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Journal of Environmental Quality Abstract -

Empirical Modeling of Spatial Vulnerability Applied to a Norflurazon Retrospective Well Study in California


This article in JEQ

  1. Vol. 28 No. 2, p. 397-403
    Received: Nov 18, 1997

    * Corresponding author(s): jtroiano@cdpr.ca.gov
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  1. J. Troiano *,
  2. J. Marade and
  3. F. Spurlock
  1. Environmental Monitoring and Pest Management Branch, California, Dep. of Pesticide Regulation, 1020 N Street. Room 161, Sacramento, CA 95814.



One goal of mandated well monitoring in California is to search for residues of active ingredients previously undetected in the state's groundwater. The realization that pesticide residues move into groundwater via several different pathways has led us to develop an empirical approach to delineate vulnerable areas; major climatic and edaphic features of areas where pesticides residues have been detected in well water have been identified on a geographic basis. The objective of this study was to evaluate the use of our empirical model in a retrospective well- sampling study for norflurazon, a preemergence herbicide with physical-chemical properties that indicated potential to move offsite with water. In our modeling approach, sections of land, which are 2.59 km2 areas, were identified as having a greater potential for contamination based on soil and depth-to-groundwater (DGW) data. Wells were sampled from a subset of these sections where use of norflurazon was historically the greatest. Norflurazon residue was detected in 8 of 43 wells sampled in Fresno County, California, and in concentrations ranging from 0.07 to 0.69 µg L−1. This result was considered highly successful because residues had not been detected in 18 previous California groundwater studies for other active ingredients, some of which had been detected in other state and federal sampling programs. Location of sampling sites in these previous 18 California studies was based only on pesticide use data. The detections of norflurazon in this study indicated that, even though using an empirical modeling approach appeared to be unorthodox, it enabled us to effectively identify vulnerable areas.

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