Frequency of occurrence of different polymer types in microplastic debris sampled at sea or in marine sediments [17].
\r\n\tIn sum, the book presents a reflective analysis of the pedagogical hubs for a changing world, considering the most fundamental areas of the current contingencies in education.
",isbn:"978-1-83968-793-8",printIsbn:"978-1-83968-792-1",pdfIsbn:"978-1-83968-794-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"b01f9136149277b7e4cbc1e52bce78ec",bookSignature:"Dr. María Jose Hernandez-Serrano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10229.jpg",keywords:"Teacher Digital Competences, Flipped Learning, Online Resources Design, Neuroscientific Literacy (Myths), Emotions and Learning, Multisensory Stimulation, Citizen Skills, Violence Prevention, Moral Development, Universal Design for Learning, Sensitizing on Diversity, Supportive Strategies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 14th 2020",dateEndSecondStepPublish:"October 12th 2020",dateEndThirdStepPublish:"December 11th 2020",dateEndFourthStepPublish:"March 1st 2021",dateEndFifthStepPublish:"April 30th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Phil. Maria Jose Hernandez Serrano is a tenured lecturer in the Department of Theory and History of Education at the University of Salamanca, where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. in Education and Training in Virtual Environments by research with the University of Manchester, UK (2009).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"187893",title:"Dr.",name:"María Jose",middleName:null,surname:"Hernandez-Serrano",slug:"maria-jose-hernandez-serrano",fullName:"María Jose Hernandez-Serrano",profilePictureURL:"https://mts.intechopen.com/storage/users/187893/images/system/187893.jpg",biography:"DPhil Maria Jose Hernandez Serrano is a tenured Lecturer in the Department of Theory and History of Education at the University of Salamanca (Spain), where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. on Education and Training in Virtual Environments by research with the University of Manchester, UK (2009). She obtained a Visiting Scholar Postdoctoral Grant (of the British Academy, UK) at the Oxford Internet Institute of the University of Oxford (2011) and was granted with a postdoctoral research (in 2021) at London Birbeck University.\n \nShe is author of more than 20 research papers, and more than 35 book chapters (H Index 10). She is interested in the study of the educational process and the analysis of cognitive and affective processes in the context of neuroeducation and neurotechnologies, along with the study of social contingencies affecting the educational institutions and requiring new skills for educators.\n\nHer publications are mainly of the educational process mediated by technologies and digital competences. Currently, her new research interests are: the transdisciplinary application of the brain-based research to the educational context and virtual environments, and the neuropedagogical implications of the technologies on the development of the brain in younger students. Also, she is interested in the promotion of creative and critical uses of digital technologies, the emerging uses of social media and transmedia, and the informal learning through technologies.\n\nShe is a member of several research Networks and Scientific Committees in international journals on Educational Technologies and Educommunication, and collaborates as a reviewer in several prestigious journals (see public profile in Publons).\n\nUntil March 2010 she was in charge of the Adult University of Salamanca, by coordinating teaching activities of more than a thousand adult students. She currently is, since 2014, the Secretary of the Department of Theory and History of Education. Since 2015 she collaborates with the Council Educational Program by training teachers and families in the translation of advances from educational neuroscience.",institutionString:"University of Salamanca",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salamanca",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"23",title:"Social Sciences",slug:"social-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6942",title:"Global Social Work",subtitle:"Cutting Edge Issues and Critical Reflections",isOpenForSubmission:!1,hash:"222c8a66edfc7a4a6537af7565bcb3de",slug:"global-social-work-cutting-edge-issues-and-critical-reflections",bookSignature:"Bala Raju Nikku",coverURL:"https://cdn.intechopen.com/books/images_new/6942.jpg",editedByType:"Edited by",editors:[{id:"263576",title:"Dr.",name:"Bala",surname:"Nikku",slug:"bala-nikku",fullName:"Bala Nikku"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"52031",title:"Microplastics in Aquatic Environments and Their Toxicological Implications for Fish",doi:"10.5772/64815",slug:"microplastics-in-aquatic-environments-and-their-toxicological-implications-for-fish",body:'\nThe production of synthetic polymers has increased more than 100‐fold since the middle of the twentieth century to reach the 280 million tonnes of plastics produced annually worldwide
most of which is destined for disposable use [1]. High production coupled with the physical characteristics of most plastics, such as their chemical inertness and very slow biodegradation rates, results in an accumulation of plastic debris in the environment [2]. Routes of discharge such as improper waste disposal, insufficient waste management and urban run‐offs [3] may lead to significant amounts of these plastics entering the aquatic environment [4, 5]. It is a long‐recognized fact that marine plastic debris contaminates the oceans and seas of all the world [3, 6, 7]. In the marine environment, plastics undergo a process of weathering and fragmentation that breaks down macrodebris into smaller micro‐ and nanodebris. This fragmentation of plastic is caused by a combination of mechanical forces, for example waves and/or photochemical processes triggered by sunlight. Some ‘degradable’ plastics are even designed to fragment quickly into small particles, although the resulting material does not necessarily biodegrade [8].
The terms ‘microplastics’ (MP) and ‘microlitter’ have been defined differently by various researchers. Gregory and Andrady [9] defined microlitter as the barely visible particles that pass through a 500‐µm sieve but are retained by a 67‐µm sieve (≈0.06–0.5 mm in diameter), while particles larger than this were called mesolitter. Others [10–12] defined the MPs as being in the size range <5 mm (recognizing 333 µm as a practical lower limit when neuston nets are used for sampling). Microplastic particles may further fragment into ‘nanoplastics’, a term that has not been defined uniformly in the literature, and may refer to <100‐µm particles of plastic [13].
\nMicroplastics have been accumulating in the environment for nearly half a century and are found in oceans worldwide [3] including in the Antarctic [7]. Despite this worldwide dissemination of plastic fragments, the global load of plastics on the open ocean surface has been estimated to be far less than might be expected, but nevertheless increasing. Thus, the potential effects of microplastics on marine ecosystems are still far from being well understood [14]. It is believed that the virging MPs are not chemical contaminants to marine organism, but they can produce physical problems such as digestive congestion. However, they can be loaded with many substances to fit the virgin MPs to industry and consumer demand (e.g. additives, preservatives, etc.). In addition, these MPs can also adsorb contaminants present in the environment and act as vectors. Therefore, in this chapter, we shall summarize some important aspects of the microplastics found in the marine environments and some of the effects described in fish biota.
\nPlastics are usually synthesized from fossil fuels, but biomass can also be used as feedstock. The most commonly used plastic materials, the also called virgin plastics, are polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polystyrene (PS) and polyethylene terephthalate (PET), which, together, represent approximately 90% of total world plastic production [15]. They are elements of high molecular weight and are non‐biodegradable and therefore extremely persistent in the environment. PE, PP, PVC, PS, PET and polyurethane (PUR) are widely used resins (29, 19, 12, 8, 6, and 7% of global production, respectively) [16]. Plastics present many advantages since they are inexpensive, water‐ and corrosion‐resistant, chemically inert, easily moulded and exhibit good thermal and electrical insulating properties. However, plastics also present many disadvantages, being non‐renewable resources and sources of contamination by additive compounds; they suffer embrittlement at low temperatures and deformation under loads; they need costly recycling processes and are highly resistant to degradation, etc. The behaviour of plastics in the environment will differ according to their chemical nature and physical properties. A reflection of this is the description of microplastics found in marine environments in different studies (Table 1).
\nPolymer type | \n% Studies (n) | \n
---|---|
Polyethylene (PE) | \n31 (33) | \n
Polypropylene (PP) | \n25 (27) | \n
Polystyrene (PS) | \n16 (17) | \n
Polyamide (nylon) (PA) | \n6.0 (7) | \n
Polyester (PES) | \n3.7 (4) | \n
Acrylic (AC) | \n3.7 (4) | \n
Polyoxymethylene (POM) | \n3.7 (4) | \n
Polyvinyl alcohol (PVA) | \n2.8 (3) | \n
Polyvinyl chloride (PVC) | \n1.8 (2) | \n
Poly methylacrylate (PMA) | \n1.8 (2) | \n
Polyethylene terephthalate (PET) | \n0.9 (1) | \n
Alkyd (AKD) | \n0.9 (1) | \n
Polyurethane (PU) | \n0.9 (1) | \n
Frequency of occurrence of different polymer types in microplastic debris sampled at sea or in marine sediments [17].
Microplastics comprise a very heterogeneous assemblage of particles that vary in size, shape, colour, chemical composition, density, and other characteristics. They can be subdivided according to their usage and source into (i) ‘primary’ MPs, produced either for indirect use as precursors (nurdles or virgin resin pellets), for the production of polymer consumer products or for direct use, for example in cosmetics, scrubs and abrasives and (ii) ‘secondary’ MPs, which result from the breakdown of larger plastic material into smaller fragments [18].
\nMicroplastics (e.g. PE spheres) are used in personal care products such as toothpaste, facial and exfoliating creams, even though many consumers are not aware of this. In some cases, these MPs have replaced natural materials, such as seeds, shells or ground pumice ingredients. Usually, they are not filtered during wastewater treatment and are usually released directly into the sea or other water bodies such as lakes and rivers. Microplastics are also found in synthetic textiles: wastewaters from washing synthetic clothes, such as shirts, contain more than 100 fibres per litre of water. According to a study by Browne et al. [19], on average, about 1900 MP fibres can be released in a single machine wash. Similar fibres have been observed in wastewater effluent and sludge near large urban centres.
\nPlastic pellets are the raw material of plastic products. They are typically spherical or cylindrical in shape and millimetres in diameter. In addition, pellets are used in various industrial applications, including as ingredients of printing inks, paints spray, injection mouldings and abrasives [20]. A proportion of MPs used in these industrial applications enters the environment. The improvement in the management of operations in which plastic pellets are used could be a clear way to prevent them from entering the environment.
