Environmental Health Perspectives • volume 118 | number 3 | March 2010 345

Research

Human exposure to arsenic (As) can occur via 

different routes. A well-known early medical 

report about As exposure and adverse health 

effects discussed cancer associated with dermal exposure to As-containing medication 

used for treating some forms of skin diseases 

(Hutchinson 1887). Later studies on occupational populations exposed to As compounds 

in industrial environments demonstrated 

that respiratory inhalation is a primary route 

of occupational As exposure, but ingestion 

and dermal exposure can be significant in 

specific situations (Occupational Safety and 

Health Administration 2005; World Health 

Organization 2004).

Compared with the simpler As chemistry 

and easily identified As exposure in medical 

and occupational fields, As chemistry and 

exposure routes for the general population are 

much more complex. General population As 

exposure varies according to local geochemistry, environmental pollution, living conditions, lifestyles, and activity patterns of the 

exposed populations. Better characterization 

of environmental As levels and human activity patterns is critical for accurately assessing the human exposure to As in the general 

population and the related health risks.

Many efforts in studying As exposure of 

and regulating As intake by the general population have been focused on the ingestion 

of As-contaminated water (Abernathy et al. 

1999, 2003; Anetor et al. 2007; Chen et al. 

1988a, 1988b; Chiou et al. 2001; National 

Research Council 2001; Tchounwou et al. 

2003). This drinking water–focused As regulation also reflects a common understanding 

that inorganic As (iAs) is more harmful than 

organic As (oAs) (Tchounwou et al. 2003). 

A recent publication concluded that typical 

and high-end background exposures to iAs in 

the U.S. population do not present elevated 

risks of carcinogenicity (Boyce et al. 2008). 

However, other reports show significant 

dietary intake of iAs via food and even show 

food as a greater source of iAs intake than is 

drinking water (Meacher et al. 2002; Schoof 

et al. 1999a, 1999b). Yost et al. (2004) estimated dietary intake of iAs in U.S. children as 

3.2 μg/day on average. A recent study shows 

that, in three U.S. counties, the food intake 

pathway is the dominant contributor to total 

As (tAs) exposure and dose (Georgopoulos 

et al. 2008).

In this study we extend findings from the 

previous studies by a) assessing the dietary 

tAs and iAs exposure using the peer-reviewed 

U.S. Environmental Protection Agency 

(EPA) Stochastic Human Exposure and Dose 

Simulation (SHEDS) model (SHEDS 2007), 

b) using more recent and larger databases representative of the U.S. population for food 

consumption and As concentrations in food 

and drinking water, and c) conducting model 

evaluation using duplicate diet and biomarker 

data. We used a population-based dietary 

exposure model, one module of the SHEDS 

model (SHEDS 2007; Xue et al. 2006; 

Zartarian et al. 2006), to estimate the exposure of As (tAs and iAs) from both food and 

drinking water. We linked the total predicted 

exposure with the Modeling ENvironment 

for TOtal Risk with Physiologically Based 

Pharmacokinetic Modeling for Populations 

(MENTOR-3P) system (Georgopoulos and 

Lioy 2006) to estimate the speciated As in 

urine. We compared the model results with 

biomarkers of tAs and As species measured 

in the 2003–2004 National Health and 

Nutrition Examination Survey (NHANES 

2003–2004). Using large data sets of food 

consumption from NHANES, As concentrations in drinking water and various foods 

from the U.S. Food and Drug Administration 

(FDA) and Natural Resources Defense 

Council databases, and urinary biomarkers from NHANES (same individuals as for 

food consumption data), we demonstrate that 

dietary exposure can be a significant route for 

human exposure to both tAs and iAs.

Address correspondence to J. Xue, U.S. EPA, 109 

T.W. Alexander Dr., MD E205-02, Research Triangle 

Park, NC 27711 USA. Telephone: 919-541-7962. 

Fax: 919-541-4787. E-mail: xue.jianping@epa.gov 

Supplemental Material is available online 

(doi:10.1289/ehp.0901205.S1 via http://dx.doi.org/).

We thank J. Burke and B. Schultz in the U.S. 

Environmental Protection Agency (EPA) Office of 

Research and Development for providing technical review of the manuscript, and S. Nako in the 

U.S. EPA Office of Pesticide Programs for collaborating on Stochastic Human Exposure and Dose 

Simulation–Dietary model development. 

The U.S. EPA through its Office of Research and 

Development conducted and partially funded the 

research described here under university partnership 

agreement CR-827033 to the Environmental and 

Occupational Health Sciences Institute. 

This research has been subjected to the U.S. EPA’s 

administrative review and approved for publication.

The authors declare they have no competing 

financial interests.

Received 15 July 2009; accepted 23 November 2009.

Probabilistic Modeling of Dietary Arsenic Exposure and Dose and Evaluation 

with 2003–2004 NHANES Data

Jianping Xue,1 Valerie Zartarian,1 Sheng-Wei Wang,2 Shi V. Liu,1 and Panos Georgopoulos3

1U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Research 

Triangle Park, North Carolina, USA; 2Graduate Institute of Environmental Health, National Taiwan University, Taipei, Taiwan; 3Environmental and Occupational Health Sciences Institute, Piscataway, New Jersey, USA

Background: Dietary exposure from food to toxic inorganic arsenic (iAs) in the general U.S. population has not been well studied.

