EPA-600/3-78-038
April 1978
Ecological Research Series
TIME SERIES EXPERIMENTS FOR STUDYING
PLANT GROWTH RESPONSE TO POLLUTION
Environmental Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Corvallis, Oregon 97330
-------
RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The nine series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
6. Scientific and Technical Assessment Reports (STAR)
7. Interagency Energy-Environment Research and Development
8. "Special" Reports
9. Miscellaneous Reports
This report has been assigned to the ECOLOGICAL RESEARCH series. This series
describes research on the effects of pollution on humans, plant and animal spe-
cies, and materials. Problems are assessed for their long- and short-term influ-
ences. Investigations include formation, transport, and pathway studies to deter-
mine the fate of pollutants and their effects. This work provides the technical basis
for setting standards to minimize undesirable changes in living organisms in the
aquatic, terrestrial, and atmospheric environments.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
-------
EPA-600/3-78-038
April 1978
TIME SERIES EXPERIMENTS FOR STUDYING PLANT
GROWTH RESPONSE TO POLLUTION
by
Larry Male and John VanSickle
Ecosystem Modeling and Analysis Branch
and
Ray Wilhour
Terrestrial Ecosystem Branch
Corvallis Environmental Research Laboratory
Con/all is, Oregon 97330
CORVALLIS ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CORVALLIS, OREGON 97330
-------
DISCLAIMER
This report has been reviewed by the Corvallis Environmental Research
Laboratory, U.S. Environmental Protection Agency, and approved for publi-
cation. Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
-------
FOREWORD
Effective regulatory and enforcement actions by the Environmental
Protection Agency would be virtually impossible without sound scientific data
on pollutants and their impact on environmental stability and human health.
Responsibility for building this data base has been assigned to EPA's Office
of Research and Development and its 15 major field installations, one of
which is the Corvallis Environmental Research Laboratory (CERL).
The primary mission of the Corvallis Laboratory is research on the
effects of environmental pollutants on terrestrial, freshwater, and marine
ecosystems; the behavior, effects and control of pollutants in lake systems;
and the development of predictive models on the movement of pollutants in the
biosphere.
This report details research aimed at establishing functional relation-
ships between agricultural crop losses and natural variations in S02 air
pollution.
A.F. Bartsch
Director, CERL
-------
ABSTRACT
This research program was initiated with the overall objective of defin-
ing experiments which could predict the yield losses of crops grown under
naturally varying sulfur dioxide concentrations.
A model for simulating realistic fluctuations in S02 air pollution was
developed. This model was used to design experiments in field growth cham-
bers for the purpose of establishing the functional and probabilistic rela-
tionship between yield loss and median S02 concentration. The stochastic
experiments are offered as a viable alternative to traditional long-term
fixed concentration experiments.
This report covers a period from April 1974 to September 1977 and was
completed as of February 1978.
IV
-------
CONTENTS
Foreword iii
Abstract iv
Figures vi
1. Introduction 1
2. General Characteristics of Observed Pollutant Levels 2
3. Experimental time series models 6
Stationary Stochastic Models 6
Periodic Models 10
4. Discussion 15
References 16
-------
FIGURES
Number Page
1 Daily treatment schedules for S02 ~~ applied continuously
through growing season 11
2 Periodic S02 treatment schedule — applied continuously
during growing season 12
3 Five-day sample from concentration time series. Series
generated by stochastic process with diurnal periodicity
in hourly geometric mean concentration 14
-------
SECTION 1
INTRODUCTION
A fundamental problem in air pollution-plant effects research is to
predict the reduction in yield of crops grown in areas of significant S02
stress. Within such areas the pollutant concentration at any point in time
is only probabilistically predictable, i.e., we can only predict the proba-
bility that the concentration will fall within a specified interval. Assuming
the S02 source strength in an area does not vary over time, then the fluctua-
tions in pollutant concentration are caused primarily by random variations in
wind speed. The time history of pollutant concentrations within an area is
called a time series. Pollutant time series for different growing seasons
will appear to be considerable different but they will usually be probabilis-
tically the same. Because of this probabilistical regularity, a model with a
maximum of two or three parameters can simulate certain pollutant time series.
