\r\n\tThus, we call for research and review papers on the chemistry and physics of dyes, pigments, and their intermediates, including chemical constituents, spectroscopic aspects, surface, solution, crystal formation, photochemical, and ecological or biological properties. The book will be of interest to a wide variety of researchers worldwide whose work involves various fields of activity such as dyes and pigment synthesis, imaging, sensor, energy, medicine, polymers, food product, toxicological properties, etc.
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1. Introduction
Atmospheric pollution may be of both anthropogenic and natural origin (Levine, 1996; Finlayson and Pitts, 1999; IPCC, 2000; Delmas et al., 2005). Concerning natural pollution, most active volcanoes emit, among others, gases (eg. sulphur dioxide) and particles into the atmosphere during eruption events (Hobbs et al., 1991; Bhugwant et al., 2009). It is thus important to conduct the monitoring of these pollutants for active volcanoes all over the world, in order to take the adequate measures of air pollution (eg. to establish evacuation plans for the surrounding population) and also to study the long-term trends and effects caused by volcanic activity. In this sense, since a decade the World Health Organization (WHO), the European Community and the French Ministry of Environment (MEDDTL: Ministère de l’Ecologie, du Développement Durable, du Transport et du Logement) have established SO2 guideline levels, following the severity of the impact of this pollutant on human health and on the environment (WHO, 2005).
Previous experimental (ground-based and air-borne) studies indicate that major volcanic eruptions such as Mount St Helens (May 1980), El Chicon (March-April 1982) and Pinatubo (June 1991) injected large amounts of solid particles and volatile gases in the troposphere and the stratosphere, up to an altitude of 25 km high (Turco et al., 1993; Trepte et al., 1993; Krueger et al., 1995; Schneider et al., 1999). Another study showed, in a statistical approach, the global distribution of volcanic SO2 degassing during the last century (1900-2000) and further indicated that each eruption (even for non monitored ones) could affect the stratosphere, based on empirical observations (Hamler et al., 2002). More recently, the eruption of the Eyjafjallajökull volcano in Iceland, which began on April 14th 2010 and ended in October 2010, caused enormous disruption to air travel across western and northern Europe, due to huge amounts of ash and particles emitted in the atmosphere.
Volcanic eruptions may cause irreversible environmental and ecological impacts (burial by ash, mud, etc.), via lava or pyroclastic flows, dusts and ash falls and/or gaseous emissions, while they may also increase the economic burden in socio-economic sectors, by causing infrastructure and habitation damages (e.g. houses, buildings, roads, fields and forests covered with ash and/or lava) (Munich Re, 1998; Brosnan, 2000). It is now clearly established that the volcanic pollutants have damaging effects on both human health and ecology. The human health effects from air pollution vary in the degree of severity, covering a range of minor effects to serious illness such as impaired pulmonary function as well as premature death in certain cases (Brantley and Myers, 2000).
The atmospheric SO2 emitted during volcanic eruptions is chemically transformed into sulphate aerosols (H2SO3 and H2SO4) during transport (McKeen et al., 1984). These sulphate aerosols may remain in suspension in the atmosphere for several years mainly in the stratosphere, while in the troposphere they are washed out in between weeks (Graf et al., 1993). A recent epidemiological study showed that the SO2 emitted during the eruption of the Piton de la Fournaise volcano might have a potential sanitary impact on the surrounding population (Viane et al., 2009).
The mean residence time of ambient SO2 concentration is about 1 day, in absence of sink processes such as rainfall (Delmas et al., 2005). However, the public may be exposed to sulfur dioxide concentrations that are higher than typical outdoor air levels. The effects of exposure to any hazardous substance depend on the dose, the duration, the way one may be exposed, the personal traits and habits. Short-term exposures to high levels of sulfur dioxide can be life-threatening (ATSDR, 1999; WHO, 2005).
The study of gaseous and particulate emissions in the atmosphere induced by natural events such as volcanic eruptions may therefore help for a better comprehension of their impact, especially on the atmospheric chemistry and on human health.
However, up to now, very few studies based on atmospheric measurements have been undertaken with links to sources such as volcanic eruptions in the southern hemisphere, especially at Reunion island, where atmospheric measurements such as SO2 and particles are still scarce (Bhugwant and Brémaud, 2001; Bhugwant et al., 2002; Halmer, 2005; Bhugwant et al., 2009).
This chapter presents the SO2 concentration measurements, undertaken over Réunion island and in the vicinity of the Piton de La Fournaise volcano during and off eruption events from 2005 to 2010. This volcano is located in the south-eastern part of the Réunion island, a tiny island found in the south-western Indian Ocean (21 S; 55,5 E). It is surrounded by quite densely inhabited regions from the north-eastern to the southern part of it. This active volcano erupts regularly, at least once a year, with notable amount of degassing. However, due to the geographic configuration, coupled with the meteorological conditions which prevail regionally (easterlies) and locally (land/sea breezes) over the island, the volcanic plumes are regularly transported towards the inhabited regions located downwind and may cause sanitary and environmental impacts.
The SO2 concentration was measured continuously with an analyser in different parts of the island and with passive diffusion samplers in the vicinity of the Piton de La Fournaise volcano since 2005. The tremor activity of the Piton de La Fournaise volcano and the meteorological data (winds and rainfall) were measured at several locations over the island for each eruption. The analysis of these datasets have contributed towards explaining the spatial distribution and the temporal evolution of the volcanic SO2 emissions, mainly due to dynamical (transport) and deposition (wet/dry) processes.
2. Guideline values for the SO2 concentration levels
It has been shown that high SO2 concentration levels present in ambient air may cause considerable environmental and sanitary impacts (EPA, 1997). It is thus important to conduct the monitoring of this pollutant, in order to take the adequate measures of air pollution and also to study its long-term trends and effects. In this sense, since a decade the World Health Organization (WHO), the European Community and the French Ministry of Environment (MEDDTL : Ministère de l’Ecologie, du Développement Durable, du Transport et du Logement) have established guidelines for the SO2 concentration levels, following the severity of the impact of this pollutant on human health and the environment.
The French National air quality criteria is mainly based on the Decree n 2002-213 of 15th February 2002 and recently on the Decree n 2010-1250 of the 21st October 2010, concerning the air quality survey, in particular for SO2, and its effects on the human health and the environment, in the air quality objectives, in the alert thresholds and in the limit values (ADMINET, 2002, 2010).
The European Council Directive (1999) also defines the limit values for sulfur dioxide in the ambient air (see Table I). The objectives of this directive are, to establish limit values and alert thresholds for sulfur dioxide concentrations in ambient air intended to avoid, prevent and/or reduce harmful effects on human health as well as on the environment.
The Table I summarizes briefly the main SO2 guideline values (critical levels) for the human health and for the vegetation, in order to establish the regulation context of this study (Council Directive, 1999 ; ADMINET, 2002; WHO, 2005).
Table 1(a).
Council directive 1999/30/EC of 22 April 1999 relating to limit values for sulfur dioxide, in ambient air.
II. Alert threshold for sulfur dioxide
500 μg/m3 measured over three consecutive hours at locations representative of air quality over at least 100 km2 or an entire zone or agglomeration, whichever is the smaller.
Table 1(b).
Decree n 2002-213 of the 15th February 2002 of the French Ministry of Environment relating to limit values for sulfur dioxide, in ambient air.
Table 1(c).
WHO Air quality guidelines, Global update 2005.
3. Experimental set-ups and measurements
The ORA measured SO2 concentrations using passive diffusion samplers on 10 inhabited sites (1 to 10) located in the vicinity of the Piton de La Fournsaise volcano from mid 2005 to late 2007. During the April-2007 eruption however, SO2 concentrations were measured using passive diffusion samplers on 12 (11 to 22) additional inhabited sites located close to the Piton de La Fournaise volcano. Since 2009, 4 more sites (23 to 26) have been added to reinforce the SO2 survey in the vicinity of the volcano (Figure 1). All the selected sampling sites are located in inhabited areas, with almost no traffic circulation and at more than 1 to 2 km away from busy highways, in order to avoid local anthropogenic contamination.
Figure 1.
Map with indication of the sampling sites (red circles) dedicated for the SO2 measurements with passive diffusion tubes from 2005 to 2010
3.1. Atmospheric measurements
3.1.1. SO2 measurement using passive diffusion samplers
Passive diffusion samplers (Ferm, 1991) were used at 10 sites located around the Piton de La Fournaise volcano for the measurement of the ambient SO2 concentration since September 2005. The measurements by passive diffusion samplers are based on the property of molecular diffusion of gases and species-specific collection on an impregnated filter specific to the SO2 pollutant measured. The passive diffusion samplers, which were exposed over a period of one week to 10 days, provided quantitative SO2 concentration measurements and the spatial distribution of this pollutant during and off volcanic eruptions of the Piton de La Fournaise volcano.
The uncertainty on the SO2 concentrations measured by passive samplers was evaluated during two inter-comparison phases in late 2005, with two series of SO2 tubes sampled on the sites. One of the series was analysed by the Joint Research Center (JRC) and the other one by the Gradko International Limited (Bhugwant et Siéja, 2005, 2006). This sampling technique has also been tested in different tropical and subtropical regions (Carmichaël et al., 2003; Ferm and Rodhe, 1997).
On the monitoring sites, the samplers are installed mesh side down in the underside of a plastic disc screwed at the top of a wooden pole and left in position for one week to 10 days (see Figure 2).
Figure 2.
Schematic representation of a SO2 passive diffusion tube (left) and the sampling system (right : shelter and mast) for protecting against meteorological conditions (UV radiation, rains …).
After they are taken off, the exposed samples are stored in a refrigerator, in order to stabilize the trapped air samples. Then, they are sent to Gradko International Limited for subsequent laboratory analysis, via Ion Chromatography, in order to calculate the SO2 concentration. The precision of the samplers, expressed as mean percentage difference between duplicates was found to be in the 10-25% range for SO2 (Ayers et al., 1998).
3.1.2. SO2 monitoring using SF-2000 analyser
The ORA also measured SO2 concentration continuously with automatic analysers in an atmospheric network composed of fixed stations at different parts over Réunion island (Figure 3). During the April-May 2007 eruption, an SO2 analyser SF-2000 (SERES) model was installed temporarily at two additional sites located in the south-western and southern parts of the island (see points 20: TR_1 and 22: TR_2 respectively, in Figure 3).
Figure 3.
Map of Réunion island, with indication of the atmospheric network composed of fixed atmospheric stations over different parts of the island
The instrument analyses ambient air by the ultraviolet (UV) fluorescence technique, at a time-base of 15 minutes, subsequently averaged to 1 hour, for analysis with other datasets such as tremor intensity, winds and rainfall.
Sulfur dioxide molecules are brought to fluorescence once irradiated by ultraviolet radiation in the 190-390 nm wavelength range.
