dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank the TBL and CFHT teams for providing service observing. We acknowledge financial support from ' Programme National de Physique Stellaire' (PNPS)  of  CNRS/INSU, France. JFD acknowledges funding from the  European Research Council  under the H2020 research & innovation programme (grants # 740651  NewWorlds). GAW acknowledges support from the  Natural Science and Engineering Research Council of Canada (NSERC).
C. Solving the Eikonal equation using the Fast Marching Method To numerically determine a front whose time evolution is governed by the principle of least time we numerically solve the Eikonal equation |∇T ( x)| = 1/v( x), which connects the (spatially varying) speed v( x) to the arrival time T ( x). The front at time t is given by contour lines of T ( x), i. e. , the front consists of all x with T ( x) = t. For numerical reasons we chose a slightly different definition of the front as described below. The Eikonal equation was solved numerically using the Fast Marching Method  [S6] , implemented in the Python module scikit-fmm version 2021. 2. 2 [S7]. In the following, we describe the parameters used to determine front speeds. Without loss of generality, we chose the size of obstacles and hotspots to be on the order of 1.
To avoid overlap of the initial front with either obstacles or hotspots, we extended the channel to one side by length 50 and placed the initial front at the far side. This region was also used to 'roughen' the front through a set of hotspots before entering a region with very weak hotspots investigated in Fig. 6C. Different parameters were used to illustrate the effect of 'refraction' at the scale of the environment in Fig. 7  of the main text. Channel length was set to 500 and channel width to 150 (no periodic boundary conditions). For Fig. 3  of the main text, we set the semi-major axis of the ellipses to 1. The tulip of Fig. S4  has a length of 2 and a width of 1. Due to the much smaller domain size, we were able to set the lattice constant to 1/100. The corresponding source code is available on Zenodo, https://doi.
org/10. 5281/zenodo. 5513567, and GitHub, https://github. com/wmoebius/inhomogeneities one2many. D. Determining front shape and front speed when following the event-based approach and when solving the Eikonal equation The front dynamics is fully described by the time T at which position (x, y) is reached. The points constituting the front between times t and t + δt are given by all (x, y) for which t ≤ T (x, y) ≤ t + δt. While this is conceptually straightforward, it can be numerically challenging. This is in particular true for perfect obstacles which have a final arrival time at their boundary, but whose interior can never be reached by the front. We therefore defined the front as h(y, t)| T = max T (x,y)≤t x. (S1) Note that the two ways to infer the front may result in different front shapes and thus different mean front positions h(t) and front roughness w(t), h(t) = 1/L h(y, t) dy, w(t) = 1/L h(y, t) -h(t) 2 dy.
The multidisciplinary project Prediction of Air Pollution in Latin America and the Caribbean (PA-PILA) is dedicated to the development and implementation of an air quality analysis and forecasting system to assess pollution impacts on human health and economy. In this context, a comprehensive emission inventory for South America was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4. 1 (developed by joining CEDS trends and EDGAR v4. 3. 2 historical data), enriching it with data derived from locally available emission inventories for Argentina, Chile, and Colombia. This work presents the results of the first joint effort of South American researchers and European colleagues to generate regional maps of emissions, together with a methodological approach to continue incorporating information into future versions of the dataset.
This version of the PAPILA dataset includes CO, NO x , NMVOCs, NH 3 , and SO 2 annual emissions from anthropogenic sources for the period 2014-2016, with a spatial resolution of 0. 1 • × 0. 1 • over a domain that covers 32-120 • W and 34 • N-58 • S. The PAPILA dataset is presented as netCDF4 files and is available in an open-access data repository under a CC-BY 4 license: https://doi. org/10. 17632/btf2mz4fhf. 3  (Castesana et al. , 2021). A comparative assessment of PAPILA-CAMS datasets was carried out for (i) the South American region, (ii) the countries with local data (Argentina, Colombia, and Chile), and (iii) downscaled emission maps for urban domains with different environmental and anthropogenic factors. Relevant differences were found at both country and urban levels for all the compounds analyzed.
