dataesr/scientific-paragraphs-categorization huggingface dataset:
5 Pmolec cm -2 (ESA, 2017a, b). Since the beginning of its nominal operation in April 2018, in-flight compliance of S5P TROPOMI with these mission requirements has been monitored routinely by means of comparisons to groundbased reference measurements in the Validation Data Analysis Facility (VDAF) of the S5P Mission Performance Centre (MPC) and by comparison with similar satellite data from OMI and GOME-2. The Copernicus S5P MPC routine operations validation service is complemented with ground-based validation studies carried out in the framework of ESA's S5P Validation Team (S5PVT) through research projects funded nationally like NIDFORVAL (see details in the Acknowledgements). Ground-based validation of satellite NO 2 data (e. g. Petritoli et al. , 2003; Brinksma et al. , 2008; Celarier et al.
S5P TROPOMI data The retrieval of NO 2 (sub)columns from TROPOMI Earth nadir radiance and solar irradiance spectra is a threestep process relying on DOAS and on a chemical transport model (CTM)-based stratosphere-troposphere separation. The TROPOMI NO 2 algorithm is an adaptation of the QA4ECV community retrieval approach  (Boersma et al. , 2018)  and of the DOMINO/TEMIS algorithm  (Boersma et al. , 2007 (Boersma et al. , , 2011)) , already applied successfully to heritage and current satellite data records (GOME, SCIAMACHY, OMI, GOME-2). In the first step, the integrated amount of NO 2 along the optical path, or slant column density (SCD), is derived using the classical DOAS approach  (Platt and Perner, 1983). In the second step, the retrieved SCD is assimilated by the TM5-MP CTM to allocate a vertical profile of the NO 2 concentration, needed for the separation between stratospheric and tropospheric SCDs.
This assimilation procedure favours observations over pristine, remote areas where the entire NO 2 SCD can be attributed to the stratospheric component. Assuming relatively slow changes in the stratospheric NO x field, the model transports information to areas with a more significant tropospheric component. In the third step, the three slant (sub)column densities are converted into vertical (sub)column densities using appropriate air mass factors (AMFs). The CTM can be run either in forecast mode, using 1 d forecast meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF), or in a more delayed processing mode, using 0-12 h forecast meteorological data. The former is used for near-real-time (NRTI) processing of the TROPOMI measurements, the latter for the offline (OFFL) production.
For full technical details, the reader is referred to the Product Readme File (PRF), Product User Manual (PUM), and Algorithm Theoretical Basis Document (ATBD), all available at http://www. tropomi. eu/ data-products/nitrogen-dioxide (last access: 5 January 2021). A detailed description and quality assessment of the derived slant column data have already been published by  van Geffen et al. (2020) , and a publication on satellite intercomparison of vertical column data is under preparation  (Eskes et al. , 2020). The current paper addresses the independent ground-based validation of vertical subcolumn densities in the troposphere and stratosphere and of the vertical total column. The S5P dataset validated here covers the nominal operational phase (Phase E2) of the S5P mission, starting in April 2018 and up to February 2020.
No data obtained during the commissioning phase of the satellite have been used. Table  1  provides an overview of the processor versions to which this corresponds. Besides very detailed quality flags, the S5P NO 2 data product includes a combined quality assurance value (qa_value) enabling end users to easily filter data for their own purpose. For tropospheric applications (when not using the averaging kernels), the guideline is to use only NO 2 data with a qa_value > 0. 75. This removes very cloudy scenes (cloud radiance fraction > 0. 5), snow-or ice-covered scenes, and problematic retrievals. For stratospheric applications, where clouds are less of an issue, a more relaxed threshold of qa_value > 0. 5 is recommended. These data filtering recommendations have been applied here, where the stricter requirement of qa_value > 0.
