dataesr/scientific-paragraphs-categorization huggingface dataset:
This work aims to understand the diurnal, synoptic and seasonal variability of the atmospheric CO 2 concentration observed at each of the five stations of the Paris megacity network from the analysis of the first ∼ 1-year time series (8 August 2010-13 July 2011). We also compare the regional CO 2 concentration datasets to those at MHD in order to assess how relevant this remote site is in defining the CO 2 background level in the Paris region. Section 2 introduces the observation network and reports the data treatment and the quality of the CO 2 time series. We also present the meteorological fields used over the period of study as well as observations of the atmospheric boundary layer (ABL) height, collected at the QUALAIR site (QUA) in the center of Paris, that cover a large part of the period of study  (8 August 2010 -31 March 2011).
Orly Airport is located about 16 km east of GIF. The TRN station, previously described by  Schmidt et al. (2014) , has been running continuously since 2007. It is located about 120 km south of the center of Paris in the region "Centre", within the Orléans forest (50 000 ha). A 200 m transmitter mast was equipped with four sampling levels: 5, 50, 100 and 180 m a. g. l. TRN is located ∼ 13 km northeast of the city of Orléans which has about 120 000 inhabitants. There are a few villages around the station, including Traînou village with 3195 inhabitants in 2012 (http://www. insee. fr/fr/themes/comparateur. asp?codgeo=com-45327). The station is surrounded by agricultural fields and a mixed forest composed of deciduous and evergreen trees. In this study, we use the datasets sampled from the 50 and 180 m levels.
Measurement system and calibration procedure The CO 2 datasets of the CO 2 -MegaParis stations (MON, GON and EIF) were collected from 8 August 2010 to 13 July 2011 using cavity ring-down spectroscopy (CRDS) analyzers (Picarro, model G1302) at 0. 5 Hz. These three stations were identically set up: atmospheric air was pumped through short inlet lines made of Synflex ® (4. 3 mm inner diameter) with a flow rate of 0. 15 L min -1. The cell temperature of the analyzers was controlled at 45 • C and the cell pressure at 140 Torr. At EIF, the analyzer was specifically designed to undergo higher temperatures inherent to the metallic structure of the tower and the cell temperature set point was set higher (60 • C). No specific impact of this set point was ob-served on the measurements. Air was not dried before analysis at the 3 stations and we applied the automatic CO 2 water correction implemented on the CRDS instruments  (Rella, 2010)  to our datasets.
The GON and MON stations were equipped with four high-pressure aluminum cylinders containing gas mixtures of CO 2 in synthetic air (matrix of N 2 , O 2 and Ar) for instrument calibration. Before on-site deployment, the CO 2 concentration of the cylinders was assigned at LSCE on the WMO-X2007 scale by a gas chromatograph (GC) described in  Lopez et al. (2012). It spanned a range from 370 to 500 ppm. At each site, three of the tanks were used for instrument calibration and measured every 2 weeks. The calibration sequence consisted of four cycles (6 h total). Each cycle measured the tanks one after the other for 30 min each. The fourth tank called "target" was run for 30 min every 12 h. The target was used to monitor the instrumental drift and to assess the dataset accuracy and repeatability.
Data processing and quality control The CRDS CO 2 data were calibrated by applying a linear fit to the CO 2 concentration of the calibration tanks as measured by the CRDS analyzer versus the CO 2 concentration as measured by the GC. Gas equilibrium issues implied retaining only the last calibration cycle of the four cycles at MON and GON (and of the two cycles at EIF) to compute the calibration equation. For all of the calibration and target gas cylinders, the CRDS CO 2 concentration was calculated as the average of the last 5 min of each gas. The accuracy of the datasets was calculated as the mean difference between the CO 2 concentration reported by the CRDS analyzer and by the GC for the target gas. The long-term repeatability of each dataset was calculated as the standard deviation of the mean concentration of the target gas reported by the CRDS analyzer over the year of observations.
