dataesr/scientific-paragraphs-categorization huggingface dataset:
Whole-procedure blank values of this sample set were measured between 0. 5 and 1pg for thorium and between 10 and 20pg for uranium. Both values are in the range typical of this method and the laboratory  (Fietzke et al. , 2005). Table  1  summarizes all measured uranium and thorium data and the calculated U/Th ages. Recommendations of  Dutton et al. (2017)  were followed for the presentation. Table  2  presents the results of dating according to all samples collected on microatolls and filling blocks inside the ahu. For uranium and thorium isotope analysis only samples with no detectable traces of calcite were used for measurements. The data show that 238U concentrations vary between 3. 945 ± 0. 008 ppm (R39, aragonitic coral) and 0. 1172 ± 0. 0003 ppm (R14, calcitic mollusc) with a mean 238U concentration of 2.
Discussion and conclusions We started our research on the marae of Taputapuātea by noting that the erected stones of the ahu were microatolls of the Porites species. Previous authors failed to identify them as such and had not considered how their analysis could provide data on construction of the marae. We had initially hypothesized that Polynesians had quarried these microatolls from the lagoon, providing a date for their extraction from the water as well as a date for the construction of the marae. It is according to this hypothesis that samples from the Porites coral erected microatolls were dated. In addition, some shells of molluscs housed in the crevices of the erected microatolls were collected and dated with the same intention. In the same way some blocks filling the ahu, small rounded blocks of Porites, were sampled for dating.
Acknowledgments This research has been funded by the  Service de la Culture et du Patrimoine of the Government of French Polynesia , agreement n°208/MCE/SCP 15th February 2016 and n°2189/MCE/SCP 5th Decem-ber 2016. Field work and sampling have been conducted by  Bernard Salvat ,  Belona Mou  and  Tamara Maric ; U/Th dating was performed by  TG  and  AE. BS , the corresponding author analysed and interpreted the data and wrote the manuscript in collaboration with the co-authors. Thanks to  Fabien Morat  and  Peter Esteve ,  Université de Perpignan , for helping in computerizing figures and tables. Mauruuru to the community of Opoa in Ra'iatea, who allowed us to work on their marae, especially the elders  Papa Maraehau ,  Timi Tavaeari 'i and  Ieremia Pani , said  Papa Pua. We are also greatly indebted to the two anonymous reviewers and editor for their constructive comments and suggestions which have significantly improved our manuscript.
Data availability. OMPS satellite observations, data from air quality stations in France and in Europe (EMEP network) as well as AERONET measurements are publicly available from the NASA Goddard Earth Sciences Data and Information Services Center, Atmo Hauts-de-France, Airparif, EBAS and AERONET websites. IASI SO 2 satellite observations can be provided on demand. While ACSM data for SIRTA are available on the EBAS website (http: //ebas. nilu. no/, last access: 14 November 2019), data for Dunkirk can be provided on request. Video supplement. Two movies illustrating the tropospheric dispersal of the Holuhraun volcanic cloud in September and October 2014 are available in the Supplement, the first including IASI SO 2 column amount and altitude observations (https://doi. org/10. 5446/40473, Boichu, 2019a) and the second OMPS SO 2 satellite observations (https://doi.
org/10. 5446/40472, Boichu, 2019b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-19-14253-2019-supplement. Author contributions. MB (text, analysis and interpretation of satellite, ACSM, EMEP, air quality monitoring, AERONET data and thermodynamical model simulations), OF (ACSM data acquisition at SIRTA, interpretation and contribution to text), VR (ACSM data acquisition at Dunkirk and interpretation), CB (analysis of AERONET AOD time series and discussions on the overall manuscript), JEP (multi-site concentration-weighted trajectory analysis), YZ (ISORROPIA thermodynamical model simulations), JS (ACSM data acquisition at SIRTA), IC (participation to AERONET data analysis), LC (processing of IASI SO 2 observations), SZ (initial processing of ACSM data in Dunkirk, air quality monitoring data acquisition at Dunkirk Port-East station), NPS (air quality monitoring data acquisition in Hauts-de-France region), ET (ACSM data acquisition at Dunkirk), HD and PG (PIs of the AERONET data acquisition at Dunkirk and SIRTA, respectively).
