DNA methyltransferase inhibitors and their analogues [74].
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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He also works as a Honorary Senior Research Fellow at Birmingham University, UK, Lecturer at the Postgraduate European Institute, and has worked as Senior Manager in Accenture (2013-2014). He obtained his European PhD with a maximum distinction. He is a holder of the Runner Prize for Management Science and Engineering Management Nominated Prize (2020), Advancement Prize (2018), First International Business Ideas Competition 2017 Award (2017), Runner (2015), Advancement (2013) and Silver (2012) by the International Society of Management Science and Engineering Management (ICMSEM), and Best Paper Award in the international journal of Renewable Energy (Impact Factor 3.5) (2015). He has published more than 150 papers (65 % ISI, 30% JCR, and 92% internationals), some recognized as follows: “Applied Energy” (Q1, as “Best Paper 2020”), “Renewable Energy” (Q1, as “Best Paper 2014”), “ICMSEM” (as “excellent”), “International Journal of Automation and Computing” and “IMechE Part F: Journal of Rail and Rapid Transit” (most downloaded), etc. He is an author and editor of 25 books (Elsevier, Springer, Pearson, Mc-GrawHill, IntechOpen, IGI, Marcombo, AlfaOmega, etc.), and 5 patents. He is also an Editor of 5 International Journals and Committee Member of more than 40 International Conferences. He has been a Principal Investigator in 4 European Projects, 6 National Projects, and more than 150 projects for universities, companies, etc. He is an European Union expert in AI4People (EISMD) and ESF. He is Director of www.ingeniumgroup.eu. His main interest are: artificial intelligence, maintenance, management, renewable energy, transport, advanced analytics, and data science.",institutionString:"University of Castile-La Mancha",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"10",totalChapterViews:"0",totalEditedBooks:"10",institution:{name:"University of Castile-La Mancha",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:{id:"155714",title:"Dr.",name:"Noor",middleName:null,surname:"Zaman",slug:"noor-zaman",fullName:"Noor Zaman",profilePictureURL:"https://mts.intechopen.com/storage/users/155714/images/5120_n.png",biography:"Dr. Noor Zaman acquired his degree in Engineering in 1998, and Master’s in Computer Science at the University of Agriculture at Faisalabad in 2000. His academic achievements further extended with Ph.D. in Information Technology at University Technology Petronas (UTP) Malaysia. He has vast experience of 16 years in the field of teaching and research. He is currently working as an Assistant Professor at the College of Computer Science and Information Technology, King Faisal University, in Saudi Arabia since 2008. He has contributed well in King Faisal University for achieving ABET Accreditation, by working as a member and Secretary for Accreditation and Quality cell for more than 08 years. He takes care of versatile operations including teaching, research activities, leading ERP projects, IT consultancy and IT management. He headed the department of IT, and administered the Prometric center in the Institute of Business and Technology (BIZTEK), in Karachi Pakistan. He has worked as a consultant for Network and Server Management remotely in Apex Canada USA base Software house and call center.\n\nDr. Noor Zaman has authored several research papers in indexed journals\\\\international conferences, and edited six international reputed Computer Science area books, has many publications to his credit. He is an associate Editor, Regional Editor and reviewer for reputed international journals and conferences around the world. He has completed several international research grants\\\\funded projects and currently involved in different courtiers. His areas of interest include Wireless Sensor Network (WSN), Internet of Things IoT, Mobile Application Programming, Ad hoc Networks, Cloud Computing, Big Data, Mobile Computing, and Software Engineering.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"King Faisal University",institutionURL:null,country:{name:"Saudi Arabia"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"759",title:"System Modeling and Analysis",slug:"system-modeling-and-analysis"}],chapters:[{id:"40445",title:"Maintenance Management Based on Signal Processing",slug:"maintenance-management-based-on-signal-processing",totalDownloads:2773,totalCrossrefCites:0,authors:[{id:"22844",title:"Prof.",name:"Fausto Pedro",surname:"García Márquez",slug:"fausto-pedro-garcia-marquez",fullName:"Fausto Pedro García Márquez"},{id:"155699",title:"Dr.",name:"Raul",surname:"Ruiz De La 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This requires extensive analysis of developing trends in scientific research in order to offer our readers relevant content. Creating the book catalogue is also based on keeping track of the most read, downloaded and highly cited chapters and books and relaunching similar topics. I am also responsible for consulting with our Scientific Advisors on which book topics to add to our catalogue and sending possible book proposal topics to them for evaluation. Once the catalogue is complete, I contact leading researchers in their respective fields and ask them to become possible Academic Editors for each book project. Once an editor is appointed, I prepare all necessary information required for them to begin their work, as well as guide them through the editorship process. I also assist editors in inviting suitable authors to contribute to a specific book project and each year, I identify and invite exceptional editors to join IntechOpen as Scientific Advisors. I am responsible for developing and maintaining strong relationships with all collaborators to ensure an effective and efficient publishing process and support other departments in developing and maintaining such relationships."}},relatedBooks:[{type:"book",id:"120",title:"Digital Filters",subtitle:null,isOpenForSubmission:!1,hash:"10692f498575728ddac136b0b327a83d",slug:"digital-filters",bookSignature:"Fausto Pedro García Márquez",coverURL:"https://cdn.intechopen.com/books/images_new/120.jpg",editedByType:"Edited by",editors:[{id:"22844",title:"Prof.",name:"Fausto Pedro",surname:"García Márquez",slug:"fausto-pedro-garcia-marquez",fullName:"Fausto Pedro García Márquez"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5223",title:"Non-Destructive Testing",subtitle:null,isOpenForSubmission:!1,hash:"1cd0602adf345e3f19f63dfbf81651d0",slug:"non-destructive-testing",bookSignature:"Fausto Pedro Garcia Marquez, Mayorkinos Papaelias and Noor 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Lung cancer is one of the leading causes of death worldwide. The five-year survival rate is approximately 15% as the condition is often diagnosed at an advanced stage. Smoking is the underlying cause of about 90% of all lung cancers, and smokers constitute the major risk group for developing lung cancer. World Health Organisation (WHO) stratifies lung cancers into two histological groups: non-small cell lung cancer (NSCLC), accounting for 85% of the cases, and small cell lung cancer (SCLC), which constitutes the remaining 15%. The most valid hypothesis for the development of these cancers suggests that multi-phase genetic alterations and a series of epigenetic events result in lung cancer [1].
Owing to the technological advances of the twenty-first century, current practices offer personalised treatment alternatives by means of detailed diagnostics and a range of treatment approaches in lung cancer. While World Health Organisation classified lung cancers almost entirely under the category of adenomas until 1960s, the utility of advanced molecular tests has allowed convenient and thorough identification of subtypes. In addition with these developments, new molecular targets have emerged, leading to novel therapies [2]. During the last 20 years, studies on molecular mechanisms seeking to elucidate the development of cancers and the underlying mechanisms have shown the importance of epigenetic changes in these processes, leading to increasing interest and studies in this field.
Like all cancers, lung cancer develops with the deviation from a normal cell structure due to a number of problems that arise during cell cycle and differentiation. Deviations from normal state to tumour formation result from alterations in cell growth-signalling pathways and apoptosis mechanisms, including a series of epigenetic modifications, all of which are critical for the cell [3].
Epigenetic modifications usually occur in two forms: (1) acetylation, which occurs mainly in histone proteins at protein level and (2) methylation, which is often seen at DNA level.
Small cell lung cancer (SCLC) accounts for approximately 16–20% of all lung cancers. Owing to the rapidly spreading pattern, SCLC is accepted as an aggressive and widespread disease; therefore, chemotherapy (CT) remains an important modality for the treatment of SCLC.
Non-small cell lung cancer (NSCLC) is responsible for about 85% of lung cancers. Postoperative adjuvant treatment approaches have become the standard of care in early stage tumours, and various systemic treatments are utilised in a metastatic setting. Although the systemic treatment approach in NSCLC according to the subtype has not changed profoundly through the years, the selection of a systemic treatment in metastatic cases has recently begun to differ based on molecular alterations and different histological subtypes of NSCLC. In addition to molecular changes that provide predictions for targeted therapies, separating NSCLC into two groups, that is, squamous and non-squamous, has been suggested to aid in selecting a more effective chemotherapy agent [4, 5, 6].