EIF MON GON Calibration sequence 2 h every 3 months 6 h every 2 weeks 6 h every 2 weeks Target sequence 30 min every 2 weeks 30 min every 12 h 30 min every 12 h Accuracy (ppm) 0. 13 -0. 04 -0. 07 Repeatability (ppm) 0. 38 0. 10 0. 07 (0. 006 • C for EIF). Furthermore, dead volumes in the setup led to instability in the response of the analyzer for 2 min after switching from one gas line to another. These 2 min periods were automatically removed from the datasets. The data were also manually inspected to remove CO 2 spikes due to very local influences (fire training at the GON station, breathing of a maintenance operator on the sampling inlet, etc. ). Very local influences were identified from the short duration of the events (a few seconds to some minutes) and from the large standard deviation of the CO 2 averages associated with these events.
This amounted to less than 1 % of the total datasets, resulting in 91 % of the data validated after the (P , T ) filtering and the manual quality control. The GIF, TRN and MHD data processing and quality check were assessed in previous studies by  Schmidt et al. (2014)  and  Messager et al. (2008) : the repeatability of the 1 h average CO 2 concentration of the target gas is 0. 05 ppm at GIF, 0. 06 ppm at TRN and 0. 05 ppm at MHD. The instrumentation at these three sites is directly linked to the WMO-X2007 scale  (Zhao and Tans, 2006). At each station, some instrument failures occurred during the period of the CO 2 -MegaParis study. The amount of available data points in the final datasets which are all provided as hourly averages is reported in Table  3  for each month and for each site, and is in most cases above 80 %.
In such cloudy weather, the cloud base height is the best estimation for the ABLH. The LBLH is calculated as in fair weather. The ABL height database was constructed by applying this detection method to hourly average lidar data, leading to hourly average ABL depth values. The data were acquired during daytime and weekdays, since an operator had to be on site to shut down the system in the case of rain. The dataset covers 70 % of the year of study.
, 1998; Noilhan and Planton, 1989). It was already validated against observations for 1 week in March 2011 in  Lac et al. (2013) , where it is described in detail. The meteorological fields were extracted for the present study from the model with an output frequency of 1 h at the sampling height of each station. About 1. 5 km north of GIF at the CEA of Saclay (SAC), a mast equipped with meteorological sensors provided wind field data at 10 m a. g. l. from August 2010 to April 2011. In that period, the observed SAC and the modeled GIF meteorological datasets match each other on average within 0. 8 m s -1 for wind speed and 3. 7 • for wind direction, giving additional confidence in the average behavior of the model, at least in such peri-urban areas. For wind fields at MHD, we use a local meteorological hourly observation dataset provided by Met Éireann (http: //www.
MHD also sometimes received continental air masses mostly in winter, spring and autumn. At this station, wind speeds ranged from 0. 1 to 25. 3 m s -1 with a mean at 7 m s -1 and the first and third quartiles at 4. 1 and 9. 5 m s -1 , respectively. Regarding temperature, field observations were available over the full period of study at 100 m a. g. l. at SAC (but not closer to the surface). Since we are mostly interested in rel- ative variations of the temperature at the seasonal scale, we use this dataset as a proxy of the air temperature for all stations located in IdF (although we know that the urban heat island can generate differences of a few degrees between the city and its surroundings, as shown in  Pal et al. , 2012). The hourly temperature dataset collected at SAC 100 m a. g. l. over the whole period of study is shown in Fig.
Air mass back trajectories and wind classification of the CO 2 concentration time series In order to get information about the origin of the air masses that reached our stations, back trajectories from the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT; https://www. arl. noaa. gov/hysplit/ hysplit/) model were calculated for the city of Paris over the full period of study. We used wind fields from the NOAA-NCEP/NCAR reanalysis data archives at 2. 5 • × 2. 5 • and 6 h resolution (http://rda. ucar. edu/datasets/ds090. 0/). The back trajectories were run for 72 h backwards and started at 10 m a. g. l. They were then aggregated on monthly plots that are shown in the Supplement (Fig. S1 ). In all cases, the monthly clusters illustrate the high variability of the origin of the air masses, which could pass over high CO 2 emission areas such as the megacity of London, the Benelux or Ruhr regions before reaching IdF.
airparif. asso. fr). However, there are some other dates (not reported by AIRPARIF as pollution events) during which the CO 2 mixing ratio peaks at the urban and periurban stations and also sometimes at the rural stations (e. g. , 20-25 August and 22-25 October). The wind classification applied on the datasets will be further used to better assess the general features of the CO 2 seasonal cycles, and a much finer wind analysis will be conducted in Sect. 3. 5. 2 to assess the role of local, regional and remote emissions on the CO 2 time series collected within the Paris observation network. 3. 2 CO 2 diurnal cycles 3. 2. 1 Mean CO 2 diurnal cycles Diurnal cycles of atmospheric CO 2 are affected by local sources and sinks, regional transport and ABL dynamics  (Fang et al. , 2014; Garcia et al.
