Geography and environment of sampling sites.
\\n\\n
These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\\n\\n\\n\\n\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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In the United States alone, this affects almost 25% of women. These disorders often affect women's daily life activities, their sexual function, their ability to exercise, and their social and psychological life. Pelvic floor disorders are usually diagnosed clinically, but in complicated cases, pelvic imaging and electromyographic studies may be required. This book attempts to discuss the pathophysiology of pelvic floor disorders, its treatment by the use of a new synthetic material, and treatment for recurrent POP. Although there are many books available on this topic, it includes some of the original research work and surgical innovation. We would like to acknowledge all the authors for their hard work in completing this book.",isbn:"978-1-78923-245-5",printIsbn:"978-1-78923-244-8",pdfIsbn:"978-1-83881-375-8",doi:"10.5772/intechopen.69095",price:100,priceEur:109,priceUsd:129,slug:"pelvic-floor-disorders",numberOfPages:82,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"e53630ad8f02658c6ca31163f9d68193",bookSignature:"Raheela M. Rizvi",publishedDate:"June 6th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6278.jpg",numberOfDownloads:7118,numberOfWosCitations:4,numberOfCrossrefCitations:9,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:8,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:21,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 19th 2017",dateEndSecondStepPublish:"July 10th 2017",dateEndThirdStepPublish:"December 1st 2017",dateEndFourthStepPublish:"January 4th 2018",dateEndFifthStepPublish:"March 5th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"185970",title:"Dr.",name:"Raheela",middleName:"Mohsin",surname:"Rizvi",slug:"raheela-rizvi",fullName:"Raheela Rizvi",profilePictureURL:"https://mts.intechopen.com/storage/users/185970/images/5330_n.jpg",biography:"Dr. Raheela Mohsin Rizvi is a gynecologist at Aga Khan University Hospital. Her education includes: diploma in medical education by Department of Education Development, Aga Khan University Karachi, Pakistan – 2011 and clinical fellowship in Urogynecology and Pelvic Reconstructive Surgery at West Mead Hospital Sydney University, Australia – 2002-2003. Her certification: FCPS (CPSP) in Obstetrics and Gynecology – 1997, MCPS (CPSP) in Obstetrics and Gynecology – 1992, M.B.B.S. (Bachelor of Medicine, Bachelor of Surgery) Rawalpindi Medical College / Punjab University, Pakistan – 1981 – 1986.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Aga Khan University",institutionURL:null,country:{name:"Pakistan"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1071",title:"Urogynecology",slug:"urogynecology"}],chapters:[{id:"61142",title:"Introductory Chapter: Pelvic Floor Disorders",doi:"10.5772/intechopen.77302",slug:"introductory-chapter-pelvic-floor-disorders",totalDownloads:922,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Raheela M. Rizvi",downloadPdfUrl:"/chapter/pdf-download/61142",previewPdfUrl:"/chapter/pdf-preview/61142",authors:[{id:"185970",title:"Dr.",name:"Raheela",surname:"Rizvi",slug:"raheela-rizvi",fullName:"Raheela Rizvi"}],corrections:null},{id:"60935",title:"Pathophysiology of Pelvic Organ Prolapse",doi:"10.5772/intechopen.76629",slug:"pathophysiology-of-pelvic-organ-prolapse",totalDownloads:1794,totalCrossrefCites:4,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Pelvic organ support is provided by interaction between the pelvic floor muscle, ligaments and its connective tissues. Failure of anatomical support may result in pelvic organ prolapse. Therefore in managing anterior, posterior, or apical compartments prolapse, conceptual understanding of pelvic floor anatomy is essential for the surgeons. To appropriately treat these entities, comprehension of the various theories of the pathophysiology of pelvic organ prolapse is of paramount importance. DeLancey has described vaginal connective tissue support of the pelvis at three levels that has helped us to understand various clinical manifestations of pelvic organ support dysfunction. Pelvic floor disorder is frequently associated with etiological risk factors which include aging, parity, obesity, connective tissue disorder, increased intra-abdominal pressure and hysterectomy. A better understanding of pathophysiology of muscular, collagen, and neuronal components of the pelvic organs and their support would provide an insight of site specific defects and its prevention.",signatures:"Lubna Razzak",downloadPdfUrl:"/chapter/pdf-download/60935",previewPdfUrl:"/chapter/pdf-preview/60935",authors:[{id:"212077",title:"Dr.",name:"Lubna",surname:"Razzak",slug:"lubna-razzak",fullName:"Lubna Razzak"}],corrections:null},{id:"61308",title:"Effects of Posture and Gravity on Pelvic Organ Prolapse",doi:"10.5772/intechopen.77040",slug:"effects-of-posture-and-gravity-on-pelvic-organ-prolapse",totalDownloads:1268,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Female pelvic floor dysfunction occurs when the integrity of the pelvic floor muscles is compromised and impacts the position and function of the pelvic organs. Physicians use international guidelines to evaluate and treat women for POP taking into account that posture and gravity impact pelvic organ position, and degree of prolapse. Our clinical focuses on the description of surface anatomy. This examination alone is insufficient. Although imaging is recommended, the modalities currently available are recognized to have flaws. MRI is performed in the supine position regardless the effect of posture and gravity on POP. A literature search was performed using databases, searching MEDLINE and PubMed using the key terms ultrasound, MRI, and CT. We describe use of a new protocol and advanced technique to evaluate the changes of POP in different positions using open MRI (MRO). POP patients underwent MRO imaging of the pelvic floor using a 0.5 T MRO scanner. The extent of displacement of prolapsed organs was determined using validated reference lines drawn on the mid-sagittal images. Manual segmentation and surface modeling were used to construct the 3D models. MRO offers new levels of anatomic detail; 3D sequences based on 2D images are an additional refinement.",signatures:"Marwa Abdulaziz, Lynn Stothers and Andrew Macnab",downloadPdfUrl:"/chapter/pdf-download/61308",previewPdfUrl:"/chapter/pdf-preview/61308",authors:[{id:"117248",title:"Dr.",name:"Andrew",surname:"Macnab",slug:"andrew-macnab",fullName:"Andrew Macnab"},{id:"183155",title:"Dr.",name:"Lynn",surname:"Stothers",slug:"lynn-stothers",fullName:"Lynn Stothers"},{id:"209987",title:"M.Sc.",name:"Marwa",surname:"Abdulaziz",slug:"marwa-abdulaziz",fullName:"Marwa Abdulaziz"}],corrections:null},{id:"60744",title:"Synthetic Materials Used in the Surgical Treatment of Pelvic Organ Prolapse: Problems of Currently Used Material and Designing the Ideal Material",doi:"10.5772/intechopen.76671",slug:"synthetic-materials-used-in-the-surgical-treatment-of-pelvic-organ-prolapse-problems-of-currently-us",totalDownloads:1341,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:1,abstract:"Synthetic materials have long been used to provide structural support when