dataesr/scientific-paragraphs-categorization huggingface dataset:
Environmental data acquisition and computations The stream hydrology was characterized as previously described  (Namour et al. , 2015; Data S1). Continuous monitoring of rainfall, stream water level, and flow discharge of the stream and the CSO was performed over 14 years covering the two CSO regimes investigated in this study  (Lagouy et al. , 2022a (Lagouy et al. , , 2022b)). Precipitation was measured at a nearby weather station (100 m East of the CSO). Another weather station (Pollionnay, France, 1. 3 km from study site; highly correlated to the Chaudanne rain gauge, r = 0. 97) was used to fill gaps in the data  (Labbas, 2015). Water flows were monitored at three instrumented bungalows located upstream, downstream, and at the CSO outlet, as previously described  (Navratil et al. , 2020).
Using a multi-parametric U-50 probe (HORIBA Europe GmbH, Oberursel, Germany), the pH, temperature ( • C), and electrical conductivity (μS/cm) of the surface waters were measured. Total suspended solids (TSS, mg/L), total organic carbon (TOC, mg/L), dissolved organic carbon (DOC, mg/L), nitrogenous compounds (Kjeldahl's nitrogen or N kj , NH 4 Statistical analyzes of the above datasets were performed in R 4. 1. 0 (2021 -05-18, R Core Team, 2021). Time step data were handled using the packages chron, lubridate, and xts  (Grolemund and Wickham, 2011; James and Hornik, 2020; Ryan and Ulrich, 2020). The numbers of CSO events were derived from the measure of CSO rate, counting one event for immediately consecutive positive (non-zero) measures (rle() function of base package; R Core Team, 2021), thus permitting the counting of multiple CSO events per day, if any.
Then, data points with LOQ were randomly imputed with a value from the same distribution bound between zero and LOQ. Differences in concentrations between campaigns (before vs. after the CSO was modified) were tested by the car package ANOVA (Anova() function (type = 3;  Fox and Weisberg, 2019). + , NO 3 -, NO 2 -, mg/L), Hydrological indices were computed using 3-week periods backwards from each sampling date (derived from  Breil et al. , 2013). Hydrological indices (Data S3) considered (i) precipitation parameters such as the number of days with measurable rainfall (day), maximum rainfall intensity (mm. h -1 ), and longest period without measurable rainfall (min), (ii) stream parameters such as average flow rate (L. s -1 ), maximum flow rate (L. s -1 ), and (iii) CSO parameters such as the number of days with at least one overflow event, cumulated duration of overflow events (min), and cumulated volume discharged to the stream (m 3 ).
The universal primer was 5'-AGATGTGTATAAGAGACAG-3′. The P5 adapter tag was 5'-TCGTCGGCAGCGTC-3′. The P7 adapter tag was 5′-GTCTCGTGGGCTCGG-3′. PCR reactions were performed using 5× Hot BIOAmp® master mix (Biofidal, France). The tpm DNA libraries were sequenced using Illumina MiSeq v3 technology and DNA sequencing services (Biofidal, France). The paired-end tpm DNA libraries were treated following standard operating procedures available in DADA2 1. 18  (Callahan et al. , 2016)  to infer Amplicon Sequencing Variants (ASVs, single-nucleotide resolution). The decontam package  (Davis et al. , 2018)  was used on prevalence and with twice the default stringency to (i) detect and discard contaminants from negative controls and (ii) detect tpm ASVs of the CSO (runoff and wastewaters) in the stream samples.
ASV DNA sequences were compared using BLASTX with those of the NCBI "non-redundant -nr" database (n = 27,545 entries, as of August 2021). ASVs that did not meet the inclusion criteria (thiopurine-S-Methyltransferase hit with >40 % identity and < 10 -5 e-value) were not considered tpm and were discarded. A two-step taxonomic assignment was then performed. ASVs were assigned to a taxa using the NCBI taxonomy status of the bacterial thiopurine-S-methyltransferase proteins of the RefSeq database with Kaiju 1. 7. 4  (Menzel et al. , 2016). ASVs were also assigned taxa using the RDP classifier implemented in DADA2  (Wang et al. , 2007)  and the BACtpm 2. 0. 1 database  (Pozzi et al. , 2021). Combining the two approaches enabled increasing both sensitivity (tpm RefSeq had 6569 protein entries belonging to 384 genera, as of August 2021) and accuracy (BACtpm had 840 nucleotide entries belonging to nearly almost 200 genera, with a particularly fine resolution for species allocated to the Pseudomonas and Aeromonas).
