Supporting Information: USEtox human exposure and toxicity factors for comparative assessment of toxic emissions in Life Cycle Analysis: Sensitivity to key chemical properties

Ralph K. Rosenbaum, Mark A.J. Huijbregts, Andrew D. Henderson, Manuele Margni, Thomas E. McKone, Dik van de Meent, Michael Z. Hauschild, Shanna Shaked, Dingsheng Li, Lois Swirsky Gold, Olivier Jolliet

S.1 Simple Model for Plant Uptake of organic chemicals by T.E. McKone

Here we provide a recommendation for a simple one-compartment vegetation model that is not intended for fate modelling, but for food exposure modelling. The USEtoxTM model comparison in Bilthoven revealed significant differences in vegetation uptake algorithms used in the multimedia fate/exposure models under consideration. This section is organized as follows: a summary as presented in main manuscript, a brief background discussion, presentation of a scientific consensus plant uptake model and parameterisation, discussion of its advantages, limitations and sensitivities.

S.1.1 Summary as presented in main manuscript (for details see below):Below-ground plant concentration:

Cplant-bg (mol/m3)= Csw x RCF x 0.8 [kg(plant)-m3(water)]/[L(water)-m3(plant)]

whereCsw = concentration of contaminant in soil solution, mol/m3

RCF = Min [200, 0.82 + 0.0303(Kow)0.77] (mol/kg per mol/L)

Above-ground plant-parts concentration The three terms below account for transfer to above-ground plant tissues from soil, from air (gas phase), and from particulate matter in air:

where Csw = concentration of contaminant in soil solution, mol/m3;

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Cair = concentration of contaminant in gas phase of the air, mol/m3; Cap = chemical concentration in air attached to particles, mol/m3;Kow = octanol-water partition coefficient;Kaw = air water partition coefficient (H/RT);Qtrans = area equivalent transpiration flow from soil through stems, m3(transpiration)/m2(land area) (default = 0.001);MTC = mass transfer coefficient at the air-leaf interface, m/d (default = 86);LAI = leaf area index, the one-sided area of plant leaf surfaces per unit land area,

m2(leaf surfaces)/m2(land area) (default = 4);λg = growth dilution rate constant, 1/d (default = 0.035);λt = rate constant for elimination by chemical transformation (i.e. metabolism) within above-ground plant tissues,

1/d (default = 0.092);Vplant = area equivalent volume of above ground plant tissues, m3(tissues)/m2 land area, assumed to be the sum of

cuticle and leaf volumes (default = 0.0125);vd = deposition ratio accounting for both wet and dry particle deposition of particles from air to plant surfaces,

mol/(m2-d) per mol/m3 or m/d (default = 500)

S.1.2 Background

Chemicals are transferred from air and soil to edible plant parts both through root uptake and through transfer from air through leave surfaces. The transfer from soil to edible plant parts has two stages. In the first stage the chemical can be transferred from soil to the vegetation via uptake through the roots. In the second stage the chemical moves from roots to the portion of the plant that is consumed (translocation). These stages are illustrated in Figure S1 where some common bioconcentration ratios are also illustrated. For many chemicals, the second stage is the dominant pathway by which chemicals are transferred from contaminated soil to edible plant parts. The transfer from air to above ground plant tissues involves transfer from air to the surface of the leaves and from there into other plant tissues.

It has long been recognized that vegetation can accumulate pollutants from air [See for example Buckley, 1982]. Field studies have revealed that, for a whole range of semi-volatile chemicals, gas-phase transfer from the atmosphere is the dominant pathway for uptake of pollutants from air into above-ground vegetation (Tolls and McLachlan, 1994; Simonich and Hites, 1994; Welsch-Pausch et al., 1995). Field studies have also been used to estimate plant-atmosphere partition coefficients (Simonich and Hites, 1994; Thomas et al., 1998a, 1998b; Hiatt, 1998, 1999). However, competing pathways, a large number of environmental variables, and the overall complexity of the soil/plant/air system make it difficult to use field studies alone to directly measure both the kinetic and the thermodynamic factors controlling pollutant uptake into plants. Experiments in exposure chambers have thus been used to measure plant uptake under controlled steady-state exposure conditions (Trapp et al., 1990; Bacci et al., 1990a, 1990b; Hauk et al., 1994; Schroll and Scheunert 1992; McCrady, and Maggard, 1993; Maddalena, 1998; Maddalena et al., 2001). These experiments have provided insight for the interpretation of field experiments, but have not provided sufficient information to interpret how transformation and translocation impact exposure. As a result, in spite of field and laboratory studies, the role of terrestrial vegetation in transferring chemicals from air into edible food commodities remains poorly understood.

The inability of field studies to accurately link soil and air concentrations to human uptake has fostered the need for models to make the link from soil and air to food.

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Fig S1 Illustration of the pathways by which chemical agents are transferred from irrigation water to soil and soil solution and then into roots, stems, leaves and edible plant tissues. The RCF refers to the root concentration factor. The BCF is the bioconcentration factor from dry soil solids to above-ground vegetation tissues.

S.1.3 Proposal for a consensus model

The proposed consensus model includes two components—the roots and the above-ground plant parts (AGPP). We begin with consideration of the root concentration algorithm and then propose the AGPP model, which is more complex and includes transfer from both roots and air to edible plant parts.

Chemicals in soil enter plants primarily through the root system. Uptake of chemicals from soil into root tissues appears to be inversely proportional to water solubility and proportional to oil solubility (as represented by the octanol/water partition coefficient). However, as the molecules become large this relationship does not hold. Thus, studies on the bioconcentration of non-ionic organic chemicals have focused on correlations between partition factors and chemical properties that express relative solubility, such as the octanol-water partition coefficients (Kow). As a result there are a number of simple models that express plant uptake in terms of the octanol-water solubility ratio. Briggs et al. (1982, 1983) have developed an estimation equations based on Kow for uptake of contaminants into (a) roots, (b) transpiration stream, and (c) stems from soil solution. Based on a review of reported measurements of bioconcentration for 29 persistent organo-chlorines in plants, Travis and Arms (1988) have correlated plant-soil bioconcentration (on a dry-mass basis) in above-ground plant parts with octanol-water partition coefficients. More recently, Dowdy and McKone (1997) compared the precision and accuracy of the molecular connectivity index (MCI) and the octanol-water partitioning coefficient (K ow) as predictors of bioconcentration from the soil matrix into above- or below- ground vegetation tissues. Polder et al. (1995, 1998) have attempted to validate the uptake of chemicals by roots and leaves as estimated by a number of models and have empirically demonstrated need for and feasibility of more simple consensus models.

Root Concentration Factor (RCF)

It appears that for the below-ground portion of plants, the RCF is the appropriate bioconcentration factor for estimating edible concentrations. Briggs and his colleagues (1982, 1983) have developed correlation models for the transfer of contaminants from soil solution to roots, transpiration stream, and stems. These models remain widely used and as reliable as any proposed alternative for below-ground components of vegetation. Based on experiments with 18 radio-labelled O–methylcarbamoyloximes and substituted phenylureas, Briggs et al. (1982) measured partitioning between barley roots and nutrient solutions. They refer to this partition coefficient as the root concentration factor, RCF. It represents the ratio of contaminant concentration in root, Xroot in mol/kg(fresh mass), to contaminant concentration in soil solution, Xsw in mol/L, and it takes the form:

log (RCF – 0.82) = 0.77 log(Kow) –1.52 ± 0.12 (n=18, r2=0.97) [S1]

which can also be written as:

Xroot/Xsw = RCF = 0.82 + 0.0303(Kow)0.77 [S2]

Our interest in concentrations expressed in mol/m3 for both soil solution and plant tissues lead to the conversion

Croot/Csw = RCF plant/[1000 L/m3] = 0.8 RCF [S3]

where

Croot = contaminant concentration in root, mol/m3;

Csw = concentration of contaminant in soil solution, mol/m3;

plant = plant root density, assumed equal to 800 kg/m3;

Setting a Limit on RCF for High-Kow Compounds

The RCF estimation equation provide in Equation [S2] tends to give unreasonably high values of RCF for compounds with a high Kow (>105). The values are high because they violate the mass balance in that RCF corresponds to a quantity of chemical in the plant that exceeds what can enter the plant through uptake. To set a limit on RCF, we recognize that the maximum amount of chemical that can enter a plant during a season (that is up to 365 days) is

Maximum uptake = Csw Qtrans 365 d [S4]

where

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Csw = concentration of contaminant in soil solution, mol/m3; and

Qtrans = area equivalent transpiration flow from soil through stems, [m3/d(transpiration stream)]/m2(land area), which we set to 0.001 m3(transpiration)/m2(land area) (Trapp and Matthies, 1995).

Equation [S4] sets the maximum value of Croot under conditions in which all of the contaminant taken into the plant by the transpiration stream is retained in the roots with no losses during a period of 365 days

Maximum uptake = Csw Qtrans 365 d/y = Croot Vroot [S5]

where

Vroot = area equivalent volume of fresh roots, m3(roots)/m2 land area, which we take as low but plausible value of 0.0025 m3(roots)/m2 land (Nobel, 1999) .

Recognizing that 0.8RCF = Croot/Csw, we rearrange Equation [S5] to obtain

RCFmax = [Qtrans 365 d]/[0.8 Vroot] = 0.001 m/d 365 d/[0.8 0.0025 m] ≤ 200

Transpiration Stream Concentration Factor (TSCF)

Briggs et al., (1982) also used experiments with 18 radio-labelled O–methylcarbamoyl-oximes and substituted phenylureas to measure partitioning between the transpiration stream in barley roots and nutrient solutions. From this work they proposed the transpiration stream concentration factor (TSCF) to express the translocation of chemicals from the root-soil pore water to the stem. The TSCF has been further characterized by Hsu et al. (1990) and Burken and Schnoor (1998). The TSCF accounts for the reduction in concentration in the pore water as it crosses the root membrane and moves through the xylem to the stem. Thus, the TSCF represents the ratio of contaminant concentration in the xylem stream of the stem, mol/m3, to contaminant concentration in soil solution, mol/m3. Briggs et al. (1982) proposed the following TSCF correlation based on Kow:

TSCF = ± 0.27 (n=18, r2=0.61) [S6]

This term is important for linking the root system with above ground components of vegetation.