This UV fluorescence is the maximum for a wavelength found in the 210-230 nm range produced by a UV radiation generator which uses a zinc vapour lamp. In the case of the SF-2000 analyser, the UV radiation of 215 nm wavelength produced by the zinc vapour lamp excites SO2 molecules contained in the sampling vat, as shown by the equation (1) :
SO2+hn→SO2*adsorption−excitedstateE1
The excited SO2* molecule regains its initial energetic state E by emitting a radiation of wavelength λ’, as indicated in the equation (2):
SO2*→SO2+hv’unexcitedstateE2
The re-emitted energy is lower than h, the excitation energy, and hence the wavelength of the UV fluorescence radiation λ’ (240-240 nm) is greater than that of the stimulation source wavelength λ (=215 nm). The fluorescence phenomenon stops when the excitation source is suppressed. The intensity of radiation of the sample is proportional to the SO2 concentration: [SO2] = k x IF, where k is the proportionality factor and IF is the measured intensity (Ruidavets et al., 2005).
The SO2 concentration measured by the SF-2000 analysers at the four locations situated in the north-western part of the island is carried out continuously since January 2001. However, only atmospheric data collected during summer 2005 and spring 2007 will be discussed in this work, as notable volcanic pollutants emitted during these eruption episodes were dispersed over different inhabited parts of the island.
3.2. Seismic network
The Observatoire Volcanologique du Piton de la Fournaise (OVPF) is in charge of the seismic and deformation network which has been implemented over the Piton de la Fournaise volcano since 1980. This network is composed, among others, of 25 seismic stations, with 1Hz and large band instruments (Aki and Ferrazzini, 2000; Staudacher at al., 2009). It records the seismic activity and deformation of Piton de la Fournaise 24 hours throughout a day and transfers data by radio in real time to the observatory. The seismic stations closer to the volcano enclosure (Staudacher at al., 2009) and to the eruption site are best representative of the eruptive tremor, and have been studied in particular for the April-May 2007 eruption (Bhugwant et al., 2005, 2009).
3.3. Meteorological data
The local meteorological parameters such as wind speed, wind direction, and rainfall are measured from the instruments onboard a tower at 10 m above the ground level, close to most of the sampling locations. These meteorological stations are managed by the French Meteorological Service (Météo-France, 2000). The wind fields calculated from the ECMWF (European Center for the Medium-Range Weather Forecast) data archives are also analysed (not shown) in order to study the dynamical processes prevailing on regional scale over Réunion Island (Bhugwant et al., 2009).
3.4. Tropospheric SO2 concentration variability measured from satellite data
The Ozone Monitoring Instrument (OMI) is dedicated to detect and measure volcanic eruptions degassing and anthropogenic pollution from space. The OMI is a hyperspectral UV-Visible spectrometer with a resolution of 13x24 km at nadir. In order to gain more information about the spatial distribution of the SO2 and the characteristics (composition, etc.) of the volcanic plumes, satellite data retrieved each day from the Aura OMI satellite following the eruptions were analysed (Carn et al., 2003, 2007; Yang et al., 2007; Bhugwant et al., 2009).
3.5. Field observations of the Piton de la Fournaise activity
It is now well established that the Piton de la Fournaise volcano is one of the most active volcanoes in the world, with one eruption every 8 months, over the last century. Since 1998, a particular intense volcanic activity is observed, with 2 to 4 eruptions per year. These eruptions are mainly basaltic, producing generally lava fountains of 50 to 100 m high and fluid lava flows of aa or pahoehoe type and of aphyric to olivine-rich composition. Most eruptions of Piton de la Fournaise last between 2 weeks and 1 month. However, the March 1998 eruption lasted 196 days. As the driving force of every eruption is the trapped gas, they always accompany each eruption. However, significant gas emissions are observed at Piton de la Fournaise during intense eruptions only (Bhugwant et al., 2001, 2009).
3.6. Sanitary and environmental survey
Following the recent population control, Réunion island holds a population of 730 000 inhabitants (INSEE, 2010). About 24% of the population of the island lives in the vicinity of the Piton de La Fournaise volcano and may thus be exposed to sanitary hazards during eruption episodes. Moreover, a large part region localised close to the Piton de La Fournaise is composed of agricultural lands and also forests with endemic species, which may be subject to environmental impacts (eg. acid rains) during eruptions.
4. Results & discussions
4.1. Spatial SO2 concentration distribution during late December 2005 eruption
The methodology applied to calculate the spatial distribution of the SO2 concentrations measured at different locations over Réunion island is the kriging interpolation method (Carletti et al., 2000).
The Figure 4 presents the GIS (geographic information system) plot of the spatial distribution of the mean SO2 concentration measured during an eruption event which occurred on late December 2005-early January 2006, i.e., from 27th December 2005 to 3rd January 2006. The mean SO2 concentration shown in this figure consists of the data obtained from both passive diffusion samplers and the automatic analysers on the atmospheric stations. It may first be observed that the highest SO2 concentration ( 70 μg/m3) is mostly confined on the southern and the south-eastern parts of the island. Then, it decreases rapidly after a few kilometres from the eruption vent. Importantly, it may be seen that the eastern to north-eastern part of the island is weakly concerned by the SO2 concentration emitted during this eruption event. The main causes are, among others, the ranges of mountains Cilaos, Mafate and Salazie (see Figure 2), which constitute a natural barrier for the transfer of atmospheric pollutants downwind, over the eastern to northern parts of the island. Moreover, due to the conjunction of the important relief of the island and the easterlies prevailing over the south-western Indian Ocean, the air masses originating from the south-eastern part of the island tend to by-pass it (Météo-France, 2000). Also, the geographic configuration of the island tends to generate microclimates over the island. Consequently, the windward side of the island is more windy with high rainfall levels throughout the year while the leeward side of it is drier with less winds. This parameter may also in part explain the low SO2 concentration observed on the eastern to northern sectors.
Figure 4.
Contour plot of the mean SO2 concentration calculated by kriging interpolation method from thirteen measurement locations during 27th December 2005-3rd January 2006 period over Réunion Island (black circles : SO2 sampling locations).
Figure 5.
Contour plot of the mean SO2 concentration calculated by kriging interpolation method from thirteen measurement locations during 3rd January 2006-12th January 2006 period over Réunion island (black circles : SO2 sampling locations).
4.2. Spatial SO2 concentration distribution during early January 2006 eruption
The Figure 5 presents the plot of the spatial distribution of the mean SO2 concentration obtained by passive diffusion samplers and automatic analysers during an eruption event which occurred from late December 2005 to mid-January 2006, i.e., from 3rd January 2006 to 12th January 2006. The highest SO2 concentration ( 370 μg/m3) is observed on the northern to south-western parts of the Piton de la Fournaise volcano. Interestingly, it may be seen that the high SO2 levels are constrained in a mountainous sector with a very low density of population. The contaminated air masses follow gullies and rivers lane found in the vicinity of the volcano (on the north-western to southern part of it) to attain the littoral regions of the island. The comparison of the two figures (4 and 5) shows that, in spite of the relief and the meteorology, the source intensity is also a dominating factor which can explain the sulfur dioxide variability observed for these two eruptive episodes.
4.3. Spatial SO2 concentration distribution during mid-April 2007 eruption
The Figure 6 presents the plot of the spatial distribution of the mean SO2 concentration obtained by passive diffusion samplers and automatic analysers during an eruption event which occurred from early April 2007 to early May 2007, i.e., from 18th April 2007 to 26th April 2007. The highest SO2 concentration ( 190 μg/m3) is mainly concentrated on the northern to south-western parts of the Piton de la Fournaise volcano while moderate levels are also measured up to the southern to south-western part of the island.
Since the SO2 surveillance in the vicinity of this volcano, it may be seen that during the previous eruptions, regularly the volcanic plumes are preferentially transported to the southern to north-western parts of Réunion island.
Figure 6.
Contour plot of the mean SO2 concentration obtained by kriging interpolation method from twenty-three measurement locations during 18th April-26th April 2007 period over Réunion island (black circles : SO2 sampling locations).
4.4. Temporal evolution of SO2 concentration in 2006 and 2007
The Figure 7 presents the monthly mean SO2 concentration measured by automatic analysers in atmospheric stations at different parts of Réunion island in 2006 and 2007. In absence of any eruption, the monthly mean SO2 concentration exhibits low levels, varying in the 3-12 μg/m3 range at all the monitoring sites.
Figure 7.
Monthly mean SO2 concentration measured continuously with automatic analysers at different atmospheric stations over Réunion island in 2006 and 2007.
The highest SO2 concentration ( 50 μg/m3) is observed at the MOB station (which is closest to the Piton de la Fournaise volcano) while the other stations are also but less impacted by the SO2 emitted in April 2007. This assessment is coherent with the results presented in Figure 6.
Importantly, it may be seen that the atmospheric stations located in the north-western parts of the island show low values while the MOB station exhibit high values during each eruption event registered in 2006 and 2007. The results also suggest a regional impact of the contaminants emitted during the Piton de la Fournaise volcano eruption and the implications on human health for the population exposed to atmospheric pollution.
4.5. Winds spatial distribution over Réunion island
Dynamical processes such as winds (direction and speed) have notable influence on atmospheric constituent variability. It is thus important to study the winds regime prevailing over the island in order to assess their influence on the SO2 concentration variability. The Figure 8 presents the wind roses annual mean distribution over Réunion island, calculated from winds data collected over meteorological stations located in different parts of the island from 1976 to 1995 (Météo-France, 2000). It may be seen that the hilly landscape of the island plays an essential role in the winds distribution. On one hand, it contributes to strengthen the winds in certain sectors, and, on the other hand, to place certain regions under cover. The land/sea breezes as well as the slope breezes, the constituent of which is perpendicular in the coasts, are clearly evidenced on most of the meteorological stations. The local winds distribution induced by the important relief of the island, coupled with the easterlies prevailing regionally over the south-western Indian Ocean, notably contribute to the dispersion of the SO2 over the island during and off eruption events.
Figure 8.
Wind roses annual mean distribution illustrating the winds spatial distribution over Réunion island obtained from winds data collected from 1976 to 1995 (Source : Météo-France, 2000).
4.6. Spatial distribution of rainfall in early winter (April)
In order to gain more information about the spatial distribution and the temporal evolution of the SO2 concentration; additional meteorological data collected over the island was also analysed. Figure 9 presents the spatial distribution of the monthly mean rainfall measured in April (from 1998 to 2008) over Réunion island. It may be seen that the maximum rainfall occurs in the southern to north-eastern regions, with a maximum rainfall of 1 200 mm in the East of the island. A comparison with the 4 to 6 suggest that this parameter is the main factor which may explain the lower SO2 levels observed in the south-eastern to the northern regions, especially during eruption events.
Figure 5.
Monthly mean rainfall measured in April from 1998 to 2008 at different parts of Réunion island (black circles : meteorological stations).
5. Conclusions
In this chapter the spatial distribution of SO2 concentration and its temporal evolution during several eruptions of the Piton de La Fournaise volcano has been analysed, in conjunction with other parameters, such as seismic and meteorological data.
A good correlation has been established between the seismic variability and the SO2 concentration variation in the vicinity of the volcano (Bhugwant et al., 2009).
