dataesr/scientific-paragraphs-categorization huggingface dataset:
This study investigates the role of biomass burning and long-range transport in the anomalies of carbon monoxide (CO) regularly observed along the tropospheric vertical profiles measured in the framework of the In-service Aircraft for a Global Observing System (IAGOS). Considering the high interannual variability of biomass burning emissions and the episodic nature of long-range pollution transport, one strength of this study is the amount of data taken into account, namely 30 000 vertical profiles at nine clusters of airports in Europe, North America, Asia, India and southern Africa over the period 2002-2017. As a preliminary, a brief overview of the spatiotemporal variability, latitudinal distribution, interannual variability and trends of biomass burning CO emissions from 14 regions is provided.
The distribution of CO mixing ratios at different levels of the troposphere is also provided based on the entire IAGOS database (125 million CO observations). This study focuses on the free troposphere (altitudes above 2 km) where the long-range transport of pollution is favoured. Anomalies at a given airport cluster are here defined as departures from the local seasonally averaged climatological vertical profile. The intensity of these anomalies varies significantly depending on the airport, with maximum (minimum) CO anomalies of 110-150 (48) ppbv in Asia (Europe). Looking at the seasonal variation of the frequency of occurrence, the 25 % strongest CO anomalies appear reasonably well distributed throughout the year, in contrast to the 5 % or 1 % strongest anomalies that exhibit a strong seasonality with, for instance, more frequent anomalies during sum-H.
Petetin et al. : The role of biomass burning and during fall in equatorial Asia. In southern Africa, the Windhoek airport was mainly impacted by fires in Southern Hemisphere Africa and South America. To our knowledge, no other studies have used such a large dataset of in situ vertical profiles for deriving a climatology of the impact of biomass burning versus anthropogenic emissions on the strongest CO anomalies observed in the troposphere, in combination with information on the source regions. This study therefore provides both qualitative and quantitative information for interpreting the highly variable CO vertical distribution in several regions of interest.
mertime in the northern United States, during winter/spring in Japan, during spring in south-east China, during the nonmonsoon seasons in south-east Asia and south India, and during summer/fall in Windhoek, Namibia. Depending on the location, these strong anomalies are observed in different parts of the free troposphere. In order to investigate the role of biomass burning emissions in these anomalies, we used the SOFT-IO (SOft attribution using FlexparT and carbon monoxide emission inventories for In-situ Observation database) v1. 0 IAGOS added-value products that consist of FLEXible PARTicle dispersion model (FLEXPART) 20-day backward simulations along all IAGOS aircraft trajectories, coupled with anthropogenic Monitoring Atmospheric Composition and Climate (MACC)/CityZEN EU projects (MACCity) and biomass burning Global Fire Assimilation System (GFAS) CO emission inventories and vertical injections.
, 2010)  or BORTAS (BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites)  (Palmer et al. , 2013). These campaigns have provided detailed information on fire plumes but remain somewhat limited by their short time coverage. Frequent profiles with high vertical resolution are essential for better characterizing biomass burning plumes and their transport. In the framework of the MOZAIC (Measurement of Ozone by Airbus In-service aircraft) programme and its successor, the IAGOS (In-service Aircraft for a Global Observing System) European Research Infrastructure (ERI) (the MOZAIC-IAGOS programmes are hereafter denoted IA-GOS), a large dataset of O 3 and CO vertical profiles (obtained during ascent and descent phases) has been available for many parts of the world since 1994 and 2002, respectively.
, 2018). Thus, this study addresses this problem by using the SOFT-IO tool  (Sauvage et al. , 2017b ) that couples FLEXible PAR-Ticle dispersion model (FLEXPART) Lagrangian backward simulations with CO emission inventories. Although some results will be shown in the tropics, this study will mainly focus on the northern midlatitudes where most IAGOS profiles are available. In total, about 30 000 CO profiles are included in this analysis. To our knowledge, this is the first study that addresses the question of the biomass burning impact on tropospheric CO based on such a large dataset of in situ measurements and over such a long period (16 years). The input data and the modelling tools used in this study are described in Sect. 2. A description of the CO vegetation fire emissions over the period 2002-2017 is provided in Sect.