We show in Sect. S5 in the Supplement the hourly means of the LBLH observed at the QUALAIR station during daytime, colored by hour and compared with the level of the EIF station. These data are summarized in Table  4. We recall that the LBLH dataset does not cover the whole period of study but the most interesting parts of it, as it includes the cold months during which the LBLH and dynamics are at their lowest. The period of August to March allows us to observe a large portion of the seasonal cycle of the LBLH which is characterized by a change in its maximum value (on average, 1200 m in summer, 400 m in winter) and in the phase of its development, which starts earlier in summer. We do not have the proper data to quantify precisely this starting time; however, we note that the LBLH is always above the level of EIF in summer, while it is below it (at 301 m on average) before 06:00 UTC in winter (see Table  4 ).
CO 2 seasonal cycle We computed the seasonal cycle of CO 2 at each site, based on the monthly means of our ∼ 1-year datasets and including all hours of the day (Fig. 8a ). The seasonal cycles of the air temperature and available LBLH data (at QUA) are also shown on the same figure. Ignoring the specific case of EIF (Sect. 3. 2. 3), throughout the year we observe that the monthly mean CO 2 concentration increases with the vicinity of the station to larger CO 2 emission sources. The maximum CO 2 enhancement compared to MHD is observed at GON which is our most anthropogenically influenced station (from 6. 8 ppm in July to 27. 5 ppm in December). Similarly to what is observed at the diurnal scale (Sect. 3. 2), differences of several ppm are also observed between our rural sites and MHD, while the differences between the rural/peri-urban/urban stations in IdF are of the same order of magnitude.
Wind speed effect Wind speed is a key factor in modulating the dispersion of CO 2 emissions (e. g. ,  Idso et al. , 2002; Moriwaki et al. , 2006, Rice and Bostrom, 2011; Garcia et al. , 2010 Garcia et al. , , 2012;; Lac et al. , 2013; Turnbull et al. , 2015). Figure  10  shows the mean hourly CO 2 concentrations and the associated standard deviations recorded at GON over the year of study for local afternoon hours only (11:00-15:00 UTC) as a function of the wind speed and colored by wind direction. The CO 2 concentrations have been seasonally adjusted to avoid biases due to seasonal variability (Sect. 3. 4), by applying the following treatment to the CO 2 hourly dataset of each station: (1) computing the annual mean of the dataset; (2) computing the monthly seasonal index for each month by calculating the ratio between the monthly mean and the annual mean of the dataset; (3) interpolating the monthly seasonal indexes at an hourly scale over the full period of study; and (4) dividing the CO 2 hourly dataset by the hourly seasonal index.
The distance of the rural stations MON and TRN to inner Paris is about 30 and 100 km, respectively. Taking into account these distances, we set the hypothesis that we can assess the influence of local emissions using hourly means observed in low wind speed conditions (less than 3 m s -1 ) while the influence of remote emissions can be analyzed using data recorded in relatively high wind speed conditions (more than 8 m s -1 ). This relies on considering the time given for atmospheric mixing of local and regional emissions (dominant at low to moderate wind speeds) versus their ventilation (dominant at high wind speeds): the integration of local and regional emissions into an air mass, which carries the signature of remote emissions when it is upwind of Paris, gets higher with decreas- ing wind speeds.
For example, for wind speeds lower than 3 m s -1 (11 km h -1 ), it takes 1 h or more for any air mass to flow over the center of Paris (∼ 10 km of diameter), allowing some time for local emissions to get mixed into the air mass, while at 8 m s -1 or more (∼ 29 km h -1 ) it takes about 20 min or less, allowing less time for the atmospheric integration of local to regional emissions. In the middle range of wind speed (3-8 m s -1 ), we expect most of the CO 2 variability to be driven by the influence of the regional emissions coming from Paris. For all of the regional stations, Fig. 11  shows the pollution roses of the mean afternoon CO 2 concentration binned by wind speed (ws) and wind direction (wd) with a resolution of 1 m s -1 for ws and 10 • for wd. We use here the CO 2 hourly concentration dataset that has been season-ally adjusted (Sect.
