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dc.date.available
2022-06-03T18:06:43Z  
dc.identifier.citation
Puliafito, Salvador Enrique; (2022): Dataset supporting the estimation and analysis of high spatial resolution inventories of atmospheric emissions from several sectors in Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. (dataset). http://hdl.handle.net/11336/158926  
dc.identifier.uri
http://hdl.handle.net/11336/158926  
dc.description.abstract
This data article provides an extensive and complete description of the high spatial resolution inventory (HSRI) estimation shown in the article "High resolution inventory of atmospheric emissions from livestock production, agriculture, and biomass burning sectors of Argentina" Puliafito et al. [1], and its comparison with several sectors in Argentina. The dataset provided are high-resolution inventories (0.025° × 0.025° lat/long) for CO2, CH4, N2O and another 8 species from livestock, biomass burning, agriculture and another 12 sectors (based on 2016 year). In addition, we also provide the database for 2014 using the same methodology. The dataset presented are necessary to improve input inventories for air quality models. Also, they are better to inform and guide the stakeholders, in making decisions related to environmental protection and health promotion, as well as assessing the environmental performance in terms of atmospheric emissions of an activity, sector or region in Argentina  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.title
Dataset supporting the estimation and analysis of high spatial resolution inventories of atmospheric emissions from several sectors in Argentina  
dc.type
dataset  
dc.date.updated
2022-06-03T15:14:51Z  
dc.description.fil
Fil: Puliafito, Salvador Enrique. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.datacite.PublicationYear
2022  
dc.datacite.Creator
Puliafito, Salvador Enrique  
dc.datacite.affiliation
Universidad Tecnológica Nacional. Facultad Regional de Mendoza  
dc.datacite.affiliation
Consejo Nacional de Investigaciones Científicas y Técnicas  
dc.datacite.affiliation
Universidad Tecnológica Nacional. Facultad Regional de Mendoza  
dc.datacite.affiliation
Universidad Tecnológica Nacional. Facultad Regional de Mendoza  
dc.datacite.affiliation
Consejo Nacional de Investigaciones Científicas y Técnicas  
dc.datacite.publisher
Consejo Nacional de Investigaciones Científicas y Técnicas  
dc.datacite.subject
Meteorología y Ciencias Atmosféricas  
dc.datacite.subject
Ciencias de la Tierra y relacionadas con el Medio Ambiente  
dc.datacite.subject
CIENCIAS NATURALES Y EXACTAS  
dc.datacite.ContributorType
DataCollector  
dc.datacite.ContributorType
DataCollector  
dc.datacite.ContributorName
Berná Peña, Lucas Luciano  
dc.datacite.ContributorName
Bolaño Ortiz, Tomas Rafael  
dc.datacite.date
01-2020  
dc.datacite.DateType
Creado  
dc.datacite.language
eng  
dc.datacite.version
1.0  
dc.datacite.description
Subject Environmental Science, Pollution Specific subject area Estimation of high spatial resolution inventory from livestock production, agriculture, and biomass burning sectors Type of dataTables and figures How data were acquiredData collection of the amount and type of production for each sector, and estimation through different methods. In addition, specific emission factors were applied for each sector for the calculation of the different polluting species.  
dc.datacite.DescriptionType
Información Técnica  
dc.relationtype.isSourceOf
https://ri.conicet.gov.ar/handle/11336/140421  
dc.relationtype.isSourceOf
https://ri.conicet.gov.ar/handle/11336/158798  
dc.relationtype.isSourceOf
https://ri.conicet.gov.ar/handle/11336/143828  
dc.subject.keyword
High-resolution emissions inventory  
dc.subject.keyword
Greenhouse gas  
dc.subject.keyword
Methane  
dc.subject.keyword
Livestock production  
dc.subject.keyword
Agriculture  
dc.datacite.resourceTypeGeneral
dataset  
dc.conicet.datoinvestigacionid
580  
dc.datacite.geolocation
: -21, -73; -56, -56; -56, -75; -21, -52;  
dc.datacite.formatedDate
2020