Objectives: The goal of this research was to quantify dietary As exposure and analyze the major 

contributors to total As (tAs) and iAs. Another objective was to compare model predictions with 

observed data.

Methods: Probabilistic exposure modeling for dietary As was conducted with the Stochastic 

Human Exposure and Dose Simulation–Dietary (SHEDS-Dietary) model, based on data from the 

National Health and Nutrition Examination Survey. The dose modeling was conducted by combining the SHEDS-Dietary model with the MENTOR-3P (Modeling ENvironment for TOtal Risk 

with Physiologically Based Pharmacokinetic Modeling for Populations) system. Model evaluation 

was conducted via comparing exposure and dose-modeling predictions against duplicate diet data 

and biomarker measurements, respectively, for the same individuals.

Results: The mean modeled tAs exposure from food is 0.38 μg/kg/day, which is approximately 

14 times higher than the mean As exposures from the drinking water. The mean iAs exposure from 

food is 0.05 μg/kg/day (1.96 μg/day), which is approximately two times higher than the mean iAs 

exposures from the drinking water. The modeled exposure and dose estimates matched well with the 

duplicate diet data and measured As biomarkers. The major food contributors to iAs exposure were 

the following: vegetables (24%); fruit juices and fruits (18%); rice (17%); beer and wine (12%); and 

flour, corn, and wheat (11%). Approximately 10% of tAs exposure from foods is the toxic iAs form.

Conclusions: The general U.S. population may be exposed to tAs and iAs more from eating some 

foods than from drinking water. In addition, this model evaluation effort provides more confidence 

in the exposure assessment tools used.

Key words: arsenic, dietary, drinking water, exposure, MENTOR, model, probabilistic, SHEDS.

Environ Health Perspect 118:345–350 (2010). doi:10.1289/ehp.0901205 available via http://dx.doi.

org/ [Online 23 November 2009]
Xue et al.

346 volume 118 | number 3 | March 2010 • Environmental Health Perspectives

Materials and Methods

Food consumption data. We used NHANES 

(2003–2004) data for model inputs regarding 

the amount of food and water consumed by 

individuals. This database contains 16,934 

person-days of real-time dietary consumption 

data—that is, amounts of food and drinking 

water recorded instantly by individuals for 

each separate eating occasion. The average 

number of eating occasions is approximately 

4.8 times per person per day. The U.S. EPA’s 

Food Consumption Intake Database (FCID) 

containing recipe files with 553 food commodities was applied where needed to break 

down NHANES food reported into raw agricultural commodities (RACs).

tAs and iAs concentrations in food and 

drinking water. We used tAs residue data 

from the FDA’s ongoing Total Dietary Survey 

(TDS), also known as the market basket study 

(FDA 1991–2004). TDS collects and analyzes approximately 280 foods for pesticide 

residues, industrial chemicals, and toxic and 

nutrient elements. Foods collected in the TDS 

are prepared as “table ready,” that is, as would 

be consumed, for realistic estimates of dietary 

intake of those targeted components. As 

water concentrations recorded in the Natural 

Resources Defense Council database (Natural 

Resources Defense Council 2000) were used 

and assumed to be tAs. This database reported 

average and maximum As concentrations (a 

total of 8,970 records) in water from 25 U.S. 

states. The As drinking water concentration 

data were weighted by population and fitted 

for the best distribution, to yield a lognormal 

distribution with 1.03 ppb as the geometric 

mean and 4.06 ppb as the geometric standard 

deviation. We derived iAs concentration in 

each food commodity by using iAs percentage in the same food category as reported by 

Schoof et al. (1999a, 1999b).

Biomarker data for As exposure. We 

compared urinary biomarker data from the 

same individuals for consumption data (2,573 

records) from the NHANES with model predictions during the same time period as the 

consumption data were collected. Detection 

rates for tAs, dimethylarsinic acid (DMA), 

arsenobetaine, and monomethylarsonic acid 

(MMA) were 98.9%, 87.4%, 66.7%, and 

36.2%, respectively. Because the detection 

rates for iAs and other species were very low 

(1–7%), our model evaluation study using 

biomarker data focused primarily on tAs.

Models used. We used a SHEDS model 

developed by the U.S. EPA Office of Research 

and Development’s National Exposure 

Research Laboratory (SHEDS 2007; Xue et al. 

2006; Zartarian et al. 2006) for calculating 

dietary and drinking water As exposures for 

each eating occasion of individuals, estimating 

the ranges of population dietary exposures,

identifying key factors and contributions of 

food types and chemicals, and quantifying 

uncertainties. The exposure outputs from this 

SHEDS-Dietary model were used for providing input for deriving target tissue doses and 

biomarker levels in the population-oriented 

physiologically based pharmacokinetic (PBPK) 

modeling of MENTOR-3P developed by 

the Environmental and Occupational Health 

Sciences Institute, University of Medicine 

and Dentistry of New Jersey, R.W. Johnson 

Medical School, and Rutgers University 

(Georgopoulos and Lioy 2006).

