EVALUATION OF HEALTH EFFECTS OF AIR POLLUTION IN THE
CHESTNUT RIDGE AREA--PRELIMINARY ANALYSIS
J. Gruhl
F.C. Schweppe
W.F. Maher
MIT Energy Laboratory
F.E. Speizer
M.B. Schenker
J. Samet
Peter Bent Brigham Hospital
MIT Energy Laboratory Report no. MIT-EL 80-019
May 1980
DOE/EV/04968-3
EVALUATION OF HEALTH EFFECTS OF AIR POLLUTION
IN THE CHESTNUT RIDGE AREA Preliminary Analysis
Progress Report
for Period September 15, 1979 to March 15, 1980
W.F. M aher
F.C. Schweppe
M.I.T. Energy Laboratory
Cambridge, Massachusetts 02139
J. Gr ·
uhl
J. Samet
M.B. Schenker
F.E. Speizer
:Peter Bent Brigham Hospital
Boston, Massachusetts 02115
May 1980
Prepared for
THE U. S. DEPARTMENT OF ENERGY
UNDER CONTRACT NO. DE-AC02-78EV04968.AO02
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ABSTRACT-::.
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This project involves several tasks designed to take advantage of
(1)a very extensive
air pollution monitoring system that is operating-'
in the Chestnut Ridge.region of Western Pennsylvania and (2) the very
well developed analytic dispersion models that have been previously
fine-tuned to this particular area.. he major task in this project
is
to establish, through several distinct epidemiologic approaches, health
data to be used to test hypotheses about'relations of air pollution
exposures to morbidity and mortality rates in this region.
Because
the air quality monitoring network involves o expense to this contract,
this project affords a very cost-effective-6pportunity-forstate-of-the-art
techniques to b used n both costly areas of air pollution and health
effects
data collection.
The closelysaced networkof monitors,
* the dispersion modelingcapabilities, allow for the investigation plus
of
health impacts
of.variouspollutant
gradients in neighboring geographic '-
areas, thus minimizing -the confounding effects of social', ethnic, and
economic factors. The pollutants that are monitored in this network
include total gaseous sulfur, sulfates, total suspended particulates,
NOx, NO, ozone/oxidants,
and coefficient
of haze.
In addition to enabling
the simulation of exposure profiles betweenmonitors, the air quality
modeling, along with extensive source and background inventories, will
allowforupgrading
the quality of the monitored data as well as
.
simulating the exposure levels for about 25 additional air pollutants.
Another mportantgoal of this project is to collect and test the many
available modelsfor associatinghealth effects.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~:':
with air pollution, to
determine their predictive validity and their usefulness in the choice
and7'iting of future
energyfacilities.
exposures~~~~~~
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-2Table of Contents
page
Abstract
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Table of Contents
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2
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Summary
1.
2. Current.Status of Chestnut Ridge Research
.
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2.1 Current Status of Existing Health-Related
93
_...
. ....
Projects
2.1.1 The Adult Women
Survey
- -. - 2.1.2 The Children School Survey .,
2.1.3. The Acute Effects Prospective Survey
..
-
2.1.4
Selected
in
Women .
.
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The Acute Children Study.
2.2 Air Pollution Data Collection.
20
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-Dis ersi
of Pollutant
2.3
. Analysis
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2.4 Analyzing Air Pollution, Eealth Impacts, and
Energy Sources.
. 3. Schedulefor Completionof Research .. - .
.
Plan of Approach, Tasks, and Rationale.
.
7
·
4.1 Rationale for Third Year Activities on Health
4
...
Data
.
-
.- NewHealth Data Aquisition and Analysis
4.2
4.3 ..Air Pollution Data.Collection ...
.
4.4 Analysis of Pollutant Dispersion .
4.5 Air Pollutant, Health, and Energy I.odels .
5. References...
.
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Sumary
-1.
The ultimate usefulness of this research project will be to
provide methodologies and information that could be used to compare
various energy options from a human health impact viewpoint. The
·.
specific products of this research will include:
.(1) information about the health impacts of various
. combinations and durations or air pollutants
·. ·
or
I
concentrationsexperienced in communities,
(2) methodologies for modeling and characterization of
human. dosages of air pollutant emissions from'.
various point and background sources,
(3) ideal formats for humandosage characterization
as
well as formats for meteorologic and demographic
data collections that could be supportive of
predictive dosage modeling, and
(4)
.
.
informations on the uncertainties and validities
of-various air pollutant/humanhealth correlative
models.
.
.
It is proposed that these broad goals be approached using several
data collection and analytic activities that aredescribed
in
greater detail in the remainderof this document.'
Generally
these
activities include:
(1) collection of health, socio-ethnic, and other confounding
information from the children and adults
in the
Chestnut Ridge area of mid-western Pennsylvania, see
Figure 1-1,
. (2) collection of air pollution, meteorologic, and emissions
data from the same region, including the emissions
from the coal-fired power plants and the coal
gasification plant that are in this area,
(3) analysis and correlation of the otherwise unexplained
health impact data with the air pollution exposure
profiles, and
(4) simulation of the emissions and health impacts of various
potential future combinations of fuels, control
equipments, and advanced coal combustion equipment.
The following section presents a short review of research
completed
to date on this contract. Considerably more detail is available
from the previously completed reports C00-4968-01 September 1979
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Figure l-1
Chestnut Ridge Area.
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(Gruhl,Speizer,Maher, Samet, Schenker, 1979) and COO -4968-2
1980 (Maher,1980).
Section 3. displays the sched[ule for
of.this third year proposed research. Section 4
January
the completion
presents the
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rationale for these new tasks.
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2.
