dataesr/scientific-paragraphs-categorization huggingface dataset:
Linked to this database is Environmental Justice, Organisations, Liabilities and Trade, a global research project supported by the European Commission that is compiling The Map of Environmental Justice, an atlas of maps documenting the distribution of pollution and environmental injustice around the world. 318 ure Earth, a New York-based environmental non-profit organisation has developed a Toxic Sites Inventory Program that includes information on about 3500 polluted sites-active and abandoned mines, smelters, factories, and hazardous waste dumps-a number that is still growing. 38 This database focuses on contaminated sites in low-income and middle-income countries and has served as a resource to the work of this Commission. In the USA, the Environmental Protection Agency has developed an open-access mapping tool, EJSCREEN, that is available on the EPA website and makes data on environmental injustice publicly available.
The precise metrics to be followed are under consideration, but possibilities include monitor ing country-by-country data on the status of regulations against each type of pollution; measuring exposures to key pollutants, country-by-country and regionally; reporting detailed country-by-country statistics on disease and premature death by pollution risk factor, to track performance towards the goals suggested in this report; tracking national and international investment into expanded research on disease and death due to pollution (especially soil pollution caused by heavy metals and toxic chemicals), including studies to discover new and previously unrecognised health effects of pollutants; tracking investments related to interventions against pollution, country-by-country (which can be broken down by source of investment and whether the investment is national or international and public or private); and developing a database to report the cost-efficacy of interventions against pollution, measured in terms of health outcomes.
New programmes to educate global leaders and government agencies about proven solutions to pollution are also in development. Activities to strengthen the involvement of the public and civil society in pollution control are essential because public concern provides a major impetus for governments to act against pollution. A new website is being developed by the Global Alliance on Health and Pollution to show current and, in some cases, real-time data related to pollution in countries across the world. This geocoded website links databases showing air pollution, water pollution, and soil contamination. Users can zoom down to the communities where they live, see the available information, and post their own stories and pictures about pollution. The website will incorporate a link for people to connect with local government organisations for solutions.
Here we present our SOPHIE radial velocity measurements, comprising 45 epochs over 2. 2 years. Both planets are detected independently in the new data set, confirming the TTV detection of the exterior planet. In order to explore the contribution of each data set, we analyse the SOPHIE radial velocities with a simple two-Keplerian model and the Kepler photometry independently of the SOPHIE data. On the other hand, and as described in detail by  Agol et al. (2005)  and  Almenara et al. (2015 Almenara et al. ( , 2016)) , photometry alone makes it possible to measure the density of the bodies of the system, that is MR -3. However, individual masses and radii cannot be constrained, and we only have access to mass ratios and radius ratios. In other words, exactly the same light curve is obtained if lengths in the system are scaled by a factor and masses are scaled by the same factor at cubic exponent.
This is called the MR -3 degeneracy. This degeneracy can be removed by constraining the system scale, for example by adding radial velocities, or by measuring the light-travel time, which provides access to absolute masses and radii. Then we combine all available data and use a photodynamical model  (Carter et al. 2011)  to derive absolute physical parameters without theoretical stellar models  (Agol et al. 2005). Finally, the results from the photodynamical modelling are used to study the evolution of the system's orbital parameters through numerical integrations over 10 kyr.
Kepler light curve Kepler observed the 13-magnitude star Kepler-419 from Q0 to Q17. We used Data Release 25 obtained from the Mikulski Archive for Space Telescopes (MAST) archive 1. Kepler 1 http://archive. stsci. edu/index. html. recorded 21 of the 22 transits of planet b that took place in the time span of its observation, the first ten and the last one in longcadence data (about one point every 29. 4 min) and the remaining ten in short-cadence data (about one point per minute). We used the simple aperture photometry (SAP) light curve, which we corrected for the flux contamination (between 0 and 2% depending on the quarter) using the value estimated by the Kepler team. We kept only the data spanning three transit durations around each transit; they were modelled after normalisation using a linear function for each transit.
