Values of coefficient “a”
\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"9971",leadTitle:null,fullTitle:"Data Science, Data Visualization, and Digital Twins",title:"Data Science, Data Visualization, and Digital Twins",subtitle:null,reviewType:"peer-reviewed",abstract:"Real-time, web-based, and interactive visualisations are proven to be outstanding methodologies and tools in numerous fields when knowledge in sophisticated data science and visualisation techniques is available. The rationale for this is because modern data science analytical approaches like machine/deep learning or artificial intelligence, as well as digital twinning, promise to give data insights, enable informed decision-making, and facilitate rich interactions among stakeholders.The benefits of data visualisation, data science, and digital twinning technologies motivate this book, which exhibits and presents numerous developed and advanced data science and visualisation approaches. Chapters cover such topics as deep learning techniques, web and dashboard-based visualisations during the COVID pandemic, 3D modelling of trees for mobile communications, digital twinning in the mining industry, data science libraries, and potential areas of future data science development.",isbn:"978-1-83962-944-0",printIsbn:"978-1-83962-943-3",pdfIsbn:"978-1-83962-945-7",doi:"10.5772/intechopen.87794",price:119,priceEur:129,priceUsd:155,slug:"data-science-data-visualization-and-digital-twins",numberOfPages:116,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"353b70c06c03295318688a64535d6d85",bookSignature:"Sara Shirowzhan",publishedDate:"February 2nd 2022",coverURL:"https://cdn.intechopen.com/books/images_new/9971.jpg",numberOfDownloads:1396,numberOfWosCitations:0,numberOfCrossrefCitations:4,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:5,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:9,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 1st 2020",dateEndSecondStepPublish:"September 29th 2020",dateEndThirdStepPublish:"November 28th 2020",dateEndFourthStepPublish:"February 16th 2021",dateEndFifthStepPublish:"April 17th 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"273838",title:"Dr.",name:"Sara",middleName:null,surname:"Shirowzhan",slug:"sara-shirowzhan",fullName:"Sara Shirowzhan",profilePictureURL:"https://mts.intechopen.com/storage/users/273838/images/system/273838.png",biography:"Dr. Sara Shirowzhan is a lecturer at the School of Built Environment (BE), University of New South Wales (UNSW), Sydney, Australia, where she teaches the City Analytics and Construction programs. She also serves as the co-chair of BE\\'s Smart Cities and Infrastructure Cluster. Dr. Shirowzhan works as tomorrow\\'s leading champion for the Chartered Institute of Building (CIOB). Her research interests include sensing technologies, enhanced GIS, BIM, digital twins, and artificial intelligence in technologies pertinent to BE informatics. She teaches and supervises students at UNSW in the areas of GIS, BIM, digital twins, AI, machine learning, city analytics, urban informatics, smart cities, infrastructure, construction informatics, and other relevant topics. She now serves on the editorial boards of the journals MDPI and Advances in Civil Engineering. She is also a topic board member of the ISPRS International Journal of Geo-Information as well as Buildings. Dr. Shirowzhan received her Ph.D. in Geomatics Engineering from the School of Civil and Environmental Engineering, UNSW.",institutionString:"UNSW Sydney",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"UNSW Sydney",institutionURL:null,country:{name:"Australia"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"89",title:"Computer Graphics",slug:"computer-and-information-science-computer-graphics"}],chapters:[{id:"76463",title:"JavaScript Implementation of Scagnostics and Its Applications",doi:"10.5772/intechopen.97490",slug:"javascript-implementation-of-scagnostics-and-its-applications",totalDownloads:153,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Scagnostics is a set of features that characterizes the 2D distributions in the underlying data. Various real-world applications have been using Scagnostics visual features to detect unusual bivariate data correlations. Concomitantly, many applications are required to be implemented on web platforms due to their accessibility and convenience. Therefore, this chapter discusses a recent JavaScript implementation of Scagnostics, an extension to higher dimensional data, and its applications in detecting abnormalities in bivariate and multivariate time series data. Its implementation in JavaScript supports the tremendous demand for visual features in the web environment. Likewise, its higher dimensional implementations allow generating Scagnostics features for the rapidly growing multivariate data. Finally, conventional ScagnosticsJS computations involve time-consuming algorithms, and they are sensitive to slight changes in the underlying data. Therefore, this chapter also discusses a recent attempt to tackle these issues using machine learning to estimate the Scagnostics scores.",signatures:"Vung Pham and Tommy Dang",downloadPdfUrl:"/chapter/pdf-download/76463",previewPdfUrl:"/chapter/pdf-preview/76463",authors:[{id:"330334",title:"Dr.",name:"Vung",surname:"Pham",slug:"vung-pham",fullName:"Vung Pham"},{id:"335450",title:"Dr.",name:"Tommy",surname:"Dang",slug:"tommy-dang",fullName:"Tommy Dang"}],corrections:null},{id:"75324",title:"Visualizing the Impact of COVID-19 in the Mobility Dynamics - A Dashboard Framework for Decision Support in Smart Cities",doi:"10.5772/intechopen.96295",slug:"visualizing-the-impact-of-covid-19-in-the-mobility-dynamics-a-dashboard-framework-for-decision-suppo",totalDownloads:183,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Being mobility one of the biggest challenge’s cities face today, the COVID-19 pandemic reinforced this challenge and caused a deep structural change in the mobility of the multilayered dynamic framework of Smart Cities. The need to supply decision support systems to city authorities is higher than ever. Planning and managing mobility in Smart Cities has become more challenging, as the amount of information available and the pressure to enforce sustainable and secure policies increases, stakeholders require faster and more targeted actions. Dashboards are powerful tools that can be used in this context to provide, in an understandable manner, multidimensional information otherwise unavailable in classically static visualizations, as these tools offer a reliable foundation for decision support systems. This chapter goes through the required visualization techniques used to produce meaningful dashboards, to both showcase spatial and temporal trends in the context of mobility in Smart Cities following the COVID-19 pandemic. A general framework for analyzing mobility patterns is suggested by gathering methods and techniques recently developed in the literature.",signatures:"Nuno Alpalhão, Miguel de Castro Neto and Marcel Motta",downloadPdfUrl:"/chapter/pdf-download/75324",previewPdfUrl:"/chapter/pdf-preview/75324",authors:[{id:"332638",title:"MSc.",name:"Nuno",surname:"Alpalhão",slug:"nuno-alpalhao",fullName:"Nuno Alpalhão"},{id:"333163",title:"MSc.",name:"Marcel",surname:"Motta",slug:"marcel-motta",fullName:"Marcel Motta"},{id:"333357",title:"Prof.",name:"Miguel",surname:"de Castro Neto",slug:"miguel-de-castro-neto",fullName:"Miguel de Castro Neto"}],corrections:null},{id:"75369",title:"3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile Communication Systems",doi:"10.5772/intechopen.96179",slug:"3d-point-cloud-based-tree-canopy-visualization-for-a-smart-deployment-of-mobile-communication-system",totalDownloads:235,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Mobile communication is one of the most important parameters of smart cities in terms of maintaining connectivity and interaction between humans and smart systems. However, In the deployment process of Mobile Communication Systems (MCS), Radio Frequency (RF) engineers use location depended empirical Signal Strength Path Loss (SSPL) models ending up with poor signal strength and slow data connection. This is due to the fact that empirical propagation models usually are restrained by the environment and do not implement state of the art technologies, including Unmanned Aerial Vehicles (UAV), Light Detection and Ranging (LiDAR), Image Processing, and Machine Learning to increase efficiency. Terrains involving buildings, hills, trees, mountains, and human-made structures are considered irregular terrains by telecommunication engineers. Irregular terrains, specifically trees, significantly affect MCS’s efficiency because of their complex pattern resulting in erroneous signal fading via multi-path reflection and absorption. Therefore, a virtual 3D environment is required to extract the required 3D terrain pattern and elevation data from the environment. Once this data is processed in the machine learning algorithm, an adaptive propagation model can be formed and can significantly improve SSPL prediction accuracy for MCS. This chapter presents 3D point cloud visualization via sensor fusion and 2D image color classification techniques, which lead to a novel propagation model for the smart deployment of MCS. The proposed system’s main contribution is to develop an intelligent environment that eliminates limitations and minimizes related signal fading prediction errors. In addition, having better connectivity and efficiency will resolve the communication problem of smart cities. The chapter also provides a case study that significantly outperforms other empirical models with an accuracy of 95.4%.",signatures:"Yunus Egi and Engin Eyceyurt",downloadPdfUrl:"/chapter/pdf-download/75369",previewPdfUrl:"/chapter/pdf-preview/75369",authors:[{id:"330309",title:"Assistant Prof.",name:"yunus",surname:"Egi",slug:"yunus-egi",fullName:"yunus Egi"},{id:"341040",title:"Dr.",name:"Engin",surname:"Eyceyurt",slug:"engin-eyceyurt",fullName:"Engin Eyceyurt"}],corrections:null},{id:"75296",title:"Digital Twin of the Mining Shaft and Hoisting System as an Opportunity to Improve the Management Processes of Shaft Infrastructure Diagnostics and Monitoring",doi:"10.5772/intechopen.96193",slug:"digital-twin-of-the-mining-shaft-and-hoisting-system-as-an-opportunity-to-improve-the-management-pro",totalDownloads:239,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:0,abstract:"The following chapter presents a concept of a virtual model of a mine shaft equipped with a hoisting system for the purpose of improving the processes of diagnostics management of shaft infrastructure and its monitoring. The chapter presents a proposal of improvement of broadly known processes such as: diagnostics and monitoring of shaft infrastructure using digital models of 3D structures, the BIM and Digital Twin idea. Implementation of such systems in the operating mine working was presented together with expected results of monitoring. As the presented solution is currently only a concept, development of such system in real application is necessary to asses real benefits of application of Digital Twin system.",signatures:"Piotr Kalinowski, Oskar Długosz and Paweł Kamiński",downloadPdfUrl:"/chapter/pdf-download/75296",previewPdfUrl:"/chapter/pdf-preview/75296",authors:[{id:"318919",title:"Ph.D.",name:"Paweł",surname:"Kamiński",slug:"pawel-kaminski",fullName:"Paweł Kamiński"},{id:"343647",title:"MSc.",name:"Piotr",surname:"Kalinowski",slug:"piotr-kalinowski",fullName:"Piotr Kalinowski"},{id:"343648",title:"MSc.",name:"Oskar",surname:"Długosz",slug:"oskar-dlugosz",fullName:"Oskar Długosz"}],corrections:null},{id:"75258",title:"Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control",doi:"10.5772/intechopen.96347",slug:"using-trend-extraction-and-spatial-trends-to-improve-flood-modeling-and-control",totalDownloads:189,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Effective management of flood events depends on a thorough understanding of regional geospatial characteristics, yet data visualization is rarely effectively integrated into the planning tools used by decision makers. This chapter considers publicly available data sets and data visualization techniques that can be adapted for use by all community planners and decision makers. A long short-term memory (LSTM) network is created to develop a univariate time series value for river stage prediction that improves the temporal resolution and accuracy of forecasts. This prediction is then tied to a corresponding spatial flood inundation profile in a geographic information system (GIS) setting. The intersection of flood profile and affected road segments can be easily visualized and extracted. Traffic decision makers can use these findings to proactively deploy re-routing measures and warnings to motorists to decrease travel-miles and risks such as loss of property or life.",signatures:"Jacob Hale, Suzanna Long, Vinayaka Gude and Steven Corns",downloadPdfUrl:"/chapter/pdf-download/75258",previewPdfUrl:"/chapter/pdf-preview/75258",authors:[{id:"188786",title:"Dr.",name:"Suzanna",surname:"Long",slug:"suzanna-long",fullName:"Suzanna Long"},{id:"333371",title:"Mr.",name:"Jacob",surname:"Hale",slug:"jacob-hale",fullName:"Jacob Hale"},{id:"333372",title:"Dr.",name:"Steven",surname:"Corns",slug:"steven-corns",fullName:"Steven Corns"},{id:"333536",title:"Dr.",name:"Vinayaka",surname:"Gude",slug:"vinayaka-gude",fullName:"Vinayaka Gude"}],corrections:null},{id:"76821",title:"Visual Data Science",doi:"10.5772/intechopen.97750",slug:"visual-data-science",totalDownloads:399,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:1,abstract:"Organizations are collecting an increasing amount of data every day. To make use of this rich source of information, more and more employees have to deal with data analysis and data science. Exploring data, understanding its structure, and finding new insights, can be greatly supported by data visualization. Therefore, the increasing interest in data science and data analytics also leads to a growing interest in data visualization and exploratory data analysis. We will outline how existing data visualization techniques are already successfully employed in different data science workflow stages. In some cases, visualization is beneficial, while still future research will be needed for other categories. The vast amount of libraries and applications available for data visualization has fostered its usage in data science. We will highlight the differences among the libraries and applications currently available. Unfortunately, there is still a clear gap between visualization research developments over the past decades and the features provided by commonly used tools and data science applications. Although basic charting options are commonly available, more advanced visualization techniques have hardly been integrated as new features yet.",signatures:"Johanna Schmidt",downloadPdfUrl:"/chapter/pdf-download/76821",previewPdfUrl:"/chapter/pdf-preview/76821",authors:[{id:"330296",title:"Dr.",name:"Johanna",surname:"Schmidt",slug:"johanna-schmidt",fullName:"Johanna Schmidt"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"9431",title:"Smart Cities and Construction Technologies",subtitle:null,isOpenForSubmission:!1,hash:"37ca01618d7f291efb11a4d115b9cb63",slug:"smart-cities-and-construction-technologies",bookSignature:"Sara Shirowzhan and Kefeng Zhang",coverURL:"https://cdn.intechopen.com/books/images_new/9431.jpg",editedByType:"Edited by",editors:[{id:"273838",title:"Dr.",name:"Sara",surname:"Shirowzhan",slug:"sara-shirowzhan",fullName:"Sara Shirowzhan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7435",title:"Computer Graphics and Imaging",subtitle:null,isOpenForSubmission:!1,hash:"889abc91038189c977749c2175bbc8e2",slug:"computer-graphics-and-imaging",bookSignature:"Branislav Sobota",coverURL:"https://cdn.intechopen.com/books/images_new/7435.jpg",editedByType:"Edited by",editors:[{id:"109378",title:"Dr.",name:"Branislav",surname:"Sobota",slug:"branislav-sobota",fullName:"Branislav Sobota"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research. This book is meant for basic scientifically inclined readers, and commences with a chapter on the basics of spaceflight and leverages this remediation to reveal very advanced topics to new spaceflight enthusiasts. The topics learned from reading this text will prepare students and faculties to investigate interesting spaceflight problems in an era where cube satellites have made such investigations attainable by even small universities. It is the fondest hope of the editor and authors that readers enjoy this book.",isbn:"978-1-78984-803-8",printIsbn:"978-1-78984-802-1",pdfIsbn:"978-1-83968-516-3",doi:"10.5772/intechopen.77574",price:119,priceEur:129,priceUsd:155,slug:"advances-in-spacecraft-attitude-control",numberOfPages:284,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"933b56622351819a21f036a4295e45c2",bookSignature:"Timothy Sands",publishedDate:"January 15th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/7761.jpg",keywords:null,numberOfDownloads:9593,numberOfWosCitations:5,numberOfCrossrefCitations:8,numberOfDimensionsCitations:11,numberOfTotalCitations:24,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 16th 2018",dateEndSecondStepPublish:"March 22nd 2019",dateEndThirdStepPublish:"June 8th 2019",dateEndFourthStepPublish:"August 16th 2019",dateEndFifthStepPublish:"October 15th 2019",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 years",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!0,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"258189",title:"Prof.",name:"Timothy",middleName:null,surname:"Sands",slug:"timothy-sands",fullName:"Timothy Sands",profilePictureURL:"https://mts.intechopen.com/storage/users/258189/images/system/258189.jpg",biography:"Dr. Timothy Sands graduated from Columbia University, Stanford University, and the Naval Postgraduate School. He is an Interna-tional Scholar Laureate of the Golden Key International Honor Society, a Fellow of the Defense Advanced Research Projects Agency, panelist of the National Science Foundation Graduate Research Fellowship program, and an interviewer for undergradu-ate admissions at Stanford University. He has published prolifically in archival journals, conference proceedings, books, and book chapters, in addition to giving plenary, keynote, and invitational presentations. He holds one patent in spacecraft attitude control. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"16220",title:"Estimated Atmospheric Emissions from Mobile Sources and Assessment of Air Quality in The Urban Area",doi:"10.5772/16887",slug:"estimated-atmospheric-emissions-from-mobile-sources-and-assessment-of-air-quality-in-the-urban-area",body:'During the last decades, the world has gone through a period of economic and technological development that brought many benefits to modern life. Due to these developments, there was a worldwide expansion of the number of automotive vehicles on the roads increasing pollutant emissions into the atmosphere, thus frequently contributing to a worse air quality, particularly in large urban areas (Colvile et al., 2001). Although a set of improvements implemented in the vehicles, such as electronic injection systems, post-combustion catalytic converter, and changes in fuel, which have contributed to a decrease in pollutant emissions into the atmosphere, the increase in the vehicle numbers has brought about a severe deterioration in the quality of the air.
Vehicular emissions are one of the major primary sources of fine particles (Sheesley et al., 2007). Emissions from diesel engines are chemically complex and contain hundreds of chemical substances divided into gaseous and particulate phases (Kagawa, 2002; Kerminen et al., 1997). The particulate phase, significantly high when considering the one produced by gasoline-powered vehicles, has a significant amount of fine and ultrafine particles, making this one of the severest problems referring to quality of air in large urban areas, as seen in Asian countries and developing countries (Kim Oanh, 2006; Gupta & Kumar, 2006).
Diesel vehicles are the major emissions sources of several compounds in the atmosphere, amongst which the polycyclic aromatic hydrocarbons (PAHs) and the nitro-polycyclic aromatic hydrocarbons (NPAHs) (Desantes et al., 2005; Maricq, 2007; Phuleria et al., 2007; Ravindra et al., 2008). PAHs are complex organic molecules, whose structure, in addition to the carbon and hydrogen atoms, contains at least two benzene rings (Karavalakis et al., 2010). Nitro-polycyclic aromatic hydrocarbons are also present in diesel-vehicle emissions, and they differ from PAHs due to the presence of at least one group –NO2 in an aromatic ring (Andrade-Eiroa et al., 2010). These compounds are widely distributed in the atmosphere and are well know as mutagenic and carcinogenic agents (Karavalakis et al., 2010; Andrade-Eiroa et al., 2010).
Type and concentration of PAHs and NPAHs emitted depend on the type of fuel used and on the operating conditions of the combustion process (Ravindra et al., 2008; Karavalakis et al., 2010). These compounds are distributed in the atmosphere in the gaseous phase, and they are mainly adsorbed onto the particulate matter (Chetwittachan et al., 2002).
Emissions from gasoline vehicles contain a great deal of fine particles, which nevertheless show small adsorption when compared to particles from diesel emissions. In addition, diesel engines have a higher rate of ultrafine particle emissions (<1.0 µm). Since these particles show a large specific area, PAHs show a trend to adsorb onto the surface of gaseous particles from diesel emissions. PAHs with five or more rings are usually associated with fine atmospheric particles from diesel exhaust.
Like in other parts of the world, in Brazil, we can consider that the prevailing urban pollution comes from vehicles, i.e., it is a direct consequence of a greater number of vehicles on the roads. In view of this, the search for a non-fossil fuel which would decrease atmospheric emissions pointed out biodiesel as the most adequate and beneficial alternative, not only in terms of the environment but of the economy as well.
Biodiesel is a fuel with a high rate of oxygenation, obtained from vegetable oils or animal fat through a reaction of transesterification, with similar properties as diesel oil (Knothe, 2007). The use of this fuel has been discussed in several countries due to an increase in environmental awareness and the high price of diesel oil. Currently, many countries such as the USA, Japan, Brazil, India, amongst others, use biodiesel and petrodiesel blends in their cars, since this blend can be used without any prejudice to the existing motors.
Agarwal (2007) reviewed the combustion characteristics with regard to fuel economy and biodiesel feasibility. There is clearly a decrease in regulated emissions –HC, CO, PM–, except for NOx, which tends to increase when the diesel engine is fueled with biodiesel. Biodiesel impact on the environment and its effects on health are also being studied by the characterization of the emissions. Lin et al. (2006) reported that PAHs emissions decreased with the increase of biodiesel amounts in the diesel-biodiesel blend. Jung et al. (2006) reported that the addition of biodiesel decreased the amount and size of particles emitted by the vehicle exhaust and increased the oxidation rate of the particles.
Biodiesel differs from diesel produced from oil in several factors, many of which directly influence the emissions. Biodiesel has two oxygen atoms per molecule more, and this represents 10-12% of its molecular weight; since it is a plant-derived fuel, it does not contain sulfur (Coronado et al., 2009); it has a greater number of cetanes (which indicates a better quality of ignition of a diesel fuel) (Knothe, 2006); and it has a higher lubricity and viscosity (Fontaras et al., 2010).
Therefore, this fuel arises as an alternative to the dependence on oil and its derivatives. Biodiesel production provides a new market for oilseeds, thereby generating a new branch of agribusiness, with a multiplier effect on various segments of the economy dealing with vegetable oils, alcohol, diesel oil, as well as inputs and byproducts from the production of the vegetable ester. In addition to all these benefits, biodiesel use also offers an excellent potential of reducing atmospheric pollutants.
The present chapter of the book aims to describe the estimation of vehicle emissions of CO, NOX, HC, SO2 and PM in an urban area located in the state of Rio Grande do Sul, from the addition of biodiesel to diesel, in accordance with Brazilian regulations as of 2008. In addition, air quality will be described according to various atmospheric parameters (PM, CO, NOx, O3, SO2, PAHs, NPAHs) and meteorological variables.
The area chosen for this study was the metropolitan area of Porto Alegre (MAPA) located at 29°30’S – 30°30’S / 50°25’W – 51°55’W in the east of the state of Rio Grande do Sul, Brazil (Figure 1). According to the Brazilian Institute of Geography and Statistics (IBGE, 2010), this region comprises an area of 9,800 km2, representing 3.76% of the total area of the state, and it has a population of 3,979,561 inhabitants, i.e., 37.21% of the total population of Rio Grande do Sul.
The metropolitan area of Porto Alegre is the most urbanized area of the state and includes 31 counties. The counties comprising the MAPA are: Alvorada, Araricá, Arroio dos Ratos, Cachoeirinha, Campo Bom, Canoas, Capela de Santana, Charqueadas, Dois Irmãos, Eldorado do Sul, Estância Velha, Esteio, Glorinha, Gravataí, Guaíba, Ivoti, Montenegro, Nova Hartz, Nova Santa Rita, Novo Hamburgo, Parobé, Portão, Porto Alegre, Santo Antônio da Patrulha, São Jerônimo, São Leopoldo, Sapiranga, Sapucaia do Sul, Taquara, Triunfo and Viamão.
The capital Porto Alegre has approximately 40% of the fleet of diesel vehicles, and its population represents 35.6% of the total population of the MAPA. Therefore, Porto Alegre is indisputably one of the most relevant cities for this estimation, since the county certainly has an intense traffic of vehicles.
The metropolitan area of Porto Alegre is characterized by different industrial typologies, including several stationary sources such as the Alberto Pasqualini oil refinery, two steel mills (Siderúrgica Riograndense and Aços Finos Piratini, which do not use coke) and two coal-fired power plants (Termochar and São Jerônimo). Despite the different industrial sources around Porto Alegre contributing to the total emissions, the major contributions come from an estimated 620,000 vehicles on local roads, representing 20% of the total 3.1 million vehicles of the state (Teixeira et al., 2008).
Due to the geographical location of the MAPA, the seasons are well defined and the rain is evenly distributed all over the year. Winter in this region is strongly influenced by cold air masses migrating from polar regions, and in summer there is a greater influence of tropical, maritime and continental air masses.
According to Köppen\'s international climate classification system, the area of study has a climate described as Cfa subtropical climate with an average temperature above 22oC during the warmest month of the year (Livi, 1999).
The prevailing wind directions are east (E), east southeast (ESE), and southeast (SE) (Livi, 1999). During the day, wind reaches its lowest speed at dawn and early morning, and highest speed in the late afternoon, between 5-7 p.m. This pattern is related to energy availability at the surface (sensible heat) during the day, intensifying local and mesoscale atmospheric circulations. The prevailing wind results from interactions of mesoscale phenomena, especially sea/land breezes (from the Atlantic Ocean and the Patos Lagoon) and valley/mountain breezes (from the nearby Serra Geral mountains to the north of the MAPA).
Location of the Metropolitan Area of Porto Alegre
The method for collecting emission data in the MAPA was based on the methods for preparing vehicular emission inventories of the US Environmental Protection Agency (US EPA, 2002). Emission factors provided by the Sao Paulo State Environmental Company (CETESB, 2008) with adjustments for the area of study, amongst which the numbers of diesel vehicles on the roads in 2008 and 2009. Thus, the emissions of the following pollutants were measured: carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and particulate matter (PM), for the scenarios of use of diesel blended with biodiesel at 2, 5, 10, and 20%.
The working method of the present study consisted of two steps:
Data collecting and organization;
Intermediate calculations to generate the variables for the general equation (1).
E = pollutant emission rate (ton/year)
Fe = pollutant emission factor (g/km)
A = vehicle autonomy (km/L)
C = fuel consumption (L/year)
N= number of vehicles
All steps of calculations were developed by using Microsoft Office Excel software. Initially, the diesel fleet was subdivided into two classes: trucks, vans and pickups, and buses and minibuses, with their corresponding values for kilometers driven and autonomy.
Fleet and fuel consumption for the 31 counties located within the MAPA in 2008 and 2009 were provided by the Traffic Department of Rio Grande do Sul (DETRAN/RS) and the Brazilian Oil Agency (ANP), respectively.
There is a significant discrepancy between the volume referred to as actual consumption and the theoretical volumes, as it is not possible to affirm that a vehicle refueling in a given county will circulate only within that area. With this in mind and based on statistical data from IBGE, a weighting was done with respect to the urban and rural population of each county in order to obtain a more realistic consumption profile of that area.
Emission factors are obtained in g/KWh in national and international approaches, and they were also applied to calculating the estimated atmospheric emissions. These values were obtained in function of average speed, air temperature, vehicle technology, amongst other variables. According to Brazilian regulations, the Sao Paulo State Environmental Company (CETESB) performs annual calculations of emission factors, which are then used as reference for all studies conducted in Brazil in this field (CETESB, 2008).
In the present study, the exponential correlation referring to the emission factors was taken into account when preparing the inventory (Figure 2). Calculation adjustment to biodiesel was done by applying Equation 2 (US EPA, 2002) added of the values of coefficient "a" (Table 1) and, by considering variables "b" to be close to null, transforming the second term in a unit value.
Results obtained by using the general equation (1) and the adjustment through equation (2) give us a forecast of the decrease in pollutant emissions in the MAPA, as will be seen later.
Mean pollutant emissions by using different biodiesel percents in diesel engines,
Coefficient “a” | |
NOX | 0.0009794 |
PM | -0.006384 |
HC | -0.011195 |
CO | -0.006561 |
Values of coefficient “a”
Figure 3 shows the block diagram in which are presented all the stages of the calculation methodology, since the data collecting and organization, through the corrections, until obtaining the final results of the emissions.
Block chart of the calculation system
The equipment used for the sampling included a sulfur oxide analyzer (using UV fluorescence method), a PM10 analyzer (using beta radiation method), a nitrogen oxide analyzer (AC31M using chemiluminescence method), a carbon monoxide analyzer (CO11M using infrared absorption), an ozone analyzer (O341M, absorption of UV light with wavelength of 254 nm, LCD/UV photometry ozone analyzer), Total hydrocarbon (HC 51 M FID Total hydrocarbon analyzer model). All equipment is manufactured by Environnement S.A.
The analysis of the 16 major PAHs (US EPA, 1999) was performed by chromatography coupled with a Shimadzu GCMS-QP5050A mass spectrophotometer detector. (For further details about the analyses, see American Society for Testing and Materials [ASTM], 2004; modified by Dallarosa et al., 2005a, 2005b; 2008). After isolation, derivatization of the extracts was performed by cleanup, according to the method proposed by Jinhui & Lee (2001). After that, NPAHs analysis was performed by gaseous chromatography by electron capture detection (GC/ECD – Varian CP-3800) and silica gel column (CP - Sil 19 CB, 30 m x 0.25 µm x 0.25 mm).
Figure 4 shows the estimated emissions by diesel vehicles on the roads in 2008 and 2009 in the MAPA. In this area, there were 80,316 and 83,548 diesel vehicles in 2008 and 2009, respectively. An increase of 3.8% in the total diesel fleet can be seen. This increase was already expected due to the increase in vehicle sales in the state of Rio Grande do Sul during the last year.
Figure 5 shows a comparison between the diesel fleets of the MAPA and of Porto Alegre. Among the counties of the MAPA, Porto Alegre is probably the major contributor of atmospheric emissions, since as already mentioned before, it represents approximately 40% of the total diesel fleet in the area.
Diesel fleet by vehicle type in the Metropolitan Area of Porto Alegre in 2008 and 2009
Distribution of type of diesel vehicles in the Metropolitan Area of Porto Alegre and the city of Porto Alegre in 2009
Below we present the variations in atmospheric emissions of NOX, CO, HC, and PM for 2008 and 2009 for different biodiesel amounts added to the diesel: 2, 5, 10, and 20%. A decrease in PM, CO, and HC emissions, as well as slight increase in NOX emissions has been observed.
Figure 6 shows an increase in NOX emissions when biodiesel was added. This might be explained due to the high oxygen levels in biodiesel molecules and to improved combustion conditions: temperature and pressure in the combustion chamber are higher, boosting the formation of larger amounts of nitrogen oxides in engines running on biodiesel, by combining oxygen with the nitrogen present in the air (Coronado et al., 2009).
Figure 7 shows the decrease in carbon monoxide emissions with the increase in biodiesel percent in the blend. This decrease might be attributed to the oxygen present in biodiesel molecules, producing a more efficient combustion reaction by moving the direction of the combustion reaction toward the production of CO2 (Maziero et al., 2006).
Figure 8 shows a decrease in unburned hydrocarbon emissions by the addition of biodiesel to the diesel. This decrease is due to the complete combustion, during which CO2 and water are mainly formed, as opposed to what occurs during diesel combustion (Coronado et al., 2009).
The decrease in particulate matter emissions, as shown in Figure 9, is due to higher oxygen levels in the biodiesel molecule, which allows the complete burning of the fuel. Consequently, there is a decrease in particulate matter emissions. The absence of sulfur in biodiesel contributes to less sulfate particles forming during combustion, thus contributing even more to a decrease in particulate matter emissions (Dwivedi et al., 2006).
Although the variation of SOX emissions with the addition of biodiesel is not provided in the present study, these emissions decreased proportionally to the percent of diesel added to the blend. This occurs because biodiesel is a vegetable-derived fuel and it is free of sulfur.
Variation of atmospheric emissions of NOX at different biodiesel blends added to diesel oil.
Variation of atmospheric emissions of CO at different biodiesel blends added to diesel oil
Variation of atmospheric emissions of HC at different biodiesel blends added to diesel oil
Variation of atmospheric emissions of PM at different biodiesel blends added to diesel oil
Table 2 shows the compared results on pollutant emissions from diesel oil added of different biodiesel blends. Results reported by Morris et al. (2003) and by the National Biodiesel Board (NBB, 2010) showed higher agreement with the estimated emission values for B20 in the present study. Data on CO, HC, NOX and PM reported by Oliveira & Costa (2002) and NBB for B20 showed the highest agreement with results of the present study. At lower percents of biodiesel in diesel, results did not show significant variations, with only slight variations in the values found. This might indicate a change of trend. The error margin of the estimated and measured values is sometimes greater than the absolute values found.
B2 | |||||
Pollutant | CO | HC | NOX | MP | SOX |
MAPA (2009) | -1.3 | -2.2 | 0.2 | -1.3 | - |
Corrêa (2005) | -0.1 | -0.4 | -1.3 | - | - |
B5 | |||||
Pollutant | CO | HC | NOX | MP | SOX |
MAPA (2009) | -3.1 | -5.4 | 0.5 | -3.2 | - |
Corrêa (2005) | -0.2 | -0.6 | -1.3 | - | - |
Oliveira & Costa (2002) | - | - | 0.7 | -2.5 | -5 |
B10 | |||||
Pollutant | CO | HC | NOX | MP | SOX |
MAPA (2009) | -6.2 | -10 | 1 | -6.4 | - |
Corrêa (2005) | -0.3 | -1.1 | -3.7 | - | - |
Oliveira & Costa (2002) | - | - | 1.3 | -5 | -9.5 |
B20 | |||||
Pollutant | CO | HC | NOX | MP | SOX |
MAPA (2009) | -12 | -20 | 2 | -12 | - |
NBB (2010) | -12 | -20 | 2 | -12 | - |
Park et al. (2009) | -20 | -35 | 3.7 | -20 | - |
Oliveira & Costa (2002) | - | - | 2.5 | -10 | -19 |
Morris et al. (2003) | -13 | - | 2.4 | -8.9 | -20 |
Compared results (%) of various studies on pollutant emissions from diesel oil added of different biodiesel rates
Table 3 shows the total average concentrations of PM10, PM2.5, CO, NOX, NO2, O3, SO2 measured in the MAPA for 2006, 2007, 2008 and 2009; and PAHs, NPAHs and PM2.5 for 2006 and 2007. An increase in the pollutant studied is seen in 2007, except for PM10 and CO, whose data are from 2006. PAHs and NPAHs were higher in 2007.
Between 2006 and 2009, CO shows a decrease in the atmosphere (Table 3). This compound originates mostly from mobile sources. Despite the greater number of vehicles on the roads in recent years, the addition of catalytic converters to automobiles has substantially reduced CO emissions from the combustion of common gasoline. However, CO emissions during the ‘‘cold start’’ of engines, when the catalyst is cold and has not reached operating temperature, can still be substantial, even for those vehicles that are equipped with catalytic converters (Gaffney & Marley, 2009).
Studies about macro-regions of Rio Grande do Sul (Teixeira et al., 2010) showed that the MAPA had higher CO concentration in the air due to gasoline-fueled vehicles. Even if the annual mean concentration of CO is decreasing, there are concerns about an increase in the number of vehicles on the roads and, consequently, in fuel consumption. Canoas, Sapucaia do Sul and Porto Alegre are the areas within the MAPA that have the greatest number of gasoline-fueled vehicles. During 2008 and 2009, gasoline consumption in these areas was 468,870 m3 and 507,331m3, respectively. These counties are probably the major contributors of atmospheric emissions (approx. 40.0%) in the MAPA, due to their great number of gasoline-fueled vehicles, with CO emissions of approximately 110,241 tons annually.
The annual SO2 mean concentration was higher in 2007, with a slight decrease up to 2009 (Table 3). Despite vehicles having shown to be major contributors of SO2 emissions, some stationary sources located in the MAPA, e.g. an oil refinery and coal-fired power plants, are probably also related to SO2 emissions and have contributed to the higher emission levels measured in 2007. Some authors (Geng et al., 2009) have reported SO2 in urban areas from vehicles and coal-fired power plants. These activities might speed up SO2 concentration in the atmosphere (Nguyen & Kim, 2006).
High SO2 and/or NO2 emissions might result in the formation of secondary particulate matter of several orders of magnitude higher than the emissions of the primary fly ash PM originating for example from a coal fire power station (Gaffney & Marley, 2009). Moreover, since these secondary particles are very small (<1 µm) they have long atmospheric residence times and can travel long distances (Finlayson-Pitts & Pitts, 2000). Highly scattering aerosols, such as sulfates and nitrates, have been shown to have an overall effect of cooling in the lower atmosphere by scattering the incoming solar radiation (Charlson & Wigley, 1994). In presence of SO2 and NOX they can also form acid rain. In the atmosphere, SO2 and NO2 react with hydroxyl radicals and, in the presence of water vapor, form H2SO4 and HNO3, respectively (Erduran & Tuncel, 2001).
Frequently, exhaust gases from motor vehicles contribute significantly to atmospheric particles emissions, mainly fine and ultrafine. The particulates associated with diesel exhaust are very small (<1 µm). Along with their small size, these particles have a very large surface area onto which other organic contaminants present in the diesel exhaust can adsorb. Polycyclic organic matter compounds with five or more membered rings are usually associated with diesel PM. Table 3 shows PM10 average concentrations in the MAPA from 2006 to 2009. The Brazilian legislation on PM10 establishes an annual average of 60 µm/m3. It can be seen that the values were above the level established in the Brazilian legislation (National Council of the Environment [CONAMA], 2002), indicating that there is a contribution from vehicles, especially from heavy traffic that is usually associated with diesel particulates. Heavy traffic of diesel-fueled vehicles is characteristic of the area of sampling.
Table 3 shows the average annual concentrations in fine particles (<2.5 µm). Although there is no legislation in Brazil concerning fine particles, an increase in theses particles was reported in 2007, confirming the influence of heavy vehicles.
As already mentioned, the area studied herein (MAPA) has a lot of traffic of heavy diesel trucks, and only in Porto Alegre diesel consumption was 171,133 m3 and 224,280 m3 in 2008 and 2009, respectively.
The heavy traffic of diesel engines significantly contributes to produce ultrafine particles (Vallius et al., 2000). These ultrafine particles have a very large surface area onto which the organic contaminants present in the diesel exhaust can adsorb. The emission depends on a number of factors such as vehicle age, engine design and operating conditions, lubricant oil and fuel quality, as well as environmental conditions (Maricq, 2007; Yanowitz et al., 2000). It is believed that with the higher percent of biodiesel added to diesel there will be a significant decrease in ultrafine particles emissions and, consequently, the quality of air will improve.
The average annual concentrations of NOX, NO2, and O3 were higher in 2007 and 2009. In recent years, despite the higher number of vehicles on the roads, there was an improvement in fuel and lubricant oil quality, vehicle maintenance and operating conditions. Not all diesel vehicles had electronic injection until 2008; therefore, they were probably contributing to a higher concentration of NOX in the atmosphere. Diesel vehicles on the roads are a major source of NOX. Studies have estimated that diesel engines produce five times the amount of NOX per mass of fuel burned when compared to gasoline vehicles (Gaffney & Marley, 2009). NOX emissions react in the presence of sunlight through a series of photochemical reactions involving hydroxyl-, peroxy- and alkoxy radicals, to form the secondary pollutant ozone (Finlayson-Pitts & Pitts, 2000). NO reacts with peroxy radicals (RO2) or O3 producing NO2, which can suffer a photolysis. The atomic oxygen thus released combines with molecular oxygen to form O3 again.
Studies by Teixeira et al. (2009) about photochemical variables in the MAPA showed that the rise of NOX and NO2 concentrations in the area of study during the first hours of the day (7:00h –10:00h) is mainly due to the increase in traffic flow (rush hours). This is also associated with weak winds and atmospheric stability (characteristic of the “nocturnal stable boundary layer”, that still persists in the first hours of the morning. These authors also report that the area of study suffers strong influence of mobile sources and NO is mainly emitted by vehicle exhausts; considering the reactivity of O3 with NO, it reacts with O3 to form NO2, which acts as an O3 sink.
Ozone is not the only oxidant formed from the reactions of NO2 in the atmosphere. Other atmospheric oxidants are also formed such as hydrogen peroxide (H2O2), which can also react with SO2 to form sulfuric acid aerosol, and others. Therefore, NOX emissions are currently regulated in vehicle exhaust in order to control the formation of ozone in the atmosphere.
Year | PM10 µg.m-3 | PM2.5 µg.m-3 | CO µg .m-3 | NOX µg.m-3 | NO2 µg.m-3 | O3 µg.m-3 | SO2 µg.m-3 | PAHs ng.m-3 PM2.5 | NPAHs ng.m-3 PM 2.5 |
2006 | 64.31 | 29.9 | 1.61 | 248 | 132 | 125 | 19.8 | 7.81 | 3.07 |
2007 | 56.25 | 39.2 | 1.42 | 313 | 178 | 155 | 41.1 | 8.54 | 7.52 |
2008 | 60.11 | -- | 0.74 | 205 | 90.0 | 110 | 22.6 | -- | -- |
2009 | 58.1 | -- | 0.77 | 257 | 110 | 118 | 8.25 | -- | -- |
Average concentration of PM10, CO, NOX, NO2, O3, SO2, measured in the MAPA for 2006, 2007, 2008 and 2009; PAHs, NPAHs and PM2.5 for 2006 and 2007.
Besides the regulated pollutants, other pollutants are also emitted by vehicles, especially diesel engines, e.g. polycyclic aromatic hydrocarbons. Some compounds have shown carcinogenic and mutagenic properties (Chang et al., 2006; Kawanaka et al., 2004; Villalobos-Pietrini et al., 2007). Studies conducted by several authors (Dallarosa et al., 2008; Sheu et al., 1997) on PAHs in fine particles have shown that the highest concentration of these compounds is found in particles <2.5 µm. Along with their small size, these particles have a very large surface area onto which other organic contaminants present in the diesel exhaust can adsorb. Polycyclic organic matter compounds with five or more membered rings are usually associated with diesel particulates.
Combustion related PAHs tend to be associated with fine mode vehicle emissions. The concentration of PAHs may vary due to meteorological conditions but high concentrations with high temperature and high solar intensity are considered favorable to photochemical and/or chemical reaction in the atmosphere (Harrison et al., 1996). Photochemical transformations are also considered significant processes for the removal of atmospheric PAHs. Ravindra et al. (2008) reported that PAHs in the vapor phase are more susceptible to such reactions than in the particulate phase. These same authors reported that at moderate temperatures the rate of PAH photo-decomposition may decrease with increased PAH particle loading.
Table 3 shows the average annual concentrations of PAHs and NPAHs in fine particles (<2.5 µm) in 2006 and 2007. Although the time span is of only two years, it is possible to see that these compounds increased in 2007 together with nitrogen oxides. These data confirm the influence of diesel vehicles, since the sampling sites are located near the BR-116 highway, which is under the strong influence of heavy traffic.
Table 4 shows a comparison of PAHs and NPAHs concentrations in the MAPA and other regions of the world. Concentrations in the MAPA were directly influenced by vehicle traffic. The area of Santiago (Chile) shows a higher PAHs concentration, because in addition to being an urban area, it is surrounded by hills and mountains ranging from 500 to 2500 m above sea level, producing limited air circulation and weak dispersion mechanisms, especially during winter, with low thermal inversion heights (Sienra & Rosazza, 2000).
NPAHs concentrations found in the MAPA (Table 4) were higher than those found by Albinet et al. (2007), even if taking into account differences in the equipment used for atmospheric particles and different meteorological conditions. Perhaps the area studied herein had a stronger influence of heavy vehicles; in addition, there might be formation of NPAHs during the sampling process (Albinet et al., 2007), since PAHs deposited on the filter are converted to NPAHs by the passage of NO2.
Various authors have demonstrated that higher PAHs concentration are seen in winter (Fang et al., 2005) and that this can be attributed to various factors, amongst which increased consumption of combustible fossil fuels, increased condensation of PAHs in the gaseous phase at low temperatures (Garban et al., 2002) and some PAHs undergoing only little photochemical degradation under solar radiation in winter (Lee et al., 2002). Moreover, PAHs concentration in winter is higher than in spring/summer, mainly because stationary and mobile source emissions are higher. Lower PAHs concentrations during the summer sampling period have also been attributed to the washing out effect of particulates during rainy days, and photochemical degradation during high solar radiation (Fang et al., 2006). Most PAHs are attached to particulates and they are washed out in rainy summer days.
Other authors (Dallarosa et al., 2005a; 2008) reported that the higher PAHs concentration in atmospheric particles (PM10) in the MAPA were related to events of thermal inversion, weak winds and low atmospheric pressure prevailing over the state. This, together with periods of heavier traffic, especially in winter, has directly influenced the accumulation of PAHs associated with particulates in the atmosphere.
Studies conducted by several authors (Dallarosa et al., 2008; Fang et al., 2006, Chang et al., 2006, Bourotte et al., 2005, and others) in different regions of world on PAHs in fine particles have shown that the highest concentration of these compounds are found in particles <2.5 μm.
NPAHs studies are based on the measuring of certain compounds in specific regions, and they try to assess the influence of emission sources and seasonality (samples collected at different seasons of the year: summer and winter) on them, as well as to propose the mechanisms by which NPAHs are formed in the atmosphere. Most studies use analytical methods already publish in the literature, only with adjustments to the particular conditions of the study.
The various papers on NPAHs published since 2000 are concentrated in the Northern Hemisphere, especially in countries as Japan (Tang et al., 2002; 2005; Kakimoto et al., 2001) and Italy (Di Filippo et al., 2007; 2009; 2010), each with three studies. In the Southern Hemisphere, we have Chile (Sienra et al., 2000; 2006) and Brazil, the latter with a study conducted in Sao Paulo by Vasconcellos et al. (2008). The latter study was based on the occurrence and the measuring of NPAHs in the air of three cities of the state of Sao Paulo (Araraquara, Piracicaba and Paulínia). Figure 10 shows a global overview on the location of the studies on NPAHs published since 2000.
Forty-seven NPAHs have been analyzed in the studies published since 2000. Despite this large number, studies have focused on a limited number of compounds: nitrofluoranthene, nitropyrene, nitroanthracene, nitronaphthalene, nitrophenanthrene, and their isomers. The most abundant NPAHs studied were: 1-nitropyrene (monitored in 90.5% of the studies: 19 out of 21 studies); 9-nitroanthracene (61.9%; 13/21); 2-nitrofluoranthene (61.9%; 13/21); 2-nitropyrene (57.1% ; 12/21); 3-nitrofluoranthene (52.4%; 11/21); 1-nitronaphthalene (47.6%; 10/21); 2-nitrofluorene (47.6%; 10/21).
The compound 1-nitropyrene (1-NP) has been detected in many types of combustion processes, including particles emitted by diesel and gasoline vehicles and by coal burning.
The compounds 2-nitrofluoranthene (2-NFl) and 2-nitropyrene (2-NP) have not been detected in processes of direct emissions, although they are amongst the most abundant NPAHs present in the atmospheric particulate matter. Their presence might be due to the atmospheric transformations of fluoranthene and pyrene.
When analyzing emissions from diesel vehicles with regard to the presence of 1-nitronaphthalene and 2-nitronaphthalene, only 1-nitronaphthalene was found (Bamford et al., 2003). Atkinson et al. (1987) observed that in the gaseous phase reactions with naphthalene initiated by OH- radicals produced 1 and 2-nitronaphthalene at almost identical amounts. Therefore, the fact that 2-nitronaphthalene having appeared in 57.1% of the studies reinforces the hypothesis of investigation on the mechanisms by which NPAHs are formed in the atmosphere, with the purpose of monitoring certain NPAHs.
PAHs are emitted directly from combustion whereas NPAHs, as already mentioned before, are primarily emitted and formed in the atmosphere, by gas and heterogeneous phase reactions of PAHs induced by atmospheric oxidants (OH, NO3, O3). PAH derivatives have a particular interest because they seem more toxic than their related parent PAHs. For instance, NPAHs could contribute with 10% to the total mutagenicity of inhalable suspended particles in polluted areas (Atkinson & Arey, 1994; Albinet et al., 2007). A correct quantification of these compounds in ambient air is very important.
Sampler type | Source | PAHs ng.m-3 | NPAHs ng.m-3 | |
MAPA-Brazil 2006/2007 | PM2.5 | urban, traffic | 7.8-8.5 | 3.07-7.52 |
Ravindra et al. 2006 | PM | different anthropogenic activities | 0.9-8.7 | -- |
Bourotte et al. 2005 | PM2.5 | urban | 10.8 | -- |
Sienra et al. 2005 | PM10 | traffic | 5.45–61.93 | -- |
Sienra et al. 2005 | PM10 | traffic | 1.79–10.91 | -- |
Albinet et al. 2007 | PM10 | traffic | 7.6 | -- |
Albinet et al. 2007 | high volume cascade impactor | traffic | 4.9 | -- |
Albinet et al. 2007 | PM10 | traffic | -- | 0.247 |
Albinet et al. 2007 | high volume cascade impactor | traffic | -- | 0.954 |
Albinet et al. 2007 | PM10 | urban | 0.5 | -- |
Albinet et al. 2007 | high volume cascade impactor | urban | 0.5 | -- |
Albinet et al. 2007 | PM10 | urban | -- | 0.152 |
Albinet et al. 2007 | high volume cascade impactor | urban | -- | 0.034 |
Compared concentrations of PAHs and NPAHs in the Metropolitan Area of Porto Alegre, Brazil, and other regions of the world.
Studies conducted in the world about NPAHs monitoring, identified in the literature and published as of 2000.
The major benefit of biodiesel to the environment is the significant decrease in atmospheric pollutant emissions compared to petroleum diesel oil. Estimated data on biodiesel addition to diesel oil indicate a decrease in pollutant levels, particularly for B20. Pollutant emissions showed a decrease in PM, CO, and HC, while NOX experienced a slight increase.
Vehicle emissions were usually the most significant source of air pollution in general in the Metropolitan Area of Porto Alegre (MAPA). This was intensified by emissions from diesel engines.
The characterization of atmospheric pollutants in the Metropolitan Area of Porto Alegre showed that it is influenced by mobile sources and, particularly, by diesel engines.
The increase in average annual concentrations of PAHs and NPAHs in the fine particles (<2.5 µm) in 2007, together with nitrogen oxides, confirms the influence of heavy traffic in the MAPA.
Studies conducted by several authors as of 2000 about NPAHs show that most of these compounds were related to direct emissions (vehicular emissions, particularly emissions from diesel engines), except for 2-nitrofluoranthene, 2-nitropyrene and 2-nitronaphthalene, which are formed by reactions of PAHs with hydroxyl radicals (OH-).
The subject of future studies will be organic compounds associated with ultrafine atmospheric particles and ways to optimize the sampling methods to achieve a more accurate assessment of the influence of mobile sources.
We are grateful to FINEP, FAPERGS and CNPq for their financial support.
The epiretinal membrane (ERM) is a layer of fibrous, contractile tissue that develops on the interface of the vitreous and the internal limiting membrane (ILM). ERM formation is associated with increased age, diabetes, retinal vein occlusions, uveitis, and other diseases of the retina and vitreous [1]. In the majority of cases, ERMs do not significantly interfere with visual functions and may be observed [2]. However, patients who experience decreased visual acuity, metamorphopsia, or diplopia secondary to the ERM may benefit from vitrectomy with membrane peeling, which is the treatment of choice for symptomatic ERMs [2]. Postoperative complications of ERM peeling include cataract formation, retinal tears/detachments, vitreous hemorrhage, macular holes, and recurrence of ERM, therefore a thorough, individualized review of clinical and imaging prognostic factors must be performed prior to consideration for surgery [2]. Clinical factors such as age, lens status, severity, and duration of symptoms may affect postoperative visual recovery [3]. Imaging modalities, such as spectral-domain optical coherence tomography (SD-OCT), fluorescein angiography (IVFA), and fundus autofluorescence (FA) can also guide clinicians in decision-making [3]. SD-OCT characteristics, such as the thickness of the ganglion cell layer and foveola, have prognostic value in postoperative visual acuity [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. OCT angiography, fluorescein angiography, fundus autofluorescence, and multifocal retinography are additional imaging modalities which have been used to predict retinal changes after ERM peeling [15, 16, 17, 18, 19, 20, 21, 22].
This chapter will review the epidemiology, classification, effect on visual function, natural disease course, and management of epiretinal membranes. Special attention will be paid to the preoperative clinical factors, imaging characteristics, and postoperative complications, most associated with epiretinal membrane peeling, that determine final visual outcome.
A Medline and Excerpta Medica database (EMBASE) search was conducted for all English language publications from 1947 to 2021 using the search term: (“ERM” OR “epiretinal membrane” OR “macular pucker“OR “pre-retinal fibrosis” OR “pre-retinal membrane” OR “cellophane maculopathy“) AND (“epidemiology” OR “classification” or “staging” OR “progression” OR “management” OR “vitrectomy” OR “peeling” OR “optical coherence tomography” OR “OCT” OR “IVFA” or “OCT-A” or “OCT angiography” OR “fluorescein angiography” OR “multifocal electroretinogram” OR “mfERG” OR “complication”). All relevant abstracts and the articles were reviewed. The search was also supplemented by manual search primarily using additional references from key articles.
An epiretinal membrane is a common condition estimated to affect approximately 7–9% of the general population [23, 24]. Certain ethnicities seem to be affected more than others; prevalence can be as high as 29% among Latinos and 39% among people of Chinese descent [25]. This condition is more common in the elderly population—one study found that 12% of people in their 70s are affected yet only 2% of people under 60 years of age are affected. Women have slightly higher rates of epiretinal membrane formation than men, and bilaterality is estimated to be 20–30% [24, 26].
An epiretinal membrane may be classified as being either idiopathic or secondary, with most cases being idiopathic [24]. Secondary causes of ERMs include retinal vein occlusions, diabetic retinopathy, uveitis, retinal tears or detachments [27]. Secondary epiretinal membranes may also be iatrogenic, triggered by both invasive (e.g., cataract, vitrectomy) and noninvasive (e.g., laser photocoagulation and cryopexy) procedures [28].
In addition to etiology, epiretinal membranes may be staged based on clinical or OCT severity. Clinically, epiretinal membranes vary from a minimal cellophane light reflex to a more opaque membrane with retinal folds [29]. The most widely used clinical classification scheme for ERMs was proposed by Gass—Grade 0: cellophane maculopathy, in which a translucent epiretinal membrane is not associated with retinal distortion; grade 1: crinkled cellophane maculopathy, in which the inner retinal surface is distorted by irregular retinal folds; grade 2: macular pucker, in which a grayish membrane causes marked retinal crinkling and macular puckering [30]. Various OCT-guided staging schemes have also been proposed based on the presence of foveal involvement, macular edema, macular holes, schisis, and the integrity of the inner foveal layers [31, 32].
Most people with epiretinal membranes have little to no visual symptoms, whereas others are more symptomatic and have progressive visual changes, including central vision loss, diplopia, metamorphopsia, and aniseikonia [2]. Metamorphopsia and diplopia can be especially debilitating, and they complicate essential daily activities such as reading and driving. Patients commonly report needing to close one eye to eliminate the distortion [2]. In addition to distortion, visual acuity is also degraded through light-filtering and scattering effects of the membrane, disruption of photoreceptor outer segments, obstruction of axoplasmic flow, tractional separation, and deformation of the outer retina [33]. Aniseikonia is caused by a traction-induced change in the distribution of photoreceptors, whereas metamorphopsia is related to tractional changes in the inner retina [33].
Anatomically, about a third of ERM cases remain stable, a third progress, and a third improve when observed for >5 years [34, 35]. Functionally, visual acuity remains stable in the majority of patients, worsens in 10% of patients, and improves in 7%, over a 2 year period [36]. Significant factors that predict progression are a lack of posterior vitreous detachment (PVD), an ERM that is attached fully (rather than focally) to the retina, and an early stage of ERM [33, 36]. In cases that do progress, ERM contraction causes retinal thickening, disappearance of the fovea pit, and disruption on the ellipsoid zone [33]. The timescale for progression is slow, spanning several years [33].
The majority of patients with ERMs do not require treatment and may be observed due to minimal symptoms at baseline and slow rate of progression [2, 34, 36]. For symptomatic patients, the mainstay of treatment is surgical requiring a pars plana vitrectomy with epiretinal membrane peeling [2]. In general, visual outcome after ERM peeling is favorable. Patients who elect to undergo vitrectomy with ERM peeling experience an average improvement in visual acuity of two or more lines and improvement in vision-related quality of life [2, 37]. Additionally, 70% of patients experience an improvement in metamorphopsia after ERM peeling, with 20% reporting a complete resolution of visual distortion [37].
Treatment with enzymatic or pneumatic vitreolysis may be used for isolated vitreomacular traction; however, these therapies have not been found to improve functional outcomes in patients with concurrent ERM [2]. Ocriplasmin, a recombinant proteolytic enzyme, has been found to release vitreomacular traction (VMT) in 8.7% of patients who have VMT in addition to an ERM (compared to 1.5% with placebo injection) without improvement in visual acuity [38]. C3F8 gas injection has been shown to release VMT in 50–83% of patients with concurrent ERMs, but also without improvement in visual acuity [39, 40].
Despite overall favorable outcomes, 10–20% of patients will have unchanged or worse vision following surgery; therefore, a review of each patient’s prognostic factors is advised prior to considering surgical intervention [2].
Both severity and duration of visual symptoms may be useful factors in predicting postoperative visual acuity. In a systematic review of prognostic factors for ERM peeling, Miguel and Legris discovered a heterogeneity regarding baseline visual acuity as a prognostic factor, finding that a low baseline VA appeared to be a poor prognostic factor in the majority of studies [3, 41]. Low baseline VA was also correlated with ERM recurrence [3, 42]. Duration of ERM-induced visual symptoms is inversely correlated to postoperative VA improvement—patients with symptoms of less than 1 year have the greatest improvement in VA [43, 44].
Age also impacts VA improvement after ERM surgery. Patients younger than 75 years old have a higher chance (42% in one study) of achieving postoperative BCVA of 20/20, compared with the patients who are older than 75 years of age [43]. However, older age should not be a deterrent for undergoing ERM peeling, since 66% of patients older than 75 years of age gained more than two Snellen lines in visual acuity postoperatively [43, 45]. In another study, which evaluated prognostic factors based on ERM etiology, younger patients with the secondary ERM as a result of previous retinal detachment surgery were found to have more significant visual improvement than the older patients [44].
Patients with preoperative pseudophakia were found to have better improvement in postoperative VA compared with phakic patients [41]. Cataract formation is the most common complication of PPV with ERM peeling (47–89% develop cataracts), so this finding may be at least partially due to phakic patients developing cataracts postoperatively [46, 47].
Spectral-domain optical coherence tomography (SD-OCT) captures the reflection of a broad-bandwidth light source to provide a high-definition image of the retinal layers [48]. Anatomical retinal changes after ERM peeling have been studied extensively by spectral-domain optical coherence tomography (SD-OCT).
The ganglion cell complex (GCC) consists of the three innermost retinal layers: the nerve fiber layer, the ganglion cell layer, and the inner plexiform layer. ERM peeling is associated with thinning of the GCC, most prominently in the temporal region [4, 5, 6]. A greater GCC reduction was found to be correlated with worse postoperative VA in some studies [4, 7] and improved postoperative VA in another study [8]. Postsurgical GCC thinning is correlated with retinal displacement of the fovea toward the optic disc [6].
The thickness of the foveola also carries a prognostic value. Eyes with the highest postoperative reduction in central foveal thickness were found to have the best improvement in VA and more significant reduction of metamorphopsia [9, 10]. In a comparison between each retinal layer thickness and its ratio of the central foveal thickness, a higher central foveal thickness/GCC ratio postoperatively was shown to be the most significant factor that is associated with the improved VA [11].
The rods and cones make up one of the outermost layers of the retina, known as the photoreceptor layer. Baseline integrity of the inner/outer segment junction (IS/OS) of the photoreceptor layer is associated with improved postoperative visual acuity and reduction in metamorphopsia. [10, 12, 13]. Similarly, postoperative incidence of an intact IS/OS junction is correlated with a higher VA improvement [14]. The gradual improvement in the IS/OS junction postsurgically most likely results from the slow functional recovery of the photoreceptors, leading to the VA improvement. The best improvement in VA after the ERM surgery usually occurs approximately 1 year after the surgery [14, 49].
Cystoid macular edema (CME) has the potential to affect all retinal layers—it can induce thinning of the GCC, cause disruption of the IS/OS junction, increase in the central foveal thickness, and lead to the development of cystic spaces primarily in the outer nuclear layer (ONL) [12, 50, 51, 52]. CME is a poor prognostic factor for VA improvement both when it is present at baseline and when it occurs postsurgically [12, 51].
Microcystic macular edema (MME), a distinct process from CME, is characterized by the absence of fluorescein leakage on FA [53]. Unlike CME, MME does not change foveal thickness and consists of more uniform, ellipsoidal cystic spaces localized to the inner nuclear layer (INL) [54]. MME typically occurs postsurgically after ERM peeling, possibly as a result of the damage to Müller cells and subsequent changes in the osmotic gradient, however, it does not seem to have an impact on visual recovery [50, 53, 54].
Lipofuscin is the by-product of the metabolism of photoreceptor external segments. Its density and distribution in the retinal pigment epithelium (RPE) can be demonstrated by fundus autofluorescence (FA) imaging [15]. Patients with normal autofluorescence were found to have the best postoperative improvement in visual acuity when compared with patients with hypoautofluorescent patterns [16]. Baseline hypoautofluorescence is associated with IS/OS segment disruption, while hyperautofluorescence is correlated with a greater reduction in postoperative central foveal thickness without affecting postoperative VA [16, 17].
OCT-A is a noninvasive technology which utilizes laser reflectance of the surface of moving red blood cells to depict vessels of the retina and choroid [18]. No differences in the superficial vascular plexus were found on OCT-A, when compared before and after the ERM surgery by Romano et al. [19]; however, a postoperative decrease in vessel density was demonstrated by Mastropasqua et al. [20]. The deep perifoveal capillary-free zone was found to increase postsurgically in patients with diabetic ERM, unlike with idiopathic ERM. Both those changes do not seem to affect postoperative VA recovery [19].
Unlike the OCT-A, fluorescein angiography (IVFA) maps out chorioretinal vasculature using the fluorescence of an intravenous dye. Patients with RPE abnormalities, demonstrated on preoperative FA, had a higher central foveal thickness preoperatively, a higher macular volume preoperatively, a more extensive ERM, and a greater BCVA improvement postoperatively [17]. This may be explained by the fact that the macular traction (with resultant macular thickness and RPE abnormalities) seems to be a reversible cause of visual loss, or by the ceiling effect, related to the data showing that patients with better preoperative BCVA have lower rates of improvement [17].
The multifocal electroretinogram (mfERG) is a noninvasive, objective measure of retinal electrical activity in response to a light stimulus. It is used for detection of localized abnormalities within the macula. It was found that mean, P1, and N1 mfERG amplitude were decreased in the eyes with ERMs preoperatively, but no significant change was noted after ERM peeling [21, 22]. There was also no correlation found between the multifocal ERG values and the central foveal thickness and visual acuity [21].
Cataract formation is the most common complication after ERM peeling—de Bustros et al. found that 47% of patients developed nuclear sclerosis within 3 years, and Reilly et al. found that 89% of patients developed nuclear sclerosis within 1 year [46, 47]. There was no change in the incidence of anterior subcapsular cataracts, and only 4% of patients developed posterior subcapsular cataracts [46].
Recurrence of ERM is another possible complication of ERM peeling. About 4–5% of all patients undergoing ERM peeling develop a recurrence within 1 year of surgery, while half of them would require a reoperation due to visually significant symptoms [42, 47].
Some surgeons choose to peel the underlying internal limiting membrane, while peeling an ERM. Although visual outcomes and anatomical retinal changes of ERM peeling with or without ILM removal seem to be equivalent, a lower ERM recurrence, a lower reoperation rate, but a higher rate of macula hole development were observed in patients who undergo ILM peeling in addition to ERM removal [55, 56]. Sandali et al. compared the two methods and found that only 2.6% of patients in the group of both ERM and ILM removal developed an ERM recurrence within 1 year, and 8.6% of ERM recurrence was noted in a group of patients who underwent only ERM peeling without ILM peeling [42]. A proposed explanation for this observation is that ILM peeling removes the scaffold for myofibroblast proliferation, and this can contribute to the recurrence of ERM [57].
Of patients who underwent ERM removal with ILM peeling, 1.7% were found to have macular holes postoperatively, while very few cases of macular hole development were noted in patients with ERM removal without ILM peeling [56]. The majority of macular holes were outside the fovea and appeared to be non-visually significant [56].
Retinal breaks and detachments are uncommon while utilizing current vitrectomy surgical methods [2]. Retinal detachments occur in 1% of cases during PPV with ERM peeling performed using a 23-gauge cannula system and in 3.5% of cases undergoing 20-gauge vitrectomy [2, 58, 59]. The incidence of retinal breaks ranges from 1 to 6% in the literature [60, 61].
Epiretinal membranes are a common finding, especially in the geriatric population. The majority of patients with ERMs do not require any treatment, but those who develop symptoms such as blurred vision or metamorphopsia may benefit from vitrectomy with ERM peeling. Vitrectomy with membrane peeling, although effective in relieving symptoms in most cases, has complications such as cataract formation, retinal breaks/detachments, ERM recurrence, and macular holes. Complications can be minimized and good outcomes maximized, through the careful consideration of prognostic factors in selecting surgical candidates. Clinical characteristics correlated with better postoperative VA include younger age, baseline pseudophakia, and shorter duration of symptoms. Physicians should also consider performing imaging tests such as OCT, IVFA, and autofluorescence (FA) to assist with decision-making. Patients who have a baseline integrity of the IS/OS junction on OCT, lack of cystic macular edema, normal autofluorescence on FA, and presence of RPE abnormalities on IVFA tend to have better postoperative visual outcomes and therefore may be good surgical candidates. With appropriate optimization and patient selection, epiretinal membrane peeling represents the best therapeutic intervention for this disabling disease. As trials continue to move forward, there remains the promise of continued improvements in technique and technologies, which will one day serve as a cure.
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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\n\nDisruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
\n\nIntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
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He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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