Data analysis The data are analysed in a systematic manner. First, the CTR simulation is compared to available observations such as wind speed and direction, temperature, relative humidity, height of the rain-snow transition, as well as precipitation amount and types collected during the field project. The time evolution of the mass content of ice crystals, cloud, rain, graupel, and snow is analysed at the grid point closest to the KES site. To analyse precipitation aloft at KES and across the Kananaskis Valley, a vertical cross section is plotted and the mass content of hydrometeors, as well as the vertical air motion, are investigated. Second, the CTR simulation is compared to the three sensitivity experiments: the simulation without the temperature change from melting of snow and graupel (NO_MLT), the simulation without the temperature change from sublimation (NO_SBL), and the simulation without graupel (NO_GRPL).
Acknowledgements. We would like to thank the  Natural Sciences and Engineering Research Council of Canada (NSERC) ,  Global Water Futures , as well as  Canadian Research Chair in Extreme Winter Weather Events  for providing support to accomplish this work. One of the authors (Émilie Poirier) would like to thank the  Fond Quebecois de la recherche sur la nature et les technologies (FRQNT)  and  NSERC  for graduate scholarships. Financial support. This research has been supported by  Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants  (grant nos. 418374-2012-RGPIN  and  2019-06654-RGPIN ) and the  NSERC, Climate Change and Atmospheric Research, Changing Cold Regions Network  (grant no. 433923-2012 ). Review statement. This paper was edited by  Chris DeBeer  and reviewed by three anonymous referees.
The originality of our study is that we used a large set of data collected from 2001 to 2014 from 16 French Mediterranean coastal lagoons, thereby covering the whole eutrophication gradient from oligotrophic to hypertrophic. Some of these lagoons have been receiving large quantities of urban nutrient inputs for decades, leading to extremely high levels of phytoplankton biomass, associated with high pico-and nano-phytoplankton abundances in the most degraded lagoons (annual median values >100 µg Chl a. L -1 , >10 9 and >10 10 cell. L -1 , respectively,  Souchu et al. 2010; Bec et al. 2011). Large-scale management actions were recently undertaken on waste water treatment systems to restore the quality and the functioning of the ecosystems  (De Wit et al. 2015; Leruste et al. 2016). The unprecedented range of anthropogenic eutrophication and the remediation works carried out during the 14-year study period allowed us to analyse recovery trajectories.
First we analysed changes in the eutrophication gradient over the study period and identified key parameters. We then focused on three contrasted ecosystems to test the following hypotheses: (i) reducing inputs of urban nutrients has a rapid effect on the nutrient levels and phytoplankton biomass in Mediterranean lagoons; and (ii) the pattern of the recovery trajectories depends on the degree of anthropogenic eutrophication before the remediation actions were undertaken. Analysis of available historical data provided useful information to improve our understanding of the patterns of change in the ecological functioning of coastal lagoons after remediation. Derolez et al. 2018. Recovery trajectories following the reduction of urban nutrient inputs along the eutrophication gradient in French Mediterranean lagoons.
OCMA_2018_443_R4 3 Data from 16 French Mediterranean coastal lagoons collected between 2001 and 2014, were analysed. Fourteen of the lagoons are located in the Gulf of Lions, from north of Perpignan to Montpellier, and two in Corsica (Fig. 1 ). The database includes polyhaline and euhaline microtidal lagoons (annual median salinity > 18) (see Supplementary Material), with surface areas ranging from 1. 4 to 75 km², depths ranging from 0. 7 to 9 m and volumes ranging from 0. 7 to 260 x 10 6 m 3  (Souchu et al. 2010). Details on the morphometric and hydrologic characteristics of the study sites are available in  Souchu et al. 2010 and Bec et al. 2011. The lagoons are all connected to the Mediterranean Sea, through direct inlets, or through indirect connections via a canal or another lagoon. The database covers the anthropogenic eutrophication gradient from oligotrophic to hypertrophic lagoons  (Bec et al.
