{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1361137044642054912.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.5194/acp-16-11083-2016"}},{"identifier":{"@type":"URI","@value":"https://acp.copernicus.org/articles/16/11083/2016/acp-16-11083-2016.pdf"}}],"dc:title":[{"@value":"Analysis of particulate emissions from tropical biomass burning using a\nglobal aerosol model and long-term surface observations"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:p>Abstract. We use the GLOMAP global aerosol model evaluated against observations of surface particulate matter (PM2.5) and aerosol optical depth (AOD) to better understand the impacts of biomass burning on tropical aerosol over the period 2003 to 2011. Previous studies report a large underestimation of AOD over regions impacted by tropical biomass burning, scaling particulate emissions from fire by up to a factor of 6 to enable the models to simulate observed AOD. To explore the uncertainty in emissions we use three satellite-derived fire emission datasets (GFED3, GFAS1 and FINN1). In these datasets the tropics account for 66–84 % of global particulate emissions from fire. With all emission datasets GLOMAP underestimates dry season PM2.5 concentrations in regions of high fire activity in South America and underestimates AOD over South America, Africa and Southeast Asia. When we assume an upper estimate of aerosol hygroscopicity, underestimation of AOD over tropical regions impacted by biomass burning is reduced relative to previous studies. Where coincident observations of surface PM2.5 and AOD are available we find a greater model underestimation of AOD than PM2.5, even when we assume an upper estimate of aerosol hygroscopicity. Increasing particulate emissions to improve simulation of AOD can therefore lead to overestimation of surface PM2.5 concentrations. We find that scaling FINN1 emissions by a factor of 1.5 prevents underestimation of AOD and surface PM2.5 in most tropical locations except Africa. GFAS1 requires emission scaling factor of 3.4 in most locations with the exception of equatorial Asia where a scaling factor of 1.5 is adequate. Scaling GFED3 emissions by a factor of 1.5 is sufficient in active deforestation regions of South America and equatorial Asia, but a larger scaling factor is required elsewhere. The model with GFED3 emissions poorly simulates observed seasonal variability in surface PM2.5 and AOD in regions where small fires dominate, providing independent evidence that GFED3 underestimates particulate emissions from small fires. Seasonal variability in both PM2.5 and AOD is better simulated by the model using FINN1 emissions. Detailed observations of aerosol properties over biomass burning regions are required to better constrain particulate emissions from fires.</jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1381137044642054915","@type":"Researcher","foaf:name":[{"@value":"Carly L. Reddington"}]},{"@id":"https://cir.nii.ac.jp/crid/1381137044642054912","@type":"Researcher","foaf:name":[{"@value":"Dominick V. Spracklen"}]},{"@id":"https://cir.nii.ac.jp/crid/1381137044642054784","@type":"Researcher","foaf:name":[{"@value":"Paulo Artaxo"}]},{"@id":"https://cir.nii.ac.jp/crid/1381137044642054913","@type":"Researcher","foaf:name":[{"@value":"David A. Ridley"}]},{"@id":"https://cir.nii.ac.jp/crid/1381137044642054916","@type":"Researcher","foaf:name":[{"@value":"Luciana V. Rizzo"}]},{"@id":"https://cir.nii.ac.jp/crid/1381137044642054914","@type":"Researcher","foaf:name":[{"@value":"Andrea Arana"}]}],"publication":{"publicationIdentifier":[{"@type":"EISSN","@value":"16807324"}],"prism:publicationName":[{"@value":"Atmospheric Chemistry and Physics"}],"dc:publisher":[{"@value":"Copernicus GmbH"}],"prism:publicationDate":"2016-09-07","prism:volume":"16","prism:number":"17","prism:startingPage":"11083","prism:endingPage":"11106"},"reviewed":"false","dc:rights":["https://creativecommons.org/licenses/by/3.0/"],"url":[{"@id":"https://acp.copernicus.org/articles/16/11083/2016/acp-16-11083-2016.pdf"}],"createdAt":"2016-09-07","modifiedAt":"2025-02-08","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1050867278040958336","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Evaluation of aerosol iron solubility over Australian coastal regions based on inverse modeling: implications of bushfires on bioaccessible iron concentrations in the Southern Hemisphere"}]},{"@id":"https://cir.nii.ac.jp/crid/1360002221111232384","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Non-methane organic gas emissions from biomass burning: identification, quantification, and emission factors from PTR-ToF during the FIREX 2016 laboratory experiment"}]},{"@id":"https://cir.nii.ac.jp/crid/1360013172240094080","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Improved representation of the global dust cycle using observational constraints on dust properties and abundance"}]},{"@id":"https://cir.nii.ac.jp/crid/1360017282189916288","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions"}]},{"@id":"https://cir.nii.ac.jp/crid/1360017282199742592","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Satellite-based evaluation of AeroCom model bias in biomass burning regions"}]},{"@id":"https://cir.nii.ac.jp/crid/1360022305565741184","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Formation and evolution of Tar Balls from Northwestern US wildfires"}]},{"@id":"https://cir.nii.ac.jp/crid/1360022307172241280","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"How emissions uncertainty influences the distribution and radiative impacts of smoke from fires in North America"}]},{"@id":"https://cir.nii.ac.jp/crid/1360290617731986432","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Recent (1980 to 2015) Trends and Variability in Daily‐to‐Interannual Soluble Iron Deposition from Dust, Fire, and Anthropogenic Sources"}]},{"@id":"https://cir.nii.ac.jp/crid/1360298760472200704","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Contrasting source contributions of Arctic black carbon to atmospheric concentrations, deposition flux, and atmospheric and snow radiative effects"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.5194/acp-16-11083-2016"},{"@type":"CROSSREF","@value":"10.5194/acp-18-3299-2018_references_DOI_3KasfMoHDUc4N5MM1jy2oB6aIAZ"},{"@type":"CROSSREF","@value":"10.1038/s41467-022-33680-4_references_DOI_3KasfMoHDUc4N5MM1jy2oB6aIAZ"},{"@type":"CROSSREF","@value":"10.5194/acp-22-11009-2022_references_DOI_3KasfMoHDUc4N5MM1jy2oB6aIAZ"},{"@type":"CROSSREF","@value":"10.5194/acp-20-2073-2020_references_DOI_3KasfMoHDUc4N5MM1jy2oB6aIAZ"},{"@type":"CROSSREF","@value":"10.1029/2020gl089688_references_DOI_3KasfMoHDUc4N5MM1jy2oB6aIAZ"},{"@type":"CROSSREF","@value":"10.1186/s40645-020-00357-9_references_DOI_3KasfMoHDUc4N5MM1jy2oB6aIAZ"},{"@type":"CROSSREF","@value":"10.5194/acp-21-8127-2021_references_DOI_3KasfMoHDUc4N5MM1jy2oB6aIAZ"},{"@type":"CROSSREF","@value":"10.5194/acp-18-11289-2018_references_DOI_3KasfMoHDUc4N5MM1jy2oB6aIAZ"},{"@type":"CROSSREF","@value":"10.5194/acp-22-8989-2022_references_DOI_3KasfMoHDUc4N5MM1jy2oB6aIAZ"}]}