IAGOS observations This study mostly relies on the CO observations available in the framework of the IAGOS ERI (https://www. iagos. org, last access: 1 April 2018)  (Petzold et al. , 2015). Observations have been performed by commercial aircraft from several airline companies since 1994 for ozone and 2002 for CO. In both the MOZAIC and IAGOS programmes, the same instruments are used in all aircraft. During the 2011-2014 overlapping years, intercomparisons have been systematically performed between MOZAIC and IAGOS, demonstrating a good consistency in the dataset  (Nédélec et al. , 2015). In MOZAIC, ozone was measured using a dual-beam UVabsorption monitor (time resolution of 4 s) with an accuracy estimated at about ±2 ppbv / ±2 %  (Thouret et al. , 1998) , while CO was measured by an improved infrared filter correlation instrument (time resolution of 30 s) with a precision estimated at ±5 ppbv / ±5 %  (Nédélec et al.
, 2003). In IA-GOS, both compounds are measured with instruments based on the same technology used for MOZAIC, with the same estimated accuracy and the same data quality control. A more detailed description of the IAGOS system and its validation can be found in  Nédélec et al. (2015). Of the 300 or so airports visited for two decades, this study focuses on those with sufficient observations to build reliable seasonally averaged climatological vertical profiles. In order to increase the amount of available data and fill data gaps, airports less than 500 km apart are combined into airport clus-ters following the description given in Table  1. The location of these airports is shown in Fig. 1. We consider only the profiles available in a validated status (i. e. after post-flight calibration) in the IAGOS database over the period 2002-2017.
Source apportionment with the SOFT-IO tool In order to get information about the recent contributions of the different CO emission sources, we use the recently developed SOFT-IO v1. 0 tool  (Sauvage et al. , 2017a, b). Here, we only give a brief overview of SOFT-IO; more details can be found in the reference paper of  Sauvage et al. (2017b). The SOFT-IO data are freely available in the IAGOS database (https://www. iagos. org, last access: 1 April 2018) (https:// doi. org/10. 25326/3, last access: 1 April 2018;  Sauvage et al. , 2017c). Along all aircraft trajectories, SOFT-IO couples FLEX-PART retroplume simulations over 20 days with anthropogenic and biomass burning CO emission inventories. The meteorological fields are taken from the ECMWF analysis and forecast. At any given point in the IAGOS trajectories, it thus provides an estimate of the primary CO contribution (in ppbv) of the recent (20 days or less) worldwide emissions.
Anthropogenic and biomass burning contributions are computed separately in order to discriminate between both origins. Additionally, the contributions are quantified for the 14 different source regions defined in Global Fire Emissions  Table  1. Description of airport clusters (the number of profiles is reported in brackets). Database (GFED) emissions (see Fig. 1 ). Among the different emission inventories available, we will use in this study the monthly Monitoring Atmospheric Composition and Climate (MACC)/CityZEN EU projects (MACCity) anthropogenic emissions  (Diehl et al. , 2012; Lamarque et al. , 2010; Granier et al. , 2011; van der Werf et al. , 2006)  and the daily Global Fire Assimilation System (GFAS) biomass burning emissions  Kaiser et al. , 2012). As GFASv1. 2 is not available in 2002, we use GFASv1.
Overview of the CO vertical profiles measured by IAGOS aircraft 4. 1 Distribution of the CO mixing ratios in the entire IAGOS database An overview of the distribution of all CO mixing ratios measured by IAGOS aircraft during the period 2002-2017 is shown in Fig. 4  (see Figs. S4-S7 for the seasonal distributions). Specifically in this section, the entire IAGOS dataset is taken into account (not only the tropospheric profiles available at the clusters of airports introduced in Sect. 2. 1) in order to give the largest view on the CO mixing ratios measured in the atmosphere since the beginning of the MOZAIC programme. Note that these distributions are not calculated based on the mean mixing ratios over 250 m thick layers but on the individual measurements. The statistical robustness is ensured by the large number of observations that reaches about 125 million.
