dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses All statistical analyses were conducted with the IBM Statistical Package for Social Sciences version 28. Continuous variables are described as means with standard deviations or medians with interquartile ranges. Bivariate analyses were conducted with Chi-square-, Kruskal-Wallis, Mann-Whitney U tests, Spearman's rank, or Pearson's correlations, as applicable. All tests were two-tailed. The significance level was set at ≤0. 05. The IDS-C total score, the YMRS total score and Sleep disturbance categories (Insomnia, Hypersomnia, No Sleep Disturbances), were tested against 1) the average number of units of nicotine used per day, both separately for cigarette and snus use and total nicotine use (cigarettes + snus) and 2) the average number of alcohol units used during a) the last two weeks and b) the last six months.
We investigated the relationship between sleep disturbances (Insomnia, Hypersomnia, No Sleep Disturbances) and nicotine use (categorized into three use levels) with a multinomial logistic regression analysis, with 'No Sleep Disturbances' as the reference group. Based on previous literature and a priori hypotheses, we considered the following variables as possible confounders of the relationship between sleep problems and alcohol or nicotine use: age, sex, BD type, current manic symptoms, current depressive symptoms, current anxiety symptoms, recent use of any illicit drugs, recent use of cannabis, and medication use. Medication use comprised three separate variables covering the use of antipsychotics, lithium, and antiepileptics (use vs. no use). In the final analysis, 32 cases were not included because of missing data for one or more variables (n = 421).
Ammonia (NH3) is an important air pollutant which, as precursor of fine particulate matter, raises public health issues. This study analyzes 2. 5-years of NH3 observations derived from ground-based (miniDOAS) and satellite (IASI) remote sensing instruments to quantify, for the first time, temporal variabilities (from interannual to diurnal) of NH3 concentrations in Paris. The IASI and miniDOAS datasets are found to be in relatively good agreement (R>0. 70) when atmospheric NH3 concentrations are high and driven by regional agricultural activities. Over the investigated period (January 2020 -June 2022), NH3 average concentrations in Paris measured by the miniDOAS and IASI are 2. 23 μg. m -3 and 7. 10x10 15 molecules. cm -  , respectively, which are lower or equivalent to those documented in urban areas.
IASI The Infrared Atmospheric Sounding Interferometer (IASI,  [Clerbaux et al. , 2009] ) was launched first in 2006 as part of the Metop satellite series to monitor atmospheric composition twice a day (at 9:30 and 21:30) globally. IASI measures atmospheric spectra in the thermal infrared region with an elliptical pixel footprint of 12 × 12 km at nadir and 20 × 39 km at the far end of the swath. In this study, we use NH3 columns derived from IASI morning (9:30) overpasses onboard Metop B and C from January 2020 to June 2022. When comparing IASI and miniDOAS NH3 concentrations in Paris, we have selected coincident observations made within the same hour. In this work, we use version 3 of the ANNI-NH3 reanalyzed dataset  [Van Damme et al. , 2021; Guo et al. , 2021; Viatte et al. , 2022].
Meteorological data from ERA-5 Meteorological parameters originate from the ERA-5 database of the European Centre for Medium-Range Weather Forecasts (ECMWF,  [Hersbach et al. , 2020] ). It is constituted from observations https://doi. org/10. 5194/egusphere-2023-963 Preprint. Discussion started: 26 May 2023 c Author(s) 2023. CC BY 4. 0 License. recalibrated on global data assimilation models at a 30km spatial resolution. In this work, we used the hourly data of the temperature at 2 m, the precipitation, the u and v components of the wind at 100 m and the height of the boundary layer, taken from the grid cells in which Paris is located.
Back-trajectories and Potential Source Contribution Factor (PSCF) analysis To study the transport affecting concentration of ammonia in Paris, we use the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT,  [Stein et al. , 2015] ) to calculate backward trajectories of air masses ending at altitudes of 100 m (above sea level which corresponds to the altitude of the miniDOAS location) between January 2020 and June 2022. Meteorological data used in the runs are from the National Centers for Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR) reanalysis at 2. 5-degree global latitudelongitude projection. We ensure by visual inspections that the back trajectories using a 2. 5° resolution meteorological dataset are similar to using a finer meteorological dataset at 0.
