Location of the Blesbokspruit gold mine tailings sediment samples.
\r\n\tThis book is intended to provide a series of peer reviewed chapters that the guest editor believe will aid in increasing the quality of the research focus across the growing field of grain and seeds compound functionality research. Overall, the objective of this project is to serve as a reference book and as an excellent resource for students, researchers, and scientists interested and working in different functional aspects of grain and seed compounds, and particularly for the scientific community to encourage it to continue publishing their research findings on grain and seed and to provide basis for new research, and the area of sustainable crop production.
\r\n\t
In proteomics analyses,protein identificationby mass spectrometry (MS) is usually performed usingprotein sequence databasessuch as RefSeq (NCBI; http://www.ncbi.nlm.nih.gov/RefSeq/), UniProt (http://www.uniprot.org/) or IPI (http://www.ebi.ac.uk/IPI/IPIhelp.html). Because these databases usuallytarget the longest (main) open reading frame (ORF) in the corresponding mRNAsequence, whether shorter ORFs on the same mRNA are actually translated still shrouds in mystery. In the first place,it had been considered that almost all eukaryotic mRNAs contains only one ORF and functions as monocistronic mRNAs.It is now known, however, that some eukaryotic mRNAshad multiple ORFs, which are recognized as polycistronic mRNAs.One of the well-known extra ORFs is an upstream ORF (uORF) and it functions as regulators of mRNA translation (Diba et al., 2001; Geballe & Morris, 1994; Morris & Geballe, 2000; Vilela & McCarthy, 2003; Zhang & Dietrich, 2005). For getting clues to the mystery of diversified short ORFs,full-length mRNA sequence databases with complete 5‘-untranslated regions (5‘-UTRs) were essentially needed (Morris & Geballe, 2000; Suzuki et al., 2001).
The oligo-capping method was developed to construct full-length cDNA libraries (Maruyama & Sugano, 1994) and the corresponding sequence were stored into the database called DBTSS (DataBase of Transcriptional Start Site; http://dbtss.hgc.jp/) (Suzuki et al., 1997, 2002, 2004; Tsuchihara et al., 2009; Wakaguri et al., 2008; Yamashita et al., 2006). Comparing the dataset of DBTSS with the corresponding RefSeq entries, it was found that about 50 % of the RefSeq entries had at least one upstream ATG (uATG) except the functional ATG initiator codon (Yamashita et al., 2003). Although it had been suggested that upstream AUGs (uAUGs) and uORFs play important roles for translation of the main ORF, none of the proteins from these uORFs was detected in biological experiments in vivo. Our previous proteomics analysis focused on small proteins revealed the first evidence of the existence of four novel small proteins translated from uORFs in vivo using highly sensitive nanoflow liquid chromatography (LC) coupled with the electrospray ionization-tandem mass spectrometry (ESI-MS/MS) system (Oyama et al., 2004). Large-scale analysis based on in-depth separation by two-dimensional LC also led to the identification of additional eight novel small proteins not only from uORFs but also from downstream ORFs and one of them was found to be translated from a non-AUG initiator codon (Oyama et al., 2007). Finding of these novel small proteins indicate the possibility of diversecontrol mechanisms of translation initiation.
In this chapter, we firstintroducewidely-recognized mechanism of translation initiation and functional roles of uORF in translational regulation. We then review how we identified novel small proteins with MS and lastly discuss the progress of bioinformatical analyses forelucidatingthe diversification of short coding regions defined by the transcriptome.
It is well known that 5‘-UTRs of some mRNAs contain functionalelements for translational regulationdefined by uAUG and uORF. In this section, we show howuAUG and uORF have biological consequences for protein synthesison eukaryotic mRNAs.
Initiation of translation on eukaryotic mRNAs occurs roughly as follows (Fig. 1) (Kozak, 1989, 1991, 1999).
A small (40S) ribosomal subunit binds near the 5‘-end of mRNA, i.e. the cap structure.
The 40S subunit migrates linearly downstream of the 5‘-UTR until it encounters the optimum AUG initiator codon.
A large (60S) ribosomal subunit joins the paused 40S subunit.
The complete ribosomal complex (40S + 60S) starts protein synthesis.
The proposed procedure for initiation of translation in eukaryotes.The black region indicates the main ORF of the mRNA.
In addition to the above mechanism, initiation of translation without the step of ribosome scanning is also known. It is called “internal initiation”, which depends on some particular structure on an mRNA termedinternal ribosome entry site (IRES).
In case that an mRNA contains a uORF, two models for the initiation of translation are suggested(Fig. 2) (Hatzigeorgiou, 2002). One is called ”leaky scanning” and the other is ”reinitiation”. If the first AUG codon is in an unfavorable sequence context defined by Kozak (see the section 3.2), a small ribosomal subunit(40S) ignores the firstAUG and initiates translation froma more favorable AUG codondownstream located. This phenomenon is known as ”leaky scanning”(Fig. 2-(A)). In case that a complete ribosomal complex translates a main ORF after termination of translation of the uORF on the same mRNA, itis termed”reinitiation” (Fig. 2-(B)).
The irregular models of ribosome scanning on eukaryotic mRNAs.(A) Leaky scanning and (B) Reinitiation.Gray regions indicate uORFs on the mRNA, whereas black ones represent the main ORFs.
Therelations between two ORFs are classified into three types as follows; (1) A distant type; in-frame/out-of-frame, (2) A contiguoustype; in-frame and (3) An overlappedtype; in-frame/out-of-frame (Fig. 3).In-frame means that a uORF and the main ORF are on the same frame of the mRNA sequence, whereas out-of-frame meansthat they are on the different frame. According to the previous analysis of the accumulated 5‘-end sequence data, the average size of uORF was estimated at 31 amino acids and 20% of ORFs were categorized into Type (3) (Yamashita et al., 2003).
The location of a uORF and the main ORF on the mRNA.(1) A distant type, (2) A contiguoustype and (3) An overlappedtype.Types (1) and (3) have two subtypes based on the frames of two ORFs. One is defined by the same reading frame (in-frame) and the other is by the different one (out-of-frame). Gray and black regions indicate uORFs and the main ORFs on mRNAs, respectively, whereas a blue one represents an overlap.
These different relations might bring about different eventsin initiatingtranslation. In eukaryotes, it hasa tendency to increase an efficiency of reinitiation if the distance betweena uORF and the main ORF is long (Kozak, 1991;Meijer & Thomas, 2002; Morris & Geballe, 2000). Therefore, the ORFs classified as Types (2) and (3) would be difficult to be regulated by reinitiation. It is also said that reinitiation occurs only when the length of uORF is short (Kozak, 1991), whereas the sequence context of an inter-ORF‘s region, that of upstream of uORF, uORF itself and even the main ORF can also affect reinitiation (Morris & Geballe, 2000). On the contrary, the ORFs of Type (3) might easily cause leaky scanning(Geballe & Morris, 1994; Yamashita et al., 2003). As a special case, when a termination codon of the uORF is nearthe AUG initiator codon of the downstream ORF, withinabout 50 nucleotides, ribosomes could scan backwards and reinitiate translation from the AUG codon of the downstream ORF (Peabody et al., 1986).
The 5‘-UTR elements such as uAUGs and uORFs are well known as important regulators for translation initiation. In case of some genes that have multiple uORFs, considerablydifferent effects can be generated on the translation of the main ORF depending on which combination of uORFs istranslated. Some uORFsseem to promotereinitiation of the main ORFs andthe others seem to inhibit it. It is supposed that these effects arecaused by the nucleotide sequences of the 3‘ ends of the uORFs, that of uORFs or protein products encoded by uORFs. Suchdifferential enhancement of translationare considered to be one ofthe responsesof adaptation to the environment (Altmann&Trachsel, 1993; Diba et al., 2001; Geballe & Morris, 1994; Hatzigeorgiou, 2002; Iacono et al., 2005; Meijer & Thomas, 2002;Morris & Geballe, 2000; Vilela & McCarthy, 2003; Wang & Rothnagel, 2004; Zhang & Dietrich, 2005). In addition to that,variousfactors or events are known to influence onthe translational inhibition of the main ORF; the presence of arginine, a stalling of a ribosomal complex at the termination or an interaction between a ribosomal complex and the peptide encoded by the uORF, which indicates that down-regulated controlsby uORFs are general (Diba et al., 2001; Geballe & Morris, 1994; Iacono et al., 2005;Meijer & Thomas, 2002; Morris & Geballe, 2000; Vilela & McCarthy, 2003; Zhang & Dietrich, 2005).
As for downstream ORFs, there is also a report that a peptide encoded in the 3‘-UTR may be expressed (Rastinejad & Blau, 1993). However, whether and how the peptides control the translation initiation of the main ORF is still unknown.
How a ribosomal complex (40S + 60S) recognizes an initiator codon on the mRNA is a matter of vital importance fordefining the proteome. Here we presenta part of already proposed elements for regulation of translation initiation.
Traditionally, the first-AUG rule iswidely recognized for initiation of translation (Kozak, 1987, 1989, 1991). It states that ribosomes start translation from the first-AUG on the corresponding mRNA. Although this rule is not absolute, 90-95% of vertebrate ORFs was established by the first AUG codon on the mRNA (Kozak, 1987, 1989, 1991). Our previous proteomics analysis of small proteins also indicated that about 84% of proteinsinRefSeq were translated from the first AUG of the corresponding mRNAs (Oyama et al., 2004). On the other hand, there are also many negative reports concerningthe rule;29% of cDNA contained at least one ATG codon in their 5‘-UTR (Suzuki et al., 2000); 41% of transcriptshad more than one uAUG and24% of genes had more than two uAUGs(Peri & Pandey, 2001); about 50% of the RefSeq entries had at least one uAUG (Yamashita et al., 2003); about 44% of 5‘-UTRs had uAUGs and uORFs(Iacono et al., 2005). There are also some reports that the first AUG is skipped if it is too close to the cap structure, within 12 (Kozak, 1991) to 14 (Sedman et al., 1990) nucleotides(see the section 3.3). In this chapter, we cited a variety of statistical data on the UTRs. Because they are based on different versions or generations of sequence databases, the data vary widely (Meijer & Thomas, 2002), which is the point to be properly considered.
The strongest bias for initiation of translation in vertebrates is the sequence context called“Kozak’s sequence”, known as GCCA/GCCATGG(Kozak, 1987). The nucleotides in positions -3 (A or G) and +4 (G) are highly conserved andgreatly effective for a ribosomal complex to start translation (Kozak, 1987, 2002; Matsui et al., 2007; Suzuki et al., 2001; Wang & Rothnagel, 2004).The context of an AUG codon in position -3 is the most highly conserved and functionally the most important; it is regarded as strong or optimal only when this position matches A or G, and that in position +4 is also highly conserved (Kozak, 2002). Some reports mentioned that only 0.86% (Kozak, 1987) to 6% (Iacono et al., 2005) of functional initiator codons lacked Kozak’ssequence in positions -3 and +4,whereas 37% (Suzuki et al., 2000) to 46% (Kozak, 1987) of uATGswould be skipped because of unfavorable Kozak’ssequencein both of the positions. On the contrary,another report mentioned that most initiator codons were not in close agreement with Kozak’sconsensus sequence (Peri & Pandey, 2001).
