Walker S, Griffin S
AGEISS Environmental, Inc., Denver, CO 80202, USA.
Environ Health Perspect. 1998 Mar;106(3):133-9. doi: 10.1289/ehp.98106133.
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.
美国环境保护局(EPA)使用暴露评估模型来估算对潜在关注化学品的每日摄入量。在蒙大拿州的阿纳康达超级基金场地,EPA暴露评估模型被用于预测尿中总砷和特定形态砷的浓度。然后将预测浓度与居住在该场地附近儿童的实测浓度进行比较。当使用土壤、室内灰尘和饮食中砷浓度的特定场地信息、特定场地摄入率以及砷吸收率时,尿砷的实测浓度和预测浓度具有合理的一致性。中心趋势暴露评估模型成功地描述了该场地大多数儿童的尿砷实测浓度。合理最大暴露评估模型成功地识别出暴露程度最高的人群。虽然尿砷实测值和预测值之间的一致性良好,但并不精确。已确定的影响一致性的变量包括土壤和灰尘样本采集方法、每日尿量、土壤摄入率以及定义暴露单位的能力。食物中的砷浓度影响尿中总砷实测值和预测值之间的一致性,但在比较尿中特定形态砷的实测值和预测值时未予考虑。尿中特定形态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场地数据,与在暴露评估模型中使用EPA默认值相比,砷暴露预测风险更低。这种差异导致修复规模和成本降低,同时仍能保护人类健康。