Statistical analyses of the tpm meta-barcoding datasets Alpha diversity indices were computed from the ASV contingency table (estimate_richness() function of the phyloseq package; McMurdie and Holmes, 2013) without rarefying as recommended by  McMurdie and Holmes (2014). Differences between samples were tested with nonparametric Kruskall-Wallis tests followed by Dunn test with dunnTest() of the FSA package, if relevant  (Ogle et al. , 2021). When low numbers of comparisons were biologically meaningful (in most cases only 4 over comparisons), p-values were not adjusted  (Rothman, 1990). Instead, we considered that under a null hypothesis and with a 5 % type I error, we could expect 0. 2 comparisons (4*0. 05) to be statistically significant (i. e. p-value <0. 05) by chance only. Thus, we ruled out that any nonadjusted significant p-value could be considered reliable.
Differences in the relative abundance of taxa of interest were tested by multiple comparison (mt() function of the multtest package  (Pollard et al. , 2005)  implemented in phyloseq). Beta diversity (differences in tpm ASVs or related groupings, i. e. inferred species, between pairs of samples) was assessed by non-metric multidimensional scaling (NMDS) ordinations based on the Bray-Curtis dissimilarities between samples (ordinate() function of phyloseq package with "NMDS" as method and "bray" as distance; McMurdie and Holmes, 2013) on relative proportions of taxa (obtained by dividing their respective counts over the total number of reads observed per sample), both at the ASV and species (ASV grouped into species) levels. Analysis of similarity (Anosim() and adonis() functions of vegan package;  Oksanen et al.
, 2020)  were used to test the significance of groupings by the controlling factors (sampling campaign, sampling season, stream section, geomorphic unit, sedimentary compartment). Permutation tests of multivariate homogeneity of group dispersions were used to compare homogeneity of the datasets between groups (permutest() and betadisper() functions). Envfit  (Oksanen et al. , 2020)  was used to test the significance of the relationships between NMDS ordinations and environmental explanatory variables. Community coalescence between exogenous and stream tpmharboring bacterial taxa was assessed by estimating the relative contributions of "source" tpm reads to a given "sink" or "target" community using fast expectation-maximization for microbial source tracking (FEAST;  Shenhav et al. , 2019).
Data availability Sequencing runs were deposited under accession numbers ERR9930363-ERR9930465 of project PRJEB53724 at the European Bioinformatics Institute (EMBL-EBI). All collected data were deposited in Zenodo  (Pozzi et al. , 2022)  and included data matrices directly usable in R (Data S5). Hydrological monitoring data were deposited in a dedicated repository  (Lagouy et al. , 2022a (Lagouy et al. , , 2022b)).
General features of the tpm metabarcoding dataset Overall, 5,559,934 raw tpm reads were generated from the 103 samples collected in this study (Table  S1 ). After processing, 2,176,055 reads were included in the final dataset, averaging 22,661 reads per stream sample (median: 22,353; range: 104-86,847) and 21,008 reads per CSO sample (median: 22,048; range: 8727-28,712). All samples except one had >3300 reads after processing. The PCR water blank samples had no read left after processing and were therefore not considered. Other blanks generated small numbers of tpm reads that belonged to five ASVs: one ASV allocated to Pseudomonas fluorescens (strain BBc6R8), 3 ASVs allocated to an unclassified Pseudomonas, and ASV to an unclassified Proteobacteria (Table  S4 ). These ASVs were identified as technical contaminants and discarded from the dataset.
A total of 9409 ASVs (tpm variants) were validated for further analyzes. Sequences of 33 variants that were below the blastx inclusion threshold were ruled non-tpm and discarded from the analysis (Table  S5 ). On average, 981 ASVs per sample (median: 978; range: 76-2126; Table  S6 ) were kept for further analyses. These ASVs were detected either only among stream samples (n = 8182) or the CSO ones (n = 211) or among both types of samples (n = 1016). Some ASVs dominated among the dataset, with the first 2, 35, 207, 906, and ASVs representing 5, 25, 50, 75, and 95 % of the reads, respectively, and 8031 ASVs representing <0. 01 % of the reads. The ASV distribution patterns were uneven; 6184 ASVs were in 10 samples, 1030 ASVs in % of these (n ≥ 24 samples), and 226 ASVs in half (n ≥ 49 samples).