Lung cancer can be histologically divided into two main groups as NSCLC and SCLC. Mutations may be seen in genes such as EGFR, RET, PIK3CA, ALK, HER2, KRAS, BRAF, MET, NRS, MEK1 and ROS1, which are often referred to as signalling pathways and considered as drivers since they cause cancer development in NSCLCs. These mutations can be seen in current smokers, ex-smokers and non-smokers, whereas mutations in EGFR, ALK, HER2, ROS1 and RET genes are seen only in people who have never smoked. Such mutations may be observed in all histological subgroups of NSCLCs including adenocarcinomas, squamous cell carcinomas (SCCs) and large cell carcinomas [7, 8, 9, 10].
Smoking causes chronic inflammatory stress on biological systems, thereby interfering with the cell cycle, cell development and differentiation. Long-term inflammation is associated with DNA methylation and contributes to lung cancer development via methylation mechanisms. For example, genes such as APC, FHIT, RASSF1A and CCND2 are inactivated only in smokers due to promoter hypermethylation. Increased promoter hypermethylation of P161NK4A, MGMT, RASSF1A, FHIT and MTHFR, depending on the intensity of smoking, shows a strong correlation with NSCLCs compared to non-smokers [11, 12]. Promoter methylation of RARβ, FHIT, P161NK4A and RASSF1A increases as smoking intensifies.
Moreover, mutations in epigenetic regulator genes create a complicated situation owing to the fact that they prevent these genes from functioning properly, which is expected to impact cell cycle and cell development, thereby resulting in the development of cancer. However, these mechanisms may also offer certain advantages in favour of treatment as they may open new ways for cancer therapies (such as DNTM inhibitors) [13].
Human genome, as that of any eukaryotic organism, is organised in a highly complicated manner. Except for the alterations in the genes effective in human development, the histone proteins that pack the genome serve to control the genome by undergoing various modifications. This is accomplished through various changes that occur during events such as DNA replication, repair and expression [14].
For instance, the amino terminal of the histone core is quite important as it contains a flexible and fairly simple tail domain, constituting a target region for several post-translational modifications. Histone modifications primarily occur in the form of addition of acetyl and methyl groups to lysine amino acids, addition of phosphorus to serine amino acids and methyl groups to arginines. These modifications play a critical role in the control of biological processes such as transcription [15].
Modifications in histone proteins develop by means of enzymatic pathways. Histone deacetylase inhibitors (HDACIs) also act as antagonists in cell differentiation by acetylating histones and non-histone proteins. Proteins in this group inhibit DNA repair, apoptosis and gene expression mechanisms. In addition, such proteins contain a zinc-binding group (ZBG) and a chain linking two proteins from these two groups referred to as the surface recognition polypeptide [16]. Therefore, molecules able to inhibit such proteins may be potential anti-cancer agents. For example, peptide-containing cyclic hydroxamic acids (CHAPs) may be suitable for therapeutic use as a group of potential anti-cancer agents. The molecule CHAP31, which acts on HDACIs, has been shown to have a highly effective anti-tumour effect on certain types of cancer such as lung, breast and gastric cancer as well as melanoma in vivo [17].
Fibrosis is one of the important factors contributing to cancer invasion and metastasis. Fibrosis results from fibroblast activation, which degrades and alters the physical structure of extracellular matrix (ECM). The fibrosis-induced increase in ECM fragility leads to pathological conditions such as epithelial-mesenchymal transition (EMT) with the transformation of normal cells into cancer. The change in the structure of collagen, one of the most important proteins in tissue structure, is another contributing factor. For this reason, collagen receptors are of particular importance regarding the progression of cancer. The discoidin domain receptors (DDRs), DDR1 and DDR2, are overexpressed receptor proteins. DDRs mechanically increase the acetylation of c-Myb, the transcription factor of histone acetyltransferase (HAT) on rigid ECM, thereby leading to c-Myb binding to DDR2 promoter together with LEF1, and result in DDR2 upregulation in a rigid environment. Silenced c-Myb may cause DDR2 inhibition and invasion of lung cancer cells, and recovery of physical characteristics of the tissue has been shown when external interventions allow DDR2 expression [18].
Proteins in the Snail group, a zinc-binding superfamily of transcription factors, are responsible for cell migration and invasion during both the embryonic period and cancer process [19]. Snail proteins bind to the E-box sequence located in the promoter region of E-cadherin gene, which encodes the protein that is responsible for cell–cell adhesion. As a result, since E-cadherin synthesis is no longer possible, cell–cell adhesion cannot be achieved, and cancer cells gain metastatic properties [20]. Snail proteins are overexpressed in several types of cancer as they are strategically important for cancer cells. Rui et al. have emphasised that Snail, acetylated by P300, may be of value in terms of developing personalised treatments for lung cancer [21].
Furthermore, HDACs allow E-cadherin expression through non-coding RNAs. In addition, miRNAs serve to control EMT in lung cancer via TGF-β-, EGF- and HGF-signalling pathways. MiR200b and miR200c are effective on H3 acetylation in E-cadherin promoter (Figure 1) [22].
IGF and p53 pathways in lung cancer.
Epigenetic readers recognise modified histones by means of a group of polypeptides referred to as ‘reader’, and these polypeptides are involved both in normal cell growth and in cancer development by controlling several processes conducted together with chromatin [23].
Recently, Wenyi mi et al. identified the YEATS domain, defined as a novel acetyl-lysine-binding module. With regard to human cancers, the functional importance of proteins containing this domain remains unknown; however, the overexpression of the YEATS2 gene has been shown in non-small cell lung cancers. This domain is thought to decrease histone acetylation, thereby inactivating key genes [24, 25].
‘Lunasin’ is a soy protein consisting of 43 amino acids known to protect mammals against chemical carcinogens. In addition, it has been shown to be potentially beneficial in several conditions [26, 27]. A derivative of this protein originating from wheat has been reported to be effective even when taken orally and potentially effective in cancer prevention, regardless of the suppressed acetylation of core H3-H4 histone proteins [28].
As is the case in any intracellular event, the acetylation process occurs by means of enzymatic pathways. The removal of acetyl groups from histones is conducted by enzymes called histone deacetylases (HDACs). Cell viability continues normally as this is carried out in a balance of acetylation-deacetylation processes [29, 30]. Sometimes, hypoacetylation may occur when the process shifts towards deacetylation, and this is accompanied by cancer development. HDAC inhibitors are thought to possess the potential to reverse this process, leading to epigenetic reactivation of the suppressed anti-tumour genes [30]. This may help the suppression of certain tumours depending on which genes are expressed and which proteins are suitable.
Long Chen et al. believe that lysine acetyltransferase accelerates tumour formation due to the acetylation of histones and non-histone proteins despite the anti-tumour effect provided by acetylation-related HDAC inhibitors, and suggest that acetylation may shift to both sides (acetylation-deacetylation) depending on which genes are active at the time [30]. They highlighted the necessity to elucidate the relationship between lysine acetyltransferases (KAT) or HDAC and other proteins such as transcription factors in order to enhance the specificity.
Human MOF (hMOF and MYST1) is a member of the histone acetyltransferase (HAT) protein family. These proteins convert histone H4 acetylation to H4K16Ac, an epigenetic marker of active genes, in particular by adding an acetyl group to the lysine-16 amino acid. Irregularities in these epigenetic markers affect cell biology, potentially leading to cancer development. In correlation with H4K16Ac, hMOF has been shown to be overexpressed in non-small cell lung cancers. Investigators have indicated that this group of proteins may have potential oncological tasks and this may be a potential therapeutic target [31].
