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Abstract The seasonal controls of hydrology, temperature, hypoxia, and biogeochemical conditions for groundwater ammonium–N (NH4+) concentrations are not well understood. Here we investigated these controls for riparian groundwaters located upstream of two milldams over a period of 4 years. Groundwater chemistry was sampled monthly while groundwater elevations, hydraulic gradients, and temperatures were recorded sub‐hourly. Distinct seasonal patterns for NH4+were observed which differed among the wells. For wells that displayed a strong seasonal pattern, NH4+concentrations increased through the summer and peaked in October–November. These elevated concentrations were attributed to ammonification, suppression of nitrification, and/or dissimilatory nitrate reduction to ammonium (DNRA). These processes were driven by high groundwater temperatures, low hydraulic gradients (or long residence times), hypoxic/anoxic groundwater conditions, and increased availability of dissolved organic carbon as an electron donor. In contrast, NH4+concentrations decreased in the riparian groundwater from January to April during cool and wet conditions. A groundwater well with elevated total dissolved iron (TdFe) concentrations had elevated NH4+concentrations but displayed a muted seasonal response. In addition to hydrologic controls, we attributed this response to additional NH4+contribution from Fe‐driven autotrophic DNRA and/or ammonification linked to dissimilatory Fe reduction. Understanding the temporal patterns and factors controlling NH4+in riparian groundwaters is important for making appropriate watershed management decisions and implementing appropriate best management practices.
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Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors
Abstract We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH4+ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH4+ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH4+concentrations. Different algorithms such as kNN, random forest, neural network, Naïve Bayes and logistic regression are compared and discussed. ML analysis have led to identify the most prominent features of an impedance spectrum that can be used as the ML predictors to estimate the real concentration of NH4+ion levels. The proposed NH4+sensor along with the decision-making model can identify and operate at specific operating frequencies to continuously collect the most relevant information from a system.
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- Award ID(s):
- 2100930
- PAR ID:
- 10361034
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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