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Probabilistic uncertainty analysis of the European Union System for the evaluation of substances multimedia regional distribution model
Author(s) -
Matthies Michael,
Berding Volker,
Beyer Andreas
Publication year - 2004
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1897/03-529
Subject(s) - range (aeronautics) , log normal distribution , percentile , standard deviation , european union , environmental science , normal distribution , cumulative distribution function , statistics , probability distribution , monte carlo method , probabilistic logic , variance (accounting) , uncertainty analysis , geometric standard deviation , mathematics , probability density function , materials science , accounting , business , composite material , economic policy
The European Union System for the Evaluation of Substances (EUSES) is a computerized model system to facilitate and harmonize health and environmental risk assessment of previously notified and new substances. For calculation of regional background exposure, a multimedia distribution model is used. In the present study, the uncertainty of this regional model is analyzed. Environmental parameters were collected for North Rhine Westphalia (Germany), which resembles the standard region of EUSES. Probability distribution functions of various types (uniform, triangular, normal, log normal) depending on data availability were derived for environmental input parameters, including geometric parameters. Generic log‐normal distribution functions with fixed standard deviations were chosen for solubility in air, water, and n ‐octanol as well as for degradation half‐lives. Monte Carlo simulations were carried out for 10 reference substances having different properties. Contribution of environmental parameter uncertainty to total output uncertainties is higher than that of substance parameters. Range of output uncertainty, defined as the ratio of the logarithms of the 90th and 10th percentiles of the cumulative probability distribution function, shows an increase from air and water to soil. The highest‐occurring range is 1.4 orders of magnitude, which means that total uncertainty of the regional model is relatively low and, usually, is lower than the range of measured values. The median of output probability distributions lies above the point estimate. Influence of input parameters was estimated as their rank correlation coefficients to output uncertainty. Substance and environmental parameters contribute differently to output variance depending on individual substance properties and environmental compartment. Hence, the present study underlines the need to perform uncertainty analyses instead of either using a set of simple rules or just looking at certain parameters.