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).
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 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 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.
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 concentrations were included in the integrated
exposure summation.
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 daily and seasonal temporal variability associated with detoxification
processes are important and cannot be ignored wne 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
should 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 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. 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. 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 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.
References
Goumenaki, E., González-Fernández,
I., and Barnes, J. D. (2021). Ozone uptake at night is more damaging
to plants than equivalent day-time flux. Planta 253, 75. https://doi.org/10.1007/s00425-021-03580-w.
Heath R. L., Lefohn A. S., and Musselman
R. C. (2009). Temporal processes that contribute to nonlinearity
in vegetation responses to ozone exposure and dose. Atmospheric
Environment. 43:2919-2928.
Lefohn A.S. and Runeckles V.C. (1987) Establishing
a standard to protect vegetation - ozone exposure/dose considerations.
Atmos. Environ. 21:561-568.
Lefohn, A.S. and Musselman, R.C. (2005)
The Strengths and Weaknesses of Exposure- and Flux-Based Ozone
Indices for Predicting Vegetation Effects. Presented at the Critical
levels of ozone: further applying and developing the flux-based
concept. Obergurgl, Tyrol, Austria. November 15-19, 2005.
Lefohn A.S., Laurence J.A. and Kohut R.J.
(1988) A comparison of indices that describe the relationship
between exposure to ozone and reduction in the yield of agricultural
crops. Atmos. Environ. 22:1229-1240.
Lefohn, A.S., Malley, C.S.,
Smith, L., Wells, B., Hazucha, M., Simon, H., Naik, V., Mills,
G., Schultz, M.G., Paoletti, E., De Marco, A., Xu, X., Zhang,
L., Wang, T., Neufeld, H.S., Musselman, R.C., Tarasick, T., Brauer,
M., Feng, Z., Tang, T., Kobayashi, K., Sicard, P., Solberg, S.,
and Gerosa. G. (2018). Tropospheric ozone assessment report:
global ozone metrics for climate change, human health, and crop/ecosystem
research. Elem Sci Anth. 2018;6(1):28. DOI:
http://doi.org/10.1525/elementa.279.
Lefohn, A.S. (2020). Comments on Draft
Review of the Ozone National Ambient Air Quality Standards -
Docket ID No. EPA-HQ-OAR-2018-0279-0421.
Musselman, R.C., Lefohn, A.S., Massman,
W.J., and Heath, R.L. (2006) A critical review and analysis of
the use of exposure- and flux-based ozone indices for predicting
vegetation effects. Atmos. Environ. 40:1869-1888.
U.S. Environmental Protection Agency (1996)
Air quality criteria for ozone and related photochemical oxidants.
Environmental Protection Agency, Office of Air Quality Planning
and Standards, Research Triangle Park, NC. U.S. EPA report no.
EPA/600/P-93/004bF.