It is our purpose in this paper to motivate the use of a time series model
for experimentally studying the long term effect on plant growth of stochas-
tically (probabilistically) varying pollutant concentrations.
As a first step we might ask "What relationship might we expect between
crop yield and a pollutant time series?" We hypothesize that plant growth
rate will fluctuate in correspondence with the pollutant time series? Assume
for the moment that growth rate is depressed by the pollutant. Plant yield
would then be the integral, over the growing season, of the resulting growth
rate time series. Since pollutant time series within different growing
seasons are probabilistically the same, we would expect growth rate time
series to be probabilistically the same. Hence we would expect yield in
different growing seasons to be probabilistically the same. Ideally, know-
ledge of the pollutant probability distribution would allow one to predict,
for any growing season, the probability that yield would be within any
specified range.
The above model assumes that there is a simple relationship between
instantaneous growth rate and pollutant concentration, but establishing this
relationship is not trivial. It is not practical to continuously monitor
growth rate in order to study its relationship with randomly varying pollu-
tant concentration. Even if it were possible to monitor growth rate it would
be an exceedingly complex task to correlate it with observed fluctuations in
-------
pollutant concentration. In order to achieve a desirable precision, a model
would have to consider response time lags, recovery rate at different concen-
trations, adaptation, and of course variation in other environmental variables
which affect growth rate. Such a model may eventually prove to be our best
predictive tool but we must wait for further technological development.
If we cannot construct a model which will predict growth rate as a func-
tion of pollutant concentration and other environmental variables then we
might ask whether there exists a single summary statistic for a pollutant
time series which would correlate well with plant yield. We cannot answer
this question here, but we can suggest an experimental approach which can be
used to define the relationship between long term growth response and summary
statistics for a pollutant time series.
One experimental technique which has been used to estimate the effect of
S02 on crop yield is the "long-term fixed concentration experiment." Plants
are subjected to a fixed S02 concentration for an entire growing season. A
variation of this experiment is the use of intermittent exposures (say,
exposure every other week). The critical assumption underlying this approach
is that reductions in yield would be the same as if the pollutant concentra-
tion had varied stochastically about an average equal to the experimentally
fixed concentration. Since this type of experiment violates our hypotheses
concerning the interactions of growth rate and pollutant concentration we
suggest abandoning it in favor of experiments which expose plants to a realis-
tic pollutant concentration schedule over the entire growing season at near-
ambient environmental conditions. The final yield of the plants would then
be correlated with summary statistics which characterize the entire pollutant
regime to which the plants were exposed.
This strategy has led to at least three different experimental methods
which appear promising. Oshima et al_. (1976) conducted growth experiments
based on environmental gradient analysis. In this design, crops are grown in
identical field plots maintained at different distances from a strong urban
oxidant source, thus providing a natural gradient in average concentration
and dose. A second method is the zonal air pollution system (ZAPS), des-
cribed by Lee et aj. (1975). The system employs grids of pipes that dispense
gaseous pollutants on field plots. Source strength is varied over several
plots to create a gradient of average concentrations, and local variations in
wind speed produce realistic short-term fluctuations in concentration.
A third approach, described here, uses closed field chambers to control
the S02 regime. Pollutant concentrations in the chambers are changed hourly,
thus plants are exposed to an arbitrary, rapidly changing time series of
pollutant concentrations. This flexibility provides a distinct advantage
over the natural gradient and ZAPS methods which depend on local meteorolo-
gical conditions for concentration variability.
These approaches are sensible only if one can quantitatively summarize
long, continuously-changing time series of pollutant concentrations. For-
tunately, traditional statistical analyses (Larsen, 1971) coupled with advan-
ces in time series analysis techniques (Box and Jenkins, 1976; Jenkins and
Watts, 1968) have begun to satisfy this need.
