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NO2 column amounts were measured for the past 2 years at Goddard Space Flight Center, Greenbelt, Maryland, using a Brewer spectrometer in direct Sun mode. A new bootstrap method to calibrate the instrument is introduced and described. This technique selects the cleanest days from the database to obtain the solar reference spectrum. The main advantage for direct Sun measurements is that the conversion uncertainty from slant column to vertical column is negligible compared to the standard scattered light observations where it is typically on the order of 100% (2sigma) at polluted sites. The total 2sigma errors of the direct Sun retrieved column amounts decrease with solar zenith angle and are estimated at 0.2 to 0.6 Dobson units (DU, 1 DU approx. equal to 2.7 10(exp 16) molecules cm(exp -2)), which is more accurate than scattered light measurements for high NO2 amounts. Measured NO2 column amounts, ranging from 0 to 3 DU with a mean of 0.7 DU, show a pronounced daily course and a strong variability from day to day. The NO2 concentration typically increases from sunrise to noon. In the afternoon it decreases in summer and stays constant in winter. As expected from the anthropogenic nature of its source, NO2 amounts on weekends are significantly reduced. The measurements were compared to satellite retrievals from Scanning Image Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY). Satellite data give the same average NO2 column and show a seasonal cycle that is similar to the ground data in the afternoon. We show that NO2 must be considered when retrieving aerosol absorption properties, especially for situations with low aerosol optical depth.
Total odd nitrogen, NO(y), may be defined as the sum of all active nitrogen species that interchange photochemically with one another on a time scale of the order of weeks or less. As noted, NO + NO2 reactions dominate the processes controlling the ozone balance in the contemporary stratosphere. The observational data from non-satellite platforms are reviewed. The growth in available satellite data in the past four years is considered. Some of the most important scientific issues are discussed, taking into account new results from atmospheric models (mainly 2-D). The model results are compared with the observational data.
We estimate the effect of lake water quality on residential housing prices using cross-sectional data on water clarity from 113 lakes across the United States. An instrumental-variables approach is developed to address potential endogeneity bias in the water-clarity variable. Three lake-based physical variables serve as instrumental variables: total nitrogen concentration in lake water, total phosphorus concentration in lake water, and water temperature near the lake surface. The econometric results include three methodological findings: the instruments are valid and strong; the ordinary-least-squares estimate of the coefficient on the water-clarity variable is unbiased; and water clarity is the attribute of water quality valued in the housing market. The estimated water-clarity effect shows that a one-tenth of a meter change in water clarity leads to a one-percent change in housing price, or an elasticity of 0.20 at mean clarity. Coupling this effect with estimates of an ecological production function, a lake-specific benefit index is developed that shows the effect on housing values of bringing lakes into compliance with the US Environmental Protection Agency's regional recommendations for phosphorous concentration in lake waters.
This dataset supports a broader study examining the effects of wetting and drying on hyporheic zone respiration across the contiguous United States (CONUS). The dataset provides data generated from a laboratory moisture manipulation experiment. The contents include time series aerobic respiration and moisture; dissolved oxygen; sediment geochemistry data; and field metadata (including qualitative information on instream and river corridor characteristics). Samples were collected as part of the WHONDRS CONUS-Scale Model-Sample Study (CM). This study was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. The data package associated with the CM study is available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689. CM sampling began in April 2022 and ended in October 2023. This study uses subsamples from a subset of CM samples collected between June 2022 and June 2023. The original field samples were labeled as CM_###. Subsequent subsamples for this study were labeled as EC_###. The labels from the field samples and the EC subsamples can be mapped directly based on the digits following the prefix and underscore (i.e., EC_001 is a subsample from CM_001). See the critical details section below for more details on sample naming. This data package was originally published in August 2024. It was updated in February 2026 (v2; new and modified files). See the change history section in the readme for more details. For details on how to navigate this data package, see this infographic from the River Corridor SFA https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) field protocol; and a (6) a subfolder with sediment sample data from the incubation experiment. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC); (2) total nitrogen (TN); (3) adenosine triphosphate (ATP); (4) percent carbon and nitrogen; (5) effect size; (6) iron (II); (7) gravimetric moisture; (8) respiration rates and raw dissolved oxygen values; (9) specific conductance; (10) pH; (11) temperature; (12) a summary containing median values of each data type for each treatment (wet and dry); (13) methods codes; (14) FTICR-MS methods; and (15) a subfolder of 9.4 Tesla FTICR-MS data. This folder contains three subfolders, one containing the sediment .xml data files, one containing the sediment CoreMS output files, the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .ref, or .xml.
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.
