dataesr/scientific-paragraphs-categorization huggingface dataset:
Terrigenous (crustal) contributions were confirmed with the empirical methods and PCA. The five-year dataset allowed for the identification of different inter-annual and seasonal trends that further enhance the knowledge on atmospheric chemical processes in southern Africa. From the results presented, it is clear that anthropogenic (fossil fuel combustion and agriculture) sources dominate the chemical composition of wet deposition at VT and AM, while also significantly influencing wet deposition chemistry at relatively remote sites (SK and LT). Although the empirical and statistical methods applied in this study could adequately identify source group contributions, it is recommended that in future studies more advanced methods such as positive matrix factorisation should be utilised in wet deposition studies from which the contribution of each source to each ion could be more accurately established  (Qiao et al.
Acknowledgments The authors would like to thanks  Sasol ,  Eskom  and the  NRF  for financial support of the South African IDAF project. The following people are also acknowledged:  Carin Van Der Merwe  for monthly site visits and maintenance,  Ville Vakkari  who compiled the overlay back trajectories shown in Fig. 2, as well as the site operators who include  Memory Deacon  (AF);  Chris James (LT) ;  Navashni Govender ,  Walter Kubheka ,  Eva Gardiner  and  Joel Tleane  (SK); Mike Odendaal (VT).
PN  (21) , CP  (22) , HCP  (23) , and PO  (24)  have been detected in the ISM. Traces of phosphorus may have been present in the dust signature of comet Halley, but no phosphorus was found in Stardust grains  (25). Until the observations in Fig. 1 , no phosphorus has been detected in the cometary coma. In the mass spectra of ROSINA (fig. S1 ), there is a clear peak at mass 30. 973 dalton, the exact mass of ionized phosphorus. However, at the moment, we cannot state what the origin of phosphorus is. No clear signatures of PO, PN, CP, or HCP have been found so far in the ROSINA data. It is therefore not clear which parent molecule produces the fragment seen in the DFMS spectra. Assuming that it is mainly PH 3 , and taking the fragmentation pattern from the National Institute of Standards and Technology (NIST) database, one arrives at an abundance of PH 3 (1.
Data sample Mass 75 Da/e was measured regularly over the course of the mission, starting with the arrival at the comet in August 2014. A full mass spectrum of DFMS between 13 and 100 Da/e takes approximately 40 min. The instrument is in standby only during thruster firings, which account for about 10% of the total time. This means that each mass in this mass range is measured 30 times per day and more than a 1000 times since arrival at the comet. However, the spacecraft is not always in a position or altitude to register rare species like glycine, mostly because of large distances to the comet forced by the dense dust coma and by pointing away from the comet to make measurements possible for other instruments. For this study, we analyzed approximately 50 mass spectra containing glycine from periods that were most suited to this analysis, namely, the two close flybys in February and March 2015 and the period in June or July when the spacecraft encountered a significant amount of dust.
Fragmentation pattern The number of detected ionized particles on the detector of ROSINA DFMS depends on the density of the species considered at the entrance of DFMS, on the fragmentation pattern, on the ionization cross section, on the geometric sensitivity of the instrument for the different masses, and on the detector yield. DFMS is less sensitive to heavy masses than to light masses because of its fixed magnetic field. Therefore, we applied a post-acceleration voltage to the ions for masses >69 dalton to improve the detection of heavier masses. With all of these complications, there is no convenient correlation between the density of the species and the number of counts on the detector. The data analysis method for ROSINA DFMS is discussed in detail by Le Roy et al. (13) , and the instrument details are discussed by Balsiger et al.
(12). The most common methods in mass spectrometry use electron impact ionization with electron energies of 70 eV to ionize neutral incoming gas, which is also the basis of the NIST Standard Reference Database No. 69 used for most of the molecules in this study. This ionization method yields not only the ionized parent molecule but also the many daughter fragments. The fragments and their abundances depend on the structure of the molecule and on the electron energy. The fragmentation pattern can thus be used to defer the structure of the molecule. The coma of the comet's several parent molecules present in a gas mixture can contribute to the same fragments. Careful bookkeeping of fragments is therefore necessary to arrive at a (unique) solution for the fragments seen in the mass spectra, which is compatible with the parent molecules present in the gas mixture.
