|
A Vignette of
Four Decades of Air Quality and Plant Response Research
By: Sagar
Krupa*
Introduction
In the early
days of air pollution science, the major emphasis of terrestrial vegetation
effects research had been on emissions from point sources, visible foliar injury
on native and cultivated plants and forest damage caused by primary pollutants
such as sulfur dioxide (SO2). Initially, I too followed that path.
However, as urban complexes and mega cities (population >10 million) grew in
numbers (from 2 to 32 in the last 50 years) and as photochemical smog (ozone, O3)
and long-range transport and deposition (both wet and dry) of secondary air
pollutants such as sulfate and nitrate aerosols became an increasing concern,
emphasis shifted from local to regional scale studies. Currently, tropospheric O3
is clearly the most important phytotoxic air pollutant worldwide. Visible O3-induced
foliar injury has been reported on cultivated and/or native vegetation from some
38 countries globally and from some 22 states in the US including Minnesota,
where we were the first to identify such injury. In addition to field surveys
for assessing the visible effects, hundreds of studies (includes our own
experiments) have been conducted under laboratory conditions in controlled
environment fumigation chambers to characterize dose-response relationships with
both primary (e.g., SO2) and secondary (e.g., O3) air
pollutants. Those were univariate, in those experiments all growth regulating
variables were maintained at relatively constant levels, except for the
pollutant [e.g., SO2, O3] of interest, studies that mainly
consisted of acute exposures (relatively high pollutant concentrations from a
few to several hours on one or more successive days). As our knowledge of air
pollutant-induced changes in plant biology and functional eco-physiology
increased, the importance of chronic or whole growth season exposures (e.g.,
with O3) and responses became the focus of numerous studies requiring
the use of greenhouse and field exposure chambers (from small cuvettes or gas
exchange chambers and Continuous Stir Tank Reactors, CSTRs to large open-top
chambers, OTCs), although many of them continued to represent univariate
experiments. In that context, we were the first to develop a computerized field
fumigation open-top chamber system to mimic the ambient conditions with the
inclusion of both SO2 and O3, but we abandoned that
approach after tornadoes destroyed our facilities two years in a row (half a
million $ each year x 2) and I realized that chambers do not represent the real
world.
Nevertheless, yield data generated from field chamber studies in large national
or continental scale networks respectively in the US (EPA’s National Crop Loss
Assessment Network, NCLAN) and Europe (European Open-Top Chambers Programme,
EOTC) were used to formulate ambient air quality regulations or standards and
objectives to protect crops and forests against the adverse effects of O3.
However, those efforts continued to generate an ongoing debate about the
validity of using chamber-based univariate data for regional scale crop loss
assessment under ambient conditions (see the discussion below). Nevertheless,
the current view is that in the US, ambient O3 causes 5 to 15% yield
loss in important agronomic and horticultural crops. Because of the debate
associated with the use of chambers, most recently, a few scientists have begun
to use chamber-less, free air, trace gas exposure systems for studying the
effects of O3 (e.g., FACE technology). In contrast, during the last
ten years, I have directed my efforts to developing numerical methods to account
or apportion the contributions of individual pollutants (e.g., O3, NOx)
in ambient air (pollutant mixtures) and individual climate variables such as air
temperature and precipitation to the overall growth and productivity of a
perennial crop such as alfalfa under field conditions (a total of 72 real world
exposure treatments were involved in our study). Such studies require intensive
measurements of the independent variables and multi-point measurements of the
crop growth rate prior to harvest at multiple locations to account for the
spatial and temporal variability. Many consider that approach daunting, but it
represents the real world. Meanwhile, in Europe, emphasis changed from the use
of air concentrations to modeling pollutant fluxes from the atmosphere on to the
plant canopy for uptake through the stomata or for computing the actual absorbed
dose. However, results from such modeling have also been the subject of some
argument, since they have not been fully validated with independent sets of
data.
In
addition to changes in the growth and yield of crops, as we and others have
shown, exposure to ambient air pollution can negatively alter the nutritive
quality and the relative food value of forage species such as alfalfa to
ruminant animals in production agriculture. This is an area that requires
significant attention.
