Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-13T08:41:49.688Z Has data issue: false hasContentIssue false
  • English
  • Français

Environmental Risk Assessment of Pesticides: Improving Simulation Model Credibility

Published online by Cambridge University Press:  12 June 2017

R. Don Wauchope
R. Don Wauchope*
Affiliation:
U.S. Dep. Agric., Agric. Res. Serv., Nematodes Weeds and Crops Research, Univ. Georgia Coastal Plain Exp. Stn., Tifton, GA 31794
Get access Rights & Permissions [Opens in a new window]

Abstract

Registrant use of computer simulation modeling is generally accepted by regulators in lieu of some experimental data for risk assessment, provided worst-case assumptions and extremely conservative criteria for negligible risk are used. These requirements taken together mean that only extreme cases escape the requirement for actual environmental data. This conservatism reflects the uncertainty in both exposure estimates by the models and in the hazard estimates from toxicologists. Until the credibility of estimated environmental concentrations given by models is improved by more experimental data and experience on the part of both registrants and regulators, the models will continue to be considered not-validated by many. There are four current developments that provide a compromise between the two impossible extremes of total model validation and total field measurement: (1) as models develop more process detail the parameters become more fundamental in nature and these are generally more amenable to independent estimation or measurement; (2) the reporting of probabilistic analyses and probable errors of results by modelers will enhance credibility; (3) the development of data sets for specific crop/site/pesticide/weather scenarios will allow users to modify those parts of the input they are concerned with while retaining confidence that parameters with which they are unfamiliar will have reasonable values; and (4) meso-scale and micro-scale physical simulation experiments can fill the gap between laboratory and field results. The most important need for enhancing model credibility is full access by the pesticide science community to all the information available, so that we may increase our understanding. Legal mechanisms should be considered for longer ownership of exclusive marketing rights of active ingredients, in exchange for complete public disclosure of the environmental fate and toxicology data submitted to regulatory agencies.

Keywords

Registrationregulationpesticide runoffpesticide leachingwater qualitynonpoint pollution
Type
Symposium
Information
Weed Technology , Volume 6 , Issue 3 , September 1992 , pp. 753 - 759
Copyright
Copyright © 1990 by the Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

