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DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Throughfall-Reduction Drying Effects on Soil Respiration, Moisture, and Temperature in Four Distinct Lowland Panamanian Forests from 2015-2022

Objectives: Climatic drying is predicted for many tropical forests, yet effects on soil properties across moisture and soil gradients within tropical forests remain poorly characterized, hampering predictions of forest-climate feedbacks. We hypothesized that throughfall reduction (i.e. chronic drying) would suppress soil CO2 fluxes (i.e., respiration) in already-drier tropical forests by further reductions in soil moisture, but increases CO2 fluxes in wetter tropical forests by alleviating anaerobiosis and soil saturation. We conducted a field study in four distinct lowland Panamanian forests that vary in background rainfall and soil fertility. In each forest we constructed throughfall reduction structures that reduce moisture inputs to plots by ~50% (see methods). In drying and paired control plots we measured soil CO2 fluxes, soil moisture (volume/volume), soil temperature (°C), air temperature (°C), and forest floor biomass during wet-dry cycles (2015 – 2022). We also collected litterfall biomass and surveyed all tree species for diameter at breast height (dbh), and identified tree species in 2018 and 2019.Results:We found that soil moisture peaked in the wet season and declined in the dry season. Measured soil CO2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in a lower soil moisture optimum for respiration than previously modeled. Chronic throughfall exclusion also suppressed soil moisture across the four forests, and also initially suppressed soil CO2 fluxes across forests. There was sustained suppression of soil CO2 fluxes after four years in the wettest forest only (-28 ± 4% during the dry season), but elevated soil CO2 fluxes in a fertile forest after four years (+75 ± 28% during the late wet season). The unexpected negative drying effect in the wettest, most infertile forest could have resulted from reduced vertical flushing of nutrients into soils, as the drying effect increased with time. Including hydro-nutrient interactions in ecosystem models could improve predictions of tropical forest-climate feedbacks. Datasets included: Datasets included here include .csv and .xls files for soil respiration (carbon dioxide flux) collected using the LiCor (see methods), air temperature °C , soil moisture (vl/vl), and soil temperature °C. Soil moisture and soil temperature were collected at 0-10cm depths using hand-held probes (see methods). Data are on ~3 week timescales from 2015-2022 with some gaps. There is also a .kml file that includes coordinates for all 32 plots included in the study of four forests (n = 4 throughfall reduction and n = 4 control plots per site). No special software is needed to open these files.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

The Catalytic Reduction of Dinitrogen Under Mild Conditions (Final Report)

In order to use molecular hydrogen as an alternative to fossil fuels, large quantities of hydrogen (or protons and electrons) would have to be generated from water using a method based on solar energy and stored as a molecule in which hydrogen is a high weight percentage. The hydrogen must then be generated as needed from the form in which it is stored. An attractive molecule for this purpose is ammonia (~17% hydrogen by weight). Among the many problems that must be addressed for a successful implementation of this approach in the long term is the reduction of nitrogen catalytically to ammonia with protons and electrons under mild conditions, a goal that is addressed in this proposal. The first abiological catalytic reduction of N 2 to NH 3 by protons and electrons under mild conditions (22 °C and 1 atm) employed molybdenum catalysts, [(HIPTNCH 2 CH 2 ) 3 N]Mo (HIPTN 3 N 3- ), where HIPT (hexaisopropylterphenyl) is 3,5- (2,4,6-i-Pr 3 C 6 H 2 ) 2 C 6 H 3 . A total of approximately four turnovers (to give a maximum of eight equivalents of ammonia) along with approximately one equivalent of hydrogen per N 2 reduced, were found. A limitation of this catalytic approach is loss of the ligand from the metal. This project is aimed at developing a complex that contains a ligand that will not be removed from the metal. A macrocyclic TREN-based ligand of this type has been synthesized recently. The project is aimed at making similar ligands and molybdenum complexes that contain them and testing the new catalysts for catalytic reduction of dinitrogen. The results of these investigations will help guide further design of ligands for more efficient reduction of nitrogen to ammonia and ultimately an integrated device in which protons and electrons are generated using solar energy and employed to reduce nitrogen to ammonia using molybdenum catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Dynamic Species Reduction for Multi-Cycle CFD Simulations (Final Technical Report)

