Introduction
One of the greatest challenges in fluoride toxicology is predicting the effects of fluoride exposure on plants or animals in natural and managed ecosystems. Although there are adequate methods for measuring fluoride in the atmosphere, they are generally expensive, lack sensitivity and are labour-intensive. Besides, even if the concentration of fluoride in the atmosphere is known, on its own this information is not usually sufficient to predict effects in an ecological context. Where plants are concerned, length and intermittence of exposure, temperature, light intensity, relative humidity and other biological factors play an important part in deter- mining the effects. For animals, such as livestock or insects, knowledge of the total dietary intake is the key requirement.
This leads to a self-evident but key question that should always be asked when monitoring is being planned: why is it being undertaken and what are the goals? In some cases monitoring may be a statutory requirement and a way of ensuring that regulations are not being breached. From an industry perspective it may be defensive, providing evidence for the regulator or public that there is no significant impact. Or it may be used to provide an early warning in order to prevent problems, such as fluorosis, which takes time to develop.
However, in too many cases the authors have seen monitoring programmes that do not seem to have an obvious purpose, that are not well planned and that in some cases are excessive. We have seen cases where data were being collected but never exam- ined or analysed, and the scientific literature has many examples of ‘monitoring’ in which the end result is data that cannot be inter- preted in terms of effects or used to prevent environmental damage. Therefore, in this chapter we discuss the methods that have been used to monitor the concentrations of fluoride in the environment and the effects on plants and animals. The intention is to help users to choose the most appropriate methods for their purposes and to avoid some of the pitfalls. The chapter is divided into four main parts: measuring atmospheric fluoride concentrations; biological indica- tors (bioindicators); biological monitors or accumulators (biomonitors); and field surveys.
Measuring Atmospheric Fluoride Concentrations
Volumetric instruments
The main advantage of using atmospheric fluoride concentrations for monitoring is that, with due care, the data are an absolute measure that is free from much of the
©L.H. Weinstein and A. Davison 2004.Fluorides in the Environment
(L.H. Weinstein and A. Davison) 147
uncertainty and problems of interpretation associated with biological methods. Reli- able records of atmospheric fluoride con- centrations are an invaluable aid to inter- preting visible injury shown by vegetation.
Several instruments based on spectroscopy are currently available that give real-time measures of the concentrations of HF and SiF4, but they are expensive and they do not have the sensitivity required for most out-plant monitoring. Their main use is for in-plant surveillance of industrial processes and determining sources of emission (e.g. LaCosse et al., 1999). For the range of concentrations that occur off industrial property and in the threshold range where effects occur, air samples have to be col- lected for periods of minutes to days, using alkaline absorbents, and then analysed, usually with a fluoride-specific ion elec- trode. Several commercial instruments are available in which the air is passed through an alkali-impregnated filter paper, through paper tape on a spool (Weinstein and Mandl, 1971) or through a liquid absorbent.
An example of the data from a paper-tape sampler was shown in Fig. 2.3 (Chapter 2).
In this case the data were very useful in demonstrating the frequency distribution of fluoride concentrations and the fact that the range is greater nearer the source. This kind of information cannot be revealed by bioindicators, although it may be possible using an appropriate biomonitor.
Monitoring using the rate of deposition of HF on alkaline media
The cost and power requirements of instru- ments mean that in most circumstances only one or two can be deployed, so they cannot show the geographical pattern of atmospheric fluoride concentrations, which is an important aid in field surveys. An alternative is to measure deposition of fluoride on an alkaline surface and to calibrate the system against a volumetric monitor (Davison and Blakemore, 1980).
This technique may have had its origins in the use of natural populations of Spanish
moss (Tillandsiaspp.) as collectors of atmo- spheric fluoride or, by analogy, with the long-established method for monitoring SO2 using lead peroxide candles, but the first use of lime-impregnated papers was in the 1950s, when it was found that there was a correlation between the fluoride depos- ited on the paper and the HF concentration (Milleret al., 1953; Robinson, 1957). These correlations have been confirmed many times (Adams, 1961; Wilson et al., 1967;
Israel, 1974a,b; Blakemore, 1978; Lynch et al., 1978; Sidhu, 1979; Davison and Blakemore, 1980). The basic technique is to impregnate a paper with an alkali, often a calcium salt or sodium formate, expose it to the air in a housing that protects it from the rain and then analyse the deposited fluoride after a period from a week to a month. Variants of the method are widely used in many countries, especially by the aluminium industry. Some have used the papers hanging in louvred boxes (Davison and Blakemore, 1980), others cement them in inverted Petri dishes (Lynchet al., 1978;
Clark, 1982) or enclose them in envelopes (Israel, 1974a) and others wrap the papers around cylinders to form candles (Wilson et al., 1967).