25° resolution (GFS). Due to the short and highly variable lifetime of NH3, ranging between 2-4 hours  [Dammers et al. , 2019]  and 12-hours  [Evangeliou et al. , 2021] , we simulated an average 6-h backward trajectories with an interval of one hour. Combining the hourly NH3 observations from the miniDOAS, the potential emission sources of NH3 were analyzed. The Potential Source Contribution Factor (PSCF) method  [Malm et al. , 1986]  is used to identify source regions affecting air quality in term of NH3 concentration in Paris between January 2020 and June 2022. This method is now commonly used in atmospheric science  [Wang et al. , 2023; Qadri et al. , 2022; Martino et al. , 2022; Biuki et al. , 2022; Ren et al. , 2021; Zachary et al. , 2018; Jeong et al. , 2011]  and combines the concentration dataset with air parcel backtrajectory to identify preferred pathways producing high observed NH3 concentrations in Paris.
, 2014]. In addition, we demonstrate that correlations between satellite and ground-based NH3 observations are independent of atmospheric temperature and planetary boundary layer height (PBLH, Figure  S1 ). A trade-off between good correlations and keeping a sufficient number of collocations is found when comparing NH3 concentrations from ground-based measurements located in the Paris city-center with the IASI dataset in a 50 km box. We chose for the rest of the analysis IASI dataset within the 50 km box to analyze spatiotemporal variabilities of NH3 in Paris.
Seasonal and monthly NH3 variabilities in Paris Unlike the weak interannual variability of NH3 concentrations in Paris, both ground-based (miniDOAS) and satellite (IASI) measurements reveal high seasonal variabilities of NH3 concentrations (Figure  3 ). In spring, NH3 concentration measured in Paris by the miniDOAS and IASI are on average 3. 34 ± 2. 67 μg. m -3 and 1. 21x10 16 ± 1. 57x10 16 molecules. cm -  , respectively. These springtime NH3 concentrations are enhanced by a factor of two compared to the other seasons, which is consistent with the fertilizer application periods over the nearby agricultural fields. Both datasets show that NH3 concentrations in March and April are 2 to 3 times higher than the other months. Precipitation for these months is also lower than in February on average (see supplementary Figure  S2 ).
Both datasets show an increase of ammonia concentrations during the week, reaching a maximum on Thursday (2. 21 μg. m 3 for the miniDOAS and 5. 90x10 15 molecules. cm -  for IASI). The weekly cycle of IASI measurements in Paris is almost analogous to the one observed over European agricultural areas with low concentrations observed on Mondays and an accumulation of ammonia during the week  [Van Damme et al. , 2022]. In addition, the IASI NH3 weekly cycle averaged over 2. 5years of measurements in Paris is very similar to the NH3 weekly cycle measured in spring (Figure  5 ) when agricultural activities intensify. Monitoring similar NH3 weekly variability in the urban area of Paris demonstrates that agricultural activities in the surrounding areas control the variability of ammonia in Paris on average over the whole season.
The NH3 weekly cycle observed over 2. 5-years of measurements from the ground-based miniDOAS and the IASI satellite observations show, however, relatively low NH3 concentrations on Saturday and Sunday. The cycle is less pronounced for IASI measurements. Ammonia concentrations observed over the weekend by the miniDOAS and IASI are lower by 25% and 20% compared to NH3 concentrations averaged over the weekdays in Paris. When considering intraweek variabilities by seasons (Figure  5 , four lower panels), one can observe that both IASI and the miniDOAS dataset reveal similar NH3 weekly cycles. The NH3 miniDOAS measurements and coincident IASI total columns measured in a 50km box around Paris exhibit lower concentrations over the weekends compared to weekdays for all seasons, except in spring for which higher NH3 concentrations are found on Wednesday and Sunday.