surgically repairing pelvic organ prolapse (POP). The most widely used synthetic material is a mesh made of polypropylene (PPL). The use of mesh is intended to improve cure rates and prevent recurrences after POP surgery – however as more mesh materials have been implanted, it has become apparent that serious complications can occur in up to 30% of women, particularly when the mesh is implanted transvaginally. Over the years many different mesh kits have been marketed and used in the treatment of POP however polypropylene mesh was never designed or tested for use in pelvic floor. Instead it was approved for clinical use based on its biocompatibility and success in abdominal hernia repairs. It is now known that PPL meshes are neither compliant with the mechanical forces in the pelvic floor nor do they integrate well into paravaginal tissues. Better materials developed specifically for use in pelvic floor are urgently needed. The aim of this chapter is to define the requirements of an ideal mesh in terms of its material properties and to summarize the ongoing research on developing the next generation pelvic floor repair materials.",signatures:"Naşide Mangir, Christopher R. Chapple and Sheila MacNeil",downloadPdfUrl:"/chapter/pdf-download/60744",previewPdfUrl:"/chapter/pdf-preview/60744",authors:[{id:"192804",title:"M.D.",name:"Naşide",surname:"Mangır",slug:"naside-mangir",fullName:"Naşide Mangır"},{id:"212217",title:"Prof.",name:"Christopher",surname:"Chapple",slug:"christopher-chapple",fullName:"Christopher Chapple"},{id:"212218",title:"Prof.",name:"Sheila",surname:"MacNeil",slug:"sheila-macneil",fullName:"Sheila MacNeil"}],corrections:null},{id:"61034",title:"Recurrent Pelvic Organ Prolapse",doi:"10.5772/intechopen.76669",slug:"recurrent-pelvic-organ-prolapse",totalDownloads:1793,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"The treatment of recurrent pelvic organ prolapse is challenging. The pelvic floor symptom needs to be treated, a high quality of life has to be ensured and complications have to be minimized. There is a wide range of surgical options that may be used. The surgeon should be able to discuss and offer native tissue procedures for prolapse. In addition, for the clinically challenging situations of recurrent prolapse, mesh augmented procedures may need to be discussed with the patient. A thorough knowledge of mesh and graft options, as well as knowledge of prolapse recurrence and adverse events rate, can help guide clinicians in counseling their patients effectively. Ultimately, this will allow surgeons to choose a personalized treatment option that best align with a woman’s lifestyle and treatment goals. In this chapter the anatomical concepts of supports of vagina are elaborated. The pelvic diaphragm, lateral attachment of vagina to arcus tendineus fascia pelvis, intrinsic and extrinsic sphincter control mechanisms are elaborated. The surgical techniques of suspending the vaginal vault with autologous tissue and synthetic mesh are discussed. Finally, the role of minimally invasive surgery of pelvic floor is discussed as an integral part of management of recurrent vaginal prolapse.",signatures:"Nidhi Sharma and Sudakshina Chakrabarti",downloadPdfUrl:"/chapter/pdf-download/61034",previewPdfUrl:"/chapter/pdf-preview/61034",authors:[{id:"220214",title:"Prof.",name:"Nidhi",surname:"Sharma",slug:"nidhi-sharma",fullName:"Nidhi Sharma"},{id:"224544",title:"Dr.",name:"Sudakshina",surname:"Chakrabarti",slug:"sudakshina-chakrabarti",fullName:"Sudakshina Chakrabarti"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"684",title:"Endometriosis",subtitle:"Basic Concepts and Current Research Trends",isOpenForSubmission:!1,hash:"1f5625375189846e4fa04200c135afcc",slug:"endometriosis-basic-concepts-and-current-research-trends",bookSignature:"Koel Chaudhury and Baidyanath Chakravarty",coverURL:"https://cdn.intechopen.com/books/images_new/684.jpg",editedByType:"Edited by",editors:[{id:"83747",title:"Prof.",name:"Koel",surname:"Chaudhury",slug:"koel-chaudhury",fullName:"Koel Chaudhury"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"707",title:"Hysterectomy",subtitle:null,isOpenForSubmission:!1,hash:"219d88512350b2e1d01cfd8faf81aa9c",slug:"hysterectomy",bookSignature:"Ayman Al-Hendy and Mohamed Sabry",coverURL:"https://cdn.intechopen.com/books/images_new/707.jpg",editedByType:"Edited by",editors:[{id:"54087",title:"Dr.",name:"Ayman",surname:"Al-Hendy",slug:"ayman-al-hendy",fullName:"Ayman Al-Hendy"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1900",title:"In Vitro Fertilization",subtitle:"Innovative Clinical and Laboratory Aspects",isOpenForSubmission:!1,hash:"212b5ed00828501488c8d7025d84a188",slug:"in-vitro-fertilization-innovative-clinical-and-laboratory-aspects",bookSignature:"Shevach Friedler",coverURL:"https://cdn.intechopen.com/books/images_new/1900.jpg",editedByType:"Edited by",editors:[{id:"111647",title:"Prof.",name:"Shevach",surname:"Friedler",slug:"shevach-friedler",fullName:"Shevach Friedler"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4641",title:"Approaches to Hysterectomy",subtitle:null,isOpenForSubmission:!1,hash:"a2a63bba8f7b17c10aff3d6d59ea0d08",slug:"approaches-to-hysterectomy",bookSignature:"Zouhair O. 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2019",book:{id:"7139",title:"Current Approaches in Orthodontics",subtitle:null,fullTitle:"Current Approaches in Orthodontics",slug:"current-approaches-in-orthodontics",publishedDate:"April 10th 2019",bookSignature:"Belma Işık Aslan and Fatma Deniz Uzuner",coverURL:"https://cdn.intechopen.com/books/images_new/7139.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"255738",title:"Associate Prof.",name:"Amila",middleName:null,surname:"Vujacic",fullName:"Amila Vujacic",slug:"amila-vujacic",email:"amilavujacic@gmail.com",position:null,institution:null},{id:"264430",title:"Prof.",name:"Jasna",middleName:null,surname:"Pavlovic",fullName:"Jasna 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Orthodontics",slug:"current-approaches-in-orthodontics",publishedDate:"April 10th 2019",bookSignature:"Belma Işık Aslan and Fatma Deniz Uzuner",coverURL:"https://cdn.intechopen.com/books/images_new/7139.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"11375",leadTitle:null,title:"Enterobacteria",subtitle:null,reviewType:"peer-reviewed",abstract:"
\r\n\tThe members of the Enterobacteria are prevalent and involved in different types of infections (nosocomial, urinary tract infections, respiratory infections, gastroenteritis, food poisoining, different outbreaks, etc.), and they need to be reviewed after a period of time as different variants and species evolve and cause different infections that need to be studied thoroughly.
\r\n\r\n\tThis book aims to cover all members of the genus Enterobacteria (E.coli, Proteus, Salmonella, Shigella, Klebsiella, Citrobacter, Edwardsiella, etc.) with respect to classification, identification, and new methods of identification for any new species identified. Explanations on the pathogenecity and variants of each of the members of enterobacteria are welcome, as well as any vaccines or prevention strategies, and outbreaks of infection reported for each of the members of Enterobacteria. The book hopes to serve as a complete resource on Enterobacteria for students, scientists, clinicians, and medical microbiologists.
",isbn:"978-1-80355-310-8",printIsbn:"978-1-80355-309-2",pdfIsbn:"978-1-80355-311-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"03a14c5427b8bbb2102fa57d3ae16de6",bookSignature:"Dr. Sonia Bhonchal Bhardwaj",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11375.jpg",keywords:"Enterobacteria, Escherichia coli, Klebsiella, Salmonella, Proteus, Enterobacteria Classification, Enterobacteria Identification, Enterobacteria Species, Enterobacteria Pathogenicity, Enterobacteria Sub-Species, Infection, Food Poisoning",numberOfDownloads:219,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 1st 2021",dateEndSecondStepPublish:"September 29th 2021",dateEndThirdStepPublish:"November 28th 2021",dateEndFourthStepPublish:"February 16th 2022",dateEndFifthStepPublish:"April 17th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"9 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Sonia Bhonchal Bhardwaj is a member of the Indian Association of Medical Microbiology (IAMM) and Gastrointestinal Infection Society of India (GISI). She has received grants from the Department of Science and Technology (India) for her research.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"178566",title:"Dr.",name:"Sonia Bhonchal",middleName:null,surname:"Bhardwaj",slug:"sonia-bhonchal-bhardwaj",fullName:"Sonia Bhonchal Bhardwaj",profilePictureURL:"https://mts.intechopen.com/storage/users/178566/images/system/178566.jpeg",biography:"Dr. Sonia Bhonchal Bhardwaj, Ph.D., is a senior assistant professor at the Department of Microbiology, Dr. Harvansh Singh Judge Institute of Dental Sciences and Hospital, Panjab University, Chandigarh, India. She has published several international publications in reputed journals as well as books, book chapters, and congress proceedings.\nDr. Bhardwaj has received grants from the Department of Science and Technology (India) and has worked on biofilm formation in Enterococcus faecalis in periodontitis, Streptococcus mutans, and phage therapy. She is a member of the Indian Association of Medical Microbiology (IAMM), Gastrointestinal Infection Society of India (GISI), Association of Microbiologists of India (AMI), and Society for Bacteriophage Therapy (SBRT).",institutionString:"Panjab University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Panjab University",institutionURL:null,country:{name:"India"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"13",title:"Immunology and Microbiology",slug:"immunology-and-microbiology"}],chapters:[{id:"80583",title:"Uropathogenic Escherichia coli",slug:"uropathogenic-escherichia-coli",totalDownloads:102,totalCrossrefCites:0,authors:[null]},{id:"80459",title:"Salmonella in Wild Animals: A Public Health Concern",slug:"salmonella-in-wild-animals-a-public-health-concern",totalDownloads:54,totalCrossrefCites:0,authors:[null]},{id:"81292",title:"Phenotypic Characterisation of Carbapenemases Produced by Enterobacteria Isolated from Patients of the Medico-Social Centre of the National Social Insurance Fund of Maroua: Cameroon",slug:"phenotypic-characterisation-of-carbapenemases-produced-by-enterobacteria-isolated-from-patients-of-t",totalDownloads:6,totalCrossrefCites:0,authors:[null]},{id:"80496",title:"Foodborne Pathogens of Enterobacteriaceae, Their Detection and Control",slug:"foodborne-pathogens-of-enterobacteriaceae-their-detection-and-control",totalDownloads:57,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"252211",firstName:"Sara",lastName:"Debeuc",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/252211/images/7239_n.png",email:"sara.d@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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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"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.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:"9111",title:"Lead Environmental Pollution in Central India",doi:"10.5772/7590",slug:"lead-environmental-pollution-in-central-india",body:'\n\t\tLead is a well known non-biodegradable toxic metal in the environment and now, it has become a global health issue (1-5). More than 15 million children in developing countries are suffering permanent neurological damage due to Pb poisoning (6). Lead toxicity in children causes serious health hazards i.e. permanent brain damage, causing learning disabilities, hearing loss, and behavioural abnormalities and in adults causes hypertension, blood pressure problems, heart disease, etc. (7-9). The elevated levels of Pb in blood of children (200 µg l-1) and dogs (250 µg l-1 ) of Indian megacities were reported (10-11). Sources of Pb pollution in India may be divided into two major categories: industrial and domestic. The industrial Pb exposures are mainly due to the particulates generated by coal burning and roasting of minerals i.e. iron pyrite, dolomite, alumina, etc. The domestic Pb exposures come mainly from cooking by use of the solid fuels (i.e. coal, biomass, agricultural waste, etc.), paints, ceramic glazes, cosmetic and folk remedies, drinking water, food, etc. The most of minerals and coal in Indian subcontinent are reserved in the central states i.e. Chhattisgarh, Jharkhand, Madhya Pradesh, Orissa, etc. Raipur city is capital of Chhattisgarh state and the environment (soil, rain, etc.) of this region is found to be contaminated with Pb and other heavy metals at elevated levels (12-13). The aim of this work is to highlight the lead levels in the various environmental compartments (i.e. air, rain, runoff water, surface soil, sludge and plants of the central India) and to discuss its sources, exposures and toxicities.
\n\t\tThe lead contamination levels were investigated in the environment of five cities: Raipur, Bhilai, Kaudikasa, Mandla and Korba, Figure 1. Their geography and environment are shown in Table 1. The population of Raipur city is ≈ 2.0 million, and being exposed to high levels of air pollution due to rapid industrialization and urbanization. Raipur city and its neighbourhood are now becoming an important regional commercial and industrial destination for the coal, power, steel and aluminium industries. Raipur is the biggest iron markets in the country. Bhilai is the second-largest city in Chhattisgarh state, and it is located 20 km away in the north-western part of Raipur. The town is famous for running of one of the largest World Steel Plant. Korba is another city famous for power supply and located in the NE direction ≈ 150 km away from Raipur. The Kaudikasa is a remote area, situated in Rajnandgaon district, Chhattisgarh and severely suffering with geogenic arsenic toxicity problem (14). The Mandla town is situated in a loop of the Narmada river in the Madhya Pradesh state and severely affected with the flourosis problem (15).
\n\t\t\tThe respirable aerosol particles, fine and coarse particulate matter (PM2.5 and PM10) were collected at residential site (i.e. Dagania) of Raipur city during period, June, 2005 – May, 2006 to know Pb-
\n\t\t\t\tRepresentation of sampling sites in India.
City | \n\t\t\t\t\t\t\tLocation | \n\t\t\t\t\t\t\tPopulation million | \n\t\t\t\t\t\t\tType | \n\t\t\t\t\t\t\tRemark | \n\t\t\t\t\t\t
Raipur | \n\t\t\t\t\t\t\t21°24’N & 81°63’E | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\tUrban & Industrial | \n\t\t\t\t\t\t\tSeveral steel, ferro-alloy and cement plants are running | \n\t\t\t\t\t\t
Bhilai | \n\t\t\t\t\t\t\t21° 13′ N & 81° 25′ E | \n\t\t\t\t\t\t\t0.5 | \n\t\t\t\t\t\t\tIndustrial | \n\t\t\t\t\t\t\tThe Asia biggest steel plant is in the operation | \n\t\t\t\t\t\t
Korba | \n\t\t\t\t\t\t\t22° 21′ N & 82° 40′ E | \n\t\t\t\t\t\t\t0.5 | \n\t\t\t\t\t\t\tIndustrial | \n\t\t\t\t\t\t\tThermal power plants of capacity, 40000 MW Yr -1 are in operation | \n\t\t\t\t\t\t
Koudikasa | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 0.01 | \n\t\t\t\t\t\t\tRural | \n\t\t\t\t\t\t\tSuffering with geogenic arsenic problem | \n\t\t\t\t\t\t
Mandala | \n\t\t\t\t\t\t\t22°60’ N & 80°38’E | \n\t\t\t\t\t\t\t0.1 | \n\t\t\t\t\t\t\tSemi-urban | \n\t\t\t\t\t\t\tSuffering with severe fluorosis problem | \n\t\t\t\t\t\t
Geography and environment of sampling sites.
contamination in the air. The Partisol Model 2300 sequential speciation air sampler (Thermo Scientific, USA) was used for collection of the PM10 and PM2.5 over the teflon PTVC filter (Whatmann, 47-mm diameter). The sampler was installed at the roof of the building, 10-m above from the ground level. The weighted filters were housed in the sampler and run for duration of 24-hrs duration from 6.00 am - 6.00 am. The loaded filters were dismounted, brought to laboratory, transferred into the desicator, and finally weighted to record the particulate contents. Each loaded filter was kept in a petri dish, and dispatched to (Department of Physics, National Institute Nuclear Physics, Florence) Italy for the analysis of the total lead content by technique i.e. proton induced X-ray emission spectroscopy (PIXE).
\n\t\t\t\tThe rain water samples were collected by using automatic collector in year 2008. Whereas, the runoff water samples were collected manually. The pH and conductivity values were measured immediately. The rain water sample was transferred into 50-ml plastic bottle and acidified with few drops of ultra pure nitric acid.
\n\t\t\t\tThe surface soil, sludge and plant samples were collected in dry season from three industrial city i.e. Raipur, Bhilai, Korba, Mandla and Koudikasa. The sample was dried, crushed, sieved out (< 0.5 mm) and digested in the microwave digestion system “MARS 5 with the aqua regia.
\n\t\t\t\tA VARIAN “SpectrAA 220Z” model graphite furnace atomic absorption spectrometer (GF-AAS) equipped with a longitudinal Zeeman Effect background corrector and THGA tube, auto sampler and automatic data processor was used for analysis of Pb at wavelength and slit width of 283.3 and 0.5 nm, respectively. The drying, ashing and atomization was carried out at 110-130, 975 and 2400 oC, respectively. The reference materials were used for the quality control.
\n\t\t\tTwo natural resourced raw materials such as iron pyrite and coal are widely used for production of steel and generation of energy, respectively. They were found to be contaminated with the toxic metals at the trace levels. The estimated Pb levels in the iron pyrite and coal were > 10 and >30 mg kg-1, respectively. The combustion of 10 MT each of pyrite and coal may emit > 400 T Pb in the air.
\n\t\t\tLead in the air is emitted as aerosol predominately by burning of solid fuel (i.e. coal and biomass) and roasting of pyrite minerals in this region. The annual concentration of PM10 and PM2.5 in the air (n = 44) was ranged from 37 -501 and 27 – 293 µg m-3 with arithmetic mean value of 209 ±38 and 95 ±18 µg m-3, respectively (16). The Pb concentration associated with the PM2.5 and PM10 are summarized in Table 2.
\n\t\t\t\tPM | \n\t\t\t\t\t\t\tRange | \n\t\t\t\t\t\t\tA. Mean | \n\t\t\t\t\t\t\tG. Mean | \n\t\t\t\t\t\t\tMedian | \n\t\t\t\t\t\t\tSTD, ± | \n\t\t\t\t\t\t
PM 2.5 | \n\t\t\t\t\t\t\t13 - 5234 | \n\t\t\t\t\t\t\t730 | \n\t\t\t\t\t\t\t230 | \n\t\t\t\t\t\t\t258 | \n\t\t\t\t\t\t\t1092 | \n\t\t\t\t\t\t
PM 10 | \n\t\t\t\t\t\t\t21 - 5582 | \n\t\t\t\t\t\t\t909 | \n\t\t\t\t\t\t\t287 | \n\t\t\t\t\t\t\t294 | \n\t\t\t\t\t\t\t1251 | \n\t\t\t\t\t\t
Concentration of Pb in air associated to PM, Raipur city, ng m-3.
The highest concentration of the PM10Pb and PM2.5Pb in the air was seen in the month of January and December of a year, respectively mainly due to the lowest wind speed, Figure 2. Meteorologically, the whole hydrological year was classified into four seasons: rainy (July - September), autumn (October - December), winter (January - March) and summer (April - June). The concentration of Pb in the rainy season was remarkably decreased, may be due to removal with the rain, Figure 3. The PM10Pb (r2 = 0.40) and PM2.5Pb (r2 = 0.17) concentration have poor correlation with the PM concentration, showing dissimilarity in their origin. While the PM10Pb and PM2.5Pb have good correlation value (r2 = 0.91), indicating similarity in their origin in the both fractions, Figure 4.
\n\t\t\t\tThe annual concentration of Pb in the PM10 and PM2.5 was ranged from 0.01 – 1.52 and 0.01 – 2.56% with mean value of 0.34 and 0.66%, respectively. The highest and lowest concentration of Pb in the PM was seen in the autumn and rainy season, respectively, Table 3.
\n\t\t\t\tMonthly mass distribution of Pb in air.
Seasonal mass distribution of Pb in air.
Correlation of PM10Pb with PM2.5Pb.
The monthly mean meteorological parameters i.e. rain fall (RF), temperature(T), humidity(H), vapour pressure(VP), wind speed(WS) and sunshine(SS) in Raipur city during period, June, 2005 – May, 2006 are summarized in Figure 5. The lowest values of the RF, T, VP and WS were observed in the winter season. The particulate Pb has poor to fare negative correlation with meteorological parameters i.e. RF, T, H, VP and WS except SS, Table 4. The concentration of Pb in the air was decreased when the value of RF, T, H, VP and WS was increased. A reverse trend was observed in the case of sunshine. The WD of the air also influenced the concentration of Pb in the air, and found to be increased remarkably due to coming of industrial effluents from north to east directions. In industrial site, the
\n\t\t\t\tPM | \n\t\t\t\t\t\t\tAnnual | \n\t\t\t\t\t\t\tWinter | \n\t\t\t\t\t\t\tSummer | \n\t\t\t\t\t\t\tRainy | \n\t\t\t\t\t\t\tAutumn | \n\t\t\t\t\t\t
PM2.5 | \n\t\t\t\t\t\t\t0.56 | \n\t\t\t\t\t\t\t0.83 | \n\t\t\t\t\t\t\t0.23 | \n\t\t\t\t\t\t\t0.16 | \n\t\t\t\t\t\t\t1.06 | \n\t\t\t\t\t\t
PM10 | \n\t\t\t\t\t\t\t0.34 | \n\t\t\t\t\t\t\t0.50 | \n\t\t\t\t\t\t\t0.07 | \n\t\t\t\t\t\t\t0.07 | \n\t\t\t\t\t\t\t0.59 | \n\t\t\t\t\t\t
Concentration of Pb in PM, Raipur city, %.
Species | \n\t\t\t\t\t\t\tRF | \n\t\t\t\t\t\t\tT | \n\t\t\t\t\t\t\tH | \n\t\t\t\t\t\t\tVP | \n\t\t\t\t\t\t\tWS | \n\t\t\t\t\t\t\tSS | \n\t\t\t\t\t\t
PM10Pb | \n\t\t\t\t\t\t\t0.25 | \n\t\t\t\t\t\t\t0.71 | \n\t\t\t\t\t\t\t0.03 | \n\t\t\t\t\t\t\t0.16 | \n\t\t\t\t\t\t\t0.78 | \n\t\t\t\t\t\t\t0.38 | \n\t\t\t\t\t\t
PM2.5Pb | \n\t\t\t\t\t\t\t0.1 | \n\t\t\t\t\t\t\t0.63 | \n\t\t\t\t\t\t\t0.16 | \n\t\t\t\t\t\t\t0.04 | \n\t\t\t\t\t\t\t0.45 | \n\t\t\t\t\t\t\t0.11 | \n\t\t\t\t\t\t
Correlation (r2) of the Pb content with meteorology.
Meteorology of Raipur city.
Pb concentration in the air was tremendously increased (> 2-folds) due to the anthropogenic emissions, Figure 6.
\n\t\t\t\tThe annual mean ratio of [PM2.5Pb]/[PM10Pb] was found to be 0.80, indicating the accumulation of 80% Pb in the aerodynamic mode. The Pb concentration in the air has good correlation with the elements i.e. S (r2 = 0. 71), Cl (r2 = 0. 80), Mn (r2 = 0. 82) and Zn (r2 = 0.78) in the fine fraction. The enrichment factor of Pb (concentration ratio of the aerosol to the soil of the element to the reference crustal element such as Al) in the PM2.5 mode was 166, and can be considered as an element of the anthropogenic origin. Lead concentration in the ambient air of Raipur city during the dry season was found to be much more higher than other part of the country (17-18).
\n\t\t\t\tSpatial distribution of Pb in PM during Feb.,2006.
The atmospheric and geospheric pollutants were washed out with precipitates (i.e. rain, fog, snow, etc.) and runoff water, respectively. The Pb contents in rain of three industrial cities i.e. Raipur, Bhilai and Korba were ranged from 28 – 849 µg l-1 with mean value of 291±130 µg l-1, respectively (19). The highest Pb level was detected in the samples of Korba city due to higher coal burning, Figure 7. Similarly, Pb-content in the runoff water was ranged from 131 - 3157 μg l-1 with mean value of 659±232 µg l-1, respectively. Almost similar spatial variation of Pb-content in the runoff water was observed, Figure 7. The Pb content in the rain of this region was found to be much higher than reported for other regions of the World (20-24).
\n\t\t\t\tSpatial distribution of Pb in rain and runoff water.
The Pb content in the surface soil of remote, urban and industrial cities (i.e. Kaudikasa, Mandla, Raipur, Bhilai and Korba) is summarized in Table 5. The highest Pb content in the surface soil of coal burning site, Korba city (over area ≈ 5000 km2) was observed, may be due to huge coal utilization (25). Similarly, high Pb- content in the soil and sludge of other industrial city: Bhilai and Raipur was measured (26). The presence of high Pb and other heavy metal contents in the Mandla city was reported (27). The most of soils were found to be associated with high heavy metal (i.e. Mn, Fe, Cu, Zn and As) contents, Table 5. Among them, Fe showed the highest fraction (1-16%) followed by Mn (3030 – 12820 mg kg-1). The higher content of As was observed in the soil of sites i.e. Korba, Mandla and Kaudikasa. The origin of As in Mandla and Kaudikasa was expected due to geogenic contamination unlikely to Korba city. The Pb contents in the soil and sludge of this region was found to be higher than reported in other parts of the World (28-31).
\n\t\t\t\tLocation | \n\t\t\t\t\t\t\tMetal, mg kg -1 | \n\t\t\t\t\t\t|||||||
Pb | \n\t\t\t\t\t\t\tAs | \n\t\t\t\t\t\t\tHg | \n\t\t\t\t\t\t\tFe, % | \n\t\t\t\t\t\t\tMn | \n\t\t\t\t\t\t\tCu | \n\t\t\t\t\t\t\tNi | \n\t\t\t\t\t\t\tZn | \n\t\t\t\t\t\t|
Raipur (n=5) | \n\t\t\t\t\t\t\t276 | \n\t\t\t\t\t\t\t15 | \n\t\t\t\t\t\t\t0.1 | \n\t\t\t\t\t\t\t16 | \n\t\t\t\t\t\t\t12820 | \n\t\t\t\t\t\t\t566 | \n\t\t\t\t\t\t\t60 | \n\t\t\t\t\t\t\t348 | \n\t\t\t\t\t\t
Bhilai (n=3) | \n\t\t\t\t\t\t\t545 | \n\t\t\t\t\t\t\t13 | \n\t\t\t\t\t\t\t4.3 | \n\t\t\t\t\t\t\t2.7 | \n\t\t\t\t\t\t\t1440 | \n\t\t\t\t\t\t\t1240 | \n\t\t\t\t\t\t\t110 | \n\t\t\t\t\t\t\t61 | \n\t\t\t\t\t\t
Korba (n=9) | \n\t\t\t\t\t\t\t1930 | \n\t\t\t\t\t\t\t45 | \n\t\t\t\t\t\t\t1.4 | \n\t\t\t\t\t\t\t21 | \n\t\t\t\t\t\t\t3400 | \n\t\t\t\t\t\t\t218 | \n\t\t\t\t\t\t\t42 | \n\t\t\t\t\t\t\t230 | \n\t\t\t\t\t\t
Mandala (n=3) | \n\t\t\t\t\t\t\t390 | \n\t\t\t\t\t\t\t53 | \n\t\t\t\t\t\t\t4.8 | \n\t\t\t\t\t\t\t1.0 | \n\t\t\t\t\t\t\t1830 | \n\t\t\t\t\t\t\t740 | \n\t\t\t\t\t\t\t670 | \n\t\t\t\t\t\t\t150 | \n\t\t\t\t\t\t
Kaudikasa (n=10) | \n\t\t\t\t\t\t\t25 | \n\t\t\t\t\t\t\t71 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | 49 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
Bhilai (n=8) | \n\t\t\t\t\t\t\t115 | \n\t\t\t\t\t\t\t17 | \n\t\t\t\t\t\t\t1.1 | \n\t\t\t\t\t\t\t16 | \n\t\t\t\t\t\t\t3030 | \n\t\t\t\t\t\t\t49 | \n\t\t\t\t\t\t\t28 | \n\t\t\t\t\t\t\t240 | \n\t\t\t\t\t\t
Lead and other heavy metals in surface soil and sludge.
