Descriptive statistics of F- mg/L for all samples.
\r\n\tIn the book the theory and practice of microwave heating are discussed. The intended scope covers the results of recent research related to the generation, transmission and reception of microwave energy, its application in the field of organic and inorganic chemistry, physics of plasma processes, industrial microwave drying and sintering, as well as in medicine for therapeutic effects on internal organs and tissues of the human body and microbiology. Both theoretical and experimental studies are anticipated.
\r\n\r\n\tThe book aims to be of interest not only for specialists in the field of theory and practice of microwave heating but also for readers of non-specialists in the field of microwave technology and those who want to study in general terms the problem of interaction of the electromagnetic field with objects of living and nonliving nature.
",isbn:"978-1-83968-227-8",printIsbn:"978-1-83968-226-1",pdfIsbn:"978-1-83968-228-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8f6a41e4f5ce0e9c48628516d7c92050",bookSignature:"Prof. Gennadiy Churyumov",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10089.jpg",keywords:"Electromagnetic Wave, Microwave Energy Application, Electromagnetic Energy Generation, Intelligent Microwave Heating, Microwave Organic Chemistry, Microwave Reactor, Microwave Discharge, Microwave Plasma, Microwave Drying System, Tissue Microwave Heating, Measurement Automation, Industrial Microwave Process",numberOfDownloads:224,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 3rd 2020",dateEndSecondStepPublish:"July 24th 2020",dateEndThirdStepPublish:"September 22nd 2020",dateEndFourthStepPublish:"December 11th 2020",dateEndFifthStepPublish:"February 9th 2021",remainingDaysToSecondStep:"7 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Prof. Gennadiy I. Churyumov is a professor at two universities: Kharkiv National University of Radio Electronics, and Harbin Institute of Technology and a senior IEEE member.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"216155",title:"Prof.",name:"Gennadiy",middleName:null,surname:"Churyumov",slug:"gennadiy-churyumov",fullName:"Gennadiy Churyumov",profilePictureURL:"https://mts.intechopen.com/storage/users/216155/images/system/216155.jfif",biography:"Gennadiy I. Churyumov (M’96–SM’00) received the Dipl.-Ing. degree in Electronics Engineering and his Ph.D. degree from the Kharkiv Institute of Radio Electronics, Kharkiv, Ukraine, in 1974 and 1981, respectively, as well as the D.Sc. degree from the Institute of Radio Physics and Electronics, National Academy of Sciences of Ukraine, Kharkiv, Ukraine, in 1997. \n\nHe is a professor at two universities: Kharkiv National University of Radio Electronics, and Harbin Institute of Technology. \n\nHe is currently the Head of a Microwave & Optoelectronics Lab at the Department of Electronics Engineering at the Kharkiv National University of Radio Electronics. \n\nHis general research interests lie in the area of 2-D and 3-D computer modeling of electron-wave processes in vacuum tubes (magnetrons and TWTs), simulation techniques of electromagnetic problems and nonlinear phenomena, as well as high-power microwaves, including electromagnetic compatibility and survivability. \n\nHis current activity concentrates on the practical aspects of the application of microwave technologies.",institutionString:"Kharkiv National University of Radio Electronics (NURE)",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"24",title:"Technology",slug:"technology"}],chapters:[{id:"74623",title:"Influence of the Microwaves on the Sol-Gel Syntheses and on the Properties of the Resulting Oxide Nanostructures",slug:"influence-of-the-microwaves-on-the-sol-gel-syntheses-and-on-the-properties-of-the-resulting-oxide-na",totalDownloads:94,totalCrossrefCites:0,authors:[null]},{id:"75284",title:"Microwave-Assisted Extraction of Bioactive Compounds (Review)",slug:"microwave-assisted-extraction-of-bioactive-compounds-review",totalDownloads:12,totalCrossrefCites:0,authors:[null]},{id:"75087",title:"Experimental Investigation on the Effect of Microwave Heating on Rock Cracking and Their Mechanical Properties",slug:"experimental-investigation-on-the-effect-of-microwave-heating-on-rock-cracking-and-their-mechanical-",totalDownloads:28,totalCrossrefCites:0,authors:[null]},{id:"74338",title:"Microwave Synthesized Functional Dyes",slug:"microwave-synthesized-functional-dyes",totalDownloads:21,totalCrossrefCites:0,authors:[null]},{id:"74744",title:"Doping of Semiconductors at Nanoscale with Microwave Heating (Overview)",slug:"doping-of-semiconductors-at-nanoscale-with-microwave-heating-overview",totalDownloads:45,totalCrossrefCites:0,authors:[null]},{id:"74664",title:"Microwave-Assisted Solid Extraction from Natural