dataesr/scientific-paragraphs-categorization huggingface dataset:
Ancillary data Two sets of ancillary data were used to better understand our results. -The meteorological data provided by the NOAA Integrated Surface database (ISD) and available at https://www. ncdc. noaa. gov/isd. -The daily burned surface area MCD64A1 satellite product derived from MODIS on AQUA and TERRA at a spatial resolution of 500 m  (Roy et al. , 2008; Roy and Boschetti, 2009). to April 2017. The AOD in Cotonou ranges between 0. 05 and 3. 5 with a mean value of 0. 68 and in Abidjan between 0. 12 and 1. 77 with a mean value of 0. 58, respectively. This difference is not expected if we consider the size of each city, Abidjan having more than 3 times more inhabitants than Cotonou. Many reasons may explain such a difference. First, the precipitation regime can partly explain that AOD is higher in Bénin than in Ivory Coast.
Conclusion In this study, the mass concentrations and particulate carbon species of PM2. 5 samples collected in coastal cities in West Africa (Abidjan and Cotonou) were investigated at two traffic sites (AT and CT), one waste burning site (AWB) and one domestic fire site (ADF) from February 2015 to March 2017. We have analyzed the weekly mean concentration of PM2. 5, EC and OC concentrations. AOD measurements were also made in Abidjan and Cotonou. Note that the AOD dataset is the first one obtained in this area. We observe large similarly between both urban site in Abidjan (AT) and Cotonou. Moreover, the waste-burning site follows the same pattern at the traffic. The mean PM2. 5 concentration for AT, CT and AWB of about 30 µg. m -3 is coherent with previous studies for sub-Saharan western Africa and is 3 times higher than the concentrations recommended by the World Health Organization.
Acknowledgements This work has received funding from the  European Union   7th Framework Programme  ( FP7/2007-2013 ) under Grant Agreement no. 603502  ( EU  project  DACCIWA: Dynamicsaerosol-chemistry-cloud interactions in West Africa ). The author would like to thank the  AUF (Agence Universitaire de la Francophonie ) for funding his stays in Laboratoire d'Aérologie of Toulouse (3 months per year during 3 years). Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2017-973Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 17 November 2017 c Author(s)  2017. CC BY 4. 0 License.
Inhalation is the main route of environmental particle exposure. It also represents an attractive pathway for local medications, such as bronchial diseases treatments, chemotherapy  1  , antibiotics  2  , and systemic medications, such as vaccinations, insulin therapy, and gene therapy 3. To relate airborne particle exposure to wanted or unwanted biological effects, it is essential to assess regional deposition within the respiratory tract. Identification of deposition sites is a major determinant of particle bio-persistence, dose in tissues, and resultant biological effects. Computational models allow calculation of inhalation dosimetry of particles  4, 5  , but there is an experimental data gap for critical parameters (submicron-sized particles, specific anatomical features). This lack of dataset restricts the calibration or verification of mathematical prediction.
A picture is available as online supplementary Figure  S2. Methods. The depressions (negative pressures approximately 8 to 10 kPa) performed in the sealed enclosure are continuously measured using a differential pressure transmitter (Autotran 860; Mesureur society, Chilly-Mazarin, France). The real-time air speed is recorded in the trachea using an anemometer (MINIAIR6 mini; Mesureur society). These data allow calculation of the air output in the trachea representing the minute ventilation. The tidal volume (VT) is obtained by integration for each respiratory cycle. An average of VT and minute ventilation is calculated for several respiratory cycles in each specimen of the model. These data are recorded with breathing rates of 10, 15, and 20 per minute, with inspiratory/expiratory (I/E) ratios of 1/1, 1/2, and 1/3 for each rate.
Statistical analysis. Results are reported as numbers (%) or average (standard deviation). Continuous variables were compared using the non-parametric two-way ANOVA and the non-parametric Tukey's multiple comparisons tests. All tests were two-sided and P < 0. 05 was considered statistically significant. The physiological data are analyzed with NextView ® 4 Lite software (BMCM, Maisach, Germany). Statistical analyses were performed using Excel 2010 (Microsoft Office ® , Redmond, WA, USA) and GraphPad Prism ® 6 (GraphPad Software, La Jolla, CA, USA).