-------
When experimental pollutant concentrations are controlled on an hour-by-
hour basis, as in the closed field chambers, one must also be able to repro-
duce realistic time series of pollutants. This paper describes models which
will generate such time series and discusses the design of plant growth
studies based on the models.
The following section is a brief, qualitative description of the pollu-
tant time series observed in the ambient environment.
The final sections provide mathematical descriptions of observed time
series and present models which simulate those observations. We also discuss
experimental design problems which arise when the models are used to generate
pollutant exposure schedules.
-------
SECTION 2
GENERAL CHARACTERISTICS OF OBSERVED POLLUTANT LEVELS
The standard method of summarizing concentration levels observed over a
long time interval is to plot their relative frequencies of occurrence.
Typically the data consist of low concentrations with infrequent occurrences
of high concentrations. Thus, the frequency curves can usually be described
by a lognormal distribution, over a broad range of pollutant types and source
configurations (Larsen, 1971; Lee et al_. 1975; Gifford, 1974; and Bencala and
Seinfeld, 1976). Gifford (1974) and Knox and Pollack (1974) provide a
simple theoretical explanation of the observed lognormality. They argue that
a pollutant from several sources, as seen in urban areas, tends to have
concentration levels which are highly correlated over short time intervals,
and they show how this leads to a lognormal frequency curve.
If concentrations are distributed lognormally, they can be summarized by
a median and shape parameter as described in Section 3. In assessing the
variable response of vegetation to ambient pollution concentration distribu-
tions, it is sufficient to correlate plant response with two parameters of
the distribution, the median concentration and the shape parameter.
A common test for lognormality is to see whether the empirical distri-
bution function plots as a straight line on logarithmic-probability paper.
The Air Quality Criteria for Sulfur Oxides (1970) shows the empirical distri-
bution functions for S02 concentration for six large cities across the United
States (Continuous Air Monitoring Project [CAMP], 1962-1967, one-hour averag-
ing time). These distributions, plotted on logarithmic-probability paper,
range from slightly curved to approximately straight lines. A general impres-
sion is that they could be approximately modeled as a family of parallel
straight lines. This is equivalent to saying that the concentrations are
lognormally distributed with a common shape parameter but with varying me-
dians.
A possible explanation for this phenomenon is that the shape parameter
is determined by meteorological factors independently of source strength of
the pollutant while the median is determined primarily by source strength.
This explanation is supported empirically by studies with the Zonal Air
Pollution Study (Lee et al_. , 1975).
-------
Another set of characteristics emerges when pollutant concentrations are
considered as a time series. They have regular fluctuations which can often
be correlated with human activity patterns (i.e., source strength) as well as
periodic weather variables (Raynor et ajL , 1974; Holzworth, 1973; Munn and
Katz, 1959). Most concentration time series show 24-hour cycles, but the
timing and shape of the diurnal patterns depend upon pollutant type, geo-
graphic location, and season. Yearly cycles as well usually appear in pollu-
tion data, but for most cases the changes are too slow to be observed over
the length of a growing season.
Concentration series are also highly correlated over short time inter-
vals. Often one can assume the time series is stationary over a growing
season—that is, the median and shape parameter of the concentration distri-
bution stay constant. In this case, the degree of serial correlation, or
autocorrelation, exhibited by a series can usually be specified by one para-
meter. The parameters can be correlated to the response of plants when they
are exposed to rapidly fluctuating pollutant levels (low autocorrelation) as
opposed to slowly-changing levels (high autocorrelation).
These observations provide a basis for simple dynamic models of pollutant
concentrations. The models are used to generate exposure schedules for plant
growth experiments in closed field exposure chambers. Several models of
different degrees of realism and complexity are being explored in an attempt
to identify those features of long-term, continuously-varying pollutant
concentrations to which plants are most sensitive. The following sections
describe observed concentration time series in more detail and present the
models used.
-------
SECTION 3
EXPERIMENTAL TIME SERIES MODELS
To perform a controlled time series experiment, a model must be used to
generate exposure schedules. Varying levels of realism can be incorporated
into this model by making it more or less complex. The first step in con-
structing the model is to decide what features of a natural time series are
important for simulation. We will first deal with stochastic time series.