Among them, we found that when comparing PAPILA total emissions versus CAMS datasets at the national level, higher levels of NO x and considerably lower levels of the Published by Copernicus Publications. P. Castesana et al. : PAPILA dataset other species were obtained for Argentina, higher levels of SO 2 and lower levels of CO and NO x for Colombia, and considerably higher levels of CO, NMVOCs, and SO 2 for Chile. These discrepancies are mainly related to the representativeness of local practices in the local emission estimates, to the improvements made in the spatial distribution of the locally estimated emissions, or to both. Both datasets were evaluated against surface concentrations of CO and NO x by using them as input data to the WRF-Chem model for one of the analyzed domains, the metropolitan area of Buenos Aires, for summer and winter of 2015.
, 2014). There is a wide range of global AEIs covering SA for different species and periods that meet the mentioned requirements. Some of the AEIs worth mentioning include the Emissions Database for Global Atmospheric Research (EDGAR)  (Janssens-Maenhout et al. , 2019; EDGAR, 2021) , the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants (ECLIPSE)  (Stohl et al. , 2015) , the Community Emissions Data System (CEDS)  (Hoesly et al. , 2018) , the integrated assessment model Greenhouse gas -Air pollution Interactions and Synergies (GAINS)  (Klimont et al. , 2017) , or the Copernicus Atmosphere Monitoring Service datasets (CAMS)  (Granier et al. , 2019). Across the region, government efforts on AEIs are mainly focused on greenhouse gases (GHGs) in line with the international commitments under the United Nations Convention on Climate Change (UNFCCC).
In this context, the HTAP_v2. 2 air pollutant grid maps were developed combining available regional information within a complete global dataset  (Janssens-Maenhout et al. , 2015)  and have been widely used even outside of HTAP. This work presents what to our knowledge constitutes the first AEIs from anthropogenic sources covering the continental SA region, which combines local available information with a global database in a proper and rigorous way. For this purpose, the dataset CAMS-GLOB-ANT v4. 1  (Granier et al. , 2019) , developed by joining CEDS trends and EDGAR v4. 3. 2 historical data, was used as a basis (hereinafter CAMS dataset), enriching it with locally developed inventories available in the literature until 2019 and selecting those with national coverage and with availability of data for the period and species of interest.
The dataset presented in this work, hereinafter called PAPILA, focuses on the group of species known as reactive gases, given their relevance in atmospheric chemistry as precursors of O 3 and PM 2. 5 : carbon monoxide (CO), nitrogen oxides (NO x ), nonmethane volatile organic compounds (NMVOCs), ammonia (NH 3 ), and sulfur dioxide (SO 2 )  (Sharma et al. , 2017). Due to the availability of data in the local AEIs and the completeness of the sectors represented, the 2014-2016 period was selected for this first version of the PAPILA dataset, including local information from the continental areas of Argentina  (Puliafito et al. , 2017; Castesana et al. , 2018) , Chile  (Mazzeo et al. , 2018; Gallardo et al. , 2018), and Colombia (IDEAM, 2017). In addition, a comparison of the performance of both AEIs (PAPILA and CAMS) is presented us-ing near-surface CO and NO x mixing ratios simulated by the Weather Research and Forecasting-Chemistry (WRF-Chem) model  (Grell et al.
, 2005)  at a high spatial resolution (3 km) against in situ observations made in Buenos Aires during February-March and August-September 2015. This work was carried out within the framework of the Prediction of Air Pollution in Latin America and the Caribbean (PAPILA, 2020) and Emission Inventories in South America (EMISA, 2020) projects. PAPILA combines, for the first time, an ensemble of state-of-the-art models, high-resolution emission inventories, space observations, and surface measurements to provide real-time forecasts and analysis of regional air pollution in the Latin American and the Caribbean regions. Thus, an important aspect of the project is the development of appropriate and consistent surface emission inventories as input data for air quality models. The EMISA initiative was created to lay the foundations for constructing robust and transparent inventories of the same set of species that have been consistently estimated across South American countries using the same methodological approach.
