas was ad-
sorbed only on the surface of the coated sensing crystal, the frequency output
was directly proportional to the concentration of gas. Rechargeable nickel-
cadmium batteries, which are capable of eight hours continuous operation,
provide all power for the electronics and for the sampling pump (Anatole J.
Sipin Co., New York). In operation, nitrogen or clean air was first allowed to
pass through the detector cell until the baseline frequency became stable. The
362
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dry N2 or air in t Nafion dryer
sampling
valve
sampled air
solid state display
sample
update
switches
Figure 4. Schematic Diagram of the Portable Piezoelectric Detector
sampling valve was then rotated to introduce the sampled air into the detector
cell, where the interaction between sampled gas and the coating occured. The
frequency response was taken as the frequency difference between the baseline
before sampling and the steady frequency after equilibrium was established. The
sampling valve was then rotated again and clean air was allowed to pass through
for desorption. For the detection of Hg, thermodesorption was used to remove Hg
on the gold coated crystal.
III. DETECTION OF MERCURY
The ability of gold to adsorb and almagamate mercury is well known (6,9-11).
Gold is also one of the metals used to make the electrode on commercially
available crystals. Mercury is adsorbed on the surface of gold electrodes,
363
-------
240
220
y 180
•H 160
o
w
14°
120
Injection
6 8 10
TIME.MINUTES
12
14
16
Figure 5. Adsorption - Desorption Curves of Mercury
causes an increase in the mass of the crystal, therefore decreasing the fre-
quency. Mercury is then desorbed by thermal desorption and the frequency
returns back to the original baseline as shown in Figure 5. Since the
sensitivity of the detector depends on the mass of mercury adsorbed on the sur-
face of gold electrode, the collection efficiency of the gold coated crystal was
studied. Gold electrode should be fresh for the best collection efficiency and
a commercially available gold electrode crystal can be used after heat cleaning
(7). The effect of flow rate on the sensitivity was also studied. The volume
of the sampled air was kept constant while the flow rates were varied from 30
ml/min to 250 ml/min. Below 100 ml/min the flow rate did not affect the sen-
sitivity. At high flow rates the collection efficiency is decreased due to
the incomplete adsorption of mercury. At 200 ml/min the collection efficiency
is about 15% lower than that at 90 ml/min. The optimum flow rate was found to
364
-------
be 100 ml/min. The configuration of the detector cell also affected the
sensitivity. Several cell configurations were investigated and the design as
shown in Figure 3, is the most sensitive one since it provides the best
contact between the gold electrode and the sampled air stream (6). For best
precision, the crystal should be desorbed after each measurement. We observed
that if the crystal is desorbed immediately after each measurement the lifetime
of the crystal is much longer. This may be due to the migration of mercury into
the inner layers of the gold film during storage; it is difficult to desorb
completely this mercury.
The linear range of the detector is from 5 ng to 100 ng as shown in
Figure 6. Increasing the surface area of the gold electrode will increase the
linear range of the detector. However, the mass sensitivity will decrease,
since the mass sensitivity of the crystal is inversely proportional to the
electrode's area. The detector responds to mass rather than to concentration
200
160
w
I 120
S
o
s
w
Cf
a
(K
80
40
Polymer Backbone
50
100
150
AMOUNT OF MERCURY, NG
Figure 6. Response of
Detector to Mercury
Figure 7. Structure of
XAD-4-Cu2+-diamine
365
-------
and the larger volume of sample, which gives the larger amount of mercury, will
increase the frequency response. Therefore, various concentrations in the ppm
and ppb ranges can be detected by varying the sampling times. This will
further enhance the usefulness of the detector. Since gold is well known as a
highly specific adsorber for mercury, the detector is free of the interferences
usually observed in cold vapor atomic absorption. l^S and Cl£ only slightly
interfer at high concentrations (> 100 ppm). However these two interferents
can be eliminated by collecting mercury from sampled air with a gold coated
quartz collector. Mercury is then released from the collector by heating and
is carried with carrier gas into the detector cell.
IV. DETECTION OF ORGANOPHOSPHORUS PESTICIDES
It has been known for many years that copper complexes can catalyze the
hydrolysis of phosphorus ester (12,13). In an aqueous solution the overall
reaction occurs in two steps: first, the copper complex binds reversibly to
the phosphorus esters and second, the adduct product is irreversibly broken
down by hydrolysis. In air, where the content of water is low, the second step
is unlikely to occur.
f\ i
Guilbault et al (14) showed that XAD-4-Cu - diamine, a polymer bonded
copper complex as shown in Figure 7 can be used as a sensitive and selective
coating for detection of organophosphorus compounds. In this study, the crystal
was first coated with polyhexadecylmethacrylate and then a finely ground powder
of XAD-4-Cu^+ - diamine was sprayed onto the crystal. The excess XAD-4-Cu2+ -
diamine was wiped out with a soft tissue paper. Since polyhexadecylmethacrylate
is a highly viscous liquid, XAD-4-Cu^+ - diamine adhered strongly onto the
surface of the crystal. For organophosphorus pesticides, diisopropyl methyl-
phosphonate (DIMP) was chosen as the model compound because of its low toxicity
and other reasons as shown elsewhere (15). The standard concentrations of
366
-------
DIMP for test atmospheres were generated by the diffusion method. No significant
interference was noted, except that responses are observed with DS-2 and high
humidity. DS-2 is a decontamination solution for organophosphorus compounds and
other chemical warfare agents. This response is expected, since DS-2 contains
amines and may form a complex with the copper coating. The calibration curve
for DIMP is shown in Figure 8.
100
100 200 300
CONCENTRATION OF DIMP, PPB
Figure 8. Frequency Response of Detector to DIMP
V. DETECTION OF FORMALDEHYDE
Due to the high sensitivity, coated piezoelectric crystals can be used as
gravimetric sensors for measuring toxic gases in the atmosphere. The only
drawback in this type of detector is the selectivity. The specificity of the
detector is dependent on the coating materials. In this study, enzymes were
immobilized on the piezoelectric crystal for assay of substrates directly in the
367
-------
gas phase, entirely analogous to their use in solution. The specificity of
enzymes is well known and this investigation represents the first attempt to
use an immobilized enzyme as a coating for piezoelectric crystal sensors. The
enzyme formaldehyde dehydrogenase catalyzes the oxidation of formaldehyde, in
the presence of co-factor NAD+, to form formic acid and NADH. Glutathione was
also used as a co-factor. Formaldehyde dehydrogenase, NAD+ and glutathione
were immobilized onto the crystal using glutaraldehyde and albumin (16). The
detector is highly selective toward formaldehyde with little response to
acetaldehyde and alcohols. As low as 0.1 ppm concentration of formaldehyde
could be detected. However, more studies are needed to improve the precision,
response time, reversibility and a stability of the enzyme coating. The
mechanism involved in this type of gas phase enzymatic reaction is not fully
explained.
