Water quality at eight points on the agricultural canals
\\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:null},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
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Interested readers will find in this book the topics of image compression, groundwater quality, establishing the downscaling and spatio-temporal scale conversion models of NDVI, modelling and optimization of 3T fractional nonlinear generalized magneto-thermoelastic multi-material, algebraic fractals in steganography, strain induced microstructures in metals and much more. The book will definitely be of interest to scientists dealing with fractal analysis, as well as biomedical engineers or IT engineers. I encourage you to view individual chapters.",isbn:"978-1-83962-483-4",printIsbn:"978-1-83962-482-7",pdfIsbn:"978-1-83962-484-1",doi:"10.5772/intechopen.87695",price:119,priceEur:129,priceUsd:155,slug:"fractal-analysis-selected-examples",numberOfPages:128,isOpenForSubmission:!1,hash:"f0c3d700a69d15b52ff8a59fe7e99062",bookSignature:"Robert Koprowski",publishedDate:"September 9th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/9886.jpg",keywords:null,numberOfDownloads:1093,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 1st 2019",dateEndSecondStepPublish:"March 3rd 2020",dateEndThirdStepPublish:"May 2nd 2020",dateEndFourthStepPublish:"July 21st 2020",dateEndFifthStepPublish:"September 19th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"50150",title:"Prof.",name:"Robert",middleName:null,surname:"Koprowski",slug:"robert-koprowski",fullName:"Robert Koprowski",profilePictureURL:"https://mts.intechopen.com/storage/users/50150/images/system/50150.jpg",biography:"Robert Koprowski, MD (1997), PhD (2003), Habilitation (2015), is an employee of the University of Silesia in Poland, Institute of Computer Science, Department of Biomedical Computer Systems. For 20 years he has studied the analysis and processing of biomedical images with particular emphasis on the full automation of measurement for a large inter-individual variability of patients. He is the author of dozens of papers with the impact factor (IF) and more than a hundred other papers, as well as the author or co-author of six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in the field of biomedical engineering.",institutionString:"University of Silesia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1407",title:"Applied Mathematics",slug:"numerical-analysis-and-scientific-computing-applied-mathematics"}],chapters:[{id:"72917",title:"Optimization of Fractal Image Compression",slug:"optimization-of-fractal-image-compression",totalDownloads:190,totalCrossrefCites:0,authors:[null]},{id:"72577",title:"Fractal Analysis for Time Series Datasets: A Case Study of Groundwater Quality",slug:"fractal-analysis-for-time-series-datasets-a-case-study-of-groundwater-quality",totalDownloads:172,totalCrossrefCites:0,authors:[null]},{id:"71255",title:"Establishing the Downscaling and Spatiotemporal Scale Conversion Models of NDVI Based on Fractal Methodology",slug:"establishing-the-downscaling-and-spatiotemporal-scale-conversion-models-of-ndvi-based-on-fractal-met",totalDownloads:207,totalCrossrefCites:0,authors:[null]},{id:"72883",title:"A New BEM for Modeling and Optimization of 3T Fractional Nonlinear Generalized Magneto-Thermoelastic Multi-Material ISMFGA Structures Subjected to Moving Heat Source",slug:"a-new-bem-for-modeling-and-optimization-of-3t-fractional-nonlinear-generalized-magneto-thermoelastic",totalDownloads:154,totalCrossrefCites:0,authors:[{id:"233766",title:"Prof.",name:"Mohamed Abdelsabour",surname:"Fahmy",slug:"mohamed-abdelsabour-fahmy",fullName:"Mohamed Abdelsabour Fahmy"}]},{id:"71839",title:"Using Algebraic Fractals in Steganography",slug:"using-algebraic-fractals-in-steganography",totalDownloads:178,totalCrossrefCites:0,authors:[null]},{id:"71305",title:"Fractal Analysis of Strain-Induced Microstructures in Metals",slug:"fractal-analysis-of-strain-induced-microstructures-in-metals",totalDownloads:195,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247865",firstName:"Jasna",lastName:"Bozic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247865/images/7225_n.jpg",email:"jasna.b@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6692",title:"Medical and Biological Image Analysis",subtitle:null,isOpenForSubmission:!1,hash:"e75f234a0fc1988d9816a94e4c724deb",slug:"medical-and-biological-image-analysis",bookSignature:"Robert Koprowski",coverURL:"https://cdn.intechopen.com/books/images_new/6692.jpg",editedByType:"Edited by",editors:[{id:"50150",title:"Prof.",name:"Robert",surname:"Koprowski",slug:"robert-koprowski",fullName:"Robert Koprowski"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6138",title:"Time Series Analysis and Applications",subtitle:null,isOpenForSubmission:!1,hash:"d33ee38578b81585416062fea4979bbf",slug:"time-series-analysis-and-applications",bookSignature:"Nawaz Mohamudally",coverURL:"https://cdn.intechopen.com/books/images_new/6138.jpg",editedByType:"Edited by",editors:[{id:"119486",title:"Dr.",name:"Nawaz",surname:"Mohamudally",slug:"nawaz-mohamudally",fullName:"Nawaz Mohamudally"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9966",title:"Dynamic Data Assimilation",subtitle:"Beating the Uncertainties",isOpenForSubmission:!1,hash:"e7fde2a36354a2f5a4282fdf9c743380",slug:"dynamic-data-assimilation-beating-the-uncertainties",bookSignature:"Dinesh G. 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However, P is difficult for plants to obtain from the rhizosphere and P deficiency is one of the major limitations on crop production. This is because soluble P in soil, the primary P source for plants, is extremely low concentration (Condron et al., 2005) and significant portions of P in the soil are various organic complexes and unavailable (Raghothama, 2005). On a worldwide scale, land covering 5.7 billion hectares is estimated to be deficient in P for optimal crop production (Batjes, 1997). Since the soluble P in the soil is easily taken up by plants and microorganisms, continuous application of P fertilizer is necessary for crop production.