1. Ahuja, L. R., Sharpley, A. N., Yamamoto, M., and Menzel, R. G. 1981. The depth of rainfall-runoff-soil interaction as determined by 32P. J. Environ. Qual. 12:34–40.Google Scholar
2. Arnold, J. G., Williams, J. R., Nicks, A. D., and Sammons, N. B. 1990. SWRRB—a basin scale simulation model for soil and water resources management. Texas A. & M. Press, 210 p.Google Scholar
3. Baker, D. B. and Richards, R. P. 1991. Herbicide concentrations in Ohio's drinking water supplies: a quantitative exposure assessment. p. 9–29 in Weigmann, D. L., ed. Pesticides in the Next decade: the Challenges Ahead. Proc. 3rd Nat. Res. Conf. on Pesticides, Nov. 8–9, 1990. Virginia Water Res. Cent., Blacksburg. 832 p. Google Scholar
4. Baker, J. L., Laflen, J. M., and Johnson, H. P. 1978. Effect of tillage systems on runoff losses of pesticides, a rainfall simulation study. Trans. Am. Soc. Agric. Eng. 1978:886–892.CrossRefGoogle Scholar
5. Bergstrom, L. 1990. Use of lysimeters to estimate leaching of pesticides in agricultural soils. Environ. Pollut. 67:325–347.Google Scholar
6. Bevin, K. J. and Jakeman, A. J. 1990. Complexity and uncertainty in models. p. 555–576 in DeCoursey, D. G., ed. Proc. Int. Symp. Water Quality Modeling on Agricultural Non-Point Sources, June 19–23, Utah State Univ., Logan, UT, U.S. Dep. Agric., Agric. Res. Serv. Rep. ARS-81, June, 1990. 881 p.Google Scholar
7. Burgoa, B. and Wauchope, R. D. 1991. Pesticides in runoff and surface waters in Roberts, T. R. and Kearney, P. C., eds. Environmental Chemistry of Pesticides, John Wiley and Sons, Sussex, England (in preparation).Google Scholar
8. Burgoa, B. and Wauchope, R. D. 1991. The effect of formulations on runoff and leaching losses of atrazine and alachlor from tilted-beds. Abstr. Weed Sci. Soc. Am. 31:63.Google Scholar
9. Coody, P. N., White, J. W., and Graney, R. L. 1990. A “small plot” approach to predicting pesticide runoff and aquatic exposure. Proc. Soc. Environ. Toxicol. Chem. 11th Annu. Mtg., Nov. 11–15, Washington, D.C. Google Scholar
10. Dean, J. D., Huyakorn, P. S., Donigian, A. S., Voos, K. A., Schanz, R. W., Meeks, Y. J., and Carsel, R. F. 1989. Risk of unsaturated/saturated transport and transformation of chemical concentrations (RUSTIC) Volume I: theory and code verification. U.S. Environ. Prot. Agency Rep. EPA/600/3-89/048a.Google Scholar
11. DeCoursey, D. G. 1991. Developing models with more process detail: do more algorithms give more truth? Weed Technol. (this issue).CrossRefGoogle Scholar
12. DeCoursey, D. G. and Rojas, K. W. 1990. RZWQM—a model for simulating the movement of water and soluted in the root zone. p. 813–821 in DeCoursey, D. G., ed. Proc. Int. Symp. Water Quality Modeling on Agricultural Non-Point Sources, June 19–23, Utah State Univ., Logan, UT, U.S. Dep. Agric., Agric. Res. Serv. Rep. ARS-81, June, 1990. 881 p. Google Scholar
13. Donigian, A. S., Meier, D. W., and Jowise, P. P. 1986. Stream transport and agricultural runoff of pesticides for exposure assessment: a methodology. Environ. Prot. Agency Rep. EPA/600/3-86/011a, March 1986, 760 p. Nat. Tech. Inf. Serv., Springfield, VA.Google Scholar
14. Ferreria, V. A. and Smith, R. A. 1990. OPUS: an advanced simulation model for nonpoint-source pollutant transport at the field scale—an overview. in DeCoursey, D. G., ed. Proc. Int. Symp. Water Quality Modeling of Agricultural Non-Point Sources, Part 2 U.S. Dep. Agric., Agric. Serv. Rep. ARS-1, June, 1980. p. 823–834.Google Scholar
15. Fontaine, D. D., Havens, P. L., Blau, G. E., and Tillotson, P. M. 1991. The role of sensitivity analysis in groundwater risk modeling for pesticides. Weed Technol. (this issue).Google Scholar
16. Giddings, J. M. 1980. Types of aquatic microcosms and their research applications. p. 248–266 in Giesy, J. P., ed. Microcosms in ecological research. CONF-781101. Tech. Inf. Cent., Oak Ridge, TN.Google Scholar
17. Goss, D. W. 1991. Screening procedure for soils and pesticides relative to potential water quality impacts. Weed Technol. (this issue). CrossRefGoogle Scholar
18. Goss, D. W. and Wauchope, R. D. 1990. The SCS/ARS/CES pesticide properties database: II. Using it with soils data in a screening procedure. p. 471–493 in Weigmann, D. L., ed. Pesticides in the Next Decade: the Challenges Ahead. Proc. 3rd Nat. Res. Conf. on Pesticides, Nov. 8–9, 1990. Virginia Water Res. Cent., Blacksburg. 831 p. Google Scholar
