dataesr/scientific-paragraphs-categorization huggingface dataset:
The interest and contribution of the network partners have enabled the database system to constantly grow, and new modules for accommodation of passive sampling, indoor environment, bioassays monitoring, antibiotic-resistant bacteria (ARBs), and antibiotic-resistant genes (ARGs) data are now under development. Besides that, the ECOTOX module already contains predicted and experimental PNECs for more than 13,000 substances and its sustainable growth is one of the priority tasks of the NORMAN network. The trend is clearly towards encouraging data sharing, improving access and use of available data along with improvement of their quality. The value of the NORMAN platform is fully recognized by the European Commission, with which NORMAN has recently started a close collaboration to achieve permanent integration of EMPODAT in IPCHEM  [4].
Evolution towards "big data" management: from hundreds to tens of thousands of candidate substances We are increasingly aware that there is a need to evolve towards a system able to deal with several thousands of compounds which may enter the environment. NORMAN is already working to replace the original "NORMAN list of emerging substances" of about 900 compounds with a much larger list of substances. The NORMAN Suspect List Exchange database (SusDat) database  [29]  has recently been launched and already includes more than 40,000 compounds as a common effort of European and North American researchers. All suspect lists currently available in SusDat can be viewed at the NORMAN website and are being progressively integrated into the US EPA CompTox Chemistry Dashboard  [22]. This large list will become the new "universe" of compounds for prioritization, and the NORMAN List will be defined as the list of top priority compounds in each prioritization action category.
New tools to improve future monitoring and regulation of CECs Non-target screening In line with the strong expertise of the NORMAN network in the field of high-resolution mass spectrometry techniques and NTS approaches, several activities have been launched over the past years and continue to be promoted to improve harmonization of liquid chromatography coupled to high-resolution tandem mass spectrometry [LC-HR-MS(MS)] and gas chromatography coupled to mass spectrometry (GC-MS) NTS protocols, in connection with the use of structure elucidation and pollution pattern recognition tools. Besides the Suspect List Exchange database  [29]  and the "Digital Sample Freezing Platform"  [39] , the NORMAN MassBank database  [40, 41]  was created in 2011 as an open-access database of mass spectra which now contains spectra of more than 1000 environmental contaminants to support the identification of "unknowns" (i.
Such data sets may need to be developed at the European level and there is an opportunity for NORMAN members to contribute to federating national on-going initiatives (such as the large demonstration project organized by AQUAREF in France in 2018-2019), to similar studies in other European countries. This would facilitate the knowledge exchange and harmonization of methodology for better comparability of data at European scale. To allow the use of passive sampling data for regulatory monitoring, it is also important to prepare the basis for archiving the generated data in appropriate databases in a harmonized format. Here, the contribution of NOR-MAN experts has resulted in harmonized guidelines for reporting of data obtained by passive sampling tools [NORMAN Data Collection Templates (DCTs)], which is expected to facilitate the wider exchange of monitoring data obtained with passive samplers  [43].
Based on these standardized DCTs, a prototype online database module for passive sampling data has recently been developed and tested with JDS3 data  [56]  within the SOLUTIONS project  [57]. Additional perspectives arise when considering the opportunities offered by the combination of passive sampling and non-target and suspect screening procedures. Relatively little work has been undertaken in this area until now. A suitable choice of polymer and extraction protocol can enable the scientist to pre-concentrate chemicals from a complex matrix while leaving behind a significant proportion of unwanted matrix affecting the performance of the analysis. This is especially relevant for complex matrices such as sediments, sludge, or biological matrices. Passive sampling of air, sediments, and water is amenable to non-target approaches, and novel applications for sampling of biota  [58, 59]  or to further our understanding of the human exposome are highly promising  [60].
Today, there is a consensus that reclaimed wastewater releases antibiotic-resistant bacteria and their genes. There is, therefore, an urgent need for better understanding of the presence and fate of micro-contaminants promoting the widespread of antibiotic-resistant bacteria and genes in wastewater treatment plant (WWTPs) effluents before their disposal or further reuse  [64]. In response to these needs, a new NORMAN activity kicked off in 2013  [65]  with the commitment to work on: (1) the definition and establishment of a harmonized protocol for measurement of antimicrobial resistance; (2) the development of a European database to compile information on the overall abundance and diversity of different genetic determinands in wastewater effluents and receiving environments; and (3) the drafting of recommendations to the European Commission  [64].
