dataesr/scientific-paragraphs-categorization huggingface dataset:
Schutgens et al. (2020)  showed that shorter time separations greatly limited the number of pairs but did not substantially alter the correlation of satellite AOD with AERONET. On the other hand, longer time separations appear to negatively affect the correlation of satellite AAOD with AERONET, see Fig. 2. The analysis shows that satellite AOD correlation with AERONET Inversion data slowly decreases as the collocation criterium is relaxed from 3 to 24 hours. However, satellite AAOD shows a sharp drop in correlation with AERONET at 6 hours (OMAERUV is the exception, the correlation is low and barely changes). We surmise this is due to plumes of absorbing aerosol drifting over the sites, requiring tight temporal constraints on collocation. Consequences of this finding will be further discussed in Sect.
7. As the FL-MOC dataset, based on CALIOP measurements, is smaller than the other satellite datasets, we were compelled to collocate FL-MOC with AERONET within 2 o instead of 1 o. Even so, the data count for the FL-MOC evaluation is low and this results in significant statistical noise. After spatio-temporally collocating two or more datasets, the data may be further averaged in space and/or time for analysis purposes. Spatio-temporally averaged SSA is always derived from averaged AOD & AAOD: SSA = 1 -AAOD/AOD. (1) During the evaluation of products with AERONET, a distinction will be made between either land or ocean grid-boxes in the common grid. A high resolution land mask was used to determine which 1 o × 1 o grid-box contained at most 30% land (designated an ocean box) or water (designated a land box).
Taylor diagrams A suitable graphic for displaying multiple datasets' correspondence with a reference dataset ('truth'), is provided by the Taylor diagram  (Taylor, 2001). In this polar plot, each data point (r, φ) shows basic statistical metrics for an entire dataset. The distance https://doi. org/10. 5194/acp-2020-1207 Preprint. Discussion started: 7 December 2020 c Author(s) 2020. CC BY 4. 0 License. from the origin (r) represents the internal variability (standard deviation) in the dataset. The angle φ through which the data point is rotated away from the horizontal axis represents the correlation with the reference dataset, which is conceptually located on the horizontal axis at radius 1 (i. e. every distance is normalised to the internal variability of the reference dataset). It can be shown  (Taylor, 2001)  that the distance between the point (r, φ) and this reference data point at (1, 0) is a measure of the Root Mean Square Error (RMSE, unbiased).
In this study, the uncertainty bars in some figures were generated by bootstrap analysis. If the sampled error distribution is different from the true error distribution, bootstrapping will likely underestimate uncertainties. Sampled error distributions may be different from the true error distribution because the act of collocating satellite and AERONET data favours certain conditions. E. g. the effective combination of two cloud screening algorithms (one for the satellite product, the other for AERONET) may favour clear sky conditions and limit sampling of errors in case of cloud contamination. This uncertainty due to sampling is unfortunately hard to assess (see e. g. Schutgens et al. (  2020 )). As an example of uncertainty due to sampling, we present Fig. 3   tively high AAOD over the Rocky mountains, the Andes and Australia.
Evaluation of satellite products with AERONET Taylor plots of the performance of the satellite products are shown in Fig. 5. Satellite AOD is evaluated against AERONET Direct Sun L2. 0. Satellite AAOD & SSA, are evaluated against AERONET Inversion L2. 0 ( which constrains AOD at 440nm > 0. 4 and provides much less data than Direct Sun). All products show high correlation with AERONET AOD (r ≥ 0. 76), although the correlations found are lower than found in  Schutgens et al. (2020)  for several MODIS Aqua products (0. 87-0. 88). Correlations for AAOD and SSA are lower than for AOD suggesting that it is more challenging to retrieve absorptive qualities. Interestingly, POLDER-SRON's SSA correlates significantly better with AERONET than POLDER-GRASP-M's but this is a sampling effect: once both products are collocated, POLDER-GRASP-M's SSA correlation with AERONET increases from 0.
