Average values of aircraft engine emission factor recalculation into actual ambient temperature.
\r\n\tThe primary objective of this book is to provide the specialists involved in the clinical management and experimental research of acute and chronic leukemias updates on the theoretical aspects as well as state-of-the-art diagnostic and clinical management of acute and chronic leukemias.
\r\n\r\n\tThe book is intended to cover a broad spectrum of leukemia-related topics such as:
\r\n\t-novel and still evolving insights into the biology and diagnosis and how these result in new drug approvals and new therapeutic options with a focus on molecular and immunotherapeutic targeted therapeutics,
\r\n\t- the molecular and functional features of leukemic stem cells and their interaction with the microenvironment,
\r\n\t- preleukemic hematopoiesis and clonal diversity,
\r\n\t- new standard treatment algorithms,
\r\n\t- mechanisms of resistance and disease progression
\r\n\t- diagnosis and management of rare acute leukemia subtypes,
\r\n\t- Covid-19 aspects in specific leukemia categories,
\r\n\t- real-world data
\r\n\t- new drugs in development
Hypertension in children and adolescence is becoming an increasing health problem. The prevalence of pre-hypertension is approximately 14% in boys and 6% in girls (age 8–17 years) [1, 2], and the prevalence of hypertension is estimated to be 3–4% (age 3–18 years) [3, 4]. One in three of the hypertensive children develops end-organ damage, including ventricular hypertrophy [5], chronic kidney disease [6] and vascular changes [7] and cognitive impairment [8, 9], all predictors for premature morbidity and mortality. Accumulating evidence supports the theory that elevated blood pressure levels in adolescence are a precursor of elevated blood pressure in adulthood, and an important risk factor for future cardiovascular diseases [10]. Another factor is the coexistent epidemic of childhood obesity, which in the US rose from 5 to 11% from the 1960s to the 1990 [11, 12], becoming a concomitant cardiovascular risk factor. Childhood BMI has strong positive concordance with blood pressure [13]. Children who are overweight demonstrate 4.5 and 2.3 times higher risk of developing increased systolic blood pressure and diastolic blood pressure, respectively [14], consistent findings were obtained by Sorof et al. [15] with a three-fold prevalence of childhood hypertension in obese versus lean at school age. Blood pressure in children may also vary by age, sex, race and height [4] but not as solid as BMI, all these inclusion criteria of risk being underdiagnosed [3]. Children within the “normotensive” range of blood pressure demonstrate elevated left ventricular mass [16] and greater risk of developing hypertension in adulthood [17]. Thus, blood pressure in children diverges from adults in that an underestimation of risk may cause severe cardiovascular diseases in adulthood [18]. Recent report indicates that more than 90% of children evaluated for hypertension have no underlying cause identified [19], which suggests that prevalence of essential hypertension is increasing. This revives the discussion of the aetiology and pathogenesis research of essential hypertension to identify important targets of prevention.
\nThe origin of paediatric hypertension evolves a cluster of metabolic and haemodynamic disorders identifies as a polyfactorial disease. Unfavourable metabolic profiles, such as hyperinsulineaemia and dyslipidaemia, at an young age with abnormalities of vascular structure and function leads to adverse cardiovascular outcome [12, 20, 21]. The adverse metabolic profile may originate from an unbalanced autonomic nervous system (ANS). ANS is known as the major adaptor for stress responses which regulate the two neural efferent pathways the parasympathetic and sympathetic system [22]. Long-term increase to stress may lead to increased sympathetic activity and decreased parasympathetic activity, contributing to obesity, insulin resistance, dyslipidaemia and hypertension [23–25]. Dysregulation of ANS may therefore predict metabolic abnormalities [26, 27] and hypertension [22]. In children, these associations have barely been investigated. Childhood obesity has been associated with lower parasympathetic activity [28–31], but more conflicting results regarding the influence of the sympathovagal balance and sympathetic hyperactivity [31–33]. Possible confounding factors and differences in methodology and sample size might explain these differences. Latchman et al. [32] showed that normotensive obese 9-year-old children exhibited reduced baroreflex sensitivity, parasympathetic control as well as increased sympathetic control compared with normotensive lean children. Thus, suggest that autonomic dysfunction may precede the hypertension in obese children. Obesity is associated with increased sympathetic nervous system (SNS) activity, with impaired heart rate variability [34]. The resting heart rate is positively correlated with sub-capsular skinfold thickness in children [35]. Similar findings have been obtained in early origin of cardiometabolic disease. Foetuses born to obese mothers demonstrate increased foetal sympathetic activation [36], which may predict long-term cardiovascular outcome.