For a discussion of the specific fragmentation pattern, see the Supplementary Materials. In principle, this fragmentation pattern depends on the specific instrument used. ROSINA DFMS uses electron energy of only 45 eV, which fragments less and therefore yields a higher amount of the ionized parent molecule compared to the daughter species. As we had to use the NIST Chemistry WebBook database because DFMS was not calibrated for most of the compounds discussed here, this therefore adds an uncertainty in the relative abundances given below. Other uncertainties are due to the detector sensitivity for the different species and due to the fact that DFMS measures single masses sequentially, which means that the subspacecraft longitude and the local time change during a measurement of a full mass spectrum, which takes up to 1 hour.
This constituted an on-going removal programme, with successful extirpation of L. gibbosus from the stream assumed to have taken place in July 2007 because L. gibbosus was not captured at any of the sites after that survey, including the single-run survey of site A in October 2009 (Table  1 ). For the present study, additional data for stable isotope analysis were collected in July 2010 from all sites to match those collected in July 2007. Fish sampling in July 2007 and 2010 was undertaken using a back-pack electrofishing unit (Bretschneider EFGI 650, Reichenbrnder Strasse 4, D-09224 Chemnitz/Gruna, Germany) whilst moving slowly in an upstream direction. A multiple-run strategy provided depletion estimates (three runs, depending on the depletion rate) that ensured thorough sampling to confirm the absence of L.
Stable isotope analysis Fish diet composition and trophic niche breadth were quantified using the stable isotope analyses of carbon and nitrogen, as carbon isotopes reflect energy origin with typical enrichment of 0-1%, whereas nitrogen isotopes indicate trophic position and show greater enrichment of 2-4% from resource to consumer  (Post, 2002; Grey, 2006). All samples for stable isotope analysis (fish fin-clips, macro-invertebrates and terrestrial basal resources) were dried at 60°C for 48 h before being processed at the Stable Isotopes In Nature Laboratory (SINLAB). The stable isotope ratios were expressed as per mille (%) using the delta notation (d). Differences in the stable isotope data and trophic niche of L. gibbosus and S. trutta were tested initially by quantifying the differences in d 13 C and d 15 N in July 2007 between the species using analyses of variance (ANOVAs).
Variation in d 13 C and d 15 N of S. trutta between years was subsequently tested using analyses of covariance (ANCOVAs), with L T as a covariate. The d 13 C and d 15 N values of both species were then used to calculate their standard ellipse areas (SEA c ), a bivariate estimate of the core isotopic trophic niche based on the distribution of individuals in the isotopic space  (Jackson et al. , 2011 (Jackson et al. , , 2012)) , for each year and   Klaar et al. (2004)  and  Copp et al. (2010)  site. This was completed using the SIAR package in R (R Core Team, 2013). The subscript 'c' indicates that a small sample size correction was used to increase the accuracy of the trophic niche estimate  (Jackson et al. , 2011). The extent to which the trophic niche overlapped in SEA c between L. gibbosus and S.
Acknowledgments Conceived within the framework of an international network initiated with a  NATO Science Programme 'Collaborative Linkage Grant ' (awarded to GHC), this study was funded mainly by a research contract to GHC from the  U. K. Department for Environment, Food and Rural Affairs (Defra). Additional support of JC was received via an '' ERG Marie Curie '' grant ( PERG08-GA-2010-276969 ) in the lab EDB, part of the  Laboratoire d'Excellence (LABEX)  entitled  TULIP  ( ANR -10-LABX-41 ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Small reservoirs have developed across semi-arid areas as a low cost solution for millions of rural small holders to harvest scarce water resources. Studies have highlighted limited agricultural water use and low water availability on individual reservoirs, but no information exists on the drought patterns of multiple small reservoirs. Their small size and dispersion prevents individualised hydrological monitoring, while hydrological modelling suffers from rainfall variability and heterogeneity across data sparse catchments and reservoirs. A semi-automated original approach exploiting free, archive Landsat satellite images is developed here for long-term monitoring of multiple ungauged small water bodies. Adapted and tested against significant hydrometric time series on three lakes, the method confirms its potential to monitor water availability on the smallest water bodies (1-10 ha) with a mean RMSE of 20,600 m 3 (NRMSE = 26%).