There are a few values outside of these ranges that are forced to the closest range bound value. To facilitate the comparison between the stations, the highest complete wind speed circle visible on the plots is set at 10 m s -1 in all cases. For MON, GON and GIF, all the data are plotted when taking this wind speed threshold. For TRN and EIF, wind speeds can reach higher values due to the elevation of these stations (during the afternoon, up to 15 m s -1 at TRN and 25. 5 m s -1 at EIF; at night, up to 15 m s -1 at TRN and 22 m s -1 at EIF). Although they represent only a minor fraction of the datasets, some of the TRN and EIF data are thus not apparent in Fig. 11 : the plots for the full wind speed ranges encountered at EIF and TRN are given in Sect. S6b (daytime) and S6c (nighttime) in the Supplement.
Influence of remote emissions (> 100 km) The back trajectories (Fig. S1  in the Supplement) show that Paris was exposed to a range of synoptic air masses over the period of study, including clean oceanic ones and others with CO 2 enriched by remote anthropogenic emissions especially from Benelux, the Ruhr area and the London megacity. Relatively high CO 2 concentrations (> 410 ppm) were observed for high wind speeds (> 8 m s -1 ) in the 0-45 • NNE sector at the three stations located relatively close to the ground level  (MON, GON and GIF). For the elevated stations (EIF and TRN), such concentration values also occur, but as expected at higher wind speeds (> 12-14 m s -1 ), reaching at least the 410 to 420 ppm range at all of the stations. The fraction of data falling in these (ws, wd) bins is large enough to consider these high concentration values to be statistically representative.
Conclusions This work forms the first study of ∼ 1 year of measurements of atmospheric CO 2 in the region of the Paris megacity. We analyzed the CO 2 diurnal, synoptic and seasonal variability at five stations in that region and carried out a comparison with the CO 2 dataset recorded at the MHD remote site. In all stations of the Paris network, the influences of anthropogenic emissions, biospheric fluxes, atmospheric dynamics and synoptic wind patterns were shown to be key factors of the diurnal, weekday/weekend and seasonal variability of the atmospheric CO 2 concentrations. At low wind speed, the stations receive local emissions from sources that could extend to a few kilometers, leading to a build-up of the CO 2 concentration, especially over Paris at the top of the Eiffel Tower during daytime and at the GON peri-urban station, where the concentration increase can reach up to 60 ppm.
Ideally, the network will also be designed to position the urban and peri-urban downwind sites on these same wind direction axes. The CO 2 datasets presented here provide the basis for a study conducted on atmospheric inversion modeling of the Paris CO 2 emissions  (Staufer et al. , 2016) , where we quantified the need for eight more sites in the suburban/urban border of Paris to improve our top-down approach. Furthermore, our analysis shows the strong coupling that exists between the CO 2 concentration diurnal cycle and the boundary layer height cycle at the elevated stations and especially at EIF. We also highlighted how the high variability observed at EIF in the afternoon reflects the coupling of the highly variable urban emissions in the vicinity of the station with fluctuations of the wind speed and direction but also possibly with atmospheric fine-scale dynamic processes.
Data availability. The CO 2 -MegaParis datasets are available from the AERIS/ESPRI data center via the following secure FTP link (http://cds-espri. ipsl. fr/espri/pubipsl/co2-megaparis/ftp. html) upon simple request to the first author. The ICOS datasets are available from the ICOS database at LSCE. Please contact the first author for further information (irene. remy-xueref@univ-amu. fr). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-18-3335-2018-supplement.
Crystallization and structure determination of the Hvo 0019-Trm112 complex Crystals of the Hvo 0019-Trm112 complex were obtained by mixing 1 L of protein complex (15 mg/ml in buffer B: 1 M NaCl, 20 mM Tris-HCl pH 7. 5, 5 mM ␤mercaptoethanol, 10 M ZnCl 2 ) with an equal volume of crystallization solution (0. 1-0. 3 M NaCl; 0. 1 M Bis-Tris pH 5. 5; 20-25% w/v PEG3350) at both 4 • C and 24 • C. Crystals appeared within 4-5 h and reached their maximum size (400 m length) within 3 days. Crystals were cryo-protected by transfer into their crystallization condition supplemented with progressively higher glycerol concentration up to 30% and then flash-frozen in liquid nitrogen. All datasets were collected on beam-line Proxima-2A (Synchrotron SOLEIL, Saint-Aubin, France) at 100 K. The structure was solved by Sulfur-SAD (Single Anomalous Dispersion) using a highly redundant dataset (seven datasets of 1400 • each collected on different regions of a single crystal) collected at 2.