4
Current Status of Chestnut Ridge Research
The Chestnut Ridge section of mid-western Pennsylvania has been
the subject area of several air pollution and health impact studies.
potential
One of the most extensive was the study of the dispersive
of the sources within this area, and the real-time control of the
pollutant emissions of those sources based upon forecasts of the
dispersive potential of the atmosphere. Results of these applied
analysis
activities are presented in
(Ruane, et al, 1977), which
is the report of that AEC-funded project. other particularly
important
studies
in the Chestnut Ridge area include the LAPPES
air pollution dispersion studies and the Seward/Florence health
effects.studies. The principal reason for the attention to this
area is the set of very large mine-mouth coal-fired power plants
and their requisite air pollutant monitoring equipments.
1-1 .showsthe general location
Figure
of four of these large facilities-
A gasification plant has been constructed, and this location can
be seen in Figure 2-1, in relation to the other sources, monitors,
* a
and centers of the study areas. Figure 2-2 presents a more graphic
display of the layout of the study areas and monitorsprincipal cooperative efforts between the
air
One of the
pollutant analysts
andtheepidemiologists on this project has been the selection of
the numbers, sizes, and boundaries of the study areas.
The major
contributing factors to these decisions have included:
(1) collapsibility into townships, if desired,
(2) aggregatibilityinto approximate regions closest
to each of the 17 pollutant monitors,
(3) school district boundaries,
(4) gradients of air pollution,
(5) population densities,
(6) easy description of the boundaries, such as along
major highways,
(7) resolution of the interpolation procedures for
pollutant modeling, and
(8) mobility of the population.
The following section presents a very short summary of the
health studies that have been conducted as a result of previous
contract work.
There is a hopefully understandable reluctance to
present the interim results of partially completed conclusions-
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Figure 2-1'
locations of the 17 monitors and the 7 major local pollution .sources.
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-9-
2.1. Current Status of Existing Health-related Projects
To date 4 separate surveys have been carried out in the Harvard
.
subcontract of this DOE contract. These have included:
1. The adult women survey -- Sept. 1978 - Jan. 1979
r.
2. The children school survey -- Feb. 1979 - May 1979
3. The Acute effects prospective survey in selected women -Sept. 1979 - Dec. 1979
and
4. The Acute pollution episode children study -- Nov. - Dec. 1979.
The current status of each data set will be briefly described
.
2.1.1The Adult women survey.
Telephone interviews were carried out using a standardized respiratory
diseasequestionnaire
and trainedinterviewers
representing
in 5,686 women aged 20 - 74,
85% of the potentially available portion of a stratified
-
randomized sample of women from the Chestnut Ridge Region. Each woman was
assigned one of 36 specifically designated geographic zones as place of residence. These 36 sites have been modeled by the M.I.T. group on a population
weighted basis for each pollutant and are being used to assign pollution
exposure scores to individuals within each area. The basic demographic
characteristics and cigarette smokingeffects in the population have been
described.
Correlation with pollution data obtained over the same period
for which questionnaire data were obtained is currently underway.
2.1.2The children school survey,
The 1 - 6th grade children from a stratified sample representing
approximately
half
of the schools in the Chestnut Ridge region had standardized
-10r.
respiratory disease questionnairescompleted by their parents, and had hei,ght,
4.
weight, and spirometry measured in school. The 3,954 children with completed
questionnaires and pulmonary function tests were over 95% of the population
sampled.
-,· s
The questionnaire data have been entered into the computer and are
currently being assessed. The spirometry data have been held up, because we
· i··
have only now acquired sufficient funds to obtain proper digitizing equipment
which will allow us to enter sufficient data into the computer to assess
f -
flow at low lung volumes, which we believe may be a more sensitive index of
small airways responsiveness than the standard tests of forced expiratory
volume.
Wewould anticipate having both these surveys fully analyzed by the
end of the current contract period (June 14, 1980).
2.l.3The acute effects prospective survey in selected women
A sample of 224 women.was selected from the initial adult women's
cross-sectional survey for intensive follow-up over this last fall and
winter season. The women were selected on the basis of residence location
and diagnosis. Controls were matched for residence, age and smoking
Of the initial sample,45 (20%) refused to participate in the
habits.
prospective
I.
survey and 24 (10.7%) indicated
a willingness
to participate
but were unable
to commit the necessary time for study participation. 35 (15.6%) could not
be contacted or had moved from the area. 1 subject was deceased.
remaining
sample
The
of 119 was distributed as follows:
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Diagnosis
Cases
Vatched Controls
Chronic wheeze, not asthma
10
4
Asthma
14
9
Chronic phlegm
15
12
Chronic bronchitis
28
17
The womenhad spirometry measured every 2 weeks and alternately
approximatelyone-half of the women measured their peak expiratory flow in
their own house twice a day for two-week periods for up to 6 cycles.
In
addition at the end of the periodthey again completedthe standardized
questionnaire. These data currently are in their raw form, awaiting conver-sion to a data tape for analysis.
2.1.4 The acute children study.;.
Duringthe last fall season, weidentified in the "high pollution area"
of the region a group of children from the original cross-sectional survey
in whom we obtained a new set of base line pulmonary function. Wlethen
monitored the air in the region and upon notification of an. "alert condition" *
we restudied the children.
These studies were repeated for the subsequent
3 weeks.
The data from these last two studies is currently being processed.
* Becausereal time data was not readily available we used real time data from
Steubenville,
Ohio, which is approximately
70 miles west of the study area and
where an exactly comparable acutechildren study was carried out simultaneously.
.,.
