Extracted values of the significant components at Midmar dam inflow (MDI).
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\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:"6343",leadTitle:null,fullTitle:"Sport and Exercise Science",title:"Sport and Exercise Science",subtitle:null,reviewType:"peer-reviewed",abstract:"Professional and semiprofessional sports as well as excessive amateur exercise inevitably lead to some degree of musculoskeletal injury once in a sportsman's career. Some injuries are represented as chronic injuries, which can result in irreversible long-term tissue changes and deformities. The subject of this book is to represent the up-to-date knowledge about etiology, pathogenesis, diagnosis, management, and prevention of chronic injuries or sport-related long-term changes in locomotor system.",isbn:"978-953-51-3795-5",printIsbn:"978-953-51-3794-8",pdfIsbn:"978-953-51-4089-4",doi:"10.5772/intechopen.69756",price:119,priceEur:129,priceUsd:155,slug:"sport-and-exercise-science",numberOfPages:124,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"f02738ce8019136d4586b616f5670e9b",bookSignature:"Matjaz Merc",publishedDate:"February 7th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6343.jpg",numberOfDownloads:8498,numberOfWosCitations:7,numberOfCrossrefCitations:4,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:6,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:17,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 18th 2017",dateEndSecondStepPublish:"June 8th 2017",dateEndThirdStepPublish:"September 4th 2017",dateEndFourthStepPublish:"December 3rd 2017",dateEndFifthStepPublish:"February 1st 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"210233",title:"Dr.",name:"Matjaz",middleName:null,surname:"Merc",slug:"matjaz-merc",fullName:"Matjaz Merc",profilePictureURL:"https://mts.intechopen.com/storage/users/210233/images/5576_n.jpg",biography:"Matjaz Merc is an orthopaedic surgeon at the Department of orthopaedics in UMC Maribor, Slovenia working also as assistant on Faculty of medicine in Maribor. He graduated in 2008 on faculty of Medicine in Ljubljana, Slovenia and earned his Ph.D in 2015 on Faculty of Medicine in Maribor. He was a visiting fellow at the Department of orthopaedics at University clinic in Basel, Switzerland, Speising Spital, Vienna, Austria and KBC Šalata, Zagreb, Croatia where he got subspecialized. His clinical work is based on foot and ankle surgery, paediatric orthopaedics as well as sports medicine. His research interests are mainly based on application of rapid prototyping technology in orthopaedics. Since 2010 he cooperates with NK Maribor football club working as a doctor in medical team.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1119",title:"Exercise Physiology",slug:"exercise-physiology"}],chapters:[{id:"58218",title:"Biology of Stress and Physical Performance",doi:"10.5772/intechopen.72425",slug:"biology-of-stress-and-physical-performance",totalDownloads:1840,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Regular physical training leads to physical capacity and optimal sports performance, and although this relationship is usually linear, the athlete’s adaptation is conditioned by multiple factors: environmental, genetic and psychological. Studies have shown that between 70 and 85% of successful and unsuccessful athletes can be identified using psychological measures of personality and mood, a level higher than chance, but insufficient for the purpose of selecting athletes. The research indicates that the mood of the athletes exhibits a dose-response relationship with their adaptation to the training load; This finding has shown potential to reduce the incidence of overtraining syndrome in athletes who undergo rigorous physical training, through early detection using scales of perception of their mood and physiological measures such as the testosterone / cortisol index. Thus, the genetic and epigenetic modifications of the factors that regulate the hypothalamic-pituitary-adrenal axis and, therefore, the response to stress, have recently been associated with a detrimental effect on physical performance and early manifestations of the overtraining syndrome and the abandonment of training and competences.",signatures:"Jorge A. Sanhueza Silva, Carlos Bahamondes-Avila, Claudio\nHernández-Mosqueira and Luis A. Salazar Navarrete",downloadPdfUrl:"/chapter/pdf-download/58218",previewPdfUrl:"/chapter/pdf-preview/58218",authors:[{id:"211582",title:"Dr.",name:"Jorge",surname:"Sanhueza",slug:"jorge-sanhueza",fullName:"Jorge Sanhueza"},{id:"221070",title:"MSc.",name:"Carlos",surname:"Bahamondes",slug:"carlos-bahamondes",fullName:"Carlos Bahamondes"},{id:"221071",title:"Dr.",name:"Claudio",surname:"Hernandez Mosqueira",slug:"claudio-hernandez-mosqueira",fullName:"Claudio Hernandez Mosqueira"}],corrections:null},{id:"58562",title:"Biokinetics: A South African Health Profession Evolving from Physical Education and Sport",doi:"10.5772/intechopen.73126",slug:"biokinetics-a-south-african-health-profession-evolving-from-physical-education-and-sport",totalDownloads:1741,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:1,abstract:"This chapter describes the South African profession of Biokinetics, which operates within the pathogenic and fortogenic health paradigms. Biokinetics is an exercise therapy profession that exclusively prescribes individulaised exercise and physical activity for rehabilitation and promotion of health and quality of life. Biokinetics differs from physiotherapy primarily due its management of injuries, illnesses and disabilities within the final-phase of rehabilitation. A brief history of the profession and its scope of profession and its alignment within the South African National Health statutory and professional bodies will be presented. The two pedagogic models adopted for the teaching and training of Biokinetics will also be discussed. Interprofessional collaborative partnerships within the medical-rehabilitation fraternity, sport, health and fitness industries and educational employment opportunities will be reviewed. Finally, the idea of internationalisation of the profession of Biokinetics to similar exercise therapy professions such as Clinical Exercise Physiology and Athletic Training will be presented.",signatures:"Terry Jeremy Ellapen, Gert Lukas Strydom, Mariette Swanepoel,\nHenriette Hammill and Yvonne Paul",downloadPdfUrl:"/chapter/pdf-download/58562",previewPdfUrl:"/chapter/pdf-preview/58562",authors:[{id:"127909",title:"Prof.",name:"Gert Lukas",surname:"Strydom",slug:"gert-lukas-strydom",fullName:"Gert Lukas Strydom"},{id:"226652",title:"Dr.",name:"Terry J.",surname:"Ellapen",slug:"terry-j.-ellapen",fullName:"Terry J. Ellapen"},{id:"233593",title:"Dr.",name:"Mariette",surname:"Swanepoel",slug:"mariette-swanepoel",fullName:"Mariette Swanepoel"},{id:"233594",title:"Dr.",name:"Henriette Valerie",surname:"Hammill",slug:"henriette-valerie-hammill",fullName:"Henriette Valerie Hammill"},{id:"233596",title:"Prof.",name:"Yvonne",surname:"Paul",slug:"yvonne-paul",fullName:"Yvonne Paul"}],corrections:null},{id:"58233",title:"Diagnosis of Motor Habits during Backward Fall with Usage of Rotating Training Simulator",doi:"10.5772/intechopen.71463",slug:"diagnosis-of-motor-habits-during-backward-fall-with-usage-of-rotating-training-simulator",totalDownloads:1226,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"There are jobs with high risk of a fall. It seems reasonable to create a device for diagnosis and improving safe fall skills for workers. The aim of the present study was a verification of usability of a rotating training simulator for assessing motor habits during a fall caused by an external force by conducting validation procedure. Material and Methods: the participants were chosen from a group of 128 students of physical education of the University of Zielona Góra. Predictive validity was determined by comparing results of immediate fall test (IFT) to forced fall test (FFT). Repeatability was determined by conduction test/retest conditions. Reliability was also determined by comparing grades given by two observers with those given by an expert. Results: the acquired results show that there were no significant differences between results of IFT and FFT tests conditions and also no significant differences between test/retest conditions separetly for IFT and FFT, alongside with moderate correlation of its results. Good and excellent reliability ICC values were obtained for observers and experts (from r = 0.853 to 1.00). Summary: the obtained results show that the rotating training simulator is a valid and reliable tool for diagnosing motor habits during a fall caused by an external force.",signatures:"Andrzej Mroczkowski and Dariusz Mosler",downloadPdfUrl:"/chapter/pdf-download/58233",previewPdfUrl:"/chapter/pdf-preview/58233",authors:[{id:"144247",title:"Dr.",name:"Andrzej",surname:"Mroczkowski",slug:"andrzej-mroczkowski",fullName:"Andrzej Mroczkowski"},{id:"221034",title:"Mr.",name:"Dariusz",surname:"Mosler",slug:"dariusz-mosler",fullName:"Dariusz Mosler"}],corrections:null},{id:"57800",title:"The Missing Science: Ethics in Practice",doi:"10.5772/intechopen.71883",slug:"the-missing-science-ethics-in-practice",totalDownloads:1245,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The Greeks argued that philosophy was the most important science even though it was a science that studied no things. Their science, philosophy, focused on the meaning of life and death, life after death, existence, knowledge, knowing the good and bad, as well as the application of right and wrong. We argue that what is right and what is wrong should underlie the development of the current book Sports and Exercise Science. The stated purposes of the book, “to present the up to date knowledge about etiology, pathogenesis, diagnosis, management and prevention of chronic injuries or sports related long term changes in locomotor