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Author Loga, Małgorzata ♦ Wierzchołowska-Dziedzic, Anna
Source Paperity
Content type Text
Publisher Springer International Publishing
File Format PDF ♦ HTM / HTML
Copyright Year ©2017
Subject Keyword Environmental monitoring/analysis ♦ Environmental management ♦ Ecotoxicology ♦ Atmospheric protection/air quality control/air pollution ♦ Ecology
Abstract Measurement uncertainties are inherent to assessment of biological indices of water bodies. The effect of these uncertainties on the probability of misclassification of ecological status is the subject of this paper. Four Monte-Carlo (M-C) models were applied to simulate the occurrence of random errors in the measurements of metrics corresponding to four biological elements of surface waters: macrophytes, phytoplankton, phytobenthos, and benthic macroinvertebrates. Long series of error-prone measurement values of these metrics, generated by M-C models, were used to identify cases in which values of any of the four biological indices lay outside of the “true” water body class, i.e., outside the class assigned from the actual physical measurements. Fraction of such cases in the M-C generated series was used to estimate the probability of misclassification. The method is particularly useful for estimating the probability of misclassification of the ecological status of surface water bodies in the case of short sequences of measurements of biological indices. The results of the Monte-Carlo simulations show a relatively high sensitivity of this probability to measurement errors of the river macrophyte index (MIR) and high robustness to measurement errors of the benthic macroinvertebrate index (MMI). The proposed method of using Monte-Carlo models to estimate the probability of misclassification has significant potential for assessing the uncertainty of water body status reported to the EC by the EU member countries according to WFD. The method can be readily applied also in risk assessment of water management decisions before adopting the status dependent corrective actions.
ISSN 01676369
Learning Resource Type Article
Publisher Date 2017-12-01
e-ISSN 15732959
Journal Environmental Monitoring and Assessment
Volume Number 189
Issue Number 12