As
noted previously, ambient atmosphere is composed of combinations of multiple
pollutants that vary significantly in their concentrations in time and in space
and therefore, any observed effect(s) on vegetation is the result of exposure to
those pollutant mixtures. However, a particular pollutant, because of its
greater phytotoxicity at its prevalent dose (concentration X exposure duration,
on one or on repeated occasions) may have a bigger impact at a given time and
location (e.g., occurrence of typical visible foliar injury). Nevertheless,
presence of two or more pollutants can result in additive, more than additive or
less than additive effects. That makes it very difficult to conduct artificial
field exposure studies that are both realistic and can explain the stochastic
(random) relationships between cause and effect. Further, such studies can only
define a portion of the total response surface. They are frequently limited by
small number of treatments due to logistic and financial considerations. That is
one of the major reasons for the approach that we have taken in our alfalfa
studies described previously.
There is a very important need to examine the entire response surface, because
many non-essential chemicals (e.g., O3, heavy metals) stimulate plant
growth and other biological processes at low doses, but inhibit such processes
at higher levels. That phenomenon known as “hormesis” represents an advantage
gained by the individual species from the overall resources and energy initially
allocated for detoxification and repair, but in excess of that needed to repair
the immediate damage. As hormetic effects vary with the plant species, it can
result in selective advantage for certain members over others in mixed
communities.
Although some air pollutant-induced hormetic plant effects have been reported in
the literature, so far experimental designs have mostly been constructed to
optimize exposure doses above an accepted or perceived level to demonstrate
adverse effects and thus, show insufficient potential for detecting or
describing “hormesis” and its impacts on the traditional dose–response
functions. That would require a change in the use of traditional experimental
designs to include cleaner air treatments. Such a shift is also critical in
examining interactive effects of multiple plant-growth-regulating variables
(both air pollutants and climate parameters such as air temperature, soil
moisture etc., that are required for normal growth and development) in the
ambient environment and in the context of climate change.
Compared to O3, sulfur is an essential plant nutrient, with soil
being its main source of supply. The stimulatory effects of atmospheric S on
plants growing on soils that have marginal sulfur content (farmers in Minnesota
could see it during the 1970s, near a new point source), is not considered to be
an “hormetic” effect by classical definition. Nevertheless, sulfurous air
pollutants can act as both stressors and as nutrients for plants. However, it is
unclear as to what extent metabolism contributes to the detoxification of
absorbed sulfur gases, as there is no clear-cut transition in the level or rate
of metabolism of the absorbed sulfur gases and their phytotoxicity. Moreover,
the effects of sulfurous air pollutants on plant functioning are strongly
dependent on the sulfur status of the soil.
On
a global scale, fossil fuel combustion is the main source for both atmospheric
SO2 and NOx. As with S, nitrogen is an essential element
and a fertilizer. Although there are reasons to believe that at low
concentrations gaseous N can be stimulatory, there are no specific studies
conducted to address that issue. With the exception of ammonia, in general
ambient concentrations of gaseous N species do not exist at phytotoxic
concentrations. Aside from its critical role in photochemistry and in the
generation of O3 and other oxidants, excess bulk (wet and dry)
deposition of total nitrogen is known to adversely alter native plant population
structure by allowing the invasion of grasses into perennial, herbaceous plant
communities, as in Europe.
Relating Source Emissions to Receptor Sites
As
indicated previously, early studies on air quality and terrestrial vegetation
effects were directed to single or specific point sources. As secondary air
pollutants and area or regional scale emissions from multiple sources became
increasingly important, source apportionment methods were developed for air
quality management (control strategies). Source apportionment is the estimation
of the contributions of elemental emissions from specific natural and
anthropogenic sources to the airborne concentrations at a given location.
Integrating source apportionment methods to ecological effects studies would
represent a major step in establishing source - effect relationships under
ambient condition, but would require very close collaboration between plant and
atmospheric scientists, as in our current efforts in Minnesota. In the overall
context, receptor models are applied to elicit information on the sources of air
pollutants from the measured constituent air concentrations. Typically,
receptor models use the chemical composition data from repeated sampling and
analysis of airborne particulate matter at a given location. In such cases, the
outcome is the identification of the pollution source types (e.g., power plant,
petroleum extraction plant, mobile sources, vegetation) and estimates of the
contribution of each source type to the observed air concentrations at the
receptor location.