system. Moreover, topics about influence of sports activities on growth and development in pediatric population and presentation of acute injuries that often develop to chronic…as well,” are topics that should be addressed through science in sports and exercise science—philosophy and ethics. Ethics should govern all science, including the growth and development of sports and exercise science. Injury often occurs because of poor coaching, poor training, or overtraining. The problem exists because of unethical practice of either coaches, parents, leaders, trainers, or a combination of all of them. This chapter focuses on ethical education for professionals, educators, practitioners, and coaches.",signatures:"Sharon Kay Stoll, Heather Van Mullem, Peter Van Mullem and\nJennifer M. Beller",downloadPdfUrl:"/chapter/pdf-download/57800",previewPdfUrl:"/chapter/pdf-preview/57800",authors:[{id:"189960",title:"Prof.",name:"Sharon Kay",surname:"Stoll",slug:"sharon-kay-stoll",fullName:"Sharon Kay Stoll"},{id:"205593",title:"Dr.",name:"Heather",surname:"VanMullem",slug:"heather-vanmullem",fullName:"Heather VanMullem"},{id:"213781",title:"Dr.",name:"Pete",surname:"Van Mullem",slug:"pete-van-mullem",fullName:"Pete Van Mullem"},{id:"213782",title:"Dr.",name:"Jennifer",surname:"Beller",slug:"jennifer-beller",fullName:"Jennifer Beller"}],corrections:null},{id:"58153",title:"Overuse Injuries in Professional Ballet",doi:"10.5772/intechopen.72428",slug:"overuse-injuries-in-professional-ballet",totalDownloads:1331,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Ballet is an athletic activity with a marked artistic component, that need a highest technical requirement and repetitive movements. In this way, Overuse injuries, as we have been able to demonstrate in our studies, will be the most frequent injuries in ballet. The technical requierements of ballet will influence both injury specificity for each discipline and for both sexes, usually with higher technical requirements among women and higher athletic requirements among men. The patellofemoral syndrome is the most frequent overuse injuries in ballet, related to decompensating mechanisms to increase a naturally weak in turnout or dehors. This injury and others as the snapping hip, are more common among women, with higher technical requirements than men, and in the more technically demanding disciplines such as classical ballet. Other important injuries in ballet are Achilles tendinopathy, the mechanical low back pain, or the Os trigonum Syndrome. It will be very important to know about, the biomechanic and pathomechanic of the Ballet specific technical gesture, the intrinsecal and environmental risk factors involved in ballet injuries, the injury-based differences among ballet disciplines and among age and professional seniority, as well as the most important preventive measures in ballet.",signatures:"Francisco J. Sobrino and Pedro Guillen",downloadPdfUrl:"/chapter/pdf-download/58153",previewPdfUrl:"/chapter/pdf-preview/58153",authors:[{id:"218702",title:"Ph.D.",name:"Francisco J.",surname:"Sobrino",slug:"francisco-j.-sobrino",fullName:"Francisco J. Sobrino"}],corrections:null},{id:"56931",title:"Sports Concussion: A Clinical Overview",doi:"10.5772/intechopen.70765",slug:"sports-concussion-a-clinical-overview",totalDownloads:1115,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Concussion is an injury risk associated with participation in collision sports. It has been identified as a research priority for many contacts and collision sports governing bodies worldwide. However, concussion remains under-researched in terms of clinical translation from both experimental models to clinical understanding, and from clinical studies to sports policy. Currently, the clinical management of concussion is largely guided by the presence or absence of symptoms with recovery indicated once all post-injury symptoms have resolved. Management of concussion includes physical and cognitive rest until acute symptoms resolve, with a graded program of exertion implemented prior to medical clearance and return-to-play. Considering the potential sequelae, the heterogeneity of symptoms, and the lack of an intervention known to prevent concussion, it is not any wonder that concussion is one of the most complex and perplexing injuries faced by medical professionals, and why making the return-to-play decision can be quite challenging. This chapter will provide an overview of the current clinical management guidelines and research literature pertaining to identification and diagnosis of injury, acute and post-acute management, and return-to-play decision-making. The traditional standard assessment process (e.g., symptom reporting, cognitive assessment, balance testing), new methods and advanced technology (e.g., ocular-motor testing, neuroimaging techniques), and biomarkers (e.g., blood plasma and serum, fluid) have led to greater insights into sports concussion and will also be briefly explored in this chapter.",signatures:"Andrew J. 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\r\n\tThe aim of this book is to exhibit broad aspects of fungicides so that readers will experience both traditional and new topics to illustrate what would be the future of fungicides. Thus, the subject matter is extensive. The book welcomes contributions related to the following topics:
\r\n\tApplied and basic studies - Field studies and lab assays of fungicides can be discussed. We also look for examples of application methods, which may include timing of application, tools for application, fungicide compatibility, phytotoxicity, etc. Field trials have to have at least two years of data;
\r\n\tAdaptation of Integrated Plant Disease Management - How the IPM practice has been adapted in the field. Application of disease risk models, or use of fungicide application aids, which can be hardware or software. The introduction of a new tool for growers can also be included;
\r\n\tNovel fungicides - In addition to the traditional chemical approach, alternative materials (enzymes, oils, extracts, etc.), biological control agents, or plant defense activators can be discussed;
\r\n\tAdaptation of new technologies - Examples will be the use of unmanned vehicles, sensor technologies, advanced sprayers, or disease forecast systems for precision agriculture;
\r\n\tFungicide resistance - Unfortunately, we cannot ignore the fact that fungicide-resistant strains are widespread. Documentation of fungicide-resistant strains, the introduction of new technologies and methods can be discussed.
Water pollution is a global challenge undermining economic growth, health of millions of people as well as the physical status of the environment in both developed and developing countries. The current global water scarcity challenge is not only related to inadequacy in terms of quantity but also related to the progressive deterioration of quality making water unfit for some given uses such as potability. The deterioration of water quality is attributed to both natural (precipitation rate, weathering processes, soil erosion, etc) and anthropogenic (urban, industrial, agricultural activities, etc) factors. Seasonal variations in precipitation, surface run-off, ground water flow, interception and abstraction strongly affect the river discharge and the concentrations of water pollutants in a basin [1]. The effect of contaminant on water depends upon the characteristics of the water itself as well as quantity and characteristics of the contaminant.
Water pollutants, which are usually introduced through surface runoff or direct discharge, may in higher concentration result in rivers failing to provide adequate attenuation of pollutants, resulting in catchments failing to meet minimum compliance of quality for various uses such as potable water production. Furthermore, water quality deterioration is often a slow process not readily noticeable due attenuation effects until an apparent change occurs. Such situations are being exacerbated by the rapid increase in the demand for freshwater in many countries including South Africa. In view of the limited quantity of freshwater resources worldwide and the effect of anthropogenic activities, protection of these resources has become a priority [2, 3, 4]. It has, therefore, become imperative to monitor the quality of water in freshwater systems in order to prevent its further deterioration and thus ultimately ensure its continuous availability in a quality that meets various uses including potable water production. Pollutants in water can cause acute or chronic illness in humans especially when polluted water is consumed or when sewage is used to irrigate vegetables meant for human consumption. In specific cases this has resulted in loss of lives. For example, as at 2015, the bacterium
With concern of the detrimental effects of pollution, various agencies have been monitoring the quality of raw water within the uMngeni, a 232 km river that is then treated to serve almost 3.8 million people within and around Durban and Pietermaritzburg (South Africa) with potable water [6, 7, 8, 9, 10]. The primary objectives of such monitoring exercise have been to identify water quality problems, describe the spatio-temporal water quality trends, determine fitness compliance for specific uses and develop monitoring tools such as water quality indices for enhancing information dissemination. Although such monitoring programs are crucial to a better knowledge of hydrology and pollution problems in catchments such as uMngeni Basin, they tend to produce large amounts of complicated data-sets of various water parameters. The data-sets are often difficult to analyse and extract meaningful information and this makes it difficult to keep the public informed, who are the custodian of the resource. By keeping the public updated, it makes them more participatory in policy formulation and decision making regarding protection of the water resource [11, 12].