19. Gustafson, D. I. 1989. Groundwater ubiquity score: a simple method for assessing pesticide leachability. Environ. Toxicol. Chem. 8:339–357.CrossRefGoogle Scholar
20. Hamaker, J. W. 1972. Decomposition: quantitative aspects. in Goring, C.A.I. and Hamaker, J. W., eds. Organic Chemicals in the Soil Environment. Marcel Dekker, Inc., New York. 440 p.Google Scholar
21. Hamaker, J. W. and Thompson, J. M. Adsorption. p. 49–144 in Goring, C.A.I. and Hamaker, J. W., eds. Organic Chemicals in the Soil Environment. Marcel Dekker, Inc., New York. 440 p. Google Scholar
22. Harper, L. A., White, A. W. Jr., Bruce, R. R., Thomas, A. W., and Leonard, R. A. 1976. Soil and microclimate effects on trifluralin volatilization. J. Environ. Qual. 5:236–242.CrossRefGoogle Scholar
23. Hollis, J. M. 1990. Assessments of the vulnerability of aquifers and surface waters to contamination by pesticides. Research Rep. for U.K. Mainstry for Forestry and Fisheries (MAFF), Soil Survey and Land Research Centre, Silsoe Campus, Silsoe, Bedford MK45 4DT, England. 27 p.Google Scholar
24. Hornsby, A. G. 1991. Site-specific pesticide recommendations: the final step in environmental impact prevention. Weed Technol. (this issue).Google Scholar
25. Hubbard, R. K., Williams, R. G., Erdman, M. D., and Marti, L. R. 1989. Chemical transport from coastal plain soils under simulated rainfall: II. Movement of cyanazine, sulfometuron-methyl, and bromide. Trans. Am. Soc. Agric. Eng. 32:1250–1257.Google Scholar
26. Johanson, R. C., Imhoff, J. C., Kittle, J. L., and Donigian, A. S. Jr. 1984. Hydrological simulation program—fortran (HSPF): user's manual for release 8.0. U.S. Environ. Prot. Agency Rep. No. EPA-600/3-84-066, June 1984. 767 p.Google Scholar
27. Jury, W. A., Focht, D. C., and Farmer, W. J. 1987. Evaluation of pesticide groundwater pollution potential from standard indices of soil chemical adsorption and degradation. J. Environ. Qual. 16:422–428.CrossRefGoogle Scholar
28. Knisel, W. G., ed. 1980. CREAMS: a field scale model for Chemicals, Runoff, and Erosion from Agricultural Management Systems. USDASEA Conserv. Res. Rep. No. 26, 643 p.Google Scholar
29. Kördel, W., Klein, M., Baust, J., and von Oepen, B. 1990. Simulation model using SESOIL for scenarios Germany, avail. Fraunhofer-Institut für Umweltchemie und Ökotoxicologie, Schmallenberg/Grafschaft, Germany. 29 p.Google Scholar
30. Leonard, R. A. 1990. Movement of pesticides into surface waters. p. 303–349 in Cheng, H. H. ed. Pesticides in the Soil Environment—Processes, Impacts, and Modelling. Soil Sci. Soc. Am. Book Ser., No. 2, Soil Sci. Soc. Am., Madison, WI 53711. 530 p. Google Scholar
31. Leonard, R. A., Knisel, W. G., and Still, D. A. 1987. GLEAMS: groundwater loading effects of agricultural management systems. Trans. Am. Assoc. Agric. Eng. 30:1403–1418.Google Scholar
32. Leonard, R. A., Langdale, G. W., and Fleming, W. G. 1979. Herbicide losses from four upland Piedmont watersheds—data and implications for modeling pesticide transport. J. Environ. Qual. 8:223–229.Google Scholar
33. McDowell, L. L., Willis, G. H., Southwick, L. M., and Smith, S. 1984. Methyl parathion and EPN washoff from cotton plants by simulated rainfall. Environ. Sci. Technol. 18:423–427.Google Scholar
34. Mills, W. C. and Leonard, R. A. 1984. Pesticide pollution probabilities. Trans. Am. Soc. Eng. 27:1704–1710.Google Scholar
35. Nash, R. G. 1983. Determining environmental fate of pesticides with microagroecosystems. Residue Rev. 85:199–215.Google Scholar
36. Obenhuber, D. C. and Lowrance, R. 1991. Reduction of nitrate in groundwater microcosms by carbon additions. J. Environ. Qual. 20:255–258.Google Scholar
37. Oliver, G., Burt, J., and Solomon, R. 1990. The use of surface runoff models for water quality decisions—a user's perspective. p. 197–204 in DeCoursey, D. G., ed. Proc. Int. Symp. Water Quality Modeling on Agricultural Non-Point Sources, June 19–23, Utah State Univ., Logan, UT, U.S. Dep. Agric., Agric. Res. Serv. Rep. ARS-81, June, 1990. 881 p. Google Scholar
