Before one can characterize
hourly average concentrations in a biologically meaningful way,
it is necessary to understand the relationship between exposure
and vegetation effects. The search for an exposure index that
relates well with plant response has been the subject of intensive
discussion in the research community. Both the magnitude of a
pollutant's concentration and the length of exposure are important
considerations when attempting to develop a realistic exposure
index. Evidence exists in the literature to indicate that the
magnitude of vegetation responses to air pollution is more related
to the magnitude of the concentration than the length of the
exposure. In other words, the concept of Haber's Rule of concentration
multiplied by time is a constant in relation to dose does not
apply when assessing the effects of ozone dose on vegetation.
The observation is the same for human health as noted by Silverman
et al. (1976), Drechsler-Parks (1990), Hazucha et al. (1992),
Adams (2003, 2006), Hazucha and Lefohn (2007), and Lefohn et
al. (2010). Should you wish to read more about this subject,
there is a discussion in Section 2.4 in Lefohn (2020).
Several different
types of exposure indices have been proposed. Both the 6-h and
7-h long-term seasonal mean ozone exposure parameter have been
used to relate vegetation effects with exposure. The 7-h (0900-1559h)
mean, calculated over an experimental period, was adopted as
the statistic of choice by the U.S. EPA's National Crop Loss
Assessment Network (NCLAN) program. Toward the end of the program,
NCLAN redesigned its experimental protocol and applied proportional
additions of ozone to its crops for 12-h periods.
The use of a long-term
average concentration, such as the 7- or 12-h average, for describing
concentration exposures does not provide accurate descriptions
of exposures that actually occur. For example, some high-elevation
sites exhibit ozone exposure characteristics that are distinctly
different from those observed at lower elevation sites. The long-term
averages calculated at some high-elevation sites tend to be higher
than the long-term averages at lower elevation sites. The
higher long-term averages reflect the infrequent number of hourly
average concentrations near the minimum detectable level and
the magnitude of the long-term average may or may not be biologically
significant for either vegetation or human health effects.
An important aspect that is overlooked is that it is important
when citing long-term average values and relating them to biological
effects, the integration period used to average the hourly average
concentrations is important. Many scientists continue to cite
7-h or 12-h average concentrations for specific experiments without
specifying the period of the averaging (e.g., 3-months, 6-month,
etc.) when attempting to assess vegetation effects. The period
of the averaging time is important because a 12-h average of
50 ppb over a 1-month period will elicit a very different vegetation
effect than a 12-h average of 50 ppb over a 6-month period. Simply
saying that a 12-h 50 ppb average elicits a 20% yield reduction
is irrelevant without citing the number of months of exposure.
As explained below, the use of a 7- or 12-h daily average accumulated
over a period of time is not an appropriate metric to use in
assessing vegetation effects. This was discussed in the US ozone
rulemaking process by the US EPA (1986).
From the middle
1960s through the middle 1980s, studies published in the literature
identified short-term, high concentration (i.e., episodic) ozone
exposures as important components of agricultural crop effects
and trees. The short-term, high concentration exposures were
identified by many researchers as being more important than long-term,
low concentration exposures.
As additional evidence
began to mount that higher concentrations of ozone should be
given more weight than lower concentrations, concerns about the
use of a long-term average to summarize exposures of ozone began
appearing in the literature. Specific concerns were focused on
the fact that the use of a long-term average failed to consider
the impact of peak concentrations. The 7-h seasonal mean contained
all hourly concentrations between 0900-1559h; this long-term
average treated all concentrations within the fixed window in
a similar manner. An infinite number of hourly distributions
within the 0900-1559h window could be used to generate the same
7-h seasonal mean, ranging from those containing many peaks to
those containing none. It was pointed out in the literature that
it was possible for two air sampling sites with the same daytime
arithmetic mean ozone concentration to experience different estimated
crop reductions.
In the late 1980s,
the focus of attention turned from the use of long-term seasonal
means to cumulative indices (i.e., exposure parameters that sum
the products of concentrations multiplied by time over an exposure
period using a threshold concentration). The use of the cumulative
exposure index with a threshold concentration had some limitations.
Depending upon the threshold concentration used, the parameter
ignored the lower hourly mean concentrations. However, the parameters
appeared to relate ozone exposure with observed functional change
at monitoring sites that experienced (1) repeated high concentration
exposures from day-to-day and (2) relatively short periods between
episodes.
