dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance of instrumentation at ambient conditions (< 10 ppb) Though there is evidence of agreement across the suite of instruments at high concentrations, in order to understand the varying performance across the instruments, the hourly ensemble median (excluding the Tiger Optics and LGR #1 due operational issues -refer to Sect. 3. 2 for details) was split into NH 3 < 10 ppb or NH 3 ≥ 10 ppb, so that a direct comparison could be made to the von Bobrutzki et al. (  2010 ) study and to assess performances for lower concentrations without the results being skewed by individual large concentration points. The intercomparison is presented for the full dataset in Fig. S3  in the Supplement. In the absence of a "perfect" reference instrument, Fig. 8  presents the summary of the instrument comparison to the ensemble median for NH 3 < 10 ppb.
Acknowledgements. The measurements were funded through the following projects, which supported the measurements during the intercomparison study: Metrology for NH 3 in ambient air (MetNH3) project as part of the European Metrology Research Programme (EMRP) of the European Union; the Academy of Finland as part of the Centre of Excellence programme (project no. 1118615); the Swiss Federal Office for the Environment (Contract 06. 9115. P2I P264-1000). For IMT Nord Europe, the participation to this work has been supported by the French Ministry of Environment (grant no. 2200995403) as a part of the activities of the National Reference Laboratory for air quality monitoring (LC-SQA). The field site infrastructure at Easter Bush is supported by the NERC UK Status, Change and Projections of the Environment (UKSCAPE) National Capability Programme (NE/R016429/1).
The data analysis and preparation of this manuscript was supported by the NERC Integrated Research Observations System for clean air (OSCA, NE/T001798/1). We dedicate this paper to Rens Zijlmans, who sadly passed away before the publication of this paper. Financial support. This research has been supported by the European Metrology Programme for Innovation and Research (grant no. JRP: ENV55 MetNH3), the Natural Environment Research Council (grant nos. NE/R016429/1 and NE/T001798/1), the Academy of Finland (grant no. 1118615), and the Bundesamt für Umwelt (grant no. 06. 9115. P2I P264-1000). For IMT Nord Europe, the participation in this work has been supported by the French Ministry of Environment (grant no. 2200995403) as a part of the activities of the National Reference Laboratory for air quality monitoring (LCSQA).
The samples were sequenced on the MiSeq Illumina platform. The 16Ss rRNA gene amplicon datasets were analyzed with a pipeline consisting of an initial step where all reads passing the standard Illumina chastity filter (PF reads) were demultiplexed according to their index sequences. This was followed by a primer clipping step in which the target forward and reverse primer sequences for bacteria and archaea were identified and clipped from the starts of the raw forward and reverse reads. Only read pairs exhibiting forward and reverse primer overlaps were kept for merging by using FLASH 2. 2. 00  (Magoč and Salzberg, 2011). This yielded a total of 1 799 339 high-quality sequence reads, with an average length after processing of 412 bp. Sequence features (herein described as representative operational taxonomic units, OTUs) were clustered using QIIME 2 (VSEARCH cluster-features-de-novo option;  Rognes et al.
, 2016). To assign taxonomic information to each OTU, we performed DC-MegaBLAST alignments of cluster-representative sequences regarding the NCBI sequence database (release 10 October 2019). A taxonomic assignment for each OTU was then transferred from the set of best-matching reference sequences (lowest common taxonomic unit of all best hits). Hereby, a sequence identity of > 70 % across at least ≥ 80 % of the representative sequence was a minimal requirement when considering reference sequences. We assigned significant tentative correspondence of OTUs to reference species provided that identity thresholds ≥ 97 % of the V3-V4 hypervariable region for bacteria and V4-V5 for archaea were meet. Further processing of OTUs and taxonomic assignments (75. 8 % of the sequences after chimera detection and filtering;  Edgar et al.
, 2011)  and read abundance estimation for all detected OTUs were performed using the QIIME 2 software package (version 1. 9. 1,  Caporaso et al. , 2010). Abundances of bacterial and archaeal taxonomic units were normalized using lineage-specific copy numbers of the relevant marker genes to improve estimates  (Angly et al. , 2014). The microbial sequence data for this study (lengths ≥ 402 bp) were deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB47217.
