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A Subregional‐Scale Method to Assess Aquifer Vulnerability to Pesticides
Author(s) -
Schlosser Stephanie A.,
McCray John E.,
Murray Kyle E.,
Austin Brad
Publication year - 2002
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/j.1745-6584.2002.tb02514.x
Subject(s) - vadose zone , aquifer , environmental science , hydraulic conductivity , hydrology (agriculture) , groundwater , soil science , pesticide , environmental engineering , soil water , geology , geotechnical engineering , ecology , biology
A method to predict aquifer vulnerability to pesticide contamination at the subregional scale was developed. The assessment method was designed to incorporate relevant hydrologic and pesticide‐transport information and to use generally available data. The method assumes steady‐state advection of pesticides in the vadose zone, including sorption and biological decay. The solution is presented as a vulnerability index (VI) that increases as the aquifer vulnerability increases. The hydrologic input data for the VI model are obtained from the soil survey geographic database. Pesticides were grouped into three leachability classes using a leachabiiity ratio (half‐life divided by organic carbon partition coefficient). Pesticide transformation is assumed to occur in the surface layer. The influence of vertical transport in the remainder of the vadose zone has been incorporated by applying a multiplying factor to the VI that varies with depth to ground water. Hydraulic conductivity is used as a surrogate for soil‐water velocity for practical purposes. The performance of the VI model is evaluated using ground water data from Weld County, Colorado. The model is demonstrated to be successful at predicting relative vulnerability, defined as the magnitude of pesticide concentration and percent of wells in a unit that exhibit a pesticide detection. Areas of low, medium, and high vulnerability are assigned. Results indicate that the vulnerability classifications are most dependent on the leachability ratio, hydraulic conductivity, and organic carbon content.