dataesr/scientific-paragraphs-categorization huggingface dataset:
For an individual observation, an IASIretrieved column is considered detectable when the vertical column density exceeds 9. 68 × 10 15 molec. cm -2 (surface concentration > 1. 74 µg m -3 ) at a thermal contrast of 20 K, while the vertical column density should be larger than 1. 69×10 16 molec. cm -2 (3. 05 µg m -3 ) at 10 K  (Van Damme et al. , 2014a). Although the retrieval algorithm uses a fixed vertical profile, extended validation of the resulting dataset has verified small uncertainties  (Van Damme et al. , 2015 , 2018; Dammers et al. , 2016; Whitburn et al. , 2016b). For instance,  Van Damme et al. (2018)  reported a difference of 2 % ± 24 % (global average) in column-integrated ammonia using different vertical profiles in the retrieval algorithm.
, 2017)  reanalysis data from NASA's Global Modeling and Assimilation Office (GMAO). Figure  S2  shows time series of column concentrations of sulfur dioxide and sulfates from OMI and MERRA-2 averaged globally, for continental regions (Europe, North America, South America, Africa), as well as for regions where ammonia emissions are particularly high (India and Southeastern Asia, North China Plain). Although column concentrations of both sulfur dioxide and sulfates present strong interannual variability (Fig. S2 ), their global concentrations show a strong decreasing trend after 2015. The observed decrease indicates that sulfate neutralizes ammonia and forms ammonium sulfate; thus, it is likely that the higher ammonia concentrations seen from IASI after 2015 are not necessarily a result of emission increases.
Comparison with traditional emission datasets In this section, we quantify the main differences of our IASI-constrained emission dataset with other state-of-theart inventories used in global models and for different applications (air quality, climate change, etc. ). Aside from comparing our emissions with those calculated using Van  Damme et al. (2018)  data with a constant lifetime (hereafter called VD0. 5), we extend our comparison to more traditional datasets such as those of ECLIPSEv5-GFED4-GEIA (EGG) for 2008-2017 and EDGARv4. 3. 1-GFED4 (Crippa et al. , 2016; Giglio et al. , 2013)    The 10-year comparison of our calculated emissions with VD0. 5 is shown in Fig. 3. The 10-year average difference amounts to 29 ± 15 Tg yr -1 (average ± SD). In all years, the largest differences could be seen over Latin America and over tropical Africa.
Our emissions (NE) show a different structure in the Indo-Gangetic Plain and situated slightly more northerly than those in VD0. 5. The difference might be due to the IDW interpolation used to process the IASI ammonia in the NE emissions compared with the oversampling method used in VD0. 5 (see Sect. 2. 3). Nevertheless, northern India has been identified as a hot-spot region for ammonia, mainly due the importance of agricultural activities in the region  (Kuttippurath et al. , 2020; Tanvir et al. , 2019). Figures S4 and S5 present a comparison of our calculated emissions (NE) with the basic state-of-theart datasets of EGG and EDGARv4. 3. 1-GFED4, respectively. In both datasets, ammonia emissions remain almost constant over time (average ± SD: 65 ± 2. 8 Tg yr -1 and 103 ± 5. 5 Tg yr -1 , respectively).
The total calculated ammonia emissions in EGG and EDGARv4. 3. 1-GFED4 are up to 3 times lower than those calculated from NE (average ± SD: 213 ± 18. 1 Tg yr -1 ) or from VD0. 5 (9-year average: 189 Tg yr -1 ). This results in 10year annual differences that are very significant (average ± SD: 150 ± 19. 3 and 111 ± 19. 2 Tg yr -1 , respectively); the largest differences appear over South America (EGG: 7. 1 ± 0. 3 Tg yr -1 , VD0. 5: 22 Tg yr -1 , NE: 28 ± 3. 0 Tg yr -1 , VDgrlf: 24 ± 1. 3 Tg yr -1 ), while European emissions are practically identical in all datasets except EGG (EGG: 6. 9 ± 1. 1 Tg yr -1 , VD0. 5: 11 Tg yr -1 , NE: 15 ± 2. 2 Tg yr -1 , VDgrlf: 11 ± 1. 0 Tg yr -1 ). Emissions from North China Plain are much higher in the two traditional datasets than those presented in this paper (EGG: 25 ± 1.