STATIONARY STOCHASTIC MODELS
In the previous section we summarized the properties of a natural pollu-
tant time series. We must now model these properties with a set of mathemati-
cal assumptions. The criteria that we feel are important for a pollutant
time series model are:
(1) Lognormally distributed concentrations.
(2) Stationarity, i.e., the distribution of concentration is time
invariant.
(3) Autoregressive, i.e., future concentrations depend upon past
behavior.
(4) Realistic autocorrelation function, i.e., similar to the natural
time series reported in Pollack, 1973, and Barlow and Singpurwalla,
1974.
In particular, we will assume that the concentration C(t) depends only on the
immediate past value, C(t-l), and a random shock. Such a model is called a
first-order, autoregressive, stochastic process.
A preliminary discussion of the lognormal distribution and auto/correla-
tion functions will simplify presentation of the stochastic model.
The lognormal distribution, LN(m, a), with median, m, and shape para-
meter, a, has a distribution function given by
-------
l~
LN(c;m,a) = f —]- exp{-% [ln{(£)1/a}]2}dy (D
Decreasing values of m cause the probability distribution to concentrate near
zero while increasing values spread (instead of shift) probability toward
higher values. Decreasing values of a cause the probability distribution to
concentrate about the median while increasing values spread the probability
asymmetrically about the median with increasing skewness toward higher values
and increasing pile-up near zero.
The mean and variance of the lognormal distribution are a function of
both m and a:
mean LN(m,o) = me20
2 °2,^2 -M (3)
variance LN(m,a) = me (ea -1)
The mean is always larger than the median because the distribution is skewed
toward the right and the difference between the mean and median increases
with a. The variance of the lognormal distribution increases with both the
median and shape parameter.
The natural logarithm of a lognormal random variable, with median m and
shape parameter a, has a normal distribution with mean In m (natural logarithm
of m) and standard deviation a, i.e., if C ~ LN(m, a) then In C N(ln m, a).
This property allows the parameters of the lognormal to be estimated from
data using standard statistics for the mean and standard deviation. The
result is that the median of the lognormal distribution is estimated by the
geometric mean of the data and the shape parameter is estimated by the stan-
dard deviation of the logarithms of the data.
Larsen (1971) defined the standard geometric deviation as the quantity
s = e . Probabilities from the lognormal distribution, LN(m, a), are related
to the standard normal distribution, N(0, 1), by
LN(msz;m,a) = N(z;0,l)
-------
In particular, 68% of the probability lies between - and ms; 95% of the
probability lies between -2 and ms2. s
The autocovariance function of a stationary stochastic process, (C(t),
t = 1, 2, ...}, measures the covariance between occurrences which are h time
units apart. For a given lag time, h, it is defined as
Cov(h) = E(C(t)-y)(C(t+h)-y)
(5)
where n = EC(t) is the mean value of the stochastic process. The auto-
correlation function at lag h, is the covariance function at lag h, normalized
by the variance of the process, i.e.,
p(h) = Cov(h)/Cov(0)
(6)
Autocorrelation functions of natural pollutant time series are charac-
terized by high correlations for small lag times and decreasing dependence
with larger lag times. Certain periodic human activities cause spikes at
daily and weekly lag times.
A stochastic model which satisfies our four conditions (lognormality-
stationarity - autoregressive - realism) can be expressed in terms of the two
parameters of the lognormal distribution, m and a, and a third parameter, \,
called the autocorrelation parameter. The concentration at time t is given
in terms of the concentration at time t-1 by
(7)
In this model Z(t), t = 1, 2, '" are independent, lognormally distributed
random variables with median equal to one and shape parameter equal to
a/FX2, i.e.,
Z(t)
-------
The parameter, X, determines the degree of dependence of C(t) on previous
concentrations C(t-l), C(t-2), '". The autocorrelation function of this
stochastic process at lag h is given by
p(h) = (e°x -l)/(eo2-1) h>o, |x|
-------
The procedure is to generate a time series from equation (7) using m = 1 and
then multiply this series by any value of m . The resulting series will have
a median, m , but the standard geometric deviation and correlation function
will not change. If several exposure schedules are generated in this manner,
using different medians, then the peaks and valleys in the different schedules
will correspond in time.
