Multi-species measurements of nitrogen isotopic composition reveal the spatial constraints and biological drivers of ammonium attenuation across a highly contaminated groundwater system

Abstract

Groundwater under industrial sites is characterised by heterogeneous chemical mixtures, making it difficult to assess the fate and transport of individual contaminants. Quantifying the in-situ biological removal (attenuation) of nitrogen (N) is particularly difficult due to its reactivity and ubiquity. Here a multi-isotope approach is developed to distinguish N sources and sinks within groundwater affected by complex industrial pollution. Samples were collected from 70 wells across the two aquifers underlying a historic industrial area in Belgium. Below the industrial site the groundwater contained up to 1000 mg N l-1 ammonium (NH4+) and 300 mg N l-1 nitrate (NO3-), while downgradient concentrations decreased to ~1 mg l-1 DIN ([DIN] = [NH4+N] + [NO3-N] + [NO2-N]). Mean δ15N-DIN increased from ~2‰ to +20‰ over this flow path, broadly confirming that biological N attenuation drove the measured concentration decrease. Multi-variate analysis of water chemistry identified two distinct NH4+ sources (δ15NNH4+ from -14‰ and +5‰) within the contaminated zone of both aquifers. Nitrate dual isotopes co-varied (δ15N: -3‰ - +60‰; δ18O: 0‰ - +50‰) within the range expected for coupled nitrification and denitrification of the identified sources. The fact that δ15NNO2- values were 50‰-20‰ less than δ15NNH4+ values in the majority of wells confirmed that nitrification controlled N turnover across the site. However, the fact that δ15NNO2- was greater than δ15NNH4+ in wells with the highest [NH4+] shows that an autotrophic NO2- reduction pathway (anaerobic NH4+ oxidation or nitrifier-denitrification) drove N attenuation closest to the contaminant plume. This direct empirical evidence that both autotrophic and heterotrophic biogeochemical processes drive N attenuation in contaminated aquifers demonstrates the power of multiple N isotopes to untangle N cycling in highly complex systems.