Local information on emissions was gathered from the countries that participate in the EMISA project: Argentina, Brazil, Chile, Colombia, and Peru. Relevant research groups in Brazil and Peru have developed emission inventories for different cities  (Policarpo et al. , 2018; Dos Santos Lucon and Moutinho Dos Santos, 2005; Vivanco and Andrade, 2006; Romero et al. , 2020; Dawidowski et al. , 2014) ; however as far as we know they have not developed inventories covering the entire countries for the species included in this study. Since national territories are the common administrative entities that can be exchanged with the global inventory, the local information on emissions from these countries was not included in this first version of the combined dataset. However, this work is expected to be the starting point for the preparation of comprehensive emission inventories in South America enriched with local information.
For this purpose, we include a flow chart with the general methodology that we have applied in combining local information with a global dataset. The paper is organized as follows. Section 2 describes, for each country, the approach and sources of information used to develop the PAPILA dataset and also discusses the application of this inventory in an air quality model. Section 3 provides the main differences of PAPILA and CAMS datasets for SA and other smaller domains and the results of the air quality simulations. Section 4 provides a description of the data availability, and finally Sect. 5 presents the main conclusions of this work.
PAPILA dataset overview The PAPILA dataset is a collection of CO, NO x , NMVOCs, NH 3 , and SO 2 inventories of annual emissions from anthropogenic sources in South America for the period 2014-2016. The inventories are presented as netCDF4 files, one for each species gridded with a spatial resolution of 0. 1 • × 0. 1 • covering the domain 32-120 • W and 34 • N-58 • S. Each file contains 12 variables corresponding to the emissions in Tg yr -1 from the following categories, which are organized and denominated using the nomenclature given by CAMS: thermal power plants (ENE); residential and commercial combustion (RES); road transportation (TRO); non-road transportation (TNR); fugitive emissions (FEF); industries, including fuel consumption in manufacturing industries and construction, refineries, industrial processes, and solvent and other product use, (IND); agricultural soils (AGS); agricultural livestock (AGL); domestic and international navigation (SHP); waste (including solid waste, wastewater, and incineration) (SWD); and the sum of all categories (SUM).
This grouping of categories was carried out following the CAMS sectoral disaggregation, except for the use of solvents, reported under IND in the PAPILA dataset. To be consistent with the base inventory (CAMS-GLOB-ANT v4. 1) used for our mosaic inventory, aviation emissions were not included in this first version of the PAPILA dataset. Agricultural fires were removed to allow the use of the inventories together with fire products such as GFEDv3 (van der  Werf et al. , 2010) , FINN v1  (Wiedinmyer et al. , 2011) , and GFAS v1  (Kaiser et al. , 2012) , avoiding double counting of these fires. It is worth mentioning that by "sum of all categories" we refer to all those included in PAPILA, both for the presentation of our results and for comparative purposes with CAMS. A broader description of the activities contemplated under each category is presented in Table  A1  in Appendix A, together with the equivalences with the IPCC 1996 reporting code.
The PAPILA dataset  (Castesana et al. , 2021)  combines surface emissions from the comprehensive CAMS dataset with local information of those countries that, at the time of development of this emission inventory, had emission estimates of the mentioned species and covering the entire national territory: Argentina, Chile, and Colombia. That information was collected and assessed in terms of species, emission categories, and spatial coverage, selecting the most appropriate and representative data for each country, as described in the following subsections and summarized in Fig. 1.
Designing and building the PAPILA dataset Figure  2  presents a flow chart of the general methodology applied in combining local information with CAMS. Data from Argentina, Chile, and Colombia were assessed in terms of species availability. In addition, and as described in the next subsections, the transparency on the methodology applied in emission estimates and the representativeness and completeness of emission categories were revised in line with the CAMS emission reporting system. For those species and/or categories with an absence of local data, the CAMS inventory was used to fill the gaps. One of the challenges of combining different local inventories into a common regional database is bringing them to a single, uniform, and homogeneous grid. For this purpose, it was necessary to resolve all conflicts arising from cells shared by more than one country or coastal cells.