The accumulation of Pb in the food grain, vegetables, spices, medicinal and wild species were investigated (26-27, 32-33). The Pb-content in 10 different rice (grown in 10 different fields) was ranged from 0.21 - 1.51 mg kg-1 with mean value of 0.64 mg kg-1, Figure 8. The Pb-content in the respective husk was ranged from 0.56 - 6.28 mg kg-1 with mean value of 1.5 mg kg-1. Among 10 rice tested, the high yield variety rice, IR-64 was found to be as good phytoextractant for Pb. The Pb-content in the leafy vegetables, medicinal, spices and wild plants was ranged from 4.6 – 54.3 mg kg-1, Figure 9. Among them, Methi was found to be as good phytoextractant. The various parts of medicinal plant: sweet Basil (
The permissible limits for Pb in the air, drinking water, soil and food reported are 0.10 – 0.30 µg m-³, 5 µg l-1, 300 µg kg-1 and 1.1 mg kg-1, respectively (43-46). The ambient air of the central India in the winter season was contaminated with the Pb at level of ≈1 µg m-³, being several folds higher than the permissible limit. The rain and runoff water of the industrial cities of central India are contaminated with several folds higher Pb than permissible limit of 5 µg l-1. The medicinal plants, species and leafy vegetables grown in the contaminated soil were found to be loaded with Pb beyond permissible limit of 1.1 mg kg-1. The humans and other animals in the industrial cities of the central India are exposed with Pb and other heavy metals via air, water, soil and food.
\n\t\t\tAccumulation of Pb in various rice.
Accumulation of Pb in spices and leaves.
Accumulation of Pb in Basil plant parts.
Accumulation of Pb in the wild plant parts.
The Pb-contamination of the central India is expected due to both geogenic and anthropogenic pollution. The coal burning is assumed as a major anthropogenic inventory for the Pb contamination in the environment. The various environmental compartments i.e. air, rain, runoff water, surface soil and sludge of the Industrial cities are contaminated with the Pb and other heavy metals at elevated levels. The leafy and medicinal plants phytoextract Pb and other heavy metals significantly, and expected one of the major entry path way route in human and other animals.
\n\t\tThe Karlsruhe University of Applied Sciences, Karlsruhe, Germany is greatly acknowledged for providing the printing charge. We are sincerely thankful to Department of Science & Technology (DST), Government of India, New Delhi for support of this work from the project grant. no. ES/48/ICRP/008/2002. One of the authors, K. S. Patel is also thankful to the Alexander von Humboldt Foundation, Bonn for granting support for presentation of this work in the WASET conference, Heidelberg, 2008.
\n\t\tConnected and Automated Vehicles (CAVs) are nowadays the area of extensive research and there are premises to suspect that the introduction CAVs may revolutionize the whole transportation area [1]. There is no lack of predictions stating that CAVs will solve many of the current problems experienced on roads today, such as congestion, traffic accidents and lost time [2].
Traffic state prediction and traffic control are two key modules in transportation systems with CAVs [3]. Traffic states such as flow, speed, congestion, etc., plays vital roles in traffic management, public service and traffic control [4]. By predicting the evolution of traffic state timely and accurately, decision-maker and traffic controller can make effective policy and control input to avoid traffic congestion ahead of time and thus ITS (Intelligent Transportation Systems), advanced traffic management systems and traveler information systems rely on real-time traffic state prediction. Traffic control can be divided into a decision-making module and a vehicle control module. The former is used to optimize the mobility, safety and energy consumption by using the vehicle trajectory prediction results to calculate vehicle platoon sizes, speed, flow, density, traffic merging, diverging flow and traffic signals, while the latter is used for vehicle path control, vehicle fleet control and steering wheel, throttle, brake, and other actuator control by using onboard units based on the control commands [3]. How to timely and accurately predict the future traffic state and deliver an effective traffic control strategy are fundamental issues in ITS.
Traffic state prediction approaches can be broadly divided into two parts: parametric and non-parametric approaches [5]. Parametric approaches utilize parametric models that capture all the information about its predictions within a finite set of parameters. The popular techniques in parametric approaches include ARIMA (Autoregressive Integrated Moving Average) [6, 7, 8, 9], linear regression [10] and Kalman Filter (KF) based method [11], which are linear models and able to have high accuracy with linear characteristics of traffic data. ARIMA model is based on the assumption that the future data will resemble the past and widely used in time series analysis, which can be made to be stationary by differencing. It can be specified three values that represent the order of autoregressive (
Non-parametric models such as DL (Deep Learning) outperform parametric models because of stochastic, indeterministic, non-linear and multidimensional characteristics of traffic data [5]. DL is a subset of machine learning (ML) which is based on the concept of deep neural network (DNN) and it has been widely used for data classification, natural language processing (NLP) and object recognition [5]. The most popular DL models used for traffic state prediction includes Convolution Neural Network (CNN) [12, 13, 14], Deep Belief Network (DBN) [15, 16], Recurrent Neural Network (RNN) [17, 18, 19] and Autoencoder (AE) [20] etc. CNN is useful for traffic prediction because of the two-dimensional characteristics of traffic data and its ability to extract the spatial feature. CNN is only connected to a smaller subset of input and thus decreases the computational complexity of the training process. DBN is a stacking of multiple RBMs (Restricted Boltzmann Machines), which can be used to estimate the probability distribution of the input traffic data. LSTM is the special type of RNN, which can capture the temporal feature of traffic data, and LSTM can overcome the gradient vanishing problem caused by the standard RNN.
Traffic control strategies can be generally divided into classical methods and learning-based methods. Classical methods develop traffic controller based on control theory or optimization-based techniques, which include dynamic traffic assignment based nonlinear controller [21], standard proportional-integral (PI) controller [22, 23], robust PI controller [24], model-based predictive control (MPC) [25, 26], linear quadratic controller [27], mixed-integer non-linear programming (MINLP) [28], multi-objective optimization based decision-making model [29]. Learning-based methods refer to the utilization of artificial intelligence technologies to achieve decision-making and control for CAVs, which can be further divided into three categories: statistic learning-based method, deep learning-based (DL) method and reinforcement learning-based (RL) method. The RL-based method is currently one of the most commonly used learning-based techniques for traffic control and decision-making because RL can solve complex control problems by using the Markov decision process (MDP) to describe the interaction states of agent and environment [4]. The most popular RL-based methods include Q-learning for adaptive traffic signal control [30, 31], multi-agent RL approaches [32, 33, 34, 35], Nash Q-learning strategy [36]. Many other RL-based approaches are also available in the literature. Q-learning based traffic signal control aims to minimize the average accumulated travel time by greedily selecting action at each iteration. Multi-agent RL approaches are more popularly used in network signal optimization and can be generally divided into centralized RL and decentralized RL, while the former considers the whole system as a single agent and the latter distributes the global control to each agent. Nash Q-learning strategy is a decentralized multi-agent RL strategy, which performs iterated updates based on assuming Nash equilibrium behavior over the current Q-values. It can be shown that traffic signal control using the Nash Q-learning strategy can converge to at least one Nash equilibrium for stationary control policies. However, Nash Q-learning is unable to achieve the Pareto Optimality without consideration of cooperation among different agents.
This chapter provides a comprehensive survey about state-of-the-art traffic state prediction and traffic control techniques. It is organized as follows: In Section 2, we firstly introduce the fundamental structure and main characteristics of two important DL models: CNN and LSTM (Long Short-Term Memory), as well as their advantages in traffic state prediction, then we introduce how to realize hybrid traffic state prediction by combining two models to achieve better accuracy. In Section 3, we detail RL fundamentals and introduce how it can be applied in traffic control and decision-making. We focus on multi-agent RL approaches. Pros and cons are discussed. Section 4 gives the summary of this chapter.