This project primarily sought to address some of the computational cost concerns of detailed simulations by developing improved methods of handling species transport, and chemical kinetics evaluation in a commercial 3D Computational Fluid Dynamics (CFD) environment. Secondary goals were to apply these techniques to fuels and conditions of interest to better understand the key species and reactions required to adequately capture cycle to cycle coupling. Improved modeling will lead to better understanding of these combustion modes and their dependence on fuel composition, which can then enable more clean and efficient engines, ultimately benefiting the consumer as well as the general public. Two approaches were used to address the computational cost. A “Dynamic Species Reduction” (DSR) procedure was developed to remove chemical species from the simulation during periods when chemical reactions were not expected to be important, particularly during gas exchange when temperatures are low and little fuel remains. This modelling procedure automatically detects relevant species to retain in the simulation domain based on their local concentration, removes species below a specified concentration threshold, and then adjusts the remaining species mass to conserve not only the number of H, C, and O atoms in each cell, but also the relative proportions between species and the heating value of the mixture in every cell. The second procedure was a “Product Directed Remapping” (PDR) updates the algorithm used to group individual computational cells for chemical kinetics evaluation to account for non-uniform temperature distributions and the CO to CO 2 ratio in the cells. The model techniques developed in this work successfully demonstrated computational performance improvements for a range of conditions relevant for Highly Dilute SI and HCCI engine operation. Runtime reductions of 10% were observed for small mechanisms, with further reductions of up to 36% observed for larger mechanisms. Improvements were primarily related to reducing the number of chemical species tracked during the gas exchange process using the Dynamic Species Reduction method. With smaller benefits observed from changes to the kinetics binning and evaluation strategy in the post combustion region using the PDR method. The results of this work show the potential for improved computational runtime for complicated simulations. They can and should be extended to additional conditions and new bio-derived and renewable fuels as they are developed and new kinetic mechanisms become available.

10 SYNTHETIC FUELS↗

DOE OSTI2023

Grid Cost and Total Emissions Reductions Through Mass Deployment of Geothermal Heat Pumps for Building Heating and Cooling Electrification in the United States

This report presents the results of a study on the potential grid impacts of national-scale mass deployment of geothermal heat pumps (GHPs) coupled with weatherization in single-family homes (SFHs) from 2022 to 2050. GHPs are a technology readiness level 10, commercially available technology across the United States. This study is an impact analysis only; installed costs and available land areas for installing GHPs are not accounted for in determining their estimated deployment. The three scenarios studied were (1) continuing to operate the grid as it is today (the Base scenario), (2) a scenario to reach 95% grid emissions reductions by 2035 and 100% clean electricity by 2050 (the Grid Decarbonization scenario), and (3) a scenario in which the Grid Decarbonization scenario is expanded to include the electrification of wide portions of the economy, including building heating (the Electrification Futures Study or EFS scenario). The analysis team modeled each of these three scenarios with and without GHP deployment to a large percentage of US building floor space. In all cases, deployment of approximately 5 million GHPs per year demonstrated system cost savings on the grid, consumer fuel cost savings through eliminated fuel combustion for space heating, and CO 2 emission reductions from avoided on-site fuel combustion—and, in the case of the Base scenario, CO 2 emissions reductions from the electric power sector. GHPs have traditionally been viewed as a building energy technology. The most notable result of this study, however, is the demonstration that GHPs coupled with weatherization in SFHs are primarily a grid cost reduction tool and technology that, when deployed at a national scale, also substantially reduces CO 2 emissions, even in the absence of any other decarbonization policy.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Electrocatalytic nitrate reduction: controlling adsorbate affinity to tailor reaction products

Every year, Haber-Bosch nitrogen fixation to form ammonia releases immense volumes of CO2. At the same time, nitrate contamination from untreated wastewater threatens human health across the US. Development of circular processes to efficiently upgrade waste nitrate for reuse is critical to sustainably address this growing environmental hazard. Electrochemical reduction of nitrate operates at ambient temperatures and pressures, and can leverage distributed renewable energy sources and water as a hydrogen source for ammonia production. However, current catalysts lack electron efficiency in reducing nitrate versus water, lack selectivity in forming ammonia as a product, or rely on expensive rare metals, making widespread implementation unfeasible. To overcome these limitations, this proposal seeks a mechanistic understanding of nitrate electrochemical reduction on earth abundant metals and their alloys, with the goal of identifying active-site properties that improve both Faradaic efficiency and product selectivity. This insight will extend to other electrocatalytic reductive process that compete with water reduction.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2024