The analysis of SO2 data collected in parallel with meteorological parameters show that the rainfall influences notably the SO2 spatial variability in particular over the eastern to northern regions of Réunion island, via scavenging processes.
The conjunction of the important relief of the island coupled with the winds (easterlies and land/sea breezes) also contribute to the spatial distribution and the temporal evolution of the SO2 concentration measured at different parts of it.
Importantly, the results of several years of SO2 measurements also show that the QO (Quality Objective) as well as the LV (Limit values for the protection of the ecosystems), based on an annual average, were not exceeded over the island during the 2005-2010 period. However, the SRI (Recommendation and information threshold) as well as the SA (alert threshold) was exceeded in some inhabited regions close to the volcano during previous eruptions, in particular during the April-May 2007 eruption (Bhugwant et al., 2009).
Recently, a preliminary epidemiological study showed that the SO2 emitted during the eruption of the Piton de la Fournaise volcano might have a potential sanitary impact on the surrounding population. Consequently, this investigation should be carried out in order to establish quantitatively the links between atmospheric pollution induced by eruption events and sanitary impacts.
This preliminary work may be of interest in particular to epidemiologists (eg. to establish the sanitary impact on the surrounding population) and to decision-makers (eg. for crisis managements during eruption episodes).
A close collaboration between the various supervisory boards (ORA, OVPF and Météo-France) may help to establish a decision-making tool to the decision-makers, in particular, to draw up evacuation plans in case of pollution alerts. The recent tools deployed, i.e., the atmospheric data collected from the satellite since a few decades is also a supplement to the ground-based measurements, for the monitoring of atmospheric plumes during future eruptions of this active volcano.
6. Acknowledgements
The present work is supported in part by the French ‘Ministère de l’Outre-Mer’ (MOM) Program. We acknowledge the French ‘Minsitry of Environment’ for the financial support of the ORA atmospheric network.
We also gratefully acknowledge the ORA staff for the technical support and for the atmospheric data sampling. We also thank the OVPF staff for preparing the tremor data. Météo-France is also gratefully acknowledged for providing the meteorological data.
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/16222.pdf",chapterXML:"https://mts.intechopen.com/source/xml/16222.xml",downloadPdfUrl:"/chapter/pdf-download/16222",previewPdfUrl:"/chapter/pdf-preview/16222",totalDownloads:2346,totalViews:268,totalCrossrefCites:1,totalDimensionsCites:1,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:31,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"October 17th 2010",dateReviewed:"April 19th 2011",datePrePublished:null,datePublished:"July 8th 2011",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/16222",risUrl:"/chapter/ris/16222",book:{id:"518",slug:"air-quality-monitoring-assessment-and-management"},signatures:"Chatrapatty Bhugwant, Miloud Bessafi and Bruno Sieja",authors:[{id:"26819",title:"Dr.",name:"Chatrapatty",middleName:null,surname:"Bhugwant",fullName:"Chatrapatty Bhugwant",slug:"chatrapatty-bhugwant",email:"chatrapatty.bhugwant@atmo-reunion.net",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"47920",title:"Mr.",name:"Bruno",middleName:null,surname:"SIEJA",fullName:"Bruno SIEJA",slug:"bruno-sieja",email:"bruno.sieja@atmo-reunion.net",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"90424",title:"Prof.",name:"Miloud",middleName:null,surname:"Bessafi",fullName:"Miloud Bessafi",slug:"miloud-bessafi",email:"bessafi@univ-reunion.fr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of La Réunion",institutionURL:null,country:{name:"Reunion"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Guideline values for the SO2 concentration levels",level:"1"},{id:"sec_3",title:"3. Experimental set-ups and measurements",level:"1"},{id:"sec_3_2",title:"3.1. Atmospheric measurements",level:"2"},{id:"sec_3_3",title:"3.1.1. SO2 measurement using passive diffusion samplers",level:"3"},{id:"sec_4_3",title:"3.1.2. SO2 monitoring using SF-2000 analyser",level:"3"},{id:"sec_6_2",title:"3.2. Seismic network",level:"2"},{id:"sec_7_2",title:"3.3. Meteorological data",level:"2"},{id:"sec_8_2",title:"3.4. Tropospheric SO2 concentration variability measured from satellite data",level:"2"},{id:"sec_9_2",title:"3.5. Field observations of the Piton de la Fournaise activity",level:"2"},{id:"sec_10_2",title:"3.6. Sanitary and environmental survey",level:"2"},{id:"sec_12",title:"4. Results & discussions",level:"1"},{id:"sec_12_2",title:"4.1. Spatial SO2 concentration distribution during late December 2005 eruption",level:"2"},{id:"sec_13_2",title:"4.2. Spatial SO2 concentration distribution during early January 2006 eruption",level:"2"},{id:"sec_14_2",title:"4.3. Spatial SO2 concentration distribution during mid-April 2007 eruption",level:"2"},{id:"sec_15_2",title:"4.4. Temporal evolution of SO2 concentration in 2006 and 2007",level:"2"},{id:"sec_16_2",title:"4.5. Winds spatial distribution over Réunion island",level:"2"},{id:"sec_17_2",title:"4.6. Spatial distribution of rainfall in early winter (April)",level:"2"},{id:"sec_19",title:"5. Conclusions",level:"1"},{id:"sec_20",title:"6. Acknowledgements",level:"1"}],chapterReferences:[{id:"B1",body:'ADMINET, http://admi.net/jo/20020219/ATEP0190081D.html, (in French), 2002. '},{id:"B2",body:'ADMINET, http://www.legifrance.gouv.fr/affichTexte.do?cidTexte=JORFTEXT000022941254 &dateTexte=&categorieLien=id, (in French), 2010.'},{id:"B3",body:'AkiK.FerrazziniV.\n\t\t\t\t\t2000\n\t\t\t\t\tSeismic monitoring and modeling of an active volcano for prediction, J. Geophys. 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Observatoire Réunionnais de l’Air, Technopole de La Réunion, Sainte-Clotilde, La Réunion island, France
Solar energy is a renewable energy source, clean and inexhaustible. It is based on the photovoltaic effect to convert solar energy into electricity through solar cells. PV panels was mainly installing in isolated areas to provide them the electricity but in the last few years a considerable amount of electricity has been generated from solar energy in different countries in the world. In 2019, the global installed solar energy capacity has reached 586.42 GW [1]. This significant growth will may be continuing in the future due to its several technological, environmental and economic benefits.
Like some other renewable energies, solar energy is intermittent. Its production is so related to the solar radiation received on the earth. Therefore, it is possible to forecast solar energy from a relevant forecasting of solar radiation. Different techniques have been developed in the literature to forecast solar radiation. Most of them treat it as time series. These techniques are based on the historical solar radiation data, they treated and followed the solar radiation evolution on the past. Based on the historical data, a model is created to characterize the solar radiation behavior in the past. Therefore, the forecasting of solar radiation on a given time interval is based on this created model. The aim of this chapter is the forecasting of solar radiation using ARMA model. Based on these results and taking into account some other parameters, the PV power is then modeled. A general overview of solar radiation and its different propagation forms is presented in the first part of this chapter. Then, a brief literature review on solar radiation forecasting techniques will be the subject of the next part. After that, the ARMA model will be used to forecast the annual solar radiation corresponds to an industrial company by considering the weekly radiation averages. PV power is modeled in the following section and based on the forecasting solar radiation results, it is presented for different PV panels number. The end section concludes and summarizes this chapter.
2. General presentation of solar radiation
The sun is a vital element, necessary for photosynthesis, important for plants and fundamental for the thermal balance of different component of the crop. 75% of its composition is Hydrogen and the rest is Helium [2]. The sun is the primary source of electromagnetic radiation in Earth. It emits energy in the form of electromagnetic waves called solar radiation which mainly composed of visible light, ultra violet and infrared radiation. Visible light is the part of electromagnetic spectrum visible with the naked eye, its wavelength is depended to the individual. The ultra violet radiation is characterized by a wavelength greater than 800 nm, it is also called black light. This type of radiation is not visible with the naked eye. The infrared is a radiation with a wavelength less than 400 nm. It is greater than that of visible light but shorter than that of micro wave. When the solar radiation passes through the atmosphere, it is reduced due to its molecular scattering and its absorption by gas molecules. Ultra violet and infrared radiations are the two most absorbed. The amount of energy received on earth is depended to the atmosphere thickness and to some other factors such as the seasonal and cloud variations.
2.1 Propagation of solar radiation in the atmosphere
By propagating in the atmosphere, solar radiation can be diffused, absorbed or reflected,
Reflected radiation: the radiation is reflected by the earth’s surface and the soil reflects the radiation in a diffuse and anisotropic manner.
Diffused radiation: the radiation is diffused in all direction, this phenomenon is occurred in a medium containing fine molecules and it strongly depends to these molecules size.
Absorbed radiation: the radiation is absorbed by gas molecules that it encounters in atmosphere, this absorption is mainly due to water vapor, carbon dioxide and ozone.
These different interaction of solar radiation with atmosphere are recapitulated in the Figure 1 [3].
Figure 1.
Interaction between solar radiation and the atmosphere [3].
2.2 Modeling of solar radiation
Several theories are developed in the literature to model solar radiation [4, 5, 6]. Therefore, at a specific moment and in a given location, the solar radiation cannot be modeled without requiring some factors such as the sky nature and the sun position. As mentioned previously, solar radiation has three different components, reflected, diffused and absorbed. All these components are modeled by the global or total solar radiation as presented in the Eq. (1).
Rtot=Rdir+Rdif+RrefE1
With Rtot represents the total solar radiation, Rdir, Rdif and Rref are respectively the directed, diffused and reflected solar radiation. Each of these radiations is sensitive to certain parameters and are calculated as presented in the following equations,
Rdir=Sh.Rout.τM.cosiE2
Rdiff=Rout.0.271−0.294.τM.sinαE3
Rref=r.Sc.0.271+0.706.τM.sinα.sin2x2E4
With Sh is a binary umbrage value, it is computed for each hour in day. Sh is assigned to 0 when the solar radiation is projected to the neighboring mountain umbrage, else it is assigned to 1. r represents the soil reflectance; it is also called the reflection factor. Sc is the constant solar equal to 1367 W/m2. To define the other parameters, a recourse to the geometry between sun and earth as well as to the characteristics of the solar flux are needed. Indeed, the position of the sun in the sky depends to the time and latitude. It is defined by two angles which characterized the altitude and the solar azimuth. The altitude angle α is defined as presented in the below Eq. [7].
sinα=sinφ.sinδ+cosφ.cosηE5
With φ and η are respectively the latitude for each cell and the solar time. δ represents the solar declinaition, this parameter depends to the year day j and expressed as written in Eq. (6),
δ=23.45.sin360.284+j365E6
The azimuth angle β is defined as presented in the Eq. (7)
cosβ=sinδ.cosφ−cosδ.sinφ.cosη/cosαE7
Rout represents the solar flux, it depends to the solar constant Sc and the year day j, it is written as indicated in the Eq. (8),
Rout=Sc.1+0.034.cos360j365E8
τM represents the transmissivity coefficient, it is defined as the fraction of the solar radiation incident on the atmosphere surface that reaches the soil along a vertical trajectory. In the mountain area, a correlation factor linked to the atmospheric pressure p/p0 must be used. The path length is presented by the lettre M and written as shown in the Eq. (9),
M=M0.PP0E9
M0 is calculated following the Eq. (10) and the p/p0 represents the correlation factor of atmospheric pressure, it is calculated as defined in the Eq. (11).