In this section, we first briefly overview the machine learning and deep learning concept. Then, we focus on introducing the architectures of two DL models: CNN and LSTM, which show good performance in processing high-dimensional and temporal correlated data. Finally, a hybrid model of CNN and LSTM is described and the research potential is about how to improve the prediction accuracy by incorporating spatio-temporal correlation.
ML approaches are broadly classified into two categories, i.e., Supervised Learning and Unsupervised Learning [5]. Supervised Learning requires input data to be clearly labeled. It involves a function
DL is a branch of ML which aims to construct a computational model with multiple processing layers to support high-level data abstraction. It can automatically extract the feature from data, without any human interference to explore hidden data relationships among different attributes of the dataset [37]. Concepts of DL are inspired by the thinking process of the human brain. Hence, the majority of DL architectures are using the framework of Artificial Neural Network (ANN), which consists of input, hidden and output layers with nonlinear computational elements (neurons and processing units). The network depth (the number of layers) can be adjusted according to the feature dimensions and complexity of the data. The number of neurons at the input layer is equal to the number of independent variables, while the number of neurons at the output layer is equal to the number of dependent variables, which can be single or multiple. Neurons of two successive layers are connected by weights which are updated while training the model. The neurons at each layer receive the output from the previous layer, which is generated by a weighted summation over inputs and then passed to an activation function (Figure 1).
(left) ANN with one input layer, two hidden layers and one output layer,
Let us take the four-layer ANN in Figure 2 for example. During the training process, the value of
CNN structure.
where
In this section, we examine two popular DL architectures: CNN and LSTM, which are used popularly for multidimensional and time sequential dataset. CNNs have been extensively applied in various fields, including traffic flow prediction [14, 40, 41], computer vision [42], Face Recognition [43], etc., while LSTMs are special kinds of RNNs, which are mainly applied in the area of temporal data processing, such as traffic state prediction [34, 44], speech processing [45] and NLP (Natural Language Processing) contexts [46], etc.
The significant difference between fully connected ANN and CNN is that CNN neurons are only connected to a smaller subset of input which decreases the total parameters in the network [47]. CNNs have the ability to extract important and distinctive features from multidimensional by making use of filtering operations. A commonly used type of CNNs, which is similar to multi-layer perception (MLP), consists of numerous convolution layers preceding pooling layers and fully connected layers. CNN structure is illustrated in Figure 2, where it consists of the input layer, convolution layer, pooling layer and fully connected layer. Convolution layer outputs higher abstraction of the feature. Each convolution layer uses several filters, which are designed to have a distinct set of weights. Filters used by the convolution layer have the smaller dimensions compared to the data size. In the training phase, filter weights are automatically determined according to an assigned task. The filters of each convolution layer are applied through the input layer by computing the sum of the product of input and filter, leading to a feature map of each filter. Each feature map detects a distinct high-level feature which is then processed by a pooling layer and a fully connected layer. ReLU activation function is applied to remove all negative values in the feature map.
The benefits of CNNs over other statistical learning methods and DL methods are listed followings [48]:
CNNs have the weight sharing feature, which reduces the number of trainable network parameters and in turn helps the network speed up the training process and avoid overfitting.
Concurrently learning the feature extraction layers and the classification layer causes the model output to be both highly organized and highly reliant on the extracted features.
Large-scale network implementation is much easier with CNN than with other neural networks.
CNN and other kinds of ANNs such has MLP are not designed for sequences and time series data because they do not have memory element. In such cases, RNN can deliver more accurate results. RNNs are widely used in traffic state prediction because traffic data has spatiotemporal characteristics, which cannot be captured by CNN or other kinds of ANNs. RNN structure is illustrated in Figure 3, where RNNs involve an internal memory element that memorizes the previous output. The current output
RNN structure.
where
LSTM is firstly proposed in [49] to overcome the gradient vanishing problems generated by other RNNs. A typical LSTM network consists of an input layer, a recursive hidden layer and an output layer. In the recursive hidden layer, each neuron is made up of four structures: a forget gate, an input gate, an output gate and a memory block. The state of the memory cell reflects the features of the input, while the three gates can read, update and delete features stored in the cell. The LSTM structure is illustrated in Figure 4.
LSTM structure.
The past information carried by the cell state
where
Generally, LSTM can address the vanishing gradient problem that makes network training difficult for a long-sequence temporal data. The long-term dependencies in the data can be learned to improve the prediction accuracy.
Although, CNN and LSTM have advantages in dealing with traffic data with spatiotemporal dependencies, due to the complex and non-linear models of traffic data, it is hard to predict accurate results by using a single model [5]. Some literature proposed that prediction accuracy can be improved by hybrid modeling such as combining CNN and LSTM [50, 51, 52, 53].
The spatial and temporal features can be fully extracted by hybrid models, where CNN in this model is used to capture spatial features of traffic data whereas LSTM is used to extract temporal features. Suppose that we have traffic state data of
Note that
There are mainly two hybridization manners: the first one is to extract spatio-temporal features by concatenating CNN and LSTM, that is, each column of
model to capture the temporal features; the second one is to parallelize CNN and LSTM modeling process by considering the extracted spatial and temporal features are of the same importance, that is, the same traffic state data is input into two models, the final prediction is obtained by passing the output of two models through a FC (Fully Connected) layer. The structure of the two hybridizations is illustrated as follows (Figure 5).
(left) concatenated hybrid model; (right) parallelized hybrid model.
For concatenated hybrid models, the real-time measured data matrix
The high-level spatial feature map output by the one-dimensional CNN can be expressed by
where
where
To extract the temporal features, the high-level spatial feature vector for single or multiple time instants will be selected for the input of each LSTM, which is denoted as
where
where
where
Concatenated hybrid models utilize a one-dimensional CNN to obtain a smaller range of spatial features, in addition, they do not contain a fully connected layer at the output of LSTM models, and thus concatenated hybrid models are with low learning complexity. However, the temporal features delivered by LSTM have a strong correlation with the spatial features output by CNN, which needs some special assumptions about the raw data.
For parallelized hybrid models, the historical data matrix
where
A LSTM is utilized to obtain the high-level temporal feature map. The output of the
By posing a fully connected layer to the output of the
where
In parallelized hybrid models, the spatial and temporal feature maps are considered to be of the same importance, and thus are extracted independently. The fully connected layer merges the output of CNN and LSTM without any special assumptions about the high-level spatial and temporal features.
Traffic state has strong periodic features because people get used to repeating some similar or same behaviors on the same time period of different days or the same day of different weeks, e.g., most people routinely go to work in the morning and go home in the evening during the peak hour [53]; most people routinely go for shopping on weekends rather than weekdays, etc. The periodic features can be used as supplementary information to predict the future traffic state. For the short-term traffic state prediction, the real-time data only contains the data before the prediction time instant, but the historical data on previous days or weeks contain the full data of that period, that means, traffic state information after the inspected time instant on previous days or weeks can be utilized to get the prediction about that on the inspected time instant. Suppose we use parallelized hybrid models, the complete prediction structure should contain CNN and LSTM for the real-time data, CNN and bidirectional LSTM for the historical data, which are connected by using a fully connected layer.