368
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REFERENCES
(1) Sauerbrey, G. Z. Phys. Verhandl 8: 113, 1957.
(2) Sauerbrey, G. Z. Z. Phys. 115: 206, 1959.
(3) Stockbridge, C. D. Resonance Frequency versus Mass Added to Quartz
Crystal. In: K. H. Behrndt (ed.), Vacuum Microbalance Techniques. Vol. 5,
Plenum, New York, 1966. p. 193.
(4) Czanderna, A. W. and Lu, C. S. Applications of Piezoelectric Quartz
Crystal Microbalances. Elsevier Scientific Publishing Co., New York,
1983.
(5) Hlavay, J., and Guilbault, G. G. Applications of the Piezoelectric
Crystal Detector in Analytical Chemistry. Anal. Chem. 49: 1890, 1977.
(6) Ho, M. H., Guilbault, G. G., and Scheide, E. P. Determination of
Nanograin Quantities of Mercury in Water with a Gold Plated Piezoelectric
Crystal Detector. Anal. Chim. Acta. 130: 141, 1981.
(7) Ho, M. H., Guiltaault, G. G., and Rietz, B. Detection of Carbon Monoxide
in Ambient Air With a Piezoelectric Crystal. Anal. Chem. 54: 1998, 1982.
(8) Nelson, G. 0. Controlled Test Atmosphere. Ann Arbor Science, Ann Arbor,
Michigan, 1980.
(9) McNerney, J. J., Buseck, P. R., and Hanson, R. C. Mercury Detection by
Means of Thin Gold Films. Science 178: 611, 1973.
(10) Bristow, Q. An Evaluation of the Quartz Crystal Microbalance as a
Mercury Vapour Sensor for Soil Gases. J. Geochem. Explor. 1:55, 1972.
(11) Scheide, E. P., and Taylor, J. K. Piezoelectric Sensor for Mercury in
Air. Environ. Sci. Tech. 8: 1097, 1974.
(12) Gustofson, R. L., Chaberek, L. S., and Martell, A. E. A Kinetic Study
of the Copper(II) Chelate Catalyzed Hydrolysis of Disopropyl phosphoro-
fluoridate. J. Amer. Chem. Soc. 58: 598, 1963.
(13) Epstein, J. and Rosenblatt, D. H. Kinetics of Some Metal Ion Catalyzed
Hydrolysis of Isopropylmethyl-phosphorofluoridate at 25°C. J. Am. Chem.
Soc. 80: 3596, 1958.
(14) Guilbault, G. G., Affolter, J., Yutaka, T., and Kolesar, E. S., Jr.
Piezoelectric Crystal Coating for Detection of Organophosphorus
Compounds. Anal. Chem. 53: 2057, 1981.
(15) Ho, M. H., and Guilbault, G. G. Portable Field Detector .for Organo-
phosphorus Pesticides. Anal. Chim. Acta, in press.
(16) Carr, P. W., and Bowers, L. D. Immobilized Enzymes in Analytical and
Clinical Chemistry. John Wiley and Sons, New York, 1980, pp. 148-191.
369
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ACKNOWLEDGEMENT
The author gratefully acknowledge the financial support of the National
Institute of Health, Division of Research (Grant S07-RR05349-20) and the UAB
Graduate School Faculty Research Grant (Grant 82-8509).
NOTICE
The work described in this paper was not funded by the U.S. Environmental
Protection Agency and therefore the contents do not necessarily reflect the
views of the Agency and no official endorsement should be inferred.
370
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REDUCED-TEMPERATURE PRECONCENTRATION
OF VOLATILE ORGANICS FOR GAS CHROMATOGRAPHIC
ANALYSIS: SYSTEM AUTOMATION*
by: W. A. McClenny
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
J. D. Pleil
Northrop Services, Inc.
Environmental Sciences
Research Triangle Park, North Carolina 27709
ABSTRACT
An automated system for unattended repetitive sampling and analysis of
volatile organic compounds in ambient air has been designed and evaluated.
The sampling and analysis scheme involves preconcentration of analytes from
whole air at reduced temperature and subsequent thermal desorption and capil-
lary column gas chromatographic analysis. This paper describes the system's
components and operating procedures. Temperature versus time profiles measured
at the trapping surface document the stability of the trap temperature during
sample collection, as well as the rapid trap temperature changes, i.e., 3.5
minutes for +120°C to -150°C during cooling of the trap and 1.0 minute for
-150°C to +100°C during thermal desorption. The system will be evaluated as a
semi-real-time monitor for volatile organics and as a central system for
analysis of air samples collected in small-volume metal containers.
INTRODUCTION
Preconcentration of target gases prior to analysis increases the effec-
tive sensitivity of standard monitoring techniques. Solid sorbants such as
Tenax are currently used to selectively sorb gaseous organics while passing
the major constituents of ambient air, including HO. Thermal desorption from
Tenax onto a cryogenic trap (cryo-focusing) is used to further concentrate the
gaseous organics. Subsequent heating of the trap releases the organics in a
sample volume many times (103 to 105) smaller than the original whole' air
volume.
""This paper has been reviewed in accordance with the U.S. Environmental Protec-
tion Agency's peer and administrative review policies and approved for presen-
tation and publication.
371
-------
Direct semi-real-time monitoring of organics can be accomplished by
sampling directly onto a reduced temperature surface, usually a section of
small diameter metal tubing filled with small glass beads. However, water
vapor condensation typically plugs a trap with only 200 to 300 ml of ambient
air collected. Recently, Nafion tubing has been used successfully to dry the
ambient air prior to condensation while quantitatively passing volatile organics
(M. W. Holdren, private communication, 1982). It is therefore possible to
directly cold-trap larger air volumes, e.g., 1 to 10 liters, in a range compar-
able to the volumes collected with solid sorbants.