41 to 0. 69. The impact of statistical noise on the AAOD evaluation is explored in Fig. 6. Using a bootstrapping technique, the spread in correlation and standard deviation were explored. For most datasets, the results seem fairly robust, except for FL-MOC which uses only 24 data points. A proper intercomparison of products requires collocation (of all the satellite data), which reduces available cases even further. Figure  S2  shows that results are not very different from Fig. 5 , but the statistical noise increases substantially. The sampling noise on such a small subset should be even larger, see also Fig. 3  and  Schutgens et al. (2020). For a sense of perspective, 53 data points represents less than 0. 0008% of the total POLDER-GRASP-M data amount used in this paper.
Evaluation and intercomparison of AOD In Fig. 7 , we provide more detail on the satellite AOD products and their evaluation against AERONET Direct Sun L2. 0 AOD. In the central column, we show the products themselves, averaged over several years. Note that the products exist for different years and even within the same years have different samplings so comparisons should be made with caution. In the left and right column, we show satellite data collocated with AERONET. On the left-hand side is a scatterplot of the raw data (with https://doi. org/10. 5194/acp-2020-1207 Preprint. Discussion started: 7 December 2020 c Author(s) 2020. CC BY 4. 0 License. associated statistics provided) and on the right-hand side is a map of multi-year difference with AERONET (provided at least 32 data points were available per site).
The scatter plots show good correlation with AERONET. The POLDER products show higher correlations and slopes closer to 1 (one) than FL-MOC and OMAERUV. Nevertheless, differences in evaluation seem rather small, which unfortunately cannot be said for the global distributions of AOD. POLDER-GRASP-M has rather high AOD over land and OMAERUV has rather high AOD over ocean (note that the satellite data themselves are not collocated). The multi-year differences with AERONET suggest that OMAERUV overestimates everywhere except in some regions with strongly absorbing aerosol. An intercomparison of satellite AOD with Aqua-DT is presented in Fig. S3  and suggests typically higher estimates over (Southern Hemisphere) Land for the POLDER products and over Ocean for OMAERUV. Note that Aqua-DT is not without significant regional biases, see  Schutgens et al.
(2020). Figure  8  shows results when bias (sign-less) and correlation per site are averaged over all sites, for all satellite products. The same 52 sites are used for all datasets although each product is individually collocated with AERONET. For FL-MOC, no site provided at least 32 observations and it is not included in the analysis. For POLDER-SRON, only 18 sites provided at least 32 collocated observations and was similarly excluded. As was also shown in  Schutgens et al. (2020) , OMAERUV shows rather large biases compared to the other AOD products. POLDER-GRASP-M, on the other hand, shows the smallest bias. The filtering of GRASP retrievals described in Sect. 2. 1 plays a significant role in this result (without filtering, POLDER-GRASP-M shows a bias twice as large).
Bootstrap analysis suggest that results are fairly robust against statistical noise (except FL-MOC, see also Fig. 6 ). The right-most column in Fig. 9  shows SSA difference as a function of (AERONET) AOD. To ensure the largest possible range in AOD values Inversion L1. 5 instead of L2. 0 is used. Especially at lower AOD, this dataset will have larger errors in AAOD and SSA than L2. 0. Interestingly, as AOD increases, all satellite products seem to agree better with AERONET (for FL-MOC, the bin with largest AOD values is affected by a very low data count). This is of course as one would expect. For smaller AOD, there is increasingly more spread although the difference distribution remains fairly unbiased. The exception is POLDER-GRASP-M which shows increasingly lower SSA than AERONET at low AOD.
OMAERUV appears to show the largest deviations from AERONET (low correlations and slopes) but its overall error statistics (mean and standard deviation) is not too different from the other products. Results for FL-MOC may be a statistical fluke due to the low data count. POLDER-GRASP-M shows quite high correlations for AOD (0. 86) and AAOD (0. 6) with reasonable slopes but has a very low correlation with AERONET for SSA (0. 41), but this seems to depend strongly on sampling as discussed at the start of this Section. In addition, it appears to systematically underestimate SSA at low AOD. Yet another aspect to this dataset (not visible in any of the analysis shown) is that it appears to have hard cut-off as SSA values larger than 0. 99 do not occur. A profound problem is the paucity of data.
