JXB Advance Access originally published online on July 4, 2006
Journal of Experimental Botany 2006 57(11):2525-2533; doi:10.1093/jxb/erl016
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FOCUS PAPER |
Parameterization, comparison, and validation of models quantifying relative change of cuticular permeability with physicochemical properties of diffusants
Lancaster Environment Centre/Biological Sciences, Lancaster University, Lancaster LA1 4YQ, UK
*E-mail: g.kerstiens{at}lancaster.ac.uk
Received 18 November 2005; Accepted 11 April 2006
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Abstract |
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Top
Abstract
IntroductionPredictions from two previously published models and a new model for the relative change in cuticular permeability with boiling point, octanol/air partition coefficient, and/or molar volume of a wide range of diffusants (not including ions and large hydrophilic compounds) are compared with each other and to experimental data sets not used for model parameterization. While the models work in a similar way for all cuticles for which data are available, it is not yet possible to predict in absolute terms the permeability of any cuticles for which no data are available—that is, while the slope of a plot representing the change in permeability with diffusant properties is predictable, the position of the linear relationship along the ordinate needs to be determined experimentally for each type of cuticle at or near the relevant temperature(s).
Key words: Air pollution, depuration, dioxins, dry deposition, ecotoxicological model, persistent organic pollutants, plant/air partitioning, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, semi-volatile organic compounds
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Introduction |
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Vegetation forms a major sink for persistent, semi-volatile organic compounds (SOCs), which include polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated dibenzo-p-dioxins (PCDDs), and dibenzofurans (PCDFs). Many of these SOCs are toxic to humans. Dry gaseous deposition of these compounds to plant surfaces represents an important point of entry into the food chain. For instance, it has been estimated that about 10% of the PCBs deposited on British pasture finds its way into cows' milk (Thomas et al., 1998). As these are highly lipophilic compounds, the cuticle will form a (and probably the) major route of uptake from the air into leaves, although stomata may also play some part (Barber et al., 2002). As it is impractical to measure cuticular permeabilities for, at best, more than a few of these compounds for any plant species of particular interest, it is highly desirable to have reliable ways to predict permeabilities from readily available physicochemical properties of the compounds. Here, estimates of cuticular permeabilities for persistent organic pollutants will be derived for one type of cuticle from published values for herbicides and other compounds of high lipophilicity.
Plant cuticles are extracellular, highly heterogeneous membranes, and there are many differently structured types of cuticles (Jeffree, 1996), depending on species, organ, and sometimes even location within the organ, such as adaxial or abaxial leaf surfaces. However, all cuticles that have been investigated to date possess, in functional rather than morphological terms, a very thin zone very near the plant/atmosphere interface distinguished by its very low permeability, which separates the atmosphere from the bulk cuticle and is called the ‘skin’. To reach the bulk cuticle directly from the atmosphere, SOCs (as any other compounds) have to diffuse across the skin. The skin dominates overall cuticular permeability of all substances for which the aqueous pathway across the cuticle is of no or little importance (Schönherr and Riederer, 1989; Schönherr, 2006). Cuticles behave as solution-diffusion membranes to such substances, which are often referred to as ‘solutes’ in the cuticle-related literature.
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Deposition and uptake |
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‘Uptake’ of a substance by vegetation can mean different things: for a physiologist, it usually means transfer from the air (or soil) into the living tissue, where an herbicide or air pollutant may interact with a plant's metabolism. For an ecotoxicologist, it is more likely to represent deposition of a substance into or onto plants from the air (or soil), so that it will be taken up by, for instance, cattle feeding on those plants. This is an important distinction as foliar uptake of SOCs from the air, in the latter sense of the term, is usually a bi- or possibly triphasic process, involving a fast step thought to be adsorption onto the waxy surface and possibly dissolution within the epicuticular wax layer, followed by much slower diffusion across the skin and then accumulation in the bulk cuticle (the so-called sorption compartment) beyond the skin and inside the living tissue. The same is true for depuration (i.e. elimination) of pre-loaded plant material, thought to involve fast desorption (within minutes to hours) from the surface, and slower (and usually incomplete) clearance of material accumulated in the bulk cuticle and living tissue within days to weeks. In mechanistic terms, the process of diffusion across the cuticle is the best-studied and best-understood part of this sequence of events, and this is what the models compared in the following sections all describe.