Interest in the utility of seaweed farms to mitigate coastal eutrophication, or nutrient loading, has grown commensurate with the recent rise of the farmed seaweed industry in the U.S. But economic valuation of this ecosystem service remains elusive in part because of challenges in quantifying this spatiotemporally variable biological process with reproducible and comparable metrics. Regulatory bodies that permit wastewater discharge or lease area for aquaculture farms require water quality testing and reporting of dissolved total nitrogen (N) in nearshore marine environments. These metrics must meet EPA standards for testing and reporting (e.g., Total Kjeldahl Nitrogen - TKN). However, these metrics are inherently highly variable over space and time in dynamic nearshore systems, and expensive to evaluate with sufficient breadth to constrain this variance, creating a critical bottleneck to direct quantification of farmed seaweed net uptake rates in situ.
Abstract BACKGROUND Pine wood nematode is a major plant quarantine object in the world. Trunk injection is an effective method for controlling pests that cause disease. To evaluate the ecological safety of trunk injection with emamectin benzoates in forests of Pinus massoniana , the community diversity and community composition of soil arthropods and flying insects (Hymenoptera) were studied at different stages of trunk injection. RESULTS The dominant taxonomic groups of soil arthropods were Collembola (30.80%), Insecta (26.42%), and Arachnida (23.84%). The taxonomic groups of flying insects (Hymenoptera) were Ichneumonidae (48.94%), Formicidae (14.10%), and Braconidae (8.44%). Trunk injection with emamectin benzoate has no significant effect on the community diversity indices of total soil arthropods and flying insects (Hymenoptera). However, it has a significant effect on the community diversity indices of detritivores for soil arthropods. It changed the community composition of soil arthropods but did not impact the community composition of flying insects (Hymenoptera). Redundancy analysis of arthropod community structure and environmental variables showed that total potassium, residual of green leaf, and residual of litter leaf have a significant impact on the community structure of soil arthropods, and total phosphorus, total nitrogen, water content, organic matter, and total potassium have a significant impact on the community structure of flying insects (Hymenoptera). CONCLUSION Trunk injection with emamectin benzoate is safe for the ecological environment. This study provides a new insight into the field for the prevention and control of pine wood nematode disease, which is of great significance to forest management and pest control. © 2022 Society of Chemical Industry.
This dataset is aimed at understanding changes in surface water chemistry associated with urbanization in the Great Lakes region. Surface water chemistry including cations, anions, organic and inorganic carbon, and nitrogen were collected from six locations along St. Louis River – a freshwater estuary entering Lake Superior (Duluth/Superior, MN/WI). The Duluth/Superior, MN/WI metropolitan area is the largest port on the United States side of Lake Superior. Samples were collected near different land uses every two weeks from late July 2023 to the onset of ice cover in November 2023. Water samples were analyzed using In-Situ AquaTroll 500 Multiparameter Sonde with Ammonium Ion Selective Electrode and Chlorophyll-a sensor (unfiltered), METTLER TOLEDO FiveEasy Plus FP20 and METTLER TOLEDO LE407 probe (unfiltered); HACH DR300 Pocket Colorimeter Iron FerroVer (filtered 0.22 μm in field); Shimadzu TOC-L Total Organic Carbon Analyzer with TNM-L Total Nitrogen Unit (filtered 0.22 μm in field); Agilent 8900 triple quadrupole ICP-MS (ICP-QQQ) (filtered 0.22 μm in field); and Dionex ICS-6000 HPIC System (filtered 0.22 μm in field).The data package is comprised of 5 files. dd.csv contains the description of columns in Geochemistry_LSNERR_water.csv and Sampling_sites.csv. flmd.csv contains descriptions of each file in the dataset. README.txt contains description of the dataset structure and file content. Geochemistry_LSNERR_water.csv contains geochemistry data for six sampling locations along the St. Louis River. Sampling_sites.csv contains latitude and longitude coordinates for each of the six sampling site locations along the St. Louis River.This data has not been previously published.
This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.