Transfers from Air and Soil to Above-Ground Plant Parts (AGPP)

To address above ground plant parts (ABPP), requires a model that can balance the transfer of chemicals both from the transpiration stream and from leaf uptake/loss. For this we find it useful to build on the work of Trapp and Matthies (1995), who developed a one-compartment differential mass balance model for uptake of chemicals into plant leaves from soil and air. Included in this model are uptake from soil through the transpiration stream, gaseous deposition, volatilization from leaves, chemical transformation, and growth dilution. Hung and Mackay (1997) followed a similar approach as Trapp and Matthies to mass balance, but used three compartments—roots, stem, and leaves—instead of one compartment. The Hung and Mackay model provides a similar opportunity for constructing a transient and plant-specific method for calculating BCFs, but requires many more plant- and chemical-specific parameters. The Trapp and Matthies model provides a method for assessing with a relatively simply model the transient or long-term bioconcentration in ABPP. It is set up by balance gains and losses from the leaf tissues as follows:

CAGPP(t) = (mass gain)/(loss rate) [1 – exp(–loss-rate x t)] [S7]

where

CAGPP(t) = concentration in plant leaves, mol/m3;

mass gain = the sum of all mass inputs to the leaf tissue--transpiration stream, gaseous diffusion from air, and dry and wet deposition of particles.

loss rate = the rate constant for all loss terms such as diffusion out of the leaves, growth dilution, and chemical transformation.

For the consensus model, we consider a steady state version of this model in which gains equal losses, that is gains = losses, such that:

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[Transfer from roots by transpiration] + [Diffusion from air to leaf] + [deposition] = [diffusion from leaf air] + [growth dilution] + [transformation] [S8]

We then replace these terms with expressions for each process to obtain:

[S9]

where

Csw = concentration of contaminant in soil solution, mol/m3;

TSCF = transpiration stream concentration factor, m3(soil solution)/m3(transpiration stream);

Qtrans = area equivalent transpiration flow from soil through stems, m3(transpiration)/m2(land area);

Cair = concentration of contaminant in gas phase of the air, mol/m3;

MTC = mass transfer coefficient at the air-leaf interface that is the air-to-leaf conductance, m/d;

Arealeaves = total area of plant leaf surfaces per unit land area, m2(plant surfaces)/m2(land area)

= 2 LAI, where LAI is the leaf area index, the one-sided area of plant leaf surfaces per

unit land area, m2(leaf surfaces)/m2(land area) (Nobel, 1999);

Cap = chemical concentration in air attached to particles, mol/m3;

vd = deposition ratio accounting for both wet and dry particle deposition of particles from air to plant surfaces, mol/(m2-d) per mol/m3 or m/d

Cplant = average concentration in above-ground plant tissues, mol/ m3;

Kpa = partition coefficient between air (gas phase) and plant tissue mol/m3(plant tissue) per mol/m3(air);

λg = growth dilution rate constant, 1/d;

λt = rate constant for elimination by chemical transformation (i.e. metabolism) within above-ground plant tissues, 1/d; and

Vplant = area equivalent volume of above ground plant tissues, m3(tissues)/m2 land area, assumed to be the sum of cuticle and leaf volumes.

Rearranging the above mass balance term gives

[S10]

Model Parameterization

The value of TSCF can be obtained from Equation 6. Over the range 0.1 ≤ Kow ≤ 109 it ranges from a minimum of 0.033 at Kow = 0.1 to a peak value of 0.78 at Kow = 65 and then to a minimum of 10-10 at Kow = 109.

For the parameters Qtrans and MTC, we use the default values first used by Trapp and Matthies (1995). These are Q trans = 0.001 m3(transpiration)/m2(land area) and MTC = 86 m/d.

For the consensus model we recommend λg = 0.01/d and set the plant metabolism/transformation parameter λ t = 0. Although the default value recommended for λg by Trapp and Matthies (1995) is λg = 0.035/d, they based this on experiments with plants in the early and rapid growth phase. We select a lower value as more representative of the annual cycle and different plant types. We set λt = 0.092, assuming that degradation on plant surfaces is 10 times higher than in soil.

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The one-sided leaf-area index (LAI) is the ratio of plant leaf-surface area and is in the range 3 to 5 with a representative value of 4 (Nobel, 1999; Maddalena, 1998). In order to estimate Arealeaves, we double this 8 to account for gas exchange from the cuticle on both sides of the leaves (Maddalena, 1998). The deposition ratio includes both wet and dry deposition and accounts for the vegetation interception fraction. Based on research on radioactive particulate matter by Whicker and Kirchner (1987), we assign this parameter value 500 m3/(m2-d).

The model of Reiderer (1990) can be used to obtain Kpa by arranging their cuticle-air partition coefficient into a form that expresses plant-air partitioning,

. [S11]

where

fpl = fraction of total plant volume that is lipid;

Vc = area equivalent volume of plant tissues that are cuticle tissue, m3(cuticle)/m2(land area);

Kaw = air water partition coefficient equal to H/RT;

fpa = fraction of total plant volume that is gas;

fpw = fraction of total plant volume that is water;

Vl = area equivalent volume of plant tissues that are leaves, m3(leaf)/m2(land area);

The standing biomass consisting of fresh-mass plant tissue is on the order of 10 kg/m2 in agricultural landscapes (Nobel, 1999; Maddalena, 1998). The density of leaf tissues is in the range from 700 to 900 kg/m3 with 800 kg/m3 as a typical value (Trapp and Matthies, 1995; Reiderer, 1990; Maddalena, 1998). Combing these values and assuming that plant biomass is equivalent to leave tissue volume gives Vl a value of 0.0125 m3(leaf tissue)/m2(land area). Combining Vl and Vc

gives a value of Vplant equal to 0.0125 m3(plant)/m2(land area).

Based on the correlation of leaf-air bioconcentration factors with air-water and octanol-water partition coefficients, as well as numerous direct measures of extractable lipid and cutin, a typical value for the volume fraction of plant tissue that is lipid (fpl) including extractable lipid and cutin, is on the order of 0.015 (Bacci et al., 1990; Reiderer, 1990; Paterson et al., 1991; Trapp et al, 1994; McCrady, 1994; Tolls and McLachlan, 1994; Trapp and Matthies, 1995; Maddalena, 1998 and Bakker et al, 1999). Measurement of the composition of citrus leaf indicates that the leaf-surface is approximately 75% lipid/cutin and 25% non-lipid material with insignificant volume fractions of water and air (Schonherr et al., 1984). The leaf surface is approximately 4 micron thick (Reiderer, 1990 and Schonherr et al., 1990). This means that, if the overall lipid content of the plant is 0.015, the lipid content of the cuticle is 0.75 and the leaf interior is 0.005 with the leaf-surface containing some 65% of the total lipid/cutin in the plant. The fraction of plant that is air (void) is 0.035 with almost 0.65 being water. The area equivalent volume of cuticle is calculated from the leaf-area index (LAI) and the cuticle thickness, which is on the order of 4 x10-6 m (Reiderer, 1990; Schonherr et al., 1990). This gives Vc on the order of 1.6 x10-5 m3/m2.

We can now make substitutions of all the parameter values above into Equation [S11] to obtain the following expression for Kpa

[S12]

which becomes

[S13]

Making substitutions into Equation 10 and arranging into terms that relate to root uptake, particle deposition, and gas-phase mass transfer gives the following expression:

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[S14]

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S.2 Supporting graphs for the results section

Kow – Kaw for air and freshwater emissions:

Fig S2 Kaw [unit less] vs. Kow [unit less]for emission to continental air, grouped by dominant intake fraction pathway

Fig S3 Kaw [unit less] vs. Kow [unit less] for emission to freshwater, grouped by dominant intake fraction pathway.

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Fate in Air: Inhalation intake fraction

Fig S4 Inhalation intake fraction for emission to continental air vs. half-life in air, grouped by Koa[unit less].

Fig S5 Inhalation intake fraction (iF [kgintake/kgemitted]) for emission to freshwater vs. Kaw [unit less], grouped by half-life in air.

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Fig S6 Ratio of Ingestion iF / inhalation iF vs. Koa [unit less], grouped by Kaw [unit less].

Fig S7 Bioaccumulation factors BAF [L/kg] for exposed produce after an emission to continental air versus K pa, grouped according to degradation rate in plant λt [1/d].

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Fig S8 Ingestion iF / inhalation iF vs. Koa, grouped by dominant intake pathway.

.Fig S9 Bioaccumulation factors BAF [L/kg] for fish after emission to freshwater, versus Koa, grouped according to the fate factor in water for a freshwater emission.

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S.3 Route to route extrapolation

S.3.1 Description of experimental data from the Carcinogenic Potency Database (CPDB)

Thus far in LCA data for different exposure routes (oral, including gavage - i.e. stomach tube, water, and diet; inhalation; and a few injection tests) have been combined in a single harmonic mean in rats and a single mean in mice. In this paper we analyze these routes separately using both theoretical and empirical bases that are further described below.