5 stitches per IT tract was realized (IQR, 12), and the median length of trachea was 7 cm (IQR, 1). Except for aspirations, flexible bronchoscopies were necessary for seven (19. 4%) IT tracts; the airways were cleaned with a saline solution. A video (Supplementary Video SV1) of this model is provided in the online data supplement. Anatomical and physiological features. The ex vivo model allows variations of inspiratory and expiratory times. Consequently, the respiratory rate and I/E ratios can be chosen as necessary for experimentation. In this study, the physiological features have been determined for the three respiratory rates and I/E ratios chosen. Each value of VT has been calculated by averaging several cycles (median, 5; IQR, 1). The average of maximal depressions measured in the box ranged from -1.
Acknowledgements The authors acknowledge the support of  Gwendoline Sarry ,  Chloé Souchère , and  Bastien Traverse  during the experiments. The  Despinasse Viande society (La Talaudière, France)  is thanked for providing the porcine respiratory tracts. Finally,  Michael Darmon  ( Intensive Care ),  Francis Dubois  ( Nuclear Medicine ), and  Frédéric Costes  (Physiology) were consulted as scientific advisors. The manuscript has been edited by  Boldface Editors.
The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (∼ 915 km) than that of the L2 SM dataset (∼1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of < 0. 05 m 3 m -3 in most of the Earth and a Pearson correlation coefficient higher than 0. 7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0. 05 m 3 m -3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America.
The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3. 5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).
, 2012). The free parameters are the SM content and the 1. 4 GHz opacity, which is mainly due to the water content of the vegetation in between the soil surface and the sensor (which some authors refer to as VOD, vegetation optical depth). Many studies have evaluated the SMOS L2 SM dataset in comparison to other remote-sensing datasets, models and in situ measurements  (Al Bitar et al. , 2012; Wanders et al. , 2012; Albergel et al. , 2012; Bircher et al. , 2013; Al-Yaari et al. , 2014a, b; Leroux et al. , 2014; Louvet et al. , 2015; Kerr et al. , 2016). SMOS shows very good global performance although other remote-sensing and model products can show better performances at some sites. In any case, datasets from the only two instruments specifically conceived to measure SM, SMOS and NASA's Soil Moisture Active Passive (SMAP), compare very well with each other  (Jackson et al.
Therefore, this processing chain does not fulfil the NRT requirements. However, as already mentioned, SMOS T b measurements are provided in NRT to ECMWF. In addition, with 6 years of SMOS measurements available, statistical algorithms can be exploited to provide faster retrievals and neural networks (NNs) have shown to be a promising technique to generate a SM dataset from SMOS T b measurements  (Rodríguez-Fernández et al. , 2015). Based on the latter, a NN processing chain to provide SMOS SM in NRT has been implemented by the ESA. The requirements are that the NRT dataset should display at least the same accuracy as the geophysical level 2 SM data product, the data should be retrieved over a large swath and the retrieval should rely on a minimum of auxiliary data files. As with the AS-CAT NRT SM processing chain cited above, the SMOS NRT SM chain handles model parameters derived offline using a database with a large number of past observations.
The advantages are that the processing is robust and very fast. In the case that significant changes in the Level 1 data used as input are available, then the model parameters should be updated correspondingly. The new SMOS NRT SM product is available from 2016 onwards and it is distributed through the World Meteorological Organization's Global Telecommunication System (GTS) and the EUMETCast service from EUMETSAT in NetCDF format. EUMETCast is a dissemination system that uses commercial telecommunication geostationary satellites and research networks to multi-cast data files to a wide user community. This paper describes the SMOS NRT SM processing chain and discusses the first evaluation results. It is organized as follows. Section 2 describes the data used for the implementation and the validation of the SMOS NRT SM product.
