Values of
\r\n\t
",isbn:"978-1-80356-495-1",printIsbn:"978-1-80356-494-4",pdfIsbn:"978-1-80356-496-8",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"2d409a285bea682efb34a817b0651aba",bookSignature:"Dr. Saeed El-Ashram, Dr. Guillermo Téllez and Dr. Firas Alali",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11635.jpg",keywords:"PCR, Genotyping, ELISA, Cell Lines, 2D Culture, 3D Culture, PRRs, CD4 Responses, CD8 Responses, Behavior Manipulation, Parasite Cysts, Psychiatric Disorders",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 4th 2022",dateEndSecondStepPublish:"May 6th 2022",dateEndThirdStepPublish:"July 5th 2022",dateEndFourthStepPublish:"September 23rd 2022",dateEndFifthStepPublish:"November 22nd 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. El-Ashram's research focuses on apicomplexan parasites, such as Toxoplasma and Eimeria. He has more than 96 SCI publications, he acted as an academic editor, reviewer, and he holds several registered patents.",coeditorOneBiosketch:"Researcher in enteric health, most notably probiotics and their relationship to nutrition and disease protection in poultry as well as the design of avian enteric inflammation models for the study of the impact of diet and microbiome on growth and development.",coeditorTwoBiosketch:"My research focuses mainly on apicomplexan parasites, such as Toxoplasma Cryptosporidium, Eimeria, and minor on nematodes. Prof.Alali has more than 30 publications and he acts as a reviewer in many journals.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"209746",title:"Dr.",name:"Saeed",middleName:null,surname:"El-Ashram",slug:"saeed-el-ashram",fullName:"Saeed El-Ashram",profilePictureURL:"https://mts.intechopen.com/storage/users/209746/images/system/209746.png",biography:"Dr. Saeed El-Ashram is a professor at Foshan University, China, and Kafrelsheikh University, Egypt, and a research professor at Zhaoqing Dahuanong Biology Medicine Co., Ltd., China. Dr. El-Ashram\\'s research focuses on parasitic diseases. He has more than 100 journal publications to his credit. He is currently an academic editor and reviewer and holds several registered patents. The primary focus of his research is to understand how the animal immune system recognizes and responds to parasitic infections with and/or without a microbial community. Some are the causative agents of significant diseases in humans, such as toxoplasmosis, cryptosporidiosis, alveolar echinococcosis, and fascioliasis. Others are a substantial financial burden to food producers because of the effects these parasites have on domestic animals, for example, coccidiosis and cryptosporidiosis (livestock and poultry).",institutionString:"Foshan University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Foshan University",institutionURL:null,country:{name:"China"}}}],coeditorOne:{id:"73465",title:"Dr.",name:"Guillermo",middleName:null,surname:"Téllez",slug:"guillermo-tellez",fullName:"Guillermo Téllez",profilePictureURL:"https://mts.intechopen.com/storage/users/73465/images/system/73465.jpg",biography:"Guillermo Tellez-Isaias received his DVM and MS in Veterinary Sciences from the National Autonomous University of Mexico (UNAM), and his Ph.D. from Texas A&M University. He worked as a professor at UNAM for sixteen years, eight as head of the Avian Medicine Department, College of Veterinary Medicine. Dr. Tellez was president of the National Poultry Science Association of Mexico and is a member of the Mexican Veterinary Academy and the Mexican National Research System. Currently, he works as a research professor at the Center of Excellence in Poultry Science, University of Arkansas. His research is focused on poultry gastrointestinal models to evaluate the beneficial effects of functional foods to enhance intestinal health and disease resistance.",institutionString:"University of Arkansas at Fayetteville",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"8",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Arkansas at Fayetteville",institutionURL:null,country:{name:"United States of America"}}},coeditorTwo:{id:"437285",title:"Dr.",name:"Firas",middleName:null,surname:"Alali",slug:"firas-alali",fullName:"Firas Alali",profilePictureURL:"https://mts.intechopen.com/storage/users/437285/images/17927_n.jpg",biography:"Academic reviewer for many journals.\r\nAssociate Professor at University of Kerbala, Iraq. Firas Alali works at the Department of Veterinary Parasitology of Veterinary Medicine college, Kerbala University. Firas does research in Parasitology, Entomology, and Vector-Borne Diseases including zoonoses.",institutionString:"University of Kerbala",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"13",title:"Immunology and Microbiology",slug:"immunology-and-microbiology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"453623",firstName:"Silvia",lastName:"Sabo",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/453623/images/20396_n.jpg",email:"silvia@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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GD have been associated with abnormal fetal development and perinatal complications such as macrosomia, neonatal hypoglicaemia, and neurological disorders (Nold & Georgieff, 2004; Pardo et al., 2012). The main risk factor to predict the GD development are increased maternal age, overweight before pregnancy, a history of GD in the first pregnancy and history of intolerance abnormal D-glucose (Morisset et al., 2010). Clinical manifestations of GD have been atribuited to conditions of hyperglicaemia, hyperlipidemia, hyperinsulinemia, and fetal endothelial dysfunction (Nold & Goergieff, 2004; Greene & Solomon, 2005; Sobrevia et al., 2011). Moreover, GD produces alterations in vascular reactivity (i.e., endothelium dependent vasodilation), which is considered a marker of endothelial dysfunction (De Vriese et al., 2000; Sobrevia et al., 2011; Westermeier et al., 2011; Salomón et al., 2012).
GD generates structural and funtional alterations, including placental microvascular and macrovascular endothelial disfunction (Tchirikov et al., 2002; Biri et al., 2006; Sobrevia et al., 2011), observations showing an altered regulation of vascular tone in the fetal-placental circulation (San Martín & Sobrevia, 2006; Casanello et al., 2007; Sobrevia et al., 2011). The distal segment of umbilical cord and the placenta correspond to vascular beds without innervation (Marzioni et al., 2004), therefore local regulation of vascular tone results from a balanced combination of the synthesis, release and bioactivity endothelium-derived vasodilators (i.e., nitric oxide (NO), prostanglandins, adenosine) and vasoconstrictors (i.e., endothelin-1, angiotensin II) (Olsson & Pearson, 1990; Becker et al., 2000). It was reported that arteries and veins in the human placenta from pregnancies with GD have an increase in NO synthesis (Figueroa et al., 2000). Furthermore, the same result was obtained from primary cultures of human umbilical vein endothelial cells (HUVEC) from pregnant women diagnosed with GD (Sobrevia et al., 1995). Therefore, vascular disfunction resulting from GD may result from a functional dissociation between NO synthesis and its bioavailability in the human placental circulation (Sobrevia et al., 2011). Even when endothelial dysfunction is associated with GD, this is referred to as an alteration of NO synthesis and the uptake of cationic aminoacid L-arginine (i.e., L-arginine/NO pathway) (Figure 1) and a lack of mechanism behind these effects of GD is still a reality (Pardo et al., 2012). However, it is accepted that GD is a result of multiple mechanisms of metabolic alteration, including human fetal endothelial sensitivity to vasoactive molecules such as adenosine (Vásquez et al., 2004; San Martín & Sobrevia, 2006; Sobrevia et al., 2011; Pardo et al., 2012).