The CPDB: The analysis by route uses the Carcinogenic Potency Database (CPDB http://potency.berkeley.edu), a resource of the results of 6540 chronic, long-term animal cancer tests on 1547 chemicals, providing qualitative and quantitative analyses of both positive and negative experiments that have been published over the past 50 years. The CPDB standardizes the diverse literature of cancer tests, which vary in protocol. For each experiment, information is included in the CPDB on species, strain, and sex of test animal; features of experimental protocol such as route of administration, duration of dosing, average daily dose rates in mg/kg body weight/day, and duration of experiment; experimental results are provided on target organ, tumour type, and tumour incidence; carcinogenic potency (TD50) and its statistical significance; shape of the dose-response, author’s opinion as to carcinogenicity, and literature citation.

The inclusion criteria for the CPDB are designed to identify reasonably thorough, chronic, long-term tests of individual chemicals. Only experiments with dosing for at least ¼ the standard lifespan of the species (24 months in rats or mice) and an experiment length at least ½ the lifespan are included. Only routes of administration with whole body exposure are included. Doses are standardized, average dose rates in mg/kg/day. A description of methods used in the CPDB to standardize the diverse literature of animal cancer tests is given on the CPDB website at http://potency.berkeley.edu/methods.html. for: 1) Criteria for inclusion of experiments 2) Standardization of average daily dose rates and 3) TD50 estimation for a standard lifespan. The standard protocol is chronic dosing for 24 months in rats and mice, and a terminal sacrifice at the standard lifespan of 24 months.

S.3.2 Methods to compare carcinogenicity by inhalation vs. oral routes:

The vast proportion of experiments in the CPDB are by an oral route. Inhalation tests for volatile chemicals are more difficult to conduct, require special chambers and equipment, and are more expensive. We searched the CPDB for chemicals tested by inhalation in at least one experiment, and identified 106. Of these, only 33 also had at least one experiment in either rats or mice by an oral route—gavage, water, or diet, and these 33 constitute the dataset for the route comparison. The most frequent oral route for volatile chemicals is gavage. A chemical is considered positive in our analysis if the author of at least one published paper evaluated the experimental results as carcinogenic. If a chemical is tested in more than one experiment by a given route, negative experiments are ignored if there is at least one positive experiment.

For the comparison of carcinogenic potency by inhalation and oral routes, when there is only one positive experiment in a species for a chemical by a given route, then the most potent TD50 value from that experiment is reported. When more than one experiment is positive, the reported TD50 value for that route in our analysis is a harmonic mean of the most potent TD50 value from each positive experiment (Table S2). The harmonic mean is similar to the most potent site for chemicals in the CPDB For the comparison of potency, we calculated separate harmonic means of TD 50 for positive tests in each species by inhalation and by oral routes. For details see http://potency.berkeley.edu/td50harmonicmean.html.

The results of the route comparison for each of the 33 chemicals may reflect variation in factors other than route, thus making conclusions difficult for this small number of chemicals. Factors that may vary by route and thus affect positivity and potency results include: (a) Variation in the power of individual experiments to detect a carcinogenic effect, e.g. the length of experiment (tumour incidence increases with age), the duration of the dosing period, the number of animals per group, the number of dose groups, whether the dose level of the maximum tolerated dose was achieved. (b) Variation in the number and type of experiments in the CPDB for a chemical by a given route, which affects the likelihood of detecting a carcinogenic effect, e.g., the number of different strains or sex groups tested, the number of experiments by each route. (c) Conventions in the CPDB for standardizing dose-rates and TD50 values when experiments differ from the standard protocol.

Differences in carcinogenic potency (TD50 values) by oral vs. inhalation routes may reflect differences in absorption and toxicity of the chemical by the 2 routes. In standard animal cancer tests, the highest dose tested is the maximum tolerated dose (MTD), which is the dose predicted from a subchronic test to result in approximately a 10% lower body weight gain in dosed animals compared to controls from causes other than tumours in a 2-year chronic study. Thus, the MTD is a

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minimally toxic dose. To the extent that there are large differences in absorption of a chemical by oral vs. inhalation route of exposure, this will be reflected in the MTD, i.e. if absorption is much less by an oral route, then the MTD administered in the oral bioassay will be larger, because more of the chemical would be required to produce a minimally toxic effect than in an inhalation bioassay. Physico-chemical properties may influence the absorbed fraction by each route of intake. These properties, especially the different partition coefficients, may also affect the subsequent distribution of the dose to the target organs. Carcinogenic potency values (TD50) estimated from positive (statistically significant) animal cancer tests are restricted to a narrow range surrounding the highest dose tested in a bioassay, the MTD (Bernstein et al, 1985) . Therefore, if the absorbed fraction and the MTD differ greatly by route of administration for a chemical, one expects to find differences between the TD50 values by the two routes as well.

The full plot of the CPDB, including the chemicals in Table S1 and S2, is available on-line and can be further examined for details of each experiment by each route. The experimental design variation such as the type of oral route used (water, diet or gavage) strain, length of test, dose, as well as the TD50 values from different experiments by any route are provided.

Table S1 summarizes the positivity results by species for the 33 chemicals tested by both oral and inhalation routes, and Table S2 provides results on positivity and TD50 for each chemical in each species. In Table S2, results are organized by whether chemicals have positive experiments in CPDB by both routes in at least one of the species, by only one route, or have only negative tests. Full results on each experiment for each chemical are available at http://potency.berkeley.edu/condensedplot.html and at http://potency.berkeley.edu/chemnameindex.html

Comparison of positivity: inhalation vs. oral routes: our analyses are restricted to chemicals with a test by both oral and inhalation routes in the same species, either rats or mice. Positivity by route is summarized in Table S1 separately for rats and mice as well as in either species. The details of positivity for each chemical are given in Table S2.

Table S1. Carcinogenicity comparison of 33 chemicals tested in the same species by both inhalation and an oral route (gavage, water, or diet).

Positivity by route

Either species by both routesN=33 (%)

Rats by both routesN=32 (%)

Mice by both routesN=18 (%)

Positive by both oral and inhalation 19 (58%) 15 (47%) 9 (50%)Positive by only one route* 9 (27%) 12 (38%) 8 (44%)Not positive by either route 5 (15%) 5 (16%) 1 (6%)Totals 33 (100%) 32 (100%) 18 (100%)Source: Carcinogenic Potency Database (http://potency.berkeley.edu). If there are both positive and negative experiments in a species, the negatives are ignored. * In rats, 4 chemicals are positive only by inhalation, and 8 only by an oral route. In mice, 4 chemicals are positive only by inhalation and 4 only by an oral route.

In earlier work Gold et al. (1987) found that even for comparisons of near-replicate experiments in which a chemical was tested twice by the same route, in the same species, strain, and sex of test animal 15% of 70 comparisons differed in positivity. This provides a benchmark for comparison to differences by route since it is a background rate of disagreement in carcinogenicity. Disagreement between routes is greater: 27% overall considering whether both routes are positive at least in one of the species. For mouse experiments, 44% disagree by route and for rats 38% disagree (Table S1).

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Table S2. Comparison of Positivity and TD50 (harmonic mean) in rats and mice for 33 chemicals tested by both inhalation and oral routes. Fractions absorbed by inhalation and by oral routex. Ratio of oral divided by inhalation TD50 and ratio of absorbed fractions by inhalation divided by oral.

TD50 rats TD50 mice x Fractions absorbed Ratio TD50oral /TD 50

inh Ratio of absorbed

A. Positive by both the inhalation and oral route in same species (N=19) CAS

[mg/kg-d]Inh Oral  

[mg/kg-d] Inh Oral

[unitless]

Inh[unitless]

Oral[unitless]Rats

[unitless]Mice

fractions

[unitless]

Acrylonitrile 107-13-1 29.5 11.8 NT 6.32 0.97

0.09 0.40 - 11

1.1720

0.60 1.3 2.0 2.0 1.9 2.1 1.533

9.21890 3.417

1.3 0.89 4.5

0.36

Benzene 71-43-2 553 169 441 64.3 0.51

0.47 0.31 0.15

Cadmium chloride* 10108-64-2 0.00910 49.5 - NT 1.00

0.0014 **5440 -

Carbon tetrachloride 56-23-5 27.8 449 18.9 150 0.29

0.49 16 8.0

Chloroform 67-66-3 - 262 362 90.3 0.59

0.45 0.25 -

1,2-Dibromo-3-chloropropane 96-12-8 0.150 0.937 1.96 4.45 0.99

0.49 6.2 2.3

1,2-Dibromoethane 106-93-4 1.58 1.43 18.3 6.22 0.89

0.45 0.91 0.34

1,4-Dichlorobenzene 106-46-7 - 644 271 398 0.93

0.49- 1.5

1,2-Dichloroethane 107-06-2 80.8 8.04 510 101 0.80

0.39 0.10 0.20

Ethylbenzene 100-41-4 48.6 4,350 1,605 NT 0.71

0.49 89 -

Ethylene oxide 75-21-8 56.0 7.43 63.7 NT 0.97

0.03 0.13 -

Formaldehyde* 50-00-0 1.08 558 43.9 NT 1.00

0.11517 -

Hydrazine* 302-01-2 0.309 42.6 - 2.93 1.00

0.0005 **138 -

Methyl tert-butyl ether 1634-04-4 297 2,201 6550 NT 0.88

0.26 7.4 -

1,2-Propylene oxide 75-56-9 95.5 39.5 912 NT 0.98

0.06 0.41 -

Tetrachloroethylene 127-18-4 145 - 240 93.6 0.63

0.49- 0.39

Trichloroethylene 79-01-6 668 - 4,402 691 0.42

0.48- 0.16

Vinyl acetate 108-05-4 1,130 201 - 3,920 0.90 0.20 0.18 -

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Vinyl chloride 75-01-4 5.93 7.50   21.8 NT 0.15

0.41 1.3 -

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B. Positive either by inhalation or oral route in rats or mice and negative by the other route in that species (N=9)