Discussion In IRES, CSO discharges can sometimes be the only significant source of water, especially in the low flow season. Thus, there is a strong need to evaluate the impact of CSO management practices on the quality of these watercourses and the benefits associated with compliance with the SBMP and UWT guidelines. Here, a field-based interdisciplinary investigation was conducted to assess the benefits of one of these operational guidelines recommending a reduction of overflows at <20 spill-days per year per CSO device, regardless of the volume or nature of the overflows. The benefits of this modification were estimated by comparing hydrological, pollutant, and microbiological datasets.
Distribution patterns of tpm-harboring indicator species according to contaminants and geomorphological units To further validate the above bacterial indicator species, their responses to the spatial distribution of monitored pollutants and the temporal change of the stream hydrological indices derived from the CSO regimes were tested using TITAN2. This approach was previously shown to be highly efficient for identifying monotonic changes (i. e. , increasing or decreasing taxa) in complex DNA meta-barcoding datasets related to particular ecological drivers  (Simonin et al. , 2019). Furthermore, the distribution patterns of these tpm indicator species were analyzed by generalized linear models (GLMs) to identify relations with the stream geomorphic units. The TITAN2 approach led to the identification of tpm community change points matching differences with the monitored contaminants.
Funding This work was funded by the Rhône-Méditerranée-Corse Water agency Project 2020 0702 "DOmic", the Agence Nationale de la Recherche (ANR) projects Chypster (ANR-21-CE34-0013) and INVA-SION (ANR-08-CESA-022), the French national research program for environmental and occupational health (ANSES) project 2011/1/137, and the Greater Lyon Urban Community through the Lyon OTHU urban drainage field observatory. This work was also performed with the support of the "EUR H2O'Lyon" School of Integrated Watershed Sciences under the terms of project ANR-17-EURE-0018, and in the frame of the CNRS International Research Programme  (IRP 2021 (IRP -2025) )  with India on the topic of river-aquifer exchanges.
Acknowledgments The authors thank the  Observatoire de Terrain en Hydrologie Urbaine (OTHU) , the  Groupe de Recherche, Animation technique et Information sur l'Eau (GRAIE) , and the  OMEAA platform (CNRS INEE)  for providing long-term support to our work. We also thank  Evelyne Borges ,  Françoise Maurin , and  Bruno Tilly  ( VetAgro Sup ) for assistance during the investigations,  Olivier Grandjouan  ( INRAE ) for assistance in data curation,  Laëtitia Bacot  ( OTHU, GRAIE ),  Hervé Matthieu  ( Syndicat d'Aménagement et de Gestion de l'Yzeron, du Ratier et du Charbonnières, SAGYRC ),  Muriel Floriat  ( Métropole de Lyon ), and  Camille Arnault  and  Fabien Abad  ( Agence de l'Eau Rhône Méditerranée Corse, AE-RMC ) for participating in the project coordination and in the dissemination of knowledge to practitioners.
Acknowledgments This project received funding from the  European Union 's  Horizon 2020 research and innovation program  ( Marie Sklodowska-Curie  grant agreement No. 707624 ). We also acknowledge support from the  European Regional Development Fund (ERDF ; project No  1166-39417 ) and the  Swiss Federal funds, Switzerland  ( AAIDI  Grant No  192292 ). AN also acknowledges support from the project  PyroTRACH  ( ERC-2016-COG ) funded from  H2020-EU. 1. 1. -Excellent  Science-European Research Council (ERC) , project  ID 726165. CP acknowledges support from the  French National Research agency  ( FIRETRAC  project,  ANR-20-CE01-0012-01 ). The authors also acknowledge  Prof. N. Mihalopoulos  and  Prof. M. Kanakidou , who generously provided the use of the  Univ. of Crete infrastructure  for sampling.
, 2016; Lauerwald et al. , 2019; Maavara et al. , 2019 , Yao et al. , 2020; Marzadri et al. , 2021; Marzadri et al. , 2022; Rosentreter et al. , 2023) ; four GHG emission databases -Emissions Database for Global Atmospheric Research EDGAR v7. 0  (Crippa et al. , 2021, https   et al. , 2015) , UNFCCC (https://unfccc. int/reports), GFED4s (van der Werf et al. , 2017) (only for biomass burning) -and one statistical model (SRNM) only for cropland soils  (Wang et al. , 2020). The TD approach consisted of four independent atmospheric inversion frameworks, namely INVICAT  (Wilson et al. , 2014) , PyVAR-CAMS  (Thompson et al. , 2014) , MIROC4-ACTM  (Patra et al. , 2022) , and GEOS-Chem  (Wells et al. , 2018). 'Natural soil baseline' emissions were obtained from the ensemble mean of the eight terrestrial biosphere models participated 395 in NMIP-2 that run with pre-industrial land cover (Table  1 ) : (1) Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC)  (Asaadi & Arora, 2021; Melton et al.