L-arginine transport in human cells corresponding to different system of amino acids transports, someone of them, it is
Human CAT-1 (hCAT-1) expression is modulated by citokines (i.e., TNFα, TGFβ) (Irie et al., 1997; Visigalli et al., 2007; Vásquez et al., 2007) and hormones (i.e., insulin) (Simmons et al., 1996; González et al., 2004, 2011a). The gene coding for this protein is called
L-Arginine transport via hCAT-1 is regulated by different conditions (Sobrevia & González, 2009; González et al., 2011a). In HUVEC, hCAT-1 expression increases by tumoral necreosis factor alpha (TNF-α) (Irie et al., 1997; Visigalli et al., 2007) and transforming growth factor beta (TGF-β) (Vásquez et al., 2007), in the presence of free radicals such as superoxide anion (O2-) (González et al., 2011b), insulin (González et al., 2011a; Guzmán-Gutiérrez et al., 2012a), activation of A2A adenosine receptors (A2AAR) (Vásquez et al., 2004; Guzmán-Gutiérrez et al., 2012a), or high extracelular D-glucose concentration (25 mM) (Vásquez et al., 2007). Interestingly, insulin, A2AAR and extracellular D-glucose have been directly associated with GD (San Martín & Sobrevia, 2006). Notably, HUVEC from GD pregnancy have increased hCAT-1 expression (Vásquez et al., 2004). Moreover, oxidized low-density lipoprotein (oxLDL) and protein kinase C (PKC) activity increase this transporter abundance in the membrane in HEK293 (Zhang et al., 2008; Vina-Vilaseca et al., 2011). Based in a series of recetn publications (reviewed in Leiva et al., 2011; Sobrevia et al., 2011; Pardo et al., 2012) it is proposed that hCAT-1 mediated L-arginine transport in HUVEC from GD could depend on the regulation of
The amino acid cationic transporters family are coding by
It has been reported that NO levels in amniotic fluid (von Mandach et al., 2003) and NO synthesis in placental vein and artery (Figueroa et al., 2000) are increased in GD. Early studies in HUVEC from GD pregnancies show increased NO synthesis and L-arginine transport (Sobrevia et al., 1995, 1997). These results were associated with an increase in eNOS number of copies for mRNA, protein level and activity (Vásquez et al., 2004; Farías et al., 2006, 2010; Westermeier et al., 2011). Moreover, HUVEC from GD pregnancies exhibit a higher number of copies of mRNA for hCAT-1 (Vásquez et al., 2004). Interestingly, HUVEC incubated with high D-glucose show increased NO synthesis and intracellular cGMP levels (Sobrevia et al., 1997; González et al., 2004, 2011a). In this phenomenon a role has been proposed for cell signaling pathways including PKC and p44/p42mapk (Montecinos et al., 2000; Flores et al., 2003). Thus, in GD there is an increase in NO level associated with an increase in hCAT-1 mediated L-arginine transport.
In HUVEC from GD pregnancies insulin reduces L-arginine transport-increased observed in this cells compared with HUVEC from normal pregnancies (Sobrevia et al., 1998). Moreover, it was observed that insulin reduce NO synthesis-increased (Sobrevia et al., 1998). Another vasoactive molecule, including adenosine, increases L-arginine transport and eNOS activity (Vásquez et al., 2004; San Martín & Sobrevia 2006; Farías et al., 2006, 2010; Westermeier et al., 2011). It was observated by assays
Adenosine is a purine nucleoside associated with several biological functions, such as nucleotides synthesis or cellular energetic metabolism (Eltzschig, 2009). Moreover, this nucleoside is a vasodilator in coronary, cerebral, and muscular circulation, in several conditions including hypoxia and exercise (Berne et al., 1983). Extracellular adenosine is a signaling molecule that activates adenosine receptors (ARs). ARs belonging purinergic receptor P1 family, are coupled to G-protein and only four subtypes ARs, A1, A2A, A2B y A3 have been described (Fredholm et al., 2001, 2007, 2011; Burnstock et al., 2006, 2010). ARs stimulation generates several biological effects which are related with the expression pattern and membrane disponibility in a certain cellular type or tissue (Liu et al., 2002; Wyatt et al., 2002; Feoktistov et al., 2002). The protein assembly exhibits a short N-terminal (7-13 amino acids) compared with the C-terminal (32-120 amino acids) (Burnstock, 2006). Humans ARs transmembrane domains have between 39–61% of identical sequence and 11-18% with P2 family (nucleotide receptors) (Burnstock, 2006). The A1AR, A2AAR y A3AR are activated by adenosine at nanomolar concentration, while A2BAR requires micromolar concentration for its activation (Fredholm et al., 2001; 2011; Schulte & Fredholm, 2003; Eltzschig, 2009; Mundell & Kelly, 2010). A1AR and A3AR are clasically associated with inhibitory signaling receptors coupled to Gi/Go protein; however, A2AAR and A2BAR are associated with stimulatory signaling receptors coupled to Gs protein (Klinger et al., 2002).
ARs activation depends on the adenosine extracellular level, a characteristic that is mainly regulated by adenosine membrane transporters (Baldwin et al., 2004; Burnstock, 2006; Westermeier et al., 2009; Burnstock et al., 2010; Sobrevia et al., 2011). In HUVEC and in human placental microvascular endothelial cells (hPMEC) the extracellular adenosine is taken up mainly via the equilibrative nucleoside transporters (ENTs) (Westermeier et al., 2009; 2011; Sobrevia et al., 2011; Salomón et al., 2012). Interestingly, the sodium dependent, concentrative nucleoside transporters (CNT) have not been described in HUVEC or hPMEC (Sobrevia et al., 2011; Pardo et al., 2012). Several studies have described endothelial effects of adenosine, including a rise in the oxygen demand/delivery relation in human heart due to A2AAR activation-associated vasodilation (Shryock et al., 1998; Sundell et al., 2003), or reduction on norepinephrine release and peripheral vascular resistance by A1AR activation in rat sympathetic nerve (Burgdorf et al., 2001, 2005). A summary of the potential biological effects resulting from activation of ARs is given in Table 2.