, 2018). As part of this, over 15 instruments of the SAOZ design (Système d'Analyse par Observation Zénitale) are distributed worldwide and provide data in near-real time through the CNRS LATMOS_RT Facility  (Pommereau and Goutail, 1988). For the current work, ZSL-DOAS validation data have been obtained: (1) through the LATMOS_RT Facility (in near-real-time processing mode), (2) from the NDACC Data Host Facility (DHF), and (3) via private communication with the instrument operator. The geographical distribution of these instruments is shown in Fig. 1 , and further details are provided in Sect. A1. Measurements are made during twilight, at sunrise, and at sunset, but only sunset measurements are used here for signal-to-noise reasons (larger NO 2 column) and as these happen closer in time to the early-afternoon overpass of S5P.
The reported systematic uncertainty may include only that from the NO 2 cross sections (approx. 3 %; UNAM, BIRA-IASB, MPIC, AUTH, IUPB), or it may include also a contribution from the VCD retrieval step (up to 14 % in JAM-STEC data and 20 % in KNMI data) and the aerosol retrieval (Chiba U;  Irie et al. , 2011). -Different SCD retrieval. Recommended common DOAS settings are used by all groups in the present study, and when doing so, instrument intercomparison campaigns like CINDI-1 and CINDI-2  (Roscoe et al. , 2010; Kreher et al. , 2020)  revealed relative biases between 3 % and 10 % in the differential slant column density (DSCD). -Different methods to retrieve VCD from DSCD (see also Table  A2 ). Using either (1) vertical profile inversion using optimal estimation (BIRA-IASB, UNAM); (2) profile inversion using (an optimal estimation of) parameterized profile shapes (JAMSTEC and Chiba U); (3) direct retrieval via the calculation of a tropospheric AMF (QA4ECV datasets); or (4) direct retrieval using a geometrical approximation can lead to systematic differences in the 5 %-15 % range  (Vlemmix et al.
, 2015; Frieß et al. , 2019). certainty in polluted conditions. Ongoing efforts to harmonize MAX-DOAS tropospheric NO 2 data processing, e. g. as part of the ESA FRM4DOAS project, should help minimize such network inhomogeneities in the near future. MAX-DOAS data have been used extensively for tropospheric NO 2 satellite validation, for instance for Aura OMI and MetOp GOME-2 (e. g. by  Celarier et al. , 2008; Irie et al. , 2012; Lin et al. , 2014; Kanaya et al. , 2014; Wang et al. , 2017; Drosoglou et al. , 2018; Liu et al. , 2019a; Compernolle et al. , 2020b; Pinardi et al. , 2020) , as well as for the evaluation of modelling results  (Vlemmix et al. , 2015; Blechschmidt et al. , 2020). Data are collected either through ESA's Atmospheric Validation Data Centre (EVDC; https://evdc. esa. int/, last access: 5 January 2021) or by direct delivery from the instrument principal investigators (e.
g. within the S5PVT NIDFORVAL AO project). Currently, 19 MAX-DOAS stations have contributed correlative data in the TROPOMI measurement period from April 2018 to February 2020. Detailed information about the stations and instruments is provided in Sect. A2. A few contributing sites measure in several geometries (e. g. Xianghe measure in both MAX-DOAS and direct Sun mode; Bremen and Athens both report MAX-DOAS and zenith-sky measurements) or have multiple instruments (e. g. Cabauw and UNAM stations host both MAX-DOAS and Pandora instruments). This allows for detailed (sub)column consistency checks and in-depth analysis of the site peculiarities, beyond the scope of the present overview paper.
, 2009; Tzortziou et al. , 2014; Kollonige et al. , 2018; Choi et al. , 2019; Judd et al. , 2019; Griffin et al. , 2019; Herman et al. , 2019; Pinardi et al. , 2020)  and TROPOMI  (Griffin et al. , 2019; Ialongo et al. , 2020; Zhao et al. , 2020). For the current work, 25 sites have contributed Pandora data, collected either from the ESA Atmospheric Validation Data Centre (EVDC) (https://evdc. esa. int/, last access: 5 January 2021) or from the PGN data archive (https:// pandonia-global-network. org/, last access: 5 January 2021). Only data files from a recent quality upgrade (processor version 1. 7, retrieval version nvs1, with file version 004 and 005; see https://www. pandonia-global-network. org/ home/documents/release-notes/, last access: 5 January 2021) were used, with 005 files (consolidated data) having precedence over 004 files (rapid delivery data).