The length of 5\'-UTR is also effective when translation occursfrom an AUG codon near the 5’ end of the mRNA (Kozak, 1991; Sedman et al., 1990).About half of ribosomes skip an AUG codon even in an optimal context if the length of 5‘-UTR is less than 12 nucleotides (mentioned in the section 3.1) and this type of leaky scanning can be reduced if the length of 5‘-UTR is more than or equal to 20 nucleotides (Kozak, 1991).In the traditional analysis based on incomplete 5‘-UTR sequences,the distance from the 5\' end to the AUG initiator codon in vertebrate mRNAs was generally from 20 and 100nucleotides (Kozak, 1987). The previous analysis using RefSeq human mRNA sequences indicated that 85% of 5‘-UTR sequences less than 100 nucleotides contain no uAUGs(Peri & Pandey, 2001). The evidence convincedusthat the first-AUG rule was widely supported in eukaryotes. In the recent analysis based on full-length 5‘-UTR sequences, it is 125 nucleotideslongon average (Suzuki et al., 2000)andtranscriptional start sites (TSSs) vary widely (Carninci et al., 2006; Kimura et al., 2006; Suzuki et al., 2001). The average scattered length of5\'-UTR was more than 61.7 nucleotides, with a standard deviation of 19.5nucleotides(Suzuki et al., 2001) and 52 % of the human RefSeq genes contained 3.1 TSS clusters on average (Kimura et al., 2006), which has an over 500 nucleotides interval (Fig. 4).In protein-coding genes, differentially regulated alternative TSSs are common (Carninci et al., 2006). Because the diversity of transcriptioninitiation greatly affects the length of the 5\'-UTR, there remainsome doubtswhether thelength of the 5\'-UTRcontributesto the efficiency of translation initiation.There is also a report that the degree of leaky scanning is not affected by the length of 5‘-UTR (Wang & Rothnagel, 2004).
The schematic representation of the 5‘ends of the TSSs.Each TSScluster consists of at least one TSS and has an over 500 nucleotides interval.
In the general translation model, a non-AUG codon is considered to be ignored by ribosomes unless a downstream AUG codon is in a relatively weak context (Geballe & Morris, 1994; Kozak, 1999). In case that an upstream non-AUG codon, such as ACG, CUG or GUG, satisfies Kozak’s consensus sequence, it possibly functions as an initiator of translation in addition to the first AUG initiator codon(Kozak, 1999, 2002). Besides Kozak’s consensus sequence, downstreamstem-and-loop and highly structured GC-rich context in the 5‘-UTRcould enhance translation initiation from a non-AUG codon(Kozak, 1991, 2002).
The recent progress of proteomic methodologies based on highly sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology have enabled us to identify hundreds or thousands of proteins in a single analysis.
Wesucceededinthe discovery of novel small proteins translated from short ORFs using direct nanoflow LC-MS/MS system (Oyama et al., 2004, 2007). Among54 proteins less than 100 amino acids that were identified by retrieving several sequence databases with a representative search engine, Mascot (Matrix Science; http://www.matrixscience.com/), four ones wereturned out to be encoded in 5‘-UTRs (Oyama et al., 2004). This showed the first direct evidence of peptide products from the uORFs actually translated in human cells. In the subsequent analysis using more sophisticated two-dimensional LC system, we also discovered eight novel small proteins (Oyama et al., 2007), five of which were encoded in the 5‘-UTR and three were encoded in the 3‘-UTR of the corresponding mRNA. Even based on the accumulated DBTSS data, two ORFs had no putative AUG codon, which indicated the possibility that they were translated fromnon-AUG initiator codon. In the article above, 197 proteins less than 20 kDa were identified by Mascot. Theprocedurefor identifying novel proteins by MS is describedas follows.
The procedure for preparing samples for proteomic analyses of small proteins.
The proteins included in cultured cell lysates were first separated according to their size. Small protein-enriched fraction through acid extraction and SDS-PAGE were treated with enzymes. In case of SDS-PAGE, the digested peptides were extracted from the gel. The samples were desalted and concentrated to introduce into the MS system. The schematic procedure is shown in Fig. 5.
The samples were analyzed using nanoflow LC-MS/MS system.The purified peptides were eluted with a linear gradient of acetonitrile and sprayed into the high-resolution tandem mass spectrometer. Acquired tandem mass (MS/MS) spectra were then converted to text files and processed against sequence databases using Mascot. Based on theprinciple that each peptide has a MS/MS spectrum with unique characteristics,the search enginecomparesmeasured data on precursor/product ionswith those theoretically calculated from protein sequence data(Fig. 6). The MS/MSspectrumfile contains mass to charge ratio (m/z) values of precursor and product ions along withtheir intensity. The measuredspectrum lists are searched against sequence databases to identify the corresponding peptide in a statistical manner. The theoretical spectrumlists are totally dependent on the contents of sequence databases themselves.
The principle ofprotein identification.A search enginecompares measuredMS/MSspectrum lists with theoretical ones. The acquired MS/MS spectra are converted into a text file that iscomposed of precursor ion data and product ion data, in a format defined by the search engine. Product ion data usually consist ofmultiplepairs ofm/zand its intensity. The theoretical m/z values are calculated virtually.
Forexploringnovel small proteins,two types of sequence databases were used;one was an artificial database computationally translated from the cDNAsequences in all the reading frames and the other was an already established protein database. In order to processthe comparison ofthe large-scale protein identification data from the two kinds of databases,severalPerl scripts have beendeveloped based on thedefinition that candidatesof novel small proteins were identified only in the cDNA database(s) (Fig. 7). In a result datasheet using RefSeq sequences, each protein was annotated with NM numbers for the cDNA database and with NP numbers for the protein database. The Perl scripts then exchanged NM to NP numbers and evaluatedthem.
Thealgorithm to compare the listsof searchresults using RefSeq cDNAand protein databases.The proteins identified from the cDNA database are annotated with NM numbers, whereas, those from the protein database are with NP numbers. To compare these results, it is needed to exchange NM to NP numbers. The NP numbersannotated only from the cDNA database are considered to becandidates of novel proteins.
In order to forward MS-based identification of novel coding regions of mRNAs, MS systems, sequence databases and bioinformatics methodologies are required toimprove together. Regardingbioinformatics, twoaspects seem to be demanded; one is for retrieving target proteins from an enormoussize ofdatabase searching results, the other is for constructingplatforms to predict novelcoding sequences (CDSs).
The recent advances in MS-based proteomics technology have enabled us to perform large-scale protein identification with high sensitivity. The accumulation of well-established sequence databasesalso made a great contribution to efficient identification in proteomics analyses. One of the representative databases is a specialized 5‘-end cDNA database like DBTSS and the other is a series of whole genomesequence databases for variousspecies. To investigatethe mechanismsintranscriptional control, DBTSS has lately attracted considerable attention because it contains accumulated information on the transcriptional regulation of each gene (Suzuki et al., 2002, 2004; Tsuchihara et al., 2009; Wakaguri et al., 2008; Yamashita et al., 2006). Based on the accumulated data,the diverse distribution of TSSs wasclearly indicated (Kimura et al., 2006; Suzuki et al., 2000, 2001). On the other hand,manywhole genome sequencing projectsare progressing all over the world (GOLD: Genomes Online Database; http://www.genomesonline.org/).Complement and maintenanceof sequence databases for variousspeciesmust help to find more novel proteins across the species. For example,there are several reports that conducted bioinformatical approaches to explore novel functional uORFs by comparing the 5\'-UTRregions of orthologs based on multiple sequence alignments (Zhang & Dietrich, 2005), using ORF Finder (http://bioinformatics.org/sms/orf find.html) and a machine learning technique, inductive logic programming (ILP) with biological background knowledge (Selpi et al., 2006), or applying comparative genomics and a heuristicrule-based expert system (Cvijovic et al., 2007). Using advanced sequence databases, new proteinCDSs were added as a result of the predictionby variousalgorithms(e.g. Hatzigeorgiou, 2002; Ota et al., 2004). Based on the well-established cDNA databases, MS couldevaluatewhether these CDSs are actually translated in a high-throughput manner. Construction of more detailed sequence databases will lead to detection of more novel small proteins in the presumed 5\'-UTRs (Oyama et al., 2004). Tomake good use of those exhaustive sequence databases, bioinformatical techniques, especiallydata mining tools such as search engines to retrieve target proteins from an enormoussize ofdatabase search results, areobviouslyindispensable.
In addition to the technological progress of MS, sequence databases and data mining tools, development of other bioinformatical techniques calledprediction tools, are also important. Ad-hoc algorithms for predicting new CDSs, as mentioned above, could be improved by usingMS-based novel protein data. Those novel onescan be applied to play a role ina collection ofsupervised training data for machine learning, pattern recognition or rule-based manual approach. There is an interesting bioinformatical reportwhich hypothesizedthat a uORF in the transcript down-regulates transcription of the corresponding RNA via RNA decay mechanisms (Matsui et al., 2007). They obtained human and mouse transcripts from RefSeq and UniGene (http://www.ncbi.nlm.nih.gov/unigene) and classified the transcripts into Level 0 (not containing uORF) and Level 1-3 (containing uORF). Then, they prepared the data of expression intensities and half-lives of mRNA transcripts mainly from SymAtlas (now linked to BioGPS; http://biogps.gnf.org/#goto=welcome) and Genome Research website (http://genome.cshlp.org/). Although they suggested that not only the expression level but also the half-life of transcriptswas obviouslydeclined in the latter group, they did not demonstrate any interaction between uORFs and transcripts.
Advanced MS instruments can not only evaluatewhether uORFs are actually translated but also quantifytime-course changes of their expression levels. Stable isotope labeling with amino acids in cell culture (SILAC) technology enables us to quantify the changes regarding all the proteins in vivo (Oyama et al., 2009).Based on time-course changes of specific peptides, we could also hypothesize some regulatory interactions.In combination with the measurement of the dynamics of the corresponding mRNAs using microarray or reverse transcription-polymerase chain reaction (RT-PCR),transcriptional regulation by short ORFs will be analyzed at the system level.
Although the roles of5‘-UTR elements, especially uORFs, had been well discussedas translational regulators for the main ORFs in the biological context, whether the proteins encoded by the uORFs were translated had not been approached for a long time. We first unraveledthe mystery by demonstrating the existence of novel protein products defined by these ORFs using advanced proteomics technology. Thanks to the progress of nanoLC-MS/MS-based shotgun proteomics strategies, thousands of proteins can now be identified fromprotein mixtures such as cell lysates. Some of the presumed UTRs areno longer“untranslated“,and other noncoding transcriptsareno longer“noncoding“. One of the novel small proteins revealed in our analysis was indeed defined by a short transcript variant generated by utilization of the downstream alternative promoters(Oyama et al., 2007). Alternative uses of diverse transcription initiation, splicing and translation start sites could increase the complexity of short protein-coding regions and MS-based annotation of these novel small proteins will enable us to perform a more detailed analysis of the real outline of the proteome, along with the translational regulationby the diversified short ORFeome systematically.
South Africa like other developing countries is faced with the challenges of environmental degradation via the continuous release into the environment of trace element-containing chemicals through urbanization, agricultural and mining activities, as well as industrialization. Trace metals (TM) are naturally occurring elements that have a high atomic weight and a density at least 5 times greater than that of water, and some of the commonly found ones particularly at contaminated sites include Arsenic (As), Cadmium (Cd), Chromium (Cr), Copper (Cu), Lead (Pb), Mercury (Hg), Nickel (Ni) and Zinc (Zn) [1, 2]. Attempts towards the assessment, mechanism and the characteristics of trace metal pollution in surrounding areas of mines has been and continue being a theme of various scientific gatherings.
Globally, the extraction and distribution of minerals from ore deposits has been one of the actions that contribute to environmental degradation due to industrialization. The extraction and beneficiation processes often result in the release of tailings that end up in natural percolations within the earth crust, thus paving a way for various kinds of risk elements entering the ecosystem. Such practices result in serious environmental complications due to the elevated concentrations and accumulation of trace metals which poses risk for human health [3, 4, 5, 6, 7].
The mining and processing of gold is associated with certain elements such as Copper (Cu), Antimony (Sb), Nickel (Ni), Selenium (Se), Mercury (Hg), Thallium (Tl), Titanium (Ti), Zinc (Zn), Silver (Ag), Cobalt (Co), Lead (Pb) and Uranium (U). Most of these metals are somewhat released into the environment via trophic links ranging from agricultural soils to plants, animals and humans [8, 9, 10].