\n\t\t\tThe global demand for P has increased 10-fold since the beginning of the 20th century (Cordell et al. 2009) and approximately 80% of the demand is for agricultural fertilizers (Steen, 1998). Thus, more P will be required as the world’s population increases. However, there is concern that world P resources will be depleted in the next 50–100 years, because the reserves of high-grade phosphate rock are limited (Runge-Metzger, 1995; Steen, 1998; Smil, 2000; Stewart et al., 2005). Therefore, the recovery of P is essential for sustaining food production.
\n\t\t\t\n\t\t\t\tFigure 1 shows a conceptual illustration of the global P cycle, which is completed by P flux from the ocean to the land, and is intimately linked to global ocean circulation. The P derived from weathering or fertilizer application on the land is washed down in rivers and enters the ocean food chain. In deep ocean water (about 2,000–3,000 m in depth), the P concentration is considerably higher than that at the surface because dead fish and plankton fall on the ocean floor. However, the P-rich water is too deep for humans to exploit. In the deep ocean, the water flows from the Atlantic Ocean to the Pacific Ocean via the Antarctic and the Indian Ocean, while the surface water flows in the opposite direction. This movement is very slow; about 2000 years is required to complete this circulation. In some areas of the Pacific Ocean, the flow rises from the bottom to the surface, but this is rare phenomenon. Because the occurrence of this rising flow depends on a complex combination of sea currents, winds, and geographical features. Consequently, these selected areas are abundant in plankton and fish. However, the P flux from ocean to land occurs only via fishery and seabirds’ droppings (guano), unlike nitrogen that can be released into the atmosphere via denitrification. In addition, the seabed is gradually transformed into land by the geological movement of the Earth\'s crust, but this occurs on a much longer time scale than do human activities. Therefore, the global P cycle is extremely limited.
\n\t\t\tConceptual illustration of the global P cycle (data from
Despite the limited nature of the P cycle, repeated applications of fertilizers and organic matter builds up nutrients in the soil. Strong relationships between the level of P monitored by soil tests and the amount of P lost in runoff have been reported (Pote et al., 1996; Sharpley, 1995). Thus, excessive application of P fertilizers contributes to eutrophication, which is sometimes responsible for the lack of clean water resources. From this viewpoint, the recovery of P is also essential.
\n\t\t\tThe behavior of P in nature has been affected by iron (Fe) oxides since ancient time (Bjerrum & Canfield, 2002). In natural water bodies such as canals, swamps, and ponds with low oxygen groundwater seeps and circumneutral conditions, the accumulation of soft, reddish-brown sediment is often observed (Fig. 2). The essential compounds in this sediment are biogenic Fe oxides produced by microaerobic Fe-oxidizing bacteria (Emerson et al., 1999; Emerson & Weiss, 2004; James & Ferris, 2004) and this ferric substance in the sediment can adsorb P in a similar manner to abiotic P adsorbents of ferric compounds (Boujelben et al., 2008; Persson et al., 1996; Seida & Nakano, 2002; Zeng et al., 2004). Therefore, biogenic Fe oxides in nature are considered as one of the P resources. However, they have not yet been recognized as such, although they have been used for ferrous Fe removal in water treatment facilities (Pacini et al., 2005; Katsoyiannis & Zouboulis, 2004; Søgaard et al., 2001). This is because biogenic Fe oxides in natural water bodies are easily dispersed by water turbulence. In addition, it is difficult to collect only the Fe oxides as a P resource, because they usually accumulate only a few centimetres, and anaerobic and malodorous mud exists underneath (see Fig. 3). Moreover, the mud deposits that have existed for a long time may accumulate harmful substances such as heavy metals.
\n\t\t\tAccumulation of reddish–brown soft sediment in an agricultural canal
Conceptual illustration of P recovery from natural water bodies using Fe-oxidizing bacteria and woody biomass.