(2018). The time series was updated to the period 2014-2016 by applying the methodology and local activity data sources detailed in the cited work. The rest of the studied species emitted from activities under AGL and AGS (i. e. , NO x and NMVOCs) were estimated according to the 2016 methodology of the European Monitoring and Evaluation Program (EMEP, 2017), following the general expression E i = AD • EF i , (1) where E i is the emission amount of the species i, AD is the activity data, and EF i represents the emission factor of the species i related to that activity. Both the activity data and their spatial distribution were based on the previous work by  Castesana et al. (2018 Castesana et al. ( , 2020)) , while the emission factors were those suggested by the EMEP according to the level of detail described for each activity in Table  1.
These emissions have been estimated ad hoc to be included as part of the PAPILA dataset. Since they have not been published thus far, interested readers may find a more complete description of the results in Appendix A, including resulting emissions from fertilizers, crop production, and animal excreta (dairy and beef cattle, poultry, swine, sheep, goats, horses, and other livestock). Consistent with the referenced studies cited above, emissions from managed excreta are reported as AGL, and those deposited in pasture during animal grazing are reported under AGS. Resulting inventories of annual emissions from agricultural activities spatially disaggregated at the district level were converted to grids with a 0. 1 • × 0. 1 • resolution.
Chile Annual Chilean emissions were taken from the CR2-MMA dataset  (CR2-MMA, 2018) , based on the works of  Gallardo et al. (2018)  and  Mazzeo et al. (2018). This dataset is presented with a spatial resolution of 0. 01 • × 0. 01 • and includes 2014 emissions of reactive gases, GHGs, and particles, reported under the following aggregation: industries (which include emissions from energy), urban and non-urban road transportation (which only includes CO and NO x for reactive gases), residential consumption, and agricultural and forest fires. Emission from industrial sources corresponds to the compilation of self-declared estimates by each facility to the Chilean Repository of Emissions and Pollutants Transport. Neither the methodology nor the emission factors used to estimate these emissions could be traced.
For the particular case of SO 2 , the local methodology for the emission estimates is based on sulfur content in fuels and in mass balances in copper production processes, which constitute the main SO 2 emitter activity in Chile (González-  Rojas et al. , 2021). For this reason, and assuming that the information on sulfur content handled locally is reliable, we have included the spatially distributed emissions as estimated in Chile in our dataset. For the rest of the species, we decided to exclusively adopt the spatial distribution and the share of the locally reported emissions and distributed the CAMS estimates by weighing them on the CR2-MMA spatial distribution as follows: E cell (i, j, k) = E local (i, j, k) N k=1 E CAMS (i, j, k) N k=1 E local (i, j, k) , (2) where E cell (i, j, k) represents the emissions of species i and category j assigned to the cell grid k, N is the total number of grid cells covering the country, E local represents the emissions locally estimated, and E CAMS is the corresponding estimate from the global database.
Emission estimates from residential sources in the CR2-MMA dataset cover only firewood combustion for all species considered in our work  (Mazzeo et al. , 2018). According to local experts, one of the most relevant aspects of air quality in the coldest regions in southern Chile is the presence of CO, NMVOCs, and particles from burning of firewood in households. Although this practice is included under the residential category of global inventories, the corresponding estimated emissions do not seem to be consistent with the magnitude of the air pollution situation observed at the local level  (Huneeus et al. , 2020b). In addition, by downscaling global inventories in the metropolitan region of Santiago (Chile's central region),  Huneeus et al. (2020a)  found out that residential emissions were strongly overestimated in global databases and attributed this inaccuracy to the use of population density as a proxy for the spatial distribution.