We are currently investigating the feasibility of reduced-temperature
trapping and have found that automation greatly facilitates run-to-run repeat-
ability and eliminates the tedious manual procedures involved in sampling and
analysis. This paper (1) describes a system design for automated operation
and (2) documents certain aspects of the system's performance. We envision
the use of this system as a semi-real-time monitor of volatile organics in
ambient air. With minor modification, the system can also be used to analyze
ambient air samples collected in reusable, small volume metal cylinders (e.g.,
2-2 aluminum cylinders) and shipped to a central location.
Previous research conducted to condense extremely large air samples for
analysis by long path absorption - Fourier transform infrared (Hanst et al.
1979) has provided useful information on the subject of cryogenic trapping.
In particular, extrapolations of the Clausius-Clapeyron equation were used to
furnish conservative estimates of target compound vapor pressures at reduced
temperatures.
EXPERIMENTAL PROCEDURE
The system includes a Hewlett-Packard (HP) Model 5880A gas chromatograph
(GC) which is operated with a preconcentrator rather than a sample loop. The
preconcentrator (Figure 1) consists of a 20-cm section of 0.32-cm OD, 0.22-cm
ID nickel tubing formed into a circular winding and threaded onto a 250-W
cylindrical resistive element. During use, a stainless steel shell encapsu-
lates the trap and is further sealed with heat-resistant tape and glass fiber
insulation to prevent escape of liquid nitrogen (N,) into the surrounding
volume. Small 25-W cartridge heaters are sandwiched between small aluminum
plates for each pair of outlet tubes to provide a heat source and thermal
inertia to eliminate "cold" spots in the outlet lines. The central 10-cm
portion of the trap is filled with 0.85 grams of 60/80 mesh Pyrex glass beads.
The trap is further characterized by the flow rate for a given pressure drop:
40 cm3/min for approximately 3 psig as measured prior to use. Each trap is
used as indicated in the flow path schematic shown in Figure 2. The trap
enclosure and a Seiscor valve are placed on an aluminum base plate that is
insulated from and placed directly over the GC oven. An aluminum box is
attached to the base plate to enclose all sample handling elements. Rope
heaters, regulated to maintain +70°C, are attached to all lines through which
the sample flows.
372
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CYLINDRICALLY WOUND
HEATER
OUTLET TUBE
HEATER
OUT
Figure 1. Unit for trapping gaseous organics at reduced temperatures with sub-
sequent thermal desorption; A indicates thermocouple positions.
373
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FLOW NEEDLE PUMP
POSITION B
Figure 2. Flow path schematic showing cross-section of a six-port Seiscor
valve in sampling (A) and desorbing (B) positions.
374
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The set point network of a Nutech Model 320A cryogenic trapping/thermal
desorption system (Nutech Corporation, 1982) controls the coooling and heating
of the trap volume by re'leasing liquid N2 into the trap enclosure or by pas-
sing current through the heater. The liquid N2 is vented from the aluminum
box.
The individual events during a system cycle are under microprocessor
control from the HP 5880A. The run table listing for the sampling/analysis
sequence is given in Table 1 along with the GC oven temperature profile. The
input parameters are specified by the operator during system initialization.
External valve controls, available on the GC as +24-V d.c. outputs, are used
to change positions of the Seiscor valve (Valve 1 in listing) and to change
the operational mode of the Nutech 320A from cooling to heating (Valve 3).
Since the Basic language programming option is not included on our unit,
Instruction 5 in the Run Table is used to reset and start successive cycles.
TABLE 1. HP 5880A LISTINGS FOR RUN TABLE AND OVEN TEMPERATURE PROFILE
Run Table
Oven temperature profile
1.
2.
3.
4.
5.
6.
7.
Initial value
Initial time
Program rate
Final value
Final time
Post value
Post time
= -50°C
= 3.00 min
= 8.00°C/min
= 150°C
= 7.00 min
= -50°C
= 10.00 min
1.
2.
3.
4.
5.
6.
Time
(min)
0.50
1.00.
5.00
10.00
35.00
35.00
Status/action
Valve 1 on
Valve 3 off
Valve 3 on
Valve 1 off
Start valve 4 on
Stop
RESULTS
Table 2 shows the events occurring during the sequence of repetitive pre-
concentration /analysis runs. The steps are also shown in Figure 3. The two
traces in Figure 3, one placed in thermal contact with the trap and one placed
in the GC oven, record thermocouple outputs.
Figure 3 shows that stable trapping temperatures of -150°C can be achieved
with cool-down times of 3.5 min. Additional thermocouples were placed (1)
near the fittings at the output of the trap, (2) near the connector leading
from the Seiscor valve into the GC oven, and (3) in the air space of the box
that covers the preconcentrator and valves. Temperature readings during
system operation indicated that these temperatures are controlled within the
following ranges: 100 ± 20°C, 55 ± 5°C, and 55 ± 5°C, respectively.
Detailed information on the system and a discussion of the condendsation
of a list of volatile organics is given by Pleil (1982).
375
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TABLE 2. EVENTS OCCURRING DURING SEQUENCE OF ANALYSIS RUNS
Time
Cycle (min) , Status/action \
A 0 Trap is at -150°C, oven at -50°C; Seiscor valve is
in position B (see Figure, 2) as sampling continues.
B 0.5 Instruction #1 of Run Table switches Seiscor valve
to position A (see Figure 2). Sampling end$ with
35.5-min sampling time. Carrier gas (N_) is directed
through trap at 3.0 cm3/min flow rate.
C 1.0 Instruction #2 of Run Table activates relay in Nutech
320A to begin rapid heating of trap. Desorbed gases
are retrapped on the column head.
D 3.0 Temperature programming begins at 8°C/min and con-
tinues for 25 min, reaching +150°C at the end.
Recording and display of chromatograph occurs on HP
thermal printer.
E 5.0 Instruction #3 of Run Table deactivates relay in
Nutech 320A to begin cooling trap.
F 10.0 Instruction #4 of Run Table switches Seiscor valve
to position B (see Figure 2) s.o that sampling begins.
G 28.0 Temperature programming ends with oven at 150°C.
Final time instruction in oven temperature profile
(OTP) holds oven temperature at 150°C for 7 min.
H Run Table instruction program is reset, post time
in OTP is set for 10 min, during which elution times
and peak areas are printed. Post value instruction
in OTP causes oven to begin cooling.
I 45.0 At end of post time, next run begins.
376
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TIME (OVEN), min
TIME (TRAP), min
Figure 3. Temperature - time profiles for trap and oven. *Trap temperatures
are approximate. tRecorder pens offset by 1.0 min. For explana-
tion of cycle steps A through I, see text.