External leaf surfaces are covered by epicuticular waxes, which may appear smooth or crystalline with varying degrees of architectural complexity and thus there is a wide range of ratios between fine-scale surface area and projected leaf area, and they may possibly expose uncovered polymeric cuticular material as well. The dynamics of ‘adsorption’ (here meaning the initial relatively fast step or steps of the uptake process, which may include dissolution within the lipids outside the skin) of lipophilic compounds from a solution or the air, and conversely the fast phase of depuration, have been investigated for a wide variety of systems, ranging from individual leaves placed in a solution (Schreiber and Schönherr, 1992a, b, 1993a, b) via individual plants exposed in the laboratory (Kömp and McLachlan, 2000; Barber et al., 2003) to an entire peat bog studied in situ (Hornbuckle and Eisenreich, 1996).
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Adsorption at the leaf surface |
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It is possible to derive an idea of the quantitative importance of adsorption to external lipids at partitioning equilibrium from the following model calculation. For barley (Hordeum vulgare) leaves it was found that, when in contact with an aqueous solution containing 10–4 mol m–3 2,4-dichlorophenoxyacetic acid (2,4-D), they carried about 1.5x10–13 mol cm–2 2,4-D adsorbed to their surface at 25 °C (Schreiber and Schönherr, 1992b). The concentration in the gas phase equivalent to the concentration applied is 3.1x10–8 g m–3 (water/air partition coefficient logKwa, 5.85; molecular weight, 221.04). With an average barley leaf thickness of 0.25 mm (Passioura and Munns, 2000) one obtains a ratio of total leaf surface area to leaf volume of 8000 m2 m–3. With this the ‘concentration’ of the adsorbed material in the leaf can be calculated as 2.65 mg m–3; the ratio of ‘concentration’ of adsorbed SOC in the plant to the concentration in the air then comes to 104.9. In a field study, compounds with logKoa (octanol/air partition coefficient) of just below 9, which is close to the logKoa of 2,4-D (8.66), were found to have attained partitioning equilibrium; the ratio of overall concentration of these compounds in the plants to the concentration in the air was approximately 107.1 (Böhme et al., 1999). Therefore, in this hypothetical case, the fraction of the total amount of 2,4-D taken up at partitioning equilibrium that was adsorbed to the external lipids would have been 104.9–7.1, or about 1%. Although the exchange of externally adsorbed SOCs with the atmosphere may be rapid, the fraction of the SOC present in the plant at partitioning equilibrium that is available for this process is likely to be quite small. This conclusion is in agreement with that of Tolls and McLachlan (1994) who investigated fluxes of a range of SOCs between the air, a ‘surface compartment’, and a ‘reservoir’ compartment in ryegrass leaves enclosed in a fugacity meter. They found that for the various compounds studied, the sorption capacity of the surface compartment ranged from 0.6% to 5.9% of the total uptake capacity of both compartments for the respective compound at the given partial pressure.
It depends on the purpose of any modelling exercise how important it is to include surface adsorption as a separate process in the modelling of SOC exchange kinetics. Tolls and McLachlan (1994) suggest that for modelling plant uptake following sudden changes in concentration, for example, after an accident, and for modelling the effect of daily temperature fluctuations on SOC distribution between vegetation and atmosphere, the rapid exchange between air and the surface compartment should in fact be taken into account. Also, studies on uptake of lipophilic compounds from aqueous solution by conifer needles suggest that some species with very elaborate wax crusts may have a particularly large capacity for surface adsorption (Schreiber and Schönherr, 1992a, 1993a).