Pollutants from various anthropogenic activities ranging from mine effluents such as wastewaters, tailings, runoff from agricultural pesticides and atmospheric deposition often contaminate the surrounding soils and water bodies thus posing threat to the ecosystem and humans. This occurs via direct ingestion or contact with contaminated soil, the food chain (soil–plant-human or soil–plant–animal-human), drinking of contaminated ground water, reduction in food quality (safety and marketability) via phytotoxicity, reduction in land usability for agricultural production causing food insecurity, and land tenure problems [11, 12]. In humans, several health challenges such as abortion, cancer, kidney damage and sometimes death, are some of the consequences of prolonged exposure to extreme concentrations of trace metals [13].
The importance of soil cannot be over emphasized as it is characterized as a complex and dynamic system that is made up of sediments that are different in relation to their physical, chemical, mineralogical and biological constituents. Soil is an essential resource for natural living conditions of plants, animals and humans. The role of soil as a collector filter of both organic and inorganic residues helps in protecting groundwater and in the sequestration of toxic materials [14]. The accumulation of excess metals and metalloids in soils over an extended period exposes humans and other animals to toxicity [15]. Assessing the spatial distribution of trace metals is soil is crucial to obtaining basic information about areas of concerns and to prioritize site mitigation strategies [16]. However, the quantification of element concentrations in soil as a single parameter is not enough in evaluating the extent of contamination due to differentiation between natural background levels and anthropogenic enrichment [3]. Indexes including geoaccumulation index (Igeo) and contamination factor (CF) which are known to provide a better picture of the status of elemental contamination compared to the background concentration were used as pointers in identifying and quantifying the level of elemental pollution as well as the intensity of anthropogenic contaminants accumulated in the soil.
There are enormous impacts of mine tailings disposal sites with over 500,000 abandoned hard rock mines located in the United States, while Mexico alone is affected by 27.1 million hectares of mining activity [17, 18, 19]. Gold mine waste was reported in 2001 by South Africa’s Department of Water Affairs and Forestry as the largest single source of waste constituting over 47% of mineral wastes generated in South Africa [20]. Previous studies indicate that there are close to 300 unlined and not vegetated tailings dumps covering over 400 km2 surface area within the Witwatersrand Basin of the Republic of South Africa. With tailings dumps being a major source of contaminants, the Witwatersrand Basin’s massive tailing dumps are a possible, environmental pollution threat [21]. Studies into the deposits in the mine regions of the Gauteng province of South Africa [17], revealed the deposits to be of great health concern; containing enormous amounts of toxic metals, such as U, As, Ra, Ni, Zn, etc.
Hence, this present study was aimed at determining the contamination level of identified trace metals in an abandoned mine tailing dump over time. In addition, findings from this study will assist the various stakeholders in resource management and policy implementation.
South Africa lies on the southernmost part of the African continent, and is known to have renowned varied topography, great natural beauty, and cultural diversity. It is a medium-sized country, with a total land area of 1,219,090 square kilometers. Ekurhuleni falls within the East Rand region and is characterized by rainfall known to be typical to the Highveld summer rainfall, which occurs from October to April. The average annual rainfall varies from 715 to 735 mm an indication that the study area has a distinct moisture deficit. Frost does occur frequently from mid-April to September, which makes temperatures below freezing common during winter times. This area is home to mild summers with temperatures seldom above 30°C. During spring and winter, northerly and north-westerly winds occur and during summer north-easterly to north–north-easterly winds occur [22]. There are many pans across the Ekurhuleni area. These pans cover a total area of 3559 hectares within the Ekurhuleni Metropolitan Municipality area and are mostly seasonal. There are also a few lakes created by mines, which are used for recreational parks. Germiston Lake, Benoni Lake and Boksburg Lake are the three main lakes used for recreational purposes within the Ekurhuleni Metropolitan Municipality area, but which fall outside the East Rand Basin area. The tailings dump has some informal settlements within its proximity with subsistence farming among the dwellers as shown in Figure 1. The specific description indicating coordinates of the sampling site located along Outeniqua Road & Cloverfield Weg in Springs, Ekurhuleni are illustrated in Table 1.
Location of the sampling site.
Station no. | Latitude (S) | Longitude (E) |
---|---|---|
1 | 260 10′ | 280 27′ |
2 | 260 15′ | 280 35′ |
3 | 260 04′ | 280 40′ |
4 | 260 17′ | 280 44′ |
5 | 260 21′ | 280 50′ |
6 | 260 30′ | 290 10′ |
7 | 260 00′ | 290 15′ |
8 | 260 27′ | 290 20′ |
9 | 260 09′ | 290 35′ |
10 | 260 38′ | 290 42′ |
11 | 260 43′ | 290 47′ |
12 | 260 34′ | 290 50′ |
13 | 260 13′ | 290 53′ |
14 | 260 19′ | 300 10′ |
15 | 260 48′ | 300 15′ |
16 | 260 36′ | 300 25′ |
17 | 260 40′ | 300 29′ |
18 | 260 14′ | 300 35′ |
19 | 260 23′ | 300 40′ |
20 | 260 54′ | 300 48′ |
Location of the Blesbokspruit gold mine tailings sediment samples.
In a bid to assess the level of trace metal contamination in the mine tailings, about 2 kilograms of 20 representative tailing samples were obtained from the dump. Preceding the removal of top tailing samples (2 cm) using an auger, samples were taken at a depth of 10 cm for every 50 m horizontal interval for a wider coverage. The collected soil samples (tailings) were kept cool in an icebox (<4°C) and transported to the laboratory for further analyses in sterile plastic bags.
20 representative tailing samples of about 5 g each were oven dried at 100°C for 24 hours and passed through a 2 mm sieve. Aliquots of approximately 2 g of the various tailing samples were weighed into a Teflon crucible and moistened with 100 mL of 1 M HCl acid for the determination of the HCl-soluble fraction of heavy metals. The mixtures were covered and placed on a shaker for 12 hours at 130 rpm. The solutions were filtered through a Whatman filter paper, and the filtrates were stored in sterile bottles prior to analysis of minerals using inductively coupled plasma-optical emission spectrometry (ICP-OES).
10 g each of the representative tailing samples were pelletized using a mold at very high pressure and then placed in the sample compartment of the X-ray fluorescence spectrometer (XRF; Rigaku ZSX PrismusII). This was done to analyze the major and trace element oxides of the tailing samples.
Physicochemical properties such as pH and EC (electrical conductivity) of the soil samples (tailings) were measured in a soil-to-water suspension (1,2.5, w/w) and a 1:5 tailings-to-water suspension using a Crison multimeter (model MM 41) respectively [23]. Loss on Ignition (LOI) analysis was used to determine the organic matter content (% OM) of the various tailing’s samples [24]. The grain size distribution of tailing samples was determined using the hydrometer method [25].
Apparatus and glassware used were acid-washed with 5% nitric acid for precision analysis while reagents were of analytical standard. The trace metals were determined using ICP-OES (Model - GBC Quantima Sequential) operated under specific conditions of 1300 W RF power, 15 L min−1 plasma flow, 2.0 L min−1 auxiliary flow, 0.8 L min−1 nebulizer flow, 1.5 mL min−1 sample uptake rate. Multiple levels of calibration standard solutions prepared from a Certipur ICP multi-element standard (Merck KGaA) was used in the calibration of the ICP-OES. Metal determination was done using Axial view, while 2-point background correction and 3 replicates were employed in the measurement of analytical signal. The emission intensities were determined for the most sensitive lines free of spectral interference. By diluting the stock multi-elemental standard solution (1000 mg L−1) in 0.5% (v/v) nitric acid, the calibration standards were prepared. The calibration curves for all the studied elements were in the range of 0.01 to 1.0 mg L−1.
The history and degree of trace metal pollution in an environment can be ascertained from the surrounding sediments by comparing the pollutant metal concentration with an unpolluted reference material. The average shale concentration as an International standard reference for unpolluted sediment was utilized [26]. This study applied pollution indices such as (i) metal contamination factor, (ii) contamination degree, (iii) index of geoaccumulation, and (iv) pollution load index to assess heavy metal contamination.
By calculating the ratio of the concentration of a specific trace metal in the study area and the concentration of the background concentration of the corresponding metal, the contamination factor was determined. Table 2 shows the various terminologies in describing contamination factor class and level [27]. CF is an effective tool for monitoring pollution over a period and for the respective metals was calculated using the equation as prescribed by [28].
CF | Description |
---|---|
CF < 1 | Low contamination factor |
1 ≤ CF < 3 | Moderate contamination factor |
3 ≤ CF < 6 | Considerate contamination factor |
CF ≥ 6 | Very high contamination factor |
Terminologies used to describe contamination factor [27].
Contamination degree (CD) refers to the sum of all the contamination factor (CF) values of a specific sampling site. It is a diagnostic tool aimed at providing a measure of the degree of overall contamination in surface layers in a sampling site or core. In this study, CD was assessed using Eq. (2).
A list of terminologies as prescribed by [29] used in describing the contamination degree of the site under investigation is summarized in Table 3.
CD | Description |
---|---|
CD < 6 | Low contamination degree |
6 ≤ CD < 12 | Moderate contamination degree |
12 ≤ CD < 24 | Considerate contamination degree |
CD ≥ 24 | Very high contamination degree |
Terminologies used to describe contamination degree for soil [29].
To quantify the level of heavy metal contamination associated with the study site, the geoaccumulation index (I-geo) was adopted. The Igeo is an important method used for the interpretation of the quality of sediments in the sampling site. It is used to assess impacts due to anthropogenic activities and was determined using Eq. (3) as prescribed by [30].
where Cn is the measure of the metal concentration in the examined metal n in the sediment, Bn is the background concentration of the element (average shale concentration) or reference value of the metal n, and 1.5 is the correction factor due to the lithogenic effect that could result in variations in the background values for a given metal in the environment. There are seven grades (0–6) ranging from unpolluted to highly polluted in the geoaccumulation index scale as described by [30] (Table 4).
Igeo Value | Class | Contamination Level |
---|---|---|
Igeo ≤ 0 | 0 | Uncontaminated |
0 < Igeo < 1 | 1 | Uncontaminated/moderately contaminated |
1 < Igeo < 2 | 2 | Moderately contaminated |
2 < Igeo < 3 | 3 | Moderately/strongly contaminated |
3 < Igeo < 4 | 4 | Strongly contaminated |
4 < Igeo < 5 | 5 | Strongly/extremely contaminated |
5 < Igeo | 6 | Extremely contaminated |
Classification for the geoaccumulation index (Igeo) [30].
Pollution load index, which is a useful tool in heavy metal pollution evaluation, refers to the number of times by which each heavy metal concentrations in the sediments (tailings) exceeded the background concentration in the soil, and it provides a summary of the overall level of heavy metal toxicity in a sample. The world average concentrations of metals using shale was used as background for identified heavy metals in this study [26]. The PLI can provide an estimate of the various metal contamination status and precautionary steps to be taking [31]. Using Eq. (4) as developed by [26], the PLI of the study site was calculated by obtaining the n-root from the n-CFs that was obtained for all the metals.
where CF is the contamination factor, CFn is the CF value of metal n, and n is the number of metals.
Interpretation of PLI values are categorized into two levels; polluted (PLI > 1) and unpolluted (PLI < 1) whereas PLI = 1 indicate trace metal loads close to the background level [32].
The potential contamination of the tailing’s sediments was evaluated using the proposed sediment quality guidelines by USEPA [19] Table 5. Illustrated the various criteria.
Metal | Not polluted | Moderately polluted | Heavily polluted | Present study |
---|---|---|---|---|
Cd | — | — | >6 | 7.1 |
Cr | <25 | 25–75 | >75 | 860.3 |
Cu | <25 | 25–50 | >50 | 0.1 |
Pb | <40 | 40–60 | >60 | 121.9 |
Zn | <90 | 90–200 | >200 | 3.9 |
USEPA guidelines for sediments (mg/kg dry weights) in comparison with gold mine tailings sediments.