\nSecondary microplastics are formed when larger plastic items are broken down. The rate at which fragmentation occurs is highly dependent on environmental conditions, especially temperature and the amount of UV light available [20]. Plastic debris can enter the ocean directly or can reach it through other water bodies or the atmosphere. The key to stopping plastic ‘ocean trash’ is to prevent such waste from entering the environment in the first place. Obviously, larger objects are easier to identify and control than smaller objects. About half of the world’s population lives within 100 km of the coast, with an increasing population in that area. It is therefore highly likely that the amount of plastic waste entering the ocean from land‐based sources will increase if significant changes are not made in the waste management on land.
\nThe accuracy of MP emission estimates is currently hindered by lack of data. More specifically, information on MP transport efficiency in run‐off and streams is missing. This is despite the large number of qualitative studies on microplastics in rivers and sediments [21–24]. Similarly, only limited assessments of MPs from sewage and canalizations, their retention by wastewater treatment plants and release by effluents are available [25, 26].
\nOne of the most important factors affecting microplastic distribution in marine waters is the density of the materials (Table 2). Materials whose specific density is less than that of marine water (∼1.02) may be located on the surface, while materials with a specific density greater than that of marine water may be sink (Table 2). Thus, being buoyant in water, PE and PP float in seawater and mainly affect ocean surfaces and deposits ashore [27, 28], while PVC, which is denser than seawater, affects the seabed, often next to the source [27].
\nCategories | \nCommon applications | \nSpecific density* | \n
---|---|---|
Polyethylene (PE) | \nPlastic bags, six‐pack rings | \n0.91–0.94 | \n
Polypropylene (PP) | \nRope, bottle caps, netting | \n0.90–0.92 | \n
Foamed polystyrene (PS) | \nCups, buoy | \n0.01–1.05 | \n
Polystyrene (PS) | \nTools, packaging | \n1.04–1.09 | \n
Polyvinyl chloride (PVC) | \nBags, tubes | \n1.16–1.30 | \n
Polyamide or nylon | \nRope | \n1.13–1.15 | \n
Polyethylene terephthalate (PET) | \nBottles | \n1.34–1.39 | \n
Polyester resin+fibreglass | \nTextiles | \n>1.35 | \n
Polycarbonate (PC) | \nElectronic compounds | \n1.20–1.22 | \n
Cellulose acetate | \nFilter cigarettes | \n1.22–1.24 | \n
Polytetrafluoroethylene | \nTeflon, tubes | \n2.1–2.3 | \n
Location | \nMicroplastic concentrations | \nReferences | \n
---|---|---|
Pacific Ocean | \n27,000–448,000 particles per km2 | \n[33, 34] | \n
370,000 particles per km2 | \n[35] | \n|
0.004–9200 particles per m3 | \n[36, 37] | \n|
Atlantic Ocean | \n2.5 particles per m3 | \n[38] | \n
Indian Ocean | \n81.43 mg per kg* | \n[39] | \n
Mediterranean Sea | \n0.16 particles per m2 | \n[40] | \n
0.62 particles per m3 | \n[41] | \n
Microplastic concentrations observed in oceans of the world.
*Sediment samples.
Moreover, the colonization of MPs by microalgae and other microorganisms increases plastic density, which has been shown to affect the vertical transport of MPs in an aquatic environment and their long‐term distribution [31]. However, the chemical composition, particularly as a result of low amounts of additives, may partially explain the changes in microbiological colonization from one type of polymer to another. Also, for the same type of polymer, the chemical composition can vary considerably depending on chemical additives and the time passed in the environment. Hence, the distribution of MPs in the ecosystems may change according to these parameters, too. Long et al. [32] recently showed that MPs could be incorporated in microalgal homo‐aggregates, demonstrating the existence of a pathway of vertical transport of MP from the surface layer to the floor of the ocean.
\nIn addition, MP concentrations and/or quantities differ between sampling sites (Table 3). A significant variation between the microplastics sampled in different oceans is evident, but there are also differences between the areas of the same ocean or sea. Published works have detected different concentrations of MPs depending on the proximity to populated and/or contaminated areas.
\n\nTo enhance the performance of plastics, additives are added during manufacture, such as reinforcing fibres, fillers, coupling agents, plasticizers, colorants, stabilizers (halogen stabilizers, antioxidants, ultraviolet absorbers and biological preservatives), adsorbed chemicals, and unreacted starting materials (monomers), processing aids (lubricants and flow control), flame retardants, peroxide, antistatic agent, and plasticizers [16, 42], which may leach out under conditions of use and accumulate in the environment [43]. Apart from the potential negative effects of the MPs per se, it is generally assumed that microplastics may increase the exposure of marine aquatic organisms to chemicals associated with the plastics, such as persistent organic pollutants (POPs) or plastic additives [44–47]. Thus, analytical study of marine MPs has revealed the composition of many toxicants adsorbed to them.
\n\nAdditive | \nCAS | \nLog KOW | \nWater solubility (mg/L) | \n
---|---|---|---|
UV stabilizers | \n|||
Benzophenone | \n119‐61‐9 | \n3.18 | \nNone | \n
Benzotriazol | \n95‐14‐7 | \n1.44 | \n1.98 × 104 | \n
Antioxidants | \n|||
Irganox 1024 | \n32687‐78‐8 | \n7.79 | \n<1 | \n
Irganox 1098 | \n23128‐74‐7 | \n– | \n0.1 | \n
Irganox 1076 | \n2082‐79‐3 | \n<6 | \n<0.01 | \n
Irganox 1010 | \n6683‐19‐8 | \n≈23 | \n<0.01 | \n
Irganox 168 | \n31570‐04‐4 | \n>6 | \n<0.005 | \n
Plasticisers | \n|||
Dimethyl phthalate | \n131‐11‐3 | \n1.61 | \n4.2 × 104 | \n
Diethyl phthalate | \n84‐66‐2 | \n2.38 | \n1.1 × 104 | \n
Di‐n‐butyl phthalate | \n84‐74‐2 | \n4.45 | \n112 | \n
Butylbenzyl phthalate | \n85‐68‐7 | \n4.59 | \n2.7 | \n
Bis(2‐ethylhexyl) phthalate | \n17‐81‐7 | \n7.5 | \n0.003 | \n
Di‐n‐octyl phthalate | \n3‐1307 | \n8.06 | \n0.02 | \n
Lubricants | \n|||
n‐Hexadecanoic acid | \n57‐10‐3 | \n7.17 | \n0.04 | \n
Oleic acid | \n112‐80‐1 | \n7.64 | \nNone | \n
Glycerol tricaprylate | \n538‐23‐8 | \n9.20 | \n0.40 (37°C) | \n
Isopropyl myristate | \n110‐27‐0 | \n7.17 | \n2.44 × 10-2 | \n
1‐Eicosanol | \n629‐96‐9 | \n8.70 | \n1.5 × 10-3 | \n
2‐Hexyl‐1‐decanol | \n2425‐77‐6 | \n6.66 | \n0.1727 | \n
Octadecanamide | \n124‐26‐5 | \n7.292 | \nNone | \n
4‐Methyl‐benzenesulfonamide | \n70‐55‐3 | \n0.82 | \n3.16 × 103 | \n
Hexacosanol | \n506‐52‐5 | \n11.65 | \n1.438 × 106 | \n
Decanedioic acid, bis(2‐ethylhexyl) | \n122‐62‐3 | \n9.63 | \nNone | \n
Fuel | \n|||
Pentadactyl ester trichloroacetic acid | \n74339‐53‐0 | \n– | \n– | \n
1,10‐[2‐methyl‐2‐(phenylthio)cyclopropenylidene] bisbenzene | \n56728‐02‐0 | \n– | \n– | \n
2,4‐dimethyl‐4‐octanol | \n568123 | \n3.51 | \n188.9 | \n
Hexadecyl ester trichloroacetic acid | \n74339‐54‐1 | \n9.1 | \n6.223 × 10-5 | \n
Intermediates | \n|||
HEHA | \n59130‐69‐7 | \n11.15 | \n4.127 × 106 | \n
2,3‐Dihydroxypropyl ester hexadecanoic acid | \n542‐44‐9 | \n4.364 | \nNone | \n
Hexadecanoic acid ethyl ester | \n628‐97‐7 | \n7.74 | \n3.71 × 103 | \n
Behenic alcohol | \n661‐19‐8 | \n9.68 | \n1.5 × 105 | \n
Nonanoic acid | \n112‐05‐0 | \n3.42 | \n284 | \n
Pimaric acid | \n127‐27‐5 | \n6.60 | \n9.232 × 102 | \n
3,5‐Di‐tert‐butyl‐4‐hydroxy phenyl propionic acid | \n20170‐32‐5 | \n4.48 | \n12.93 | \n
Abietic acid | \n514‐10‐3 | \n6.51 | \n8.96 × 102 | \n
Dehydroabietic acid | \n1740‐19‐8 | \n6.35 | \n8.161 × 102 | \n
Monomers and oligomers | \n|||
Bisphenol A | \n80‐05‐7 | \n3.32 | \n300 | \n
4‐Hydroxyacetophenone | \n99‐93‐4 | \n1.42 | \n2.32 × 104 | \n
4‐Hydroxyacetophenone | \n99‐96‐7 | \n1.58 | \n5 × 103 | \n
Flame retards | \n|||
PCBs | \n1336‐36‐3 | \n3.76–8.26 | \n2.7–1.5 × 104 | \n
PBBs | \n67774‐32‐7 | \n6.5–9.4 | \n– | \n
PBDE | \n\n | 5.52‐11.22 | \n5.6 × 10-10–0.13 | \n
‐tetraBDE | \n40088‐47‐9 | \n5.87–6.16 | \n1.1 × 10-2 | \n
‐pentaBDE | \n32534‐81‐9 | \n6.57 | \n13.3 × 10-3 | \n
‐hexaBDE | \n36483‐60‐0 | \n6.86–7.92 | \n4.2 × 10-6 | \n
‐heptaBDE | \n68928‐80‐3 | \n9.44 | \n2.2 × 10-7 | \n
‐octaBDE | \n32536‐52‐0 | \n6.29 | \n5 × 10-4 | \n
‐nonaBDE | \n63936‐56‐1 | \n11.22 | \n5.6 × 10-10 | \n
‐decaBDE | \n1163‐19‐5 | \n6.265 | \n– | \n
α‐HBCD | \n134237‐50‐6 | \n5.07 | \n48.8 | \n
β‐HBCD | \n134237‐51‐7 | \n5.12 | \n14.7 | \n
γ‐HBCD | \n134237‐52‐8 | \n5.47 | \n2.1 | \n
TBBP‐A | \n79‐94‐7 | \n4.5 | \n720 | \n
BTBPE | \n37853‐59‐1 | \n7.88 | \n19 | \n
DBDPE | \n84852‐53‐9 | \n11.1 | \n21 | \n
Anti‐DP syn‐DP | \n13560‐89‐9 | \n9.3 | \n250 | \n
Others | \n|||
7,9‐Di‐tert‐butyl‐1‐oxaspiro(4,5)deca‐6,9‐diene‐2,8‐dione | \n82304‐66‐3 | \n3.59 | \n15.5 | \n
Glycerol 1‐palmitate | \n32899‐41‐5 | \n6.17 | \n0.1252 | \n