Although the interferon regulatory factor 3 (IRF-3), an important transcription factor for interferon genes, is often functional in viral infections, the regulation mechanism of IRF-3 expression in cancers has not been fully understood. The concurrent use of histone deacetylase inhibitors and Trichostatin A (TSA) has been shown to increase IRF-3 expression in lung adenocarcinoma A549 cells by altering GATA-1 acetylation, and targeting IRF-3 is therefore thought to be a novel therapeutic approach [32].
In light of all this information, one may conclude that some genes or proteins contribute to carcinogenesis when they function towards acetylation, while others contribute to carcinogenesis when they function towards deacetylation [22].
In living organisms, all molecular structures and events are generated by specific sequences called genes that are found in the DNA molecule. However, these genes need to be governed and controlled so that they can participate in biological processes at the optimal time. Genes are actively controlled by specific genes and proteins referred to as transcription factors. However, there is a different mechanism that also determines gene expression, which can be transmitted from generation to generation and from cell to cell. This is called the epigenetic code [12]. DNA sequence does not undergo any changes during the formation of this code, but the relevant part of the DNA fragment becomes no longer meaningful. The most common epigenetic modifications are the changes in histone proteins and DNA methylation. The most widely studied and the most well-established epigenetic mechanism is DNA methylation. It is an enzymatic change where cytosines are converted to 5′-methylcytosine. The cytosine-end methylation seen in mammalian genome often occurs at the nucleotide pairs which are also called the CpG dinucleotides. Detection of these methylated gene segments on the genome is highly informative in terms of the effects of genes in several biological processes from carcinogenesis to ageing [33, 34].
Methylation-related changes may occur anywhere in the genetic material of a eukaryotic cell. In eukaryotic cells, genetic material is found in two organelles: the nucleus, which contains almost all of the genetic material, and mitochondrion, which has a very small genome compared to the nucleus. Certain methylations are specific to the genomes of these two organelles [1].
For several years since being discovered, the pattern of DNA hypomethylation in CpG dinucleotides has been shown to be highly important in cancer cells [35, 36].
A methylated gene becomes inactivated and therefore cannot synthesise the product, that is, the RNA or protein. Gene methylation occurs more commonly in promoter regions. Methylation in the promoters of tumour suppressor genes is mostly associated with carcinogenesis and often occurs in the form of hypermethylation [12].
Apart from global DNA hypomethylation reported in earlier stages, hypomethylation may also occur in the CpG islands in promoter regions of several specific genes or in the nucleotide pairs of genes that are activated by hypomethylation and silenced by hypermethylation. Methylation of a promoter region does not necessarily produce any protein or RNA [37]. The SHOX2 gene, a member of the Homeobox gene family, has been shown to be a specific biomarker with 60% sensitivity and 90% specificity when investigated by bisulphite modification-PCR in blood plasma samples during a case–control study of approximately 400 subjects with lung cancer. Also, in the same study, comparison against the results from another study conducted with bronchial aspiration samples revealed a higher level of sensitivity compared to results from blood plasma samples [38]. In bronchoalveolar lavage samples obtained from NSCLC patients, 24% methylation was observed in the promoter region of the CDKN2A gene, also known as P16INK4A, in addition to microsatellite instabilities and p53 mutations [39, 40].
The lungs are highly exposed to external factors owing to the nature of their functions. This constitutes a major risk factor in terms of epigenetic modifications as well as being associated with pulmonary diseases caused by environmental factors and smoking in particular. DNA methylation, histone modification and non-coding RNA have been shown to be increased in smokers [11].
Lung cancer develops upon the accumulation of numerous genetic and epigenetic alterations in the respiratory epithelium. Early promoter methylation and tumour suppressor gene inactivation are considered as signs of pulmonary carcinogenesis [12].
Defects in the apoptotic pathway are among the main reasons contributing to the high fatality of lung cancers. Apoptosis, also known as programmed cell death, has a wide range of physiological effects from embryonic stages to tumour formation. Inhibition of apoptosis is particularly detrimental in cancer treatments. The main reason of this is the fact that most treatments exert their effects by activating apoptotic mechanisms. Targeting the apoptotic pathway ensures the effectiveness of anti-cancer treatments [41, 42].
Survivin, one of the apoptosis inhibitor proteins, plays a critical role in cell division and in the continuation of cell survival [43, 44]. Since increased expression of survivin in human tumours leads to aggressive tumour development and resistance to main cancer treatments such as chemotherapy and radiotherapy, survivin gene and protein have been found to be important markers regarding the outcome of treatment [45, 46].
Computer analyses have shown a potential methylation region in exon 1 of the survivin gene; however, no methylation was found in lung cancer patients, and it has been shown that survivin gene expression in any cell may be effective not only with methylation but also through other transcription factors [47].
Apoptosis occurs during the normal development of multicellular organisms and continues throughout life. This mechanism is responsible for embryonic development and organ formation through cell differentiation. For example, toes are separated from one another by means of apoptosis.
Apoptosis also controls the immune system. T-lymphocytes are involved in the destruction of infected or damaged cells during cellular immunity. The T-lymphocytes produced in the thymus gland need to be active against foreign antigens before being released into bloodstream and should not show any activity against normal cells. Any inactive or semi-active T-cell is bound to be destroyed by apoptosis before they may begin their task.
Inhibitors of apoptosis proteins from the anti-apoptotic protein family have been identified in vertebrate and invertebrate species, and they are known to be negative regulators of programmed cell death. Some homologues identified in mammals include XIAP, cIAP1, cIAP2, NAIP, Bruce, Survivin and IAP. Most of these block cell death by directly binding to and inhibiting caspase-3, caspase-7 and caspase-9 [32].
Various diseases may arise in the event of any defect within the regulation of apoptosis. Among these, cancer is a condition characterised by little or no apoptosis. The mutations in cancer cells result in different cell-signalling and cell growth processes compared to normal cells. Under normal circumstances, when cells become damaged, they become apoptotic while cancer cells do not undergo apoptosis as a result of the cancerous mutations, which leads to uncontrolled cell differentiation and tumour formation. It is often difficult to eliminate such tumours with cell-damaging treatments such as chemotherapy and radiotherapy. In addition, some cancer cells develop resistance to treatments that target tumours with mutations in apoptotic pathways. Further understanding of the regulation of apoptosis in cancer cells is expected which allow developing novel therapies. While apoptosis is reduced in cancer, conditions with increased apoptosis lead to different problems. For example, neurodegenerative diseases such as Alzheimer’s and Parkinson may be the result of increased cell death [43, 44, 45, 46, 48].
In early studies, caspase-3 suppression was suggested to be directly responsible for the anti-apoptotic mechanism of action of survivin. However, three-dimensional structural studies have shown that BIR (baculovirus IAP repeat domain) of survivin is not long enough to block the enzymatically active region of caspase-3. Survivin is thought to directly bind to caspase-9 as three-dimensional studies have revealed the similarity between the BIR domain of survivin and BIR domain of another IAP, XIAP, which directly binds to and inhibits caspase-9 in vitro. Another relevant mechanism is the inactivation of a pro-apoptotic molecule called SMAC/Diablo released together with cytochrome-c during mitochondrial apoptosis. SMAC/Diablo binds to IAPs and prevents their caspase-suppressing effect. Theoretically, survivin binds to SMAC/Diablo. Survivin bound to SMAC/Diablo protects other IAPs from the inhibitory effect of this protein. Thus, caspase suppression continues, leading to apoptosis blockade. There is evidence indicating that survivin plays an important role in p53-associated apoptosis. p53 blocks survivin transcription both through direct and indirect pathways. Conversely, overexpression of survivin inhibits p53-dependent apoptosis.
Phosphorylation of threonine-34 residues is necessary for survivin to bind to caspase-9. This is conducted by a kinase called p34cdc2-cyclin B1. Survivin-caspase interaction is shown in Figure 2 [38].
Apoptotic pathways.