Dichlorvos 62-73-7 - 4.16 NT 70.4 1.00 0.39Epichlorohydrin 106-89-8 - 2.96 NT NT 0.99 0.13Ethyl acrylate 140-88-5 - 119 - 324 0.93 0.36Methylene chloride 75-09-2 724 - 1,098 - 0.59 0.34Styrene 100-42-5 23.3 - 210 - 0.83 0.49Telone II, technical grade(no epichlorohydrin) 542-75-6 - 100 118 - 0.61 0.46Toluene 108-88-3 - 3,065 - NT 0.60 0.49Toluene diisocyanate, commercial grade (2,4 (80%)- and 2,6 (20%)-) 26471-62-5 - 33.7 - 250 1.00 0.49

Vinylidene chloride 75-35-4 - -   34.6 -  0.18 0.47   C. Negative by both inhalation and oral route in the species tested by both routes (N=5)Chlorine 7782-50-5 - - - NT 0.008 0.16N-Methyl-2-pyrrolidone 872-50-4 - - NT 2,047 0.99 0.021-Nitropropane 5522-43-0 - - NT NT 1.00 0.49Phosphine 7803-51-2 - - NT NT 0.15 0.03

Trichlorofluoromethane 75-69-4 - I   - -  0.08 0.48    NT=Not Tested by that route in that species. - = all tests are negative by that route in that species,I = inadequate study.*Chemicals for which inhalation and oral TD50s differ by more than a factor 100 in rats.** Chemicals for which inhalation and oral absorbed fractions differ by more than a factor 100.xCalculated for female rats according to Eq. S16 and S17 for QAO= 0.00088[kg/day], QAW=27800 [kg/day], = 0.0009 [kg/day],

= 0.015 [kg/day]. . Detailed individual chemical properties are available in Huijbregts et al. (2010).

Comparison of carcinogenic potency: inhalation vs. oral routes. Potency values for different experiments can vary by route for a given chemical based on experimental and calculation factors other than route. The route comparison of potency needs to be viewed in that context: strain, sex, experiment length, exposure duration, doses administered, and conventions in the CPDB to standardize the experimental literature. The TD50, which is a dose-rate in mg/kg/day, may also be different for different routes due to variation in the fraction of the administered dose that is absorbed by each route.

Table S2 reports the harmonic mean of TD50 for all positive chemicals in each species by route Section A of the table gives the 19 chemicals for which potency by the different routes can be compared because there is at least one positive experiment by both routes.

Table S2 indicates that in mice, 9 chemicals are positive by both routes. The carcinogenic potency values estimated from oral vs. inhalation tests in mice are within a factor of 2 of each other in 11% (1/9) of the route comparisons, within a factor of 5 in 67%, and all are within a factor of 10. The differences in potency by route are greater in rats: TD 50 values estimated from oral vs. inhalation tests for the 15 chemicals positive by both routes were within a factor of 2 in 13% (2/15) of the comparisons, within a factor of 5 in 33%, within a factor of 10 in 67% , within a factor of 100 in 80%. There are 3 outliers in the potency comparison between oral and inhalation routes for which TD50 values vary by more than a factor 100. All are in rats, and in all 3 cases the oral TD50 is weaker (larger) than the inhalation TD50. The TD50 values by inhalation are more potent than oral administration by a factor of 138 for hydrazine, a factor of 517 for formaldehyde, and a factor of 5,440 for cadmium chloride (See Ratios of oral TD50 to inhalation TD50 columns in Table S2) .

As shown in the last column of Table S2, hydrazine and cadmium chloride, for which the inhalation TD50 is more than 100-fold more potent than the oral TD50, the absorption by inhalation is more than 500-fold greater than by oral route. These outliers have the greatest difference in absorption fraction of all chemicals that are positive by both routes. Since, the fraction absorbed by the oral route is lower than by inhalation this difference in absorption by route may in part account for the higher (weaker) TD50 values by oral administration, as discussed above under Methods.

The fraction absorbed and its role in USEtox is discussed further below in S.3.2

For the outliers that differ by more than a factor of 100 in TD50, TD50 differences by route may also to some extent reflect other factors related to experimental design and toxicity:

16

- For hydrazine, experimental factors may account for some of the 138 fold more potent TD50 in rats by inhalation compared to administration in water: in the positive tests by inhalation, exposure to hydrazine was stopped after 12 months, and the animals survived to 30 months; since the daily dose rate used to estimate TD50 averages the administered dose over the length of the experiment, the dose rate used for TD50 estimation is lower (more potent) than the actual dose administered, and the TD50 is therefore more potent. In contrast, in the experiment with administration in water the TD 50

value is weaker, in part, because there was no terminal sacrifice and animals were permitted to die; the experiment length to the death of the last animal was 36 months, and the TD50 becomes weaker when extrapolated to a standard lifespan of 24 months. Thus experimental design also contributes to the difference in TD50 by route for hydrazine.

- For cadmium chloride the TD50 by inhalation is more potent than orally by a factor of 5,440, and the fraction absorbed by inhalation is more than 500 greater than orally. In the positive experiments, the doses administered by diet are in the low milligram range and doses by inhalation in the microgram range. Like the experimental design issue for hydrazine, exposure by inhalation was stopped early, and the average daily dose rates are lower than the doses administered, which is reflected in lowered TD50 values compared to the administered maximum tolerated dose.

- For formaldehyde the absorbed fraction is more similar by oral and inhalation routes, differing by only a factor of 9 (Table S2). The TD50 by inhalation is 517 times more potent. Formaldehyde is positive by inhalation in many studies, and in one of two studies by drinking water. Factors contributing to the difference in TD50 values by route include: The dose administered by inhalation is about 50- fold lower than the dose given by water, and the tumor incidence rate is much higher in the inhalation tests that in the positive drinking water tests. Formaldehyde by inhalation in rats and mice only induces nasal tumors which are at the site of administration.

S.3.2 Theoretical Toxicokinetic Approach For Route To Route Extrapolation

Background: First, one can expect important variations in sensitivity if observed tumours are directly related to a given exposure route, e.g. for lung, nasal or gastro-intestinal cancers.

Second, as inhalation and oral doses are based on intake rather than on absorbed doses, physico-chemical properties may influence the absorbed fraction by each route of intake. These properties and especially the different partition coefficients may also affect the subsequent distribution of the toxic to the target organs. Therefore, one can expect that inhalation and oral TD50s are related as follows:

[S15]

We will identify in a first step the potential factors of influence affecting absorption by oral and inhalation routes and determine these absorption coefficients, as a basis to elaborate criteria to detect chemical for which exposure could highly vary depending on the exposure route.

Key properties affecting absorption by inhalation: We can use knowledge from Physiologically Based ToxicoKinetic modelling (PBTK) to propose a relationship between inhalation and absorption. According to Price et al. (2003), absorption by the inhalation route is driven by the blood:air partition coefficient ( ). Using the equation proposed by Poulin and Krishan (1996):

[S16]

where:

and are respectively the dimensionless octanol:air and water:air partition coefficient and

and are respectively the fraction of lipids and water in blood.

A similar equation is formulated for the tissue:air partition coefficients, based on the fractions of lipids and water in tissues. Béliveau et al. (2005) propose a similar equation for tissues and blood, while providing typical compositions for rats and humans. For blood, a term is added to represent the protein content of blood as a fraction of the blood volume. This later term would be of interest to consider in subsequent studies. It has however not been retained in the present analysis due to the lack of data on the protein:air partition coefficient.

17

Building on the PBPK model proposed by Chiu and White (2007), the absorption fraction through inhalation can be expressed as:

[S17]

where the Qp represent the alveolar flow = 7.31 [L/h], Q liver the liver blood flow = 1.57 [L/h] and Vmax and Km are the metabolic rate constants for the considered substance. This inhalation absorption fraction is maximum at high degradation rates when Vmax/Km tends towards infinity as illustrated in the right term of equation [S17] that provides a maximum limit to the absorption by inhalation as a function of the blood:air partition coefficient as a main chemical characteristic.

Key properties affecting absorption by oral route: For the ingestion route, Moser and MacLachlan (2002) and MacLachlan (1994) show that the transfer from gut to blood significantly depends on the octanol:water partition coefficient . Rosenbaum et al. (2009) shows that the absorbed fraction from gut to blood can be obtained from a steady-state mass balance applied to gut and blood, yielding:

[S18]

where and the output fluxes of water and lipid phase respectively through the faeces [kg/day], and with QAO in kg/day, the octanol film diffusion transfer coefficient and QAW in kg/day, the water film diffusion transfer coefficient. These last two parameters were extrapolated from Moser and McLachlan (2002, Eq. .23 and 24), assuming these parameters are proportional to BW0.75, using the human and rat body weights reported in table S3.

The second term of equation [S18] represents the fraction that is not degraded in the first passage in the liver in order to ensure a balanced comparison with the inhalation pathway. Assuming a blood volume of 0.016 L/female rat, one get:

. Considering the liver blood flow of Qliver = 1.57 [L/h], it is only for a very short half-lives that the fraction degraded in the liver is important, e.g. a 50% degradation in the first passage in the liver for a 25s half-life.

Table S2 provides the resulting fraction absorbed by inhalation and oral routes as calculated on the basis of the blood-air and octanol-water partition coefficients for female rats. The largest difference between oral and ingestion TD50s occurs for two chemicals with the largest difference between absorbed fraction by inhalation compared to absorbed fraction by ingestion. The two chemicals for which absorption by inhalation is more than 100-fold higher than by ingestion (marked as ** in Table S2) are cadmium chloride and hydrazine, two of the outliers, for which inhalation TD 50 is more than 100-fold more potent than oral TD50 (marked as * in Table S2).