Exposure modeling. For estimating daily 

dietary As exposure, the detailed NHANES 

food diaries were used by the SHEDS-Dietary 

model to simulate food ingestion exposures 

by separate eating occasions for a simulated 

individual (Figure 1). The SHEDS-Dietary 

model can use residues for food items as 

consumed, as well as residues of RACs. The 

reported NHANES food items were matched 

with food items in the TDS where possible (Figure 1, step 1). If TDS residues for 

As were available for a particular food (e.g., 

rice, chicken), then SHEDS-Dietary randomly drew a TDS tAs or iAs residue from 

that corresponding residue distribution of the 

same food. Otherwise, the model applied the 

FCID recipe files to the NHANES food items 

and randomly selected a residue for each of 

the RAC ingredients according to the recipe 

(Figure 1, step 2).

Through the recipe files, the unmatched 

foods consumed were matched by RAC so 

that residues for those foods could be calculated. The SHEDS-Dietary model drew the 

same residue value if that RAC was found in 

the same foods. Assignment of residues for 

nondetect values depended on the commodity: if there was at least one detection, half the 

limit of detection was assigned; if no As values 

were detected, zero values were assigned. For 

each NHANES food diary, SHEDS-Dietary 

was applied using Monte Carlo simulation 

by selecting a residue value from an empirical distribution for each TDS food or RAC. 

Although a particular commodity may be used 

in multiple foods, the cooking method may 

differ, so it will have a different food form. 

Process factors can then be applied (Figure 1, 

step 3). These factors account for food changes 

and related concentration changes due to dilution, drying, and so on, but were not used here 

because of the lack of sufficient such information for our study. Each simulated individual’s 

exposure for each commodity was calculated 

by multiplying total daily consumption with 

corresponding residues. Aggregate daily exposure was calculated by summing exposures 

across all commodities:

Exposure (mass chemical/eating occasion) 

= Σamount of food item consumed (mass)

 × As concentration in the food item 

 (mass chemical/mass food)

 × process factors. [1]

Summation of As exposures from every eating 

occasion for 1 day yielded the individual’s daily 

tAs exposure (Figure 1, step 4). In principle, 

both food residues and drinking water concentrations may vary by eating occasion and/

or across foods consumed within an eating 

occasion.

For modeling drinking water As expoFigure 1. SHEDS-Dietary module overview. USDA, U.S. Department of Agriculture. sures, we used the NHANES data to assess 

NHANES

consumption

Residue

consumption

Distribution fitting

Food item

match

Commodity

match No exposure

Recipe files (EPA FCID)

NHANES consumption:

Residue concentration:

Distribution fitting:

Food item:

Commodity:

Usage factors:

Process factors:

Recipe files (EPA FCID):

Food consumption data from NHANES

Total As residue data by food item or commodity from TDS

Fittings of residue data into suitable statistical distribution

Food products people in the survey consumed such as pizza, raw apple

Raw agriculture commodity (RAC)

Pesticide usage percentages by RAC from USDA

Concentration or dilution factors due to processes of food from RAC into food products

Data base for percents of various RACs for the food products

Dietary intake estimation Modified

matched data

No 

Step 1

Step 3

Step 2

Step 4

No 

Yes

Yes Matched data

Usage factors

Process factors
Modeling and evaluation of arsenic exposure and dose

Environmental Health Perspectives • volume 118 | number 3 | March 2010 347

the timing and amounts of direct and indirect 

drinking water intake within a simulated person-day. Total drinking water consumed (both 

direct and indirect water, from tap, bottled, 

and other sources) was assumed to contain 

the same concentration level; that is, only one 

concentration value was selected in the Monte 

Carlo simulation for each eating occasion. 

Water used in cooking is one example of indirect water. The modeled drinking water exposure algorithm in SHEDS-Dietary is similar to 

that used for food exposure (Equation 1). One 

residue value is randomly selected and multiplied by total water intake to obtain drinking 

water exposures. Although SHEDS-Dietary 

can be used to model longitudinal dietary 

exposure as well as cross-sectional exposure, 

we addressed only the cross-sectional exposure 

based on single-day data.

PBPK modeling. We used MENTOR-3P 

to represent absorption, distribution, metabolism, and excretion processes of As inside the 

human body by lumping together similar tissues as a set of physiologic compartments. A 

‘‘flow-limited’’ PBPK formulation, representing a simplification of a generalized PBPK 

model of MENTOR-3P (Figure 2), was 

adopted here. This simplified PBPK model 

for As employed the model parameters in the 

work of Yu (1999a, 1999b), including fractional blood flow rates, metabolism parameters, and tissue/blood partition coefficients. 

The modification of calculating tissue volumes 

and blood flow rate based on body weight 

was added to this simplified populationoriented PBPK model (see Georgopoulos et al. 

2008 and references therein), such that the 

interindividual variability of these physiologic 

parameters can be captured. The dynamics of 

four As circulating species in body compartments (arsenates, arsenites, and the As metabolites MMA and DMA) were captured using 

this PBPK model. Also characterized were the 

corresponding biomarker levels in urine.