Such a baseline and a better process understanding (and corresponding models) will help to assess the potential risk of antibiotic resistances in the aquatic environment and water reuse. Through its activities and collaborations with other relevant EU-funded projects (NEREUS  [67]  and ANSWER  [68] ), NORMAN developed in 2017 a new Data Collection Template used as a basis for a new EMPODAT database module concerning ARBs and ARGs which will be fed by this project.
Indoor environment There is potential for extending the scope of NORMAN activities to other environmental matrices and compartments (air, sediments, biota, etc. ). Indoor environment appears as a relevant key domain for NORMAN's missions when looking at the concerns associated with emerging contaminants in human matrices. Articles and consumer products used indoors may contain a variety of both well-known chemicals and emerging substances  [69] [70] [71]. Chemicals are emitted in the indoor environment and indoor air and dust is an important pathway of chemical exposure for humans. A new NORMAN activity for the indoor environment was launched in 2014 aimed at identifying CECs for the indoor environment and at storing respective data in a harmonized way in EMPODAT. Measuring goes along with prioritization of relevant compounds in the indoor environment, the identification of emission sources of CECs, and relevant exposure pathways.
Strategies for prioritization of CECs indoors are currently being discussed and a subgroup for this task has recently been formed, in connection with the already operational NORMAN Prioritization Working Group. The generation of high quality and comparable monitoring data-still scarce and highly scattered in the indoor environment-and minimum quality requirements for their harmonized storage in a common database is crucial to support prioritization activities. Thanks to NORMAN activities, a new Data Collection Template with relevant metadata for indoor air and dust has been developed for the indoor environment module of the NORMAN EMPODAT database. Finally, NORMAN is committed to improving harmonization of sampling protocols for dust and air. The NOR-MAN indoor environment working group made a first inventory of sampling protocols used to collect indoor dust and air.
EU Commission DG ENV According to DG ENV, the tools developed by NOR-MAN are useful to the Commission services and to the Member States. NORMAN has contributed significantly to the European prioritization process of the WFD with unique data sets (15% of the monitoring data used in the review process for the EU priority substances have been retrieved from the NORMAN EMPODAT database). However, challenges still remain for representative monitoring data of sufficient quality and for more holistic monitoring approaches. J. Romero (DG ENV) highlighted the following four main challenges. As regards river basin-specific pollutants (RBSP), the efforts of the Member States clearly indicate that there is a need for improved and more comparable approaches between countries, in terms of both identification and monitoring of RBSP.
National agencies: two examples NORMAN in support of environmental legislation The feedback from AFB in France was that NOR-MAN helps water managers. The added value of NOR-MAN for national activities is that NORMAN draws together expertise from across the EU and beyond, and promotes synergies across research teams: this adds significant value to the CIS in support of the WFD. Furthermore, NORMAN's strategic focus and its ability to help expertise and data sharing stimulate In France, national authorities adopted NORMAN products to develop the national strategies for water management. The mechanism currently used in France for the national review of the list of River Basin-Specific Pollutants (RBSPs) and the launch of regular screening studies on emerging substances is based on the principles of the NORMAN prioritization scheme.
The need for monitoring data in chemicals legislation The positive impact of NORMAN as regards the generation of environmental occurrence data was addressed by the German Environment Ministry with reference to the case of biocides in the aquatic environment and the need to improve the legislation for safe marketing of biocidal products. In 2016, NORMAN organized a workshop on environmental monitoring of biocides in Europe  [74]. Monitoring data can tell us whether there are shortcomings in the authorization procedure, and whether risk mitigation measures are designed in a reasonable manner. They can also serve as a means to better focus surveillance and control measures. As yet, far less data for biocides in the environment are available in comparison with plant protection products and pharmaceuticals.