These residual uncertainties are notably caused by difficulties in detecting cirrus clouds and shadows and can lead to significant overestimations  [12]. (ii) Landsat data using a site-specific rating curve; (iii) Landsat data using the power relation adapted for silting; (iv) Landsat data using the power relation not adapted for silting. Smaller reservoirs like Fidh Ali were more affected by residual uncertainties due to the greater proportion per lake of mixed pixels, including flooded vegetation and waters, as well as the proportionally greater error from clouds and their shadows. Furthermore, the absence of frequent observations reduced the performance, leading to irregular flood rises and rapid declines due to rapid infiltration or smaller volumes (50,000 m 3 ), which were not reliably monitored  [12].
The SHIVA (Stratospheric Ozone: Halogen Impacts in a Varying Atmosphere) ship, aircraft and ground-based campaign during November and December 2011 in the Southern South China and Sulu seas investigated oceanic emission strengths of marine VSLSs, as well as their atmospheric transport and chemical transformation from the ocean surface to the upper troposphere. For more details about the SHIVA campaign see the ACP special issue  (Sturges et al. , 2012). In this study, we present campaign data from the research vessel (R/V) Sonne and the research aircraft (R/A) Falcon. We identify the contribution of oceanic emissions to the MABL and their exchange into the FT applying in situ observations, trajectory calculations and source-loss estimates. The results are crucial for a better process understanding and for chemical transport model validation  (Hossaini et al.
Measurements on board R/V Sonne Meteorological parameters (temperature, air pressure, humidity and wind) were recorded at 20 m height every second. A 10 min running mean of these data is used for this study. An optical disdrometer ("ODM-470") measured the amount and intensity of precipitation during the cruise at 15 m height every minute (see Supplement for further details). To obtain atmospheric profiles of air temperature, relative humidity and wind from the surface to the stratosphere 67 GRAW DFM-09 and 6 GRAW DFM-97 radiosondes were launched every 6 h at standard UTC times (00:00, 06:00, 12:00, 18:00) from the working deck of R/V Sonne at about 2 m a. s. l. At the 24 h stations, the launch frequency was increased to 2-3 h to analyse short-term diel variations of the atmospheric boundary layer.
VSLS measurements and flux calculation VSLSs in marine surface air and seawater were sampled synchronously on R/V Sonne along the cruise track. From these data the oceanic emissions of the compounds during the SHIVA campaign were calculated (Sect. 2. 3. 3). Addition-ally, VSLSs were measured in the MABL and the FT by R/A Falcon  (Sala et al. , 2014; Tegtmeier et al. , 2013).
Additionally, 700 mL glass flasks were filled with ambient air to a pressure of 2. 5 bar with the R/A Falcon whole air sampler (WASP) and analysed within 48 h by a ground-based gas chromatography/mass spectrometry (GC/MS) instrument (Agilent 6973) of the University of East Anglia  (Worton et al. , 2008). During the flights GhOST measurements were conducted approximately every 5 min with a sampling time of 1 min, while WASP samples were taken every 3-15 min with a sampling time of 2 min. Further details on the instrumental precision and intercalibration on R/A Falcon are given in  Sala et al. (2014). Given that the ground-based GC/MS investigated only brominated compounds, CH 3 I data are not available from WASP. Measurements from R/V Sonne and R/A Falcon were both calibrated with NOAA standards.
Acknowledgements. This work was supported by the  EU  project  SHIVA  under grant agreement no. FP7-ENV-2007-1-226224  and by the  BMBF  grants  SHIVA-Sonne  (FKZ:  03G0218A ),  SOPRAN II  ( BMBF-FKZ :  03F0611A ). SF was also partly funded by the grants  ROMIC Threat  ( BMBF-FKZ :  01LG1217A ) and  OASIS-Sonne  ( BMBF-FKZ :  03G0235A ). We thank the authorities of  Malaysia  and the Philippines for the permissions to work in their territorial waters, as well as the SHIVA coordinators  Klaus Pfeilsticker  and  Marcel Dorf  and all other SHIVA contributors. We acknowledge the  ECMWF  for the provision of ERA-Interim reanalysis data and the FLEXPART development team for the model used in this publication. We would also like to thank the captain and crew of  R/V Sonne  and the pilot in command and crew of  R/A Falcon  as well as the  Projektträger Jülich (PTJ)  and the  Deutscher Wetterdienst (DWD)  for the support.