377
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REFERENCES
1. Brown, R. H. and C. J. Purnell (1979) J. Chromatog., 178:179.
2. Hanst, P. L., L. L. Spiller, D. M. Watts, J. W. Spence, and M. F. Miller
(1975) J. Air Poll. Control Assoc., 25:1220.
3. Nutech Corporation (1982) Nutech Model 320A Operating Manual, Nutech Cor-
poration, 2806 Creek Road, Durham, North Carolina 27704.
4. Pleil, J. D. (1982) Technical Report TN-82-02, July 1982, Northrop Services,
Inc.-Environmental Sciences, P. 0. Box 12313, Research Triangle Park,
North Carolina 27709.
378
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DETECTION AND DETERMINATION OF POL YCHLORINATED
BEPHENYLS IN AMBIENT AIR
E. Singer, M. Sage, T. Jarv and R. Corkum
Ministry of the Environment of Ontario, Air Resources Branch
Toronto, Ontario, Canada, M5S 1Z8
Abstract
High resolution gas chromatography with simultaneous analysis on two columns of
different polarity with computer assisted data reduction and correlation was applied to
the analysis of Polychlorinated Biphenyls (PCB's) in ambient air.
A method of identification of individual PCB congeners based on retention indices
(RFs) was developed, as well as quantification of congeners, which are unavailable to be
used as standards.
A standard, based on a mixture of commerical Arodors, was synthesized and all
major peaks in the mixture were identified and quantified.
A set of rules for the reduction of data from two columns of different polarity was
established. As well, a programme was written for the miroprocessor controlled HP 5880
gas chromatograph. The program controls the gas chromatograph, the HP Autosampler
7672A, initiates the recalibration, performs the data reduction, correlation of the peaks,
prints the reports and stores all the GC data and reports on a magnetic tape.
Ambient air samples from a survey of PCB's across the Province of Ontario were
analyzed using methodologiies employing packed columns, single capillary columns, and
dual capillary columns of different polarity and some of the results are reported.
379
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The detection and determination of PCB's in environmental samples is a highly
complex task. There are 209 possible congeners of PCB's, 102 of which have been
identified in commercial Aroclor mixtures, and are therefore likely to be encountered in
environmental samples. The major approach to the analysis was until recently low or
medium resolution gas chromatography (1,2,3) utilizing packed columns coupled with
detection by electron capture (EC) detector or mass-spectrometry (MS). The results
were then quantified by peak or pattern matching with commercial Arodors or their
mixtures. The disadvantages of this technique, when applied to environmental (and
mainly air) samples are obvious.
1. It is assumed that the ratio of individual PCB congener in the vapour phase is
identical to the ratio in the liquid phase, which is unlikely. The boiling points of
PCB congeners spread over the range of more than 150°C and it is likely that the
vapour pressure will spread over a range of several orders of magnitude as well.
2. It is assumed that the ratio of individual PCB congener does not change after
exposure to the environment. However, individual PCB congeners have different
chemical and physico-chemical reactivities, so it is likely that the composition will
change with the length of the exposure to the environment.
3. It is assumed that all interfering compounds can be removed from the sample
during the clean-up procedure and if any stay in, they can be resolved from the
PCB congeners and thus the PCB peaks can be identified and quantified with high
degree of confidence.
380
-------
However, the efficiency of the clean-up procedures, even for samples of "dean"
air, can be questioned as well. It has been estimated (4) that in the concentration range
individual PCB congeners might be present in "clean" ambient air, about 106 organic
compounds could be present at similar levels. Only a fraction of these low level
impurities were identified. It is a reasonable assumption that a significant number may
not be removed by the clean-up procedure and some, which were not removed, may have
on a given column retention times close to the retention times of the PCB congeners and
may be detected by the EC detector as well.
The identification and quantification of individual PCB congeners in a complex
matrix was not too practical until rescently. With the commercial availability of
capillary columns of good and reproducible quality, gas chromatographs designed for
work with capillary columns, and computer controlled instrumentation and signal
processing, the introduction of high resolution gas chromatography into routine
laboratory work became realistic although there were still some open questions. Mainly,
1. Only a limited number of individual PCB congeners, as they were identified in
Arodors, were commercially available.
2. The commercially available fused silica capillary columns, suitable for this analysis
could not resolve all the PCB congeners, unless the run would be extended over
extremdy long periods of time.
3. There still remained the possibility that some impurities, present in the air sample
and not removed by the dean-up procedure may be identified as PCB's.
381
-------
The first problem was identification of the individual PCB congeners. Sissu and
Welti (5) reported in 1971 that the RI of PCB's can be calculated by summing up the K2RI
for the two substituted rings. We decided to extend their idea to capillary columns under
temperature programmed condition using the formula of Said and Hussein (6). The
calculated HRFs for different chlorine substitution and two different liquid phases are in
Table 1. A comparison of measured and calculated RFs of a number of PCB congeners is
in Table 2.
Table 1. HALF RETENTION INDICES OF POLYCHLORINATED BIPHENYLS
Cl-
Substitution
0-
2-
3-
4-
2,3-
2,4-
2,5-
2,6-
3,4-
3,5-
2,3,4-
2,3,5-
2,3,6-(2,5,6)
2,4,5-
2,4,6-
3,4,5-*
2,3,4,5-
2,3,5,6-
2,3,4,6-
2,3,4,5,6-
ASYM
PARENT
676
791
860
866
983
955
954
905
1056
1013
1166
1140
-
1128
1042
-
1331
1242
1254
1448
OV-1
SYM
PARENT
676
790
860
870
984
954
947
897
1066
-
1174
1141
1065
1124
1028
-
1330
1207
1251
1414
ASYM
PARENT
690
811
877
885
1005
972
972
926
1076
1030
1188
1159
-
1145
1057
-
1350
1260
1272
1464
SE-54
SYM
PARENT
690
808
878
889
1004
972
965
917
1088
-
1197
1162
1085
1140
1042
-
1349
1222
1279
1428
not commercially available
382
-------
TABLE 2. COMPARISON OF MEASURED AND CALCULATED RI's
FOR SOME PCB's
OV-1
SE-54
CONGENOR
MEASURED CALCULATED
MEASURED CALCULATED
ASYM.
SYM.
ASYM. SYM.