\nA large body of epidemiological literature supports the link between an adverse intrauterine environment and disease in later life, showing inverse association between low birth weights (poor nutrition) with subsequent hypertension [37–40]. Similar findings have been demonstrated in animal models of maternal malnutrition and uterine growth restriction (IUGR) [41]. Despite the worldwide obesity epidemic, relative few studies have investigated the influence of maternal obesity on offspring health, with only recent emerging human data suggesting detrimental effects with preterm mortality in the offspring [42]. Epidemiological study demonstrates associations between maternal BMI and increased systolic blood pressure (SBP) in 7-year-old children [43]. The Amsterdam Born Children and their Development (ABCD) study recently reported that maternal pre-pregnancy BMI, in 3074 women, was positively associated with childhood diastolic blood pressure (DBP) and SBP at the age of 5–6 [44]. In the Jerusalem perinatal study (JPS), both gestational weight gain (GWG) and pre-pregnancy BMI were related to cardiovascular risk factors including SBP and DBP in adult offspring, at the age of 32 [45]. A stronger association for maternal pre-pregnancy BMI than paternal BMI with adverse cardiometabolic health in offspring suggests a direct intrauterine mechanisms, instead of life-style-related characteristics or genetic factors [46]. However, the causation is difficult to establish in human cohort studies. Interestingly, a recent study demonstrated an increased risk for cardiovascular mortality in children born to obese mothers, and this association remained after removing the child BMI [42]. Thus, suggests a direct effect of maternal obesity on child cardiovascular function, independent of childhood obesity [42]. The WHO Global Burden of Disease database currently identifies a rapid rise in maternal obesity in the past two decades. In the US, 64% of women of reproductive age are overweight and 35% are obese [47], with a similar pattern in Europe [48] and the rest of the world [49–51]. Obese pregnant women not only develop a higher risk of preeclampsia, preterm labour, stillbirth, caesarean deliveries, there are also a higher incidence of developing diabetes and hypertension in the offspring [52]. The increasing rate of maternal obesity may therefore provide a major challenge to future generations’ health. Children born to obese mothers not only are prone to develop obesity but also essential hypertension which is the primary risk factor for developing other cardiovascular diseases leading to premature death. Whilst further randomised controlled clinical trials of improved design are indicated, there is an important task to revisit the basic science of autonomic function using experimental models that mimics the human condition of essential hypertension.
\nThe autonomic nervous system (ANS) has two principal divisions, the parasympathetic pathway and the sympathetic pathway which acts either in synergy or in opposition synergy. The autonomic system continuously controls heart rate and blood pressure, respiratory rate and gut motility, body temperature and other essential functions. The autonomic function interacts with the primitive brain, including the limbic system (memory function), brain stem and hypothalamus [53]. Neurons within hypothalamic nuclei, particularly the paraventricular nucleus (PVN) and dorsomedial hypothalamus (DMN), make direct or indirect connection with sympathetic and parasympathetic preganglionic neurons and interfere with autonomic balance, sympathetic hyperactivity and neurogenic hypertension [53]. Early stages of hypertension, particularly in children, are defined by autonomic dysfunction [54]. Excessive sympathetic activity and/or withdrawal of parasympathetic balance are assessed by HR variability (HRV), using the ratio of low to high frequency (LF/HF) power. Pioneering studies conducted by Urbina et al. showed altered HRV in 39 male adolescences and reported trends of higher LF/HF ratio with higher BP, but did not reach statistical significance [65]. In a larger cohort, Sorof et al. [20] reported increased HR and BP variability in obese children with isolated systolic hypertension assessed by office HR/BP measurement and ambulatory blood pressure monitoring (ABPM). Interestingly, obese hypertensive children had higher HR than non-obese hypertensive children, suggesting that obesity is independently related with SNS activation [20]. These initial findings of SNS hyperactivity in hypertensive children, measured by indirect methods, were later confirmed by direct measurement of sympathetic activity using microneurography [55]. Zhou et al. [56] demonstrated altered vagal and sympathetic activity in hypertensive children, with a greater influence of