An Arsenic Exposure Model: Probabilistic Validation Using Empirical Data

Abstract

This paper describes development of a multi-pathway arsenic exposure model. The model uses information on arsenic concentrations in food, water, soil, and dust, combined with estimates of intake and medium-specific absorption. Urinary arsenic is predicted assuming that 60% of absorbed arsenic is excreted in urine under steady state conditions. Fecal arsenic is predicted assuming all unabsorbed arsenic is excreted in feces. We applied this model at a former copper smelter site. Site specific distributions were available for the following parameters: soil and dust arsenic concentration (geometric mean approximately 100 to 200 ppm and 50 to 100 ppm, respectively); the combined childhood soil and dust ingestion rate (geometric mean of 20 mg/d); soil and dust arsenic relative bioavailability (geometric mean 0.20 and 0.28, respectively); exposure duration; water arsenic concentration; air arsenic concentration; and total arsenic in food. Monte Carlo simulation was used to predict daily arsenic uptake and excretion in urine and feces for children. Predicted urine arsenic levels were less than measured levels (73% to 88% of measured values, depending on region of site). On the other hand, predicted fecal arsenic levels exceeded measured levels by a factor of 1.7 to 4.6. We were able to improve the correspondence between predicted and measured arsenic excretion rates by decreasing the assumed value of the combined soil and dust ingestion rate, and increasing the assumed bioavailability of arsenic in soil and dust.

Keywords:

• model calibration
• monte carlo
• biomarker
• arsenic
• superfund