A new method for the recovery of P from natural water bodies using Fe-oxidizing bacteria and woody biomass as a carrier has been proposed (Fig. 3). A woody carrier is immersed in water in which Fe-oxidizing bacteria are abundant and then removed several weeks later. In this chapter, this method was tested in an agricultural area, dominated by rice paddy fields, located in the eastern part of Shimane Prefecture, Japan. As the woody carrier, sawdust from the Japanese cedar and Japanese cypress were used. Since the accumulation of biogenic Fe oxides was observed throughout the year at several locations, the water quality at these points was monitored. In addition, heavy metals on the immersed carrier were also measured, because biogenic Fe oxides have the potential to also adsorb heavy metals such as arsenic (As), cadmium (Cd), chromium (Cr), mercury (Hg), lead (Pb), zinc (Zn), and nickel (Ni).
\n\t\tSamples for water quality monitoring were collected at eight points on agricultural drainage canals (Fig. 4) on December 13, 2008. These points were located in the downstream area of the Hii River, Japan, at approximately 35° 24′ N and 132° 50′ E. The pH and oxidation-reduction potential (ORP) were monitored with a portable analyzer (Kasahara Chemical Instruments, KP-5Z). The Fe, P, and nitrogen (N) concentrations were analyzed in accordance with Japanese Industrial Standard (JIS) K 0102 (Namiki, 2003): total Fe (T–Fe) and dissolved Fe (D–Fe) were measured by the 1,10-phenanthroline method; total phosphorus (T–P) was measured by the ascorbic acid reduction molybdenum blue method after potassium peroxodisulfate decomposition; phosphate phosphorus (PO4–P) was measured by the ascorbic acid reduction molybdenum blue method; total nitrogen (T–N) was measured by UV absorption spectroscopy after alkaline potassium peroxodisulfate decomposition; ammonium nitrogen (NH4–N) was measured by the indophenol blue method; nitrate nitrogen (NO3–N) was measured by ion chromatography (Shimadzu HIC–6A). The total organic carbon (TOC) concentration was measured by Shimadzu TOC-Vcsn system and suspended solids (SS) were measured by gravimetric analysis using glass-fiber filters (pore size = 0.45 μm; Advantec GS25).
\n\t\t\tAlthough the precise mechanism of Fe oxidation-deposition by Fe-oxidizing bacteria is not sufficiently understood (Pacini et al. 2005) and some of the species are characterized as autotrophic (Hallbeck & Pedersen, 1991; Imai, 1984), a substantial accumulation of biogenic Fe oxides was found on the surface of submerged aquatic plants in an agricultural drainage canal (Fig. 5). On the basis of this finding and some trial-and-error experiments, woody biomass (conifer heartwood) was used as the carrier for collecting biogenic Fe oxides. In particular, sawdust (particle size: 0.2–2 mm) of the Japanese cedar (
Map of study site
Accumulation of biogenic Fe oxides on the surface of submerged aquatic plant
Sheathed bacteria,
The immersion test was conducted in an agricultural drainage canal (at point 1 in Fig. 4) where reddish-brown sediment accumulated, and sheathed bacteria (
In this test, the woody carrier was placed in a container of non-woven bag and lowered to the bottom of the canal. The carrier in the container was removed from the water after immersion for 4 weeks. The Fe collected on the immersed carrier was analyzed by the 1,10-phenanthroline method (Stucki & Anderson, 1981), and the P adsorbed on the Fe oxides was analyzed by the Bray-2 method (Byrnside & Sturgis, 1958). The Bray–2 P is a portion of the soil P and is one of the indexes of available P for plant uptake. In this study, the adsorbed P is expressed as g/kg instead of the conventional expression of Bray-2 P (mg P2O5/100 g dry material). In addition, the water samples were collected at weekly intervals and the water quality of D-Fe and PO4-P was analyzed by the above-mentioned methods.
\n\t\t\tElemental analysis of the immersed carrier was carried out by X-ray fluorescence spectrometry system (Shimadzu, EDX-720) at a voltage of 50 kV and a current of 1 mA.
\n\t\t\t\n\t\t\t\t\tTable 1 presents the water quality at eight points on the agricultural canals. At all points, the D–Fe concentration was much lower than the T–Fe concentration, and the same relationship was found between the PO4–P concentration and T–P concentration. Therefore, most of the Fe and P in the water were associated with particulate matter. The average concentrations of T–N and TOC were 2.864 and 2.180 mg/L, respectively, and the NO2–N concentration was much lower than the T–N concentration.
\n\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t
Water quality at eight points on the agricultural canals
Data plot on pH–OPR diagram (from
Biogenic Fe oxides (brown mass) on woody carrier
Since the Fe oxidation was characterized with a pH–ORP diagram (Mouchet, 1992), the data from this study were plotted on it (Fig. 7). In this diagram, the pH–ORP area is divided into physical-chemical oxidation, biological oxidation, and stability of ferrous Fe. The data from this study were within the range of pH = 6.7 to 7.1 and ORP = −0.03 to 0.05 V, and were located near the boundary between the biological oxidation and the stable ferrous Fe area. Since the suitable aquatic conditions for the growth of Fe-oxidizing bacteria have been reported to be low concentration of oxygen and circumneutral pH (James & Ferris, 2004), the results of present study agree with this knowledge.