The vasodilatory effect of adenosine, which is endothelial-derived NO-dependent, is mediated by activation of ARs (Sobrevia & Mann, 1997; Edmunds & Marshall, 2003; Vásquez et al., 2004; San Martín & Sobrevia, 2006; Ray & Marshall, 2006; Casanello et al., 2007; Escudero et al., 2008, 2009; Westermeier et al., 2009; Sobrevia et al., 2011). This is also seen in primary cultures of HUVEC from GD (Vásquez et al., 2004; San Martín & Sobrevia, 2006; Casanello et al., 2007; Westermeier et al., 2009; Farías et al., 2006, 2010) or in HUVEC from normal pregnancies exposed to high D-glucose (Muñoz et al., 2006; Puebla et al., 2008). The functional link between adenosine and L-arginine/NO pathway in HUVEC has been referred as the ALANO signalling pathway (i.e.,
The increased activity of ALANO pathway in GD involves extracellular adenosine accumulation resulting from reduced of adenosine uptake into endothelial cells (Vásquez et al., 2004; Farías et al., 2006, 2010). This means that changes in plasma adenosine concentration in the fetoplacental circulation could result in an altered blood flux control in the human placenta (Westermeier et al., 2009; Sobrevia et al., 2011). It was demonstrated that resistance of umbilical vessels from GD do not change with respect to vessels from normal pregnancies (Brown et al., 1990; Biri et al., 2006; Pietryga et al., 2006). It has been reported that plasma adenosine level in umbilical vein whole blood is higher in GD with respect to normal pregnancies (Westermeier et al., 2011). In addition, umbilical vein blood contained more adenosine compared with umbilical cord arteries in GD, thus suggestsing that an altered placental metabolism of this nucleoside is likely in this syndrome (Salomón et al., 2012). These results complement other studies showing increased adenosine concentration in umbilical vein blood from GD compare to normal pregnancies (Maguire et al., 1998) or in the extracellular medium in primary cultures for HUVEC and hPMEC from GD (Vásquez et al., 2004; Farías et al., 2006, 2010; Westermeier et al., 2011; Salomón et al., 2012). Even when all these observation have been made, there is not a full consense between the findings showing increased plasma level of adenosine and endothelial dysfunction in GD pregnancies (Baldwin et al., 2004; San Martín & Sobrevia, 2006; Casanello et al., 2007; Westermeier et al., 2009; Sobrevia et al., 2011; Pardo et al., 2012).
Insulin is a polypeptide hormone of 51 amino acid residues, synthesized and secreted by β cells in the Langerhans islets of pancreas as an inactive single polypeptide, i.e., preproinsulin, with an N-terminal signal sequence that determines its incorporation to secretory vesicles (Mounier et al., 2006). The proteolytic elimination of the signal sequence and the formation of three di-sulfur bridges yield the proinsulin. This molecule goes to the Golgi apparatus where it is modified and stored in secretory vesicles (Shepherd, 2004). The raise of D-glucose in the blood triggers insulin production through conversion of proinsulin to active insulin by proteases that will cut two peptide bonds to form the mature form of insulin in equimolar quantities of C peptide (Shepherd, 2004). Insulin is the archetypal growth hormone during fetal development, promotes the deposit of carbohydrates, lipids and protein in the tissues and D-glucose uptake. This hexose is the main source of energy in the fetus and its metabolism responds to fetal insulin since the 12th week of gestation (first trimester) (Desoye at al., 2007). Intracellular hormones and signals regulate insulin secretion, also the autonomous nervous system and the interaction of substrates like amino acids and mainly D-glucose (Shepherd, 2004). Once secreted from the pancreas, insulin exerts several effects on its target cells and regulates a myriad of processes in the organism (Muniyappa et al., 2007).
The studies ‘Summary and Recommendations of the Fourth International Workshop-Conference on Gestational Diabetes Mellitus’ (Metzger & Coustan, 1998) and ‘Gestational Diabetes Mellitus, Position Statement of the American Diabetes Association’ (2004) list different priority areas regarding gestational diabetes research, proposing the characterization of regulatory mechanisms of fetal blood flow as a necessary attention sector, based in the lack of information about the effect of gestational diabetes over the fetoplacentary circulation. Furthermore, some reports (i.e., ‘Summary and Recommendations of the Fifth International Workshop-Conference on Gestational Diabetes Mellitus’) (Metzger et al., 2007) include recommendations for research in several aspects of placental function in the context of gestational diabetes. These recommendations include characterization of insulin resistant mechanisms and identification of cellular mechanism that reduces insulin signal in GD (Metzger et al., 2007). Although the role of insulin is accepted in GD, cellular signaling and the fetoplacental tissue response to insulin in this syndrome is not well understood (Hiden et al., 2009; Westermeier et al., 2009; Sobrevia & González, 2009; Sobrevia et al., 2009). Even when insulin receptors are expressed in human placental vasculature (Hiden et al., 2009; Westermeier et al., 2011; Salomón et al., 2012), there is limited information available about the biological action of insulin receptors activation and the vascular effects of insulin in the placental circulation in GD (Desoye & Hauguel-de Mouson, 2007; Barret et al., 2009; Genua et al., 2009; Sobrevia et al., 2011).
GD leads to abnormalities in the transplacental transport, an event that happens, among others factors, due to a lost in the hormonal balance induced by changes in the synthesis and signaling of insulin (Kuzuya & Matsuda, 1997; Metzger & Custan, 1998; Greene & Solomon, 2005; Biri et al., 2006; Sobrevia & González, 2009; Barret et al., 2009). Insulin causes vasodilation in normal subjects via a mechanism that is dependent on endothelium-derived NO (Steinberg & Baron, 2002; Sundell & Knuuti, 2003; Barret et al., 2009; Sonne et al., 2010; Timmerman et al., 2010). Furthermore,
In primary cultures of HUVEC in euglycemia conditions (i.e., containing 5 mM D-glucose) and in the presence of physiological concentrations of insulin (0.1-1 nM) it has been observed an increase of the maximum velocity of L-arginine transport (
Insulin generates its biological effects via activation of insulin receptors in the plasma membrane of endothelial cells of human umbilical vein (Zheng & Quon, 1996; Nitert et al., 2005) and placental microvasculature (Desoye & Hauguel-de Mouzon, 2007; Hiden et al., 2009). The gene coding the human insulin receptor is located on the short arm of chromosome 19 and consists of 22 exons and 21 introns (Seino et al., 1989). The mature insulin receptor is a glycoprotein composed of two β subunits (transmembrane domain) joined by disulfide bridges. The
With the cloning of the two isoforms of the insulin receptor, i.e., IR-A and IR-B, it the possibility of a differential response to insulin by selective activation (or semi selective) of these isoforms has been proposed (Ullrich et al., 1985; Ebina et al., 1985; Frasca et al., 1999; Sesti et al., 2001; Belfiore et al., 2009; Genua et al., 2009; Sciacca et al., 2003, 2010; Thørsoe et al., 2010; Sen et al., 2010; Westermeier et al., 2011; Sobrevia et al., 2011; Leiva et al., 2011; Pardo et al., 2012; Salomón et al., 2012). The IR-A cDNA (exon 11-) lacks exon 11, and IR-B (exon 11+) contains exon 11 (Genua et al., 2009; Thørsoe et al., 2010; Sen et al., 2010). Both isoforms are expressed in insulin-sensitive tissues (liver, muscle and adipose tissue) (Moller et al., 1989; Mosthaf et al., 1990), but IR-A is predominantly expressed in the fetus and placenta, where it plays a role in embryonic development (Frasca et al., 1999). These isoforms are also expressed in adult tissue, especially in the brain (Belfiore et al., 2009). Moreover, IR-B is expressed mainly in differentiated adult tissues, such as the liver, and associates with increased metabolic effects of insulin (Sciacca et al., 2003, 2010; Genua et al., 2009; Sen et al., 2010). Dysregulation of the insulin receptor splicing in key tissues responsive to insulin may occur in patients with insulin resistance, but this role is unclear in diabetes mellitus (Belfiore et al., 2009) and not reported in GD (Sobrevia et al., 2011; Leiva et al., 2011; Pardo et al., 2012). A recent study shows that IR-A activation by insulin activates a predominant metabolic signaling pathway (p42/p44mapk/Akt activity ratio >1) instead of a predominant mitogenic signaling pathway (p42/p44mapk/Akt activity ratio <1), as described in response to IR-B activation in the R- cell line of mouse embryonic fibroblasts (Sciacca et al., 2010). These results suggest differential cell signaling pathways activated by these insulin receptor subtypes (Genua et al., 2009; Sciacca et al., 2010). In fact, recently was shown that hPMEC from GD exhibit a predominant metabolic phenotype compared with cells from normal pregnancies, and that this phenotype could be reversed to a mitogenic, normal phenotype (Salomón et al., 2012). Thus, a modulation of the expression level will, perhaps, has a consequence in the metabolism of the endothelial cells of the fetoplacental unit in GD. Other evidence suggests that a decrease in insulin response, as in Type 2 Diabetes Mellitus where the predominant isoform is IR-A (Norgren et al., 1994), and in states of insulin resistance where IR-A/IR-B increases in the skeletal muscle of patients with myotonic dystrophy type 1 (Savkur et al., 2001) and 2 (Savkur et al., 2001; Phillips et al., 1998).