Textural properties obtained from sieve analysis of the gold mine tailings sediments using classification as prescribed by [25] are presented in Table 6. These results reveal that fine sand (0.150–0.075 mm) and clay (0.075–0.053 mm) were the principal fractions of all sediment samples, with an average composition of 66.03% for fine sand, 23.08% clay and 10.89% silt respectively. With the larger portion of the sediments being fine sand, there is a likelihood for nutrients accumulation is high due to the higher surface-to-volume ratios [33].
Sample no. | Sieve size (ASTM) % Materials; Retains (gms) | ||||||||
---|---|---|---|---|---|---|---|---|---|
No. 100 | No. 140 | No. 200 | No. 270 | PAN | TOTAL | % Sand | % Silt | % Clay | |
1 | 5.68 | 45.51 | 15.84 | 10.25 | 22.72 | 100 | 67.03 | 10.25 | 22.72 |
2 | 5.75 | 46.82 | 13.79 | 10.58 | 23.41 | 100 | 66.01 | 10.58 | 23.41 |
3 | 5.40 | 46.52 | 13.61 | 10.62 | 23.85 | 100 | 65.53 | 10.62 | 23.85 |
4 | 5.37 | 45.84 | 14.71 | 11.25 | 22.83 | 100 | 65.92 | 11.25 | 22.83 |
5 | 5.42 | 45.93 | 13.93 | 11.81 | 22.91 | 100 | 65.28 | 11.81 | 22.91 |
6 | 5.39 | 47.88 | 13.01 | 11.20 | 22.52 | 100 | 66.28 | 11.20 | 22.52 |
7 | 5.42 | 48.23 | 11.87 | 10.78 | 23.70 | 100 | 65.52 | 10.78 | 23.70 |
8 | 5.88 | 46.38 | 13.42 | 10.44 | 23.88 | 100 | 65.68 | 10.44 | 23.88 |
9 | 5.94 | 46.82 | 13.00 | 10.32 | 23.92 | 100 | 65.76 | 10.32 | 23.92 |
10 | 5.66 | 44.46 | 16.15 | 10.58 | 23.15 | 100 | 66.27 | 10.58 | 23.15 |
11 | 5.86 | 47.20 | 14.22 | 9.88 | 22.84 | 100 | 67.28 | 9.88 | 22.84 |
12 | 5.42 | 45.30 | 15.83 | 11.32 | 22.13 | 100 | 66.55 | 11.32 | 22.13 |
13 | 5.38 | 45.92 | 13.68 | 11.84 | 23.18 | 100 | 64.98 | 11.84 | 23.18 |
14 | 5.62 | 46.34 | 13.74 | 10.68 | 23.62 | 100 | 65.70 | 10.68 | 23.62 |
15 | 5.48 | 46.82 | 13.81 | 10.31 | 23.58 | 100 | 66.11 | 10.31 | 23.58 |
16 | 5.23 | 46.92 | 14.81 | 10.22 | 22.82 | 100 | 66.96 | 10.22 | 22.82 |
17 | 5.98 | 48.22 | 11.62 | 11.69 | 22.49 | 100 | 65.82 | 11.69 | 22.49 |
18 | 5.36 | 48.80 | 11.78 | 11.38 | 22.68 | 100 | 65.94 | 11.38 | 22.68 |
19 | 5.92 | 48.24 | 11.34 | 11.75 | 22.75 | 100 | 65.50 | 11.75 | 22.75 |
20 | 5.68 | 47.36 | 13.37 | 10.94 | 22.65 | 100 | 66.41 | 10.94 | 22.65 |
Sieve analysis of the gold mine tailings sediment samples.
Geochemical properties of the sediments such as the pH, EC and carbonate content (see Table 7) helps in ascertaining vital information to comprehend the soils potential to withhold heavy metals [34]. The results obtained for the sediment pH measurements, showed that the study area is very strongly acidic ranging from 3.86 to 4.34. The low pH values in the study area were related with heterogeneous deposits of sulfidic residues from the mine surroundings, which resulted in low pH values that is attributed to microbial sulfide oxidation and the resultant formation of sulfuric acid [35]. Nutrient uptake by plants may be inhibited by the level of acidity as most plant nutrients are optimally available to plants within 6.5 to 7.5 pH range which also support plant root growth [36]. The low CEC values which correlates with the high proportion of sand fragment is an indication that the sediments may likely not have reliable soil sorption capacity [37]. LOI of studied soils were in the range of (5.0–5.4%)-dry weight, which could be, attributed to growing plants within the tailing’s sediments.
Station no. | pH | C.E (mS/cm) | CEC (meq/100 g) | LOI (%) |
---|---|---|---|---|
1 | 3.86 | 1.30 | 8.5 | 5.1 |
2 | 4.34 | 1.50 | 8.8 | 5.4 |
3 | 4.28 | 1.80 | 9.0 | 5.0 |
4 | 4.30 | 1.90 | 8.3 | 5.1 |
5 | 3.92 | 1.40 | 9.1 | 5.3 |
6 | 4.34 | 1.60 | 8.8 | 5.1 |
7 | 3.89 | 1.40 | 8.5 | 5.4 |
8 | 3.87 | 1.40 | 9.1 | 5.1 |
9 | 3.86 | 1.40 | 9.0 | 5.2 |
10 | 4.27 | 1.80 | 8.8 | 5.2 |
11 | 4.28 | 1.80 | 9.4 | 5.4 |
12 | 4.28 | 1.80 | 8.5 | 5.1 |
13 | 3.88 | 1.40 | 9.3 | 5.2 |
14 | 3.86 | 1.40 | 8.7 | 5.2 |
15 | 4.30 | 1.60 | 8.3 | 5.4 |
16 | 3.87 | 1.40 | 9.1 | 5.1 |
17 | 3.86 | 1.40 | 9.0 | 5.1 |
18 | 4.31 | 1.50 | 8.5 | 5.2 |
19 | 4.27 | 1.90 | 8.8 | 5.1 |
20 | 4.28 | 1.80 | 9.3 | 5.2 |
Geochemical properties of gold mine tailings sediments.
The summary of the determined heavy metal concentrations within the sediments of the study area by using ICP-OES are presented in Table 8. The concentration of various heavy metal varies from 860.3–862.6 mg/kg for Cr; 324.9–328.4 mg/kg for Al; 200.9–203.4 mg/kg for As; 130.1–136.2 mg/kg for Fe; 121.9–125.8 mg/kg for Pb; 27.3–30.2 mg/kg for Co; 23.8–26.8 mg/kg for Ni; 7.2–9.2 mg/kg for Ti; 7.1–9.2 mg/kg for Cd; 4.0–5.6 mg/kg for Zn and 0.1–0.6 mg/kg for Cu. Chromium (Cr) was identified as the most abundant heavy metal in the sediment samples. Mean concentration of the metals were Cr: 861.5 mg/kg; Al: 326.8 mg/kg; As: 202.2 mg/kg; Fe: 134.3 mg/kg; Pb: 123.7 mg/kg; Co: 28.8 mg/kg; Ni: 25.4 mg/kg; Ti: 8.5 mg/kg; Cd: 8.3 mg/kg; Zn: 4.5 mg/kg and Cu: 0.2 mg/kg dry weights. The average order of metal concentration is Cr > Al > As > Fe > Pb > Co >Ni > Ti > Cd > Zn > Cu. The mineral composition of the sediments and mining activities that took place within this region may be attributed to the high element concentrations in the soil samples.
Station no. | Cr | Al | As | Fe | Pb | Co | Ni | Ti | Cd | Zn | Cu |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 862.6 | 327.4 | 201.7 | 134.1 | 125.6 | 28.4 | 26.1 | 9.0 | 9.2 | 4.7 | 0.6 |
2 | 860.4 | 327.9 | 203.4 | 136.2 | 122.9 | 30.2 | 25.3 | 8.3 | 8.8 | 4.0 | 0.1 |
3 | 861.3 | 328.0 | 202.9 | 133.7 | 123.1 | 29.5 | 26.4 | 9.2 | 8.1 | 4.1 | 0.2 |
4 | 862.4 | 328.4 | 202.4 | 130.1 | 124.7 | 28.8 | 24.7 | 8.1 | 7.9 | 3.9 | 0.2 |
5 | 862.1 | 326.5 | 202.1 | 132.5 | 121.9 | 29.6 | 23.8 | 8.7 | 7.2 | 5.6 | 0.3 |
6 | 861.5 | 325.7 | 201.7 | 134.9 | 122.1 | 29.3 | 25.1 | 7.9 | 8.3 | 4.3 | 0.1 |
7 | 860.6 | 324.9 | 203.0 | 135.3 | 123.5 | 28.7 | 25.7 | 8.5 | 7.5 | 5.2 | 0.1 |
8 | 861.1 | 328.1 | 201.9 | 135.1 | 123.2 | 29.2 | 26.3 | 9.0 | 7.9 | 4.9 | 0.3 |
9 | 860.7 | 327.9 | 202.6 | 135.9 | 124.1 | 27.5 | 26.8 | 9.2 | 9.0 | 4.2 | 0.1 |
10 | 860.3 | 326.3 | 202.1 | 132.7 | 124.8 | 29.1 | 25.2 | 8.1 | 8.5 | 5.1 | 0.1 |
11 | 860.6 | 325.4 | 201.7 | 136.0 | 122.3 | 27.3 | 25.7 | 8.3 | 7.1 | 5.3 | 0.1 |
12 | 861.0 | 326.7 | 200.9 | 131.8 | 122.5 | 27.9 | 23.9 | 8.7 | 8.7 | 5.0 | 0.2 |
13 | 862.1 | 326.1 | 201.2 | 135.9 | 124.9 | 28.7 | 24.3 | 7.6 | 8.3 | 5.5 | 0.6 |
14 | 860.5 | 327.9 | 201.4 | 134.1 | 123.1 | 28.3 | 26.0 | 9.1 | 9.1 | 4.5 | 0.1 |
15 | 862.5 | 328.2 | 203.0 | 133.7 | 122.7 | 28.0 | 25.8 | 9.2 | 8.3 | 5.0 | 0.2 |
16 | 862.3 | 326.3 | 202.6 | 134.9 | 125.8 | 29.1 | 24.6 | 8.3 | 8.5 | 5.3 | 0.3 |
17 | 862.4 | 325.9 | 201.5 | 133.5 | 123.7 | 29.5 | 24.2 | 8.0 | 8.1 | 4.1 | 0.1 |
18 | 861.9 | 327.4 | 202.3 | 134.2 | 125.1 | 29.7 | 26.5 | 7.2 | 8.0 | 4.8 | 0.4 |
19 | 861.6 | 326.1 | 201.9 | 134.9 | 124.3 | 28.1 | 25.7 | 8.4 | 8.5 | 4.4 | 0.5 |
20 | 862.0 | 325.6 | 203.1 | 135.7 | 125.3 | 28.4 | 26.3 | 8.7 | 8.8 | 4.6 | 0.1 |
Mean | 861.5 | 326.8 | 202.2 | 134.3 | 123.7 | 28.8 | 25.4 | 8.5 | 8.3 | 4.5 | 0.2 |
Max | 862.6 | 328.4 | 203.4 | 136.2 | 125.8 | 30.2 | 26.8 | 9.2 | 9.2 | 5.6 | 0.6 |
Min | 860.3 | 324.9 | 200.9 | 130.1 | 121.9 | 27.3 | 23.8 | 7.2 | 7.1 | 3.9 | 0.1 |
Bn ISQG | 90 52.3 | 88,000 NA | 13 7.24 | 47,200 NA | 20 30.2 | 19 NA | 50 NA | 4600 NA | 0.3 0.7 | 95 124.0 | 45 18.7 |
Heavy metals concentration (mg/kg dry weight) in gold mine tailings sediments.
In comparison to the interim sediment quality guidelines (ISQG) proposed by the Canadian Council of Ministers of the Environment [38], the elemental pollution status of the tailings (soil) were assessed Table 8. The heavy metals from the studied tailings sediments except for Zn and Cu all exceeded the ISQG. This implies that the sediments are toxic and could result in the introduction of sediment contaminants into the aquatic food web through predation by organisms at higher trophic levels.
In trace amounts, Arsenic is one of the priority toxic metals due to its several deteriorating effects to both plants and animals. The level of identified arsenic in the sediment is worrisome. As a non-essential element, Arsenic is not required for the growth of living organisms, though recent discovery reports a bacterium that replaces phosphorus with As for a number of cellular functions [39]. Plants often accumulate As by root uptake from soil or by absorption of airborne As deposited on their leaves [40]. Arsenate, a dominant specie of Arsenic in soils, based on its similarity to phosphate usually compete for the same uptake carriers in the root plasmalemma of most plants. In so doing interrupts with several metabolic processes that end up inhibiting plant growth and development through arsenic-induced phytotoxicity [41, 42]. Some of the toxicity symptoms may include inhibition of seed germination, decrease in plant height, depressed tillering, reduction in root growth and some necrosis, decrease in shoot growth, lower fruit and grain yield, reductions in chlorophyll and protein contents, and in photosynthetic capacity and even death [41, 42, 43, 44, 45, 46]. Due to migration and expansion of residential areas into former mining territories, the danger of human exposure to soil As has risen in the last two decades which have affected adversely the health of many [47]. Continued exposure to As results in several clinical manifestations such as melanosis (hyperpigmentation), keratosis, and leukomelanosis (hypopigmentation) of which cutaneous lesions are the highest reported [48, 49]. As is also a well-known carcinogen, causing skin, lung, bladder, liver, and kidney cancers [50, 51].
The average concentration of Copper (Cu) being 0.2 mg/kg was within the maximum acceptable concentration of 6.6 mg/kg for agricultural soil and safe limit of the Republic of South Africa [52].
As an important micronutrient, Cu is required for the growth of both plants and animals. In humans, it aids in the production of blood hemoglobin while plants utilize it in seed production, disease resistance, and regulation of water. In high levels, Cu could cause anemia, liver and kidney damage, as well as stomach and intestinal irritation [53]. Cu typically occurs in drinking water from Cu pipes, as well as from additives intended to control algal growth. The interaction of Cu with the environment is complex, however different studies revealed that most Cu introduced into the environment rapidly becomes, stable and results in a form which does not pose a danger to the environment [54, 55].
Zinc is an important metal due to its enzymatic and regulatory functions in biological systems. Being a readily mobile element, Zinc (Zn) when in high doses exhibit toxic and carcinogenic effects that could result in serious hematological and neurologic complications, liver and kidney disorders, hypertension, gastrointestinal misery, loose bowels, pancreatic harm and a host of other ailments in both humans and animals [56]. On the earth crust, Zinc is found in an average concentration of 80 mg/kg in association with ores of other metals such as Pb, Cu and Cd [57].
Chromium (Cr) has an average concentration of 100 mg/kg in the earth crust and the only known ore of commercial value is chromite (FeO.Cr2O4). Contamination by Cr could result in toxicity in plants depending on its state of valency since Cr (VI) due to its being highly mobile is toxic, while Cr (III) as less mobile is less toxic. The subsequent uptake, translocation, and accumulation of Cr by plants is dependent on its speciation. Cr in its trivalent (III) and hexavalent (VI) forms are known to be of biological importance. Generally, Cr poses the greatest threat to humans, animals and plants. Decreased seed germination, reduction of growth, decreased yield, inhibition of enzymatic activities, impairment of photosynthesis, nutrient and oxidative imbalances, and mutagenesis are some of the symptoms of Cr toxicity in plants [58]. In a previous study by López-Luna et al. [59], the toxicity of Cr (VI), Cr (III), and Cr tannery sludge were compared with respect to Cr mobility in soil and toxicity in wheat, oat, and sorghum plants and findings were that Cr(VI) was more mobile in soil and caused higher toxicity on those plant seedlings, while tannery sludge was the least toxic [60]. In humans, prolonged exposure results in kidney and liver disorders [61].
Lead (Pb) is the largely known immobile nonessential element among the heavy metals with most of its compounds being noxious in nature. Pb on the earth crust has an average concentration of 0.1 mg/kg. There is a gradual phase out of Pb from the materials regularly used by humans due to it being a metal toxicant. Mostly via food chain, Pb penetrates human or animal metabolism. The observed Pb content within the samples was very high and have been reported globally to be very harmful to humans and other animals as a long-term exposure could result in the bioaccumulation and biomagnification that end up in serious neurological health challenges. In plants, concentrations above 5 mg/kg of Pb causes severe growth retardation, discoloration, and morphological deformities. Pb accumulates in the body organs (i.e., brain), which may lead to poisoning (plumbism) or even death. The presence of lead often affects the gastrointestinal tracts, kidneys, and central nervous system. Infants exposed to lead are likely to suffer impaired development, lower IQ, shortened attention span, hyperactivity, and mental deterioration [62]. Adults usually suffer decreased reaction time, loss of memory, nausea, insomnia, anorexia, and weakness of the joints when exposed to Pb [63]. Lead performs no known essential function in the human body, it can merely do harm after uptake from food, air, or water.
Industrial waste materials, lime, fertilizer and sewage sludge constitute the major sources of nickel into soils [64]. Till date, nickel (Ni) remains a heavy metal of environmental concern as a result of decreased soil pH, due to reduced use of soil liming in agricultural soils and mobilization arising from increased acid rain in industrialized areas [65]. With decreasing pH, Ni exhibits increased solubility and mobility, thus, soil pH is the major factor controlling its solubility, mobility and sorption, while clay content, iron- manganese mineral and soil organic matter are of secondary importance [66]. Nickel (Ni) concentrations were observed to be high which could result in toxic effects to both plants and animals due to its ability to replace other metal ions in enzymes, proteins or bind to cellular compounds [65]. Nickel (Ni) is reported to interact with at least 13 essential elements namely calcium, chromium, cobalt, copper, iodine, iron, magnesium, manganese, molybdenum, phosphorus, potassium, sodium and zinc [67]. As a result, prolong exposure of humans to oxides and sulfides of nickel is linked with possible risk to lung and nasal tumors, skin allergies, nasal sinusitis, rhinitis and dermatitis [68]. Symptoms of nickel toxicity in plants besides inhibited growth include chlorosis, stunted root growth and brown interveinal necrosis [69].
Cadmium (Cd) is being discussed on a global platform as one of the most eco-toxic metals with a tendency of adversely affecting biological activities, plant metabolism, soil health and human health. The usage of Cadmium (Cd) is widely seen in Ni/Cd batteries, as rechargeable or secondary power sources exhibiting high output, long life, low maintenance, and high tolerance to physical and electrical stress. Observed levels of Cadmium was high and of great concern because it is very biopersistent and, once absorbed by an organism, remains resident for many years. In humans, Cadmium is known to affect several enzymes. Previous research revealed that renal damage that results in proteinuria is the consequence of Cd adversely affecting enzymes responsible for reabsorption of proteins in kidney tubules [70]. A prolong exposure to this metal even at very low concentration also reduces the activity of delta-aminolevulinic acid synthetase, arylsulfatase, alcohol dehydrogenase, and lipoamide dehydrogenase, which often cause anemia, cardiovascular disorders and hypertension whereas it enhances the activity of delta-aminolevulinic acid dehydratase, pyruvate dehydrogenase, and pyruvate decarboxylase [71].
The assessment of the overall contamination of the studied area was based on the contamination factor Table 9. The average contamination factor for single metal from this study revealed the sediments as slightly contaminated with Ni and Zn, moderately contaminated with Co and highly contaminated with Cr, As, Pb and Cd. The highest average contamination factor value was that of Cd (27.63). Overall, the degree of contamination values of the sediments from the study site indicate very high contamination.
Station no. | Contamination factor of single metal | Degree of contamination | |||||||
---|---|---|---|---|---|---|---|---|---|
Cr | As | Pb | Co | Ni | Cd | Zn | |||
1 | 9.58 | 15.52 | 6.28 | 1.49 | 0.52 | 30.67 | 0.05 | 64.11 | Very high |
2 | 9.56 | 15.65 | 6.15 | 1.59 | 0.51 | 29.33 | 0.04 | 62.83 | Very high |
3 | 9.57 | 15.61 | 6.16 | 1.55 | 0.53 | 27.00 | 0.04 | 60.46 | Very high |
4 | 9.58 | 15.57 | 6.24 | 1.52 | 0.49 | 26.33 | 0.04 | 59.77 | Very high |
5 | 9.58 | 15.55 | 6.10 | 1.56 | 0.48 | 24.00 | 0.06 | 57.33 | Very high |
6 | 9.57 | 15.52 | 6.11 | 1.54 | 0.50 | 27.67 | 0.05 | 60.96 | Very high |
7 | 9.56 | 15.62 | 6.18 | 1.51 | 0.51 | 25.00 | 0.05 | 58.43 | Very high |
8 | 9.57 | 15.53 | 6.16 | 1.54 | 0.53 | 26.33 | 0.05 | 59.71 | Very high |
9 | 9.56 | 15.58 | 6.21 | 1.45 | 0.54 | 30.00 | 0.04 | 63.38 | Very high |
10 | 9.56 | 15.55 | 6.24 | 1.53 | 0.50 | 28.33 | 0.05 | 61.76 | Very high |
11 | 9.56 | 15.52 | 6.12 | 1.44 | 0.51 | 23.67 | 0.06 | 56.88 | Very high |
12 | 9.57 | 15.45 | 6.13 | 1.47 | 0.48 | 29.00 | 0.05 | 62.15 | Very high |
13 | 9.58 | 15.48 | 6.25 | 1.51 | 0.49 | 27.67 | 0.06 | 61.04 | Very high |
14 | 9.56 | 15.49 | 6.16 | 1.49 | 0.52 | 30.33 | 0.05 | 63.60 | Very high |
15 | 9.58 | 15.62 | 6.14 | 1.47 | 0.52 | 27.67 | 0.05 | 61.05 | Very high |
16 | 9.58 | 15.58 | 6.29 | 1.53 | 0.49 | 28.33 | 0.06 | 61.86 | Very high |
17 | 9.58 | 15.50 | 6.19 | 1.55 | 0.48 | 27.00 | 0.04 | 60.34 | Very high |
18 | 9.58 | 15.56 | 6.26 | 1.56 | 0.53 | 26.67 | 0.05 | 60.21 | Very high |
19 | 9.57 | 15.53 | 6.22 | 1.48 | 0.51 | 28.33 | 0.05 | 61.69 | Very high |
20 | 9.58 | 15.62 | 6.27 | 1.49 | 0.53 | 29.33 | 0.05 | 62.82 | Very high |
Average | 9.57 | 15.55 | 6.19 | 1.51 | 0.51 | 27.63 | 0.05 | 61.01 | Very high |
Contamination factor (CF) and degree of contamination at various sampling station at the Blesbokspruit abandoned gold mine tailings site.
The average index of geoaccumulation values and contamination levels from the various sampling points within the study area as shown on Table 10 reveals an uncontaminated status for Co (0.01), Ni (−1.09) and Zn (−3.39) respectively. However, Cr and Pb within the area showed a moderately contamination level with average Igeo values being 1.85 and 1.42 respectively. The site was however moderately to strongly contaminated with As (2.34) and Cd (2.91).