Model results evaluation. We conducted 

two types of model evaluation: a) SHEDSDietary predictions were compared with 

National Human Exposure Assessment 

Survey (NHEXAS) duplicate diet data; and 

b) linked SHEDS–MENTOR predictions 

were compared with NHANES biomonitoring data. Duplicate food study subjects in 

NHEXAS (n = 156) were matched by age, 

sex, and location with modeled results from 

SHEDS-Dietary (based on NHANES consumption diaries). To account for variability, 

we ran the model 200 times for 156 matched 

subjects, and selected three cumulative distribution functions according to the 5th, 50th,

and 95th percentiles of the 200 simulations. 

Modeled estimates of tAs dose from the linked 

SHEDS–MENTOR predictions were compared with the NHANES urinary biomarker 

data for tAs. For the matched NHANES 

dietary consumption with NHANES biomarker data, 2,355 records were available.

Results

Using the SHEDS-Dietary model, we calculated that the tAs exposure from food is 

0.36, 1.28, and 1.40 μg/kg/day for the mean, 

SD, and 95th percentile, respectively, for the 

entire simulated population (Table 1). The 

tAs exposure from food for young children 

(≤ 5 years of age) is higher (means ranged 

between 0.54 and 0.62 μg/kg/day) than that 

shown for other age groups (means ranged 

between 0.25 and 0.37 μg/kg/day) (Table 1). 

Based on mean values in Tables 1 and 2, the 

tAs exposure from food predicted by SHEDSDietary is, on average, approximately 14 times 

higher than the tAs exposure from drinking 

water. iAs exposures from drinking water are 

0.025, 0.104, and 0.107 μg/kg/day for the 

mean, SD, and 95th percentile, respectively 

(Table 2). There is no clear age group difference in the drinking water As exposure.

The iAs exposure from food for young 

children (≤ 5 years of age) is higher (means 

ranged between 0.08 and 0.23) than that 

shown for other age groups (means ranged 

between 0.03 and 0.04) (Table 1). The iAs 

exposure from food predicted by SHEDSDietary model (Table 1) is on average two 

times higher than the tAs exposure from 

drinking water (Table 2). Thus, even if we 

assume all As in the drinking water exists in 

the iAs forms, the dietary food iAs exposure 

by the modeled general U.S. population is 

still greater than the drinking water exposure. 

Summarizing the iAs contribution by food 

commodities, we estimate that about 10% of 

tAs exposure from foods is the toxic iAs form.

Among biomarkers analyzed for As exposure in the NHANES subjects, arsenobetaine 

and DMA had high concentrations, with means

of 8.4 and 5.4 μg/L, respectively, whereas the 

mean concentration for tAs in the urine was 

18.4 μg/L [see Supplemental Material, Table 4s

(available online (doi:10.1289/ehp.0901205.

S1 via http://dx.doi.org)].

Compared with the NHEXAS duplicate 

diet data, our SHEDS-Dietary modeling of 

tAs exposure from foods performed reasonably 

well (Figure 3). Among 156 paired comparisons, the mean ± SD of SHEDS-Dietary estimates for tAs exposure from food was 0.192 ± 

0.561 μg/kg/day, compared with 0.185 ± 0.3 

Figure 2. Structure of PBPK modeling of exposure to As in the MENTOR framework.
Xue et al.

348 volume 118 | number 3 | March 2010 • Environmental Health Perspectives

shown by the NHEXAS duplicate diet analysis 

(Table 3).

The linked SHEDS–MENTOR model 

also predicted well the tAs in urine (Figure 4). 

The SAS (version 9.2; SAS Insitute Inc., Cary, 

NC) regression analysis showed a good fit 

with a slope of 1.4 and R2 of 0.91 for the 

logarithmic-transformed predicted and measured values. The means of model predictions 

and NHANES urine measurements of tAs are 

18.32 and 18.06 μg/L, respectively (Table 3). 

The five major food contributors to tAs 

exposure were fish (60%), shellfish (9%), rice 

(7%), fruit juices and fruits (5%), and meats 

(5%) [see Supplemental Material, Figure 1s 

(doi:10.1289/ehp.0901205.S1)]. The major 

food contributors to iAs exposure were vegetables (24%), fruit juices and fruits (18%), 

rice (17%), beer and wine (12%), and flour, 

corn, and wheat (11%) (Figure 5).

Discussion

It is challenging to study As exposure in the 

general human population because many variables affect the processes, and obtaining relevant information has numerous limitations. 

Unlike the study of occupational As exposure, where populations are relatively homogeneous, As compounds are easy to identify, 

and exposure routes are limited. As exposure 

in the general population is complicated with 

subject heterogeneity, different As species, 

and multiple exposure routes. Some information easily obtainable from industrial settings 

may be difficult or too expensive to obtain in 

general environmental settings. Another challenge is that As from the diet exists in many 

forms, most as oAs, which is much less toxic 

than iAs. Thus, it is important to consider the 

different As species in As exposure and risk 

analysis. Using some modeling approaches 

to estimate general human exposure to As 

and to identify some data gaps or assumption 

deficiencies is helpful for understanding As 

exposure in the general population.