Machine-learning based model reduction for partial differential equations

We develop a novel synergistic approach between model reduction and machine learning. The specific goal of this project is to aid in the construction of reduced order models for basis functions that are custom-made to represent the solution of partial differential equations. Partial differential equations (PDEs) are one of the main mathematical tools for describing physical phenomena. However, due to either efficiency or necessity, for many real-world problems, we are interested in constructing reduced order models (ROMs) which focus only on the explicit computation of subsets of the active spatio-temporal scales in the problem, while treating the interaction with the rest of the scales approximately. The task of accurate representation of such interactions (usually called memory terms) constitutes a vast area of research known as model reduction. PI Stinis has significant expertise in the construction of ROMs for complex systems. In addition, in recent work with the project key participant Qadeer, they have utilized machine learning to acquire custom-made basis functions (CBFs) to expand the solutions of PDEs. In the proposed work, we will merge the two concepts by constructing ROMs for subsets of the CBFs needed to represent the solution of a PDE. Specifically, we will use the Mori-Zwanzig model reduction formalism to construct ROMs for subsets of CBFs for nonlinear PDEs of various complexity, as well as investigate the usage of CBFs in the spectral vanishing viscosity method for problems that can form shocks in finite time. The outcome of the research is aimed to be proof-of-concept about a novel synergistic approach between model reduction and machine learning, thus advancing the field of scientific machine learning. Such a capability will benefit the efficient modeling of physical systems appearing in various areas of interest to the DOE.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Cell-free bioelectrocatalytic platform for carbon dioxide reduction (Final Technical Report)

The University of Minnesota (UMN) EcoSynBio Team aimed to develop a cell-free, enzyme-based platform for the electro- biocatalytic conversion of CO2 into formate as a platform chemical for further upgrading. This type of bio electrocatalytic process delivers a clean product stream without the need for extensive separation from the electrolyte as in electrochemical synthesis and microbial processes. The reduction reaction is catalyzed by metal-dependent formate dehydrogenases (mFDHs) that are capable of efficient electrocatalytic CO2 reduction without the need of costly co-factors. The development of an efficient, scalable electrobiocatalytic process with high total turnover numbers and viable space time yields, however, was not without its challenges. The UM team has developed a protein-based scaffolding system that facilitates enzyme stabilization and attachment to electrodes along with electron transfer. Yet, although FDHs are highly promising enzymes for cell-free, electrobiochemical CO2 reduction, they are also greatly understudied and especially for applications in electrocatalysis. The UM team used the best described mFDH from Clostridium as its benchmark system and spent significant time and effort in attempting to replicate published data and finally, redesigned a recombinant production system for proper metal co-factor incorporation. The UM team has also identified a small set of new enzyme homologs from extreme microorganisms with superior stabilities that have yielded initial structural data for further engineering. In addition, a new bioelectrocatalytic reactor system has been developed that can be 3D printed and used for enzyme attachment to electrodes. In summary the project has generated critical basic information for the further development of this class of enzymes for the electricity driven reduction of CO2 into formate as platform chemical for upgrading into various other chemicals, including fuels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Xcel Energy’s electric carbon emissions reduction trajectories in the context of 1.5°C and 2°C warming pathways

In 2015 the Paris Agreement established the goals of limiting global average warming to well below 2°C and pursuing efforts to limit warming to below 1.5°C. A large and growing number of scenarios have been developed by the climate research community that explore global energy and emissions pathways that would achieve those goals. We draw on the most recent database of such scenarios to update a previous analysis of Xcel Energy’s emissions reduction goals in light of evolving climate science. We assess the outlook for the role of the US electricity sector in current economy-wide and global emissions pathways and compare it to Xcel Energy’s near-term resource plans to 2030. We find that global scenarios that achieve the 1.5°C goal span a range of US/North America electricity sector emissions reductions by 2030 of about 65-85%. Xcel Energy’s emissions reductions to date have exceeded those of the US electricity sector as a whole, and its projected trajectory to 2030 under current approved resource plans falls within this range. Scenarios achieving the 2°C goal have a wider range of reductions (about 40-85%). In scenarios achieving either goal, electricity sector emissions fall faster than economy-wide emissions, a robust feature of mitigation scenarios, which typically rely on low carbon electricity to achieve climate targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Control of hydrogen concentrations by microbial sulfate reduction in two contrasting anoxic coastal sediments