38. Philip, J. R. 1991. Soils, natural science, and models. Soil Sci. 151:91–98.Google Scholar
39. Pionke, H. B., Glotfelty, D. E., Lucas, A. D., and Urban, J. B. 1988. Pesticide contamination of ground waters in the Mahantango Creek Watershed. J. Environ. Qual. 17:76–84.Google Scholar
40. Skipper, H. D., Mueller, J. G., Ward, V. L., and Wagner, S. C. 1986. Microbial degradation of herbicides. p. 457–475 in Camper, N. D., ed. Research Methods in Weed Science, 3rd Edition, Southern Weed Science Society, Champaign, IL 61820. 486 p. Google Scholar
41. Smith, C. N., Brown, D. S., Dean, J. D., Parrish, R. S., Carsel, R. F., and Donigian, A. S. 1985. Field Agricultural runoff monitoring (FARM) manual. Environ. Prot. Agency Rep. No. EPA/600/3-85/043, June, 1985. 230 p. Nat. Tech. Inf. Serv., Springfield, VA.Google Scholar
42. Smith, C. N., Leonard, R. A., Langdale, G. W., and Bailey, G. W. 1978. Transport of agricultural chemicals from small upland Piedmont watersheds. Environ. Prot. Agency Rep. No. EPA/600/3-78-056. Nat. Tech. Inf. Serv., Springfield, VA.Google Scholar
43. Spencer, W. F., Cliath, M. M., Blair, J. W., and LeMert, R. A. 1985. Transport of pesticides from irrigated fields in surface runoff and tile drain waters. U.S. Dep. Agric., Agric. Res. Serv. Conserv. Res. Rep. No. 31, Nat. Tech. Inf. Serv., Washington, D.C. 71 p.Google Scholar
44. Suntio, L. R., Shiu, W. Y., Mackay, D., Seiber, J. N., and Glotfelty, D. 1988. Critical review of Henry's law constants for pesticides. Residue. Rev. 103:1–59.Google Scholar
45. Taylor, A. W., Glotfelty, D. E., Glass, B. L., Freeman, H. P., and Edwards, W. M. 1976. Volatilization of dieldrin and heptachlor from a maize field. Agric. Food Chem. 24:625–631.CrossRefGoogle ScholarPubMed
46. Taylor, A. W. and Spencer, W. F. 1990. Volatilization and vapor transport processes. p. 213–269 in Cheng, H. N., ed. Pesticides in the Soil Environment—Processes, Impacts, and Modelling. Soil Sci. Soc. Am. Book Ser., No. 2, Soil Sci. Soc. Am., Madison, WI 53711. 530 p. Google Scholar
47. Touart, L. W. 1988. Aquatic mesocosm tests to support pesticide registrations. Hazard Evaluation Division Technical Guidance Document, Office of Pesticide Programs, U.S. Environ. Prot. Agency Rep. EPA/540/09-88-035. Nat. Tech. Inf. Serv., Springfield, VA.Google Scholar
48. Wagenet, R. J. and Rao, P.S.C. 1990. Modeling pesticide fate in soils. p. 351–399 in Cheng, H. H., ed. Pesticides in the Soil Environment—Processes, Impacts, and Modelling. Soil Sci. Soc. Am. Book Ser., No. 2, Soil Sci. Soc. Am., Madison, WI 53711. 530 p. Google Scholar
49. Wauchope, R. D. 1978. The pesticide content of surface water draining from agricultural fields—a review. J. Environ. Qual. 7:459–472.Google Scholar
50. Wauchope, R. D. 1981. Runoff studies and pesticide registration, p. 200–205 in Zweig, G. and Beroza, M., eds. Test Protocols for Environmental Fate and Movement of Toxicants. Assoc. Off. Anal. Chem., Arlington, VA. 330 p.Google Scholar
51. Wauchope, R. D., Buttler, T. M., Hornsby, A. G., Augustijn-Beckers, P.W.M., and Burt, J. P. 1991. The SCS/ARS/CES Pesticide Properties Database for Environmental Decision-Making. Rev. Environ. Contam. Toxicol. (in press).Google Scholar
52. Wauchope, R. D. and DeCoursey, D. G. 1986. Measuring and predicting losses of herbicides in runoff water from agricultural areas. p. 135–154 in Camper, N. D., ed. Research Methods in Weed Science, 3rd Edition, Southern Weed Science Society, Champaign, IL 61820, 486 p. Google Scholar
53. Wauchope, R. D. and Leonard, R. A. 1980. Maximum pesticide concentrations in agricultural runoff: a semiempirical prediction formula. J. Environ. Qual. 9:665–672.Google Scholar
54. Weber, J. B. 1985. Soils, herbicide sorption and model plant-soil systems. p. 155–188 in Camper, N. D., ed. Research Methods in Weed Science, 3rd Edition, Southern Weed Science Society, Champaign, IL 61820. 486 p. Google Scholar
55. Williams, J. R., Jones, C. A., and Dyke, P. T. 1984. A modeling approach to determining the relationship between erosion and soil productivity. Trans. Am. Soc. Agric. Eng. 27:129–144.Google Scholar
56. World Wildlife Fund. 1991. Improving Aquatic Risk assessment under FIFRA: report of the Aquatic Effects Dialog Group. In Press.Google Scholar