The temperature sensitivity parameterized in this correction is approximately 0. 32 % K -1  (Zara et al. , 2017). A posteriori temperature correction of the ground-based data is beyond the scope of this paper, so it must be kept in mind that this may contribute to differences between S5P and ground-based columns. Specifically, we could expect a small seasonal cycle in the stratospheric column comparisons of a few percent due to the seasonal variation in stratospheric temperature not being accounted for in the ZSL-DOAS data processing. PGN columns may either be overestimated by up to 10 % when the column is mostly stratospheric or underestimated by a similar order of magnitude when large tropospheric amounts are present. The MAX-DOAS data may be biased in either direction by a few percent when tropospheric and/or stratospheric temperatures differ strongly from the 298 and 220 K default temperatures.
Comparison results An illustration of the daily comparisons between TROPOMI and ground-based MAX-DOAS measurements between May 2018 and the end of January 2020 is presented in Fig. 8 for the Uccle station (Brussels, B, with moderate pollution levels). The two datasets have a correlation coefficient of 0. 75 and a regression slope and intercept of 0. 47 and 1. 0 Pmolec cm -2 respectively. The (median and mean) difference of about -2. 3 to -3. 1 Pmolec cm -2 corresponds to a median relative difference of about -30 %. Results for the entire MAX-DOAS network are presented in Fig. 9. This figure reveals mostly (but not only) negative differences, with a fairly significant variability but no clear seasonal features. No impact of the TROPOMI ground pixel size change on 6 August 2019 is observed. Box-and-whisker plots for the whole network are shown in Fig.
Bias Dispersion Stratosphere -0. 2 Pmolec cm -2 ; -9 % 0. 3 Pmolec cm -2 Troposphere -low NO 2 -0. 3 Pmolec cm -2 ; -23 % 0. 7 Pmolec cm -2 -high NO 2 -2 Pmolec cm -2 ; -37 % 3. 4 Pmolec cm -2 -extreme NO 2 -12 Pmolec cm -2 ; -51 % 7 Pmolec cm -2 Total column -low NO 2 0. 1 Pmolec cm -2 ; 2 % 1 Pmolec cm -2 -high NO 2 -3. 6 Pmolec cm -2 ; -30 % 3 Pmolec cm -2 spect to the NDACC ZSL-DOAS network, of the order of -0. 2 Pmolec cm -2 on average, with some station-station inhomogeneities and with larger differences in the highly variable conditions of the denoxified polar stratosphere in local winter. This median difference remains within the S5P mission requirements and is similar to the conclusions derived for similar satellite data from other sounders (e. g. Compernolle et al. , 2020b). In view of the sources of systematic uncertainties in the different components of the comparison (satellite data, reference data, photochemical cycle adjustment, irreducible mismatch errors), this result is entirely within expectations.
While comparisons to mountaintop PGN instruments confirm these values, using cross sections at a more appropriate (lower) temperature in the PGN data processing would lead to somewhat smaller columns and therefore a less significant negative median difference than that observed with respect to the ZSL-DOAS instruments. This probably reflects the true accuracy of the ground-based data, which should thus be taken to be of the order of ±10 % at best. For the tropospheric and total columns, averaging results over the networks with the hope of obtaining a meaningful global estimate is of limited use as the results depend strongly on the amount of tropospheric NO 2. Overall, mission requirements in terms of bias are mostly met, the only exception being the tropospheric columns at extremely polluted sites, which have a bias on the threshold of 50 %.