0664 Å to get strong anomalous signal from sulfur atoms (see Supplementary Table  S3  for statistics). A 1. 35 Å resolution dataset was obtained by merging two datasets collected at different crystal-detector distances on another crystal (Supplementary Table  S3 ). Data were processed with XDS (66) and scaled using XS-CALE. All crystals belonged to space group P2 1 2 1 2 1 with two copies of Hvo 0019-Trm112 complex per asymmetric unit. Twenty-four sulfur sites (eleven per Hvo 0019-Trm112 complex and two from S-adenosyl-L-homocysteine (SAH) molecules bound to Hvo 0019 methyltransferase) were successfully located using SHELXD  (67). Experimental phasing followed by density modification were performed with the PHASER EP and RESOLVE programs implemented in the PHENIX suite  (68) (69) (70) (71).
The final model was obtained by iterative cycles of building and refinement performed using COOT  (72)  and BUSTER  (73)  programs, respectively (Supplementary Table  S3 ). The final model for Hvo 0019-Trm112 complex contains Hvo 0019 residues 2-227 (including the first histidine from the His 6 -tag) and 2-12, 15-231 (including five histidine residues from the His 6 -tag) for protomers A and B, respectively as well as HvoTrm112 residues 1-59 and 1-25, 29-58 for protomers C and D, respectively. In addition, 387 water molecules, two SAH molecules and two glycerol molecules from the cryoprotectant solution have also been modeled. The coordinates and structure factors files are available from the Protein Data Bank (PDB) under accession code 6F5Z.
Anita Marchfelder, Ulm University, Germany) as negative control. Before cell lysis, an in vivo cross-linking step was performed in the co-IP protocol by adding 1% formaldehyde as described by Fischer and colleagues (65). This step was added for the following reasons. First, an initial co-IP experiment with no cross-linking resulted in a low number of specific peptide spectra in the mass spectrometry analysis (data not shown). Second, HvoTrm112 could interact with some of its partners in a transient manner. Third, protein-protein interactions in this organism might be maintained by high salt concentration while the low salt concentration used during the co-IP protocol could induce complex dissociation and/or result in protein aggregation (Figure  1A ). Proteins co-immunoprecipitated with HvoTrm112-Flag or with the Flag-tag control were identified by mass spectrometry as described in the Supplemental methods.
Structure of a Trm112-MTase complex from H. volcanii To gain insight into the molecular bases responsible for the interaction between HvoTrm112 and its interacting MTase partners, different HvoTrm112-MTase complexes were subjected to crystallization trials in the presence or in the absence of SAM. Crystals diffracting up to 1. 35 Å resolution were obtained for the Hvo 0019-Trm112 complex. The structure of this complex was solved at 2. 5 Å resolution by the Sulfur-SAD method (Supplementary Figure  S8 ) by taking advantage of the high number of sulfur atoms in this complex (11, excluding the initial methionine from Hvo 0019, which is very likely to be excised in E. coli  (80) ). The structure was further refined at high resolution using a 1. 35 Å resolution native dataset to yield a final model with R and R free values of 18.
HvoTrm112 structure consists of only the zinc knuckle domain previously observed in eukaryotic Trm112 proteins (rmsd values of 1. 1-1. 4 Å over around 55 C␣ atoms;  (25, 27, 33, 47) ), including a small Nterminal ␣-helix (␣1) and a four-stranded anti-parallel ␤sheet (Figure  4B ). Contrary to eukaryotic Trm112 proteins and some structurally similar bacterial proteins (PDB code: 2KPI) of known structures, there is no zinc atom bound to HvoTrm112 in agreement with the absence of conservation of the cysteine residues involved in zinc coordination. The interface between HvoTrm112 and Hvo 0019 has an overall area of 975 Å2 and is slightly smaller than the previously described interfaces between eukaryotic Trm112 and its MTase partners  (33, 47). This most likely results from the absence of the helical domain, specific to eukaryotic Trm112 proteins, that was in part contributing to these interactions.