dataesr/scientific-paragraphs-categorization huggingface dataset:
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 base 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 estimates CO, NO x and SO 2 emissions from solid waste, wastewater and waste incineration, SWD emissions were taken from CAMS.
Comparison of local and global datasets A spatial analysis was performed following a similar approach to that by  Trombetti et al. (2018)  in their work on spatial intercomparison of top-down emission inventories in European urban areas, in which the analysis was made in terms of normalized emission values by group of categories and for different urban domains in order to become independent from emission levels and to show the relative contribution of a certain group of emission activities in different areas. However, we only need to compare two inventories and are also interested in observing the differences in terms of magnitude, we therefore propose a comparison of normalized emissions by category and urban domain normalizing them with respect to those from the CAMS dataset, such 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 "Other categories", and applied the analysis to (i) SA region, (ii) countries with local data (Argentina, Chile and Colombia), and (iii) urban domains from those countries with local information on the spatial disaggregation of emissions.
Dataset performance evaluation: case study in MABA The performance of the PAPILA dataset in comparison with that of CAMS as input data to air quality models was assessed using the Weather Research and Forecasting Chemistry (WRF-Chem v4. 1. 2) regional model. The site chosen for this case study was the MABA, a megacity strongly influenced not only by mobile and residential sources, but also by the presence of four big thermal power plants, an important industrial park and an international port. Simulations were conducted using the model over three nested domains with a highest horizontal resolution of 3 km centered in MABA. Two time periods were selected to cover summer (from 7 February to 5 March 2015) and winter (from 26 August to 17 September 2015) to assess the role of the emission estimates from the two inventories in the simulated air pollutant concentrations in the different seasons.
The chemistry in these simulations was modelled using the GOCART bulk aerosol scheme along with RADM2 for gas-phase chemistry for aerosol phase chemistry. The initial and boundary conditions were taken from the NCEP Final Operational Global Analysis data (FNL), available at a resolution of 1 degree every 6 hours  (NOAA, 2000). FINN Fire Database was used for fire emissions  (Wiedinmyer et al. , 2011) , MEGAN Biogenic database 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 resolution of each of the domains. For reactive gases we used the emission inventory presented in this article. The impact of aerosols emissions was also included in the analysis, enriching PAPILA database with EDGARv4.
3. 2 Global emission database for PM 2. 5 and PM 10 and CAMSv4. 1 for OC, BC and VOCs. Temporal emission patterns were computed by an iterative method, focusing on capturing daily and seasonal variations of CO, NO x and PM surface mixing ratios observed. For ENE, TRO and RES monthly emission patterns were defined to breakdown total annual emissions into monthly fluxes (see Fig. A2 ). Emissions from other categories were evenly distributed throughout the year. RES monthly cycle was established from the reports on natural gas consumption reported in national statistics (ENARGAS, 2021) for residential and commercial activties in the MABA. This profile shows a maximum during winter linked to the increase in residential heating. Similarly, TRO monthly cycle was defined from the total fossil fuel consumption from the road transport reported in the statistics for the entire country (Secretaría de Energía, 2021).
Model evaluation Highest resolution model outputs using these two emission inventories were evaluated against CO and NO x ground-based observations from the available monitoring stations in MABA (See locations of the sites in Figure  A2 ). Air pollutant data from the Environmental Protection Agency of Buenos Aires (APRA) include hourly measurements of NO x and CO in two sites, Cordoba (34. 60º S, 58. 39º W) and Parque Centenario (34. 61º S, 58. 44º W). Cordoba's site is located in a commercial area with high vehicular flow and very low incidence of stationary sources while Parque Centenario is located in a residential area next to an arboreal space with medium vehicular flow and also very low incidence of stationary sources. As the air quality database is at hourly resolution, the model was also sampled at every hour.
RESULTS AND DISCUSSION Table  2  reports the resulting 2015 annual emissions of CO, NO x , NMVOCs, NH 3 and SO 2 corresponding to the sum of all categories in the PAPILA dataset, for different domains: South America, Argentina, Chile, Bahía Blanca, MABA, Mendoza, Antofagasta, MRS and Osorno, in comparison with the corresponding emissions levels from CAMS. In addition, Figure  3  depicts the shares of each group of categories (ENE + IND, RES, TRO, and Others) to the total emissions of each species and for all the aforementioned domains. They are expressed in a normalized way with respect to the sum of all categories here analysed in CAMS for each corresponding species and domain. In this way, the emissions of each species in CAMS correspond to the sum of the share of each group of categories, adding up to a total of 1.