, 2000; Bian and Prather, 2002) , which is already used in various chemistry-transport models (CTMs)  (Telford et al. , 2013; Real and Sartelet, 2011) , has been included into the CHIMERE chemistry-transport model  (Menut et al. , 2013). With this new development, the CHIMERE model is able to simulate the radiative impact of aerosols on photochemistry. The Fast-JX module takes into account the values provided in real time by the CTM for all aerosol species as well as for tropospheric ozone up to the top of the CHIMERE domain. The real-time model values of the meteorological variables (temperature, pressure and cloud cover) are also used by the Fast-JX module. Monthly climatological distributions for stratospheric ozone are taken from the  McPeters et al. (1997)  climatology. As the CHIMERE model takes into account all the major anthropogenic and natural sources of aerosols and trace gases in a realistic way for the Mediterranean basin  (Menut et al.
5 • , and the error distribution peaks in the vicinity of 0, showing that the synoptic wind patterns are reproduced rather well by the model. Atmospheric chemistry has been modelled with the CHIMERE chemistry transport model  (Menut et al. , 2013). We used the MELCHIOR-2 (Modèle de la chimie de l'ozone à l'échelle régionale 2) chemical mechanism along with the aerosol scheme by  Bessagnet et al. (2004). For this study, the emissions are taken from the HTAP (Hemispheric Transport of Air Pollution) inventory provided by the EDGAR (Emissions Database for Global Atmospheric Research) team 1 and adapted to the model grid as described in  Menut et al. (2013). The resulting mean NO x emissions over the simulation domain are shown in Fig. 2. The boundary conditions for all gaseous and particulate species are taken from LMDz-INCA (Laboratoire de Météorologie Dynamique Zoomé -INteraction avec la Chimie et les Aérosols) climatology  (Hauglustaine et al.
The photolysis rates in CHIMERE are updated every 5 min by calling the Fast-JX model. The AOD for each model layer is an intermediate result of the Fast-JX model, which we sum over the model layers and export for the five wavelengths used by Fast-JX in order to compare it to available observations. One key parameter in simulating J (O 1 D) is the total atmospheric ozone column. Within the simulation domain (from the surface to 300 hPa, Fast-JX uses the ozone concentrations calculated within CHIMERE. Above this level, that is, for upper tropospheric and stratospheric ozone, Fast-JX uses tabulated climatological ozone concentrations. These climatological concentrations from  McPeters et al. (1997) , included within the Fast-JX module in its standard configuration, are given with a vertical resolution of about 2 km up to an altitude of about 60 km, with monthly values for every 10 • latitude band.
The AERONET AOD as well as MFRSR AOD has been interpolated at the wavelength of 400 nm, which is one of the five wavelengths for which Fast-JX computes the AOD. The interpolation was performed following an Angström power law based on the nearest available wavelengths in the measured data: 380 and 440 nm for the AERONET data and 416 and 440. 6 nm for the MFRSR data. Actinic flux spectra were measured using a Metcon diode array spectrometer  (Casasanta et al. , 2011). The actinic flux measurements were calibrated at the beginning of SOP1a by using NIST traceable 1000 watt lamps. The value of J (O 1 D) was derived from the actinic flux measurements as described by  Casasanta et al. (2011). J (NO 2 ) was calculated from the measured actinic flux spectra by using the temperature dependent NO 2 absorption cross sections by  Davidson et al.
These three AERONET stations have been selected in the western Mediterranean according to the data availability for June 2013 and their position on the trajectory of the dust plume of 13-25 June as seen by MODIS. As discussed in Sect. 2, the three selected stations are Lampedusa (Italy), Oujda (Morocco) and Palma de Mallorca (Spain). The AERONET data for Lampedusa were not available for 1 to 16 June, so that the time series have been completed using the MFRSR data at Lampedusa station for those 16 days as well as for 27 June. The comparison of the AOD measured in these three AERONET stations to the AOD of CHIMERE is shown in Fig. 7  at 400 nm. Statistical scores have also been calculated for 10 additional AERONET stations from the Saharan area to northern Europe (Table  3 ). The dust peak observed from 21 to 24 June in Lampedusa is simulated realistically by CHIMERE (Fig.