Recognizing the
disadvantage of using a threshold concentration with the cumulative
index, a modification was suggested that applied differential
weighting to the hourly mean concentrations of ozone and summing
over time. Lefohn and Runeckles (1987) proposed a sigmoidal weighting
function that was used in developing a cumulative integrated
exposure index. The sigmoidal weighting function was multiplied
by each of the hourly mean concentrations; thus, the lower, less
biologically effective hourly average concentrations were included
in the integrated exposure summation, but were provided less
weight than the higher concentrations.
The form of the
sigmoidally weighted index was tested using NCLAN data. Lefohn
et al. (1988) showed that exposure indices that weight peak concentrations
of ozone differently than lower concentrations of an exposure
regime could be used in the development of exposure-response
functions.
Based on evidence
published in the literature, as well as special analytical studies
sponsored by the U.S. EPA (1996), many in the research community
have concluded that the use of cumulative indices to describe
exposures of ozone for predicting trees and agricultural crop
effects appears to be a more rational approach than the use of
long-term seasonal averages.
Exposure-based metrics
are traditionally used to relate O3 to vegetation response. Flux-based
models have been developed to predict the effects of O3 on vegetation.
Because plant response is more closely related to O3 absorbed
into leaf tissue than to exposure, it is often assumed that flux-based
models offer less uncertainty in predicting vegetation effects
than the use of exposure-based metrics. Lefohn and Musselman
(2005) and Musselman et al. (2006) discussed the advantages
and limitations associated with the use of flux-based models
for predicting vegetation effects. An important aspect associated
with adequately predicting the effects of O3 on vegetation is
identification and quantification of the detoxification processes.
The daily and seasonal temporal variability associated with detoxification
processes are important and cannot be ignored when using flux-based
models to predict vegetation effects (Musselman et al., 2006;
Heath et al., 2009). As discussed in Musselman et al. (2006),
the use of a constant (i.e., threshold) in flux-based models
cannot adequately serve as a methematical surrogate for detoxification
considerations. An important paper by Goumenaki et al. (2021)
appears to substantiate the observations described in Heath et
al. (2009).
While future research
continues to focus on the use of flux-based indices that include
daily and seasonal temporal variability associated with detoxificaton
processes, it is important to continue to identify the family
of cumulative indices that best describe the relationship between
ozone exposure and vegetation effects. One needs to be aware
that both exposure indices and flux-based indices will continue
to produce inconsistent results when trying to predict growth
losses. Most exposure indices are insensitive to diurnal periods
of maximum sensitivity of the plant. The sensitivity of vegetation
as a function of the time of day has not been well defined and
is an important consideration for both exposure indices, as well
as flux-based indices. In addition, as described in the literature,
the distribution patterns of the hourly average concentrations
for some high-elevation and low-elevation sites are different.
Most cumulative-type and other exposure indices, as well as flux-based
indices cannot adequately describe some of the subtle differences
in the two different types of exposure regimes. Besides sensitivity,
the majority of exposure indices used today do not address (1)
the amount and chemical form of the pollutant that enters the
target organism (i.e., stomata considerations), (2) the length
of the exposure within each episodic event, or (3) the time between
exposures (i.e., the respite or recovery time). It is unclear
how important sensitivity and the amount and chemical form of
the pollutant that enters the target organism are in an overall
weighting scheme when predicting vegetation effects. If both
the sensitivity of the target organism and the actual dose that
enters the organism are as important as ambient air pollutant
exposure, then a given pollutant exposure will elicit varying
biological responses at different times for the same crop. While
recognizing the limitations of applying exposure indices as dose
surrogates, at this time, the cumulative exposure index appears
to be the best family of indices available for relating exposure
and biological response. Results published in the literature
under experimental and ambient forest conditions in the 1980s
and 1990s provide researchers with clear guidance on the importance
of the weighting of the higher hourly average ozone concentrations
in comparison to the mid- and low-level values (please see Section
2 of Lefohn et al., 2018 for a list of studies). Models that
question the fundamental principle of the importance of the higher
hourly average ozone concentrations should be further investigated
to better understand the limitations associated with the adequacy
of the predictive capability of these models. Recent research
results have indicated the importance of the diurnal variability
of detoxification and how the selection of constant thresholds
in flux-based models to represent detoxification may not be appropriate.
Clearly, more work is required in this important research area.
Today, many vegetation scientists use cumulative
exposure indices that weight the higher hourly average concentrations
more than the mid- and lower-level values. The mid- and low-level
concentrations are not ignored, but rather weighted differently
that the higher hourly average concentrations. The U.S. EPA,
U.S. Forest Service, and National Park Service use the sigmoidally
weighted W126 exposure index to assess the potential impact of
ozone on vegetation.
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