The classification and interpretation of monitoring stations are the most important steps in the assessment of water quality. Numerous studies have confirmed multivariate statistical techniques (cluster analysis, principal component analysis, factor analysis and discriminant analysis) as excellent tools for exploring and presenting the bulk and complex water quality data-sets [13, 14, 15]. These techniques allow for the determination of spatio-temporal water quality variability, classification of sampling stations and the identification of pollution sources [15, 16, 17, 18]. Furthermore, by eliminating subjective assumptions, multivariate techniques tend to reduce biasness when selecting parameters for developing tools such as water quality index. This ultimately assists in improving the accuracy of such monitoring tool. Nevertheless, the selection of a multivariate technique to apply depends on the nature of data-set and research objectives. While there are a number of multivariate techniques, studies have extensively applied the Principal Component Analysis (PCA) and Cluster Analysis (CA) due to their suitability in extracting information on various situations [15, 19, 20, 21, 22].
The application of principal component analysis (PCA) for the interpretation of a large and complex volume of data offers a better understanding of water quality, the ecological status of the basin being studied, while also allowing for the identification of possible factors/sources that influence the surface water systems [16]. Principal Component Analysis (PCA) aims to find combinations for certain variables to determine indices which describe the variation in the data while retaining as much information as possible. This reduction is achieved by transforming original variables into a new set of variables, known as principal components (PCs). These PCs, which are uncorrelated with the first few, retain most of the variation present in the original variables. The PCA technique transforms original variables into new uncorrelated variable known as principal components (PCs) [23, 24, 25]. The derived few variables can be used to provide a meaningful description of the entire data-set with a minimal loss of original information. The eigenvalues indicate the significance of each PC and a greater value, indicating the parameter’s importance [26]. Correlation of PCs and original variables are given by the loadings [27]. While loadings reflect the relative importance of a variable within the component, it should be highlighted that these values does not show the importance of the component itself [28].
The PCA has been successfully applied on hydrogeological and hydrogeochemical studies. The application of PCA by Razmkhah, Abrishamchi [29] distinguished the anthropogenic and natural polluting activities along Jairood River in Iran. The results identified 5 factors which explained 85% the variation in water quality. Mazlum, Ozer [30] applied the PCA to determine factors causing water quality variability along a tributary, Porsuk, in Turkey. The study identified four PCs which explained 70% of the total water quality variance. The factors were related to the discharge of domestic wastewater, nitrification, industrial wastewater and the seasonal effect. Haag and Westrich [31] applied PCA to analyse the water quality along Neckar River in Germany based on ten parameters monitored from 1993 to 1998. The four principal components extracted accounting for 72% of total variance were interpreted as; (i) dilution by high discharge (ii) biological activity, (iii) seasonal effects and (iv) wastewater impact [31].
The limitations with the PCA technique include ignoring the degree of data dispersion as well as a weakness in processing nonlinear data. The result of PCA can also be influenced by uneven sampling interval, missing values or observations below detection limits of analytical methods, which can be changed during the data collection period. It is thus important to treat water quality data before modelling in order to improve the accuracy [32].
On the other hand, cluster analysis (CA) is an unsupervised pattern recognition multivariate technique which group objects (e.g., water quality variables) based on either their similarities or dissimilarities [33, 34]. Its objective is to sort cases into groups or clusters, so that the degree of association is strong between members of the same cluster and weak between members of different clusters. Most studies have applied the hierarchical clustering (HC) technique to sequentially category objects [13, 15, 34, 35]. Based on the hierarchical CA, water quality characteristics of each sampling location can be classified depending on pollution level. The results of a HC analysis are displayed graphically using a tree diagram commonly known as a dendrogram [13, 14, 36, 37]. The technique firstly groups the objects according to similarity. These groups are further merged according to their similarities or dissimilarities and eventually merge into a single cluster as the similarity among the subgroups decreases. The cluster analysis approach offers a reliable classification of surface water making it possible to design a future spatial sampling strategy that is cost-effective, with reduced number of sampling sites without losing any significant information [38].
uMngeni Basin, the study area, is situated in KwaZulu-Natal (KZN) Province, which lies along the eastern seaboard of the Republic of South Africa (Figure 1). uMngeni River (the main river in the basin), at 232 km long, is the primary source of raw water, which is then treated to serve a population of almost 3.8 million (as at 2013), in and around Durban metro as well as the city of Pietermaritzburg (PMB).
uMngeni Basin, KZN Province, Drakensberg Mountains and South Africa.
Key activities that generate point and non-point pollution within the catchment include agriculture and animal faming while concentrated urban settlements provide a variety of supportive economic activities that generate solid and liquid waste. A consequence of the concentrated development in the catchment area has been the high levels of pollutants entering the water system, which are eventually flushed out to sea. The basin receives much of its rain in summer, with occasional snow falls in some of its high lying areas such as the Drakensberg Mountain [39]. The geology of uMngeni Basin varies from basalts, granites, sandstones, shale and tillites [40]. About half of uMngeni Basin sits on top of the Karoo in the KZN part of the Drakensberg Mountains. The other potion extends east on top of the South African Coastal Plate (Figure 2).
Human settlements dominate from the central parts of the basin (PMB and its periphery) up to the Indian Ocean.
The 2009 Landuse map (Figure 2) indicates that there are mixed activities, where cultivation and plantations are located predominantly from central to the north-west of the basin.
Six water quality monitoring points shown in Figure 2, namely Midmar Dam Inflow (MDI)
Multivariate statistical methods have been widely applied in environmental data reduction and interpretation of multi-constituent chemical and physical biological measurements. These techniques have been applied to identify factors that influence water systems, to assist in reliable water resource management as well as determine rapid solutions for pollution problems [16, 41]. This study applied PCA and CA techniques to extract information from the raw data regarding the significant parameters influencing the variation of water quality at each of the six stations studied. The Kaiser-Meyer-Olkin (KMO), which test the sampling adequacy, was used to determine the suitability of water quality data for PCA analysis [42, 43, 44]. Kaiser [43] recommended 0.5 as a minimum (barely accepted), values between 0.7–0.8 acceptable, and values above 0.9 as depicting excellence. The current study employed the PCA technique to determine the most significant parameters that would explain the variation in water quality.
PCA is a very powerful multivariate statistical analysis technique used to reduce the dimensionality of a data set consisting multiple inter-related variables, while retaining data variability [45]. The technique extracts primary information representative of the typical characteristics of the water environment from a large amount of data and then represents it as a new set of independent variables of the principal component. PCA reduces the dimensionality of a multivariate data-set to a small number of independent principal components. Each principal component contains all the variable information, thus reducing the omission of information.
The PCA method is composed of five main operational steps, as follows:
The original data matrix is shown in Eq. 1:
where
Standardising the original data with Z-score standardisation formula to eliminate the impact of dimension (Eq. 2).
where
Calculating the correlation coefficient matrix, R, with standardised data and determining the correlation between indicators (Eq. 3).