Most users regard the data as a relative measure of the fluoride concentration and report some form of ‘fluoridation index’ or
‘fluoridation rate’, and there is a tendency to regard the system as a ‘black box’, even though the physics and chemistry of deposi- tion are well known. A few authors have calibrated their systems, notably Robinson (1957), Adams (1961), Mukai and Ishida (1970), Israel (1974a), Lynch et al. (1978) Sidhu (1979) and Davison and Blakemore (1980). The latter authors reviewed the pub- lications up to 1980 and showed that the rates of deposition differed from about 25 to 73mg F/dm2/week at an atmospheric con- centration of 1mg HF/m3. The differences were due to the fact that the different meth- ods used to protect the papers from rain had different effects on the turbulence. The sys- tems that exposed the papers to wind speeds above about 3 m/s had consistent, high rates of deposition, around 65–70mg/dm2/week per 1mg F/m3. The two studies that were
done indoors with low wind speeds had rates of 25 and 33mg/dm2/week. Davison and Blakemore (1980) concluded that com- parability could be obtained by standardiz- ing the housing used to shelter the papers.
With adequate control of the turbulence by sheltering the surfaces and after calibrating the individual system, the technique gives a useful estimate of the HF concentration.
Figure 6.1 shows data of Davison and Blakemore (1980) collected in England near an aluminium smelter, using paper suspen- ded in a louvred box. Also shown are the data of Lynchet al. (1978) collected by CaO plated into upturned dishes exposed near a phosphate fertilizer factory in the USA. The individual regressions are: Davison and Blakemore, HF = 8.85×deposited F (mg/
cm2/day), r2= 0.749, n= 98, and Lynch et al., HF = 5.81×deposited F (mg/cm2/day) + 0.411,r2= 0.611,n= 18. Considering the differences in the techniques, sources and
locations, the two data sets are remarkably compatible.
An important question about the use of alkaline papers is whether they collect only HF or whether they collect a significant fraction of the particulate fluorides as well. Particles land on the exposed paper surfaces but the rate of deposition depends on the particle size and the wind speed (Table 2.1, Chapter 2). Larger particles (> 10mm) have a higher deposition velocity than HF, especially at higher wind speeds, so, if they form a significant fraction of the total fluoride, this will be reflected in the total amount deposited. However, particles as large as that only occur very close to certain sources and the louvred housing or upturned Petri dish that is used to shelter alkaline papers reduces the turbulence significantly, so interference from large particles is not a serious problem in most cir- cumstances. For smaller particles (< 5mm),
Fig. 6.1. The regression of weekly mean atmospheric HF concentration (mg/m3) on the rate of fluoride deposition on alkaline media.r= data of Davison and Blakemore (1980) from 2 years of continuous monitoring near an aluminium smelter: HF = 8.85×deposited F,r2= 0.749,n= 98.q= data from Lynch et al. (1978): HF = 5.81×deposited F + 0.411,r2= 0.611,n= 18. (From Davison, A.W. and Blakemore, J.
(1980) Rate of deposition and resistance to deposition of fluorides on alkali impregnated papers.Environ- mental Pollution Series B1, 305–319, with permission from Elsevier.)
the rate of deposition on a paper in a housing is lower than that of HF, so the contribution to the total deposit is small. This interpreta- tion is supported by field data (Davison and Blakemore, 1980).
Alkaline papers can be deployed at any place where the site is clear of major obstruc- tions, such as trees or buildings, and where they are out of the reach of inquisitive animals, including humans. Knowledge of the sources of emissions – stacks, vents and doors or settling ponds – can be used to optimize the locations. As fluoride concentrations decrease in a non-linear way with distance, it may be best to site the samplers more densely close to the source or in the locations where stack fallout is expected. An initial survey of the fluoride content of vegetation (i.e. a biomonitor survey – see later) can be used to assist in choosing sites. Clearly, the method does
not give an exact estimate of the HF con- centration but it is sufficiently reliable to paint a broad, comparative picture, which can be used to support other investi- gations and interpret vegetation injury. For example, Fig. 2.2 (Chapter 2) shows how alkaline plates were used by Sidhu (1979) to monitor the HF concentrations near a phosphate plant. This demonstrated how far downwind the HF concentration was elevated above background. A second example, Fig. 6.2, shows the pattern of estimated HF concentrations around an aluminium smelter in Europe. The map was used to predict the locations where there might be the greatest effects on vegetation and therefore where to focus field surveys. Any symptoms recorded out- side the lowest contour were regarded as probably not being due to HF and needing further investigation.