However, for many substances, there are no-or only insufficient-monitoring data. New analytical techniques such as NTS are likely to generate much more chemical monitoring data in the future. A wider picture of contaminants in the environment will become a challenge for environmental legislation, e. g. , the WFD and chemical legislation such as REACH and the overarching Commission's 2018 Strategy for a nontoxic environment  [15]. NORMAN is establishing collaborations with EU regulatory bodies, e. g. , ECHA. A reliable assessment of chemicals of emerging concern in environmental media requires exposure data to be linked with information on marketed substances. Following this line, it is important to establish a mutual data exchange between NOR-MAN databases and IPCHEM. Some of the data that have been generated (e.
g. , produced by research studies) are still kept in databases with restricted access, so that they cannot be used for assessment of occurrence levels. For data produced with public funds, there should be a mechanism to make them available to the public authorities and institutional bodies by default. Furthermore, the example of per-and polyfluorinated alkyl substances (PFAS) indicates the need to look beyond the list of registered substances: only 5% of PFAS have a CAS Registry Number as a unique, unmistakable identifier for chemical substances. Novel analytical techniques such as NTS can identify the presence of these compounds in the environment. There is still a need to better investigate individual chemicals of emerging concern and their transformation products by developing analytical methods to determine occurrence of these compounds in the environment; understanding how they may be released or formed; and identifying their potential environmental effects.
Acknowledgements. This work has been supported by the  French Programme National de Télédétection Spatiale  (PNTS, http://www. insu. cnrs. fr/pnts,last access:  21 June 2018 ), grant no. PNTS-2014-02  and by the  Centre National d'Etudes Spatiales (CNES). The authors are very grateful for the interesting and pertinent comments made by the reviewers. They contributed to greatly improve the manuscript. Edited by: Piet Stammes Reviewed by:  Zhibo Zhang ,  Feng Xu ,  Gerard van Harten , and one anonymous referee.
Methods Imagery data were gathered from the following sources: Landsat-7 satellite from Earth Explorer, Land Information New Zealand aerial imagery, and Sentinel 2A satellite imagery from Earth Explorer. Three spectral bands (red, green, and blue) were used in the land use classification (see Table  1 ). Other studies have used Landsat imagery of this resolution to map changes in land cover over time, setting a strong precedent for its use in this study  (Peterson & Aunap 1998; Seto et al. 2002; Li & Yeh 2004; Hansen et al. 2013). Although the satellite imagery varies in its resolution, the scale at which land use changed was large, making it easily detectable. Additionally, although studies have found that 1 m resolution imagery is ideal, 30 m resolution imagery is still relatively accurate for land use change of this magnitude (see  Fisher et al.
g. size distribution, particle type) and field observations to estimate atmospheric deposition fluxes  (Mahowald et al. , 2005; Zender et al. , 2003; Zhang et al. , 2015). Often, modelling approaches simulate atmospheric deposition fluxes more accurately in regions downwind from the main aerosol sources  (Huneeus et al. , 2011; Wagener et al. , 2008)  because the aerosol characteristics, due to extensive datasets in these regions, are better constrained in proximity to their source. Modelled atmospheric deposition fluxes rely on satellite-derived climatologies which suffer from interferences from cloud coverage. In this manuscript we present surface ocean dAl concentration data for the Atlantic Ocean and use these to calculate atmospheric deposition fluxes using the MADCOW model  (Measures and Brown, 1996).
Baker et al. (2013)  provide a good spatial coverage of the Atlantic Ocean, thus allowing us to assign values of Al fractional solubility for different biogeochemical regions (Figure  1 ) based on field observations. However, the aerosols Al fractional solubility might not be representative over an annual timescale due to, for example, the pulses of Saharan dust and where the mineral dust falls which is related to the position of the ITCZ. Yet, it is still the largest dataset on aerosol Al fractional solubility over the Atlantic Ocean. We produced a single weighted averaged aerosol Al fractional solubility percentage value for each area of interest based on the Al fractional solubility for each air mass to the relative contribution of that air mass to a certain area (Table  S3 ). Furthermore, we assumed that the chosen Al solubility was representative for the whole annual cycle as our atmospheric aerosol deposition flux estimates are given in g m -2 y -1.