Introduction Molecular hydrogen is produced by the fermentation of organic matter and consumed by organisms including hydrogenotrophic methanogens and sulfate reducers in anoxic marine sediment. The thermodynamic feasibility of these metabolisms depends strongly on organic matter reactivity and hydrogen concentrations; low organic matter reactivity and high hydrogen concentrations can inhibit fermentation so when organic matter is poor, fermenters might form syntrophies with methanogens and/or sulfate reducers who alleviate thermodynamic stress by keeping hydrogen concentrations low and tightly controlled. However, it is unclear how these metabolisms effect porewater hydrogen concentrations in natural marine sediments of different organic matter reactivities. Methods We measured aqueous concentrations of hydrogen, sulfate, methane, dissolved inorganic carbon, and sulfide with high-depth-resolution and 16S rRNA gene assays in sediment cores with low carbon reactivity in White Oak River (WOR) estuary, North Carolina, and those with high carbon reactivity in Cape Lookout Bight (CLB), North Carolina. We calculated the Gibbs energies of sulfate reduction and hydrogenotrophic methanogenesis. Results Hydrogen concentrations were significantly higher in the sulfate reduction zone at CLB than WOR (mean: 0.716 vs. 0.437 nM H 2 ) with highly contrasting hydrogen profiles. At WOR, hydrogen was extremely low and invariant (range: 0.41–0.52 nM H 2 ) in the upper 15 cm. Deeper than 15 cm, hydrogen became more variable (range: 0.312–2.56 nM H 2 ) and increased until methane production began at ~30 cm. At CLB, hydrogen was highly variable in the upper 15 cm (range: 0.08–2.18 nM H 2 ). Ratios of inorganic carbon production to sulfate consumption show AOM drives sulfate reduction in WOR while degradation of organics drive sulfate reduction in CLB. Discussion We conclude more reactive organic matter increases hydrogen concentrations and their variability in anoxic marine sediments. In our AOM-dominated site, WOR, sulfate reducers have tight control on hydrogen via consortia with fermenters which leads to the lower observed variance due to interspecies hydrogen transfer. After sulfate depletion, hydrogen accumulates and becomes variable, supporting methanogenesis. This suggests that CLB’s more reactive organic matter allows fermentation to occur without tight metabolic coupling of fermenters to sulfate reducers, resulting in high and variable porewater hydrogen concentrations that prevent AOM from occurring through reverse hydrogenotrophic methanogenesis.

Microbiology↗

DOE OSTI2022

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Synergizing Fe 2 O 3 Nanoparticles on Single Atom Fe‐N‐C for Nitrate Reduction to Ammonia at Industrial Current Densities

The electrochemical reduction of nitrates (NO 3 − ) enables a pathway for the carbon neutral synthesis of ammonia (NH 3 ), via the nitrate reduction reaction (NO 3 RR), which has been demonstrated at high selectivity. However, to make NH 3 synthesis cost-competitive with current technologies, high NH 3 partial current densities (j NH3 ) must be achieved to reduce the levelized cost of NH 3 . Here, the high NO 3 RR activity of Fe-based materials is leveraged to synthesize a novel active particle-active support system with Fe 2 O 3 nanoparticles supported on atomically dispersed Fe–N–C. The optimized 3×Fe 2 O 3 /Fe–N–C catalyst demonstrates an ultrahigh NO 3 RR activity, reaching a maximum j NH3 of 1.95 A cm −2 at a Faradaic efficiency (FE) for NH 3 of 100% and an NH 3 yield rate over 9 mmol hr −1 cm −2 . Operando XANES and post-mortem XPS reveal the importance of a pre-reduction activation step, reducing the surface Fe 2 O 3 (Fe 3+ ) to highly active Fe 0 sites, which are maintained during electrolysis. Durability studies demonstrate the robustness of both the Fe 2 O 3 particles and Fe–N x sites at highly cathodic potentials, maintaining a current of −1.3 A cm −2 over 24 hours. This work exhibits an effective and durable active particle-active support system enhancing the performance of the NO 3 RR, enabling industrially relevant current densities and near 100% selectivity.

active support↗

DOE OSTI2021

Photochemical CO 2 Reduction Using Rhenium(I) Tricarbonyl Complexes with Bipyridyl-Type Ligands with and without Second Coordination Sphere Effects

