[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128523-en":3,"doc-seo-128523-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128523,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Assessing over Decadal Biomass Burning Influence on Particulate Matter Composition in Subequatorial Amazon - Literature Review, Remote Sensing, Chemical Speciation and Machine Learning Application","Study of aerosol burdens in the Brazilian subequatorial Amazon compared Tangará da Serra (TS) and Alta Floresta (AF) with a background site (Manaus, AM), focusing on decadal biomass-burning impacts during the dry season. Chemical characterization quantified water-soluble ions (WSI) and black carbon (BC) in fine and coarse particulate matter. Machine learning methods ranked explanatory variables and clarified pathways for PM variability across groups and periods. BC and SO4^2− dominated (27%–68%). Higher PM2.0, BC, and most WSI occurred in the dry season, confirming biomass-burning influence.","An Acad Bras Cienc (2023) 95(Suppl. 2) e20220932 DOI 10.1590/0001-3765202320220932  \nAnais da Academia Brasileira de Ciências | Annals of the Brazilian Academy of Sciences Printed ISSN 0001-3765 I Online ISSN 1678-2690 [www.scielo. br/aabc | www.fb.com/aabcjournal](www.scielo. br/aabc | www.fb.com/aabcjournal)  \nFORESTRY SCIENCE  \nAssessing over decadal biomass burning influence on particulate matter composition in subequatorial Amazon: literature review, remote sensing, chemical speciation and machine learning application  \nADRIANA 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  \nARTAXO  \nAbstract: 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 SO42- 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.  \nKey words: biomass burning, CIT, particulate matter, random forests, secondary inorganic aerosol.  \nINTRODUCTION  \nThe Brazilian Amazon is the world’s largest tropica l–forest reserve representing approximately one-third of forests on the globe. Forest exploitation in the Amazon, has significantly increased in the last few decades since the timber supply in Southern Brazil has come to exhaustion. Moreover, infrastructure developments, such as the construction of roads, hydroelectric power plants, and large settlements, have also become increasingly  \ncommon in the Amazon. Additionally, current deforestation practices, often linked to wildfire, degradation caused by natural resources extraction, and also by new settlements disorderly implemented, are among the main causes of anthropic disturbance in forest sites. These practices, along with natural forest events, have a profound impact on ecological damage  \nand carbon emissions (Bullock et al. 2020) .  \nThe Amazon region has a wet tropical climate, with two well-defined seasons: the  \nAn Acad Bras Cienc (2023) 95(Suppl. 2)  \nADRIANA GIODA et al. BIOMASS BURNING INFLUENCE IN THE AMAZON  \nrainy summer season and the dry winter season. Biomass burning is often observed over the Amazon in the dry season, and this is the main anthropogenic source of particle and gas emissions. Huge amounts of greenhouse gases and aerosol particles are released during wildfire events, and they threaten biodiversity and local population. In addition, intense carbon emissions have a negative impact on the Amazon climate by altering biogeochemical processes (Cammelli et al. 2020, Covey et al. 2021, Silva et al. 2021) . Cloud droplets are formed by water vapor condensation in aerosol particles emitted from fire events. These particles can affect cloud properties and, consequently, atmospheric dynamics and radiative balance (Takeishi et al. 2020) . Therefore, large amounts of aerosol particles from forest fire eve","cbCaikOSOFu71DKq","https://ap.wps.com/l/cbCaikOSOFu71DKq","pdf",888212,4,1,20,"English","en",105,"# Introduction\n## Amazon climate, seasons, and biomass burning\n## Impacts on aerosols, health, and biogeochemical cycles","[{\"question\":\"What regions and comparison sites were analyzed in the study?\",\"answer\":\"Tangará da Serra (TS) and Alta Floresta (AF) in the Brazilian subequatorial Amazon were analyzed and compared with a background site with different characteristics in Manaus, AM.\"},{\"question\":\"Which particulate components were chemically characterized?\",\"answer\":\"Fine and coarse particulate matter were chemically characterized to quantify water-soluble ions (WSI) and black carbon (BC).\"},{\"question\":\"How did the machine learning results support biomass-burning influence?\",\"answer\":\"Machine learning was used to assess the importance of explanatory variables and revealed biomass-burning-driven patterns: PM2.0, BC, and most WSI showed higher concentrations in the dry season across key sample groups and periods.\"}]","Assessing over Decadal Biomass Burning Influence on Particulate Matter Composition in Subequatorial Amazon - Literature Review, Remote Sensing, Chemical Speciation and Machine Learning Application | PDF",1786001533,50,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"assessing-over-decadal-biomass-burning-influence-on-particulate-matter-composition-in-subequatorial-amazon-literature-review-remote-sensing-chemical-speciation-and-machine-learning-application","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/assessing-over-decadal-biomass-burning-influence-on-particulate-matter-composition-in-subequatorial-amazon-literature-review-remote-sensing-chemical-speciation-and-machine-learning-application/128523/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What regions and comparison sites were analyzed in the study?","Question",{"text":76,"@type":77},"Tangará da Serra (TS) and Alta Floresta (AF) in the Brazilian subequatorial Amazon were analyzed and compared with a background site with different characteristics in Manaus, AM.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which particulate components were chemically characterized?",{"text":81,"@type":77},"Fine and coarse particulate matter were chemically characterized to quantify water-soluble ions (WSI) and black carbon (BC).",{"name":83,"@type":74,"acceptedAnswer":84},"How did the machine learning results support biomass-burning influence?",{"text":85,"@type":77},"Machine learning was used to assess the importance of explanatory variables and revealed biomass-burning-driven patterns: PM2.0, BC, and most WSI showed higher concentrations in the dry season across key sample groups and periods.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":30,"slug":114},6,"Technology","technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":22,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":22,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":107,"slug":137},19,"General","general"]