dataesr/scientific-paragraphs-categorization huggingface dataset:
The Seine counts about 1700 smaller capacity WWTPs spread throughout the basin (Fig. 1b ). The basin comprises 56. 8% arable land (mainly under intensive agriculture), 25. 8% forests, 9. 7% grasslands and 7. 0% urban areas (CLC database, IFEN 2012, Fig. 1c ). Wetlands have been estimated at between 10. 9% and 15. 6% of the surface area of the basin  22. The Strahler stream order  23  of the main stream of the basin is 6 th order for the Marne River and 7 th order for the Seine River downstream of Paris (Fig. 1a ). The sedimentary basin of the Seine River is characterized by geological formations with low slope gradients resulting in concentric lithology dominated by carbonate and limestone in the central part of the basin, a wide band of Cretaceous chalk and a narrow band of clay followed by Jurassic limestone at the periphery (Fig.
TA (µmol kg -1 ) was analyzed using an automatic titrator (TitroLine ® 5000) on three 20 mL replicates of filtered water (GF/F: 0. 7 μm), with hydrochloric acid (0. 1 M). Values of total suspended solids (TSS) were determined as the weight of material retained on a Whatman GF/F membrane per volume unit after drying the filter for 2 h at 120 °C. Chlorophyll a concentrations (Chl. a) were determined according to Lorenzen  25. Aquifer waters were also sampled during the same periods. Groundwater was pumped from the piezometers using a peristaltic pump. Before the samples were collected, the piezometers were emptied by flushing to remove the standing water (5-10 L in each piezometer)  26. The same variables were measured or analyzed, except Chl. a. pCO 2 calculations from existing data. pCO 2 were computed with the CO 2 SYS software  27  using the water temperature and two of the three following measurements: pH, TA and DIC.
We also used the database (42,108 data with simultaneous water temperatures, pH and TA measured between 1971 and 2015) provided by the French water authorities (AESN) to compute and analyze the pCO 2 dynamics in the Seine basin in space and over time since the 1970s. These pCO 2 data series were corrected by the relationship previously established and then averaged by months and years at each monitoring station. Within this timeframe (1970-2015), two periods of interest (ii) 1989-1991 (92 monitoring stations) and (ii) 2013-2015 (234 monitoring stations) were defined as representative of the changes that occurred recently in the Seine river basin. The former period (1989-1991) represents the period of highest organic pollution from WWTPs, only treated by activated sludge. The most recent period (2013-2015) illustrates the state after a full implementation of the Urban Wastewater Treatment Directive (1991, 91/271/CEE), including the reduction of point sources of organic carbon (industrial and domestic) discharge into the river, as well as phosphorus and nitrogen  20, 21, 29.
Determination of gas transfer velocities. Raymond et al. 30  pointed out that gas transfer velocity equations including slope and water velocity enable easy measurements and recommended the use of these equations at large spatial scales. We selected the equation requiring only the slope and the water velocity and that had the highest squared-R in the Raymond et al. 30  study (Equation 5 in Table  2  in Raymond et al. (2012)) as we wanted to compare the variability of CO 2 evasion in the basin in space and over time. We used kinematic water viscosity coefficients and Schmidt numbers calculated according to Wanninkhof et al. 31. 1. Summary of the field data set. Median, 10 th and 90 th percentiles pH (measured on the NBS scale), water temperature, total alkalinity, dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), total suspended solids (TSS), chlorophyll a (Chl.
Results Measured versus calculated pCO 2. The streams and rivers sampled during our field campaigns were neutral or basic and carbonate-buffered (Fig. 2a, c ), excluding the overestimation of calculated pCO 2 already shown to be linked to the low buffering capacity of the carbonate system  12. A logarithmic transformation was performed on both measured and calculated pCO 2 to obtain normal distribution (Shapiro-Wilk test, p > 0. 01) to calculate a linear regression. A positive relationship was established (R 2 = 0. 56, n = 130, p < 0. 01). Field campaign dataset overview. Average water temperatures ranged between 6. 9 °C and 18 °C which corresponds to the expected seasonal range for the Seine basin (Table  1 ). pH values were generally neutral to basic, with median values of pH and TA in all streams and rivers ranging respectively from 7.
