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Site-specific data confirm arsenic exposure predicted by the U.S. Environmental Protection Agency.

机译:特定地点的数据证实了美国环境保护署预测的砷暴露。

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摘要

The EPA uses an exposure assessment model to estimate daily intake to chemicals of potential concern. At the Anaconda Superfund site in Montana, the EPA exposure assessment model was used to predict total and speciated urinary arsenic concentrations. Predicted concentrations were then compared to concentrations measured in children living near the site. When site-specific information on concentrations of arsenic in soil, interior dust, and diet, site-specific ingestion rates, and arsenic absorption rates were used, measured and predicted urinary arsenic concentrations were in reasonable agreement. The central tendency exposure assessment model successfully described the measured urinary arsenic concentration for the majority of children at the site. The reasonable maximum exposure assessment model successfully identified the uppermost exposed population. While the agreement between measured and predicted urinary arsenic is good, it is not exact. The variables that were identified which influenced agreement included soil and dust sample collection methodology, daily urinary volume, soil ingestion rate, and the ability to define the exposure unit. The concentration of arsenic in food affected agreement between measured and predicted total urinary arsenic, but was not considered when comparing measured and predicted speciated urinary arsenic. Speciated urinary arsenic is the recommended biomarker for recent inorganic arsenic exposure. By using site-specific data in the exposure assessment model, predicted risks from exposure to arsenic were less than predicted risks would have been if the EPA's default values had been used in the exposure assessment model. This difference resulted in reduced magnitude and cost of remediation while still protecting human health.
机译:EPA使用暴露评估模型来估计潜在关注化学品的每日摄入量。在蒙大拿州的Anaconda Superfund站点,使用EPA暴露评估模型来预测总和特定的尿砷浓度。然后将预测的浓度与居住在该地点附近儿童的浓度进行比较。当使用关于土壤,内部灰尘和饮食中砷浓度的特定地点信息,特定地点的摄入率和砷吸收率时,测量和预测的尿中砷浓度在合理范围内。集中趋势暴露评估模型成功地描述了该地点大多数儿童测得的尿砷浓度。合理的最大暴露评估模型成功地确定了最高暴露人群。虽然测得的和预测的尿中砷之间的一致性很好,但并不精确。确定的影响一致性的变量包括土壤和灰尘样品的收集方法,每日尿量,土壤摄入率以及定义暴露单位的能力。食物中砷的浓度会影响测得的和预测的总尿砷之间的一致性,但在比较测得的和预测的特定尿砷时未考虑。推荐的尿砷是近期无机砷暴露的推荐生物标志物。通过在暴露评估模型中使用特定于地点的数据,砷暴露的预测风险要小于如果在暴露评估模型中使用了EPA的默认值,则砷的预测风险将小于预期风险。这种差异导致降低的补救幅度和成本,同时仍保护人类健康。

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