As a starting point, the three outliers can therefore be identified by applying the two following criteria: a) the primary target site is specifically related to the route of entry (case of formaldehyde linked to nasal cancer) and b) The expected fraction absorbed via inhalation is much higher than the fraction absorbed via ingestion, e.g. 500-fold. This factor of 500 is rare but indicates that exposure by inhalation may be far more toxic than by ingestion.

In order to identify outliers a priori, the difference between inhalation and oral TD50, values has been studied as a function of the properties identified in the physico-chemical analysis presented in section S.3.2

Discussion and recommendation for route-to-route extrapolations

In all cases, it appears to be useful to give separate factors for oral and inhalation routes when available. However, if the available inhalation tests are negative and one or more oral ones are positive, a more thorough examination of experimental data is required as the negativity could just be linked to lower dosage in the test. This could be reflected in the uncertainty range on these TD50s.

Different strategies can be formulated to extrapolate missing data from one route (generally inhalation) based on data from another route (mostly oral):

18

1. Just use the 1:1 equivalency, without considering any correction factor. This has the advantage of simplicity, but could lead to underestimating the inhalation route by 3 to 4 orders of magnitude in specific cases.

2. Use the 1:1 equivalency with a correction factor based on the calculated ratio of pharmacokinetic absorption: . This would be difficult to apply to the extended set of TD50 due to the limited

availability of metabolism rate and may introduce other biases at the present level of knowledge.

3. As an intermediary recommendation between these two approaches, we propose at this stage to use the 1:1 equivalency, but flag chemicals as interim that have an inhalation absorbed fraction more than 500-fold higher than by oral route and reflect this with a higher uncertainty range for these chemicals. In the chemicals examined so far, the remaining chemicals will be off by less than 2 orders of magnitude, with most chemicals within one order of magnitude of the measured TD50.

Direct Cut-off criteria as a function of physico-chemical properties: It is interesting to understand the influence of the underlying chemical properties on the difference between and ratio and to study whether the cut-of criteria of 500-fold between absorption by the two routes can be directly translated in term of physicochemical properties. Figure S10 plots the ratio as a function of the Kow, differentiating between values of Kba – the two main factors underlying equations [S17] and [S18].

At low Kba, inhalation is reduced and the absorption may be higher through ingestion than inhalation, but the ratio remains relatively low, rarely higher than a factor 10. On the other hand, the high ratio for which inhalation may be

higher than ingestion are mostly determined by low values of in two Kow zones corresponding to the U-shape of figure

S10: The resulting absorption coefficient from gut to blood is reduced at low Kow. At higher Kow, the water film resistance (1/QAW) becomes dominant and the transfer from gut to blood also saturates. As the faeces excretion continues to increase with Kow, the resulting absorbed fraction is expected to decrease at log Kow higher than 6.5.

In practice in the observed range of TD50s, there is little difference between the 500-fold cut-off criterion between absorption fractions by route of exposure and a direct cut-off rule based on lower and upper values of Kow. This advocates to use the simplest metric at this stage with Kow smaller than 2.5 10-2 or Kow larger than 1010 to identify outliers in which the inhalation TD50 may significantly differ from the oral TD50. In these cases, the interim characterisation factor can underestimate the potential impact by inhalation.

1.0E-3

1.0E-2

1.0E-1

1.0E+0

1.0E+1

1.0E+2

1.0E+3

1.0E+4

1.0E+5

1.0E+6

1.0E+7

1.0E-4 1.0E-2 1.0E+0 1.0E+2 1.0E+4 1.0E+6 1.0E+8 1.0E+10 1.0E+12 1.0E+14

f abs

max

inh

/ fab

soral

(-)

Kow (-)

Kba < 1.0E+0

1.0E+0 ≤ Kba < 1.0E+2

1.0E+2 ≤ Kba < 1.0E+4

1.0E+4 ≤ Kba < 1.0E+7

1.0E+7 ≤ Kba

Fig S10 ratio as a function of Kow, differentiating between values of Kba. Calculated for QAO= 0.00088[kg/day],

QAW=27800 [kg/day], = 0.0009 [kg/day], = 0.015 [kg/day].

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S.3.3 Hierarchical procedure and a full set of TD50 for use in USEtox

Finally applying the above-described approach, a hierarchical procedure is established to determine cancer and non-cancer effect factors, and a full set of TD50s is derived using the full CPDB database. Factors are also differentiated between recommended and interim factors for which uncertainty is high.\

Human carcinogenic toxicity: The following order of preference in toxicity data has been used in the USEtoxTM

calculations of carcinogenic effect factors:

1. The carcinogenic effect factor takes as a point of departure the effect dose 50% (ED 50) which is preferably estimated from human data from high exposures in the workplace, using the low-dose, slope factor (q1*), for the few substances for which human data are available. The slope factors for acrylonitrile, arsenic, benzene, benzidine, beryllium, 1,3 butadiene, cadmium, chromium VI and nickel for humans after inhalation were available via the IRIS database (http://www.epa.gov/iris/). Low-dose slope factors for inhalation are reported in units of m3/g. The ED50 is derived by 0.8/ q1* where 0.8 is a 1/ q1*-to-ED50 conversion factor. After that, the unit was converted from g/m3 to kg/person/lifetime, using a lifetime of 70 years and an inhalation rate of 13 m3/day (N=9).

2. In case no quantitative effect information from human data was available from the IRIS database, TD50s from the carcinogenic potency database were taken (CPDB; http://potency.berkeley.edu/). ED50s for ingestion and inhalation are reported in the CPDB in units of mg/kg/day and are converted into an ED50 in units of kg/person/lifetime in USEtoxTM, using a lifetime of 70 years and a body weight of 70 kg. For cancer, the harmonic mean of all positive TD 50s in the CPDB is retained for the most sensitive species in animal cancer tests after application of an allometric interspecies conversion factor proportional to bodyweight to the power of 0.25. Table S3 provides an overview of interspecies conversion factors applied in constructing the USEtoxTM chemical database (Huijbregts et al., 2005). Experimental TD50 data in the CPDB are available for rats, mice, hamsters, dogs, and monkeys. For carcinogenic and non-carcinogenic effects, the ED50h,j adjusted for humans (kg/person/lifetime) is derived from the animal TD50 as follows:

[S19]

where ED50a,t,j is the daily dose for animal a (e.g. rat) and time duration t (e.g. subchronic) per kg body weight that

causes a disease probability of 50% for exposure route j (mg.kg -1.day-1), is the lowest species-corrected

harmonic mean of tumourigenic dose-rate for 50% of animals in a chronic, lifetime cancer test. AFa the extrapolation factor for interspecies differences (see Table S3), AFt is the extrapolation factor for differences in time of exposure, i.e. a factor of 2 for subchronic to chronic exposure and a factor of 5 for subacute to chronic exposure (Huijbregts et al., 2005), BW is the average body weight of humans (70 kg), LT is the average lifetime of humans (70 years), N the number of days per year (365 days.year-1).

For example for acrylonitrile, the first chemical in Table S2, harmonic means of oral TD 50s are 11.8 mg/kg-d for rats and 6.32 mg/kg-d and mice. Applying the species extrapolation factor of table S3 of 4.1 for rats and 7.3 for mice and an extrapolation factor for differences in time of exposure of AFt=1 (TD50s reported in CPDB have already been adjusted for time of exposure), the lowest species-corrected harmonic mean of TD50s is found for mice: 6.32/(7.3∙1)=0.87 mg/kg-d, against 11.8/4.1=2.88 mg/kg-d for rats. The resulting human adjusted ED50 is given by:

[S20]

If the inhalation animal toxicity data is given as an air concentration (EC50, mg.m-3), the human-equivalent ED50

(kg/person/lifetime) can be estimated by:

[S21]

where INH is the average daily human inhalation rate (13 m3/d). Note that the AFa, the extrapolation factor for interspecies differences, is by default 1 if the ED50 is given as concentration in the air. Metabolic activity and inhalation rate are assumed to have the same ratio for all species (N=584).

20

3. In case no quantitative effect information was available from the CPDB, the carcinogenic ED 50 has been estimated from the low-dose, slope factor (q1*) by a 1/q*-to-ED50 conversion factor of 0.8, based on animal data. The slope factors were again taken from the IRIS database (http://www.epa.gov/iris/) (N=10).

4. In case no data was available for a specific exposure route, a route-to-route extrapolation has been carried out, assuming equal TD50 or slope factor between inhalation and ingestion route (N=572, 534 extrapolated from oral route to inhalation and 38 from inhalation to oral route). Chemicals with all negative carcinogenic effect data were also included as true zero carcinogenic effect factors and distinguished from missing data (N=417).

Non-cancer human toxicity: In the case of effects other than cancer, for most of the substances, insufficient data were available to recalculate an ED50 with dose–response models. For chemicals with no evidence of carcinogenicity, the ED 50

has been estimated from no-observed effect level (NOEL) by a NOEL-to-ED50 conversion factor of 9. In case only a LOEL was available, a LOEL-to-ED50 conversion factor of 2.25 has been applied. NOELs and LOELs were derived from the IRIS database and from the World Health Organisation (WHO) with priority for data from the WHO. If relevant, conversion factors to extrapolate from sub-chronic to chronic exposure and sub-acute to chronic exposure were applied as well (see Huijbregts et al. 2005 for further details). The same approach as for carcinogenic effect was used to determine ED 50s. For some toxicity data after inhalation, however, substance-specific interspecies differences were derived by the US-EPA via pharmacokinetic modelling. In these specific cases, the interspecies conversion factors reported by the US-EPA were applied. As for carcinogenic effects, in case no data is available for a specific exposure route, a route-to-route extrapolation has been carried out, assuming equal ED50 between inhalation and ingestion route.