variabilities of NH3 concentrations in Paris are assessed using joined observations of ground-based (miniDOAS) and satellite (IASI) remote sensing observations from January 2020 to June 2022. We present the first relatively long (2. 5-years) and continuous record of hourly NH3 concentrations in Paris to determine temporal variabilities of ammonia at different scales (from interannual to diurnal variability) to unravel emission sources (traffic and agriculture). Qualitative comparison of NH3 derived from the ground-based miniDOAS located in Paris city-center and IASI satellite observations reveals an overall moderate agreement with Pearson's correlation coefficients of 0. 66, 0. 69 and 0. 70 when considering IASI observations in a 100km, 50km, and 30km box around Paris. The best agreement between both datasets is found during springtime when NH3 concentrations are 2 to 3 times higher than during the other seasons due to spreading practices occurring in the surrounding agricultural regions of Paris.
Further long-term NH3 monitoring in urban areas is needed to better estimate NH3 emissions from the on-road sector and their impact on secondary particle formation. We have shown that the planetary boundary layer height greatly influences diurnal variabilities derived from surface measurements. Future work will be carried to compare these NH3 datasets in Paris to atmospheric model outputs to evaluate the timing and the absolute value of emission inventories, as https://doi. org/10. 5194/egusphere-2023-963 Preprint. Discussion started: 26 May 2023 c Author(s) 2023. CC BY 4. 0 License.
Acknowledgments IASI is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). The IASI Level 1C data are distributed in near real time by  EUMETSAT  through the EUMETCast system distribution. The authors acknowledge the AERIS data infrastructure (https://www. aeris-data. fr)for providing access to the IASI Level 1 radiance and Level 2 NH3 concentration data used in this study. CNES and the  AC-SAF  ( CDOP3 ) project provided financial support for the miniDOAS acquisition. We thank the  NOAA's Air Resources Laboratory (ARL)  for providing the HYSPLIT model. Research at  ULB  was supported by the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex HIRS)  and the  Air Liquide Foundation  ( TAPIR  project). LC is Research Associate supported by the  Belgian F.
‡ 154 First, I would like to acknowledge the huge and heroic effort carried out by our colleagues that have rst built the reaction networks on which the two above mentioned databases are based on, especially Eric Herbst and Tom Millar. That said, the KIDA and UMIST database networks suffer some problems that cannot be ignored, and which I here identify as reactions that (i) "should not be there because they are wrong" and (ii) "are missing". Reactions that should not be there. As indicated in the two databases, the large majority $80% of the included reactions has never been studied in laboratory experiments or by theoretical computations. Even in the cases of reactions that have been investigated, the experimental conditions rarely reproduce those typical of solar-type star forming regions, either regarding the temperature or the density.
Another example is provided by acetaldehyde, also discussed later (Section 5): about half of the acetaldehyde formation reactions listed in the KIDA database have either the wrong rate coefficient or products. 158 eactions that are missing. Despite the large dimensions of the available gasphase reaction networks, there are no doubts that some important, and maybe also crucial, reactions are missing. One obvious class of missing reactions are those in which neutrals react with the most abundant ions in molecular gas, namely HCO + , H 3 + , He + and H +. Tinacci and colleagues reported that, out of 238 neutral species present in the GRETO-BAPE network, 111 of them do not have reactions with at least one of the abovementioned ions, and 10 not even with one ion. 155 Finally, it is more than probable that important gas-phase reactions are missing.
Some of them may even be present in the literature but just overlooked. One example is the formation of methyl formate from dimethyl ether which was only introduced in 2015 (ref. 159), although the chain of reactions were measured and computed since 2005. Another possibility is just that reactions were not thought of, like the case of the formation of glycolaldehyde, for which no gasphase formation routes were present in the database or literature: ad hoc new QM computations showed that there exists at least one formation route of glycolaldehyde in the gas-phase. 160 s said, both laboratory experiments and QM computations are very time consuming, so it is important that chemists and astronomers work together. It is possible that articial intelligence (machine learning) techniques could help to reduce the burden.