Calculating the eigenvalues and eigenvectors of the correlation coefficient matrix, R, to determine the number of principal components. The eigenvalues of the correlation coefficient matrix, R, are represented by i (i = 1, 2 _ _ _ n) and their eigenvectors are ui (Ui = Ui1, Ui2, …………Uin) (i = 1, 2 _ _ _n). The value corresponds to the variance of the principal component, and the value of variance is positively correlated with the contribution rate of the principal components. Further, the cumulated contribution rate of the first m principal components should be more than 80%, which means that as explained in Eq. 4:
The principal component is represented by Eq. 5.
where xi is the standardised indicator variable as shown in Eq. 6:
The obtained principal components are weighted and summed to obtain a comprehensive evaluation function, as shown in Eq. 7:
Principal components with an eigenvalue greater than 1 were related to the major pollution sources in uMngeni Basin. Water quality parameters with loadings of greater than 0.5 (highlighted in bold in the results tables) were regarded as significantly influencing water quality variation in uMngeni Basin.
Thereafter, cluster analysis (CA) was applied to determine the spatial similarity of the six water sampling stations studied. The hierarchy cluster analysis was employed using the Ward’s method with Euclidean distances as a measure of dissimilarity [37, 46]. The number of subgroups for analysis were determined by drawing a line across the dendrogram and examining the main clusters branching out beneath that line [47]. Determination of the subgroups for analysis was subjective based on available information regarding pollution activities the along uMngeni River.
Eight-year (2005–2012) water quality data-sets obtained from then Umgeni Water was used in this study. Since monitoring generally depends on the pollution problem at any given time and space, the number and type of parameters monitored at each of the stations varied. As the study was data-driven, a monthly median was used for in-depth analysis. The adoption of median instead of the mean was in consideration that the latter is normally influenced by the outliers which are common in water quality data-sets while the former is resistant. The period studied was determined in consideration of a criteria explained by Schertz, Alexander [48] and Lettenmaier, Conquest [49]. These studies reported that at least a five-year monthly data and two-year monthly data should be sufficient for a defensible monotonic and step-trend (abrupt shift) study, respectively.
The Kaiser-Meyer-Olkin (KMO) results for the six stations ranged from 0.610 to 0.786 showing the fitness of the data-sets for PCA analysis. The component matrix tables shown in the different sections of the results only depict PCs with eigenvalue of greater than one (1). Only parameters with a correlation coefficient of great than 0.5 (highlighted in bold black) with its respective principal component were considered as significantly influencing water quality variability at any given station.
Table 1 show the extracted seven PCs with eigenvalues of greater than 1 that explain 75% of the water quality variation at the Midmar Dam Inflow sampling station. While considering the high positive correlations of nutrient, metal ion and organic related parameters with component 1 (Table 2), it can be hypothesised that 20.6% of the water quality variation at this station is a result of both anthropogenic and natural processes. The nutrient and organic related parameters can be explained by piggery, dairy and maize farming activities surrounding Midmar Dam [6]. Animal manure enters surface water, both accidentally and deliberately, from households, villages, communal farms and feedlots. Without treatment, manure runoff tends to result in algae blooms which can lead to human health problems if consumed. The second component explained 15.7% (Table 1) of the total variance at MDI and showed a positive correlation to Suspended Solids (SS), iron (Fe) and turbidity (Table 2). Turbidity and suspended solids can be related to surface runoff from agricultural activities along uMngeni River whilst Iron (Fe) can be attributed to weathering processes.
Midmar dam inflow: total variance explained | |||||||||
---|---|---|---|---|---|---|---|---|---|
Component | Initial eigenvalues | Extraction sums of squared loadings | Rotation sums of squared loadings | ||||||
Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
1 | 5.740 | 24.955 | 24.955 | 5.740 | 24.955 | 24.955 | 4.740 | 20.607 | 20.607 |
2 | 5.002 | 21.750 | 46.705 | 5.002 | 21.750 | 46.705 | 3.621 | 15.742 | 36.349 |
3 | 1.903 | 8.272 | 54.977 | 1.903 | 8.272 | 54.977 | 3.011 | 13.090 | 49.440 |
4 | 1.325 | 5.760 | 60.737 | 1.325 | 5.760 | 60.737 | 1.787 | 7.771 | 57.211 |
5 | 1.165 | 5.065 | 65.802 | 1.165 | 5.065 | 65.802 | 1.412 | 6.139 | 63.350 |
6 | 1.103 | 4.795 | 70.598 | 1.103 | 4.795 | 70.598 | 1.390 | 6.043 | 69.393 |
7 | 1.011 | 4.394 | 74.992 | 1.011 | 4.394 | 74.992 | 1.288 | 5.598 | 74.992 |
Extracted values of the significant components at Midmar dam inflow (MDI).
Extraction Method: Principal Component Analysis.
Component Matrixa | |||||||
---|---|---|---|---|---|---|---|
Component | |||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | |
Potassium (K) | −0.094 | −0.027 | 0.111 | 0.090 | −0.054 | −0.074 | |
Sulphate (SO4) | −0.027 | −0.213 | −0.078 | −0.128 | −0.018 | 0.072 | |
Chloride (Cl) | −0.322 | −0.169 | −0.099 | −0.129 | −0.008 | −0.071 | |
Total Dissolved Solid | −0.199 | −0.161 | −0.197 | −0.161 | −0.001 | −0.106 | |
Nitrate (NO3) | −0.243 | −0.231 | −0.097 | −0.295 | 0.049 | −0.014 | |
Total Organic Carbon(TOC) | 0.464 | 0.012 | −0.053 | 0.074 | 0.003 | −0.156 | |
0.440 | 0.356 | 0.035 | 0.431 | −0.153 | 0.135 | −0.085 | |
Suspended Solid | 0.416 | 0.005 | 0.172 | 0.181 | 0.113 | 0.319 | |
Iron (Fe) | 0.401 | −0.084 | 0.037 | 0.133 | −0.101 | 0.152 | |
Turbidity | 0.400 | 0.064 | 0.209 | 0.190 | 0.141 | 0.404 | |
Calcium (Ca) | 0.304 | 0.091 | 0.266 | 0.098 | 0.045 | ||
Magnesium (Mg) | 0.168 | 0.025 | 0.089 | 0.073 | 0.009 | ||
Sodium (Na) | 0.156 | 0.010 | 0.207 | 0.085 | 0.059 | ||
Alkalinity (Alk) | −0.036 | 0.451 | 0.090 | 0.338 | 0.157 | 0.149 | |
Total Phosphate | 0.192 | 0.556 | 0.415 | −0.092 | −0.089 | 0.260 | −0.284 |
Colour | 0.246 | 0.545 | 0.225 | −0.183 | 0.355 | −0.198 | 0.026 |
Dissolved Oxygen (DO) | −0.048 | −0.075 | 0.410 | 0.017 | 0.262 | 0.093 | |
Temperature (Temp) | 0.230 | 0.453 | 0.460 | −0.383 | 0.017 | −0.222 | −0.137 |
Silicon (Si) | 0.347 | 0.321 | −0.379 | −0.460 | −0.026 | 0.059 | 0.121 |
pH | −0.059 | −0.147 | −0.433 | −0.345 | 0.222 | −.199 | |
Ammonia (NH3) | 0.085 | 0.160 | 0.470 | −0.089 | −0.376 | 0.619 | 0.009 |
Conductivity | 0.286 | −0.324 | 0.225 | 0.099 | −0.252 | −0.528 | 0.339 |
Soluble Reactive Phosphate (SRP) | 0.313 | 0.250 | 0.019 | 0.131 | −0.223 |
The correlation among the parameters measured and the extracted significant components at MDI.
7 Components extracted.Bold: Significant contributors to the respective principal component in their respective there column.
Extraction Method: Principal Component Analysis.
Agriculture, a sector responsible for the usage of 70% of water being abstracted globally, plays a major role in water pollution [50, 51]. Runoff from agricultural activities such as agrochemicals, organic matter, drug residues, sediments and saline drainage into water bodies can lead to nutrient enrichment and eutrophication. The resultant water pollution poses a risk to aquatic ecosystems, human health and productive activities. Poor land management practises and deforestation can also explain the water quality variation at MDI. It is important for communities to practise improved land management through planting vegetation such as trees and plants to cover the ground. The negative relationship of metal ions with component 2 can be explained by the seasonality effect. Increased flow in the wet season turns to reduce the concentration of mineral salt content in a river system as a result of the dilution effect.
The results in Table 3 indicate that the first seven principal components with eigenvalues of greater than one (1) account for 73.7% of the total variance in the water-quality data set at Midmar Dam Outflow station. Component 1 which explains 27.1% of the total variance at MDO (Table 3) is mainly influenced by parameters related to human activities (turbidity and ammonia) as well as natural geological processes (silicon and calcium) (Table 4). Silicon is part of various essential plant minerals and it is released during weathering processes. Sodium and potassium which showed a moderate positive correlation to component 5 reflects the natural processes such as weathering.
Midmar dam outflow: total variance explained | |||||||||
---|---|---|---|---|---|---|---|---|---|
Component | Initial eigenvalues | Extraction sums of squared loadings | Rotation sums of squared loadings | ||||||
Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
1 | 6.729 | 32.044 | 32.044 | 6.729 | 32.044 | 32.044 | 5.690 | 27.096 | 27.096 |
2 | 2.042 | 9.726 | 41.770 | 2.042 | 9.726 | 41.770 | 1.871 | 8.910 | 36.006 |
3 | 1.759 | 8.378 | 50.148 | 1.759 | 8.378 | 50.148 | 1.794 | 8.542 | 44.548 |
4 | 1.399 | 6.660 | 56.808 | 1.399 | 6.660 | 56.808 | 1.769 | 8.424 | 52.971 |
5 | 1.271 | 6.053 | 62.861 | 1.271 | 6.053 | 62.861 | 1.659 | 7.902 | 60.873 |
6 | 1.196 | 5.694 | 68.555 | 1.196 | 5.694 | 68.555 | 1.508 | 7.181 | 68.054 |
7 | 1.078 | 5.133 | 73.687 | 1.078 | 5.133 | 73.687 | 1.183 | 5.634 | 73.687 |
Extracted values of the significant components at Midmar dam outflow.
Extraction Method: Principal Component Analysis.
Component Matrixa | |||||||
---|---|---|---|---|---|---|---|
Component | |||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | |
Calcium (Ca) | −0.100 | 0.117 | −0.076 | 0.134 | −0.065 | 0.054 | |
Conductivity | 0.028 | 0.083 | 0.114 | 0.030 | 0.060 | −0.050 | |
Magnesium (Mg) | −0.131 | 0.109 | −0.236 | 0.171 | −0.045 | 0.039 | |
Alkalinity | −0.208 | 0.103 | −0.103 | −0.092 | 0.080 | 0.075 | |
Ammonia (NH3) | −0.136 | 0.201 | −0.051 | −0.181 | 0.072 | −0.036 | |
Turbidity | 0.442 | −0.045 | −0.171 | −0.183 | −0.146 | 0.062 | |
Suspended Solids (SS) | −0.039 | −0.187 | −0.118 | −0.163 | 0.079 | ||
Sulphates (SO4) | 0.052 | −0.267 | 0.088 | 0.314 | 0.107 | 0.315 | |
Silicon (Si) | 0.253 | 0.099 | 0.390 | 0.123 | −0.164 | 0.086 | |
Dissolved Oxygen (DO) | 0.260 | 0.062 | −0.368 | 0.195 | −0.143 | −0.019 | |
Nitrate (NO3) | −0.334 | 0.068 | 0.130 | −0.303 | 0.288 | −0.061 | |
Total Phosphate (TP) | 0.205 | −0.285 | −0.332 | 0.016 | 0.291 | −0.048 | |
% NH3% | −0.247 | 0.292 | 0.065 | 0.097 | 0.314 | 0.154 | |
pH | −0.336 | 0.463 | 0.005 | 0.061 | 0.103 | 0.130 | |
Total Organic Carbon (TOC) | 0.212 | 0.412 | −0.169 | 0.459 | −0.116 | −0.355 | 0.306 |
Potassium (K) | 0.399 | 0.046 | −0.1608 | 0.308 | −0.105 | −0.160 | |
Sodium (Na) | 0.278 | −0.360 | 0.245 | −0.421 | −0.148 | −0.084 | |
Chloride (Cl) | 0.384 | 0.277 | −0.303 | −0.104 | 0.390 | 0.211 | 0.193 |
Temperature `C | 0.320 | −0.159 | −0.057 | 0.437 | 0.209 | 0.558 | 0.160 |
SRP | 0.063 | 0.434 | −0.279 | −0.095 | −0.048 | 0.488 | −0.443 |
−0.031 | 0.230 | 0.225 | 0.355 | 0.127 | −0.195 | ||
The correlation among the parameters measured and the extracted significant components at MDO.
7 Components extracted.Bold: Significant contributors to the respective principal component in their respective there column.
Extraction Method: Principal Component Analysis.
The PCA technique identified five components, which cumulatively explained 67.4% of the total variance at NDI (Table 5). Component 1 which explained 24.7% of the total variance is significantly affected by parameters (highlighted in bold black) which normally originate from surface runoff of agriculture areas and effluent from wastewater treatment plants (Table 6). The pollution of water bodies when practicing agriculture is mainly due to fertiliser runoff after rainfall, nutrients (such as nitrogen) that percolate through the soil and contaminated groundwater, as well as sediment that is eroded from fields and washed into watercourses during and after rainfall. While studying the limnology of South Africa’s major impoundments, Walmsely and Butty [52] described Nagle Dam as a phosphate limited oligotrophic system.
Nagle dam inflow: total variance explained | |||||||||
---|---|---|---|---|---|---|---|---|---|
Component | Initial eigenvalues | Extraction sums of squared loadings | Rotation sums of squared loadings | ||||||
Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
1 | 4.960 | 30.998 | 30.998 | 4.960 | 30.998 | 30.998 | 3.962 | 24.760 | 24.760 |
2 | 2.164 | 13.523 | 44.521 | 2.164 | 13.523 | 44.521 | 2.073 | 12.957 | 37.717 |
3 | 1.429 | 8.934 | 53.455 | 1.429 | 8.934 | 53.455 | 1.973 | 12.332 | 50.049 |
4 | 1.161 | 7.257 | 60.713 | 1.161 | 7.257 | 60.713 | 1.558 | 9.736 | 59.785 |
5 | 1.074 | 6.710 | 67.422 | 1.074 | 6.710 | 67.422 | 1.222 | 7.638 | 67.422 |
Extracted values of the significant components at Nagle dam inflow (NDI).
Extraction Method: Principal Component Analysis.
Component Matrixa | |||||
---|---|---|---|---|---|
Component | |||||
1 | 2 | 3 | 4 | 5 | |
Turbidity | −0.253 | −0.036 | −0.214 | 0.052 | |
Total Phosphate | −0.084 | −0.350 | 0.307 | 0.068 | |
Soluble Reactive Phosphate (SRP) | −0.207 | −0.323 | 0.299 | 0.080 | |
Suspended Solids (SS) | 0.001 | −0.213 | −0.333 | 0.093 | |
Total Organic Carbon (TOC) | 0.099 | −0.132 | −0.410 | 0.152 | |
−0.404 | 0.047 | 0.273 | 0.234 | ||
Nitrate (NO3) | −0.278 | 0.337 | −0.076 | −0.251 | |
Conductivity | 0.020 | 0.383 | −0.180 | −0.336 | |
Temperature | 0.395 | 0.205 | 0.131 | −0.325 | |
% NH3% | 0.363 | −0.137 | −0.088 | 0.152 | |
pH | 0.145 | −0.094 | 0.010 | 0.228 | |
Scenedesmus | 0.160 | 0.336 | −0.119 | 0.246 | −0.283 |
Nitzschia | 0.206 | 0.153 | −0.130 | 0.087 | |
Algal count cell | 0.253 | 0.301 | 0.093 | 0.486 | −0.334 |
Navicula | 0.143 | 0.016 | 0.517 | 0.409 | |
Ammonia- N (NH3) | −0.051 | −0.185 | −0.106 | 0.183 | −0.206 |
The correlation among the parameters and the significant components at NDI.
5 Components extracted.Bold: Significant contributors to the respective principal component in their respective there column.
Extraction Method: Principal Component Analysis.
Seven significant components which explained 75% of the total variance were extracted at Nagle Dam Outflow station (Table 7). The first component which contributed 23% (Table 8) of the total variance is dominated with metal ions which can be related on natural geological processes such as weathering. The second component which explains 15.5% of the total variation is dominated by
Nagle dam outflow: total variance explained | ||||||
---|---|---|---|---|---|---|
Component | Initial eigenvalues | Extraction sums of squared loadings | ||||
Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
1 | 4.832 | 23.011 | 23.011 | 4.832 | 23.011 | 23.011 |
2 | 3.262 | 15.532 | 38.542 | 3.262 | 15.532 | 38.542 |
3 | 2.186 | 10.409 | 48.951 | 2.186 | 10.409 | 48.951 |
4 | 1.879 | 8.946 | 57.897 | 1.879 | 8.946 | 57.897 |
5 | 1.390 | 6.618 | 64.515 | 1.390 | 6.618 | 64.515 |
6 | 1.185 | 5.643 | 70.158 | 1.185 | 5.643 | 70.158 |
7 | 1.072 | 5.103 | 75.261 | 1.072 | 5.103 | 75.261 |
Extracted values of the significant components at Nagle dam outflow.
Extraction Method: Principal Component Analysis.
Component Matrixa | |||||||
---|---|---|---|---|---|---|---|
Component | |||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | |
% NH3% | −.158 | .051 | .364 | .483 | −.092 | .164 | |
Alkalinity | .342 | −.099 | .339 | −.377 | .102 | .149 | .346 |
Calcium (Ca) | −.537 | −.454 | .154 | .103 | −.007 | .030 | |
Chloride (Cl) | −.154 | .499 | −.032 | .055 | .037 | −.281 | |
Conductivity | −.017 | .430 | −.134 | .114 | .024 | −.039 | |
Dissolved Oxygen (DO) | −.496 | −.280 | .224 | −.228 | .016 | .255 | −.213 |
.330 | −.300 | −.071 | .287 | .227 | −.175 | ||
Potassium (K) | −.235 | −.219 | .259 | −.269 | −.213 | .031 | |
Magnesium (Mg) | −.557 | −.120 | .036 | .126 | .087 | .050 | |
Sodium (Na) | .549 | −.591 | −.482 | .257 | .017 | −.040 | −.050 |
Ammonia (NH3) | −.027 | .091 | −.381 | .117 | −.047 | .453 | .426 |
Nitrate (NO3) | .498 | .053 | .232 | −.337 | .048 | −.408 | |
pH | −.350 | −.039 | .181 | −.032 | −.052 | ||
Silicon (Si) | .442 | .404 | .255 | −.162 | .043 | .011 | .364 |
Sulphate (SO4) | .326 | −.363 | .139 | −.445 | .266 | .000 | |
Soluble Reactive Phosphate (SRP) | .056 | .148 | −.166 | −.385 | .302 | −.028 | |
Suspended Solids (SS) | .312 | −.238 | .193 | .046 | .404 | −.197 | |
Temperature | .429 | .292 | .184 | −.183 | −.177 | .441 | |
Total Organic Carbon | .489 | .497 | .031 | .116 | −.047 | −.133 | −.031 |
Total Phosphate (TP) | .127 | .354 | −.303 | −.227 | .283 | −.347 | −.130 |
Turbidity | .402 | −.282 | .006 | .084 | .109 | −.165 |
The correlation among the parameters and the significant components at NDO.
7 Components extracted.
Extraction Method: Principal Component Analysis.
At Inanda Dam Inflow station, seven components which explained 76% of the total variance were extracted (Table 9). Component 1 (Table 10) which is mainly metal ions and explains 11% of the total variance was comprised of metal ions (highlighted in bold black) which suggest that geological processes in the area could be significantly attributing to the water quality variation. The high positive correlation of chloride and component 1 also reflects the effect of anthropogenic pollutants on this station. Both legal and illegal effluent discharges from industrial areas such as Willowton, are the predominant pollution sources that affect Inanda Dam [53]. In developing countries, 70 percent of industrial wastes are dumped untreated into waters, impacting on the usability of the water resource [54].
Inanda dam inflow: total variance explained | |||||||||
---|---|---|---|---|---|---|---|---|---|
Component | Initial eigenvalues | Extraction sums of squared loadings | Rotation sums of squared loadings | ||||||
Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
1 | 7.005 | 31.840 | 31.840 | 7.005 | 31.840 | 31.840 | 5.946 | 27.025 | 27.025 |
2 | 2.888 | 13.127 | 44.967 | 2.888 | 13.127 | 44.967 | 2.443 | 11.104 | 38.129 |
3 | 1.865 | 8.478 | 53.445 | 1.865 | 8.478 | 53.445 | 2.019 | 9.179 | 47.309 |
4 | 1.427 | 6.485 | 59.930 | 1.427 | 6.485 | 59.930 | 1.922 | 8.737 | 56.045 |
5 | 1.363 | 6.196 | 66.127 | 1.363 | 6.196 | 66.127 | 1.695 | 7.704 | 63.749 |
6 | 1.171 | 5.325 | 71.451 | 1.171 | 5.325 | 71.451 | 1.385 | 6.297 | 70.046 |
7 | 1.012 | 4.599 | 76.050 | 1.012 | 4.599 | 76.050 | 1.321 | 6.004 | 76.050 |
Extracted values of the significant components at Inanda dam inflow.
Extraction Method: Principal Component Analysis.
Component Matrixa | |||||||
---|---|---|---|---|---|---|---|
Component | |||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | |
% NH3% | −0.155 | 0.269 | 0.193 | 0.209 | −0.185 | −0.170 | |
Alkalinity | −0.186 | 0.182 | −0.241 | 0.254 | 0.045 | −0.065 | |
Calcium (Ca) | 0.215 | −0.059 | −0.247 | 0.030 | 0.093 | 0.063 | |
Chloride (Cl) | 0.140 | 0.089 | −0.054 | 0.122 | 0.024 | −0.090 | |
Conductivity | 0.169 | 0.009 | −0.048 | 0.108 | 0.027 | −0.096 | |
Dissolved Oxygen (DO) | 0.313 | −0.033 | 0.128 | 0.464 | 0.168 | 0.017 | |
−0.297 | 0.400 | −0.055 | 0.080 | −0.122 | −0.355 | ||
Flourine (F) | 0.204 | 0.307 | 0.318 | 0.329 | −0.067 | 0.236 | −0.319 |
Potassium (K) | 0.496 | −0.065 | −0.122 | −0.012 | −0.240 | 0.116 | |
Magnesium (Mg) | 0.145 | 0.075 | −0.161 | 0.074 | 0.411 | 0.176 | |
Sodium (Na) | 0.174 | 0.100 | −0.017 | 0.045 | 0.009 | −0.006 | |
Ammonia (NH3) | 0.163 | 0.330 | 0.194 | 0.239 | −0.195 | 0.012 | |
Nitrate (NO3) | 0.493 | 0.194 | −0.375 | 0.336 | −0.191 | −0.131 | 0.144 |
pH | −0.075 | 0.227 | 0.019 | −0.260 | −0.408 | −0.102 | |
Silicon (Si) | −0.581 | 0.216 | 0.178 | 0.174 | 0.322 | 0.192 | 0.306 |
Sulphate (SO42+) | .339 | 0.088 | 0.076 | 0.071 | 0.061 | 0.005 | |
Soluble Reactive Phosphate (SRP) | 0.086 | −0.461 | 0.396 | 0.271 | −0.278 | −0.102 | |
Suspended Solids | −.522 | −0.048 | −0.382 | −0.219 | 0.006 | 0.108 | |
Temperature | −0.506 | 0.187 | 0.303 | 0.088 | 0.184 | 0.215 | |
Total Organic Carbon (TOC) | −0.190 | 0.036 | −0.192 | −0.249 | −0.049 | ||
Total Phosphate (TP) | −0.303 | −0.351 | 0.229 | 0.227 | −0.142 | −0.158 | |
Turbidity | −0.585 | −0.028 | −0.241 | −0.184 | 0.328 | −0.010 |
The correlation among the parameters and the significant components at IDI.
7 Components extracted.Bold: Significant contributors to the respective principal component in their respective there column.
Extraction Method: Principal Component Analysis.