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-12-
2.2 Air Pollutant Data Collection
The monitoring and collection of air pollution data in the
Chestnut Ridge area are activities carried out outside the tasks
of this.project. The owners of the power plants in this region,
see describing data in Table 2-1, have set up and maintained
pollutant monitoring grid as part
the
of their initial licensing
obligations. They have added additional monitoring capabilities,
see Table 2-2, that are however beyond the state siting requirements., f
The electric utilities participate in the.Pennsylvania Electric
- Association's (PEA) data base program, aimed at collecting all the
meteorologic and pollutant information in Pennsylvania in a common
accessible data base. Figure 2-3 shows a sample format sheet from
DeNardo & McFarland, the contractors for maintaining the PEA data
base,.-whichshows some of the monitors and pollutants that have
been collected from the Chestnut Ridge monitoring network.
Extensive additional information about this monitoring system cari
be found in the previously cited project documents.
The pollutant concentrations
in many cases are
collected
in
hourly averages over the course of each year. This chronological
data can be extremely cumbersomeand unenlightening to casual or
even intensive examinations. For this reason time-collapsed
formulations
such as the arrowhead curve exposure profiles
have :
been developed and fine-tuned as part of the previous work on this
contract
(Maher,1980).
Figure 2-4 shows the typical form of the.-
exposure profile, with the various concentrationquantiles collected
and connected for easy display and interpretability. Figure 2-5
displays a common variation of the arrowhead exposure profile, here
showing lower overall concentrationsand significant long cleansing
periods in the lower right-hand portion of the plot. Figure 2$&
contains some profiles for other pollutants collected in the
Chestnut Ridge area.'
Some of the pollutant information in support of the individual
health studies has been collected outside the PEA data base due
!' Power Plant Parameters
Table
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Monitor 0 Arrowhead Profile
1978
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r
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'·
r
r·
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'.
P.
.'
-·t
r
·
.r
r
r
_
r
...
-.-
r
-i
,-
--
-
-t-
1000
----
a
-
-
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-
10000
-29-
2.3
Analysis of Pollutant Dispersion
The recent project report (Maher,1980)is primarily aimed
at the analysis of pollution data. Thus this report will offer
a few of the findings 'in the other document, but will concentrate
mainly on newer material.
is
The end result of this analysis
to be the annual exposure profiles for several pollutants
in each
of the Chestnut Ridge area. Table 2-3 shows some of these various
exposure profiles that will be attempted. In addition there may
be successful attempts at separating the sources into separate
exposure profiles for each of the.districts. This would have the
natural advantage of providing an important tool for predicting
exposure profiles for proposed-energyfacilities at new sitesThe possibility
for estimating and' separating exposure
profiles
depends upon a knowledge of the mathematics of time-collapsed
representations. For example, if to time-collapsed exposure profiles
are perfectly correlated in the time domain then their values can be
added on a pointwise basis to determine their sum.
Scalar multiples
of time-collapsed representations are obviously the appropriate
to represent scalar multiples'of the chronologic waveforms.
way
The
(Maher,1980)document deals more extensively with the 'arrowhead'
mathematics, and hopefully this will later lead to a development of
an arrowhead dispersion formula.. This would make possible the development
of exposure profiles directly,
and more accurately, from the time-
collapsed representationsof mixing depth,
Emission
so on.
patterns
wind speed,
turbulence,
and
that come in sine or square waves, such as
powerplants,have peculiarities
intermediate
arrowhead curves, see Figures 2-17 and 2-18.
to their
emissions
ethods are being
invest-
igated for pushing these time-collapsed emissions representations,
and their correlation characterization'(to meteorological processes)
through
time-collapsed
One of the
dispersionrepresentations.
key advantages of operating in time-collapsed formats,
besides computational speed and ease of data handling, is the greater
accuracy with which interpolations can be made between monitors. The
reason for this is that even closely neighboring monitors
have
surpris-
ingly uncorrelated concentrations. That is, a peak occurs at one but
-30-
Table 2-3: Diflerent Types of Resolution That ould Be Ideal for Exposure
I'rofile Characterization (ArrowheadCurve Type Profiles)
Averaqing Times:
'
1 hour, 2 hours,
3 hours,
.
- )b
. o.
8 hours,
1 day, 2 days,
1Imonth, 3 months, 6 months, 1 year
3
,
-.
days, l eek,
.
Years:
c.
.
i
,..--
.
'
Great detail
and accuracy:
1974, 1975, 1978, 1979
*
Lesser detail and accuracy: 1968-1973, 1976, 1977
Coarser estimations: 1949-1967.
.
.
-
.
.
·..
'
~ .
.
.
.
Pollutants:
..
,-
t
·
.
:.
.
.
.
.
.
. .: .
Initial effort: SO
.-
-.
Next effort: Particulates"
Lesser detail: NO, NO2 , NO3, 0,
sulfates
Estimations: CO, trace elements, hydrocarbons
Source Separation:
.. .;
..·
..
.
.
-.
. .
..
''' '
''
4
,
4 -.Individual Coal-Fired Power Plants
...
I
-1- CO-Acceptor Gasification Plant
20 - Local Sources
6 to 10 - Background Sources
Regions
36 - Local
4
.~
- Generic Outside Situations
Probabilistic Discretization:
Maximum - 100% lower than
1.
·
.
this level
2nd highest value over course of year
I Deviation High - 84%lower than this level
Median - 50% lower than this level
1 Deviation Low- 16% lower than this level
Minimum - 0% lower than this level
Quality of Data:
Deterministic Estimate
One Geometric Deviation
-·..-..
·:
··
i-
·.
.·
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-33-
not at the same time at the other monitor.
Chronologic
interpolators
would thus grossly underestimatethe peaks. On the other hand,
interpolation in the time-collapsedformat would tend to preserve the
annual statistics displayed by neighboring monitors, which are mostly
quite similar.