Previous studies have shown that, for most 

people in the general population, diet may be 

the largest source of exposure to As (MacIntosh 

et al. 1996). For example, MacIntosh et al.

(1997) reported that mean dietary intakes of 

tAs is 50.6 μg/day for females and 58.5 μg/

day for males. Some recent studies suggested 

that dietary exposure to As may exceed the 

maximum As intake from drinking water in 

areas where elevated As levels were found in 

rice (Williams et al. 2007). Other studies have 

shown a greater intake of toxic iAs from food 

compared with that from drinking water (e.g., 

Meacher et al. 2002). Schoof et al. (1999a,

1999b) estimated that intake of iAs in the 

U.S. diet ranges from 1 to 20 μg/day, with a 

mean of 3.2 μg/day. An estimation of dietary 

iAs intake by U.S. children was 3.2 μg/day on 

average, with a range of 1.6–6.2 μg/day (Yost 

et al. 2004). These estimations are close to 

values reported in another study that showed 

average iAs intake ranges from 1.34 μg/day in 

infants to 12.54 μg/day in 60- to 65-year-olds 

(Tao and Bolger 1998). However, these studies of dietary As exposure are usually based on 

the same assumed food intake values per person, so they lack characterization of interindividual variability of exposures. Lack of data 

about the actual amount of food consumed 

accounted for at least 80% of the total uncertainty for As exposure estimation (MacIntosh 

et al. 1996). MacIntosh et al. (1997) also 

pointed out that the food consumption–food 

composition approach adopted in their earlier 

study (MacIntosh et al. 1996) did not capture 

all the As exposure as reflected in the empirically weighted toenail As concentration data 

used for validation.

In the present study we used data from 

NHANES, thus far the most comprehensive 

survey including food intakes, which has the 

unique advantage of containing biomarker 

information for the same subjects in the survey (NHANES 2003–2004). Biomarkers of 

exposure are independent measurements that 

Table 1. SHEDS modeled As exposure from food using NHANES (2003–2004) data (μg/kg/day).

As species/ Percentile

age group (years) n Mean ± SD 5th 25th 50th 75th 95th 99th

tAs

0 to < 1 757 0.62 ± 0.53 0.05 0.27 0.56 0.84 1.45 2.08

1–2 1,068 0.54 ± 1.23 0.05 0.13 0.26 0.46 2.06 5.06

3–5 953 0.54 ± 1.74 0.03 0.11 0.2 0.36 2.2 5.6

6–12 2,190 0.37 ± 1.69 0.02 0.05 0.1 0.22 1.24 4.28

13–19 3,576 0.25 ± 1.04 0.01 0.03 0.06 0.14 1.02 3.58

20–49 4,221 0.33 ± 1.09 0.01 0.03 0.07 0.17 1.41 4.12

≥ 50 3,804 0.32 ± 1.2 0.01 0.03 0.05 0.15 1.35 4.91

All ages 16,931 0.36 ± 1.28 0.01 0.04 0.08 0.24 1.4 4.45

iAs

0 to < 1 757 0.23 ± 0.19 0.01 0.09 0.21 0.31 0.53 0.8

1–2 1,068 0.1 ± 0.12 0.01 0.04 0.07 0.12 0.29 0.59

3–5 953 0.08 ± 0.11 0.01 0.03 0.05 0.09 0.21 0.4

6–12 2,190 0.04 ± 0.06 0 0.01 0.03 0.05 0.13 0.25

13–19 3,576 0.03 ± 0.05 0 0.01 0.01 0.03 0.09 0.21

20–49 4,221 0.03 ± 0.07 0 0.01 0.02 0.04 0.11 0.28

≥ 50 3,804 0.03 ± 0.07 0 0.01 0.01 0.03 0.09 0.22

All ages 16,931 0.05 ± 0.09 0 0.01 0.02 0.05 0.19 0.41

Table 2. SHEDS modeled As exposure from drinking water (μg/kg/day).

Percentile

Age group (years) n Mean ± SD 5th 25th 50th 75th 95th 99th

0 to < 1 756 0.014 ± 0.083 0.000 0.000 0.000 0.000 0.053 0.412

1–2 1,064 0.031 ± 0.108 0.000 0.000 0.002 0.019 0.150 0.397

3–5 944 0.036 ± 0.150 0.000 0.000 0.004 0.021 0.152 0.539

6–12 2,179 0.030 ± 0.156 0.000 0.000 0.003 0.016 0.108 0.441

13–19 3,566 0.019 ± 0.092 0.000 0.000 0.002 0.011 0.076 0.281

20–49 4,218 0.026 ± 0.087 0.000 0.000 0.002 0.016 0.113 0.414

≥ 50 3,797 0.025 ± 0.084 0.000 0.000 0.004 0.019 0.107 0.344

All ages 16,883 0.025 ± 0.104 0.000 0.000 0.002 0.016 0.107 0.374

Figure 3. SHEDS-Dietary exposure model evaluation with NHEXAS duplicate food survey (nondetects 

replaced with one-half the limit of detection).

