Forest biomass and its density by region, 2010 [2].
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"9407",leadTitle:null,fullTitle:"Biochemical Toxicology - Heavy Metals and Nanomaterials",title:"Biochemical Toxicology",subtitle:"Heavy Metals and Nanomaterials",reviewType:"peer-reviewed",abstract:"Biochemical Toxicology - Heavy Metals and Nanomaterials provides an overview of biochemical contamination, nanomaterials and toxic metals, and measurement techniques. It explains and clarifies important studies and compares and develops new and groundbreaking measurement techniques in the fields of organic and inorganic pollution and nanoscience. It is highly recommended for professionals and readers interested in the environment and human health.",isbn:"978-1-78984-697-3",printIsbn:"978-1-78984-696-6",pdfIsbn:"978-1-83880-921-8",doi:"10.5772/intechopen.85340",price:119,priceEur:129,priceUsd:155,slug:"biochemical-toxicology-heavy-metals-and-nanomaterials",numberOfPages:230,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"892372c885d7a41fc85666f573c7a32b",bookSignature:"Muharrem Ince, Olcay Kaplan Ince and Gabrijel Ondrasek",publishedDate:"July 29th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/9407.jpg",numberOfDownloads:12727,numberOfWosCitations:10,numberOfCrossrefCitations:28,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:71,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:109,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 29th 2019",dateEndSecondStepPublish:"October 9th 2019",dateEndThirdStepPublish:"December 8th 2019",dateEndFourthStepPublish:"February 26th 2020",dateEndFifthStepPublish:"April 26th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"258431",title:"Prof.",name:"Muharrem",middleName:null,surname:"Ince",slug:"muharrem-ince",fullName:"Muharrem Ince",profilePictureURL:"https://mts.intechopen.com/storage/users/258431/images/system/258431.jpg",biography:"Professor Muharrem Ince received his Ph.D. in Analytical Chemistry from Firat University, Turkey, in 2008. From 2009 to 2012, he worked as a research analytical chemist at Mus Alparslan University, Turkey. He is currently a professor at Munzur University. From 2013 to 2016, he served as head of the Department of Chemical Engineering, Munzur University. He is an editorial board member of several international journals as well as an author and co-author of more than forty papers published in respectable international journals. His expertise is in analytical method development, spectroscopic and chromatographic techniques, environmental sciences, water pollution identification and prevention, food analysis and toxicology, green and sustainable chemistry, nanoscience, and nanotechnology.",institutionString:"Munzur University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"Munzur University",institutionURL:null,country:{name:"Turkey"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"266549",title:"Dr.",name:"Olcay",middleName:null,surname:"Kaplan Ince",slug:"olcay-kaplan-ince",fullName:"Olcay Kaplan Ince",profilePictureURL:"https://mts.intechopen.com/storage/users/266549/images/system/266549.jpg",biography:"Prof. Dr. Olcay Kaplan Ince received a BS from Hacettepe University, Turkey, and a Ph.D. in Analytical Chemistry from Firat University, Turkey, in 2008. She is a research analytical chemist and previous head of the Food Engineering Department, Munzur University, Turkey. She serves as editor-in-chief of the International Journal of Pure and Applied Sciences. She is the author of more than forty papers published in respectable journals. Her research interests include trace and toxic element analysis, analytical chemistry, instrumental analysis, problem-solving in analytical chemistry, food science and chromatography, nanoscience and cytotoxicity, and deep eutectic solvents.",institutionString:"Munzur University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Munzur University",institutionURL:null,country:{name:"Turkey"}}},coeditorTwo:{id:"46939",title:"Prof.",name:"Gabrijel",middleName:null,surname:"Ondrasek",slug:"gabrijel-ondrasek",fullName:"Gabrijel Ondrasek",profilePictureURL:"https://mts.intechopen.com/storage/users/46939/images/system/46939.jpg",biography:"Gabrijel Ondrasek, PhD, is employed at the University of Zagreb, Faculty of Agriculture, Croatia, as a full professor at the Department of Soil Amelioration. His academic and scientific opus is orientated towards sustainable management of natural resources (water, soils) in the agro-environment, notably exposed to disturbed water balance on the soil–plant route, excessive salinity, metal contamination, and their environmental implications.",institutionString:"University of Zagreb",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Zagreb",institutionURL:null,country:{name:"Croatia"}}},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1206",title:"Environmental Toxicology",slug:"environmental-toxicology"}],chapters:[{id:"68822",title:"Heavy Metal Removal Techniques Using Response Surface Methodology: Water/Wastewater Treatment",doi:"10.5772/intechopen.88915",slug:"heavy-metal-removal-techniques-using-response-surface-methodology-water-wastewater-treatment",totalDownloads:2179,totalCrossrefCites:9,totalDimensionsCites:18,hasAltmetrics:0,abstract:"Advanced water/wastewater treatment techniques including ion exchange separation, filtration separation, and adsorption are essential in the removal of nonbiodegradable toxic wastes from water. In the current study, removal of heavy metal ions from water/wastewater and the use of response surface methodology (RSM) for experimental optimization were examined thoroughly. The objective of this work was to summarize the removal of heavy metal ions from water/wastewater using various chemical techniques and to emphasize the superiority of RSM in these studies.",signatures:"Muharrem Ince and Olcay Kaplan Ince",downloadPdfUrl:"/chapter/pdf-download/68822",previewPdfUrl:"/chapter/pdf-preview/68822",authors:[{id:"258431",title:"Prof.",name:"Muharrem",surname:"Ince",slug:"muharrem-ince",fullName:"Muharrem Ince"},{id:"266549",title:"Dr.",name:"Olcay",surname:"Kaplan Ince",slug:"olcay-kaplan-ince",fullName:"Olcay Kaplan Ince"}],corrections:null},{id:"67798",title:"Ecotoxicology of Glyphosate-Based Herbicides on Aquatic Environment",doi:"10.5772/intechopen.85157",slug:"ecotoxicology-of-glyphosate-based-herbicides-on-aquatic-environment",totalDownloads:1185,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:1,abstract:"Glyphosate-based herbicides (GBHs) are chemicals developed to control unwanted plants such as weeds or algae. These chemicals act on EPSPS enzyme that blocks the production of tyrosine, phenylalanine, and tryptophan amino acids causing plant death. This biochemical pathway exists only in plant organisms. Despite the target use, GBHs have been related to toxic effects on nonplant organisms, such as invertebrates, fishes, amphibians, reptiles, birds, and mammals, including humans. This chapter is focused on ecotoxicological effects of GBHs on aquatic environment, showing a perspective of studies since this kind of product was developed until nowadays, an analysis of how many studies for each taxonomic group. Furthermore, we analyzed specifically the toxic effect of GBHs on each taxon, and finally, we discuss future perspectives and suggestions for a better regulation and application for this chemical.",signatures:"Bruno Bastos Gonçalves, Percilia Cardoso Giaquinto, Douglas dos Santos Silva, Carlos de Melo e Silva Neto, Amanda Alves de Lima, Adriano Antonio Brito Darosci, Jorge Laço Portinho, Wanessa Fernandes Carvalho and Thiago Lopes Rocha",downloadPdfUrl:"/chapter/pdf-download/67798",previewPdfUrl:"/chapter/pdf-preview/67798",authors:[{id:"207211",title:"Dr.",name:"Carlos",surname:"de Melo e Silva Neto",slug:"carlos-de-melo-e-silva-neto",fullName:"Carlos de Melo e Silva Neto"},{id:"262395",title:"Dr.",name:"Bruno Bastos",surname:"Gonçalves",slug:"bruno-bastos-goncalves",fullName:"Bruno Bastos Gonçalves"},{id:"262400",title:"Dr.",name:"Thiago Lopes",surname:"Rocha",slug:"thiago-lopes-rocha",fullName:"Thiago Lopes Rocha"},{id:"262402",title:"Dr.",name:"Percilia Cardoso",surname:"Giaquinto",slug:"percilia-cardoso-giaquinto",fullName:"Percilia Cardoso Giaquinto"},{id:"262403",title:"Dr.",name:"Jorge Laço",surname:"Portinho",slug:"jorge-laco-portinho",fullName:"Jorge Laço Portinho"},{id:"286563",title:"Dr.",name:"Douglas",surname:"dos Santos Silva",slug:"douglas-dos-santos-silva",fullName:"Douglas dos Santos Silva"},{id:"286564",title:"BSc.",name:"Amanda",surname:"Alves de Lima",slug:"amanda-alves-de-lima",fullName:"Amanda Alves de Lima"},{id:"286565",title:"Dr.",name:"Adriano Antonio Brito",surname:"Darosci",slug:"adriano-antonio-brito-darosci",fullName:"Adriano Antonio Brito Darosci"},{id:"286566",title:"Dr.",name:"Wanessa Fernandes",surname:"Carvalho",slug:"wanessa-fernandes-carvalho",fullName:"Wanessa Fernandes Carvalho"}],corrections:null},{id:"66089",title:"Water Resource Pollution by Herbicide Residues",doi:"10.5772/intechopen.85159",slug:"water-resource-pollution-by-herbicide-residues",totalDownloads:1274,totalCrossrefCites:6,totalDimensionsCites:10,hasAltmetrics:1,abstract:"Herbicides are frequently used in the chemical control of weeds in various crops in Brazil and worldwide, so they are more frequently detected outside the application areas, contributing to the risk of environmental contamination. The importance of knowledge of the physicochemical properties of the environment and the pesticide used in the agricultural area is in order to understand its effects on terrestrial and aquatic ecosystems and the search for the prevention of future bioaccumulation potentials (bioconcentration and/or biomagnification) of molecules of pesticides in living nontarget organisms, minimizing their negative effects on the environment. The understanding of analytical techniques for measuring the quality of water resources as well as techniques for the remediation of contaminated water is essential to minimize the possible impacts caused by the application of pesticides to the environment.",signatures:"Kassio Ferreira Mendes, Ana Paula Justiniano Régo, Vanessa Takeshita and Valdemar Luiz Tornisielo",downloadPdfUrl:"/chapter/pdf-download/66089",previewPdfUrl:"/chapter/pdf-preview/66089",authors:[{id:"162791",title:"Prof.",name:"Valdemar",surname:"Tornisielo",slug:"valdemar-tornisielo",fullName:"Valdemar Tornisielo"},{id:"197720",title:"Ph.D.",name:"Kassio",surname:"Ferreira Mendes",slug:"kassio-ferreira-mendes",fullName:"Kassio Ferreira Mendes"},{id:"258779",title:"Dr.",name:"Ana Paula",surname:"Justiniano Régo",slug:"ana-paula-justiniano-rego",fullName:"Ana Paula Justiniano Régo"},{id:"277330",title:"MSc.",name:"Vanessa",surname:"Takeshita",slug:"vanessa-takeshita",fullName:"Vanessa Takeshita"}],corrections:null},{id:"70500",title:"Challenges for Assessing Toxicity of Nanomaterials",doi:"10.5772/intechopen.89601",slug:"challenges-for-assessing-toxicity-of-nanomaterials",totalDownloads:1100,totalCrossrefCites:4,totalDimensionsCites:11,hasAltmetrics:1,abstract:"On the development of nano-world, nanotechnology provides enormous opportunities in daily routine products and further future sustainable innovations. The nanotechnology extends its benefits to various fields such as engineering, medical, biological, environmental, and communication. However, the exponential growth of nanomaterials production would lead to severe complications related to their hazardous effects to the human health and environment. Moreover, negative impact of nanomaterials toxicity on human health is one of the significant issues on exhausting nano-products. The most vulnerable situation is associated with the use of nanomaterials in the biomedical application. The several efforts have been ongoing to study the nanotoxicity and its interaction with the biomolecules. Nevertheless, it is hard to assess and validate the nanotoxicity in a biological system. This chapter aims to study the challenges in determining the toxicity of nanomaterials. The toxicity assessment and hurdles in determining the impact on biological systems are epoch making. In-vitro, in-vivo, and in-silico studies are summarized in this chapter in assessing the toxicity of engineered nanomaterials. The different approaches of toxicity assessment have their difficulties faced by researchers while characterizing nanomaterials in powder form, solution-based, and interacting with biological systems. The assessment tools and characterization techniques play a vital role in overcoming the challenges, while the cytotoxic assays involve nanoparticle shape, morphology, and size consideration.",signatures:"Akanksha Gupta, Sanjay Kumar and Vinod Kumar",downloadPdfUrl:"/chapter/pdf-download/70500",previewPdfUrl:"/chapter/pdf-preview/70500",authors:[{id:"309802",title:"Dr.",name:"Vinod",surname:"Kumar",slug:"vinod-kumar",fullName:"Vinod Kumar"},{id:"311316",title:"Dr.",name:"Akanksha",surname:"Gupta",slug:"akanksha-gupta",fullName:"Akanksha Gupta"},{id:"311317",title:"Mr.",name:"Sanjay",surname:"Kumar",slug:"sanjay-kumar",fullName:"Sanjay Kumar"}],corrections:null},{id:"65983",title:"Residue of Selected Persistent Organic Pollutants (POPs) in Soil of Some Areas in Vietnam",doi:"10.5772/intechopen.84918",slug:"residue-of-selected-persistent-organic-pollutants-pops-in-soil-of-some-areas-in-vietnam",totalDownloads:846,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"This chapter evaluates the contamination of selected persistent organic pollutants (S-POPs) in soil of some typical areas in Vietnam (mangrove forest, industrial, and urban areas in northern part). S-POPs are composed of polychlorinated biphenyls (PCBs) and polyaromatic hydrocarbons (PAHs). The collected data and analyzed results indicated the wide occurrence of significant S-POPs residues in study areas. The main sources of S-POPs are discussed by using composition analyses and diagnostic ratios of S-POPs indicator. Risk assessment of S-POPs in soil is assessed by using the guidance of the US Environmental Protection Agency. The obtained results have contributed to assess the S-POPs fate in the soil environment in Vietnam.",signatures:"Toan Vu Duc, Chi Do Thi Lan and Mai Ngo Tra",downloadPdfUrl:"/chapter/pdf-download/65983",previewPdfUrl:"/chapter/pdf-preview/65983",authors:[{id:"209088",title:"Prof.",name:"Toan",surname:"Vu Duc",slug:"toan-vu-duc",fullName:"Toan Vu Duc"},{id:"209091",title:"Dr.",name:"Mai",surname:"Ngo Tra",slug:"mai-ngo-tra",fullName:"Mai Ngo Tra"},{id:"279762",title:"Dr.",name:"Chi",surname:"Do Thi Lan",slug:"chi-do-thi-lan",fullName:"Chi Do Thi Lan"}],corrections:null},{id:"69211",title:"Formaldehyde Advantages and Disadvantages: Usage Areas and Harmful Effects on Human Beings",doi:"10.5772/intechopen.89299",slug:"formaldehyde-advantages-and-disadvantages-usage-areas-and-harmful-effects-on-human-beings",totalDownloads:1279,totalCrossrefCites:0,totalDimensionsCites:6,hasAltmetrics:0,abstract:"Formaldehyde, a simple but important member of aldehydes, is highly reactive due to its strong electrophilic properties. It is a colorless, pungent, low molecular weight poisonous gas that can rapidly pass into gaseous phase at room temperature, can burn, and can dissolve very well in water. Formaldehyde, which is found in the natural structure of the organism, is used in many places from industrial areas to household materials and from the production of coatings in dentistry to the determination of cadavers in laboratories. In addition to having such a wide range of uses, it has harmful effects on human health as it can react spontaneously with various cellular elements. In this review, which is based on various sources, detailed information about the definition, properties, usage areas, and harmful effects of formaldehyde will be given.",signatures:"Nuriye Tuna Subasi",downloadPdfUrl:"/chapter/pdf-download/69211",previewPdfUrl:"/chapter/pdf-preview/69211",authors:[{id:"279801",title:"Dr.",name:"Nuriye Tuna",surname:"Subaşı",slug:"nuriye-tuna-subasi",fullName:"Nuriye Tuna Subaşı"}],corrections:null},{id:"69512",title:"Biomedical Applications of Nanomaterials: Nanotubes and Metal-Organic Frameworks (MOFs)",doi:"10.5772/intechopen.89626",slug:"biomedical-applications-of-nanomaterials-nanotubes-and-metal-organic-frameworks-mofs-",totalDownloads:773,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Nanomedicine plays an important role in the diagnosis, treatment, monitoring and control of biological systems in the area of nanotechnology and has been referred by the National Institute of Health (NIH) as an emergent way of medicine. Nanoparticles are new delivery vehicles with the ability to release drugs to a specific cell type or tissue, which may also improve the pharmacological activity of those drugs by controlling their release, as well as prolonging their short half-lives in blood. The aim of this review is to gather several options of MOFs and nanotubes synthesised with different nanoparticles and processes, some including compound loading and release studies, with particular focus on 13 anti-cancer compounds e.g. doxorubicin, curcumin, methotrexate, etc.; 3 anti-inflammatory compounds, namely ibuprofen, salicylic acid and chlorogenic acid; and with 5 miscellaneous bioactive compounds, including rifampicin, griseofulvin, enoxacin, etc. Finally, other biomedical applications for these composites are shown, like being enzyme immobilisation agents, for water treatment e.g. in swimming pools, and other becoming support to carry & secure integrity of drugs.",signatures:"Miguel Martell-Mendoza, Cuauhtémoc Pérez-González, Hiram I. Beltrán, Roberto Serrano-Vega and Carlos Alberto Méndez-Cuesta",downloadPdfUrl:"/chapter/pdf-download/69512",previewPdfUrl:"/chapter/pdf-preview/69512",authors:[{id:"287822",title:"Dr.",name:"Roberto José",surname:"Serrano-Vega",slug:"roberto-jose-serrano-vega",fullName:"Roberto José Serrano-Vega"},{id:"287825",title:"Dr.",name:"Cuauhtemoc",surname:"Pérez-González",slug:"cuauhtemoc-perez-gonzalez",fullName:"Cuauhtemoc Pérez-González"},{id:"300158",title:"Dr.",name:"Carlos Alberto",surname:"Méndez-Cuesta",slug:"carlos-alberto-mendez-cuesta",fullName:"Carlos Alberto Méndez-Cuesta"},{id:"300160",title:"Mr.",name:"Miguel",surname:"Martell-Mendoza",slug:"miguel-martell-mendoza",fullName:"Miguel Martell-Mendoza"},{id:"308388",title:"Dr.",name:"Hiram Isaac",surname:"Beltrán",slug:"hiram-isaac-beltran",fullName:"Hiram Isaac Beltrán"}],corrections:null},{id:"71555",title:"Biochemical Toxicology: Heavy Metals and Nanomaterials",doi:"10.5772/intechopen.90928",slug:"biochemical-toxicology-heavy-metals-and-nanomaterials",totalDownloads:697,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"The synthesis and application of nanoparticles have been actively studied in the modern era as it holds promises for effective and targeted strategies to deliver drugs inside the human body. Nanoparticles (NPs) play a big role in cancer diagnosis and have various advantages over other conventional chemotherapeutic drug delivery systems. But, the application of emerging engineered NPs to heavy toxic metals such as zinc, cobalt, and iron has resulted in a major source of toxicity. The toxicity of nanomaterials is majorly determined by their physical and chemical properties such as size, charge, and surface area. Also, the mechanism of nanotoxicity is majorly via the production of reactive oxygen species that create oxidative stress, thereby activating inflammatory cytokines and the mechanism of DNA damage that ultimately results in the cell death. So, mechanistic study needs to be done on nanomaterials to elucidate the mechanism involved in nanotoxicity and to generate less toxic and efficient nanomaterials.",signatures:"Sibi Raj and Dhruv Kumar",downloadPdfUrl:"/chapter/pdf-download/71555",previewPdfUrl:"/chapter/pdf-preview/71555",authors:[{id:"312126",title:"Dr.",name:"Dhruv",surname:"Kumar",slug:"dhruv-kumar",fullName:"Dhruv Kumar"},{id:"312212",title:"Ms.",name:"Sibi",surname:"Raj",slug:"sibi-raj",fullName:"Sibi Raj"}],corrections:null},{id:"70615",title:"Few-Layered Hexagonal Boron Nitride: Functionalization, Nanocomposites, and Physicochemical and Biological Properties",doi:"10.5772/intechopen.90528",slug:"few-layered-hexagonal-boron-nitride-functionalization-nanocomposites-and-physicochemical-and-biologi",totalDownloads:946,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Hexagonal boron nitride (h-BN) is an analogue of graphite called “white graphene.” In the structure of h-BN, B and N atoms substitute C atoms. The boron and nitrogen atoms are linked via strong B-N covalent bonds and form interlocking hexagonal rings. h-BN is used in different areas due to its interesting physical and chemical properties, e.g., in electronics as an insulator and in ceramics, resins, plastics, and paints. Therefore, boron nitride (BN) is also a popular inorganic compound in cosmetic industry (the highest BN concentration up to 25% can be found in eye shadow formulation). It is also widely used in dental cement production (for dental and orthodontic applications). Boron nitride seems to be suitable for biomedical applications; therefore, the cytotoxicity in vitro and in vivo observations of h-BN nanoplates and novel few-layered h-BN-based nanocomposites are still needed. The short-time studies confirm their low cytotoxicity and suggest that BN can be used as a novel drug delivery system; however, medical application needs additional verification in long-term studies.",signatures:"Magdalena Jedrzejczak-Silicka, Martyna Trukawka, Katarzyna Piotrowska and Ewa Mijowska",downloadPdfUrl:"/chapter/pdf-download/70615",previewPdfUrl:"/chapter/pdf-preview/70615",authors:[{id:"186478",title:"Dr.",name:"Magdalena",surname:"Jedrzejczak-Silicka",slug:"magdalena-jedrzejczak-silicka",fullName:"Magdalena Jedrzejczak-Silicka"},{id:"231014",title:"Prof.",name:"Ewa",surname:"Mijowska",slug:"ewa-mijowska",fullName:"Ewa Mijowska"},{id:"312078",title:"MSc.",name:"Martyna",surname:"Trukawka",slug:"martyna-trukawka",fullName:"Martyna Trukawka"},{id:"312079",title:"Dr.",name:"Katarzyna",surname:"Piotrowska",slug:"katarzyna-piotrowska",fullName:"Katarzyna Piotrowska"}],corrections:null},{id:"71950",title:"Nanosafety",doi:"10.5772/intechopen.92290",slug:"nanosafety",totalDownloads:466,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:1,abstract:"The nanomaterials resembling nanotubes, nanospheres, nanofertilizer, nanoherbicide, nanoinsecticide, and nanosheets have the physical, chemical, biological, mechanical, electrical and thermal properties. Still, the nanoparticles have very minute dimensions, enormous area and high reactivity they need the potential ability to penetrate in living cells quite rapidly. The petite size nanoparticles contain lofty surface area may cause higher reactivity with nearby particles. It is broadly predictable that there is a critical need for more information and facts about the implications of manufactured nanomaterials on personal fitness and surroundings. Concerns about potential risks to health that may arise during the making, management, use, and discarding of these nanomaterials have been spoken over the past few years. Consequently, strong research action is being undertaken in various institutions, and industries across the world to appraise their toxicity and spread of nanoparticle.",signatures:"Muthuraman Yuvaraj, Venkatesan Yuvaraj, Venugopal Arunkumar, Muthaiyan Pandiyan and Kizhaeral Sevathapandian Subramanian",downloadPdfUrl:"/chapter/pdf-download/71950",previewPdfUrl:"/chapter/pdf-preview/71950",authors:[{id:"280193",title:"Dr.",name:"Muthuraman",surname:"Yuvaraj",slug:"muthuraman-yuvaraj",fullName:"Muthuraman Yuvaraj"},{id:"280247",title:"Prof.",name:"Kizhaeral Sevanthiyppan",surname:"Subramanian",slug:"kizhaeral-sevanthiyppan-subramanian",fullName:"Kizhaeral Sevanthiyppan Subramanian"}],corrections:null},{id:"70717",title:"Biological Role of Withania somnifera against Promiscuity of Zinc Oxide Nano Particles and Its Interaction with Macrophages",doi:"10.5772/intechopen.90128",slug:"biological-role-of-em-withania-somnifera-em-against-promiscuity-of-zinc-oxide-nano-particles-and-its",totalDownloads:743,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"In agriculture and food industry, nanotechnology can be utilized to improve crop yield, food quality, shelf life, safety, cost and nutritional benefits. Zinc is a trace element and its deficiency causes health problems in human beings and animals. The use of zinc oxide nanoparticles (ZnO NPs) is growing exponentially in food industry, biomedicine and nanofertilizer segment. A remarkable presence of nanomaterials in ecosystem and consumer products can cause adverse effects. Hence, it is an important challenge for the use of nanoparticles in agriculture as fertilizer to enhance plant yield on one hand and their interaction with the cells of the innate immune system in animals on the other hand. So, public concern about their potential toxicity is increasing. ZnO NPs interact with cells and produce harmful effects in a dose dependent manner. The reactive oxygen species generation might be a reason for the toxicity of ZnO NPs. The toxicity is caused due to dissolved Zn++ ions by absorption which causes adverse effect on phagocytosis and oxidative stress by free radical while Withania somnifera induced the phagocytosis activity by antioxidant mechanism thus having protective effects. 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Many PAHs have toxic, mutagenic, and/or carcinogenic functions. PAHs are highly lipid soluble which lead to a fast absorption by the gastrointestinal tract of marine mammals. They are immediately distributed in a vast variety of tissues with a notable tendency for localization in body fat. Metabolism of PAHs is obtained via the cytochrome P450-mediated mixed function oxidase system with oxidation or hydroxylation as the first step. PAHs are environmental contaminants that pose significant risk to health of fish. The effect of PAHs on fish is a topic of rising attention in a lot of countries. Different studies using the bile metabolites separated by high-performance liquid chromatography with fluorescence detection were presented. The aim is to compare the levels of PAH metabolites in fish from different areas and fish species. The major metabolite present in all fish was 1-hydroxypyrene. 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Since 2000, the DFT has grown exponentially in several computational areas because of its versatility and reliability to calculate energy from electronic density. The fast DFT’s calculations show how scientists develop more codes focused to simulate molecular and material properties reaching better conclusions than with previous theories. More powerful computers and lower computational costs have certainly assisted the increased growth of interest in this theory. 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From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Hashim"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"52687",title:"Methods of Estimating Forest Biomass: A Review",doi:"10.5772/65733",slug:"methods-of-estimating-forest-biomass-a-review",body:'\nCurrently, CO2 and other greenhouse gas are inducing global warming, and vegetation is the only natural ecosystems to fix atmospheric CO2. Forest is the main component of vegetation. Accordingly, forest ecosystem is destined to be paid more attention by governments, academics, and the general public [1]. According to the Global Forest Resources Assessment 2010 [2], the global forest biomass (including above- and belowground) is 600 Pg, with a mean biomass density of 148.8 t/ha (Table 1). It is estimated that carbon sequestered in forest can account for about 77% of terrestrial ecosystem [3].
\nRegion | \nBiomass (×106 t) | \nBiomass density (t/ha) | \n
---|---|---|
Eastern and Southern Africa | \n33,385 | \n124.8 | \n
Northern Africa | \n3711 | \n47.1 | \n
Western and Central Africa | \n816.3 | \n248.7 | \n
\n | \n\n | \n\n | \n
East Asia | \n18,429 | \n72.4 | \n
South and Southeast Asia | \n51,933 | \n176.4 | \n
Western and Central Asia | \n3502 | \n80.5 | \n
\n | \n\n | \n\n | \n
Europe excl. Russian Federation | \n25,602 | \n130.7 | \n
\n | \n\n | \n\n | \n
Caribbean | \n1092 | \n157.5 | \n
Central America | \n3715 | \n190.5 | \n
North America | \n76,929 | \n113.3 | \n
\n | \n\n | \n\n | \n
\n | \n\n | \n\n | \n
\n | \n\n | \n\n | \n
Forest biomass and its density by region, 2010 [2].
Global deforestation is undergoing seriously [2], which contributes to a quarter of carbon released into the atmosphere each year [4]. Land use/land use change (mainly deforestation) is considered to be an important approach to the release of CO2, which affects the carbon cycle on various spatial and temporal scales, and then global climate change [5, 6]. Therefore, the scientific and real-time monitoring of forest cover change and more accurate estimates of forest biomass and its magnitude is of significance to clarify the contribution of forests in global climate change.
\nIn addition, the current world is facing the dual pressures of economic growth and environmental protection. Adjusting and optimizing energy structure, vigorously developing biomass energy has become the main developing trend of energy in the future. As energy plant, the most important characteristic is to possess a large net accumulation of biomass. Therefore, the scientific evaluation of the size and potential of biomass needs a suitable method used to estimate its biomass potential.
\nHowever, a large uncertainty exists in biomass estimation, which is unable to meet the requirement of the accurate carbon accounting required by Kyoto Protocol. The use of a suitable and rigor method to accurately estimate the size and distribution of forest biomass is of significance and also urgently needed. And also, the method used to estimate forest biomass is more likely to vary frequently with scale.
\nGiven this, we reviewed the commonly used methods to estimate forest biomass across the scale in this chapter, for the purpose of operation and guidance, which includes allometric equation, mean biomass density, biomass expansion factor, forest identity, remote sensing- and geostatistics-based estimation methods, etc. For each method, we will present background, rational, applicability, as well as estimation procedure by exemplifying a case. At the end of this chapter, we argued that the new developed techniques such as geostatistics and remote sensing technique (e.g., LIDAR) would be the key tools to improve forest biomass estimation with a high accuracy. However, prior to this, spatial variation in forest biomass at various levels should be first explored using multi-source data and multi-approaches.
\nIf one organ is correlated to another of a plant, or a certain attribute is to plant size, we can call it allometry [7, 8], which is frequently expressed with a power relationship below [9, 10]:
or
\nwhere
In botany, the allometric relationship is able to be used to calculate biomass and other ecological factors by measuring the easily measured diameter at breast height (and/or height). Theoretically, tree
where
Tree
Such allometric relationship is based on the measured sample tree and aims to estimate vegetation biomass as the mathematical model (hereafter also referred to as a biomass allometric equation). Apparently, plant allometry is the theoretical basis of vegetation biomass estimation, which makes biomass estimate possible. Recently, remote sensing technique has been increasingly applied to estimate the biomass [23, 24]. However, data derived from allometric equation must be verified with field data in the method [25]. Generally, the use of allometric equation is indispensable to estimate biomass for both tree and forest.
\nMulti-scale aboveground biomass estimation is demonstrated as an example to show the procedure (Figure 1). First, a number of plots are set, where field survey is performed (step 1); then several sample trees are cut to fit individual-level allometric equation (step 2); the use of developed allometric equation, together with filed survey data (mainly
While estimating forest carbon stock, most scholars assumed that carbon content in plant biomass is constant (approximately 50%) [27–29]. Therefore, we estimate forest biomass first, multiplied by 50%, and can calculate the corresponding forest biomass carbon stock. It is not difficult to conclude that the method used to estimate forest carbon stock is almost entirely consistent with one used for biomass estimation; thus, the method of estimating forest biomass was addressed below, which can also be used to estimate forest carbon stock.
\nProcedure for biomass estimate and its error propagation [
Destructive sampling method (or harvesting method) and developed allometric equation can both be used to estimate individual-level biomass. For tree biomass estimate, destructive sampling method is more accurate than the use of developed allometric equation, because all the developed allometric equations are fitted (derived) from the biomass data based on the destructive sampling method. However, destructive method needs to cut down several sample trees and is thus expensive and time-consuming; moreover, it is not practical to weigh all the biomass for each tree in a stand or forest.
\nThe relationship between the total biomass (
The general procedure for estimating biomass using destructive sampling method is to cut down several sample trees and weigh its different components (e.g., foliage, branch, stem, and root), respectively. After field survey, the components of the sample trees are collected and immediately taken to the laboratory to determine the water content. Subsequently, the (total) biomass can be determined by multiplying the fresh weight by the dry/fresh weight ratio. Then allometric equation can be fitted between the sampling biomass and
The choice of stand-level biomass estimation is varied with the proportion of stand species composition (i.e., mixed or pure forests). Mixed stand-level biomass estimates may be estimated using allometric equation and then obtained by the addition of entire stands.
\nThis stand-level method is similar to the large-scale mean biomass density method described above, which does not take variations in biomass within a stand into account. In addition, the aforementioned simple allometric equation method is unable to fully reflect the developments and changes in stand structures. The corporation of the commonly used simple allometric equation and diameter-distribution functions (e.g., normal, lognormal, gamma, logistic, exponential, Richards, or Weibull functions) into a model (hereafter referred to as a diameter-distribution model) would likely improve the biomass estimation accuracy and strengthen the power of forest dynamics analyses.
\nThe paper reported by Qi et al. [30] has exemplified the diameter-distribution model (Eq. (5)), which combined a three-parameter diameter-distribution function with an allometric equation to estimate the biomass of pure moso bamboo forests in China. The study found that a three-parameter Weibull distribution best characterized the diameter distribution of the moso bamboo stands. The biomass derived using the allometric equation was estimated 52.39 t/ha, smaller than 53.25 t/ha estimated using the Weibull distribution model (Table 2); this implied that the use of the common allometric equation alone to estimate forest biomass and carbon stocks may lead to an underestimate. It is concluded that using the diameter-distribution model to estimate forest biomass and carbon stock is expected to improve the accuracy.
\nPlot | \nBDWD (t/ha) | \nBDAE (t/ha) | \nRE (%) | \nPlot | \nBDWD (t/ha) | \nBDAE (t/ha) | \nRE (%) | \n
---|---|---|---|---|---|---|---|
PG1 | \n45.54 | \n46.29 | \n−1.64 | \nPH15 | \n47.48 | \n46.16 | \n2.79 | \n
PG2 | \n35.88 | \n34.79 | \n3.04 | \nPH16 | \n31.43 | \n32.17 | \n−2.34 | \n
PG3 | \n43.00 | \n42.19 | \n1.87 | \nPH17 | \n34.99 | \n34.59 | \n1.14 | \n
PG4 | \n41.46 | \n38.87 | \n6.25 | \nPH18 | \n24.87 | \n23.78 | \n4.37 | \n
PH5 | \n18.96 | \n18.02 | \n4.94 | \nPH19 | \n61.27 | \n59.91 | \n2.23 | \n
PH6 | \n51.25 | \n50.72 | \n1.03 | \nPH20 | \n13.07 | \n12.68 | \n3.00 | \n
PH7 | \n40.39 | \n39.02 | \n3.38 | \nPHS21 | \n87.39 | \n87.36 | \n0.03 | \n
PH8 | \n53.54 | \n52.05 | \n2.78 | \nPHS22 | \n63.389 | \n63.28 | \n0.16 | \n
PH9 | \n64.17 | \n62.37 | \n2.81 | \nPHS23 | \n49.339 | \n48.96 | \n0.76 | \n
PH10 | \n44.38 | \n43.79 | \n1.32 | \nPHS24 | \n61.22 | \n60.13 | \n1.78 | \n
PH11 | \n52.57 | \n50.57 | \n3.80 | \nPHS25 | \n100.93 | \n100.79 | \n0.14 | \n
PH12 | \n43.42 | \n41.94 | \n3.40 | \nPHS26 | \n102.58 | \n102.10 | \n0.47 | \n
PH13 | \n62.56 | \n60.81 | \n2.79 | \nPHS27 | \n102.44 | \n101.54 | \n0.87 | \n
PH14 | \n60.40 | \n59.69 | \n1.18 | \nAll | \n53.25 | \n52.39 | \n1.91 | \n
Comparison of biomass density (BD, t/ha) based on both the Weibull-distribution model and allometric equation alone for the 27 moso bamboo stands.
\n
where
Early in the International Biosphere Plan (IBP) period, Whittaker et al. [31, 32] have assessed forest biomass and carbon stock on the regional and global scales, via mean biomass density method, where one can estimate biomass for a stand or forest by the mean biomass density multiplied by the area.
\nShi [33] selected 36 plots of moso bamboo forests to first calculate the mean
Plot | \nBiomass density (t/ha) | \nCarbon density (t/ha) | \nPlot | \nBiomass density (t/ha) | \nCarbon density (t/ha) | \n||
---|---|---|---|---|---|---|---|
P1 | \n9.56 | \n45.94 | \n22.97 | \nP20 | \n4.76 | \n16.56 | \n8.28 | \n
P2 | \n7.82 | \n33.03 | \n16.52 | \nP21 | \n4.62 | \n8.81 | \n4.4 | \n
P3 | \n9.31 | \n37.99 | \n18.99 | \nP22 | \n6.68 | \n14.66 | \n7.33 | \n
P4 | \n6.4 | \n34.26 | \n17.13 | \nP23 | \n6.71 | \n16.64 | \n8.32 | \n
P5 | \n6.7 | \n23.33 | \n11.66 | \nP24 | \n8.12 | \n34.5 | \n17.25 | \n
P6 | \n8.79 | \n47.79 | \n23.89 | \nP25 | \n10.57 | \n52.75 | \n26.37 | \n
P7 | \n7.73 | \n60.46 | \n30.23 | \nP26 | \n8.11 | \n21.46 | \n10.73 | \n
P8 | \n8.54 | \n64.65 | \n32.33 | \nP27 | \n6.12 | \n37.49 | \n18.75 | \n
P9 | \n8.89 | \n72.69 | \n36.35 | \nP28 | \n5.56 | \n22.51 | \n11.26 | \n
P10 | \n7.82 | \n38.89 | \n19.45 | \nP29 | \n5.98 | \n25.26 | \n12.63 | \n
P11 | \n8.09 | \n46.84 | \n23.42 | \nP30 | \n7.14 | \n24.35 | \n12.18 | \n
P12 | \n7.18 | \n43.36 | \n21.68 | \nP31 | \n5.91 | \n18.44 | \n9.22 | \n
P13 | \n7.81 | \n30.61 | \n15.31 | \nP32 | \n7.86 | \n31.12 | \n15.56 | \n
P14 | \n9.81 | \n52.73 | \n26.37 | \nP33 | \n9.00 | \n60.95 | \n30.48 | \n
P15 | \n7.73 | \n45.80 | \n22.9 | \nP34 | \n10.51 | \n49.96 | \n24.98 | \n
P16 | \n8.02 | \n27.09 | \n13.54 | \nP35 | \n10.77 | \n57.13 | \n28.57 | \n
P17 | \n8.72 | \n29.62 | \n14.81 | \nP36 | \n8.17 | \n39.05 | \n19.53 | \n
P18 | \n6.33 | \n22.09 | \n11.05 | \n\n | \n | \n | \n |
P19 | \n9.07 | \n54.91 | \n27.46 | \nMean | \n | 37.33 | \n18.66 | \n
Mean biomass method-derived biomass and carbon stock at the stand level in South Anhui Province, China.
\n
However, the measured biomass density (i.e., data or plot) is more limited in this method, and plot location is frequently in the well-growing stand, easily leading to an overestimation [37, 38].
\nAs a spatial statistics, geostatistics has become an indispensable method used to study the nature with the dual characteristics of randomness and regularity over the past 50 years [39]. Forest is affected by physical, climate, and other natural disturbances with a high degree of heterogeneity and relevance; therefore geostatistics is also gradually used in forest ecology, including forest biomass estimate [40, 41].
\nZhang et al. [42] confirmed the feasibility of geostatistical methods used in estimating bamboo forest biomass. They chose Huangkeng Town, southern Wuyishan Mountain, as the study area and cut 103 sample bamboo culms to develop an allometric equation for moso bamboo of the region, combined with data of field survey and leaf area index data to estimate the biomass of a total of 209 plots at stand level. By means of ARCGIS software, statistical technique was used to estimate bamboo forest biomass of the whole town, and spatial distribution of biomass was also visualized.
\nMany countries have implemented national forest inventory (NFI) regularly or irregularly in order to grasp the forest resources and their dynamics, as well as more scientific development of forest policies [43]. Subsequently, many scholars estimated forest biomass using NFI data at the region level, and then a biomass expansion factor (BEF) method came into being [28, 44]. This method assumes that there is a certain relationship between the forest growing stock and biomass; thus, biomass can be estimated based on the growing stock (derived from NFI) multiplied by the BEF conversion factor. Some researchers hold that BEF is a constant; we can call it mean BEF method. Actually, BEF varied frequently with forest age, site class, and stand density [45–47]. Hence, an improved method (called continuous BEF) [38, 45, 48, 49] is gradually being accepted by many scientists.
\nCurrent researches regarding forest biomass change are mainly based on NFI data [46, 47, 50–54]. But for the nation with a larger land area, the NFI points are limited, and remote areas are also difficult to reach, thereby often creating a bias in estimates. Moreover, forest resource assessment is incomparable; for example, the error of tropical deforestation rate estimated by FAO may be up to 50%, which is mainly due to the differences in national inventory methods and the definition of forest in tropical region [3]. Additionally, since NFI data have no information recording the spatial distribution of plots; therefore, spatial variation cannot be analyzed while using NFI data.
\nRemote sensing technique developed rapidly in the late twentieth century, and remote sensing data with high spatiotemporal resolution, wide coverage, and timely updates has been widely used in the assessment of forest biomass and carbon stock on various scales [55–57]. Currently, remote sensing-derived biomass estimation has become the leading method of large-scale forest biomass and carbon stock estimation. The use of remote sensing technique to assess forest biomass is mainly based on the normalized difference vegetation index (NDVI) datasets. While using NDVI datasets, most researches frequently overlay the vegetation maps of a region and NDVI datasets to explore the spatiotemporal changes. But this method only pays attention to changes in productivity, without considering the change in area caused by land-use change [58, 59].
\nThe method can be exemplified with the study conducted by Shi [24]. In this paper, National Forest Inventory and normalized difference vegetation index (NDVI, comes from Global Inventory Monitoring and Modeling Studies) datasets were integrated via matching forest type for Heilongjiang, Liaoning, and Jilin Provinces while fitting the inverse model (Figure 3). The developed inverse models were used to estimate forest growing stock and carbon stock, respectively. Consequently, changes in growing stock and biomass between 1982 and 2006 were analyzed.
\nRelationship between forest timber volume density (m3/ha) (a), biomass density (t/ha) (b), and annual mean NDVI (NDVIave) of northeast China (including Heilongjiang, Liaoning, and Jilin provinces), respectively; four forest types for each inventory period (four inventory periods of and a total of 48 data points) are used in the regression.
For any forest, area, growing stock, biomass, and carbon can be linked using Eqs. (6)–(8). The forest identity method defines these four valued attributes by using measurable variables, and it quantitatively and logically integrates their changes into a causal relationship (i.e., Eqs. (9)–(11)) [60, 61].
Then,
Let
Then,
\nSimilarly,
\nAnd also,
\nwhere
Shi et al. [35] applied both forest identity and regression approaches to explore the temporal changes in China’s forest. This paper showed that China’s forest area and growing stock density increased by 0.51 and 0.44% annually over the past three decades, while the conversion ratio of forest biomass to growing stock declined by 0.10% annually. These developments resulted in a net annual increase in 0.85% in forest carbon sequestration, which is equivalent to a net biomass carbon uptake of 43.8 Tg per year (1 Tg = 1012 g).
\nIn the paper, two regression equations below (i.e., Eqs. (12) and (13)) were used to obtain the derivatives of the forest attributes with respect to time:
where
Each estimation method has its advantages and disadvantages. None of these methods mentioned in this chapter is the best from individual to large scale (Table 4). Fitting allometric equation requires some down sampling trees, but the number is small relative to all the standing trees. The equation (coefficients) varies frequently with species, terrain, temperature, and rainfall. To improve its prediction power, combining the field survey with LIDAR and incorporating the variation into allometric coefficients are the two key elements. It is fully convinced that allometric equation, together with LIDAR, is increasing widely used in estimate forest biomass at individual and stand levels in the future.
\nMethod | \nScale | \nMajor limitation | \nImprovement practices | \n
---|---|---|---|
Allometric equation | \nIndividual or stand | \nVarying frequently with species, terrain, temperature, and rainfall; less sampling trees | \nIncorporating these factors into allometric coefficients; combine with LIDAR | \n
Mean biomass density | \nStand or region | \nEasily leading to an overestimation | \nRandomly set more plots | \n
Biomass expansion factor | \nStand | \nVarying frequently with species, terrain, temperature, and rainfall | \nIncorporating these into conversion factor | \n
Forest identity | \nRegion | \nComprehensive analysis | \n– | \n
Remote sensing | \nStand or region | \nSaturation and bidirectional reflectance of surface features | \nHigher spatiotemporal resolution, advanced algorithm and technology | \n
Geostatistics | \nRegion | \nMore field data | \nConstructing the biomass database | \n
Comparison of the forest biomass estimation methods.
Mean biomass density easily leads to an overestimation. However, the uncertainty will be reduced if we randomly select some plots across the study region as more as possible.
\nLike allometric equation, biomass expansion factor also varies frequently with species, terrain, temperature, and rainfall. It is of significance to explore the variation of conversion factor with species and environment; then the conversion factor can be estimated as possible as we can.
\nForest identity is a tool used to comprehensively analyze the forest change, which is more likely be employed only by a small number of scientists.
\nRemote sensing technique has been booming since the 1980s, but saturation of vegetation index (e.g., widely used NDVI) and bidirectional reflectance of surface features still occur. Exploring the higher spatiotemporal resolution, advanced algorithm and technology are needed in the future.
\nGeostatistics is an important technique to upscale forest biomass but needs more field data. More field data implies more cost and time-consuming. It is, therefore, important to construct the biomass database.
\nMany scholars have conducted researches to estimate forest biomass and carbon stock on various scales, but estimates have a large uncertainty [62–65]. Dixon et al. [37] used mean biomass density method to estimate China’s forest, whose carbon storage and carbon density were 7.0 Pg C and 114 t/ha, while biomass was estimated 6.2 Pg C and 57.07 t/ha based on the 720 plots conducted by Zhou et al. [66]. Estimation error of tropical deforestation rates may be as high as 50% due to the difference by national forest inventory method in tropical regions and different definitions of forest [3]. With regard to carbon stock for the Amazon rain forest, the estimation results varied from 58 [67] to 134 Pg C [68], as well as 89 Pg C estimated by FAO [2]. Given the large uncertainty, Houghton et al. [69] conducted a study regarding the comparison of different methods by different scholars and concluded that carbon storage estimates are rarely considerable, even using similar data and the same estimation method.
\nAs addressed above, the procedure of estimating forest biomass includes four steps (Figure 1). Each step can produce uncertainty: step 1 induces measure error, step 2 causes model error, step 3 involves sampling error, and step 4 often ignores biomass spatial variability and increases uncertainty. Uncertainty resulting from the previous steps will affect (propagate) the following biomass estimation process (result). Compared to measure and sampling errors, model and spatial variability errors are much larger but easier to solve. Taking the inverse model (see Figure 3), for example, the densities of growing stock and biomass both have significant relationships with NDVI, but determination coefficients (
In ecological researches, a number of parameters (such as biomass) are not easy to be measured directly; thus, the indirect measurement is available, namely, measuring certain indirect variables first and calculating direct one based on the relationship between indirect and direct variables. Direct measurement error will be transformed and propagated to indirect measurement error by a certain function, namely, error propagation [70]. Overall, the uncertainty is mainly due to the spatial variability of forest carbon stock and error propagated from the estimation process [26, 71, 72]. In general, the total uncertainty can be obtained by summing the error produced during the procedure [73].
\nCurrently, little research has been conducted on the uncertainty of biomass estimation [73], and only a few studies have focused on one or several errors. For example, Ketterings et al. [74] have focused on the error induced by step 2 and presented a simple method to reduce error based on the tree
With the increasing value of climate change by world dignitaries and growing global carbon market, the accuracy problems in estimating forest carbon stock on various scales have become increasingly prominent. The uncertainty in forest carbon reserve estimation can involve the climate negotiations and the development of carbon trading market to some extent, thereby affecting the development of government policies and international negotiations regarding the forest response to climate change. Therefore, the scientific community has become increasingly concerned about the uncertainty in the estimation of forest carbon stock. Changes in publication papers from 1990 to date regarding biomass estimation uncertainty can be evident (Figure 4); prior to 1997, the number of papers is relatively stable, but article number increases exponentially since 1997 (when Kyoto Protocol effects).
\nArticles regarding the uncertainty analysis of forest carbon stock versus time. The number is obtained via searching forest, biomass, and uncertainty in core journal of ISI Web of Knowledge.
Although remote sensing, NFI, geographic information system, and other advanced techniques have been applied to estimate forest biomass, but the accuracy of estimate of forest biomass needs to be improved. In recent years, with the advent of LIDAR, multispectral data, and geostatistical technique, most scholars paid more attention to the application of such advanced technology in biomass estimates but ignored the acquisition and compiling field biomass data. Although acquisition of field biomass data using harvesting method is time-consuming and laborious, the fitted inverse model based on remote sensing data must be verified using the measured data on biomass. Matching remote sensing data and ground-truth data is often a difficult problem during biomass estimation. In various biomass estimation methods, most researchers assume that the ground-truth biomass data are the most accurate. It is thus important to carry out field biomass measurement globally using a unified investigation specification, as well as the establishment of a unified biomass database.
\nScale has been one of the core focuses of ecology. Scale and upscale affect the accuracy of forest biomass estimation and are also an important issue in biomass estimation. Biomass estimation method varied with scale. The mean biomass density method, BEF method, remote sensing, and geostatistic method are used to estimate large-scale forest biomass and carbon stock, which involves scale biomass (upscaling); allometric equation is used for small-scale biomass estimation. However, large-scale biomass estimate must be based on the allometric equation. If forest biomass estimation method you use is different from another, estimate may be different even if the same data is used. The rapid development of remote sensing data, geostatistical technique, and others make scaling possible while estimating biomass. Therefore, upscaling forest biomass from stand scale to regional and even global scale should be one of the key elements for future biomass researches. Only by addressing the scaling issue and clarifying the spatial variation of forest biomass can a unified framework or system for estimating forest biomass be presented. Then national and international standards of estimating forest biomass can be developed to improve biomass estimation with a high accuracy.
\nRemote sensing data with a high spatiotemporal resolution, wide coverage, and timely updates have been widely used in the assessment of forest biomass and carbon stock on various scales, which play an important role in improving estimation accuracy.
\nIn addition, scaling biomass cannot do without application of remote sensing data. However, remote sensing data have no continuity of the time series, the resolution is not high enough, and the fitted inverse model is often saturated. Moreover, the quality of the current multispectral data is not high, and LIDAR data and UAV technology applied in forest biomass estimate are not yet mature. Improving the quality of multispectral data, LIDAR and UAV technology are two of the most active frontiers in the future.
\nAs mentioned above, allometric equation is indispensable in biomass estimation. However, the coefficients of the equation varied frequently with species, terrain, temperature, and rainfall [77–79]. Small variation in the allometric coefficients is likely to result in a larger biomass estimation error [80]. Therefore, the clarification of the variation in the allometric coefficients along different gradients is also the element to improve the accuracy of biomass estimation.
\nBiomass estimate may be different using different allometric equations while estimating vegetation biomass. Thus, it is likely to come to inconsistent results, even when different scholars estimate the same forest. In other words, the choice of the appropriate predictors and optimization of allometric equation and its parameters can contribute to reduce biomass estimation uncertainty and improve the ability to predict the biomass and carbon stock. It is worth noting that recently developed metabolic rate theory (fractal networks) is contrary to conventional wisdom. The theory inference indicated that the forest biomass (
Plant allometry is the theoretical basis of vegetation biomass estimation. Generally, the use of allometric equation is indispensable to estimate biomass for both tree and forest. By reviewing the methods used for estimating forest biomass, we can conclude that each estimation method has its advantages and disadvantages, and none of these methods mentioned is always the best from individual to large scales. A large uncertainty exists in biomass estimation, which is unable to meet the requirement of the accurate carbon accounting required by Kyoto Protocol. It is of significance and urgently needed to develop the more suitable and rigor method to accurately estimate the size and distribution of forest biomass. To achieve this goal, we argued that the new developed technique such as geostatistics and remote sensing technique (e.g., LIDAR) would be the key tools to improve forest biomass estimation with a high accuracy. However, prior to this, spatial variation in forest biomass at various levels should be explored using multi-source data and multi-approaches.
\nThis study was supported by the Special Fund for Basic Scientific Research of International Center for Bamboo and Rattan (1632015005) and the National Natural Science Foundation of China (31300177).
\nThe energy demand is increasing concurrently with the increase of the world’s population and meeting the increasing energy demands including managing social, economic, and ultimate environmental issues are one of the greatest challenges of the present time. Solar energy, as one of the promising renewable energy sources, is becoming an important source of energy all over the world. Its huge development potential has attracted a lot of attention and the photovoltaic (PV) industry has been experiencing a large-scale development to replace traditional energy. Also, a significant increase in energy conversion efficiency and the decrease in the price of the solar panels along with various national policies over the world enhanced the solar PV-based energy generation with the least Levelized-cost-of-energy (LCOE). However, for getting optimum output, proper resource estimation is necessary to assess the feasibility of solar PV systems in any area. The output of any PV system’s output indeed depends on the weather of its surroundings will be elaborated in this chapter.
In contrast, there are several types including different technologies of photovoltaic modules in the market. However, the technological choices are very critical in the sense of the lack of guide and forecasting tools suited to the climates and environment of the installation sites. There have been many PV system projects going to fail due to the bad choice of PV technology where failure causes are influenced by the environmental parameters, such as heat, humidity, shadow, and dust, etc. Manufacturers provide a characteristic of PV modules measured in standard test conditions (STC), however, the performance cannot reach that level in real operating conditions. Besides, the PV module’s performances and aging strongly depend on the climate and the surrounding environment of the installation site.
The investigation of PV performance under real external conditions became an important factor as a result of increasing trends of PV capacity over the world. Particularly, the performance of the PV module influence by the number of different external issues, such as, (i) spectral irradiance, i.e., the wavelength of incident light and light intensity, the efficiency of PV certainly varied with the variations in the spectrum of sunlight [1, 2, 3, 4] and light intensity directly affect the short circuit current [5]; (ii) reflectivity of the module surface, however, the reflectivity that occurred in the module surface depends on the angle between the module surface and the incident angle [6]; and (iii) module temperature, particularly temperature of the module surface increases to 60-80 °C at noon and cause of the reduction in open-circuit voltage which also depend on the light intensity and airflow [7]. Thus, in each PV field, the factors that contribute to solar cell efficiency are different and the important considerations applied in each area are different. On the other hand, some types of PV modules show the degradation of power conversion efficiency under the long-term light exposer in the field and/or elevated temperatures. Particularly, due to the above-mentioned effects, the module efficiency and/or electrical parameters are observed to deviate from the nameplate value measured under Standard Test Conditions (STC) [8] in the real external condition. Besides, there are some other causes for which the energy production capability of a PV module is affected, such as installation angle; possible shadow, dust or snow deposition, etc. However, these mostly depend more on the details of the installation, not inherent to the module type and the physical properties of the module. It could be mention that the power output could vary as an impact of the above-listed causes while different types of the module installed in the same way; alternatively, similar types of module generate different power output due to the installation in a different way or different places. The variation of PV performance has been investigated by several authors in terms of geographical variability and technology. Some authors only focused on the effect of solar irradiation while other authors consider some of the above-mentioned factors. In this study, we also only reviewed the study that focuses on the effect of solar irradiance on the different PV technology.
Solar irradiance on different locations of the earth is shown in Figure 1 [9]. The maps highlight the global horizontal irradiation (GHI) which means that the overall irradiance from the sun reaches the earth’s horizontal surface. It is related to the diffuse horizontal irradiance (DHI) and direct normal irradiance (DNI) as follows [10],
Global horizontal irradiation (GHI) over the world [
Where θ is the solar zenith angle. Areas with a high proportion of GHI include South-East China, Northern Europe, and the tropical belt around the equator.
Since the electrical performance of PV devices is greatly affected by the incident light spectrum, hence, significant efforts have given by the PV community to develop methods and evaluate the impact of the spectral variations on the PV device performance over the last three decades. The parameters that have the highest impact on the spectrum distribution as well as on the PV module performance are (i) the air mass (AM), (ii) the perceptible water (PW), and (iii) the aerosols optical depth (AOD) [11].
The AM is a measure of the atmospheric absorption that affects the spectral content and the intensity of the solar radiation coming to the earth’s surface. The impact of AM on the solar spectral distribution is shown in Figure 2 [11]. Particularly, the solar spectral distribution just above the Earth’s atmosphere-in the relative vacuum of space is commonly referred to as an air mass zero (AM0). And the AM = 1.0 at sea level when the sun is directly overhead (zenith angle, θz = 0). As the θz increases, the path passes by the sun spectrum through the atmosphere become longer, and AM increases. The AM could estimate simply using a trigonometric function of the zenith angle:
Impact on the direct spectral irradiance of air mass (AM) simulated with the SMARTS model [
where θz is the angle of incidence or solar zenith angle.
The above equation is quite accurate for θz ≤80 degrees, however, more complex and precise models are necessary when the sun goes near the horizon. Moreover, the distribution of the outdoor solar spectrum varied during the day due to the presence of water vapors and aerosol in the air. Thus, the real spectrum at the earth’s surface is infrequent to fit with the AM1.5 standard solar spectrum as defined in standard IEC 60904–3 and/or ASTM GE173–03 [12]. Specifically, the spectral power distribution observed in the sun at an angle of about 48.2o is specified as AM 1.5 spectrums (as in Figure 3(a)). The power density of AM1.5 light is about 1,000 W/m2. The standard AM 1.5 spectrum is known as solar constant and is normally used in solar cell analysis. Figure 3(b) shows the spectral distribution of sunlight under the different air masses.
(a) The path length (in units of air mass) changes with the zenith angle (b) spectral distribution of solar energy [
Another important parameter that needs to be considered for understanding solar irradiance on the earth’s surface is the clearness index (KT). Particularly, KT is defined by the ratios of the solar radiation for a particular day and the extraterrestrial solar radiation for that day. It could also be defined by hourly as shown below:
Where H and I represent the total measured and Ho and Io are represent the extra-terrestrial solar radiation which could be calculated using several approaches [14]. This value of KT or kT lies between zero and one which contingent on atmospheric conditions. For clear sky conditions, KT is near 1 and if the sky is very cloudy and/or turbid and/or heavily overcast, KT becomes less than 0.4. Several laboratories have been developed computational models considering spectral direct beam during the clear sky and hemispherical diffused irradiances on a surface either horizontal or tilted condition for a certain location and time [15]. Other than the above parameters, the outdoor energy yield and performance of the PV modules further depend on a large number of on-site factors or local factors such as ambient temperature, wind, and rain. These undefined factors may also influence significantly amount of solar radiation that arrives on the surface of the PV module. Certainly, it is important to analyze the influences of all the above on-site environmental factors on the outdoor performance of different types of PV modules, for finding out the best-suited technology for a specific location and enabling more widespread deployment.
The electrical power generated in a solar cell or PV device can be modeled with a well-known equivalent circuit as shown in Figure 4 which includes a shunt resistance parallel with a diode and a series resistance [16].
Equivalent circuits for a solar cell in a single diode model, including series and shunt resistance [
This equivalent circuit can be used for either an individual cell, a multi-cell module, or an array consisting of multiple modules. Using this model and considering constant temperature and solar radiation, the current–voltage equation for a solar cell or module could be expressed as shown in Eq. (4).
Where, IL is the light generated current, Io is the dark saturation current, Rs is the series resistance, Rsh is the shunt resistance, A is the modified cell or module ideality factor that can be expressed as:
where Ns is the number of cells or modules that are connected in series, ni is the diode ideality factor for a cell, K is the Boltzmann constant, qe is the electron charge, T is the cell or module temperature.
Figure 5 shows the current–voltage (I-V) characteristic curves of a solar cell or a module. Particularly, the power generated by the solar cell or module is the product of the current (Imp) and voltage (Vmp). It should be noted that five parameters, such as IL, Io, Rs, Rsh, and A, determine the current and voltage generated in a cell or module, thus the impact of external factors, such as solar radiation and temperature could be analyzed from the change of these values. In general, the FF is directly affected by series resistance, and it is found that the fill factor of a solar cell decreases by about 2.5% for each 0.1 Ω increase in series resistance [17]. On the other hand, Rsh is reduced if the leakage current is increased in a solar cell. If there any light and temperature-activated defects available in a solar cell, then leakage current could be increased, alternatively Rsh could be reduced as the increase of irradiance intensity or temperature. Finally, FF and Voc will be reduced. For an ideal case, Rs = 0, Rsh = ∞ and ni = 1, the open-circuit voltage, Voc could be expressed as,
Typical current–voltage (I-V) and power-voltage characteristic curves of a solar cell.
For a very small applied voltage (V ≈ 0), the diode current, Io is negligible or zero, then from Eq. (6), we can find,
Where Isc is a short circuit current. Now Eq. (9) becomes,
The Voc and Isc rectangle description as shown in Figure 5 offers a useful means for characterizing the maximum power point [18]. The fill factor (FF) is defined as the ratio of the maximum power to the product of Voc and Isc and is less than one at all times. FF indicates the squareness of the I-V curves and can be defined from the ratios of two rectangles (Figure 5) as,
Where Pmp denotes the maximum power of the solar cell or module, Imp and Vmp are the current and the voltage values at the maximum power point, respectively. Moreover, the most significant Figure of merit for a solar cell or PV module is its power conversion efficiency, η, which is specified as,
Where Pin denoted the power of incident light that is determined by the characteristics of the light spectrum incident onto the solar cell or PV module. The power of the incident light spectrum, Pin can be express as,
Where A is the surface area of the solar cell or PV module and G is the total spectral irradiance, which could be defined as [19],
Where, f(λ) is the flux density (number of incident photon per unit area and unit time) for a specific wavelength of the photon with energy, ελ and wavelength, λ.
Particularly, the light to the electrical power conversion efficiency of a solar cell or a module is an inherent property that depends on the type of semiconductor material and the manufacturing process. However, this efficiency also depends on the environment of the installation site, especially on the hours of equivalent peak spectral irradiance in a day and/or temperature. The PV module characteristics that we find in the nameplate are typically measured at standard testing conditions (STC), the irradiance of 1000Wm−2 at AM 1.5 and 25 °C of cell temperature. In fact, these conditions hardly exist because the outdoor spectrum is far different from the STC condition, which also varied by location and season. The response to the spectral variation by different types of PV modules vastly depends on its material properties and structure. This response is primarily determined by the bandgap of the materials used in fabrication, which sets the upper wavelength limit of the spectral response (SR). More specifically, SR is depending on the PV material’s bandgap, cell thickness, and carrier transport mechanisms in the device. Secondly, device structure, means the position of the absorber material and other supporting layers has a significant effect on the spectral response. Also, the variation of electrical parameters of different types of PV module/device as an impact of various environmental factors depends on the technology (device structure and materials). On the whole, the PV device performance and SR is proportional for specific PV devices, where SR is defined as:
Where JL(λ) represents the light-generated current density for a specific wavelength “λ” and G(λ) is the spectral irradiance of the incident light measured in W/m2-nm. However, in state-of-the-art solar cell or PV modules, the spectral response is defined as the short-circuit current, Isc(λ), resulting from a single wavelength of light normalized by the maximum possible current [20, 21, 22, 23].
Where, q is the electronic charge 1.6 x 10–19 C, A is the surface area of the PV device and f(λ) is the incident photon flux (number of photons incident per unit area per second per wavelength). Besides, the SR of the PV devices is also estimated in terms of quantum efficiency (QE), which indicates that how efficiently a PV device converts the incident light to a charge carrier that flows through the external circuit [24], details on QE has been discussed next section. In that case,
In the case of PV modules, JL is approximately the same in value as the short-circuit current density (Jsc) [25]. Thus, with the help of the above equations, Jsc can be expressed as,
It could be seen in Eq. (16) that Jsc can be estimated by the SR for PV modules which certainly have prime importance in evaluating PV materials and device characteristics. Particularly, the degree to which the SR and the incident irradiance spectrum varies gives rise to a spectral effect on the device current and efficiency. The SR of different types of the module at AM1.5G spectrum (up to 1300 nm) is shown in Figure 6 to confirm the response is different for different technologies [10]. As seen in Eq. (16), Isc is affected by the spectrum. Particularly, the spectrum variations are also influenced the other PV output parameters, viz. FF, Voc, and η. To determine the magnitudes of these effects on different technology-based PV devices, various performance review studies were carried out [26, 27, 28, 29, 30, 31].