M0=1229+614.sinα2−614.sinαE10
PP0=288−0.0065.h2885.256E11
An incidence angle i between the sun ray and the soil surface must be taken into account when the solar radiation is converged to sloping areas. This angle is depended to the sun position and to the topography and it is written as described the below equation,
cosi=cosα.sinx.cosβ−βs+sinα.cosxE12
With x and βs represent respectively the slope and the exposure, they are taken in degrees. It should be noted that the Eq. (1) describes the solar radiation without taking into account the clouds effects. To take them into account, a coefficient Kc must be added. So the expression of solar radiation in the presence of clouds Rtotc will be written as presented in the Eq. (13).
Rtotc=Kc.RtotE13
The Kc coefficient is depended to the cloudiness N and calculated as described in the Eq. (14).
Kc=1−0.75.N83.4E14
3. Forecasting of solar radiation
3.1 Forecast horizon
Before forecasting, it must specify firstly the horizon forecasting. The choice of this horizon is relative to the problem to be treated. They are four forecasting horizon categories which are the very short term, the short term, the medium term and the long term. Each of these horizons is characterized by a time interval as described in the following paragraph,
Very short term: the time horizon of this forecasting category does not exceed a few hours, usually it is used for the intra-day market.
Short term: the time horizon of this category is between 48 hours and 72 hours. This type of forecasting horizon is useful for the daily dispatching electrical power.
Medium term: the time horizon of this forecasting term is done for more than one week to one month. It intervenes in the planning of the power system. It is also used for the dispatching of the conventional power plants.
Long term: the time horizon of this type is done from one month to one year. It is useful for long term planning operations such as expansion projects for power generation units.
3.2 Solar radiation forecasting techniques
In the literature, different techniques are proposed to the forecasting of solar radiation [7]. It is possible to classify them into four groups, the naïve models, the conditional probability models, the reference models and the connectionist models. A description of each of these techniques is described in the following sub sections.
3.2.1 Naïve model
They are the smallest techniques for time series forecasting. For a given horizon, the forecasting is based on the last observed variable [8]. The mean, the persistence and the k nearest neighbors are registered under these models.
3.2.1.1 Mean forecasting method
The mean forecasting method consists to substitute the variable to be forecasted by the mean available data assigned to this variable. It is a simple technique to apply but it is so expensive in terms of history [7]. If N corresponds to the number of historical data, the forecasting of a variable x at a given horizon h is described as presented in the Eq. (15).
x̂t+h=1N∑i=1NxiE15
3.2.1.2 The persistence
This technique is based on the repetition of a measurement from time t to time t+h [7]. If the considered horizon h is 1, the forecasting of a variable data at time time t+1 is defined as written in the Eq. (16).
x̂t+1=xtE16
This predictor type is often used in time series forecasting because it is so easy to implement and it does not require a large historical data base. On the other hand, it is imprecise and it does not lead to an improvement in time series.
3.2.1.3 The k nearest neighbors
This technique is derived from the artificial intelligence, it consists to find in time series, a set of k data similar to those that to be predicted [9]. The determination of k is done by different algorithm [7]. This technique is, in general, efficient in the time series forecasting, however, it is sensitive to the dimensionality and to the irrelevant variables.
3.2.2 Conditional probability models
We cite as an example for these types of models those of Marcov chains and Bayesian inferences.
3.2.2.1 Marcov chain
This technique is rarely used for the forecasting of solar radiation [10]. It is a stochastic process that has the Markovian property [11]. A future state is modeled by a probabilistic process which depends only to the present states. Following Markov chain, the forecasting of a variable at a given horizon h is defined as presented in the Eq. (17).
Xt+h=Xt.RMhE17
With RM represents the transition matrix of Markov chain, its dimension depends to several factors such as the number of available data and the precision nature [12].
3.2.2.2 Bayesian inferences
This method is mainly based on the conditional probability; it is rarely used for the forecasting of solar radiation. This method is very difficult to handle and it requires several parameters. The estimation of the probability of a series at a given horizon can be done by Bayes theorem as described in the Eq. (18).
pAB=pBA.pApBE18
3.2.3 Connectionist models
The first artificial neuron was created by Warren McCulloch and Walter Pitts in 1943 [13]. The structure of this neuron is imitated from the biological neuron as presented in the Figure 2 [14]. An artificial neural network (ANN), is an assembly strongly connected of formal neurons. It is characterized by an excellent capacity of learning and generalization as well as a speed of processing. Its ability to learn and generalize makes it a very powerful tools. It has proven, in recent years, its effectiveness in various research fields. ANNs are subdivided into two large families, static and dynamic neural network. The choice of the one or the other of these two networks depends to the application to be processed, the available information and the complexity model [15].
Figure 2.
Schematic diagram of an ANN structure neuron model [14].
3.2.4 Conditional probability models
These are models from the large family of Auto Regressive and Moving Average (ARMA). ARMA is the combination of two models, the Auto Regressive (AR) and Moving Average (MA). It is characterized by its ability to extract useful statistical properties. Thus, it is among the most widely used models for time series forecasting. Its effectiveness to forecast solar radiation is well proven in certain research work [16]. AR model assumes that each point can be forecasted by the sum of p previous points plus a random error term. The expression of AR model with an order p (AR(p)) is written as presented in the Eq. (19),
xt=α1.xt−1+α2.xt−2+…αp.xt−p+εtE19
with αi represent the AR coefficients and εt is a white noise.
The moving average process assumes that each point is the sum of q previous errors plus its own error. The expression of MA model with an order q (MA(q)) is written as presented in the Eq. (20).
xt=β1.et−1+β2.et−2+…βq.et−qE20
With βi are the MA coefficients. A combination of these two models forms the ARMA model with order p and q, its expression is described in the Eq. (21).
The major requirement of ARMA model is that the time series studied must be stationary. A series is considered stationary when its statistical properties such the mean and the variance are constant over time [17]. The distribution of a stationary series at time t is identical to that at time t-1. The unit root is among the stationarity tests. Autos-correlations and partial autos-correlations diagrams can be used also to prove the stationarity of time series.
If the time series is proved stationary, an approach must be followed to define the p and q orders. Box and Jenkins methodology is used to determine them, it contains four steps, identification of the model, estimation of the parameters, the validation of the selected model and finally the use of this model for forecasting.
Identification: this is the most important step, it aims to identify the p and q orders. This is done by examining the auto correlation and the partial auto correlation diagrams of the time series.
Estimation of parameters: the determination of p and q orders does not reflect the validation of this model. It is necessary to estimate the ARMA(p,q) selected. This estimation can be made by the student test.
Validation model: this validation is carried out by applying two tests on the residues, the Ljung-Box test and homoscedasticity test to ensure that the residuals are white noises.
The use of model: the selected ARMA model can be used in forecasting. However, in order to ensure the validity of this model, it must be tested on a data base already known. It should find good forecasting performances by comparing the data forecasted by this model and those already known.
3.3 Solar radiation forecasting using ARMA model
The objective of this section is to forecast the solar radiation using ARMA model. The data base solar radiation considered for the forecasting is the set of solar radiation measurements corresponds to an industrial company located in Barcelona north [18]. The time interval of these measurements is five minutes, they are taken every day for a whole year as presented in the Figure 3.
Figure 3.
Annual solar radiation evolution.
To refine the representative curve, just the weekly solar radiation averages are taken into account as presented in the Figure 4.
Figure 4.
Weekly solar radiation averages.
To apply ARMA model, it must study the stationarity of this series. Correlograms corresponding to the auto-correlations and to the partial auto-correlation to this series are presented in the Figure 5.
Figure 5.
(a) Auto-correlation and partial auto-correlation (b) correlograms of annual solar radiation.
The auto-correlation coefficient of order 1 is close to 1 and the correlogram shows a slow regression which is typical of non-stationary series. Dickey Fuller test is thus applied using EViews software; it proves the weak stationary of this series as shows in the Figure 6.
Figure 6.
Dickey fuller test results for weekly solar radiation series.
The differentiation of this series is necessary in order to make it stationary. The following Figure 7 shows the evolution of the differentiated weekly solar radiation.
Figure 7.
Differentiated weekly solar radiation evolution.
The Dickey Fuller is thus applied and it proves the stationary of this series as shown in the Figure 8. Thereafter, the different Box and Jenkins methodology steps are followed to obtain finally the optimal ARMA model that reproduces the best the behavior of this series. Orders p and q, coefficients α1, α2 and β1 of the ARMA model are recapitulated in the Table 1.
Figure 8.
Dickey fuller test results for the differentiated weekly solar radiation series.
ARMA (p,q)
Order
Coefficients
p = 2
α1 = −1.0342; α2 = −0.4023
q = 1
β1 = 0.7483
Table 1.
Orders and coefficients of ARMA (2,1).
In this paragraph, ARMA (2,1) model is used to forecast the differentiated weekly solar radiation averages. The real solar radiation curve and the forecasted one are presented in the Figure 9. It is clear that an approximation is observed between the two curves for certain time intervals, especially when the solar radiation does not present large fluctuations. For other moments time, the forecasted solar radiation curve diverges from the real one. This is particularly observed when the solar radiation presents large fluctuations. To confirm these results, the ARMA model errors are presented in Figure 10.
Figure 9.
Solar radiation modeled by ARMA (2,1).
Figure 10.
Error (a) and relative error (b) of solar radiation modeled by ARMA (2,1).
Following the Figure 10b, it is clear that the relative error is small, it does not exceed 15%. It is thus observed two peaks, the first one corresponds to the 16th week of the year and the second is in the 36th week. Therefore, when we refer to the real annual solar radiation curve, we observe a sudden fluctuation during these two weeks. Indeed, 16th and 17th weeks correspond respectively to the last week of April month and the first week of May. A considerable decrease of temperature is observed during this period; this is may be the main reason to the sudden decrease of radiation. On the other hand, 37th and 38th weeks correspond to the two first weeks of September month. At the end of this period, it is observed also a sudden decrease of the temperature which affects considerably the solar radiation. Furthermore, as the weekly solar radiation averages are considered for the forecasting, it is obvious to have these large solar radiation variations especially in the switching periods from one season to another one. The influence of temperature on solar radiation evolution for April, May, September and October months are presented in the Figure 11.
Figure 11.
Influence of temperature on solar radiation (a) April and may weeks (b) September and October weeks.
After forecasting solar radiation or any other parameters, a forecasting error should always be calculated. An error in the forecasting context does not indicate a fault or an anomaly as it is known in several other fields but rather a criterion to evaluate the forecasting performances. In this study, Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are calculated as written in the Eqs. (22)–(24) [19]. ei (i=1….n) represents the error measured between the actual value and the forecasted one for sample i and n is the total number of samples. Results are recapitulated in the Table 2, the MSE presents the lowest one (0.2182), it is a small value which reflects the performances of ARMA (2,1) model to forecast the solar radiation.