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Cuticular permeabilities to individual compounds |
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Transport of lipophilic compounds across cuticles has been studied mostly by applying the compounds in aqueous solution. It appears safe to assume that the presence of water has no direct, physical effect on cuticular permeability to such compounds. Cuticular permeability (or permeance) is determined as the ratio of diffusant flux density (in units of mass per unit area and unit time) and diffusant concentration difference (in units of mass per unit volume) across the cuticle. Cuticular permeability referenced to concentrations in the air, Pa, is obtained from permeability referenced to concentrations in water, Pw, by multiplication with the water/air partition coefficient, Kwa (Lendzian and Kerstiens, 1991):
 | (1) |
Table 1 presents an overview of cuticular permeabilities for compounds with logKoa values in the range 
7–13, typical of SOCs (as well as for some less highly lipophilic test compounds used in model parameterization). The values of Pa fall into a wide range from 1.3x10–5 to 0.13 m s–1. It should be noted that where permeabilities for one particular type of cuticle were taken from different sources, the properties of the experimental plant material representing this cuticle type in the different experiments may have varied considerably. Results for a given type of cuticle and compound may vary between experiments by up to about an order of magnitude (cf. values for water permeability; Table 1 in Kerstiens, 1996). As by far the greatest number of permeability data is available for adaxial leaf cuticles of the evergreen tree Citrus aurantium this species was chosen for parameterization of the models to be compared here. It should be noted that the permeability of this type of cuticle is relatively low compared with that of the majority of more mesophytic plant species (cf. Table 1 in Kerstiens, 1996).
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Prediction of variation of cuticular permeability with diffusant properties |
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Model 1
The permeability of a membrane to a given compound is proportional to the partition coefficient K between the membrane and the medium containing the compound (diffusant, solute), and the mobility D of the compound in the membrane (Schönherr and Riederer, 1989). This relationship can be written as
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 | (3) |
Combining the two estimates of the regression coefficient into one interval, equation 3 can be rewritten as:
 | (3a) |
Furthermore, it has been shown that the logarithm of the mobility of organic compounds in plant cuticles decreases linearly with molar volume (Vx), with relatively little variation in the steepness of the relationship (the so-called size selectivity, ß') between different types of cuticle and compound groups, ranging from –12 to –7 mol l–1 with an average of 9.5 mol l–1 (Baur et al., 1996; Buchholz et al., 1998):
 | (4) |
It is worth noting that Popp et al. (2005) recently found that a similar value for ß' (8.8 mol l–1) applied to another set of lipophilic compounds, but discovered that size selectivity was greater (17 mol l–1) for a range of small, hydrophilic, organic non-electrolytes likely to diffuse along an alternative, hydrophilic pathway (a finding that is at odds with the much less severe size selectivity found for ions; Schreiber, 2005).
By substituting equations 3a and 4 into equation 2, one obtains:
 | (5) |
Note that the ‘constant’ term in the different equations refers to numerically different constants. The constant in equation 5 may vary by 3 integers between different types of cuticle and can only be determined experimentally at present (for a suggested method, see Kerstiens et al., 2006).
While Baur et al. (1996), Buchholz et al. (1998), and Popp et al. (2005) used McGowan molar volumes (Vx), the LeBas molar volumes (Vm) are more readily available for SOCs (Mackay et al., 1992–1995). While there is no clear theoretical relationship between the two parameters, which refer to the molar volume at either a given surface tension (Vx) or the respective compound's boiling point (Vm), respectively, a small regression exercise for a range of compounds resulted in a good linear relationship (r2=0.98) going through the origin, with Vx=0.45xVm. Disregarding the greater size selectivity for small hydrophilic compounds, as this is of little relevance to uptake of SOCs, equation 5 becomes
 | (5a) |
Multiple linear regression of the experimentally determined permeabilities of Citrus aurantium from Table 1 on logKoa and Vm produced the following relationship (r2=0.96):
 | (6) |
The regression coefficients for logKoa and Vm are significantly different from zero and are in excellent agreement with the results from the independent sorption and mobility experiments (i.e. ranges of coefficient values in equation 5a).