First analysis: Keplerian model We first fitted the radial velocities with a two-planet Keplerian model, i. e. neglecting the mutual gravitational interactions between both planets. The goal here was to have a first idea of the information included in the radial velocity data set. Figure  4  shows Generalised Lomb-Scargle (GLS) periodograms  (Zechmeister & Kürster 2009)  of the radial velocities. In each panel the periods of the two planets are indicated with the green and the red lines (70 and 673 days, respectively). The first panel shows the periodogram of the mean-corrected SOPHIE and HIRES velocities together. It reveals no significant signals, but there is a peak close to the rotational period of the star, 4. 5 days. The periodogram of the HIRES data is presented in second panel of Fig.
4. No significant power is seen at the periods of any of the planets, and there is a forest of peaks around the rotational period, including a seemingly significant one around 7 days. We cannot explain this signal easily, but as it is only detected in the HIRES data, we are inclined to ascribe it to instrument systematics (see also discussion at the end of the section). The third panel of Fig. 4  presents the periodogram of SOPHIE data only. The pvalues of the peaks at the planet periods, computed by randomly shuffling the model residuals, remain above the customary 1%. However, when the Keplerian orbit is eccentric, the GLS periodogram power is transferred partially to the harmonics of the orbital period, leading to a reduced peak amplitude. More robust methods should be used to assess the question of the significance of these signals in detail, but this is beyond the scope of this paper.
4 , lower panel). The scatter of the residuals is 38 m s -1 , in good agreement with the precision of the observations and the expected stellar jitter 4. This is similar to the dispersion measured in the HIRES residuals after a similar fit. As the HIRES data have a smaller internal dispersion (12 m s -1 ), this hints at some level of systematics in the HIRES data. 4 28 m s -1 2 + 27 m s -1 2 39 m s -1.
Final analysis: photodynamical model Finally, we employed a photodynamical model of the observed photometry and radial velocity measurements, accounting for the gravitational interactions of all the known components of the system. Our model is described in detail in  Almenara et al. (2015 Almenara et al. ( , 2016)). In brief, we obtain the positions and velocities of the three bodies in the system in time through numerical integration of the system. The sky-projected positions are used to com- pute the light curve (Mandel & Agol 2002) using a quadratic limb-darkening law  (Manduca et al. 1977). To account for the integration time, the model was oversampled by a factor of 30 and 3, for the long-and short-cadence data respectively, and then binned back to match the cadence of the data points (Kip- ping 2010).
3. 2. The model is parametrised using osculating astrocentric asteroidal orbital elements (Table  2 ) at the time immediately before the first transit observed by Kepler t ref = 2 454 958 BJD TDB , given in Barycentric Dynamical Time. Due to the symmetry of the problem, we fixed the longitude of the ascending node of the interior planet Ω b at t ref , we limited the inclination of the outer one i c < 90 • , and rejected models where planet c transits 5. Our model has 26 free parameters. In addition to the physical parameters, we considered a radial velocity offset for each instrument with respect to the systemic velocity (assumed to be zero), a global light curve normalisation factor for long-and short-cadence data, and a multiplicative jitter parameter for each data set. A non-informative uniform prior distribution was chosen, and the joint posterior distribution was sampled using the emcee algorithm  (Goodman & Weare 2010; Foreman-Mackey et al.
The substantial power in n = 10 is due to both eccentricities (see Table  3 ). To verify this, we simulated a light curve with 54 transits of planet b using the MAP values in Table  2  with a sampling and white noise amplitude equal to that of the Kepler SC data. The TTV periodogram for the simulated LC is also shown in Fig. 11. It reveals that the dominant harmonic is n = 10, and that the powerful n = 9 harmonic is not resolved in the 22-transit data set 11. This lack of resolution results in a poor constraint on the mass of the transiting planet for the following reasons. For a coplanar system there are seven unknowns (the masses of the two planets, the two eccentricities and corresponding longitudes of periastron, and the mean longitude of planet c at epoch). Therefore, information from at least four well-resolved harmonics (each with an amplitude and a phase) is needed in order to place reasonable bounds on all seven parameters from TTV data alone.