7a ). The peak value of the AOD is about 0. 5 in the model and 0. 35 in the observations. Three other sharp peaks in AOD are represented in CHIMERE for 6, 9-10 June and 2-4 July. The peak of 6 June is the most intense in the simulation period and has a rather short duration (about 24 h). The maximal value of the AOD during this peak is between 0. 8 and 0. 9 in both the MFRSR data and the model in the afternoon of 6 June. The AOD value then steadily decreases on 7 June, ranging be-    3 ). For the Oujda station (Fig. 7b ), a period of strong AOD is represented in both the model and observations from 12 to 17 June, with a similar timing and duration between the model and the observations and a stronger value for the maximal AOD in the model than in observations (0. 6 vs. 0. 4). Another strong AOD peak is simulated by CHIMERE from 28 June to 2 July but with no available data at the time, and a last AOD peak is modelled and observed on 11-12 July.
This compensation effect between the effects of changes in AOD and in total ozone column on surface UV irradiance, and thus also on J (O 1 D), has been discussed by di  Sarra et al. (2002) , who have shown that during spring and summer at Lampedusa, the synoptic conditions leading to dust transport also induce thinner ozone columns. Figure  15a  confirms that the representation of the diurnal cycle of J (O 1 D) at Lampedusa by the Fast-JX module within CHIMERE is very satisfactory. The linear correlation coefficient between the observed and modelled value for the REF simulation is of 0. 981, slightly stronger than the value of 0. 972 obtained for the NA simulation (Table  4 ). The high values of these correlation coefficients for both simulations confirm that the general shape of the diurnal cycle of J (O 1 D) is captured very well by both simulations, confirming that the dependence of J (O 1 D) on the solar zenith angle is represented correctly by the CHIMERE model.
(2012)  for cases when dust predominates rather than to the blue regression line given for cases when non-dust aerosols predominate. Regarding J (NO 2 ), Fig. 16b  reproduces the linear relationships given in  Gerasopoulos et al. (2012)  (their Fig. 6 ) for J (NO 2 ) vs. AOD at 60 • zenith angle. The red line concerns the relationship they establish when the AOD is predominantly due to dust, and the blue line is for AOD predominantly due to other aerosols. From the location of our modelled points relative to these linear relationships established from measurement data, it can be said that the quasilinear dependence between J (NO 2 ) and the AOD for a fixed zenith angle is reproduced very well by the Fast-JX module in CHIMERE. It can also be inferred from this figure that the relationship between J (NO 2 ) and the AOD proposed by  Gerasopoulos et al.
97, reflecting the fact that the diurnal cycle of J (O 1 D) and J (NO 2 ) is represented very realistically by the Fast-JX module within the CHIMERE model. If we remove the impact of the diurnal cycle by comparing the daily maxima of J (O 1 D) and J (NO 2 ) in both simulations to measurements, it becomes clear that the day-today variability of J (O 1 D) is represented much better in the REF simulation than in the NA simulation. While the simulation without effect of the aerosols is not able to reproduce any of the observed day-to-day variations in J (O 1 D), the daily maxima of J (O 1 D) REF simulation are significantly correlated to the observed values. Therefore, despite the strong dependence of J (O 1 D) on the total ozone column, it is safe to state that the inclusion of the optical effect of aerosols improves the representation of the evolution of J (O 1 D) in the CHIMERE model.
Acknowledgements. We thank the  ChArMEx program  (sponsored by  CNRS-INSU ,  ADEME ,  Météo-France  and  CEA ) as well as the  ADRIMED program  (sponsored by  ANR ) for permitting the collection of the relevant field data by the principal investigators of the three AERONET stations that have been used in this study:  Daniela Meloni for Lampedusa ,  Diouri Mohammed  and  Djamaleddine Chabane for Oujda  and  Juan Ramon Moreta Gonzalez  for  Palma de Mallorca. Measurements at Lampedusa during ChArMEx  were partly supported by the  Italian Ministry for University and Research  through the NextData and Ritmare projects. The contributions by  Lorenzo De Silvestri  are gratefully acknowledged. The authors also wish to thank the editor for his careful editorial and scientific review of the initial manuscript as well as the three anonymous reviewers who helped to significantly improve this study.