2,2',5
2, 3', 5
2, 4', 5
2, 3', 4'
2,2',4,6
2,2',3,5'
2,2',4,5'
2, 3', 4', 5
2, 2', 3, 5, 6
2,2',3,4,5'
2, 2', 4, 4', 6
2, 2', 4, 6, 6'
2 2' 3 3' 4 5
2 2" 3 4 5 5'
2'3,3'X4;,5
2,2',3,5,5',6
2,2',3,4,5,5',6
2,2',3,4,4',5,6'
2,2',3,3',4,4',6
2,2',3,4,4',5,6,6'
2, 2', 3, 3', 4, 5', 6, 6'
2, 2', 3, 3', 4, 4', 5, 6,6'
2,2',3,3',4,4',5,5',6
1739
1810
1824
1844
1828
1931
1903
2010
2021
2117
1990
1927
2312
2270
2405
2170
2375
2354
2405
2435
2430
2635
2752
1745
1813
1820
1847
1833
1936
1908
2009
2034
2120
1996
1947
2313
2284
2386
2196
2401
2372
2421
2489
2497
2702
2778
1737
1808
1817
1856
1818
1931
1902
2014
1997
2121
1982
1925
2314
2278
2396
2154
2361
2359
2424
2442
2458
2664
2744
1774
1845
1860
1881
1859
1970
1939
2048
2054
2157
2022
1961
2352
2305
2446
2203
2405
2384
2444
2463
2460
2665
2783
1783
1849
1856
1887
1868
1977
1944
2048
2071
2160
2029
1982
2354
2322
2426
2231
2436
2406
2460
2521
2532
2736
2814
1773
1843
1854
1895
1849
1970
1937
2053
2029
2162
2013
1959
2353
2314
2437
2187
2393
2391
2475
2469
2501
2706
2777
Although the agreement between the measured and calculated values of RI is very good -
generally better than 0.5% - there are some differences depending on whether symmetric
or asymetric parents are used. To get the best possible answer we set in Table 3 simple
rules for the calculation of RPs using either symetric or asymetric parents, depending on
the structure of the polychlorinated biphenyl.
383
-------
TABLE 3.
RULES FOR PREDICT ING THE RI'sOF
PCB CONGENORS
PCB CONGENOR
OV-1
ASYM. SYM.
PARENTS PARENTS
SE-54
ASYM.
PARENTS
SYM.
PARENTS
Mono-chloro
x=l;
Di-chloro
x=2; y=0
x=l; y=l
Tri-chloro
x=3; y=0
x=2; y=l
Tetra-chloro
x=4; y=0
x=3; y=l
x=2; y=2
Penta-chloro
x=5; y=0
x=3; y=2
Hexa-chloro
x=5; y=l
x=^; y=2
x=3;6=3
Hepta-chloro
3,4-
*
*
*
*
*
*
Octa-chloro
x=5; y=3
Nona-chloro
x=5;y=*
Deca-chloro
x=5; y=5
*
*
Note: x =
Y =
*
-; 3,4-
no. of chlorines on one ring
no. of chlorines on other ring
use these types of parents to generate the retention index for the PCB
whose composition is denoted by x and y.
these are exceptions to the * rule, use the parents of the column they
are located in to generate the best retention index.
384
-------
The next problem was the quantification of the unavailable congenors. Some
relative response factors were already reported in literature (7). We investigated the
relative response factors of the commercially available PCB congenors and some of the
data are presented in Table 4.
TABLE 4. RELATIVE RESPONSE FACTORS OF SOME PCB CONGENORS
CONGE NOR
2-
3-
4-
2,2'-
2,5-
2,3-
3,5'-
3,3'-
3,V-
2,4,6-
2,2',5-
2,4,5-
2,3',5-
2,4',5-
2,2', 6, 6'-
2,2',4,6-
2,2',6'-
2, 3', 4, 6-
7 2' 5 V-
t-i*- i->i->
2,2',4',5-
2,4,^,6-
2,3,5,6-
7 7' 3' 5-
^,^ j-5 >J
7 3' 5 V-
Z-!J >J>J
2,2'3,3'-
2,3,^,5-
2,3'*,'5-
3,3',(f,V-
REL. RESPONSE
21
1
11
AVG.=11
*8
206
22^
150
82
166
AVG.=146
289
211
326
319
308
AVG.=290
22^
352
2^8
320
320
335
527
361
388
351
371
296
358
287
AVG.=338
CONGE NOR
2,2', H, 6,6'-
2,2'4, 5/6-
2,2',M',6-
2,2',3,*,6-
2, 2', 3,5, 6-
2, 3' A 5', 6-
2,2',if,5,5'
2,3',4,V,6-
2,2',3,*',5-
2, 2', 4, 4', 6,6'-
2,?,w,y,6-
2,2',3,5,5',6-
2,2',3,4,V,6-
2, 2', 3, 4, 5, 6'-
7 7' L. ii' S S1
•<:»/: j^,41 ,-',^
7 7" 3 U 5 5'-
^ J *• 5 -3, ^> >, J
? ?' 1 It U* 1-
^•>*- ? -),^j^ jj
7 2' 3 3' 4 5-
^ , ^ j -", -J ? ^» ->
7 7' 3 3' U U*
£,£ , J, J ,4-,t
2, 3, 3', 4,4', 5-
2, 2', 3, 4, 5,5', 6-
2, 2', 3, 3, 5,5', 6, 6'-
2,2'3,3',4,5',6,6'-
2, 2', 3, 4, 4', 5,6,6'-
REL. RESPONSE
313
340
355
351
364
381
395
369
427
AVG.=336
367
351
378
341
399
401
400
440
370
344
291
AVG.=371
402
378
290
366
AVG.=345
385
-------
In this table the response of 3-monochlorobiphenyl is taken as unity. The relative
response factors of tri- to octachloro biphenyls are fairly close, in the average about
340, however, there are large differences between relative response factors of mono-and
dichlorobiphenyls. Luckily all mono- and a significant number of dichlorobiphenyls are
commercially available. For the quantification of the rest of the PCB's we decided to
take either the measured response factor (if available) or the average of the response
factors of the two available PCB congeners bracketing in the congener whose standard is
not available.
The goals were not only positive identification and quantification of the individual
biphenyls but also automation of the procedure, so it could be run on a microprocessor
controlled GC - in our case a HP 5880A with dual terminals and level IV programming.
This goal required a standard with as many identified and quantified PCB congenors as
possible. This was achieved by combining Arodors 1016, 1221 and 1254 (EPA Depository
#9324, 9382 and 9533) in a single solution in the ratio of 2:3:2. For the identification and
quantification, these Arodors and their mixture were run on two capillary columns of
different polarity simultaneously. Individual PCB congenors were identified with the
help of K2RPs and quantified as mentioned before.