, 2023 , also see Supplementary Information SI-7), namely an observationbased estimate that relied on a random-forest (RF) algorithm to interpolate N2O data  (Yang et al. , 2020) , based on a synthesis of over 158,000 observations of N2O mixing ratio, partial pressure, and concentration in the surface ocean from the MEMENTO database (MEM-RF)  (Kock and Bange, 2015) , an estimate relying on the high-resolution configuration  (Berthet et al. , 2019)  of the global ocean-biogeochemical component of CNRM-ESM2-1 (CNRM-0. 25°), and two estimates relying on the ECCO-Darwin model run at 1/3° (ECCO-Darwin1) and 1/6° (ECCO-Darwin2), respectively. Considering that ECCO-Darwin1 and Darwin2 relied on the same model, their mean N2O fluxes were used.
Emissions from other direct anthropogenic sources. This category includes 'Fossil fuel and industry', 'Waste and wastewater', and 'Biomass burning'. Both emissions from 'Fossil fuel and industry' and 'Waste and wastewater' were calculated as the ensemble means of EDGAR v7. 0 and UNFCCC databases. The 'Biomass burning' emission is the ensemble mean of FAOSTAT, DLEM, and GFED4s databases. In EDGAR v7. 0, 'Waste and wastewater' includes 'Waste incineration' and 'Wastewater handling'. We merged 'Transportation', 'Energy', 'Industry', and 'Residential and other sectors' to represent the total emission from 'Fossil fuel and industry'. In addition to the IPCC agriculture burning categories 'Burning crop residues' and 'Burning savannah', we included FAOSTAT estimates for N2O emissions from deforestation fires, forest fires and peatland fires  (Prosperi et al.
, 2020). The FAOSTAT emissions database of the Food and Agriculture Organization of the United Nations (FAO) covers emissions of N2O from categories, i. e. , cultivation of histosols, biomass burning, etc. , applying only Tier-1 coefficients  (Tubiello et al. , 2022; 2021; Conchedda and Tubiello, 2020; Prosperi et al. , 2020).
Uncertainties in N2O emission factors Four inventories of N2O emissions (EDGAR, FAOSTAT, GFED and UNFCCC) are integrated into the current synthesis of anthropogenic N2O emissions. These emission factor (EF)-based inventory datasets used the IPCC default EFs at regional and global scales. However, the poorly captured dependence of EFs on regional climate, management practices such as tillage, legume effect, and soil physical and biochemical conditions are key causes of the large uncertainty in the estimates of agricultural N2O emissions  (Shcherbak et al. , 2014; Tian et al. , 2019; Lu et al. , 2022) , particularly for croplands where EFs has high spatial heterogeneity  (Shang et al. , 2019; Wang et al. , 2020). There is evidence of greater-than-linear dependence of emissions on N-input where there is an excess of N, which is not represented in inventories which assume a linear dependence on N-input  (Cui et al.
2021). Higher IPCC-tier GHG inventories using the alternative EFs that are disaggregated by environmental factors and management-related factors  (Buendia et al. , 2019)  could provide more accurate estimates, especially for regions where N input surplus is high such as Eastern China and India. Establishing national and regional N2O flux measurement networks could improve the accuracy of EFs estimates for regions with different vegetation types and management measures. Furthermore, inventory datasets based on EF methods also suffer from large uncertainties induced by the underlying agriculture and rural data and statistics used as input, including statistics on fertilizer applications, livestock manure availability, storage and applications, and nutrient, crop and soils management. According to the ensemble of process-based land model emissions derived from NMIP2, we estimate that the emission factor (EF) of fertilizer and manure applied on global croplands was 1.