22 M , may have had a similar origin to Kepler-419, but found itself with a value of bc conducive to circularisation. Just as for the Kozai migration mechanism, such an evolutionary history would leave little room for intervening planets. However, unlike the Kozai mechanism, the present mechanism produces hot Jupiters without the need of mutual inclination. collected by the Kepler mission. Funding for the Kepler mission is provided by the NASA Science Mission directorate. Some of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). This research has made use of the Exoplanet Orbit Database and the Exoplanet Data Explorer at exoplanets. org. This research was made possible through the use of the AAVSO Photometric All-Sky Survey (APASS), funded by the Robert Martin Ayers Sciences Fund.
This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. Simulations in this paper made use of the REBOUND code which can be downloaded freely at http://github. com/hannorein/rebound. These simulations have been run on the Regor cluster kindly provided by the Observatoire de Genève.
(CH) dedicated to extrasolar planets data visualisation, exchange, and analysis. DACE is a platform of the Swiss National Centre of Competence in Research (NCCR) PlanetS, federating the Swiss expertise in Exoplanet research. The DACE platform is available at https://dace. unige. ch. This work has been carried out within the framework of the National Centre for Competence in Research Plan-etS supported by the Swiss National Science Foundation. The authors acknowledge the financial support of the SNSF. S. C. C. B. acknowledges support by the Fundação para a Ciência e a Tecnologia (FCT) through the Investigador FCT Contract IF/01312/2014/CP1215/CT0004 and also acknowledges support from FCT through national funds and by FEDER through COMPETE2020 by these grants UID/FIS/04434/2013 & POCI-01-0145-FEDER-007672 and PTDC/FIS-AST/1526/2014 & POCI-01-0145-FEDER-016886.
Acknowledgements. We thank  Rebekah I. Dawson  and  Geoffrey W. Marcy  for helpful discussions. We thank the technical team at the  Observatoire de Haute-Provence  for their support with the SOPHIE instrument and the 1. 93m telescope and, in particular, for the essential work of the night assistants. Financial support for the SOPHIE observations from the  Programme National de Planètologie (PNP)  of  CNRS/INSU, France , is gratefully acknowledged. We also acknowledge support from the  French National Research Agency  ( ANR-08-JCJC-0102-01 ). We thank  L. Kreidberg  for her  Mandel & Agol  code and  Y. Revaz  for his assistance with the computing cluster used in this work. This paper includes data.
Participants All data collection took place at the Laboratory for Social and Neural Systems research (SNS-Lab) at the University of Zurich, Switzerland. Thirty-two right-handed participants were included in the experiment. All of them were fluent English speakers recruited from the University of Zurich database and gave informed written consent in accordance with local ethics, approved by the Kantonale Ethikkommission Zu ¨rich (2010-0326/3). Participants were included in the study following established TMS safety procedures  (Keel et al. , 2001). Data of subjects with fixed responses (accept/reject every response) was not included in the analysis due to biased preferential responses. One subject was excluded prior to the study due to recent migraine symptoms and two others were excluded after the experiment as they showed the fixed response pattern of rejecting all trials in the task.
Acknowledgements This work was financed in the framework of the  Laboratory of excellence LABEX ANR-11-LABEX-0042 of Universitede Lyon, within the program 'Investissements d'Avenir'  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR)  and this research has benefited from the financial support of  IDEXLYON from Universitede Lyon  (project  INDEPTH ) within the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ). This work was also supported by grants from the  ANR-14-CE13-0006 BrainChoice , and the  NSF-ANR CRCNS 'Social POMDP'  ( ANR-16-NEUC-0003-01 ) to J-C D. CCR received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  725355 ,  ERC  consolidator grant BRAINCODES).