The vegetation optical depth (τ ) and the SM content are free parameters that are allowed to vary to minimize the difference of the simulated T b values and SMOS Level 1C T b measurements. In the case of forest, two contributions to the opacity are taken into account: one from the trees, which is estimated from the maximum leaf area index (LAI;  Ferrazzoli et al. , 2002) , and another from the understory vegetation. Soil temperature is obtained from ECMWF IFS data. For footprints with mixed land cover, the SM content of the minor land cover is estimated from ECMWF IFS and its contribution to the T b is fixed. For such cases, the SMOS SM retrieval is only performed for the dominant land cover class within the footprint  (Kerr et al. , 2012). ESA Level 2 SM data are provided in an icosahedral equal area (ISEA) 4H9 grid  (Sahr et al.
, 2003)  with a sampling space of 15 km. The version of the SMOS L2 SM dataset used in this study is v620, which became operational in May 2015. In order to have enough data for a robust training of the NN, an additional dataset from 1 June 2010 to 30 June 2012 was reprocessed with the same version v620 of the L2 SM algorithm. The evaluation of the NRT SM product has been done from May 2015 to the time of the NRT SM implementation (end of 2015).
SMOS NRT SM The SMOS NRT SM product was obtained by training a NN using SMOS T b measurements and soil temperature from ECMWF models as input. SMOS T b measurements are provided by ESA to ECMWF in NRT (less than 3 h after sensing) for operational monitoring within the IFS  (Muñoz-Sabater, 2015). The training dataset used for the supervised learning phase of the NN was the SMOS Level 2 SM product. The training was done using data from June 2010 to June 2012 and it is described in Sect. 3. The NRT SM processing chain was evaluated using data from May to November 2015. Taking into account the satisfactory results of the evaluation (presented in Sect. 5), the SMOS NRT SM product became operational in January 2016. The SMOS NRT SM product is computed at ECMWF and delivered to ESA, where the data are sent to EUMETSAT for dissemination via EUMETCast.
In situ SM measurements The SMOS NRT SM product was evaluated against in situ measurements of SM over a large number of sites. The same evaluation was done with the Level 2 SM product. The in situ data used for those evaluations are described below. The Soil Climate Analysis Network (SCAN) of the United States Department of Agriculture  (Schaefer et al. , 2007)  has been widely used to evaluate modelled and remote-sensing SM datasets and it contains over 100 sensors or sites. The sensors are located in agricultural regions with a relatively homogeneous landscape in many cases. The sensors used in this study are placed horizontally at 5 cm depth. The United States Climate Reference Network (USCRN,  Bell et al. , 2013)  is a network of climate monitoring stations with sites across the USA, managed and maintained by the National Oceanic and Atmospheric Administration (NOAA).
The SMOS NRT SM processor The SMOS NRT SMOS processor is based on a NN approach to retrieve SM from SMOS observations. Rodríguez-Fernández et al. (2015)  showed that SMOS T b values binned in 5 • width incidence-angle bins (the L3TB product; Al Bitar et al. , 2017) can be used to retrieve SM on a daily basis. They used ECMWF SM model fields as reference dataset to train the NN. Rodríguez-Fernández et al. (2013)  had previously shown that SMOS Level 3 SM  (Al Bitar et al. , 2017)  can also be used as reference data to train the NN instead of ECMWF modelled SM fields. They also showed that the additional input dataset with the most significant impact on the retrieval is the soil temperature dataset. In the context of the ESA operational NRT SM processor, the goal was to obtain a SM dataset as similar as possible to the ESA Level 2 SM dataset but in NRT.
Therefore, the ESA SMOS Level 2 SM dataset  (Kerr et al. , 2012)  was used as reference dataset for the training phase. Finally, taking into account operational constraints, the only complementary data used for the retrieval are soil temperature estimations for the first layer (0-7 cm) of ECMWF models, which is the complementary dataset with the most significant impact (∼ 3 %) on the retrieval  (Rodríguez-Fernández et al. , 2013). This dataset was chosen because it is the same model data used by the Level 2 SM algorithm (Sect. 2).