The efficiency is given for three assumed values of the diffusion-overbinding energy and it goes from unity, for values larger than 0. 4, and crashes when it is 0. 3, showing the dramatic dependence of the acetaldehyde formation efficiency on a parameter that is practically unknown (see above). Finally, it is important to emphasize that, in practice, each case is peculiar and requires a dedicated study. At present about two dozen systems have been studied on the water surfaces 186 and much less on CO-rich ices. 187 nly one public database exists so far with a list of grain-surface reactions, posted on the KIDA website. The list is based on Garrod and collaborators' reaction network  129, 183  and it is presented in a work by Ruaud and collaborators. 130.
Acknowledgements I would like to warmly thank my chemist friends and collaborators  Nadia Balucani ,  Piero Ugliengo ,  Albert Rimola : without their help and teaching, this review would not have existed. I also thank my astronomer friends and collaborators, especially  Claudio Codella ,  Ana López-Sepulcre ,  Charlotte Vastel  and  Bertrand Leoch. A nal very deep thank goes to the young researchers with whom I had the luck to work, especially  Eleonora Bianchi ,  Mathilde Bouvier ,  Marta De Simone ,  Joan Enrique-Romero ,  Cecile Favre ,  Lorenzo Tinacci  and  Fanny Vazart. I acknowledge the funding within the  European Union 's  Horizon 2020 research and innovation programme  from the  European Research Council (ERC)  for the project "The  Dawn of Organic Chemistry" (DOC) , grant agreement No.
Model output statistics (MOS) approaches relying on machine learning algorithms were applied to downscale regional air quality forecasts produced by CAMS (Copernicus Atmosphere Monitoring Service) at hundreds of monitoring sites across Europe. Besides the CAMS forecast, the predictors in the MOS typically include meteorological variables but also ancillary data. We explored first a "local" approach where specific models are trained at each site. An alternative "global" approach where a single model is trained with data from the whole geographical domain was also investigated. In both cases, local predictors are used for a given station in predictive mode. Because of its global nature, the latter approach can capture a variety of meteorological situations within a very short training period and is thereby more suited to cope with operational constraints in relation to the training of the MOS (frequent upgrades of the modelling system, addition of new monitoring sites).
Every year there are between one and two upgrades to the set-up of the CAMS individual models producing air quality forecasts at regional scale over Europe. Therefore, an alternative global approach, building one single model for all the monitoring sites with a very short training period (a few days preceding the forecast), but using data from the whole geographical domain was also tested for comparison. In the following article, we present first, in Sect. 2, the observations and model datasets used to train and test the predictive models. Then, MOS approaches and algorithms are presented in Sect. 3. Finally, Sect. 4 explores the sensitivity of the two MOS approaches to training data, and Sect. 5 compares and discusses their performances in the frame of the selected scenarios.
Training data The MOS development is based on 3 years of air pollution and meteorological data covering the 2017-2019 period. These data include hourly in situ observations of PM 10 , PM 2. 5 , NO 2 and O 3 concentrations at hundreds of urban, suburban and rural background regulatory monitoring stations and is retrieved from the up-to-date (UTD) dataset of the Air Quality E-reporting database (https:// www. eea. europa. eu/data-and-maps/data/aqereporting-9, last access: 13 April 2023) of the European Environment Agency. Daily mean, daily 1 h maximum and daily 1 h minimum were calculated when 75 % of the hourly data were available for the considered dates (i. e. at least 18 h over 24 h). All the stations located in the European region, over a domain ranging from -25 • W to 45 • E longitude and from 30 • S to 70 • N latitude, have been considered in this work.