Atmospheric carbon-dioxide enrichment is known to affect the yield of lettuce and radish grown in controlled environments, but little is known about CO2 enrichment effects on the chemical composition of lettuce and radish. These crops are useful model systems for a Controlled Ecological Life-Support System (CELSS), largely because of their relatively short production cycles. Lettuce (Lactuca sativa L.) cultivar 'Waldmann's Green' and radish (Raphanus sativus L.) cultivar 'Giant White Globe' were grown both in the field and in controlled environments, where hydroponic nutrient solution, light, and temperature were regulated, and where CO2 levels were controlled at 400, 1000, 5000, or 10,000 ppm. Plants were harvested at maturity, dried, and analyzed for proximate composition (protein, fat, ash, and carbohydrate), total nitrogen (N), nitrate N, free sugars, starch, total dietary fiber, and minerals. Total N, protein N, nonprotein N (NPN), and nitrate N generally increased for radish roots and lettuce leaves when grown under growth chamber conditions compared to field conditions. The nitrate-N level of lettuce leaves, as a percentage of total NPN, decreased with increasing levels of CO2 enrichment. The ash content of radish roots and of radish and lettuce leaves decreased with increasing levels of CO2 enrichment. The levels of certain minerals differed between field- and chamber-grown materials, including changes in the calcium (Ca) and phosphorus (P) contents of radish and lettuce leaves, resulting in reduced Ca/P ratio for chamber-grown materials. The free-sugar contents were similar between the field and chamber-grown lettuce leaves, but total dietary fiber content was much higher in the field-grown plant material. The starch content of growth-chamber lettuce increased with CO2 level.
This data package includes processed and undiluted measurements for metal, total carbon, total nitrogen, and anion concentrations from pore water (groundwater) and surface water samples from the Slate River and East River floodplains of Crested Butte, CO, focus field sites for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? Samples were collected between May and October of 2022. These measurements were all recorded at the Arizona Laboratory for Emerging Contaminants (ALEC) at the University of Arizona located in Tucson, AZ. Groundwater samples were extracted from a network of installed rhizon (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the river floodplain. All water samples were shaded from sun exposure during extraction from the subsurface and preserved at 4C until measured at ALEC.Analysis by ICP-MS (metals):Measurements for total metals were made on the Agilent 7700x ICP-MS (for total metals) – Agilent Technologies, Santa Clara, CA. The analytical QA/QC protocol was adapted from US EPA Method 200.8 for analysis by ICP-MS. Calibration standards were prepared from multi-element stock solutions (SPEX Certiprep, Metuchen, NJ). Calibration curves include at least 7 points with correlation coefficients > 0.995. The QC protocol includes a continuing calibration blank (CCB), a continuing calibration verification (CCV) solution and at least one quality control sample (QCS) to be analyzed just after calibration and again after every 12 samples and at the completion of the run. The QCS solutions are from an independent source, such as NIST SRM 1643e - Trace elements in water, or QCS solutions from High Purity Standards (Charleston, SC). Acceptable QC responses must be between 90 and 110% of the certified value. Lastly, a suitable internal standard (usually Rh, In, Ga or Ge) is added using on-line addition into the sample line and mixing tee.Analysis by Shimadzu TOC-L (TOC/TN):The TOC-L system is a combustion technique where liquid samples are injected and combusted into CO2 for carbon detection by non-dispersive infrared (NDIR) and NO for detection by chemiluminescence. A calibration curve using five standard solutions between 0.1 and 7 ppm for carbon and 0.05 and 3.5 ppm for nitrogen is made for each type of measurement with a linearity >0.99. All samples, standards, and QC’s are prepared in 24mL scintillation vials that have been baked for 4hrs at 475 Cº and made using RO water (18.2mΩ). QC’s include a calibration blank check (CCB), continuing calibration check (CCC), and a certified reference material check (CRM). All QC’s are within ±10% error and are run before and after each batch of samples. Samples are diluted and rerun if any measurement concentrations are above the highest standard.Analysis by Ion Chromatography (Anions):The instrument used is the Thermo Scientific Dionex ICS-6000 using AS+AG22 column set for anion analysis with sodium carbonate eluent. A calibration curve using five standard solutions between 5 and 250 umol/L is made with a linearity >0.99. Standards and QC’s are prepared in 15mL polypropylene conical tubes, pipetted along with the samples into 1.5mL polypropylene vials. Dilutions are made using RO water (18.2mΩ). QC’s include a calibration blank check (CCB), continuing calibration check (CCC), and a certified reference material check (CRM). All QC’s are within ±10% error and are run before and after each batch of samples. Samples are diluted and rerun if any measurement concentrations are above the highest standard.All files are in csv format.
Abstract Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. Efforts to quantify the occurrence, drivers, and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll‐aconcentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in‐lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll‐aacross the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll‐aconcentrations across US EPA's National Lakes Assessment (n = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of variation in chlorophyll‐a. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll‐afor ∼112,000 lakes across the CONUS. Risk for high chlorophyll‐aconcentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in nutrient pollution hot spots across the country. These catchment and lake‐specific results can help managers identify potential nutrient pollution and chlorophyll‐ahot spots that may fuel blooms, prioritize at‐risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals.