Insulin, insulin-like growth factor 1 (IGF1) and 2 (IGF2) generate various metabolic and mitogenic effects through activation of receptors associated with tyrosine kinase activity on the surface of the target cells. These hormones have high structural homology. The two receptors may act as ligands for these molecules. At physiological concentrations insulin and IGF1 are attached only to the insulin and IGF receptors, respectively. While, IGF2 receptor binds to IGF1 (IGFR1) and IR-A (Frasca et al., 1999). The affinity of IGF2 by IR-A is less than that of insulin for this receptor (
Insulin sensitivity is increased in rats supplemented with adenosine in the diet (Ardiansyah et al., 2010). These data are complemented by similar observations described in diabetic rat adipocytes (Joost & Steinfelder, 1982), nondiabetic rat skeletal muscle (Vergauwen et al., 1995), and patients with TIDM who received an infusion of adenosine (Srinivasan et al., 2005) (Table 3). In other studies, adenosine, agonists and antagonists concentration of adenosine receptors, and insulin used was greater than 100 nM, suggesting for adenosine receptors, that the activation and inhibition of this receptors was complete, and for insulin, involving IR-A, IR-B, and IGF receptors in the system. However, in some studies the concentration of insulin that was used is relatively selective for the receptors of insulin, suggesting the possibility that activation of adenosine receptors increases insulin effect (Webster et al., 1996; Ciaraldi et al., 1997; Sundell et al., 2002; Srinivasan et al., 2005). Similarly, oyther groups show that inhibition of adenosine receptors blocks the effect of insulin mediated only by the insulin receptor (Pawelczyk et al., 2005; Dhalla et al., 2008). Moreover, the expression and activation of adenosine receptors reduces plasma levels of D-glucose, due to increased release and the biological effect of insulin in diabetic rats (Johansson et al., 2006; Németh et al., 2007; Töpfer et al., 2008). Activation of A1AR (Vergauwen et al., 1994) or decreased expression A2BAR (Ardiansyah et al., 2010; Figler et al., 2011), results in an increased sensitivity to insulin, but there is no information about the specific mechanisms explaining the biological actions of adenosine (Burnstock et al., 2006; San Martín & Sobrevia, 2006; Mundell & Kelly, 2010). The activation of the A2BAR, but to a lesser degree than the A1AR, prevents the development of diabetes in mouse (Németh et al., 2007). However, a study in C57BL/6J mice suggests that insulin sensitivity decreases by activation of A2BAR, except in the knockout mouse for this receptor, suggesting that A2BAR is involved in the phenomenon of insulin resistance (Figler et al., 2011). This finding opens the possibility that the increase and/or inhibition of the expression or activity of ARs may be associated as a protective mechanism against this syndrome. Recently, we have published that A2AAR activation in HUVEC from normal pregnancies modulate insulin effect on hCAT-1-mediated L-arginine transport and expression (Guzmán-Gutiérrez et al., 2012a). Interestingly, we saw that in HUVEC from GD insulin reversed GD-increased hCAT-1-mediated L-arginine transport, a mechanism that is dependent on A1AR activation (Guzmán-Gutiérrez et al., 2012b). Based on these findings, a possible cross talk between the adenosine receptors and insulin receptors is feasible. This phenomenon could create a potential regulatory mechanism of the biological actions of insulin in the fetoplacental vasculature in GD (Figure 5).
GD associates with endotelial dysfunction in the fetoplacental macro and microcirculation associated with an increase in NO synthesis and hCAT-1 mediated L-arginine transport. Hyperinsulinemia and high plasma adenosine in umbilical blood in GD, suggest the involvement of these molecules in this syndrome. A2AAR and insulin receptors increase hCAT-1 and eNOS activity and expression in HUVEC from normal, while HUVEC from GD, activation of A2AAR would be part of mechanism that explain the increase of NO synthesis (i.e., ALANO pathway). In other hands, it has been proposed that insulin acts as a factor that reverses GD-increased NO synthesis and L-arginine transport to values in cells from normal pregnancies. This insulin dual effect can be explained for a differential expression of IR-A and IR-B in normal and GD pregnancies. Insulin effects are dependet on activation of ARs in several cell types, suggesting that adenosine should be act as an isoform insulin receptor activity regulator. Thus, regarding the GD association with increased hCAT-1 expression and activity, there are several not still answered, for example, how is insulin decreasing hCAT-1 activity and expression?, and is adenosine a modulator of the expression and associated signaling of the isoforms of insulin receptors in GD?. Answering these (and other) questions will help us understand insulin mechanisms, opening the possibility to study potential treatment for insulin resistence pathologies including GD.
We are thankfull to the personnel at the Hospital Clínico Pontificia Universidad Católica de Chile labour ward for their support in the supply of placentas.This research was supported by Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT 1110977, 11110059, 3130583), Programa de Investigación Interdisciplinario (PIA) from Comisión Nacional de Investigación en Ciencia y Tecnología (CONICYT, Anillos ACT-73) (Chile) and CONICYT Ayuda de Tesis (CONICYT AT-24120944). EG-G and PA hold CONICYT-PhD (Chile) fellowships. FP was the recipient of a postdoctoral position (CONICYT PIA Anillos ACT-73 postdoctoral research associate at CMPL, Pontificia Universidad Católica de Chile (PUC)). PA is the recipient of a Faculty of Medicine (PUC) PhD fellowship.
Uruguay is a small country located in South America. According to the 2010 Strategic Energy Development Plan for Uruguay, the diversifying of the energy matrix should be prioritized. Alternative and renewable energy sources rose to be exploited at a major scale. Because of its windy climate, wind energy is now one of the most important sources in our country: currently, Uruguay has more than 1500 MW of installed power lying on this green energy source.
