Pesticide hotspot probability model
Description
A high density of shallow pore water screening samples taken for a pesticide contamination study were applied to the development of a probability model spreadsheet. The JAGG model 1.5, developed by the Danish Environmental Protection Agency, was modified to pore water sampling instead of soil gas, to output the number of pore water samples necessary to detect a pesticide hotspot with a defined level of certainty. The spreadsheets calculate the grid size (representing one pore water sample per grid cell) required to detect a pesticide of a defined concentration at a defined certainty. In this study, the probability model was run for several concentration levels of two dominant pesticides, desphenyl choridazone (DPC) and mechloroprop (MCPP). The results of the spreadsheet provide a starting point for similar studies to locate and delineate pesticide contamination areas.
Files
Steps to reproduce
User input, indicated by gray cells with blue text, includes the assumed aerial rectangular investigation area (length and width in meters), price per sample in Danish kroner (DKK), radius of contamination (meters) and desired probability of overlapping the contamination hotspot. Iso-contours produced from the sample data provided an initial estimate of the contamination area, which was transformed into an equivalent cylindrical volume of soil. Contamination is assumed to spread concentrically from the source. An assumed pore water volume is removed corresponding to the volume of pore water extracted from a spherical volume of soil. Contamination is detected by the model if a grid cell overlaps the contaminated volume of soil. The model calculates the grid size (i.e. sampling density) required to locate a cylindrical volume containing contaminated soil to the user-defined certainty. The count based on the actual area is then normalized to sample count per 100 m2. The model is run at several concentration levels for each pesticides. The number of pore water sampling points required to detect an unknown hotspot for a certain contaminant concentration is found by adjusting grid spacing and observing the resultant probability that the hotspot fell within that area. Several concentration levels are included as screening criteria for discovery to find the optimum number of samples needed to theoretically detect an unknown hotspot.