The interpolation schemes compared in (Maher,1980) range from
chronologic, to time-collapsedlinear (3 nearest monitors or 3 closest
surrounding monitors), to time-collapsed nonlinear. These interpolation
schemes can be useful not only for estimatingpast exposures in
unmonitored areas, but also for estimating the exposure profiles of
prospective sites for new energy facilities, see Figure 2-19. Another
interesting conclusion presented in the (Maher,1980)document discusses
the advantages of various resolutions in the collapsing of chronologic
functions, see Table 2-4.
Given the exposure
profiles
for
the various
in the analysis is to devise 'air scores.'
years, the next step
These air scores can be
functions of any of the points in any of the years for any of the
pollutants. A first set of air scores was created in one of the
monthly joint .4IT-Harvardproject meetings. These scores relate
only to 1978 and only to SOx, and were created based upon hypotheses
about persistancies and durations of concentrations that were probably
important for human health, see Table 2-5. One of the consistent
assumptions in epidemiologicalwork -in the past has been that human
exposure can be accurately characterizedby a 24-hour maximum or an
annual
average.
Ifthis assumptionis correct then regardless of the
'air score' used the districts should not shift much in their cleanestto-dirtiest rankings. Amazingly, as shown in Table 2-6 the rankings
vary tremendously.
District 32 is one of the cleanest disticts
using
the 24-hour maximum, but one of the dirtiest annual averages. And
district 36 is vice versa. District 1 is dirty on the annual basis,
medium for mid-term and clean for short-term. District 32 is dirty
for the annual, clean for the midrange and dirty for the short-term.
Cleansing periods are sometimes consistent with acute
levels,
in terms
of rankings, but they are sometimesopposite. The conclusion here is
that the assumptionof 'air score' will drastically affect the rank
of regions. In the follow-on work we intend to carefully examine
-34-
-A
I ,
.
.
- -
-,7
-
''
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.
I
t
-- -
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X
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z·
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. .
O
-L
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.-
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4 ,
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I
S
,·~~~~~~~~
s-o
S.,
.,
-
-S."-
.r~~~~~~~~~~~~S
'I
~~~ ~ ~ ~ ~ ~ ~ ~~~.
::
.
o existing monitoring data
-- district centers
·
.-
:-
[ point source
-.:. ._
Figure2-t9ior each pollutant, backgroundlevels are available at'a set
of area monitors. Interpolation will probably be linear within triangles
[defined as triangles with minimumclosure(largest' edge) that encloses
or comes closest to enclosing the center to be estimated]. Extrapolation
will probably be optional, among options of linear, flat, or half-linear
extrapolations from nearest triangle.
s
.
.
,
...
- 7
.
-35.
Table 2-4
Running versus Sparse Uniform Averaging
1975 SOx Concentrations - Monitor A
''
r
.* .Averaging
v
Time (Hours)
Averaging
Averages
Scheme
Calculated
-
e
·r,
*
-
-
24331'
Running92
Maximu
Percent
TI
Value (ppb')
_I
^
.
_
215
93
7287
196
'-
DifferenceI
60
':
.. 7
J I
:
8
~
.....
c
I
l[
I l
I
.
-
]l
[gII
_
_
243
Running
....
.
t ...
. ..
-
.
__
_
206
90
7272
130
I
w
31
..
'. e
· .
;:
I .
.
-
II
I
I
#
|
-
.
24
122 .
Running
100
75
7180
81
6
"
182
168
152
Running
·7283
45
48
.
.
52
47
7596
Running
..
.
.6
--
-.-
I
...
730
.
37 39
-.
.e
. ....
.
.
..
'_,
.".
i
,
8760
,
- -
,
1
5327
Running
,
.
i
0-
27
27
...
_
_
. .. , ,
Notes
1. Sparse averaging scheme- these.averages are distributed
throughout the year.
2.
8760
running averages are ideally calculated.
uniformly.
.
--
-
Table
Index
.
2 -5Trial
VL,
Pollution Indexes for SOx
Names
General
Description
-Sum of 99, 84-, 50, 16
and O
percentiles for lhr, 3hr,
8hr, 1 day, 3 day, 1 week',
1 mo, 3 mno,and
year
2
Short-rerm
Acute
-Sum of 99 and 8- percentiles
fcrlhr,3hr, 8hr, 24hr,
3-hort-Term
for lhr, 3hr, 8r- and 2hr
-Sum of 99,
84, and 50 percentiles
-.
High
of 99 percentile to . hr
4I
3Hr Standard
-Ratio
5--
241trStandard
-Ratio of 99 percentile- to 24bthreshold standard
-
6
thres hold standard
.
:
' Annual Standar I -Ratio of 99 percentile to lyr
thresholdIstandard .
7
8
.:Z3hort-Term
Cleansing
Long-Term-
Cle ansing
-Sum of 16 and O percentiles
lhr, 3hr, 8hr, and 24hr
times
for
averaging
-S1umof 16 and 0 percentiles for.
3 day, week, 1 mo, 3 ma
averaging times
* Note in all-cases 99 percentile means second highest value,
as written into the threshold standards
--
·,
Ii
-- -
-·
Table 2-6Comparison of the Rai'-ingsof Each District for the
Various Different Sample Indexes (lowest ranks are cleanest)
Rank.for index
5
6
7
8
6
11
6
7. 17
11 . 5 22
31
27
22-
19
14
12
20
18
18
16- 28
22
4
7
16
8
8
10
15
29
13
34
7
32
7. 6
32 32
3 1
13
29
25
33
23
20
20
16
29
18 16
9
28
15
na
na
1
2
2
17
15
35
District
1
4
5
6
8.
