Note: Data files will be made available upon manuscript publication This dataset contains all code and data needed to reproduce the analyses in the manuscript: IDENTIFICATION OF A KEY TARGET FOR ELIMINATION OF NITROUS OXIDE, A MAJOR GREENHOUSE GAS. Blake A. Oakley (1), Trevor Mitchell (2), Quentin D. Read (3), Garrett Hibbs (1), Scott E. Gold (2), Anthony E. Glenn (2) Department of Plant Pathology, University of Georgia, Athens, GA, USA. Toxicology and Mycotoxin Research Unit, U.S. National Poultry Research Center, United States Department of Agriculture-Agricultural Research Service, Athens, GA, USA Southeast Area, United States Department of Agriculture-Agricultural Research Service, Raleigh, NC, USA citation will be updated upon acceptance of manuscript Brief description of study aims Denitrification is a chemical process that releases nitrous oxide (N2O), a potent greenhouse gas. The NOR1 gene is part of the denitrification pathway in Fusarium. Three experiments were conducted for this study. (1) The N2O comparative experiment compares denitrification rates, as measured by N2O production, of a variety of Fusarium spp. strains with and without the NOR1 gene. (2) The N2O substrate experiment compares denitrification rates of selected strains on different growth media (substrates). For parts 1 and 2, linear models are fit comparing N2O production between strains and/or substrates. (3) The Bioscreen growth assay tests whether there is a pleiotropic effect of the NOR1 gene. In this portion of the analysis, growth curves are fit to assess differences in growth rate and carrying capacity between selected strains with and without the NOR1 gene. Code All code is included in a .zip archive generated from a private git repository on 2022-10-13 and archived as part of this dataset. The code is contained in R scripts and RMarkdown notebooks. There are two components to the analysis: the denitrification analysis (comprising parts 1 and 2 described above) and the Bioscreen growth analysis (part 3). The scripts for each are listed and described below. Analysis of results of denitrification experiments (parts 1 and 2) NOR1_denitrification_analysis.Rmd: The R code to analyze the experimental data comparing nitrous oxide emissions is all contained in a single RMarkdown notebook. This script analyzes the results from the comparative study and the substrate study. n2o_subgroup_figures.R: R script to create additional figures using the output from the RMarkdown notebook Analysis of results of Bioscreen growth assay (part 3) bioscreen_analysis.Rmd: This RMarkdown notebook contains all R code needed to analyze the results of the Bioscreen assay comparing growth of the different strains. It could be run as is. However, the model-fitting portion was run on a high-performance computing cluster with the following scripts: bioscreen_fit_simpler.R: R script containing only the model-fitting portion of the Bioscreen analysis, fit using the Stan modeling language interfaced with R through the brms and cmdstanr packages. job_bssimple.sh: Job submission shell script used to submit the model-fitting R job to be run on USDA SciNet high-performance computing cluster. Additional scripts developed as part of the analysis but that are not required to reproduce the analyses in the manuscript are in the deprecated/ folder. Also note the files nor1-denitrification.Rproj (RStudio project file) and gtstyle.css (stylesheet for formatting the tables in the notebooks) are included. Data Data required to run the analysis scripts are archived in this dataset, other than strain_lookup.csv, a lookup table of strain abbreviations and full names included in the code repository for convenience. They should be placed in a folder or symbolic link called project within the unzipped code repository directory. N2O_data_2022-08-03/N2O_Comparative_Study_Trial_(n)_(date range).xlsx: These are the data from the N2O comparative study, where n is the trial number from 1-3 and date range is the begin and end date of the trial. N2O_data_2022-08-03/Nitrogen_Substrate_Study_Trial_(n)_(date range).xlsx: These are the data from the N2O substrate study, where n is the trial number from 1-3 and date range is the begin and end date of the trial. Outliers_NOR1_2022/Bioscreen_NOR1_Fungal_Growth_Assay_(substrate)_(oxygen level)_Outliers_BAO_(date).xlsx: These are the raw Bioscreen data files in MS Excel format. The format of each file name includes the substrate (minimal medium with nitrite or nitrate and lysine), oxygen level (hypoxia or normoxia), and date of the run. This repository includes code to process these files, but the processed data are also included on Ag Data Commons, so it is not necessary to run the data processing portion of the code. clean_data/bioscreen_clean_data.csv: This is an intermediate output file in CSV format generated by bioscreen_analysis.Rmd. It includes all the data from the Bioscreen assays in a clean analysis-ready format.