(Z)‐13‐docosenamide | \n112‐84‐5 | \n5.3 | \n0.2 | \n
Di‐tert‐dodecyl disulfide | \n27458‐90‐8 | \n6.1 | \nNone | \n
1‐Hexadecanol | \n36653‐82‐4 | \n6.83 | \n4.1 × 10-2 | \n
Oleic acid eicosyl ester | \n22393‐88‐0 | \n13.609 | \n– | \n
Octadecanoic acid | \n57‐11‐4 | \n8.23 | \n0.568–0.597 | \n
Octadecanoic acid 4‐hydroxy‐methyl ester | \n2420‐38‐4 | \n– | \n– | \n
Tridecanoic acid 4,8,12‐trimethyl‐methyl ester | \n5129‐58‐8 | \n– | \n– | \n
Succinic acid | \n110‐15‐6 | \n-0.59 | \n8.32 × 104 | \n
Triclosan | \n3380‐34‐5 | \n4.76 | \n12 | \n
In recent model analyses, however, it was shown that the effects of plastic on the bioaccumulation of POPs may be small, due to a lack of gradient between POPs in plastic and biota lipids, and that a cleaning mechanism is likely to dominate at higher log KOW (octanol/water partition coefficient) values [44, 48, 49] (Table 4). In the case of additives, monomers or oligomers, which are components of the plastics, this issue has hardly been addressed. Many substances such as plasticizers may have biological effects even at low concentrations in the ng/L or μg/L range [50]. Although it has been argued that exposure to additives will probably be low because of the low diffusivities of the chemicals, bioaccumulation could increase the concentration in animal tissues. Moreover, POPs, like the bisphenols or nonylphenols found in plastics, have been suggested to be a relevant environmental problem [51]. It has been reported that the concentrations of bisphenol A in wild freshwater fishes oscillated from undetected to 25.2 μg/kg biomass, while nonylphenol levels varied from 1.01 to 277 μg/kg [52]. So, the substances can enter and be accumulated by animals, and the log KOW could give an idea of the behaviour of additives in aquatic environments and their solubility in water (Table 4).
\nHydrocarbons are organic compounds comprising only carbon and hydrogen atoms. The molecular structure comprises a frame of carbon and hydrogen atoms and grouped into saturated (straight, substituted and cyclic alkanes), unsaturated (alkenes with straight, branched and cyclic), halogenated and aromatic hydrocarbons. The hydrocarbons can be classified into two types—aliphatic and aromatic. Aliphatic hydrocarbons in turn can be classified into alkanes, alkenes and alkynes as link types that bind the carbon atoms. The general formulas of alkanes, alkenes and alkynes are CnH2n+2, CnH2n and CnH2n-2, respectively. Many alkanes with a chain length varying from C‐11 to C‐31 have been found in plastics from coastal debris [16]. These are other oligomers originating from polyolefins (polypropylene, polyethylene and poly(acetylene: styrene)) during recycling [63]. Octadecane (n = 20/43), hexadecane (n = 19), eicosane (n = 18), tetradecane (n = 18), heptacosane (n = 14), heptadecane (n = 13), pentadecane (n = 11), tetracosane (n = 10), docosane (n = 8), dodecane (n = 7), hexacosane (n = 7), 2,6,10‐trimethyl‐tetradecane (n = 10) and heptadecane, 3‐methyl‐ (n = 6) were the most frequently detected in the plastic debris from near the coasts [16].
\nLinear alkanes, together with iso‐alkanes, originate from the paraffin wax that is used as an external lubricant in PVC and other polymers, where they help the polymers to slide over other surfaces. Alkanes are also used as a solvents, such as hexane and heptane. Alkenes (squalene and others) and cycloalkenes are used as starting compounds for several additives and polymers and are formed as by‐products during olefin polymerization.
\nAromatic hydrocarbons, such as benzene and anthracene derivatives, have also been found in MP debris. Benzene is an important organic chemical compound used mainly as an intermediate to make other chemicals, mainly ethylbenzene, cumene, cyclohexane, nitrobenzene, and alkylbenzene. More than half of the entire benzene production is processed into ethylbenzene, a precursor of styrene, which is used to make polymers and plastics like polystyrene and expanded polystyrene. Around 20% of benzene production is used to manufacture cumene, which is needed to produce phenol and acetone for resins and adhesives. The plastics may also carry halogenated hydrocarbons, which have been considered as POPs and are of proven toxicity [64, 65].
\nBenzophenone and its derivatives are used as photo‐initiators in the UV curing of inks and as UV absorbers. These compounds absorb the harmful UV light that would eventually change the physical and optical properties of the polymer and make the material lose colour or fade. This substance can also be added to plastic packaging as a UV blocker to prevent photo‐degradation of the packaging polymers or contents. Its use allows manufacturers to package the product in clear glass or plastic [66] since, without the UV blocker, opaque or dark packaging would be required. These plastic additives are used in PP, PE (2–3%) and acrylonitrile, butadiene and styrene (ABS) copolymer products. Benzotriazole UV stabilizers (BUVS) are emerging contaminants that are mutagenic, toxic, pseudopersistent, bioaccumulated and show significant estrogenic activity [67–70]. Great amounts of BUVS have been detected in rivers from Japan and China coming from wastewater treatment plants [71–73]. Due to their common use, BUVS have been found in aquatics environments [69, 72, 74], organisms [71, 72, 74, 75], tap water and well water [76]. Recent findings in German rivers and previously reports suggest that BUVSs have a potential of long‐range transport, similar to several POPs [74].
\nAntioxidants are widely used in plastic polymers to delay oxidation and to improve polymer properties [77]. Several types of antioxidants can be used to prevent the aging of plastic, such as phenolic antioxidants, organophosphorus compounds and different amines. However, antioxidants can migrate from the plastics into the food and contaminate it during production or storage, potentially giving rise to food safety issues [78, 79]. Antioxidants are used in almost all commercial polymers in small amounts up to 2% (w/w) (20,000 mg/kg or ppm) [16]. The polymers can be oxidized during synthesis, processing, transfer or final use, resulting in loss of chemical, optical and mechanical properties, among others. Thermal oxidation results in the formation of free radicals that react with oxygen to form hydroperoxides. In order to inhibit the onset of thermal oxidation of polymers and/or slow down degradative processes, the antioxidant additives are added during manufacture, processing and/or during the manufacture of the products. In the specific case of the polypropylene, antioxidant additives are important because the chemical structure of this type of polyolefin tends to degrade easily. The plastic antioxidants identified in the literature are usually limited to the commonly used Irganox series (including Irganox 1010, Irganox 1076, Irganox 168) [80–84].
\nPlastic as a material may contain a variety of chemicals, some potentially hazardous. Plasticizers, which are used to make the plastic soft and flexible, are mainly used in PVC, but they are detected in other polymer plastics. Several types of plasticizers are found in plastic debris, but phthalates predominate [85]. The phthalates found in plastics include dimethyl phthalate (DMP), diethyl phthalate (DEP), di‐n‐butyl phthalate (DBP), butyl benzyl phthalate (BBP), bis(2‐ethylhexyl) phthalate (DEHP) and di‐n‐octyl phthalate (DNOP) [86]. Their concentrations in different plastics vary widely; for example, in foodstuffs, the content of phthalates varies from 658 to 1610 ng/g fresh weight [87]. Phthalates are produced in large quantities around the world and are also widely used in cosmetics, plastics, carpets, building materials, toys, medical and cleaning products.
\nSeveral cross‐sectional and case‐control studies have reported an association between exposure to phthalates and the development of certain human allergies and respiratory diseases [88]. A recent systematic review based on less than ten relatively small (N < 400) studies found that the findings from these studies are inconsistent, with both decreases in birthweight and null associations, and both longer and shorter gestational periods being recorded [89]. A prospective birth cohort study researched the association between butyl benzyl phthalate and an early‐onset eczema, although not the late‐onset eczema, finding that prenatal exposure to butylbenzyl phthalate may influence the risk of developing eczema in early childhood [90]. Three studies reported a positive relation between prenatal exposition and the risk of wheeze, asthma and respiratory infections in children aged 5–11 years [91–93], although, even here, there were inconsistencies concerning the phthalate congeners implicated [94].
\nOn the other hand, phthalates have been related with hormone disorders [95], abortion [96], metabolic diseases [97], hormone disturbances, reprotoxicity or even suspected cancer [98–100]. Other plasticizers are often used as substitutes for phthalates, but their effects on the health are not always clear, usually because of the limited data available. Therefore, because the amount of plasticizers could increase the 50% of the total weight, and the possibility that these substances will leach when the plastics come into contact with seawater is greater [16], the substances called plasticizers should be considered in a hazard category and need be reviewed.
\nUsually, lubricants are used to minimize adhesion and viscosity of plastic polymers. Internal lubricants can facilitate the production process by providing lubrication at molecular level between the polymer chains [101]. Commonly, they are composed of an oil base accompanied by a variety of additives that confer desirable properties. Lubricants are based in one type of base oil, but in commercial requirements, it usually makes that a mixture are used [102]. n‐Hexadecanoic acid, oleic acid, glycerol tricaprylate, isopropyl myristate, 1‐eicosanol, 2‐hexyl‐1‐decanol, octadecanamide, 4‐methyl‐benzenesulfonamide, 1‐hexacosanol and decanedioic acid, bis(2‐ethylhexyl) ester can be found in plastic debris [16]. The transfer of additives such as lubricants to the medium or to the substances which are in contact with the plastics has been reported previously [103].
\nChemicals like pentadactyl ester trichloroacetic acid, 1,10‐[2‐methyl‐2‐(phenylthio) cyclopropenylidene] bisbenzene and 2,4‐dimethyl‐4‐octanol are often found in plastic debris [16]. These substances and others, like hexadecyl ester trichloroacetic acid, have been considered as fuel precursor based on plastic wastes additives, due to the large amount and variety of additives that plastics can contain [63]. Waste plastics are considered a promising source for fuel production because of their high combustion heat and their increasing availability in local communities [104].
\nIn manufacture of plastics, it is normal to use stabilizers (DEHA or DEHP) and plasticizers that contain intermediate substances like hexanoic acid 2‐ethyl‐hexadecyl ester (HEHA), 2,3‐dihydroxypropyl ester hexadecanoic acid, hexadecanoic acid ethyl ester, behenic alcohol, nonanoic acid, pimaric acid, 3,5‐di‐tert‐butyl‐4‐hydroxyphenyl propionic acid, abietic acid and dehydroabietic acid [16]. HEHA has been classified as belonging to reprotoxic category 3 by Council Directive 67/548/EEC [105].
\nFlame retardants are a group of chemical compounds that are used in plastics with the aim of diminishing the flammability of combustible materials, like synthetic polymers and plastics. To make sure that flame retardants remain in the polymers, these compounds are designed to be stable for many years, which means they will remain in the environment long past the time when the material itself was used [106]. Thus, these compounds can enter aquatic environments via the atmospheric deposition of fine particles, direct discharges of municipal and industrial wastewater effluents, and through run‐off and other human activities [107]. Flame retardants include α, β, γ‐diastereoisomers of hexabromocyclododecane (HBCD), tetrabromobisphenol‐A (TBBP‐A), anti‐ and syn‐isomers of dechlorane plus (DP) and two novel compounds, decabromodiphenylethane (DBDPE) and 2‐bis(2,4,6‐tribromophenoxy) ethane (BTBPE). Among the most widely used flame retards are polybrominated diphenyl ethers, which have been in use since the late 1970s. Polybrominated diphenyl ethers are a class of brominated compounds widely used as flame retardants including in polymers such as low density polyethylene or silicone rubber [45, 108]. Polybrominated diphenyl ethers are very hydrophobic, with log KOW above 5.5 and molecular weights (MW) in the range of 300–1000 g/mol which means that these compounds are likely to have diffusion coefficients significantly lower than those measured for polycyclic aromatic hydrocarbons and polychlorinated biphenyls. This implies that the polymer diffusion coefficients for these plastic additives used as flame retardants need to be taken into account when considering the risk posed by microplastic particle ingestion by marine organisms [109]. Many studies on polybrominated diphenyl ethers [110–118] have shown that these compounds are ubiquitous, toxic, persistent and bioaccumulated in the environment. As a result, some flame retardants have been prohibited in the USA and European Union [119, 120], such a penta‐ and octabrominated diphenyl ether. Nevertheless, new compounds have replaced the forbidden polybrominated diphenyl ethers, such as 1,2‐bis(pentabromodiphenyl) ethane, which is used in solid plastics, wire, cable and electronics, high impact polystyrene and thermoplastics [121].
\nBisphenol A (2,2‐(4,4‐dihydroxydiphenyl) propane) is used as a monomer in polycarbonate, for the production of polycarbonate plastics and epoxy resins. It has been found in samples of PE, PP and acrylate‐styrene, where it is probably used as chain terminator, to finish the polymerization of polymers or as antioxidant for polymers or plasticizers [16]. Bisphenol A is also used to manufacture a great variety of products, including CDs, food can linings, thermal paper, safety helmets, plastic windows, car parts, adhesives, protective coatings, powder paints, and the sheathing of electrical and electronic parts [122]. As a result of its wide usage, bisphenol A is frequently detected in wastewaters [123].
\nBisphenol A has been identified as an endocrine disruptor [124], and several studies have demonstrated reproductive, metabolic and neurodevelopmental problems in animals exposed to environmentally relevant levels of this substance [125–127]. In addition, an increased risk for cardiovascular disease, altered immune system activity, miscarriages, decreased birthweight at term, metabolic problems and diabetes in adults, breast and prostate cancer, reproductive and sexual dysfunctions and cognitive and behavioural development in young children have been associated with the human exposure to bisphenol A [128–134].
\nIt is known that plasticizers may have biological effects even at low concentrations in the ng/L range, especially for molluscs, crustaceans and amphibians [50]. Although it has been argued that one should expect levels of exposure to plastic additives to be low due to the low diffusivities of chemicals like bisphenol A or nonylphenol in plastics [51], as we said above, their bioaccumulation could play an important role, increasing physiological concentrations in the food chain. In an attempt to solve these problems, physicochemical processes for the removal of bisphenol A from wastewaters have been studied [135, 136]. However, possible solutions presented several problems related to the cost of chemicals, the generation of bisphenol A‐containing sludge and the conditions necessary to optimize the bisphenol A elimination process. The most frequently detected metabolic products of the aerobic biodegradation pathway of bisphenol A include 4‐hydroxyacetophenone and 4‐hydroxybenzoic acid [137]. Both bisphenol A and 4‐hydroxybenzoic acids have shown a certain degree of biodegradability [138], and these compounds are not expected to be persistent in an activated sludge system, although the information concerning 4‐hydroxyacetophenone is scarce.
\nDegradation products, antifogging, antiblocking, colouring, heat stabilizers, fatty acids and their derivatives have also been found in plastics debris [16]. This heterogeneous group includes 7,9‐di‐tert‐butyl‐1‐oxaspiro(4,5)deca‐6,9‐diene‐2,8‐dione, glycerol 1‐palmitate, (Z)‐13‐docosenamide, 2,3‐dichloro‐1,10‐biphenyl, trans‐13‐docosenamide, di‐tert‐dodecyl disulfide, 1‐hexa‐decanol, 2,4‐bis [2‐(4‐methoxyphenyl‐2‐propyl)] methoxybenzene, oleic acid eicosyl ester, octadecanoic acid, octadecanoic acid 4‐hydroxy‐methyl ester, octadecanoic acid 2‐hydroxy‐1‐(hydroxymethyl)ethyl ester, tridecanoic acid 4,8,12‐trimethyl‐methyl ester, heptanedioic acid 4‐(ethoxycarbonylmethylene)‐diethyl ester and succinic acid [16]. Fatty acids and their esters could originate from several kinds of oils, such as coconut oil (lauric acid) or palm oil (palmitic acid), acids which, along with their esters, are usually used as internal lubricants. Besides, metallic salts of fatty acids are normally used as stabilizers and plasticizers in the production of the plastics.
\nOther substances can stick or bind to plastics, such as disinfectants, aromatic compounds, soaps used to clean the plastics. In this respect, triclosan (5‐chloro‐2‐[2,4‐dichloro‐phenoxy]‐phenol) is an additive that has been reported to be toxic [139–141]. Triclosan is an antimicrobial that is effective against bacteria of the adult oral cavity and skin. It is currently used in antibacterial soaps, deodorants, skin creams, toothpastes and plastics. Triclosan is an ionizable chlorinated biphenyl ether of low water solubility, with a pKa of 8.1, and a vapour pressure of 4 × 10-6 mm Hg [139]. Triclosan readily bioaccumulates within aquatic organisms and has been found to be toxic to fish. In larval fishes, exposure to triclosan disrupts a variety of developmental processes, impairs hatching success, and causes pericardial oedema, having the potential to cause subtle cardiac toxicity [142]. Browne et al. [47] showed that triclosan added to MPs diminished the ability of worms to engineer sediments and caused mortality, each by >55%, while PVC alone made worms >30% more susceptible to oxidative stress. Triclosan persists in water and is difficult to eliminate from wastewaters [143, 144]. The ingestion of MPs by organisms can transfer pollutants and additives (such as triclosan) to their tissues at concentrations sufficient to disrupt ecophysiological functions linked to health and biodiversity. Biomarkers of endocrine disruption found in fish indicated long‐term exposure to estrogenic chemicals in the wastewater [145].
\nThe accumulation of microplastic waste could affect the functioning of marine ecosystems. However, the mechanisms by which these effects will be manifested have not been identified. Impacts on biota and marine environmental quality are well documented [146], with damage for the global economy estimated to be in the range of $13 billion per year [147].
\n\nPrincipal effects of microplastics on fish.
Negative effects include entanglement in plastic wires or nets, or to ingestion, which has been reported in benthic invertebrates, birds, fish, mammals and turtles [148–151]. This is especially true for eggs, embryos and larvae of aquatic organisms, which are particularly vulnerable to water‐borne pollutants owing to their limited ability to regulate their internal environment [152]. In particular, the early life stages of fishes are subjected to strong selection forces, driven by high rates of predator‐induced mortality [153, 154]. So, it has been reported that there is a clear overlap between areas with high levels of microplastics pollution and the feeding grounds of fin whales in the Mediterranean Sea, which could mean that fin whales are subjected to a high level of exposure to MPs ingestion during feeding in the areas [155]. The bioaccumulation of MPs and the substances which they could carry seem to be an increasing problem due to MPs which has been detected from little fish species to the top of food web.
\nThe ingestion of the MPs can influence marine animals in different ways (Figure 1). It can affect to the immune system, both chemically (caused by the substances that MPs might contain, absorb or release, which may be toxic) [156] and physically blocking the digestive organs and preventing the animals from feeding [157]. Ecology and behaviour could also be affected.
\n\nInteractions between plastic microparticles and aquatic organisms have been reported, and several recent studies have addressed the effects of nanoplastic material on different organisms and their health status. This research suggests that nanoplastics can enter different organisms and may interact with the immune system [158–161].
\nIn fish, cellular innate immunity effectors act as one of the first organ defences against various agents, which makes these effectors the possible target for interaction with nanoplastic particles. Neutrophil activation is critical for the host defences, and their function is a valuable tool to assess the health status of individuals and animal populations [162]. So, fish neutrophils can extravasate, migrate chemotactically, degranulate, release neutrophil extracellular traps and phagocytize particulate matter such as bacteria [163]. Hypotheses existed about the interactions between MPs or nanoplastics and the neutrophils until recently, it has been reported that polystyrene and polycarbonate nanoplastic can act as stressors to the innate immune response of fish [164]. Therefore, nanoplastic could potentially interfere with innate immune responses in fish populations by altering organismal defence mechanisms.
\nIn addition, plastic fragments found in the marine habitat have been shown to absorb POPs, so effects on the immune system may be caused by particle toxicity, plastic‐associated chemicals and absorbed environmental chemicals.
\nEvidence points to the potential role of microplastics as vectors of chemical pollutants, either used as additives during polymer synthesis, or adsorbed directly from seawater [27, 45, 165]. The hydrophobicity of organic xenobiotics and the surfaces of polymers facilitate the adsorption of the chemicals on MPs at concentrations with orders of magnitude higher than those usually detected in seawater [166].
\nSeveral of these plastic‐associated chemicals have been linked to endocrine‐disrupting effects in fish. Styrene [167], a monomer of several plastic types including polystyrene, rubber and acrylonitrile–butadiene–styrene, and bisphenol‐A [168] a monomer of polycarbonate, can disrupt the endocrine system function, as mentioned above. In addition, there is evidence that UV stabilizers, phthalates and nonylphenol, additives to plastic, are estrogenic and/or antiandrogenic [169, 170]. Furthermore, chemicals historically known to promote adverse effects in the endocrine system functions, including heavy metals, organochlorine pesticides and petroleum hydrocarbons [171, 172], have been found attached to plastic debris around the world [173, 174].
\nThe ingestion of plastic debris has been documented in fish [175, 176], which may introduce a ‘cocktail’ of endocrine‐disrupting chemicals [47, 150, 177]. Significantly higher concentrations of several polybrominated diphenyl ethers, such as polychlorinated biphenyl congener (PCB#28) and the polycyclic aromatic hydrocarbon chrysene, have been recorded in Japanese medaka (Oryzias latipes) exposed to polyethylene that had been deployed in the marine environment compared to fish exposed to a virgin polyethylene and a control treatment [177].
\nFish are useful as sensitive indicators of endocrine‐disrupting chemicals in aquatic habitats, as exposure can result in changes in gonadal growth, gonadal degeneration, sex‐specific gene protein and intersex induction [178]. Finally, recent research showed that ingestion of plastic debris at environmentally relevant concentrations may alter the endocrine system function in adults [179], where the presence of abnormal germ cell proliferation observed may be related to plastic. In this respect, ovary structure protein 1 (OSP1) gene has been proposed as a suitable indicator of the early stages of intersex development and suggested to be a more sensitive early‐warning signal than histopathological observation [180].
\nIt has been shown in various marine organisms that ingestion of MPs occurs in animals with different feeding strategies and may negatively influence both the feeding activity and nutritional value, especially in species which cannot vary their food source [181, 182]. Different studies have pointed to the obstruction and damage of digestive tracts or even animals starving to death caused by stomachs filled with plastic [18]. In addition, MP ingestion by marine biota has been detected in benthic fish species [183, 184], and different sized plastic items were identified in the stomachs of three large pelagic fish in the Mediterranean Sea [185].
\nIn a study made in Spanish coastal waters and which constitutes the first report of MPs ingestion by demersal fishes, red mullets (Mullus barbatus) from Barcelona presented the highest abundance of microplastics, followed by dogfish (Scyliorhinus canicula) from the Cantabrian coast and the Gulf of Cadiz, whereas dogfish from the Galician coast presented the lowest levels [186]. In agreement with previous studies, the detected MPs were mostly fibres (71%) [174, 184, 187], and the most frequent colour was black (51%) (Table 5).
\n\nBecause of their small size, MPs may be ingested by marine organisms, regardless of their feeding mechanisms, and may enter their circulatory system and accumulate in different types of tissues, as has been proven in laboratory experiments [182]. These reported data, along with the fact that MPs serve as dispersal vectors for invasive species [188] and the toxic and bioaccumulative substances bound to the plastics [149], together with the research that indicates that MPs may have the ability to enter and disseminate though the marine food web [189, 190], suggest grave ecological implications of microplastics across the food web.
\nForm | \nPercentage (%) | \n
---|---|
Fibre | \n71.0 | \n
Sphere | \n24.2 | \n
Film | \n3.2 | \n
Fragment | \n1.6 | \n
Types of plastics found in fish and their relative abundance in Spanish coastal waters [188].
Behaviour is a crucial determinant for essential parameters such as overall health, growth, reproduction and survival [191]. During the life cycle of fish, a critical point is the early stage of development. Survival depends, in many cases, on the capacity of the organism to evade predators. An innate ability to detect and act accordingly is therefore vital [153, 154, 192].
\nIn this regard, it has been suggest that olfactory sense in fish larvae could suffer damage mediated by an immunological response produced by the pollutant from microplastics. Lönnstedt and Eklöv [193] found that not only was crucial behaviour, such as activity and feeding, affected by microplastics, but that innate responses to olfactory threat cues were also impaired. Such a loss of predator avoidance behaviour greatly increased predator‐induced mortality rates of larvae. Finally, survival of fishes could be seriously affected by the presence of MPs, with their significant impact on the life cycle of the fish.
\nMicroplastics in the aquatic environment have been demonstrated to be a significant problem. The great amount of research on this topic, as well as the quantity of the results that describe the problem of MPs and their effects on fishes and aquatic life, have thrown some light on this issue. Among the effects that MPs have are stress, intestinal obstruction and the alteration of health, while further studies are in progress to ascertain the full potential risks of MPs in aquatic organisms with special attention paid to fish. A huge number of substances are added to plastics, which can bioaccumulate throughout the trophic chain. Besides the problems that MPs represent for marine life in general, the MPs could begin act as disruptors of the welfare and health of fishes, both wild and cultivated. This is clearly a growing problem not only for the environment but also for human health. For these reasons, further efforts are needed to know the exact effects that microplastics, and their constitutive and adsorbed contaminants, may have on aquatic environments.
\nFinancial support by grants PCIN‐2015‐187‐C03‐02 (MINECO, JPIOceans: Microplastics, EPHEMARE) and 19883/GERM/15 (Fundación Séneca de la Región de Murcia, Spain) is gratefully acknowledged.\n
\nTaiwan is on the path of western Pacific typhoon path and on the circum-Pacific earthquake belt, indicating that Taiwan suffered from two or more natural disasters, which was the highest in the world [1]. Besides, most of the land in Taiwan, about 70% of total area, is hillside. Given the conditions of increasing impacts of climate change and extreme weathers, the rainfall-induced landslide has become a serious issue in Taiwan.
Most landslide researches used the landslide susceptibility analysis (LSA) to develop landslide evaluation model [2]. The LSA models basically use factors and observed data to construct the description of landslides. The factors include rainfall intensity, accumulated rainfall, slope degree, vegetation, etc. The common models developed for landslide hazard or landslide evaluation are usually deterministic analysis, including the traditional slope stability analysis [2]. Recently, a novel concept of applying probability to landslide evaluation had been proposed. The fragility curves, which are commonly used in the earthquake-induced structure analysis, had been adopted to represent the probability of landslide [3, 4, 5]. The process of applying fragility curve to landslide evaluation is to consider and estimate the recurrence and the probability of exceedance of a damage level for a landslide [3, 4].
In this chapter, the preparation of landslide fragility curves was introduced. The procedure of developing the landslide fragility curve (LFC) model was the researches of rainfall-induced shallow landslide in the past years [2, 3, 4, 5]. The proposed LFC model considered the impacts of rainfall and the vulnerability of environment. Instead of using one-variable triggering factor (rainfall intensity or accumulation) in the previous research [2], the newly improved LFC model used bivariate approach in the model [3, 4]. The improved LFC model introduced the landslide fragility surface (LFS) by considering the influence of both rainfall intensity and accumulation at the same time [4, 5].
The spatial statistics and geographic information system (GIS) were used for data processing. The data of each factor used in the model was further divided into groups. Classification of factors represented the environmental characteristics of a specific area. The analysis basis was conducted spatially on the slope units, which are topographically defined as the parts of a watershed [5]. With the LFS model, the risk assessment of landslide then was analyzed in association with the rainfall hazard potential [4, 5]. The Shenmu area of Chen-Yu-Lan watershed was selected as the study area, and historical cases were used to illustrate the application of LFS model.
When considering the factors to be used in the landslide problem, these factors are generally classified as triggering and environmental factors [6, 7, 8]. Among these factors, the rainfall is usually the major concern, and for environmental vulnerability, many factors can be chosen from. Not every chosen environmental factor can be used in developing a landslide model because of (1) few data in the database, (2) lack of data, and (3) low influence in the model. In this chapter, the cumulative rainfall and maximum hourly rainfall (rainfall intensity) were used for triggering factors, whereas slopes, slope aspects, landslide area, incremental landslide area, ratio of incremental landslide area, normalized difference vegetation index, distance to the nearest river, and geology were used for environmental factors of hillside slope in the study. A GIS database to describe landslide areas was created and was later applied in developing the proposed fragility curve model. These indexes, factors, and symbol definitions are explained in the following:
Maximum rainfall intensity (Imax): the maximum rainfall intensity is the rainfall in the form of rainfall per unit time. In this study, Imax refers to the maximum hourly rainfall (Figure 1) and was used as a triggering factor for LFC model.
Effective accumulated rainfall (Rte): the Rte is defined as the accumulated rainfall before the maximum rainfall intensity in a continuous raining event (Figure 1), by considering the influence of antecedent 7-day rainfall.
Hillside slope (S): the dynamic behavior of the landslide has close relationship with the slope. Hence, the degree of slope may be a prominent factor of triggering landslides. In this study, the slope was classified based on the Soil and Water Conservation Bureau manual [9]. There are seven slope levels of 5% or less, 5–15%, 15–30%, 30–40%, 40–55%, 55–100%, and slope exceeding 100%. The slopes <15% are recognized as flat ground or very gentle slopes and not included in this study. Slopes of levels 3–7 were studied in the landslide model.
Slope aspect (A): the slope aspect represents the vulnerable directions of occurring landslide when given a known topography. This factor may represent the “weak” aspect of a slope in terms of landslide.
Landslide area (LA): observing the landslide distribution through image classification results can obtain the information about the land cover change. The change from events of Typhoon Sinlaku (in 2008) and Typhoon Morakot (in 2009) was identified using GIS software.
Incremental landslide area (IA): to understand the landslide increment, the images before and after a landslide were considered. The landslides are classified into five categories (shown in Figure 2): (1) the original landslide area (number 1 + 2), (2) the original landslide area extension (number 2), (3) new landslide area on single period (number 3), (4) new landslide area on pre-/post periods (number of 2 + 3), and (5) vegetation restoration area (number of 1). In this study, the new landslide area on pre-/post periods (number of 2 + 3) was considered.
Ratio of incremental landslide area (RIL): to obtain the ratio of incremental landslide area, this study used the incremental landslide area from image of two periods to determine this factor.
Vegetation index (N): to determine the density of vegetation on a patch of land, researchers must observe the distinct colors (wavelengths) of visible and near-infrared sunlight reflected by the plants [10]. Nearly almost satellite vegetation indices employ the difference formula,
Distance to the nearest river (R): the landslide may be triggered due to the erosion by the river at the toe section. The distance to the river reflects the potential of landslide contributed from the river system.
Geology (G): the geological time scale of the area and the rock types of the site were combined into consideration as the geology factor. In the past studies, the geology-related information (like the rock types and rock strength) was not usually available. Therefore, to simplify the classification, the geological time scale was chosen to represent the possible influence of geology.
The definition of rainfall indices: Imax and Rte (modified after [2, 3, 4]).
Concept of mapping landslide area change: differences between two periods of SPOT image [2].
To explain the landslide fragility model, the Shenmu area in Taiwan was used as a case to demonstrate the development of LFC of a given site. The Shenmu area locates in the watershed of Chen-Yu-Lan River. Chen-Yu-Lan watershed is at the central part of Taiwan (Figure 3). The Chen-Yu-Lan River originates from the north peak of Yu Mountain and is one of the upper rivers of the Zhuoshui River system, which is the largest river system in Taiwan. Chen-Yu-Lan River has a length of 42.4 km with an average declination slope of 5%, and its watershed area is about 450 km2. This area was fragile after the Chi-Chi Earthquake (occurred on September 21, 1999).
Chen-Yu-Lan watershed [2].
The Shenmu area is a location where debris flows frequently occurred [5]. The local village is adjacent to the confluence of three streams: Aiyuzi Stream (DF226), Huosa Stream (DF227), and Chushuei Stream (DF199). In Shenmu, the debris flows usually occurred at the Aiyuzi Stream due to its shorter length and large landslide area (Table 1) in its upstream [5]. Figure 4 shows the terrain of three streams.
Debris flow no. | Stream | Length (km) | Catchment area (km2) | Landslide area (km2) |
---|---|---|---|---|
DF199 | Chushuei stream | 7.16 | 8.62 | 0.33 |
DF227 | Huosa stream | 17.66 | 26.20 | 1.49 |
DF226 | Aiyuzi stream | 3.30 | 4.00 | 1.00 |
The landslide area in Shenmu after 2009 [5].
The terrain and landslide areas of Shenmu area.
In addition to the basic terrain data of Shenmu area, the hydrologic and geographic factors are needed in modeling. To obtain these factors, an environment database of Chen-Yu-Lan watershed was prepared. Among the data collection, the landslide increment (i.e., new landslides) after a rainfall event was also obtained by image processing method in this study.
To develop the LFC model, the local environmental data was collected for the study area, and GIS was used to process the data. The environment database of Chen-Yu-Lan watershed includes data of geology, geological layers, rock property, slope and slope aspects, and DEM, as shown in Figures 5–8.
Chen-Yu-Lan watershed: (a) geological time scale and (b) rock types.
Five-meter DEM of Chen-Yu-Lan watershed (after [2]).
The slope of Chen-Yu-Lan watershed.
The slope aspects of Chen-Yu-Lan watershed.
The new landslide areas (Figures 9 and 10) were identified by using pre- and post-event satellite images of Typhoon Sinlaku in 2008 and Typhoon Morakot in 2009 (Table 2). These landslide areas were used for later LFC model analysis. Another important factor in the LFC model is the vegetation conditions. The information of vegetation status was also obtained by image processing the same as the determination of new landslides.
Satellite images of pre- (a) and post-event (b) Typhoon Sinlaku and the new landslide areas (c) in Chen-Yu-Lan watershed.
Satellite images of pre- (a) and post-event (b) Typhoon Morakot and the new landslide areas (c) in Chen-Yu-Lan watershed.
Watershed | Event | Image time | Satellite | Incremental area (km2) |
---|---|---|---|---|
Chen-Yu-Lan 448.14 km2 | Pre-Sinlaku | February 21, 2008 | SPOT5 | 9.52 (2.12%) |
Post-Sinlaku | November 28, 2008 | SPOT5 | ||
Pre-Morakot | November 28, 2008 | SPOT5 | 10.21 (2.28%) | |
Post-Morakot | October 14, 2009 | SPOT5 |
Satellite images of events at Chen-Yu-Lan watershed.
In addition to the hydrologic and geographic data, the landslide triggering factors were also considered in data preparation. Table 3 defines the rainfall indices. It should be noted that the effective accumulated rainfall was calculated by including the antecedent 7-day accumulated rainfall. The antecedent 7-day accumulated rainfall is the total weighted rainfall counted from the 7-day duration before the starting of current rainfall event. Take Typhoon Sinlaku (September 11–16, 2008) for example. The starting date of Typhoon Sinlaku was September 11, 2008, and the antecedent 7-day accumulation rainfall was the total weighted rainfall during September 3 to September 10, as described as Ra in Table 3.
Index | Symbol | Definition |
---|---|---|
Max. hourly rainfall | Imax | The maximum hourly rainfall in a rainfall event |
Effective accumulated rainfall | Rte | The antecedent 7-day accumulated rainfall (with reduction factor of 0.7*) before the starting of current event and the accumulated rainfall before the max. hourly rainfall in current event |
The rainfall indices.
Antecedent 7-day accumulated rainfall (Ra) can be calculated by
Figures 11 and 12 show the rainfall interpolation of the events of Typhoon Sinlaku (September 11–16, 2008) and Typhoon Morakot (August 5–10, 2009). The red spots in the figure are the locations of rainfall stations. It was noted that the rainfall intensity and the cumulative rainfall of event of Typhoon Morakot were much higher than those of Typhoon Sinlaku. Both events had caused serious landslides in the central Taiwan.
Rainfall indices of Typhoon Sinlaku: (a) Imax and (b) Rte.
Rainfall indices of Typhoon Morakot: (a) Imax and (b) Rte.
Finally, the database was used to analyze the study area on the basis of slope units. The slope unit was defined as in Figure 13. A slope unit is defined as one slope part or the left/right part of a watershed. Slope units can be topologically divided by the watershed divide and drainage line, with the help of GIS tool [12]. The application of slope unit in the development of LFC was based on the physical interpretation of slopes in the mountain area. The environmental database was applied in accordance with the slope units at the site of interest. Figure 14 shows the slope unit distribution (total 5872 units) of Chen-Yu-Lan watershed.
Slope unit delineation, the left and right slope units of a watershed [3, 4].
The slope units of Chen-Yu-Lan watershed.
To develop the empirical landslide fragility model, a probability distribution was chosen to describe the potential of landslide fragility. When the probability distribution was determined, the parameters of probability, the median and standard deviation, were obtained by fitting the data from the environmental database and the landslide areas. The use of slope unit was adopted here, and the classification of environmental factors was applied to represent the conditions of landslide given rainfall intensity and accumulated rainfall. The procedure of developing the empirical landslide fragility curve was described in the following.
The fragility analysis is usually used to describe the potential of hazard in terms of potential levels or probability of exceedance of a level. To describe the probability about a hazard fragility, a feasible probability distribution can be assumed and applied in the model. The fragility curve of landslide, therefore, was assumed to be a lognormal distribution [12, 13]. The lognormal distribution can be constructed simply by the values of median and lognormal standard deviation and are called bivariate parameters (Eq. (1)):
where fj is the probability density function of lognormal distribution,
Eq. (2) represents the jth fragility, and erf() is the Gaussian error function. When the median and log-standard deviation are determined, the fragility curve of jth level can be obtained. The maximum likelihood estimation (MLE) can be applied to determine the median and log-standard deviation [13]. The aforementioned equations are suitable for one-variable estimation model.
Since both the rainfall intensity and rainfall accumulation contribute to the probability of triggering a landslide, the bivariate lognormal distribution was applied in the developing LFC model [4, 14], as in Eq. (3):
where
Eq. (4) represents the j-th fragility curve of landslide, including four fragility parameters. The cumulative density function of Eq. (4) is a fragility surface of probability.
The parameters in Eq. (4) can be obtained by using the least square estimate. When the landslide locations and areas are available, meaning the classification of landslide based on the factors (see next section), the fragility curve of landslide (a surface) of a specific classification can be determined.
The environmental factors, geology, slope, distance to river, slope aspect, and vegetation index, were classified into levels in order to group similar slope units. The triggering factors of rainfall intensity and effective accumulated rainfall were also redistributed onto slope unit scale. These factors were classified into groups, i.e., two groups of G, three of S, two of R, two of A, and two of N (Tables 4–8), based on the available data and appropriate judgment to simplify the process. There were total of 48 combinations of classification, as described below.
Classification | Geology time scale | Rock type |
---|---|---|
G1 | Eocene | Dark gray slate and phyllite slate, interbedded with quartz sandstone |
Eocene | Slate and phyllite quartzite sandstone | |
Oligocene | Hard shale sandwiched to thick sandstone | |
Oligocene | Thick or massive white medium to very coarse quartzite and hard shale | |
G2 | Miocene | Hard shale, slate, phyllite sandstone |
Mid-Miocene | Sandstone and shale interbed, coal seam | |
Late Miocene | Sandstone and shale interbed, coal seam | |
Miocene to Pliocene | Sandstone and shale interbed, coal seam | |
Pliocene | Shale, sandy shale, mudstone | |
Pliocene | Sandstone, mudstone, shale interbed | |
Pliocene to Pleistocene | Gravel | |
Pleistocene | Gravel, sand, and clay |
The geology classification.
Classification | SWCB slope level | Technical regulations for soil and water conservation | |
---|---|---|---|
Slope range | degree (°) | ||
S1 | 3 | 15% < S ≦ 30% | 8.53 < S ≦ 16.70 |
4 | 30% < S ≦ 40% | 16.70 < S ≦ 21.80 | |
S2 | 5 | 40% < S ≦ 55% | 21.80 < S ≦ 28.81 |
S3 | 6 | 55% < S ≦ 100% | 28.81 < S ≦ 45.00 |
7 | S > 100% | S > 45.00 |
The slope classification.
Classification | Definition | Distance (m) |
---|---|---|
R1 | Close | ≤300 m |
R2 | Not close | >300 m |
The classification of distance to river.
Classification | Definition |
---|---|
A1 | Weak aspect: the four slope aspects of higher ratio of incremental landslide area. In this study, A1 are E, SE, S, and SW |
A2 | Strong aspect: the four slope aspects of lower RIL. In this study, A2 are W, NW, N, and NE |
The classification of slope aspects.
Image process | Classification | ||
---|---|---|---|
Low vegetation | Mid-to-high vegetation | ||
−1 < NDVI ≦ NDVIc* | NDVIc* < NDVI ≦ 1 | ||
Pre-event image | Barren land | N1 | N1 |
Non-barren land | N1 | N2 |
The vegetation classification.
NDVIc is the threshold value to classify low and mid-to-high vegetation index. In this study, the NDVIc was −0.35.
The geology is an important factor when considering the potential of landslide. However, the geological conditions, like soil layer depth, rock type, and strength at the site, are not usually available to researchers. Therefore, a simplified step can be used at the geology time scale to generally represent the older and younger stratum of the study area. For Chen-Yu-Lan watershed, the rock type of the area was first used to highlight the geological time scale. The same geology era contained different rock formations, and the factor of geology was classified into two groups, as shown in Table 4 and Figure 15. It was noted that there are 1798 slope units of G1 and 2463 slope units of G2.
The geology classification of Chen-Yu-Lan watershed.
Based on the Soil and Water Conservation Bureau manual, the hillside slope is classified as seven levels. In the fragility model, level 3 to level 7 slopes were considered and simply further classified as three groups, as shown in Table 5. Figure 16 shows the classification results in the Chen-Yu-Lan watershed, and 137 slope units were classified as S1, 827 as S2, and 3297 as S3.
The slope classification of Chen-Yu-Lan watershed.
The distance to the nearest river channel was classified into two groups, with the threshold value of 300 m. Table 6 and Figure 17 show the classification results, in which there are 2482 and 1779 slope units of R1 and R2, respectively.
The classification of distance to the river of Chen-Yu-Lan watershed.
The slope aspect was considered in the beginning to distinguish the range of frequent landslide on a given mountain slope. There are eight slope aspects (Figure 18) used in the study that were grouped into two classes as shown in Table 7 and Figure 19, in which there are 2051 and 2210 slope units of A1 and A2, respectively.
The slope aspects.
The slope aspect classification of Chen-Yu-Lan watershed.
The land cover status was also an important factor when estimating the landslide potential. The normalized difference vegetation index was used to represent the land cover status of a given site. Satellite images of SPOT (February 21, 2008, November 28, 2008, and October 14, 2009) were used to calculate the NDVI of the ground surface, and an empirical NDVI threshold was applied to classify barren land and non-barren land. Table 8 summarized the classification, and Figure 20 shows the results, in which there are 2765 and 1496 slope units of N1 and N2, respectively.
The vegetation index classification of Chen-Yu-Lan watershed.
The rainfall data from Typhoon Sinlaku in 2008 and Typhoon Morakot in 2009 was applied to obtain the rainfall intensity and effective accumulated rainfall in the Chen-Yu-Lan watershed. The hourly rainfall data measured at the surrounding weather stations was used to get the rainfall of each slope unit by interpolation. Figures 21 and 22 show the rainfall distribution during the two typhoon events.
The rainfall of Chen-Yu-Lan watershed during Typhoon Sinlaku: (a) max. hourly rainfall (Imax) and (b) effective accumulated rainfall (Rte).
The rainfall of Chen-Yu-Lan watershed during Typhoon Morakot: (a) max. hourly rainfall (Imax) and (b) effective accumulated rainfall (Rte).
Based on the site investigation in the past after typhoon events, the expected average landslide volume (V) was set as V = 6000 m3. By applying the relationship of
Slope S1: the slope unit is counted as a landslide when its landslide area ratio (LAR) is equal to or higher than 5% or the projected landslide area on the slope is greater than 2800 m2 (0.28 ha). Otherwise, the slope unit is not counted as a landslide area.
Slope S2: the slope unit is counted as a landslide when its landslide area ratio is equal to or higher than 5% or the projected landslide area on the slope is greater than 2400 m2 (0.24 ha). Otherwise, the slope unit is not counted as a landslide area.
Slope S3: the slope unit is counted as a landslide when its landslide area ratio is equal to or higher than 5% or the projected landslide area on the slope is greater than 2200 m2 (0.22 ha). Otherwise, the slope unit is not counted as a landslide area.
The landslide area classification of Chen-Yu-Lan watershed is shown in Figure 23. There were 1810 slope units of landslide after Typhon Sinlaku and 1544 ones after Typhoon Morakot, as shown in colored slope units in Figure 23.
The landslide area of Chen-Yu-Lan watershed during (a) Typhoon Sinlaku and (b) Typhoon Morakot.
The environmental database and rainfall data of typhoon events were applied to classify the slope units and the landslide areas. With the classification described in previous sections, there were a total of 48 classes with combinations of factors G, S, A, R, and N. Each classification was in association with two rainfall indices, the rainfall intensity and effective accumulated rainfall. The fragility of landslide, or the probability of exceeding a level of hazard, was constructed and used for landslide potential assessment. Tables 9 and 10 summarized the fragility parameters obtained from the two events, and some examples of fragility curves were shown in Figure 24. It should be noted that during the classification, insufficient samples of certain classification had led to difficulty of finding parameters needed. Therefore, these samples were combined with other classifications in order to get reasonable probability values of median and standard deviation.