Model 2
Kerler and Schönherr (1988b) reported an empirical relationship between directly determined cuticular permeability of Citrus aurantium and logKow (r2=0.83):
 | (7) |
Two more models with somewhat greater correlation coefficients than model 2 were presented in the same paper, one in which Kow was replaced by the cuticle/water partition coefficient Kcw (r2=0.90), and one in which Vm was introduced as a further independent variable (r2=0.96). However, equation 7 is given preference here over the other two models from Kerler and Schönherr (1988b) as values of Kcw are not normally available, and one of the eight model compounds was not included in the model incorporating Vm. Equation 7 can be expressed in terms of logPa, logKow and logKoa, as follows:
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Model 3
Models 1 and 2 result in wildly inaccurate estimates of cuticular permeability to other pollutants such as O3 or SO2, as well as other gases (not shown). However, the linear relationship between logPa and boiling point Tb (Kerstiens et al., 1992) provides a third way of estimating Pa, which is also applicable for a subset of SOCs. This relationship was originally described for Citrus aurantium with a data set obtained with inorganic compounds (mostly air pollutants) whose boiling points ranged from about –180 °C to +100 °C. Of the seven compounds from Table 1 for which the boiling points are known, permeabilities to the four with the lowest boiling points (between 160 °C and 310 °C) fit the relationship very well (r2=0.96; Fig. 1), whereas those that boil at higher temperature fall well outside the linear relationship. Parameterized with all available data for compounds with Tb <310 °C it takes the following form:
 | (8) |
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Comparison of predictive models
Figure 2 shows the predicted values of logPa in Citrus aurantium, obtained from equations 6, 7a, and 8, for the model compounds from Table 1. The average distance between predicted and experimentally determined values is 0.185 log-units for model 1 (equation 6), 0.424 log-units for model 2 (equation 7a, and 0.159 log-units for model 3 (equation 8).
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Predicted permeabilities of Citrus aurantium adaxial leaf cuticles to 28 PAHs, 27 PCBs, two chlorobenzenes, 15 PCDDs, and seven PCDFs, for which the necessary physicochemical data could be obtained, were calculated from equations 6 and 7a and are shown in Fig. 3, together with predictions from model 3 for those compounds from this set for which boiling points were also available. All three models predict relatively similar slopes of logPa versus logKoa, ranging from 0.61 (model 3) to 0.84 (model 2), but the absolute values are widely apart.
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Validation of predicted changes of logPa with logKoa with results from independent studies |
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Experiments that provide (or lend themselves to the calculation of) atmosphere–plant mass transfer coefficients of SOCs are a way to test the predictions from the different models. Figure 4 presents results from four such experiments (Bacci et al., 1990a, b; Kömp and McLachlan, 2000; Barber et al., 2003, 2004). Only results for the slow phase (which is thought to represent diffusion across the cuticular barrier) are shown. The mass transfer coefficients, MTC, are defined in a way analogous to Pa (i.e. mass flux density divided by concentration difference, referenced to the gas phase, across the plant–atmosphere interface) but include the conductance of the stomatal pathway in parallel to the cuticular one and an atmospheric resistance element in series with that of the leaf surface. For the purpose of this analysis, the atmospheric resistance can be considered to be of relatively minor importance under the reasonably well-ventilated conditions of the experiments. The role of stomatal uptake is unclear. Any possible contribution to SOC elimination from wax erosion is considered negligible as none of the species considered showed an unusual prominence of epicuticular waxes, and the considerations above indicate that in such foliage no more than a few percent of the total SOC load will be present in the erodable phase. The examples demonstrate that the steepness of the relationships predicted from models 1 and 2 depends not only on the model but also on the range of chemicals under consideration, and that the same is true for the difference in absolute terms between predictions for Pa of Citrus cuticle from models 1 and 2.
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Bacci et al. (1990a, b; Fig. 4A) studied uptake of a range of herbicide and insecticide vapours, as well as other SOCs, by the evergreen shrub Azalea indica in a well-mixed fumigation chamber. The slope of logMTC versus logKoa (0.570) is in excellent agreement with that from model 2 (0.654), but model 1 predicts a significantly steeper slope (0.795). There is a certain discrepancy between the calculated MTC values and the models' predictions for Pa of the same substances in Citrus cuticle, but this is well within the natural variability in cuticular permeability between different species (cf. Table 1 in Kerstiens, 1996).
