- Dairy waste lagoons are considered to be point sources of groundwater contamination by chloride (Cl(-)), different nitrogen-species and pathogens/microorganisms. The objective of this work is to introduce a methodology to assess the past and future impacts of such lagoons on regional groundwater quality. The method is based on a spatial statistical analysis of Cl(-) and total nitrogen (TN) concentration distributions in the saturated and the vadose (unsaturated) zones. The method provides quantitative data on the relation between the locations of dairy lagoons and the spatial variability in Cl(-) and TN concentrations in groundwater. The method was applied to the Beer-Tuvia region, Israel, where intensive dairy farming has been practiced for over 50 years above the local phreatic aquifer. Mass balance calculations accounted for the various groundwater recharge and abstraction sources and sinks in the entire region. The mass balances showed that despite the small surface area covered by the dairy lagoons in this region (0.8%), leachates from lagoons have contributed 6.0% and 12.6% of the total mass of Cl(-) and TN (mainly as NO3(-)-N) added to the aquifer. The chemical composition of the aquifer and vadose zone water suggested that irrigated agricultural activity in the region is the main contributor of Cl(-) and TN to the groundwater. A low spatial correlation between the Cl(-) and NO3(-)-N concentrations in the groundwater and the on-land location of the dairy farms strengthened this assumption, despite the dairy waste lagoon being a point source for groundwater contamination by Cl(-) and NO3(-)-N. Mass balance calculations, for the vadose zone of the entire region, indicated that drying of the lagoons would decrease the regional groundwater salinization process (11% of the total Cl(-) load is stored under lagoons). A more considerable reduction in the groundwater contamination by NO3(-)-N is expected (25% of the NO3(-)-N load is stored under lagoons). Results demonstrate that analyzing vadose zone and groundwater data by spatial statistical analysis methods can significantly contribute to the understanding of the relations between groundwater contaminating sources, and to assessing appropriate remediation steps.