A simple empirical model to predict forest insecticide ground-level deposition from a compendium of field data.
Deposit data from 205 aerial forest insecticide applications conducted in field trials by the Canadian Forest Service, Great Lakes Forestry Centre over a 15-year period are summarized. Deposit measurements were taken under "worst case" scenarios in the sense that direct applications were made over water bodies, and ground samplers were intentionally placed in open or cleared areas of forest. The median % deposit on shoreline collectors (32 separate applications) was 5.7%, on mid-stream collectors (44 separate applications) was 6.2%, and on forest floor collectors (129 separate applications) was 4.9%. Forest floor deposit was most closely associated with application rate and droplet size (r = 0.624, p < 0.001 and r = 0.662, p = 0.011, respectively) but these variables combined only explained 44% of the variation in deposit. Data from all three collector types were grouped by 10% deposit increments and combined to provide a data set from all deposition scenarios. A negative exponential model was fitted to the proportion of these combined sites regressed on % deposit in 10% increments and plotted as a deposit probability distribution curve (p < 0.001, r2 = 0.992). The probability distribution curve indicated that 5-10% deposit would be expected about 57-91% of the time, whereas 50% deposit or greater would be expected about 2% of the time or less. In a probabilistic risk assessment for aerially applied insecticides in a conifer-dominated forest environment, the probability distribution curve based on empirical data presented here can be used to refine the characterization of exposure scenarios from which effects estimates can be derived.
Kreutzweiser
David P
DP
Canadian Forest Service, Natural Resources Canada, Sault Ste Marie, Ontario, Canada. dave.kreutzweiser@nrcan.gc.ca
Nicholson
Chantal L
CL
D004784:Environmental Monitoring / Q000379:methods*; D007306:Insecticides / Q000009:adverse effects / Q000032:analysis*; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D011634:Public Health; D018570:Risk Assessment; D012989:Soil Pollutants / Q000009:adverse effects / Q000032:analysis*; D014197:Trees; D014874:Water Pollutants, Chemical / Q000009:adverse effects / Q000032:analysis*; D014881:Water Supply
D007306:Insecticides; D012989:Soil Pollutants; D014874:Water Pollutants, Chemical