Thirdly, we look at POLDER SSA statistics that are collocated with AERONET observations. Figure  10  shows the associated difference distributions. Using various non-parametric statistical tests (Mann-Whitney U, Student's t, Kolmogorov-Smirnov) we can show that the distribution means for the first and third sampling are fundamentally different. Not only that, but the mean difference in SSA for the first sampling is 2. 6 as large (-0. 043 vs. -0. 017) as for the third sampling (and is statistically significant). As POLDER-SRON is biased high and POLDER-GRASP-M is biased low vs AERONET, the corrollary to this is of course that at least one of the products has a larger bias vs the truth globally than can be seen in the AERONET observations. Conversely this suggests that the AERONET Inversion dataset does not allow a truly global evaluation of satellite datasets: it provides a sub-sample with skewed statistics of SSA.
Incidentally, it is the temporal subsampling enforced by collocation with AERONET observations that causes the largest shift in the difference distribution (POLDER measurements over AERONET sites show a similar SSA distribution as the global dataset). It is possible that the SSA difference is partly driven by cloud contamination which we know is present in these satellite datasets  (Schutgens et al. , 2020)  and may be ameliorated when a third cloud masking (from AERONET) is applied (through the collocation of data).
Maps of the SSA difference between the POLDER products as a function of minimum AOD can be seen in Fig. S5. A higher minimum AOD mostly constrains data to a smaller portion of the globe but does not affect local biases greatly. In this study, we evaluate several remote sensing datasets of AAOD and SSA, from a variety of sensors (CALIOP on CA-LYPSO, OMI on Aura, POLDER on PARASOL), and use them to evaluate AEROCOM models. This is the first global study to intercompare satellite remotely sensed products of AAOD (and SSA). The evaluation of the products (daily aggregates over 1 o × 1 o ) is done through comparison with AERONET Direct Sun (AOD) and Inversion (AAOD and SSA) observations. To minimize sampling issues, satellite products and AERONET data are collocated in time and space, within 3 hours and 1 degree.
The most prominent example is a much reduced systematic difference (-0. 017) between POLDER-GRASP-M and POLDER-SRON SSA as seen in an evaluation with AERONET Inversion L2. 0 observations, than is present in the global satellite dataset (-0. 04). This suggest that biases inferred from an AERONET evaluation will be smaller than those actually present in the satellite products. It will not be easy to increase Inversion L2. 0 observations due to the technical limitations in the sensors  (Sinyuk et al. , 2020). However, an alternative L1. 5 product with similar cloud screening as L2. 0 might go a long way, especially if follow-up studies can show that inversion errors at individual sites behave as random errors (amenable to temporal averaging) and not systematic biases. This paper is one part of a two paper study into the use of satellite AAOD and SSA for aerosol model evaluation.
In its companion paper, we use the datasets introduced in the current paper to evaluate AEROCOM (AEROsol Comparisons between Observations and Models) models. It turns out that robust and consistent evaluation of the models is possible, notwithstanding the biases in the satellite data we have detailed in the current paper. The main reason seems to be that model biases (and the diversity in those biases) are even larger than satellite biases. Hence these satellite AAOD and SSA products are very useful: in regions with AERONET sites, they provide spatial detail lacking in a surface network; in regions without AERONET sites, they are the only datasets of observed AAOD and SSA available. locations and are called collocated datasets. By choosing a larger temporal extent of the grid-box, the collocation criterion can be relaxed.
AOD study This study As the super-observations are on a regular spatio-temporal grid and collocation requires further aggregation to another regular but coarser, grid, the whole procedure is very fast. It is possible to collocate all 7 products from afternoon platforms over three years using an IDL (Interactive Data Language) code (that served as a prototype for CIS) and a single processing core in just 30 minutes. This greatly facilitates sensitivity studies. Starting from super-observations, a 3-year average can easily be constructed by once more performing an aggregation operation but now with a grid-box of 3 yr × 1 o × 1 o. If two collocated datasets are aggregated in this fashion, their 3-year average can 750 be compared with minimal representation errors. This allows us to construct global maps of e.
Acknowledgements. We thank the  PI(s) and Co-I(s)  and their staff for establishing and maintaining the many AERONET sites used in this investigation. The figures in this paper were prepared using  David W. Fanning 's Coyote Library for IDL. The work by  N. Schutgens  is part of the  Vici research programme  with project number  016. 160. 324 , which is (partly) financed by the  Dutch Research Council (NWO). NS thanks  Tom Eck ,  Greg Schuster  and  Kostas Tsigaridis  for insightful discussions on the use of AERONET observations.