The last goal was the increase in confidence of positive identification and
quantification of the PCB congenors. RT or RI on a single phase does not unequivocally
identify an organic compound. It is estimated that the confidence of positive
identification is about 60% for a packed column and relatively simple matrix and does
not increase significantly for a high resolution capillary column. However, the
confidence increases dramatically to about 90% if the sample is run on two columns of
different polarity.
386
-------
During our work fused silica columns came on market which simplified significantly
our task. With this type of columns, we were able to mount two columns of identical
lengths and I.D. but with different liquid phase into the same injection port and attach to
two different EC detectors. This, with proper programming, simplified significantly our
task. The correlation of the data from the two chromatograms was performed by the GC
-microprocessor as well.
The programme, written for the HP 5880A, does the following:
1. controls theGC parameters
2. controls the HP-Autosampler 7672A
3. recalibrates the GC after a preset number of samples with weight averaging of the
retention times and reponse factors
4. after analysis prints the reports from each column with the tentatively identified
PCB congeners
5. correlates the gas chromatograms and prints the final report
6. stores all data and reports on a magnetic tape
For the correlation of the two gas chromatograms, we decided on a set of rules,
under which the programme handles and correlates the peaks, tentatively identified and
quantified on each column as PCB congeners. The rules are:
1. The retention time of the peak must be within narrow limits (usually +0.1%) of the
expected retention time of the PCB congener, otherwise the peak is rejected.
387
-------
2. If the peak for a given PCB congener is identified on both columns and the
quantities are within limits (usually +20%), the average is calculated and printed
followed by the word "confirmed".
3. If the peak for a given PCB congenor is identified on both columns, but the
quantities are not within given limits, then the lower value is taken as the result,
followed by word "interference" (we assume that the higher value is caused by a
coeluting impurity).
k. If a peak is identified as a given PCB congenor on one column only, the value is
rejected and identified by the words "not PCB".
5. Although the used capillary columns have very high resolving power, some PCB
congeners can not be resolved at all or can be resolved on one column only. If this
happens, then the determined quantities of unresolved PCB congeners on one or
both columns are summed up and evaluated under identical criteria.
The method was tested on individual Arodors, Aroclor mixtures and synthetic
ambient air samples. The results were within^15% of the expected values.
We performed in Ontario two surveys for PCB's, in 1979 and 1980, to increase our
data base for the background levels in ambient air.
All samples were collected on "Florisil" cartridges over 24 hour period at flow
rates between 10-15 litre/min. The cartridges were extracted by pentane, boiled down in
a Kuderna-Danish apparatus to a volume of 2 ml, cleaned up on a "Florisil" microcolumn,
388
-------
and the final extract was boiled down in a Kuderna-Danish apparatus to a 1 ml volume
with iso-octane (1 ml) as keeper. Processed samples were sealed in glass ampoules
before they were analysed. Table 5 shows some data, obtained from the 1979 survey, as
analysed by different gas chromatographic techniques. The processed samples were
analysed by packed column (Dexil 400/Anachrom Q, 9 feet long, 1/8" O.D.), fused silica
capillary columns - 50 m, 0.2 mm I.D. - with different liquid phases and by the dual
column technique.
TABLE 5. COMPARISON OF RESULTS BETWEEN PACKED COLUMN,
GC2 ANDGC2 X 2
SAMPLE SAMPLE
// CODING
PACKED
COLUMN
Concentration ng/m^
SINGLE CAPILLARY COLUMN DUAL COLUMN
SP-2100 OV-1 SE-54 OV-l/SE-54
1
2
3
4
5
6
7
8
9
10
11
12
13
If
15
16
17
18
19
20
21
22
23
24
HAM-I
HAM-I
HAM-I
HAM-I
HAM-U
HAM-U
HAM-U
HAM-U
KIN-U
KIN-U
LON-S
MIS-U
MIS-U
MIS-U
MIS-U
MOO-R
MOO-R
NAN-R
NAN-R
SAR-I
SAR-U
SAR-U
STC-R
STC-R
317
173
52
68
28
128
31
25
3.6
9.3
35
155
40
20
6.7
33
27
96
28
1615
115
58
78
15
11
4.8
8.7
20
1.6
2.4
3.6
4.4
4.3
3.3
11
7.3
5.1
1.4
2.4
2.5
4.7
4.9
11
6.8
3.5
4.6
14
1.9
11
5.2
0.7
5.9
2.5
3.8
2.3
2.2
12
5.2
9.4
3.7
3.2
3.3
3.4
2.9
3.1
3.7
4.6
4.3
3.7
6.1
4.2
4.7
7.0
4.1
1.6
9.7
1.0
3.0
1.7
1.6
6.5
2.7
9.1
2.7
2.8
1.9
3.3
1.1
1.7
1.7
2.4
6.5
14
4.3
2.5
4.2
3.3
1.4
0.2
1.9
0.02
0.9
0.2
0.2
0.08
0.3
6.3
0.9
0.05
0.3
0.2
0.1
0.3
0.1
1.1
2.1
0.6
0.6
0.5
2.0
389
-------
The data for total PCB's on the medium resolution packed column are about an
order of magnitude, or more, higher than the data from the single capillary columns.
The reason for it is, in our opinion, the low resolving power of the packed column with
many impurities, which were not removed by the clean-up procedure, identified as PCB's.
There seems to be a fair agreement between the data for total PCB's obtained on
capillary columns with different liquid phases. However, on closer examination we see
that some PCB congeners are identified on one type of column only and their presence
can not be confirmed on the other types of columns. The reason for it is, in our opinion,
that although capillary columns have very high resolving power, many impurities present
in ambient air may not be removed by the clean-up procedure and because of very close
RT under given gas chromatographic conditions on a given column, they may be
identified as PCB congenors and thus the reported data might be biased high.
The data from the dual column analysis with the elimination of PCB congenor
which can not be confirmed on both columns and quantification based on individual PCB
congenors are, in our opinion, closer to the true value than the results from a single
capillary column analysis.
The work described in this paper was not funded by the U.S. Environmental
Protection Agency and therefore the contents do not necessarily reflect the views of the
Agency and no official endorsement should be inferred.
390
-------
REFERENCES
1. Association Off. Anal. Chem. Methods 29.018.
2. Sawyer, L.D. Quantitation of Polychlorinated Biphenyl Residues by Electron
Capture Gas-Liquid Chromatography: Reference Material Characterization and
Preliminary Studies. 3. Assoc. Off. Anal. Chem. 61_, 272 (1978).
