Table 1. Physical Properties of PCE, Water and Soil at 25oC (Degrees Celsius)(Brewster & Annan, 1994), (Von Hippel, 1953), (Hipp, 1974), (Weedon & Rappaport, 1997), (Rappaport et al., 1999).
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"IntechOpen Maintains",originalUrl:"/media/original/113"}},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{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"}]},book:{item:{type:"book",id:"6560",leadTitle:null,fullTitle:"Plant Competition in Cropping Systems",title:"Plant Competition in Cropping Systems",subtitle:null,reviewType:"peer-reviewed",abstract:"In the coming years, farmers will face difficult challenges throughout the world in terms of climate change, water scarcity, and environmental issues caused by conventional agricultural technologies. Effective management of natural resources can be encouraged by orienting the common agricultural practices towards the functional biodiversity concept in designing and implementing sustainable and eco-friendly cropping systems. In the framework of polycrop science, this book provides basic principles and several case studies of polycrop utilization in various regions of the world as a method of functional biodiversity amplification through species associations that maximize the productivity per unit of land area, suppress the growth and development of weeds, and reduce the amount of harmful pests and insects.",isbn:"978-1-78984-342-2",printIsbn:"978-1-78984-341-5",pdfIsbn:"978-1-83881-553-0",doi:"10.5772/intechopen.71291",price:119,priceEur:129,priceUsd:155,slug:"plant-competition-in-cropping-systems",numberOfPages:102,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"664e0a97f4494932f6c0461f9a6e7bd6",bookSignature:"Daniel Dunea",publishedDate:"October 31st 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6560.jpg",numberOfDownloads:6596,numberOfWosCitations:2,numberOfCrossrefCitations:9,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:11,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:22,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 14th 2017",dateEndSecondStepPublish:"December 5th 2017",dateEndThirdStepPublish:"February 3rd 2018",dateEndFourthStepPublish:"April 24th 2018",dateEndFifthStepPublish:"June 23rd 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"180202",title:"Associate Prof.",name:"Daniel",middleName:null,surname:"Dunea",slug:"daniel-dunea",fullName:"Daniel Dunea",profilePictureURL:"https://mts.intechopen.com/storage/users/180202/images/system/180202.png",biography:"Daniel Dunea is a professor in the Department of Environmental Engineering, Valahia University of Targoviste, Romania. He received his Habilitation degree and Ph.D. from the University of Agronomical Sciences and Veterinary Medicine of Bucharest, Romania. He completed a Marie Curie Fellowship at Wageningen University & Research (WUR), Netherlands. In 2020, he was appointed a member of the prestigious National Council for Attestation of University Degrees, Diplomas and Certificates – Romanian Ministry of Education. He has published several books and more than fifty scientific articles indexed in the Web of Science (WoS). He has also reviewed manuscripts for more than thirty-five WoS journals. His main research interests are environmental monitoring and assessment and associated aspects.",institutionString:"Valahia University of Targoviste",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Valahia University of Targoviste",institutionURL:null,country:{name:"Romania"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"307",title:"Agroecology",slug:"agroecology"}],chapters:[{id:"63445",title:"Introductory Chapter: Plant Competition in Multiple Cropping Systems beyond Conceptual Knowledge",doi:"10.5772/intechopen.81076",slug:"introductory-chapter-plant-competition-in-multiple-cropping-systems-beyond-conceptual-knowledge",totalDownloads:737,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Daniel Dunea",downloadPdfUrl:"/chapter/pdf-download/63445",previewPdfUrl:"/chapter/pdf-preview/63445",authors:[{id:"180202",title:"Associate Prof.",name:"Daniel",surname:"Dunea",slug:"daniel-dunea",fullName:"Daniel Dunea"}],corrections:null},{id:"61656",title:"The Ecological Role of Biodiversity for Crop Protection",doi:"10.5772/intechopen.78228",slug:"the-ecological-role-of-biodiversity-for-crop-protection",totalDownloads:1138,totalCrossrefCites:3,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Agricultural system is a complex community sheltering different ecological units. The units of this complex structure are in balance with each other showing fluctuations to ensure effective regulations from time to time depending on the abundance of both undesirable and beneficial organisms. This balance is a major case for biological activity playing an important role to maintain biological diversity. Once this natural balance is impaired due to abiotic and biotic factors occurring in biosystems, the economic and environmental problems appear becoming significant for the economical dimension in agriculture. The most important components showing deficiencies in systemically agroecostructure problems result from soil fertility, pest and disease management. Large interactions, which are concomitantly persisting with biological processes, are on plant and animal biodiversity, which have been affected by miss-treatments in crop protection and plant nutrition. Hence, food-web and biodiversity are indirectly seriously damaged in nature, such as recycling of nutrients and changes of microclimate. In this chapter, we have discussed the major effects of crop protection on biodiversity in detail regarding the persistence of biodiversity that needs to be mediated, considering the preserving of ecological properties and sustainable maintenance of biological integrity in agroecosystems.",signatures:"Ömür Baysal and Ragıp Soner Silme",downloadPdfUrl:"/chapter/pdf-download/61656",previewPdfUrl:"/chapter/pdf-preview/61656",authors:[{id:"175718",title:"Distinguished Prof.",name:"Ömür",surname:"Baysal",slug:"omur-baysal",fullName:"Ömür Baysal"},{id:"238542",title:"Dr.",name:"Ragıp Soner",surname:"Silme",slug:"ragip-soner-silme",fullName:"Ragıp Soner Silme"}],corrections:null},{id:"61059",title:"Understanding Species Traits and Biodiversity Indices to Solve Problems Associated with Legume Persistence in Cropping Systems",doi:"10.5772/intechopen.76523",slug:"understanding-species-traits-and-biodiversity-indices-to-solve-problems-associated-with-legume-persi",totalDownloads:971,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Shading and competition for mineral nutrients by grass impair legume functions and production in mixed cropping systems. Sustained stress from competition and adverse environments contribute to shortened legume life spans in such cropping systems. This creates negative consequences to forage productivity. There are opportunities to solve the challenge of legume persistence by understanding species traits and plant community dynamics that foster coexistence and complementary resource use. Together with species’ unique ability to tolerate adverse soil factors such as water stress, acidity and salinity, self-seeding, and shade tolerance are positive traits among legume species that grow in mixed crops. In communities, converging leaf and shoot conformations as well as asynchrony in dry matter distribution among species can avert negative effects of species competition. While seeding ratios can influence forage production and quality, management including harvest frequency and optimizing phosphorus (P) and potassium (K) fertilizers have crucial roles in perpetuating legume growth and function in mixtures with grass. Some facts on species competition for light, water, and nutrient resources; shade avoidance; and biodiversity mechanisms are highlighted in this chapter.",signatures:"M. Anowarul Islam and Dennis Shibonje Ashilenje",downloadPdfUrl:"/chapter/pdf-download/61059",previewPdfUrl:"/chapter/pdf-preview/61059",authors:[{id:"220180",title:"Dr.",name:"M. Anowarul",surname:"Islam",slug:"m.-anowarul-islam",fullName:"M. Anowarul Islam"},{id:"220183",title:"Mr.",name:"Dennis",surname:"Ashilenje",slug:"dennis-ashilenje",fullName:"Dennis Ashilenje"}],corrections:null},{id:"61957",title:"Competitive Ability of Rice Cultivars in the Era of Weed Resistance",doi:"10.5772/intechopen.78342",slug:"competitive-ability-of-rice-cultivars-in-the-era-of-weed-resistance",totalDownloads:1280,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Almost all plants are negatively affected by neighboring plants, which impose some degree of competition within the population, depending mainly on the quantity and quality of natural resources available in the environment. In rice cultivation, the occurrence of a high and diverse infestation of weeds results in high competition levels among the species. In addition, the high and growing number of cases about herbicide-resistant weeds, especially the widespread distribution of Imidazolinone-resistant weedy-rice and the high infestation of weeds belonging to the Echinochloa genus, has increased the competition levels within rice cultivation due to the lack of control. Therefore, the inclusion of rice cultivars with greater competitive ability represents a promising tool for weed management, since new cases of resistance to herbicides are often reported and alternative control strategies are scarce. The use of rice cultivars with a greater ability to suppress weeds can alleviate the competitive effect of these species, giving priority to the crop for the use of environmental resources due to the faster occupation of the ecological niches. Thus, this chapter aims to explore the competitive ability of rice cultivars against troublesome weed species, accounting for the role of their morphological and physiological traits as a function of environment-friendly crop practices.",signatures:"Fábio Schreiber, Ananda Scherner, André Andres, Germani\nConcenço and Francisco Goulart",downloadPdfUrl:"/chapter/pdf-download/61957",previewPdfUrl:"/chapter/pdf-preview/61957",authors:[{id:"160203",title:"Dr.",name:"André",surname:"Andres",slug:"andre-andres",fullName:"André Andres"},{id:"191276",title:"Dr.",name:"Germani",surname:"Concenço",slug:"germani-concenco",fullName:"Germani Concenço"},{id:"200031",title:"Dr.",name:"Fábio",surname:"Schreiber",slug:"fabio-schreiber",fullName:"Fábio Schreiber"},{id:"214193",title:"Dr.",name:"Ananda",surname:"Scherner",slug:"ananda-scherner",fullName:"Ananda Scherner"},{id:"247906",title:"Mr.",name:"Francisco",surname:"Goulart",slug:"francisco-goulart",fullName:"Francisco Goulart"}],corrections:null},{id:"63235",title:"Enhancing Productivity in Rice-Based Cropping Systems",doi:"10.5772/intechopen.76904",slug:"enhancing-productivity-in-rice-based-cropping-systems",totalDownloads:1444,totalCrossrefCites:5,totalDimensionsCites:5,hasAltmetrics:0,abstract:"In India, the rice-based cropping system is a major food production system with rice as the first food crop. The cereal-based cropping system is low-yielding and highly nutrient exhaustive resulting in the declining of soil fertility. Summer/pre kharif fallowing leaves on the land fallow for entire season and production of the cropping system is declined. Hence, crops that can improve the fertility status should be included in the cropping system. Development of short duration thermal insensitive rice varieties has encouraged multiple cropping involving a wide range of crops. Diversification of rice-based cropping systems with inclusion of pulses/legumes and oilseeds in summer fallows is one of the options for horizontal expansion, as they are known to improve soil organic matter through biological nitrogen fixation, root exudates, leaf shedding and higher below ground biomass. The strategy for higher yields in the cropping system should be formulated using the combined application of organics, inorganics and biofertilizers coupled with the inclusion of crops in summer fallows for sustainable yields and preservation of soil health.",signatures:"Uppu Sai Sravan and Koti Venkata Ramana Murthy",downloadPdfUrl:"/chapter/pdf-download/63235",previewPdfUrl:"/chapter/pdf-preview/63235",authors:[{id:"238178",title:"Ph.D. Student",name:"Sai",surname:"Sravan",slug:"sai-sravan",fullName:"Sai Sravan"}],corrections:null},{id:"61908",title:"Land Utilization Pattern in the Indonesian Forest: Cassava Cultivation in an Agroforestal System",doi:"10.5772/intechopen.76928",slug:"land-utilization-pattern-in-the-indonesian-forest-cassava-cultivation-in-an-agroforestal-system",totalDownloads:1027,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The potential forestland for agroforestry implementation in Indonesia is teak forest (Tectona grandis). The teak forest is less dense during the dry season, allowing sunlight to enter through the trees gap to the ground under the canopy. Therefore, some people use that condition as “palawija” farming land (palawija/phaladwija, in Java-Indonesia represents the type of non-rice agricultural crops). It is done to prevent the growth of weeds that can disturb the teak growth. The phenomenon of land utilization under the stands (PLDT) is an alternative in accessing forestland use by the community, a part of intercropping location. Theoretically, if the implementation was correct, it could be an effort to restore the forest ecological function. The pattern of the PLDT model on teak forests needs to select correct plants according to temporal dynamics, namely the season (dry or rainy) and the plants age. Land use representation could be seen from the cultivation pattern and crops variety that is cultivated under the forest stands at three research locations called Development Areas wilayah pengembangan (WP). The palawija crops that exist on all three WP were cassava (Manihot esculenta Crantz).",signatures:"Eva Banowati and Satya Budi Nugraha",downloadPdfUrl:"/chapter/pdf-download/61908",previewPdfUrl:"/chapter/pdf-preview/61908",authors:[{id:"238963",title:"Dr.",name:"Eva",surname:"Banowati",slug:"eva-banowati",fullName:"Eva Banowati"},{id:"238984",title:"Dr.",name:"Satya",surname:"Nugraha",slug:"satya-nugraha",fullName:"Satya Nugraha"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"11074",title:"Water Quality",subtitle:"Factors and Impacts",isOpenForSubmission:!1,hash:"c3f3c2405260fed102e4ef982cff54c6",slug:"water-quality-factors-and-impacts",bookSignature:"Daniel Dunea",coverURL:"https://cdn.intechopen.com/books/images_new/11074.jpg",editedByType:"Edited by",editors:[{id:"180202",title:"Associate Prof.",name:"Daniel",surname:"Dunea",slug:"daniel-dunea",fullName:"Daniel Dunea"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9345",title:"Sustainable Crop Production",subtitle:null,isOpenForSubmission:!1,hash:"5135c48a58f18229b288f2c690257bcb",slug:"sustainable-crop-production",bookSignature:"Mirza Hasanuzzaman, Marcelo Carvalho Minhoto Teixeira Filho, Masayuki Fujita and Thiago Assis Rodrigues Nogueira",coverURL:"https://cdn.intechopen.com/books/images_new/9345.jpg",editedByType:"Edited by",editors:[{id:"76477",title:"Prof.",name:"Mirza",surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4566",title:"Agroecology",subtitle:null,isOpenForSubmission:!1,hash:"9e35a4ff7bee4ab82eab2c6b3f441789",slug:"agroecology",bookSignature:"Vytautas Pilipavičius",coverURL:"https://cdn.intechopen.com/books/images_new/4566.jpg",editedByType:"Edited by",editors:[{id:"169359",title:"Dr.",name:"Vytautas",surname:"Pilipavicius",slug:"vytautas-pilipavicius",fullName:"Vytautas Pilipavicius"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6952",title:"Irrigation in Agroecosystems",subtitle:null,isOpenForSubmission:!1,hash:"1afe3f365612ea9b4f35942c69792f63",slug:"irrigation-in-agroecosystems",bookSignature:"Gabrijel Ondrašek",coverURL:"https://cdn.intechopen.com/books/images_new/6952.jpg",editedByType:"Edited by",editors:[{id:"46939",title:"Prof.",name:"Gabrijel",surname:"Ondrasek",slug:"gabrijel-ondrasek",fullName:"Gabrijel Ondrasek"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6485",title:"Sustainability of Agroecosystems",subtitle:null,isOpenForSubmission:!1,hash:"4ed7b8c6bce44bfaddb83c0365793742",slug:"sustainability-of-agroecosystems",bookSignature:"Alexandre Bosco de Oliveira",coverURL:"https://cdn.intechopen.com/books/images_new/6485.jpg",editedByType:"Edited by",editors:[{id:"77880",title:"Dr.",name:"Alexandre",surname:"De Oliveira",slug:"alexandre-de-oliveira",fullName:"Alexandre De Oliveira"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6301",title:"Vegetation",subtitle:null,isOpenForSubmission:!1,hash:"5c1b7f22f2f926f8d59ea56f2fe84c6f",slug:"vegetation",bookSignature:"Allan Sebata",coverURL:"https://cdn.intechopen.com/books/images_new/6301.jpg",editedByType:"Edited by",editors:[{id:"143409",title:"Dr.",name:"Allan",surname:"Sebata",slug:"allan-sebata",fullName:"Allan Sebata"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10499",title:"Next-Generation Greenhouses for Food Security",subtitle:null,isOpenForSubmission:!1,hash:"456f82c97eafad5734cd36c48e167781",slug:"next-generation-greenhouses-for-food-security",bookSignature:"Redmond R. 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Soil is a complex, potentially heterogeneous, lossy, and dispersive medium. Modeling the propagation and scattering of electromagnetic (EM) waves in soil is, hence, more challenging than in air or in other less complex media. This chapter will explain fundamentals of the numerical modeling of EM wave propagation and scattering in soil through solving Maxwell’s equations using a finite difference time domain (FDTD) method. The chapter will explain how: (i) the lossy and dispersive soil medium (in both dry and fully water-saturated conditions), (ii) a fourth phase (anomaly), (iii) two different types of transmitting antennae (a monopole and a dipole), and (iv) required absorbing boundary conditions can numerically be modeled. This is described through two examples that simulate the detection of DNAPL (dense nonaqueous-phase liquid) contamination in soil using Cross-well radar (CWR). CWR —otherwise known as cross-borehole GPR (ground penetrating radar)—modality was selected to eliminate the need for simulation of the roughness of the soil-air interface. The two examples demonstrate the scattering effect of a dielectric anomaly (representing a DNAPL pool) on the EM wave propagation through soil.
The objective behind selecting these two examples is twofold: (i) explanation of the details and challenges of numerical modeling of EM wave propagation and scattering through soil for an actual problem (in this case, DNAPL detection), and (ii) demonstration of the feasibility of using EM waves for this actual detection problem.
In addition, the results of an experimental simulation of Example 1 (the case with the monopole antenna) will be analyzed and discussed. The results of the corresponding numerically simulated example will then be compared and validated against the above-mentioned experimental results. A conclusion section will close the chapter.
Before explaining the numerical modeling and its challenges, some background about DNAPLs and their detection technologies, including the CWR method, is explained in the following section.
Cross-well radar (CWR), otherwise known as cross-borehole ground-penetrating radar (cross-borehole GPR) is a minimally invasive method that uses high frequency electromagnetic (EM) waves transmitted and received by antennae in the subsurface to image objects of contrasting dielectric properties. In order to assess the feasibility of using CWR for detection of dense nonaqueous-phase liquid (DNAPL) pools in deeper layers, this technique should be numerically simulated and evaluated. CWR is more appropriate for deep investigations. For near-surface sensing such as bridge-deck health monitoring, methods such as GPR are more appropriate (Belli et al., 2009 and 2009a). GPR studies require addressing the scattering due to the rough soil-air interface in the forward model as well as through the inversion process (Firoozabadi et al., 2007).This chapter describes a numerical modeling approach to Maxwell\'s equations using a finite difference time domain (FDTD) solution with both monopole and dipole antennae to simulate the scattering and propagation of EM waves in DNAPL-contaminated media. An FDTD code, originally developed for detection of mines using two-dimensional (2D) surface-reflection ground-penetrating radar (GPR) through non-dispersive media, was revised and upgraded for three-dimensional (3D) cross-borehole wave propagation in heterogeneous soils. The three dimensional FDTD code was enhanced to accommodate dispersive media and was used to model EM wave propagation in heterogeneous soils. This chapter describes the effect of the radiation patterns of two different antennae on propagation and scattering of EM waves through the soil subsurface and its potential for detection of DNAPL pools. In order to evaluate the feasibility of using the CWR method to detect DNAPL pools, illustrative examples with and without the presence of the DNAPL pool were analyzed. The results show considerable diagnostic potential to detect contaminated zones with DNAPLs using EM waves through CWR.
DNAPLs are separate-phase hydrocarbon liquids denser than water, such as chlorinated solvents (tetrachloroethene (PCE) and trichloroethylene (TCE)), wood preservatives, coal tar wastes, and pesticides. DNAPLs may not usually be found as a free phase in soil cores or accumulated in monitoring wells. Based on this lack of observable evidence of DNAPL pools, investigators may conclude that no DNAPL is present, when it may be present in substantial quantities at residual saturation as large as 70% to 80% of the total porosity (ITRC, 2000).
There are varieties of invasive techniques to detect DNAPLs such as direct push probe techniques (e.g., direct soil sampling or indirect sampling such as negative ion sensor) and use of in situ tracers (e.g., PITT, or partitioning interwell tracer test), excavating test pits, and groundwater profiling. Most invasive techniques just provide point-sources of information and may help the DNAPL pool to spread through the substantial number of required boreholes and excavated trenches, while noninvasive geophysical techniques noninvasively or minimally invasively detect DNAPLs. They can use different types of waves (e.g., electromagnetic, acoustic, etc.) to indirectly reconstruct images to characterize or detect anomalies as DNAPLs within heterogeneous media. These methods are minimally invasive techniques that discriminate the contrast between local physical properties of the background and target to produce images of the subsurface.
Cross-well P-wave transmission at 90 kHz was used in a sand pack before and after introducing NAPLs by Geller et al. (2000). The results indicated that small NAPL saturations may be more easily detected with amplitude than with travel time data, but the relationships between the amplitude changes and NAPL saturation may be more complex than those for velocity.
Cross-hole complex resistivity was also applied to a contaminated vadose zone by Grimm and Olhoeft (2004) to predict the general distribution of DNAPLs at parts per thousand concentrations, specifically widespread near-surface contamination and in the vadose zone immediately underneath the source.
Complex resistivity (CR) is a technique that so far has shown a potential in detecting DNAPL pools (Blackhawk Geoservices Inc., 2008).
Smith-Rose (1993& 1935) is among the earliest scientists who studied the dielectric permittivity of different soils. Some physical properties of a typical DNAPL (i.e., PCE) are compared with those of different soils, water, and air in Table 1.
As seen, dielectric permittivity and effective (= DC + alternating field) electrical conductivity of PCE are lower than those of water and relatively similar to air or some dry soils. Thus, a PCE-contaminated region of the saturated zone should provide a contrast with a similar magnitude of the unsaturated-saturated zone interface (water table). The values of dielectric permittivity for water and soil are dispersive (depend on frequency), but most DNAPLs are nonpolar molecules with minimal frequency dependence and therefore with almost no dispersive characteristics. In frequencies greater than 10 MHz and less than 1 GHz, the dielectric permittivity is controlled by the polarization of individual water molecules and is therefore dependent on moisture content (Binley et al., 2001). Dielectric permittivity in the lower MHz range of frequency (< 10 MHz) depends on particle shape and mineralogy, electrolyte type and concentration, particle orientation, and soil electrolyte interaction (Rinaldi & Francisca, 1999). In the upper MHz range of frequency (10 to 1000 MHz), the real part of dielectric permittivity is affected by the polar contribution of bound and free water molecules (Hoekstra & Doyle, 1971; Hoekstra & Delaney, 1974; Selig & Mansukhani, 1975; Dobson et al., 1985; Hallikainen et al., 1985; and Arulanandan, 1964). Sachs and Spiegler (1964) presented a model for dielectric permittivity of soil mixtures at different frequencies. This model is based on an equivalent circuit for conductive particles in electrolytes and fitted in the dielectric plane by adjustment of three parameters. Arulanandan and Smith (1973) explained some aspects of dielectric dispersion based on the Sachs and Spiegler (1964) model in a frequency range from 106 to 108 Hz. Another model that explains the dispersion of the soil relative dielectric permittivity, otherwise known as dielectric constant, was presented by Thevanayagham (1995), which considers the effect of particle orientation relative to the electric field. Weedon and Rappaport (1997) and Rappaport et al. (1999) presented a model to predict the dispersive nature of the dielectric permittivity and electrical conductivity of soils, using a rational Z-transform approximation function of the conductivity. This technique uses the values of dielectric permittivity and electrical conductivity of the bulk mixture, and does not deal with the components and their volumetric content. It is basically a technique to convert the bulk values for the matrix to a form applicable to the FDTD method to take the dispersive nature of the resultant mixture into account.
Material | Density ρ (g/cm3) | Real Part of Dielectric Constant* (εr) | **** Effective Electrical Conductivity σe (1/Ω.m) | ||
f = 100 MHz | f = 1.5 GHz | f = 100 MHz | f = 1.5 GHz | ||
PCE | 1.62 | 2.28 | 2.28 | 2.63 × 10-6 | 1.52 × 10-4 |
Water** | 1.00 | 78 | 77.14 | 2.2 × 10-3 | 0.509 |
Dry Sand | 1.30 | 2.55 | 2.55 | 1.8 × 10-4 | 2.1 × 10-3 |
Soil*** | 1.60 | 6.912 | 6.50 | 0.1329 | 0.1 |
Table 1. Physical Properties of PCE, Water and Soil at 25oC (Degrees Celsius)(Brewster & Annan, 1994), (Von Hippel, 1953), (Hipp, 1974), (Weedon & Rappaport, 1997), (Rappaport et al., 1999).
Dielectric constant is also known as relative dielectric permittivity** Pure water *** 20% moist Bosnian clay loam**** Effective electrical conductivity (
The loss (attenuation) in soil is a function of a variety of factors such as soil type and mineralogy, moisture content, electrical conductivity, and frequency. Higher frequency EM waves attenuate faster. In other words, soil acts as a low-pass filter. However, higher frequency EM waves provide higher image resolution. There is a tradeoff between the penetration depth and image resolution. Therefore, feasibility of the CWR to detect an average size DNAPL pool using proper frequency range is the first step to detection, which is the main goal of this chapter.
Soil, water, and DNAPL mixed at DNAPL saturations of 0% to 50% (i.e., water saturation of 50% to 100% below water table; i.e., no air) have been evaluated by Ajo-Franklin et al. (2004) to have a dielectric constant of 9 (50% water saturation soil with 50% DNAPL saturation) to 24 (fully (100%) water-saturated soil with 0% DNAPL saturation). These values are different from the dielectric constant of pure DNAPL (
There are some expected limitations to using CWR to detect DNAPLs in soil and groundwater such as: (a) use of CWR to detect DNAPLS may not perform as well in dry or partially saturated zones as in water saturated zones, due to the weaker contrast in electromagnetic properties of DNAPLs and dry soils compared to the one between DNAPLs and water saturated soils, (b) the concentration (or contaminant saturation) must be fairly high, which makes the detection of DNAPLs using CWR often limited to identifying DNAPL pool sources (high DNAPL saturation) and not plumes, and (c) the spacing between the wells strongly influences the effectiveness of CWR (as the separation between transmitting and receiving antennae increases, the radar wave amplitude attenuates, which creates greater difficulty in distinguishing the wave from background noises) (ITRC, 2000).
Therefore, in this chapter a simple case of DNAPL dominantly replacing water in the pores (DNAPL saturation of 80% to 100%) is simulated numerically. Based on Bruggeman-Hanai-Sen (BHS) model (Sen et al., 1981), due to low dielectric constant of soil grains (≈ 5, (Ajo-Franklin et al., 2004)), the bulk dielectric constant of the soil mixture within the DNAPL pool (close to 100% DNAPL saturation) values between 2.3 and 5. Hence, for comparison, acrylic was selected as the dielectric object of
The U.S. Geological Survey used 500-MHz surface GPR among other methods to monitor the location and migration of the subsequent plume (Sneddon et al., 2000).
Bradford and Wu (2007) tested 3D multi-fold GPR on a small scale controlled DNAPL release to detect contaminated soil with demonstrated success.
While GPR is the least invasive radar-based method, it is not a practical one for deep investigations. Cross-Well Radar (CWR), otherwise known as cross-borehole GPR, may be a more effective method for deep contaminant detection. CWR is a method that uses EM wave transmission measurement across borehole antennae as opposed to reflection measurements used in surface-reflection imaging methods, such as GPR. CWR overcomes some of depth limitations of surface-reflection imaging GPR. CWR uses antennae that are lowered into sampling wells with cables. Radar waves are emitted from a transmitting antenna in one well and propagate through the ground to a receiving antenna in a second well. The subsurface geology and pore fluids scatter, refract, or reflect the waves. DNAPL pools also provide dielectric permittivity and electrical conductivity contrasts within relative moist soils, which in turn, cause refractions or reflections on EM waves. These scatterings and reflections make DNAPLs amenable to detection by radar. Other than the magnitude of the scattering field, phase, and hence, travel time can also be used for other techniques such as travel time tomography to reconstruct images of the background medium and anomalies within the medium.
Success of radar-based methods as well as any other detection technique depends on the strength of the contrast in properties of the background and scatterer (e.g., DNAPLs, moisture). Dielectric permittivity and electrical conductivity are the main parameters controlling such differences for radar-based techniques.
CWR relies on a one-way travel time whereas the surface-reflection imaging GPR relies on the 2-way travel time. Therefore, the effective antenna separation of CWR can be twice as far as the penetration depth of surface-reflection GPR, theoretically. GPR antennae are designed to be relatively impedance-matched to the ground, and the reflection coefficient at the air-soil interface is close to unity leading to the potential for strong internal reflection. However, there is still the disadvantage of reflections at the air-soil interface, which reduces the amount of received waves reflected by the underground objects. The performance of detection using CWR is highly dependent on seasonal conditions, in particular soil moisture (Daniels et al., 1992). Soil mineral composition and physicochemical properties also influence radar signals. For example, clays tend to attenuate these signals and therefore may limit the skin depth and effectiveness of the method (Anderson & Peltola, 1996). Considering these limiting factors, feasibility of the technique for DNAPL detection should be evaluated by simulating implementation of ultra-wideband CWR. Further understanding of the behavior of 3D EM waves in soil is necessary for the future implementation and feasibility evaluation of CWR for detecting DNAPLs. To achieve this goal, the detection technique using monopole and dipole antennae is modeled numerically in the time domain using an FDTD technique. A critical issue addressed in this research, by direct simulation of the antennae, is the wave interaction at the antenna/soil interface. The technique models the transmitting antenna but measures the electrical field at all grid points on any desired cross-sectional or depth slice (instead of modeling receiving antennae). This is equivalent to having hundreds of receiving antennae in soil, which is not practical. In practical techniques such as cross-well tomography, few antennae are installed and used alternatively as transmitters or receivers to collect data in a multiple-depth, multiple-location manner, and the outcome is used for inversion and image processing techniques (the authors are working on different aspects of cross-well tomography and results will be published in the future). Obviously, the simulation technique presented in this chapter is used only for preliminary evaluation of the feasibility of detecting DNAPL pools using CWR. The outcome can also be used as a forward modeling for future inversion techniques.
Besides, another goal of this chapter is to evaluate the feasibility of DNAPL detection and to investigate if there are points with strong signature by the object on the scattered field, not to discuss inverse modeling. Therefore, receiving antennae are not modeled. The results model no noise. Nevertheless, comparison between the values of the scattered field (by the target) with those of the incident field will provide helpful information that can lead to the critical noise/signal ratio, even without applying any noise. This is discussed in more detail in the following sections. Experimental works are being conducted to evaluate typical levels of noise, compare them to the levels derived from this technique, then apply the noise to the simulation and reevaluate the feasibility (Farid et al., 2006).
Finite Difference Time Domain (FDTD) is a relatively powerful and very popular method because of its simplicity and despite its instability problems. Therefore, a 3D FDTD code was developed and used to simulate the antenna performance, coupling, and interactions in soils. The FDTD technique was originally introduced by Yee (1966) and is based on time and spatial discretization of Maxwell’s equations to obtain solutions for the EM field in the time domain. The technique is numerically implemented by continuously sampling the electromagnetic field over the wave propagation medium discretized into a grid. The differential form of Maxwell’s equations in the time domain is as follows (Grant & Philips, 1990; Sheriff, 1989; and Balanis, 1989).
along with the constitutive relations,
where E is the electric field density, H is the magnetic field intensity, D is the electric displacement or electric flux density, B is the magnetic flux density,
interlocking FDTD cell (
The most advantageous characteristic of the FDTD method is its simplicity. Solving Maxwell’s equations using FDTD is a simple iterative procedure. However, there are several limitations to the numerical implementation of the FDTD technique through the above-mentioned difference equations. These limitations have been and are subject of research. Some of these challenging limitations impose restrictions on the grid size and time step increments that affect stability and accuracy of the method (Kunz & Luebbers, 1993).
One fundamental restriction requires the longest side of the grid cell to be much shorter than the shortest wavelength of the wave within the cell. A very common restriction assumed in practice is / 10, where is the shortest appreciable wavelength in the excitation signal. This constraint is imposed by both sampling limitation and grid dispersion errors (Kosmas, 2006).
The second restriction develops from the scale and geometry of the problem. The method uses a uniform grid to model small antennae along with a large soil medium with or without contrasting objects. Accordingly, the geometry imposes challenging limitations, especially in computation cost. For example, in the case of a DNAPL contaminated site, dimensions of the background in horizontal directions may be of the order of tens of meters by tens of meters in the XY-plane, while the antenna thickness may be of the order of millimeters to centimeters. This requires solving a large uniform grid with a small grid-cell size in the X and Y directions. At the same time, a small vertical (Z-direction) cell size is required to account for thin DNAPL pools. One way to solve this problem is by using a non-uniform grid, which adds more difficulties to the task of satisfying stability conditions (e.g., Courant condition).
The third limitation is the time-step restriction required to satisfy the Courant condition. The Courant condition restricts time increments to a range in which waves do not travel too far in each time increment, or:
where N is the grid dimension, x
In the case of N-dimensional isotropic cells, Equation (9) can be simplified to
Soil is a complex medium to model because of its heterogeneous, lossy, and dispersive nature. Accurate computation of the behavior of soil over a wideband of frequency requires either several individual frequency domain calculations or a robust deconvolution of E(t) from D(t) in the time domain (Rappaport et al., 1999). The convolutional approach to modeling dispersion in soil approximates the frequency domain dispersive complex dielectric constant with rational functions of jω and multiplies the constitutive relations by the denominator. Then, the results are inverse-Fourier-transformed. This model is called the Debye or Lorentz model (Kashiwa & Fukai, 1990; and Gandhi, 1993). Weedon and Rappaport (1997) and Rappaport and Winton (1997) simplified the problem by modeling the conductivity as a simple rational function of Z-transform. Modeling conductivity in terms of the Z-transform variable (Z
Using this function, both real and imaginary components of conductivity depend on the sampling interval Δt and the coefficients of the rational Z-transform function (a
As in simulation of any diffusion application (e.g., heat transfer or water flow through porous media), wave propagation through infinite media is practically modeled in a finite grid. To model the infinity of the flow or wave propagation through the required infinite media by the finite number of grid cells, appropriate boundary conditions are required. The lattice termination absorbing boundary condition used in the FDTD code was based on the second-order one-way wave propagation equation of Mur (1981) and adjusted for lossy soils by Talbot and Rappaport (2000) using the rational function approximation of Equation (11).
There are some factors that categorize the excitation, such as the source geometry, excitation signal type, and hard or soft source of propagation.
Hard and Soft Sources: The code uses soft sources for all different antenna types. A hard source specifies the total field at the excitation point. These types of sources are usually avoided as they cause undesirable reflections at the source point. Alternatively, a soft source specifies the additional field supplied above the existing background field at the source point. The soft source can be specified at points on a radiating aperture, while hard sources are used for current sources flowing along metal structures. In 2D cases, soft sources propagate well, but extensive testing of 3D FDTD cases has shown difficulties involved in propagation from a soft source unless the physical body of the antennae is modeled. This paper uses soft sources for both monopole and dipole antenna cases of the following sections.
Excitation Signal: A cosine-modulated Gaussian signal (Equation (13)) was used to excite both antennae because of its simplicity and frequency content
where E
To model the DNAPL detection and evaluate its feasibility, two different antenna types were modeled, a monopole and a dipole. Some fundamental parameters and facts about these simulations are explained in the following.
The monopole antenna is simply a coaxial cable, schematically shown in Fig. 2(a) with its shield removed at a specific length from the tip (referred to as extended dielectric length). In a simple monopole antenna, the most efficient radiation occurs when the exposed dielectric and center conductor length is ¼ of the excitation wavelength. For the range of frequency of interest of this and other ongoing and further research (400-2200 MHz), and based on the dielectric permittivity of the dielectric part of the antennae (≈ 2.1), this length should be between 2.35 cm and 12.94 cm. Different types of antennae have different radiation patterns. The radiation pattern is a visual way of representing how an antenna distributes energy into the surrounding space. Different methods (parameters) can be used to demonstrate the wave propagation through space. One popular method is to use power as the key parameter. This method visualizes how and in which direction power is concentrated by the antenna to propagate away. This is possible by drawing contours of equal power. Other parameters, such as field magnitude or phase, can be used for visualization purposes. Contours of these parameters are drawn to visualize radiation patterns. A monopole antenna radiates waves in all different directions.
A dipole antenna consists of two straight metallic or dielectric parts connected at the center to a feed line. A typical dipole antenna is shown in Fig. 2(b). This antenna type constitutes the main RF radiating and receiving element in various sophisticated types. Dipoles are inherently balanced antennae due to bilateral symmetry. The best dipole length is usually ½ wavelengths.
As mentioned before, two different antennae are simulated in the code to model the problem and evaluate the feasibility of the use of the CWR method to detect dielectric objects in soil. The first case models a small-scale modeled monopole antenna as a soft source of the wave within a fully water-saturated sandy soil medium (degree of water-saturation = S
Antennae: (a) Monopole antenna derived from a coaxial cable by removing a part of outer conductor, (b) a UHF–Half–Wave Dipole (Wikipedia, 2011).
The pilot-scale simulation of the SoilBED facility (Farid et al., 2006) is explained in this section. In the first case, a 5 mm-thick monopole antenna is modeled within a fully saturated sandy soil background. The size of the medium under study was selected to satisfy limitations of the FDTD code as well as the experiment. Table 2(a) summarizes details about the geometry and grid size of the soil medium. The simulation is driven by a cosine modulated Gaussian time pulse at a reasonably high frequency (1.5 GHz). To accommodate the simulation of the dispersive soil and stability of the FDTD code at this frequency, the time increment Δt = 2 psec was used. The dispersive properties of the soil for this choice of center frequency and time-step are modeled with the Z-transform function coefficient set
To relate the results to the field site, the model can be scaled up in size while scaling down the frequency. To evaluate the feasibility of the DNAPL detection method using monopole antennae, wave propagation through the background soil and scattered EM wave propagation by a DNAPL pool were modeled and analyzed. The geometry details of the monopole transmitting antenna modeled in this case are tabulated in Table 2(c). The drive signal excites the top of the simulated coaxial cable feeding the monopole antenna in a conventional radial field pattern. The electric field components on all grid points of different cross-sectional and depth slices of the medium were computed and then visualized using MATLAB.
First, the background medium was analyzed. Then, a rectangular acrylic plate as a representative of a DNAPL pool was modeled within the soil medium. Fig. 3 schematically shows the simulated geometry (the monopole antenna, the DNAPL pool, and the soil medium). Details of the geometry of the DNAPL pool scatterer are listed in Table 2(d).
SoilBED, antenna, observation slice and rectangular DNAPL pool (3 cm × 3 cm × 1 cm).
Geometry | Size |
Simulated grid | 149 × 149 × 29 |
Grid cell size | 0.2 cm × 0.2 cm × 1 cm |
Entire grid size | 29.6 cm × 29.6 cm × 28 cm |
Soil thickness | 21 cm |
Air thickness | 7 cm |
Details of the simulated medium
Parameter | Value |
20.9 | |
a1 | -0.8985 |
b0 | -34.3627 |
b1 | 68.7577 |
b2 | -34.3945 |
Table 2.b. Soil properties, used for the simulation of the fully saturated sandy soil at f = 1.5 GHz, t = 2 psec, and 17% gravimetric moisture content (w)
Due to solving the problem at Δt = 2 psec, the FDTD code is very sensitive, and all digits are necessary to satisfy the stability conditions
Antenna Details | Size |
Antenna depth | 120 mm |
Perfectly conducting core wire thickness | 1 mm |
Extended dielectric length | 20 mm |
Extended dielectric thickness | 3 mm |
Perfectly conducting outer conductor (shield) thickness | 3 mm |
Frequency | 1.5 GHz |
Gaussian width | 0.667 nsec |
Gaussian peak | 5 nsec |
Table 2.c. Geometry details of the simulated monopole antenna
The dielectric constant and effective electrical conductivity of the extended dielectric of the antenna are respectively assumed to be 2.1 and zero (Ω-1).
DNAPL Pool Geometry | Size |
Horizontal cross-section | 3 cm × 3 cm × 1 cm |
Depth | 9 cm |
Clear separation from the antenna | 3.8 cm |
Coordinate of the pool center* | 6 cm, 0 cm, -2 cm |
Table 2.d. Details of the DNAPL pool scatterer
With respect to the center of the grid
To evaluate the wave propagation, the following observation slices were selected. Different components of electric field were computed and visualized on these slices.
A cross-sectional (horizontal: XY-plane) slice, cutting through the antenna and DNAPL pool at the depth of 9 cm. Z and X components of the electric field (E
Up to this point, only the three vector components of the electric field were visualized. Now, the power is depicted. The intensity of a rapidly varying field is often displayed on a dB scale, enabling the visualization of small amplitude levels. This scale is given by 20 log10|E / E
A depth (vertical: XZ-plane) slice, passing through the antenna and DNAPL pool. This slice (XZ-plane) was chosen because the YZ-plane does not intersect the DNAPL pool. Due to symmetry, E
Electric field simulated on the cross-sectional slice (XY-plane) at t = 3.6 nsec (the extent of the DNAPL pool is marked by a yellow box): Z-component of the electric field: a) Incident, b) Total, and c) Scattered; and X-component of the electric field: d) Incident, e) Total, and f) Scattered.
Electric field [Sign(Ez,Total - Ez,Incident)] × 20 log10(|E| or |Ex
This case was initially analyzed without DNAPL contamination (incident field or background) and then with the DNAPL pool (total field). The scattered field by the DNAPL pool target can be computed by subtracting the two previous fields. Three figures are shown for each slice and for each electric field component and for: (i) “incident” (i.e., background, no target), (ii) “total” = background + DNAPL pool target as the scatterer, and (iii) “scattered” (i.e., signature of the target). All results shown in Fig. 4 are captured at t = 3.6 nsec. As seen, incident results of Figs. 4(a) and 4(d) are symmetric, while the total field results shown in Figs. 4(b) and 5(e) are not symmetric. The resulting scattered field information shown in Figs. 4(c) and 4(f) is asymmetric as well.
The incident, total, and scattered (target signature) fields are shown in Fig. 5. The monopole antenna was modeled as a Z-polarized antenna. Therefore, the Z-component of the electric field is the major component, but the scattered field by the DNAPL pool is also readily visible on the X and Y component plots. Since E
This figure shows that there is a considerable magnitude and travel time difference between the total and incident fields received at a receiver located right above the DNAPL pool. The strong magnitude difference (more than 100%) and time difference (around 100 psec) between the two signals illustrate the potential of the cross-borehole GPR method to detect DNAPL pools. The early arrival of the total field is caused by the increase in the velocity of EM waves through the DNAPL pool due to its lower dielectric permittivity compared to the saturated soil. The increase in the magnitude of the total field is, on the other hand, caused by lower loss through the DNAPL pool due to its lower electrical conductivity. This illustrates a great potential for DNAPL detection using CWR in saturated soils, if the thickness and size of the pool is a reasonable fraction of the wavelength.
Z-component of total and incident electric fields due to the monopole antenna, received at a receiver located right above the DNAPL pool.
The above-mentioned small size monopole case can be scaled up to a more realistic size contaminated site. However, scaling up the results may cause some problems that do not allow a simple and direct generalization from small numerical models to real size contaminated sites. For example, in a non-dispersive medium, linear enlargement of the size can be simply interpreted to a linear increase in the wavelength and decrease in the frequency. However, in a dispersive medium, any change in the frequency causes variations in the dielectric properties of the medium. This change in the dielectric constant causes variations in the wave velocity, which in turn adds nonlinearity to the scaling process from the simulated medium up to the real size.
Therefore, to evaluate the scaling issues in a dispersive medium and study the effect of different radiation patterns of different antennae, another case with a more realistic size of soil medium surrounding a dipole antenna was modeled. The dipole is also larger than the monopole, since the smallest object to be modeled (the antenna) controls the uniform grid size in X and Y directions and size limitations of the FDTD code. The details about the grid size and the geometry of the soil medium for this case are tabulated in Table 3(a).
To decrease the computation cost, a much larger grid cell (3 cm in X and Y directions, and 5 cm in Z direction) was modeled (Table 3(a)). To satisfy sampling limitations (grid size < λ / 10) and study the scaling effect, the wavelength should be larger. Therefore, the frequency was selected to be 100 MHz (lower than 1.5 GHz in Case A). To satisfy the Courant’s condition for the new grid size, the time increment was increased to Δt = 50 psec.
Geometry | Size |
Simulated grid | 149 × 149 × 69 |
Grid cell size | 3 cm × 3 cm × 5 cm |
Entire grid size | 444 cm × 444 cm × 340 cm |
Soil thickness | 305 cm |
Air thickness | 35 cm |
Details of the simulated medium
The soil medium is exactly the same fully water-saturated sandy soil modeled in the previous case with 17% gravimetric moisture content. However, dielectric properties of the dispersive soil at the different frequency and time increments (f = 100 MHz, and Δt = 50 psec) are different. Therefore, the dielectric constant and coefficients (ε
Parameter | Value |
14.9251 | |
a1 | -0.8985 |
b0 | 1.04948 |
b1 | -1.9896 |
b2 | 0.94093 |
Table 3.b. Soil parameters, used for the simulation of the fully saturated sandy soil at f = 100 MHz, Δt = 50 psec, and 17% gravimetric moisture content
Due to solving the problem at Δt = 50 psec, the FDTD code is very sensitive, and all digits are necessary to satisfy the stability conditions.
modeling and because the wall of the PVC is very thin compared to the wavelength (780 mm) of the EM wave. The dipole antenna is Z-polarized and the excitation signal is a 100 MHz cosine-modulated Gaussian pulse, progressively delayed along the antenna in the Z-direction (i.e., points along the Z-directed dipole are excited with a progressive phase delay proportionate to the traveling time of the current fed through the midpoint and along the dielectric portion of the dipole). Table 3(c) summarizes the details about the structure of the dipole antenna.
Antenna Details | Size |
Antenna depth | 1800 mm |
Borehole diameter | 240 mm |
Perfectly conducting core wire thickness | 22 mm |
Extended dielectric length | 500 mm |
Extended dielectric thickness | 64 mm |
Perfectly conducting outer conductor (shield) thickness | 43 mm |
Depth of water-filled borehole | 500 mm |
Frequency | 100 MHz |
Gaussian width | 10 nsec |
Gaussian peak | 75 nsec |
Table 3.c. Details of the simulated dipole antenna
The dielectric constant and effective electrical conductivity of the extended dielectric are respectively assumed to be 2.1 and zero (Ω-1).
First, the wave propagation through the soil background was analyzed. Then, a rectangular DNAPL pool was modeled within the soil medium. Fig. 7 schematically shows the geometry of this DNAPL pool. The details about the geometry of the DNAPL pool scatterer are listed in Table 3(d).
DNAPL Pool Geometry | Size |
Horizontal area | 45 cm × 45 cm × 15 cm |
Depth | 90 cm |
Clear distance to the antenna | 22.5 cm |
Coordinate of the pool center* | 90 cm, 0 cm, 45 cm |
Table 3.d. Details about the DNAPL pool scatterer
With respect to the center of the grid
Similar to Case A, the transmitting antenna is modeled in the code, but rather than modeling receiving antennae, the three different components of the electric and magnetic fields are computed at all grid points on the following depth and cross-sectional slices.
A cross-sectional (horizontal: XY-plane) slice, cutting through the antenna and DNAPL pool at the depth of 90 cm. Z and X components of the electric field (E
A depth (vertical: XZ-plane) slice, passing through the antenna and DNAPL pool. The magnitude of the power, derived from both E
Schematic representation of the borehole dipole antenna geometry and DNAPL pool (45 × 45 cm × 15 cm).
Electric field simulated on the cross-sectional slice (XY-plane) at t = 90 nsec (the extent of the DNAPL pool is marked by a yellow box): Z-component of the electric field: a) Incident, b) Total, and c) Scattered X-component of the electric field: d) Incident, e) Total, and f) Scattered.
Electric field, [Sign(E
As before, for each slice and each electric field component, three figures are shown: (i) incident (background) field, (ii) total field, and (iii) scattered field. As seen in Figs. 8(a) and 8(d), background results are symmetric. The total fields of Figs. 8(b) and 8(e) and the scattered field shown in Figs. 8(c) and 8(f) are asymmetric. The interesting and encouraging point is the visibility of the DNAPL pool over the entire medium within the total and scattered fields, even on the far side of the pool from the antenna. This predicts that dipole antenna boreholes of the CWR method can be drilled far from DNAPL-contaminated zones to reduce the risk of further vertically downward penetration of DNAPLs associated with drilling through contaminated zones. This appears to be more valid at higher degrees of water-saturation. Again, this potential can also be demonstrated in a different form as shown in Fig. 9.
Previously, in the case of the monopole transmitter, the received total and incident signals were computed at a receiver located right above the DNAPL pool. Now, the two are computed for a receiver located 175 cm above the pool to examine the possibility of minimizing the destructive effect of placing the receiving antenna too close to the pool. This figure again demonstrates a strong magnitude and travel time difference between the total and incident fields received at the receiver located far above the DNAPL pool. The strong magnitude difference (around 40%) and time difference (around 2.5 nsec.) between the two signals, once again, embraces the potential of the use of the cross-borehole GPR method to detect DNAPL pools. As in the monopole case, the early arrival of the total field and its higher magnitude can be respectively justified by the higher velocity of EM waves through the DNAPL pool due to its lower dielectric permittivity (relative to the water-saturated soil) and the lower loss due to the pool’s lower electrical conductivity. The dry soil case has been studied (it does not fit in the extent of this chapter) and proved to have weaker scattered signals, but still strong enough to have the potential to detect DNAPL pools using relatively widely spaced antennae.
As expected for the radiation pattern of dipoles, most of the energy is transmitted perpendicularly to the antenna through the mid-part of the antenna into the soil. The modeled dipole antenna is Z-polarized, and thus the Z-component of the electric field is the major component. The monopole antenna behaves similarly to the dipole antenna, the only difference being the strong signature of the DNAPL pool on the Z-component as well as X and Y components in the case of the dipole antennae. This wider spread perturbation due to the clutter promises more potential detection key-points for the dipole antenna installation. It is noteworthy that the perturbation due to the object on the X and Y components of the electric field of both monopole and dipole antennae appears more to the sides of the contaminated zone and perpendicular to the line connecting the center of the contaminated zone and the antennae. The perturbation on the Ez component in the monopole antenna case is distributed throughout the contaminated zone, while for the dipole one, this perturbation spreads to the far side across the contaminated zone as well.
In this section, the numerically simulated monopole case is compared with experimentation for validation. The experimental setup (for more information refer to Farid et al., 2006) uses two PVC-cased ferrite-bead-jacketed monopole antennae connected to a vector network analyzer (Agilent 8714ES), and frequency-response measurements were collected for a homogeneous water-saturated sandy soil background. Fig. 11 shows a schematic of the experiment.
Z-component of total and incident electric fields due to the dipole antenna, received at a receiver located 175 cm above the DNAPL pool.
Pulse traveling through transmitter, soil and then receiver
The frequency-response measurements are collected across the frequency range of 0.4 to 2.2 GHz. The frequency-response measurements are, then, transformed to the time domain using an inverse fast Fourier transform (IFFT) and an assumption of a narrow-width, wideband Gaussian pulse as the transmitted signal. Both the experiment and the FDTD model use the same Gaussian pulse source. Due to the frequency range used in the experimentation (0.4 GHz to 2.2 GHz), the width of the Gaussian signal should not exceed a maximum of: W
X- and Y-components of the radial excitation at the end of the feed cable into the top of the transmitting antenna (E
The FDTD-simulated received signal at the bottom of the receiving antenna is shown in Fig. 13(a). As seen, this received signal is distorted and does not resemble the Gaussian transmitted one. Therefore, its peak is not easily distinguishable, since the received signal is modulated and noisy. To resolve this issue, the received signal should first be demodulated and then low-pass-filtered. The demodulation frequency can be found by observing the received signal in the frequency domain (computed via a fast Fourier transform). This demodulation frequency is observed to be dependent on the separation between the transmitting and receiving antennae. A MATLAB code was prepared to automatically observe the received signals in the frequency domain, find the proper demodulation frequencies, and find the proper low-pass filter to filter the noise. The processed received signal is shown in Fig. 13(b).
The travel time of the FDTD model can be simply calculated as the difference between the peak times of the received (Fig. 13(b)) and transmitted (Fig. 12) signals.
To transform the experimental frequency-response to the travel-time, the transmitted signal is fast Fourier transformed to the frequency domain and multiplied by the frequency-response (Fig. 14) to find the received signal at the receiver in the frequency domain. Then, the result is transformed back to the time domain using an inverse fast Fourier transform. The result (received signal in the time domain) is shown in Fig. 15(a).
This signal does not resemble the transmitted Gaussian signal. Therefore, it needs to be processed (demodulated and low-pass-filtered). The processed received signal is shown in Fig. 15(b). The demodulation frequency and filter design vary with the distance between the transmitting and receiving antennae, which is automatically calculated using the above-mentioned MATLAB code.
Received signal (E
The travel time of the experimental model can be simply calculated as the difference between the peak times of the received (Fig. 15(b)) and transmitted (Fig. 12) signals.
Since the vector network analyzer is calibrated at the end of the cables (the connection points to the monopole antennae), the experimental travel-time is measured between these two points. On the other hand, the FDTD travel-time (t
Experimental frequency-response in water-saturated background soil
cable and the bottom of the receiving antenna. Therefore, it needs to be adjusted for this difference.
Up to this point, the FDTD travel-time (t
The travel time computed from the forward model is (4500 + 900 - 1000) × 2 psec = 8.8 nsec, which closely agrees with the one indirectly computed from the experimentally collected frequency-response data: (5700 - 1000) × 1.87 psec = 8.6 nsec. The difference is due to the slight, potential discrepancy between the dielectric constant assigned to the forward model (used from the results of another work by the authors (Zhan et al., 2007)) and the real values of the experimentation.
The intensities of the unprocessed received signals from the FDTD simulation (Fig. 13(a)) and experimentation (Fig. 15(a)) agree relatively well, but not perfectly. The reason is the potential slight discrepancy between the electrical conductivity assigned to the FDTD model compared to the actual one of the experiment. However, due to the difference between the necessary processing methods (different filters), the intensity of the processed received signals for the FDTD simulation (Fig. 13(b)) and the one of the experiment (Fig. 15(b)) do not agree as closely.
This comparison consists of the incident field for the homogeneous background soil. The comparison for the total and scattered fields at the presence of any anomalies (e.g., dielectric objects) will be conducted in the future.
Received signal (E
A finite difference time domain (FDTD) model was developed for monopole and dipole antennae. Then, the scattering due to dielectric materials (to simulate DNAPL pools) in soils was modeled and analyzed. Results of the two simulated cases using the FDTD model demonstrate strong perturbation by the DNAPL pool on the electric field in the fully water-saturated sandy soil. In the case of the monopole antenna, the DNAPL pool target is more visible on the X and Y components of the electric field compared to the major component Z. The perturbation on the intensity of the electric field (|E|) transmitted by the monopole antenna is not as strongly visible as in the dipole case. In the dipole case, X and Y components are those parallel to likely hydraulic-conductivity contrast planes (e.g., usually horizontal clay lenses within a thick sand layer), which are potential locations to accumulate DNAPLs.
Different components of the electric field can selectively be collected using receiving antennae with different polarizations from the polarization of the transmitting antenna (e.g., a horizontally-polarized receiving monopole antenna and a vertically-polarized transmitting monopole antenna). Therefore, designing the receiving antenna alignment and polarization to selectively collect electric field components parallel to a possible DNAPL pool may help to compensate for a stronger perturbation on the minor components (X and Y) of the electric field emitted from a Z-polarized monopole antenna. These minor components should be of a high enough signal to noise ratio.
In the case of the dipole antenna, all three components of the electric field in the fully water-saturated soil have almost equal detection potential. In both of the above cases, there is a strong dielectric contrast between the DNAPL pool and the water-saturated soil. However, different radiation patterns of the dipole antenna compared to the monopole antenna may make the dipole antenna more desirable for DNAPL detection.
Field problems can be scaled down in size along with scaling up the frequency in non-dispersive soils to achieve the proper geometry and frequency for simulation purposes. This linear scaling of frequency and size may not work as well for dispersive soils, since frequency-dependent dielectric properties of dispersive soils add nonlinearity to the scaling problem. Other conclusions follow.
Images provided by such simulations show the field distribution that exists throughout the subsurface (i.e., similar to filling the entire volume with receiver antennae), but the field can only be observed practically by placing a reasonable number of receiving antennae at key underground positions with the appropriate polarization. This research can be used to find the radiation patterns of different antenna types and the interaction of the radiated field with soil heterogeneities, which leads to a better understanding of subsurface wave behavior at these key positions and aids the selection of optimum antenna patterns to cover these key positions.
While the depth of contamination is a problem for surface-reflection methods (e.g., GPR), there are no theoretical depth limitations for CWR, except practical drilling limitations and cost. The separation limitations between transmitting and receiving antennae used for CWR still exist. However, CWR has the advantage of using a one-way traveling path (transmission), unlike the two-way traveling path of surface-reflection GPR. In addition, the strong reflecting air-soil interface in the surface-reflection GPR technique is eliminated in the CWR technique and replaced with a better-controlled coupling between the borehole antennae and surrounding soil.
The perturbation due to the DNAPL target is stronger for the greater dielectric permittivity contrast between DNAPL pools and highly moist soil, as opposed to DNAPL plumes with low DNAPL saturation and dryer soils.
The signal to noise ratio of the scattered field by DNAPL pools should be high enough for measurements. As seen in the figures, the scattered field is comparable to the incident field. Therefore, if the signal to noise ratio of the incident field is high enough for measurement, the scattered field will probably have a large enough signal to noise ratio to be measurable as well.
The results of this forward model with monopole and dipole antennae show that the field perturbation (scattered = total - incident) for relatively large DNAPL pools at high enough DNAPL saturation, is of the same order of magnitude as the incident signal. This proves DNAPL detection using CWR in water-saturated soils feasible. The simulation tool can also be used as a forward model to develop an inverse scheme for DNAPL imaging.
Armed with the background data as well as the radiation patterns of different antennae (via simulations like those in this chapter), the existence of DNAPL pools can be confirmed with efficient inverse models and judicious placement of receiving antennae (i.e., pattern of antenna installation) where stronger perturbation and reception by receiving antennae are expected.
CWR may be a feasible and reasonable method to monitor DNAPL pools in a suitable environment. This most suitable environment is a medium consisting of a low-loss, low-heterogeneity porous material. In other media, it is more difficult to distinguish DNAPL accumulation from geologic variations, which are more complicated due to heterogeneity. Nevertheless, soil heterogeneity may not pose a crucial problem under water-saturated conditions since different soils behave similarly at relatively high degrees of water-saturation and high frequencies (the case is different for low frequencies). Monitoring DNAPL movement may well be possible or easier in an even less saturated heterogeneous environment because of the static nature of stratigraphic events and the dynamic nature of DNAPL flow. Several features of DNAPL pools may help to distinguish them from stratigraphic events, such as their irregular shapes with sharp lateral boundaries.
Finally, the FDTD model was compared for the incident field due to the monopole case in a homogeneous water-saturated sandy soil background with the experimental results. The reasonable agreement between both the travel time and intensity of the unprocessed, simulated and experimental results validates the FDTD model. The comparison and validation for the total and scattered fields at the presence of any anomalies (e.g., dielectric objects) need to be studied in the future.
This research was supported in part by the Gordon Center for Subsurface Sensing and Imaging Systems (CenSSIS), under the Engineering Research Centers Program of the National Science Foundation (NSF: Award Number EEC-9986821).
The authors would like to express gratitude for financial and scientific support provided by the Gordon CenSSIS and NSF.
The oceans act as a natural sink for carbon dioxide and other greenhouse gases. However, anthropogenic activities have severely polluted the marine environment in the past few decades. Pollutants including plastic, oil, toxic chemicals, radioactive waste, and domestic and industrial sewage can be found in marine waters. Marine pollution is also caused by the discharge of sewage into rivers and excessive nutrients entering marine waters from agricultural fertilizers and pesticides [1]. These pollutants have adverse impacts on marine ecosystem including but not limited to sensitive coral reefs, mangroves, and aquaculture [2]. Therefore, in addition to reducing pollutant flow into oceans, it is essential to map and monitor marine pollutants to ensure a sustainable marine ecosystem.
Scientists and researchers have been working on detailed ocean monitoring for a sustainable blue economy. A variety of sensing systems are now available for ocean monitoring including autonomous underwater vehicles (AUVs), profiling floats, gliders, drifters, volunteer measurements from ships, and sensing nodes with cable networks [3]. These approaches to marine monitoring usually measure temperature, conductivity, pH, salinity, dissolved oxygen, fluorescence due to chlorophyll, turbidity, and color dissolved organic matter (CDOM). The most common approach for marine pollution measurements is to use conventional method of collecting in situ water samples using boats/ships from different depths of water with water samplers. The water samples are analyzed in the laboratory to determine the physical and chemical properties of the water. Such methods are accurate but time-consuming and geographically constrained and require trained professionals and laboratory analysis. However, real-time or near real-time measurements of marine pollutants and toxins across a range of spatial scales are necessary for monitoring and managing the environmental impacts and understanding the processes governing their spatial distribution [3].
To overcome these problems, remote sensing technology provides spatially synoptic and near real-time measurements that can be effectively used to detect, map, and track many pollutants such as oil and chemical spills, algal blooms, and high suspended solid concentrations. Aerial and satellite remote sensing has been demonstrated as an effective tool in detecting and mapping pollutant spills and for providing useful input data for oil spill models, to track pollutants through space and time [4, 5, 6]. An added advantage of remote sensing is that it provides information from remote areas. However, existing remote sensing technology still has some limitations, such as estimating pollutants over the vertical dimension of the water column.
The initial premise of watercolor remote sensing was to determine optical water quality variables such as chlorophyll-a (Chl-a) concentration, diffuse attenuation coefficient, and water-leaving radiance spectra [7]. The optical properties of water depend on many factors, e.g., suspended organic and inorganic particles and dissolved substances. There have been many successful applications of using remote sensing sensors for water color monitoring. The coastal zone color scanner (CZCS), having a spatial resolution of 825 m for six spectral bands from 443 to 750 nm, was the earliest satellite sensor designed and launched in 1978 specially to study ocean color. The sea-viewing wide field-of-view sensor (SeaWiFS) was the successor to CZCS with a spatial resolution of 1.1 km for eight spectral bands from 402 to 885 nm. Currently, many satellite sensors provide ocean color data for marine monitoring such as the moderate resolution imaging spectroradiometer (MODIS), the geostationary ocean color imager (GOCI), the visible infrared imager radiometer suite (VIIRS), the ocean and land color imager (OLCI), the Landsat operational land imager (OLI), and the Sentinel-2 multispectral instrument (MSI), all of which have suitable spectral and spatial resolutions capable of detecting marine pollutants and other water quality parameters (Table 2).
In order to track marine pollutants, prior understanding of marine dynamics is important, such as ocean current direction and magnitude, direction and speed of surface winds, sea surface temperature (SST), and sea surface salinity (SSS). Remote sensing now provides multiple satellite and airborne sensors to acquire information about marine dynamics over the vast marine regions. Apart from optical data, scanning radiometers and microwave sounders measure SST data, altimeters collect wave height data, and synthetic aperture radar (SAR) can measure the sea surface roughness patterns from which information on sea surface winds can be derived [31]. These datasets are of critical importance for detection and tracking of pollutants.
Remote sensors capture the response of the electromagnetic interaction with water (Figure 1). Absorption and scattering are inherent optical properties (IOP) of water; and variations in IOP change the reflectance of water which is captured by a remote sensing sensor, and this is known as the apparent optical properties (AOP) of water (Figure 2). Reflection, absorption, and transmittance of electromagnetic radiation are highly dependent on the concentrations, types, and presence of substances in water. Total absorption is the sum of absorption by phytoplankton (microalgae), non-algal pigments (NAP), color dissolved organic matter (CDOM), and absorption by water, whereas light scattering by water is mainly controlled by suspended sediments (SS) present in water. Hence, ocean color represents the responses in , green, and red region, and data can be used to estimate the concentrations of water constituents [7].
Interaction of light with the water surface. a is absorption (aph, absorption by phytoplankton; anap, absorption by non-algal pigments; aCDOM, absorption by color dissolved organic matter; and aw, absorption by water), b is backscattering (bb, backward scattering; bf, forward scattering), Rrs is remote sensing reflectance recorded by sensor, Ed is downwelling irradiance, Lu is upwelling radiance, and Lw is water-leaving radiance [
Reflectance (Rrs) by clear water (blue), water with chlorophyll content (green), water with CDOM (black), and sedimented water (orange) [
Generally, clear water has low reflectance in the visible spectrum and has no reflection in near infrared (NIR) region, as it is absorbed by clear water. However, high reflectance measurements in red (600–700 nm) and NIR region (750–1400 nm) show a strong correlation with SS concentrations. A high concentration of suspended sediments blocks the transmittance to and from lower depths and therefore increases reflectance from the water surface. Similarly, high concentrations of chlorophyll (a photosynthetic pigment in phytoplankton and macroalgae) in water cause high reflectance in the green region (500–600 nm) and high absorption in the blue and red regions due to photosynthetic activity (Figure 2).
A portion of absorbed incident energy by the earth’s features is also re-emitted in the thermal infrared region of the electromagnetic spectrum. Many satellite sensors such as MODIS, VIIRS, the advanced very high-resolution radiometer (AVHRR), and the sea and land surface temperature radiometer (SLSTR) measure the emitted thermal energy to determine sea surface temperature (SST). SST is an important parameter for understanding ocean water circulation. In case of large oil spills, these data can be effective for pinpointing the oil spilled areas, as they appear cooler than water surface due to their lower emissivity [31].
Fluorescence is another type of energy emitted by a substance when it comes to a lower energy level from a higher energy level. The emitted energy is in a longer wavelength than the excitation wavelength. Algae absorb visible light for the photosynthesis process and emit excessive energy in the form of fluorescence signal (681 nm, the fluorescence band) when chlorophyll molecule comes to the non-excitation state during the photosynthesis process. The fluorescence can be detected by optical sensors with fine spectral resolution in the far-red and NIR and has a potential source for monitoring changes in the photosynthesis process in plants. Furthermore, in laser fluorometry, laser light is used to excite molecules [33]. This technique is common to detect oil and chemical spills [31].
There are now several remote sensing platforms for monitoring water pollutants, and they can be categorized into two types: airborne and spaceborne.
An aircraft flies at relatively low altitudes (a few hundred meters to a few kilometers above the surface); therefore, the acquired data always have higher levels of detail. Airborne data are particularly useful for real-time monitoring of oil and chemical spills. Four common airborne sensors used for spill surveillance [34] are listed below:
Infrared/ultraviolet line scan (IR/UVLS)
Side-looking airborne radar (SLAR)
Microwave radiometer (MWR)
Laser fluorosensor (LF)
Airborne hyperspectral sensors with fine spatial resolution are able to capture detailed spectral variations. Therefore, they help to select the appropriate spectral region to study a specific water quality parameter, design satellite sensors, and improve already developed algorithms. Some airborne hyperspectral sensors particularly useful for studying coastal/river water quality are described in Table 1.
Sensor | Manufacturer | Number of bands | Spectral range (nm) | Spatial resolution (m) | Studied parameter |
---|---|---|---|---|---|
Airborne visible infrared imaging spectrometer (AVIRIS) | NASA Jet Propulsion Lab | 224 | 400–2500 | 17 | Bottom albedo, water absorption, backscattering coefficients [35], Chl-a, CDOM, TSS [36] |
HyMap | Earth Search Sciences Inc. | 128 | 400–2500 | 3–10 | Heavy metals [37] |
Portable remote imaging spectrometer (PRISM) | NASA Jet Propulsion Lab | — | 350–1050, SWIR band (1240 and 1640) | 0.3 | Sediment, CDOM, chlorophyll fluorescence [38] turbidity, Chl-a, dissolved organic carbon [39] |
Airborne prism experiment (APEX) | VITO (Belgium) | 313 | VIS and NIR (380–970), SWIR (970–2500) | 2–5 | Chlorophyll fluorescence, SS [40] |
Hyperspectral airborne sensors used in water quality assessment.
Spaceborne sensors can cover extensive and remote areas for water quality monitoring. Optical spaceborne sensors used for marine monitoring are mostly in sun-synchronous orbit; only GOCI, designed specifically for marine monitoring, is placed in geostationary orbit. The spatial coverage of these sensors ranges from tens to hundreds of kilometers, and the temporal frequency is from hourly to weekly monitoring.
Many algorithms have been developed to retrieve water quality information such as primary productivity, Chl-a variability, SS, total suspended solids (TSS), turbidity, total nitrogen, total phosphorus, CDOM, and SST. Table 2 shows the satellite sensors most used for the study of water quality parameters related to marine pollution. The major application areas of active spaceborne sensors include, but are not limited to, sea surface currents, oil spills, biogenic films (algal blooms), and river plumes (Table 5).
Satellite sensor | Launch date | Spectral bands (nm) | Spatial resolution (m) | Swath width (km) | Marine parameter accessed |
---|---|---|---|---|---|
Landsats 4 and 5 TM | 1 March 1984 | 5 (450–1750), 1 (2080–2350), and 1 (1040–1250) | 30–120 | 185 | Chl-a, SS, Secchi depth [8] |
Landsat 7 ETM+ | 15 April 1999 | 6 (450–1750), 1 Pan (520–900), 1 (2090–2350), and 1 (1040–1250) | 15–30–60 | 183 | Chl-a, SS, Secchi depth, turbidity [9] |
Terra Aster | 18 December 1999 | 3 (520–860), 6 (1600–2430), and 5 TIR (8125–11,650) | 15–30–90 | 60 | Chl-a [10] |
EO-1 ALI | November 2000 | (443–2350) | 30 | Turbidity [11], SS [12] | |
EO-1 Hyperion | 1 November 2000 | 242 (350–2570) | 30 | 7.5 | Chl-a, SS, CDOM [13, 14] |
PROBA CHRIS | 22 October 2001 | 19 (400–105) | 18–36 | 14 | Chl-a, phycocyanin [15] behenic macroalgae [16] |
HICO | 10 September 2009 | 128 (350–1080) | 100 | 45–50 | Chl-a, turbidity, CDOM [17], SS [18] |
Landsat 8 OLI/TIRS | 11 February 2013 | 1 cirrus cloud detection (1360–1380), 5 (430–880), 1 Pan (500–680), 2 (1570–2290), 2 TIRS (10,600–12,510) | 15–30–100 | 170 | Chl-a, SS, turbidity, TN, TP [19] |
Sentinel-2 MSI | 23 June 2015 | 8 (490–865), 1(443) coastal aerosol, and 3 (1375–2190) | 10–20–60 | 290 | Chl-a, CDOM, DOC [20], SS [21] |
Orb View 2 SeaWiFS | 1 August 1997 | 8 (402–885) | 1130 | 2806 | Chl-a [22] |
Terra, Aqua MODIS | 18 December 1999 | 2 (620–876), 5 (459–2155), 29 (405–877), and thermal | 250–500–100 | 2330 | Chl-a, CDOM SS [23], turbidity [24], TP [25] |
ENVISAT-1 MERIS | 1 March 2002 | 15 (390–1040) | 300–1200 | 1150 | Chl-a, SS [26, 27] |
GOCI | 26 June 2010 | 8 (412–865) | 500 | 2500 | Chl-a, SS, turbidity [28] |
Suomi NPP VIIRS | 28 October 2011 | 5 bands (640–1145), 16 bands (412–12,013), DNB (500–900) | 375–750 | 3060 | Chl-a [29] |
Sentinel-3 OLCI | 16 February 2016 | 21 (400–1020) | 300 | 1270 | Chl-a, SS, CDOM, and Secchi depth [30] |
Satellite sensors mostly used to retrieve marine water quality parameters.
Most algal species are nontoxic and are always present in coastal and open oceans. Planktons are the base of the marine food chain [22]. But, algae do not have to produce toxins to be harmful to the environment. The accelerated growth of algae produces a large amount of biomass which blocks sunlight and produces an anoxic or hypoxic condition (dissolved oxygen is depleted from the water column), which is hazardous to marine life. Algal blooms also affect coastal operations such as movement of ships, coastal tourism, and coastal sports (Figure 3). Algal blooms can persist from a few days to more than a month and spatially they may extend from a few meters to tens of kilometers.
Spread of green algae along the coast of Qingdao in 2008, when summer Olympics was planned in this coast (source: Corey Sheran/Flickr) (right) and algae visible in MODIS false color image (shortwave, NIR, and Red) (source: MODIS rapid response project at NASA/GSFC) (left).
The impact of algal blooms on marine life depends largely on the algal species involved. In situ field data collected using vessels are important for determining the algal species and level of toxicity during the bloom. However, field data are always limited for estimating the spatial extent as well as the dispersion. Detection of algal bloom by estimating the Chl-a concentrations using satellite imagery has been well-researched, as remote sensing has been used to observe ocean primary productivity since the launch of CZCS in 1978. High spatial and temporal resolutions are the main requirements of remote sensing data to study the variability in ocean and coastal Chl-a. By comparing a time series of satellite images, researchers can evaluate the spatial and temporal variations in Chl-a concentration during the bloom. This can also help to understand the dynamics of blooms. However, there are still certain conditions for using optical remote sensing to detect Chl-a, including (i) no or low cloud cover, (ii) the bloom should be near to the surface, and (iii) the bloom must cause the coloration of the water.
Optical remote sensing can observe the coloration of water due to algal pigments. In the open ocean, the color of water is mainly determined by phytoplankton; hence, it is relatively simple to develop algorithms using a bio-optical approach and remote sensing reflectance [22]. In the open ocean, Chl-a can be retrieved from the ratio of blue and green wavelengths as Chl-a absorption is sensitive to blue wavelength and reflectance peak occurs in the green wavelength region [22]. However, in coastal waters, the color of water also depends on organic matter such as NAP, CDOM, and inorganic solids, and consequently it is more complex to determine accurate Chl-a concentrations in coastal/turbid waters. Researchers have demonstrated that waters with increased Chl-a concentrations show a lower spectral response at short wavelengths especially in the blue wavelength regions [41]. This is due to increased absorption of red and blue wavelengths during photosynthetic process. Figure 4 shows the reflectance of water with increasing Chl-a concentrations. Thus, in coastal waters, the red/NIR ratio is more effective for retrieval of Chl-a due to the presence of suspended solids and the increased spectral response of Chl-a pigments at longer wavelengths [43].
Changing spectral response of water with different levels of chlorophyll concentration [
Narrow spectral bandwidth is a necessity for accurate retrieval of Chl-a concentrations [7]. The height of the spectral peak between 700 and 710 nm is used as a proxy for phytoplankton biomass [44]. Many researchers have used broad wavelength data (i.e., Landsat, HJ-1A/1B) as input to regression and neural network approaches for estimating Chl-a, achieving reasonable accuracy (70–90%) [9, 19, 45, 46]. Table 3 shows some studies and datasets used to study Chl-a in marine regions. Lim and Choi [19] found that green and NIR bands of OLI are highly correlated with Chl-a (R = 0.71) in Korean waters. Nazeer and Nichol [46] also used the red/blue ratio to retrieve Chl-a with high accuracy (R = 0.85). Gurlin et al. [43] calibrated three models for Chl-a concentrations from 0 to 100 mg m−3 using two bands (red and NIR) of MERIS and MODIS reflectance data. They found that a simple two-band model achieved a higher accuracy than a complex three-band model. Moses et al. [51] also calibrated a red-NIR algorithm for high Chl-a concentrations in productive turbid waters. Figure 5 shows Chl-a concentrations in highly turbid Pearl River Estuary and connecting rivers, derived using high-resolution MSI data with the method of Moses et al. [51].
Band combination | Sensor | Reference | |
---|---|---|---|
All bands (neural network and other machine learning methods) | GOCI | [28] | |
TM, SAR | [45] | ||
Multiple bands and their ratios (multiple regression) | OLI band (2–5) | [19] | |
OLCI | [30] | ||
TM | [8] | ||
HICO | [17] | ||
Blue (400–500 nm) and green (500–600 nm) ratio | In situ | [22] | |
Blue (400–500 nm) and red (600–700 nm) ratio | TM, ETM+, HJ-1A/1B CCD | [9, 46] | |
Green (500–600 nm) and red (600–700 nm) ratio | TM | [47] | |
In situ (0.70/0.56 μm) | [44] | ||
Red (600–700 nm) and NIR (700 μm–900 μm) ratio | MERIS, MODIS | [43] | |
HICO | [48] | ||
Using a single band | Green (500–600 nm) | Daedalus Airborne Thematic Mapper | [49] |
Red (600–700 nm) | AVHRR | [50] |
Methods used to retrieve Chl-a using remote sensing data in the river and marine waters.
Chl-a concentration observed in the Pearl River Estuary and its connecting rivers on 31 December 2017.
Recently, machine learning approaches taking advantages of reflectance in all bands have also been applied using Landsat [45, 52] and GOCI data [28]. Our work also shows the potential use of Landsat TM, ETM+, and OLI with a machine learning approach to estimate Chl-a in coastal waters (Figure 6). We have evaluated three machine learning models to estimate Chl-a in the coastal waters of Hong Kong, of which artificial neural networks (ANN) performed best resulting in higher R (0.91) and lower RMSE (1.4 μg/L) than models based on support vector regression (SVR) and random forest (RF) algorithms. Chlorophyll indices such as the cyanobacteria index [53], maximum chlorophyll Index [54], and maximum peak height algorithm [55] have been demonstrated the robustness for detecting algal blooms and surface scum in coastal waters. Lunetta et al. [56] described the potential of using cyanobacteria index to measure cyanobacteria cell counts in bloom situations using MERIS data. Nazeer et al. [57] used board waveband band data (Landsat TM, ETM+, and HJ-1A/1B CCD) along with meteorological data as inputs to an artificial neural network model to map phytoplankton cell counts during a bloom in the complex coastal waters of Hong Kong and validated the model in two lakes in the United States and Japan.
Comparison of measured and predicted values from three machine learning models. (a) Chl-a concentration using artificial neural network, (b) Chl-a concentration using support vector regression, and (c) Chl-a concentration using random forest.
Synthetic aperture radar (SAR) data can also be used to detect large algal blooms in cloudy weather as algal blooms may appear as an area of low backscatter compared to surrounding water surfaces [50].
Turbidity is an optical property of water and is highly influenced by concentrations of suspended and dissolved organic and inorganic materials in water, including Chl-a, SS, and CDOM. SS is mainly responsible for the light scattering, whereas CDOM and Chl-a control the light absorption properties of water [58].
Turbidity and TSS are two important variables of marine systems studies because of their direct linkages with photosynthetically available radiation, which affects the growth of plankton and other algae [41]. Turbidity has also been used to measure fluvial SS concentrations in rivers and river plumes [59]. These fluvial SS loads are rich in nutrients and considered a cause of eutrophication. So, it is vital to have time series records of suspended sediment concentrations for better understanding of land-ocean interactions. High SS loads negatively affect aquaculture [59] and are hazardous to benthic invertebrates [60]. These parameters are also associated with the diffuse attenuation coefficient (penetration of light, in the blue-green region of the spectrum, through water column) and Secchi disk depth (a measure of water transparency) [41]. For all these reasons, turbidity and TSS concentrations are considered to be critical parameters in the study of marine systems.
Ocean color remote sensing techniques are widely used to monitor spatiotemporal variations in SS concentration and for mapping of water turbidity. Figure 7 shows the changes in ocean color due to high sediment loads in the Yangtze River Estuary [60] and the Pearl River Estuary [61]. It is suggested that an algorithm using single bands provides a good estimation of TSS concentrations if an appropriate band is used [62]. Moreover Novo et al. [63] and Curran et al. [64] have demonstrated that a single-band approach may be adopted when water reflectance in the single band has a linear relationship with TSS concentrations. However, coastal water often consists of a complex mixture of substances and results in large variations in reflectance. In this case, multiple spectral bands should be adopted for TSS retrieval [62, 65, 66]. These methods using band arithmetic can achieve high accuracy around 80% for retrieving TSS concentrations in complex waters [67, 68]. The peak of the reflectance curve shifts from the green region to the red region with increasing concentration of dissolved and suspended matter; and water starts reflecting significantly in NIR region [21] (Figure 8). For water with high TSS concentrations, the spectral region between 600 and 900 nm should be used. Several studies using Landsat TM, ETM+, and OLI show that the blue, green, red, and NIR bands are useful for the determination of TSS [8, 19, 68, 69, 70]. Literature also shows that TM, ETM+, OLI, and MODIS are the most frequently used sensors for developing algorithms to study seasonal TSS variability in coastal and estuarine areas, due to the large amount of archived remote sensing data [24, 71, 72]. The recently launched MSI sensor onboard Sentinel-2A and Sentinel-2B provide high spatial resolution of 10–20 m with a high temporal resolution of 5 days. The high spatial resolution (10 m) red and NIR bands are capable of routine monitoring of TSS concentration and turbidity in narrow bays, rivers, and inlets. Figure 9 shows the suspended matter concentrations, and Figure 10 shows turbidity in the Pearl River Estuary and connecting rivers using MSI data with algorithms of Nechad et al. [62] and Nechad et al. [73], respectively.
Terra-MODIS true color image, captured on 16 September 2000, shows the sediment plume of the Yangtze River Estuary (left). The Sentinel-2 true color image, captured on 31 December 2017, shows high sediment concentrations in the Pearl River Estuary (right).
Remote sensing reflectance (Rrs) spectra of water containing different suspended solid concentration (mg/L) [
High levels of suspended matter concentration were observed in the Pearl River Estuary and its connecting rivers on 31 December 2017.
High levels of turbidity were observed in the Pearl River Estuary and its connecting rivers on 31 December 2017.
Methods and algorithms for estimation of TSS and turbidity have been evolved from simple methods such as linear/nonlinear regression and principal component analysis (PCA) to relatively complex techniques such as genetic algorithms and ANN. Nazeer and Nichol [68] initially developed a regression model resulting in an RMSE of 2.60 mg/L. Later, Nazeer et al. [52] evaluated the potential of a machine learning model for estimating TSS in the complex coastal area of Hong Kong achieving an RMSE of 4.59 mg/L. Our work of machine learning models with Landsat TM, ETM+, and OLI data in the same area also shows promising results for estimation of TSS (Figure 11). In our work, ANN outperformed the other two machine learning approaches, SVR (support vector machine) and RF (random forest), resulting in the lowest RMSE of 2.8 mg/L. Table 4 includes some studies and methods used to study TSS in rivers, bays, estuaries, and relatively open coastal waters.
Comparison of measured and predicted values from three machine learning models. (a) TSS concentration using artificial neural network, (b) TSS concentration using support vector regression, and (c) TSS concentration using random forest.
Band combination | Sensor | Reference | |
---|---|---|---|
All bands (neural network and other machine learning methods) | GOCI | [28] | |
Landsat TM, ETM+, OLI, HJ-1 A/B CCD | [52] | ||
TM, SAR | [45] | ||
Multiple bands and their ratios (multiple regression) | Landsat OLI band (2–5) | [19] | |
Landsat ETM+ | [9] | ||
Multiple green (500–600 nm) and red (600–700 nm) ratio | Landsat TM, ETM+ | [68] | |
Green (500–600 nm) and red (600–700 nm) ratio | HJ-1A/1B CCD | [67] | |
Red (600–700 nm) and NIR (700–900 nm) ratio | MODIS | [65] | |
Single band algorithms | Green (500–600 nm) | SeaWiFS | [58] |
EO-ALI | [12] | ||
Red (600–700 nm) | Landsat TM, ETM+, HJ-1 | [47, 68] | |
AVHRR, SeaWiFS | [58] | ||
MODIS, MERIS, SeaWiFS | [24, 62, 65] | ||
HICO | [17] | ||
NIR (700–900 nm) | MODIS, MERIS, SeaWiFS | [62] |
Methods used to retrieve TSS using remote sensing data in marine waters.
Stormwater runoff is also a large source of marine pollution as runoffs and pollutants from the urban watershed enter into the coastal environment after rainstorms. Stormwater runoff and municipal wastewater plumes may sometimes be overlooked due to persistent cloud cover in optical imagery. These types of runoff are often detectable via SAR as they deposit surfactants on the sea surface, smoothing the small gravity waves and thus producing an area of low backscatter in comparison to the surrounding sea surface [74]. DiGiacomo et al. [74] used high-resolution SAR to monitor such plumes in the Southern California Bight. In their study, the dynamics of runoff plume was modeled using SAR images together with meteorological data as a function of cumulative event discharge, timing of the peak flow, and total storm precipitation. Holt et al. [75] used multi-platform SAR data along with MODIS and precipitation data to study a stormwater plume and its flow direction.
A large oil spill from tankers causes not only significant economic loss but also destruction to the aquatic ecosystem. After the spill, oil undergoes several processes such as spreading, evaporation, dissolution, drifting, photolysis, biodegradation, and the formation of oil-in-water and water-in-oil emulsions [76].
Owing to the dynamic spreading nature of the spill, both remote and station-based sensors are essential for comprehensive and effective monitoring. Airborne survey of an oil spill can be carried out by side-looking airborne radar (SLAR), laser fluorosensor (LF), and ultraviolet and thermal infrared video cameras. Ultraviolet, microwave, thermal, and optical airborne sensors all exhibit the ability to detect oil spills [6]. Ultraviolet sensors are sensitive to oil thickness of 0.01–0.05 μm. Oil appears as a bright target in this region of the spectrum, and brightness increases with the thickness of the oil. Optical sensors can measure thicker oil (2–500 μm) and are able to detect oil dispersed in water, whereas thermal infrared sensors measure oil with a thickness of about 10–50 μm [34]. Airborne LF and microwave radiometers (MWR) are considered to be the most appropriate sensors for oil spill detection. SLAR, ultraviolet, and thermal video cameras were used to identify areas of thick oil during the Sea Empress oil spill in 1996. Oil also undergoes weathering and aging. Multispectral satellite images, taking advantage of fluorescence characteristics of oil, can detect spills and assess the levels of weathering of the oil [31].
Spaceborne synthetic aperture radar (SAR) is commonly used for ocean pollution monitoring, especially oil spills. Table 5 includes some SAR-equipped satellites used for oil spill detection. The advantage of SAR is the capability to take measurements during all day and all-weather conditions. Therefore, they are considered superior to optical sensors in this application [5]. The spreading trend of oil highly depends on wind direction and speed. An oil spill would break up and disperse if the wind speed is greater than 10 m/s [74]. DiGiacomo et al. [74] used ERS-2 SAR and RADARSAT-1 SAR images to map oil spills in the Southern California Bight. Shirvany et al. [77] evaluated the potential of different polarizations using RADARSAT-2 data for oil spill detection in the Gulf of Mexico. In another study, ENVISAT data was used effectively as an input to a hydrodynamic model to track the fate of oil after the Kerch Strait oil spill in 2007 [78]. Figure 12 shows an incident of large oil spill on the Galicia coast [79] and the Korean coast [80] for which spaceborne SAR data was used to access the coverage areas and the damage caused by the spills.
Satellite sensor | Operation |
---|---|
Sentinel-1A | 2014, operating |
Sentinel-1B | 2016, operating |
TerraSAR-X | 2007, operating |
ENVISAT advanced synthetic aperture radar (ASAR) | 2002, not operating |
RADARSAT-1 | 1995, not operating |
European remote sensing (ERS) satellites: ESR-2 | 1995, not operating |
Active spaceborne sensors mostly used in oil spill detection.
(a) ASAR wide-swath image of northwest coast of Spain, captured on 17 Nov 2002, at 10:45 UTC showing oil from the wrecked tanker approaching Spanish coast (source, ESA), (b) ASAR image of South Korea, captured on 11 Dec 2007, at 01:40 UTC, showing oil spill from 146,000 ton damaged crude oil tanker (source ESA).
With the increasing amount of marine plastic litter, its adverse chemical, biological, and ecological impacts on the marine ecosystem have raised the public concerns [81]. It is estimated that 4.8–12.7 million metric tons of plastic is dumped in the sea every year [82] due to increased use of plastic in industry and daily life [83, 84]. Although some surveys have been undertaken [85] to estimate the density and weight of floating plastic in the oceans globally, there is a lack of long-term and large-scale monitoring.
Some research has been conducted using remote sensing technology for the detection of floating marine plastic [86]. However, this research domain is still in its early stages. The reflectance from water captured by sensors is different from that of floating plastic objects. There are several reasons for this, (1) the physical properties of water are different from that of plastic, and they have significant distinct reflectance; (2) the transmitting ability of light through water is different from that through plastic; (3) the absorption of light by water is different from plastic [87]. Figure 13 shows different pathways of incident light after interacting with the surface (with and without marine plastic). Some studies have used hyperspectral remote sensing to study marine macroplastics [87] and microplastics [88]. Goddijn-Murphy et al. [87] considered the spectral signatures and geometric optics of plastic and seawater to develop a reflectance model for detecting macroplastics. The key is to determine the appropriate reflectance peak of plastic and consider its ratio with wavelength bands where water-leaving reflectance is low. Their model considers reflectivity of only one type of plastic litter in two dimensions. However, there are some constraints for detecting marine plastics in a real scenario since there have no standard shape, dimension, color, chemical composition, etc. Nevertheless, this study demonstrated the possibility of using remote sensing as a useful means for mapping and tracking of marine plastic.
Schematic of solar radiance interacting with (A) an open water body and (B) the same water body but with floating plastic. Ld is total downwelling radiance (solar beam + diffuse skylight), Lds is subsurface downwelling radiance, Lws is subsurface upwelling radiance, Lwr is radiance reflected directly off the water surface, Lwt is subsurface upwelling radiance transmitted through the water-air interface, Lpr is radiance reflected off the plastic, and Lpt is subsurface upwelling radiance transmitted through the plastic. Lw is total water-leaving radiance, Lwr + Lwt, and Lp is total plastic leaving radiance, Lpt + Lpr; subscript ‘0’ indicates all the variables in the absence of plastic and FOV is a field of view [
Detecting coastal litter near land surface is easier than in open ocean, as its reflectance and shape characteristics are not affected by its pitching and rolling on ocean waves. Moy et al. [89] used aerial imagery along with spatial analysis to categorize and map marine litter deposited along the coasts of the Hawaiian Islands. Very high-resolution aerial imagery allowed precise measurements of the quantity, location, type, and size of dumped litter (>0.05 m2) (Figure 14). In another study, Martin et al. [90] discussed the potential of combining images from unmanned aerial vehicles (UAV) and a machine learning approach, to detect and map marine litter. Machine learning algorithms are able to detect and classify objects when training samples with known training objects are provided. Their results showed that a UAV-based beach survey is 39 times faster than beach screening on foot and the large footprint of a UAV can cover entire coastlines and beaches including those in remote areas.
Distribution and density of marine litter along the coasts of the main Hawaiian Islands. Areas with 100 and more item densities are shown as hotspots of high marine litter [
Increased levels of marine pollution due to anthropogenic activities are adversely affecting marine sustainability of marine ecosystems. Reviewed literature suggested that aerial and spaceborne sensors provide holistic information for monitoring many of the major marine pollutants. These include oil and chemical spills, sewage, high suspended solids, and algal blooms. Solid waste deposited in coastal areas can also be mapped using similar geospatial technology. However, there are some technical limitations in assessing detailed information about pollutants. These limitations stem from their dynamic nature, limited information of specific spectral response of pollutants, substrate response in optically shallow waters, and complex physics of light interaction through the water column. Despite these limitations, remote sensing is still capable of providing useful information about pollution events in sensitive marine areas.
Active and hyperspectral airborne sensors are often considered superior to spaceborne sensors for monitoring coastal and estuarine pollutants due to their real-time and detailed monitoring capability. Spaceborne sensors are more reliable for large-scale ocean, but with the recent development of sensor technology, especially hyperspectral and active sensors with high temporal resolution, the applications of spaceborne sensors in coastal regions are also increasing. Presently, monitoring of marine waters is offered through numerous satellite sensors such as MODIS, VIIRS, AVHRR, OLCI, GOCI, Landsat, and Sentinel-2 with spectral and spatial resolutions able to measure marine pollutants and other marine parameters. Active satellite sensors such as SAR, altimeters, scanning radiometers, and microwave sounders, which are mostly used in physical oceanography, also possess the potential for detection of marine pollution under specific meteorological conditions and provide useful data to track and model the impact of these pollutants.
Heavy metal pollution in coastal and estuarine region is another major concern of marine managers and researchers. Studies have attempted to use airborne hyperspectral data for this task, but satellite remote sensing is not yet able to detect these loads directly. However, the core factors causing these pollutants such as river plumes, sewerage, and industrial waste entering into these sensitive systems can be monitored using satellite remote sensing. If the point source of heavy metals is traced by remote sensing, policies and management practices can be applied according to the specific pollutants, and their mobilization and transfer of heavy metal to sensitive coastal environments can be avoided. Multiple approaches have proven reliable for this task.
In addition, recent developments in software and computation power have led to the increased use of data captured by remote sensing systems. Computer systems can now store and analyze large datasets. Therefore, marine protection agencies and government can utilize the full potential of remote sensing data in geographic information systems (GIS) and decision support systems (DSS) to manage marine resources and pollution. Collaboration between the research community and government is of utmost importance for using the full potential of this data in marine pollution management. Different applications of remote sensing such as detection of floating marine plastic litter and the use of active remote sensing for detecting algal blooms are still in the research. With the advancement of remote sensing sensors, sophisticated methods will be developed in the future for monitoring marine pollution.
Authors would like to acknowledge the General Research Fund (project id: 15246916), the Hong Kong Ph.D. Fellowship Scheme from the Research Grants Council of Hong Kong. The authors would also like to acknowledge US Geological Survey for providing Landsat (TM, ETM+, and OLI) image archive, the Copernicus Open Access Hub for providing Sentinel-2 data, and the Hong Kong Environmental Protection Department for providing station-based coastal water quality data for developing numerical models.
Our journals are currently in their launching issue. They will be applied to all relevant indexes as soon as they are eligible. These include (but are not limited to): Web of Science, Scopus, PubMed, MEDLINE, Database of Open Access Journals (DOAJ), Google Scholar and Inspec.
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Conversely, dismissive attitude by decision-makers towards architectural practice and education, even among architects, in recognizing architecture as data-driven and source of data deserve rethink. Here the chapter demonstrates architects as data scientists and the symbiotic relationship that exist between architecture and 3D computer graphics while highlighting emerging data sciences opportunities and threats. The chapter adopted principally reviews of scholarly literatures, draws from authors’ 20-years personal experiences, and industry leaders’ views. The language is accessible yet academically concise. The chapter concluded with recommendations, including highlights of big data technologies potential transformation of 3D computer graphics. 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In a series of four studies, we collected data and compared results from the performance in a COTS game to scores on a traditional multiple-choice assessment written for the purposes of each study. Each assessment was written to evaluate the same content presented in the game for each respective study. Three of the four studies demonstrated a significant correlation between the COTS game and the traditional multiple choice assessment scores. The non-significant value in Study 4 was likely due to a small sample size (n < 100). The results of these studies support our hypothesis and demonstrate that COTS games may be a useful educational tool for training or assessment purposes. We recommend that future research focuses on specific applications of COTS games to explore further opportunities for utilizing COTS in education and assessment.",book:{id:"11192",title:"Computer Game Development",coverURL:"https://cdn.intechopen.com/books/images_new/11192.jpg"},signatures:"Diana R. Sanchez, Amanda Rueda, Leila Jimeno Jimènez and Mahsa Norouzi Nargesi"},{id:"80731",title:"The Use of Serious Games for Learning Cardiopulmonary Resuscitation Procedures: A Systematic Mapping of the Literature",slug:"the-use-of-serious-games-for-learning-cardiopulmonary-resuscitation-procedures-a-systematic-mapping-",totalDownloads:70,totalDimensionsCites:0,doi:"10.5772/intechopen.102399",abstract:"This article presents a systematic mapping, with an analysis of 35 selected works according to established criteria, seeking to connect the points and find relevant information for the following research areas: basic life support, cardiopulmonary resuscitation, serious games, and games for healthcare. Among the main results found, we can mention the representativeness of works by regions and their most productive years, the most common platforms, noting a focus on VR technologies, in addition to identifying the preference for the Unity 3D tool for implementations. It was also possible to show that serious games can be very effective in teaching CPR.",book:{id:"11192",title:"Computer Game Development",coverURL:"https://cdn.intechopen.com/books/images_new/11192.jpg"},signatures:"Ingrid Nery Mendes, Maicon de Araújo Nogueira, Filipe Valente Mendes, Otavio Noura Teixeira and Viviane Almeida dos Santos"},{id:"80515",title:"View Synthesis Tool for VR Immersive Video",slug:"view-synthesis-tool-for-vr-immersive-video",totalDownloads:142,totalDimensionsCites:0,doi:"10.5772/intechopen.102382",abstract:"This chapter addresses the view synthesis of natural scenes in virtual reality (VR) using depth image-based rendering (DIBR). This method reaches photorealistic results as it directly warps photos to obtain the output, avoiding the need to photograph every possible viewpoint or to make a 3D reconstruction of a scene followed by a ray-tracing rendering. An overview of the DIBR approach and frequently encountered challenges (disocclusion and ghosting artifacts, multi-view blending, handling of non-Lambertian objects) are described. Such technology finds applications in VR immersive displays and holography. Finally, a comprehensive manual of the Reference View Synthesis software (RVS), an open-source tool tested on open datasets and recognized by the MPEG-I standardization activities (where “I” refers to “immersive”) is described for hands-on practicing.",book:{id:"11192",title:"Computer Game Development",coverURL:"https://cdn.intechopen.com/books/images_new/11192.jpg"},signatures:"Sarah Fachada, Daniele Bonatto, Mehrdad Teratani and Gauthier Lafruit"}],onlineFirstChaptersTotal:8},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"10",title:"Physiology",doi:"10.5772/intechopen.72796",issn:"2631-8261",scope:"Modern physiology requires a comprehensive understanding of the integration of tissues and organs throughout the mammalian body, including the cooperation between structure and function at the cellular and molecular levels governed by gene and protein expression. While a daunting task, learning is facilitated by identifying common and effective signaling pathways mediated by a variety of factors employed by nature to preserve and sustain homeostatic life. \r\nAs a leading example, the cellular interaction between intracellular concentration of Ca+2 increases, and changes in plasma membrane potential is integral for coordinating blood flow, governing the exocytosis of neurotransmitters, and modulating gene expression and cell effector secretory functions. Furthermore, in this manner, understanding the systemic interaction between the cardiovascular and nervous systems has become more important than ever as human populations' life prolongation, aging and mechanisms of cellular oxidative signaling are utilised for sustaining life. \r\nAltogether, physiological research enables our identification of distinct and precise points of transition from health to the development of multimorbidity throughout the inevitable aging disorders (e.g., diabetes, hypertension, chronic kidney disease, heart failure, peptic ulcer, inflammatory bowel disease, age-related macular degeneration, cancer). With consideration of all organ systems (e.g., brain, heart, lung, gut, skeletal and smooth muscle, liver, pancreas, kidney, eye) and the interactions thereof, this Physiology Series will address the goals of resolving (1) Aging physiology and chronic disease progression (2) Examination of key cellular pathways as they relate to calcium, oxidative stress, and electrical signaling, and (3) how changes in plasma membrane produced by lipid peroxidation products can affect aging physiology, covering new research in the area of cell, human, plant and animal physiology.",coverUrl:"https://cdn.intechopen.com/series/covers/10.jpg",latestPublicationDate:"July 20th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:14,editor:{id:"35854",title:"Prof.",name:"Tomasz",middleName:null,surname:"Brzozowski",slug:"tomasz-brzozowski",fullName:"Tomasz Brzozowski",profilePictureURL:"https://mts.intechopen.com/storage/users/35854/images/system/35854.jpg",biography:"Prof. Dr. Thomas Brzozowski works as a professor of Human Physiology and is currently Chairman at the Department of Physiology and is V-Dean of the Medical Faculty at Jagiellonian University Medical College, Cracow, Poland. His primary area of interest is physiology and pathophysiology of the gastrointestinal (GI) tract, with the major focus on the mechanism of GI mucosal defense, protection, and ulcer healing. He was a postdoctoral NIH fellow at the University of California and the Gastroenterology VA Medical Center, Irvine, Long Beach, CA, USA, and at the Gastroenterology Clinics Erlangen-Nuremberg and Munster in Germany. He has published 290 original articles in some of the most prestigious scientific journals and seven book chapters on the pathophysiology of the GI tract, gastroprotection, ulcer healing, drug therapy of peptic ulcers, hormonal regulation of the gut, and inflammatory bowel disease.",institutionString:null,institution:{name:"Jagiellonian University",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"10",title:"Animal Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/10.jpg",isOpenForSubmission:!0,editor:{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null},{id:"11",title:"Cell Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/11.jpg",isOpenForSubmission:!0,editor:{id:"133493",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",profilePictureURL:"https://mts.intechopen.com/storage/users/133493/images/3091_n.jpg",biography:"Prof. Dr. Angel Catalá \r\nShort Biography Angel Catalá was born in Rodeo (San Juan, Argentina). He studied \r\nchemistry at the Universidad Nacional de La Plata, Argentina, where received aPh.D. degree in chemistry (Biological Branch) in 1965. From\r\n1964 to 1974, he worked as Assistant in Biochemistry at the School of MedicineUniversidad Nacional de La Plata, Argentina. From 1974 to 1976, he was a Fellowof the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor oBiochemistry at the Universidad Nacional de La Plata, Argentina. He is Member ofthe National Research Council (CONICET), Argentina, and Argentine Society foBiochemistry and Molecular Biology (SAIB). His laboratory has been interested for manyears in the lipid peroxidation of biological membranes from various tissues and different species. Professor Catalá has directed twelve doctoral theses, publishedover 100 papers in peer reviewed journals, several chapters in books andtwelve edited books. Angel Catalá received awards at the 40th InternationaConference Biochemistry of Lipids 1999: Dijon (France). W inner of the Bimbo PanAmerican Nutrition, Food Science and Technology Award 2006 and 2012, South AmericaHuman Nutrition, Professional Category. 2006 award in pharmacology, Bernardo\r\nHoussay, in recognition of his meritorious works of research. Angel Catalá belongto the Editorial Board of Journal of lipids, International Review of Biophysical ChemistryFrontiers in Membrane Physiology and Biophysics, World Journal oExperimental Medicine and Biochemistry Research International, W orld Journal oBiological Chemistry, Oxidative Medicine and Cellular Longevity, Diabetes and thePancreas, International Journal of Chronic Diseases & Therapy, International Journal oNutrition, Co-Editor of The Open Biology Journal.",institutionString:null,institution:{name:"National University of La Plata",institutionURL:null,country:{name:"Argentina"}}},editorTwo:null,editorThree:null},{id:"12",title:"Human Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/12.jpg",isOpenForSubmission:!0,editor:{id:"195829",title:"Prof.",name:"Kunihiro",middleName:null,surname:"Sakuma",slug:"kunihiro-sakuma",fullName:"Kunihiro Sakuma",profilePictureURL:"https://mts.intechopen.com/storage/users/195829/images/system/195829.jpg",biography:"Professor Kunihiro Sakuma, Ph.D., currently works in the Institute for Liberal Arts at the Tokyo Institute of Technology. He is a physiologist working in the field of skeletal muscle. He was awarded his sports science diploma in 1995 by the University of Tsukuba and began his scientific work at the Department of Physiology, Aichi Human Service Center, focusing on the molecular mechanism of congenital muscular dystrophy and normal muscle regeneration. His interest later turned to the molecular mechanism and attenuating strategy of sarcopenia (age-related muscle atrophy). His opinion is to attenuate sarcopenia by improving autophagic defects using nutrient- and pharmaceutical-based treatments.",institutionString:null,institution:{name:"Tokyo Institute of Technology",institutionURL:null,country:{name:"Japan"}}},editorTwo:{id:"331519",title:"Dr.",name:"Kotomi",middleName:null,surname:"Sakai",slug:"kotomi-sakai",fullName:"Kotomi Sakai",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000031QtFXQA0/Profile_Picture_1637053227318",biography:"Senior researcher Kotomi Sakai, Ph.D., MPH, works at the Research Organization of Science and Technology in Ritsumeikan University. She is a researcher in the geriatric rehabilitation and public health field. She received Ph.D. from Nihon University and MPH from St.Luke’s International University. Her main research interest is sarcopenia in older adults, especially its association with nutritional status. Additionally, to understand how to maintain and improve physical function in older adults, to conduct studies about the mechanism of sarcopenia and determine when possible interventions are needed.",institutionString:null,institution:{name:"Ritsumeikan University",institutionURL:null,country:{name:"Japan"}}},editorThree:null},{id:"13",title:"Plant Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/13.jpg",isOpenForSubmission:!0,editor:{id:"332229",title:"Prof.",name:"Jen-Tsung",middleName:null,surname:"Chen",slug:"jen-tsung-chen",fullName:"Jen-Tsung Chen",profilePictureURL:"https://mts.intechopen.com/storage/users/332229/images/system/332229.png",biography:"Dr. Jen-Tsung Chen is currently a professor at the National University of Kaohsiung, Taiwan. He teaches cell biology, genomics, proteomics, medicinal plant biotechnology, and plant tissue culture. Dr. Chen\\'s research interests include bioactive compounds, chromatography techniques, in vitro culture, medicinal plants, phytochemicals, and plant biotechnology. He has published more than ninety scientific papers and serves as an editorial board member for Plant Methods, Biomolecules, and International Journal of Molecular Sciences.",institutionString:"National University of Kaohsiung",institution:{name:"National University of Kaohsiung",institutionURL:null,country:{name:"Taiwan"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:16,paginationItems:[{id:"82135",title:"Carotenoids in Cassava (Manihot esculenta Crantz)",doi:"10.5772/intechopen.105210",signatures:"Lovina I. Udoh, Josephine U. Agogbua, Eberechi R. Keyagha and Itorobong I. 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Buchholz and Erik J. Behringer",hash:"e373a3d1123dbd45fddf75d90e3e7c38",volumeInSeries:1,fullTitle:"Calcium and Signal Transduction",editors:[{id:"89438",title:"Dr.",name:"John N.",middleName:null,surname:"Buchholz",slug:"john-n.-buchholz",fullName:"John N. Buchholz",profilePictureURL:"https://mts.intechopen.com/storage/users/89438/images/6463_n.jpg",biography:"Full Professor and Vice Chair, Division of Pharmacology, Loma Linda University, School of Medicine. He received his B.S. Degree in Biology at La Sierra University, Riverside California (1980) and a PhD in Pharmacology from Loma Linda University School of Medicine (1988). Post-Doctoral Fellow at University of California, Irvine, College of Medicine 1989-1992 with a focus on autonomic nerve function in blood vessels and the impact of aging on the function of these nerves and overall blood vessel function. Twenty years of research funding and served on NIH R01 review panels, Editor-In-Chief of Edorium Journal of Aging Research. Serves as a peer reviewer for biomedical journals. Military Reserve Officer serving with the 100 Support Command, 100 Troop Command, 40 Infantry Division, CA National Guard.",institutionString:null,institution:{name:"Loma Linda University",institutionURL:null,country:{name:"United States of America"}}}]},{type:"book",id:"6925",title:"Endoplasmic Reticulum",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6925.jpg",slug:"endoplasmic-reticulum",publishedDate:"April 17th 2019",editedByType:"Edited by",bookSignature:"Angel Català",hash:"a9e90d2dbdbc46128dfe7dac9f87c6b4",volumeInSeries:2,fullTitle:"Endoplasmic Reticulum",editors:[{id:"196544",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",profilePictureURL:"https://mts.intechopen.com/storage/users/196544/images/system/196544.jpg",biography:"Angel Catalá studied chemistry at Universidad Nacional de La Plata, Argentina, where he received a Ph.D. in Chemistry (Biological Branch) in 1965. From 1964 to 1974, he worked as an Assistant in Biochemistry at the School of Medicine at the same university. From 1974 to 1976, he was a fellow of the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor of Biochemistry at the Universidad Nacional de La Plata. He is a member of the National Research Council (CONICET), Argentina, and the Argentine Society for Biochemistry and Molecular Biology (SAIB). His laboratory has been interested for many years in the lipid peroxidation of biological membranes from various tissues and different species. Dr. Catalá has directed twelve doctoral theses, published more than 100 papers in peer-reviewed journals, several chapters in books, and edited twelve books. He received awards at the 40th International Conference Biochemistry of Lipids 1999 in Dijon, France. He is the winner of the Bimbo Pan-American Nutrition, Food Science and Technology Award 2006 and 2012, South America, Human Nutrition, Professional Category. In 2006, he won the Bernardo Houssay award in pharmacology, in recognition of his meritorious works of research. Dr. Catalá belongs to the editorial board of several journals including Journal of Lipids; International Review of Biophysical Chemistry; Frontiers in Membrane Physiology and Biophysics; World Journal of Experimental Medicine and Biochemistry Research International; World Journal of Biological Chemistry, Diabetes, and the Pancreas; International Journal of Chronic Diseases & Therapy; and International Journal of Nutrition. He is the co-editor of The Open Biology Journal and associate editor for Oxidative Medicine and Cellular Longevity.",institutionString:"Universidad Nacional de La Plata",institution:{name:"National University of La Plata",institutionURL:null,country:{name:"Argentina"}}}]},{type:"book",id:"6924",title:"Adenosine Triphosphate in Health and Disease",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6924.jpg",slug:"adenosine-triphosphate-in-health-and-disease",publishedDate:"April 24th 2019",editedByType:"Edited by",bookSignature:"Gyula Mozsik",hash:"04106c232a3c68fec07ba7cf00d2522d",volumeInSeries:3,fullTitle:"Adenosine Triphosphate in Health and Disease",editors:[{id:"58390",title:"Dr.",name:"Gyula",middleName:null,surname:"Mozsik",slug:"gyula-mozsik",fullName:"Gyula Mozsik",profilePictureURL:"https://mts.intechopen.com/storage/users/58390/images/system/58390.png",biography:"Gyula Mózsik MD, Ph.D., ScD (med), is an emeritus professor of Medicine at the First Department of Medicine, Univesity of Pécs, Hungary. He was head of this department from 1993 to 2003. His specializations are medicine, gastroenterology, clinical pharmacology, clinical nutrition, and dietetics. His research fields are biochemical pharmacological examinations in the human gastrointestinal (GI) mucosa, mechanisms of retinoids, drugs, capsaicin-sensitive afferent nerves, and innovative pharmacological, pharmaceutical, and nutritional (dietary) research in humans. He has published about 360 peer-reviewed papers, 197 book chapters, 692 abstracts, 19 monographs, and has edited 37 books. He has given about 1120 regular and review lectures. He has organized thirty-eight national and international congresses and symposia. He is the founder of the International Conference on Ulcer Research (ICUR); International Union of Pharmacology, Gastrointestinal Section (IUPHAR-GI); Brain-Gut Society symposiums, and gastrointestinal cytoprotective symposiums. He received the Andre Robert Award from IUPHAR-GI in 2014. Fifteen of his students have been appointed as full professors in Egypt, Cuba, and Hungary.",institutionString:"University of Pécs",institution:{name:"University of Pecs",institutionURL:null,country:{name:"Hungary"}}}]},{type:"book",id:"8008",title:"Antioxidants",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/8008.jpg",slug:"antioxidants",publishedDate:"November 6th 2019",editedByType:"Edited by",bookSignature:"Emad Shalaby",hash:"76361b4061e830906267933c1c670027",volumeInSeries:5,fullTitle:"Antioxidants",editors:[{id:"63600",title:"Prof.",name:"Emad",middleName:null,surname:"Shalaby",slug:"emad-shalaby",fullName:"Emad Shalaby",profilePictureURL:"https://mts.intechopen.com/storage/users/63600/images/system/63600.png",biography:"Dr. Emad Shalaby is a professor of biochemistry on the Biochemistry Department Faculty of Agriculture, Cairo University. He\nreceived a short-term scholarship to carry out his post-doctoral\nstudies abroad, from Japan International Cooperation Agency\n(JICA), in coordination with the Egyptian government. Dr.\nShalaby speaks fluent English and his native Arabic. He has 77\ninternationally published research papers, has attended 15 international conferences, and has contributed to 18 international books and chapters.\nDr. Shalaby works as a reviewer on over one hundred international journals and is\non the editorial board of more than twenty-five international journals. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. 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He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. 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Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. 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. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. 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. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"27",type:"subseries",title:"Multi-Agent Systems",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11423,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. 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Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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