The ECMWF IFS soil temperature in the 0-7 cm layer is used as input to the NN, as it increases the performances of the retrieval  (Rodríguez-Fernández et al. , 2016). SMOS data from a given grid point are not used if snow is found in that point based on the latest ECMWF snow depth forecast field or if the soil temperature forecast of the top 7 cm of soil is below 274 K. The SMOS NRT SM dataset is a land-only product and a SM retrieval is not provided if more than 50 % of the SMOS footprint is covered by water. This filter avoids spurious SM values near the coastlines, for instance. Of course, even if less than 50 % of the footprint surface is covered by water, the SM retrieval can still be affected by the free water. Users interested in regions close to the coast or to water bodies are advised to use the land-sea mask available on the ESA SMOS data products portal.
06 m 3 m -3 to use the most reliable data for the training. The input vectors contain T b values, I indexes for H and V polarizations, the three angle bins from 30 to 45 • , and the soil temperature from 0 to 7 cm from the ECMWF IFS. Therefore, input vectors have a total of 13 elements. All 13 elements must be well-defined to train the NN and there must be a well-defined, associated SM value. One-fifth of the vectors in the training database were selected randomly to have ∼ 3 × 10 5 vectors. A subset of 60 % of those vectors is used for the actual training, 20 % is used for evaluation of the NN performances during the training and to avoid over-training, the final 20 % is used to test the performances of the trained NN a posteriori. Gradient back-propagation and minimization with the Levenberg-Marquardt algorithm has been used.
One single hidden layer with 5 neurons has been used, as it has been shown by  Rodríguez-Fernández et al. (2016)  that it is enough to capture the relationship between the input data and the reference SM, while keeping the NN as simple as possible. No signs of overtraining were found and the training was stopped after 50 iterations when the mean squared difference was asymptotically approaching a minimum. When the trained NN was applied to the test subset and the NN output was compared to the SMOS L2 SM, the Pearson correlation R was 0. 86, the standard deviation of the difference (STDD) was 0. 068 m 3 m -3 and the root mean square error or difference (RMSE) was also 0. 068 m 3 m -3 , which implies that there was not a significant bias between both SM datasets. These results show that the NN ability to capture the dynamics of the current L2 SM dataset is very good.
Global evaluation Several metrics have been used to evaluate the NRT SM dataset from 15 May to 25 November 2015 against the SMOS L2 SM dataset. For all grid points λφ, the temporal means of both SM datasets, SM L2 λφ and SM NRT λφ , have been computed as the following: SM NRT λφ = 1 N t N t i=1 SM NRT λφ (t i ) (2) and SM L2 λφ = 1 N t N t i=1 SM L2 λφ (t i ) , (3) using only times (t i ) for which a well-defined value is present simultaneously in both datasets. This number is in principle different for each λφ grid point, but it will be noted as N t in the following instead of N λφ t to simplify the notation. A bias map has been computed from the local (λ and φ) mean of each dataset as follows: Bias λφ = SM NRT λφ -SM L2 λφ. (4) In order to compare the temporal dynamics of the two datasets, the Pearson correlation R has also been computed as follows: R λφ = N t i=1 SM NRT λφ (t i ) -SM NRT λφ SM L2 λφ (t i ) -SM L2 λφ N t i=1 SM NRT λφ (t i ) -SM NRT λφ 2 N t i=1 SM L2 λφ (t i ) -SM L2 λφ 2 , (5) where the sum runs for all the points available at a given position λφ: N t.
The absolute values of the two datasets have been evaluated using the standard deviation of the difference as a metric. The local time series difference D of the two datasets was defined as D λφ (t) = SM NRT λφ (t) -SM L2 λφ (t). (6) The standard deviation of the difference time series (STDD) has been computed as the following: STDD λφ = D 2 λφ -D λφ 2 = 1 N t N t i=1 D 2 λφ (t i ) - 1 N t N t i=1 D λφ (t i ) 2. (7) In some studies, the STDD is calculated indirectly from the bias and the root mean squared difference (RMSD) and called unbiased-RMSD (ubRMSD).
