Assessing over decadal biomass burning influence on particulate matter composition in subequatorial Amazon: literature review, remote sensing, chemical speciation and machine learning application
Abstract A study on aerosols in the Brazilian subequatorial Amazon region, Tangará da Serra (TS) and Alta Floresta (AF) was conducted and compared to findings in an additional site with background characteristics (Manaus, AM). TS and AF counties suffer from intense biomass burning periods in the dry...
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| Format: | Article |
| Language: | English |
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Academia Brasileira de Ciências
2023-12-01
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| Series: | Anais da Academia Brasileira de Ciências |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652023000501601&lng=en&tlng=en |
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| author | ADRIANA GIODA VINICIUS L. MATEUS SANDRA S. HACON ELIANE IGNOTTI RUAN G.S. GOMES MARCOS FELIPE S. PEDREIRA JOSÉ MARCUS GODOY RIVANILDO DALLACORT ANA LÚCIA M. LOUREIRO FERNANDO MORAIS PAULO ARTAXO |
| author_facet | ADRIANA GIODA VINICIUS L. MATEUS SANDRA S. HACON ELIANE IGNOTTI RUAN G.S. GOMES MARCOS FELIPE S. PEDREIRA JOSÉ MARCUS GODOY RIVANILDO DALLACORT ANA LÚCIA M. LOUREIRO FERNANDO MORAIS PAULO ARTAXO |
| author_sort | ADRIANA GIODA |
| collection | DOAJ |
| description | Abstract A study on aerosols in the Brazilian subequatorial Amazon region, Tangará da Serra (TS) and Alta Floresta (AF) was conducted and compared to findings in an additional site with background characteristics (Manaus, AM). TS and AF counties suffer from intense biomass burning periods in the dry season, and it accounts for high levels of particles in the atmosphere. Chemical characterization of fine and coarse particulate matter (PM) was performed to quantify water-soluble ions (WSI) and black carbon (BC). The importance of explanatory variables was assessed using three machine learning techniques. Average concentrations of PM in AF and TS were similar (PM2.0, 17±10 µg m-3 (AF) and 16±11 µg m-3 (TS) and PM10-2.0, 13±5 µg m-3 (AF) and 11±7 µg m-3 (TS)), but higher than the background site. BC and SO4 2- were the prevalent components as they represented 27%–68% of particulates chemical composition. The combination of the machine learning techniques provided a further understanding of the pathways for PM concentration variability, and the results highlighted the influence of biomass burning for key sample groups and periods. PM2.0, BC, and most WSI presented higher concentrations in the dry season, providing further support for the influence of biomass burning. |
| first_indexed | 2024-03-09T02:53:23Z |
| format | Article |
| id | doaj.art-adf1a15e7da04d38b950fb2a6020e44a |
| institution | Directory Open Access Journal |
| issn | 1678-2690 |
| language | English |
| last_indexed | 2024-03-09T02:53:23Z |
| publishDate | 2023-12-01 |
| publisher | Academia Brasileira de Ciências |
| record_format | Article |
| series | Anais da Academia Brasileira de Ciências |
| spelling | doaj.art-adf1a15e7da04d38b950fb2a6020e44a2023-12-05T07:57:12ZengAcademia Brasileira de CiênciasAnais da Academia Brasileira de Ciências1678-26902023-12-0195suppl 210.1590/0001-3765202320220932Assessing over decadal biomass burning influence on particulate matter composition in subequatorial Amazon: literature review, remote sensing, chemical speciation and machine learning applicationADRIANA GIODAhttps://orcid.org/0000-0002-5315-5650VINICIUS L. MATEUShttps://orcid.org/0000-0002-7732-6207SANDRA S. HACONhttps://orcid.org/0000-0002-8222-0992ELIANE IGNOTTIhttps://orcid.org/0000-0002-9743-1856RUAN G.S. GOMEShttps://orcid.org/0000-0002-1376-0995MARCOS FELIPE S. PEDREIRAhttps://orcid.org/0000-0002-5755-7438JOSÉ MARCUS GODOYhttps://orcid.org/0000-0001-6135-090XRIVANILDO DALLACORThttps://orcid.org/0000-0002-7634-8973ANA LÚCIA M. LOUREIROhttps://orcid.org/0009-0009-2773-1181FERNANDO MORAIShttps://orcid.org/0000-0002-7207-4450PAULO ARTAXOhttps://orcid.org/0000-0001-7754-3036Abstract A study on aerosols in the Brazilian subequatorial Amazon region, Tangará da Serra (TS) and Alta Floresta (AF) was conducted and compared to findings in an additional site with background characteristics (Manaus, AM). TS and AF counties suffer from intense biomass burning periods in the dry season, and it accounts for high levels of particles in the atmosphere. Chemical characterization of fine and coarse particulate matter (PM) was performed to quantify water-soluble ions (WSI) and black carbon (BC). The importance of explanatory variables was assessed using three machine learning techniques. Average concentrations of PM in AF and TS were similar (PM2.0, 17±10 µg m-3 (AF) and 16±11 µg m-3 (TS) and PM10-2.0, 13±5 µg m-3 (AF) and 11±7 µg m-3 (TS)), but higher than the background site. BC and SO4 2- were the prevalent components as they represented 27%–68% of particulates chemical composition. The combination of the machine learning techniques provided a further understanding of the pathways for PM concentration variability, and the results highlighted the influence of biomass burning for key sample groups and periods. PM2.0, BC, and most WSI presented higher concentrations in the dry season, providing further support for the influence of biomass burning.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652023000501601&lng=en&tlng=enbiomass burningCITparticulate matterrandom forestssecondary inorganic aerosol |
| spellingShingle | ADRIANA GIODA VINICIUS L. MATEUS SANDRA S. HACON ELIANE IGNOTTI RUAN G.S. GOMES MARCOS FELIPE S. PEDREIRA JOSÉ MARCUS GODOY RIVANILDO DALLACORT ANA LÚCIA M. LOUREIRO FERNANDO MORAIS PAULO ARTAXO Assessing over decadal biomass burning influence on particulate matter composition in subequatorial Amazon: literature review, remote sensing, chemical speciation and machine learning application Anais da Academia Brasileira de Ciências biomass burning CIT particulate matter random forests secondary inorganic aerosol |
| title | Assessing over decadal biomass burning influence on particulate matter composition in subequatorial Amazon: literature review, remote sensing, chemical speciation and machine learning application |
| title_full | Assessing over decadal biomass burning influence on particulate matter composition in subequatorial Amazon: literature review, remote sensing, chemical speciation and machine learning application |
| title_fullStr | Assessing over decadal biomass burning influence on particulate matter composition in subequatorial Amazon: literature review, remote sensing, chemical speciation and machine learning application |
| title_full_unstemmed | Assessing over decadal biomass burning influence on particulate matter composition in subequatorial Amazon: literature review, remote sensing, chemical speciation and machine learning application |
| title_short | Assessing over decadal biomass burning influence on particulate matter composition in subequatorial Amazon: literature review, remote sensing, chemical speciation and machine learning application |
| title_sort | assessing over decadal biomass burning influence on particulate matter composition in subequatorial amazon literature review remote sensing chemical speciation and machine learning application |
| topic | biomass burning CIT particulate matter random forests secondary inorganic aerosol |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652023000501601&lng=en&tlng=en |
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