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


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.


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.


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.


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.

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This study was carried out under the ISM Cooperative Research Program (2009-ISMCRP-2039) and supported by Global COE Program E03 (Eco-risk Asia) of the Ministry of Education, Culture, Sports, Science and Technology of Japan. This work was also supported by the Steel Industry Foundation for the Advancement of Environmental Protection Technology. We thank two anonymous reviewers for very helpful comments on the manuscript. We thank K.D. Schamphelaere, M. Kamo, and T. Nagai for providing helpful information about ecotoxicity data correction by biotic ligand models. We thank Y. Sugaya for providing general information about risk assessment of nickel.

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Correspondence to Takehiko I. Hayashi.

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Responsible editor: Philippe Garrigues

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Hayashi, T.I., Kashiwagi, N. A Bayesian approach to probabilistic ecological risk assessment: risk comparison of nine toxic substances in Tokyo surface waters. Environ Sci Pollut Res 18, 365–375 (2011). https://doi.org/10.1007/s11356-010-0380-5

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