Matrices",slug:"microwave-assisted-solid-extraction-from-natural-matrices",totalDownloads:25,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"252211",firstName:"Sara",lastName:"Debeuc",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/252211/images/7239_n.png",email:"sara.d@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"63701",title:"Wetland Monitoring and Mapping Using Synthetic Aperture Radar",doi:"10.5772/intechopen.80224",slug:"wetland-monitoring-and-mapping-using-synthetic-aperture-radar",body:'\nWetlands are defined based on the Canadian Wetland Classification System as land that is saturated with water long enough to promote wetland or aquatic processes as indicated by poorly drained soils, hydrophytic vegetation and various kinds of biological activity which are adapted to a wet environment [1]. Wetlands are important ecological systems which play a critical role in hydrology and act as water reservoirs, affecting water quality and controlling runoff rate [2]. Also, they are amongst the most productive ecosystems, providing food, construction materials, transport, and coastline protection. They provide many important environmental functions and habitat for a diversity of plant and animal species [2]. Furthermore, wetlands bring economic value with social benefits for people, providing significant tourism opportunities and recreation that can be a key source of income. For these reasons, the continuous and accurate monitoring of wetlands is necessary, especially for better urban planning and improved natural resources management [3]. The formation of wetlands requires the presence of the appropriate hydrological, geomorphological and biological conditions [2].
\nThe Canadian Wetland Classification System divides wetlands into five classes based on their developmental characteristics and the environment in which they exist [1]. As shown in Figure 1, these classes are: bogs, fens, marches, swamps, and shallow water. Bogs (Figure 2a) are peatlands with a peat layer of at least 40 cm thickness, consisting partially decomposed plants. Bogs surface is usually higher relatively to the surrounding landscape and characterized by evergreen trees and shrubs and covered by sphagnum moss. The only source of water and nutrients in this type of wetlands is the rainfall [4]. Bogs are extremely low in mineral nutrients and tend to be strongly acidic [1].
\nWetland classes hierarchy.
Wetland classes as defined by the Canadian Wetland Classification System: (a) bog, (b) fen, (c) marsh, (d) swamp and (e) shallow open water.
Like bogs, fens (Figure 2b) are also peatlands that accumulate peats. Fens occurs in regions where the ground water discharges to the surface [1]. This type of wetlands is usually covered by grasses, sedges, reeds, and wildflowers. Typically, fens have more nutrients than bogs, and the water is less acidic [4]. Marshes (Figure 2c) are wetlands that are periodically or permanently flooded with standing or slowly moving water and hence are rich in nutrients [4]. Some marshes accumulate peats, though many do not. Marshes are characterized by non-woody vegetation, such as cattails, rushes, reeds, grasses and sedges [1]. Similar to marshes, swamps (Figure 2d) are wetlands that are subject to relatively large seasonal water level fluctuations [4]. Swamps are characterized by woody vegetation, such as dense coniferous or deciduous forest and tall shrubs. Some marshes accumulate peats, though many do not [1]. Shallow open water wetlands (Figure 2e) are ponds of standing water bodies, which represent a transition stage between lakes and marshes. This type of wetlands is free of vegetation with a depth of less than 2 m [1].
\nSpaceborne remote sensing technology is necessary for effective monitoring and mapping of wetlands. The use of this technology provides a practical monitoring and mapping approach of wetlands, especially for those located in remote areas [5].
\nWetlands are usually located in remote areas with limited accessibility. Thus, remote sensing technology is attractive for mapping and monitoring wetlands. Synthetic Aperture Radar (SAR) systems are active remote sensing systems independent of weather and sun illumination. SAR systems transmit electromagnetic microwave from their radar antenna and record the backscattered signal from the radar target [6]. The sensitivity of SAR sensors is a function of the: (1) band, polarization, and incidence angle of the transmitted electromagnetic signal and (2) geometric and dielectric properties of the radar target [7]. Radar targets can be discriminated in a SAR image if their backscattering components are different and the radar spatial resolution is sufficient to distinguish between targets [6]. Conventional SAR systems are linearly polarized radar systems which transmit horizontally and/or vertically polarized radar signal and receive the horizontal and/or vertical polarized components of the backscattered signal (Figure 3). In SAR systems, polarization is referred to the orientation of the electrical field of the electromagnetic wave.