8
2
9
.9 4
10
11
. 12
.4
"7? 5
5 1
5'
4
3
4
4 O10 20
25
8
7
16 18
2
6 12 13
4
1
3
5
2
na
7
29
17. 10
1
na'
na na
13
..
29
28
28
28
25
8
15
26
.14
27
24- 24
25
20
28
18
14
.15
3
2
2
3
5
2
2
3
16
10
12
13
14
12
6
11
21
17
18
14 15
15
11
14
23
7
5
11
6
13
10
12
9
9
13
9
2
17
10
8
6
3
1
12
35
33
na
14 14 15.
33 33 33
31 31 31
na na na
8
35
33
na
9 12
314 34
19 28
na na
8
4
22
9
20
10
20
12
19
11
26
1
32
-2.
18
26
16
26
16
21
18
34
32
35
13
28
27
27
27
24
24
31
19
21
30
17
19
30
17
19
30
17
20
27 14
31 11 27
19 21 32
25
22
22
23
23
26
24
30.
30
21 21 22 16 26
25 25 26 23 18 21 17
23 23 '24 21 15 19 11
6 9
5
29 29 29 30
19
20
21
22
23
24
25
26
27
28.
29
30
32
.33
3"
35
36
j.4
3
4
30
1
35
27
na
24
13
32
35
34
10
24
26 23
30 31
31
33
22
25
33
.·
-38-
the types of 'air scores' that most closely correlate with
'health
scores.'
process,
There is of course some danger in this selection
given enough 'air scores' there are bound to be near perfect fits
to 'health scores.' We will attempt to use data splitting and
hypothesis testing techniques to avoid invalid associations.
The air scores themselves have been normalized and 7 of the
8 air
scores
are printed on the regional map in Figure 2-20.
The
interpolation scheme used here was the linear, 3 nearest monitor,
technique. A comparison of these scores and rankings is underway
based on the other two important interpolation techniques.
4
I
I
.0
_j
0
0
C>
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8
i.
na
na
/!
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f _. .56
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89 t 2,'j~'
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5- .93
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-
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a
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Figure
`-
Normalized Air Scores Using 7 Different
I
-
''
''
_
Indexes
---- . --44_o9o
.
-40-
2.4 Analyzing Air Pollution, Health Impacts and Energy Sources
As discussed in (Gruhl, et al, 1979) the 'health scores'
will be held on disk at the MIT Information Processing Center.
These 'health scores' will be a priori specifications of the manner
in which the health data to be collected and weighted will represent
a particular
then
health effect.
Associated with each 'case'therewill
be a 'health score' or set of scores and a residence history.
Given the formula for the 'air score', using the residence history
for informationabout the relevant districts in the past years,
an 'air
score'
can be developedfor each individual. The TROLL
statistical
andgraphics
packages
are intended for use in modeling
relationships between the health and air scores. Some of the
modeling has been accomplished, and in fact there are examples in
(Gruhl, et al, 1979)
,
Additional progress has been made on the AEGIS, Alternative
Electric Generation Impact Simulator. Table 2-7 shows a crude
population density situation that has been set up for AEGIS.
Tables 2-8 through 2-12 show performances for a hypothetical
coal plant.
Additional detail on'the meteorbogic dispersion
for the Chestnut Ridge area, additional health models, and refined
plant data are all on the agenda for the next six month time
period.
-41-
Table
2-7
Population Density Miodel,
AEGIS Example
r - Radial Distance From
Population within
Power'PlantSite (k)
(k"
.r
.
4
..4
8 . .' - '-'
'
:'' -
1,125
';.
.
.
-9,750
16
24-
(km)of Plant Site
.
.
..
36
49,500
'
72,000
.,
135,000
48 .
260,000
64
405,000
80
800,000
S
p
I
-42-
-P
-
I----I
I
- - -
ASSUMPTIONS
-----------
i
sZE<iiE>
1200.0
YEAR .COMPL
1980.0
TYPE
IFUEL
;
HIDWEST PENN BITUi COAL
'·PRECL TYPE
NONE
GENERATYFE
.
COAL DIRECT
DES CAPAt
-FAC<>
: .,STORAGE
1
-I
72,000
'
CONV COMBUST
CAP<MWH>
SURB.ENT TYPE
ABATE TYPESt
...
PARTTYPE
--
..
NONE
'-
-- . 96% ELECTRUSTATfC
PRECIP
SCRUB TYPE
STACK HT<M>
-- '
NONE
'
MET STE
TYPE . ·
AEROCHEi
MODELS
.~-
256,00
STANDARD DILUTION SCALE
- SULFATION- TYPE ''NO
SNOG TYPE
DENSITY PATTERN.
SCALED BY
*HEALTH/IHPACTS
·-
SULFATION
NO IC NOX OR
'OXD
COMBIN
HOMER CITY IN 1980 ESTi
1 0000
·CHEh HEALTH HDTJ -LAH-LIN
RAD HEALTH
OD
AD
-f
NONE
POLLUTION INDEX
NODEL
NONE
,-t
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3.
Schedule for Completion of Research
The tasks represented in the proposed research are listed and
numbered in table 4-1.
The lead institutions for each of these
tasks are also mentioned, although many of the tasks will involve
considerable interaction.
Figure 4-1 shows the proposed time
schedule and milestones for the proposed third year.
-48Table
-1
Tasks for the Proposed Third Year
I-
__
_
1.
Continued analysis of previous health data
Harvard/MIT
2.
New health data collection - Children's spirometry
Harvard
3.
New health data collection - Children's surveillance
Harvard
4.
Analysis of health data
Harvard/MIT
5.
Atmospheric and emission data collection
MIT
6.