Survivin molecule is expressed in special tissues and cells during certain phases of cell cycle. In humans, survivin expression occurs in the heart, liver, gastrointestinal tract and other foetal tissues from embryonic development until the end of foetal period, and in stem cells, epithelial cells and pancreatic endocrine cells during adulthood. Increased survivin expression has been shown in solid tumour tissues of adults, for example, lung, breast, brain, stomach, oesophagus, pancreas, liver, uterus and ovarian tumours. Increased survivin expression has also been reported in certain tumour tissues such as neuroblastoma, colorectal cancer and gastric cancer and has been associated with poor prognosis in these patients [49, 50]. Some studies have indicated the promotion of tumour development via survivin overexpression. There are numerous studies showing that survivin plays an important role not only in cell division and inhibition of apoptosis but also in cancer development [50, 51].
The human survivin gene is located on the telomeric position of chromosome 17 with a size of 14.7 kb. Survivin gene consists of four exons and three introns and encodes the survivin protein of 16.5 kD. While other IAPs contain more than one BIR in their structure, the survivin gene contains only one BIR region at the N-terminal domain and also contains an alpha-helix structure at the C-terminal domain. Survivin protein interacts with caspase-7 and caspase-9 via the BIR region while its alpha-helix structure interacts with tubulin subunits during mitosis [52, 53].
Survivin expression is suppressed during the G1 and S phases of the cell cycle but increases during G2/M. This control mechanism mostly functions at transcription level, occurring through cell cycle-dependent elements (CDEs) and cell cycle homology regions (CHRs). The CDE/CHR suppressor protein binds to this region, thereby suppressing gene expression. The CHR region is located at the proximal end of the survivin gene promoter [52, 53].
Cell-culture studies have demonstrated the association between SurKex methylation region and histone acetylation in the promoter region of the survivin gene. These studies have also reported a decreased mRNA expression in the survivin gene with the methylation of survivin promoter [54].
The methylated survivin promoter region has also been shown to inhibit p53 binding, which may render p53 ineffective in cell cycle [55].
For this reason, the studies on survivin gene region and protein have been intensified, with a steady increase in the number of studies investigating the expression analysis of survivin as well as survivin promoter-related polymorphisms and mutations.
Mitochondrial functions provide several protein components synthesised from mitochondrial DNA (mtDNA) for the oxidative phosphorylation mechanism. In mammals, 12S and 16S rRNAs are encoded with 13 proteins from mtDNA. These genes are effective on cell homeostasis and apoptotic pathways [56].
Since mitochondria are dominant organelles regarding intracellular energy and because they have their own independent DNA genome, it is important to look at the genomic alterations of this organelle in certain diseases. These alterations are associated particularly with various pulmonary diseases and lung cancers [57, 58].
As mentioned earlier, mitochondrial methylation may also be affected in lung cells, which are considerably exposed to environmental effects. In their study dated 2013, Byun et al. have shown that mitochondrial RNA may undergo methylation [59].
Several treatment modalities are utilised in different subtypes and stages of lung cancer. Surgery is the first-choice treatment if tumour margins are well defined while adjuvant or neoadjuvant chemotherapy and hormone therapies are also applied with or after surgery. Treatment often continues in the form of chemotherapy, with practices that differ from country to country. The major chemotherapeutic agents approved for lung cancer include cyclophosphamide, doxorubicin, vincristine, cisplatin and mitomycin-C [60, 61, 62]. Almost all of these agents share the common feature that they induce apoptosis in the cell by triggering DNA damage in various ways.
Cyclophosphamide (Cytoxan, Neosar) is an immunosuppressant used for the treatment of lung cancer, and its metabolite, phosphoramide mustard, is the molecular structure form which exerts the actual effect. This agent alters DNA structure by forming irreversible cross-links between N-7 atoms of the guanine base in the DNA strand. This altered DNA structure stimulates intracellular apoptotic pathways, allowing the cell to undergo apoptosis [63].
Doxorubicin, sold under the commercial name Adriamycin, is another chemical agent that can be administered via intravascular route and interacts with DNA by intercalation. This agent prevents the biosynthesis of DNA macromolecules, inhibits DNA replication by stabilising topoisomerases and thereby shows the anti-cancer effect [64, 65].
Cisplatin is a platinum molecule with two chloride ions. This agent interferes with DNA, prevents replication and induces apoptosis in cells that are rapidly proliferating. Because of the different chloride concentration in intracellular and extracellular environment, cisplatin readily enters the cell and interacts with the water molecule to form a complex. This complex replaces the N-heterocyclic bases in DNA and has a particularly strong binding effect on guanine. This new structure forms the cis-[PtCl(NH3)2(N7-ACV)]+ structure. In this situation, DNA repair mechanisms cannot work, and degradation of DNA is initiated in apoptotic cells [66, 67].
Mitomycin-C is another bactericidal chemotherapeutic agent. The mechanism of action of this agent is to generate DNA damage by alkylating the guanine nucleotide in 5’-CpG-3′ sequence via cross-links. Mitomycin-C exerts the anti-cancer effect by activating apoptotic pathways [68].
Another treatment modality employed for the treatment of lung cancers is radiation therapy. Radiotherapy is a radiation-based application that utilises ionised radiation, such as high-energy X or gamma radiation. Radiotherapy can be applied before or after surgery and can also be combined with chemotherapy, depending on the localisation and stage of the tumours that are being treated. In this therapy, high-energy radiation beams cause DNA damage in the cell and result in apoptosis [69, 70].
The abovementioned therapeutic approaches share a common mechanism of action, in that almost all chemotherapeutic agents and radiotherapies induce DNA damage by activating apoptotic pathways and destroy the tumour with the help of apoptosis [71]. However, this requires the presence of intact apoptotic pathways; in other words, apoptotic pathways should not have been inhibited in order for these treatments to be effective. If anti-apoptotic mechanisms are activated, these treatments often fail, and drug resistance may develop. Specific targets of certain chemotherapeutic agents may be located in base sequences that undergo methylation, and motif changes may occur in the relevant DNA sequence due to methylation. In this case, chemotherapeutic agents may prove to be ineffective.
DNA methylations, chromosome acetylations and inhibited apoptotic pathways are among the reasons of resistance to therapeutic agents and radiotherapy. Studies have shown that epigenetic agents are highly promising in terms of overcoming the resistance to chemotherapy in various tumours [72]. A good understanding of acetylation, methylation and apoptosis mechanisms will allow developing more effective and targeted novel molecules.
Excision of the tumour tissue and the surrounding lymph nodes with the most recent surgical approach remains the optimal treatment in lung cancers. However, this may not always be possible owing to the anatomic location, spread pattern and metastasis status of the cancer. Chemotherapy or radiotherapy or both may be used in such cases. Response to treatment, however, is often not promising [73]. It is at this point where epigenetic alterations during cancer development emerge as therapeutic options. Due to their reversible characteristics, epigenetic modifications are therapeutic targets which may prove to have very good anti-cancer effects. For this reason, the US Food and Drug Administration (FDA) and European Medicines Evaluation Agency (EMEA) have started granting approval for certain drugs such as histone deacetylation inhibitors and DNA methyltransferase inhibitors. Among these, inhibitors of DNA methylation are the most effective treatment options and they appear to be effective in lung cancer as well [36].
DNA DNMTs are molecules that transfer methyl groups to cytosines via S-adenosyl methionine (SAM). Hypo- and hyper-methylation of DNA may occur in any cancer cell and silence tumour suppressor genes or inactivate T-cell recognition genes, which provide immune response, or affect the genes that trigger metastasis, angiogenesis and invasion [74]. The most important investigational DNA methyltransferase inhibitors and their analogues are presented in Table 1 [74].
DNA methyltransferase inhibitors and their analogues [74].
CI-994 is one of the candidate therapeutics in clinical testing phase. CI-994 is an orally bioavailable histone deacetylase (HDAC) inhibitor that causes histone hyperacetylation in viable cells. CI-994 shows inhibitory effects based on the concentration of HDAC1 and HDAC2. Specifically, it mediates the arrest of cell cycle at G1, inhibits proliferation and induces apoptosis both in vitro and in vivo [75, 76].
FDA-approved epigenetic therapy agents are shown in Table 2 together with their indications and year of approval [77].