S.3.4 Interspecies extrapolation and relationship between exposure dose and concentration

Table S3 Interspecies conversion factors to humans for various species (Vermeire et al., 2001).

Type CF interspecies (-) Average bodyweight (kg)human 1.0 70pig 1.1 48dog 1.5 15monkey 1.9 5cat 1.9 5rabbit 2.4 2mink 2.9 1guinea pig 3.1 0.750rat 4.1 0.250hamster 4.9 0.125gerbil 5.5 0.075mouse 7.3 0.025

The need for interspecies correction is obvious when assuming that the toxic air concentration for which 50% over background of the individual are affected is equal between humans and the considered animals:

. In that case, the corresponding toxic doses 50% are related as follows:

and

thus

where VR is the ventilation rate for humans and animals. Since this ventilation rate is approximately proportional to the body weight BW0.75 for both animals and humans, this equation is transformed as follows:

demonstrating that the toxic dose 50% is approximately proportional to BW0.25, as also observed experimentally by Watanabe et al., 1992.

21

According to Vermeire et al. (2001), this leads to extrapolation factors of 4.1 for rats to humans and 7.3 for mice to humans.

S 3.5 Comparison with Kramer et al. (1996) for non cancer acute to chronic extrapolation

22

S.4 Acute to chronic extrapolation

S.4.1 Cancer data

Table S4 List of data used in the acute-to-chronic extrapolation for carcinogens: 107 orally administered rodent carcinogens rat and mouse TD50 values from CPDB and derived human adjusted ED50s from USEtox, compared to LD50

from HSDB database.

USEtox

CAS NameRat oral TD50

(mg/kg/day)Mouse oral TD50

(mg/kg/day)Oral ED50

(kg/lifetime)Rat oral LD50

(mg/kg)Mouse oral LD50

(mg/kg)1746-01-6 2,3,7,8-Tetracdd 0.0000235 0.000156 0.0000103 0.022 0.1141162-65-8 Aflatoxin B1 0.0032 0.0014 4.8 9

56-53-1 Diethylstilbestrol 0.223 0.0391 0.0096 3000 300010595-95-6 N-Methyl-N-Nitrosoethylamine 0.0503 0.022 90

684-93-5 N-Methyl-N-Nitrosourea 0.0927 1.23 0.040 74.962-75-9 N-Nitrosodimethylamine 0.0959 0.189 0.042 2759-89-2 N-Nitrosomorpholine 0.109 0.048 282

303-47-9 Ochratoxin A 0.136 6.41 0.059 3.9 46148-82-3 Phenylalanine mustard 0.066 13

10048-13-2 Sterigmatocystin 0.152 0.908 0.066 120 800621-64-7 N-Nitrosodipropylamine 0.186 0.081 480

57-63-6 Ethinyl estradiol 0.2 0.087 5000 2500151-56-4 Aziridine 0.377 0.092 15

98-07-7 Benzotrichloride 0.396 0.097 6000606-20-2 2,6-Dintrotoluene 0.292 0.127 177 621615-53-2 Ethyl N-Methyl-N-Nitrosocarbamate 0.128 237.8764-41-0 1,4-Dichloro-2-Butene 0.130 89 190930-55-2 N-Nitrosopyrrolidine 0.799 0.679 0.166 900303-34-4 Lasiocarpine 0.408 0.178 150509-14-8 Tetranitromethane 0.195 130 375143-50-0 Kepone 2.96 0.982 0.241 95924-16-3 Dibutylnitrosamine 0.691 1.09 0.267 1200100-75-4 N-Nitrosopiperidine 1.43 1.3 0.319 200

70-25-7 N-Nitroso-N-Methyl-N'-Nitroguanidine 0.803 2.03 0.350 377.5115-09-3 Methyl mercury chloride NEG 1.45 0.355 29.915 57.6315-22-0 Monocrotaline 0.94 0.410 66

92-67-1 4-Aminobiphenyl 2.1 0.515 500 20579-44-7 Dimethylcarbamyl chloride 0.628 1000

302-01-2 Hydrazine 42.6 2.93 0.718 60 5957-14-7 1,1-Dimethyl Hydrazine NEG 3.96 0.970 122 26595-80-7 2,4-Diaminotoluene 2.47 26.7 1.08 500

11096-82-5 Aroclor 1260 2.81 1.23 1300139-65-1 4,4-Thiodianiline 3.71 33.2 1.62 1100 620319-84-6 Alpha-hch 11.2 6.62 1.62 177101-90-6 Oxirane, 2,2'- 1,3-Phenylenebis(Oxymethylene) bi 3.78 24.3 1.65 2570 980126-72-7 Tris (2,3-Dibromopropyl) phosphate 3.83 128 1.67 1010

1120-71-4 Propane sultone 3.84 1.68 1200050-06-6 Phenobarbital NEG 7.37 1.81 162 13760-34-4 Methylhydrazine 7.55 1.85 33 33

10034-93-2 Hydrazine sulfate 40.8 7.59 1.86 601 740126-99-8 Chloroprene 2.00 450 146

91-94-1 3,3'-Dichlorobenzidine 28.1 2.12 3820 35262-55-5 Thioacetamide 11.5 8.81 2.16 301

446-86-6 Azathioprine NEG 8.92 2.19 535 13898001-35-2 Toxaphene NEG 9.09 2.23 80107-30-2 Chloromethyl methyl ether 2.40 500101-14-4 4,4'-Methylenebis(2-Chloroanaline) 19.3 2.53 1140 640

21725-46-2 Cyanazine 6.33 2.76 139 380111-44-4 Bis(2-Chloroethyl)ether 11.7 2.87 75 136612-83-9 3,3'-Dichlorobenzidine dihydrochloride 12.3 3.01 3820

21436-96-4 2,4-Dimethylaniline hydrochloride NEG 12.4 3.04 47072-55-9 P,P'-DDE NEG 12.5 3.06 880 700

Chemicals HSDB aCPDB a

23

75-21-8 Ethylene oxide 7.43 3.24 72 280107-06-2 1,2-Dichloroethane 8.04 101 3.51 670 413608-73-1 1,2,3,4,5,6-Hexachlorocyclohexane 14.8 3.63 100 59

51-79-6 Ethyl carbamate 41.3 16.9 4.14 1809 2500101-80-4 4,4'-Diaminodiphenyl ether 9.51 33.6 4.15 725 685

75-60-5 Cacodylic acid 11.4 NEG 4.97 644 65288-12-0 N-Vinyl-2-Pyrrolidinone 5.23 940 147050-18-0 Cyclophosphamide 12.8 5.58 160 13751-52-5 Propylthiouracil 13.7 409 5.98 1980

2303-16-4 Diallate 26.7 6.54 395319-85-7 Beta-Hexachlorocyclohexane 27.8 6.81 6000 1500

72-54-8 DDD NEG 30.7 7.52 113 146672178-02-0 Fomesafen 7.53 1250

66-27-3 Methyl methane sulfonate 31.8 7.79 225604-75-1 Oxazepam NEG 35.8 8.77 5000

79-34-5 1,1,2,2-Tetrachloroethane NEG 38.3 9.38 25095-06-7 Sulfallate 26.1 42.2 10.3 85094-59-7 Safrole 441 51.3 12.6 2950 2350

3688-53-7 2-(2-Furyl)-3-(5-No2Furyl)Acrylamide 29.4 131 12.8 340 475134-29-2 O-Anisidine hydrochloride 29.7 966 13.0 2000 1400121-82-4 1,3,5,-Trinitrohexahydro-1,3,5-Triazine 13.0 100 59120-71-8 P-Cresidine 98 54.3 13.3 1450

57-41-0 Phenytoin NEG 59.1 14.5 1635 150100-44-7 Benzyl chloride NEG 61.5 15.1 1231 1150

75-52-5 Nitromethane 17.6 940 950115-28-6 Chlorendic acid 40.8 141 17.8 1770636-21-5 2-Methylbenzenamine hydrochloride 43.6 840 19.0 940 1100122-60-1 Phenyl glydidyl ether 19.2 2500 1400611-23-4 Benzene, 1-Methyl-2-Nitroso- 50.7 22.1 940 1100

96-09-3 Styrene oxide 55.4 118 24.2 3000100-40-3 4-Vinylcyclohexene 106 26.0 1600

1836-75-5 Nitrofen 420 115 28.2 116 45087-68-3 Hexachlorobutadiene 65.8 28.7 90 8794-58-6 Dihydrosafrole 143 125 30.6 2260 3700

120-80-9 Catechol 71.5 244 31.2 300 260140-57-8 Aramite 96.7 158 38.7 3900 2000

62-56-6 Thiourea 93.5 NEG 40.8 20 8500117-10-2 9,10-Anthracenedione, 1,8-Dihydroxy- 245 201 49.2 7000123-91-1 1,4-Dioxane 267 204 50.0 5700140-88-5 Ethyl acrylate 119 324 51.9 760 1800

74-96-4 Bromoethane 65.0 135078-79-5 Isoprene 67.1 204399-55-8 5-Nitro-O-Toluidine NEG 277 67.9 574

2475-45-8 1,4,5,8-Tetraaminoanthraquinone 156 NEG 68.1 120060-35-5 Acetamide 180 3010 78.5 7000 12900

39156-41-7 C,I, Oxidation base 12A 183 906 79.8 4000100-00-5 P-Chloronitrobenzene NEG 473 116 294 650

76-03-9 Trichloroacetic acid NEG 584 143 400 4970149-30-4 2-Mercaptobenzothiazole 344 NEG 150 1490

79-01-6 Trichloroethylene NEG 691 169 4920 240225013-16-5 Butylated Hydroxyanisole 405 5530 177 2200 2000

88-06-2 2,4,6-Trichlorophenol 405 1070 177 820109-99-9 Tetrahydrofuran 178 1650120-32-1 5-Chloro-2-Hydroxydiphenylmethane NEG 1350 331 1700 65

62-44-2 Phenacetin 1250 2140 524 1650a: Blank indicates there are no data in the CPDB (or HSDB) database; "NEG" indicates there are tests but results are negative.