\nHorizontally and vertically polarized radar signal.
A single polarized SAR system is a SAR system which transmits one horizontally or vertically polarized signal and receives the horizontal or vertical polarized component of the returned signal. A dual polarized SAR system is a SAR system which transmits one horizontally or vertically polarized signal and receives both the horizontal and vertical polarized components of the returned signal. A single or dual polarized SAR system acquires partial information with respect to the full polarimetric state of the radar target. A fully polarimetric SAR system transmits alternatively horizontally and vertically polarized signal and receives returns in both orthogonal polarizations, allowing for complete information of the radar target [6, 8]. While full polarimetric SAR systems provide complete information about the radar target, the coverage of these systems is half of the coverage of single or dual polarized SAR systems. Also, the energy required by the satellite for the acquisition of full polarimetric SAR imagery and the pulse repetition frequency of the SAR sensor are twice the single or dual polarized SAR systems.
\nA new SAR configuration named compact polarimetric SAR is currently being implemented in SAR systems, where a circular polarized signal (Figure 4) is transmitted and two orthogonal polarizations (horizontal and vertical) are coherently received [9]. Thus, the relative phase between the two receiving channels is preserved and calibrated, but the swath coverage is not reduced.
\nCircular polarized radar signal.
In comparison to the full polarimetric SAR systems, compact polarimetric SAR operates with half pulse repetition frequency, reducing the average transmit power and increasing the swath width. Consequently, this SAR configuration is associated with low-cost and low-mass constraints of the spaceborne polarimetric SAR systems. The wider coverage of the compact SAR system reduces the revisit time of the satellite, making this system operationally viable [10]. These advantages come with an associated cost in the loss of full polarimetric information. Hence, generally, a compact polarimetric SAR system cannot be “as good as” a full polarimetric system [11]. Such SAR architecture is already included in the current Indian Radar Imaging Satellite-1 (RISAT-1) and the Japanese Advanced Land Observing Satellite-2 (ALOS-2) carrying the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2). Also, compact polarimetric SAR will be included in the future Canadian RADARSAT Constellation Mission (RCM).
\nFully polarimetric SAR systems measure the complete polarimetric information of a radar target in the form of a scattering matrix [S]. The scattering matrix [S] is an array of four complex elements that describes the transformation of the polarization of a wave pulse incident upon a reflective medium to the polarization of the backscattered wave and has the form [6]:
\nwhere H and V refer to horizontal and vertical polarized signals, respectively. The elements of the scattering matrix [S] are complex scattering amplitudes. For most natural targets including wetlands, the reciprocity assumption holds where HV = VH. The diagonal elements HH and VV are called co-polarized elements, while the off-diagonal elements HV and VH are called cross-polarized elements. Two polarimetric scattering vectors can be extracted from the target scattering matrix, which are the lexicographical scattering vector and the Pauli scattering vector [12]. Assuming the reciprocity condition, the lexicological scattering vector has the form:
\nwhere the superscript T denotes the vector transpose. The multiplication of the cross-polarization with 2 is to preserve the total backscattered power of the returned signal. The Pauli scattering vector can be obtained from the complex Pauli spin matrices [6] and, assuming the reciprocity condition, has the form:
\nDeterministic scatterers can be described completely by a single scattering matrix or vector. However, for remote sensing SAR applications, the assumption of pure deterministic scatterers is not valid. Thus, scatterers are non-deterministic and cannot be described with a single polarimetric scattering matrix or vector. This is because the resolution cell is bigger than the wavelength of the incident wave. Non-deterministic scatterers are spatially distributed. Therefore, each resolution cell is assumed to contain many deterministic scatterers, where each of these scatterers can be described by a single scattering matrix [Si]. Therefore, the measured scattering matrix [S] for one resolution cell consists of the coherent superposition of the individual scattering matrices [Si] of all the deterministic scatterers located within the resolution cell [6, 12].
\nAn ensemble average of the complex product between the lexicological scattering vector \n
where \n
The relationship between the covariance matrix [C] and the coherency matrix [T] is linear. Both matrices are full rank, hermitian positive semidefinite and have the same real non-negative eigenvalues, but different eigenvectors. Moreover, both matrices contain the complete information about variance and correlation for all the complex elements of the scattering matrix [S] [12].