Concentrations less than the median occur half of the time and concentra-
tions higher than the median occur half the time, but the frequency at which
low and high concentrations occur is determined by the shape parameter, a.
Varying the median concentration corresponds to varying the source strength
of a diffuse source, while varying the shape parameter corresponds to varying
meteorological conditions. Monitoring data taken from widely separated
locations show surprisingly small variation in the shape parameter (Larsen,
1971). However, small variations in a could be extremely important to plants
because of its effect on the frequency of high concentrations.
Varying the correlation parameter, A., changes the time behavior of the
process. With a small value of A a strip chart of concentration vs. time
would look like a comb, while a large value of \ would cause the strip chart
to look more like hills and valleys. Two conflicting hypotheses about the
affect of X on plant growth seem reasonable: 1) a highly correlated (A near
1) concentration regime would reduce growth more than an uncorrelated regime
(\ near 0) because the highly correlated series would have incidences of high
concentrations which could last for several time units; 2) a highly correlated
series would reduce growth less than an uncorrelated series because plants
possess adaptive mechanisms that could operate efficiently if the concentra-
tion did not change rapidly.
PERIODIC MODELS
The simplest long-term pollutant schedule is a constant level applied
night and day throughout the growing season. Such treatments are used for
control purposes to quantify variablity within and between experimental
chambers.
At the next level of complexity are the exposure schedules of Figure 1.
The daily, on-off levels of these schedules are similar to those seen in
conventional chronic exposure studies and provide a calibration of our own
experimental system with previous studies. The constant levels are set to
occur during morning hours, when higher S02 levels are usually observed.
A further increase in realism is taken with the schedule seen in
Figure 2. As we indicated earlier, the shape of regular fluctuations in
pollutant strength depends upon human activity patterns and local meteorologi-
cal conditions. In the case of S02, however, some generalization is possible.
A few available studies of diurnal patterns in S02 show that urban areas tend
to have a bimodal daytime concentration curve, with peaks at midmorning and
late afternoon, followed by low, constant nighttime levels (Holzworth, 1973;
Munn and Katz, 1959; and Lee et al_. , 1975).
10
-------
o
< 4C
o:
i-
UJ
0
0
° 2C
UJ
>
< c
UJ
a:
0
, 3 hr
sr
. 6 hr
^
12 hr
S*
12 4 8 12 4 8 1
A.M. P.M.
TIME OF DAY
Figure 1. Daily treatment schedules for S02 -- applied
continuously throughout growing season.
11
-------
ro
or 3C
LU
O
O
O
LU
2C
LU
o:
I . I . I . I
I . I . I . I
I
I . I
5 7
A.M.
5 7
P.M.
TIME OF DAY
II I
Figure 2. Periodic S02 treatment schedule — applied continuously during growing season.
-------
This pattern is the basis for Figure 2. Plant responses to schedules in
Figures 1 and 2 can be compared on the basis of total dose received.
It is possible that plants exposed to the schedules of Figures 1 and 2
may become adapted to the regular fluctuations. In the real world, however,
air pollutants are not so predictable, and hour-to-hour changes in concentra-
tion appear more random than periodic. It is important, therefore, to deter-
mine whether random shocks of pollutant stress reduce growth to a different
extent than do predictable stresses. The next model superimposes stochastic
fluctuations on periodic patterns such as the one in Figure 2. In this model
we assume that the concentration at hour i, i = 1, 2, ... , 24 of any day is a
lognormally distributed random variable. Now let Figure 2 be a graph of the
median concentrations, m, for every hour of the day. Also assume that all
hourly distributions have the same shape parameter, a. Then a time series of
hourly concentrations is generated for hour i of each day by choosing a
random sample from a distribution which is LN(m(i), a). Figure 3 shows a
typical sample time series generated by this method. Bimodal daily peaks can
be discerned in the figure, following the pattern of Figure 2. Monte Carlo
studies with this model have shown that the empirical distribution function
for all the hourly levels taken together is well fit by a lognormal curve.