g. l. are considered. In addition, this study will focus on the strongest positive CO anomalies. Different thresholds, p, expressed as a percentile of the CO anomalies distribution at a given airport cluster will be discussed and, for clarity, the corresponding subset will be annotated CO >p. For instance, CO >75 and CO >90 represent the 25 % and 10 % highest CO anomalies among the whole database available at a given airport cluster, respectively (and thus CO >0 represents the whole anomalies dataset at this airport cluster). Note that all 250 m width layers are treated independently from each other. This means, for instance, that on a given profile, one large pollution plume observed between 5 and 6 km altitude will be treated as four (250 m thick) anomalies. For each CO anomaly, both biomass burning (C BB ) and anthropogenic (C AN ) contributions (in ppbv) are calculated with the SOFT-IO tool (Sect.
SOFT-IO contributions The SOFT-IO tool was evaluated in  Sauvage et al. (2017b)  over the entire IAGOS dataset. Evaluation results have shown that SOFT-IO detects more than 95 % of all observed CO plumes. The biases in the CO enhancements are usually lower than 10-15 ppbv in most regions, although the agree-ment is lower in the middle troposphere possibly due to numerous thin plumes of low intensity  (Sauvage et al. , 2017b). Note that, as previously explained in Sect. 5. 1, the way we define the CO anomalies in our study (departure from the seasonal climatological profile) differs from  Sauvage et al. (2017b)  (departure from a linear fit of the CO vertical profile above 2 km, plus additional conditions on the excess of CO; more details can be found in Sect. 3. 4 of  Sauvage et al. , 2017b).
Vertical distribution of SOFT-IO contributions The climatological vertical distribution of the different anthropogenic and biomass burning contributions is shown in Fig. 23  for the entire dataset (CO >0 ). At the Germany cluster, EURO (TENA) contributions are the strongest below 6 km (10 km). The BONA contributions are the strongest in the lower free troposphere and decrease with altitude more quickly than the TENA contributions. The contributions from Asian source regions (CEAS, SEAS) reach their maximum higher in altitude, roughly between 6 and 12 km. In particular, the SEAS contribution peaks at about 10 km. At the clusters in North America, the TENA emissions mainly impact the lower altitudes, while strongest contributions from CEAS and SEAS are found higher in the troposphere, between 4 and 12 km (with a maximum between 6 and 10 km).
7±1. 0 % yr -1 ) and in Southern Hemisphere South America (-5. 1 ± 3. 8 % yr -1 ) maybe due to a reduced deforestation over the recent years. We provided an altitude-dependent distribution of CO mixing ratios based on the entire IAGOS dataset (about 125 million observations) in order to give the most general view of the CO levels typically encountered in the troposphere. Concerning the vertical distribution of the extreme CO mixing ratios registered by IAGOS over 2002-2017, the 99th (99. 9th) percentile decreases with altitude from 750 (1619) ppbv below 1 km to 162 (229) ppbv above 12 km altitude. The overview of all individual CO vertical profiles at the different airport clusters highlights frequent but irregular strong CO plumes in the free troposphere at most locations. In order to investigate the role of biomass burning relative to anthropogenic emissions, we simulated the recent primary CO contribution from both types of sources along all IA-GOS trajectories with the recently developed SOFT-IO tool.
Data availability. No new measurements were made for this review article. The IAGOS data are available on http://www. iagos. fr (IA-GOS CO profiles, last access: 1 May 2018) or directly via the Aeris website (http://www. aeris-data. fr). The SOFT-IO products will be made available through the IAGOS central database and are part of the ancillary products (https://doi. org/10. 25326/3, Sauvage et al. , 2017a, b, c). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-18-17277-2018-supplement. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. The authors acknowledge the strong support of the  European Commission, Airbus  and the airlines (Lufthansa,  Air France ,  Austrian ,  Air Namibia ,  Cathay Pacific ,  Iberia  and  China Airlines , so far) who have carried the MOZAIC or IAGOS equipment and performed the maintenance since 1994. In its last 10 years of operation, MOZAIC has been funded by  INSU-CNRS (France) ,  Météo-France ,  Université Paul Sabatier (Toulouse, France)  and  Jülich Research Center (FZJ, Jülich, Germany ). IA-GOS has been additionally funded by the  EU projects IAGOS-DS and IAGOS-ERI. The MOZAIC-IAGOS database is supported by  Aeris  ( CNES  and  INSU-CNRS ). Edited by: Jens-Uwe Grooß Reviewed by: two anonymous referees.