Total hemispherical emissivity to nitrogen temperature
This dataset is associated with the publication “Disinfection byproducts formed during drinking water treatment reveal an export control point for dissolved organic matter in a subalpine headwater stream” published in Water Research X (Leonard et al. 2022; https://doi.org/10.1016/j.wroa.2022.100144). The associated study analyzed temporal trends from the Town of Crested Butte water treatment facility and synoptic sampling at Coal Creek in Crested Butte, Colorado, US. This work demonstrates how drinking water quality archives combined with synoptic sampling and targeted analyses can be used to identify and understand export control points for dissolved organic matter. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) metadata and international geo-sample number (IGSN) mapping file; (4) dissolved organic carbon (DOC), ultraviolet absorbance at 254 nanometers (UV254), total nitrogen (TN), and specific ultraviolet absorbance (SUVA) data; (5) disinfection bioproduct formation potential (DBP-FP) data; (6) readme; (7) methods codes; (8) water collection protocol; (9) folder of high resolution characterization of organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory); and (10) folder of excitation emissions matrix (EEM) spectra. The FTICR folder contains a file of DOC (measured as non-purgeable organic carbon; NPOC) used for FTICR sample preparation. The FTICR folder also contains three subfolders: one subfolder containing the raw .xml data files, one containing the processed data, and the other containing instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .dat, .R, .ref, or .xml
This dataset supports a broader study examining drivers of organic matter chemistry in variably inundated hyporheic zone sediments and further linking that chemistry to biogeochemical rates. The dataset provides physical and chemical sediment data collected from 48 rivers across North America. There are related data that will be published, including surficial sediment temperature time series that can be used to infer the dynamics of inundation prior to the collection of sediments. This dataset is comprised of two folders: (1) ECA2_Sediment and (2) ECA2_SitePhotos. The ECA2_Sediment folder contains: (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) non-purgeable organic carbon (NPOC), total nitrogen (TN), and sediment moisture; (5) sediment sampling protocol, (6) readme, and (7) folder of high resolution characterization of organic matter from sediment extracts via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). The FTICR-MS files are .xml, and the data package includes instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. All other files are .csv, .txt, or .pdf. The ECA2_SitePhotos folder contains a readme and site photographs taken in the field. All files are .txt or .jpg.
This dataset supports a broader study examining the effects of intermittency on sediment respiration. The dataset provides sediment and surface water geochemistry and in situ sensor data from 7 perennial and 7 intermittent streams in San Antonio, Texas. Each stream/site was visited both in summer during base flow (July-September 2023) and winter during peak flow (January-February 2024). Related data were collected and will be published separately in collaboration with A. Veach. The data package was originally published in April 2025. It was updated in June 2025 (v2; modified and new files) and September 2025 (v3; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocol; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) sediment grain size data; (4) sediment iron (II) data and averages; (5) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment percent carbon and nitrogen; (11) sediment X-ray diffraction (XRD) data; (12) gravimetric moisture and averages; (13) a subfolder with sediment incubation respiration data, scripts, and plots; (14) surface water and sediment FTICR methods; and (15) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: The data processing methods for FTICR described in “v3_WHONDRS_AV1_Methods_Codes.csv” mistakenly indicate that users should process the data in Formultitude. The corrected description should read: “Both unprocessed and processed data are provided to allow users flexibility in data processing. Instructions and scripts for processing the data using CoreMS are included.” CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package.
Abstract Nitrogen recovery from wastewater represents a sustainable route to recycle reactive nitrogen (Nr). It can reduce the demand of producing Nr from the energy‐extensive Haber‐Bosch process and lower the risk of causing eutrophication simultaneously. In this aspect, source‐separated fresh urine is an ideal source for nitrogen recovery given its ubiquity and high nitrogen contents. However, current techniques for nitrogen recovery from fresh urine require high energy input and are of low efficiencies because the recovery target, urea, is a challenge to separate. In this work, we developed a novel fresh urine nitrogen recovery treatment process based on modular functionalized metal–organic frameworks (MOFs). Specifically, we employed three distinct modification methods to MOF‐808 and developed robust functional materials for urea hydrolysis, ammonium adsorption, and ammonia monitoring. By integrating these functional materials into our newly developed nitrogen recovery treatment process, we achieved an average of 75 % total nitrogen reduction and 45 % nitrogen recovery with a 30‐minute treatment of synthetic fresh urine. The nitrogen recovery process developed in this work can serve as a sustainable and efficient nutrient management that is suitable for decentralized wastewater treatment. This work also provides a new perspective of implementing versatile advanced materials for water and wastewater treatment.