PCR Product Sequencing The positive PCR products of E. coli pathotypes, Cryptosporidium spp. , and Giardia spp. were then sequenced in Functional Biosciences (Madison, WI, USA) using a Big Dye Terminator V3. 1 and run-on ABI 3730xl instruments. Forward and reverse sequence segments were overlapped using PreGap4 and Gap4 (Staden Package, Rodger Staden's group, Cambridge, England), and the primer sequences were removed. Elucidated nucleotide sequences were compared to the GenBank collection of sequences using the Standard Nucleotide of Basic Local Alignment Search Tool (BLAST). The accuracy of the data was based on the degree of sequence identity.
Statistical Analysis The information obtained from the microbial, physico-chemical, and metal analysis of the collected samples was evaluated by using the statistical software package SPSS version 23. 0 (IBM Corp, 2013; Armonk, NY, USA). Several linear and multiple regressions were performed between the concentration of E. coli and total coliforms, physical-chemical parameters, and major and trace element concentrations. In all the hypothesis tests, a significance level of 5% was used as the standard. In all tests, a p-value < 0. 05 was considered to be statistically significant.
Detection of Microbial Genera, Candida Albicans, and Escherichia coli Pathotypes Molecular analysis was conducted by PCR to confirm the presence or absence of the following microbial genera: Legionella, Pseudomonas, Salmonella, Shigella, Cryptosporidium, and Giardia. Concerning parasites genera, three rivers showed the presence of Cryptosporidium spp. , which were the following, Mindo, Pisque, and Alambi Rivers. Meanwhile, eight rivers showed the presence of Giardia spp. , more precisely, the Machángara, San Pedro, Monjas, Blanco, Mindo, Pisque, Pilatón, and Guachalá Rivers. Sequencing and comparison of these products to the GenBank nucleotide collection using BLASTN did not produce any significant alignments for Cryptosporidium spp. ; however, the positive products for Giardia spp. showed 100% homology to Giardia intestinalis (syn.
However, Candida albicans was only detected in three rivers by PCR: the Pita, Monjas, and Blanco Rivers. Furthermore, the detection of four E. coli pathotypes was done for all the analyzed rivers: enteroaggregative E. coli (EHEC), enteropathogenic E. coli (EPEC), enteroinvasive E. coli (EIEC) and enteroaggregative E. coli (EAEC). Our analysis showed the EIEC pathotype as the most prevalent pathogen in the study set, illustrating positive results in the Machángara, Guayllabamba, and Monjas Rivers. Meanwhile, EHEC and EAEC were each only detected in one river individually, more precisely, the Monjas and Machángara Rivers, respectively. Sequencing and comparing these products to the GenBank nucleotide collection using BLASTN did not produce any significant alignments for these pathotypes due to the low size of the consensus sequences and a great number of gaps.
Acknowledgments: We thank the volunteers that made this study possible, and also the people who work in the  Institute of Microbiology of USFQ , in particular  Christina Álvarez  who helped in the development of the analysis in the laboratory. We also thank  Izan Chalen  for creating the map for this study. Finally, we would like to thank the  Laboratory of Environmental Engineering at USFQ (LIA-USFQ ) for metal analysis (ThermoScientific iCAP 7400 ICP-OES) and  Universidad San Francisco de Quito  for financial support.
A simple mass balance gives the fraction of dissolved 232 Th in seawater coming from the dissolution of particulate  Th 35 (Roy-Barman et al. , 2002) : This estimate is independent on the concentration data that may be biased by readsorption. We evaluate fdissol_isot based on average ratios of the C series for original seawater and the average ratio of the last samples of the D and G treatments to integrate dissolution over the course of the whole experiment (Tab. ES3). For the particulate ratio, we tentatively 5 used a ratio of 8. 5×10 -6 mol/mol (value best defined by the y-axis value for 1/ 232 Th = 0 of the dissolution and scavenging trends at ION, Fig. 5 ). 𝑓 𝑙𝑖𝑡ℎ𝑜 = ( 230𝑇ℎ 232𝑇ℎ ) 𝐷 𝑜𝑟 𝐺 _ ( The resulting average fdissol_isot are below 3% for FAST D, FAST G, ION D and ION G, confirming the low solubility of Th (Tab.