Al residence time in the surface mixed layer The residence time is defined as the ratio of the dAl inventory in the surface mixed layer to the rate of input or removal. Residence times of dAl in the surface mixed layer were obtained from  Han et al. (2008)  and were derived using model simulations using the Biogeochemical Elemental Cycling (BEC) ocean model  (Moore et al. , 2004). The dAl residence times provided by  Han et al. (2008)  take into account Al sources to the surface mixed layer from atmospheric deposition, advection, and mixing. We have chosen the residence times from  Han et al. (2008)  as they are based on an extensive surface water dAl observational database, including a total of 41 research expeditions (22 in the Atlantic Ocean) up to the year 2002. The MADCOW model used in our study does not account for advection and mixing processes which, for example, occurs in equatorial regions as waters of higher dAl concentrations are advected into low dAl concentration waters  (van Hulten et al.
of the MADCOW model has limitations (i. e. tropical Atlantic Ocean). In these regions, more work is needed to constrain parameters such aerosol Al fractional solubility and the residence time of Al within the surface mixed layer. Realistic residence time values would significantly improve our atmospheric deposition flux results. We suggest that forthcoming expeditions should use more than one technique to calculate atmospheric deposition fluxes as different methods provide us with complimentary information. The new Intermediate Data Product from the GEOTRACES Programme along with upcoming dAl datasets will give us a better coverage of under-sampled regions which combined with historical data will help us to refine atmospheric deposition fluxes (i. e. capturing seasonal and inter-annual variability).
Acknowledgments We are greatly thankful to the captains and crews of expeditions  GA01 ,  GA06 ,  GA08  and  GA10  for their support during the cruises. We would like to give a special thanks to  Pierre Branellec ,  Michel Hamon ,  Catherine Kermabon ,  Philippe Le Bot ,  Stéphane Leizour ,  Olivier Ménage ,  Fabien Pérault  and  Emmanuel de Saint Léger  for their technical expertise during GA01 clean CTD deployments and to  Catherine Schmechtig  for the GA01 database management. Greg Cutter  is also strongly acknowledged for his help in setting up the new French clean sampling system. A special thank goes to the trace metal colleagues on board  GA01 ,  GA06 ,  GA08 , and  GA10. This work was funded by a PhD fellowship to  Jan-Lukas Menzel Barraqueta  from the  Department of Scientific Politics of the Basque Government , with further financial support from  Biogeosciences Discuss.
Freshwater fish No A graded study reports BMAA in freshwater fish and only data from B graded studies are reported here. A single carp was caught in the Mascoma Lake (New Hampshire), a lake associated with a high ALS incidence in the population living nearby  [51]. The concentrations of BMAA and DAB (total fractions) in the carp brain was 0. 086 and 0. 002 mg kg -1 FW, respectively. BMAA was also detected in the carp liver (0. 26 mg kg -1 FW) and muscles (0. 25 mg kg -1 FW), but not DAB (Table  3 ). The BMAA contents patterns in the brain, muscle, liver and kidney of several fish species from different trophic levels (e. g. , plankti-benthivorous and piscivorous fish) were assessed in the Swedish Finjasjön Lake suffering from major blooms of cyanobacteria  [48]. Authors reported a BMAA concentration varying from 0.
00002 to 0. 0016 mg kg -1 FW in the muscle of 16% of the fish (bream, perch, pike, pike-perch, roach, ruffe and tench, with higher percentage for bream feeding on benthic preys) (Table  3 ). (   No acidic extraction (6 N HCl) is mentioned in the material and method section. (2) Limits of detection/quantification determined for matrices other than water; sb = soluble bound; pb = precipitated bound; f = free; ts = total soluble; t = total; NA = data not available; NQ = not quantified; ND = not detected; DW = dry weight; WW = wet weight; Micro = microalgae-dominated fraction of the seston; Zoo = zooplankton-dominated fraction of the seston. In µg g -1 DW fBMAA 0. 2-1 tBMAA 2. 5-20 - [21] A Lagoon periphyton France tsBMAA 1. 3-4. 3 µg g -1 DW tsAEG ND-0. 8 µg g -1 tsDAB 1. 3-4. 8 µg g -1 DW fDAB: mean 0.