2 Tg yr -1 , VD0. 5: 36 Tg yr -1 , NE: 38 ± 2. 8 Tg yr -1 , VDgrlf: 39 ± 1. 8 Tg yr -1 ). Ammonia emissions derived over China in this work (NE) are among the highest worldwide (Fig. S1 ), which agrees well with the 9-year average emissions calculated in the VD0. 5 inventory over China (see Fig. 3 ). To assess to which extent emissions from EGG and EDGARv4. 3. 1-GFED4 are underestimated can only be done by comparing ammonia with ground or satellite observations. The comparison of the annual ammonia emissions in the NE dataset to the modified VDgrlf emissions is shown in Fig. S6. The latter showed a better agreement to the emissions presented in this study with mean annual difference of 14 ± 19 Tg yr -1 (average ± SD). Previously observed emission differences in the two state-of-the-art inventories over South America and Africa have been now minimized, and the displacement north of the Indo-Gangetic Plain emissions remains important.
KDE is a fundamental data smoothing tool that attempts to infer characteristics of a population, based on a finite dataset. It weighs the distance of all points in each specific location along the distribution. If there are more points grouped locally, the estimation is higher as the probability of seeing a point at that location increases. The kernel function is the specific mechanism used to weigh the points across the data set, and it uses the bandwidth to limit the scope of the function. The latter is computed using Scott's factor  (Scott, 2015). We also provide the mean fractional bias (MFB) for modelled and measured concentrations of ammonia as follows: MFB = 1 N N i=1 (C m -C o ) N i=1 C m +C o 2 × 100 %, (8) where C m and C o are the modelled and measured ammonia concentrations, respectively, and N is the total number of observations.
MFB is a symmetric performance indicator that gives equal weights to under-or overestimated concentrations (minimum to maximum values range from -200 % to 200 %). Furthermore, we assess the deviation of the data from the line of best fit using the root mean square error (RMSE) defined as RMSE = N i=1 (C m -C o ) 2 N. (9) From 134 European stations, nearly 300 000 measurements made at a daily to weekly temporal resolution over the period of study  (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018)  are presented in Fig. 5. All emission datasets underestimate ammonia surface concentration over Europe. The most accurate prediction of concentrations was achieved using the traditional EGG emissions that underestimated observations by 67 %, also being the least scattered from the best fit (RMSE EGG = 4.
06 µg N m -3 ), followed by the emissions presented in this paper (MFB NE = -72 %, RMSE NE = 4. 65 µg N m -3 ), although they were more variable. VD0. 5 or VDgrlf emissions further underestimated observations, although they were less sparse (Fig. 5d ). About 12 % of the modelled concentrations using EGG were outside of the 10-fold limit from the observations, in contrast to only 17 % and 15 % in VD0. 5 and VDgrlf and 20 % in NE. In regard to the spatial comparison with the observed concentrations, all datasets cause overestimations in the ammonia concentrations predicted in western Europe. EGG appears to be the most accurate in central Europe (all stations with suffix DE00), NE emissions in all Spanish stations (suffix ES00) and VD0. 5 and VDgrlf emissions in Italian stations (Fig. S7 ). The comparison of simulated ammonia concentrations to observations over North America includes 119 stations, which represent nearly 27 000 observations (Fig.
6 ) with a weekly, biweekly or monthly resolution. The only emission dataset that lead to an underestimation of ammonia concentrations was EGG (MFB EGG = -28 %). Two others, VD0. 5 and VDgrlf, caused ammonia observations to be strongly overestimated (MFB VD0. 5 = 52 % and MFB VDgrlf = 54 %), while NE only slightly (MFB NE = 32 %). All inventories resulted in about the same variability in ammonia concentrations with RMSEs between 4. 15 and 4. 17 µg N m -3 (Fig. 6 ). About 10 % of the predicted concentrations using EGG emissions were at least 10 times off from the measured ones, which is more than twice the number of measurements compared to the other dataset. NE emissions better capture levels in the easternmost stations of the US (AL99, AR15, CT15, IL37, IN22, MI52, NY56, ON26) and in California (CA83) and Oklahoma (OK98), which are close to hot-spot regions (see  Sect.
8. Although the traditional EGG emissions capture many stations very well, there are large MFB values observed in eastern and western USA (AMoN) and northern Europe (EMEP), whereas large overestimations are observed in most of the Southeastern Asian stations (EANET). The large bias at several AMoN stations decreases when using satellite-derived emissions. All datasets miscalculated surface concentrations in Southeastern Asia, although some stations present lower MFBs when using IASI constrained emissions. Note that large differences when comparing bias from all measurements versus station-by-station bias have been calculated as a result of the different frequencies of measurements in each station. To further show whether the satellite-derived emissions presented here (NE) capture surface concentrations of am- monia or not, we used surface ammonia concentrations from CrIS from 1 May 2012 to 31 December 2017.