FDA-approved epigenetic therapy agents [77].
Acetylation-based drug study is an early phase 2 trial of vorinostat (Zolinza®) [78]. Vorinostat is an HDAC inhibitor from the hydroxamate group. In this phase 2 study, vorinostat was evaluated in breast, colorectal, and non-small lung cell cancers; however, adequate clinical response was not obtained, and authors reported that studies are to continue to determine appropriate doses [78]. While having a wide spectrum of tolerable side effects, it is a promising molecule in terms of chemotherapeutic utility [79].
In addition to survivin gene vaccines, a study has been conducted to target the methylated oligonucleotides of the survivin gene in non-small cell lung cancer. The study in question aimed to break down the apoptosis resistance of NSCLC by interfering with the survivin gene expression via oligonucleotides called SurKex1, which are specific to the promoter region of the methylated survivin gene. Data from this study were the first to show the utility of SurKex1 owing to its downregulating effects on survivin expression by means of DNMT1 activation [80, 81]. This study, although conducted in a cell-culture setting, is promising with regard to targeted survivin gene therapy in the near future.
Chemotherapy combined with immunotherapy may also be effective in treatment. High rates of response to treatment were demonstrated through PD-1 blockade via activated IFN signals by hypomethylation in treatments combined with IRF1/7 following treatment with decitabine (5-aza-2′-deoxycytidine or 5-Aza-Cdr or DAC), which was the first cytosine analogue synthesised by Pliml and Sorm in 1960. DAC, known to inhibit DNMT during cell division, is also a candidate for use in cancer therapy as an FDA-approved promoter hypomethylation inhibitor [82, 83].
Cystatin A (CSTA), a member of the type 1 cystatin superfamily, is essential to protect cells from cytoplasmic proteolysis and is mainly expressed in epithelial and lymphoid tissue. Furthermore, while cathepsins B, H and L, CSTA and cytoskeleton are known to be involved as tumour suppressors in oesophageal cancers, they have been found to exert such effects also in lung cancers. Histone methylation and acetylation play an important role in CSTA gene silencing in lung cancers. DAC treatments have an inhibitory effect on DNMT1, which is responsible for replicating DNA in a methylated form during replication. While limited CSTA expression is associated with high grades in squamous cell carcinoma (SCC), silencing in CSTA promoter region has also been demonstrated in the absence of CpG islands through epigenetic mechanisms such as partial methylation [84]. This renders DNMT1s a good target for novel treatment approaches.
In lung cancers, miR-9-3 hypermethylation occurs and the resulting downregulation of miR-9-3 expression leads to poor prognosis. Sulforaphane (SFN), a natural plant-derived molecule with anti-cancer properties, has been reported to decrease miR-9-3 methylation by attenuating DNMT activity in lung cancers and has a potential effect in improving the cancer prognosis [85].
It has been shown that Runx transcription factors (Runx1, Runx2 and Runx3), which play a critical role in organogenesis and cell differentiation pathways, are involved in lung cancers as they cause epigenetic silencing of a tumour growth inhibitor called BMP-3B. In this respect, downregulation of BMP-3B and lung cancers are closely related. Therefore, Runx transcription factors now appear to be a potential epigenetic target in lung cancers [86].
MARVELD1, a recently identified nuclear factor, is known to be extensively expressed in all human tissues and downregulated via promoter methylation in multiple cancer tissues. By working in combination with DNA methylation and histone acetylations, the epigenetic silencing of MARVELD1 leads to a decreased expression, causing unfavourable effects on histopathology and malignancy in lung cancers. This decreased MARVELD1 status in lung cancers eliminates the NMD complex-forming activity with UPF1/SMG1, resulting in premature termination codons and non-functional RNA. Epigenetic MARVELD1 silencing, which may serve as a diagnostic biomarker in lung cancers, appears to be a good target for anti-tumourigenesis [87].
Highly tumourigenic stem-like cells, which are thought to be tumour-initiating cells, cause the initiation, recurrence and drug resistance of cancers. Ca + 2/calmodulin-dependent protein kinase IIy (CaMKIIy), which is abnormally overexpressed in highly tumourigenic stem-like cells, is also associated with poor prognosis in lung cancers. Oct4 is one of the mRNA expression factors for pluripotent stem cells and regulates the differentiation of these cells by inhibiting CaMKIIy through epigenetic regulation. Therefore, Oct4 may be considered as a novel target approach in lung cancers [88].
The long non-coding RNAs are now also thought to offer a potential biomarker in non-small cell lung cancers. Studies have shown that they are particularly increased in non-small cell lung cancers. Because such RNAs occur mainly through methylation at DNA-gene level, long non-coding RNAs appear to be good markers and targets for novel treatment approaches in non-small cell lung cancers with regard to epigenetic mechanisms [89].
Among lung cancers, the metastatic risk is high in adenocarcinomas. In a study conducted to reveal possible biomarkers and therapeutic agent targets in these carcinomas, DNA methylation profile was downloaded from Gene Expression Omnibus (GEO) database, and DNA methylation profile of lung adenocarcinoma was investigated. This study concluded that methylated PTPRF, HOXD3, HOXD13 and CACNA1A genes may be potential biomarkers for the diagnosis and treatment of lung adenomas [90].
In light of all the information described earlier, one may conclude that acetylation at histone level and DNA methylations may be potential biomarkers and also good target molecules for treatment, particularly in lung cancers. Especially, the promoter regions of several tumour suppressor genes, and regions such as exon 1, although to a smaller extent, are inactivated through methylation. While the DNMT1 enzyme is in the position of a general target for inhibition to prevent these methylations, approaches such as inactivation of certain specific genes, oncogenes or apoptosis-inhibiting genes by means of methylated DNA oligo-primers appear to be a considerably good option. In the future, a combination of all these possibilities may allow treatments with significantly reduced side effects compared to current treatments as well as improved targeted approaches which destruct or prevent the progression of tumours.
There is no conflict of interest.
Despite significant economic and social benefits the aviation brings, its activities also contribute to local air quality impact and correspondingly affect the health and quality life of people living near the airports. The number of flights has increased by 80% between 1990 and 2014 and is forecasted to grow by a further 45% between 2014 and 2035. Consequently, the future growth in the European aviation sector will be inextricably linked to its environmental sustainability [1].
\nDuring the last decade, a lot of studies have also focused on the aircraft emissions impact on local and regional air quality in the vicinity of airport [2, 3, 4, 5, 6, 7]. The basic objects of attention are extremely high concentration of toxic compounds (including nitrogen oxides (NOx), particle matter (PM with various sizes: PM10, PM2.5, and ultrafine), unburned hydrocarbons (UHC), and carbon monoxide (CO)) due to airport-related emissions and their significant impact on the environment [2, 8] and health of the people living near the airport [3, 4].
\nGround-level emissions associated with the airport have the biggest impact on local air quality, whereas elevated aircraft emissions have less impact because they take place at increasing height. Figure 1 shows aircraft produce approximately 54% of ground-level emissions, whereas airport-related traffic is estimated to emit a further 28% [5].
\nEstimated ground-level airport-related emissions from Heathrow Airport.
Analysis of inventory emission results at major European (Frankfurt am Main, Heathrow, Zurich, etc.) and Ukrainian airports highlighted that aircrafts (during approach, landing, taxi, takeoff and initial climb of the aircraft, engine run-ups, etc.) are the dominant source of air pollution in most cases under consideration [6, 9, 10], Figures 2 and 3. More than 50% of total NOx emissions inventory inside airport area is released by aircraft engines. As shown in Figures 2(b) and 3(b), the contribution of aircraft emission to total airport PM emissions is sufficiently high.
\nThe emissions inventory of NOx [(a) annual emissions: 3.284 tons/year] and PM10 [(b) total emissions: 25 tons/year] within the Frankfurt International Airport for 2005 with an intensity of takeoffs and landings, 1300 per day.
The emissions inventory of NOx (a) and PM10 (b) within Boryspil International Airport with an intensity oftakeoffs and landings 50,000 per year.