Station no. | Cr | As | Pb | Co | Ni | Cd | Zn | PLI | Description of PLI |
---|---|---|---|---|---|---|---|---|---|
1 | 1.85 | 2.34 | 1.43 | 0.00 | −1.05 | 3.02 | −3.51 | 2.72 | Polluted |
2 | 1.85 | 2.34 | 1.41 | 0.06 | −1.09 | 2.97 | −3.51 | 2.63 | Polluted |
3 | 1.85 | 2.34 | 1.41 | 0.04 | −1.05 | 2.89 | −3.51 | 2.61 | Polluted |
4 | 1.85 | 2.34 | 1.43 | 0.01 | −1.11 | 2.87 | −3.51 | 2.56 | Polluted |
5 | 1.85 | 2.34 | 1.40 | 0.04 | −1.14 | 2.77 | −3.22 | 2.67 | Polluted |
6 | 1.85 | 2.34 | 1.40 | 0.03 | −1.11 | 2.91 | −3.51 | 2.67 | Polluted |
7 | 1.85 | 2.34 | 1.42 | 0.01 | −1.08 | 2.81 | −3.22 | 2.64 | Polluted |
8 | 1.85 | 2.34 | 1.41 | 0.02 | −1.05 | 2.87 | −3.51 | 2.68 | Polluted |
9 | 1.85 | 2.34 | 1.42 | −0.04 | −1.02 | 3.00 | −3.51 | 2.63 | Polluted |
10 | 1.85 | 2.34 | 1.43 | 0.02 | −1.08 | 2.94 | −3.22 | 2.68 | Polluted |
11 | 1.85 | 2.34 | 1.41 | −0.04 | −1.08 | 2.76 | −3.22 | 2.66 | Polluted |
12 | 1.85 | 2.33 | 1.41 | −0.02 | −1.14 | 2.96 | −3.22 | 2.65 | Polluted |
13 | 1.85 | 2.34 | 1.43 | 0.01 | −1.14 | 2.91 | −3.22 | 2.73 | Polluted |
14 | 1.85 | 2.34 | 1.41 | −0.01 | −1.05 | 3.01 | −3.51 | 2.71 | Polluted |
15 | 1.85 | 2.34 | 1.41 | −0.02 | −1.08 | 2.91 | −3.22 | 2.67 | Polluted |
16 | 1.85 | 2.34 | 1.43 | 0.02 | −1.11 | 2.94 | −3.22 | 2.75 | Polluted |
17 | 1.85 | 2.34 | 1.42 | 0.04 | −1.14 | 2.89 | −3.51 | 2.57 | Polluted |
18 | 1.85 | 2.34 | 1.43 | 0.04 | −1.05 | 2.88 | −3.51 | 2.69 | Polluted |
19 | 1.85 | 2.34 | 1.42 | −0.01 | −1.08 | 2.94 | −3.51 | 2.68 | Polluted |
20 | 1.85 | 2.34 | 1.43 | 0.00 | −1.05 | 2.97 | −3.51 | 2.71 | Polluted |
Geoaccumulation index (Igeo) and pollution load index (PLI) at various sampling station at the Blesbokspruit abandoned gold mine tailings site.
As indicated in Table 10, Pollution load index (PLI) ranged from 2.56–2.75, with mean value 2.67. PLI values of the different stations are above 1 which strongly indicate that the sediments are all polluted by heavy metals, an indication of deterioration of the study site.
It is evident from the present study that the abandoned gold mine tailings site is not polluted with Zn and Cu, but heavily polluted with Cd, Cr and Pb when evaluated by comparison with the sediment quality guideline proposed by USEPA.
The successful assessment of trace metal contamination of the abandoned gold mine tailings at Blesbokspruit-Ekurhuleni was done using indices such as geoaccumulation index, contamination factor, and degree of contamination and pollution load index. The sediment was mostly dominated by fine sand and silt/clay. Based on sediment quality guidelines proposed by the USEPA, the contamination of the sediment by Zn and Cu was negligible while Cd, Cr and Pb were detected at high concentrations. The evaluated pollution load index indicated that the sediments in the tailings dump are polluted while the geoaccumulation index revealed that Cr, Pb, and As contaminated the site, thus indicating very high degrees of contamination of the sediments at the mine dump. The high metal contaminants could be attributed to anthropogenic activities from previous extensive gold mining activities that took place within the area. Considering agricultural activities and human dwellers within the surrounding areas of the mine tailings, there are high tendencies of deleterious impacts. As a further precaution, this study strongly supports the call for analysis of the stream and drinking water quality, including the staple crops that are cultivated within the vicinity of the dump site, to ascertain the levels of heavy metals within such crops. Stringent mitigation plans or conversion of the tailings into value-added products should be considered.
We thank the Mineral Processing and Technology Research Centre of the University of Johannesburg for providing the resources used in conducting this study.
The authors declare no conflict of interest.
You have been successfully unsubscribed.
",metaTitle:"Unsubscribe Successful",metaDescription:"You have been successfully unsubscribed.",metaKeywords:null,canonicalURL:"/page/unsubscribe-successful",contentRaw:'[{"type":"htmlEditorComponent","content":""}]'},components:[{type:"htmlEditorComponent",content:""}]},successStories:{items:[]},authorsAndEditors:{filterParams:{sort:"featured,name"},profiles:[{id:"6700",title:"Dr.",name:"Abbass A.",middleName:null,surname:"Hashim",slug:"abbass-a.-hashim",fullName:"Abbass A. Hashim",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/6700/images/1864_n.jpg",biography:"Currently I am carrying out research in several areas of interest, mainly covering work on chemical and bio-sensors, semiconductor thin film device fabrication and characterisation.\nAt the moment I have very strong interest in radiation environmental pollution and bacteriology treatment. The teams of researchers are working very hard to bring novel results in this field. I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). I am a Reviewer for several refereed journals and international conferences, such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Industrial Electronics, Optic Letters, Measurement Science Review, and also a member of the International Advisory Committee for 2012 IEEE Business Engineering and Industrial Applications and 2012 IEEE Symposium on Business, Engineering and Industrial Applications.",institutionString:null,institution:{name:"Joseph Fourier University",country:{name:"France"}}},{id:"55578",title:"Dr.",name:"Antonio",middleName:null,surname:"Jurado-Navas",slug:"antonio-jurado-navas",fullName:"Antonio Jurado-Navas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/55578/images/4574_n.png",biography:"Antonio Jurado-Navas received the M.S. degree (2002) and the Ph.D. degree (2009) in Telecommunication Engineering, both from the University of Málaga (Spain). He first worked as a consultant at Vodafone-Spain. From 2004 to 2011, he was a Research Assistant with the Communications Engineering Department at the University of Málaga. In 2011, he became an Assistant Professor in the same department. From 2012 to 2015, he was with Ericsson Spain, where he was working on geo-location\ntools for third generation mobile networks. Since 2015, he is a Marie-Curie fellow at the Denmark Technical University. His current research interests include the areas of mobile communication systems and channel modeling in addition to atmospheric optical communications, adaptive optics and statistics",institutionString:null,institution:{name:"University of Malaga",country:{name:"Spain"}}}],filtersByRegion:[{group:"region",caption:"North America",value:1,count:5681},{group:"region",caption:"Middle and South America",value:2,count:5161},{group:"region",caption:"Africa",value:3,count:1683},{group:"region",caption:"Asia",value:4,count:10200},{group:"region",caption:"Australia and Oceania",value:5,count:886},{group:"region",caption:"Europe",value:6,count:15610}],offset:12,limit:12,total:117095},chapterEmbeded:{data:{}},editorApplication:{success:null,errors:{}},ofsBooks:{filterParams:{sort:"dateendthirdsteppublish"},books:[],filtersByTopic:[{group:"topic",caption:"Agricultural and Biological Sciences",value:5,count:9},{group:"topic",caption:"Biochemistry, Genetics and Molecular Biology",value:6,count:17},{group:"topic",caption:"Business, Management and Economics",value:7,count:2},{group:"topic",caption:"Chemistry",value:8,count:7},{group:"topic",caption:"Computer and Information Science",value:9,count:10},{group:"topic",caption:"Earth and Planetary Sciences",value:10,count:5},{group:"topic",caption:"Engineering",value:11,count:15},{group:"topic",caption:"Environmental Sciences",value:12,count:2},{group:"topic",caption:"Immunology and Microbiology",value:13,count:5},{group:"topic",caption:"Materials Science",value:14,count:4},{group:"topic",caption:"Mathematics",value:15,count:1},{group:"topic",caption:"Medicine",value:16,count:60},{group:"topic",caption:"Nanotechnology and Nanomaterials",value:17,count:1},{group:"topic",caption:"Neuroscience",value:18,count:1},{group:"topic",caption:"Pharmacology, Toxicology and Pharmaceutical Science",value:19,count:6},{group:"topic",caption:"Physics",value:20,count:2},{group:"topic",caption:"Psychology",value:21,count:3},{group:"topic",caption:"Robotics",value:22,count:1},{group:"topic",caption:"Social Sciences",value:23,count:3},{group:"topic",caption:"Technology",value:24,count:1},{group:"topic",caption:"Veterinary Medicine and Science",value:25,count:2}],offset:0,limit:12,total:null},popularBooks:{featuredBooks:[{type:"book",id:"9343",title:"Trace Metals in the Environment",subtitle:"New Approaches and Recent Advances",isOpenForSubmission:!1,hash:"ae07e345bc2ce1ebbda9f70c5cd12141",slug:"trace-metals-in-the-environment-new-approaches-and-recent-advances",bookSignature:"Mario Alfonso Murillo-Tovar, Hugo Saldarriaga-Noreña and Agnieszka Saeid",coverURL:"https://cdn.intechopen.com/books/images_new/9343.jpg",editors:[{id:"255959",title:"Dr.",name:"Mario Alfonso",middleName:null,surname:"Murillo-Tovar",slug:"mario-alfonso-murillo-tovar",fullName:"Mario Alfonso Murillo-Tovar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7769",title:"Medical Isotopes",subtitle:null,isOpenForSubmission:!1,hash:"f8d3c5a6c9a42398e56b4e82264753f7",slug:"medical-isotopes",bookSignature:"Syed Ali Raza Naqvi and Muhammad Babar Imrani",coverURL:"https://cdn.intechopen.com/books/images_new/7769.jpg",editors:[{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9376",title:"Contemporary Developments and Perspectives in International Health Security",subtitle:"Volume 1",isOpenForSubmission:!1,hash:"b9a00b84cd04aae458fb1d6c65795601",slug:"contemporary-developments-and-perspectives-in-international-health-security-volume-1",bookSignature:"Stanislaw P. Stawicki, Michael S. Firstenberg, Sagar C. Galwankar, Ricardo Izurieta and Thomas Papadimos",coverURL:"https://cdn.intechopen.com/books/images_new/9376.jpg",editors:[{id:"181694",title:"Dr.",name:"Stanislaw P.",middleName:null,surname:"Stawicki",slug:"stanislaw-p.