Sediment grain size regulates the biogeochemical processes of nitrate in the riparian zone by influencing nutrient concentrations and microbial abundance.

Riparian zones play a crucial role in reducing nitrate pollution in both terrestrial and aquatic environments. Complex deposition action and dynamic hydrological processes will change the grain size distribution of riparian sediments, affect the residence time of substances, and have a cascade effect on the biogeochemical process of nitrate nitrogen (NO ₃ ^- -N). However, simultaneous studies on NO ₃ ^- -N transformation and the potential drivers in riparian zones are still lacking, especially neglecting the effect of sediment grain size (SGS). To fill this knowledge gap, we first systematically identified and quantified NO ₃ ^- -N biogeochemical processes in the riparian zone by integrating molecular biotechnology, ¹⁵ N stable isotope tracing, and microcosmic incubation experiments. We then evaluated the combined effects of environmental variables (including pH, dissolved organic carbon (DOC), oxidation reduction potential, SGS, etc.) on NO ₃ ^- -N transformation through Random Forest and Structural Equation Models. The results demonstrated that NO ₃ ^- -N underwent five microbial-mediated processes, with denitrification, dissimilatory nitrate reduction to ammonium (DNRA) dominated the NO ₃ ^- -N attenuation (69.4 % and 20.1 %, respectively), followed by anaerobic ammonia oxidation (anammox) and nitrate-dependent ferric oxidation (NDFO) (8.4 % and 2.1 %, respectively), while nitrification dominated the NO ₃ ^- -N production. SGS emerged as the most critical factor influencing NO ₃ ^- -N transformation (24.96 %, p < 0.01), followed by functional genes (nirS, nrfA) abundance, DOC, and ammonia concentrations (14.12 %, 16.40 %, 13.08 %, p < 0.01). SGS influenced NO ₃ ^- -N transformation by regulating microbial abundance and nutrient concentrations. RF predicted that a 5 % increase in the proportion of fine grains (diameter < 50 μm) may increase the NO ₃ ^- -N transformation rate by 3.8 %. This work highlights the significance of integrating machine learning and geochemical analysis for a comprehensive understanding of nitrate biogeochemical processes in riparian zones, contributing valuable references for future nitrogen management strategies.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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