Errors
Performances
MSE
0.2182
MAE
0.2999
RMSE
0.4671
Table 2.
Errors of solar radiation forecasting using ARMA (2,1).
MSE=1n∑i=1nei2E22
RMSE=1n∑i=1nei2E23
MAE=1n∑i=1n∣ei∣E24
4. Modeling and forecasting of PV power
The forecasting of PV power has a great importance to the best management of grid connected PV systems as well as to the isolated micro grid which include PV system as renewable energy source. Based on the literature, it is possible to forecast the PV power by direct or indirect methods [20]. Direct methods consist to describe models to directly forecast the amount of PV power or forecast the PV power without using other metrological data. In this context, different approaches are suggested which mainly the ANN and the machine learning techniques [20, 21, 22]. On the other hand, the indirect methods consist to forecast the PV power based on the forecasting of another meteorological data such as the solar radiation or the temperature [20, 23]. Different physical and statistical approaches are proposed in this field. The choice for the one or the other method is depended to the available data and the forecast horizon term. In physical approaches, the PV power forecasting is based on weather variables predicted by numerical weather prediction (NWP) models and they are more suitable for the long term horizon. The statistical approaches are based on past measured time data series and generally they are appropriate for short term horizon. Moreover, the statistical approaches are simpler than the physical approaches since they require less input data and lower computation [24].
In the following section, the PV power will be modeled and forecasted based on the results of solar radiation forecasting, presented in the precedent section. Indeed, the PV power generators are very often operating with a maximum power called Maximum Power Point Tracker (MPPT) [25]. The maximum power PPV delivered by a PV generator composed of N PV panels can be expressed as indicated in the Eq. (25) [26].
PPV=ηg.N.A.GE25
With A represents the area of a single PV panel, it is expressed in m2. G is the solar radiation measured in w/m2. ηg is the PV generator efficiency and it is described as written in the Eq. (26) [27].
ηg=ηr.ηpt.1−βt.Tc−TrE26
ηr represents the reference efficiency of PV generator, it depends to the PV cells materials. ηpt is the efficiency of power tracking equipment, it is equal to 1 if the MPPT is perfectly used, βt is the temperature coefficient, it is expressed in °C. The typical value of this coefficient varies between 0.004 and 0.006, usually, it is taken in the range of 0.005°C [26]. Tc and Tr represent respectively the tcemperature measured in the PV cells and the reference temperature. Tc depends to the ambient temperature Ta and the radiation G as presented in the Eq. (27) [26].
Tc=Ta+G.NOCT−20800E27
The typical NOCT value for polycrystalline cells is around 45°C. Taking into account Eq. (26) and Eq. (27), the PV power is described as presented in the Eq. (28).
PPV=ηr.ηpt.1−βt.Ta+G.NOCT−20800−Tr.N.A.GE28
As shown in the Eq. (28), the evolution of PV power depends to several parameters such as the temperature, the solar radiation and the PV panels number. Therefore, it is possible to forecast the PV power from the solar radiation forecasting. So, if the PV cells used is the pollicrystalline and the area of a single PV panel is 2.25m2, the evolution of PV power for different PV panels number and based on the solar radiation forecasting results is described as presented in the Figure 12.
Figure 12.
PV power forecasting for different PV panels number (a) N=5 (b) N=20 (c) N=50 (d) N=100.
5. Conclusion
This chapter focuses to model and to forecast the PV power based on the solar radiation forecasting results. Some physical equations are presented firstly to define in general the three different forms of solar radiation. They are explained taking into account some topographical factors and geometric relations.
For solar radiation forecasting, a set of solar radiation measurements corresponds to an industrial company is considered as data base. ARMA model is used to forecast the weekly solar radiation averages. The simulation results obtained are proven the effectiveness of this model to forecast the small variation of solar radiation. On the other hand, it is observed the deterioration of ARMA model with the large solar radiation fluctuations. The forecasting of PV power is carried out based on the obtained solar radiation forecasting results and taking into account some other parameters such as the temperature, the PV cells materials and the PV panels number.
Acknowledgments
This work was supported by Tunisian Ministry of Higher Education and Scientific Research under Grant LSE-ENIT-LR 11ES15.
\n',keywords:"solar radiation, PV power, forecasting, ARMA, fluctuation",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/78152.pdf",chapterXML:"https://mts.intechopen.com/source/xml/78152.xml",downloadPdfUrl:"/chapter/pdf-download/78152",previewPdfUrl:"/chapter/pdf-preview/78152",totalDownloads:155,totalViews:0,totalCrossrefCites:0,dateSubmitted:"October 4th 2020",dateReviewed:"July 16th 2021",datePrePublished:"October 22nd 2021",datePublished:null,dateFinished:"August 18th 2021",readingETA:"0",abstract:"Solar radiation is characterized by its fluctuation because it depends to different factors such as the day hour, the speed wind, the cloud cover and some other weather conditions. Certainly, this fluctuation can affect the PV power production and then its integration on the electrical micro grid. An accurate forecasting of solar radiation is so important to avoid these problems. In this chapter, the solar radiation is treated as time series and it is predicted using the Auto Regressive and Moving Average (ARMA) model. Based on the solar radiation forecasting results, the photovoltaic (PV) power is then forecasted. The choice of ARMA model has been carried out in order to exploit its own strength. This model is characterized by its flexibility and its ability to extract the useful statistical properties, for time series predictions, it is among the most used models. In this work, ARMA model is used to forecast the solar radiation one year in advance considering the weekly radiation averages. Simulation results have proven the effectiveness of ARMA model to forecast the small solar radiation fluctuations.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/78152",risUrl:"/chapter/ris/78152",signatures:"Ines Sansa and Najiba Mrabet Bellaaj",book:{id:"9862",type:"book",title:"Solar Radiation - Measurements, Modeling and Forecasting for Photovoltaic Solar Energy Applications",subtitle:null,fullTitle:"Solar Radiation - Measurements, Modeling and Forecasting for Photovoltaic Solar Energy Applications",slug:null,publishedDate:null,bookSignature:"Dr. Mohammadreza Aghaei",coverURL:"https://cdn.intechopen.com/books/images_new/9862.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83968-859-1",printIsbn:"978-1-83968-858-4",pdfIsbn:"978-1-83968-860-7",isAvailableForWebshopOrdering:!0,editors:[{id:"317230",title:"Dr.",name:"Mohammadreza",middleName:null,surname:"Aghaei",slug:"mohammadreza-aghaei",fullName:"Mohammadreza Aghaei"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. General presentation of solar radiation",level:"1"},{id:"sec_2_2",title:"2.1 Propagation of solar radiation in the atmosphere",level:"2"},{id:"sec_3_2",title:"2.2 Modeling of solar radiation",level:"2"},{id:"sec_5",title:"3. Forecasting of solar radiation",level:"1"},{id:"sec_5_2",title:"3.1 Forecast horizon",level:"2"},{id:"sec_6_2",title:"3.2 Solar radiation forecasting techniques",level:"2"},{id:"sec_6_3",title:"3.2.1 Naïve model",level:"3"},{id:"sec_6_4",title:"3.2.1.1 Mean forecasting method",level:"4"},{id:"sec_7_4",title:"3.2.1.2 The persistence",level:"4"},{id:"sec_8_4",title:"3.2.1.3 The k nearest neighbors",level:"4"},{id:"sec_10_3",title:"3.2.2 Conditional probability models",level:"3"},{id:"sec_10_4",title:"3.2.2.1 Marcov chain",level:"4"},{id:"sec_11_4",title:"3.2.2.2 Bayesian inferences",level:"4"},{id:"sec_13_3",title:"3.2.3 Connectionist models",level:"3"},{id:"sec_14_3",title:"3.2.4 Conditional probability models",level:"3"},{id:"sec_16_2",title:"3.3 Solar radiation forecasting using ARMA model",level:"2"},{id:"sec_18",title:"4. Modeling and forecasting of PV power",level:"1"},{id:"sec_19",title:"5. 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Université de Tunis El Manar, Ecole Nationale d’Ingénieurs de Tunis, LR11ES15, Laboratoire des Systèmes Electriques, Tunisie
Université de Tunis El Manar, Institut Supérieur d’Informatique, Tunisie
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Optional Services
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We feel that financial barriers should never prevent researchers from publishing their work. Please consult our Open Access Funding page to explore funding opportunities and learn more about how you can finance your IntechOpen publication.