Laboratory analyses and data collection Between 2001 and 2014, water was sampled each year at monthly intervals (June, July and August) in summer, when primary production is at maximum in Mediterranean lagoons  (Souchu et al. 2010; Bec et al. 2011). We avoided collecting samples during periods of sediment resuspension by not sampling for three days after any period in which wind speed exceeded 12. 5 m s -1. Temperature (Temp. , °C), salinity (Sal. ) and dissolved oxygen (O2, mg L -1 ) were recorded in-situ with field sensors. At each station, on each sampling occasion, one water sample was collected in a 1 L polypropylene bottle at the subsurface (in the middle of the water column for depths below 2 m and at -1 m from the surface at deeper stations) for laboratory analysis. The following parameters were analysed in the laboratory: dissolved inorganic nitrogen (DIN = NH4 + + NO3 -+ NO2, µM), dissolved phosphorus (DIP, µM), dissolved silicate (Si, µM), total nitrogen and phosphorus (TN and TP, µM) and turbidity.
2001). The withingroup PCA of the dataset was centred by group (same dimensions as the original dataset: 5 columns (environmental variables), 924 lines). The within-group analysis searched for information shared by the groups, while the between-group analysis searched for differences between groups. For example, in our study, between-station analysis was a PCA of the dataset containing of station means: 5 columns (environmental variables) and 22 lines (stations). In order to highlight the annual variability of each station with respect to its average position on the inertia axes, the initial data were used as supplementary individuals in the between-station PCA. Between-group and within-group PCAs were performed to study station, year and month effects, using a reduced dataset with five selected parameters (Chl a, TN, TP, DIN and DIP).
These PCAs made it possible to split the total inertia (i. e. the total information contained in the dataset) into between-and within-group inertia. Monte Carlo permutation tests were conducted on stations, sampling years and sampling months to identify the statistical significance of between-group analyses  (Manly 2007). The distances of the trajectories recorded year to year (di, with i: period from year i to i+1) were calculated at MEW, BGN and AYR stations using the first two dimensions of the between-station PCA. The cumulative year-to-year distance on the first two dimensions was calculated (Δtotal=Ʃ|di|, from i=2001 to 2013). At MEW and BGN, annual average distances were also compared for the periods before (Δbefore) and after (Δafter) the works on the sewerage systems were completed.
Key parameters highlighted a eutrophication gradient The dataset revealed several significant correlations (Table  2 ) (see Supplementary Material for the ranges of the environmental parameters). Temperature, salinity, O2, Si, DIN and NANO were weakly correlated with other parameters (ρ < 0. 5). Chl a concentrations were highly correlated with TP (ρ = 0. 82, n = 900), TN (ρ = 0. 70, n = 899), autotrophic picoeukaryote abundances (ρ = 0. 67, n = 886) and turbidity (ρ = 0. 66, n = 903). These five variables were significantly correlated with each other, demonstrating their link with the primary production process. Total phosphorus was significantly and much more strongly correlated with its inorganic forms DIP (ρ = 0. 61, n = 902) than total nitrogen with DIN (ρ = 0. 23, n = 901). The contribution of DIP to TP had a median value of 11.
This led us to average the dataset per year and per station for the five water quality parameters. Then, as the spatial pattern was stronger than the temporal pattern (percentages of total inertia of 52. 3% and 10. 2%, respectively, Table  3 ), between-station PCA was selected to identify an anthropogenic eutrophication gradient among the 22 sampling stations and to reveal trajectories of change over the study period. The first factorial axis of the between-station PCA (PC1, 81% of the between-station inertia) was highly correlated with Chl a, TN, TP, and to a lesser extent with DIP (Fig. 2a ). This axis ordinated stations according to a clear anthropogenic eutrophication gradient (Fig. 2b ). The second axis (PC2) accounted for only 11. 5% of the betweenstation inertia and was correlated with DIN (Fig.
Integrative parameters revealed a eutrophication gradient The rare dataset we used to monitor coastal lagoons whose status ranged from oligotrophic to hypertrophic  (Souchu et al. 2010; Bec et al. 2011) , allowed us to analyse eutrophication and oligotrophication phenomena in these ecosystems. Some of these lagoons received large inputs of urban nutrients for several decades, but major management actions have recently been implemented on the waste water treatment systems in most of their watersheds  (Meinesz et al. 2013; Gowen et al. 2015; Leruste et al. 2016). Fourteen years after the monitoring of French Mediterranean lagoons began  (Souchu et al. 2010 ), an anthropogenic eutrophication gradient is still highlighted by the water quality parameters. The five parameters selected in our study to identify the anthropogenic eutrophication gradient are representative of nutrient cycling in the water column of coastal lagoons as they incorporate either the causes (DIN and DIP), or the consequences of the eutrophication process (Chl a), or both (TP and TN, which were closely correlated with each other and with Chl a).