3

2

1

0

Dietary intake (μg/kg/day)

Percentile

0 10 20 30 40 50 60 70 80 90 100

Model 5th

Model 50th

Model 95th

NHEXAS
Modeling and evaluation of arsenic exposure and dose

Environmental Health Perspectives • volume 118 | number 3 | March 2010 349

can be used to evaluate the validity of dietary 

assessment methods and food composition 

data. Using the biomarker data from the same 

survey for model evaluation is more reliable, 

because it does not suffer from other complications such as differences between study groups 

related to location, lifestyle, living conditions, 

and other potential confounding factors.

The NHANES data are also more recent 

than data such as the Continuing Survey of 

Food Intakes by Individuals 1994–1996, 1998 

(Agricultural Research Service 2009) used in 

previous studies. Compared with previous 

As exposure modeling, the SHEDS-Dietary 

model we used in this study performed food 

item matching and incorporated usage factors 

in the modeling. We also based the dietary 

intake estimation on actual eating occasions 

(Figure 1).

Our modeling approach yielded estimates 

that are very compatible with the duplicate 

diet data (Figure 3). The mean and 95th

percentile of modeled tAs exposure (0.192 

and 0.723 μg/kg/day, respectively) were very 

comparable to As intakes from the NHEXAS 

duplicate food study (0.185 and 0.612 μg/kg/

day, respectively) for the same age, sex, and 

location. The combination of the SHEDSDietary model with MENTOR-3P also predicted urine tAs concentrations that compared 

well with biomarker monitoring data in the 

NHANES (slope = 1.4 and R2 = 0.91 with 

logarithmic-transformed data) (Figure 4). 

Thus, it seems that our modeling approach 

has overcome some previous deficiencies and 

yielded more reliable estimates.

Because of the low detection rates of iAs 

(1–7%) in the NHANES urine data, the 

evaluation of SHEDS–MENTOR modeling results for iAs could not be conducted. 

However, the Yu et al. PBPK model adapted 

for MENTOR-3P has been validated with 

experimental observations from the literature 

for urinary biomarker levels of speciated arsenic such as in Buchet et al. (1981), Pomroy 

et al. (1980), and Johnson and Farmer (1991) 

as described by Yu (1999a, 1999b). Because

the TDS study provided only tAs concentrations in foods, we used the iAs percentage in 

the same food category as reported by Schoof 

et al. (1999a, 1999b) to derive iAs food concentrations. This assumption could result in 

uncertainties of estimated iAs exposure from 

foods, which could be carried into the subsequent PBPK modeling analysis for estimating 

target tissue doses and biomarker levels of iAs.

Our results in general are consistent 

with those reported in previous studies. For 

example, a duplicate diet study of children in 

Germany showed weekly As intake as 2.31 μg/

kg body weight/week, which is equivalent to 

0.33 μg/kg/day and is close to our estimate of 

0.39 μg/kg/day (Wilhelm et al. 2003). These 

are compatible with our estimates of 7.2 and 

3.5 μg/day for 1- to 2-year-olds and 10.8 and 

4.1 μg/day for 3- to 5-year-olds. Another study 

showed that average intake of tAs for the general U.S. population estimated by the Dietary 

Exposure Potential Model is 0.653 μg/kg/day 

(Moschandreas et al. 2002), which is similar to 

our result of 0.39 μg/kg/day for the same population. Even when iAs is specifically considered, 

our results are also within the wider range of 

iAs exposures reported in previous such studies. For example, Schoof et al. (1999a, 1999b) 

estimated the iAs intake from U.S. diet to be 

1–20 μg/day with a mean of 3.2 μg/day, and 

Tao and Bolger (1998) reported it as 1.34 μg/

day in infants and 12.54 μg/day in adults 

60–65 years of age. Our results of the major 

food contributors to As exposure are consistent 

with the As levels measured in various foods in 

U.S. markets (Tao and Bolger 1998).

Our modeling assessment advances the science by using the large and recent databases 

from NHANES, TDS, NHEXAS, and the 

Natural Resources Defense Council to estimate As intake for the U.S. general population 

from food and drinking water. Other unique 

aspects of research presented in this article are 

Table 3. Comparison of tAs intake and urinary excretion with SHEDS results and the PBPK model.

Percentile

Data source n Mean ± SD 25th 50th 75th 95th

Comparison of tAs intake (μg/kg/day) of NHEXAS duplicates and SHEDS results

NHEXAS 156 0.185 ± 0.3 0.049 0.095 0.174 0.612

SHEDS 156 0.192 ± 0.561 0.024 0.052 0.115 0.723

Comparison of tAs in urine (μg/L) from NHANES data and PBPK model

PBPK model 2,355 18.32 ± 46.86 4.7 8.1 16.1 58.9

Measured concentration 2,355 18.06 ± 42.12 2.5 4.89 14.64 74.84

Figure 4. tAs model evaluation for SHEDS and MENTOR PBPK with NHANES urine data.

100

80

60

40

20

0

Percentile

0 100 200

Urine concentration (μg/L)

300 400 500

Mean model prediciton

NHANES urine data

Figure 5. Contributions of iAs intake by foods.

