Pesticide hotspot probability model
Description
A high density of shallow pore water samples taken for a pesticide contamination study were applied to the development of a probability model spreadsheet calculating the number of pore water samples necessary to detect a pesticide (DPC and MCPP) hotspot with a defined certainty. The spreadsheets calculate the grid size (representing one pore water sample per grid cell) which would detect a pesticide of a defined concentration. Contamination detection is simulated by the model if a grid cell overlaps the contaminated volume of soil. The model was simulated at several pesticide concentration levels to find the optimum number of samples. The results of the spreadsheet indicate a starting point for similar studies to locate and delineate pesticide contamination areas.
Files
Steps to reproduce
The JAGG model can calculate the probability of locating a cylindrical volume containing contaminated soil from borehole inputs. Iso-contouring using the sample data in Surfer provided an estimate of the contamination area, which was transformed into an equivalent cylindrical volume of soil as model input. Probability calculations were made at three different concentration levels for both pesticides, spread concentrically from a source. An assumed pore water volume is removed corresponding to the volume of pore water extracted from a spherical volume of soil. Contamination of the pore water is detected by the model if it overlaps that soil volume from the sampling point. The number of pore water sampling points required to detect an unknown hotspot for a certain contaminant concentration was found by adjusting grid spacing and observing the resultant probability that the hotspot fell within that area. This method was applied to several concentration levels to find the number of samples needed to theoretically detect 90% of an unknown hotspot using the respective pore water concentrations as screening criteria for discovery. The probability based on the actual area was then normalized to sample count per 100 m2.