\nA compact polarimetric SAR system transmits a right- or left-circular polarized signal, providing a scattering vector of two elements:
\nwhere R refers to a transmitted right-circular polarized signal. A four-element vector called Stokes vector [g] can be calculated from the measured compact polarimetric scattering vector, as follow [11]:
\nwhere Re and Im are the real and imaginary parts of a complex number. The first Stokes element g0 is associated with the total power of the backscattered signal while the fourth Stokes vector is associated with the power in the right-hand and left-hand circularly polarized component [13]. The elements of the Stokes vector can be used to derive an average coherency matrix, which takes the form [14]:
\nRadar backscattering is a function of the radar target properties (dielectric properties, roughness, target geometry) and the radar system characteristics (polarization, band, incidence angle). Three major backscattering mechanisms can take place during the backscattering process. These are the surface, double bounce and volume scattering mechanism (Figure 5).
\nThe three major scattering mechanisms: surface, double bounce and volume.
In the case of surface scattering mechanism (Figure 5), the incident radar signal features one or an odd number of bounces before returns back to the SAR antenna. In this case, a phase shift of 180o occurs between the transmitted and the received signal [6]. However, a very smooth surface could cause the radar incident signal to be reflected away from the radar antenna, causing the radar target to appear dark in the SAR image. In this case, scattering is called specular scattering. An example of such surfaces is the open water in wetlands [12]. In the case of double bounce scattering mechanism (Figure 5), the incident radar signal hits two surfaces, horizontal and adjacent vertical forming a dihedral angle, and almost all of incident waves return back to the radar antenna. Thus, the scattering from radar targets with double bounce scattering is very high. The phase difference between the transmitted and the received signal is equal to zero. Double bounce scattering mechanism is frequently observed in open wetlands, such as bog and marsh, as the results of the interaction of the radar signal between the standing water and vegetation [15]. In the case of volume scattering mechanism (Figure 5), the radar signal features multiple random scattering within the natural medium. Usually, a large portion of the transmitted signal is returned back to the SAR sensor, causing rise to cross polarizations (HV and VH). Thus, illuminated radar targets with volume scattering appear bright in a SAR image. Volume scattering is commonly observed in flooded vegetation wetlands due to multiple scattering in the vegetation canopy.
\nIn general, the penetration capabilities and the attenuation depth of radar signal in a medium, such as flooded vegetation, increases with the increasing of the wavelength [6, 12]. Figure 6 presents the penetration of radar signals for different bands. As shown in Figure 6, X-band SAR has a short wavelength signal with limited penetration capability, while L-band SAR has long wavelength signal with higher penetration capability. C-band SAR is assumed as a good compromise between X- and L-band SAR systems. As shown in Figure 6, the scattering mechanism of a radar target could be affected by the penetration depth of the radar signal. Thus, dense flooded vegetation could present volume scattering mechanism in X- or C-band SAR (return from canopy), but double bounce scattering mechanism in L-band due to scattering process from trunk-water interaction (Figure 6) [12].
\nThe radar signal penetration for different bands.
Different decomposition methods have been proposed to derive the target scattering mechanisms for both full polarimetric [6, 16, 17, 18, 19, 20, 21, 22, 23, 24] and compact polarimetric [11, 25] SAR data. One of the earliest and widely used decomposition methods is the Cloude-Pottier decomposition [17]. This method is incoherent decomposition method based on the eigenvector and eigenvalue analysis of the coherency matrix [T]. Given that [T] is hermitian positive semidefinite matrix, it can always be diagonalized using unitary similarity transformations. That is, the coherency matrix can be given as
\nwhere [Λ] is the diagonal eigenvalue matrix of [T], λ1 ≥ λ2 ≥ λ3 ≥ 0 are the real eigenvalues and [U] is a unitary matrix whose columns correspond to the orthogonal eigenvectors of [T]. Based on the Cloude-Pottier decomposition, three parameters can be derived [17]. The polarimetric entropy H (0 ≤ H ≤ 1) is defined by the logarithmic sum of the eigenvalues
\nwhere \n
The anisotropy A provides additional information only for medium values of H because in this case secondary scattering mechanisms, in addition to the dominant scattering mechanism, play an important role in the scattering process [6]. The alpha angle α (0 ≤ α ≤ 90o) provides information about the type of scattering mechanism
\nwhere cos(αi) in the magnitude of the first component of the coherency matrix eigenvector ei (i = 1, 2, 3).
\nAnother widely used polarimetric decomposition method is the Freeman-Durden method [18]. Contrary to the Cloude-Pottier decomposition, which is a purely mathematical construct, the Freeman-Durden decomposition method is a physically model-based incoherent decomposition based on the polarimetric covariance matrix. It relies on the conversion of a covariance matrix to a three-component model. The results of this decomposition are three coefficients corresponding to the weights of different model components. A polarimetric covariance matrix [C] can be decomposed to a sum of three components, corresponding to volume, surface, and double bounce scattering mechanisms [18]:
\nwhere fv, fs, and fd are the three coefficients corresponding to volume, surface, and double bounce scattering, respectively. The Freeman-Durden decomposition is particularly well adapted to the study of vegetated areas [18]. Thus, it is widely used for multitemporal wetland monitoring to track changes of shallow open water to flooded vegetation [26].
\nScattering mechanism information can also be obtained using compact polarimetric SAR data. Two decomposition methods are commonly used. The first is the m-δ decomposition method [11], which is based on the degree of polarization of the backscattered signal \n
where Vd, Vv, and Vs refer to double bounce, volume, and surface scattering mechanisms, respectively. The second decomposition method is the m-χ decomposition [25], which is based on the degree of polarization m and the ellipticity \n
where Pd, Pv, and Ps refer to even bounce, volume, and odd bounce scattering mechanisms, respectively.
\nThe accurate, effective, and continuous identification and tracking of changes in wetlands is necessary for monitoring human, climatic and other effects on these ecosystems and better understanding of their response. Wetlands are expected to be even more dynamic in the future with rapid and frequent changes due to the human stresses on environment and the global warming [27]. Different methodologies can be adopted to detect and track changes in wetlands using SAR imagery, depending on the type of the change and the available polarization option. For example, a change in the surface water level of a wetland area due to e.g. heavy rainfall could extend the wetland water surface, causing flooding in the surrounding areas. Such a change can be easily detected using SAR amplitude images before and after the event acquired with similar acquisition geometry. The specular scattering of the radar signal can highlight the open water areas (dark areas due to low returned signal). Spatiotemporal changes in wetlands as dynamic ecosystems could be interpreted using SAR amplitude imagery only. This is because changes within wetlands could change the surface type illuminated by the radar. Sometimes, the change could be more complex with alternations in surface water, flooded vegetation and upland boundaries. In this case, the additional polarimetric information from full or compact polarimetric SAR is necessary for the detection and interpretation of changes within wetlands.
\nAs shown in Figure 7, a change within a wetland from wet soil with a high dielectric constant to open water is usually accompanied with a change in the radar backscattering from surface scattering with a strong returned signal (Figure 7a) to specular reflection with a weak returned signal (Figure 7b). The change in wetland could also be due to its seasonal development over time. Hence, intermediate marsh with large vegetation stems properly oriented could allow for double bounce scattering mechanism (Figure 7c). As the marsh develops, the strong observed double bounce scattering mechanism gradually decreases in favor of the volume scattering (Figure 7d) from the dense canopy of the fully developed marsh [28]. Thus, polarimetric decomposition methods enable the identification of wetland classes (e.g. flooded vegetation) and monitoring changes within these classes by means of the temporal change in the backscattering mechanisms. The role of decomposition methods for identification and monitoring of wetlands was highlighted in a number of recent studies [26, 29, 30, 31]. Another way of monitoring changes within wetlands could be through polarimetric change detection methodologies using full [10], compact [10, 32], or even coherent dual [33] polarized SAR imagery. These methodologies are based on polarimetric coherency/covariance matrices. Herein, changes are flagged without information about the scattering mechanisms, which occurred during the scattering process. Test statistics, such as those proposed in [34, 35], were proven effective for polarimetric change detection over wetlands.
\n(a) Surface scattering mechanism from wet soil, (b) radar signal reflection from shallow open water, (c) double bounce scattering mechanism from signal interaction with vegetation stems and water surface and (d) volume scattering due to random scattering within the dense flooded vegetation canopy.
Ever since the launch of the Earth Resources Technology Satellite (ERTS) in 1972 there has been interest in using satellite remote sensing as a tool for wetland mapping and classification because the traditional air photo and field visit approaches are too costly and time consuming [36]. Wetlands are difficult to map and classify due to a large degree of spatial and temporal variability as well as structural and spectral similarities between wetland classes. Over the last decade or so a state-of-the-art approach for wetland classification has emerged. This is an object based classification approach using multi-source input data (optical and SAR) with a machine learning classification algorithm and a quality Digital Elevation Model (DEM) for identifying terrain suitability for wetlands or surface water [37, 38, 39, 40]. Using this approach, greater than 90% accuracy is often achieved for a wide variety of wetland classification systems [41].
\nThe early satellite SAR systems produced single channel intensity only output data, which limited its value for land cover and wetland classification. This type of data when used synergistically with optical data improved the wetland classification compared to using optical data alone but only to a minor degree [37, 42, 43, 44, 45]. This is largely due to the ability of the SAR wavelengths to penetrate wetland vegetation and “see” the underlying water, thereby improving the flooded vegetation class discrimination. The flooded vegetation tends to produce a double bounce scattering mechanism, as explained earlier, which increases the intensity of the backscatter. HH polarization is best for this due to the enhanced penetration in vegetation.
\nAs one goes up the polarization hierarchy from single channel intensity only data to dual-channel, compact polarimetry, and full polarimetry data sets the information content increases and the wetland classification subsequently improves [11, 46, 47, 48, 49, 50]. In general, dual channel SAR’s and polarization ratios outperform single channel intensity only data systems and compact polarimetric data is better than dual channel data. Fully polarimetric data consistently shows the best information content for wetland classification by using polarimetric parameters derived from the data matrices, or polarimetric decompositions such as the Cloude and Pottier [17], Freeman-Durden [18], or Touzi [24] decompositions. You can use the decompositions or polarimetric parameters such as the polarization phase difference to identify flooded vegetation due to the double bounce effect increasing intensity and producing the phase shift. The Shannon Entropy has also proven useful for wetland mapping [51] and may have some benefit for finding the transition from flooded to saturated soil and between flooded vegetation and open water. This is a two-parameter model with one parameter relating to intensity and the other polarization diversity, and it may be simpler than using the decompositions.
\nThere have been numerous frequency effect evaluations since the early observations of enhanced scattering from flooded vegetation on SEASAT imagery [52]. This effect for swamps and many vegetated wetlands with high biomass is quite evident in L-band data due to the increased canopy penetration and better interaction with the water/trunk/stem interface, resulting in the double bounce scattering mechanism [53, 54]. It is also evident at C-band and in some cases at X-band depending on the biomass and density of the canopy and the subsequent wavelength dependent penetration [41, 55, 56, 57, 58, 59, 60]. In general, X- and C-band are preferred for herbaceous wetlands and less dense canopies while L-band is preferred for woody wetlands such as swamps and other wetland classes with high biomass.
\nSAR data has also proven effective for mapping peatlands, which is becoming more important because of climate change and carbon emission issues [61, 62, 63, 64]. Due to the penetration of these longer wavelengths and the ability to penetrate beneath the plant canopy, there have been some indications that L-band polarimetric SAR can be used to differentiate between bog and fen peatlands due to the sensitivity of the water flow characteristics beneath the surface [65].
\nSAR does not penetrate water so provides little information on invasive aquatic submersive plants, but L-band and to a lesser degree C-band have shown some success at identifying invasive Phragmites [66]. This tall dense invasive provides significant SAR backscatter and can be separated from other land-cover due to this characteristic and its location in the landscape. It helps to use LiDAR as well as SAR due to the relative height and landscape position of the Phragmites [67].
\nIn general, one wants to use a steep incidence angle for woody wetlands or flooded vegetation mapping in order to enhance the penetration to reach the water surface and realize the enhanced scattering effect due to double bounce scattering between the vegetation and the water surface. A shallow angle may be preferred if the focus is on the open water mapping as this can enhance the contrast between the specular scattering of surface water and the flooded vegetation with volume and double bounce scattering. Recent reviews of wetland remote sensing and SAR are provided in [40, 41, 68, 69, 70].
\nThe specular backscatter from calm water surfaces allows for easy discrimination of open water from upland and flooded vegetation using SAR data. At the same time, the double bounce scattering from flooded vegetation allows discrimination from upland and open water as described earlier. This, combined with the all-weather data collection capabilities, makes SAR an ideal sensor for mapping flood as well as dynamic surface water and flooded vegetation [40].
\nFlood mapping is operational with SAR data in many countries using data from a variety of SAR systems from X- to L-band (see for example [71, 72, 73, 74]). Intensity thresholding techniques have traditionally been used for open water mapping [75]. Texture, cross-polarization data, and other techniques are being developed to solve the problem when the water is brighter due to wind or current induced roughness, as well as to automate the process [76, 77, 78, 79].
\nAs described in the section on wetland mapping the double bounce effect and enhanced scattering from flooded vegetation makes SAR a good sensor for mapping flooded vegetation from non-flooded vegetation [40, 59, 80]. This allows the delineation of wetland extent and with multi-temporal data can be very useful for monitoring seasonal and/or annual changes in the wetland size and extent. [81] showed that flooded vegetation tends to remain coherent using InSAR techniques and this can then be used to map wetland type and extent. Thus, SAR is an ideal sensor for monitoring the spatially and temporally dynamic flooded vegetation components of wetlands.
\nThe development of standard coverages, like that used for the Sentinel program, results in stacks of data with the same geometry and facilitates the use of temporal filters for speckle noise reduction. The use of a multi-temporal filter rather than the conventional spatial filtering approach can be an effective way to reduce the speckle while maintaining the spatial resolution and the ability to detect small objects and edges [82, 83]. This also allows the use of intensity metrics rather than thresholds to separate water from land, which can also help solving the wind roughness problem [84]. The multi-temporal coverage provided by SAR systems enables generating hydro-period and dynamic surface water as well as flooded vegetation masks [85, 86]. This enables better mapping of temporary, seasonal and ephemeral water bodies as well as the permanent water bodies, which are static and much easier to map. A recent review of SAR flood mapping and flood studies with SAR is provided in [87], while [88] provides a review of flooded vegetation mapping with SAR.
\nWetland interferometric synthetic aperture radar (InSAR) is a relatively new application of the InSAR technology that detects water level changes over wide areas with 5–100 m pixel resolution and several centimeters vertical accuracy [89, 90, 91]. The wetland InSAR technique works where vegetation emerges above the water surface due to the “double bounce” effect, in which the radar pulse is backscattered twice from the water surface and vegetation [53]. InSAR observations were successfully used to study wetland hydrology in the Everglades [90, 91, 92, 93], Louisiana [94, 95, 96] and the Sian Ka’an in Yucatan [97].
\nOne of the key issues in using the InSAR observations for assessing wetland hydrology is the calibration of the InSAR observations, which are relative in both space and time. In time, the measurements provide the change in water level (not the actual water level) that occurred between the two data acquisitions. In space, the measurements describe the relative change of water levels in the entire interferogram with respect to a zero change at an arbitrary reference point, because the actual range between the satellite and the surface cannot be determined accurately. However, the relative changes between pixels can be determined at the cm-level. In many other InSAR applications, such as earthquake or volcanic induced deformation, the reference zero change point is chosen to be in the far-field, where changes are known to be negligible [98]. However, in wetland InSAR, the assumption of zero surface change in the far-field does not hold, because flow and water levels can be discontinuous across the various water control structures or other flow obstacles.
\nThe calibration stage requires additional information on water level changes, which can be derived from various sources. In areas monitored by stage (water level) stations, as in the Everglades, the stage data can be used for the InSAR calibration, as conducted by [90]. Another calibration technique relies on spaceborne radar altimetry, which detects absolute water level changes over a few km wide footprints with accuracy of 5–10 cm [94]. However, the altimetry observations are limited in space and time, as the radar altimeter data can only be acquired along the satellite tracks, which are spaces roughly 100 km apart. Also, the altimetry data is not always synchronized with the InSAR observations, which are acquired by different satellites.
\nWetland biomass is of increasing interest due to methane emission contributions to climate change from degraded and thawing wetlands. Wetland change can also be used as an indicator of climate change impacts. Wetland vegetation biomass can therefore be an important indicator of carbon sequestration in wetlands and is essential for understanding the carbon cycle of these ecosystems. SAR data has the potential to estimate vegetation biomass in wetlands because radar is particularly sensitive to the vegetation canopy over an underlying water surface [99]. The biomass of totora reeds and bofedal in water-saturated Andean grasslands was mapped with ERS-1 data in [100]. The goal was to protect this ecosystem from overgrazing. They found that the backscatter signal of ERS-1 was sensitive to the humid and dry biomass of reeds and grasslands and their biomass maps were useful for the livestock management in the study region. [101] developed regression and analytical models for estimating mangrove wetland biomass in South China using RADARSAT images. [102, 103] also found that L-band ALOS PALSAR can be used to estimate the aboveground biomass because of the correlation between HH and HV backscatter signals. C-band backscatter characteristics from RADARSAT-2 data were used by [104] to estimate the biomass of the Poyang Lake wetlands in China. Also, [105] used ENVISAT ASAR data to estimate wetland vegetation biomass in Poyang Lake. These studies have shown that it is possible to estimate above water biomass in wetlands with SAR data.