00. On the other hand, the shares of each sector in PAPILA can be compared with those in CAMS and add up to a total greater or less than 1. 00 according to the differences in the sum of all the categories indicated in Table  2. The spatial distribution of 2015 annual emissions of CO, NO x , NMVOCs, NH 3 and SO 2 is shown in Figure  4  for the sum of all categories. In addition, this figure includes maps with the differences between PAPILA and CAMS datasets for each species, depicting the differences in terms of intensity and location of emission sources. For comparative purposes, emissions from agricultural fires have been subtracted from the sum of all categories in CAMS database. In what follows results are presented firstly by species, highlighting the most relevant aspects of the 2015 emission (section 3.
In Antofagasta, although total emissions levels from both datasets were similar, there were substantial differences in the contributions by category: emissions from ENE + IND are almost seven times larger in PAPILA than in CAMS, and while RES and TRO emissions are negligible in the local estimates, according to global estimates they contribute to almost 90% of the domain's emissions. On the contrary, in Osorno local estimates for the sum of all categories were seven times larger than those in CAMS, emissions coming almost entirely from residential firewood burning.
As seen in Figure  3 , all urban domains showed higher local emissions, except Antofagasta with a barely noticeable difference. In B. Blanca, the most relevant differences were the larger emissions in ENE + IND and SHP, category for which CAMS reported zero emissions while according to the local data it represented 12% of the domain's emissions, despite the omission of the international port as a source of emission. Relevant larger emissions existed for TRO, ENE and RES in the MABA and Mendoza, together with significantly smaller emissions from IND. The larger emissions from other categories in the MABA and B. Blanca are mainly attributable to SHP, and although the impact of Other sectors on the total budget in these domains was negligible, local estimates showed considerably higher levels than CAMS for emissions from agricultural activities and FEF, category for which the global database attributed zero emissions in the three urban domains.
Smaller emissions from FEF were observed in B. Blanca and Mendoza; while TRO contributed to these differences in the first domain and counteracted them in the second. In the MABA emissions from FEF and TRO were considerably larger than those in CAMS. Even when estimates from RES in PAPILA were around 80% lower than those of CAMS they exhibited lesser impact on the differences between the two datasets and on the total emissions in each domain. Local estimates for the MRS and Antofagasta were significantly smaller (around 80%) than the global ones, the difference is mainly attributable to the adopted local information on locations and emission shares for ENE + IND. On the contrary, local estimates for Osorno showed emissions more than 24 times larger than those of CAMS, almost exclusively attributed to the incorporation of local information on firewood consumption in the RES category in cold areas of the country.
Ammonia Similarly to NMVOCs, the only two countries with local data on NH 3 are Chile and Argentina. At national level, the inclusion of local information is reflected in differences of -7% of NH 3 emissions in Chile (only attributable to RES) and -40% in Argentina, where smaller emissions from AGS were the main responsible for that difference partially offset by larger emissions from AGL. These two categories represent the main sources of NH 3 emissions in the country with a contribution of 72% from soils and 24% from livestock, according to the local estimates. Smaller emissions in local estimates of Argentina and Chile were reflected in a difference with CAMS of only -4% of total emissions in South America. Although the impact of emissions from urban domains on the total levels of each country was negligible (around 1%), big differences were found at the category level between the two datasets.
As in Argentina, the sulfur content in the coal used was highly variable, due to the different sulfur levels that the country's coal fields present. In the same way as Colombia, Chile showed larger emissions from the sum of all categories as a consequence of the inclusion of local data in ENE + IND, differences mainly related with sulfur emissions from the relevant copper mining activities that take place along the country, which were non fully covered by CAMS. The lower TRO emissions reported by CAMS for Argentina and Colombia seem to be related in part to the methodology used for projections, that assumes a sustained reduction in sulfur content from 2012 to 2015. Nevertheless, this reduction did not occur in any of the countries: while Colombia introduced prior to 2012 strong restrictions to fuel quality, in Argentina these restrictions for the fuels used by heavy duty trucks (the main emitters) did not take place.
Model evaluation and results Table  3  summarizes the overall model performance of PAPILA and CAMS-based results for hourly CO and NO x concentrations. For the winter period, PAPILA-based results had lower normalized mean error than CAMS-based results; the negative bias was larger for the CAMS emission run, exceeding in all cases more than 12% for both CO and NO x except for NO x in Parque Centenario. FAC2 was also better in PAPILA simulation. Differences in the concentrations resulting from both runs were consistent with those exhibited between the inventories. In terms of CO emissions, PAPILA dataset emissions were 12% higher than CAMS being road transportation the main responsible followed by industry and residential sources. On the other hand, NO x emissions were 46% higher in PAPILA, with significant discrepancies in emissions mainly from TRO followed by ENE and IND.