3. Sawyer, L.D. Quantitation of Polychlorinated Biphenyl Residues by Electron
Capture Gas-Liquid Chromatography: Collaborative Study. J. Assoc. Off. Anal.
Chem. 61, 282 (1978).
4. Lewis, R.G. Accuracy and Trace Organic Analysis. National Bureau of Standards
Special Publication 422, pp. 17-20.
5. Sissons, D. and Welti, D. Structural Identification of Polychlorinated Biphenyls in
Commercial Mixtures by Gas-Liquid Chromatography, Nuclear Magnetic Resonance
and Mass-Spectrometry. J. Chromatog. 60, 15 (1971).
6. Said, A.S. and Hussein, F.H. The Absolute Retention Index in Chromatography.
Part I. J. High Res. Chromatog. and Chromatog. Comm. 257 (1978).
7. Hutzinger, O., Safe, S. and Titko, V. The Chemistry of PCB's. CRC Press Inc.
1974.
6AR2-21
391
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A Cost-Effective Procedure to Screen Air Samples
for Polyaromatic Pollutants4
T. Vo-DinhD, G. C. Colovosc, T. J. Wagner , and R. H. lungers'
Health and Safety Research Division
Oak Ridge National Laboratory
Oak Ridge, TN 37830
ABSTRACT
The use of simple and cost-effective luminescence techniques for
screening ambient air particulate samples is described. Two analytical
methods, synchronous luminescence and room temperature phosphorescence,
are employed to monitor the content of polynuclear aromatic (PNA)
species in air particulate extracts collected at two wood-burning
communities. The validity and efficacy of this cost-effective screening
approach are demonstrated via comparison of the screening data with
results obtained by detailed gas chromatography/mass spectrometry and
high-performance liquid chromatography. The results of this field study
demonstrate that a simple and cost-effective screening procedure can be
used to obtain PNA spectral profiles as a basis to rank air samples
according to their PNA content and/or to determine whether these samples
have similar PNA compositions.
This paper has been reviewed in accordance with the U.S.
Environmental Protection Agency's peer and administrative review
policies and approved for presentation and publication.
Research sponsored jointly by the US Environmental Protection
Agency under Interagency Agreement No. ERD-82-190 and the Office of
Health and Environmental Research, US Department of Energy under
Contract No. W-7405-eng-26 with the Union Carbide Corporation.
Instrumentation and Measurements Group, Health and Safety Research
Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830.
C
Rockwell International, Energy Systems Group, Environmental Monitoiing
Service Center, Newbury P*r>, CA 91320
PEDCo Environmental, Inc., Cincinnati, OH 45246.
Data Management and Analysis Division, US Environmental Protection
Agency, Environmental Monitoring Systems Laboratory, Research
Triangle Park, NC 27711.
392
-------
I. INTRODUCTION
Many routine air monitoring programs involving large numbers of
samples require that the decision as to the levels of accuracy and
sensitivity of the analytical methods be governed by cost-benefit
consideration. Before selection of the groups of samples to be analyzed
in detail or the types of methods to be employed, it is desirable to
have some screening procedure available that can provide an overall
profile of major components. Such a screening step may obviate the need
to perform unnecessary and costly detailed analyses.
This field study evaluated the efficacy of two simple and rapid
analytical techniques, synchronous luminescence (SL) and room
temperature phosphorescence (RTF), for routine screening of samples for
PNA content (3-6). The SL and RTF techniques were used to monitor PNA
compounds in cyclohexane extracts of air particulate samples collected
in two wood-burning communities. The luminescence screening procedures
conducted at Oak Ridge National Laboratory ranked all samples on the
basis of their PNA content (7). The efficacy of the screening procedure
was evaluated by comparing the SL and RTF results with data obtained
from independent gas chromatography/mass spectrometry (GC/MS) and high-
performance liquid chromotography (EPLC) analysis performed by PEDCo
Environmental, Inc. and by Rockwell International, respectively. The
results of this field evaluation showed that the luminescence screening
procedure constituted an effective tool for ranking air samples for
their PNA content and could serve to reduce the number of GC/MS analyses
needed to characterize PNA compounds in ambient air.
393
-------
II. EXPERIMENTAL
Ranking Procedures
The screening procedure evaluated in this research project is based
upon RTP and SL (3-6). Because most of the PNA compounds fluoresce
and/or phosphoresce, this ranking protocol is based upon the gross
intensity of the total peak height of the SL and RTP bands. The full
details of the screening protocol are described elsewhere (7). In this
study the SL technique was applied to fluorescence measurements.The four
major steps involved in the protocol are: 1) serial dilution of the
sample extracts, 2) SL measurements, 3) RTP measurements, and 4) ranking
the samples.
A simple computer program was developed to calculate a ranking
index for SL and RTP screening. This program receives as inputs the
peak height intensities of a preselected number of emission bands in the
SL and RTP spectra, the sensitivity factor of the detector, the dilution
factor of the sample, and the peak height of a given band of a known
reference standard sample. These peak height intensities are then
corrected to the reference standard, normalized to the unity sensitivity
scale of the spectrometer, and original dilution (1:1) of the sample,
and summed. The summed value of the peak height for each sample is
stored in a one-dimensional array and used as a basis for ranking.
The SL ranking index is a relative number proportional to the total
peak height of the six SL emission bands monitored at 316, 336, 360,
404, 434, and 472 nm. The RTP ranking index is obtained as a relative
394
-------
number proportional to the average peak height of RTF emission using
excitation at 290, 343, and 390 nm. These three wavelengths were found
to excite most PNA compounds of interest in this study.
III. RESULTS AND DISCUSSION
Figures 1 and 2 shew some typical SL spectra using AX = 3 nm for
six samples in the first series and illustrate the principle of SL
screening. The background signal of the solvent blank is given in
Figure 2d for comparison purposes. The samples, identified by a 5-digit
alphanumeric code, correspond to diluted cyclohexane extracts from air
particulate samples collected outdoors at different locations in a
residential community. Visual examination of Figures 1 and 2 led
readily to the qualitative ranking of
OG654>CG543>CG626>CG634>CG573>CG656. Sample CG654 had the highest PNA
content since a 50-fold diluted sample exhibited an SL emission
intensity equivalent to that of a 10-fold diluted solution in other
samples. It is interesting to note that samples CG543 showed three
emission bands between 400 and 500 nm similar to those in sample OG626
with some additional bands at the spectral region <400 nm. This feature
is an indication that sample OG626 contains fewer small-ring species
(less than 4 benzene rings) than sample CG543.
