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


CONCLUSIONS
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.
Hayashi
Takehiko I
TI
Research Center for Environmental Risk, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan. hayashi.takehiko@nies.go.jp
Kashiwagi
Nobuhisa
N
D000641:Ammonia / Q000032:analysis / Q000633:toxicity; D001499:Bayes Theorem; D001559:Benzhydryl Compounds; D002725:Chloroform / Q000032:analysis / Q000633:toxicity; D004784:Environmental Monitoring / Q000379:methods*; D005618:Fresh Water / Q000737:chemistry*; D015386:Hazardous Substances / Q000032:analysis* / Q000633:toxicity; D019216:Metals, Heavy / Q000032:analysis / Q000633:toxicity; D009010:Monte Carlo Method; D010636:Phenols / Q000032:analysis / Q000633:toxicity; D011336:Probability; D018570:Risk Assessment / Q000379:methods; D013045:Species Specificity; D014041:Tokyo; D014874:Water Pollutants, Chemical / Q000032:analysis* / Q000633:toxicity; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data*; D000098826:Bisphenol A Compounds
D000641:Ammonia; D001559:Benzhydryl Compounds; D002725:Chloroform; D015386:Hazardous Substances; D019216:Metals, Heavy; D010636:Phenols; D014874:Water Pollutants, Chemical; C006780:bisphenol A; D000098826:Bisphenol A Compounds