Uncertainty analysis A sensitivity analysis was also performed in order to calculate the level of uncertainty that each of the parameter gives to the modelled surface concentrations of ammonia. The relative uncertainty was calculated as the standard deviation of ammonia's surface concentrations from a model ensemble of 10 members (Table  1 ) divided by the average. The first six members are the surface concentrations that resulted from    1 ) divided by the average. simulations of ammonia emissions after perturbation of the Euclidian distance d k in the parameters of the IDW interpolation. The remaining four members are simulated concentrations using the previously reported emissions datasets (EGG, VD0. 5, NE and VDgrlf). The results are shown as a 10-year (2008-2017) annual average relative uncertainty in Fig.
Conclusions In the present paper, satellite measurements from IASI were used to constrain global ammonia emissions over the period 2008-2017. The data were firstly processed to monthly ammonia column concentrations with a spatial resolution of 2. 5 • × 1. 3 •. Then, using gridded lifetime for ammonia calculated with a CTM, monthly fluxes were derived. This contrasts with previously reported methods that used a single constant lifetime. This enables a more accurate calculation in regions where different abundances of atmospheric sulfuric and nitric acid, as well as in their precursors (sulfur and nitrogen dioxide, respectively), can neutralize ammonia through heterogeneous chemical reactions to sulfate and nitrate aerosols. The calculated ammonia emission fluxes were then used to simulate ammonia concentrations for the period 2008-2017 (referred to as NE).
4 Tg yr -1 ). These emission values amount to twice as much as those published from datasets, such as EGG (65 ± 2. 8 Tg yr -1 ) and EDGARv4. 3. 1-GFED4 (103 ± 5. 5 Tg yr -1 ). In the North China Plain, a region characterized by intensive agricultural activities, a small increase of ammonia emissions is simulated after 2015. This is attributed to decreases in sulfur species, as revealed from OMI and MERRA-2 measurements. Less sulfates in the atmosphere leads to less ammonia neutralization and hence to larger loads in the atmospheric column as measured by IASI. In Europe, the 10-year average of ammonia emissions was estimated at 15 ± 2. 2 Tg yr -1 (NE), twice as much as those in EGG (6. 9 ± 1. 1 Tg yr -1 ) and similar to those in VD0. 5 (11 Tg yr -1 ) or VDgrlf (11 ± 1. 0 Tg yr -1 ). The strongest emission fluxes were calculated over Belgium, the Netherlands, Italy (Po Valley), northwestern Germany and Denmark.
A strong seasonality in the emissions was observed with a late summer peak in most years, due to rice cultivation, synthetic fertilizer and livestock manure applications, and in spring due to wheat cultivation. A large bias was calculated in several ground-based stations when using the state-of-the-art emissions EGG. The bias decreased substantially when satellite-derived emissions were used to simulate surface concentrations of ammonia. Data availability. All data and python scripts used for the present publication can be obtained from the corresponding author upon request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-4431-2021-supplement. Author contributions. NE performed the simulations and analyses and wrote and coordinated the paper.
Acknowledgements. We thank  Espen Sollum  for his continuous contribution and help with big data analysis. Financial support. This study was supported by the  Research Council of Norway  (project  ID :  275407 ,  COMBAT -Quantification of Global Ammonia Sources constrained by a Bayesian Inversion Technique ). Lieven Clarisse  and  Martin Van Damme  are supported by the  F. R. S. -FNRS. Review statement. This paper was edited by  Drew Gentner  and reviewed by two anonymous referees.
Acknowledgments: The REALCAT platform is benefiting from a state subsidy administrated by the  French National Research Agency (ANR) , within the frame of the " Future Investments" program  (PIA), with the contractual reference " ANR-11-EQPX-0037 ". The  European Union , through the ERDF funding administered by the  Hauts-de-France Region , co-financed the platform. Centrale Lille , the  CNRS and Lille University , as well as the  Centrale Initiative Foundation , are thanked for their financial contribution to the acquisition and implementation of the equipment of the REALCAT platform. The  Métropole Européen de Lille (MEL)  for the " CatBioInnov " project is also acknowledged.