Given the high positive correlation coefficient of Soluble Reactive Phosphate (SRP), Suspended Solids (SS), Total Organic Carbon (TOC), Total Phosphate (TP) and turbidity on the component 2, it can be claimed that pollutants in this group which explained 9% of the variation in water quality are emanating from agricultural activities (Table 10). The negative correlation noted between dissolved oxygen and component 5 indicates deterioration in the water quality. Since component 5 also exhibited a positive correlation to temperature, it can be deduced that climatic conditions could explain the 7.7% variation noted at this station. Component 6 which is mainly influenced by
At Inanda Dam Outflow station, eight components explaining 75% of the total variance were extracted as depicted in Table 11. We hypothesised that pollutants in component 1 depicted in Table 12 (11.8% and highlighted in bold black) were mainly contributed by metal ions which reflects the geology of a catchment area. The positive correlation of sulphate and component 1 reflects the effect of anthropogenic polluting activities. Component 3 (Table 12) is mainly attributable to agricultural pollutant sources due to moderate positive high correlations with turbidity, nitrate and suspended solids. It is most plausible to suggest that turbidity and suspended solids is a result of surface runoff due to rainfall.
Inanda dam outflow: total variance explained | ||||||
---|---|---|---|---|---|---|
Component | Initial eigenvalues | Extraction sums of squared loadings | ||||
Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
1 | 5.575 | 25.342 | 25.342 | 5.575 | 25.342 | 25.342 |
2 | 2.597 | 11.804 | 37.146 | 2.597 | 11.804 | 37.146 |
3 | 2.066 | 9.390 | 46.536 | 2.066 | 9.390 | 46.536 |
4 | 1.582 | 7.192 | 53.728 | 1.582 | 7.192 | 53.728 |
5 | 1.360 | 6.183 | 59.911 | 1.360 | 6.183 | 59.911 |
6 | 1.217 | 5.534 | 65.445 | 1.217 | 5.534 | 65.445 |
7 | 1.135 | 5.161 | 70.606 | 1.135 | 5.161 | 70.606 |
8 | 1.009 | 4.589 | 75.194 | 1.009 | 4.589 | 75.194 |
Extracted values of the significant components at Inanda dam outflow.
Extraction Method: Principal Component Analysis.
Component Matrixa | ||||||||
---|---|---|---|---|---|---|---|---|
Component | ||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
Magnesium (Mg) | −0.053 | −0.074 | −0.034 | −0.141 | 0.023 | 0.013 | −0.014 | |
Sodium (Na) | 0.007 | −0.096 | −0.078 | −0.102 | 0.030 | −0.107 | −0.029 | |
Calcium (Ca) | −0.033 | 0.056 | 0.108 | −0.146 | 0.174 | −0.189 | 0.000 | |
Chloride (Cl) | −0.050 | −0.031 | 0.029 | 0.071 | 0.170 | 0.257 | 0.001 | |
Potassium (K) | −0.076 | −0.093 | −0.100 | −0.138 | −0.411 | −0.045 | −0.011 | |
Conductivity | −0.096 | −0.186 | 0.042 | 0.241 | 0.182 | 0.046 | −0.131 | |
Alkalinity | −0.334 | −0.367 | 0.268 | 0.138 | 0.059 | −0.009 | 0.158 | |
Sulphate (SO4) | 0.374 | 0.080 | −0.262 | −0.118 | 0.055 | 0.095 | −0.324 | |
% NH3% | 0.165 | −0.177 | −0.033 | −0.212 | −0.037 | −0.011 | 0.376 | |
pH | 0.212 | −0.046 | −0.084 | −0.295 | 0.039 | −0.090 | 0.310 | |
Temperature | 0.027 | −0.134 | −0.120 | 0.347 | −0.151 | 0.470 | 0.041 | |
Turbidity | 0.327 | 0.099 | 0.153 | 0.143 | −0.057 | 0.028 | 0.046 | |
Nitrate (NO3) | −0.009 | 0.192 | 0.039 | −0.279 | 0.294 | −0.290 | −0.197 | |
Suspended Solids | 0.325 | 0.347 | 0.121 | 0.317 | −0.194 | 0.170 | −0.178 | |
Ammonia (NH3) | 0.271 | −0.384 | 0.430 | −0.071 | −0.094 | 0.011 | 0.004 | 0.290 |
Total Phosphate (TP) | −0.008 | −0.226 | 0.106 | −0.032 | 0.172 | −0.051 | 0.217 | |
Soluble Reactive Phosphate (SRP) | 0.017 | −0.366 | 0.232 | −0.017 | −0.043 | 0.160 | 0.371 | |
Flourine (F) | 0.469 | −0.136 | −0.003 | −.103 | 0.538 | −0.089 | −0.300 | 0.130 |
Dissolved Oxygen (DO) | 0.041 | −0.213 | −0.176 | 0.398 | −0.508 | 0.042 | 0.083 | 0.045 |
−0.094 | 0.149 | −0.026 | 0.171 | 0.282 | 0.139 | 0.199 | ||
Silicon (Si) | 0.095 | −0.283 | 0.327 | 0.458 | −0.161 | −0.146 | 0.524 | 0.300 |
TOC | −0.047 | 0.169 | 0.064 | 0.407 | 0.283 | −0.203 | −0.496 | 0.363 |
The correlation among the parameters and the significant components extracted at IDO.
8 Components extracted.Bold: Significant contributors to the respective principal component in their respective there column.
Extraction Method: Principal Component Analysis.
Cluster analysis was used to detect similarities among the sampling stations in the study area. The dendrogram shows that the six sampling stations in the area studied could be grouped into two significant clusters (A and B) as illustrated by Figure 3. Such is the case of the relatively large linkage distance at which the two groups combine, which indicates the Euclidean distances [47]. Cluster A mainly consists of four sampling stations that were located mostly in the outflow of the river (NDO, IDO, MDO and NDI) while Cluster B mainly consist of two stations mainly dam inflow stations (MDI and IDI). Except for Nagle Dam Inflow, Cluster A basically comprised of dam outflow stations. These stations can be described as less polluted due to the dilution and retention effect. On the other hand, Cluster B composed of dam inflow stations. These stations can be described as more polluted as a result of activities along the river course. The PC results explained Section 4.1 of this chapter showed that poor agriculture practises resulting in runoff of agrochemicals, organic matter, drug residues, sediments and saline drainage as well as sewage and industrial effluent discharges are key factors being reflected by the poor water quality results of the dam’s inflow stations (Cluster B). These practices pose a risk to aquatic ecosystems, human health and productive activities. The significant presence of
Dendrogram of the stations along uMngeni basin.
Understanding the primary effects of anthropogenic activities and natural factors on river water quality is important in the study and efficient management of water resources. Hence, the PCA method assisted in the identification of significant parameters influencing water quality variations at the six stations studied in uMngeni Basin. The PCs extracted suggest that pollution sources along uMngeni Basin can be attributed to geological processes, sewage effluent, agricultural runoff and surface runoff pollutants. The findings could assist in reducing the number of parameters being monitored at any station and thus ultimately reducing the associated cost monitoring cost. It is recommended that, effluents be treated before discharge into the river. Additionally, it is recommended that buffer zone policies be enforced.
The result of the cluster analysis should also assist in categorising sampling sites according to pollution levels. Classification of sampling stations based on pollution level can assist in the designing of an optimal sampling strategy, which could reduce the number of sampling stations and associated costs. This study highlights the usefulness of multivariate statistical assessment such as PCA and CA in analysing complex databases, especially in the identification of pollution sources and to better comprehend the spatial and temporal variations for effective river water-quality management. It is worthwhile to conclude that PCA and CA are better tools for better understanding concealed information about parameter variance and datasets. The study recommends the application of PCA and CA for interpreting bulk surface water quality data-sets.
The authors gratefully acknowledge Durban University of Technology for hosting and funding the main author during his Master’s Degree study.