The bidirectional LSTM is composed of two independent forward and backward LSTMs, whose inputs are the time series before and after the inspected time instant. The final prediction of bidirectional LSTM is obtained by concatenating the forward and backward LSTMs. The structure of bidirectional LSTM is depicted in Figure 6.
Bidirectional LSTM structure.
Suppose that additionally, we have historical traffic state data
where
Using Eq. (12), the output of the
Then, the
An accurate and efficient traffic state prediction can provide continuous and precise traffic status and vehicle states based on past information. How to utilize the current and predicted traffic states to make a real-time optimum decision is the main task of the traffic signal control module in ITS. The objectives of traffic signal control include minimizing the average waiting time at multiple intersections, reducing traffic congestion and maximizing network capacity. There exist real-time linear feedback control approaches and MPC (Model-based Predictive Control) that are specifically designed for traffic signal control systems to achieve the targets. The drawback of linear feedback control techniques that have been tried is that the system should always remain in the linear region at all times for the controller. Although, MPC has some advantages such as imposing constraints, the main shortcoming is it needs an accurate dynamic model, which is difficult to be obtained for traffic control systems. Data-driven approaches such as DRL (Deep Reinforcement Learning) based traffic control techniques are widely presented for ITS in recent years because RL can solve complex control problems and deep learning helps to approximate highly nonlinear functions from the complex datasets. In this section, we firstly briefly review the fundamental principles of RL. Then, we focus on multi-agent DRL based traffic signal control techniques such as decentralized multi-agent advantage actor-critic, which can converge to the local optimum and overcome the scalability issue by considering the non-stationarity of MDP transition caused by policy update of the neighborhood; and Nash Q-learning strategy, which can converge to Nash equilibrium by only considering the competition among agents.
Reinforcement Learning (RL) is a promising data-driven approach for decision-making and control in complex dynamic systems. RL methodology formally comes from a Markov Decision Process (MDP), which is a general mathematical framework sequential decision-making algorithms, and consists of five elements [54]:
A set of states
A set of actions
Transition probability
Reward function
The discount factor
RL aims to maximize a numerically defined reward by interacting with the environment to learn how to behave in an environment without any prior knowledge by learning. In traffic signal control systems, RL is used to find the best control policy
where
RL generally can be classified into model-based RL which knows or learns the transition model from state
where
The stochasticity in Eq. (21) comes from the control policy
where
The learning rate
Value-based RL does not work well for continuous control problems with infinite-dimensional action space or high-dimensional problems because it is difficult to explore all the states in a large and continuous space and store them in a table. In such a case, policy-based RL can provide better solutions than value-based RL. By treating the policy
The optimum policy parameters
Policy-based RL tries to select the optimum actions by using the gradient of the objective function with respect to
where
where
where
Actor-critic RL combines the characteristics of policy-based methods and value-based methods, in which an actor is used to control the agent’s behaviors based on policy, critic evaluates the taken action based on value function. From Eq. (27), the objective function can be rewritten as
The loss function for policy and value updating can be respectively defined as
where
Recall that
where
A real traffic network consists of multiple signalized intersections, each of which can be considered as an agent. The states for the
A set of states space of the
A set of action space of the
Transition probability
Control policy of the
The instantaneous reward function of the
The centralized multi-agent RL considers the multi-agent systems as a single-agent system with joint state space
Suppose we have a multi-intersection traffic network, which can be modeled as
where the global states and policies can be communicated from all other agents in the system as well as the neighborhood
We assume Eq. (33) has continuous state-action space and thus multi-agent A2C can be applied to search the optimum policy parameter. From Eq. (31), the Advantage value for the
where
If each agent follows Eqs. (35) and (36) in a decentralized manner, a local optimum policy
In practice, the information exchange among multiple intersections may not be synchronized and communication delay should be considered, which causes policy changing within the same episode and thus leads to non-stationarity. There is some research that try to stabilize convergence and relieve non-stationarity. Tesauro proposes a “Hyper-Q” learning, in which values of mixed strategies rather than base actions are learned and other agents’ strategies are estimated from observed actions via Bayesian inference [55]. Foerster et al. include low-dimensional fingerprints, such as
To relieve non-stationarity, the key is to keep policies from neighboring agents fixed within one episode. We can apply a DNN network to approximate the local policy
Then, the loss function for policy updating can be rewritten by
Even if the policies from the neighbors are fixed and are considered to be additional input, it is still difficult to approximate
where
Then, the cumulative discounted reward can be obtained by
and the local return and Advantage value
and Eq. (38) can be rewritten as
The loss function for value updating can be expressed as
The decreolized MA2C can overcome the scalability issue and achieve local optimum (Pareto Optimality). How to achieve the global optimum using a decentralized approach when the global reward function is non-convex in the future research direction.
Compared to decentralized MA2C, Nash Q-learning does not consider cooperation among agents and thus it has lower computational complexity but can only achieve the Nash equilibrium. Nash Q-learning aims to find the optimal global control policy
where
Eqs. (45) and (46) show that at each iteration
In traffic signal control application, the state space
By conducting a simulation on SUMO for a two-intersection case, we can observe in Figure 7 that the centralized DQN outperform the centralized Q-learning in terms of reward value (Average Waiting Time/s) and convergence rate (the Number of Iterations). When the number of agents is small (two, in this case), by using the centralized methods, the average waiting time can converge to the local optimum, which is more optimal than the Nash equilibrium delivered by Nash Q learning. However, the convergence rate of Nash Q learning is higher than that of centralized methods.
Comparison of different multi-agent RL methods for traffic signal control.
In this chapter, we introduced deep learning-based traffic state prediction technique, which can provide accurate future information for traffic control and decision making. The traffic state data depicts a strong correlation in the spatial and temporal domain, which can be utilized by applying CNN and LSTM techniques to improve the prediction accuracy. CNN technique is used to capture high-level spatial features while LSTM can provide excellent performance in dealing with time-sequential data by extracting high-level temporal features. We firstly reviewed the fundamentals of deep learning and presented the architecture of CNN and LSTM. Then, we introduced how to combine these two models to form concatenated hybrid models and parallelized hybrid models. Finally, we proposed bidirectional LSTM models to enhance prediction performance by learning additional high-level temporal features from the historical data in previous days.
Furthermore, we introduced the decentralized multi-agent advantage Actor-Critic technique and Nash Q learning for traffic signal control applications. We firstly briefly review the fundamental principles of RL. Then, we focus on multi-agent DRL-based traffic signal control techniques such as decentralized multi-agent advantage actor-critic, which can converge to the local optimum and overcome the scalability issue by considering the non-stationarity of MDP transition caused by policy update of the neighborhood.
The main contribution of this chapter can be summarized as followings:
We reviewed the state-of-the-art technique in traffic state prediction and traffic control strategies, and provide readers with a clear framework for understanding how to apply deep learning models to traffic state prediction and how to deal with multi-agent traffic control by using RL strategies.
We proposed the hybrid prediction models, which can utilize CNN and LSTM to capture the spatio-temporal feature of traffic data.
We proposed a multi-agent deep RL (MARL) strategy, which conducts in a decentralized manner and considers the cooperation among agents and thus can overcome the scalability issue and achieve local optimum.
We compared the centralized RL Q-learning, DQN to the Nash Q-learning strategy in terms of the reward value and convergence rate.
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