Long-term pCO 2 variability (1970-2015). Long-term analysis of French water authority (AESN) databases showed supersaturation of CO 2 of the Seine River dating back to 1970 (98. 5% data suggested supersaturation with respect to atmospheric equilibrium -pCO 2 median = 3030 ppm; mean = 4765 ppm). From that period on, the Seine River has been a source of CO 2 to the atmosphere even when frequent phytoplankton blooms occurred before wastewater treatment was improved. However, focusing on the bloom events (Chl. a >50 µg l -1 , Fig. 5 ), we observed the opposite pattern between Chl. a and pCO 2 dynamics, with depletion of pCO 2 concomitantly with peaks of phytoplankton. This consumption of CO 2 was not sufficient to cause undersaturation of CO 2 in the river (Fig. 5 ). When we compared the two contrasted periods with respect to water sanitation and associated organic carbon releases, we found a similar range of temperature and discharge values and a seasonal pattern typical of temperate oceanic hydro-climatology regimes, i.
Acknowledgements The project including this study received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No. 643052. Audrey Marescaux  received a PhD grant. Many thanks to  Abdelkader Azougui ,  Anunciacion Martinez Serrano ,  Benjamin Mercier  and  Sébastien Bosc  for their participation in the fieldwork and for their help with chemical analyses in the lab. Vincent Thieu  (assistant professor at  Sorbonne University Paris ) and  Josette Garnier  (Research Director at the.
, https://doi. org/10. 5194/bg-2017-571 Manuscript under review for journal Biogeosciences Discussion started: 9 January 2018 c Author(s) 2018. CC BY 4. 0 License. these issues as reflectance from the floating algae can be wrongly interpreted as aerosols or clouds. It is a main concern in this region as the blooming period of Trichodesmium (mainly November to March  (Dupouy et al. , 2011) ) coincides with the South Pacific Convergence Zone, i. e. heavy cloudiness making difficult the identification of coincident in-situ mats in satellite imagery. The local aggregation and small width of Trichodesmium mats (~ 50 m typically) also calls into question the influence of ocean color sensors resolution on the detection quality of these mats. The aim of this study is to manage a systematic detection of Trichodesmium blooms in the vast WTSP Ocean between latitudes 26° and 10°S and longitudes between 155° and 190° E, building on previously published algorithm and, in particular, provide an updated in situ database on which these algorithms can be evaluated with newly acquired datasets and particularly during the "Oligotrophy from Ultra-oligoTrophy PACific Experiment"(OUTPACE) cruise (DOI: http://dx.
doi. org/10. 17600/15000900) in March-April 2015  (Moutin et al. , 2017). To achieve this objective, a large database of mat observations in this region was created in order to evaluate retrievals from MODIS reflectance. Because of the specificities of the MODIS latest release and the presence of numerous clouds in the WTSP, the existing algorithms had to be adapted whenever possible. From this experience, a new algorithm was then created for the detection of Trichodesmium in the WTSP, more robust to cloud cover and tested on high resolution MODIS imagery, building on the algorithms of  (McKinna et al. , 2011)  and  (Hu et al. , 2010). The paper is organized as follows: Section 2 presents in-situ data and satellite image used in this study. Section 3 covers the methods used in order to extract Trichodesmium spectral signature and their limitations, as well as the three detection algorithms evaluated in this study.
In situ observations The in-situ database used to train and test the Trichodesmium detection model is a combination of three datasets intersecting with the MODIS acquisition period  (2000-present). It includes the Trichodesmium mat observations published in  Dupouy et al. (2011). These observations were done between 1998 and 2010, from aircraft, French Navy ships, research vessels (e. g. R/V Alis) and ships of opportunity. Some of these visual observations were confirmed by water samples analyzed with photomicrographs confirming the presence of abundant Trichodesmium  (Dupouy et al. , 2011). Airborne visual observations were also gathered in December 2014 in the vicinity of New Caledonia during the REMMOA program  (Laran et al. , 2016). This second dataset provides a large number of Trichodesmium mat observations along numerous and repetitive transects, which is most favorable for satellite data validation.
