Predicting runoff-induced pesticide input in agricultural sub-catchment surface waters: linking catchment variables and contamination.
An urgent need exists for applicable methods to predict areas at risk of pesticide contamination within agricultural catchments. As such, an attempt was made to predict and validate contamination in nine separate sub-catchments of the Lourens River, South Africa, through use of a geographic information system (GIS)-based runoff model, which incorporates geographical catchment variables and physicochemical characteristics of applied pesticides. We compared the results of the prediction with measured contamination in water and suspended sediment samples collected during runoff conditions in tributaries discharging these sub-catchments. The most common insecticides applied and detected in the catchment over a 3-year sampling period were azinphos-methyl (AZP), chlorpyrifos (CPF) and endosulfan (END). AZP was predominantly found in water samples, while CPF and END were detected at higher levels in the suspended particle samples. We found positive (p < 0.002) correlations between the predicted average loss and the concentrations of the three insecticides both in water and suspended sediments (r between 0.87 and 0.94). Two sites in the sub-catchment were identified as posing the greatest risk to the Lourens River mainstream. It is assumed that lack of buffer strips, presence of erosion rills and high slopes are the main variables responsible for the high contamination at these sites. We conclude that this approach to predict runoff-related surface water contamination may serve as a powerful tool for risk assessment and management in South African orchard areas.
Dabrowski
James M
JM
Department of Zoology, University of Stellenbosch, Matieland, South Africa.
Peall
Sue K C
SK
Van Niekerk
Adriaan
A
Reinecke
Adriaan J
AJ
Day
Jenny A
JA
Schulz
Ralf
R
D000383:Agriculture; D005544:Forecasting; D040362:Geographic Information Systems; D019015:Geologic Sediments / Q000737:chemistry; D010575:Pesticides / Q000032:analysis*; D018570:Risk Assessment; D012989:Soil Pollutants / Q000032:analysis*; D013019:South Africa
D010575:Pesticides; D012989:Soil Pollutants