This information could be used, for example, to weight individual hydrographic profiles when building gridded products from observations; spatiotemporal variances are sometimes used to estimate these weights, but ePDFs may be better adapted for this purpose because they are available at each location and time. Simulated equivalents of all hydrographic profiles contained in the EN4 database  (Good et al. , 2013)  were extracted online from the 50 members during the OCCIPUT simulation. Each set of these "ensemble synthetic observations" is associated with an observed T/S profile: it can be used to address the issues mentioned above, and to probabilistically evaluate the simulation. These results suggest that the observed evolution of regional temperatures (e. g. , from Argo or CTD data) should be interpreted carefully.
ACKNOWLEDGMENTS This work is a contribution to the  OCCIPUT  and PIRATE projects. PIRATE (https://sealevel. jpl. nasa. gov/science/ostscienceteam/scientistlinks/ scientificinvestigations2017/ penduff) is funded by  CNES  through the  Ocean Surface Topography Science Team (OSTST). OCCIPUT (http://meom-group. github. io/projects/occiput) is funded by  ANR  through contract  ANR-13-BS06-0007-01. We acknowledge that the results of this research have been achieved using the  PRACE Research Infrastructure resource CURIE based in France at TGCC. Some of the computations presented in this study were performed at IDRIS and TGCC under allocations granted by  GENCI. The data set used for this study is available upon request (contact: thierry. penduff@cnrs. fr).
, 2011; Surratt et al. , 2010)  though there have been no recent observations over the SWA. Therefore, the large dataset gathered during the DACCIWA aircraft field campaign allows for unprecedented characterization of the aerosol population, comprising insights on the complex interplay between anthropogenic and biogenic sources on this highly sensitive, understudied and rapidly changing environment. In this paper, two important processes that lead to the formation of BSOA from BVOCs are discussed. These processes are briefly outlined here.
Instrumentation The chemical composition and mass concentration of the non-refractory submicron particulate matter (NR-PM1) was measured with an Aerodyne compact time-of-flight aerosol mass spectrometer (C-ToF-AMS) with a time resolution typically of 10 or 20 s without particle sizing information. Less often, measurements were carried out at 40 s time resolution with sizing information (PTOF); however, these will not be discussed here. Before aerosol particles were sampled by the C-ToF-AMS, they passed through a pressure-controlled inlet (PCI), regulated at about 400 hPa, avoiding pressure changes to the aerodynamic inlet of the instrument during airborne sampling  (Bahreini et al. , 2008; Freney et al. , 2014). In order to extract chemically resolved mass concentrations of individual species, the C-ToF-AMS raw data were evaluated using the standard fragmentation table  (Allan et al.
87; R 2 : 0. 83; Fig. S1  in the Supplement). The density used for each species was 1. 78, 1. 72, 1. 72, 1. 52 and 1. 77 g cm -3 for sulfate, nitrate, ammonium, chloride and BC, respectively  (Lide, 1991; Park et al. , 2004). The density of organics was estimated based on the oxygen-to-carbon (O : C) and hydrogen-to-carbon (H : C) ratios  (Canagaratna et al. , 2015; Kuwata et al. , 2012) , yielding a campaign average of 1. 67 g cm -3. Aerosol loadings from the C-ToF-AMS were also compared with measurements of C-ToF-AMS from the two other aircraft that took part in the campaign, the German DLR Falcon and the British Twin Otter (TO). For this, data were selected around take-off and landing at Lomé airport. Results between the instrument used here (ATR42) and the TO were generally in good agreement.