In this study, the effect of phenyl, phenol, aniline, amino, and CF 3 substituents of the 2,2'-bipyridine ligand in fac-ReCl(L)(CO) 3 (L=2,2'-bipyridine derivative) catalysts on photochemical CO 2 reduction in dimethylacetamide is examined, in order to understand the structure-function relationships and to compare the catalytic activities with the previously published electrochemical results. All complexes including ReCl(bpy)(CO) 3 have similar excited-state lifetimes and emission spectra, but complex 1 with the Ph-NH 2 moiety exhibits a significantly larger molar absorption coefficient for its metal-to-ligand charge transfer (MLCT) band. All complexes we tested produce CO with only a negligible amount of H 2 and formate in self-sensitized systems in the presence of triethanolamine (TEOA) and in some cases, BIH (1,3-dimethyl-2-phenyl-2,3-dihydro-1H-benzo[d]-imidazole). The presence of the Ph-NH 2 moiety (complex 1) has a beneficial effect on both electrochemical and photochemical activity, allowing a turnover number (TON) of 32 and 120 for photochemical CO production (without and with BIH, respectively). In the case of the Ph-OH group in the second coordination sphere (complexes 4 and 6), these complexes are active for photochemical CO 2 reduction, despite the formation of a stable 6-coordinate Re-OPh intermediate via reductive deprotonation as previously observed under electrochemical conditions. Overall, BIH accelerates the rate of formation of the one-electron reduced species (OERS) of the Re catalysts and allows higher turnover frequency (TOF) and TON for CO formation. The X-ray structures of complexes 1 and 4 were determined to have distorted octahedral Re centres, and show π-π stacking interactions with neighboring molecules as well as intramolecular hydrogen bonds to the internal chloride ligands. The unusually high absorptivity of the MLCT absorption of complex 1 has been explained by TD-DFT calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Selective reduction of CO to acetaldehyde with CuAg electrocatalysts

Electrochemical CO reduction can serve as a sequential step in the transformation of CO 2 into multicarbon fuels and chemicals. In this study, we provide insights on how to steer selectivity for CO reduction almost exclusively toward a single multicarbon oxygenate by carefully controlling the catalyst composition and its surrounding reaction conditions. Under alkaline reaction conditions, we demonstrate that planar CuAg electrodes can reduce CO to acetaldehyde with over 50% Faradaic efficiency and over 90% selectivity on a carbon basis at a modest electrode potential of −0.536 V vs. the reversible hydrogen electrode. The Faradaic efficiency to acetaldehyde was further enhanced to 70% by increasing the roughness factor of the CuAg electrode. Density functional theory calculations indicate that Ag ad-atoms on Cu weaken the binding energy of the reduced acetaldehyde intermediate and inhibit its further reduction to ethanol, demonstrating that the improved selectivity to acetaldehyde is due to the electronic effect from Ag incorporation. These findings will aid in the design of catalysts that are able to guide complex reaction networks and achieve high selectivity for the desired product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Power System Network Reduction for Power Hardware-in-the-Loop Simulation

This paper proposes single-port equivalent and two-port equivalent network reduction methods to respectively reduce single-port and two-port areas in a large power network. Parameters of the reduced systems are rigorously derived, which guarantees that the electrical quantities at the port(s) remain unchanged over the reduction, including voltage magnitude and phase and active and reactive power injections into the area to be reduced. The proposed techniques are applied to reduce a practical Maui grid, where the total numbers of buses, lines and transformers are respectively reduced from 212, 106 and 108 to 45, 30 and 13. Dynamic behaviors between the full model and the reduced model are compared in detail to illustrate the efficacy and accuracy of the proposed network reduction.

model reduction↗

DOE OSTI2026

Surface water nitrogen and sediment potential nitrate reduction rates, nutrient stocks, and stable isotopes from nine wetlands at the Tanglewood Biological Station, Alabama

This dataset supports a broader study investigating wetland hydrologic and biogeochemical responses to inundation disturbances. Bimonthly surface water and sediment sampling events were conducted at nine wetland sites situated within the Tanglewood Biological Station in Alabama from April 2023 to February 2024. The contents included in the data package include surface water nitrogen (nitrogen oxides and ammonium) and sediment potential nitrate reduction rates (measured as potential denitrification and dissimilatory nitrate reduction to ammonium processing), nutrient stocks (total carbon, total nitrogen, and organic matter), and stable isotopes (carbon and nitrogen). Water level data related to each wetland location can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/2530253 (Kirker et al., 2024) and related water geochemistry data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3001967 (Forbes et al., 2025). In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) field metadata and international generic sample numbers (IGSNs); (4) readme; (5) the field protocol; and (6) a subfolder with sample data. The sample data subfolder contains (1) sediment potential denitrification rate, (2) sediment potential dissimilatory nitrate reduction to ammonium (DNRA) rate, (3) sediment total carbon and nitrogen content, (4) sediment stable isotopes (delta nitrogen-15 and delta carbon-13), (5) sediment percent organic matter, (6) surface water nitrous oxides, (7) surface water ammonium, and (8) methods codes. All files are .csv or .pdf.

Ammonium↗