AERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition

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dc.catalogadordfo
dc.contributor.authorMahowald, Natalie M.
dc.contributor.authorLi, Longlei
dc.contributor.authorVira, Julius
dc.contributor.authorPrank, Marje
dc.contributor.authorHamilton, Douglas S.
dc.contributor.authorMatsui, Hitoshi
dc.contributor.authorMiller, Ron L.
dc.contributor.authorLu, P. Louis
dc.contributor.authorAkyuz, Ezgi
dc.contributor.authorMeidan, Daphne
dc.contributor.authorHess, Peter
dc.contributor.authorLihavainen, Heikki
dc.contributor.authorWiedinmyer, Christine
dc.contributor.authorHand, Jenny
dc.contributor.authorAlaimo, Maria Grazia
dc.contributor.authorAlves, Célia
dc.contributor.authorAlastuey, Andres
dc.contributor.authorArtaxo, Paulo
dc.contributor.authorBarreto, Africa
dc.contributor.authorBarraza, Francisco
dc.contributor.authorLambert, Fabrice
dc.contributor.authorBecagli, Silvia
dc.contributor.authorCalzolai, Giulia
dc.contributor.authorChellam, Shankararaman
dc.contributor.authorChen, Ying
dc.contributor.authorChuang, Patrick
dc.contributor.authorCohen, David D.
dc.contributor.authorColombi, Cristina
dc.contributor.authorDiapouli, Evangelia
dc.contributor.authorDongarra, Gaetano
dc.contributor.authorEleftheriadis, Konstantinos
dc.contributor.authorEngelbrecht, Johann
dc.contributor.authorGaly-Lacaux, Corinne
dc.contributor.authorGaston, Cassandra
dc.contributor.authorGomez, Dario
dc.contributor.authorGonzález Ramos, Yenny
dc.contributor.authorHarrison, Roy M.
dc.contributor.authorHeyes, Chris
dc.contributor.authorHerut, Barak
dc.contributor.authorHopke, Philip
dc.contributor.authorHüglin, Christoph
dc.contributor.authorKanakidou, Maria
dc.contributor.authorKertesz, Zsofia
dc.contributor.authorKlimont, Zbigniew
dc.contributor.authorKyllönen, Katriina
dc.contributor.authorLiu, Xiaohong
dc.contributor.authorLosno, Remi
dc.contributor.authorLucarelli, Franco
dc.contributor.authorMaenhaut, Willy
dc.contributor.authorMarticorena, Beatrice
dc.contributor.authorMartin, Randall V.
dc.contributor.authorMihalopoulos, Nikolaos
dc.contributor.authorMorera-Gómez, Yasser
dc.contributor.authorPaytan, Adina
dc.contributor.authorProspero, Joseph
dc.contributor.authorRodríguez, Sergio
dc.contributor.authorSmichowski, Patricia
dc.contributor.authorVarrica, Daniela
dc.contributor.authorWalsh, Brenna
dc.contributor.authorWeagle, Crystal L.
dc.contributor.authorZhao, Xi
dc.date.accessioned2025-06-23T19:54:33Z
dc.date.available2025-06-23T19:54:33Z
dc.date.issued2025
dc.description.abstractAerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations - and, especially, particle composition - to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit similar to 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers.
dc.fechaingreso.objetodigital2025-06-23
dc.fuente.origenWOS
dc.identifier.doi10.5194/acp-25-4665-2025
dc.identifier.eissn1680-7324
dc.identifier.issn1680-7316
dc.identifier.urihttps://doi.org/10.5194/acp-25-4665-2025
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/104744
dc.identifier.wosidWOS:001481380800001
dc.information.autorucInstituto de Geografía; Lambert Fabrice; 0000-0002-2192-024X; 250043
dc.issue.numero9
dc.language.isoen
dc.nota.accesocontenido completo
dc.pagina.final4702
dc.pagina.inicio4665
dc.revistaAtmospheric chemistry and physics
dc.rightsacceso abierto
dc.rights.licenseAtribución/Reconocimiento 4.0 Internacional
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subject.ddc550
dc.subject.deweyCiencias de la tierraes_ES
dc.subject.ods15 Life on land
dc.subject.odspa15 Vida de ecosistemas terrestres
dc.titleAERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition
dc.typepreprint
dc.volumen25
sipa.codpersvinculados250043
sipa.trazabilidadWOS;2025-05-24
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