Influence of threshold value in the use of statistical methods for groundwater vulnerability assessment

Statistical techniques can be used in groundwater pollution problems to determine the relationships among observed contamination (impacted wells representing an occurrence of what has to be predicted), environmental factors that may influence it and the potential contamination sources. Determination of a threshold concentration to discriminate between impacted or non impacted wells represents a key issue in the application of these techniques. In this work the effects on groundwater vulnerability assessment by statistical methods due to the use of different threshold values have been evaluated. The study area (Province of Milan, northern Italy) is about 2000 km² and groundwater nitrate concentration is constantly monitored by a net of about 300 wells. Along with different predictor factors three different threshold values of nitrate concentration have been considered to perform the vulnerability assessment of the shallow unconfined aquifer. The likelihood ratio model has been chosen to analyze the spatial distribution of the vulnerable areas. The reliability of the three final vulnerability maps has been tested showing that all maps identify a general positive trend relating mean nitrate concentration in the wells and vulnerability classes the same wells belong to. Then using the kappa coefficient the influence of the different threshold values has been evaluated comparing the spatial distribution of the resulting vulnerability classes in each map. The use of different threshold does not determine different vulnerability assessment if results are analyzed on a broad scale, even if the smaller threshold value gives the poorest performance in terms of reliability. On the contrary, the spatial distribution of a detailed vulnerability assessment is strongly influenced by the selected threshold used to identify the occurrences, suggesting that there is a strong relationship among the number of identified occurrences, the scale of the maps representing the predictor factors and the model efficiency in discriminating different vulnerable areas.