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A methodology to develop a GIS-based system for the surface water risk assessment of agricultural chemicals is described. It is based on the integration of relational and spatial databases, GIS incorporating raster and vector, mass balance models, and pesticide risks indicators. Surface water pollution was modeled by taking into account two main processes: the load due to drift and the load due to a rainfall-runoff event. The former is immediately consequent to pesticide application; the second occurs a short period afterward. Thus two distinct PEC (predicted environmental concentration) values were estimated, differing in time. A pilot approach was applied to the herbicide alachlor on corn in Lombardia region (northern Italy) and represents the first stage of a wider project. Although the resultant alachlor PEC and risk maps represent a static image of a worst-case simulation, the main objective was to provide information for the territory with respect to relative risks at the watershed level, which is important in managing risks to the aquatic environment. The driving forces and spatial variability of the above-mentioned processes were investigated to improve knowledge about the territory and to indicate the need for more detailed site-specific studies.

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The approach adopted, taking into account the variability in farm structure, cropping pattern, risk attitude and economic availability, is not an instrument to identify the most suitable protection strategy for a given crop in a given period, but to help professional users to improve their practices in managing PPPs on farms and to make the most appropriate choices leading to reduced environmental and human risk, without compromising the profitability of agricultural production and food standards. This work has, as an underlying principle, a holistic approach to link the different elements of the three pillars of sustainability (environment, economy and society) and to enhance knowledge, which represents one of the main aspects of the Directive.