Considered problems are intensified in connection with rising tensions of expansion of airports and growing cities closer and closer each other (the most urgent is for Ukrainian airports, such as Zhulyany, Boryspil, Lviv, Odessa, and Zaporizhzhia) and accordingly growing public concern with air quality around the airport.
\nAircrafts are a special source of air pollution due to some features.
\nFirst of all, aircrafts are a moving (on the ground and in flight) pollution source with varying emission factors during landing and takeoff (LTO) as well as ground operation (engine start after maintenance and run-ups to check the correct operation of the flight system). At the airport, engine operation may change from idle to maximum thrust. Accordingly, temperature, exhaust gas velocity, and emissions of an aircraft engine may change within a wide range [11].
\nSecond, the most important feature is the presence of a jet of exhaust gases, which can transport pollutants over rather large distances because of high exhaust velocities and temperatures (Figure 4). Such a distance is determined by the engine power setting and installation parameters, mode of airplane movement, and meteorological parameters. The results of jet model calculations show that depending on initial data, the jet plumes from aircraft engines range from 20 to 1000 m and sometimes even more [11].
\nJet structure for jet transport model. ΔhA, XA are the height and longitudinal coordinate of jet axis rise due to buoyancy effect, m; hEN is the height of engine installation, m; RB is the radius of jet expansion, m; X1 is the longitudinal coordinate of first contact point of jet with ground, m; and X2 is the longitudinal coordinate of a point of jet lift-off from the ground due to buoyancy effect, m.
So, to evaluate the aircraft contribution in Local Air Quality assessment of the airports accurately, it is important to take in mind few features of the aircraft during their landing-takeoff cycle (LTO), which define emission and dispersion parameters of the considered source.
\nModeling of airport air pollution includes two parts: emission inventory and dispersion calculation.
\nICAO Doc 9889 [12] recommends few tools for air quality analysis—to model emission inventory from every character groups of the spatially distributed sources as well as atmospheric concentrations resulting from emission dispersion: EDMS is based on Gaussian plume model (AERMOD) [13], LASPORT is based on Lagrangian particle model (LASAT) [14], and ALAQS–AV provides to use both Gaussian and Lagrangian approaches for dispersion calculations [12].
\nA complex model Pollution and Emission Calculation (PolEmiCa) for assessment of air pollution and emission inventory analysis, produced within the airport boundaries, has been developed at National Aviation University (Kyiv, Ukraine) [15]. It consists of the following basic components:
engine emission model—emission assessment for aircraft engines, including influence of operational factors;
jet transport model—transportation of the contaminants by the jet plume from the engine exhaust nozzle; and
dispersion model—dispersion of the contaminants in atmosphere due to turbulent diffusion and wind transfer.
The emission inventory of aircraft emissions are usually calculated on the basis of certificated emission indexes, which are provided by the engine manufacturers and reported in the database of the International Civil Aviation Organization (ICAO) [16].
\nThe emission indices rely on well-defined measurement procedure and conditions during aircraft engine certification. Under real circumstances, however, these conditions may vary and deviations from the certificated emission indices may occur due to the impact factors such as
the life expectancy (age) of an aircraft—emission of an aircraft engine might vary significantly over the years (the average period is 30 years); usually aging aircraft/engine provides higher emission indices in comparison with same type but new ones;
the type of an engine (or its specific modification, for example with different combustion chambers) installed on an aircraft, which can be different from an engine operated in an engine test bed (during certification); and
meteorological conditions—temperature, humidity, and pressure of ambient air, which can be different for certification conditions.
So, the analysis of several measurement campaigns for idling aircraft at different European airports (London-Heathrow in 1999 and 2000, Frankfurt/Main in 2000, Vienna in 2001, and Zurich in 2003) [7] concludes that the largest difference between emission indices’ measurement data and the ICAO data for CO for the RB211-524D4 engine was caused due to quite long life expectancy of B747-236 (aging aircraft and engines) (Figure 5). The oldest aircraft with an emission index of 52.9 g/kg was 25 years old; the other two were built in 1987 and 1983. Mean values of the measured emission indices for three engine types (CFM56-5B1, CFM-5B4/2P, and CFM56-5B3/P) are nearly identical although the ICAO data of the CFM56-5B family differ by a factor of 2 (Figure 6) [7].
\nComparison measured EICO by FTIR emission and absorption spectrometry during measurement campaign for idling aircraft at the European airports.
Comparison EICO determined for CFM-5Bx engines with ICAO values for idling aircraft at European airports.
The dependences of engine thermodynamic parameters and EE index for NOx (assessed in g/kgfuel) and factor Q (assessed in g/sec) for the aircraft engine D-36 (installed on Yakovlev-42 and on Antonov-74, -148, and -158 aircrafts) are shown in Figure 7 as the functions from ambient temperature ТА (basic engine control law for D-36 provides the constant value of compressor pressure ratio π∑* in a broad range of ambient temperatures). Values of an emission index ЕINOx vary up to 50% in relation to value at International standard atmosphere conditions inside the range of ambient temperatures between −30 and + 30°C [17, 18].
\nDependences of EE index EI [gemission/kgfuel], factor Q [g/s] for NOx, and temperature behind the compressor Tc for D-36 engine from ambient temperature.
A gradient of change of the factor Q\nNOx at T\nA < T\nALIM is also large enough (with Т\nA growth, a factor Q\nNOx monotonically increases). In case of change of engine automatic control mode at T\nA > T\nALIM, the propellant consumption drops with the growth of temperature; therefore, monotonic character for Q\nNOx dependence disappears and at T\nA > 30°C, the factor Q\nNOx decreases [17, 18].
\nSo, under operating conditions, engine emission characteristics are subject to changes as a result of influence of the meteorological factors.
\nBased on the obtained research outcomes of aircraft engine emission derivation, due to meteorological factor influences, the model was developed to recalculate the emission indices for ISA conditions EI\niISA into actual meteorological conditions EI\nit [17, 18]:
\nFor emission factor (in g/s or kg/hour), the recalculation into actual meteorological conditions are determined under the formula:
\nIn Table 1, the correction coefficients for NOx emission factor KQnox and for products of incomplete fuel combustion KQco for average parameters of the engines while in operation are adduced.
\nTemperature, °C | \n−20 | \n−10 | \n0 | \n+ 10 | \n+ 20 | \n
---|---|---|---|---|---|
Factor KQnox\n | \n0,74 | \n0,81 | \n0,88 | \n0,96 | \n1,0 | \n
Factor KQco\n | \n1,3 | \n1,2 | \n1,1 | \n1,04 | \n1,0 | \n
Average values of aircraft engine emission factor recalculation into actual ambient temperature.
Current calculation method, realized in software PolEmiCa, also implemented the recommendations of ICAO Doc9889 [12] for emission factor assessment, including the recommendations for aircraft engine emission.
\nThe efficiency of the temperature (seasonal) factor account for pollution inventory produced by aircraft in airport area is shown in Table 2 by matching the outcomes of calculation from previous and new calculation techniques [19].
\nTechniques | \nCO | \nHC | \nNOx\n | \nPM | \n
---|---|---|---|---|
Previous | \n307,000.1 | \n104,200. | \n16,700.0 | \n3400.0 | \n
ICAO LTO | \n282,754.6 | \n97,139.2 | \n18,621.1 | \n2859.4 | \n
Actual LTO for considered airport | \n185,055.1 | \n59,556.4 | \n16,869.1 | \n2207.3 | \n
Actual LTO for considered airport + temperature factor | \n190,246.1 | \n61,254.1 | \n15,984.1 | \n2207.3 | \n
Calculated aircraft engine pollution, kg.
