This exercise deals with the assessment of aquifer vulnerability to pollution in the Piana Campana, southern Italy. Maps of aquifer vulnerability to pollution are becoming more and more in demand because, on the one hand groundwater represents the main source of drinking water, and on the other hand high concentrations of human/economic activities, e.g. industrial, agricultural, cattle-breeding, represent real or potential sources of groundwater contamination.
The main objectives of this case study are:
Area description and methodology
The study area is approximately 410 kmē and covers the south-east part of the Piana Campana, to the north of Naples, southern Italy. The Piana Campana is an alluvial plain with interbedded pyroclastic deposits. The area is strongly vulnerable to pollution due to the presence of intensive agricultural activity and industries. Increasing groundwater demand makes the protection of the aquifer from pollution crucial. Physical and hydrogeological characteristics make the aquifer susceptible to pollution in different ways.
The concept of groundwater vulnerability is based on the assumption that the physical environment may provide some degree of protection to groundwater against natural impacts, especially with regard to contaminants entering the subsurface environment. Consequently, some land areas are more vulnerable to groundwater contamination than others. The ultimate goal of vulnerability maps is a subdivision of the area into several units which have different levels of vulnerability. Aquifer pollution vulnerability can be assessed using several approaches.
In this case study the DRASTIC method, a standardized system for evaluating groundwater pollution potential is used. DRASTIC has been widely used in many countries because the inputs required for its application are generally available or easy to obtain from public agencies. The DRASTIC model is based on seven factors, corresponding to seven layers to be produced as input parameters for modelling.
The acronym DRASTIC corresponds to the initials of the seven base maps:
Each of the parameters is mapped and classified either into ranges or into significant media types which have an impact on pollution potential. Each factor or parameter is assigned a subjective rating between 1 and 10. Weight multipliers are then used for each factor to balance and enhance its importance. The final vertical vulnerability, the DRASTIC index Di is computed as the weighted sum overlay of the seven layers:
Di = Dr*Dw + Rr*Rw + Ar*Ar + Sr*Sw + Tr*Tw + Ir*Iw + Cr*Cw [formula 1]
where D, R, A, S, T, I, and C are the seven parameters, r is the rating value and w is the weight associated to each parameter.
Preparing the data layers
In this exercise two of the seven layers are calculated. Depth to water, the first data layer, represents the depth of the water table from the topographic surface and gives an idea of the minimum distance that a pollutant has to travel to reach the saturated zone. Depth to water is computed using piezometric field data. First, the point data are interpolated using the Moving Average interpolation technique in order to obtain a piezometric surface map. Then, using MapCalc, Depth to water is computed as the difference between the DEM and the piezometric level.
Topography, the second data layer that is calculated, is represented by steepness. Areas with low slope tend to retain water longer. This allows a greater infiltration of recharge water and a greater potential for contaminant migration. Areas with steep slopes, having large amounts of run-off and smaller amounts of infiltration, are less vulnerable to groundwater contamination. The slope map is calculated by filtering the DEM in x and y direction and by using the MapCalc statement Slope = ((HYP (Demdx,Demdy)) / 30)*100.
Computing the vulnerability
The vulnerability index can be computed when all seven data layers are ready.
The first step is to assign the ratings to the data layers. For example, the Soil media layer is reclassified by assigning a rating to each soil class. A new column has to be created in the attribute table that contains the ratings. Now, the input Soil map can be reclassified using this rating column; the obtained attribute map represents one of the seven layers.
Finally, the vulnerability map is computed as the weighted sum of the seven base maps.
Performing a sensitivity analysis
Subjectivity is unavoidably associated to the selection of the ratings and weights that have to be assigned to the seven base maps which represent the seven parameters. Such a selection can strongly affect the result of the final vulnerability map. Given the fact that at present it is not possible to avoid subjectivity, the way to deal with it is by performing a sensitivity analysis.
A sensitivity analysis studies the contribution of individual input parameters on the resultant output of an analytical model. Many factors influence the result such as the type of overlay operation performed, the value of the weights, the number of data layers and of map units in each layer, the error or uncertainty associated to each map unit, and so on. This exercise shows a procedure to detect the influence, i.e. the actual weight, of the ratings assigned to each data layer.
First, all the input maps are crossed to obtain "unique condition subareas". A "unique condition subarea" is defined as a set of one or more areas consisting of pixels with a unique combination of Di, Ri, Ai, Si, Ti, Ii, and Ci where Di, Ri, Ai, Si, Ti, Ii, and Ci, are the rating values of the seven layers used to compute the vulnerability index, and 1 <= i Then, some TabCalc operations are performed to calculate the effective weight Wpi (in %) for each subarea using: Wpi = (PRi*PWi/vulni)*100 [formula 2] where PRi and PWi are the ratings and the weights respectively of the layer P assigned to the subarea i, and vulni is the vulnerability index as computed in formula 1. Finally, the seven attribute maps showing the actual weight of each data layer are prepared. For more information on this case study, contact: Paola Napolitano
Then, some TabCalc operations are performed to calculate the effective weight Wpi (in %) for each subarea using:
Wpi = (PRi*PWi/vulni)*100 [formula 2]
where PRi and PWi are the ratings and the weights respectively of the layer P assigned to the subarea i, and vulni is the vulnerability index as computed in formula 1.
Finally, the seven attribute maps showing the actual weight of each data layer are prepared.
For more information on this case study, contact: