[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125760-en":3,"doc-seo-125760-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125760,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Black Carbon characterization with Raman spectroscopy and machine learning techniques - first results for urban and rural area","Among the chemical substances of particulate matter (PM), black carbon (BC) represents a significant fraction linked to adverse public health effects and climate change. This study develops an innovative source apportionment approach for BC within PM by combining Raman spectroscopy with machine learning. Raman spectra featuring the characteristic G and D bands are fitted to extract source-relevant information. A K-Nearest Neighbors (KNN) model is used to assess clustering accuracy and to assign BC in PM to its emission sources, revealing diesel engine exhaust as a major contributor across urban and alpine-valley samples.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nBlack Carbon characterization with Raman spectroscopy and machine learning techniques: first results for urban and rural area  \nOriginal  \nBlack Carbon characterization with Raman spectroscopy and machine learning techniques: first results for urban and rural area / Drudi, Lia; Giardino, Matteo; Janner, DAVIDE LUCA; Pognant, Federica; Matera, Francesco; Sacco, Milena; Bellopede, Rossana. - (2023) . (Intervento presentato al convegno International Conference on Environmental Science and Technology tenutosi a Athens (Greece) nel 30 August to 2 September 2023) [10 .30955/gnc2023 .00088] .  \nAvailability:  \nThis version is available at: 11583/2983533 since: 2024-06-06T12:40:52Z  \nPublisher: CEST  \nPublished  \nDOI:10.30955/gnc2023.00088  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n18 September 2024  \nBlack Carbon characterization with Raman spectroscopy and machine learning techniques: first results for urban and rural area  \nDRUDI L.1,*, GIARDINO M.2,3, JANNER D.2,3, POGNANT F.4, MATERA F.4, SACCO M.5, BELLOPEDE R.1.  \n1Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy  \n2Department of Applied Science and Technology (DISAT), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy  \n3Consorzio Interuniversitario Nazionale per la Scienza e Tecnologia dei Materiali (INSTM), Via G. Giusti 9, 50121 Florence, Italy  \n4Evironment Direction, Regione Piemonte, Via Principe Amedeo, Turin, Italy  \n5Arpa Piemonte, North West Piedmont Department, Via Pio VII, 9, 10135, Turin, Italy  \n*corresponding author:  \ne-mail:lia.drudi@polito.it  \nAbstract  \nAmong the chemical substances of Particulate Matter (PM), there is a considerable quantity of black carbon (BC), which is linked to adverse public health effects and climate change. This study aims to develop an innovative method for the source apportionment of BC inside the PM, using Raman spectroscopy and machine learning techniques.  \nDifferent BC sources, including biomass ashes and vehicle emissions, and different PM samples from air quality monitoring stations have been analyzed with a Raman spectrometer. The PM samples used in the present study are collected from two different locations: an urban environment (Turin, Italy) and an alpine valley context (Oulx, Italy) .  \nTo each obtained spectrum, which presents the characteristic G and D bands, a five-band fitting has been applied to gather information that can lead to the identification of the different BC sources. Machine learning techniques, including the K-Nearest Neighbors (KNN) algorithm, have been applied to calculate the cluster resolution through a value of accuracy. Finally, the same algorithm, trained on the BC emission sources'data, tries to associate each BC in the PM to its source. In particular, a large amount of BC from diesel engine car exhaust emissions is found in all the considered PM samples.  \nKeywords: Fossil fuel, Biomass burning, Source apportionment, D Band, G Band  \n1. Introduction  \nBlack Carbon is a well-known pollutant produced fossil fuel, biomass and biofuels combustion. It is the second strongest contributor to global warming only after carbon dioxide (Ramanathan & Carmichael, 2008) . Still the  \nimpact of BC on the climate is different from the other greenhouse gases: it has a short lifetime in the atmosphere (Bond et al. , 2013), of about 1 week (Mingjiang et al, 2014 ), and it has the capacity to absorb solar radiation. This causes a modification of the atmospheric radiative properties resulting in a substantial warming climate effect. Furthermore, BC can affect the meteorological condition by modifying the air relative humidity causing a decrease in precipitation (Ramanathan & Carmichael, 2","cbCaibvvYK9oorgu","https://ap.wps.com/l/cbCaibvvYK9oorgu","pdf",271191,1,5,"English","en",105,"# Introduction\n## Black carbon as a climate and health relevant pollutant\n## Measurement approaches and limits\n## Rationale for Raman spectroscopy and machine learning","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To develop an innovative method for attributing black carbon (BC) sources within particulate matter using Raman spectroscopy and machine learning techniques.\"},{\"question\":\"Which data and locations were used for the PM samples?\",\"answer\":\"PM samples were analyzed from two locations: an urban environment in Turin, Italy, and an alpine valley context in Oulx, Italy.\"},{\"question\":\"How is the BC source attribution performed?\",\"answer\":\"Raman spectra with characteristic G and D bands are processed with a five-band fitting, then a KNN machine learning approach is applied to evaluate accuracy and to associate BC in PM with its emission sources.\"}]","Black Carbon characterization with Raman spectroscopy and machine learning techniques - first results for urban and rural area | PDF",1785901062,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"black-carbon-characterization-with-raman-spectroscopy-and-machine-learning-techniques-first-results-for-urban-and-rural-area","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/black-carbon-characterization-with-raman-spectroscopy-and-machine-learning-techniques-first-results-for-urban-and-rural-area/125760/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To develop an innovative method for attributing black carbon (BC) sources within particulate matter using Raman spectroscopy and machine learning techniques.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data and locations were used for the PM samples?",{"text":80,"@type":76},"PM samples were analyzed from two locations: an urban environment in Turin, Italy, and an alpine valley context in Oulx, Italy.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the BC source attribution performed?",{"text":84,"@type":76},"Raman spectra with characteristic G and D bands are processed with a five-band fitting, then a KNN machine learning approach is applied to evaluate accuracy and to associate BC in PM with its emission sources.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]