The better agreement here is therefore consistent with the good agreement found for the TROPOMI stratospheric NO 2 column vs. ZSL-DOAS and also vs. PGN at pristine mountain sites (Sect. 4). For sites characterized by a higher total NO 2 column, the tropospheric contribution becomes more important, and some of the same effects that make satellite-MAX-DOAS comparisons difficult, such as the smoothing difference error, the lower sensitivity of the satellite close to the surface, and the approximate S5P a priori profile, come into play as well. In conclusion, the first 2 years of Copernicus S5P TROPOMI NO 2 column data produced both with the NRTI and OFFL versions 01. 0x. xx of the operational processors do meet mission requirements for the bias and, to some extent, with precaution for the uncertainty (dispersion).
The different data products available publicly through the Copernicus system are mutually consistent, are in good geophysical and quantitative agreement with ground-based correlative data of documented quality, and can be used for a variety of applications, on the condition that the features and limitations exposed here are taken into proper consideration and that the S5P data are filtered and used according to the recommendations provided in the official Product Readme File (PRF) and associated documentation, also available publicly. Ground-based validation activities relying on the correlative measurements contributed by the NDACC ZSL-DOAS, MAX-DOAS, and PGN global monitoring networks have progressed significantly in recent years and have demonstrated their capacity but also their current limitations in an operational context such as the Copernicus programme.
Room does exist for further improvement of both the satellite and ground-based datasets, as well as the intercomparison methodology and its associated error budget. Beyond the methodology advances published here and in aforementioned papers, special effort is needed to understand fully and ever reduce comparison mismatch errors, which so far make the accurate validation of S5P data uncertainty bars difficult. Several updates of the calibration of TROPOMI spectra and of the TROPOMI NO 2 data retrieval processors are already in development and in implementation. Upcoming data versions should be validated with the same system as used in the current paper, allowing the necessary independent assessment of the S5P data product evolution. A1. ZSL-DOAS hosting stations, ordered by latitude, that contribute to the stratospheric NO 2 column validation.
7, 3. 4, and 7 Pmolec cm -2. Note that the median values for the high tropospheric columns (Athens to Xianghe) are almost the same as the statistics found for the whole network. The site-site bias dispersion is 0. 2, 1. 2, and 3. 3 Pmolec cm -2 for each regime. A3. PGN stations, ordered by median PGN NO 2 column value, that contribute to the total NO 2 validation. Mountaintop stations (not sensitive to lower lying tropospheric NO 2 ) are marked with an asterisk. In the last row, we indicate where the data can be obtained (EVDC or directly from the PGN website). Note that only PGN data from a recent quality upgrade (with file version 004 or 005, where 005 has precedence) was used. The bias over all stations (median over all station medians) is -0. 5 Pmolec cm -2 (-7 %), while the overall dispersion (median over all 1/2IP68) is 1.
Acknowledgements. Part of the reported work was carried out in the framework of the  Copernicus Sentinel-5 Precursor Mission Performance Centre  ( S5P MPC ), contracted by the  European Space Agency  and supported by the  Belgian Federal Science Policy Office (BELSPO) , the  Royal Belgian Institute for Space Aeronomy (BIRA-IASB) , the  Netherlands Space Office (NSO) , and the  German Aerospace Centre (DLR). Part of this work was carried out also in the framework of the  S5P Validation Team (S5PVT) AO projects NIDFORVAL  (ID no. 28607 ,  PI Gaia Pinardi, BIRA-IASB ) and CESAR (ID no. 28596 ,  PI Arnoud Apituley, KNMI ). The authors express special thanks to  Ann Mari Fjaeraa ,  José Granville ,  Sander Niemeijer , and  Olivier Rasson  for post-processing of the network and satellite data and for their dedication to the S5P operational validation.
JPMJCR15K4 ). The University of Toronto ZSL-DOAS measurements at  Eureka  were made at the  Polar Environment Atmospheric Research Laboratory (PEARL)  by the  Canadian Network for the Detection of Atmospheric Change (CANDAC) , with support from the  Canadian Space Agency  ( AVATARS  project), the  Natural Sciences and Engineering Research Council  ( PAHA  project), and  Environment and Climate Change Canada. Financial support. This research has been supported by the  ESA/ESRIN  (grant no. 4000117151/16/I-LG ) and the  BEL-SPO/ESA ProDEx  ( TROVA-E2  (PEA grant no. 4000116692 )). Review statement. This paper was edited by  Jhoon Kim  and reviewed by two anonymous referees.
Data availability. No data were produced specifically for this research. The S5P data used here are publicly available from the Copernicus Open Access Hub (https://scihub. copernicus. eu/, last access: 5 January 2021, ESA, 2021). The ZSL-DOAS and MAX-DOAS data are publicly available from the NDACC Data Host Facility (ftp://ftp. cpc. ncep. noaa. gov/ndacc, last access: 5 January 2021, NDACC, 2021). The PGN data are publicly available from the Pandonia data archive (http://data. pandonia-global-network. org/, last access: 5 January 2021, PGN, 2021).
03. 01 7907 23 April 2019 8814 26 June 2019 01. 03. 02 8815 26 June 2019 current version RPRO 01. 02. 02 2836 1 May 2018 5235 17 October 2018 They constitute as continuous a dataset as possible from May (NRTI) or October (OFFL) 2018 onwards. Combining interim reprocessing (RPRO) (May-October 2018) with OFFL, a coherent dataset with the OFFL processor v01. 02. 02 or higher can be obtained.
7 0. 85 Kasuga 33. 52 130. 48 28 Chiba U PP, ascii (f, g, h) 7. 3 -3. 1; -50. 4 % 4 0. 46 Mainz 49. 99 8. 23 150 MPIC QA4ECV dataset, 7. 3 -3. 3; -41 % 3. 3 0. 75 GEOMS Cabauw 51. 97 4. 93 3 KNMI PP, GEOMS (j) 6. 7 -2. 5; -36. 5 % 3. 5 0. 40 Uccle 50. 80 4. 36 120 BIRA-IASB OE (bePRO), (k) 5. 7 -2. 3; -37 % 3. 3 0. 75 GEOMS De Bilt 52. 10 5. 18 20 KNMI PP, GEOMS (j) 5. 4 -0. 95; -16. 8 % 2. 8 0. 64 Bremen 53. 10 8. 85 27 IUPB QA4ECV dataset, 5. 2 -2. 1; -37 % 2. 3 0. 59 GEOMS Pantnagar 29. 03 79. 47 237 Chiba U PP, ascii (f, g, h, l) 4. 6 -2. 6; -56 % 1. 6 0. 33 Thessaloniki_lap 40. 63 22. 96 60 AUTH QA4ECV dataset, (m) 4. 6 -1. 5; -43. 8 % 4. 1 0. 69 GEOMS Thessaloniki_ciri 40. 56 22. 99 70 AUTH QA4ECV dataset, (m) 3. 6 -1. 3; -34. 9 % 2 0. 73 GEOMS Athens 38. 05 23. 86 527 IUPB QA4ECV dataset, 3.
All fish were then exposed in the same aquaria for 5 days (exactly 134 hours) to the highest concentration of M. aeruginosa, 100 µg. L -1 Chla (d39, Fig. 1 ). At d39, four fish, 150 mL of water and faeces were sampled in each aquarium. Along the two successive exposures, 10 mL of M. aeruginosa culture were sampled every two days but only one sample per week was analysed further. Dataset S1 provides details of sampled individuals and performed analyses.