(  2017 ),  Kerr et al. (2016) ,  Leroux (2012) ,  Van der Schalie et al. (2016)  and  Jackson et al. (2012). The SMOS NRT SM and the SMOS L2 SM datasets have been evaluated against the in situ measurements discussed in Sect. 2 in a consistent manner. First, for each station available, a quality check of the data was performed. Sites with suspicious data (e. g. measurement discontinuity, spurious jumps) were eliminated. The locations of the 127 retained stations are shown in Fig. 4. The same metrics discussed in the previous section have been computed for the NRT SM dataset with respect to the in situ measurements and for the L2 SM dataset with respect to the in situ measurements. The Pearson correlation was used to compare temporal dynamics of the two SM datasets. The long-term (seasonal) dynamics were compared by computing the Pearson correlation coefficient R of SM L2 and SM NRT with respect to SM inSitu , site per site.
In addition, the short-term (1-30 days) dynamics were evaluated by computing site per site the Pearson correlation of the anomalies time series. Following  Albergel et al. (2009) , the SM anomaly at a given time t, SM a (t), was computed using a 31-day window centred at t as follows: SM a (t) = SM(t) -Mean(SM(t -15, t + 15)) STD(SM(t -15, t + 15)) , (8) where SM(t -15, t + 15) represents the ensemble of measurements in the 31-day window. The Pearson correlation coefficient R computed with the anomalous time series will be referred to as R a in the following. The metrics were computed independently for the NRT and the L2 datasets in a first step. In a second step, the metrics were recomputed only using times for which both the NRT and the L2 were simultaneously available, and thus, using the same number of points for the two time series.
In the Sahara, the low correlation is probably not significant because the SM values are very low and the variance is driven by the noise. Actually, Fig. 3f  shows that the STDD is also very low in this region. Therefore, L2 SM and NRT SM have actually similar values. In contrast, dense forest regions show a high   STDD in addition to a low R. Therefore, both products show some differences in these regions. Unfortunately, in situ measurements are not available to perform an independent evaluation of both datasets for dense-forest sites. In conclusion, both products show similar dynamics over large parts of the globe. The bias map (Fig. 3d ) shows that the NRT SM product shows a tendency to underestimate the L2 SM dataset, which is an expected behaviour as it has been obtained using a regression technique and extreme values are underrepresented in the reference dataset.
value is also given). Both SMOS products show a similar mean bias with respect to the in situ measurements, while the mean STDD and RMSD are slightly lower for the NRT SM product. In order to get further insight into the intrinsic quality differences of both datasets, the same statistics have been computed but only using times for which both SMOS products are retrieved. The results are also shown in Table  2. The differences in the evaluation of both products decreases, but the NRT product still shows a larger correlation and lower STDD with respect to the in situ measurements than the L2 product. Since the mean or median values alone do not show the full picture of the evaluation for more than 100 sites, Fig. 5a  and b  show box plots for the Pearson correlation coefficient of the time series (R) and the anomalous time series (R a ), respectively.
When comparing to in situ measurements, the best NN models showed a higher Pearson R and a lower STDD than those obtained for the ECMWF SM model simulations. These results can be understood because, provided that the training is done with a large number of statistically representative sam- The mean values are also shown as black crosses. The upper and lower bars represent the minimum and maximum values of the distribution excluding outliers. Points are considered as outliers if they are larger than q 3 + 1. 5(q 3 -q 1 ) or smaller than q 1 -1. 5(q 3 -q 1 ). ples, the NN will not be significantly affected by outliers or inconsistent values during the training phase. The NN output is the most likely (in the sense of the Bayes theorem) SM value taking into account a given set of input data. Thus, a good NN model can show slightly better quality metrics when compared to in situ measurements than the dataset used as reference to train the NN.
Conclusions This paper describes the ESA SMOS NRT SM processor and the first evaluation of this new operational dataset. This processor is based on a NN algorithm that uses SMOS NRT brightness temperatures and ECMWF IFS soil temperature in the 0-7 cm layer as input. It has been trained with SMOS Level 2 SM data as reference. The SMOS NRT brightness temperatures have been transformed from the antenna reference frame to the ground reference frame to express the polarization as horizontal and vertical components. In addition, they have been binned in 5 • width incidence-angle bins. Soil temperature and snow cover forecasts from ECMWF IFS are used to filter out frozen soil or soil covered by snow. The uncertainties of the NRT SM data were estimated from the input brightness temperature uncertainties.