Using information on uncertainty to improve environmental fate modeling: A case study on DDT

Environmental Science and Technology

Volumn 43, Issue 1, 2009, Pages 128-134

  Schenker, Urs ^a   Scheringer, Martin ^a   Sohn, Michael D ^b   Maddalena, Randy L ^b   Mckone, Thomas E ^b,c   Hungerbühier, Konrad ^a  

^a ETH ZURICH   (Switzerland)

^b LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN; BAYESIAN UPDATING; CONFIDENCE INTERVALS; DEGRADATION RATE CONSTANTS; EMISSION RATES; ENVIRONMENTAL FATES; ENVIRONMENTAL MEDIAS; ENVIRONMENTAL PARAMETERS; GEOGRAPHICAL LOCATIONS; MODEL INPUTS; MODEL RESULTS; MODEL UNCERTAINTIES; MONTE CARLO APPROACHES; MONTE CARLO SIMULATIONS; MULTIMEDIA MODELS; ORDERS OF MAGNITUDES; PROPERTY DATUM; RANK CORRELATIONS; TEMPERATURE DEPENDENCIES; UNCERTAIN MODELS;

BAYESIAN NETWORKS; DEGRADATION; MONTE CARLO METHODS; RATE CONSTANTS; SENSITIVITY ANALYSIS;

UNCERTAINTY ANALYSIS;

CHLORPHENOTANE;

ATMOSPHERIC MODELING; BAYESIAN ANALYSIS; CONCENTRATION (COMPOSITION); DDT; EMISSION; ENVIRONMENTAL FATE; MONTE CARLO ANALYSIS; UNCERTAINTY ANALYSIS;

ARCTIC; ARTICLE; ATMOSPHERE; BAYES THEOREM; CONFIDENCE INTERVAL; GEOGRAPHY; HALF LIFE TIME; INFORMATION; MONTE CARLO METHOD; SENSITIVITY ANALYSIS; STATISTICAL MODEL; TEMPERATURE DEPENDENCE; UNCERTAINTY; AIR; ENVIRONMENT; SOIL;

AIR; ARCTIC REGIONS; BAYES THEOREM; DDT; ENVIRONMENT; MODELS, STATISTICAL; MONTE CARLO METHOD; SOIL; UNCERTAINTY;

EID: 61849102895     PISSN: 0013936X     EISSN: 15205851     Source Type: Journal    
DOI: 10.1021/es801161x     Document Type: Article

References (42)