The fast growing development of wind power in Uruguay has encouraged research on many environmental issues, especially those related to wind turbines operation. In many countries—Uruguay included—the method of ISO Standard 9613-2 is the preferred tool for predicting environmental sound pressure levels due to stationary noise sources. However, it is well known that it can incur on great underestimations when sources are large wind turbines, especially under certain atmospheric conditions [1].
This paper is focused on the prediction of environmental sound pressure levels due to the operation of large wind turbines, emphasizing in the prediction model developed by the Research Group on Noise Pollution at the Faculty of Engineering (UdelaR). For its development, the theoretical analysis of the phenomena involved on noise emissions was complemented with tests at the University wind tunnel and, of course, with sound pressure levels measurements at some wind farms in Uruguay. The aerodynamic phenomena involved in acoustic emissions were analyzed with the Research Group on Wind Energy at the Faculty, which has been working on wind energy for nearly 30 years.
The application of the prediction model allows obtaining the expected sound pressure levels at different points. Only airborne sound propagation is considered, as the importance of ground propagation does not involve an important amount of acoustic energy for onshore wind farms.
Environmental sound pressure levels related to stationary sources are usually predicted as prescribed by the ISO Standard 9613-2. It is not only a standardized method of calculation, but it has been for a long time the recommended one in the European Union [2]. This is a strong argument at some developing countries. Convincing the decision-makers about the need of developing another prediction method to achieve more reliable results in the case of wind turbines is not an easy task.
This section aims to point out the main hypothesis of the ISO Standard 9613-2 [3] and to discuss their applicability to wind turbine noise.
In 1981, CONCAWE (a group of oil companies, aiming toward the research on the conservation of water and air quality in Europe) hired C. J. Manning for developing a prediction model of environmental sound pressure levels [4]. Some novel prediction methods were inspired on it, as the ISO Standard 9613-2 was.
According to CONCAWE, the environmental sound pressure levels at remote places due to a noise source can be obtained by solving the following expression:
Where Lp is the sound pressure level in the short time for the octave band
The ISO Standard 9613-2 general expression is just the same:
The definitions of Eq. (1) are valid for Eq. (2). Ai are the attenuation terms (atmosphere absorption, ground absorption, presence of obstacles or noise barriers, etc.). The subscript
Both calculation methods assume the divergence law to be quadratics, thus the emitter is supposed to be a point source. Also, both calculation methods promote their application by frequency octave bands. Nevertheless, if there is not enough information to work by bands, ISO Standard 9613-2 will accept calculating in A-weighted sound pressure levels using all formulae and coefficients corresponding to the octave band centered at 500 Hz. If the acoustic emissions have high energy content in low frequencies, this way of calculating will cause a great underestimation of immission sound pressure levels.
CONCAWE’s model uses the meteorological categories proposed by Parkin and Scholes instead of the currently preferred Pasquill-Gifford ones. ISO Standard 9613-2 does not consider calculating differences due to different meteorological conditions when the main calculation hypothesis is satisfied: wind speed between 1 and 5 m/s at a height between 3 and 11 m above the ground and averaged over a short period of time or moderate temperature inversion with its base at ground level. These conditions are not always met when the source is a wind turbine.
In this century, it has been verified that the differences between environmental sound pressure levels predicted by ISO Standard 9613-2 and those that do occur due to the operation of wind turbines would be very important: underestimations of 15 dB or more have been reported during the occurrence of certain combination of environmental conditions [5, 6].
Then, ISO Standard 9613-2 calculation method has been submitted to a deeper analysis.
Aerodynamic noise generation during operation of wind turbines is inherent to them in nature: the major acoustic emissions from large wind turbines are caused by the interaction between the air flow and the blades. The acoustic emissions occur all along each blade, most of them at low frequencies. Then, the height of the noise source is from about 40–130 m above the ground. The incident wind speed largely varies between these two heights, so that the wind turbine becomes a heterogeneous and complex sound emission source.
There are some limitations for the use of ISO Standard 9613-2 to predict environmental sound pressure levels due to large size wind turbines [1]. Some of the general ones are the following:
The hypothesis about wind speed and atmospheric stability is not always fulfilled.
Atmospheric conditions (neutral, instability, or under an inversion layer) have a great incidence both on generation and on propagation of noise [6].
At the typical distances of interest, a wind turbine cannot be supposed to be a point source [7].
The Standard supposes the distance from source to receiver to be between 100 and 1000 m.
The average height of source and receiver should be between 0 and 30 m (then, the maximum height of the source will not exceed 60 m if the receiver is at 0 m height).
Source and receiver should be placed over a plain surface (a surface with a continuous slope, i.e., the method is not valid for complex terrain).
If the calculations are done based on A-weighted sound pressure levels (as it is allowed by the Standard), a greater underestimation should be done at low frequencies.
Some experimental findings also refer to better results when not considering ground attenuation effects during propagation [8].
But there are also two of the major assumptions that are at the very beginning of the conceptual framework of environmental acoustics that are not fulfilled by the physic/fluid mechanic phenomena involved in the aerodynamic sound generation from wind turbines [9]:
The hypothesis that acoustic processes are adiabatic, because they occur very fast and involve only very small amounts of energy. Most of vibration phenomena can be well described as adiabatic ones, but this is not the case of the aerodynamic noise related to wind turbines’ operation. Noise generation is related to turbulent phenomena, which are not adiabatic but very dissipative ones.
The hypothesis of ideal fluid, which is opposite to the main phenomena that are related to release of eddies from a boundary layer; these phenomena only can occur if the air is considered as a viscous or real fluid.
These are thought to be the root causes for both CONCAWE and ISO methods not to being appropriate for predicting the environmental sound pressure levels related to wind turbines’ operation, as they cannot describe the main involved phenomena on a right way [1, 9, 10, 11].
In order to improve the current prediction method, we have proposed several modifications. We have focused on the noise generation phenomena, but we have also worked on two other points: the explicit consideration of the atmospheric stability condition and the dissipative nature of the main phenomena during propagation.
The wind velocity is usually measured at 10 m height above the ground. One of the main causes of underestimating the environmental sound pressure levels is related to calculating the wind speed at the hub height using a neutral atmospheric profile with basis on its value at 10 m. To avoid this problem, the atmospheric stability class (according to Pasquill-Gifford) has to be explicitly taken into account for this calculation. The atmospheric stability does not only influence the wind speed profile but also the turbulence intensity and, therefore, the acoustic energy depletion law.
If a stable or thermal inversion atmospheric condition occurs, not including it in the prediction method will conduct to:
A great underestimation of the wind speed at the hub height, which would result in the underestimation of the emitted acoustic power level.
A great overestimation of the sound pressure levels depletion, due to the lower atmospheric turbulence and hence, the lower energy dissipation during propagation.
If the atmospheric thermic profile is not known, the wind speed at the hub height should be obtained by supposing a strong atmospheric stability profile (class F according to Pasquill-Gifford), to be in the most demanding hypothesis for protecting the health of noise receivers.