Advanced interpolationschemes and uncertainty measures MIT,
7.
Pollution/health analysis in TROLL
Harvard/MIT
8.
Uncertainty in exposure characterization
MIT
9.
10.
11.
Dispersion modeling in time-collapsed format
MIT
Comparison of different dispersion formulas
MIT
Energy/health model assessments
MIT/Harvard
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4.
Plan of Approach, Tasks, and Rationale
The Chestnut Ridge region emissions sources and dispersion
patterns
are well characterized, and extensive monitoring equipment are available.
This presents the opportunity to identify health impacts in a region
where air pollution exposures of the general population
well known.
are relatively
Dose-response relationshipsmay become apparent and with
modeling could be used to direct further health research, direct
searches and choices of new technologies, and direct energy policy
decisions.
The general plan for approaching this project has been to begin
with a listing of the pollutant and non-pollutanthealth effects
variables.
From this list strategies have been devised for collecting
the appropriate air pollution and demographic data.
In addition,
specific or systemic targets have been identified, and strategies
devised for collecting information about these impacts.
Once all
of the collection of the air pollutant variables and the health
and confounding variables has been completed then a search will be
conducted to find particular functional combinations of pollutants
that are both reasonable and validly correlated with the otherwise
unexplained health impacts that have been observed.
-514.1Rationale
for 3rd year Activities on Health Data.
To complete the above-mentioned studies and bring the data analyses to
and
their full potential will require a considerable period of programming
data analysis time. We recognized further that many of these analyses will
raise additional questions for which the Chestnut Ridge region may be a useful
However, if we were to wait until the
resource in which to pursue answers.
completion of the analyses before proposing some of these questions we might
miss some unique opportunities for further data collection.
For example,
even without knowing the outcome of the Acute Children Study, we can say
categorically that it would be worth repeating the study just to be sure that
whatever the results may be they are not due to a possible intercurrent
"miniepidemic" of viral illness.
These kinds of "epidemics" are common
among children, but the chance of/a similar episode occurring two years in
a row is relatively small, and one would have more confidence
in the results
if they were consistent over 2 years.
Similarly because these children have been identified, and the pollution
monitoring is on-going, additional study of those children who might be considered most susceptible
(i.e. those with historically reported respiratory
disease) would provide :.isef.ul
data.
.
.
Therefore, in spite of there being sufficient existing data already
available which we believe would justify extension of the contract we are
proposing additional data collection to be carried out over the next year
while we continue to examine the data we have collected thus far.
4.2 New Data Acquisition
Acute Effects of Air Pollution in Children
Background:
In several recent studies of acute air pollution
episodes,
investi-
gators serially monitored the pulmonary function of cohorts of children.
Stebbings initiated spirometry at the time of the 1975 Pittsburgh
air pollution
-52-
episode and found that the forced vital capacity increased on successive
as pollution levels declined (1).
days
He interpreted these data as demonstrating
a reversible effect of pollution on small airways function.
A similar tech-
nique was utilized by the investigators in the six cities study in Steubenville,
Ohio with similar findings (2).
such studies.
Children appear to be ideal candidates
They can be feasibly studied in the school setting.
no occupational respiratory hazard and most are non-smokers.
for
There
is
Additionally,
they may be more likely than adults to have demonstrable changes in pulmonary
function after pollution exposure.
Because the diameter of children's small
airways is less than that of adults', mucosal injury with swelling results in
a proportionally greater loss of airways area in children.
We propose to conduct a study, modeled after those above, to detect
acute changes in pulmonary function in children.
from September through December, 1979.
Wie conducted a similar study
The data are not yet analyzed, but
follow-up was over 95% complete in the 120 children tested.
Methods:
Air pollution monitoring:
The on-line monitoring of air pollutants
Chestnut Ridge region will provide notification of levels.
in the
We will initiate
testing for levels exceeding Federal standard values for TSP and/or SO 2 and
a meteorological pattern which predicts stagnant air.
Pulmonary function testing:
reside in the area.
Personnel trained to perform spirometry
After obtaining appropriate consent, baseline
currently
spirometric
testing and respiratory questionnaires will be obtained for approximately
children who attend a single school.
100
The chosen school will be in the Seward-
New Florence area which we anticipate will experience the highest pollution
levels.
-53-
Upon notification of an alert, the field personnel will begin spirometric
testing of the children at the school.
The testing will be carried out during
the duration of the episode and then weekly for 3 - 4 weeks.
We were able to
execute this technique successfully in the fall of 1979.
Additionally, we plan follow-up and testing in the home of non-attendees
during the episode.
While this may only be a few subjects, the recent investi-
gation in Steubenville suggested that the pulmonary function effects in these
sick children may be greater than in their healthy peers.
Analysis:
If pollution affects the pulmonary function of the study children,
we anticipate a decline from baseline followed by improvement.
calculate the serial changes in each spirometric parameter
FEV0.75
FEV1 , FV/FVC,
the study period.
Wle will thus
(including FVC,
V5 0 , V25) and determine the pattern of change during
The questionnaire data will be used to identify character-
istics of susceptible and non-susceptible subgroups.
Children's Surveillance Study
Introduction:
In the U.S., most people spend most of the day indoors.
Their actual time of potential exposure to outdoor, ambient air pollutant
levels is thus far less than their exposure to indoor pollutant
levels.
The
latter are'jointly determined by outdoor concentrations, rate of air turnover,
and indoor sources.
At this time, identified domestic pollutant
sources
include cigarette smoking, a source of particulates, and gas cooking stoves,
a source of NO2
.
Both exposures have been associated in children with small
decreases in ventilatory function and increases in respiratory morbidity.
