Lee, R.C. and Kissel, J.C. (1995). "Probabilistic Prediction of Exposures
to Arsenic-contaminated Residential Soil," Environ. Geochem.
Health 17:159-168.
Abstract
Probabilistic modeling using Monte Carlo simulation has been proposed
as a more scientifically valid method of estimating soil contaminant exposures
than conservative deterministic methods currently used by regulatory agencies.
A retrospective application of probabilistic modeling to an exposure scenario
involving arsenic-contaminated residential soil near the former ASARCO smelter
near Tacoma, Washington is presented. The population of interest is children,
aged 2-6 years, living within one-half mile (0.3 km) of the smelter site.
Models that predict urinary arsenic levels based on unintentional soil ingestion
and inhalation exposure pathways are used. Distributions of exposure variables
are based on site-specific data and previous exposure studies. Simulated
urinary arsenic levels are compared with data from two biomonitoring studies
performed during the late 1980s. Arsenic distributions produced by simulation
and biomonitoring are significantly different, and likely contributors to
this difference are discussed. However the probabilistic model provides
closer estimations of urinary arsenic levels than conservative deterministic
models similar to those used by regulatory agencies, and provides useful
information regarding parameter uncertainty. Soil ingestion rate was a driving
variable in the probabilistic models. Further quantification of soil ingestion
rates is warranted.
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