Spectral response characteristics of different solar module technologies, modified from [
Particularly, The SR shows represent the current produced by a solar cell for per watt of irradiance at each wavelength of the photon. As seen in Figure 7 that SR towards the higher wavelength region is lower because photons in this region have energy less than the material bandgap threshold. As a result, the effect of spectral variation on the output of PV devices is most pronounced in narrow SR technologies such as a-Si and CdTe. Especially narrowest SR is seen for the a-Si that is also discussed in the literature [32, 33, 34, 35]. For simplification of SR and PV performance, research is commonly used one-dimensional terms, such as spectral mismatch factor (MMF) [32, 33, 34, 36], the useful fraction (UF) [37], average photon energy (APE) [38, 39]. In the case of MMF and UF, their values should be a known factor for a specific module understudy, however, the SR data is not available publicly and analysis complexity arises. Besides, APE is denoted by the unit of an electron volt (eV) which signifies the average incoming photon energy. The equation for calculating is as follow:
(a) variation of EQE, IQE, and reflectance with the wavelength of a c-Si solar cell (collected and modified from Wikipedia), (b) EQE of different PV solar cell technology [
Where, E(λ) represents the energy of the incident photon and f(λ) is the incident photon flux at wavelength λ, and pλ and qλ are the integration limits indicate the lower and higher absorption wavelength, which are 300 and 1200 nm as shown in Figure 7. Particularly, APE varies on a daily and seasonal basis due to the increase of air mass at sunrise and sunset compared to noon and in winter compared to summer. For example, when the sun is above the horizon, the spectral irradiance is red-shifted and the APE becomes low. APE rises again to a high around noon during the day. Moreover, the APE is higher in the summer months than in winter because the zenith angle of the sun is higher in summer. Besides, the atmospheric water, cloud cover, and/or aerosol content affect the APE due to light absorption and scattering. For most of the PV modules, the APE effect on performance seems to be linear. The spectral photon flux density denoted in joules can be expressed as below for a specific wavelength λ:
where ‘h’ is the Planck constant and ‘c’ is the light velocity in vacuum.
The SR and QE are conceptually similar to each other. Particularly, SR is the ratio of the generated current in a solar cell per unit incident power, while QE denoted the ratio of the number of generated carriers and the number of the incident photon on the solar cell. In another way, the QE of a solar cell represents the amount of current the cell produces for a particular wavelength of an incident photon. Knowing the QE of a particular PV technology is important because by integrating QE for the whole solar spectrum, the current generation capability of PV solar cells could be realized. Interestingly, the QE value could exceed 100% for a PV solar cell in the case of multiple excitation and generation (MEG). In that case, one incident photon could generate several electron–hole pairs as an impact of multiple excitations. The MEG properties are typically seen in quantum-dot solar cells [40]. However, all the incident photons on the cell surface cannot be absorbed due to surface optical properties, such as absorption and reflection. Thus, QE is divided into two terms, (i) external QE (EQE) and (ii) internal QE (IQE) which simply differ by the photons reflection properties of a PV solar cell. In the case of EQE, all photons that impinge on the cell surface are taken into account, while in the case of IQE, only photons that are absorbed (not reflected) by the solar cell are considered. The graphical representation of EQE and IQE is shown in Figure 7.
High EQE is a precondition for high-power PV applications, which depends on the absorption coefficient of the absorber material of a PV solar cell, the carrier excitation quality, and carrier recombination rate or the amount of electron transport to the electrodes. The mentioned QE in Eq. (15) is typically EQE, which is directly related to the current generation by a solar cell [41]:
Where, with q is the charge of electron and φλ(λ) the incident spectral flux density, indicating the incident number of photons of wavelength λ on the cell surface per unit of area, per unit of time and EQE could be defined as:
The relation between IQE and EQE could be defined as:
Where L is the total optical loss that occurred in a solar cell either through reflection or transmission or both. Particularly, for maximizing EQE, the optical loss should be minimized. To reduce the optical loss, anti-reflection coating, and back-reflection coating is applied in the current PV technologies.
Solar irradiance and surface air temperature are two key factors for investigating the PV module performance. Particularly, the increase in solar irradiation is a cause of the increase in air temperature and vice versa. On the other hand, the increase in solar irradiance is proportionally increased the power output of the PV module, however, module output decrease with the increase of temperature [42]. Usually, the output and temperature of the PV modules are considered to be linear. The effect of temperature mostly depends on the absorber material and its quality. From the module electrical properties, the temperature effect could be realized by observing the variation of the device parameters:
In the case of Isc and FF, there is very little change that occurred with temperature for crystalline silicon and thin-film devices. Alternatively, the Voc is highly dependent on the temperature variation, which can be described via the Voc as calculated from the one diode model as shown below:
And,
Where VT is known as thermal voltage, T is the solar cell temperature, k is the Boltzmann constant, q is the elemental charge q, n is the ideality factor and I0 is the diode saturation current. From the above diode equation, it could seem that the Voc is positively changing with the temperature, because the above-simplified diode equation typically overlooked the parasitic factors, such as solar cell series and shunt resistance. Particularly, this parasitic resistance is changed significantly over thermal variation [43] and greatly impacts the voltage and diode saturation current as reported elsewhere [44]. For understanding the impact of temperature on Voc, we have to consider the temperature-dependent diode saturation current, which in turn:
Where B is a temperature-independent empirical factor but controlled by the quality of absorber material, γ is also an empirical factor that relies on the specific carrier loss mechanism and Eg is the absorber material bandgap. The influence of irradiance and module temperature can be explored by combining the data according to these dependencies. The resulting matrix can then be used to model the annual yield for various technologies at different locations [45]. The main uncertainties, in this case, are kWp standardization and input irradiance [46].
As there are several uncertainty factors are involving, for the easy and efficient deployment of PV solar cell system, it is essential to measure and develop a model for the spectral distribution of solar radiation. Colle et al. [47] have shown that there has a linear relationship between the uncertainty of solar irradiation and the uncertainty of solar thermal and PV systems. This is a big challenge in the 21st century to develop a more efficient and robust model that could reduce the solar radiation misprint include will need fewer input parameters, will have smaller residual and can be used in a wide variety of conditions.
Indeed, the solar spectrum depends on the place, time, and condition of the atmosphere. The global solar spectrum may be divided into two spectrum models, one for direct beam radiation and the other for diffuse radiation. Particularly, the spectrum of solar incident radiation wavelengths on the PV modules corresponds to the appropriate spectral response range of the PV cells. Several reports on the effect of spectral irradiance variation and PV solar cell performance can be found elsewhere [48, 49]. The longer irradiation hours provided the better annual average electricity outputs [50]. The effect of solar spectral irradiation on the yield of several PV technologies has been documented by Nann and Emery at four separate locations [51]. Eke et al., on the other hand, found that the spectrum variance had a very limited effect on the low bandgap absorber content in PV solar cells [52]. Figures 6 and 7 shows the spectral response characteristics and EQE of different PV technologies which indicate that how the performance of PV module could change upon the variation spectral distribution.
Several solar spectrum models, including SPECTRAL2 [53], LOWTRAN2 [54], REST2 [55], and SMARTS2 [56], have been developed yet to date over time for clear skies. These models are usually computer programs developed to evaluate the shortwave spectrum components of surface solar irradiance in the range of 280 to 4000 nm. Some of them have high spectral resolutions, however, they need very complex calculations making them less efficient. In the case of LOWTRAN(2), detailed inputs are needed, which increases the execution time and creates some performance limitations, that’s why the use of this model is limited in engineering applications [57]. On the other hand, even a low number of parameters are needed for SPECTRAL2, however, the mean deviation associated with different aerosol models is higher than SMART2 [58]. On the other hand, transmittance parameterizations based on the SMARTS spectral model are used to build the high-performance REST2 model [57]. Particularly, more updated parametric functions and constants are used in the SMARTS2 model, for which it has a higher resolution and is showing lower deviation in the spectral analysis. SMARTS program is written in FORTRAN and depends on simplifications of the radiative transfer equation which allow very quick calculations of the irradiance of the surface. The newest versions, such as SMARTS2.9.2 and SMARTS 2.9.5 are hosted by NREL.
The SMART model uses different inputs to define the conditions of the atmosphere under which the irradiance spectra are to be measured. Ideal conditions can also be selected by the user, based on various potential model atmospheres and aerosol models. Moreover, it is also possible to determine practical conditions as inputs, based, for example, on aerosol and water vapor data supplied by a sun photometer [59]. Besides, the spectrally integrated (or ‘broadband’) irradiance values are given by this model, which can later be compared with measurements from a pyranometer (for diffuse or global radiation) or pyrheliometer (for direct radiation). Solar geometry is another vital input in this model in addition to the atmospheric condition, which is typically specified by the position of the sun (zenith angle and azimuth), the location, the air mass (AM), or by specific time and date. More details on the usage of the SMARTS model for PV applications can be found elsewhere [60, 61, 62, 63]. Particularly, this model is frequently employed to evaluate PV modules’ efficiency and mismatch factors in real-world conditions [64, 65, 66]. Figure 8 shows the direct normal irradiance spectra with SMART 2.9.5 for different air mass.
Direct normal irradiance spectra calculated with SMARTS 2.9.5 for increasing air mass (0 to 10), using the same atmospheric conditions as the ASTM G173 standard. Air mass 0 corresponds to the extraterrestrial spectrum, marked as top of atmosphere (TOA), modified from [
Crystalline silicon (c-Si) is the most prevalent PV technology on the market (c-Si). In considering crystal size and crystallinity, c-Si can be divided into two major categories, mono or single-crystalline Si (sc-S) and multi or polycrystalline Si (mc-Si). The power conversion efficiency of sc-Si is higher than mc-Si solar cells, alternatively, sc-Si is costly than mc-Si. The typical efficiency of commercial c-Si modules is between 11% and 20% which power generation varies by temperature (temperature coefficients) in the range of 0.3–0.5%/K [68]. Commercial c-Si modules consisting of 200–500 μm thick PV cells that are connected in series and/or parallel for attaining expected voltage and current. It is important to note that c-Si solar cells or PV modules can generate electrical energy for a wide range of the spectrum (350–1200 nm) as illustrated in Figure 9 [69]. However, the absorption coefficient of c-Si is below 104 cm−1 for all wavelengths larger than 500 nm as shown in Figure 9. This means that all the potential photons below 500 nm are absorbed close to the surface of the cell. Thus, it is important for the c-Si solar cell that the active region has to be located near the cell surface for absorbing all potential photons and achieving optimum efficiency. Also, it could be seen in Figure 9 that the absorption coefficient is below 2.0 x 104 for wavelength above 650 nm. As the absorption coefficient of c-Si is below 103 for wavelengths above 700 nm which indicates that photons in this range can penetrate the bulk and generate electron–hole pairs. However, their contribution to the photocurrent is very hard in the case of conventional c-Si solar cells. Thus, for collecting these bulk carriers, the configuration of conventional c-Si structure modified, by names they are passivated emitter and rear contact (PERC) [70], passivated emitter and rear locally-diffused PERL [71], interdigitated back-contact (IBC) c-Si [72] solar cells.
Absorption spectrum of Si, CdTe, and CIGS solar cells, modified from [
As it has been mentioned earlier that the response to spectral variation by different types of PV modules vastly depends on its material properties and structure, c-Si solar cells also showed different characteristics depending on the irradiation properties. Several studies have been reported on the in-field energy output analysis of c-Si PV systems by Panchula et al. [73] based in Ontario, Canada; Dolara et al. [74] based in Tuscania, Italy; Fiances et al. [75] Based on a different place in Peru, Kazem et al. [76] based on the desert area of Sohar, Oman, Fuentes et al. [77] and Muñoz et al. [78] under warm climate of Spain, Bahaidarah et al. [79] based on Dhahran, Saudi Arabia and Edalati et al. [80] based on Kerman, Iran. In the above reports, they typically estimate the performance of the system based only on average monthly or yearly insolation and performance ratio varied by the location ranging from around 0.7 to 0.85. Fiances et al. [75] studied different Si technology includes sc-Si, mc-Si, a-Si, and μc-Si PV modules in the climate of Peru, and finalize that a-Si/μc-Si PV modules perform much better than others with an annual performance ratio of 0.97. Ahmed Ghitas [81] reported the effects of the spectral variations on the mc-Si module performance based on outdoor measurements in daily irradiation changes. They only consider cloud-free days in Helwan, Egypt in their measurements and also did not consider the temperature effect. The variation of Voc, Jsc, and power concerning radiation intensity is shown in Figure 10. It is evident from Figure 10 that the most affected device parameter is Isc, and output power in the case of the mc-Si PV module.