Acknowledgements This study was based on data collected during the projects " Réseau de Suivi Lagunaire " and  Water Framework Directive , which received financial support from  Ifremer ,  Agence de l'Eau Rhône Méditerranée Corse ,  Région Languedoc-Roussillon/Occitanie  and Cépralmar. The authors are extremely grateful to  Martine Fortuné  and  Grégory Messiaen  for the analyses of nutrients andto  Elise Hatey  for the analyses of chlorophyll a during all the years of the monitoring program; this article would not exists without their rigorous work in the laboratory. We thank our colleagues from Ifremer LERLR, LERPAC and  LERPAC-Corse ,  Laurent Benau  ( Narbonne Regional Park ), lagoon management structures for their help during field investigations. The authors would like to thank  Maela Kloareg  and  Claire Bissery/Kuzulia  for their help Derolez et al.
, 2019; Xiao et al. , 2018; Zhu et al. , 2015). In this context, atmospheric concentrations of specific sugar alcohols and/or primary monosaccharides (including glucose) have been previously quantified at sites in several continental, agricultural, coastal, or polar regions  (Barbaro et al. , 2015; Chen et al. , 2013; Glasius et al. , 2018; Li et al. , 2018; Pietrogrande et al. , 2014; Verma et al. , 2018; Wan et al. , 2019; Yan et al. , 2019; Yttri et al. , 2007). However, large datasets investigating their (multi)annual cycles and seasonal and simultaneous short-term variations at multiple spatialscale resolutions (i. e. , from local to continental) are still lacking  (Liang et al. , 2013; Nirmalkar et al. , 2018; Pietrogrande et al. , 2014; Yan et al. , 2019). Such records are essential to better understand the spatial behavior of primary sugar compound (SC) concentrations (i.
A large database on SC concentrations was obtained over France in the last decade. It already allowed the investigation of the size distribution and seasonal variabilities of SC concentrations in aerosols at 28 French sites, notably showing that SCs are ubiquitous primary aerosols, accounting for a significant proportion of PM 10 organic matter (OM) mass  (Samaké et al. , 2019). Results confirmed that their ambient concentrations display a well-marked seasonality, with maximum concentrations from late spring to early autumn, followed by an abrupt decrease in late autumns and a minimum concentration during wintertime in France. This study also showed that the mean PBOA chemical profile is largely dominated by organic compounds, with only a minor contribution of dust particle fraction. The latter result indicated that ambient polyols could most likely be associated with direct biological particle emissions (active spore discharge, microbiota released from phylloplane or phyllosphere, etc.
Sampling sites Daily PM 10 concentrations reported in the present work were obtained from different research and monitoring programs conducted over the last 6 years in France. Within the framework of the present study, we carefully selected sites sharing at least 1 complete year of concurrent monitoring with another one, to be representative of the annual variation cycles. The final dataset includes data from 16 sites, which are distributed in different regions of France (Fig. 1 ) and cover several main types of environmental conditions in terms of site topography, local vegetation, and climate. The characteristics and data available at each sampling site are listed in Table  S1  of the Supplement, together with the information on the annual average concentrations of aerosol chemical composition (Table  S2 ).
Detailed information on the sampling conditions can be found in  Samaké et al. (2019) , such as the campaign periods, number of collected PM samples, sampling flow rates, sample storage and handling, etc. Note that, the previous database  (Samaké et al. , 2019)  has been updated here with arabitol and mannitol in PM 10 collected at the suburban site of Nogent-sur-Oise for a series covering the years 2013 to 2017.
Trichoderma reesei cellulase (> 700 U g -1 , Sigma Aldrich) and Aspergillus niger glucosidase (> 750 U g -1 , Sigma Aldrich) were used as saccharification enzymes. The protocol is detailed in Sect. S2 in the Supplement. Field blank filters (about 10 % of samples) were handled as real samples for quality assurance. The present data have been corrected from field blanks. The reproducibility of the analysis of primary sugar compounds (polyols, glucose) and cellulose, estimated from the analysis of sample extracts from 10 punches of the same filters, were in the range of 10 %-15 %. The quantification limits of primary sugar compounds and cellulose ranged from 0. 63 to 0. 89 ng m -3. About 2800 samples are considered in this work for the polyols and glucose series, while 290 samples (from the sites of Grenoble_LF and OPE-ANDRA) are considered for the cel-lulose series.