\n\t\t\tThe color on the woody carrier changed from light yellow to dark brown. Observation using a microscope revealed that biogenic Fe oxides produced by Fe-oxidizing bacteria had accumulated on the woody carrier (Fig. 8(a)). In many cases, the woody carriers were not easily visible because they had been completely covered by a mass of Fe oxides (Fig. 8(b)).
\n\t\t\t\t\n\t\t\t\t\tFigure 9 shows the Fe collected on the woody carrier and the D-Fe concentrations of the water at the site of the immersion test. The average accumulation of Fe on the Japanese cedar was 7.91 g/kg during the irrigation period and 6.74 g/kg during the non-irrigation period. The respective values for the Japanese cypress were 7.67 and 5.54 g/kg. There were no significant differences between the values during the irrigation and the non-irrigation period. The average D–Fe concentration during the irrigation period (0.952 mg/L) was much higher than that during the non-irrigation period (0.338 mg/L). There were no significant differences during the irrigation and non-irrigation period between the collected Fe for the Japanese cedar and the Japanese cypress (Fig. 10). When these values are expressed in parts per million (ppm), the Fe collected during the irrigation period was 7,910 ppm for the Japanese cedar and 7,670 ppm for the Japanese cypress, while the D–Fe concentration was 0.952 ppm. Therefore, the concentration of the Fe on the woody carrier was 8,000- to 8,300-fold greater than the Fe dissolved in the water. For the non-irrigation period, the degree of Fe concentration was 16,000- to 20,000-fold greater.
\n\t\t\t\t\n\t\t\t\t\tFigure 11 shows the P adsorbed on the woody carrier and the PO4-P concentration. The average P adsorbed on the Japanese cedar carrier was 0.350 g/kg during the irrigation period and 0.187 g/kg during the non-irrigation period. The respective values for the Japanese cypress were 0.332 and 0.172 g/kg. The differences between the values during the irrigation and non-irrigation periods were significant (p < 0.05). The average PO4–P concentration of the water during the irrigation period (0.058 mg/L) was much higher than that during the non-irrigation period (0.022 mg/L). This is probably because the anaerobic conditions caused by flooded water on the paddy fields during the irrigation period lead to the reduction of ferric phosphate (FePO4) compounds and the release of Fe2+ and phosphate (PO4\n\t\t\t\t\t3-) ions. There were no significant differences in the adsorbed P during the irrigation and the non-irrigation period between the Japanese cedar and the Japanese cypress (Fig. 12). When these values are expressed in ppm, the P adsorbed during the irrigation period was 350 ppm for the Japanese cedar and 332 ppm for the Japanese cypress, while the PO4–P concentration was 0.058 ppm. Therefore, the concentration of the P on the woody carrier was 5,700- to 6,000-fold greater than the P dissolved in the water, and for the non-irrigation period, it was 7,800- to 8,500-fold greater.
\n\t\t\t\tFe content after the immersion test. (a), (b): collected Fe after 4 weeks immersion; (c): D-Fe concentration of the water (means and standard errors, n=8)
Comparison of collected Fe between Japanese cedar and Japanese cypress (n=8)
P contents from the immersion test. (a), (b): adsorbed P after 4 weeks immersion; (c): PO4–P concentration of the water (means and standard errors, n=8)
Comparison of adsorbed P between Japanese cedar and Japanese cypress (n=8)
P fertility of the immersed carrier in the relationship between the Bray-2 P in arable soils and the rice yield index (adapted from
\n\t\t\t\t\tFigure 13 shows the P fertile position of the immersed carrier on the relationship between the Bray-2 P in arable soils and the rice yield index (adapted from Komoto, 1984). In low-fertility soil (Fig. 13(a)), the yield index increases with Bray-2 P, but does not increase over the fertile level of 0.025 g/kg of Bray-2 P. As shown in Fig. 13(b), soils containing greater than 0.1 g/kg are categorized as high-fertility soil. The P values from this study were between 8- and 17-fold higher than the required level (0.025 g/kg) and categorized in the range of high-fertility soil. Therefore, the immersed carrier had obtained sufficient P fertility.
\n\t\t\t\n\t\t\t\t\tFigure 14 shows an example of an X-ray fluorescence spectrum of the immersed carrier. Fe was the main species detected, although silicon (Si), calcium (Ca), aluminum (Al), P, sulfur (SO4), potassium (K), chlorine (Cl) were also present. Heavy metals were not detected on most of the carriers, but traces of Pb and Zn were detected in some samples (Table 2). However, they were well below regulation levels set out in the Fertilizers Regulation Act (Ministry of Agriculture, Forestry and Fisheries, 2007) and the Guidelines against Heavy Metal Accumulation in Arable Soil (Environment Agency, 1984). This was probably because the study site was in a rural area that had not been contaminated by heavy metals and also because the immersion period was too short for these metals to accumulate.