PAPILA-CAMS main differences Our results show relevant differences between the PAPILA and CAMS datasets, both in terms of emission levels and their spatial distribution. They also served to exemplify the goodness of the PAPILA-based simulated surface mixing ratios in the city where concentrations were analysed. The reasons behind the observed differences are diverse and are mainly linked to the activity data and to the methodologies applied to estimate and spatially distribute the emissions. Given the limited availability of local data, the emission factors for the compounds covered in this work were mostly based on default values in both the global and local datasets considered. In general, it is observed that the CAMS-GLOB-ANTv4. 1 applies linear trends on the emissions of the aggregated categories, based on the estimates of the EDGARv4.
3. 2 of the year 2012. This is different from what has been done in this work, since for the three countries 2014 was taken as the base year, and while the same methodology than that used by CAMS was applied to extrapolate to 2015 and 2016 for Chile and some categories for Argentina, locally estimated trends were applied for Colombia. In this way, local inventories are not only based on region-specific information, but also the extrapolation of a shorter time period reduces the uncertainties associated with the activity data strongly linked to short-term variations and technological changes. For those activities related to fuel consumption, local inventories used the information reported in the national energy balance and other national energy statistics while global inventories are based on the information reported by the countries to the International Energy Agency, which is consistent but not exactly the same than that reported in the national statistics since the IEA processes the information received (IEA, 2020).
Another relevant aspect of national and therefore international statistics is the lack of reliable information on firewood consumption, widely used in rural and urban areas of the cold regions of Chile. This fact also impacts on the correct representation of the replacement of firewood by LPG or Natural Gas that took place in Argentina in the last decade, due to higher production of non-conventional shale gas in Vaca Muerta basin  (El Pais, 2015)  and the resulting reduction in fossil fuel prices. In addition, the use of the population as a driver for the spatial distribution of the emissions from some categories, such as RES, implies another difficulty that is linked to the resolution used. Generally, international databases use country and subnational borders as proxy population data for the spatial disaggregation  (Janssens-Maenhout et al.
Added to this, substantial differences have been observed in terms of the location of the IND sources in both inventories, probably attributable to the drivers used by the global databases for this purpose, with a very low presence of sources throughout the Argentine territory in CAMS, and a striking abundance of sources distributed over the central and northern region in Chile, which however do not reflect the heterogeneous share of emissions shown by the local distribution. With the advancement of satellite tools, there are many aspects related to the spatial disaggregation of emissions that are improving over time, as the allocation of agricultural emissions. However, there are many aspects that the global inventory development methodologies have so far failed to replicate, whereas the incorporation of information generated from a local perspective has provided local inventories with greater precision for estimating emissions.
In addition to the specific agricultural practices of the region, such as the predominant use of grazing for cattle farming or the use of large proportions of urea for the fertilization of crops, during the last decades economic, natural and technological changes have occurred that have impacted on the sector's emissions and which are not reflected in global inventories  (Castesana et al. , 2018 (Castesana et al. , , 2020)). Lastly, and with the aim of contributing some aspects that may improve both global inventories and the dataset presented here, we list some information gaps identified when using the version CAMS-GLOB-ANT v4. 1 as a base inventory for the region: (i) emissions from domestic and international civil aviation are not included; (ii) domestic and international waterborne navigation are not reflected by downscaling the port city B.
, 2019). For its part, the item (v) brings to light the problems around waste management in the region, where waste is often disposed of in open dumpsites and, particularly regarding CO, NO x and SO 2 emissions, uncontrolled open burning occurs, these practices are not reflected in local or global emission maps (UNEP, 2018). All these gaps in the base inventory were replicated in PAPILA dataset, except for the case of FEF emissions and NMVOCs emissions from agricultural activities in Argentina.
Data availability Gridded maps with all inventories per species and year are available as netCDF4 (Network Common Data Format) files for the regional domain (32°W-120°W, 34°N-58°S) at a resolution of 0. 1°× 0. 1°(PAPILA dataset) and can be accessed through the open access data repository http://dx. doi. org/10. 17632/btf2mz4fhf. 2, under a CC-BY 4 license  (Castesana et al. , 2021). The dataset includes all necessary attributes to be easily used in air quality simulations, such as molecular weight, projection and units. In addition, temporal profiles applied in the inventory evaluation for the Metropolitan Area of Buenos Aires, Argentina, are available in Appendix A of this work.
