| Literature DB >> 26745303 |
Sebastian Stehle1, James Michael Dabrowski2, Uli Bangert3, Ralf Schulz3.
Abstract
Regulatory risk assessment considers vegetated buffer strips as effective risk mitigation measures for the reduction of runoff-related pesticide exposure of surface waters. However, apart from buffer strip widths, further characteristics such as vegetation density or the presence of erosion rills are generally neglected in the determination of buffer strip mitigation efficacies. This study conducted a field survey of fruit orchards (average slope 3.1-12.2%) of the Lourens River catchment, South Africa, which specifically focused on the characteristics and attributes of buffer strips separating orchard areas from tributary streams. In addition, in-stream and erosion rill water samples were collected during three runoff events and GIS-based modeling was employed to predict losses of pesticides associated with runoff. The results show that erosion rills are common in buffer strips (on average 13 to 24 m wide) of the tributaries (up to 6.5 erosion rills per km flow length) and that erosion rills represent concentrated entry pathways of pesticide runoff into the tributaries during rainfall events. Exposure modeling shows that measured pesticide surface water concentrations correlated significantly (R(2)=0.626; p<0.001) with runoff losses predicted by the modeling approach in which buffer strip width was set to zero at sites with erosion rills; in contrast, no relationship between predicted runoff losses and in-stream pesticide concentrations were detected in the modeling approach that neglected erosion rills and thus assumed efficient buffer strips. Overall, the results of our study show that erosion rills may substantially reduce buffer strip pesticide retention efficacies during runoff events and suggest that the capability of buffer strips as a risk mitigation tool for runoff is largely overestimated in current regulatory risk assessment procedures conducted for pesticide authorization.Entities:
Keywords: Exposure modeling; Field survey; Monitoring; Risk assessment; Runoff; South Africa
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Year: 2015 PMID: 26745303 DOI: 10.1016/j.scitotenv.2015.12.077
Source DB: PubMed Journal: Sci Total Environ ISSN: 0048-9697 Impact factor: 7.963