Kömp and McLachlan (2000) (Fig. 4B) quantified elimination of PCBs from contaminated Welsh ryegrass (Lolium multiflorum) under field conditions. Again, the correspondence between the slopes of measured logMTC versus logKoa (0.704) and predicted logPa versus logKoa (0.676 and 0.725 for models 1 and 2) is very good. The discrepancy in the absolute values is much greater than with Azalea; for model 1, this is still within a plausible range but the prediction from model 2 is very low.
For the two depuration experiments with the evergreen shrub Skimmia japonica and mixed grassland swards (Barber et al., 2003, 2004; Fig. 4C, D) the overlap with the SOCs included in Fig. 3 is very large and, therefore, the slopes of logPa versus logKoa shown there can be used for comparison with the observed slopes of logMTC versus logKoa. For Skimmia, agreement with the prediction from model 1 is very good (Fig. 4C). Model 2 predicts a slightly steeper but not significantly different relationship. The absolute values of the measurements are lower than the predictions for Citrus in this case, but again the difference is within the natural range of permeabilities.
Model 3 predicts far higher values than the observed MTC. Given the unexplained breakdown of the linear relationship between logPa and boiling point at around 300 °C (Fig. 1) and the fact that few SOCs boil below this temperature (Mackay et al., 1992–1995), use of this model for SOCs is not advisable.
The depuration from the grassland sward (Fig. 4D) raises an interesting point with regard to calculating MTC in depuration experiments. The calculation of this variable, when it is to be referenced to the gas phase for comparison with MTC or Pa derived from uptake experiments, requires knowledge of the plant/air partition coefficient Kpa, and determination of Kpa requires partition equilibrium between plants and atmosphere to be reached. For highly lipophilic compounds and lipid-rich foliage (e.g. needles of some conifers) the vegetation's uptake capacity may be so large that this would take months or even years. If equilibrium in the uptake phase preceding the depuration experiment is not reached for all SOCs and a best estimate for Kpa is obtained from concentrations before the uptake phase is terminated, Kpa of the most lipophilic SOCs are the ones most likely to be considerably underestimated, resulting in an underestimate of MTC (cf. equation 5 in Barber et al., 2004). This is a likely cause of the comparatively low regression coefficient between logMTC and logKoa in this experiment (Fig. 4D). However, it should be noted that the (non-significant) difference between the regression coefficients from the depuration experiments following 4 d or 14 d of contamination (at concentrations 200–2000 times of that in the depuration environment) does not support this theory; the slopes were 0.519 and 0.345, respectively (not shown). Skimmia (Fig. 4C) was contaminated for 28 d in the same polluted environment as the swards (Barber et al., 2003). The grass cultures used by Kömp and McLachlan (2000) were contaminated for only 3 d, but at concentrations 1000–100 000 times higher than in the depuration environment.
It is striking that in both the sward and Skimmia depuration studies, the slopes of logMTC versus logKoa for the fast phase (not shown) were in good agreement with the slope (0.746±0.009) from model 1, namely 0.648 ±0.048 for the sward and 0.681±0.071 for Skimmia (Barber et al., 2003, 2004). This appears to indicate that the fast step does not just involve surface adsorption but probably dissolution and diffusion within surface waxes (external to the cuticular skin) as well.