systolic blood pressure (SBP) than diastolic blood pressure (DBP) on HRV [57]. Genovesi et al. [58] demonstrated baroreflex impairment, in both hypertensive and pre-hypertensive children. Autonomic dysfunction is therefore considered a critical feature in pre-hypertensive children which may predict future cardiovascular health. In children with arterial hypertension, the increase of sympathetic activity during sleep correlate with increase left ventricular mass and left ventricular mass index [59]. Moreover, HRV can predict the severity of children with pulmonary arterial hypertension [60]. This is particular worrisome as historical reference data on child HRV by Massin et al. [61] with current child HRV in Germany [62] showed change in children’s ANS in the last 15 years. These changes constitute reduced vagal activity and a shift towards sympathetic dominance [62]. The authors suggest that these changes might be related to the rise in childhood obesity, with a negative association between BMI and ANS activity [62]. The historical samples of Kauzuma et al. [63], however, featured a comparable overweight rate (17%), but still reported much lower mean sympathetic activity. Additional factors including physical inactivity or nutrient composition may influence ANS [64, 65]. Maternal BMI, which recently been associated with the offspring ANS activity, may be another important determinant [66]. Several different mechanisms leading to and maintaining central sympathetic hyperactivity in essential hypertension have been identified. An impaired vagal heart rate control exerted by arterial baroreflex impaired volume-sensitive cardiopulmonary reflex, arterial chemoreceptors as well as humoral factors such as leptin and angiotensin II with direct central sympathoexcitatory effects have all been shown to play at least partial roles in essential hypertension.
\nExperimental models of maternal obesity in sheep, non-human primate and rodents provide evidence for the adverse influence on offspring cardiovascular function [67]. In rodents, the perinatal exposure to metabolic milieu of maternal obesity may permanently change the central pathways involved in blood pressure regulation [66]. Leptin, an adipocyte-derived hormone, promotes weight loss by reducing appetite and increasing energy expenditure through hypothalamic sympathetic stimulation to brown adipose tissue [68] and kidney [69] which results in increased arterial pressure [70]. This has been confirmed in chronic infusion of leptin in rats developing increased blood pressure [71]. Transgenic mice overexpressing leptin develops overt obesity with elevations of blood pressure [72]. Selective leptin resistance of the appetite and weight reducing effect of leptin [73], and preservation of the sympathetic action of leptin, been implicated in obesity-related hypertension [74]. In humans, high plasma leptin concentration has been associated with arterial pressure [75] and muscle sympathetic nerve activity [76]. Leptin is also thought to have a neurotrophic role in the development of the hypothalamus [77], and altered neonatal leptin profiles secondary to maternal obesity are associated with permanently altered brain hypothalamic structure and function. In rodent studies, maternal obesity confers persistent sympathoexcitatory hyper-responsiveness and hypertension acquired in the early stages of development [78]. Unrevealing the mechanisms controlling hypothalamic development may help to identify the nature of the hypothalamic dysfunction and develop future therapies. High leptin in cord blood from foetuses of obese mothers [79] might cause permanent changes of the hypothalamic circuits leading to heightened leptin-induced sympathetic activity and blood pressure in juvenile offspring, prior to obesity and metabolic dysfunction [70].
\nThe melanocortin system is an essential pathway in central regulation of metabolic and cardiovascular function. Central pro-opiomelanocortin (POMC) containing neurons in the arcuate nucleus (ARC) of the hypothalamus and the brain stem (e.g. nucleus of the tractus solitaries, NTS) project to other brain regions involved in energy homeostasis but also cardiovascular regulation [80]. The POMC neurons stimulate melanocortin receptor subtype 3 (MC3R) and 4 (MC4R) and reduce appetite and increase energy expenditure, SNS activity and BP [80]. Mutation of the melanocortin-4 receptor (MC4R) or pro-opiomelanocortin (POMC) gene estimates for 5–6% of early onset obesity in human [81]. Pharmacological blockade of MC4R causes pronounced obesity in rodents [82], whereas activation of MC4R promotes weight loss by reducing appetite and increase energy expenditure [83, 84]. Conversely, chronic MC4R activation causes sustained increased in BP despite reducing food intake and promoting weight loss [85]. MC4R-deficient rodents demonstrate reduced SNS activity and BP, independent of obesity [86]. Similar observations have been shown in humans, and MC4R deficiency leads to obesity but exhibits lower BP and reduced 24-h noradrenaline excretion compared with obese subjects with normal MC4R function [87, 88]. We and others have also demonstrated a critical role for the POMC-neurons MC4R axis in mediating appetite-suppressing and blood pressure effects of leptin [89, 90]. Rahmouni et al. [89] showed that acute effect of leptin-induced hypophagia and renal SNS activity which were attenuated and abolished in heterozygous and homozygous MC4R knockout mice, respectively. Intact POMC neurons-MC4R axis is also required in chronic leptin-induced SNS activity and BP regulation [91]. MC4R antagonism markedly reduced BP in juvenile offspring born to obese dams (OffOb) [90] and spontaneous hypertensive rats (SHR) [92] two experimental models of hypertension that is associated with increased SNS activity in the absence of obesity [70, 93]. MC4R antagonism also attenuates or abolishes the acute pressor responses to leptin that raises BP by SNS stimulation [92]. Collectively, these observations suggest that the MC4R plays a key role in contributing to elevated BP in several forms of hypertension that accompany SNS overactivity. Greatest abundance of MC4R is the paraventricular nucleus of the hypothalamus (PVN), lateral hypothalamus (LH), the amygdala, the NTS and the preganglionic sympathetic neurons, which are all important sites for regulation of autonomic function [80]. Although the specific contribution of MC4R in distinct CNS nuclei in mediating the actions of the brain melanocortin system on energy balance, appetite and glucose homeostasis has been the subject of intense investigation, the particular regions of the brain, where MC4R is the most important in regulation of SNS activity and BP, are still unclear. We have recently shown that the activation of MC4R in the PVN (using sim-cre genetic-modified mice) demonstrated increased BP in offspring of obese dams that were protected in the MC4R-mutated mice; suggest an important role for MC4R in PVN in contributing to early onset hypertension [90]. One study has also observed that specific neuronal populations including cholinergic preganglionic parasympathetic and sympathetic neurons are involved in MC4R-mediated hypertension [94]. The specific stimuli that mediate the effect of MC4R to evoke sustained increases in SNS activity to cardiovascular-relevant tissue and promote chronic increase in BP are still unknown and remain an important area for future investigations.
\nThere has been a great progress in elucidating molecular targets for hypertension from monogenic disorders [95]. Among the most significant findings has been from single-gene disorders with primary effect on blood pressure that acts via common pathway alterations including renin-angiotensin and melanocortin system [95]. Recent genome-wide association studies (GWASs), conducted mostly in Europeans, have identified >30 genomic loci associated with systolic/diastolic BP [96], including candidate genes angiotensinogen [97], angiotensin-converting enzyme (ACE) [98], and alpha 2 adrenergic receptor genes (ADRA2A) [98]. The GWAS analysis is, however, inconsistent between populations, with a great gene-environment interaction, that significantly contributes to the increased risk of hypertension [99]. Obesity is one of the most dominant risk factor of childhood hypertension with a common genetic traits in FTO [100] and downstream of MC4R [101]. Hypertension has been associated with the risk allele A for FTO rs9939609 and the risk allele C for MC4R rs17782313, independent of BMI [102, 103]. Recent study by Sun et al. demonstrated an association of the FTO rs9939609 and MC4R rs17782313 genes with nocturnal blood pressure in the Chinese Han population [104]. The effect sizes are, however, small for each individual genetic variant, typically 1 mmHg for SBP and 0.5 mmHg for DBP [105]. Even collectively, the 30 variants tested in one experiment explain only 1–2% of SBP and DBP variance [105]. Heritability of hypertension is estimated to be between 30 and 40% which is approximately 25 times larger than the phenotypic variation and disease risk currently explained by GWAS SNPs. The observation that only little of the total heritability can be currently be explained by the GWAS has led to the term “missing heritability” [106]. It is expected that many more yet undiscovered loci, possible including variants in the rare allele spectrum that might have larger effects sizes, will contribute to explain the missing heritability [106]. It has been suggested that epigenetic changes may account for the missing heritability determinants of complex diseases, such as hypertension.
\nRecent years have shown a dramatic interest in the epigenetic trait of human disease. Phenotypic variation is regulated independent of changes in DNA sequence, such as DNA methylation, histone modification, chromatin remodelling and the action of small noncoding RNAs (microRNA) [107]. These epigenetic modifications change the accessibility of gene promoter sequence (by methyl donor) and binding domain [107]. Several animal studies have characterise epigenetic modification influenced by the intrauterine environment (maternal stress, nutrition and behaviour) [107]. In cardiovascular disease, recent studies of low-protein diet during pregnancy showed early onset hypertension in the offspring [108]. The renin-angiotensin system showed to be a main target as angiotensin receptor (AT1R) antagonist reversed the hypertension in the offspring [108]. Consistent with these finding, offspring showed a hypomethylated AT1R gene promoter along with the increased expression of AT1R [109], suggesting a role for specific AT1R hypomethylation in regulating elevated blood pressure in this model. Similar epigenetic modification has been shown in the hypothalamic POMC neurons in a rat model of neonatal overfeeding [110]. Hypothalamic POMC showed hypermethylated in the overfed neonates and consequently influence the set point of the melanocortin system which is critical for metabolic and cardiovascular regulation [110]. Fewer studies of epigenetic changes have been conducted in primates, and there is little direct evidence relating this to humans. One study showed a correlation of epigenetic RXRA (retinoid X receptor alpha—induces transcription of PPARs) promoter methylation with increased adiposity in children of mothers with lower carbohydrate intake in two independent cohorts [111]. Although this fails to confirm a causal relationship, it may provide an objective marker in identifying children at risk of obesity and hypertension-induced cardiac hypertrophy [112].
\nMechanistic overview of the developmental origin of hypertension.
Several reports have demonstrated enhanced inflammatory profile with paediatric hypertension [113, 114]. The C-reactive protein (CRP) which normally is involved in innate immune responses is heightened both in hypertensive and pre-hypertensive obese children, suggesting that systemic low-grade inflammation may precede hypertension [115]. This has been further confirmed in animal models of hypertension. Spontaneous hypertensive rats (SHR) a genetic model of essential hypertension demonstrate increased renal infiltration of lymphocytes and macrophages and activation of nuclear factor –kappa B (NF-kB) in 3-week-old pre-hypertensive rats [116]. Serum CRP has also been associated with cardiovascular risk factors in children including BP variability [117], intima media thickness [118], arterial stiffness [119], left ventricular hypertrophy [120]. Obese children and adolescence also demonstrate elevated serum concentration of pro-inflammatory cytokines interleukin-6 (IL-6) IL-1β and ICAM-1 (intercellular cell adhesion molecule-1) with increased ambulatory BP [121]. The pro-inflammatory cytokines may also be increased due to obesity alone, independent of essential hypertension [122]. However, the highest concentration of these molecules was found in children with co-existing hypertension [114]. Mounting evidence suggests that the pro-inflammatory condition in mother may induce inflammation-induced hypertension in their offspring [123]. An overactive immune response during pregnancy, as shown in obese pregnancy [124], can lead to chronic neuro-inflammation in the foetus [125]. Activated microglia, resident immune cells in the brain, increases pro-inflammatory cytokines release from the PVN, which stimulate preganglionic nerve fibres and sympathetic nerve activity (SNA) [126]. Vice versa, SNA has a direct impact on microglia via adrenergic receptors [127] or indirect via regulating distribution and production of lymphocytes, or modulating the release of pro-inflammatory peptides. SNA is also involved in inflammatory cell recruitment and redistribution, and SNA mobilise inflammatory cells from spleen and bone marrow [128]. In addition, parasympathetic nervous system has anti-inflammatory effects [129]. Vagal afferents sense peripheral inflammation and feedback via the cholinergic anti-inflammatory pathway [129]. There are also important direct effects of cytokines and angiotensin II on the brain that certainly could contribute to SNA [129, 130]. Catheter-base renal denervation is a promising therapeutic approach to treat hypertension [131], and recent animal studies suggest an improvement of renal inflammation with reduced renal macrophages and levels of cortical TNF-alpha and suggest a potential target for renal injury and dysfunction [132]. Minocyclin treatment, an anti-inflammatory antibiotic that crosses the blood brain barrier, has shown to prevent autonomic dysfunction and hypertension in experimental models of hypertension [126]. The reduction in blood pressure was associated with “de-activation” of the microglia in the PVN [126]. Overall, all these studies suggest a potentially important link between inflammation, melanocortin system, developing brain and autonomic dysfunction in the environmental and genetic predisposition of hypertension arising from maternal diet-induced obesity (Figure 1).