| Species richness data Species richness data for land snails for each island of the dataset were compiled from existing literature and species checklists (references in Supporting Information Table  S1 ). Infraspecific entities were grouped into their respective specific taxonomic rank. Species status was standardized based on MolluscaBase (2017; https:// www. molluscabase. org). We considered only islands for which complete lists were available. Recorded extinct species were included in the dataset, while species presumed to be introduced were excluded  (Cameron et al. , 2013; Triantis, Rigal, et al. , 2016). Land snails were classified according to chorotype (a group of species with their distribution restricted to a certain region; see Table  1  for an overview of chorotype acronyms) as: native non-endemic (N S ), multiple-island endemic (MIE S ) and single-island endemic (SIE S ).
5% of the last ~800 kyr sea levels were higher; Figure  1 ). We obtained the palaeo-configuration of all islands at the respective sea level stands (SLI MED , SLI FREQ , SLL -122 and SLL GM ) from the Palaeo-Islands and Archipelago Configuration (PIAC) database  (Norder et al. , 2018). For each palaeo-configuration, we calculated delta area (dA; km 2 ) per reference sea level as the log-transformed absolute difference between current area and palaeo-area. Palaeo-connectedness (PC) was calculated for each respective sea level stand as the number of present-day islands that were connected within a single palaeo-island at a lower sea level. For the highest sea level we used the current area (CA) from the Database of Global Administrative Areas (GADM; https://www. gadm. org/version1), as reported in  Weigelt et al.
(2016). Current isolation (CI) was calculated as the distance to the nearest other island for which species data were available. As island age is known to influence endemism patterns on individual islands and archipelagos  (Peck, 1990; Whittaker et al. , 2008) , we tested for correlation (herein Pearson's correlation) between each of the aforementioned archipelago configuration variables and island age (island ages and sources in Supporting Information Table  S4 ). All correlations were low and non-significant (Supporting Information Table  S5  and Figure  S1a-h ). This is unsurprising; although volcanic and erosional processes show some age-progressive trends  (Whittaker et al. , 2008) , it is problematic to stereotype the consequences of such trends for area and connectivity through sea-level fluctuations.
At a sea level of -122 m MSL (SLL -122 ) and during the LGM (SLL GM ), 12 and 13 islands were connected, respectively. Pearson's correlations of the predictor variables PC and palaeo-area range from r = 0. 28 to r = 0. 55, with the highest values for palaeo-area and PC at the same sea-level stand (Supporting Information Table  S6 ). PC values at different sea levels are strongly correlated, with lowest correlations between SLI MED and SLL GM (r = 0. 67) and highest correlations between SLL -122 and SLL GM (r = 0. 97). The correlations between palaeo-area at different sea levels show the same pattern: palaeo-areas at SLI MED and SLL GM are least correlated (r = 0. 87), while the palaeo-areas at SLL GM and SLL -122 can be considered identical (r > 0. 99) for our dataset of 53 volcanic oceanic islands.
For land snails, our data represented 1,903 species, consisting of 1,430 SIE S , 302 MIE S and 171 native species N S. In total, 1,627 SIE P were included in our dataset. Native species richness for angiosperms could not be calculated from the available data because we only had data on species richness per island but no species identities (see  Weigelt et al. , 2016). For land snails, the mean proportion of each chorotype across islands was 28. 7% for N S , 34% for MIE S and 37. 2% for SIE S. Mean inter-island chorotype proportion for angiosperms was 92. 9% for N P and 7. 1% for SIE P.
Hence land snails can produce more SIE in any given island area, and show stronger effects of island area being formerly larger than do angiosperms. Conversely, a greater mobility of plants could connect "would-be endemics" or replace them with fresh colonists and as such reduce the effect of area change. This reasoning seems in line with the chorotype proportions for SIE in our dataset, which are high for land snails and low for angiosperms. An interesting avenue for future research would be therefore to further explore the underlying mechanisms that might explain the differential response of both taxa to palaeo-area. Our results indicate a negative relationship between PC and SIE richness of both taxa. The decreasing number of SIE S and SIE P with PC might result from higher levels of gene flow, hindering diversification into distinct lineages (cf.