, 2022). In addition, developing forcing datasets with high quality and high spatiotemporal resolution is also important for reducing uncertainties in simulated N2O fluxes. Among various input variables, precise information regarding fertilizer and manure application (including crop-specific application rate, type, timing, and frequency) is pivotal for improving the accuracy of model simulations. However, this crucial information was not unified in NMIP2 simulations, leading to increased modeling uncertainty. To mitigate this issue, it is strongly recommended to use improved fertilizer and manure datasets that provide detailed information on crop-specific application rate, timing and frequency to drive models in future intercomparison projects. Moreover, with the availability of additional high-precision datasets from manipulation field experiments (e.
g. , microbial data), we could use these datasets to constrain our models and delve deeper into the underlying mechanisms that regulate N2O fluxes (e. g. , the role of soil microbes) and further incorporate these mechanisms into models to reduce uncertainties. uncertainties. Currently, most available field N2O observations (see Supplementary Information) are made in Europe, the USA, and China and are scarce in most developing countries (such as Sub-Saharan Africa). Therefore, extending the global coverage of direct and indirect N2O flux measurements to encompass all major agricultural land-use types and climates, land-use changes and management practices and conducting long-term high-frequency monitoring are particularly important to increase the reliability of EFs as well as upscale results from site to regional scales.
Acknowledgements This paper is the result of a collaborative international effort under the umbrella of the  Global Carbon Project  (a project of  Future Earth and a research partner of  the  World Climate Research Programme ) in partnership with  International Nitrogen Initiative  (INI). We acknowledge all the people and institutions who provided the data used in the global nitrous oxide budget as well as the institutions funding parts of this effort (see Table A5). We acknowledge the modelling groups for making their simulations available for this analysis. H. T. and S. P. acknowledges computational and administrative support from  Schiller Institute for Integrated Science and Society at Boston College , and  International Center for Climate and Global Change Research at Auburn University.
J. G. C. thanks the  Australian National Environmental Science Program -Climate Systems Hub  for supporting the  GHG budget activities of  the  Global Carbon Project (GCP ), including the Global and regional N2O Budgets work. We are grateful to the  EDGAR  team ( M. Crippa ,  D. Guizzardi ,  E. Schaaf ,  M. Muntean ,  E. Solazzo ,  F. Pagani  and  M. Banja ) for the work needed to publish the EDGARv7. 0 Global Greenhouse Gas Emissions dataset (https://edgar. jrc. ec. europa. eu/dataset_ghg70). AKJ thanks  Shijie Shu  and  Tzu-Shun Lin  for their involvement in developing https://doi. org/10. 5194/essd-2023-401 Preprint. Discussion started: 9 October 2023 c Author(s) 2023. CC BY 4. 0 License.
-based upscaling approach: Measurement-based upscaling estimates are subject to uncertainties due to various factors. One major reason is the limited recording of microscale variables and incomplete https://doi. org/10. 5194/essd-2023-401 Preprint. Discussion started: 9 October 2023 c Author(s) 2023. CC BY 4. 0 License. quantification of local EFs related to microbial N2O production. Sampling limitations also contribute to uncertainties, as the frequency and repeatability of measurements may not fully capture the high spatiotemporal variability of N2O flux. The lack of the history of control sites further complicates the exclusion of observation data with significant legacy fluxes, thereby biasing our estimates. Additionally, gaps in global agricultural management datasets, particularly regarding fertilization details,enlarge the prediction interval of EFs and introduce uncertainties.
We then used a Monte Carlo simulation to estimate three sources of uncertainty for predicting EFs based on flux upscaling approach: i) the fixed coefficients, ii) the random coefficients, and iii) input data. The uncertainty from sampling frequency and replication is reflected in the first source, while the uncertainty from unquantified sources related to field measurements is reflected in the second source. Each of the crop-specific SRNM models was run by randomly generating the fixed and random coefficients from their fitted multivariate normal distribution, as well as climate, soil, and other relevant factors following independent normal distributions with the mean of the value in our dataset and standard deviation of the absolute difference between the dataset used in this study and other global datasets.
https://doi. org/10. 5194/essd-2023-401 Preprint. Discussion started: 9 October 2023 c Author(s) 2023. CC BY 4. 0 License. understanding of the precise contribution of termites to the atmospheric N2O budget on a global scale remains limited and not considered in our analysis 5 Data Availability The accompanying database includes two Excel files and 27 txt files. The two Excel files are organized into the following spreadsheets. The Global N2O Budget 1980-2020: Global emission data includes the following items: 1. Summary. 2. Bottom-up estimates: global BU N2O budget from 1980 to 2020, including 20 individual sources and sinks. 3. Top-down estimates: N2O emissions from land, ocean, and global during 1997-2020 estimated by the four atmospheric inversion models. 4. Atmospheric_Chemical_sink: Global atmospheric chemical sink estimated by the four atmospheric inversion models (1997-2020) and one satellite and photolysis model (2005-2020).