Mineralogy The mineralogy of the sediment was determined, semiquantitatively, via X-ray diffraction (XRD). Powder XRD data were collected on a D8 Advance powder diffractometer (Bruker) with a LYNXEYE XE detector, under a Bragg-Brentano geometry and Cu K 1 radiation (λ = 1. 5405 Å). Collection in the 2 range 4-80 • was performed using 0. 015 • step-size increments and 0. 8 s collection time per step size. Qualitative phase analyses were performed by comparison with diffraction patterns from the PDF-2 database. A semi-quantitative phase analysis was performed by the Rietveld refinement method  (Post and Bish, 1989) , as implemented in the computer code TOPAS 5 (Bruker). The crystal structures of the mineral phases used for refinement were obtained from the Inorganic Crystal Structure Database (ICSD).
Conclusions We investigated biomineralization reactions occurring and prokaryotes thriving in the ferruginous and sulfate-rich water column of a post-mining lake. For this purpose, we considered the pools and fluxes of iron, manganese, carbon, nitrogen and sulfur in the bottom redox-stratified water column and upper reactive sediments (Fig. 7 ). Discrete spectroscopic datasets were combined with a 16S rRNA gene-aided inference of the planktonic prokaryote community structure to unravel the mechanisms procuring and/or consuming bioactive nitrogen, iron and sulfur species in the redox-stratified ecosystem. Integration of these datasets provides evidence for niche differentiation, but despite marked redox gradients in the water column, we observed sustained genetic potential for anoxygenic sulfide oxidation and intermediate sulfur disproportionation.
Acknowledgements. We are grateful to  Alexandra A. Phillips  and the two anonymous reviewers for constructive criticisms and suggestions that improved an early (preprint) version of this paper. The editorial input of  Denise Akob  is also acknowledged. We sincerely thank  Jiří Jan  and  Jakub Borovec  ( BC-CAS ) for technical support while sampling the bottom waters and anoxic sediments of Lake Medard and for support during HP-LC analyses. We are thankful to  Karelys Umbria-Salinas  for wet-lab assistance. Stefan V. Lalonde  ( European Institute for Marine Studies, Brest ) is thanked for ICP-MS measurements of the water samples and  V. Chrastný  ( Czech University of Life Sciences, Prague ) for ICP-MS measurements of iron and manganese in the sediment reactive fractions. František Bůžek  ( Czech Geological Survey ) is thanked for stable isotope analyses of siderite and gypsum.
D. A. Petrash et al. : Aqueous system-level processes Code availability. Software codes and their sources are fully detailed in Sect. 3 and Supplement 1. Data availability. Datasets and their sources are fully detailed in Tables  1  and 2 , Appendix B, Fig. B2 , and Supplement 1. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-19-1723-2022-supplement.
Determination of the reproducibility of the calibration method LNE used METAS ammonia analyzer, based on cavity ring-down spectroscopy (G2103, Picarro Inc. , CA, USA), for a bilateral comparison. This device is specifically manufactured for the measurement of NH3. Moreover, the analyzer was upgraded in 2019, when the cavity and the sampling handling were replaced by SilcoNert® 2000 coated elements, thus the RegPoly software developed at LNE. This software allowed the integration of uncertainties of the X and the Y axis. The procedure was the same as when determining repeatability: generation during 2 hours and calculation of the amount fractions average and associated standard deviations over the last 30 minutes. Tables  6  and 7  summarize the results obtained. The reproducibility of the determination of the calibration function was around 0.
The tubing used during the generation was 1/4 in. coated (SilcoNert® 2000) stainless steel. The analyzer's calibration function estimated using the RegPoly software developed by LNE followed Eq. (  13 ):  13)  where: 𝑌 = 1. 046 • 𝑋 + 0. 22 ( Y reading of the analyzer X reference amount fraction METAS' analyzer was then calibrated four times by LNE at the end of May 2020 according to the developed method. The linear regression of each calibration was calculated from RegPoly software. The uncertainties of X and Y were taken The normalized deviation (En) between the slopes of the regression lines determined by METAS and by LNE, was equal to 0. 54 (Table  10 ). This value lower than 1 demonstrated the good agreement between the NH3 reference generators developed by METAS and by LNE. This comparison validated the LNE reference generator and the analyzer's calibration method.
Acknowledgment. We thank  Mr Steve Belliride from Sensor  and  Mr Marc Monnet , director of  2M  PROCESS company for their strong involvement in this study. Owing to their experience and their skills in the field of measuring instruments used for gas analysis, the ammonia reference gas generator was developed with accurate metrological performances in a short time. The research leading to these results was performed within the  French National laboratory  for monitoring air quality https://doi. org/10. 5194/amt-2022-6Preprint. Discussion started: 13 January 2022 c Author(s)  2022. CC BY 4. 0 License.