\n\t\t\t\tX-ray fluorescence spectrum of immersed carrier
\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t
Heavy metal concentrations in immersed carrier (maximum for n=45)
The findings reported in this chapter have been obtained from a specific region in Japan. However, Fe is the third most abundant metal found in the soil (Spark, 1995), and Fe-oxidizing bacteria are not rare (Emerson et al., 1999; Emerson & Weiss, 2004; James & Ferris, 2004). Thus, this method can be applicable in many places, provided suitable aquatic conditions supporting the growth of Fe-oxidizing bacteria (low concentration of oxygen and circumneutral pH) are available. In addition, the immersed woody carrier can be applied directly to agricultural land in the form of a fertilizer, without P extraction procedures, which are commonly required for P recovery methods. Therefore, this method is a low-cost technique that should contribute to P resource recycling and the improvement of the aquatic environment, if adopted on a large scale.
\n\t\t\tA new method of P recovery from natural water bodies using Fe-oxidizing bacteria and woody biomass (Japanese cedar and Japanese cypress) was applied in an agricultural canal during irrigation and non-irrigation periods. The amounts of P adsorbed on the carrier during these periods were 0.332–0.350 and 0.172–0.187 g/kg, respectively, while the PO4–P concentrations of the water were 0.058 and 0.022 mg/L. Expressed these values in parts per million, the P adsorbed on the carrier was 5,700- to 8,500-fold more concentrated than the P dissolved in water. The P on the carrier was 8- to 17-fold higher than the required level for sufficient fertility to support rice production, and it was categorized in the range of high-fertility soil. Some traces of heavy metals adsorbed on the carrier were detected, but they were much lower than the regulation levels. In addition, the woody carrier can be applied directly to agricultural land without P extraction. Therefore, this method is a low-cost technique that should contribute to P resource recycling and the improvement of aquatic environment.
\n\t\tThis study was partially supported by a grant from the Shimane University Priority Research Project and a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (#20380179).
\n\t\tBitcoin is a digital currency built on a peer-to-peer network and on the blockchain, a public ledger where all transactions are recorded and made available to all nodes. Opposite to traditional banking transactions, based on trust for counterparty, Bitcoin relies on cryptography and on a consensus protocol for the network. The entire system is founded on an open source software created in 2009 by a computer scientist known under the pseudonym Satoshi Nakamoto, whose identity is still unknown (see [1]). Hence, Bitcoin is an independent digital currency, not subject to the control of central authorities and without inflation; furthermore, transactions in the network are pseudonymous and irreversible.
\nBitcoin and the underlying blockchain technology have gained much attention in the last few years. Research on Bitcoin often deals with cybersecurity and legitimacy issues such as the analysis of double spending possibilities and other cyber-threats; recently, high returns and volatility have attracted research toward the analysis of Bitcoin price efficiency as well as its dynamics (see, among others, [2, 3, 4]). Moreover, many contributions claim that Bitcoin price is driven by attention or sentiment about the Bitcoin system itself; see [5, 6, 7, 8]. Possible driving factors for the sentiment about the Bitcoin system are the volume of Google searches or Wikipedia requests as in [5], or more traditional indicators as the number or volume of transactions, as suggested in [6]. In [9], the author suggests a time series model in order to identify the dynamic relation between speculation activity and price.
\nIn this chapter, after having introduced the basic concepts underlying Bitcoin, we sum up and describe to a broader audience the recent outcomes of the research reported in [10], by avoiding unnecessary technicalities. Some new insights are also given by looking at possible extensions in order to take into account the presence of bubble effects or the special feature of Bitcoin being traded in different online platforms (exchanges) that will be further investigated in our future research.
\nWe recall that Bitcoin was first introduced as an electronic payment system between peers by Satoshi Nakamoto (pseudonym) in [1]. Opposite to traditional transactions, which are based on the trust in financial intermediaries, this system relies on the network, on the fixed rules and on cryptography. Bitcoins can be purchased on appropriate websites that allow to change usual currencies in the cryptocurrency.
\nThe Bitcoin network has several attractive properties for its users:
No central bank authority for money supply and no regulator;
Transactions are 24/7 and without any country border;
Transaction cost are almost negligible with respect to traded amount;
Transaction are anonymous;
The security of each transaction is guaranteed by cryptography and digital signature;
The security of the whole network is guaranteed by construction unless more than 50% of the network nodes agree on a deceptive action.
As a digital payment system, Bitcoins may be used to pay for several online services and goods. Special applications have been designed for smartphones and tablets for transactions in Bitcoins and some ATMs have appeared all over the world (see Coin ATM radar) to change traditional currencies in Bitcoins. Accepting Bitcoins as a payment method is also related to an advertisement opportunity for companies. However, the high returns achieved in the last few years have transformed Bitcoin in a speculative asset affecting its use as a form of payment.
\nThe Bitcoin system has been subject to many cracks but has proven to be very resilient as the value of the cryptocurrency was able to rise again after all the falls. Nevertheless, at the time of writing, Bitcoin was experiencing a fall in its exchange rate with main fiat currencies.
\nTwo of the main crackdowns were China enforcement in December 2013 and Mt. Gox bankruptcy in February 2014.
\nBesides technical and regulation issues, the Bitcoin system also faces reputational concerns.
\nIn fact, the ambiguity of anonymous transactions has blamed the network of allowing several criminal activities such as buying illegal goods, money laundering or the financing of terrorism actions. As a representative example, we recall that
It is worth noticing that while counterparties are represented by secret addresses and are anonymous, all transactions are recorded and might be traced. Investigation is hence favored by this feature of the network.
\nDespite the flaws in the system, Bitcoin has achieved a notwithstanding rise in recent years.
\nIn Figure 1, we report Bitcoin price and returns from January 2012 to December 2017 (source https://blockchain.info/en/charts).
\nBitcoin price (top) and returns (bottom) from January 2012 to December 2017.
The model we suggest in what follows is motivated by findings in [5, 6, 8, 11] where it is showed that Bitcoin price is related to investors’ attention measured by the trading volume and/or the number of searches in engines such as Google and Wikipedia. Bitcoin is treated as a financial stock as suggested in [12] and the suggested model may be applied in principle to other assets that are proven to depend on market attention.
\nConsider a probability space \n
Let us denote the Bitcoin price process as \n
where \n
It is well known that the above dynamics for the attention factor is a geometric Brownian motion, the solution of which is given by \n
We collect in this subsection the properties of the logarithmic returns obtained by the price process defined in Eq. (1).
\nConsider the discrete process \n
Theorem 2.1.
As for the unconditional distribution, it is easy to obtain, for \n
where \n
Proposition 2.2. The joint probability density of the vector \n
where \n
The proof follows from Bayes’ rule and application of Theorem 2.1.
\nIt is worth to remark that the probability density \n
Precisely, we have that \n
We apply the outcomes above in order to estimate model parameters according to the maximum-likelihood method (see for example [14, 15]) where the likelihood is approximated by applying the Levy approximation [13].
\nParameter estimates are obtained as
\nwhere
The first step in our procedure is to identify possible measures of investors’ attention. As already mentioned in the introduction, we consider the total trading volume on Bitcoin available from https://blockchain.info as well as the search volume index (SVI) for Google searches on the topic “bitcoin” provided by https://trends.google.it/trends/.
\nThe trading volume of exchange is a classical measure of the attractiveness of a traded asset for an investor; besides, in [16], the authors find evidence that the latter captures the attention of retail/uniformed investors.
\nWe consider daily data from January 1, 2015, to June 30, 2017, for the total volume and the SVI Index. As for the daily value of the Bitcoin, we have considered the average mean across main exchanges represented by the Index in https://blockchain.info.
\nIn Table 1, the outcomes for parameter estimates, obtained by maximizing the approximate likelihood given the observed time series, are summed up.
\n\n | \n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|
0.9571 | \n1.1346 | \n0.0218 | \n0.0829 | \n|
1.3584 | \n1.0687 | \n0.0743 | \n0.1559 | \n
Parameter estimates for the model in Eq. (1) fitted on daily observations from January 2015 to June 2017.
In this section, we show how to characterize the price of European call options on Bitcoins in the underlying market model. Let us fix a finite time horizon \n
where \n
The proof can be deduced from that of Lemma 1.4 in [10], where they also account for a possible delay between the attention factor and its effect on Bitcoin prices trend. The process \n
is an \n
Equivalently, we can write the discounted Bitcoin price process \n
Clearly, under the minimal martingale measure \n
where \n
Now, we compute the fair price of a Bitcoin European call option via the risk-neutral evaluation approach, so it can be expressed as expected value of the terminal payoff under the selected pricing measure, that is, the minimal martingale measure. Let
where
\nand \n
Here, \n
The following result provides the risk-neutral price of the option under the minimal martingale measure \n
Hence, the resulting risk-neutral pricing formula when evaluated in \n
In order to appreciate the performance of the pricing formula in Eq. (19), we compute model prices for option traded on the online platform http://www.deribit.com on July, 30, 2017, by plugging in the estimated parameters. The outcomes are compared with the Black & Scholes benchmark (see [20]) as a reference price, computed by plugging the volatility parameter estimated on the same time series of the trading volume/SVI index, and with the bid-ask prices provided in the website. Best overall pricing values are obtained when market attention is measured by volume; in the case of the SVI Google index, near-term options are very close to the mid-value of the bid-ask, while next-term options are overpriced. One possible explanation is that investors that get information about Bitcoin on search engines are more likely to be uninformed/retail investors that are self-exciting and may add spurious noise to the Bitcoin price volatility leading to an increase in call option prices (Table 2).
\nT-K | \nMarket bid | \nMarket ask | \nModel volume | \nModel Google SVI | \nBenchmark BS | \n
---|---|---|---|---|---|
Aug-2200 | \n0.1662 | \n0.2318 | \n0.2029 | \n0.2282 | \n0.1967 | \n
Aug-2300 | \n0.1670 | \n0.2072 | \n0.1737 | \n0.2032 | \n0.1655 | \n
Aug-2400 | \n0.1390 | \n0.1845 | \n0.1469 | \n0.1802 | \n0.1369 | \n
Aug-2500 | \n0.1142 | \n0.1638 | \n0.1228 | \n0.1591 | \n0.1112 | \n
Aug-2600 | \n0.0922 | \n0.1376 | \n0.1014 | \n0.1399 | \n0.0887 | \n
Aug-2700 | \n0.0749 | \n0.1202 | \n0.0828 | \n0.1226 | \n0.0695 | \n
Aug-2800 | \n0.0572 | \n0.1047 | \n0.0684 | \n0.107 | \n0.0535 | \n
Aug-2900 | \n0.0442 | \n0.0983 | \n0.0549 | \n0.0931 | \n0.0405 | \n
Sept-2200 | \n0.1991 | \n0.2648 | \n0.2546 | \n0.3204 | \n0.2173 | \n
Sept-2300 | \n0.1766 | \n0.2432 | \n0.2321 | \n0.3019 | \n0.1906 | \n
Sept-2400 | \n0.1890 | \n0.2230 | \n0.2113 | \n0.2844 | \n0.1662 | \n
Sept-2500 | \n0.1375 | \n0.2042 | \n0.1919 | \n0.2679 | \n0.1439 | \n
Sept-2600 | \n0.1207 | \n0.1828 | \n0.1741 | \n0.2523 | \n0.1239 | \n
Sept-2700 | \n0.1120 | \n0.1668 | \n0.1576 | \n0.2377 | \n0.1060 | \n
Sept-2800 | \n0.0953 | \n0.1504 | \n0.1463 | \n0.2239 | \n0.0903 | \n
Sept-2900 | \n0.0848 | \n0.1422 | \n0.1325 | \n0.2109 | \n0.0764 | \n
Comparison between model prices computed according to formula in Eq. (19), Black & Scholes formula in [20], and the bid and ask prices provided in http:\\\\www.deribit.comfor options traded on July, 30, 2017, and expiring on August 25, 2017, and on September 28, 2017.
Motivated by empirical evidences (see for example [21, 22]), we discuss a generalization of the model introduced in Section 3.1, which is capable to describe speculative bubbles in Bitcoin markets.
\nPrecisely, we fix a finite time horizon \n
Without loss of generality, we assume that the interest rate is fixed and equal to zero. In this setting, the discounted Bitcoin price trend and the market attention factor dynamics are described by
\nwhere we have set \n
By simulating trajectories for the asset price \n
Simulated trajectories with
Indeed, we will show formally that the possibility of Bitcoin speculative bubbles is related to the sign of the correlation parameter \n
The mathematical theory of financial bubbles is developed, among others, in [23, 24, 25]. Precisely, we introduce the following definition from [23].
\nThe term strict \n
Recall that the absence of arbitrage opportunities is “essentially” equivalent to the existence of a probability measure \n
Then, to exclude arbitrage opportunities from the market, we define the process \n
where \n
To ensure that \n
and we can consider the corresponding family of equivalent (local) martingale measures \n
where the \n
\n\n
Now, suppose that the risk perception process is zero, that is, \n
is a true \n
The proof is based on the application of some of Sin’s results given in [27], where the existence of risk-neutral measures for the Hull-White stochastic volatility model [19] and for similar frameworks is determined by the possibility of explosion in finite time for solutions of certain auxiliary stochastic differential equations. Precisely, it is possible to show that the martingale property of the discounted stock price \n
Let us generalize the model introduced in Eq. (1) by assuming a possible delay \n
where \n
Analogous results as those in Section 2 can be derived by similar computations, and model parameters, for a fixed delay, can be estimated by means of the maximum likelihood method. In order to estimate the delay parameter, we maximize the profile likelihood as defined in [15]. Details of this procedure can be found in [10]. The estimation results of model in Eq. (27) on the same daily data considered in Section 2 are summed up in Table 3.
\n\n | \n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|---|
1 day | \n0.4881 | \n1.0459 | \n0.0282 | \n0.0924 | \n|
7 days | \n1.0964 | \n0.9946 | \n0.1005 | \n0.1885 | \n
Parameter estimates for model in Eq. (27) fitted on daily observations from January 2015 to June 2017.
In Figure 3, we plot simulated trajectories of the price process in Eq. (27) by letting the delay parameter vary.
\nSimulated trajectories of
The different delays result in a shift to the south-east between the faster and slower reacting trajectories; in the picture, this behavior is sharp since the other model parameters are kept constant. By looking at the picture, the idea to model the price of Bitcoin in different exchanges by the same model in Eq. (27) but allowing different parameters naturally arises.
\nIn particular, considering for instance two exchanges, we have
\nwhere \n
Note that within this model, prices for Bitcoin traded in different exchanges are perfectly correlated. Indeed, this is what happens in observed data; considering daily prices from January 2015 to June 2017 for Bitstamp, Kraken, Cex.io, Gdax, and The Rock exchanges we get cross-correlation values larger than 0.999.
