Addressing Nitrate Contamination in Groundwater: The Importance of Spatial and Temporal Understandings and Interpolation Methods

Abstract

Iranian water security is threatened by groundwater (GW) degradation. The excessive use of GW for agriculture in Iran is degrading these resources. Livestock waste disposal and sewage irrigation are also major contributors. Nitrate (NO3) contamination in GW is a growing global concern, posing serious health and environmental risks. Soil can easily leach NO3 into GW, causing long-term contamination. Understanding the temporal and spatial patterns of NO3 pollution is vital in protecting human health and establishing safe drinking water limits. Choosing an appropriate interpolation method is crucial for creating a reliable spatial variability map, which is essential for environmental research and decision-making. This study used 85 GW samples collected over four periods to create interpolated maps and examine the spatial variability of NO3 levels. Spatial interpolation methods were performed using the geostatistical tool within ArcGIS Software. The results showed that Empirical Bayesian Kriging (EBK) was the most effective of the five evaluated interpolation methods, although the performance of each method varied depending on the period sampled. Therefore, the choice of interpolation method should be tailored to the study’s specific needs and the characteristics of the data being interpolated. The EBK method produced interpolation maps that illustrated the spatial distribution of NO3 concentrations, both within and exceeding the recommended guidelines. Interpolation methods can assist in creating spatial maps of NO3 concentrations, identifying pollution sources, and developing targeted management strategies. These maps demonstrate the potential impact of human activities on the observed patterns. A thorough understanding of Iran’s current GW quality is very important and valuable for management and policymakers.