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Your published content is immediately available to read, share and download for free
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Chapter and book statistics performance reports allowing you to examine the reach of your content
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Rapid publishing process with personal support
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CO2 capture, utilisation and storage (CCUS) is considered a means to deliver low carbon energy, decarbonising industries, power plants and facilitates the net removal of CO2 from the atmosphere. The stages involved include CO2 capture, transport of the captured CO2, utilisation and secure storage of the captured CO2. This chapter reports the use of eggshell and seashells biomaterials as an adsorbent to separate CO2 from other gases generated by power plants and industrial processes. The capture of carbon dioxide by adsorption is based on the ability of a material to preferentially adsorb or carbonate CO2 over other gases. In light of this, calcined eggshell and seashells biomaterial rich in calcium carbonate from which calcium oxide (94%) can be obtained have demonstrated a strong affinity for CO2. These biomaterials are abundant and low-cost alternative to zeolite, activated carbon and molecular sieve carbon. The mechanism of CO2 capture by eggshell and seashells derived CaO adsorbent comprises of a series of carbonation-calcination reactions (CCR): calcium oxide (CaO) reacts with CO2 resulting in calcium carbonate (CaCO3), which releases pure CO2 stream upon calcinations for sequestration or utilisation, and as a consequence, the biomaterial is regenerated. Findings reveal that these biomaterials can hold up to eight times its own weight of CO2 from flue gas stream. It was also found that the combination of 2 M acetic acid and water pretreatment improved the reactivity and capture capacity of the biomaterial for successive regeneration over four cycle’s usage. Unlike activated carbon, these biomaterials are considered stable for high-temperature adsorption through carbonation.",book:{id:"8509",slug:"carbon-capture",title:"Carbon Capture",fullTitle:"Carbon Capture"},signatures:"Abarasi Hart and Helen Onyeaka",authors:[{id:"326770",title:"Dr.",name:"Helen",middleName:null,surname:"Onyeaka",slug:"helen-onyeaka",fullName:"Helen Onyeaka"},{id:"327022",title:"Dr.",name:"Abarasi",middleName:null,surname:"Hart",slug:"abarasi-hart",fullName:"Abarasi Hart"}]},{id:"73496",doi:"10.5772/intechopen.93893",title:"COVID-19: A Learning Opportunity to Improve Environmental Sustainability",slug:"covid-19-a-learning-opportunity-to-improve-environmental-sustainability",totalDownloads:654,totalCrossrefCites:2,totalDimensionsCites:5,abstract:"In just a few months, COVID-19 transformed from a dangerous regional health threat into a widespread global pandemic and economic disaster. Thus the world is expecting a great recession once again. The rapid spread of COVID-19 has had far-reaching consequences for people’s daily lives in almost all parts of the world. Climate change and biodiversity depletion have now reached global boundaries; thus, human activity has surpassed Earth’s capacities. Earth capacities can be explained in terms of extreme climate change. This chapter is intended to investigate the link between the outbreak of Covid-19 and its effect on environmental and society. The discussion reveals that environmental pollution is minimized as a result of global lockdown. Furthermore, our review also shows that in terms of environment, Covid-19 provide an opportunity to transform our polluted economy toward the green economy through adoption of renewable energy sources and green practices in our businesses.",book:{id:"8509",slug:"carbon-capture",title:"Carbon Capture",fullTitle:"Carbon Capture"},signatures:"Syed Abdul Rehman Khan, Laeeq Razzak Janjua and Zhang Yu",authors:[{id:"254664",title:"Prof.",name:"Syed Abdul Rehman",middleName:null,surname:"Khan",slug:"syed-abdul-rehman-khan",fullName:"Syed Abdul Rehman Khan"},{id:"300373",title:"Dr.",name:"Zhang",middleName:null,surname:"Yu",slug:"zhang-yu",fullName:"Zhang Yu"},{id:"328959",title:"Dr.",name:"Laeeq Razzak",middleName:null,surname:"Janjua",slug:"laeeq-razzak-janjua",fullName:"Laeeq Razzak Janjua"}]}],mostDownloadedChaptersLast30Days:[{id:"74218",title:"Energy-Efficient Landscape Design",slug:"energy-efficient-landscape-design",totalDownloads:654,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Buildings that are carefully designed using passive strategies for natural ventilation and day lighting reduces our dependency on electrical energy meanwhile ensuring thermal comfort inside the building. Similarly, carefully planned vegetation around the building helps in reducing the urban heat island effect and electricity consumption. Methodology adopted for presenting this study as book chapter, first by understanding the concept of landscape with respect to typologies and components. Secondly discussing the physical parameters in terms of temperature, precipitations and humidity of varied prevailing climatic conditions and varied methods adopted through landscape interventions and techniques to overcome the extreme conditions throughout the year, which in turn helps in reducing the consumption of energy.",book:{id:"9838",slug:"design-of-cities-and-buildings-sustainability-and-resilience-in-the-built-environment",title:"Design of Cities and Buildings",fullTitle:"Design of Cities and Buildings - Sustainability and Resilience in the Built Environment"},signatures:"Prashanti Rao and Janmejoy Gupta",authors:[{id:"330405",title:"Dr.",name:"Janmejoy",middleName:null,surname:"Gupta",slug:"janmejoy-gupta",fullName:"Janmejoy Gupta"},{id:"330407",title:"Dr.",name:"Prashanti",middleName:null,surname:"Rao",slug:"prashanti-rao",fullName:"Prashanti Rao"}]},{id:"75082",title:"Traffic Flow Analysis and Management",slug:"traffic-flow-analysis-and-management",totalDownloads:651,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This research is about joint government founded program between Japan and India or Science and Technology Research Partnership for Sustainable development (SATREPS). The purpose of this research is to establish Low Carbon Transportation in developing countries and we choose one of major city in India, where it is Ahmedabad city of Gujarat state—west cost of India. In order to approach the target, we need to understand the current situation of traffic condition in the city. The current traffic condition in India is some chaotic because of their different driving behavior compared with the advanced countries. It is becoming the chaotic traffic condition in India by not only diving behavior during investigation of this research. The main reason of the traffic congestion comes from the unbalance between growing transportation demand and its insufficient infrastructure preparation. In this chapter, it introduces the current traffic condition based on four years monitoring of the traffic by the traffic monitoring cameras and comparison by the traffic flow theory at first. Then it introduces the new traffic analysis method especially for its traffic congestion analysis and its parameters. After the traffic congestion analysis, it summarizes conclusion and our next step from the experience.",book:{id:"9838",slug:"design-of-cities-and-buildings-sustainability-and-resilience-in-the-built-environment",title:"Design of Cities and Buildings",fullTitle:"Design of Cities and Buildings - Sustainability and Resilience in the Built Environment"},signatures:"Tsutomu Tsuboi",authors:[{id:"327074",title:"Ph.D.",name:"Tsutomu",middleName:null,surname:"Tsuboi",slug:"tsutomu-tsuboi",fullName:"Tsutomu Tsuboi"}]},{id:"76112",title:"Introductory Chapter: Intelligence, Sustainable and Post-COVID-19 Resilience Built Environment: An Agenda for Future",slug:"introductory-chapter-intelligence-sustainable-and-post-covid-19-resilience-built-environment-an-agen",totalDownloads:381,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"9838",slug:"design-of-cities-and-buildings-sustainability-and-resilience-in-the-built-environment",title:"Design of Cities and Buildings",fullTitle:"Design of Cities and Buildings - Sustainability and Resilience in the Built Environment"},signatures:"Samad Sepasgozar, José David Bienvenido-Huertas, Sara Shirowzhan and Sharifeh Sargolzae",authors:[{id:"221172",title:"Dr.",name:"Samad M.E.",middleName:null,surname:"Sepasgozar",slug:"samad-m.e.-sepasgozar",fullName:"Samad M.E. Sepasgozar"}]},{id:"29930",title:"Life Cycle Analysis of Wind Turbine",slug:"life-cycle-analysis-of-wind-turbine",totalDownloads:8101,totalCrossrefCites:6,totalDimensionsCites:21,abstract:null,book:{id:"896",slug:"sustainable-development-energy-engineering-and-technologies-manufacturing-and-environment",title:"Sustainable Development",fullTitle:"Sustainable Development - Energy, Engineering and Technologies - Manufacturing and Environment"},signatures:"Chaouki Ghenai",authors:[{id:"14569",title:"Prof.",name:"Chaouki",middleName:null,surname:"Ghenai",slug:"chaouki-ghenai",fullName:"Chaouki Ghenai"}]},{id:"74716",title:"Climate Resilience, Megalopolis Vulnerability and Spatial Distribution",slug:"climate-resilience-megalopolis-vulnerability-and-spatial-distribution",totalDownloads:376,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This chapter takes three megalopolises including Guangdong-Hong Kong-Macao, Yangtze River Delta and Beijing-Tianjin-Hebei as research objects, firstly analyzes the connection relationship and megalopolis vulnerability among core cities in the context of regional integration. Secondly, we calculate there megalopolises to obtain the vulnerability of each city in 2018 Sex index. The results show that the central cities and economically underdeveloped cities of the three megalopolises are relatively vulnerable areas in the urban agglomerations, and areas have low sensitivity and high response. Finally, policy suggestions for megalopolis are given to improve the adaptive capacity of tackling climate change. The innovation of this chapter is to use spatial data to comprehensively evaluate and analyze the vulnerability, and to realize visualization in the map, which better reflects the distribution law and proposes a response to megalopolis vulnerability.",book:{id:"9838",slug:"design-of-cities-and-buildings-sustainability-and-resilience-in-the-built-environment",title:"Design of Cities and Buildings",fullTitle:"Design of Cities and Buildings - Sustainability and Resilience in the Built Environment"},signatures:"Tao Ma, Nairong Tan, Xiaolei Wang, Hao Wang and Mingxi Zhou",authors:[{id:"322664",title:"Prof.",name:"Tao",middleName:null,surname:"Ma",slug:"tao-ma",fullName:"Tao Ma"},{id:"328882",title:"MSc.",name:"Nairong",middleName:null,surname:"Tan",slug:"nairong-tan",fullName:"Nairong Tan"},{id:"328887",title:"Ms.",name:"Xiaolei",middleName:null,surname:"Wang",slug:"xiaolei-wang",fullName:"Xiaolei Wang"},{id:"332546",title:"MSc.",name:"Hao",middleName:null,surname:"Wang",slug:"hao-wang",fullName:"Hao Wang"},{id:"332548",title:"MSc.",name:"Zhou",middleName:null,surname:"Mingxi",slug:"zhou-mingxi",fullName:"Zhou Mingxi"}]}],onlineFirstChaptersFilter:{topicId:"783",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[],lsSeriesList:[],hsSeriesList:[],sshSeriesList:[],testimonialsList:[]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:null,scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"June 28th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:0,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. Her publications cover a wide range of scientific and technical research publications that include edited books, book chapters, refereed journals, refereed conference papers and reports for local, state and federal government clients. She has also produced podcasts for various organisations and participated in media interviews. She has received state, national and international funding worth over USD $25 million. Usha has been awarded the Quarterly Franklin Membership by London Journals Press (UK). Her biography has been included in the Marquis Who's Who in the World® 2018, 2016 (33rd Edition), along with approximately 55,000 of the most accomplished men and women from around the world, including luminaries as U.N. Secretary-General Ban Ki-moon. In 2017, Usha was awarded the Marquis Who’s Who Lifetime Achiever Award.",institutionString:null,institution:{name:"RMIT University",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:5,paginationItems:[{id:"91",title:"Sustainable Economy and Fair Society",coverUrl:"https://cdn.intechopen.com/series_topics/covers/91.jpg",isOpenForSubmission:!0,editor:{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo",profilePictureURL:"https://mts.intechopen.com/storage/users/181603/images/system/181603.jpg",biography:"Antonella Petrillo is a Professor at the Department of Engineering of the University of Naples “Parthenope”, Italy. She received her Ph.D. in Mechanical Engineering from the University of Cassino. Her research interests include multi-criteria decision analysis, industrial plant, logistics, manufacturing and safety. She serves as an Associate Editor for the International Journal of the Analytic Hierarchy Process. She is a member of AHP Academy and a member of several editorial boards. She has over 160 Scientific Publications in International Journals and Conferences and she is the author of 5 books on Innovation and Decision Making in Industrial Applications and Engineering.",institutionString:null,institution:{name:"Parthenope University of Naples",institutionURL:null,country:{name:"Italy"}}},editorTwo:null,editorThree:null},{id:"92",title:"Health and Wellbeing",coverUrl:"https://cdn.intechopen.com/series_topics/covers/92.jpg",isOpenForSubmission:!0,editor:{id:"348225",title:"Prof.",name:"Ann",middleName:null,surname:"Hemingway",slug:"ann-hemingway",fullName:"Ann Hemingway",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035LZFoQAO/Profile_Picture_2022-04-11T14:55:40.jpg",biography:"Professor Hemingway is a public health researcher, Bournemouth University, undertaking international and UK research focused on reducing inequalities in health outcomes for marginalised and excluded populations and more recently focused on equine assisted interventions.",institutionString:null,institution:{name:"Bournemouth University",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null},{id:"93",title:"Inclusivity and Social Equity",coverUrl:"https://cdn.intechopen.com/series_topics/covers/93.jpg",isOpenForSubmission:!0,editor:{id:"210060",title:"Prof. Dr.",name:"Ebba",middleName:null,surname:"Ossiannilsson",slug:"ebba-ossiannilsson",fullName:"Ebba Ossiannilsson",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6LkBQAU/Profile_Picture_2022-02-28T13:31:48.png",biography:"Professor Dr. Ebba Ossiannilsson is an independent researcher, expert, consultant, quality auditor and influencer in the fields of open, flexible online and distance learning (OFDL) and the 'new normal'. Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. She is regularly invited as a keynote speaker at conferences. She is a guest editor for several special issues and a member of the editorial board of several scientific journals. She has published more than 200 articles and is currently working on book projects in the field of OFDL. Ossiannilsson is a visiting professor at several international universities and was recently appointed Professor and Research Fellow at Victoria University of Wellington, NZ. Ossiannilsson has been awarded the following fellowships: EDEN Fellows, EDEN Council of Fellows, and Open Education Europe. She is a ICDE OER Ambassador, Open Education Europe Ambassador, GIZ Ambassador for Quality in Digital Learning, and part of the Globe-Community of Digital Learning and Champion of SPARC Europe. On a national level, she is a quality developer at the Swedish Institute for Standards (SIS) and for ISO. She is a member of the Digital Skills and Jobs Coalition Sweden and Vice President of the Swedish Association for Distance Education. She is currently working on a government initiative on quality in distance education at the National Council for Higher Education. She holds a Ph.D. from the University of Oulu, Finland.",institutionString:"Swedish Association for Distance Education, Sweden",institution:null},editorTwo:null,editorThree:null},{id:"94",title:"Climate Change and Environmental Sustainability",coverUrl:"https://cdn.intechopen.com/series_topics/covers/94.jpg",isOpenForSubmission:!0,editor:{id:"61855",title:"Dr.",name:"Yixin",middleName:null,surname:"Zhang",slug:"yixin-zhang",fullName:"Yixin Zhang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYWJgQAO/Profile_Picture_2022-06-09T11:36:35.jpg",biography:"Professor Yixin Zhang is an aquatic ecologist with over 30 years of research and teaching experience in three continents (Asia, Europe, and North America) in Stream Ecology, Riparian Ecology, Urban Ecology, and Ecosystem Restoration and Aquatic Conservation, Human-Nature Interactions and Sustainability, Urbanization Impact on Aquatic Ecosystems. He got his Ph.D. in Animal Ecology at Umeå University in Sweden in 1998. He conducted postdoc research in stream ecology at the University of California at Santa Barbara in the USA. After that, he was a postdoc research fellow at the University of British Columbia in Canada to do research on large-scale stream experimental manipulation and watershed ecological survey in temperate rainforests of BC. He was a faculty member at the University of Hong Kong to run ecological research projects on aquatic insects, fishes, and newts in Tropical Asian streams. He also conducted research in streams, rivers, and caves in Texas, USA, to study the ecology of macroinvertebrates, big-claw river shrimp, fish, turtles, and bats. Current research interests include trophic flows across ecosystems; watershed impacts of land-use change on biodiversity and ecosystem functioning; ecological civilization and water resource management; urban ecology and urban/rural sustainable development.",institutionString:null,institution:{name:"Soochow University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null},{id:"95",title:"Urban Planning and Environmental Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/95.jpg",isOpenForSubmission:!0,editor:{id:"181079",title:"Dr.",name:"Christoph",middleName:null,surname:"Lüthi",slug:"christoph-luthi",fullName:"Christoph Lüthi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHSqQAO/Profile_Picture_2022-04-12T15:51:33.png",biography:"Dr. Christoph Lüthi is an urban infrastructure planner with over 25 years of experience in planning and design of urban infrastructure in middle and low-income countries. He holds a Master’s Degree in Urban Development Planning from the University College of London (UCL), and a Ph.D. in Urban Planning & Engineering from TU Berlin. He has conducted applied research on urban planning and infrastructure issues in over 20 countries in Africa and Asia. In 2005 he joined Eawag-Sandec as Leader of the Strategic Environmental Sanitation Planning Group. 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She is also Invisalign certified. She’s working as a Senior Lecturer in the Department of Orthodontics, SRM Dental College since November 2019. She is actively involved in teaching orthodontics to the undergraduates and the postgraduates. Her clinical research topics include new orthodontic brackets, fixed appliances and TADs. She’s published 4 articles in well renowned indexed journals and has a published patency of her own. Her private practice is currently limited to orthodontics and works as a consultant in various clinics.",institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"323731",title:"Prof.",name:"Deepak M.",middleName:"Macchindra",surname:"Vikhe",slug:"deepak-m.-vikhe",fullName:"Deepak M. 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Presently he is working as a associate professor in the Dept of Prosthodontics, Rural Dental College, Loni and maintains a successful private practice specialising in Implantology at Rahata.\n\nEmail: drdeepak_mvikhe@yahoo.com..................",institutionString:null,institution:{name:"Pravara Institute of Medical Sciences",country:{name:"India"}}},{id:"204110",title:"Dr.",name:"Ahmed A.",middleName:null,surname:"Madfa",slug:"ahmed-a.-madfa",fullName:"Ahmed A. Madfa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204110/images/system/204110.jpg",biography:"Dr. Madfa is currently Associate Professor of Endodontics at Thamar University and a visiting lecturer at Sana'a University and University of Sciences and Technology. He has more than 6 years of experience in teaching. His research interests include root canal morphology, functionally graded concept, dental biomaterials, epidemiology and dental education, biomimetic restoration, finite element analysis and endodontic regeneration. Dr. Madfa has numerous international publications, full articles, two patents, a book and a book chapter. Furthermore, he won 14 international scientific awards. Furthermore, he is involved in many academic activities ranging from editorial board member, reviewer for many international journals and postgraduate students' supervisor. Besides, I deliver many courses and training workshops at various scientific events. Dr. Madfa also regularly attends international conferences and holds administrative positions (Deputy Dean of the Faculty for Students’ & Academic Affairs and Deputy Head of Research Unit).",institutionString:"Thamar University",institution:null},{id:"210472",title:"Dr.",name:"Nermin",middleName:"Mohammed Ahmed",surname:"Yussif",slug:"nermin-yussif",fullName:"Nermin Yussif",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210472/images/system/210472.jpg",biography:"Dr. Nermin Mohammed Ahmed Yussif is working at the Faculty of dentistry, University for October university for modern sciences and arts (MSA). Her areas of expertise include: periodontology, dental laserology, oral implantology, periodontal plastic surgeries, oral mesotherapy, nutrition, dental pharmacology. She is an editor and reviewer in numerous international journals.",institutionString:"MSA University",institution:null},{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. He is now Head of the TMD Clinic at Prosthodontic Department of Faculty of Dentistry , Istanbul Aydın University , Turkey.",institutionString:"Istanbul Aydin University",institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"240870",title:"Ph.D.",name:"Alaa Eddin Omar",middleName:null,surname:"Al Ostwani",slug:"alaa-eddin-omar-al-ostwani",fullName:"Alaa Eddin Omar Al Ostwani",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/240870/images/system/240870.jpeg",biography:"Dr. Al Ostwani Alaa Eddin Omar received his Master in dentistry from Damascus University in 2010, and his Ph.D. in Pediatric Dentistry from Damascus University in 2014. Dr. Al Ostwani is an assistant professor and faculty member at IUST University since 2014. \nDuring his academic experience, he has received several awards including the scientific research award from the Union of Arab Universities, the Syrian gold medal and the international gold medal for invention and creativity. Dr. Al Ostwani is a Member of the International Association of Dental Traumatology and the Syrian Society for Research and Preventive Dentistry since 2017. He is also a Member of the Reviewer Board of International Journal of Dental Medicine (IJDM), and the Indian Journal of Conservative and Endodontics since 2016.",institutionString:"International University for Science and Technology.",institution:{name:"Islamic University of Science and Technology",country:{name:"India"}}},{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null},{id:"178412",title:"Associate Prof.",name:"Guhan",middleName:null,surname:"Dergin",slug:"guhan-dergin",fullName:"Guhan Dergin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178412/images/6954_n.jpg",biography:"Assoc. Prof. Dr. Gühan Dergin was born in 1973 in Izmit. He graduated from Marmara University Faculty of Dentistry in 1999. He completed his specialty of OMFS surgery in Marmara University Faculty of Dentistry and obtained his PhD degree in 2006. In 2005, he was invited as a visiting doctor in the Oral and Maxillofacial Surgery Department of the University of North Carolina, USA, where he went on a scholarship. Dr. Dergin still continues his academic career as an associate professor in Marmara University Faculty of Dentistry. He has many articles in international and national scientific journals and chapters in books.",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"178414",title:"Prof.",name:"Yusuf",middleName:null,surname:"Emes",slug:"yusuf-emes",fullName:"Yusuf Emes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178414/images/6953_n.jpg",biography:"Born in Istanbul in 1974, Dr. Emes graduated from Istanbul University Faculty of Dentistry in 1997 and completed his PhD degree in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery in 2005. He has papers published in international and national scientific journals, including research articles on implantology, oroantral fistulas, odontogenic cysts, and temporomandibular disorders. Dr. Emes is currently working as a full-time academic staff in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery.",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"192229",title:"Ph.D.",name:"Ana Luiza",middleName:null,surname:"De Carvalho Felippini",slug:"ana-luiza-de-carvalho-felippini",fullName:"Ana Luiza De Carvalho Felippini",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192229/images/system/192229.jpg",biography:null,institutionString:"University of São Paulo",institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"256851",title:"Prof.",name:"Ayşe",middleName:null,surname:"Gülşen",slug:"ayse-gulsen",fullName:"Ayşe Gülşen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256851/images/9696_n.jpg",biography:"Dr. Ayşe Gülşen graduated in 1990 from Faculty of Dentistry, University of Ankara and did a postgraduate program at University of Gazi. \nShe worked as an observer and research assistant in Craniofacial Surgery Departments in New York, Providence Hospital in Michigan and Chang Gung Memorial Hospital in Taiwan. \nShe works as Craniofacial Orthodontist in Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi, Ankara Turkey since 2004.",institutionString:"Univeristy of Gazi",institution:null},{id:"255366",title:"Prof.",name:"Tosun",middleName:null,surname:"Tosun",slug:"tosun-tosun",fullName:"Tosun Tosun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255366/images/7347_n.jpg",biography:"Graduated at the Faculty of Dentistry, University of Istanbul, Turkey in 1989;\nVisitor Assistant at the University of Padua, Italy and Branemark Osseointegration Center of Treviso, Italy between 1993-94;\nPhD thesis on oral implantology in University of Istanbul and was awarded the academic title “Dr.med.dent.”, 1997;\nHe was awarded the academic title “Doç.Dr.” (Associated Professor) in 2003;\nProficiency in Botulinum Toxin Applications, Reading-UK in 2009;\nMastership, RWTH Certificate in Laser Therapy in Dentistry, AALZ-Aachen University, Germany 2009-11;\nMaster of Science (MSc) in Laser Dentistry, University of Genoa, Italy 2013-14.