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 an
effect of the magnitude of the concentration than the length
of the exposure. Should you wish to read more about this subject,
there is a discussion in Section 2.4 in Lefohn (2020).
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 lack of hourly average concentrations
near the minimum detectable level and may or may not be biologically
significant. An important aspect that is overlooked is that it
is important when citing long-term average values and relating
them to biological effects, the period that is used to average
is important. Many scientists continue to cite 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 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.
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
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
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 concentrations were included in the integrated
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 can 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 detoxification processes, including their temporal variability
and relevance, are important and cannot be ignored when predicting
vegetation effects (Musselman et al., 2006; Heath et al., 2009).
As discussed in Musselman et al. (2006), the use of a "threshold"
in flux-based models will not serve as a methematical surrogate
for the detoxification process. An important paper by Wang et
al. (2015) appears to substantiate the conclusions of Heath et
While future research
should focus on the use of flux-based indices that include 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 exposure 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.
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 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. 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. Resultes 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 Lefohn
et al., 2018 for a list of studies). Models that question the
fundamental principle of the importance of the higher 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 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.
Heath R. L., Lefohn A. S., and Musselman
R. C. (2009). Temporal processes that contribute to nonlinearity
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