(a) Daily profile of the measured solar module short circuit current, open-circuit voltage, and electrical output power, (b) daily profile of incident solar radiation along with module output power, and (c) spectral irradiance variation versus time (a.ms) on a clear sky measurement day [
Eke and Demircan [82] have been studied mc-Si PV module performance based on winter (January) and Summer (August) for Mugla, Turkey. The operating temperature at this location is 50.5 °C in January and 80.5 °C on August 16. The power generation of the module is 30% lesser in summer than winter because of the significant difference in operating temperature. The power generation every day in January and August is shown in Figure 11. Bora et al. [83] also studied the pc-Si PV module along with a-Si, HIT-Si PV modules under the climate condition of the different parts of India. They find that all these three types of Si-based PV modules produce the highest energy yield in the cold and sunny zone.
PV module performance in January (a) and august (b) 2008 for Mugla, Turkey environment [
It is important to mention that the energy yield analysis of a PV system is incomplete if their low light condition analysis is missing. Reich et al. [84] have reported the performance of c-Si at low light conditions, however, the impact of temperature is missing as they conducted the study focusing on indoor performance. The finalized that the obtained efficiency via indoor measurement and rated efficiency has a significant difference. Certainly, temperature is a dominant factor in the performance of the PV system in outdoor conditions. It should be noted that solar irradiance and ambient temperature are proportional. Chander et al. [85], and Atsu and Dhaundiyal [86] studied output yield using a detailed model that includes temperature and wind speed variation. Chander et al. [85] reported that the performance parameters of the sc-Si module such as Voc, Pmax, FF, and efficiency are decreased with temperature while the Jsc is increased. Bahaidarah et al. [73] also suggested that for achieving the highest PV performance yield in Saudi Arabia, a suitable and uniform cooling system is necessary due to the climatic conditions. A detailed study on performance variation by low light conditions along with the temperature variation effect has been presented by Pervaiz and Khan [87]. In their modeling, they used various insulation profiles for a different location in the US collected from NREL. They reported that the energy harvesting of a PV system for a specific location depends on the average peak sunlight hours of that location as shown in Figure 12. The use of the following equation for calculating energy difference concerning the variation of Equivalent Peak Sunlight Hours (EPSH).
Energy difference (D) in percent for Washington, Seattle, and Austin for years 2000–2003 [
Where Ei is the energy harvested during one year considering a constant efficiency and Ec is the energy harvested incorporating a change in efficiency. The finalized that the reduction in energy yield is reliant on the EPSH of a region where reduction factor could range from 1.5 to 5% for various regions concerning the value of the EPSH.
Cotfas and Cotfas [88] have been studied details on the performance of sc-Si and a-Si PV modules under the natural condition via years of observation, in Brasov, Romania. They reported that the average Pmax of the sc-Si module is two times greater than the a-Si module, however, on clear winter days, the values even increase near to three times greater. Also, at low irradiance, under 100 W/m2, the power gain is of sc-Si is 1.9 times greater than a-Si. The gain is over 1.9 times even for very low irradiance, under 100 W/m2. The detailed performance of the a-Si PV module including other thin-film modules as an impact of irradiance and temperature are covered in the next section. Under the Mediterranean climatic conditions of the north of Athens, Greece [89], the performance of the p-Si photovoltaic system has been investigated. There is a linear relationship between the module surface temperature and the irradiance where the average temperature about 49.9 °C in summer and 16.8 °C in winter. The efficiency of the p-Si module has been significantly dropped in summer where it ranging from 6.2% to 10.4% concerning the module temperature.
The SR of PV cells depends on the absorption coefficient and/or bandgap of the absorber materials. Similarly, the performance variation by increase or decrease of temperature also depends on the bandgap [90]. The semiconductor material with a wider bandgap, such as 1.04–1.68 eV for CIGS [91], 1.45–1.5 eV for CdTe [92, 93], and (1.7–1.9 eV for a-Si [94] shows higher temperature resistance to the increase of module temperature. As a consequence, they have a lower temperature coefficient than sc-Si and pc-Si PV modules [95], and thus, device performance is significantly affected by the temperature. The details on the effects of irradiance, spectrum, and temperature on thin-film PV modules were investigated by Gottschalk et al. [96] under the UK environment. It has been reported that the performance of a-Si is highly spectral dependent as shown in Figure 13. The relative change in short circuit current (Isc) is +10% to −20% observed for a-Si whereas the change is only ±3% for c-Si and CIGS. Environmental effects have also been shown to cause up to 15% of losses to the annual PV production. The spectral impact on different PV technologies for all single months has been investigated under the German climate condition [32]. Similar to the other reports, the spectral impact changes more for bigger bandgap a-Si PV modules as shown in Table 1. The average gains over the year are 3.4% for a-Si, 1.1% for c-Si, 0.6% for CIGS, and 2.4% for CdTe. It has been reported that CIGS and c-Si modules exhibit high gains in winter and a-Si and CdTe shows an advantage in summer attributed mostly to spectrum variation [32]. The study carried in the Netherlands [97] showed that low irradiance caused a decrease in annual energy yield of 1.2% for the CIGS modules and 1% for CdTe. This experimental study also indicated a strong effect of spectral variation on the performance of the a-Si modules.
Calculated spectral effects for the devices under test in the UK environment. The graph compares the normalized ISC divided by the irradiance measured with the pyrometer [
Gi [kWh] average monthly irradiation from the reference period | Average, relative monthly spectral impact | |||||
---|---|---|---|---|---|---|
a-Si (%) | CdTe (%) | c-Si (%) | High-eff. c-Si (%) | CIGS (%) | ||
1 | 38 | −2.0 | 1 | 1.9 | 2.4 | 2.6 |
2 | 65 | −1.3 | 0.1 | 1 | 1.4 | 1.6 |
3 | 122 | 0.1 | 0.6 | 0.7 | 0.8 | 0.9 |
4 | 141 | 3.5 | 1.9 | 1.2 | 0.9 | 0.4 |
5 | 166 | 4.2 | 2.3 | 1.5 | 0.9 | 0.3 |
6 | 166 | 5.1 | 2.8 | 1.4 | 0.8 | 0 |
7 | 184 | 5.3 | 3.4 | 1.5 | 0.8 | 0 |
8 | 168 | 5.3 | 3.5 | 1.6 | 0.9 | 0.1 |
9 | 136 | 4.3 | 3.1 | 1.5 | 1 | 0.4 |
10 | 91 | 2.8 | 3 | 1.9 | 1.7 | 1.3 |
11 | 43 | 0.8 | 2.3 | 2.1 | 2.2 | 2.1 |
12 | 35 | −2.2 | 1.8 | 2.4 | 3 | 3.3 |
Calculation of annual spectral impact based on the monthly sums of irradiance of a reference year and the determined average monthly spectral impact assessed in Germany [32].
The detail on performance variation by the influence of temperature of the different types of PV modules has been conducted by Gutkowski et al. under the low insolation climate of Poland [95]. They observed a significant difference in performance by different PV modules at temperatures 15-48 °C as shown in Figure 14(a). It is clear from Figure 14(a) that under real conditions of the high-temperature region, the power generated by CIGS thin-film technologies is higher compare to the pc-Si PV modules. Ozden et al. [98] also experimentally investigate the a-Si and CdTe thin-film PV module performance under the Turkey climate zone along with sc-Si and mc-Si. They found a significant difference in performance in that module for the sunny and cloudy days as shown in Figure 14(b). The output performance of sc-Si and mc-Si is found to be the same, but the output difference between CdTe and sc-Si modules is 60% for a sunny day and which reduces to 35% for a cloudy day indicating the impact of irradiance as well as temperature on these technologies. Moreover, the performance ratio (PR) of sc-Si is in the range of 70%–90%, a-Si is about 70% and CdTe is only 42%–72%. Alternatively, Kesler et al. [99] also conducted a performance analysis between the c-Si and thin film for another location, Antalya, Turkey, and reported that performance of the both technology is very close to each other. Even they specified the reason is the high ambient temperature of that area, however, the rated efficiency of that technologies may play an important role in this case, which means that if the rated efficiency is almost the same, their performance will be close to each other.
(a) Normalized DC power generated by the PV systems of each studied technology [
Sharma et al. [100] studied three different PV technologies, such as a-Si, pc-Si, and HIT under the tropical climate of India. They found that the best-suited PV technology for this climate is HIT and a-Si. The overall performance ratio for a-Si is 90% and for pc-Si is 83% in this region as shown in Figure 15. Interestingly, the energy yield of a-Si is 14% greater during summer, but 6% lower in winter. The effect of seasonal which in turn the effect of irradiance and temperature on the performance of a-Si may be related to its thermal annealing process [101]. The HIT modules have consistently performed better (≥ 4–12%) than p-Si over the year. Aste et al. [102] investigate PV module performance under temperate climatic conditions (Italy) where the more distinct seasonal change and/or wide temperature variation have occurred. They found that a-Si is much more sensitive to the seasonal solar spectrum rather than c-Si and HIT technology. The highest 93% of performance ratio has been reported for c-Si in this study. However, the c-Si technology has also shown seasonal variation as an exceptional case [75] and the performance ratio found 20% lower in summer than in winter as a role of temperature variation. In summer, the a-Si/mc-Si stack cell showed higher performance than the other technologies tested in this study, which may be due to its low-temperature coefficient and thermal annealing.
Comparison of measured monthly and yearly performance ratio of each technology array tested in Indian climate condition [
The assessment carried in the Netherlands [103] showed that the CIGS modules are strongly affected by irradiance and temperature variations with a decrease in annual energy yield of 1.2%. Moreover, CdTe modules also exhibited a decrease in energy yield of about 1.0%. This experimental study also showed a significant influence of spectral variation on the efficiency of a-Si modules. Zdyb and Gutkowski studied four different types of PV modules, such as pc-Si, a-Si, CIGS, and CdTe at high latitude under East Poland climate conditions [104]. In their study, a-Si and CIGS shows the gain in performance ratio (about to 73.4% for a-Si and 90.7% for CIGS) during summer sunny and warm environment. The increase of performance ratio of a-Si PV modules has also been reported by Makrides et al. [101] studied under the Cyprus environment. On the other hand, the performance ratio of pc-Si PV modules exhibited over 80% except for December and always remain the highest among the investigated PV modules over the year as shown in Figure 16.
Performance ratio for each studied PV technology investigated in East Poland (data collected in 2018) [
The effect of spectral irradiance distribution on the performance of a-Si/mc-Si stacked photovoltaic modules has been analyzed by Minemoto et al. [105] installed at Kusatsu-city (Japan). Their study revealed that these stacked PV modules are extremely spectrally sensitive compared to pc-Si PV modules installed on the same site. Akhmad et al. [106] have been compared the performance of poly-silicon (pc-Si) and amorphous silicon (a-Si) at Kobe, Japan, and found a-Si modules are better for this region. K. Nishioka et al. [107] compared sc-Si, pc-Si module, and heterojunction silicon at Nara Institute of Science and Technology (NAIST) under Japanese climate. They reported that the HIT technology is better suited for this region due to its low-temperature dependency. Poissant [108] has evaluated four different novels PV module technologies, (i) H-Si, (ii) IBC, (iii) a-Si/uc-Si, and (iv) c-Si under the climate of Canada. His study confirmed that the heterojunction silicon and a-Si/uc-Si technologies are less affected by temperature than the other two crystalline silicon technologies. Canete et al. [109] also performed a comparative study of four different photovoltaic module technologies, (i) amorphous silicon (a-Si), (ii) tandem structure of amorphous silicon- microcrystalline silicon (a-Si/mc-Si), (iii) polycrystalline silicon module (pc-Si) and (iv) cadmium telluride (CdTe). Their results show that the performance of thin-film modules is better than that of pc-Si modules for the location of Southern Spain. The performances of c-Si and a-Si PV modules under South Africa climate conditions have been evaluated by Maluta and Sankaran [110]. They found that both technologies give a similar and suitable performance for the climate of this region. Three different PV technologies (monocrystalline, polycrystalline, and amorphous silicon) have been evaluated under the desert climate by M. Shaltout et al. [111]. They reported that the polycrystalline silicon cells are more suitable in such a climate. All these above-mentioned studies indicate the difficulty when it comes to choosing the appropriate PV technology for a given site. Thus, the prediction of PV energy potentials before installation helps us to understand the economic advantages associated with it and for policy regulation for electric utilities.
Table 2 shows the summary of a few reported works for finding out the best PV technology by location and its climate. It should be noticed that the results reported by the various researcher as mentioned above are very difficult to compare because the work has been conducted focusing on different locations and various time scales (instantaneous, monthly, annual), different energy effects, and even the works are different by used metrics and calculation. However, it is well agreed that the impact of spectral irradiance variations on PV device performance mostly depends on its spectral response, which in turn depends on its absorber material properties and quality. Moreover, the influence of spectral irradiance on PV performance is dependent on installation sites, for instance, the spectral distribution, climate, environment, latitude, longitude, albedo, etc. of the location. Besides, the spectral distribution of specific sites again depends on the cloudiness, water-vapor and aerosol content in the sky of that sites. The analysis considering all the above factors certainly will be too difficult, thus, the researcher considers only some of the factors for simplifying their work.