-stawicki",fullName:"Stanislaw P. Stawicki"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7831",title:"Sustainability in Urban Planning and Design",subtitle:null,isOpenForSubmission:!1,hash:"c924420492c8c2c9751e178d025f4066",slug:"sustainability-in-urban-planning-and-design",bookSignature:"Amjad Almusaed, Asaad Almssad and Linh Truong - Hong",coverURL:"https://cdn.intechopen.com/books/images_new/7831.jpg",editors:[{id:"110471",title:"Dr.",name:"Amjad",middleName:"Zaki",surname:"Almusaed",slug:"amjad-almusaed",fullName:"Amjad Almusaed"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9279",title:"Concepts, Applications and Emerging Opportunities in Industrial Engineering",subtitle:null,isOpenForSubmission:!1,hash:"9bfa87f9b627a5468b7c1e30b0eea07a",slug:"concepts-applications-and-emerging-opportunities-in-industrial-engineering",bookSignature:"Gary Moynihan",coverURL:"https://cdn.intechopen.com/books/images_new/9279.jpg",editors:[{id:"16974",title:"Dr.",name:"Gary",middleName:null,surname:"Moynihan",slug:"gary-moynihan",fullName:"Gary Moynihan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7807",title:"A Closer Look at Organizational Culture in Action",subtitle:null,isOpenForSubmission:!1,hash:"05c608b9271cc2bc711f4b28748b247b",slug:"a-closer-look-at-organizational-culture-in-action",bookSignature:"Süleyman Davut Göker",coverURL:"https://cdn.intechopen.com/books/images_new/7807.jpg",editors:[{id:"190035",title:"Associate Prof.",name:"Süleyman Davut",middleName:null,surname:"Göker",slug:"suleyman-davut-goker",fullName:"Süleyman Davut Göker"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7796",title:"Human 4.0",subtitle:"From Biology to Cybernetic",isOpenForSubmission:!1,hash:"5ac5c052d3a593d5c4f4df66d005e5af",slug:"human-4-0-from-biology-to-cybernetic",bookSignature:"Yves Rybarczyk",coverURL:"https://cdn.intechopen.com/books/images_new/7796.jpg",editors:[{id:"72920",title:"Prof.",name:"Yves",middleName:"Philippe",surname:"Rybarczyk",slug:"yves-rybarczyk",fullName:"Yves Rybarczyk"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9711",title:"Pests, Weeds and Diseases in Agricultural Crop and Animal Husbandry Production",subtitle:null,isOpenForSubmission:!1,hash:"12cf675f1e433135dd5bf5df7cec124f",slug:"pests-weeds-and-diseases-in-agricultural-crop-and-animal-husbandry-production",bookSignature:"Dimitrios Kontogiannatos, Anna Kourti and Kassio Ferreira Mendes",coverURL:"https://cdn.intechopen.com/books/images_new/9711.jpg",editors:[{id:"196691",title:"Dr.",name:"Dimitrios",middleName:null,surname:"Kontogiannatos",slug:"dimitrios-kontogiannatos",fullName:"Dimitrios Kontogiannatos"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"10178",title:"Environmental Emissions",subtitle:null,isOpenForSubmission:!1,hash:"febf21ec717bfe20ae25a9dab9b5d438",slug:"environmental-emissions",bookSignature:"Richard Viskup",coverURL:"https://cdn.intechopen.com/books/images_new/10178.jpg",editors:[{id:"103742",title:"Dr.",name:"Richard",middleName:null,surname:"Viskup",slug:"richard-viskup",fullName:"Richard Viskup"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8511",title:"Cyberspace",subtitle:null,isOpenForSubmission:!1,hash:"8c1cdeb133dbe6cc1151367061c1bba6",slug:"cyberspace",bookSignature:"Evon Abu-Taieh, Abdelkrim El Mouatasim and Issam H. Al Hadid",coverURL:"https://cdn.intechopen.com/books/images_new/8511.jpg",editors:[{id:"223522",title:"Dr.",name:"Evon",middleName:"M.O.",surname:"Abu-Taieh",slug:"evon-abu-taieh",fullName:"Evon Abu-Taieh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9534",title:"Banking and Finance",subtitle:null,isOpenForSubmission:!1,hash:"af14229738af402c3b595d7e124dce82",slug:"banking-and-finance",bookSignature:"Razali Haron, Maizaitulaidawati Md Husin and Michael Murg",coverURL:"https://cdn.intechopen.com/books/images_new/9534.jpg",editors:[{id:"206517",title:"Prof.",name:"Razali",middleName:null,surname:"Haron",slug:"razali-haron",fullName:"Razali Haron"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"2160",title:"MATLAB",subtitle:"A Fundamental Tool for Scientific Computing and Engineering Applications - Volume 1",isOpenForSubmission:!1,hash:"dd9c658341fbd264ed4f8d9e6aa8ca29",slug:"matlab-a-fundamental-tool-for-scientific-computing-and-engineering-applications-volume-1",bookSignature:"Vasilios N. Katsikis",coverURL:"https://cdn.intechopen.com/books/images_new/2160.jpg",editors:[{id:"12289",title:"Prof.",name:"Vasilios",middleName:"N.",surname:"Katsikis",slug:"vasilios-katsikis",fullName:"Vasilios Katsikis"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],offset:12,limit:12,total:5126},hotBookTopics:{hotBooks:[],offset:0,limit:12,total:null},publish:{},publishingProposal:{success:null,errors:{}},books:{featuredBooks:[{type:"book",id:"9208",title:"Welding",subtitle:"Modern Topics",isOpenForSubmission:!1,hash:"7d6be076ccf3a3f8bd2ca52d86d4506b",slug:"welding-modern-topics",bookSignature:"Sadek Crisóstomo Absi Alfaro, Wojciech Borek and Błażej Tomiczek",coverURL:"https://cdn.intechopen.com/books/images_new/9208.jpg",editors:[{id:"65292",title:"Prof.",name:"Sadek Crisostomo Absi",middleName:"C. Absi",surname:"Alfaro",slug:"sadek-crisostomo-absi-alfaro",fullName:"Sadek Crisostomo Absi Alfaro"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9139",title:"Topics in Primary Care Medicine",subtitle:null,isOpenForSubmission:!1,hash:"ea774a4d4c1179da92a782e0ae9cde92",slug:"topics-in-primary-care-medicine",bookSignature:"Thomas F. Heston",coverURL:"https://cdn.intechopen.com/books/images_new/9139.jpg",editors:[{id:"217926",title:"Dr.",name:"Thomas F.",middleName:null,surname:"Heston",slug:"thomas-f.-heston",fullName:"Thomas F. Heston"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8697",title:"Virtual Reality and Its Application in Education",subtitle:null,isOpenForSubmission:!1,hash:"ee01b5e387ba0062c6b0d1e9227bda05",slug:"virtual-reality-and-its-application-in-education",bookSignature:"Dragan Cvetković",coverURL:"https://cdn.intechopen.com/books/images_new/8697.jpg",editors:[{id:"101330",title:"Dr.",name:"Dragan",middleName:"Mladen",surname:"Cvetković",slug:"dragan-cvetkovic",fullName:"Dragan Cvetković"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9785",title:"Endometriosis",subtitle:null,isOpenForSubmission:!1,hash:"f457ca61f29cf7e8bc191732c50bb0ce",slug:"endometriosis",bookSignature:"Courtney Marsh",coverURL:"https://cdn.intechopen.com/books/images_new/9785.jpg",editors:[{id:"255491",title:"Dr.",name:"Courtney",middleName:null,surname:"Marsh",slug:"courtney-marsh",fullName:"Courtney Marsh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9343",title:"Trace Metals in the Environment",subtitle:"New Approaches and Recent Advances",isOpenForSubmission:!1,hash:"ae07e345bc2ce1ebbda9f70c5cd12141",slug:"trace-metals-in-the-environment-new-approaches-and-recent-advances",bookSignature:"Mario Alfonso Murillo-Tovar, Hugo Saldarriaga-Noreña and Agnieszka Saeid",coverURL:"https://cdn.intechopen.com/books/images_new/9343.jpg",editors:[{id:"255959",title:"Dr.",name:"Mario Alfonso",middleName:null,surname:"Murillo-Tovar",slug:"mario-alfonso-murillo-tovar",fullName:"Mario Alfonso Murillo-Tovar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8468",title:"Sheep Farming",subtitle:"An Approach to Feed, Growth and Sanity",isOpenForSubmission:!1,hash:"838f08594850bc04aa14ec873ed1b96f",slug:"sheep-farming-an-approach-to-feed-growth-and-sanity",bookSignature:"António Monteiro",coverURL:"https://cdn.intechopen.com/books/images_new/8468.jpg",editors:[{id:"190314",title:"Prof.",name:"António",middleName:"Cardoso",surname:"Monteiro",slug:"antonio-monteiro",fullName:"António Monteiro"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8816",title:"Financial Crises",subtitle:"A Selection of Readings",isOpenForSubmission:!1,hash:"6f2f49fb903656e4e54280c79fabd10c",slug:"financial-crises-a-selection-of-readings",bookSignature:"Stelios Markoulis",coverURL:"https://cdn.intechopen.com/books/images_new/8816.jpg",editors:[{id:"237863",title:"Dr.",name:"Stelios",middleName:null,surname:"Markoulis",slug:"stelios-markoulis",fullName:"Stelios Markoulis"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7831",title:"Sustainability in Urban Planning and Design",subtitle:null,isOpenForSubmission:!1,hash:"c924420492c8c2c9751e178d025f4066",slug:"sustainability-in-urban-planning-and-design",bookSignature:"Amjad Almusaed, Asaad Almssad and Linh Truong - Hong",coverURL:"https://cdn.intechopen.com/books/images_new/7831.jpg",editors:[{id:"110471",title:"Dr.",name:"Amjad",middleName:"Zaki",surname:"Almusaed",slug:"amjad-almusaed",fullName:"Amjad Almusaed"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9376",title:"Contemporary Developments and Perspectives in International Health Security",subtitle:"Volume 1",isOpenForSubmission:!1,hash:"b9a00b84cd04aae458fb1d6c65795601",slug:"contemporary-developments-and-perspectives-in-international-health-security-volume-1",bookSignature:"Stanislaw P. Stawicki, Michael S. Firstenberg, Sagar C. Galwankar, Ricardo Izurieta and Thomas Papadimos",coverURL:"https://cdn.intechopen.com/books/images_new/9376.jpg",editors:[{id:"181694",title:"Dr.",name:"Stanislaw P.",middleName:null,surname:"Stawicki",slug:"stanislaw-p.-stawicki",fullName:"Stanislaw P. Stawicki"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7769",title:"Medical Isotopes",subtitle:null,isOpenForSubmission:!1,hash:"f8d3c5a6c9a42398e56b4e82264753f7",slug:"medical-isotopes",bookSignature:"Syed Ali Raza Naqvi and Muhammad Babar Imrani",coverURL:"https://cdn.intechopen.com/books/images_new/7769.jpg",editors:[{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],latestBooks:[{type:"book",id:"8468",title:"Sheep Farming",subtitle:"An Approach to Feed, Growth and Sanity",isOpenForSubmission:!1,hash:"838f08594850bc04aa14ec873ed1b96f",slug:"sheep-farming-an-approach-to-feed-growth-and-sanity",bookSignature:"António Monteiro",coverURL:"https://cdn.intechopen.com/books/images_new/8468.jpg",editedByType:"Edited by",editors:[{id:"190314",title:"Prof.",name:"António",middleName:"Cardoso",surname:"Monteiro",slug:"antonio-monteiro",fullName:"António Monteiro"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9523",title:"Oral and Maxillofacial Surgery",subtitle:null,isOpenForSubmission:!1,hash:"5eb6ec2db961a6c8965d11180a58d5c1",slug:"oral-and-maxillofacial-surgery",bookSignature:"Gokul Sridharan",coverURL:"https://cdn.intechopen.com/books/images_new/9523.jpg",editedByType:"Edited by",editors:[{id:"82453",title:"Dr.",name:"Gokul",middleName:null,surname:"Sridharan",slug:"gokul-sridharan",fullName:"Gokul Sridharan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9785",title:"Endometriosis",subtitle:null,isOpenForSubmission:!1,hash:"f457ca61f29cf7e8bc191732c50bb0ce",slug:"endometriosis",bookSignature:"Courtney Marsh",coverURL:"https://cdn.intechopen.com/books/images_new/9785.jpg",editedByType:"Edited by",editors:[{id:"255491",title:"Dr.",name:"Courtney",middleName:null,surname:"Marsh",slug:"courtney-marsh",fullName:"Courtney Marsh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9018",title:"Some RNA Viruses",subtitle:null,isOpenForSubmission:!1,hash:"a5cae846dbe3692495fc4add2f60fd84",slug:"some-rna-viruses",bookSignature:"Yogendra Shah and Eltayb Abuelzein",coverURL:"https://cdn.intechopen.com/books/images_new/9018.jpg",editedByType:"Edited by",editors:[{id:"278914",title:"Ph.D.",name:"Yogendra",middleName:null,surname:"Shah",slug:"yogendra-shah",fullName:"Yogendra Shah"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8816",title:"Financial Crises",subtitle:"A Selection of Readings",isOpenForSubmission:!1,hash:"6f2f49fb903656e4e54280c79fabd10c",slug:"financial-crises-a-selection-of-readings",bookSignature:"Stelios Markoulis",coverURL:"https://cdn.intechopen.com/books/images_new/8816.jpg",editedByType:"Edited by",editors:[{id:"237863",title:"Dr.",name:"Stelios",middleName:null,surname:"Markoulis",slug:"stelios-markoulis",fullName:"Stelios Markoulis"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9585",title:"Advances in Complex Valvular Disease",subtitle:null,isOpenForSubmission:!1,hash:"ef64f11e211621ecfe69c46e60e7ca3d",slug:"advances-in-complex-valvular-disease",bookSignature:"Michael S. Firstenberg and Imran Khan",coverURL:"https://cdn.intechopen.com/books/images_new/9585.jpg",editedByType:"Edited by",editors:[{id:"64343",title:null,name:"Michael S.",middleName:"S",surname:"Firstenberg",slug:"michael-s.