A thorough discussion of the normalized cross-correlation method can be found elsewhere  (Bardal and Saetran, 2016; Dai and Zhou, 2017; Eisner et al. , 2009; Kaso, 2018; Lainer et al. , 2016; Le Pichon et al. , 2019). To achieve pair-wise correlation analysis between the sampling sites collected during the same periods, the original raw daily measurements were processed as follows: starting on identical days for each pairs of sites, the arrangement of the original daily data into consecutive 3 d intervals (or 6 d intervals in the case of OPE-ANDRA) and the calculation of the average concentration values for the middle day were performed. The resultant data were used for correlation analysis between site pairs (Table  S3 ). Multiple linear regression (MLR) was used to assess the strength of the relationships between atmospheric concentrations of particulate SC and local environmental factors including the daily mean relative humidity, nighttime temperature, average and maximum temperature, wind speed, solar radiation, rain levels, and LAI.
Because the LAI is a 4 d composite, daily values of the other variables were rescaled to consecutive 4 d averaged values. The linear regression (linear model or lm) package in R was employed for multiple regression analyses. The concentration data were log-transformed to obtain regression residual distributions as close as possible to the normal Gaussian one (Fig. S2 ). Stepwise forward selection was used to select the predictors that explain well the temporal variation in SC concentrations at the site of Marnaz. It should be noted that due to the limited availability of external parameters, the environmental factors driving SC atmospheric levels have been extensively investigated for only two monitoring sites with contrasted characteristics: the urban background site of Marnaz located in an Alpine valley, and the rural OPE-ANDRA site surrounded by field crop areas spreading over several tens of kilometers.
Example of spatial coherence of the concentrations at different scales Our previous work  (Samaké et al. , 2019)  showed that particulate polyols and glucose are ubiquitous primary compounds with nonrandom spatial and seasonal variation patterns over France. Here, an inter-site comparison of their short-term concentration evolutions has been carried out at different space scales (from local to national) for the pairs that can be investigated in our database. Figure  2  presents some of these comparisons for three spatial scales (15, 120, and 205 km). The daily average concentrations of polyols (defined as the sum of arabitol and mannitol) and glucose display highly synchronous evolutional trends (i. e. , homogeneity in the concentrations, the timing of concentration peaks, simultaneity of the daily specific episodes of increase or decrease in concentrations) over three neighboring monitoring sites located 15 km apart in the Grenoble area (Fig.
Multiple linear regression for ambient polyol and glucose concentrations and their effective environmental factors at the Marnaz site. Contributions of predictor are normalized to sum 1. The "Relaimpo package under R" was used to compute bootstrap confidence intervals for the importance of effective predictors (n = 1000)  (Grömping, 2006)  lease microorganisms along with plant debris into the atmosphere, strong air motions induce higher dispersion. These observations are in good agreement with those previously reported  (Jones and Harrison, 2004; Liang et al. , 2013; Zhang et al. , 2010 Zhang et al. , , 2015;; Zhu et al. , 2018b). For instance  Liang et al. (2013)  have found a negative correlation between wind speed and polyol concentrations, and the highest atmospheric fungal spores concentrations were observed for a wind speed range of 0.
EP, OF, and VR supervised the PhD of DMO, who investigated the sites in northern France. OF, JLJ, JLB, AA, and NM were the coordinators or partners of the different initial programs for the collection and chemical analysis of the samples. VJ developed the analytical techniques for polyol and cellulose measurements. TC performed the cellulose measurements. Sample analyses at LSCE were performed by NB. AC gave advice on the statistical aspects of the data processing. AS and JLJ processed the data and wrote up the paper. SW participated in the visualization of the results. SC supervises the OPE station and collected the agricultural activities records. All authors from AASQA (author affiliation nos. 9 to 16) are representatives of each network that conducted the sample collection and the general supervision of the sampling sites.