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S.4.2 Non cancer data

Table S5 List of data used in the acute-to-chronic extrapolation for non-carcinogens: 207 chemicals NOELs, LOELs and derived human adjusted non cancer ED50s from USEtox, compared to mice and rats LD50 from HSDB database.

USEtox

CAS Name NOEL/LOEL a

(mg/kg/day)NOEC/LOEC a

(mg/m3)Test animal

Oral ED50

(kg/lifetime)Rat oral LD50

(mg/kg)Mouse oral LD50

(mg/kg)

78-00-2 Tetraethyl lead 0.0012 * rat 0.000590 1.2 13532-27-4 2-Chloroacetophenone 0.18 * rat 0.022 127

2104-64-5 EPN 0.01 hen 0.031 7 12.262-38-4 Phenylmercuric acetate 0.0084 rat 0.033 22

13071-79-9 Terbufos 0.016 rat 0.063 2 5.46923-22-4 Azodrin 0.006 human 0.097 18

81-81-2 Warfarin 0.029 human 0.12 1.6 60919-86-8 Demeton-S-Methyl 0.03 rat 0.12 30786-19-6 Carbophenthion 0.01 human 0.16 20107-02-8 Acrolein 0.05 rat 0.20 29470-90-6 Chlorfenvinphos 0.05 rat 0.20 9.66

24017-47-8 Triazophos 0.0125 human 0.20 667786-34-7 Mevinphos 0.016 human 0.26 4.31113-02-6 Dimethoxon 0.025 dog 0.27 25 24115-90-2 Fensulfothion 0.025 dog 0.27 1.872-20-8 Endrin 0.025 dog 0.27 3 1.4

150-50-5 Merphos 0.1 hen 0.31 910298-04-4 Disulfoton 0.03 dog 0.33 2.3141-66-2 Dicrotophos 0.1 rat 0.39 16 11

5598-13-0 Chlorpyrifos methyl 0.1 rat 0.39 1500 2032639-58-7 Triphenyltin chloride 0.1 rat 0.39 190 18900-95-8 Fentin Acetate 0.1 rat 0.39 125 81.3116-06-3 Aldicarb 0.025 human 0.40 0.65 0.3333-41-5 Diazinon 0.025 human 0.40 66 17640-15-3 Thiometon 0.12 rat 0.47 120

10311-84-9 Dialifor 0.03 human 0.48 5 3925311-71-1 Isofenphos 0.05 dog 0.55 28 91.3

298-02-2 Phorate 0.05 dog 0.55 1.1 2.2510265-92-6 Methamidphos 0.04 human 0.64 25 14

300-76-5 Naled 0.2 rat 0.79 250 360563-12-2 Ethion 0.2 rat 0.79 24.499-65-0 1,3-Dinitrobenzene 0.4 rat 0.79 5985-00-7 Diquat dibromide 0.22 rat 0.87 194 125

22224-92-6 Fenamiphos 0.083 dog 0.91 2.717109-49-8 Edifenphos 0.25 rat 0.98 100 143

298-00-0 Parathion-methyl 0.25 rat 0.98 143689-24-5 Sulfotepp 0.5 rat 0.98 5 21.5

70-30-4 Hexachloroprene 0.75 * dog 1.03 67 6755-38-9 Fenthion 0.07 human 1.13 190

83121-18-0 Teflubenzuron 2.1 * mouse 1.16 4640 464038260-54-7 Etrimfos 0.3 rat 1.18 1600 437

576-26-1 2,6-Dimethylphenol 0.6 rat 1.18 296 4501563-66-2 Carbofuran 0.22 dog 1.20 8 2

78-34-2 Dioxathion 0.075 human 1.21 2357-24-9 Strychnine 2.5 * rat 1.23 2.35 2

2275-23-2 Vamidothion 0.08 human 1.29 64 4022781-23-3 Bendiocarb 0.38 rat 1.50 40 45

83-79-4 Rotenone 0.38 rat 1.50 132 35016752-77-5 Methomyl 0.1 human 1.61 17 102921-88-2 Chloropyrifos 0.1 human 1.61 82 60

56-38-2 Parathion 0.1 human 1.61 2 594-74-6 2-Methyl-4-Chlorophenoxyacetic Acid 0.15 dog 1.64 700 439

126-98-7 Methacrylonitrile 0.34 dog 1.86 25 2086-50-0 Methyl azinphos 0.48 rat 1.89 4.4 15

122-14-5 Fenitrothion 0.5 rat 1.97 500 13362597-03-7 Fenthoate 0.5 rat 1.97 77.7 350299-84-3 Ronnel 0.5 rat 1.97 1250122-34-9 Simazine 0.52 rat 2.05 5000 5000944-22-9 Fonophos 0.2 dog 2.19 8 14115-29-7 Endosulfan 0.6 rat 2.36 18 7.36

Main database, without chemicals with cancer effect ED50 or criterion of ED50

HSDB bChemicals US EPA or WHO

25

26

27

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S5. Additional references supporting information

Chiu and White (2007), Gold et al. (1984), Huijbregts et al. (2005), Moser and MacLachlan (2002), Poulin and Krishan (1996), Price et al. (2003) and Rosenbaum et al. (2009) are given in the reference section of the main paper.

Bacci, E., and C. Gaggi (1986) “Chlorinated Pesticides and Plant Foliage Translation Experiments,” Bull. Environ. Contam. Toxicol. 37, 850–857.

Bacci, E., D. Calamari, C. Gaggi and M. Vighi (1990a) "Bioconcentration of organic chemical vapors in plant leaves: Experimental measurements and correlation", Environ. Sci. Technol. 24:885-889.

Bacci E., M.J. Cerejeira, C. Gaggi, G. Chemello, D. Calamari, and M. Vighi (1990b) ”Bioconcentration of organic chemical vapours in plant leaves: The azalea model” Chemosphere 21:525-535.

Béliveau M, Lipscomb J, Tardif R, Krishnan K (2005) Quantitative structure-property relationships for interspecies extrapolation of the inhalation pharmacokinetics of organic chemicals. Chemical Research in Toxicology, 18 (3), pp. 475-485.

Bernstein L, Gold LS, Ames BN, Pike MC and Hoel DG (1985) Some tautologous aspects of the comparison of carcinogenic potency in rats and mice. Fundamental and Applied Toxicology 5: 79-86.

Briggs, G. G., R. H. Bromilow, and A. A. Evans (1982) “Relationship Between Lipophilicity and Root Uptake and Translocation of Non-Ionized Chemicals by Barley,” Pestic. Sci. 13, 495–504.

Briggs, G. G., R. H. Bromilow, A. A. Evans, and M. Williams (1983) “Relationships Between Lipophilicity and the Distribution of Non-Ionized Chemicals in Barley Shoots Following Uptake by the Roots,” Pestic. Sci. 14, 492–500.

Buckley, E.H. (1982) “Accumulation of airborne polychlorinated biphenyls in foliage,” Science, 216:520-522.Burken, J.G., and Schnoor, J.L. (1998) "Predictive Relationships for Uptake of Organic Contaminants by Hybrid Poplar

Trees," Environmental Science and Technology, 32, 3379-3385.Calamari, D., M. Vighi, and E. Bacci, (1987) “The use of terrestrial plant biomass as a parameter in the fugacity model,”

Chemosphere, 16(10-12):2359-2364.Chiou, C.T., G. Sheng and M. Manes (2001) "A Partition-Limited Model for the Plant Uptake of Organic Contaminants

from Soil and Water," Environmental Science and Technology, 35, 1437-1444.Dowdy, D.L., T.E. McKone, and D.P.H. Hsieh (1996) “The Use of the Molecular Connectivity Index for Estimating

Biotransfer Factors,” Environmental Science and Technology, 30, 984-989.Dowdy, D.L., and T.E. McKone (1997) “Predicting the Bioconcentration of Organic Chemicals from Soil or Air into Plants

Using Quantitative Structure Activity Relationships,” Environmental Toxicology and Chemistry, 16(12) 2448-2456.Gold LS, Wright C, Bernstein L and deVeciana M (1987) Reproducibility of results in “near-replicate” carcinogenesis

bioassays. JNCI 78: 1149-1158.Hauk, H., G. Umlauf, and M.S. McLachlan (1994) “Uptake of gaseous DDE in spruce needles,” Environmental Science

and Technology, 28:2372-2379.Hiatt, M.H. (1998) “Bioconcentration factors for volatile organic compounds in vegetation,” Analytical Chemistry, 70:851-

856.Hiatt, M.H. (1999) “Leaves as an indicator of exposure to airborne volatile organic compounds,” Environmental Science

and Technology, 33:4126-4133. Hsu, F.C., Marxmiller, R.L., and Yang, A.Y.S., (1990) "Study of Root Uptake and Xylem Translocation of Cinmethylin

and Related Compounds in Detopped Soybeans Roots Using a Pressure Chamber Technique," Plant Phyiology, 93, 1573-1578.

Huijbregts M, Jolliet O, Margni M, McKone T, van de Meent D, Rosenbaum R, Hauschild M (2010). USEtoxTM - Chemical database: inorganics and organics. Excel and word files available at www.usetox.org.

Hung, H. and D. Mackay (1997) “A novel and simple model of the uptake of organic chemicals by vegetation from air and soil,” Chemosphere 35:959-977.

Kraajj, H., and Connell, D.W. (1997) "Bioconcentraiton and Uptake Kinetics of Chlorobenzenes in Soy-Bean Roots," Chemosphere, 34(12):2607-2620.