Residential emissions from firewood burning are less relevant in the more temperate northern areas where air pollution is mostly linked to emissions of SO 2 and particles from the metal industry  (Huneeus et al. , 2020b). From this and assuming that (i) residential firewood burning is a predominant source in the southern region and (ii) in central and northern regions this work improves the representation of the diversity of sources and local practices for the other fuel combustion categories, we have decided to replace the residential emissions of CAMS with those of the CR2-MMA, at the risk of underestimating residential emissions in the central and northern regions by omitting those from fuels other than firewood (see  Sect. 3. 1). Local estimates of CO and NO x emissions from urban and non-urban road transportation were aggregated and reported in the PAPILA dataset under the TRO category.
Colombia For the purpose of the PAPILA dataset, the only available information for Colombia was the emission estimates of CO, NO x , and SO 2 at the national level from all the categories of interest, except agricultural soils. These estimates were developed for the Third National Communication of Colombia to the UNFCCC, covering the period  2010 -2014 (IDEAM, 2017)). Annual emissions from Colombia were extrapolated to 2015 and 2016 by applying linear regression forecast using the local time series and disaggregated using the spatial distribution of sources of the CAMS inventory as follows: E cell (i, j, k) = E CAMS (i, j, k) N k=1 E local (i, j, k) N k=1 E CAMS (i, j, k) , (3) where variables and indexes are those described in Eq. (  2 ). Although in this context the country reports CO, NO x , and SO 2 emissions from SWD, CAMS reports them as zero.
Comparison of local and global datasets A spatial analysis was performed following a similar approach to  Trombetti et al. (2018)  in their work on spatial inter-comparison of top-down emission inventories in European urban areas, in which the analysis was made in terms of normalized emission values by a group of categories and for different urban domains in order to become independent of emission levels and to show the relative contribution of a certain group of emission activities in different areas. Since in our work we are interested in comparing only two inventories without losing sight of the differences in terms of magnitude, we have adapted this approach by comparing normalized emissions by category and urban domain, normalizing them with respect to those from the CAMS dataset, as shown in Eq.
(  4 ). In this way, we were able to compare both datasets in relative terms and without losing information on the shares of each group of categories and the differences in the emission levels of each dataset. ∀i, ∀J : E * i,J (d, area) = N k=1 E i,J (d, k) N k=1 E i,J (CAMS, k) , (4) where E * i,J (d, area) and E i,J (d, k) are the normalized emissions and the emission levels, respectively, of the species i and group of categories J corresponding to the dataset d and the area (region, country, or urban domain) covered by the total number N of cell grids k. For this analysis, we grouped categories as ENE + IND, RES, TRO, and "others", and applied the analysis to (i) the SA region, (ii) countries with local data (Argentina, Chile, and Colombia), and (iii) urban domains from those countries that have implemented their own methodologies for the spatial distribution of emissions.
WRF-Chem simulations: case study in Buenos Aires The performance of the PAPILA dataset in comparison with CAMS can be assessed using both inventories as input data of a regional model, implemented in the whole domain where local data have been integrated into the global dataset. This vast region, which includes the tropical Andes in Colombia, the dry Andes in southern Chile, and the Argentinean plateau towards the Atlantic coast, is characterized by diverse topographic features and vegetation patterns. In order to capture the differences in boundary layer process and surface energy budget in the whole area, a high-resolution model is needed, set up in each area where the main changes between the PAPILA and CAMS datasets have been made. As a first step of this verification exercise, here we present a study focused on Buenos Aires, using the Weather Research and Forecasting Chemistry regional model version 4.
The chemistry in these simulations was modeled using the GOCART bulk aerosol scheme for aerosol-phase chemistry along with RADM2 for gasphase chemistry. The initial and boundary conditions were taken from the NCEP Final Operational Global Analysis data (FNL), available at a resolution of 1 • every 6 h  (NOAA, 2000). The FINN fire database was used for fire emissions  (Wiedinmyer et al. , 2011) , the MEGAN biogenic database was used for biogenic emissions  (Guenther et al. , 2006) , and sea salt emissions from GOCART were also included. Reported annual emissions in 2015 from the two inventories were processed to produce hourly-resolved emissions at the resolution of each of the domains. Since the PAPILA dataset only includes reactive gases, for aerosol emissions the ones of the CAMS simulation were used: EDGAR v4.