Acknowledgments We thank the  MesoNH team  for their frequent help. We also acknowledge the support of the  New Zealand eScience Infrastructure (NeSI)  high-performance computing facilities and consulting support as part of this research. New Zealand's national facilities are provided by  NeSI  and funded jointly by  NeSI 's collaborator institutions and through the  Ministry of Business, Innovation and Employment's Research Infrastructure  programme (https://www. nesi. org. nz). G. B. was supported by the  Glavish-Buckley Lectureship  and D. C. by the  Glavish Postdoctoral Fellowship  at the  University of Auckland. The MesoNH model can be downloaded from the MesoNH webpage (http://mesonh. aero. obs-mip. fr/mesonh55) and simulations used in this study are available via the  Open Science Foundation  (https://osf.
ROMS MODEL The numerical tool used for the above-mentioned objective, ROMS  [Shchepetkin and McWilliams, 2003, 2005; Penven et al. , 2006; Debreu et al. , 2012] , is a free-surface, terrain-following coordinate model with split-explicit time stepping and with Boussinesq and hydrostatic approximations. This model has been employed in many regions of the world ocean by a broad, international community. For our simulations we used the ROMS version developed at the 'Institut de Recherche pour le Développement' (IRD), where the ROMSTOOLS package  [Penven et al. , 2006]  is available for creating the initial configurations and for the subsequent visualization and analysis of its output. This ROMS version also allows the nesting of various grids ones into the others, thus increasing their individual resolutions, through the adoption of the AGRIF library J o u r n a l P r e -p r o o f Journal Pre-proof (Adaptative Grid Refinement in Fortran)  [Blayo and Debreu, 1999; Debreu, 2000; Debreu and Vouland, 2003; Debreu and Blayo, 2008].
It is indeed quite straightforward to realize sensitivity tests with this model by differently tuning a wide range of parameters, which enables the challenging pursuit of process-oriented studies. ROMS has also been designed for parallelization and computing optimization, so that our nested numerical experiments have been compiled using the MPI (Message Passing Interface) library. This numerical code has been satisfactorily implemented on the French supercomputing clusters of IDRIS, of which we used the latest machine named Jean-Zay (http://www. idris. fr/annonces/annonce-jean-zay. html). As it regards the main datasets used to force and initialize our numerical experiments, at the lateral boundary and initial conditions all variables were constrained by the monthly mean of the 2009 World Ocean Atlas, WOA2009  [Locarnini et al.
, 2010; Antonov et al. , 2010]  with a resolution of 1°. The surface forcings were obtained from the monthly mean climatology of the Comprehensive Ocean-Atmosphere Data Set (COADS05) [Da  Silva et al. , 1994]  with 0. 5° of resolution. For the river discharge, we took advantage of the climatology of  Dai and Trenberth [2002] , which includes monthly means' values for the biggest South-American rivers' runoff. To verify if this dataset well reproduces the mean values of discharge for the two rivers under study, we have plotted a monthly time series for both the Sao Francisco and the Parnaiba (Supplementary Material 1) displaying a range of seasonal values well comparable to the ones reported in the regional literature (not shown). Tides are a key process in mixing the river freshwater plumes with the open ocean, and were obtained from the TPXO7  [Egbert et al.
Taking into account that ROMS is a sigma coordinate model, a smoothing over the bathymetry, derived from the ETOPO2  [Smith and Sandwell, 1997]  database with 2 min of resolution and a value of minimum depth at the shore (hmin) of 10 m, has been necessary to minimise pressure gradient errors. However, in order to avoid an excessive filtering of the topography, this process has been evaluated through neutral stratification tests run using an idealized stratification of the water column. This showed the need of activating the diffusive component of the advection scheme, designed to reduce artificial effects of spurious diapycnal mixing that could appear in the tracers fields (T and S). The first mesoscale-resolving simulations were integrated for a period of 10 years, setting the output at a daily frequency, in order to evaluate not only the initial adjustment of the model but also the period necessary to achieve a statical steady-state of the simulated energy budget.