\n\nDr.Tosun worked as Research Assistant in the Department of Oral Implantology, Faculty of Dentistry, University of Istanbul between 1990-2002. \nHe worked part-time as Consultant surgeon in Harvard Medical International Hospitals and John Hopkins Medicine, Istanbul between years 2007-09.\u2028He was contract Professor in the Department of Surgical and Diagnostic Sciences (DI.S.C.), Medical School, University of Genova, Italy between years 2011-16. \nSince 2015 he is visiting Professor at Medical School, University of Plovdiv, Bulgaria. \nCurrently he is Associated Prof.Dr. at the Dental School, Oral Surgery Dept., Istanbul Aydin University and since 2003 he works in his own private clinic in Istanbul, Turkey.\u2028\nDr.Tosun is reviewer in journal ‘Laser in Medical Sciences’, reviewer in journal ‘Folia Medica\\', a Fellow of the International Team for Implantology, Clinical Lecturer of DGZI German Association of Oral Implantology, Expert Lecturer of Laser&Health Academy, Country Representative of World Federation for Laser Dentistry, member of European Federation of Periodontology, member of Academy of Laser Dentistry. Dr.Tosun presents papers in international and national congresses and has scientific publications in international and national journals. He speaks english, spanish, italian and french.",institutionString:null,institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"171887",title:"Prof.",name:"Zühre",middleName:null,surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/171887/images/system/171887.jpg",biography:"Zühre Akarslan was born in 1977 in Cyprus. She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"256417",title:"Associate Prof.",name:"Sanaz",middleName:null,surname:"Sadry",slug:"sanaz-sadry",fullName:"Sanaz Sadry",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256417/images/8106_n.jpg",biography:null,institutionString:null,institution:null},{id:"272237",title:"Dr.",name:"Pinar",middleName:"Kiymet",surname:"Karataban",slug:"pinar-karataban",fullName:"Pinar Karataban",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272237/images/8911_n.png",biography:"Assist.Prof.Dr.Pınar Kıymet Karataban, DDS PhD \n\nDr.Pınar Kıymet Karataban was born in Istanbul in 1975. After her graduation from Marmara University Faculty of Dentistry in 1998 she started her PhD in Paediatric Dentistry focused on children with special needs; mainly children with Cerebral Palsy. She finished her pHD thesis entitled \\'Investigation of occlusion via cast analysis and evaluation of dental caries prevalance, periodontal status and muscle dysfunctions in children with cerebral palsy” in 2008. She got her Assist. Proffessor degree in Istanbul Aydın University Paediatric Dentistry Department in 2015-2018. ın 2019 she started her new career in Bahcesehir University, Istanbul as Head of Department of Pediatric Dentistry. In 2020 she was accepted to BAU International University, Batumi as Professor of Pediatric Dentistry. She’s a lecturer in the same university meanwhile working part-time in private practice in Ege Dental Studio (https://www.egedisklinigi.com/) a multidisciplinary dental clinic in Istanbul. Her main interests are paleodontology, ancient and contemporary dentistry, oral microbiology, cerebral palsy and special care dentistry. She has national and international publications, scientific reports and is a member of IAPO (International Association for Paleodontology), IADH (International Association of Disability and Oral Health) and EAPD (European Association of Pediatric Dentistry).",institutionString:null,institution:null},{id:"202198",title:"Dr.",name:"Buket",middleName:null,surname:"Aybar",slug:"buket-aybar",fullName:"Buket Aybar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202198/images/6955_n.jpg",biography:"Buket Aybar, DDS, PhD, was born in 1971. She graduated from Istanbul University, Faculty of Dentistry, in 1992 and completed her PhD degree on Oral and Maxillofacial Surgery in Istanbul University in 1997.\nDr. Aybar is currently a full-time professor in Istanbul University, Faculty of Dentistry Department of Oral and Maxillofacial Surgery. She has teaching responsibilities in graduate and postgraduate programs. Her clinical practice includes mainly dentoalveolar surgery.\nHer topics of interest are biomaterials science and cell culture studies. She has many articles in international and national scientific journals and chapters in books; she also has participated in several scientific projects supported by Istanbul University Research fund.",institutionString:null,institution:null},{id:"260116",title:"Dr.",name:"Mehmet",middleName:null,surname:"Yaltirik",slug:"mehmet-yaltirik",fullName:"Mehmet Yaltirik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/260116/images/7413_n.jpg",biography:"Birth Date 25.09.1965\r\nBirth Place Adana- Turkey\r\nSex Male\r\nMarrial Status Bachelor\r\nDriving License Acquired\r\nMother Tongue Turkish\r\n\r\nAddress:\r\nWork:University of Istanbul,Faculty of Dentistry, Department of Oral Surgery and Oral Medicine 34093 Capa,Istanbul- TURKIYE",institutionString:null,institution:null},{id:"172009",title:"Dr.",name:"Fatma Deniz",middleName:null,surname:"Uzuner",slug:"fatma-deniz-uzuner",fullName:"Fatma Deniz Uzuner",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/172009/images/7122_n.jpg",biography:"Dr. Deniz Uzuner was born in 1969 in Kocaeli-TURKEY. After graduating from TED Ankara College in 1986, she attended the Hacettepe University, Faculty of Dentistry in Ankara. \nIn 1993 she attended the Gazi University, Faculty of Dentistry, Department of Orthodontics for her PhD education. After finishing the PhD education, she worked as orthodontist in Ankara Dental Hospital under the Turkish Government, Ministry of Health and in a special Orthodontic Clinic till 2011. Between 2011 and 2016, Dr. Deniz Uzuner worked as a specialist in the Department of Orthodontics, Faculty of Dentistry, Gazi University in Ankara/Turkey. In 2016, she was appointed associate professor. Dr. Deniz Uzuner has authored 23 Journal Papers, 3 Book Chapters and has had 39 oral/poster presentations. She is a member of the Turkish Orthodontic Society. Her knowledge of English is at an advanced level.",institutionString:null,institution:null},{id:"332914",title:"Dr.",name:"Muhammad Saad",middleName:null,surname:"Shaikh",slug:"muhammad-saad-shaikh",fullName:"Muhammad Saad Shaikh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jinnah Sindh Medical University",country:{name:"Pakistan"}}},{id:"315775",title:"Dr.",name:"Feng",middleName:null,surname:"Luo",slug:"feng-luo",fullName:"Feng Luo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sichuan University",country:{name:"China"}}},{id:"423519",title:"Dr.",name:"Sizakele",middleName:null,surname:"Ngwenya",slug:"sizakele-ngwenya",fullName:"Sizakele Ngwenya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"419270",title:"Dr.",name:"Ann",middleName:null,surname:"Chianchitlert",slug:"ann-chianchitlert",fullName:"Ann Chianchitlert",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419271",title:"Dr.",name:"Diane",middleName:null,surname:"Selvido",slug:"diane-selvido",fullName:"Diane Selvido",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419272",title:"Dr.",name:"Irin",middleName:null,surname:"Sirisoontorn",slug:"irin-sirisoontorn",fullName:"Irin Sirisoontorn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"355660",title:"Dr.",name:"Anitha",middleName:null,surname:"Mani",slug:"anitha-mani",fullName:"Anitha Mani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"355612",title:"Dr.",name:"Janani",middleName:null,surname:"Karthikeyan",slug:"janani-karthikeyan",fullName:"Janani Karthikeyan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"334400",title:"Dr.",name:"Suvetha",middleName:null,surname:"Siva",slug:"suvetha-siva",fullName:"Suvetha Siva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}}]}},subseries:{item:{id:"10",type:"subseries",title:"Animal Physiology",keywords:"Physiology, Comparative, Evolution, Biomolecules, Organ, Homeostasis, Anatomy, Pathology, Medical, Cell Division, Cell Signaling, Cell Growth, Cell Metabolism, Endocrine, Neuroscience, Cardiovascular, Development, Aging, Development",scope:"Physiology, the scientific study of functions and mechanisms of living systems, is an essential area of research in its own right, but also in relation to medicine and health sciences. The scope of this topic will range from molecular, biochemical, cellular, and physiological processes in all animal species. Work pertaining to the whole organism, organ systems, individual organs and tissues, cells, and biomolecules will be included. Medical, animal, cell, and comparative physiology and allied fields such as anatomy, histology, and pathology with physiology links will be covered in this topic. 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Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,series:{id:"10",title:"Physiology",doi:"10.5772/intechopen.72796",issn:"2631-8261"},editorialBoard:[{id:"306970",title:"Mr.",name:"Amin",middleName:null,surname:"Tamadon",slug:"amin-tamadon",fullName:"Amin Tamadon",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002oHR5wQAG/Profile_Picture_1623910304139",institutionString:null,institution:{name:"Bushehr University of Medical Sciences",institutionURL:null,country:{name:"Iran"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón",slug:"juan-carlos-gardon",fullName:"Juan Carlos Gardón",profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",institutionString:"Catholic University of Valencia San Vicente Mártir, Spain",institution:null},{id:"245306",title:"Dr.",name:"María Luz",middleName:null,surname:"Garcia Pardo",slug:"maria-luz-garcia-pardo",fullName:"María Luz Garcia Pardo",profilePictureURL:"https://mts.intechopen.com/storage/users/245306/images/system/245306.png",institutionString:null,institution:{name:"Miguel Hernandez University",institutionURL:null,country:{name:"Spain"}}},{id:"283315",title:"Prof.",name:"Samir",middleName:null,surname:"El-Gendy",slug:"samir-el-gendy",fullName:"Samir El-Gendy",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRduYQAS/Profile_Picture_1606215849748",institutionString:null,institution:{name:"Alexandria University",institutionURL:null,country:{name:"Egypt"}}}]},onlineFirstChapters:{},publishedBooks:{},testimonialsList:[{id:"27",text:"The opportunity to work with a prestigious publisher allows for the possibility to collaborate with more research groups interested in animal nutrition, leading to the development of new feeding strategies and food valuation while being more sustainable with the environment, allowing more readers to learn about the subject.",author:{id:"175967",name:"Manuel",surname:"Gonzalez Ronquillo",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",slug:"manuel-gonzalez-ronquillo",institution:{id:"6221",name:"Universidad Autónoma del Estado de México",country:{id:null,name:"Mexico"}}}},{id:"18",text:"It was great publishing with IntechOpen, the process was straightforward and I had support all along.",author:{id:"71579",name:"Berend",surname:"Olivier",institutionString:"Utrecht University",profilePictureURL:"https://mts.intechopen.com/storage/users/71579/images/system/71579.png",slug:"berend-olivier",institution:{id:"253",name:"Utrecht University",country:{id:null,name:"Netherlands"}}}},{id:"8",text:"I work with IntechOpen for a number of reasons: their professionalism, their mission in support of Open Access publishing, and the quality of their peer-reviewed publications, but also because they believe in equality.",author:{id:"202192",name:"Catrin",surname:"Rutland",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",slug:"catrin-rutland",institution:{id:"134",name:"University of Nottingham",country:{id:null,name:"United Kingdom"}}}}]},submityourwork:{pteSeriesList:[],lsSeriesList:[],hsSeriesList:[],sshSeriesList:[],subseriesList:[],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/16222",hash:"",query:{},params:{id:"16222"},fullPath:"/chapters/16222",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()