The time series in Figure 3 can thus be summarized statistically by a
median and shape parameter, permitting comparison of treatment responses with
those obtained from the stationary model, equation (7). Exposure responses
can also be compared with those obtained from the purely deterministic treat-
ments of Figures 1 and 2 on the basis of total dose or, in the case of Figure
2, on the basis of median concentration.
13
-------
TIME (DAYS)
Figure 3. Five-day sample from concentration time series. Series
generated by stochastic process with diurnal periodicity
in hourly geometric mean concentration.
14
-------
SECTION 4
DISCUSSION
The stochastic models developed here are good descriptors of natural
concentration time series from diffuse sources, such as urban locations. It
is not clear that the models are useful in simulating levels from a single
point source such as a power plant in rural areas. There are few long-term
studies of time series from a point source. Gifford (1974) has suggested, on
empirical grounds, that a negative exponential provides the best fit to point
source data and Knox and Pollack (1974) argue theoretically that the distri-
bution is Chi Square with two degrees of freedom. The most probable concen-
tration near a point source is zero, and high levels of pollutant are rare
and unpredictable.
Exposure schedules which simulate a point source raise problems of
replication and prediction of response. Imagine a concentration time series,
the length of a growing season, which is dominated by zero levels and has
only two or three concentration spikes high enough to affect plant growth.
The problem is that the plant growth response to such a schedule may vary
widely, depending on the timing of the spikes relative to each other and
relative to the developmental stages of the plant.
A realistic appraisal of the effect upon plant growth of an air pollutant
may be obtained by exposing plants to simulated ambient fluctuation in pollu-
tant concentration for an entire growing season. A gradient in pollutant
source strength, meteorological effects on the time series, and periodic
changes in magnitude can be accomplished by varying the parameters of a
model. Experiments currently underway are designed to test the sensitivity
of plant growth to both probabilistic and deterministic variations in concen-
tration time series. If plant growth does correlate well with parameters of
concentration frequency distributions, then the output of regional air pollu-
tion models can be applied immediately as input to plant response models.
In summary, stochastic experiments provide a viable alternative to long-
term fixed concentration experiments for predicting crop losses caused by S02
air pollution. The probabilistic nature of S02 time series are fairly well
understood and a model which will reasonably simulate realistic exposures has
been suggested. Parameters of this model can be varied in field growth
chambers to establish their correlation with crop yield. By designing the
experiment so that a large number of different time series from the same
model are tested then relatively precise probabilistic predictions of crop
losses are possible.
15
-------
REFERENCES
Air Quality Criteria for Sulfur Oxides. 1970. Nat. Air Poll. Control
Admin. Pub. AP-50.
Barlow, R. E. and N. D. Singpurwalla. 1974. Averaging Time and Maxima for
Dependent Observations. In Proc. Symp. on Statistical Aspects of Air
Quality Data. Env. Prot. Agency Report EPA-650.
Bencala, K. E. and J. H. Seinfeld. 1976. On Frequency Distributions of Air
Pollutant Concentrations. Atmos. Env. 10:941-950.
Box, G. E. .P. and G. M. Jenkins. 1976. Time Series Analysis: Forecasting
and Control. Holden-Day, San Francisco.
Gifford, F. A., Jr. 1974. The Form of the Frequency Distribution of Air
Pollution Concentrations. In Proc. Symp. on Statistical Aspects of Air
Quality Data. Env. Prot. Agency Report EPA-650.