1. UNEP, Stockholm Convention on Persistent Organic Pollutants, http://www.pops.int/documents/convtext/convtext-en.pdf.; accessed March 31, 2008.
2. Schenker, U.; Scheringer, M.; Hungerbühler, K. Investigating the global fate of DDT: model evaluation and estimation of future trends. Environ. Sci. Technol. 2008, 42, 1178-1184.
3. Li, Y. F.; Bidleman, T. F. Usage and emissions of organochlorine pesticides, Canadian Arctic contaminants assessment report II, Physical environment, part II, chapter A.2; Minister of Indian Affairs and Northern Development, 2003.
4. Li, Y. F.; Macdonald, R. W. Sources and pathways of selected organochlorine pesticides to the Arctic and the effect of pathway divergence on HCH trends in biota: a review. Sci. Total Environ. 2005, 342, 87-106.
5. Li, Y. F.; Venkatesh, S.; Li, D. Modeling global emissions and residues of pesticides. Environ. Model. Assess. 2004, 9,237-243.
6. Li,Y. F.; Zhulidov,A.V.; Robarts, R. D.; Korotova, L. G.; Zhulidov, D. A.; Yurtovaya, T. Y.; Ge, L. P. Diehlorodiphenyltrichloroefhane usage in the former Soviet Union. Sci. Total Environ. 2006, 357, 138-145.
7. Schenker, U.; MacLeod, M.; Scheringer, M.; Hungerb̈hler, K. Improving accuracy and quality of data for environmental fate models: A least-squares adjustment procedure for harmonizing physicochemical properties of organic compounds. Environ. Sci. Technol. 2005, 39, 8434-8441.
8. Shen, L.; Wania, F. Compilation, evaluation, and selection of physical-chemical property data for organochlorine pesticides. LChem. Eng. Data 2005, 50, 742-768.
9. Kriiger, H. U.; Gavrilov, R.; Liu, Q.; Zetzsch, C. Entwicklung eines Persistenz-Messverfahrens fur den troposphärischen Abbau von mittelfluchtigen Pflanzenschutzmitteln durch OH- Radikale, Deutsches Umweltbundesamt, 2005.
10. Liu, Q.; Krüger, H. U.; Zetzsch, C, Degradation study of the aerosol-borne insecticides dicofol and DDT in an aerosol smog chamber facility by OH radicals in relation to the POPs convention. Proceedings to European Geosciences Union, Vienna, 2005.
11. Mackay, D.; Shiu, W. Y.; Ma, K. C. Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals; Lewis: Boca-Raton, FL, 1997; Vol. 5.
12. Aislabie,). M.; Richards, N. K.; Boul, H. L. Microbial degradation of DDT and its residues - A review. New. Zeal. J. Agric. Res. 1997, 40, 269-282.
13. ow,) data for hydrophobic organic compounds: DDT and DDE as a case study, U. S. Geological Survey: Washington, DC, 2001.
14. Fenner, K.; Scheringer, M.; Hungerbühler, K. foint persistence of transformation products in chemicals assessment: case studies and uncertainty analysis. Risk Anal. 2003, 23, 35-53.
15. Hertwich, E. G.; McKone, T. E.; Pease, W. S. Parameter uncertainty and variability in evaluative fate and exposure models. Risk Anal. 1999, 19, 1193-1204.
16. Luo, Y. Z.; Yang, X. S. A multimedia environmental model of chemical distribution: Fate, transport, and uncertainty analysis. Chemosphere 2007, 66, 1396-1407.
17. MacLeod, M.; Fraser, A. J.; Mackay, D. Evaluating and expressing the propagation of uncertainty in chemical fate and bioaccu- mulation models. Environ. Toxicol. Chem. 2002, 21, 700-709.
18. Maddalena, R. L.; McKone, T. E.; Hsieh, D. P. H.; Geng, S. Influential input classification in probabilistic multimedia models. Stock. Environ. Res. Risk \ 2001. 15, 1-17.
19. McKone, T. E. Uncertainty and variability in human exposures to soil contaminants through home-grown food - a Monte Carlo assessment. Risk Anal. 1994, 14, 449-63.
20. Meyer, T.; Wania, F. What environmental fate processes have the strongest influence on a completely persistent organic chemical's accumulation in the Arctic? Atmos. Environ. 2007. 41, 2757-2767.
21. Slob, W. Uncertainty Analysis in Multiplicative Models. Risk Anal. 1994, 14, 571-576.
22. Morgan, M. G.; Henrion, M.; Small, M. Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis; Cambridge University Press: Cambridge, 1990.
23. Scheringer, M.; Wegmann, F.; Fenner, K.; Hungerbühler, K. Investigation of the cold condensation of persistent organic pollutants with a global multimedia fate model. Environ. Set Technol. 2000, 34, 1842-1850.
24. Wegmann, F.; Scheringer, M.; Möller, M.; Hungerbühler, K. Influence of vegetation on the environmental partitioning of DDT in two global multimedia models. Environ. Sci. Technol. 2004, 38, 1505-1512.
25. Dubus, I. G.; Brown, C. D.; Beulke, S. Sources of uncertainty in pesticide fate modelling. Sci. Total Environ. 2003, 317, 53- 72.
26. Hertwich, E. C; McKone, T. E.; Pease, W. S. A systematic uncertainty analysis of an evaluative fate and exposure model. Risk Anal. 2000, 20, 439-454.
27. Wegmann, F. The global dynamic multicompartment model CliMoChem for persistent organic pollutants: Investigations of the vegetation influence, the cold condensation and the global fractionation. DSc Thesis, ETH Zurich, 2004.
28. DeFries, R. S.; Townshend, I. R. G. NDVI-Derived Land-Cover Classifications at a Global-Scale. Int. J. Remote Sens. 1994, 15, 3567-3586.
29. Legates, D. R.; Willmott, C. ]. Mean Seasonal and Spatial Variability in Gauge-Corrected, Global Precipitation. Int.. J. Cli- matol. 1990, 10, 111-127.
30. Helton, I. C; lohnson, I. D.; Sallaberry, C. J.; Storlie, C. B. Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliab. Eng. Syst. Saf. 2006, 91, 1175-1209.
31. Helton, ]. C; Davis, F. ]. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab. Eng. Syst.) Saf 2003. 81, 23-69.
32. Helton, I. C; Davis, F. I. Illustration of sampling-based methods for uncertainty and sensitivity analysis. Risk Anal. 2002, 22, 591-622.
33. CrystalBall. Crystal Ball Homepage, http://www.crystalball.com/ crystal-ball/index.html; accessed Dec. 15, 2007.
34. Schenker, U.; Wegmann, F.; Scheringer, M.; Hungerbühler, K. Emission scenario comparison for DDT; ETH Zurich; Unpublished, available upon request. 2006.
35. McKone, T. E.; Bodnar, A. B.; Hertwich, E. G.: Development and evaluation of state-specific landscape data sets for multimedia source-to-dose models; Ernest Orlando Lawrence Berkeley National Laboratory, 2001.
36. Smith, A. E.; Ryan, P. B.; Evans, J. S. The Effect of Neglecting Correlations When Propagating Uncertainty and Estimating the Population-Distribution of Risk. Risk Anal. 1992, 12, 467-474.
37. CrystalBall. How does Crystal Ball calculate sensitivity? http:// www.decisioneering.com/support/simulation/cbl-gen-004A. html; accessed January 12, 2008.
38. Beven, K.; Binley, A. The Future of Distributed Models - Model Calibration and Uncertainty Prediction, llvdrol. Processes 1992, 6, 279-298.
39. Patwardhan, A.; Small, M. J. Bayesian Methods for Model Uncertainty Analysis with Application to Future Sea-Level Rise. Risk Anal 1992, 12, 513-523.
40. Brand, K. P.; Small, M. J. Updating uncertainty in an integrated risk assessment: Conceptual framework and methods. Risk Anal. 1995, 15, 719-731.
41. Sohn, M. D.; Small, M. J.; Pantazidou, M. Reducing uncertainty in site characterization using Bayes Monte Carlo methods. I Environ. Eng. 2000, 126, 893-902.
42. U.S. EPA.EPIWinSuite, http://www.epa.gov/opptintr/exposure/ pubs/episuitedl.htm; accessed February 2, 2008.