Fig. 6.2. A contour map of the monthly mean HF concentrations around an aluminium smelter, drawn using data from alkali-impregnated papers. The sampling sites are shown as X. The data were converted tomg/m3, using the regression equation of Davison and Blakemore (1980), and mapped, usingSURFER software. The prevailing wind was from the west. The main aim of the exercise was to determine the area where a vegetation survey should be concentrated and to assist interpretation of visible symptoms shown by the plants. (The data were converted tomg/m3using the regression equation of Davison and Blakemore, 1980)
Precipitation and bulk deposition Several researchers have found that the fluoride content of precipitation is higher near sources and sometimes it correlates with the fluoride content of vegetation (see Chapter 2). Therefore, one possibility for monitoring is to record total or wet deposi- tion. However, as explained in Chapter 2, the problem with this approach is that the amount of fluoride collected in a deposition gauge depends upon the geometry of the gauge and the wind speed, so the data are not absolute measurements. Also, a deposi- tion gauge does not measure the amount deposited on vegetation and it is impossible to estimate the latter on a routine basis, so deposition measurements are not recommended.
Bioindicators and Biomonitors – Definitions
There is such a plethora of definitions of these two terms that they confuse students and practitioners alike. A bioindicator plant has been defined as a sensitive species that responds in a characteristic and predictable manner to the conditions that occur in a particular region or habitat (Mellanby, 2000). These responses are generally visible changes in leaves, flowers or fruits, and include formation of chlorotic (yellow) or necrotic lesions, other pigment changes, such as the production of red pigments (anthocyanosis), numerous kinds of leaf distortions, fruit deformities, reduced growth, alteration in plant form and other effects. A bioindicator has also been defined as a plant that exhibits a specific array of symptoms when exposed to a particular phytotoxicant (Feder, 1978;
Feder and Manning, 1979; Manning and Feder, 1980), which is similar to Mellanby’s (2000) definition. One can compare a bio- indicator species with Rich’s statement that ‘crops are like canaries’ (Rich, 1964) – bioindicators that have been used since Roman times as indicators of carbon monoxide in coal mines. The canary, which
became unconscious, could be revived in clean air and used over and over again.
Another use of bioindicators is the measurement of changes in metabolic, physiological, histological and genetic characteristics resulting from exposure to fluoride, although this approach is time- consuming and not pollutant-specific.
Some investigators refer to ‘sentinel organ- isms’, which can be defined as ‘the most sensitive organisms to fluoride in each plant or animal category’. Thus, sentinel organisms are included as the quintessen- tial bioindicators. In recent years, the term biomarker has been introduced as ‘systems that generally include a subsystem of a whole organism to identify a specific endpoint’ (Huggett et al., 1992). The US Environmental Protection Agency (EPA) definition of a biomarker is ‘any assessment of pollutants or biological effects of pollut- ants which enter the organism’s organs or tissues’ (Fowle and Sexton, 1992). From the latter definition, one might conclude that the biomarker could include both bio- indicators and biomonitors. Jamil (2001) said that bioindicator refers to ‘the presence, absence or abundance of certain species indicating information about envi- ronmental quality’, and biomonitor to ‘the measurement among individuals of their molecular, biochemical, or physiological parameters’. Peakall and Shugart (1991) dis- cussed ‘biomarkers of exposure, biomarkers of effect’ and ‘biomarkers of susceptibility’.
We prefer to use biomarker to describe the more subtle changes in organisms, such as alterations in enzyme activity or metabolic processes due to chronic or acute exposures to a toxicant, but which may or may not demonstrate toxicity at the organismic level.
The second category is the biomonitor, which is often defined as ‘a plant that is tol- erant to fluoride exposure and accumulates it in foliar or other tissues’. Leaf analysis can provide an estimate of the length and inten- sity of exposure and a rough approximation of the concentration of fluoride encountered – provided that the dynamics of fluoride uptake and loss are known (Davison, 1982, 1983, 1987), and that is an uncommon
situation. However, Burton (1986) defined biomonitor in its broadest sense as ‘the mea- surement of growth and effects on metabo- lism and of concentrations of contaminants in living organisms’, thus including both bioindicators and biomonitors. Using bio- monitors is also time-consuming, but, as part of a continuing and multifaceted pro- gramme, it can be invaluable. The presence and amount of fluoride in plant tissues can also help to verify that visible plant injuries are caused by fluoride and not any of a host of edaphic, climatic and biological factors that can mimic fluoride injury, provided that the data are interpreted with due care.
Bioindicators
Having conducted field studies for a number of years and in many ecological situations, ranging from tropical to cool, temperate rainforests, from high to low deserts and from semi-tropical to temperate forests, it is the authors’ experience that at least one species of plant in each area has stood out as a sensitive bioindicator. It is only in heavily industrialized areas that sensitive indicators tend to be totally absent or greatly restricted in their distribution.
