Sensitive parameters in predicting exposure contaminants concentration in a risk assessment process

TitleSensitive parameters in predicting exposure contaminants concentration in a risk assessment process
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2005
AuthorsAvagliano, S., Vecchio A., and Belgiorno V.
JournalEnvironmental Monitoring and Assessment
Volume111
Pagination133-148
ISSN01676369
Keywordsaccuracy, analytic method, article, Benzene, Benzo(a)pyrene, concentration (parameters), contaminated land, Contaminated site remediation, contamination, decision making, Engineering, environmental exposure, environmental management, Environmental parameters, financial management, ground water, groundwater, Humans, Infiltration, information processing, leaching, Leaching ingestion, Materials testing, Mathematical models, methodology, Models, Naphthalenes, natural attenuation, Natural attenuation factors, Organic carbon, organic compound, partition coefficient, prediction, remediation, Risk analysis, Risk assessment, Sensitivity analysis, Sensitivity and Specificity, Soil, Soil Pollutants, Soil pollution, Soils, Standardization, Tetrachloroethylene, Theoretical, time management, Uncertainty, variance, Variance based method, Velocity, Water Movements, water supply
Abstract

A sensitivity analysis (SA) was conducted on the analytical models considered in the risk-based corrective-action (RBCA) methodology of risk analysis, as developed by the American Society for Testing of Materials (ASTM), to predict a contaminant's concentration in the affected medium at the point of human exposure. These models are of interest because evaluations regarding the best approach to contaminated site remediation are shifting toward increased use of risk-based decision, and the ASTM RBCA methodology represents the most effective and internationally widely used standardized guide for risk assessment process. This paper identifies key physical and chemical parameters that need additional precision and accuracy consideration in order to reduce uncertainty in models prediction, thereby saving time, money and engineering effort in the data collection process. SA was performed applying a variance-based method to organic contaminants migration models with reference to soil-to-groundwater leaching ingestion exposure scenario. Results indicate that model output strongly depends on the organic-carbon partition coefficient, organic-carbon content, net infiltration, Darcy velocity, source-receptor distance, and first-order decay constant. © Springer Science + Business Media, Inc. 2005.

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-29444437549&doi=10.1007%2fs10661-005-8218-1&partnerID=40&md5=8c66e66d099228112ab0223db83995d6
DOI10.1007/s10661-005-8218-1