Elemental Tracers of
Source Emissions and their Accumulation in Receptors
Under ambient conditions responses of sensitive plant species can be used to
assess relative air quality. Development of pollutant specific foliar injury
symptoms on sensitive plant species has been used as a biological indication of
the relative air quality at a given location and time (for example, at more than
20,000 sites in the UK during 1990). In some cases progressive disappearance of
a particular species in a given geographic area has also been used as an
indication of deteriorating air quality. Another indicator is shifts in the
plant populations within a community.
Traditionally, in addition to foliar injury surveys, a number of investigators
(including our group) have used sulfur accumulation in plant tissues at various
distances along directional transects from a point source (e.g., coal-fired
power plant, metal smelter, petroleum refinery, natural gas extraction plant) to
map zones of impact or no impact in predominant upwind and downwind areas. We
have used differences in the concentrations and ratios of total (absorbed),
inorganic (stored) and organic (assimilated) S in the plant tissue to
differentiate the relative point source plume impacts versus the contribution of
the soil. We have also used an Elemental Enrichment Analysis (EEA) to separate
the contributions of the atmosphere from those of the soil to Austrian pine (Pinus
nigra) foliar concentrations of total S and other elements. Based on the
least amount of variance between several elemental concentrations in P. nigra
needles and the corresponding soils in the plots, aluminium was chosen as the
normalization element for computing Elemental Enrichment Factors. Those results
identifying sites with various levels of impacts were in close agreement with
the results of measured plume transport and deposition in the complex terrain of
the study area, before and after the installation of a SO2 control
system. Those results were verified independently by plume tracking over a
number of years using ground-based, but mobile Correlation Spectroscopy (CoSpec)
and a fast response pulse fluorescence SO2 analyzer. Thus, EEA offers
a significant advancement in the traditional application of S accumulation in
plant tissues to map plume impacts.
With a different, but a more sophisticated, highly successful approach, in the
West White Court Case Study in Alberta, Canada, abundance of stable S isotopes (32S:
34S) was used as plume tracers and S deposition into the ecosystem.
However, caution is warranted in using single elemental isotopes as tracers in
impact assessment. For example, the 32S: 34S in the
emission must be distinctly different from the background value as was the case
in the studies at West White Court. In contrast, we were unable to find a
similar differentiation regarding a coal-fired power plant plume in Minnesota,
USA. The probability of success is increased with the application of
multi-element stable isotopes. Dual elemental isotopes of 13C and
18O in stem cellulose have been used to examine the stress responses
of tress to air pollution in an urban corridor in Quebec, Canada. Another step
in the application of stable elemental isotopes in environmental research is the
use of three elements, for example S, N and O (i.e., SO42-
and NO3-). In addition to the West White Court Case Study
with S, recently the stable isotope 17O signal of NO3-
has been used as a tracer of atmospheric NO3- and was
found to be a more robust tracer of atmospheric NO3- than
15N and 18O methods. 17O can also be used as a
tracer of fresh (local) versus aged (transported) O3. Certainly the
use of stable elemental isotopes allows the separation of anthropogenic from the
influence of natural sources. It also allows the tracing of the fate of the
element through the ecosystem components and consequently its impacts. However,
where multiple source plumes are involved with not so distinctly different
stable isotopic signals, other source signatures must be used.
One aspect of receptor modeling involves the use of US EPA’s “Speciate” source
finger print library (available on the Internet) on inorganic elemental
composition of emissions from various types of sources. Here elements such as
As, Be, Cd, Cr, Hg, Ni, Pb, Rb, Se, Sr, Ti,V and others are included in the
source apportionment methodology. Many of these and other elements (both
essential and non-essential) accumulate in plant tissues, particularly in lower
plants such as the lichens.
The concept of source apportionment and receptor modeling are based on data
gathered separately on the chemical composition of fine (< 2.5 µm) and coarse
(>2.5 µm) particles. Where opportunities for those types of data collection do
not exist, because of logistic and financial restrictions, accumulation of
elements in plant tissues (receptor accumulation) can be used in source
apportionment. An excellent example relates to the use of spatial variability in
the elemental composition of lichens throughout the Netherlands to map air
quality as influenced through source apportionment.