Then, the wind speed at the hub height should be met by converting the measured wind speed data—that are usually taken at 10 m over the ground—using a proper method.
Using the logarithmic profile approach for wind velocity (Eq. (3)) is better than using the potential profile approach (Eq. (4)), even though there are good experimental values for the potential approach. Indeed, since several authors refer that the usual values of
Pasquill stability class | m | ||
---|---|---|---|
Class | Description | Usual bibliography values | Van den Berg experimental values [from 6] |
A | Highly unstable | 0.09 | 0.15 |
D | Neutral | 0.28 | 0.40 |
F | Highly stable | 0.41 | 0.65 |
Values of
Where:
u(hhub) wind velocity at hub height
u* friction velocity
k von Karman’s constant
z0 roughness length
ψm thermal stratification function
L* Monin-Obukhov length
Where:
uhub wind velocity at hub height hhub
uref measured wind velocity at a reference height href
m coefficient depending on Pasquill-Gifford class of atmospheric stability (see Table 1)
The calculation procedure that we recommend to meet the wind velocity at hhub height, taking into account its value at any other height href, is as follows [10, 11, 12]:
If the stability class to which uref corresponds is known, the velocity at the hub height can be met by applying either Eqs. (3) or (4).
If the stability class to which uref corresponds is not known, a stable atmospheric profile should be assumed for calculating the velocity at hub height with basis on the wind velocity at a reference height (usually 10 m above the ground).
Once the wind velocity at the hub height (uhub) has been obtained, a “corrected” wind velocity at 10 m in height should be calculated. This is the 10 m height wind velocity to be used for meeting the acoustic power level of the wind turbine from tables or charts provided by the manufacturer. Figure 1 sketches the procedure.
How to reach the wind speed at 10 m height to obtain LW,A (redrawn from [
Please note:
If the wind speed at the hub height is known, the wind velocity at 10 m must always be obtained assuming a neutral atmosphere (even when the stability class is known not to be neutral).
For obtaining the sound pressure level resulting from a wind velocity value measured at a height “H” (other from hhub) in any given atmospheric condition “X”: uhub should be calculated assuming the class of stability “X”; then, the “corrected” wind speed at 10 m in height should also be calculated by assuming neutral atmosphere (class D). The acoustic power shall be read from the datasheet provided by the manufacturer; it will also be associated with that atmospheric stability class:
Wind turbine manufacturers often provide tables or graphs relating the wind velocity at 10 m in height (u10) to the acoustic power level (in dBA) emitted by the wind turbine in neutral atmosphere conditions. However, providing emission spectra in frequency bands is not so common. If this information is not available, a reference spectrum should be used, e.g., spectrum in Table 2.
f (Hz) | 16 | 31.5 | 63 | 125 | 250 | 500 | 1000 | 2000 | 4000 | 8000 |
---|---|---|---|---|---|---|---|---|---|---|
Add to LWA (dB) | −44 | −26 | −21 | −14 | −7 | −6 | −6 | −9 | −12 | −22 |
Reference spectrum of acoustic power of 2 MW wind turbines in octave bands (based on [13]).
Table 2 (based on [13]) presents the values to be added arithmetically to the acoustic power level of the wind turbine (LWA) to obtain the acoustic power levels in each octave band, also in dBA (LW,f,A).
We aim to obtain the sound pressure levels due to the operation of a typical three-blade wind turbine, at a generic receiver point located downwind at a distance
Aerodynamic noise is generated by the interaction of wind with the blades of the machine. Most of the acoustic emissions occur in low frequencies, so the acoustic print of wind turbines can be found at large distances from the sources, making the problem more complex to manage.
There are three main processes causing the fluctuation of the pressure field and then the acoustic emissions [14]:
The turbulence of the incoming wind, which causes pressure fluctuations around the blades; it is variable over time and it is called “incoming flow noise.”
The viscous forces in the boundary layer over the solid surfaces of the turbine, such as blades, tower, and hub. Viscous forces in this layer are not negligible compared with the inertial forces (related to the medium air flow). The release of eddies with negative gauge pressure at their cores, developed on solid surfaces such as blades, tower, and hub due to viscous stresses, causes a continuous noise called “trailing edge noise.”
The power exchange between the wind and the machine that produces the release of two families of eddies linked to each blade; one of them has helical motion and the other one is centered on the rotation axis, and its length scale is about the length of the diameter of the rotor.
These phenomena are related to three different geometric scales [14, 15]:
Macroscale: it is the scale related to the largest eddies. If U, L, and T are the scales of velocity, length, and time associated to these eddies, the Reynolds number of the biggest eddies is the same as for the main flow.
Intermediate scale: it includes lower scales than the macroscale ones; there is still no power dissipation. The range of scales included here is called “inertial range.”
Microscale: it is the lowest scale, in which the energy dissipation occurs. Unlike what happens in the macroscale, the smallest eddies are isotropic, as if the flow has “forgotten” where it comes from.
The turbulent cascade hypothesis is then to be considered. According to it, the larger eddies are dissipating into smaller scale eddies with increased kinetic energy. However, there is a length scale at which the power transfer to a smaller eddies scale is not possible. At this point, the turbulent cascade ends and the energy from the last eddies is finally dissipated. The smallest eddies scale is the Kolmogorov scale; the so-called Kolmogorov frequency or dissipation frequency is the generation frequency of these smallest eddies [15]. According to their frequency and energy, the released eddies are able to produce audible phenomena, i.e., they can become noise sources (Figure 2).
Atmospheric conditions for propagation. From: [
The passage of the blades ahead the tower imposes a fluctuation of the sound level pressures emitted by the abovementioned phenomena. It results in an amplitude-modulated noise, called the “blade passage noise.” It has a double nature, one related to the flow and one related to the geometry of the source. The modulation is the most related process to annoyance in wind turbine noise. This process is not modeled in detail: the informed sound pressure levels are the highest of those corresponding to the fluctuation.
A first approach to describe the wind turbines operation is to model the rotor as an active disk, which absorbs kinetic energy from the incoming wind, resulting in a reduction in the flow speed downstream of the turbine.
If v1 is the incoming velocity and v2 is the outcoming velocity, the velocity induction coefficient “
Applying mass and energy balances to the incoming flow, and supposing an adiabatic and incompressible flow, the maximum amount of power absorbed by the disk is:
In Eq. (6), ρ is the air mass density, and A is the swept area or rotor area.
The power absorbed is maximized when
The wind turbine operates at its maximum power for each wind velocity, while the wind velocity is under the rate value. As consequence of the power exchange process between the wind and the machine, the flow downwind the rotor rotates around the turbine axis.
The force over the blade, consequence of the interaction between the flow and the blade, is split in two components: the drag effort (D) and the lift one (L) (Figure 3). The magnitude of these components strongly depends on the angle of attack (α), which is the angle between the chord of the blade and the incoming flow relative to the blade. When the drag component increases, the noise generation increases too. This usually occurs when the angle of attack α increases.
Drag FD and lift FL efforts over the blade (from [
There are several analytical expressions to describe the universal shape of the turbulence spectrum. One of that, the Von Karman’s spectrum, expresses the spectrum as function of a nondimensional ratio built with turbulence integral length scale Lu and the main flow speed
According to Von Karman spectrum, we propose to estimate the energy content in a third-octave band centered at a frequency
To meet the acoustic power level, we accept that each blade is composed of a group of discrete thin elements or slices. Each one would be sufficiently thin to be thought as a noise point source. Then, the total acoustic power emitted by one blade element should be obtained as the superposition of the acoustic power emitted by the incoming flow (LW,IF) and the trailing edge (LW,TE) as following (Eq. (10)):
The incoming flow noise is the result of the fluctuation of the lift effort on the blade, which makes the drag effort to fluctuate. We propose estimate of the pressure field fluctuation using McLaurin series, where first-order terms are related to the turbulent fluctuation.
The length scale of interest, for the incoming turbulence, is similar to the length of the blade chord, which corresponds to the length scale of the eddies that produce the greatest amplitude fluctuation on the pressure field. Van den Berg proposes to use a length scale equal to 60% of the blade chord for this length [8]. Smaller eddies would produce lower fluctuations on the pressure field. Then, the shape of the spectrum of the incoming edge noise, associated to eddies with length scale larger than the blade chord, will be the same as Von Karman’s spectrum.
The trailing edge noise is due to the turbulent boundary layer separation over the blade. The length scale of interest in this case is about the boundary layer thickness.
We built a routine for obtaining the emitted acoustic power level by third-band octave. Its aim is to obtain the predicted sound pressure levels at a point placed at 100 m downwind of the wind turbine tower. We discretize the blade in infinitesimal length blade elements. The coordinates of each slice in every moment could be thought as [x(t), y(t), z (t)]. Then, the propagation into the first 100 m from the tower axis is done assuming that each one of the blade elements is a nonstationary noise point source (Figure 4).
Sketch of calculation of sound pressure levels in the first 100 m (from [
For the propagation from each blade element to the receiver location, our routine only considers the geometrical divergence and the atmospheric sound absorption as indicated at ISO Standard 9613-1 [16]. The output of this routine is the input for the propagation module [10, 11].
For computing sound propagation, the input data are the results of the computing at 100 m far from the tower of the wind turbine. Not only geometric divergence but also atmospheric absorption and turbulent dissipation are considered; both phenomena depend on the frequency. The final sound pressure levels at a given reception point are obtained by superposing the sound pressure levels due to different wind turbines operation. All computations are done in octave bands, and the final results are expressed as LAeq values [11].
The analysis of the evolution of eddies generated due to wind turbine operation requires the use of the cascade process as it is usual in turbulent flow studies. According to it, the larger eddies are melting into smaller ones, increasing its kinetic energy. At some point, small eddies cannot continue to transfer power to smaller ones; so, they dissipate their remaining energy, thus ending the cascade process. The scale of these last eddies is the order of the Kolmogorov’s scale.
Under atmospheric instability condition, the turbulence is very high and the eddies scale interval is broad; the cascade process is very efficient to dissipate the produced turbulence. For distances greater than the one at which that dissipation occurs, it shall be assumed that the flow conditions are the same as upstream the wind turbine. The ratio between the current wind velocity up and downstream the machine tends to 1 for greater distances, and the difference between them is practically negligible at a distance of about 6 or 7 rotor diameters downstream of the wind turbine (i.e., about 600 m).
In strong atmospheric instability conditions, the prior distance is the shortest one to fully carry out the whole energy cascade process. For any other atmospheric conditions, the dissipation process occurs in greater distances.
Under strong atmospheric stability conditions (i.e., class “F” according to Pasquill-Gilfford stability classes), the effect of turbulence should be negligible. The only mechanism that affects the energy depletion process in any frequency band—in addition to the geometric divergence or attenuation by distance—is the atmospheric absorption.
The effect of atmospheric absorption can be considered as the depletion of the acoustic energy of a wave over a given distance, due to energy loss caused by the viscosity of the propagation medium (currently, the atmosphere). To estimate the effect of the atmospheric absorption, the computation method of the ISO Standard 9613-1 was used [16].
The attenuation due to atmospheric absorption in dB of a pure tone with frequency
Where Γi is the atmospheric absorption in the
The generation and propagation phenomena of eddies can be described from a wave approach. Then, close to the source, the sound pressure levels should be estimated considering energy depletion by geometric divergence (
The threshold of perception at each frequency band should be another criterion for determining the distance upon which the sound is still audible. Hearing threshold levels were retrieved from ISO Standard 226 [17].
For the depletion of sound pressure levels due to distance, the adjustment is focused on the exponent (
As one of the main hypotheses of linear acoustics was broken (nonviscous effect), we intend not to be mandatory for
Here,
For the calculations of geometric divergence, the turbulent cascade approach is taken into account (Figure 2). The released eddies can propagate along great distances while the turbulent cascade occurs [15]. These distances are related to a certain energy level and a length scale. They are also closely related to atmospheric stability.
We calculated the length scale where the turbulent cascade is expected to end by considering it to depend on the incoming wind velocity and the frequency of the released eddies. This is based on prior consideration of the atmospheric stability during the calculation of noise emissions [10, 11].
Different sets of
Field data were taken at a height of 1.2 m with class 1 sound pressure meters, close to three different wind farms over plain terrain; the distances of measurements covered from 100 m to about 2000 m.
The best set of values was found to be the one obtained for
The values of n(f, d, u) are given in octave bands according to whether the calculation distances are closer or further than 750 m and that the wind speed at the hub height is less or greater or equal to than 6.5 m/s (Table 3).
f (Hz) | 16 | 31.5 | 63 | 125 | 250 | 500 | 1000 | 2000 | 4000 | 8000 |
---|---|---|---|---|---|---|---|---|---|---|
Closer than 750 m, less than 6.5 m/s | 0.37 | 0.01 | 0.05 | 0.66 | 1.60 | 1.67 | 1.94 | 1.61 | 0.85 | 0.43 |
Closer than 750 m, 6.5 m/s or more | 0.52 | 0.02 | 0.01 | 0.22 | 0.93 | 0.79 | 1.04 | 1.07 | 1.06 | 0.65 |
Further than 750 m | 0.45 | 0.01 | 0.06 | 0.72 | 1.58 | 1.71 | 2.03 | 1.60 | 0.67 | 0.34 |
Divergence coefficient “
Several sound pressure level measurement campaigns were conducted at three different wind farms with large wind turbines (rate power of 1.8 MW), covering several operating conditions.
Sound pressure level records were taken at 1.2 m height, simultaneously with records of wind speed and direction at 10 m height. In addition, the records of wind speed and direction were obtained from the wind farm anemometer, located at 66 m high and close to the turbines. To carry out the measurements, we used two sound level meters class 1 (Brüel and Kjaer 2250 and Casella 633C), an anemometer (Extech EN-300), a GPS, and two computers.
The measurement points covered four different geographical locations:
A hilly zone, far from external sources such as houses or roads, to avoid introducing disturbances to the data obtained.
A flat zone close to the sea, where 10 large wind turbines are installed.
Another flat zone where two 1.8 MW wind turbines are installed.