24%

17% 18%

11%

12%

5% 13%

Beer, wine

Flour, corn, wheat

Fruit, fruit juice

Others

Poultry, pork, beef, egg

Rice

Vegetables
Xue et al.

350 volume 118 | number 3 | March 2010 • Environmental Health Perspectives

evaluation of tAs intake estimates using duplicate food survey data from NHEXAS, and 

using urine biomarker data from NHANES 

to evaluate the SHEDS–MENTOR model 

predictions. The integrated exposure and dose 

modeling application presented in this article for As has not been attempted before for a 

large general population (e.g., the U.S. general 

population), to our knowledge, in the exposure-related literature. The SHEDS-Dietary 

model and the linked SHEDS-Dietary–

MENTOR-3P model predictions compared 

well with the measured duplicate diet data and 

urine biomarker data, respectively; thus, this 

was an important model evaluation effort to 

provide more confidence in these predictive 

exposure assessment tools.

Conclusions

The relationship between As intake from drinking water and related health effects has been 

well studied previously. Using rich data sets 

and state-of-the-science models, we found that 

the general U.S. population may be exposed 

to tAs and toxic iAs through the dietary route 

more from eating some As-containing foods 

than from drinking As-containing water. 

The major food contributors to tAs exposure 

were fish, shellfish, rice, fruit juices and fruits, 

and meats; the major food contributors to 

iAs exposure were vegetables, fruit juices and 

fruits, rice, beer and wine, and flour, corn, and 

wheat. Approximately 10% of tAs exposure 

from foods is the toxic iAs form.

Our study reinforces and expands on previous observations that dietary As exposure via 

food is an important route for As intake by 

the general population and that in some cases 

it can be even a greater source of As exposure 

than drinking water. Thus, for complete exposure analysis and risk assessment in the general population, iAs intake from food should 

be considered in addition to iAs intake from 

drinking water.

References

Abernathy CO, Liu YP, Longfellow D, Aposhian HV, Beck B, 

Fowler B, et al. 1999. Arsenic: health effects, mechanisms 

of actions, and research issues. Environ Health Perspect 

107:593–597.

Abernathy CO, Thomas DJ, Calderon RL. 2003. Health effects 

and risk assessment of arsenic. J Nutr 133(5 suppl 

1):1536S–1538S.

Anetor JI, Wanibuchi H, Fukushima S. 2007. Arsenic exposure 

and its health effects and risk of cancer in developing 

countries: micronutrients as host defence. Asian Pac J 

Cancer Prev 8(1):13–23.

Agricultural Research Service. 2009. Continuing Survey of 

Food Intakes by Individuals (CSFII), 1994–1996, 1998. 

Available: http://sodapop.pop.psu.edu/data-collections/

csfii [accessed 16 November 2009].

Boyce CP, Lewis AS, Sax SN, Eldan M, Cohen SM, Becj BD. 

2008. Probabilistic analysis of human health risks associated with background concentrations of inorganic arsenic: use of a margin of exposure approach. Hum Ecol Risk 

Assess 14:1159–1201.

Buchet JP, Lauwerys R, Roels H. 1981. Comparison of the urinary excretion of arsenic metabolites after a single dose 

of sodium arsenite, monomethylarsenate, or dimethylarsenate in man. Int Arch Occup Environ Health 48:71–79.

Chen CJ, Kuo TL, Wu MM. 1988a. Arsenic and cancers. Lancet 

1(8582):414–415.

Chen CJ, Wu MM, Lee SS, Wang JD, Cheng SH, Wu HY. 1988b. 

Atherogenicity and carcinogenicity of high-arsenic artesian well water. Multiple risk factors and related malignant neoplasms of blackfoot disease. Arteriosclerosis 

8(5):452–460.

Chiou HY, Chiou ST, Hsu YH, Chou YL, Tseng CH, Wei ML, et al. 

2001. Incidence of transitional cell carcinoma and arsenic 

in drinking water: a follow-up study of 8,102 residents in 

an arseniasis-endemic area in northeastern Taiwan. Am J 

Epidemiol 153(5):411–418.

FDA (Food and Drug Administration). 1991–2004. Total Diet Study—

Analytical Results. Available: http://www.fda.gov/Food/

FoodSafety/FoodContaminantsAdulteration/TotalDietStudy/

ucm184293.htm [accessed 16 November 2009].

Georgopoulos PG, Lioy PJ. 2006. From theoretical aspects of 

human exposure and dose assessment to computational 

model implementation: the modeling environment for total 

risk studies (MENTOR). J Toxicol Environ Health B Crit Rev 

9(6):457–483.

Georgopoulos PG, Wang SW, Yang YC, Xue J, Zartarian VG, 

McCurdy T, et al. 2008. Biologically based modeling of 

multimedia, multipathway, multiroute population exposures 

to arsenic. J Expo Sci Environ Epidemiol 18(5):462–476.

Hutchinson J. 1887. Arsenic cancer. Br Med J 2:1280–1281.

Johnson LR, Farmer JG. 1991. Use of human metabolic studies 

and urinary arsenic speciation in assessing arsenic exposure. Bull Environ Contam Toxicol 46:53–61.