-firstenberg",fullName:"Michael S. Firstenberg"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10150",title:"Smart Manufacturing",subtitle:"When Artificial Intelligence Meets the Internet of Things",isOpenForSubmission:!1,hash:"87004a19de13702d042f8ff96d454698",slug:"smart-manufacturing-when-artificial-intelligence-meets-the-internet-of-things",bookSignature:"Tan Yen Kheng",coverURL:"https://cdn.intechopen.com/books/images_new/10150.jpg",editedByType:"Edited by",editors:[{id:"78857",title:"Dr.",name:"Tan Yen",middleName:null,surname:"Kheng",slug:"tan-yen-kheng",fullName:"Tan Yen Kheng"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9386",title:"Direct Numerical Simulations",subtitle:"An Introduction and Applications",isOpenForSubmission:!1,hash:"158a3a0fdba295d21ff23326f5a072d5",slug:"direct-numerical-simulations-an-introduction-and-applications",bookSignature:"Srinivasa Rao",coverURL:"https://cdn.intechopen.com/books/images_new/9386.jpg",editedByType:"Edited by",editors:[{id:"6897",title:"Dr.",name:"Srinivasa",middleName:"P",surname:"Rao",slug:"srinivasa-rao",fullName:"Srinivasa Rao"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9139",title:"Topics in Primary Care Medicine",subtitle:null,isOpenForSubmission:!1,hash:"ea774a4d4c1179da92a782e0ae9cde92",slug:"topics-in-primary-care-medicine",bookSignature:"Thomas F. Heston",coverURL:"https://cdn.intechopen.com/books/images_new/9139.jpg",editedByType:"Edited by",editors:[{id:"217926",title:"Dr.",name:"Thomas F.",middleName:null,surname:"Heston",slug:"thomas-f.-heston",fullName:"Thomas F. Heston"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9208",title:"Welding",subtitle:"Modern Topics",isOpenForSubmission:!1,hash:"7d6be076ccf3a3f8bd2ca52d86d4506b",slug:"welding-modern-topics",bookSignature:"Sadek Crisóstomo Absi Alfaro, Wojciech Borek and Błażej Tomiczek",coverURL:"https://cdn.intechopen.com/books/images_new/9208.jpg",editedByType:"Edited by",editors:[{id:"65292",title:"Prof.",name:"Sadek Crisostomo Absi",middleName:"C. Absi",surname:"Alfaro",slug:"sadek-crisostomo-absi-alfaro",fullName:"Sadek Crisostomo Absi Alfaro"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},subject:{topic:{id:"515",title:"Computer Gaming",slug:"computer-gaming",parent:{title:"Artificial Intelligence",slug:"computer-and-information-science-artificial-intelligence"},numberOfBooks:2,numberOfAuthorsAndEditors:1,numberOfWosCitations:64,numberOfCrossrefCitations:41,numberOfDimensionsCitations:102,videoUrl:null,fallbackUrl:null,description:null},booksByTopicFilter:{topicSlug:"computer-gaming",sort:"-publishedDate",limit:12,offset:0},booksByTopicCollection:[{type:"book",id:"3751",title:"Machine Learning",subtitle:null,isOpenForSubmission:!1,hash:"5094182fa13e485c45ab489be59beed4",slug:"machine-learning",bookSignature:"Yagang Zhang",coverURL:"https://cdn.intechopen.com/books/images_new/3751.jpg",editedByType:"Edited by",editors:[{id:"2987",title:"Dr.",name:"Yagang",middleName:null,surname:"Zhang",slug:"yagang-zhang",fullName:"Yagang Zhang"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3752",title:"New Advances in Machine Learning",subtitle:null,isOpenForSubmission:!1,hash:"5b5624fb61f120a1a6dbdecc4cc48dfb",slug:"new-advances-in-machine-learning",bookSignature:"Yagang Zhang",coverURL:"https://cdn.intechopen.com/books/images_new/3752.jpg",editedByType:"Edited by",editors:[{id:"2987",title:"Dr.",name:"Yagang",middleName:null,surname:"Zhang",slug:"yagang-zhang",fullName:"Yagang Zhang"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:2,mostCitedChapters:[{id:"10694",doi:"10.5772/9385",title:"Types of Machine Learning Algorithms",slug:"types-of-machine-learning-algorithms",totalDownloads:9622,totalCrossrefCites:12,totalDimensionsCites:41,book:{slug:"new-advances-in-machine-learning",title:"New Advances in Machine Learning",fullTitle:"New Advances in Machine Learning"},signatures:"Taiwo Oladipupo Ayodele",authors:null},{id:"10448",doi:"10.5772/9157",title:"Adaptive Basis Function Construction: An Approach for Adaptive Building of Sparse Polynomial Regression Models",slug:"adaptive-basis-function-construction-an-approach-for-adaptive-building-of-sparse-polynomial-regressi",totalDownloads:1601,totalCrossrefCites:12,totalDimensionsCites:15,book:{slug:"machine-learning",title:"Machine Learning",fullTitle:"Machine Learning"},signatures:"Gints Jekabsons",authors:null},{id:"10698",doi:"10.5772/9389",title:"Ant Colony Optimization",slug:"ant-colony-optimization",totalDownloads:2153,totalCrossrefCites:1,totalDimensionsCites:7,book:{slug:"new-advances-in-machine-learning",title:"New Advances in Machine Learning",fullTitle:"New Advances in Machine Learning"},signatures:"Benlian Xu, Jihong Zhu and Qinlan Chen",authors:null}],mostDownloadedChaptersLast30Days:[{id:"10432",title:"Automated Detection and Analysis of Particle Beams in Laser-Plasman Accelerator Simulations",slug:"automated-detection-and-analysis-of-particle-beams-in-laser-plasman-accelerator-simulations",totalDownloads:1841,totalCrossrefCites:0,totalDimensionsCites:1,book:{slug:"machine-learning",title:"Machine Learning",fullTitle:"Machine Learning"},signatures:"Daniela M. Ushizima, Cameron G. Geddes, Estelle Cormier-Michel, E.Wes Bethel, Janet Jacobsen, Prabhat, Oliver Rubel, Gunther Weber, Bernd Hamann, Peter Messmer and Hans Haggen",authors:null},{id:"10694",title:"Types of Machine Learning Algorithms",slug:"types-of-machine-learning-algorithms",totalDownloads:9622,totalCrossrefCites:12,totalDimensionsCites:41,book:{slug:"new-advances-in-machine-learning",title:"New Advances in Machine Learning",fullTitle:"New Advances in Machine Learning"},signatures:"Taiwo Oladipupo Ayodele",authors:null},{id:"10447",title:"Machine Learning: When and Where the Horses Went Astray?",slug:"machine-learning-when-and-where-the-horses-went-astray-",totalDownloads:1625,totalCrossrefCites:1,totalDimensionsCites:3,book:{slug:"machine-learning",title:"Machine Learning",fullTitle:"Machine Learning"},signatures:"Emanuel Diamant",authors:null},{id:"10690",title:"An Intelligent System for Container Image Recognition using ART2-based Self-Organizing Supervised Learning Algorithm",slug:"an-intelligent-system-for-container-image-recognition-using-art2-based-self-organizing-supervised-le",totalDownloads:1945,totalCrossrefCites:2,totalDimensionsCites:2,book:{slug:"new-advances-in-machine-learning",title:"New Advances in Machine Learning",fullTitle:"New Advances in Machine Learning"},signatures:"Kwang-Baek Kim, Sungshin Kim and Young Woon Woo",authors:null},{id:"10683",title:"Machine Learning Overview",slug:"machine-learning-overview",totalDownloads:3078,totalCrossrefCites:3,totalDimensionsCites:3,book:{slug:"new-advances-in-machine-learning",title:"New Advances in Machine Learning",fullTitle:"New Advances in Machine Learning"},signatures:"Taiwo Oladipupo Ayodele",authors:null},{id:"10703",title:"Introduction to Machine Learning",slug:"introduction-to-machine-learning",totalDownloads:2914,totalCrossrefCites:1,totalDimensionsCites:7,book:{slug:"new-advances-in-machine-learning",title:"New Advances in Machine Learning",fullTitle:"New Advances in Machine Learning"},signatures:"Taiwo Oladipupo Ayodele",authors:null},{id:"10684",title:"Knowledge Structures for Visualising Advanced Research and Trends",slug:"knowledge-structures-for-visualising-advanced-research-and-trends",totalDownloads:1569,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"new-advances-in-machine-learning",title:"New Advances in Machine Learning",fullTitle:"New Advances in Machine Learning"},signatures:"Maria R. Lee and Tsung Teng Chen",authors:null},{id:"10428",title:"SOMs for Machine Learning",slug:"soms-for-machine-learning",totalDownloads:1451,totalCrossrefCites:1,totalDimensionsCites:0,book:{slug:"machine-learning",title:"Machine Learning",fullTitle:"Machine Learning"},signatures:"Iren Valova, Derek Beaton and Daniel MacLean",authors:null},{id:"10446",title:"Relational Analysis for Clustering Consensus",slug:"relational-analysis-for-clustering-consensus",totalDownloads:1558,totalCrossrefCites:1,totalDimensionsCites:1,book:{slug:"machine-learning",title:"Machine Learning",fullTitle:"Machine Learning"},signatures:"Mustapha Lebbah, Younes Bennani, Nistor Grozavu and Hamid Benhadda",authors:null},{id:"10687",title:"Methods for Pattern Classification",slug:"methods-for-pattern-classification",totalDownloads:1710,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"new-advances-in-machine-learning",title:"New Advances in Machine Learning",fullTitle:"New Advances in Machine Learning"},signatures:"Yizhang Guan",authors:null}],onlineFirstChaptersFilter:{topicSlug:"computer-gaming",limit:3,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[{type:"book",id:"10176",title:"Microgrids and Local Energy Systems",subtitle:null,isOpenForSubmission:!0,hash:"c32b4a5351a88f263074b0d0ca813a9c",slug:null,bookSignature:"Prof. Nick Jenkins",coverURL:"https://cdn.intechopen.com/books/images_new/10176.jpg",editedByType:null,editors:[{id:"55219",title:"Prof.",name:"Nick",middleName:null,surname:"Jenkins",slug:"nick-jenkins",fullName:"Nick Jenkins"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],offset:8,limit:8,total:1},route:{name:"profile.detail",path:"/profiles/17823/filipe-tavares",hash:"",query:{},params:{id:"17823",slug:"filipe-tavares"},fullPath:"/profiles/17823/filipe-tavares",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)}()