Quantification of spatial and seasonal variations in the proportional contribution of nitrate sources using a multi-isotope approach and Bayesian isotope mixing model.

Abstract

Spatial and seasonal variations in nitrate contamination are a globally concern. While numerous studies have used δ15N-NO3 and δ18O-NO3 to elucidate the dominant sources of nitrate in groundwater, this approach has significant limitations due to the overlap of nitrate isotopic ranges and the occurrence of nitrate isotopic fractionation. This study quantitatively assessed the spatial and seasonal variations in the proportional contributions of nitrate sources from different land uses in the Tarom watershed in North-West Iran. To achieve this aim, orthogonal projection of the hydrochemical and isotopic dataset of the principal component analysis (PCA) as well as correlation coefficient matrix (Corr-PCA) were evaluated to reduce the dimensionality of the inter-correlated dataset. Next, a nitrate isotopic biplot accompanied with a Bayesian isotope mixing model (SIAR) were applied to specify the spatial and seasonal trends in the proportional contribution of three dominant sources of nitrate (fertilizers, animal manure and residential waste) in the watershed. Finally, in order to provide a sensitive framework for nitrate source appointment and overcome the associated limitations of dual nitrate isotope application, the integration of boron isotope (δ11B) and strontium isotopic ratio (87Sr/86Sr) was introduced. The results revealed that the mean contribution of residential sewage increased (17%-27.5%), while the mean contribution of fertilizers decreased (28.3%-19%), from late spring to early autumn. Also, fertilizer was the highest contributor (42.1% ± 3.2) during late spring, especially in regions with more than 75% agricultural land. Meanwhile, the mean contribution of sewage was highest in early autumn (32.1% ± 2.8) in the areas with more than 20% residential land. These results were confirmed by coupled application of δ11B and 87Sr/86Sr. This study provides a useful insight for environmental managers to verify groundwater pollution contributors and to better apply remedial solutions.