Kramer HJ, van den Ham WA, Slob WHJ., Pieters MN (1996) Conversion Factors Estimating Indicative Chronic No-Observed-Adverse-Effect Levels from Short-Term Toxicity Data. Regulatory toxicology and pharmacology 23, 249–255.

Kipopoulou, A.M., E. Manoli, and C. Samara (1999) "Bioconcentration of Polycyclic Aromatic Hydrocarbons in Vegetables Grown in an Industrial Area," Environmental Pollution, 106:369-380.

McLachlan M.S (1994) Model of the Fate of Hydrophobic Contaminants in Cows. Environmental Science and Technology, 28, 2047-2414.

Peto R, Pike MC, Bernstein L, Gold LS and Ames BN (1984) The TD50: A proposed general convention for the numerical description of the carcinogenic potency of chemicals in chronic-exposure animal experiments. Environmental Health Perspectives 58: 1-8.

29

Maddalena, R. (1998) Model Directed Investigation and Measurement of the Fate of Polycyclic Aromatic Hydrocarbons Among the Atmosphere and Vegetation, PhD Dissertation in Agricultural and Environmental Chemistry, University of California, Davis, CA.

Maddalena, R.L., T.E. McKone, and N.Y. Kado (2001) “Exposure chamber measurements of mass transfer and partitioning at the plant/air interface,” accepted for publication in the journal Environmental Science and Technology.

McCrady, J.K. (1994) “Vapor-phase 2,3,7,8-TCDD sorption to plant foliage—A species comparison,” Chemosphere. 28:207-216.

McCrady, J.K. and S.P. Maggard (1993) “Uptake and photodegradation of 2,3,7,8-tetrachlorodibenzo-p-dioxin sorbed to grass foliage,” Environmental Science and Technology, 27(2):343-350.

McFarlane, J. C., T. Pfleeger, and J. Fletcher (1987) “Transpiration Effect on the Uptake and Distribution of Bromacil, Nitrobenzene, and Phenol in Soybean Plants,” J. Environ. Qual. 16, 372–376.

McFarlane J.C. (1995) “Anatomy and physiology of plant conductive systems” in Plant Contamination Modeling and Simulation of Organic Chemical Processes, S. Trapp, and J.C. McFarlane, (Eds.), New York: Lewis Publishers, 13-36.

McKone, T.E., and P.B. Ryan (1989) “Human exposures to chemicals through food chains: an uncertainty analysis,” Environmental Science and Technology 23;1154-1163.

McKone, T.E. (1993) “The Precision of QSAR Methods for Estimating Intermedia Transfer Factors in Exposure Assessments,” SAR and QSAR in Environmental Research, 1, 41-51.

Nobel, P.S. (1999) Physicochemical and Environmental Plant Physiology, 2nd Edition (Academic Press, New York).Paterson, S., and D. Mackay (1989) “Modeling the uptake and distribution of organic chemicals in plants,” in Intermedia

Pollutant Transport Modeling and Field Measurements D.T. Allen, Y. Cohen, and I.R. Kaplan (eds.), Plenum Press, New York.

Paterson S., D. Mackay, D. Tam and W.Y. Shiu (1990) “Uptake of organic chemicals by plants: a review of processes, correlations and models,” Chemosphere 21:297-231.

Paterson, S., D. Mackay, and A. Gladman (1991a) “A fugacity model of chemical uptake by plants from soil and air,” Chemosphere, 23(4):539-565.

Paterson S., D. Mackay, E. Bacci and C. Calamari (1991b) "Correlation of the equilibrium and kinetics of leaf-air exchange of hydrophobic organic chemicals," Environmental Science and Technology, 25:866-871.

Paterson, S., D. Mackay, and C. McFarlane (1994) "A model of organic chemical uptake by plants from soil and the atmosphere,” Environmental Science and Technology, 28(13):2259-2266.

Pinder, J.E. and K.W. McLeod (1989) “Mass loading of soil particles on plant surfaces,” Health Physics, 57(6):935-942.Polder, M.D., E.M. Hulzebos, and D.T. Jager (1995) "Validation of models on uptake of organic chemicals by plant roots,"

Environmental Toxicology and Chemistry, 14(9) 1616-1623.Polder, M.D., E.M. Hulzebos, and D.T. Jager (1998) "Bioconcentration of gaseous organic chemicals in plant leaves:

comparison of experimental data with model predictions," Environmental Toxicology and Chemistry, 17(5) 962-968.Riederer, M., 1990, “Estimating Partitioning and Transport of Organic Chemicals in the Foliage/Atmosphere System:

Discussion of a Fugacity-Based Model,” Environ. Sci. Technol. 24, 829-837. Riederer, M. and G. Schneider (1990) “The effect of the environment of the permeability and composition of citrus leaf

cuticles: II. Composition of soluble cuticular lipids and correlation with transport properties” Planta 180:154-165.Riederer, M., (1995) “Partitioning and transport of organic chemicals between the atmospheric environment and leaves,” in

Plant Contamination, Modeling and Simulation of Organic Chemical Processes, S. Trapp and J. McFarlane, (Eds) Lewis Publishers: Boca Raton. p. 153-190.

Ryan, J.A., R.M. Bell, J.M. Davidson, and G.A. O’Connor (1988) ”Plant Uptake of Non-ionic Organic Chemicals from Soils” Chemosphere 17:2299-2323.

Schonherr J., F. Kerler, M. Riederer (1984) "Cuticular Lipids as an Interface Between Plant and Environment" in S tructure, Function and Metabolism of Plant Lipids, P.A. Siegenthaler and W. Eichenberger, Eds.

Schroll S., and I. Scheunert (1992) “A laboratory system to determine separately the uptake of organic chemicals from soil by plant roots and by leaves after vaporization,” Chemosphere 24:97-108.

Schynowski, F. and W. Schwack (1996) “Photochemistry of parathion on plant surfaces: relationship between photodecomposition and iodine number of the plant cuticle” Chemosphere 33:2255-2262.

Schuler, F., P. Schmid, and Ch. Schlatter (1998) “Photodegradation of polychlorinated dibenzo-p-dioxins and dibenzofurans in cuticular waxes of laurel cherry (prunus laurocerasus),” Chemosphere 36:21-34.

Severinsen, M. and T. Jager, T. (1998) “Modelling the influence of terrestrial vegetation on the environmental fate of xenobiotics,” Chemosphere, 37(1):41-62.

Simonich, S.L. and R.A. Hites (1994) “Vegetation-Atmosphere partitioning of polycyclic aromatic hydrocarbons,” Environmental Science and Technology, 28, 939-943.

Sicbaldi, F., G. A. Sacchi, M. Trevisan and A.A.M. Del Re1 (1997) "Root Uptake and Xylem Translocation of Pesticides from Different Chemical Classes," Pestic. Sci. 50, 111-119.

Thomas, G., A.J. Sweetman, WA Ockenden, D. Mackay, and K.C. Jones (1998a) “Air-pasture transfer of PCBs,” Environmental Science and Technology, 32:936-942.

30

Thomas, G.O., K.E.C. Smith, A.J. Sweetman, and K.C. Jones (1998b) “Further studies of the air-pasture transfer of polychlorinated biphenyls,” Environmental Pollution, 102:119-128.

Tolls, J. and M.S. McLachlan (1994) “Partitioning of semivolatile organic compounds between air and Lolium multiflorum (Welsh Ray Grass),” Environmental Science and Technology, 28:159-166.

Topp, E. I. Scheunert, A. Attar, and F. Korte (1986) “Factors Affecting the Uptake of 14C-Labeled Organic Chemicals by Plants from Soil,” Ecotoxicol. Environ. Saf. 11, 219-228.

Trapp, S., M. Matthies, I. Scheunert, and E. M. Topp (1990) “Modeling the bioconcentration of organic chemicals in plants,” Environmental Science and Technology, 24, 1246–1252.

Trapp, S., C. McFarlane, and M. Matthies (1994) “Model for uptake of xenobiotics into plants - validation with bromacil experiments,” Environmental Toxicology and Chemistry, 13(3):413-422.

Trapp, S. and M. Matthies (1995) “Generic one-compartment model for uptake of organic chemicals by foliar vegetation,” Environmental Science and Technology, 29(9):2333-2338.

Travis, C. C., and A. D. Arms (1988) “Bioconcentration of Organics in Beef, Milk, and Vegetation,” Environ. Sci. Technol. 22, 271–274.

Umlauf, G. and M. McLachlan (1994) "Deposition of semivolatile organic compounds to spruce leaves," Environmental Science and Pollution Research, 1(3):146-150.

U.S. EPA (1998) “Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions, U.S Environmental Protection Agency, Office of Research and Development, report EPA 600/R-98/137

U.S. EPA (2000) "Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo-Dioxin (TCDD) and Related Compounds Part I: Estimating Exposure to Dioxin-Like Compounds Volume 3: Properties, Environmental Levels, and Background Exposures," US Environmental Protection Agency Report EPA/600/P-00/001Bc (DRAFT) September 2000.

Watanabe K, Bois FY, Zeise L (1992) Interspecies extrapolation: A reexamination of acute toxicity data. Risk Analysis, 12 (2), pp. 301-310

Welsch-Pausch, K., M.S. McLachlan, and G. Umlauf, (1995) “Determination of the principal pathways of polychlorinated dibenzo-p-dioxins and dibenzofurans to Lolium Multiflorum Welsh Ray Grass,” Environmental Science and Technology, 29(4): 1090-1098.

Whicker, F.W. and T.B. Kirchner (1987) "PATHWAY: A dynamic foodchain model to predict radionuclide ingestion after fallout deposition," Health Physics, 52(6):717-737.

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