\nPaediatric hypertension has been gaining significant attention in the last decade, mainly due to the increased prevalence worldwide. The estimated prevalence of paediatric hypertension is from 1 to 10%, with a steady rise over time. Alarming rate of childhood obesity and metabolic syndrome with the precondition of maternal obesity may worsen the future cardiovascular morbidity and mortality. This could be hypothetically prevented by early diagnosis and management in children before they even develop the pathophysiological progression state of hypertension. In fact, certain drugs may fail to reduce sympathetic hyperactivity as other stimuli of SNA have become predominant in elevating SNA, which are independent of the standard antihypertensive strategies. The progress and impact of preventive blood pressure screening for children could also inhibit adult hypertension and cardiovascular disease. Therefore, increased alertness to paediatric hypertension including several risk parameters (genetic, maternal, inflammatory, adiposity) and standardise sequential ABPM monitoring to avoid “white-coat” and “masked” hypertension in the diagnosis could improve future statistics in adverse cardiovascular outcome. Research effort should continue with the goal to clarify the aetiology, complexity and inheritable factors of paediatric hypertension. Research efforts should also focus on optimal treatment of these children and on effective preventive measures starting in the pregnant mother to the child at a young age.
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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.
\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:
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
Comparison 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
So, 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
For 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
\n
\n
where \n
The stage of a jet, which is defined by boundary
At point (
where
For 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
where 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
where
where
According to considered formula (11), a dispersion model integrates engine emission model and jet transport model via including the following parameters:
\n\n
\n
In 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).
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",metaTitle:"Publishing Process Steps and Descriptions",metaDescription:"This is a brief overview of the main steps involved in publishing with InTechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Publishing Process Manager who will be your single point of contact and lead you through all the described steps below.",metaKeywords:null,canonicalURL:"page/publishing-process-steps",contentRaw:'[{"type":"htmlEditorComponent","content":"1. SEND YOUR PROPOSAL
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\n\n2. SUBMIT YOUR MANUSCRIPT
\n\nAfter approval, you will proceed in submitting your full-length manuscript. 50-130 pages for compacts, 130-500 for Monographs & Edited Books.Your full-length manuscript must follow IntechOpen's Author Guidelines and comply with our publishing rules. Once the manuscript is submitted, but before it is forwarded for peer review, it will be screened for plagiarism.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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Also, they can change their position frequently in a specific sensing area. The applications of the MWSNs can be widely divided into time-driven, event-driven, on-demand and tracking based applications. Mobile sensor node architecture, residual energy utilization, mobility, topology, scalability, localization, data collection routing, Quality of Service (QoS), etc., are the key factors to design an energy efficient MWSNs for some specific purpose. This chapter deals with an overview of the MWSNs and a few significant phenomena to design an energy efficient MWSNs to the large-scale environment.",book:{id:"6038",slug:"wireless-sensor-networks-insights-and-innovations",title:"Wireless Sensor Networks",fullTitle:"Wireless Sensor Networks - Insights and Innovations"},signatures:"Velmani Ramasamy",authors:[{id:"206195",title:"Dr.",name:"Velmani",middleName:null,surname:"Ramasamy",slug:"velmani-ramasamy",fullName:"Velmani Ramasamy"}]},{id:"56541",doi:"10.5772/intechopen.70208",title:"Routing Protocols for Wireless Sensor Networks (WSNs)",slug:"routing-protocols-for-wireless-sensor-networks-wsns-",totalDownloads:5806,totalCrossrefCites:19,totalDimensionsCites:28,abstract:"Wireless sensor networks (WSNs) are achieving importance with the passage of time. Out of massive usage of wireless sensor networks, few applications demand quick data transfer including minimum possible interruption. Several applications give importance to throughput and they have not much to do with delay. It all rest on the applications desires that which parameter is more favourite. The knowledge of network structure and routing protocol is very important and it should be appropriate for the requirement of the usage. In the end a performance analysis of different routing protocols is made using a WLAN and a ZigBee based Wireless Sensor Network.",book:{id:"6038",slug:"wireless-sensor-networks-insights-and-innovations",title:"Wireless Sensor Networks",fullTitle:"Wireless Sensor Networks - Insights and Innovations"},signatures:"Noman Shabbir and Syed Rizwan Hassan",authors:[{id:"206600",title:"Mr.",name:"Noman",middleName:null,surname:"Shabbir",slug:"noman-shabbir",fullName:"Noman Shabbir"},{id:"206601",title:"Mr.",name:"Syed Rizwan",middleName:null,surname:"Hassan",slug:"syed-rizwan-hassan",fullName:"Syed Rizwan Hassan"}]},{id:"32626",doi:"10.5772/33373",title:"Augmented Human Engineering: A Theoretical and Experimental Approach to Human Systems Integration",slug:"agmented-human-engineering-a-theoretical-and-experimental-approach-to-human-system-integration",totalDownloads:2583,totalCrossrefCites:8,totalDimensionsCites:11,abstract:null,book:{id:"1664",slug:"systems-engineering-practice-and-theory",title:"Systems Engineering",fullTitle:"Systems Engineering - Practice and Theory"},signatures:"Didier Fass",authors:[{id:"95113",title:"Dr.",name:"Didier",middleName:null,surname:"Fass",slug:"didier-fass",fullName:"Didier Fass"}]},{id:"56436",doi:"10.5772/intechopen.70138",title:"Low-Cost Energy-Efficient Air Quality Monitoring System Using Wireless Sensor Network",slug:"low-cost-energy-efficient-air-quality-monitoring-system-using-wireless-sensor-network",totalDownloads:2256,totalCrossrefCites:5,totalDimensionsCites:10,abstract:"Due to rapid industrialization and urbanization, Mauritius is witnessing an unprecedented increase in air pollution. The release of hazardous gases such as carbon monoxide and sulphur dioxide are not only harmful to the health of the population but are also causing irreversible impact to the environment. Currently, there are only two fixed air quality monitoring units on the island and therefore, air pollution cannot be monitored in real-time. The objective of this chapter is to describe the implementation of a low-cost and energy-efficient air quality monitoring system using wireless sensor network (WSN) that can be easily deployed in highly polluted areas of Mauritius. A Hierarchical Based Genetic Algorithm (HBGA) is proposed to address the issue of sensor nodes with limited energy. Based on hierarchical routing and genetic algorithm, HBGA has been designed to extend the lifetime of the network by minimizing the energy consumption. The proposed air quality monitoring system uses an air quality index that can be easily interpreted. The evaluation results confirm the potential of the proposed system for real-time temporal and spatial monitoring of air quality. Moreover, it possible for the general public to have access to the air quality monitoring results in real time.",book:{id:"6038",slug:"wireless-sensor-networks-insights-and-innovations",title:"Wireless Sensor Networks",fullTitle:"Wireless Sensor Networks - Insights and Innovations"},signatures:"Kavi Kumar Khedo and Vishwakarma Chikhooreeah",authors:[{id:"28763",title:"Associate Prof.",name:"Kavi",middleName:null,surname:"Khedo",slug:"kavi-khedo",fullName:"Kavi Khedo"},{id:"206500",title:"Mr.",name:"Vishwakarma",middleName:null,surname:"Chikhooreeah",slug:"vishwakarma-chikhooreeah",fullName:"Vishwakarma Chikhooreeah"}]}],mostDownloadedChaptersLast30Days:[{id:"56541",title:"Routing Protocols for Wireless Sensor Networks (WSNs)",slug:"routing-protocols-for-wireless-sensor-networks-wsns-",totalDownloads:5817,totalCrossrefCites:19,totalDimensionsCites:28,abstract:"Wireless sensor networks (WSNs) are achieving importance with the passage of time. Out of massive usage of wireless sensor networks, few applications demand quick data transfer including minimum possible interruption. Several applications give importance to throughput and they have not much to do with delay. It all rest on the applications desires that which parameter is more favourite. The knowledge of network structure and routing protocol is very important and it should be appropriate for the requirement of the usage. 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The unintentional introduction of stoats and rats into a once pristine ecosystem has resulted in the devastation of large parts of New Zealand’s native flora and fauna. Other equally harmful mammalian species, including possum, for their fur, and domestic cats, were introduced intentionally. Abundant vegetation and a lack of predators lead to rampant population growth, further exacerbating their destructive impact. Effective monitoring, trapping and control of mammalian pests have proven difficult, time-consuming and expensive, primarily relying on socially controversial methods such as aerially delivered toxins. Despite advances in technology, costly and time-intensive manual checking of lures, toxins, traps and tracking devices remains a limiting factor. Together