Satellite imagery The satellite data used in this study was all MODIS-Aqua and MODIS-Terra data corresponding in time and space to the SOB database and the OUTPACE campaign. Image were downloaded at level L1A (oceandata. sci. gsfc. nasa. gov) and processed with SeaDAS v7. 0. 2 to produce a L2 MODIS Data basis at 250 m and 1 km resolution. Standard SeaDAS masks were applied during this processing: atmospheric correction failure, land, sunglint, very high or saturated radiance, sensor view zenith angle exceeding threshold, stray light contamination and cloud contamination. In order to reduce the influence of these phenomena, only the observations with less than 60 % of mask coverage over a 0. 5° radius area around point location were kept. We used MODIS radiance in visible, near-infrared (NIR) and short wavelength infrared (SWIR) at different resolutions: 250 m resolution for bands 1 (645 nm) and 2 (859 nm), 500 m resolution (bands 3-7, visible and SWIR land/clouds dedicated bands) and 1 km resolution (bands 8-16, VNIR ocean color dedicated bands).
A total of 468 satellite pixels were found coincident to the SOB database. Only 50 remain after the mask application. Thus, approximately 90 % of in-situ In order to increase the number of useful observations, the coincidence detection was extended to a temporal window of +/-4 days and the search area up to +/-50 km (200 pixels at 250 m resolution) considering that the drifting speed of the algae mat could be up to 0. 5 m/s when the weather condition is favorable, i. e. wind speed sufficiently low to keep Trichodesmium aggregates at surface. Also, some in-situ observations close spatially and temporarily (in the same tile and at intervals of +/-4 days) increased our degree of confidence in identifying the filamentous patterns as Trichodesmium. From the 6 MODIS tiles, two types of spectral signatures have been identified and extracted.
, 2014). At present, we do not know any such study that included Trichodesmium but we have hopes that with our new in situ database and our understanding of advances can be made in that that regard. This will be undertaken in the future. Finally  (Dutheil et al. ,  This issue) explore the regional and seasonal budget of the N2 fixation due to Trichodesmium in a numerical model based on physical and biogeochemical properties that does not take into consideration the part of Trichodesmium that aggregates in mats. One interesting aspect will be to find a way to integrate our results in such model to better estimate the regional effects of that species.
using the current MODIS data archive, cannot be directly used to detect Trichodesmium mats automatically in the South Pacific as they either miss the mats due to algorithms failures (Section 3. 3) and/or do not eliminate numerous false positive in the presence of clouds. In our paper, we have devised a new algorithm building on the previous ones, which allows a cleaner detection of those mats. One of the strengths of our study is the validation of our method with a new, updated database of mats in the South Pacific. This algorithm can however detect only the densest slick but achieves the goal of limiting the detection of false positive due to clouds. During the OUTPACE cruise, we show that satellite detections could help to confirm the presence of Trichodesmium slicks at much wider spatial range than what is possible to observe from a ship.
Based on LMDZ4 daily temperature dataset, equidistant cumulative distribution function matching method (EDCDFm) and cumulative distribution function-transform method (CDF-t) are used to evaluate the ability of models in simulating extreme temperature over central and eastern China. The future temperature change is then projected. The results show that the EDCDFm and CDF-t methods function effectively correct the spatial distribution of daily mean temperature and extreme temperature, significantly reduce the biases of the model simulation and effectively improve the capacity of models for spatial pattern of extreme temperature. However, the cold bias of the CDF-t method in winter is obviously higher than that of the EDCDFm method, and the temperature change curve of the EDCDFm method is closer to the observation than that of the CDF-t method.
Datasets The dynamic downscaling LMDZ4 is the atmospheric component of the coupled model IPSL-CM5A that participates in the Coupled Model Intercomparison Project Phase 5 (CMIP5)  (Hourdin et al. , 2006; Taylor et al. , 2012; Dufresne et al. , 2013). The model center is set at 30 N, 110 E; the range of encryption is 5 e55 N, 85 e135 E; and the simulated horizontal resolution is approximately 60 km and with 19 layers in the vertical direction. Dynamical downscaling daily mean, minimum, and maximum temperature data from LMDZ4, which is nested into five GCMs (namely, bcc-csm1-1-m, CNRM-CM5, FGOALS-g2, IPSL-CM5A-MR, and IPSL-CM5A-MR) are chosen. Basic information on the models is shown in Table  1. The data are divided into two phases: historical (1961e2005) and estimated period (2006e2100). The previous data are used for calibration and verification of the bias correction, and the posterior data are used for projection of future temperature change.