Satellite Products -Sea Surface Temperature (SST) product, from the TropFlux (https://incois. gov. in/tropflux/) reanalysis of air-sea heat fluxes for the global tropical oceans from 2007 to 2018: this dataset provides daily, timely, accurate air-sea heat and momentum flux data for the entire 30°N-30°S region at 1° of spatial resolution. It is largely derived from a combination of ERA-I re-analysis data for turbulent and longwave fluxes (https://www. ecmwf. int/), and ISCCP surface radiation data for shortwave flux (https://isccp. giss. nasa. gov/). A precise description of the flux computation procedure is provided in Praveen  Kumar et al. [2012]. -Sea Surface Salinity (SSS) from the Soil Moisture and Ocean Salinity (SMOS, https://earth. esa. int/web/guest/missions/esa-operational-eo-missions/smos) data from 2010 to 2017 at 1/3° spatial resolution.
The Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) radiometer picks up faint microwave emissions from Earth's surface to map levels of soil moisture, sea surface salinity, sea ice thickness and other geophysical variables such as wind speed over ocean and freeze/ thaw soil state. We used the level 2 SSS products version v662. -U and V velocities from the Ocean Surface Current Analysis Real-time (OSCAR, https://data. planetos. com/datasets/nasa_oscar_global_5day) from 1992 to 2018: this dataset is generated by Earth Space Research (ESR, http://www. esr. org/oscar_index. html) and contains near-surface ocean current estimates, derived using quasi-linear and steadyflow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and temperature.
03 PSU between the ROMS estimate and the SMOS one. Overall, the two graphs of Figure4show a fair comparison between numerical results and satellite measurements, but in order to have a statistical confirmation of model ability to reproduce observations, we performed the twosample 't-test. Prior to performing the latter, observations and numerical output were normalised since the initial dataset showed non-normal distributions as verified by the onesample Kolmogorov-Smirnov test. Finally, the t-test results indicated no statistically significant differences between the temporal distributions of the thermo-haline variables issued of the ROMS tidal simulation and the ones retrieved from satellite data, as the values of the 'p' coefficients range from 0. 9941 (for the SST in the Parnaiba zoom, Fig.
Evolution of seismic monitoring systems in La Réunion island The Piton de la Fournaise Volcano Observatory was created in 1979 following an eruption outside the Enclos Fouqué caldera in 1977 that damaged the village of Piton Sainte-Rose. For decades, the observatory concentrated its seismic instruments on the Piton de la Fournaise volcano, with only a few stations outside the Enclos Fouqué caldera because of the difficulty to transmit data in real time using frequency modulation. At the time, most of the sensors were 1 Hz vertical Mark Product seismometers. Only three stations were equipped with 3-component Mark Product sensors running with Lennartz digitizers. Since 1992, events were detected by an analog LTA/STA and digitized on the hard disk of an HP1000 computer which could handle 200 events of 40 s regularly transferred on magnetic tapes.
ipgp. fr), and validated data can be accessed through the RESIF data portal (http://resif. fr). Earthquake locations are also available as soon as validated by an operator on the RENASS website (http://renass. unistra. fr/fr/ zones/la-reunion). From 1996 to 2012, earthquakes were located using hypo71 modified by  Nercessian et al. [1996]  to take into account the travel time along the ray to correct for the station elevation, and a 1D velocity model consisting of eight layers  [Battaglia et al. , 2005c , Nercessian et al. , 1996]. Since 2012, earthquakes are located using NonLinLoc software  [Lomax et al. , 2001]  and a velocity gradient following the topography of the island (P wave velocity of 3. 3 km/s at the free surface increasing to 8 km/s at a depth of ∼16 km). Since the beginning of 2018, people who felt an earthquake in La Réunion can testify on the website of the French central seismological office (Bureau Central Sismologique Français, http: //www.
Acknowledgments We thank  Aline Peltier  for enlightening discussion. Information to access OVPF data is available on the VOLOBSIS portal (http://volobsis. ipgp. fr). Validated data can be accessed through the  RE-SIF data portal  (http://resif. fr). OVPF earthquake locations are available on the RENASS website (http://renass. unistra. fr/fr/zones/la-reunion). This project was supported by the  European Research Council (ERC)  (under the European Union's  Horizon 2020 research and innovation program  under grant agreement No. 805256 ). This study also benefited from support from the  Research Federation Observations des Milieux Naturels et des Changements Globaux (OMNCG)  and  Observatoire des Sciences de l'Univers -Réunion  ( OSU-R) at University of La Réunion , the  Région Réunion  and the  CNRS-INSU (Institut National des Sciences de l'Univers ) funding from the  TelluS-SYSTER  and  TelluS-ALEAS  programs.
