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Vol. 15 No. 1 - January 2009

A Vignette of Four Decades of Air Quality and Plant Response Research

 By: Sagar Krupa*


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 <krupa001@umn.edu>

This article has been reproduced from the archives of EnviroNews - Newsletter of ISEB India.

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