Funding: This research was funded by  REGION HAUTS-de-FRANCE , grant number  3859523  FDCA  STARTAIRR  project and  I-SITE ULNE grant V-Start 'AIRR-18-001-Wojcieszak This study was supported by the  French government  through the  Programme Investissement d'Avenir (I-SITE ULNE/ ANR-16-IDEX-0004  ULNE) managed by the  Agence Nationale de la Recherche.
Conclusion and perspectives A methodology for accurate determination of soot temperature, volume fraction, and maturity in laminar axisymmetric coflow flames using multiwavelength attenuation and emission measurements was developed and demonstrated. The ability to fully characterize soot properties is crucial for improving our understanding of soot particles formation and maturation processes in flames. Comprehensive and high-quality experimental dataset of soot parameters is highly desirable for validating robust soot models that are indispensable for the design of future cleaner combustion devices. Up to now, the two-color emission method has been commonly used to infer soot temperature based on the hypothesis of a constant, both spectrally and spatially, soot absorption function. However, even a weakly spectral dependence of soot absorption function can produce a fairly large error in the measured temperature.
Finally, the technique developed in this study will be used to obtain comprehensive experimental dataset for validation of soot formation models. direction; thus, the flow inside fuel tube and heat transfer between the flame base and the fuel tube are not modeled. A non-uniform numerical grid is used, which consists of 202 (axial,z) × 94 (radial,r) control volumes (CVs) extending to 14. 3 cm and 2. 74 cm, respectively. The mesh size remains constant at 0. 02 cm (r) × 0. 05 cm (z) in the near burner region to capture the steep gradients and becomes coarsened at a constant stretching factors of 1. 05 outside this region in both the streamwise and radial directions. Pure ethylene is injected through the central tube of the burner at 3. 465 cm/s and ambient air (assumed as mixture of 21%O 2 and 79%N 2 ) is injected through the co-annular region between both concentric tubes at 61.
Acknowledgments The authors gratefully acknowledge the support provided by  France 's  ANR ASTORIA  under the research grant  ANR-18-CE05-0015  and the  Region of Normandy RIN Gaspropres project and Chile's National Agency for Research and Development (ANID)  through research program Fondecyt/Regular 1191758. Also, this work was performed by the mobility  France-Chile joint program  receiving a research grant provided by  Ecos/ANID   C19E01.
This scale allows the application of mathematical operations, e. g. , developing equations to relate OI to chemical concentration  (Kim, 2010 (Kim, , 2011;; Wu et al. , 2016 Wu et al. , , 2017)). It should be mentioned that if the odor intensity of odorant A is equal to 1 and the OI of B = 5, this does not indicate that OI B = 5 × OI A  (Gostelow et al. , 2001). The category scale is easy to use and does not require much training. When Sucker et al. assessed the intensity, hedonic tone, and frequency of odors emanating from six different industries, they gathered data from 10 to 16 selected assessors per site in addition to the data gathered from 1456 residents (approximately 200 per site). The results of the OI assessment from assessors and residents are shown in Fig. 1. The averaged results show close OIs between panelists and residents for rusk, cast iron, and seed oil industries but a difference of approximately one magnitude for sweets, textile, and fat industries.
, 2014). Cross modality matching is not only based on comparing the OI to taste intensity. Indeed, Jiang et al. used a scale with matching temperatures (Table  3 ) in a study on the relation between the odor concentration and the odor intensity when analyzing odors issued from alumina refineries  (Jiang et al. , 2006). 3. 1. 1. 4. Choosing the appropriate scale. Choosing the right method depends on several factors, e. g. , the intended study, the aims, logistics, etc. In this part, the scales described earlier are compared. Table  4  summarizes the different methods presenting their advantages and disadvantages. In 2002, Devos et al. performed a comparative study of Stevens' coefficients obtained by different psychophysical methods. Devos et al. analyzed sensory data of 249 odorants from 87 papers (21,663 potential data) in which odor intensity was assessed using different odor intensity scales.