Author(s) | Location | Environmental Parameters | Tested Technologies | Best Perform Technology |
---|---|---|---|---|
Dirnberger et al., 2015 [32] | Breisgau, Germany | Maritime climate, 5-25 °C, 1,117 kW/m2/year (approx.) | a- Si, sc-Si, CIGS and CdTe | a-Si |
Francis et al., 2019 [75] |
|
|
| a-Si/μc-Si |
Edalati et al., 2015 [81] | Kerman, Iran | Dry climate 68.64–198.72 kW/m2, 20 °C | sc-Si, and pc-Si | pc-Si |
Bora et al., 2018 [83] | Different parts of India | 0.82–0.87 kW/m2/day not mentioned | a-Si, HIT, and pc- Si | All (cold and sunny zone) |
Cotfas and Cotfas, 2019 [88] | Brasov, Romania | Temperate-continental climate, 2.1–1.82 Wh/m2/day, −4.0 – 24 °C | sc-Si and a-Si | sc-Si |
Louwen et al., 2016 [89] | Utrecht, Netherlands | Oceanic climate, 20.5–29.5 °C, 950–1050 W/m2 | SHJ, a- Si, sc-Si, pc-Si, CIGS, CIS and CdTe | sc-Si and SHJ |
Gulkowski et al., 2019 [95] | Lublin, Poland | Temperate climate, 950–1250 (kWh/m2)/year, 15–48 °C | CdTe, CIGS, and pc-Si | CIGS |
Aste et al., 2014 [94] | Milan, Italy | Temperate climatic, 1270 kW/m2/year, −5-32 °C | c-Si, a-Si/uc-Si, HIT | HIT |
Zdyb and Gulkowski, 2020 [103] | Lublin, Poland | Temperate climate, 950–1250 (kWh/m2)/year, 15–48 °C | pc-Si, a-Si, CIGS, and CdTe | pc-Si and CIGS |
Makrides et al., 2018 [104] | Cyprus | Mediterranean climate, 1988–2054 kWh/m2, 10-40 °C | sc-Si, pc-Si, a-Si, CIGS and CdTe | a-Si |
Minemoto et al., 2007 [105] | Kusatsu-city, Japan | Subtropical climate, 200 kW/m2, 9-33 °C | pc-Si, and a-Si | pc-Si |
Poissant, 2009 [108] | Montreal, Canada | Continental climate, 950–1050 W/m2, max. 20 to 22 °C | SHJ, IBC, a-Si/uc-Si, and c-Si | a-Si/uc-Si |
Cañete et al., 2014 [109] | Southern Spain | Dry Mediterranean climate, 3.7–7.4 kWh/m2/day, 15–30 °C | a-Si, a-Si/μc-Si CdTe, and pc-Si | a-Si and CdTe |
Summary of few reported works for finding out the best PV technology by location and climate.
The weather and/or solar irradiance of the earth is significantly different from one location to another. Again, solar irradiance varies for a specific location by season and/or common weather phenomenon, such as dust, rain, wind, cloud, fog, and snow, etc. Thus, every year solar irradiance also not the same in amount and as an impact of the above factors, the energy yield of different PV technology is affected differently and prediction is very complicated. However, numerous studies could help us to predict which PV technology is better suited for a certain location. It should be noticed that all the incident solar radiations absorbed by PV cells are not able to convert into electricity, some of them are increase temperature, thus the performance varied. As discussed in this book chapter, most of the study showed that summer months when irradiation becomes high that leads to an increase of module temperature, a-Si technology show better performance than c-Si PV modules. It may be due to the metastable defects generated during the dangling bond compensation are decreased upon module temperature increase and as a result, the module performs better in elevated temperature. Also, CIGS PV modules show similar behavior to the a-Si PV modules. The performance gain observed in CIGS technology in summer or at elevated temperatures may be related to the larger bandgap and lower temperature coefficient. Particularly, the optical bandgap of CIGS thin film is higher than a-Si and the higher bandgap has a lower temperature coefficient. Also, CIGS modules can convert the blue light part of the solar spectrum due to a larger bandgap that may assist to perform better in hot summer. Alternatively, c-Si have a narrow bandgap, as the defect density increases upon high irradiance and high temperature in hot summer, the dark saturation current and/or leakage current is increased. Consequently, the performance decrease in summer. However, it has been seen that c-Si perform very in high irradiance with cold weather. It should be noticed that the module with a higher leakage current is highly affected by low irradiance. Since a-Si solar cells inherently have high defect density and/or high leakage current than c-Si solar cells, thus the power gain by c-Si at very low irradiance is significantly higher than a-Si as discussed in the above section. Overall, CdTe modules are performed much poorer than others probably due to the consequence of early degradation of the module as reported in the previous section. All these above-mentioned studies specify the difficulty of choosing an appropriate PV technology for a given site. Thus, the prediction of PV energy potentials before installation is very important concerning the economic advantages and for policy regulation for electric utilities.
The authors wish to thank the Ministry of Higher Education of Malaysia (MoHE) for providing the Long Term Research Grant Scheme (LRGS) with the code of LRGS/1/2019/UKM-UNITEN/6/2 to support this research. The authors also acknowledge the publication support from the iRMC of Universiti Tenaga Nasional (@UNITEN), Malaysia. The authors would also like to acknowledge the Faculty of Engineering, University of Malaya (@UM) for other supports.
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Polymorphism at the DNA level includes a wide range of variations from single base pair change, many base pairs, and repeated sequences. Genomic variability can be present in many forms, including single nucleotide polymorphisms (SNPs), variable number of tandem repeats (VNTRs, e.g., mini- and microsatellites), transposable elements (e.g., Alu repeats), structural alterations, and copy number variations. Different forms of DNA polymorphisms can be tracked using a variety of techniques; some of these techniques include restriction fragment length polymorphisms (RFLPs) with Southern blots, polymerase chain reactions (PCRs), hybridization techniques using DNA microarray chips, and genome sequencing. During the last years, the recent advance of molecular technologies revealed new discoveries of DNA polymorphisms. DNA polymorphisms are endless, and more discoveries continue at a rapid rate. Mapping the human genome requires a set of genetic markers. DNA polymorphism serves as a genetic marker for its own location in the chromosome; thus, they are convenient for analysis and are often used as in molecular genetic studies.",book:{id:"6719",slug:"genetic-diversity-and-disease-susceptibility",title:"Genetic Diversity and Disease Susceptibility",fullTitle:"Genetic Diversity and Disease Susceptibility"},signatures:"Salwa Teama",authors:[{id:"249329",title:"Dr.",name:"Salwa",middleName:null,surname:"Teama",slug:"salwa-teama",fullName:"Salwa Teama"}]},{id:"58467",title:"Generation of Antibody Diversity",slug:"generation-of-antibody-diversity",totalDownloads:3163,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Because of the huge diversity, the immunoglobulin repertoire cannot be encoded by static genes, which would explode the genomic capacity comprising about 20,000–25,000 human genes. The immunoglobulin repertoire is provided by the process of somatic germ line recombination, which is the only controlled alteration of the genomic DNA after meiosis. It takes place in mammalian B lymphocyte (B cells) precursors in the bone marrow. The genome germ line sequence of undeveloped B cells is organized in gene segments and compromise V (variable), D (diversity), and J (joining) gene segments constituting the variable domain of the heavy chain and only V and J genes for building up the variable domain of the light chain. The rearrangement of the variable region follows a strict order. The following processes that participate in the generation of antibody diversity were summarized—allelic, combinational, and junctional diversity, pairing of IgH and IgL, and receptor editing—which all together produce the primary antigen repertoire (pre-antigen stimulation). When a B cell encounters a foreign antigen, affinity maturation and class switch are induced. Thereby the antibody repertoire increases. The resulting secondary immunoglobulin repertoire reveals in humans at least 1011 specificities for different antigens.",book:{id:"5784",slug:"antibody-engineering",title:"Antibody Engineering",fullTitle:"Antibody Engineering"},signatures:"Oliver Backhaus",authors:[{id:"177685",title:"M.Sc.",name:"Oliver",middleName:null,surname:"Backhaus",slug:"oliver-backhaus",fullName:"Oliver Backhaus"}]},{id:"61204",title:"Polymorphisms",slug:"polymorphisms",totalDownloads:2057,totalCrossrefCites:3,totalDimensionsCites:10,abstract:"Polymorphism or variation in DNA sequence can affect individual phenotypes such as color of skin or eyes, susceptible to diseases, and respond to drug, vaccine, chemical, and pathogen. It occurs more often than mutations (frequency ≥ 1%). The common polymorphism is single nucleotide polymorphism (SNP) which is a single base change in a DNA sequence that occurs most commonly in the human genome. SNPs have been used as molecular markers in a wide range of studies. Genome-wide association studies (GWAS) searches for SNPs that occur more frequently in person with a particular disease than in person without the disease and pinpoint genes or regions that may contribute to a risk of disease. This topic describes about polymorphisms, SNPs, GWAS, linkage disequilibrium (LD), minor allele frequency, haplotype, method for SNP genotyping, and application of SNPs and genome-wide association study in human diseases and drug development.",book:{id:"6719",slug:"genetic-diversity-and-disease-susceptibility",title:"Genetic Diversity and Disease Susceptibility",fullTitle:"Genetic Diversity and Disease Susceptibility"},signatures:"Wasana Sukhumsirichart",authors:[{id:"238537",title:"Associate Prof.",name:"Wasana",middleName:null,surname:"Sukhumsirichart",slug:"wasana-sukhumsirichart",fullName:"Wasana Sukhumsirichart"}]},{id:"21711",title:"Screening of Bacterial Recombinants: Strategies and Preventing False Positives",slug:"screening-of-bacterial-recombinants-strategies-and-preventing-false-positives",totalDownloads:28495,totalCrossrefCites:1,totalDimensionsCites:5,abstract:null,book:{id:"375",slug:"molecular-cloning-selected-applications-in-medicine-and-biology",title:"Molecular Cloning",fullTitle:"Molecular Cloning - Selected Applications in Medicine and Biology"},signatures:"Sriram Padmanabhan, Sampali Banerjee and Naganath Mandi",authors:[{id:"46458",title:"Dr.",name:"Sriram",middleName:null,surname:"Padmanabhan",slug:"sriram-padmanabhan",fullName:"Sriram Padmanabhan"},{id:"136523",title:"Prof.",name:"Sampali",middleName:null,surname:"Banerjee",slug:"sampali-banerjee",fullName:"Sampali Banerjee"},{id:"136524",title:"Prof.",name:"Naganath",middleName:null,surname:"Mandi",slug:"naganath-mandi",fullName:"Naganath Mandi"}]},{id:"49200",title:"Human Embryology",slug:"human-embryology",totalDownloads:3573,totalCrossrefCites:5,totalDimensionsCites:7,abstract:"The study of human embryology has a very long history. Modern embryology owes its initial development to the key embryo collections that began in the 19th century. The first large collection was that of Carnegie, and this was followed later by the major 7 collections. The second role of the Carnegie collection was for researchers to establish a defined set of Carnegie stages based on embryo morphological features. Today, embryos are imaged three-dimensionally (3D) by a range of imaging modalities including, magnetic resonance microscopy (MRM), episcopic fluorescence image capture (EFIC), phase-contrast X-ray computed tomography (pCT), and optical projection tomography (OPT). Historically, embryo serial images were reconstructed using wax-plate and model techniques. The above new 3D imaging techniques now allow 3D computer reconstructions, analysis, and even 3D printing. This chapter will describe how the classical embryology collections and techniques have developed into today’s imaging and analysis techniques, giving new insights to human embryonic development.",book:{id:"4588",slug:"new-discoveries-in-embryology",title:"New Discoveries in Embryology",fullTitle:"New Discoveries in Embryology"},signatures:"Shigehito Yamada, Mark Hill and Tetsuya Takakuwa",authors:[{id:"49486",title:"Prof.",name:"Shigehito",middleName:null,surname:"Yamada",slug:"shigehito-yamada",fullName:"Shigehito Yamada"},{id:"90205",title:"Prof.",name:"Tetsuya",middleName:null,surname:"Takakuwa",slug:"tetsuya-takakuwa",fullName:"Tetsuya Takakuwa"},{id:"175453",title:"Dr.",name:"Mark",middleName:null,surname:"Hill",slug:"mark-hill",fullName:"Mark Hill"}]}],onlineFirstChaptersFilter:{topicId:"54",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:31,numberOfPublishedChapters:314,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,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:19,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:14,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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At the Ministry of Justice of Slovenia, she is a member of examination boards for court expert candidates and judicial appraisers in the following areas: economy/finance, valuation of companies, banking, and forensic investigation of economic operations/accounting. 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She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",institutionURL:null,country:{name:"Turkey"}}}]},{type:"book",id:"7139",title:"Current Approaches in Orthodontics",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7139.jpg",slug:"current-approaches-in-orthodontics",publishedDate:"April 10th 2019",editedByType:"Edited by",bookSignature:"Belma Işık Aslan and Fatma Deniz Uzuner",hash:"2c77384eeb748cf05a898d65b9dcb48a",volumeInSeries:2,fullTitle:"Current Approaches in Orthodontics",editors:[{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. 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