Mass spectrometry data processing and analysis Each metabolite extract from fish livers, guts and muscles was analysed by Ultra high-performance liquid chromatography (UHPLC; ELUTE, Bruker) coupled with a high-resolution mass spectrometer (ESI-Qq-TOF Compact, Bruker) at 2 Hz speed, on simple MS mode then on broad-band Collision Ion Dissociation (bbCID) or autoMS/MS mode on the 50-1500 m/z range. Three feature peak lists were generated from MS spectra within a retention time window of 1-15 minutes and a filtering of 5000 counts using MetaboScape 4. 0 software (Bruker). The three peak lists consisted of the area-under-the-peaks of extracted analytes from the three tissues (gut, liver, muscle) sampled at d28, d33 and d39, resulting in 1672, 909 and 3127 analytes, respectively. The genuine metabolite content of the culture was investigated on metabolite extracts using LC-MS/MS approach, combined with molecular network analysis and metabolite annotation using a cyanobacterial metabolite reference database, as previously described  (29).
Prior to analyses, Pareto scaling was applied on the datasets. Principal Component Analyses (PCA) were performed to compare the metabolite composition among groups using the mixOmics package (30) in R 4. 1. 0 (R Core Team, 2021). The variance among groups was compared conducting PERMANOVA (999 permutations) based on euclidean distance with the vegan (31) followed by Bonferroni-adjusted pairwise comparisons with the RVAideMemoire  (32).
Bacterial 16S rRNA gene sequencing and analyses The V4-V5 variable region of the 16S rRNA gene was amplified using 479F (5'-CAGCMGCYGCNGTAANAC-3') and 888R primers (5'-CCGYCAATTCMTTTRAGT-3')  (33) , and sequenced (Illumina MiSeq paired-end, 2x250 bp, GenoScreen, France). Paired-end reads were demultiplexed, quality controlled, trimmed and assembled with FLASH  (34). Sequence analysis was performed using the QIIME 2 2020. 11 pipeline  (35). Chimeras were removed and sequences were trimmed to 367 pb then denoised using the DADA2 plugin, resulting in Amplicon Sequence Variants (ASVs)  (36). ASVs were affiliated from the SILVA database release 138 (37) using the feature-classifier plugin and classify-sklearn module  (38, 39). Sequences assigned as Eukaryota, Archaea, Mitochondria, Chloroplast and Unassigned were removed from the dataset then the sample dataset was rarefied to a list of 6,978 sequences.
Microbiome-metabolome integrative analysis The integration of datasets, i. e. the area-under-the-peaks in metabolite profiles and the ASV counts describing the bacterial communities in the same sample, was performed using the mixOmics package in R. Pareto scaling was applied on the metabolome data, and a centred log-ratio transformation then a prefiltering keeping only abundant ASVs, (i. e. representing at least 1% of the reads in at least one sample), were applied on the microbiome data. Following unsupervised analyses on each dataset, completed to explore and visualize any similar changes according to treatments, the integration was carried out. A supervised Projection to Latent Structures Discriminant Analysis (PLS-DA) was performed using DIABLO (Data Integration Analysis for Biomarker discovery using Latent cOmponent)  (45) , enabling to identify highlycorrelated variables (metabolites and ASVs) also discriminating the different treatments.
Metagenomic sequencing and analysis Shotgun metagenome sequencing was performed on DNA from 10 gut samples collected at d28, 5 in treatment d28_0 and 5 from d28_100 (Illumina HiSeq, 2x150bp, GenoScreen, France). Reads corresponding to animal sequences were identified by aligning each dataset against Oryzias latipes available at the NCBI, using BBMap/bbsplit  (47) , and discarded. Remaining reads from each sample were assembled using metaSPAdes with default parameters  (48). Scaffolds were first taxonomically annotated using Contig Annotation Tool (CAT)  (49)  and Kaiju (50) allowing to detect sequences from O. latipes retroviruses and Microcystis genome which were then discarded. All scaffolds were clustered using MyCC (51) (k-mer size = 4, minimal sequence size = 1000) and bins were taxonomically annotated using Bin Annotation Tool  (49).