The implications for health of the patterns of interaction between
and outdoor pollution are currently unclear.
indoor
(3,4)
-54-
The purpose of this proposed study of children in the Chestnut Ridge
region is to assess the relationship between several respiratory parameters
and ambient air pollution in children selected from homes with differing
domestic pollutant sources. Additionally,we plan to select subjects with
a varying range of airways reactivity or disease:
children who reported
persistent wheezing or were physician diagnosed asthmatics on the 1979
questionnaire, children who reported chronic cough or phlegm production
on
the 1979 questionnaire, and asymptomatic children.
Methods: The cohort for this study will be selected from the 3954 children
on whom completed questionnaires and spirograms were obtained in the crosssectional classroom survey in 1979. To select
children in the geographic
area with the highest air pollution levels and for practical
considerations
in
reaching subjects in their homes, only children living in the lower half of
Indiana county will be considered. This will reduce the sample to approximately 2000 children. This population will be further reduced to 1700 by
selection of only children 8 through 12 years old.
A preliminary frequency distribution of 1000 children surveyed
in the cross-sectional classroom survey has yielded the following
frequencies.
The projected number available is based on a population size of 1700.
Projected number
available
Symptomor diagnosis
Rate
Persistent wheeze
6.9%
117
Asthma, M.D. diagnosed
3.1%
53
Chronic cough
7.0%
119
Chronic phlegm
5.0%
85
in 1979
-55-
While these diagnostic categories are not mutually exclusive,
there
appears to be an adequate number of subjects for 60 children in each of two
categories: 1) persistent wheeze or asthma, and 2) chronic cough or phlegm.
These two categories correspond to the two major categories of adult chronic
lung disease and represent subjects most probable to show increased sensitivity
to air pollution.
An equal number of control children matched for age, sex,
area of residence and indoor pollution factors
(number of smoking parents,
.gas cooking stoves) would also be selected.
Parents of all potential subjects would be contacted in mid-summer
by
a letter briefly describing the proposed study. This would be followed by
a telephone call requesting permission for a home visit.
At the home visit,
pre-mailed respiratory questionnaires will be completed and spirometry
will
be performed on Stead-Wellsportable spirometers by all family members ages
6 through 75.
The field worker will also assess relevant aspects of the
indoor environment such as type of fuel used for cooking and use of wood
or coal stoves for heat.
All subjects will have symptom diaries left for completion over the
succeeding six months
(September through February).
These diaries will focus
on symptoms of lower respiratory tract infection and airway obstruction.
Criteria for lower respiratory tract infection will be those developed
for the
Tecumseh studies (5,6) and used in the East Boston children's lung studies
(7).
(See sample diary attached.)
In addition, one third of the families will have Mini-Wright Peak flow
(MWPF) meters left to be used by the child twice-daily for a two month period.
The MWPF meters will be collected (by home visit or mail) and rotated to the
other two-thirds of the cohort for two months each. After the six-month
period each child and family member would have repeat spirometry
Wills portable spirometer.
on a Stead-
-56-
At two-week intervals each of the families will be contacted by
telephone to ascertain symptoms not recorded on the diaries and to determine
if problems exist in using and recording results with the MWPF meters.
Telephone monitoring has been found to be necessary in similar studies on
children (7).
The children's surveillance study will thus have the following
design:
-57-
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4.
-58-
Prior to the actual study a pilot evaluation of the use of Mini-Wright
flow meters in children will be undertaken.
This will consist of a comparison
of measurements made at the same time on different aged children with an Mini-
Wright peak flow meter, repeated on two occasions within the same week and a
Stead-Wills water-filled spirometer. A pilot study would provide data on
reproducibilityof the Mini-Wright flow meters in different age children and
appropriate instructions
pective
adult
women's
for the use of these meters in children. The prosstudy has shown that the Mini-Wrights are able to with-
stand daily repeated measurements in the home. Published studies have been
done demonstrationg their
in the range of peak flows that
accuracy
will
be
seen in children over the age of eight (8).
Analysis:
The analyses will focus on demonstrating correlation between changes in
Because the pollutant
peak expiratory flow rates (PEFR) and pollutant levels.
data are time-series observations,the successive observations may be correlated (auto-correlation) and time series techniques, rather than standard
statistical methods, may be needed.
The initial step in the data analysis will
thus be an assessment of the extent of autocorrelation
in the pollution
measurements.Subsequent analyses will be determined by the results.
If serious auto-correlation is absent, standard techniques
will be used.
For each day, we will have 6 measurements
of correlation
each for 60 children.
One approach is to calculate the individual correlation coefficients
them utilizing the Fisher
statistic.
and combine
An alternative is to calculate the
interclass correlation of pollutant with PEFT measurements (as with familial
data).
Procedures are available for significance testing
(9).
If the pollution data demonstrate auto-correlation,we will choose appropriate time series techniques with assistance from our statistician.
-59-
4.3
Air Pollution Data Collection
The principal task in the area of further air pollution data
collection would be the continued compilation of the several air
pollutant concentrations that are available from the various
monitors.
These efforts would be primarily aimed at the completion
of the 1979 data base and the collection of 1980 and 1981 data.
In addition there would be short term tasks involving the collection
of real-time pollutant information, in direct support of
the
specific health data collection projects.
Previous research on this contract has shown the possible
potential of schemes for the use of time-collapsed data, such as
arrowhead curves for meteorological data, in air pollutant
dispersion formulas.
There would be additional data collection
requirements to support this modeling task, namely the assemblage
of chronological and time-collapsed characterizations of:
(1) air pollutant concentrations,
(2) wind speeds,
(3) wind directions,
(4) mixing depths,
(5) stability classes,
(6) local pollutant emissions, and
(7) background pollutant levels.