According to their RTF intensities, the six samples in this first
series were ranked as follows: CG654>CG543>CG634>CG626>CG573>CG656.
Table I summarizes the results of gross ranking for the six air
particulate extracts investigated in this first sample series and
compares the SL and RTF screening results with data obtained by PEDCo
395
-------
using GC/MS measurements. The SL/RTP ranking results were in good
agreement with GC/MS data for the six samples from this first series of
field samples.
Another series of field samples consisted of ten air particulate
extracts randomly picked among the samples sent to ORNL. The SL spectra
were similar to those observed with the first sample series, showing six
major emission bands at approximately 316, 336, 360, 404, 434, and
472 nm. The RTF spectra of the field samples were also similar to those
studied previously and exhibited a broadband and featureles's structure
with the maximum emission between 500 and 600 nm. Table II gives the
results of the calculation of the SL Ranking Index for this series of
field samples.
This second series of field samples were independently analyzed by
PEDCo using GC/MS for ten specific PNA compounds used previously in the
standard reference mixture, viz, phenanthrene, anthracene, fluoranthene,
pyrene, benz[a]anthracene, chrysene, triphenylene, benzo[a]pyrene,
benzo [e jpyrene, and perylene. The "total" content for the ten PNA
compounds obtained by GC/MS was compared to the SL/RTP composite ranking
index as shown in Table II. The correlation between the SL/RTP ranking
results and the GC/MS data is illustrated in Figure 3. As depicted in
Figure 3, there is a "linear" correlation between the SL/RTP ranking
index and the total content for the ten PNAs analyzed by GC/MS.
Further information of analytical interest could also be provided
by the spectral structure of the SL profiles. Examination of the SL
profile of samples CG642 and CG635 shows that the intensity is higher
396
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for wavelengths >360 nm where the emission of most PNAs having more than
three benzenoid rings occurs. The GC/MS data on Table II for samples
OG642 and CG635 are in good agreement with this qualitative prediction.
Since the GC/MS analyses dealt mainly with high-number ring PNAs, the
total PNA content obtained by GC/MS for CG642 and CG635 are relatively
higher than those obtained with the other samples. Note that the data
for samples CG642 and CG635 (triangular dots on Figure 3) correspond to
another correlation line with a higher slope. This result demonstrates
that the SL profile can be used not only to provide an index for ranking
but also to provide useful information concerning the ring size of the
PNA constituents in the sample.
Another series of air particulate sample extracts, previously
ranked by SL and RTP, were also analyzed by Rockwell International using
HPLC. The comparative results are illustrated in Figure 4. The results
of this field study demonstrate the efficacy of the SL/RTP procedures to
provide a ranking based solely on the total intensity of the
luminescence profile without requiring any physical sample separation.
Use of this ranking procedure will reduce the cost of pollution control
and human exposure assessment by reducing the number of unnecessary
analyses by more sophisticated and more expensive techniques.
397
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References
[1] Greenberg, A., Yokoyama, R. , Giorgio, P., and Canova, F., Analysis
of Polynuclear Aromatic Hydrocarbons on the Airborne particulates
of Urban New Jersey, in Polyaromatic Hydrocarbons, ed. Bjorseth
and A. J. Dennis, Batelle Press, p. 193 (1980) .
[2] Funke, W. , Romanowski, T. , Konig, J. , and Balfanz, E. , Detection
of High Molecular Polycyclic Aromatic Hydrocarbons in Airborne
Particulate Matter Using MS, GC, and GC/MS, in Polyaromatic
Hydrocarbons. eds. M Cooke, A. J. Dennis, and G. L. Fisher,
Batelle Press, p. 305 (1982).
[3] Vo-Dinh, T. Multicomponent Analysis by Synchronous Luminescence
Spectrometry, Anal. Chem. 50: 396(1978).
[4] Vo-Dinh, T. Synchronous Excitation Spectroscopy, in Modern
Fluorescence Spectroscopy. Volume 4, ed. by E. L. Wehry, Plenum
Press, NY, 1981.
[5] Vo-Dinh, T. Room Temperature Phosphorimetry for Chemical
Analysis, John Wiley and Sons , Inc. Publishers, New York (1984).
[6] Vo-Dinh, T., and Gammage, R. B. Singlet-Triplet Energy Difference
as a parameter of Selectivity in Synchronous Phosphorimetry, Anal.
Chem. 50: 2054 (1979) .
[7] Vo-Dinh, T. , and Bruewer, T. B. Field Evaluation of a Cost-
Effective Screening Procedure for Polynuclear Aromatic Compounds
in Ambient Air Samples, (Final Report, Interagency Agreement No.
ERD-82-170, U.S. Environmental Protection Agency) Oak Ridge
National Laboratory, Oak Ridge, TN (1983).
398
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Table I. Comparison between SL/RTP result and GC/MS data.-
SL ? RTF
Ranking
(ORNL)
CG654) CG543> CG626> CG634> OG573> CG656
GC/MS Analysis
(PEDCo)-
Phenanthrene
Anthracene
Fluoranthene
Pyrene
Benz[a]anthracene
Chrysene/
Triphenylene
Benzo[e ]pyrene
Benzo[a]pyrene
Perylene
52
0
1015
1253
1608
2437
1120
1539
166
180
0
446
479
408
731
278
297
51
70
0
238
221
135
260
152
0
0
44
0
96
86
0
66
0
0
0
42
0
63
65
0
0
0
0
0
19
0
60
61
0
59
0
0
0
—SL/RTP measurements were conducted at
conducted at PEDCo.
^GC/MS data = ug/mL.
ORNL; GC/MS measurements were
Table II. Comparison between the SL/RTP ranking
results and GC/MS data for the 2nd sample series.
Sampl e
CG616
CG545
CG627
CG560
CG619
CG629
CG624
CG533
CG642-
CG635-
SF
Index
90
75
65
55
65
20
15
15
45
20
RTP
Index
35
25
20
20
10
15
5
1
15
2
SL/RTP
Ranking Index
125
100
85
75
75
35
20
16
60
22
GC/MS Data
Hg/mL
2.197
2.264
1.119
0.727
1.094
0.056
0.134
0.260
2.390
0.414
SF profile of these two samples indicate a
composition with relatively higher content of PNA with 4
different PNA
rings and up.
399
-------
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