MacIntosh DL, Spengler JD, Ozkaynak H, Tsai L, Ryan PB. 

1996. Dietary exposures to selected metals and pesticides. 

Environ Health Perspect 104:202–209.

MacIntosh DL, Williams PL, Hunter DJ, Sampson LA, Morris SC, 

Willett WC, et al. 1997. Evaluation of a food frequency 

questionnaire-food composition approach for estimating 

dietary intake of inorganic arsenic and methylmercury. 

Cancer Epidemiol Biomarkers Prev 6(12):1043–1050.

Meacher DM, Menzel DB, Dillencourt MD, Bic LF, Schoof RA, 

Yost LJ, et al. 2002. Estimation of multimedia inorganic 

arsenic intake in the U.S. population. Hum Ecol Risk 

Assess 8:1697–1721.

Moschandreas DJ, Karuchit S, Berry MR, O’Rourke MK, Lo D, 

Lebowitz MD, et al. 2002. Exposure apportionment: ranking food items by their contribution to dietary exposure. 

J Expo Anal Environ Epidemiol 12:233–243.

National Research Council. 2001. Arsenic in Drinking Water: 

2001 Update. Washington, DC:National Academy Press.

Natural Resources Defense Council. 2000. Arsenic in Drinking 

Water. Available: http://www.nrdc.org/water/drinking/

qarsenic.asp [accessed 16 November 2009].

NHANES. 2003–2004. National Health and Nutrition Examination 

Survey. Available: http://www.cdc.gov/nchs/nhanes/

nhanes2003-2004/nhanes03_04.htm [accessed 1 February 

2010].

Occupational Safety and Health Administration. 2005. Inorganic 

Arsenic. Occupational Safety and Health Standards. 

29 CFR 1910.1018.

Pomroy C, Charbonnaeu SM, McCullough RS, Tam GKH. 

1980. Human retention studies with 74As. Toxicol Appl 

Pharmacol 53:550–556.

Schoof EA, Eickhoff J, Yost LJ, Crecelius EA, Cragin DW, 

Meacher DM, et al. 1999a. Dietary exposure to inorganic 

arsenic. In: Arsenic Exposure and Health Effects (Chappell 

WR, Abemathy CO, Calderon RL, eds). New York:Elsevier, 

81–88.

Schoof RA, Yost LJ, Eickhoff J, Crecelius EA, Cragin DW, 

Meacher DM, et al. 1999b. A market basket survey of inorganic arsenic in food. Food Chem Toxicol 37(8):839–846.

SHEDS. 2007. Scientific Advisory Panel Review. Available: 

http://www.epa.gov/scipoly/SAP/meetings/2007/081407_

mtg.htm#materials [accessed 16 November 2009].

Tao SS-H, Bolger PM. 1998. Dietary arsenic intakes in the 

United States: FDA Total Diet Study, September 1991–-

December 1996. Food Addit Contam 16:465–472.

Tchounwou PB, Patlolla AK, Centeno JA. 2003. Carcinogenic 

and systemic health effects associated with arsenic exposure—a critical review. Toxicol Pathol 31(6):575–588.

Wilhelm M, Wittsiepe J, Schrey P, Lajoie-Junge L, Busch V. 

2003. Dietary intake of arsenic, mercury and selenium by 

children from a German North Sea island using duplicate 

portion sampling. J Trace Elem Med Biol 17(2):123–132.

Williams PN, Raab A, Feldmann J, Meharg AA. 2007. Market 

basket survey shows elevated levels of As in South 

Central U.S. processed rice compared to California: consequences for human dietary exposure. Environ Sci Technol 

41(7):2178–2183.

World Health Organization. 2004. Environmental Health 

Criteria 224: Arsenic and Arsenic Compounds. Available: 

http://www.who.int/ipcs/publications/ehc/ehc_224/en/ 

[accessed 16 November 2009].

Xue J, Zartarian VG, Ozkaynak H, Dang W, Glen G, Smith L, 

et al. 2006. A probabilistic arsenic exposure assessment 

for children who contact chromated copper arsenate 

(CCA)-treated playsets and decks, part 2: sensitivity and 

uncertainty analyses. Risk Anal 26(2):533–541.

Yost IJ, Tao S-H, Egan SK, Barraj LM, Smith KM, Tsuji JS, et al. 

2004. Estimation of dietary intake of inorganic arsenic in 

U.S. Children. Hum Ecol Risk Assess 10:473–483.

Yu D. 1999a. A pharmacokinetic modeling of inorganic 

arsenic: a short-term oral exposure model for humans. 

Chemosphere 39(15):2737–2747.

Yu D. 1999b. A physiologically based pharmacokinetic model 

of inorganic arsenic. Regul Toxicol Pharmacol 29(2 pt 

1):128–141.

Zartarian VG, Xue J, Ozkaynak H, Dang W, Glen G, Smith L, 

et al. 2006. A probabilistic arsenic exposure assessment 

for children who contact CCA-treated playsets and decks, 

part 1: model methodology, variability results, and model 

evaluation. Risk Anal 26(2):515–531.