Isoprene epoxydiols secondary organic aerosol Isoprene epoxydiol SOAs (IEPOX-SOA) have previously been identified from AMS spectra through an enhanced signal at ion C 5 H 6 O + , or m/z 82 in unit mass resolution systems  (Allan et al. , 2014; Budisulistiorini et al. , 2015; Robinson et al. , 2011; de Sá et al. , 2017). More recently,  Hu et al. (2015)  proposed a diagnostic tracer for IEPOX-SOA based on datasets from a wide range of environments, such as biomass-burning, urban or monoterpene-impacted areas, for high or unit mass resolution instruments. Results have shown that the relative contribution of m/z 82 to total organic aerosol concentration, i. e. f 82 = (m/z 82/OA), is a suitable tracer, with uncertainties up to 30 %. Furthermore,  Hu et al. (2015)  proposed an estimation of IEPOX-SOA taking into account mass concentration at m/z 82 (m 82 ), total OA concentration (OA), a reference f 82 value for IEPOX-SOA (f IEPOX-SOA 82 = 22 ‰) and a background value for f 82 (f BK 82 ): IEPOX-SOA = m 82 -OA × f BK 82 f IEPOX-SOA 82 -f BK 82 in µg m -3.
This is strongly associated with the fact that the dataset originates from airborne measurements and therefore (i) the dataset has a somewhat limited temporal cover-age (about 70 h in total, compared to weeks/months/years of ground-based campaigns); (ii) the dataset lacks diurnal variation, as most of the flights were either carried out during morning or afternoon hours; and (iii) the aircraft samples air masses with large co-variability (e. g. biomass-burning emissions from within the city itself along traffic emissions, as discussed above). The outcomes of PMF results are twofold: the different components of OA obtained from the use of PMF will be used in a systematic identification of in-plume and background measurements, and the identification of processes of interest (IEPOX-SOA and pON) shall be carried out using the diagnostic tracers detailed in Sect.
9 µg m -3 ). A similar analysis for pON has identified no quantifiable change between incoming oceanic (upwind Abidjan) and continental air masses (0. 18 µg m -3 ), leading to the conclusion that this species is not locally formed, but mostly advected into the region. A systematic analysis of in-plume and regional background air masses has been carried out using the ATR42 dataset below 2000 m. Regional background concentrations are fairly polluted, with average concentration of CO of 131 ppb, ozone of 32 ppb and aerosol number concentration of 735 cm -3. Regarding PM 1 composition, OA was the most abundant species, contributing 54 %, followed by SO 4 (24 %) and minor contribution of other species. Mean background PM 1 in the region was 5. 9 µg m -3. During in-plume measurements there was a marked enhancement of pollutant levels, e.
Data availability. The data used here can be accessed using the DACCIWA database at http://baobab. sedoo. fr/DACCIWA/. An embargo period of 2 years after the upload applies. After that, external users can access the data in the same way as DACCIWA participants before that time. Before the end of the embargo period, external users can request the release of individual datasets. It is planned for DACCIWA data to get DOIs, but this has not been realized for all datasets yet. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-18-757-2018-supplement. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "Results of the project 'Dynamics-aerosol-chemistry-cloud interactions in West Africa' (DACCIWA) (ACP/AMT inter-journal SI)".
Acknowledgements. The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603502. The authors would also like to extend special thanks to the pilots and flight crew from SAFIRE for all their enthusiasm and support during the measurement campaign aboard the ATR42 aircraft. The authors acknowledge  Christiane Schulz  and  Johannes Schneider  for the scientific input and text revision. Pamela Dominutti  thanks the  CNPq and PVE-CAPES  programme for financial support during her international exchange. Cyrielle Denjean  thanks the  Centre National des Etudes Spatiales (CNES)  for financial support. Anneke M. Batenburg  acknowledges internal funds of the  Max Planck Institute for Chemistry in Mainz  as well.
In addition, a Bayesian approach (e. g. ,  Robert, 1994; Gelman et al. , 2013)  is used to assess uncertainties in return levels. The comparison of the results between the different time periods and regions is used to assess the significance of the differences (e. g. , how significant are the changes in return levels between  1973-1994 and 1995-2016). We use BA from the Prométhée fire database as a surrogate for fire risk, based on the assumption that the largest fires generate the highest impacts and costs for suppression and are among the most devastative  (Lahaye et al. , 2018). Our final goal is to provide risk managers with crucial information on the future likelihood of very large fires in southern France. 2 Materials and methods.