The 0. 5 Â 0. 5 daily maximum and minimum temperature datasets, which are based on the interpolation of 753 meteorological station observations from the China Meteorological Administration, are used as observations  (Xu et al. , 2009). For convenience of comparison, the bilinear interpolation method is used to interpolate the model outputs to the same grid as observed data. Considering the obvious variation of terrain in different regions of central and eastern China, we divided the study area into seven regions to compare the difference between the global model and the downscaled model for extreme temperature  (Zou and Zhou, 2013; Dong et al. , 2015) ; these regions are Northeast China (NE, 42 e55 N, 105 e132 E),.
Isotopic composition Water samples for isotopic analysis were collected in 13 mL screw-top plastic test tubes. The stable oxygen isotopic composition (δ 18 O) of the water samples was analyzed at the GEOTOP-UQAM stable isotope laboratory. 200-μL aliquots of the water samples and two laboratory internal reference waters were transferred into 3-mL vials stoppered with a septum cap. The vials were then placed in a heated rack maintained at 40°C. Commercially available CO2 gas was introduced in all the vials using a Micromass AquaPrep and allowed to equilibrate for 7 hours. The headspace CO2 was then sampled by the Micromass AquaPrep, dried on a -80°C water trap, and analyzed on a Micromass Isoprime universal triple collector isotope ratio mass spectrometer in dual inlet mode. Data were normalized against the two internal reference waters, both calibrated against V-SMOW and V-SLAP.
The solution includes two physically realistic constraints: (i) the contributions from all sources must add up to 100%, and (ii) all contributions have to be nonnegative. Based on the description of the water circulation in the LSLE, four different source-water types contribute to the water column properties at the head of the Laurentian Channel: the freshwaters of the St. Lawrence and Saguenay Rivers, the saline and cold intermediate layer water and the upwelling saline hypoxic bottom water of the LSLE. Even though the properties of the source-water types are variable in both space and time, we use the dataset acquired during this study as well as data acquired a week before on another cruise along a seaward transect through the Lower Estuary and Gulf of St. Lawrence to define the most representative properties for each source-water type (Table  1 ).
Acknowledgements- We wish to thank the  Captain and crew of the Coriolis II  for their help during a trying research cruise. We also wish to acknowledge the help of  Gilles Desmeules  and  Sylvain Blondeau  for their dedicated electronic and field sampling support as well as  Dr. Michel Starr ,  Qiang Chen ,  Liliane St-Amand ,  Sonia Michaud ,  Hourssem Gaalloul ,  Jory Cabrol ,  Robin Bénard ,  Honghai Zhang  for their help in field data acquisition. Most of the plots in this study were created with the ODV Software [Schlitzer, 2009]. This research was funded by a  Team grant  from the  Fonds Québécois de recherche nature et technologies  ( FQRNT-Équipe-165335 ) and a  Ship-Time Allocation grant  from the  Natural Sciences and Engineering Research Council of Canada  ( NSERC-STAC-436804-2013 ).
This setup allowed for a fast methane elution time (< 1 minute). Samples were taken every 1-2 minutes at all GC-FID sites. The raw data were analysed using the commercially available software 'Igor Pro' (WaveMetrics, 2012), which automatically detects and measures the desired peak height. Relative standard deviation (precision) values were defined half hourly. Average values showed precision to be below 0. 3% (of 5 the relative standard deviations).
The solution grid resolution is determined through two factors: the dilution matrix and the NAEI methane emissions values. For the dilution grid matrix, areas where trajectories spent relatively long periods of time will have a finer spatial resolution, as more data are available and thus there is greater sensitivity to resolve the emissions from that area. A predefined dilution threshold subdivides regions into finer grids based on the dilution matrix  (Manning et al. , 2011). 35  Generally, areas nearer to the measurement sites are more finely resolved than more distant areas. Complementing this, the NAEI methane emissions are also incorporated to define the solution grid resolution. The NAEI emission magnitudes are used as a linear scaling factor to define the grid resolution (as in other inversion techniques such as  Rigby et al.