\nWe fit model in Eq. (28) for the Bitstamp and Gdax exchanges on daily observations of Bitcoin price from January 2015 to June 2017 obtaining the outcomes reported in Table 4, when the attention is measured by the trading volume, and in Table 5, when attention is measured by the Google SVI index.
\nExchange | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|---|
Bitstamp | \n1 | \n0.4994 | \n1.0461 | \n0.0281 | \n0.0896 | \n
Gdax | \n2 | \n0.4997 | \n1.0420 | \n0.0326 | \n0.1036 | \n
Model fitting with delay parameter: outcomes for Bitstamp and Gdax exchanges when attention is measured by the trading volume.
Exchange | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|---|
Bitstamp | \n7 days | \n1.0934 | \n0.9946 | \n0.0992 | \n0.1782 | \n
Gdax | \n7 days | \n1.0964 | \n0.9946 | \n0.1160 | \n0.2087 | \n
Model fitting with delay parameter: outcomes for Bitstamp and Gdax exchanges when attention is measured by the SVI index.
It is evident from the outcomes in Table 4 that the model parameters are not significantly different while the delay might be quite different as if the reaction to the attention factor is faster for some exchanges and slower for others. On the contrary, when attention is measured by the Google SVI Index, the delay is unchanged, but the difference between estimated parameters for the price dynamics is nonnegligible.
\nBy analyzing the outcomes and considering the shift effect as depicted in Figure 3, it is tempting to conjecture that the faster reaction determines the leader exchanges and that the slower exchange will then follow. If we could forecast that the next day price of the slower exchange will reach the price today for the faster one, we could obtain a profit by suitably investing in the two exchanges. However, it is worth noticing that the estimation of the delay parameter is obtained by maximizing the likelihood over a whole time series and is a product of averaging so arbitrage cannot be achieved in a direct way.
\nNevertheless, in a multivariate setting as ours, the theory guarantees that arbitrage opportunities are ruled out if the market price of risk in the market is unique. Without entering technical details and assuming \n
It is evident that these values are not equal if we plug parameter estimates in Eq. (30); hence, arbitrage opportunities are not ruled out at least from a theoretical point of view. We will address this issue more precisely in future research.
\nIn this chapter, we have introduced a model in continuous time in order to describe the dynamics of Bitcoin price depending on an exogenous stochastic factor, which represents market attention on the Bitcoin system. Market attention is measured either by the total trading volume in Bitcoins or by means of the Google Search Volume Index, which, as suggested in [16], is a direct measure of the revealed attention for uniformed retail investors. More precisely, the attention factor affects directly the instantaneous mean and volatility of logarithmic returns; in addition, it may be also correlated with the price changes. An estimation procedure to fit the model to observed data is also suggested and, under the assumption of no correlation, a closed formula for standard European option prices on Bitcoin is provided.
\nBy applying outcomes within the mathematical theory of bubbles [23, 24, 25, 27], we are able to show that Bitcoin boosts in a bubble if and only if there is a positive correlation between changes in the price and in the attention factor. This finding is reasonable and claims that a stronger positive dependence between the two processes in Eq. (21) may result in an explosion of the price process.
\nFinally, we allow for a delay on the effect of market attention on the Bitcoin price, and, based on this generalized model, we introduce a multivariate setting for our model (Eq. (28)) in order to take into account the special feature of multiple exchanges where it is possible to trade in Bitcoins. Preliminary results indicate that arbitrage opportunities may arise in two exchanges that are characterized by different delays.
\nWe gratefully acknowledge Marco Patacca for having provided the routines in Matlab® to develop the empirical sections of the paper and for useful suggestions and comments. We also acknowledge funding from Fondazione Cassa di Risparmio di Perugia (Grant 2015:0459013) and Bank of Italy (Grant 407660/16).
\nThe authors declare no conflict of interest.
Gianna Figà-Talamanca gratefully acknowledges interesting discussions on the topic of this chapter with colleagues of the Department of Finance and Risk Engineering where she was visiting professor while preparing this research. The authors also wish to thank Stefano Bistarelli for having introduced them to the intriguing and worth to explore world of cryptocurrencies.
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',metaTitle:"Horizon 2020 Compliance",metaDescription:"General requirements for Open Access to Horizon 2020 research project outputs are found within Guidelines on Open Access to Scientific Publication and Research Data in Horizon 2020. The guidelines, in their simplest form, state that if you are a Horizon 2020 recipient, you must ensure open access to your scientific publications by enabling them to be downloaded, printed and read online. Additionally, said publications must be peer reviewed. ",metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"Publishing with IntechOpen means that your scientific publications already meet these basic requirements. It also means that through our utilization of open licensing, our publications are also able to be copied, shared, searched, linked, crawled, and mined for text and data, optimizing our authors' compliance as suggested by the European Commission.
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\n\nRead more about Open Access in Horizon 2020 here.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). 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