Holzworth, G. C. 1973. Variations of Meteorology, Pollutant Emissions, and
Air Quality. American Chemical Society, American Inst. of Aeronautics
and Astronautics, American Meteorological Society, U.S. Dept. of Trans-
portation, Environmental Protection Agency, Inst. of Electrical and
Electronic Engineers, Instrument Society of America, National Aero-
nautics and Space Administration, and National Oceanographic and Atmos-
pheric Administration, 2nd Joint Conf. Sensing Environ. Pollut., Washing-
ton, D.C. , p. 247-255.
Jenkins, G. M. and D. G. Watts. 1968. Spectral Analysis and Its Appli-
cations. Holden-Day, San Francisco.
Knox, J. B. and R. I. Pollack. 1974. An Investigation of the Frequency
Distribution of Surface Air-Pollutant Concentrations. In Proc. Symp. on
Statistical Aspects of Air Quality Data. Env. Prot. Agency Report EPA-
650.
Larsen, R. I. 1971. A Mathematical Model for Relating Air Quality Measure-
ments to Air Quality Standards. Environmental Protection Agency Report
AP-89.
16
-------
Lee, J. J. , R. A. Lewis, D. E. Body. 1975. A Field Experimental System for
the Evaluation of the Bioenvironmental Effects of Sulfur Dioxide. Li
the Fort Union Coal Symposium, V. 5, W. C. Clark, ed. Montana Acad.
Sci., E. Mont. Coll., Billings.
Munn, R. E. and M. Katz. 1959. Daily and Seasonal Pollution Cycles in the
Detroit-Windsor Area. Internat. J. Air Poll. 2:51.
Oshima, R. J. , Poe, M. P., Braegelmenn, P. K., Baldwin, D. W., and VanWay, V.
1976. Ozone Dosage-Group Loss Function for Alfalfa: A Standardized
Method for Assessing Crop Losses from Air Pollutants. APCA Journal
26:861-865.
Pollack, R. I. 1973. Studies of Pollutant Concentration Frequency Distri-
butions. Lawrence Livermore Lab. UCRL-51459. Livermore, CA.
Raynor, G. S. , M. E. Smith, and I. A. Singer. 1974. Temporal and Spatial
Variation in Sulfur Dioxide Concentrations on Suburban Long Island, New
York. JAPCA 24:586-590.
17
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing/
1. REPORT NO.
EPA-600/3-78-038
riTLE AND SUBTITLE
2.
"Time Series Experiments for Studying Plant Growth
Response to Pollution"
3. RECIPIENT'S ACCESSION NO.
5. REPORT DATE
April 1978
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
Larry Male. John Van Sickle, and Ray Wilhour
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Assessment & Criteria Development Division
Ecosystems Modeling & Analysis Branch
Corvallis Environmental Research Laboratory
200 S.W. 35th St. -- Corvallis, OR 97330
10. PROGRAM ELEMENT NO.
1AA602
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Environmental Protection Agency - Corvallis, OR
Corvallis Environmental Research Center
200 S.W. 35th St.
Con/all is, OR 97330
13. TYPE OF REPORT AND PERIOD COVERED
Inhouse
14. SPONSORING AGENCY CODE
EPA/600/02
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This research program was initiated with the overall objective of defining experi-
ments which could predict the yield losses of crops grown under naturally varying
sulfur dioxide concentrations.
A model for simulating realistic fluctuations in S02 air pollution was developed.
This model was used to design experiments in field growth chambers for the purpose
of establishing the functional and probabilistic relationship between yield loss
and median S02 concentration. The stochastic experiments are offered as a viable
alternative to traditional long-term fixed concentration experiments.
This report covers a period from April 1974 to September 1977 and was completed
as of February 1978.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
c. COSATI F-'ield/Group
Sulfur Dioxide
Time Series Analysis
Experimental Design
Field 06/F
06/S
18. DISTRIBUTION STATEMENT
Release Unlimited
19. SECURITY CLASS (This Report)
UNCLASSIFIED
21. NO. OF PAGES
24
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION is OBSOLETE
18
6 U.S. GOVERNMENT PRINTING OFCIC6: 1978—796-747/139 REGION 10
------- |