Acknowledgements This study is a contribution to the  PEACETIME  project (http://peacetime-project. org), a joint initiative of the MERMEX and ChArMEx components supported by  CNRS-INSU ,  IFREMER ,  CEA , and  Météo-France  as part of the 5  MISTRALS program  coordinated by  INSU  (PEACETIME cruise https://doi:  10. 17600/17000300 ). All data have been acquired during the PEACETIME oceanographic expedition on board R/V Pourquoi Pas? in May-June 2017. PEACETIME was endorsed as a process study by GEOTRACES. M. B was funded from the  European Union   Seventh Framework Program  ([FP7/2007-2013]) under grant agreement no. [PIOF-GA-2012-626734] ( IRON-IC  project)). We thank the captain and the crew of the RV Pourquoi Pas? for their professionalism and their work at sea. 10 https://doi. org/10.
Data availability Underlying research data are being used by researcher participants of the "Peacetime" campaign to prepare other 30 manuscripts and therefore data are not publicly accessible at the time of publication. Data will be accessible (http://www. obs-vlfr. fr/proof/php/PEACETIME/peacetime. php, last access: 22 June 2020) once the special issue is completed (all papers should be published by fall 2020). The policy of the database is detailed here http://www. obsvlfr. fr/proof/dataconvention. php (last access: 22 June 2020).
Water resources and environment quality are nowadays under high pressure because of climate change, land use practices, as well as human actions. A comprehensive gridded dataset becomes a necessary instrument to assess the risk level at regional scale, and also for territorial planning, the defining strategies to address future natural and anthropological challenges. In order to obtain a complete database with the most important parameters at spatial scale, this study is constructed as a preparation of layers used for various environmental risks, but mostly with the climate change effect on the water resources from the Grand Est region, France. In addition, geological formations, terrain data, and land cover were harmonized as grid format for the study area. Thus, the temperature and precipitation parameters, related to the 1961-1990 (1990s), 2011-2040 (2020s),  and 2041-2070 (2050s), become useful data for evapotranspiration, water availability, and effective precipitation calculations.
In order to support the preservation of the environment and create a holistic image of the geographical region, the objectives of this work are focused to calculate the main parameters that influence the water resources in the Grand Est region and to integrate the geospatial data at 1 km 2 of these parameters for long-term periods of 1990s, 2020s, and 2050s. The product is important for the assessment of climate change effects on the water resources, groundwater vulnerability determination, and future plans of water management. Through this study, the drought areas and high land use impact from the Grand Est region could be easily identified and used for advance analyses. The complexity of the survey is much consistent with integration of the terrain morphology and geology. This dataset is prepared for a risk assessment framework and it provides both spatial and temporal scales, especially for past, current, and mid 21st century.
Climate Data The temporal frames used in this study for the climate data are 1961-1990 (for the 1990s), 2011-2040 (for the 2020s), and 2041-2070 (for the 2050s). The annual temperature, precipitation, and ET0 data were processed at high-spatial resolution (1 km 2 ). The main reason for the climate data analysis in such an investigation on water resources and risk is that the aridity-humidity status of the area is directly correlated with the water availability. In addition, the climate data and water availability variation in time over the Grand Est region is closely related to the assessment of risk of runoff leading to flooding, and the risk of infiltration, important for civil engineering projects and for predicting landslides and slope instability. Both aridity index-here De Martonne Aridity Index (Figure ??j-l)-and water availability (Figure  ??m-o ) were prepared at 1 km 2 resolution for the Grand Est region.
Terrain Data and Potential Infiltration Map The digital elevation model (DEM) of the Grand Est region was used for the slope angle and potential infiltration map calculation. The Rhine Valley in the eastern part and the Marne and Aube Valleys in the western part represent the lowlands in the Grand Est region. The highest elevations are recorded in the south-east side of the region, where the Vosges Mountains are present. The altitudes in the study area vary from 51 m to 1298 m above sea level (Figure ??e), thus the relief morphology influences greatly the runoff flow. By combining relief morphology and lithology, the groundwater flow directions and accumulation could be estimated at spatial scale. The DEM layer is important in water resources and environmental risks studies, not only because of the influence of variations of relief topography and slopes' angle gradient on the water flow direction and infiltration control, but also because of their role in flooding, flow accumulation, and slope stability.