This package contains data to an article about denitrification in groundwater. The data included are: 1. continuously analyzed (noble) gases and excess air model results of three different piezometers over a six-month period; 2. hydraulic conductivity and microbial activity analyzed at the piezometers (and the stream); 3. key parameters associated with denitrification (nitrate, alkalinity, O2, DOC, sulfate); 4. precipitation data and water level data of the stream and two piezometers.

Nitrous oxides (N2O) emissions contribute to climate change and stratospheric ozone depletion. Wastewater treatment is a significant source of N2O remissions but likely underestimated, as recent, long-term monitoring campaigns demonstrated. However, the available data are insufficient to representatively estimate countrywide emission given the duration of most monitoring campaigns. Here, we show that the emission estimates can be significantly improved using an advanced approach based on multiple continuous, long-term monitoring campaigns. In 14 full-scale wastewater treatment plants (WWTPs), we found a strong variability for the yearly emission factors (0.1 to 8% of the incoming nitrogen load), which exhibited a high correlation with effluent nitrite compared to the inflow nitrogen load and also good correlation with nitrogen removal efficiencies. But, countrywide data on nitrite effluent concentrations is very limited and not available for emission estimation in many countries. We propose to calculate a countrywide emission factor based the weighted emission factors of three nutrient removal WWTP categories (carbon removal: 0.1-8%, nitrification only: 1.8%, and full nitrogen removal: 0.9%). However, emission factors of carbon removal WWTPs are still highly uncertain given the expected performance variability. The newly developed approach allows representative, country-specific estimations of the N2O emissions from WWTP. Applied to the case of Switzerland, the estimations result in an average EF of 0.9-3.6% and total emissions of 410 to 1690 tN2O/year, which corresponds to 0.3-1.4% of the total Emission in Switzerland. Our results demonstrate that a better data availability and an improved understanding of long-term monitoring campaigns is crucial to improve current emissions estimations. Year-round denitrification, limiting nitrite accumulation, and a stringent control of sludge age in carbon removing plants are key measures to mitigate N2O emissions from wastewater treatment.

Nitrous oxide (N2O) is a strong greenhouse gas and causal for stratospheric ozone depletion. During biological nitrogen removal in wastewater treatment plants (WWTP), high N2O fluxes to the atmosphere can occur, typically exhibiting a seasonal emission pattern. Attempts to explain the peak emission phases in winter and spring using physico-chemical process data from WWTP were so far unsuccessful and new approaches are required. The complex and diverse microbial community of activated sludge used in biological treatment systems also exhibit substantial seasonal patterns. However, a potentially causal link between the seasonal patterns of microbial diversity and N2O emissions has not yet been investigated. Here we show that in a full-scale WWTP nitrification failure and N2O peak emissions, bad settleability of the activated sludge and a turbid effluent strongly correlate with a significant reduction in the microbial community diversity and shifts in community composition. During episodes of impaired performance, we observed a significant reduction in abundance for filamentous and nitrite oxidizing bacteria in all affected reactors. In some reactors that did not exhibit nitrification and settling failures, we observed a stable microbial community and no drastic loss of species. Standard engineering approaches to stabilize nitrification, such as increasing the aerobic sludge age and oxygen availability failed to improve the plant performance on this particular WWTP and replacing the activated sludge was the only measure applied by the operators to recover treatment performance in affected reactors. Our results demonstrate that disturbances of the sludge microbiome affect key structural and functional microbial groups, which lead to seasonal N2O emission patterns. To reduce N2O emissions from WWTP, it is therefore crucial to understand the drivers that lead to the microbial population dynamics in the activated sludge.

The dataset contains lab analyzed water chemistry and field collected data from hand held sondes. This dataset is associated with the following publication: Narr, C.F., H. Singh, P. Mayer, A. Keeley, B. Faulkner, D. Beak, and K.J. Forshay. Quantifying the Effects of Surface Conveyance of Treated Wastewater Effluent on Groundwater, Surface Water, and Nutrient Dynamics in a Large River Floodplain. ECOLOGICAL ENGINEERING. Elsevier Science Ltd, New York, NY, USA, 129: 123-133, (2019).

A package describing a off-gas monitoring system for wastewater treatment plants. The system uses floating flux chambers for a spatially highly resolved monitoring of compounds in wastewater reactor systems.