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Differences between species and influence of temperature |
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Species
Several lines of experimental evidence suggest that the relative changes in cuticular permeability with physicochemical properties of the diffusing compounds are similar for different types of cuticles, even though the absolute permeabilities to a given compound may differ by several orders of magnitude between the cuticles; in terms of model formulation, the regression coefficients tend to differ relatively little between species, whereas the constants will vary very considerably. Firstly, Table 1 shows that, on the whole, relative differences between permeabilities to different compounds are similar in different types of cuticles. Secondly, the regression coefficient in equation 8 was almost identical for Citrus and for the second type of cuticle studied, the structurally very different tomato (Lycopersicon esculentum) fruit cuticle of much higher permeability (Kerstiens et al., 1992). Thirdly, the ratio between the permeabilities to water and to benzoic acid changed only modestly across an array of 12 different types of cuticle whose permeabilities covered a range of two orders of magnitude (Niederl et al., 1998). Fourthly, the coefficients of logKoa and Vx in equation 5 did not differ significantly (for the same range of compounds) between different types of cuticles (Kerler and Schönherr, 1988b; Buchholz et al., 1998). And lastly, a strong correlation was found between water permeability and the mobility of a lipophilic molecular probe (octadecanoic acid) across two orders of magnitude in 24 different types of cuticles (Schreiber and Riederer, 1996). Elsewhere in this issue (Kerstiens et al., 2006), a method is proposed that should allow a relatively straightforward determination of cuticular permeability to a moderately lipophilic model substance in virtually any kind of leaf, which, in combination with any of the models presented here, would permit a first approximation estimate of permeability to any lipophilic compound in that type of cuticle.
Temperature
Both partitioning and mobility depend exponentially on temperature. The temperature effect on partitioning is negative, whereas for mobility it is positive (Baur et al., 1997; Merk and Riederer, 1997). Activation energies for the two individual processes or their combination—i.e. permeation—vary between cuticle types (Riederer and Schönherr, 1986; Baur et al., 1997; Schreiber, 2001). Therefore, the ratio of permeabilities to different substances will be more or less the same for any given pair of cuticles only if both cuticle types are at around the same temperature.
The temperature dependence of mobility has been studied for a range of lipophilic compounds and different cuticle types. Across all combinations the activation energies of diffusion varied by a factor of 2.5 (Baur et al., 1997). This variable increased with molecular size and differed, for a given substance, between different cuticle types by up to a factor of 1.8. Size selectivity linearly decreased with increasing temperature (Buchholz et al., 1998).
It has been found that the cuticle/air partitioning coefficient of volatile organic compounds (C1 to C6 alkanols) falls by roughly 50% for every increase in temperature of 10 K (tomato fruit cuticular matrix; Merk and Riederer, 1997). Plant/atmosphere partitioning of a range of PCBs in Welsh ryegrass was experimentally demonstrated to follow, as expected for an exponential dependence of K on temperature, an inverse linear relationship between logK and temperature. However, only the dichlorinated biphenyls displayed a decrease in K with rising temperature similar to that found by Merk and Riederer (1997), whereas the most highly substituted compounds showed, for instance, a nearly 50-fold decrease in K between 10 °C and 30 °C (Kömp and McLachlan, 1997a).
The enthalpy of transfer from the air into the plant varied, by up to about 30%, between different plant species (Kömp and McLachlan, 1997b). No comparative data for different types of cuticles are available, with the one exception of partitioning of 4-nitrophenol between aqueous solution and cuticles of tomato fruit or rubber tree (Ficus elastica) leaves studied by Riederer and Schönherr (1986). The enthalpy of transfer from the solution differed by about 30% between cuticle types at low concentrations of the solute, such as would be found with SOCs. As our understanding of the temperature dependence of octanol/air partitioning of SOCs is progressing (Chen et al., 2004) it will hopefully become possible to apply these approaches to cuticle/air partitioning as well.
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Conclusions |
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The predicted relationships for logPa versus logKoa from models 1 and 2, parameterized with permeability data obtained for the adaxial leaf cuticle of the evergreen tree Citrus aurantium, are generally in good agreement with the observed relationships between logMTC and logKoa in foliage-SOC uptake and elimination experiments under well-ventilated conditions. Absolute values obtained from the models are in most cases within a plausible range of the observed values of MTC, given the large differences between species, but there are also quite large differences between predictions from models 1 and 2 (typically by around an order of magnitude) for many compounds. The remaining inconsistencies cannot be resolved at the moment, but on the whole both models give valid first approximation estimates of how the kinetics of the slow SOC uptake or clearance phases change with the physicochemical properties of the pollutants. The mechanistic basis of the models should facilitate the incorporation of future insights from controlled model studies into how temperature and other factors affect these processes in the field.
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