But suitability as a bioindicator varies with the location; the most sensitive species in one area may be considered as tolerant in another. A good example of this isEucalyp- tus globulus, which is regarded as being of intermediate tolerance in its native Austra- lia (Doley, 1986). However, it is grown as a timber tree in much cooler and more humid conditions in western Europe and there it appears to be less tolerant, possibly because of the lack of water stress and greater stomatal conductance over the whole of the year. For unknown reasons, an occasional species may be sensitive in some locations and relatively insensitive in others. For example,Hypericum perforatum(St John’s wort) is usually a bioindicator of choice because it is widely distributed in the USA, Canada and Europe. In nearly all areas, the species is sensitive to fluoride, expressing
symptoms as orange-red tip necrosis of young leaves. However, in at least one area of the Pacific coastal USA (Tacoma, Washington State), H. perforatum has been exposed to HF for many years and is relatively tolerant. North of Tacoma in Bellingham, Washington State, also a Pacific coastal city, the species is once again very sensitive. There is also similar variation in parts of Europe. Why is this?
Perhaps some populations are more tolerant because of heritable differences in stomatal conductance, leaf structure or water use, or perhaps it is due to local soil conditions characteristic of the area. Determining the causes of this kind of variation needs an experimental approach to determine the contribution made by genetic and environ- mental factors but it has rarely been done.
Whatever the reason, several of the species listed in Table 6.1 may also exhibit differences in sensitivity in some locations.
Many authors have provided informa- tion on reactions of plants to fluoride and those species suitable as plant bioindicators.
Thus, for North American species, the reader is referred to Treshow and Pack (1970), Weinstein (1977) and Weinstein et al. (1998); for South America, Arndtet al.
(1995) and Weinstein and Hansen (1988);
and for Australia and New Zealand, Doley (1986). For Europe, several authors have reported on relative sensitivities, notably Borsdorf (1960), Bolay and Bovay (1965), Bossavy (1965), Guderianet al. (1969) and Dässler et al. (1972). But all of these lists should be used with great caution.
Comparing the rankings is difficult, in part because few species are common to all lists, but also because the sensitivity classes are subjective and based on different criteria.
For example, Borsdorf (1960) divided species into four classes:hochempfindlich (highly sensitive), empfindlich (sensitive), wenig empfindlich (slightly sensitive) and unempfindlich (non-sensitive). The highly sensitive category included plants that showed obvious injury at the furthest dis- tance from the source that he investigated, while the non-sensitive (tolerant) plants showed little or no injury even when adjacent to the source. The criteria for
Latin binomial Common name Response USA, UK, Europea
Abies alba Abies balsamea Abies concolor Acer campestre Acer ginnala
*Acer negundo
Acer palmatum(and cvs) Acer pennsylvanicum Acer platanoides Acer saccharinum Acer saccharum Achilleaspp.
Agavespp.
*Allium ursinum
*Alliumspp.
Allyssum saxatile Alnus rubra Alnus rugosa Althea rosea
Amaranthus retroflexus Ambrosia artemisifolia Ambrosia trifida
*Amelanchier canadensis Anthemis cotula
Apium graveolens Arctiumspp.
Arctostaphylos uva-ursi Asclepias syriacus Asparagus officinalis Asterspp.
Avena sativa Avena sativa Berberis julianae
Berberis nervosa, Berberis repens Berberis thunbergii
Berberis verruculosa
*Berberis vulgaris Beta vulgaris Betula lutea Betula nigra Betula papyrifera Betula pendula Betula populifolia Brassica kaber
Brassica oleracea(and cvs) Bromus tectorum
Buxus sempervirens Callistyphus chinensis Camellia japonica Campanulaspp.
Capsicum frutescens
Silver fir Balsam fir Colorado fir Hedge maple Amur maple
Box elder or Manitoba maple Japanese maple
Striped maple Norway maple Swamp or silver maple Sugar maple
Yarrow Agave Wild garlic Onion Golden tuft Red alder Speckled alder Hollyhock Pigweed
Common ragweed Giant or great ragweed Service-berry, saskatoon-berry Burdock
Celery Burdock
Bear-berry or kinnikinick Milkweed
Asparagus Aster
Oat (mature plant) Oat (young) Wintergreen barberry Oregon grape Japanese barberry Warty barberry Common barberry Beet
Yellow birch Black birch White birch European birch Grey birch Common mustard
Cabbage, kale, Brussels sprouts Downy brome grass
Boxwood
Rainha-margarida or China aster Camellia
Bellflower Pepper
S S S I I S S–I
I I I I T I S S–I
T I–T I–T T T T T I T T T T I T I T S T S I T S T T T T T T T T I I I–T
T T T continued Table 6.1. Relative sensitivities of higher plants to atmospheric fluorides, based on foliar symptoms.