The total numbers of stations available for training and testing the MOS are 1535 for O 3 , 957 for PM 10 , 1468 for NO 2 and 498 for PM 2. 5. Hourly concentrations from the CAMS European ensemble forecast have been retrieved from the Atmosphere Data Store (https://ads. atmosphere. copernicus. eu/cdsapp#!/home, last access: 13 April 2023). During the 2017-2019 period, 1  the CAMS ensemble was defined as the median of seven individual models covering the European region at the resolution of 0. 1 • and developed by several European modelling teams, namely CHIMERE  (Mailler et al. , 2017) , EMEP  (Simpson et al. , 2012) , EURAD-IM  (Hass et al. , 1995) , LOTOS-EUROS  (Schaap et al. , 2009) , MATCH  (Robertson et al. , 1999) , MOCAGE  (Guth et al. , 2016)  and SILAM  (Sofiev et al. , 2015). Note that the CAMS ensemble was upgraded during the month of June 2019 with the use of a new anthropogenic emissions dataset, extension of the geographical domain, and provision of dust (within PM 10 ) and secondary inorganic aerosols (aggregation of ammonium sulfates and nitrates within PM 2.
Design of the MOS approaches The MOS strategy can be called "hybrid" modelling in the sense that it uses both a deterministic forecast (here the CAMS regional ensemble) and other relevant predictors to produce a statistically corrected output concentration. In machine learning terminology it corresponds to a supervised learning problem as we use a training dataset composed of a number of predictor variables (also called features) labelled with the corresponding pollutant concentration observations. The model fitted with the training data is then applied to future situations (new predictors values) to forecast pollutant concentrations. Three distinct problems have been considered in this work: prediction of daily mean, daily maximum and hourly concentrations. The quality of the predictions is explored for the first day (D+0) or first 24 h of the forecast in this work, but the methodologies proposed are adapted to tackle longer forecast leads.
2): PRSS = N i=1 y i -α 0 - p j =1 α j x ij 2 + λ p j =1 f (α j ), (2) where y i denotes an observed concentration, and x ij is the associated value for the predictor j ; N is the number of observations in the training dataset; λ is a penalty coefficient; and f denotes either the absolute value or the square function. In the case where λ is set to zero, the regularization term on the rightmost part of the equation nullifies, and we obtain a standard linear model (LM) based on the minimization of residual sum of squares. Otherwise, λ will have to be tuned (see below), and depending on the choice of f -absolute value or square function -we obtain a LASSO or a ridge linear model, respectively. The ridge and the LASSO regressions were introduced separately by Hoerl and  Kennard (1970)  and  Tibshirani (1996) , respectively.
For both the ridge and LASSO approaches, the regularization term in Eq. (  2 ) favours solutions with coefficient values of small amplitude, thus reducing the risk of overfitting, i. e. of producing a model that sticks too close to the training data and has poor generalization skills. In contrast to the ridge regularization, the LASSO tends to produce exactly zero values for those coefficients associated with the less important predictors, offering a way to deal with variable selection and improving model interpretability. In this study, we used the implementation of the ridge and LASSO regression in the "glmnet" package in the R language  (Friedman et al. , 2010). The other two predictive models are based on the decision trees described by  Breiman et al. (1984). These trees are based on a series of nodes that represent both a predictor and an associated threshold value.
In this study we used the "randomForest"  (Liaw and Wiener, 2002)  and "gbm"  (Greenwell et al. , 2019 ) R packages for the implementation of the RF and GBM algorithms, respectively. A key challenge with statistical learning methods is to learn as much as possible from the training data, without losing generalization skills. To reach an optimal balance and optimize the predictive performances, a learning algorithm may be tuned by choosing values for some parameters often referred to as hyper-parameters. The method used for tuning these hyper-parameters consists of a grid search, where possible values for each hyper-parameter are predefined. A model is trained and tested for every possible combination of hyper-parameter value using a fivefold cross-validation procedure. The best combination of hyper-parameter is then selected to train the final model but this time using the full training dataset.
The tuning of these hyper-parameters is performed at every monitoring site for the local MOS approach or every day for the global MOS approach (local and global approaches are defined in Sect. 3. 2). The number of parameters to be tuned depends on the algorithm. It is limited to one for the LASSO and ridge model, two for the random forest model, and four for the GBM model. To limit the number of combinations and computation time, the grids of possible values for each parameter were kept simple, with very few values to test, and remained the same in all the learning configurations of this study. The grids of tuning values for each algorithm are described in Appendix A. The tuning of the learning algorithms was performed using the caret R package  (Kuhn, 2008).