Enough of this data would be collected to provide sets of data for
empirical calibration of the dispersion models and sets of data for
validation.
-60-
4.4
Analysis of Pollutant Dispersion
The principal analysis tasks proposed for the continuation
of this project would be the time-collapsing of the new pollutant
data and the interpolation between the 17 monitors
so as to
provide continued pollutant profiles for each of the 36 districts.
In addition, several interesting data-splitting validation
efforts
were operated on the interpolation schemes in the previous
contract
work and it would be well worthwhile to continue these tasks so as
to be able to quantitativelyreflect the degree of validity of the
more recent pollutant exposure profile characterizations. These
validation efforts involve prediction of an exposure profile that
has been set aside for validation purposes. Past validation
exercises have shown that chronological interpolation
schemes tend
to be too conservative, with direct interpolation of profiles
being superior.
There is still room for analytic
improvement
of these interpolation schemes, and perhaps some new insights
will come from the following task.
Previous research on this contract has suggested that there
may well be an analytic foundation for simulating time-collapsed
air pollutant concentrationsusing time-collapsed input data and
formulas.
time-collapsed analogues to the air pollutant dispersion
The key to the solution of this problem lies in as yet undiscovered
techniques for the characterization of the correlations
various input time series.
It is essential to know,
of the
for example,
whether or not certain stability classes are correlated with wind
directions.
It seems beyond hope to expect enough randomness
these input data to allow for general uncorrelated
between
assumptions,
thus
correlation characterizationand measurement would be required.
Time-collapsingthe wind dir. data presents some unique problems.
It might be possible to collect all other input data in arrowhead
curves for just those times that are in each of the 16 wind directions.
This, however, would necessitate 16 times the number of time-collapsed
inputs, and would additionally raise the need for a peculiar
data format that does not currently exist in any pollution
It would thus appear that a more direct analytic approach
characterization of wind directions should be sought.
type of
data bases.
to the
-61-
Another analytic exercise that would be included in the
proposed third year would be the characterization of uncertainty
in the time-collapsed concentration profiles.
An obvious format
for characterization of uncertainty would involve the estimation
and collection of a separate arrowhead curve of deviations.
It
would be desirable to seek a more analytically usable and accurate
characterization of uncertainty.
Uncertainty measures could be developed to reflect the errors
involved in the interpolation schemes used and to reflect the
errors in the measurement or estimation of the input data.
Another
source of error in this modeling process is in the dispersion
modeling itself.
It would be desirable to make comparisons of
several chronologic and time-collapsed dispersion
formulas to
gather estimates of the uncertainties associated with their use.
Such uncertainties could then be appropriately introduced
into
the other uncertainties associated with the use of the exposure
profiles.
4,
-62-
4.5
Air Pollutant, Health Impact, and Energy Model Studies
There are two tasks that would be included in this section of
the proposed project continuation,and they are identical to the
tasks that are currently conducted in this category of research:
(1) air pollution and health impact data-would be collected
in a statistical and graphics program so as to
facilitate the hypothesis testing and validation
procedures associated with the pollutant/health
modeling, and
(2) site-specificfacility simulation would continue to
be supported and updated as a result of the proposed
project continuation.
-63-
5.
1.
References
Stebbings JH, Jr., Fogleman D.
Identifying a Susceptible Subgroup:
Effects of the Pittsburgh Air Pollution Episode Upon Schoolchildren.
Am J Epidemiol 1979; 110(6):27-40.
2.
Samet JM, Speizer FE, Bishop Y, Ferris BG.
The Relationship Between
Air Pollution and Emergency Room Visits in an Industrial Community.
(Submitted for publication)
3.
Tager IB, Weiss ST, Rosner B, Speizer F.
Effect of Parental Cigarette
Smoking on the Pulmonary Function of Children.
Am J Epidemiol 1979;
110(1):15-26.
4.
Speizer FE, Ferris B Jr., Bishop YMM, Spengler J.
Indoor N02 Exposure: Preliminary Results.
Oxides and Their Effects on Health.
Health Effects of
In: Lee SD, ed., Nitrogen
Ann Arbor, Michigan: Ann Arbor
Science Publishers, 1980: 343-359.
5.
Monto AS, et al.
The Tecumseh Study of Respiratory Illness.
Am J
Epidemiol 1971; 94:269.
6.
Monto AS, et al.
Acute Respiratory Illness in an American Community.
JAMA 1974; 227:164.
7.
Tager IB, Speizer FE.
Surveillance Techniques for Respiratory Illness.
Arch Environ Health 1976; 31:25-28.
8. Wright BM. A Miniature Wright peak-flow meter.
9.
Rosner B, Donner A, Hennekens CH.
Correlations from Familial Data.
Br Med J 1978; 2:1627-1628.
Significance Testing of Interclass
Presented at the Annual Meeting
of the American Statistical Association, Washington, D.C., August 1979.
a,
-64-
;-64
10. Ruane, M.P. et al, 1977. The Chestnut Ridge SCS, plus Appendixes.
ERDAReport
C0O-2428-5, MIT Energy Lab, Cambridge MA, March.
11. Gruhl,J., F. Speizer,
W. Maher, J. Samet, and M. Schenker,
1979.
Evaluation of Health Effects of Air Pollution in the Chestnut
Ridge Area - Initial
Data Collection and Methodological
Developments,.
DOEReport C00-4968-1, October (forthcoming)
12. Maher, W.F., 1980.
CharacterizingAir Pollution for Health Impact
and Energy Policy Evaluations, forthcoming DOE Report COO-4968-2,
M.I.T. M.S. Thesis Department of Electrical
185pp.,
Engineering
and Computer Science.
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