There are different types of engines installed on civilian aircraft currently: turbojet (TJE), turbofan (TFE), turboprop (TPE), and piston (PE). The process of contaminant transport by engine jet is described by the theory of turbulent jets [20]. The restrictions on the use of this theory are satisfied completely in the current task [21]: efflux from a jet engine is a very complex fast flow of hot gas, it is nonuniform, turbulent, and has various velocity scales and chemical reactions; the gas flow in jet is usually isobaric process, the pressure in the jet flow is equal to the atmospheric pressure, which is corresponding to the nature of incompressible flow; the Mach number of jet flow at outlet nozzle of the engine does not exceed 1; and the Reynolds number for the flow is rather large U0D0/ν > 105, and the initial turbulence in the jet flow is quite moderate. For majority of the calculations, the simplifying preconditions were formulated and used: radial velocity profile has a self-preserving pattern; mechanisms of boundary layer formation near ground surface are not taken into account in this calculation; the external borders of a jet represent linear dependencies; the structure of shear layer is similar to free jet [11].
\nThe conditions of jet outflow define the type of its physical model and appropriate algorithm of its parameters calculation. The choice of the model depends on the direction of the jet at exhaust nozzle relative to the direction of the wind and/or airplane motion and from the speeds of the jet, airplane, and wind. The initial parameters for jet calculations are: slipstream flow parameter m = UH/U0, where U0 is the velocity of the jet at engine nozzle, m·s−1 and UH is the speed of an external air flow, m·s−1; UH = UW + UPL, where UW is the wind speed, m·s−1 and UPL is the airplane speed, m·s−1; Nen – number of the engines in operation, angle between vectors of wind and jet speeds ψ, grad. For ground stages of LTO cycle in airport area, the slipstream parameter m < < 1; therefore, in most cases, it is possible to take advantage of semiempirical modeling of the nonisothermal-free jets.
\nTurbulent-free jet can be divided into three stages: initial (potential core), transitive (flow development region), and developed (fully developed flow) [20]. Their boundaries along the length of jet axis S and their expansion R (on considered sites) are defined by the formulas [11, 20, 22]:
for an initial stage:
\n
for a transitive stage:
\n
for the fully developed stage:
where \n
The stage of a jet, which is defined by boundary S\nB (6), determines a point (X\nE, Y\nE, Z\nE) on a jet axis, where centerline flow speed Um\n and the wind speed UW\n become equal. From this point, it is assumed that atmospheric turbulence and wind play a dominant role in the plume behavior and its further dispersion, while the jet parameters influence is not already sufficient at this stage of plume development.
\nAt point (X\nE, Y\nE, Z\nE), a jet center-line due to buoyancy effect takes height of plume rise (it is equal to effective height of source H in (1) and (2)) [11, 20, 22]:
\nwhere h\nEN is a height of engine installation (of their axis above a ground surface), m and ΔhA\n is a height of jet rise, m.
\nFor an estimation of the buoyancy characteristics, the Archimedes number is introduced:
\nThe height of the jet is given by the empirical relationship [23]:
\nwhere \n
The concentration is changed along the length of jet in dependence with its type. Taking into account that flow parameter m in jet is rather small, the concentration C of the contaminant on a surface (X, Z) is defined as [20, 22]:
\nwhere C0 is the concentration at the exhaust nozzle of the engine, μg·m−3; KC = 9.5 for the free jet, KC = 6.5—for an opposite jet; and KE takes into account influence of a reflecting surface on straightline characteristics of a jet: ĥEN < 20 KE = 1–0.025hEN, at ĥEN ≥ 20, KE = 1, where ĥ = h/R0.
\nConsidered version of complex model PolEmiCa is based on a semiempirical model of turbulent jets and not taking into account ground surface impact on jet structure and its behavior [11]. It was argued that development of three-dimensional model of exhaust gases jet from aircraft engine near the ground is an important research topic for airport LAQ [24, 25, 26].
\nA three-dimensional model of a jet was generated in Fluent 6.3 by using large Eddy simulation (LES) method to reveal the unsteady ground vortices and turbulence characteristics of fluid flow, to investigate transient parameters of hot gases in jet and their dispersion.
\nThe jet from aircraft engine exhaust near ground surface is corresponding to a wall jet if an aircraft is moving on this surface. Numerical simulation of wall jets was performed in Fluent 6.3 for engine NK-8-2 U of the aircraft Tupolev-154 for different operational conditions.
\nFor the considered task, a computational domain was built to simplify the problem and optimize the mesh distribution where it is needed mostly (i.e., near the engine exhaust and ground surface) (Figure 8).
\nGeometry model and computational mesh visualization in vertical plane.
The zone of ground vortices formation—between ground surface and aircraft engine exhaust nozzle—is characterized by structured mesh with higher resolution, with an aim to investigate the ground vortices generation processes and basic mechanisms of boundary layer formation, ground surface impact on fluid flow mechanics, and particularly Coanda effect occurrence. Zone of engine nozzle exhaust is discretized using a very fine structured mesh to capture the jet development pattern and its vortices structure [24, 25].
\nFor considered task, the boundary conditions were specified to the boundaries of the computational domain of jet flow field (Figure 9).
\nBoundary conditions for CFD simulations of exhaust gases of jet from aircraft engine near ground.
LES provides an approach inside which large eddies are explicitly resolved in time-dependent simulation using low-pass-filtered Navier-Stokes equations [25]. Smagorinsky’s subgrid model was set to model the smaller eddies (fluctuation component of instantaneous velocity of modeling fluid flow) that are not resolved in the LES. All the calculations were made with a second-order discretization.
\nComparison of results from numerical simulations of free and wall jets for engine idle operation (U0 = 50 m·s−1; T0 = 343 K) revealed some differences in their structures and properties.
\nAxial velocity profiles based on Fluent 6.3 results show (Figure 10) a substantial difference between the wall and free jet. First, the decay rate is 40–50% higher for free jet than for the wall jet. In the case of wall jet, the maximum velocity is high and equal to 50% of initial velocity at a distance of 90 diameters of the jet penetration, whereas the free jet is relatively slow and equal only to 10% of the velocity at exhaust nozzle of the engine, Figure 10. Second, the wall jet penetrates deeper (SBwall ≈ 150 m) than the free jet (SBfree ≈ 100 m) (Figure 11). As shown in Figure 12, jet arises over the ground surface due to buoyancy effect much faster (longitudinal coordinate, XA = 65 m) and higher for free jet (height of plume rise, ΔhA = 17.8 m), than in case of wall jet (XA = 135 m, ΔhA = 14 m).
\nMaximum velocity decay along the axis of the free and wall jets.
Mean velocity contours for (a) free jet and (b) wall jet in streamwise direction after 10 s.
Buoyancy effect of free and wall jets: longitudinal and vertical coordinates of jet axis.
The same differences in the structure and properties of free and wall jets were revealed for different operational conditions (U0 = 100 m·s−1; T0 = 343 ÷ 673 K).
\nThe ground surface sufficiently impacts on jet’s structure and behavior. Numerical simulations of wall jet by Fluent 6.3 defined a decrease of buoyancy effect of height rise, which is 3–5 times less (Figure 13a) and an increase of longitudinal coordinate of jet penetration by 30%, (Figure 13b).
\nComparison of buoyancy effect parameters calculated by Fluent 6.3 and complex model PolEmiCa: longitudinal coordinate (a) and height of jet rise (b).
Comparison of the calculated parameters of the jet (height and longitudinal coordinate of jet axis arise due to buoyancy effect, length of the jet penetration) by Fluent 6.3 and semiempirical model for aircraft engine jets implemented in complex model PolEmiCa proves the found trend of the jet behavior. Thus, the including the ground impact on the jet structure and its behavior by Fluent 6.3, provides longitudinal coordinate increase and height reduction of buoyancy effect.
\nThe basic model equation for definition of instantaneous concentration C at any moment t in point (x,y,z) from a moving source from a single exhaust event with preliminary transport by jet on distance XA\n and rise on total altitude H (Figure 4) and dilution of contaminants by jet (σ\n0\n) has a form [11, 19]:
\nwhere KX, KY, KZ\n are the diffusion factors (m2·s−1) for atmosphere turbulence along three axes, axis OX is directed along wind direction. Aircraft is considered as a moving emission source, thus current coordinates (x’, y’, z’) of the emission source in movement during time t’ are defined as:
\nwhere x0, y0, z0\n are initial coordinates of the source, m; uPL, vPL, wPL\n are vector components of source speed, m·s−1; aPL, bPL, cPL\n are vector components of source acceleration, m·s−2; and uw\n is the wind speed, m·s−1.