with WSN-based remote monitoring capability, these advances look set to have a significant impact. This chapter discusses opportunities for WSN in conservation management. It outlines a mammalian pest management project utilising a series of possum-specific self-resetting traps. A WSN designed for remotely monitoring possum trap activity is detailed, and the process for reconfiguring and presenting field-trial data via alpha-numeric and graphical user interface applications is described.",book:{id:"6038",slug:"wireless-sensor-networks-insights-and-innovations",title:"Wireless Sensor Networks",fullTitle:"Wireless Sensor Networks - Insights and Innovations"},signatures:"Akbar Ghobakhlou and Shane Inder",authors:[{id:"209937",title:"Dr.",name:"Akbar",middleName:null,surname:"Ghobakhlou",slug:"akbar-ghobakhlou",fullName:"Akbar Ghobakhlou"},{id:"214817",title:"Dr.",name:"Shane",middleName:null,surname:"Inder",slug:"shane-inder",fullName:"Shane Inder"}]},{id:"56523",title:"Fuzzy Adaptive Setpoint Weighting Controller for WirelessHART Networked Control Systems",slug:"fuzzy-adaptive-setpoint-weighting-controller-for-wirelesshart-networked-control-systems",totalDownloads:1167,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"Gain range limitation of conventional proportional‐integral‐derivative (PID) controllers has made them unsuitable for application in a delayed environment. These controllers are also not suitable for use in a Wireless Highway Addressable Remote Transducer (WirelessHART) protocol networked control setup. This is due to stochastic network‐induced delay and uncertainties such as packet dropout. The use of setpoint weighting strategy has been proposed to improve the performance of the PID in such environments. However, the stochastic delay still makes it difficult to achieve optimal performance. This chapter proposes an adaptation to the setpoint weighting technique. The proposed approach will be used to adapt the setpoint weighting structure to variation in WirelessHART network‐induced delay through fuzzy inference. Result comparison of the proposed approach with both setpoint weighting and proportional‐integral (PI) control strategy shows improved setpoint tracking and load regulation. For the first‐, second‐ and third‐order systems considered, analysis of the results in the time domain shows that in terms of overshoot, undershoot, rise time, and settling times, the proposed approach outperforms both the setpoint weighting and the PI controller. The approach also shows faster recovery from disturbance effect.",book:{id:"6038",slug:"wireless-sensor-networks-insights-and-innovations",title:"Wireless Sensor Networks",fullTitle:"Wireless Sensor Networks - Insights and Innovations"},signatures:"Sabo Miya Hassan, Rosdiazli Ibrahim, Nordin Saad, Vijanth Sagayan\nAsirvadam, Kishore Bingi and Tran Duc Chung",authors:[{id:"206524",title:"MSc.",name:"Sabo",middleName:"Miya",surname:"Hassan",slug:"sabo-hassan",fullName:"Sabo Hassan"},{id:"206525",title:"Dr.",name:"Rosdiazli",middleName:null,surname:"Ibrahim",slug:"rosdiazli-ibrahim",fullName:"Rosdiazli Ibrahim"},{id:"206529",title:"Dr.",name:"Nordin",middleName:null,surname:"Saad",slug:"nordin-saad",fullName:"Nordin Saad"},{id:"206530",title:"Dr.",name:"Vijanth Sagayan",middleName:null,surname:"Asirvadam",slug:"vijanth-sagayan-asirvadam",fullName:"Vijanth Sagayan Asirvadam"},{id:"206532",title:"Mr.",name:"Tran",middleName:"Duc",surname:"Chung",slug:"tran-chung",fullName:"Tran Chung"},{id:"206540",title:"Ph.D. Student",name:"Kishore",middleName:null,surname:"Bingi",slug:"kishore-bingi",fullName:"Kishore Bingi"}]},{id:"56900",title:"Mobile Wireless Sensor Networks: An Overview",slug:"mobile-wireless-sensor-networks-an-overview",totalDownloads:2730,totalCrossrefCites:23,totalDimensionsCites:30,abstract:"Mobile wireless sensor networks (MWSNs) have emerged and shifted the focus from the typical static wireless sensor networks to networks with mobile sensor nodes that are capable to sense the various types of events. 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He received his MSc and PhD in Biomedical Engineering respectively from the Federal University of Uberlândia (UFU, Brazil) in 2000 and from the University of Reading (UK) in 2005. He completed a one-year Post-Doctoral Fellowship awarded by the DFAIT (Foreign Affairs and International Trade Canada) at the Institute of Biomedical Engineering of the University of New Brunswick (Canada) in 2010. Currently, he is Professor in the Faculty of Electrical Engineering (UFU). He has authored and co-authored more than 200 peer-reviewed publications in Biomedical Engineering. He has been a researcher of The National Council for Scientific and Technological Development (CNPq-Brazil) since 2009. He has served as an ad-hoc consultant for CNPq, CAPES (Coordination for the Improvement of Higher Education Personnel), FINEP (Brazilian Innovation Agency), and other funding bodies on several occasions. 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