Conclusions This work presents the results of the first joint effort of South American countries to generate regional maps of emissions inventories. Although it includes the emissions estimates made for a limited number of countries, it shows the enormous potential for the improvement of air quality studies in the region, by including local data into global databases and building together an inventory of the reactive gases CO, NO x , NMVOCs, NH 3 and SO 2. The results obtained through modelling using the inventory presented here as input are promising. Although there is room for improvements that may be linked to both emissions and other processes, the CO and NO x results in the Metropolitan Area of Buenos Aires were able to reasonably reproduce surface observations, improving those obtained from the use of global inventories.
This work highlights the strengths and weaknesses of not only global inventories but also local ones. Although the latter improve the representativeness of the estimates, the groups that generate information on emissions in the region do not necessarily have the same objectives (some are mainly oriented to the generation of input information for models, others towards mitigation measures that respond to air pollution concerns of their region) which is reflected in the uneven levels of development of local inventories. In this sense, it is worth mentioning that although the resources in the region often limit their growth, the capacities of the groups are growing, which is partly reflected in the development of local databases published in this same special issue  (Puliafito et al. , 2021; Álamos et al.
, this issue; Osses et al. , this issue). In addition to individual advances, we want to emphasize the role of the PAPILA project and the EMISA initiative, which promote collaboration between groups in the region, enhancing efforts aimed at the development of appropriate and consistent surface emission inventories. In this auspicious context, we trust that this work will be a starting point for the development of comprehensive emission inventories in South America enriched with local information. To this end, the first step will be to join the efforts of other countries in this endeavor, encouraging those with inventory capabilities to broaden their focus beyond cities by building national emission maps. In addition to the aspects that can be improved both in local and global inventories, we found it relevant to point out the key role of transparency in the allocation of sources in the categories for the development of coherent databases.
restuans, Cx. quinquefasciatus, Cx. tarsalis, and Cx. Coronator. Asian and American genotypes of ZIKV may occasionally infect Culex mosquitoes but not African strains  14. From our dataset  [15] [16] [17] [18]  , we showed that ZIKV can accumulate in Cx. pipiens saliva only after forced infection by intrathoracic inoculation, short-circuiting the midgut barrier  17  ; after oral infection, the virus was unlikely to disseminate in the mosquito ruling out the role of Culex mosquitoes in ZIKV transmission. Viral replication was not detected in Culex which was unable to trigger the RNAi pathway as no viral replication signatures such as 21 nt viral siRNAs were detected  19. Even though vectors of the flavivirus West-Nile virus, a phylogenetically ZIKV-related arbovirus, mosquitoes of the Cx. pipiens complex are not competent for ZIKV transmission and are very unlikely to play a role of vector during a Zika outbreak.
Owing to complex hierarchical experimental design, each mosquito species was modeled separately. When several mosquito populations were sampled at different locations within a given country, the variations resulting from the population effect was modeled by a random effect nested within the country, in a mixed regression framework. The contribution of each fixed-effect term was assessed by refitting univariable models and measuring the semi-partial R-squared value (as defined by Stoffel et al. 51  ). Likewise, the contribution of random effects was measured as the adjusted intra-class correlation coefficient (as defined by Nakagawa et al. 52  ). Analyses were conducted using the R software (v. 4. 2. 0)  53 with the following packages: lme4 (v. 1. 1-27. 1)  54 , emmeans (v. 1. 7. 2), performance (v.
Galactic SEDs in the mm-to-cm range Our knowledge of the Spectral Energy Distribution (SED) of nearby galaxies has greatly improved in recent times, thanks to multi-wavelength surveys and in particular to the Herschel Space Observatory. Herschel has extended the SED coverage to far-infrared (FIR) and submm wavelengths, a range where dust, i. e. solid grains in the interstellar medium (ISM), emits the radiation it has absorbed from starlight (∼1/4 of the total luminosity for late type galaxies;  [1] ). Using a dust model and fitting tools such as, e. g. , CIGALE  [2]  and HerBIE  [3] , a wealth of information can be extracted from the SED: star-formation rates (SFRs), stellar and dust masses, gas-to-dust ratios, etc. (  [4] [5] [6] [7] , just to mention a few recents works). Figure  1  shows an example of the current SED knowledge: it uses data from DustPedia  [8] , a database collecting photometry and imagery for all the large (D 25 > 1 ) and nearby (d < 30 Mpc) galaxies observed by Herschel; and mid radio continuum flux densities from  [9].