Classification | Imax (mm) | Rte (mm) | Combined with* | ||
---|---|---|---|---|---|
Median | Std. deviation | Median | Std. deviation | ||
G1S1A1R1N1 | 64.40 | 0.21 | 485.00 | 0.28 | With 21111 |
G1S1A1R1N2 | 27.53 | 1.24 | 383.77 | 0.29 | With 21112 |
G1S1A1R2N1 | 33.70 | 0.31 | 1112.62 | 0.10 | With 21121 |
G1S1A1R2N2 | 37.94 | 0.16 | 239.39 | 0.27 | With 21122 |
G1S1A2R1N1 | 44.40 | 1.10 | 290.86 | 0.24 | With 21211 |
G1S1A2R1N2 | 43.91 | 0.16 | 1007.19 | 0.71 | With 21212 |
G1S1A2R2N1 | 32.48 | 0.77 | 320.60 | 0.39 | With 21221 |
G1S1A2R2N2 | 40.58 | 0.45 | 332.07 | 0.22 | With 21222 |
G1S2A1R1N1 | 40.44 | 0.58 | 235.49 | 0.79 | With 22111 |
G1S2A1R1N2 | 72.70 | 0.32 | 384.00 | 0.67 | With 22112 |
G1S2A1R2N1 | 22.60 | 0.34 | 407.35 | 0.26 | With 22121 |
G1S2A1R2N2 | 74.16 | 1.17 | 527.59 | 1.20 | With 22122 |
G1S2A2R1N1 | 22.41 | 0.70 | 399.60 | 1.23 | With 22211 |
G1S2A2R1N2 | 42.39 | 0.28 | 252.25 | 0.62 | With 22212 |
G1S2A2R2N1 | 14.08 | 0.11 | 706.36 | 0.80 | With 22221 |
G1S2A2R2N2 | 115.74 | 0.61 | 207.21 | 0.77 | With 22222 |
G1S3A1R1N1 | 18.81 | 0.21 | 135.69 | 1.06 | |
G1S3A1R1N2 | 14.51 | 0.12 | 295.58 | 0.29 | |
G1S3A1R2N1 | 75.05 | 0.29 | 225.74 | 0.88 | |
G1S3A1R2N2 | 28.07 | 0.38 | 269.76 | 0.55 | |
G1S3A2R1N1 | 35.79 | 0.57 | 967.74 | 0.35 | |
G1S3A2R1N2 | 44.53 | 1.54 | 554.12 | 1.26 | |
G1S3A2R2N1 | 29.66 | 0.72 | 298.05 | 0.30 | |
G1S3A2R2N2 | 34.00 | 0.89 | 269.00 | 0.69 |
Fragility parameters of G1 classification.
Due to the insufficient data, some classifications were combined together in order to obtain reasonable parameters.
Classification | Imax (mm) | Rte (mm) | ||
---|---|---|---|---|
Median | Std. deviation | Median | Std. deviation | |
G2S1A1R1N1 | 64.40 | 0.21 | 485.00 | 0.28 |
G2S1A1R1N2 | 27.53 | 1.24 | 383.77 | 0.29 |
G2S1A1R2N1 | 33.70 | 0.31 | 1112.62 | 0.10 |
G2S1A1R2N2 | 37.94 | 0.16 | 239.39 | 0.27 |
G2S1A2R1N1 | 44.40 | 1.10 | 290.86 | 0.24 |
G2S1A2R1N2 | 43.91 | 0.16 | 1007.19 | 0.71 |
G2S1A2R2N1 | 32.48 | 0.77 | 320.60 | 0.39 |
G2S1A2R2N2 | 40.58 | 0.45 | 332.07 | 0.22 |
G2S2A1R1N1 | 40.44 | 0.58 | 235.49 | 0.79 |
G2S2A1R1N2 | 72.70 | 0.32 | 384.00 | 0.67 |
G2S2A1R2N1 | 22.60 | 0.34 | 407.35 | 0.26 |
G2S2A1R2N2 | 74.16 | 1.17 | 527.59 | 1.20 |
G2S2A2R1N1 | 22.41 | 0.70 | 399.60 | 1.23 |
G2S2A2R1N2 | 42.39 | 0.28 | 252.25 | 0.62 |
G2S2A2R2N1 | 14.08 | 0.11 | 706.36 | 0.80 |
G2S2A2R2N2 | 115.74 | 0.61 | 207.21 | 0.77 |
G2S3A1R1N1 | 16.70 | 0.13 | 604.42 | 0.53 |
G2S3A1R1N2 | 72.54 | 0.58 | 305.93 | 0.41 |
G2S3A1R2N1 | 21.81 | 1.31 | 387.14 | 0.84 |
G2S3A1R2N2 | 56.01 | 1.07 | 527.88 | 0.69 |
G2S3A2R1N1 | 23.20 | 0.78 | 378.00 | 0.66 |
G2S3A2R1N2 | 14.50 | 0.11 | 151.30 | 0.10 |
G2S3A2R2N1 | 23.76 | 0.66 | 270.92 | 0.28 |
G2S3A2R2N2 | 29.86 | 1.02 | 249.28 | 0.80 |
Fragility parameters of G2 classification.
Examples of fragility curves of Chen-Yu-Lan watershed: (a) G1S3A1R1N1, (b) G2S2A1R1N1, (c) G1S3A1R2N1, and (d) G2S3A1R2N1.
The fragility curves of 48 classification slope units represented the local environmental characteristics of a given area. Instead of directly using 48 set fragility curves, it should be practical to obtain one set of representative fragility curve for a given site or location. To achieve this goal, the weighted fragility curves were introduced and applied to the Shenmu village. The weighted fragility parameters were determined using the following equations:
where x and y are rainfall indices,
After the weighted calculation, the fragility parameters of Shenmu area are median
The fragility surface and fragility curves of Shenmu area.
The risk of landslide was demonstrated by using the critical values of rainfall hazard and landslide fragility. The concept of landslide warning was adopted in this study, and by combining both Hc and Fc, the warning status includes safe stage and unsafe stages, as illustrated in Figure 26. It should be noted that there are two stages of unsafe status, Red I and Red II. Red I stage indicates that the situation has pass Hc and a rainfall hazard could occur. Red II stage implies the most serious condition that in addition to the rainfall hazard, a landslide could occur as well. Both stages are determined with a probability when given a rainfall condition. The procedure of determining safe stage was designed to match the needs of disaster preparation and prediction of government.
The warning conditions based on landslide fragility (Fc) and rainfall hazard (Hc).
Cases of landslides and debris flows in Shenmu were collected from the disaster notices issued by Soil and Water Conservation Bureau of Taiwan. As shown in Table 11 and Figure 27, a total of seven cases were used to determine the critical values of Hc (=0.91) and Fc (=0.23) of Shenmu. These cases were used in the assumption that whenever there was a debris flow, there should be landslides at the upper stream areas before or during the debris flow.
Year | Event | Disaster | Village | Imax (mm) | Rte (mm) |
---|---|---|---|---|---|
2009 | Typhoon Morakot | Debris flow, flood | Tongfu | 85.5 | 1130 |
2009 | Typhoon Morakot | Debris flow | Wangmei | 85.5 | 1130 |
2009 | Typhoon Morakot | Landslide | Shenmu | 47.5 | 829.5 |
2009 | Typhoon Morakot | Debris flow | Shenmu | 42.5 | 750 |
2009 | Typhoon Morakot | Debris flow | Shenmu | 33.5 | 641 |
2009 | Typhoon Morakot | Landslide | Shenmu | 20 | 476.5 |
2009 | Typhoon Morakot | Debris flow | Shenmu | 38.5 | 877 |
2012 | 0610 Heavy rainfall | Debris flow, flood | Shenmu | 18.5 | 450.6 |
The disaster notices around Shenmu area.
The probability thresholds of rainfall hazard and landslide fragility in Shenmu area: (a) rainfall warning threshold and (b) landslide warning threshold.
The rainfall history of Typhoon Morakot in 2009 and 0601 Heavy Rainfall in 2016 were used to evaluate the landslide risk assessment in Shenmu. Figure 28 shows the results of event, and the dots in the figure represent the rainfall condition (hourly rainfall and cumulative rainfall) and the probability of hazard. It was noted that the dots behaved like a “snake” line going from Safe stage to Red I and Red II stages. Also, the snake line stayed shortly at Red I stage for both events and passed to Red II in a jump. This condition implied that when the situation was beyond the Hc line, the landslide hazard was very likely to occur. The results conformed to the records of Typhoon Morakot. Severe landslides occurred at the upper stream areas in Shenmu during the typhoon. Therefore, the proposed risk assessment and warning stages of landslide were reasonably useful in this case.
The change of probability in Shenmu area during (a) Typhoon Morakot (2009) event and (b) 0601 heavy rainfall in 2016 (after [4, 5]).
This study had developed the landslide fragility curve model by using the spatial data and statistical methods. The fragility curves of the study area were derived for all combinations of environmental and triggering factors. The data sets included the geomorphological and vegetation condition factors, based on the landslides at the Chen-Yu-Lan watershed in Taiwan, during Typhoon Sinlaku (September 2008) and Typhoon Morakot (August 2009). This study also proposed landslide risk assessment using rainfall hazard potential and landslide fragility curves and concluded findings as follows:
Overall, the proposed model provides considerably accurate and reliable results on landslide estimations in terms of spatial distribution.
Adoption of slope unit was physically proper in modeling landslide locations.
The classifications of slope unit can be applied to different areas, and the fragility curve of each classification can be used directly.
The procedure of risk assessment was useful for practical landslide disaster preparation and prediction.
The LFC model was developed using two typhoon events. More events and landslide cases are needed to improve the LFC model in the future. Furthermore, the classification of upstream areas based on their environment is suggested for better possible estimation.
The applicability of factors should be considered before developing the model. The concerns about the model factors and the limits of satellite images can be resolved by using different methods to obtain necessary data. For example, the information of LIDAR may be used with the satellite images to provide better description on landslide identification. Therefore, the LFC model could be improved when more factors are available and applicable.
The authors would like to express their gratitude to research assistant Xingping Wang, for helping in collecting all the data relevant to the landslides in the Chen-Yu-Lan watershed. The authors also would like to thank the Soil and Water Conservation Bureau in Taiwan for supporting this research.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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