8-352. 2 (15/16) (In mg kg -1 DW) tAEG (5) 1298. 4-1765. 1 (16/16) (In mg kg -1 DW) etDAB (5)  (1) Except for Mondo et al. [55]  whose results are reported in mg kg -1 DW. (2) LODs and LOQs determined for a crayfish matrix. (3) LODs and LOQs determined for a sea hare matrix. (4) LODs and LOQs determined for a mussel matrix. (5) Two methods were used in this study but only samples analyzed by UPLC-MS/MS are considered here. Sb = soluble bound; pb = precipitated bound; f = free; ts = total soluble; t = total; NA = data not available; NQ = not quantified; ND = not detected; DW = dry weight; FW = wet weight. Table  3. Concentrations (mg kg -1 FW) (1) of free, bound or total BMAA and isomers (DAB, AEG) in edible parts of aquatic organisms reported in B graded studies. When concentrations were reported in mg kg -1 DW by authors, an estimate in mg kg -1 FW was calculated.
(1 Two methods were used in this study but only samples analyzed by UPLC-MS/MS are considered here. (2) Sum of values for hydrolysed supernatant and pellet. Sb = soluble bound; pb = precipitated bound; f = free; ts = total soluble; t = total; NA = data not available; NQ = not quantified; ND = not detected; DW = dry weight; FW= wet weight.
The absence of total BMAA was also reported for salmon (Salmo salar), cod (Gadus morhua) and perch (Perca fluviatilis) from trade  [50]. However, some total BMAA was quantified in plaices Pleuronectes platessa (in all three samples) from Sweden and in herrings Clupea harengus (one out of three samples) from Baltic Sea, but at low concentrations, close to the LOD and LOQ (from <LOD to 0. 02 mg kg -1 FW)  [50]. Among marine taxa, the BMAA content appears to be much higher in sharks to date. Total BMAA concentrations between 19. 2 and 33. 15 mg kg -1 FW were reported in fins and muscle of four shark species  [59]. From this study, we only considered data of quantification performed by ultra-pressure liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) and not the one acquired by liquid chromatography with fluorescence detection (HPLC-FLD), the latter technique being less specific.
Discussion This systematic review only reports data on the occurrence of BMAA, DAB, and AEG in aquatic environments (water or phytoplankton samples) and in aquatic animal species of the food web (sampled in situ), extracted from original papers (no review or book chapter) and written in English. A drastic selection among studies was performed according to strict qualitative and quantitative criteria concerning the chemical analytical method used. The studies using LC-MS/MS and satisfying both the qualitative (e. g. , retention time, ion ratio, specific transitions, and LOD) and quantitative (e. g. , LOQ, recovery of the method, and use of an internal standard) criteria were graded A, those satisfying only the qualitative criteria were graded B, and those that do not fulfil any of the above criteria or do not provide sufficient information to estimate the validity of the method were graded C.
The Strategy for the Extensive Literature Search The literature review was performed according to the EFSA systematic review guidance  [74]. The literature databases Scopus and PubMed were consulted to retrieve the studies reporting the occurrence of BMAA and its isomers DAB, AEG and BAMA in environment and aquatic foods. The search was carried out in English with the entire and abbreviated names of BMAA and isomers connected by the Boolean connector "OR": "BMAA" OR "β-N-methylamino-L-alanine" OR "DAB" OR "2,4-Diaminobutyric Acid" OR "AEG" OR "N-(2-aminoethyl)glycine" OR "β-amino-N-methyl-alanine" OR "BAMA. The entire names or abbreviations of the toxin and its isomers were combined with the words describing the matrix by the Boolean operator "AND" : AND "Lake" OR "pond" OR "reservoir" OR "sea" OR "ocean" OR "drinking" OR "freshwater" OR "water for aquatic environments and "accumulation" OR "bioaccumulation" OR "food web" OR "shellfish" OR "mussel" OR "oyster" OR "shrimps" OR "clam" OR "seafood" OR "fish for aquatic food sources for humans.