We would like to acknowledge the  INSU-RESIF/SISMOB  for providing five seismic stations deployed in the " Rivière des Pluies " network http://dx. doi. org/10. 15778/RESIF. ZF2015. Seismic data will be available at the French RESIF archive center (http://seismology. resif. fr)under the ZF experiment code [Fontaine et al. , 2015a]. This study has been also supported by the  ReNovRisk-Erosion FEDER  project funded by the  Région Réunion and European Community. Thanks to the  University of La Réunion  and the  Laboratoire Géo-Sciences Réunion  for their supports. We thank the editor  Frédéric Masson  and two anonymous reviewers for their comments, which helped us improve this manuscript. This is IPGP Contribution Number  4236.
FAST-LVOS measurements and models The FAST-LVOS field experiment was also organized around the TOPAZ lidar, which was upgraded with a new data acquisition system in 2015. This upgrade more than doubled the useful operating range of the lidar from ≈ 3 to 8 km, which allowed it to reach higher altitudes than was possible during LVOS 2013, even while operating from a much lower-elevation site at the North Las Vegas Airport (NLVA; 36. 2 • N, -115. 2 • E; 680 m a. s. l. ), located about 8 km NW of downtown Las Vegas. NOAA CSL also brought a vertically staring Doppler lidar to the NLVA to characterize mixing between the boundary layer and free troposphere and a mobile laboratory with a more extensive suite of in situ measurements to the summit of AP. These primary measurements were supplemented by measurements from a single-engine Mooney TLS Bravo aircraft operated by Scientific Aviation, Inc.
Ozone was also measured by a commercial (2B Tech, Model 205) ultraviolet photometer, and these data were used to fill in the gaps during brief periods when the CRDS instrument was offline. There were no aerosol measurements. The mobile laboratory carried the same automated weather station (Airmar 150WX) as the TOPAZ truck and was also equipped with differential GPS and sonic anemometers to measure absolute wind speed and direction while the van was moving. The laboratory was parked on the southeastern edge of the AP summit overlooking Kyle Canyon, the primary corridor for upslope transport from the LVV, for most of the campaign. This is the same location (Fig. 2 , bottom) occupied by the TOPAZ truck during LVOS 2013 and provides an unobstructed fetch to the west, south, and east. Northerly winds (≈ 335 and 65 • ) were perturbed by one of the nearby buildings, but winds from this direction were uncommon during the study.
Fig. 1b ), during the four FAST-LVOS IOPs, logging a total of 90 flight hours over 15 d. The aircraft carried a pilot and flight scientist, along with a 2B Technologies Model 205 O 3 monitor, an Aerodyne Research cavity attenuated phase shift (CAPS) NO 2 monitor, and a Picarro 2301f wavelength-scanned cavity ring-down spectrometer (WS-CRDS) to measure  CO 2 , CH 4 , ethane  (C 2 H 6 ),  and H 2 O   (Trousdell et al. , 2016). The 2B O 3 data were sampled at 2 s intervals, which corresponds to a mean distance of 150 m at the typical level flight speed of 75 m s -1. The standard TLS flight plan (Fig. 1b  and c ) began with a vertical profile to about 5 km a. s. l. above North Las Vegas after take-off. The aircraft then flew to AP and spiralled down around the summit to ≈ 3 km a. s. l. (cf. Fig.
, 2015; Uhl and Moore, 2018). Data availability. FLEXPART simulations presented in this paper are available upon request to the corresponding author (andrew. o. langford@noaa. gov). Field measurements during FAST-LVOS are available at https://csl. noaa. gov/projects/fastlvos/data. html (last access: 31 December; NOAA, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-1707-2022-supplement. Author contributions. AOL and CJS conceived this study and planned the field campaign; AOL, CJS, RJA, SPS, AMW, IP, PDC, CWS, JP, TBR, SSB, ZCJD, GK, SAC, and DJC prepared the instruments and carried out the field measurements; SB and TAB analyzed the Doppler lidar data; KCA compiled and archived the mobile laboratory measurements; LZ performed the GFDL-AM4 simulations and all analysis under the supervision of ML; RBP and MP conducted the RAQMS and RAP-Chem model forecasts, respectively; JB and SE executed the FLEXPART model calculations; AOL did the analyses and wrote the article with inputs from all coauthors.