Grid measurement methodology is a technique in which the assessors register odor data following measuring grids within the area under study for a fixed period of time  (NF EN 16841-1, 2016; Sówka, 2010; Guillot et al. , 2012)  (Fig. 4 ). A large period is considered (6 months to one year), as well as subsequently a multiplicity of meteorological conditions. In addition, the method allows us to cover a large impacted area and to assess areas that are not exposed or are low exposed, as well as highly exposed areas  (Sucker et al. , 2008b). The grid edges are recommended to be 250 m, but they can be adjusted to 50, 125, or even 500 m depending on the conditions and needs  (Yusoff and Zaman, 2017). 2003)  assessed the odors at each grid point and noted the intensity and character of the smelled odors.
e. , the impacted zone and odor concentrations during a definite percentage of time (Fig. 7 ). At the source, odorous air is sampled in bags over a definite period. Afterward, the samples are analyzed in a laboratory to determine the odor concentration emitted during the sampling time to calculate the odor concentration emission rate (ou E /s)  (Capelli et al. , 2013a). Odor concentrations at the source are high; hence, the use of laboratory-based olfactometry is pertinent and not limited by the factors mentioned for laboratory assessment. Then, the most common approach is using a dispersion model that considers the odor concentration emission rate along with meteorological and topological data such as elevation, wind speed, wind direction, etc. Different models have been used, such as analytical stationary plume models, puff models, Langrangian models, etc.
Moreover, previous experiences and the social background of the individual may affect his/her appreciation of the odor  (Candau, 2001). For example, a person who lived in an agricultural society may not be offended by odorous emissions from agricultural domains (livestock or natural fertilizers) as a person living in modern metropoles. Hence, the HT is by essence subjective. Therefore, if needed to be assessed, HT may be used to study the impact of olfactory annoyance on a population. Community assessment  (Brancher and Lisboa, 2014; Sucker et al. , 2008b)  in this case seems to be a more suitable strategy. Residents are reliable specialists to evaluate odor annoyance due to their exposure history. Indeed, the laboratory assessment of OC alone is not sufficient because data on the perception of the odor by the community are missing  (Drew et al.
MD simulations MD simulations were carried out using Gromacs 5. 1 package  [45]  within the NPT ensemble by adopting the leapfrog algorithm to integrate the equations of motion at a fixed temperature (298 K) and pressure (1 bar). [46] The non-polarizable CL&P OPLS all atom force field was used for [N 4444 ] + ,  [47]  and the force field parameters for the nitrate anion were taken from Sambasivarao and Acevedo. [48] he optimized OPLS-AA forcefield of Bonthuis et al. [49]  was used for H 3 O + , forcefield parameters from Price et al. [50]  were used for undissociated HNO 3 and water molecules were represented by the SPC/E model. [51] Hydrogen bonds were constrained by the LINCS algorithm  [52]  whilst LJ and coulombic interactions were computed up to a cut-off radius of 1. 2 nm. Long-range coulombic interactions were evaluated by particle mesh Ewald (PME).
[53] The temperature and pressure were controlled through the Nose-Hoover thermostat  [53]  and the Parrinello-Rahman barostat,  [54]  respectively. All simulations were started from a randomly distributed configuration, and production runs were carried for a minimum of 90 ns with a time step of 2 fs. The system compositions and final box volumes provided in Table  S3. MD simulation outputs were visualized using the VMD software package. [55] Radial distribution functions (RDFs), coordination numbers (CNs), partial densities, and SASA of the components were calculated using the Gromacs inbuilt analysis tools.
Acknowledgements The authors thank  Maria C. C. Azevedo  and  Marcia C. Neves  for help with the Raman and SEM data acquisition, respectively. This work was developed within the scope of the project  CICECO-Aveiro Institute of Materials ,  UIDB/50011/2020 & UIDP/50011/2020 , financed by national funds through the  Foundation for Science and Technology/MCTES. N. S. acknowledges the national funds (OE), through  FCT À Fundação para a Ciência e a Tecnologia , I. P. , in the scope of the framework contract foreseen in the numbers 4, 5, and 6 of the article 23, of the Decree-Law  57/2016 , of August 29, changed by Law  57/2017 , of July 19. H. Passos acknowledges FCT under the  Scientific Employment Stimulus-Individual Call -CEECIND/00831/2017.
Experimental setup and theoretical backgrounds for ignition delay evaluation An in-line four-cylinder, four-stroke, naturally aspirated, direct injection, Diesel engine was tested on a test bed provided with an eddy current dynamometer AVL Alpha 160 and a gas analyzer HORIBA Mexa 7170D and a smoke meter AVL 415S. The highspeed data acquisition was made using AVL pressure transducers and a Lucas needle lift sensor. Line pressures, cylinder pressure traces and the needle lift have been registered by statistical acquisition of 200 consecutive cycles. The NOx and soot emissions have been registered automatically as average values by the gas analyzer, under AVL PUMA control, in a time interval of 30 s. Details of the experimental setup used for this work are given in references  (Aldhaidhawi et al.