\nAccording to considered formula (11), a dispersion model integrates engine emission model and jet transport model via including the following parameters:
\n\nQ—emission rate is provided by engine emission model and includes influence operational and meteorological conditions [17, 18, 19].
\n\nH—height of buoyancy effect and horizontal σ2\nx, σ2\ny and vertical σ2\nz dispersion are provided by jet transport model [24, 25, 26].
\nIn other words, engine emission model and jet transport model provide input data to calculate concentration values by the dispersion model.
\nThe development of three-dimensional model of wall jet by using CFD tool (Fluent 6.3) allows to include the ground impact on basic parameters of the exhaust gases jet (i.e., plume buoyancy effect, length, and dispersion characteristics) for further dispersion modeling (11). It may be concluded that using the CFD tool allows us to improve the PolEmiCa model by taking into account the impact of ground surface on the jet structure and its behavior. So, it means that the improvement is achieved with input parameters for further dispersion calculation.
\nThe verification of the PolEmiCa model with measurement data was done initiatively for trials made in airports of Athens (Greece, 2007) [27] and Boryspil (Ukraine, 2012) [28]. In both cases, the comparisons were quite good, showing appropriate correspondence of the model to subject of assessment.
\nComparison between calculated and measured NOx concentrations (averaged for 1 min) in aircraft engine plume under real operation conditions (aircraft accelerating on the runway during takeoff stage of flight) at Athens airport is shown in Table 3 and Figure 14.
\n№ | \nAircraft | \nEngine | \nCalculated concentration | \nMeasured concentration | \n||
---|---|---|---|---|---|---|
NOx (delta), μg/m3\n | \nNOx (delta), μg/m3\n | \n|||||
With jet | \nWithout jet | \nValue | \nError | \n|||
1 | \nB737-3YO | \nCFM56-3C1 | \n27,43 | \n30,01 | \n31,8 | \n3,2 | \n
2 | \nB737-3Q8 | \nCFM56-3B2 | \n30,7 | \n33,50 | \n28,0 | \n2,8 | \n
3 | \nВ737-45S | \nCFM56-3B2 | \n29,76 | \n27,95 | \n23,6 | \n2,4 | \n
4 | \nB737-4Q8 | \nCFM56-3B2 | \n31,28 | \n34,93 | \n56,9 | \n5,7 | \n
5 | \nA-310 | \nCF6-80C2A8 | \n88,86 | \n122,12 | \n86,1 | \n8,6 | \n
6 | \nA-319 | \nCFM56-5B5 | \n29,85 | \n32,27 | \n26,9 | \n2,7 | \n
7 | \nB747–230 | \nCF6-50E2 | \n163,63 | \n205,37 | \n82,5 | \n8,2 | \n
8 | \nA-321-211 | \nCFM56-5B-3 | \n81,78 | \n89,74 | \n43,3 | \n4,3 | \n
9 | \nA320–214 | \nCFM56-5B-4 | \n49,99 | \n52,29 | \n16,4 | \n1,6 | \n
10 | \nB737-33A | \nCFM56-3B1 | \n25,5 | \n27,95 | \n11,5 | \n1,1 | \n
Measurement results by TE42C-TL96 system and calculation results by PolEmiCa model of NOx concentration in plume from aircraft engine emission for maximum operation mode.
Comparison of measured and modeled averaged concentrations of NOx (for a period of 1 min) under takeoff conditions (maximum operation mode of aircraft engine).
Besides, results were defined for the cases with and without jets from the engines to show that with jets, they are more equal (by 17%) to measured data, because impact of jet basic parameters (buoyancy effect and dispersion characteristics) on concentration distribution was estimated by complex model PolEmiCa (Table 3 and Figure 14). Comparison between measurements and the PolEmiCa/Fluent 6.3 model is significantly better (by 20%), because lateral wind and ground impact on jet parameters (height of buoyancy effect, jet length penetration, and plume dispersions) were included in the model.
\nThe better agreement was obtained between the calculated and measured instantaneous concentration (averaged for 3 s) in aircraft engine jet under real operation conditions (aircraft accelerating on the runway and takeoff) at Boryspil airport.
\nAs shown from Table 4 and Figure 15, the modeling results for each engine are in good agreement with the results of measurements by the AC3 2 M system due to taking into account the jet- and plume regime during experimental investigation at Boryspil airport. Also, using CFD code (Fluent 6.3) allows to improve results by 30% (coefficient of correlation, r = 0.76) by taking into account lateral wind and ground impact on jet parameters.
\nAircraft | \nAircraft engine | \nELAN | \nAC3 2 M | \nPolEmiCa CFD (Fluent 6.3) | \nPolEmiCa | \n|||||
---|---|---|---|---|---|---|---|---|---|---|
Peak 1 | \nPeak 1 | \nBackground | \n3 м | \n6 м | \n1 engine | \nAll engines | \n1 engine | \nAll engines | \n||
NOx | \nNOx | \nNOx | \nNOx | \nNOx | \nNOx | \nNOx | \nNOx | \nNOx | \n||
BAE147 | \nLY LF507-1H | \n38 | \n35 | \n1,70 | \n22,067 | \n33,9 | \n35,1 | \n70,46 | \n48,9 | \n202,3 | \n
A321 | \nCFM56-5B3/P | \n39 | \n39 | \n0,72 | \n44,00 | \n54,2 | \n90,85 | \n182,90 | \n184,2 | \n371,2 | \n
B735 | \nCFM-563C1 | \n40 | \n45 | \n0,77 | \n94,095 | \n76,57 | \n60,03 | \n120,91 | \n35,3 | \n71,10 | \n
B735 | \nCFM56-3B1 | \n45 | \n41 | \n1,74 | \n29,20 | \n23,4 | \n42,34 | \n85,30 | \n33,7 | \n67,76 | \n
Comparison measured (AC3 2 M, ELAN) and calculated concentration (averaged for 3 s) of NOx produced by aircraft engine emissions at accelerating stage on the runway.
Comparison of the PolEmiCa and PolEmiCa/CFD model results with the measured NOx concentration at different heights for selected aircraft engines under maximum operation mode.
Analysis of inventory emission results at the major European and Ukrainian airports highlighted that aircrafts (during approach, landing, taxi, takeoff and initial climb of the aircraft, engine run-ups, etc.) are the dominant source of air pollution in most cases under consideration. The aircraft is a special source of air pollution. Thus, the method for LAQ assessment of the airports has to take in mind few features of the aircraft during their landing-takeoff cycle (LTO), which defines emission and dispersion parameters of the considered source.
\nCFD numerical simulations of aircraft engine exhaust jet near to ground surface show that structures, properties, and fluid mechanics of jets are influenced by the ground surfaces, providing longer penetration, less rise, and appropriate dispersion parameters of the jets, and accordingly little bit higher concentrations of air pollution. So, using results obtained from CFD simulations (Fluent 6.3) of aircraft engine jet dynamics allow us to improve LAQ modeling systems (improved version of PolEmiCa).
\nComparison of measured and modeled NOx concentrations in the plumes from aircraft engines was significantly improved (by 20%—at Athens and by 30%—at Boryspil airports) by taking into account lateral wind and ground impact on jet parameters (height of buoyancy effect, jet length penetration, and plume dispersions).
\nIntechOpen’s Academic Editors and Authors have received funding for their work through many well-known funders, including: the European Commission, Bill and Melinda Gates Foundation, Wellcome Trust, Chinese Academy of Sciences, Natural Science Foundation of China (NSFC), CGIAR Consortium of International Agricultural Research Centers, National Institute of Health (NIH), National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), German Research Foundation (DFG), Research Councils United Kingdom (RCUK), Oswaldo Cruz Foundation, Austrian Science Fund (FWF), Foundation for Science and Technology (FCT), Australian Research Council (ARC).
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