5 Ma (Hawaii Island) and 5. 1 Ma (Kauai). Hawaii itself is the only island that is volcanically active, all others being disconnected from the hotspot and inactive for at least 0. 75 Myr. This contrasts with the complex geological setting of the Azores, with a western group of two islands located on the North American plate, and a central and eastern group (of five and two islands, respectively) located at the junction between the Eurasian and Nubian lithospheric plate  (Ramalho et al. , 2017) , and no linear age progression from one side of the archipelago to the other  (Ávila et al. , 2016). In addition, some islands in our dataset are younger than the last nine glacial-interglacial cycles (~800 ka) we used to calculate the most frequent and median sea levels. However, due to the recurrent character of sea-level oscillations, later stages of these cycles will nonetheless have affected younger islands.
DATA ACCE SS I B I LIT Y The data supporting the results are archived in ISLANDLAB (https://islandlab. uac. pt/software/ver. php?id=32) and PANGAEA (https://doi. pangaea. de/10. 1594/PANGAEA. 893265) under the CC BY 4. 0 license (https://creativecommons. org/licenses/by/4. 0/).
In 2005, the European Commission funded the NORMAN project to promote a permanent network of reference laboratories and research centers, including academia, industry, standardization bodies, and NGOs. Since then, NORMAN has (i) facilitated a more rapid and wide-scope exchange of data on the occurrence and effects of contaminants of emerging concern (CECs), (ii) improved data quality and comparability via validation and harmonization of common sampling and measurement methods (chemical and biological), (iii) provided more transparent information and monitoring data on CECs, and (iv) established an independent and competent forum for the technical/scientific debate on issues related to emerging substances. NORMAN plays a significant role as an independent organization at the interface between science and policy, with the advantage of speaking to the European Commission and other public institutions with the "bigger voice" of more than 70 members from 20 countries.
In the light of the experience acquired, NORMAN is now committed to the further development of the current scheme with the extension of the original NOR-MAN list of substances (ca. 900 compounds) to a much larger list of several thousands of compounds. This goes along with the establishment of dynamic links with existing databases (such as, for example, the US EPA Comp-Tox Chemistry Dashboard  [22] ) for a more powerful and systematic retrieval of supporting data for prioritization of substances, and the introduction of new indicators for better integration of the results from novel monitoringbased approaches, such as suspect and non-target screening (NTS) as well as effect-based methods (EBM) in the prioritization process. NORMAN fosters an integrative approach for the prioritization of CECs  [23] , which relies on three pillars: the first is EMPODAT  [24] , a powerful database system which has been developed to store the monitoring data collected by NORMAN members and as a tool for use by regulators and scientists alike for the prioritization of CECs.
Its added value will be further increased in the future thanks to its full integration into the European Information Platform for Chemical Monitoring (IPCHEM)  [4] , which will improve systematic exploitation of raw monitoring data to support prioritization exercises. This is closely connected with the second pillar, the EMPODAT ECOTOX module, a platform for systematic collection and evaluation of the relevance and reliability of ecotoxicity studies which aims to become an essential tool for the European community of ecotoxicologists for the derivation and harmonization of predicted no-effect concentration (PNEC) threshold levels. NTS, the third pillar, includes recent workflows for the application and evaluation of high-resolution mass spectrometry for identification of suspects and unknowns.
Data collection and data management Reliable identification and prioritization of relevant CECs is strongly dependent on the quality and quantity of archived monitoring data. The development of databases such as EMPODAT and improvement of data exchange have been NORMAN's core business since the start of the project. A clear need was identified at the time of the launch of the NORMAN project, which was confirmed by the first NORMAN Databases workshop in 2011  [27] , where experts concluded that, in spite of the numerous chemical monitoring activities carried out in the EU and worldwide and the significant amounts of data generated by the scientific community within research projects, environmental monitoring data were not systematically collected at the EU level  [28]. EMPODAT  [24]  is today the largest database on emerging substances worldwide, with about ten million data records for more than 500 emerging substances.