Relating
Elemental Accumulation to Vegetation Effects
With the exception of O3, many other air pollutants such as SO2
(S), NOx - NOy (N), HF hydrogen fluoride (F) and trace
metals accumulate in foliar tissues. As noted previously, normally soil is the
predominant source for many of the elements measured in plants. As discussed
previously, we have used the Elemental Enrichment Analysis (EEA) to separate the
role of the soil from the atmosphere. But in receptor modeling, patterns and
variability of multi-elemental accumulation in plants over a region, can be used
in source apportionment. Here, the key is the use of multiple elements as source
fingerprints (predominantly source specific spectral patterns of elements) and
not a single element such as S. The critical requirement here is, to demonstrate
co-linearity between the occurrences of the phytotoxic element such as S (SO2)
and the elements (trace metals) used in the receptor modeling.
At
this time, there are many studies for example, on the accumulation of S or its
metabolic products and plant physiological responses such as changes in
photosynthesis.
However, to my knowledge there are no studies relating the dynamics of
atmospheric deposition, tissue elemental accumulation and irreversible effects
such as yield reductions. Such studies will involve repeated measurements (time
series of relating tissue elemental accumulation to growth or yield) and
multi-point modeling of chronic relationships of cause and effect. On the other
hand, repeated measurements are one of the backbones of atmospheric receptor
modeling. In air pollution – plant biomass effects literature, virtually all of
the studies relate to single point, season end harvests and their correlations
with some exposure statistic, making it scientifically unsound in attempting to
account for the random relationships of cause and effect.
Lower plants such as bryophytes and lichens are excellent accumulators of
various elements and can be used in receptor modeling. Particularly species that
are epiphytic or those that are representative of ombotrophic plant communities
derive virtually their entire tissue elemental signature through atmospheric
uptake. The major limitation here is a need for repeated measurements over
multiple years to establish a recurring or changing patterns of multiple source
contributions to the receptor.
5.
Conclusions
While elemental accumulation in biological receptors such as in the lichens, can
be used in source apportionment, there are issues associated with biodiversity
[loss of sensitive lichen species themselves, as with nitrophobic or SO2
sensitive species]. A major objective should include long-term impacts on
the primary producers (the starting point of energy flow) in the ecosystem.
There is evidence that patterns of pollutant accumulation in epiphytic lichens
and in their higher plant associates are comparable. Under various SO2
exposure regimes, tissue S accumulation rates in lichens and strawberry and
white pine foliage was found to be similar. Comparison of the S uptake with
concentration (c) and exposure time (t) under similar products of
c and t showed that pollutant uptake of lichens was more dependent
on the exposure time than on concentration. A main difference between the
lichens and the higher plants is the lack of ability of lichens to dilute the
absorbed pollutants through the formation of significant amounts of new plant
material with low natural levels of the particular element in question. Thus,
lichens are more sensitive and can be used as an early warning system in spatial
mapping of impact and no impact zones to assist in the assessment of long-term
air pollutant effects on the growth and productivity of the primary producers in
the ecosystem.
In the context
of the primary producers, even repeated measurements of tissue elemental
concentrations by themselves are not likely to account satisfactorily for the
stochastic behavior of growth and biomass relationships under ambient
conditions, because the combined effects of more than one or multiple growth
regulating factors (air pollutant mixtures, air temperature, precipitation,
diseases etc.) are in effect. Few scientists have addressed this complex problem
of the ambient environment, although there are ways to do so. Many mechanistic
or process models have been developed, but most of them have not been validated
with independent sets of data. Most recently we offered potential approaches to
addressing this issue. Although those are first order efforts, they represent
general time series methods that are multi-variant models that can accommodate
repeated measurements of tissue elemental accumulation rates. That is in
addition to other independent variables such as air temperature, precipitation
depth etc. that effect changes in plant growth and biomass under ambient (not
experimental) conditions. Thus, repeated measurements of biological responses
can be coupled to repeated measurements of elemental deposition/ accumulation in
receptor modeling in deriving great benefits in source apportionment studies
that have so far eluded plant scientists. Equally importantly, such efforts can
allow an assessment of the efficacy of air quality regulatory policies (data
from before and after the implementation of control strategies).
*Professor,
Department of Plant Pathology, University of Minnesota, St. Paul, MN 55108, USA
<[email protected]> |