Alizarin is a stable organic compound, prominently known as a red dye with significant industrial applications, particularly its use in dying textile fabrics. The application in textile coloration industry is inspired by the fact that alizarin is a natural compound often referred a natural dye, initially extracted from the roots of plants of the madder genus [1], before it was synthetically made [2], thus, it is a molecular species from nature, exhibiting and portraying green chemistry properties. The molecular structural framework of alizarin is characterized by the anthraquinone moiety bearing two para-positioned intermolecular hydroxyl groups both on one carbocyclic ring adjacent to the quinone ring. A typical excited state intermolecular proton transfer system (ESIPT), alizarin is a natural dye which has been widely used in pigments, as anticancer agents as well as chemical reagents for use in data recording and storage materials due to its tunable electronic properties [3, 4]. In addition, the natural dye has strong antigenotoxic activity, ascribed to the transfer of ultrafast electrons to TiO2-based materials, which can also perfectly fit as an excellent photosensitizer in dye-sensitized solar cells. Thus, alizarin chromophore has been favored by many researchers, both experimentally and theoretically [5, 6, 7, 8, 9, 10].
Ideally, alizarin forms an intramolecular hydrogen bond between a hydroxyl and a carbonyl group, in the ground and excited states, whereby upon photoexcitation, a proton transfer from the hydroxyl to the carbonyl group is observed, which normally results in dual emission bands of the locally excited (LE) and proton-transferred (PT) tautomers [9, 11, 12, 13]. Characteristically, this process in known as ESIPT, which is viewed as a very fast photo-tautomerization process taking place along an intramolecular hydrogen bond between two atoms that are significantly tuned by electronic excitation. In recent studies, the practical and applications of the ESIPT mechanism based on their photophysical characteristics and properties have been extensively explored and investigated, especially in laser dyes, OLEDs, molecular switches, fluorescence sensors, and particularly biological systems [14, 15, 16, 17, 18, 19, 20]. More importantly, the ESIPT based reactions increase the acidity of the proton donor groups, due to the change of electron density after electron excitation, and the basicity of the acceptor groups is significantly increased to promote the formation of tautomer by intramolecular proton transfer [4, 21, 22, 23, 24, 25, 26].
On the other hand, molecular recognition has been the epic center of supramolecular chemistry due to its significant role in biological and environmental systems, through the host-guest interaction chemistry. Consequently, chemosensors are designed for specific target analytes based on their chemical make-up and complementarity towards each other. The impact of sensing biologically important anions such as acetate, cyanide, fluoride, dihydrogen phosphate, etc., have been receiving attention in literature and many industrial applications. A large volume of colorimetric and fluorometric probes for anions such as fluoride (F-), cyanide (CN-), acetate (AcO-), dihydrogen phosphate (H2PO4−), hydroxide (OH-) and others have been developed. Hydroxide ions play a very significant role in environmental and physiological systems, thus monitoring its concentration in these systems must be highly prioritized [27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]. Moreover, the presence of soft (donor) atoms such as oxygen from hydroxyl and quinone groups of the carboxylic ring raises the prospect of dual sensing, for both cations and anions, which stems from the presence of both, the anion receptors (-OH) and the cationphilic groups, through coordination induced interaction [38, 39, 40, 41].
Herein, we have conducted a comparative study for the two alizarin-based derivatives,
The molecular 2-D structures of (a) alizarin (
Compound
All UV–Vis spectra were recorded in acetonitrile (CH3CN) solvent on a Perkin Elmer Lambda 35 spectrometer by adding Tetrabutylammonium salts while keeping the concentration of
In order to establish the occurrence of chemical interactions between
Observable colorimetric changes of different anions upon interacting with (a)
Moreover, comparative colorimetric studies were conducted for
Furthermore, the multi-colorimetric sensor (
Observable colorimetric changes of different cations upon interacting with (a)
Spectrally, the two probes were characterized by more or less similar absorption spectra, both of them defined by the π → π* transitions in the ultraviolet region, as well as the internal charge transfer band (ICT) in the visible region. Specifically,
Absorption spectra of (a)
The interaction of
The absorption titration spectra of
On the other hand, the molar titrations of
The absorption titration spectra of
Notingly, among the rest of the anions, H2PO4− was still able to induce changes when added to
The absorption titration spectra of
Complementary to colorimetric experiments, UV–Vis spectroscopic experiments were conducted to investigate how absorption properties of
The absorption titration spectra of
Furthermore, the only other cations that could induce significant changes when introduced to
Contrastingly, the introduction of cations to
The absorption titration spectra of
Moreover, it was noticeable that the interaction behaviors of Fe3+, Ni2+ and Zn2+ are of the same nature, based on the characteristics of their respective absorption spectra. The molar introduction of these cations to
The selectivity of
The combined absorption titration spectra of (a)
Furthermore, the two sensors were responsive commonly to four anions (F−, CN−, OH−, AcO−) as displayed above. However, upon the addition of 1 equiv. of each anion, the spectra intensities of AcO− and OH− were similarly high than all others for
In previous studies, an alizarin molecule (
Upon the molar titration with cations, the emission spectrum of
The fluorescence titration spectra of
Proposed binding mechanisms for (a)
Furthermore, the interaction of cations with
The fluorescence titration spectra of
The interaction of biological anions with
The fluorescence titration spectra of
Moreover, the effect of an electron withdrawing sulfonyl group was apparent from the activities of emission spectra of
The fluorescence titration spectra of
Conclusively, the comparative studies on the chemosensing property studies of the two Alizarin probes (
This work was supported by the Department of Physics, Chemistry & Material Science, University of Namibia, Namibia. The work was also partly supported by the Royal Society-DFiD Africa Capacity Building Initiative, New Materials for a Sustainable Energy Future.
There are no conflicts of interest to declare.
IntechOpen implements a robust policy to minimize and deal with instances of fraud or misconduct. As part of our general commitment to transparency and openness, and in order to maintain high scientific standards, we have a well-defined editorial policy regarding Retractions and Corrections.
",metaTitle:"Retraction and Correction Policy",metaDescription:"Retraction and Correction Policy",metaKeywords:null,canonicalURL:"/page/retraction-and-correction-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\\n\\n1. RETRACTIONS
\\n\\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
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\\n\\nPublishing of a Retraction Notice will adhere to the following guidelines:
\\n\\n1.2. REMOVALS AND CANCELLATIONS
\\n\\n2. STATEMENTS OF CONCERN
\\n\\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\\n\\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\\n\\n3. CORRECTIONS
\\n\\nA Correction will be issued by the Academic Editor when:
\\n\\n3.1. ERRATUM
\\n\\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\\n\\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n3.2. CORRIGENDUM
\\n\\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n4. FINAL REMARKS
\\n\\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\\n\\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\\n\\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\\n\\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\\n\\nPolicy last updated: 2017-09-11
\\n"}]'},components:[{type:"htmlEditorComponent",content:'IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\n\n1. RETRACTIONS
\n\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\n\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\n\nPublishing of a Retraction Notice will adhere to the following guidelines:
\n\n1.2. REMOVALS AND CANCELLATIONS
\n\n2. STATEMENTS OF CONCERN
\n\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\n\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\n\n3. CORRECTIONS
\n\nA Correction will be issued by the Academic Editor when:
\n\n3.1. ERRATUM
\n\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\n\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n3.2. CORRIGENDUM
\n\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n4. FINAL REMARKS
\n\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\n\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\n\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\n\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\n\nPolicy last updated: 2017-09-11
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He received his Ph.D. in Environmental Analytical Chemistry from Assiut University, Egypt, in 1989. His research interest is in analytical and environmental chemistry with special emphasis on: (1) monitoring and assessing biological trace elements and toxic metals in human blood, urine, water, crops, vegetables, and medicinal plants; (2) relationships between environmental heavy metals and human diseases; (3) uses of biological indicators for monitoring water pollution; (4) environmental chemistry of lakes, rivers, and well water; (5) water and wastewater treatment by adsorption and photocatalysis techniques; (6) soil and water pollution monitoring, control, and treatment; and (7) advanced oxidation treatment. Prof. Rashed has supervised several MSc and Ph.D. theses in the field of analytical and environmental chemistry. He served as an examiner for several Ph.D. theses in analytical chemistry in India, Kazakhstan, and Botswana. 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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. 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. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 18th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:287,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. 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. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"onlineFirst.detail",path:"/online-first/81250",hash:"",query:{},params:{id:"81250"},fullPath:"/online-first/81250",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()