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

Environmental Science and Pollution Research

Volumn 18, Issue 3, 2011, Pages 365-375

  Hayashi, Takehiko I ^a   Kashiwagi, Nobuhisa ^b  

^a NATIONAL INSTITUTE FOR ENVIRONMENTAL STUDIES   (Japan)

^b INSTITUTE OF STATISTICAL MATHEMATICS   (Japan)

Author keywords

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

Indexed keywords

BAYESIAN ANALYSIS; BIOAVAILABILITY; MONTE CARLO ANALYSIS; PROBABILITY; RISK ASSESSMENT; SUBLETHAL EFFECT; SURFACE WATER; TOXIC SUBSTANCE; UNCERTAINTY ANALYSIS; WATER POLLUTION;

HONSHU; JAPAN; KANTO; TOKYO [KANTO];

EID: 79951770716     PISSN: 09441344     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11356-010-0380-5     Document Type: Article

References (35)

1. Aldenberg T, Jaworska JS, Traas TP (2002) Normal species sensitivity distributions and probabilistic ecological risk assessment. In: Posthuma L, Suter GW II, Trass TP (eds) Species sensitivity distributions in ecotoxicology. Lewis, Boca Raton, FL, pp 49-102.
2. CERI and NITE (2008) Screening-level assessment of chemicals. Ver. 1. 0. No. 116. Manganese and its compounds. Available at http://www. safe. nite. go. jp/risk/files/ (in Japanese).
3. Duboudin C, Ciffroy P, Magaud H (2004) Effects of data manipulation and statistical methods on species sensitivity distributions. Environ Toxicol Chem 23: 489-499.
4. EUFRAM (2006) Concerted action to develop a European framework for probabilistic risk assessment of the environmental impacts of pesticides. Available at http://www. eufram. com.
5. European Commission (2003) Technical guidance document on risk assessment. European Commission, Italy.
6. European Union (2008) European Union risk assessment report. Voluntary risk assessment of copper, copper II sulphate pentahydrate, copper(I)oxide, copper(II)oxide, dicopper chloride trihydroxide. Available at http://www. echa. europa. eu/chem_data/transit_measures/vrar_en. asp.
7. European Union (2009) European Union risk assessment report. Nickel and nickel compounds. Available at http://www. mst. dk/English/Chemicals/Substances_and_materials/Nickel/.
8. Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7: 457-472.
9. Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis. CRC, Boca Raton.
10. Hayashi TI, Kashiwagi N (2009) Analyzing spatial and temporal variability and uncertainty: a hierarchical Bayesian approach to environmental monitoring data set containing non-detected observations. Japanese J Risk Anal 19: 47-54 (in Japanese).
11. Hayashi TI, Kashiwagi N (2010) A Bayesian method for deriving species-sensitivity distributions: selecting the best fit tolerance distributions of taxonomic groups. Hum Ecol Risk Assess 16: 251-263.
12. Imaizumi Y, Suzuki N, Shiraishi H (2006) Bootstrap methods for confidence intervals of percentiles from dataset containing nondetected observations using lognormal distribution. J Chemom 20: 68-75.
13. Kikuchi M, Wakabayashi M (1997) Environmental risk assessment of ammonia pollution of river waters. Annual report of Tokyo Metropolitan Research Institute for Environmental Protection, pp 143-148 (in Japanese).
14. Lunn DJ, Thomas A, Best N, Spigelhalter D (2000) WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10: 325-337.
15. Nakanishi J, Ono K (2008) Series of detailed risk assessment 21: chromium hexavalent. Maruzen, Tokyo, in Japanese.
16. Nakanishi K, Miyamoto K, Kawasaki H (2005) Series of detailed risk assessment 6: bisphenol-A. Maruzen, Tokyo, in Japanese.
17. Nakanishi J, Kobayashi N, Naito K (2006) Series of detailed risk assessment 9: lead. Maruzen, Tokyo, in Japanese.
18. Nakanishi J, Ishikawa Y, Kawasaki H (2008a) Series of detailed risk assessment 15: chloroform. Maruzen, Tokyo, in Japanese.
19. Nakanishi J, Naito K, Kamo M (2008b) Series of detailed risk assessment 20: zinc. Maruzen, Tokyo, in Japanese.
20. OECD (1995) Guidance document for aquatic effects assessment. OECD Environment Monographs No. 92, Paris.
21. Ouyang T, Torigai M, Okita S, Baba K, Yun S-J, Iwakawa K (1998) Concentration, speciation and source estimation of Cr, Mo and Sb in Tamagawa river water. Japanese J Environ Chem 8: 33-45, in Japanese.
22. Posthuma L, Suter GW II, Traas TP (2002) Species sensitivity distributions in ecotoxicology. Lewis, Boca Raton, FL.
23. Qian SS, Schulman A, Koplos J, Kotros A, Kellar P (2004) A hierarchical modeling approach for estimating national distributions of chemicals in public drinking water systems. Environ Sci Technol 38: 1176-1182.
24. R Development Core Team (2007) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www. R-project. org.
25. Sinha P, Lambert MB, Trumbull VL (2006) Evaluation of statistical methods for left-censored environmental data with nonuniform detection limits. Environ Toxicol Chem 25: 2533-2540.
26. Solomon KR, Takacs P (2002) Probabilistic risk assessment using species sensitivity distributions. In: Posthuma L, Suter GW II, Trass TP (eds) Species sensitivity distributions in ecotoxicology. Lewis, Boca Raton, FL, pp 285-313.
27. Spigelhalter D, Best N, Carlin B, Linde A (2002) Bayesian measures of model complexity and fit. J Roy Stat Soc B 64: 583-639.
28. Spigelhalter D, Thomas A, Best N, Lunn D (2003) WinBUGS user manual. Version 1. 4. Available at http://www. mrc-bsu. cam. ac. uk/bugs.
29. Suter GW II (2006) Ecological risk assessment. CRC, New York, NY.
30. US EPA (1998) update of ambient water quality criteria for ammonia. US EPA, Washington, DC.
31. US EPA (2003) Occurrence estimation methodology and occurrence findings report for the six-year review of existing national primary drinking water regulations; EPA-815-R-03-006. Office of Water: June 2003, 874 p. http://www. epa. gov/safewater/standard.
32. van den Brink PJ, Blake N, Brock TCM, Maltby L (2006) Predictive value of species sensitivity distributions for effects of herbicides in freshwater ecosystem. Hum Ecol Risk Assess 12: 645-674.
33. van Sprang SV, Verdonck FAM, Vanrolleghem PA, Vanrolleghem PA, Vangheluwe ML, Janssen CR (2004) Probabilistic environmental risk assessment of zinc in Dutch surface waters. Environ Toxicol Chem 23: 2993-3002.
34. Vose D (2007) Risk analysis: quantitative guide. Wiley, West Sussex.
35. Yamazaki M, Ando H (1997) Analysis of dissolved trace elements in river water by inductively coupled plasma-mass spectrometry and instrumental neutron activation analysis. Annual report of Tokyo Metropolitan Research Institute for Environmental Protection, pp 113-120 (in Japanese).