Baseline A baseline that represents the atmospheric methane concentration arriving at the edge of the inversion domain (Figure  4 ) 10 must be defined within InTEM2014. For this study, a statistical baseline was calculated from the measured observations that also incorporated the particle trajectory analysis from NAME. Methane concentrations from the four measurement sites were divided into time series depending on whether their trajectory origin was dominated by a certain direction (e. g. , from the NNE, ENE, ESE, NNW etc. ). These eight individual time series, representing concentrations from the eight different compass directions, were used to estimate eight statistically derived baselines, each calculated by passing a 15 rolling 18 th percentile, spanning one week, through each dataset.
InTEM2014 emission estimate results 25 The following results were derived using a one year dataset from all four sites covering the period from June 2013 to May 2014 (inclusive) with hourly observations. This period was chosen as it marks the first full year where all four sites were operational. A subset of the resulting measured and pseudo-observations for 01-30 July 2013 is shown in Figure  5  Figure  5  shows a large fraction of the a posteriori estimates lie outside the uncertainty range of the measured observations, although without a more thorough description of a priori errors this is difficult to fully diagnose. As it stands, this implies that either the prescribed InTEM2014 uncertainties are too small or the resulting emission field needs be more resolved (in time and space) in order to better represent the concentrations being measured (currently, the spatial resolution of the resulting emission grid could be too coarse to fully capture the peaks and troughs of the measured time series).
Acknowledgements This project was supported by the  UK Natural Environment Research Council (NERC)  through the  Greenhouse gAs UK and Global Emissions (GAUGE)  project on grant number  NE/K002570/1. We also thank the  Department of Environment, Farming  and  Rural Affairs  and the  Royal Society  for seed funding and  NERC  for additional support through grants  NE/G014655/1 ,  NE/J006246/1  and a PhD studentship for Sarah Connors. We acknowledge the  UK Government Department for Business, Energy & Industrial Strategy  and the former  UK Department for Energy and Climate Change  for the use of the national methane measurement network data and we would like to thank the  National Centre for Atmospheric Sciences (NCAS)  for access to the  Weybourne Atmospheric Observatory  and for providing data.
The data were corrected for multiple scattering and shadowing effects according to  Bond et al. (1999)  and  Müller et al. (2009). Data with a filter transmission under 0. 7 are removed as corrections are not applicable. Furthermore, PSAP measurements were used to compute the AAE. The particle extinction coefficient is measured with a cavity attenuated phase shift particle light extinction monitor (CAPS-PMex, Aerodyne Research) operated at the wavelength of 530 nm. Data were processed with a time resolution of 1 s. An integrated nephelometer (Ecotech, model Aurora 3000) provided aerosol light scattering at three wavelengths (450, 550, and 700 nm), which was used to correct for the impact of aerosol scattering based on the correction scheme by Anderson and Ogren (1998) and using correction factors obtained by  Müller et al.
(2011)  without a submicron size cut-off. Uncertainties in the absorption coefficient are of the order of 30 %  (Müller et al. , 2011). The nephelometer was calibrated with particle-free air and high-purity CO 2 prior to and after the campaign. Prior to the campaign, the CAPS data were evaluated against the combination of the nephelometer and the PSAP measurements. The instrument intercomparison has been performed with purely scattering ammonium sulfate particles and with strongly absorbing black carbon particles. Both types of aerosols were generated by nebulizing a solution of the respective substances and size-selected using a differential mobility analyser. For instrument intercomparison purposes, the extinction coefficient from the nephelometer and PSAP was adjusted to that for 530 nm by using the scattering and absorption Angstrom exponent.
5 µm and between 2. 5 and 10 µm, are computed from the OPC, and are referred to as N PM 1 , N PM 2. 5 , and N PM 10 , respectively, in the following. The GRIMM OPC was calibrated with size-standard particles prior to and after the field campaign. Sampling with all the above-mentioned instruments is achieved through the Community Aerosol Inlet of the ATR 42. Regarding gas-phase chemistry, we make use of an O 3 analyser and a NO x analyser from Thermo Environmental Instruments (TEI Model 49i and TEI 42CTL, respectively). The associated uncertainty is of the order of 5 and 10 %, respectively. Carbon monoxide (CO) measurements are performed using the near-infrared cavity ring-down spectroscopy technique (G2401, Picarro Inc. , Santa Clara, CA, USA), with a time resolution of 5 s. All in-cloud measurements are removed from the data shown here.