Environmental Science and Pollution Research

, Volume 18, Issue 3, pp 365–375 | Cite as

A Bayesian approach to probabilistic ecological risk assessment: risk comparison of nine toxic substances in Tokyo surface waters

Research Article

Abstract

Background, aim, and scope

Quantitative risk comparison of toxic substances is necessary to decide which substances should be prioritized to achieve effective risk management. This study compared the ecological risk among nine major toxic substances (ammonia, bisphenol-A, chloroform, copper, hexavalent chromium, lead, manganese, nickel, and zinc) in Tokyo surface waters by adopting an integrated risk analysis procedure using Bayesian statistics.

Methods

Species sensitivity distributions of these substances were derived by using four Bayesian models. Environmental concentration distributions were derived by a hierarchical Bayesian model that explicitly considered the differences between within-site and between-site variations in environmental concentrations. Medians and confidence intervals of the expected potentially affected fraction (EPAF) of species were then computed by the Monte Carlo method.

Results

The estimated EPAF values suggested that risk from nickel was highest and risk from zinc and ammonia were also high relative to other substances. The risk from copper was highest if bioavailability was not considered, although toxicity correction by a biotic ligand model greatly reduced the estimated risk. The risk from manganese was highest if a conservative risk index estimate (90% upper EPAF confidence limit) was selected.

Conclusion

It is suggested that zinc is not a predominant risk factor in Tokyo surface waters and strategic efforts are required to reduce the total ecological risk from multiple substances. The presented risk analysis procedure using EPAF and Bayesian statistics is expected to advance methodologies and practices in quantitative ecological risk comparison.

Keywords

Ecological risk assessment Probabilistic risk analysis Quantitative risk comparison Bayesian statistics Uncertainty analysis Species sensitivity distribution 

Supplementary material

11356_2010_380_MOESM1_ESM.pdf (3.1 mb)
ESM 1(PDF 3.09 MB)

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Research Center for Environmental RiskNational Institute for Environmental StudiesTsukubaJapan
  2. 2.The Institute of Statistical MathematicsTachikawaJapan

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