A location in the countryside close to a private company, which has only one wind turbine. This location was particularly interesting for this study, since the records are not affected by other turbines or any external sources.
A set of 59 measurements was used during the calibration and validation processes. The main findings showed that the model gave a good approach for the environmental sound pressure levels related to the operation of wind turbines for wind speeds over 5 m/s at the hub height.
In order to validate the model, another set of field data was used. It was another set of data of 49 cases from 10 wind farms in different locations in Uruguay:
The four abovementioned places.
A flat zone in the northern of the country, where 35 wind turbines are installed.
Two hilly wind farms placed on the Southern part of the country, each one with around 25 wind turbines
Three rather flat zones in the center/south-western part of the country, having from 20 to 35 wind turbines each one.
Some adjustments were needed for improving the prediction of noise propagation from wind farms built on uneven terrain.
The results obtained in the verification of the performance of the model are presented in Tables 4–6. Almost 80% of the cases are reproduced within ±3 dB range.
Number of cases | Cases in ±3 dB | ||
---|---|---|---|
Number | % | ||
d closer than 750 m | 30 | 24 | 80 |
d further than 750 m | 18 | 14 | 78 |
Total | 48 | 38 | 79 |
Quality of results according to distance to the wind turbine.
Number of cases | Cases in ±3 dB | ||
---|---|---|---|
Number | % | ||
u lower than 6.5 m/s | 12 | 10 | 83 |
u higher than 6.5 m/s | 36 | 28 | 78 |
Total | 48 | 38 | 79 |
Quality of results according to wind velocity at the hub height.
Number of cases | Cases in ±3 dB | ||
---|---|---|---|
Number | % | ||
d closer than 750 m, u lower than 6.5 m | 8 | 7 | 88 |
d closer than 750 m, u higher than 6.5 m | 22 | 17 | 77 |
d further than 750 m, u lower than 6.5 m | 4 | 3 | 75 |
d further than 750 m, u higher than 6.5 m | 14 | 11 | 79 |
Total | 48 | 38 | 79 |
Quality of results according to distance to the wind turbine and wind velocity at the hub height.
As important as the percentage of accurate predictions is to state that not only the levels in scale A are predicted in a reasonably adjusted way, but particularly that the spectra obtained with the proposed model are also rightly adjusted to the measured ones [10].
Figure 5 shows some results for short and long distances. The blue bar is the measured sound pressure level; the pink bar is the computed sound pressure level using ISO 9613-2 with attenuation only due to geometric divergence and atmospheric absorption; and the dark red bar is the result of our prediction proposal. As it can be seen, our model achieves a good performance as a prediction tool.
Comparison of results; left bar: Measured sound pressure levels; center bar: ISO 9613-2 predicted sound pressure levels; left bar: Our prediction proposal. All sound pressure levels are in dBZ. (Adapted from [
It is not usual to find references that use a variable depletion law according to the frequency.
We compared our attenuation curves with those presented in a paper published in 2021 [18]. The authors estimate the sound pressure level related to wind turbines with a nonnegative matrix factorization (NMF), a machine learning technique.
They present some attenuation filters in third-octave bands from 31.3 to 2000 Hz, for attenuation only and for attenuation considering three kinds of residual noise designed by the authors with basis on real noise samples. The filters were published in graphic format for three distances: 500, 1000, and 1500 m. We read the graphs and compared the attenuations proposed in [18] with the attenuation achieved for our prediction model in the same frequencies range.
The comparison was done using the Wilcoxon’s test for differences between pairs. H0 was the equivalence of the compared curves; accepting H0 at 95% confidence means that our attenuation curves are equivalent to those from [18]. Test results are summarized in Table 7. We conclude that each one of our attenuation curves reasonably fit at least one case of the filters suggested by [18], the filter with residual noise 1 being the most similar to our proposal.
Only attenuation | With residual noise 1 | With residual noise 2 | With residual noise 3 | |
---|---|---|---|---|
500 m, u less than 6.5 m/s | Accept H0 | Reject H0 | Reject H0 | Reject H0 |
500 m, u 6.5 m/s or higher | Reject H0 | Accept H0 | Reject H0 | Reject H0 |
500 m (all together) | Reject H0 | Accept H0 | Reject H0 | Reject H0 |
1000 m | Reject H0 | Accept H0 | Accept H0 | Accept H0 |
1500 m | Reject H0 | Accept H0 | Accept H0 | Accept H0 |
Comparison between our attenuation curves and those from [18].
Our proposed calculation process has two steps: at first, modeling the noise generation and its propagation in the short scale (less than 100 m); and propagating the output of the first step from short to large distances far away from the source. At the beginning of the process, the atmospheric stability class is to be taken into account by a correction to the wind velocity; this is very important, to avoid underestimations.
The first step of the calculation process divides the blade in infinitesimal length slices that behave as point sources, and for them the sound pressure level associated to the three mentioned generation processes for each source is calculated and summed logarithmically.
The propagation model takes into account the geometric divergence and the atmospheric absorption, considering
Figure 6 shows a sketch of the procedure.
Calculation process of this prediction proposal (adapted from [
We presented an alternative proposal for predicting sound pressure levels from wind turbines. It is a simple method that has shown very good results, it is easy to build, and it does not need special hardware or software requirements.
The whole model was calibrated, validated, and verified, with field measurements made in different wind farms located in Uruguay. Field data for calibration and validation were taken at distances between 300 and 2000 m from the tower of the wind turbine.
Although this method can be used for different turbines, our results were obtained for 2 MW power rate turbines with between 80 and 90 m of hub height.
More, our prediction model seems to be a good one to be used in noise impact studies related to environmental impact assessments for getting the environmental license of wind farms before their construction.
We presented the accuracy of the predicted A-weighted sound pressure levels, which are good or very good in most of the cases; we also showed the obtained spectra fit accurately to the measured ones.
This fitting is more noticeable at low frequencies, the most problematic ones for noise phenomena in wind turbine, because the energetic content in those frequencies is potentially related to people’s annoyance.
This chapter is the result of many years of research. It has been supported by funds from cooperation with the National Program of Wind Energy (National Energy Directory of the Ministry of Industry, Energy and Mining of Uruguay, DNE – MIEM), by the Sectorial Energy Fund of the National Agency of Research and Innovation (Project ANII_FSE_1_2013_1_10942) and the Research Commission of the Universidad de la República through their Fund for Research and Development Groups (CSIC I + D Groups 2014).
Many people have been involved in our team along the years. Here they are, in alphabetical order: Fabiana Bianchi Falco, Pablo Bonilla Medina (in memoriam), Nicolás Cunha Apatie, Matteo Deambrosi Papini, Pablo Gianoli Kovar, Matías Hernández Castellani, Marcos Raúl Lisboa, Joaquín Montero Croucciée, Luciana Olazábal Barrios, Juan Ignacio Pais, Martín Paz Urban, Nicolás Rezzano Tizze, Felipe Silva Rodríguez, and Guillermo Sugasti Sánchez. We thank all of them for taking part in this research.
The authors declare they have no conflict of interest.
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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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