\nAs shown in the previous section, spaceborne SAR remote sensing technology is recognized as essential tool for effective wetland observation. With the presence of global warming and its associated risks on Earth systems, there is an expressed interest in increased temporal and spatial resolution of satellite measurements. Thus, a trend toward increased temporal and spatial resolution of SAR imagery is noted in recent and future SAR missions. The Sentinel-1 SAR mission with its two identical SAR satellites (Sentinel-1A&B) is a good example of a recent SAR mission with a spatial resolution ranging from 5 m to 100 m and a revisit time of 6 days. This high temporal and spatial resolution is expected to be even higher in the near future with the launch of the RCM in late 2018. The RCM is expected to provide SAR imagery in a spatial resolution ranging from 1 m to 100 m, in a revisit time of only 4 days [32]. The increased temporal and spatial resolution would be required to adequately monitor wetlands and characterize the actual implications of climate change. Also, it is expected to further improve our understanding of climate change in wetlands and water quality, allowing ecosystem managers and decision makers to have sufficient information regarding wetland preservation.
\nWith the availability of different remote sensing data with various information contents, the application of multi-source data for advanced wetland applications is demonstrated in a number of studies; see for example [2, 44, 61, 67, 106]. In addition to SAR imagery, experiments on the integration of topographic and remote sensing data, such as optical imagery and LiDAR data, were conducted. The ultimate objective of these experiments was the improved mapping accuracy of wetlands. The integration of SAR imagery with optical and topographic data from multiple sensors was shown in [44, 106] to be necessary for improved wetland mapping and classification during the growing season. However, the integration of SAR imagery and LiDAR data did not improve significantly the classification accuracy of wetland in [61, 67]. The modern advances in remote sensing technology and the availability of multi-source information are shifting the manner in which Earth observation data are used for wetland monitoring, indicating the need for automated and efficient techniques. Different studies, such as [2, 44, 61, 106], have highlighted the effectiveness of machine learning algorithms for automated wetland classification. An example of these algorithms is the Random Forest (RF) classification algorithm proposed in [107]. This shift toward the automated machine learning algorithms comes to fulfill the requirement for operational wetland monitoring systems.
\nThe continuing advancements in computer processing power and software development as well as the trend toward free and open access to remote sensing imagery, such as those from the current Sentinel satellites and the future RCM, are enabling the ingestion of data into a centralized archive. This also supports the application of a standard rapid processing chain to generate analysis-ready wetland products. The provision of analysis-ready products to a wide range of users would revolutionize the role of remote sensing in Earth system science [108].
\nThis chapter highlighted the SAR remote sensing technology and its potential for wetland monitoring and mapping. It was shown that a wide range of wetland applications can be addressed using SAR remote sensing imagery. SAR data with enhanced target information provided by full or compact polarimetric SAR systems can provide information for advanced wetland applications. In many studies, the information about the polarimetric scattering mechanisms was found necessary for observing the temporal development of wetlands and detecting their changes. This chapter shows that the fusion of multi-source data improves wetland mapping, especially during the growing season. Furthermore, a relatively new application of the InSAR technology is currently implemented for water level monitoring. Given the problem of climate change, wetland biomass estimation using SAR imagery is becoming necessary for the evaluation of methane emission contributions to climate change from degraded and thawing wetlands. The current advanced computing capabilities along with the shift toward free and open access remote sensing data are enabling analysis-ready products for a wide range of users.
\nAuthors would like to thank Mr. Jean Granger and Dr. Bahram Salehi from the Memorial University of Newfoundland and Dr. Koreen Millard form Environment and Climate Change Canada for their help in providing pictures of different wetland classes.
\nAuthors declare no conflict of interest.
SAR | synthetic aperture radar |
HH | horizontal transmitted horizontal received signal |
VV | vertical transmitted vertical received signal |
HV | horizontal transmitted vertical received signal |
VH | vertical transmitted horizontal received signal |
RH | right circular transmitted horizontal received signal |
RV | right circular transmitted vertical received signal |
RISAT-1 | Radar Imaging Satellite-1 |
ALOS-2 | Advanced Land Observing Satellite-2 |
PALSAR-2 | Phased Array type L-band Synthetic Aperture Radar-2 |
RCM | RADARSAT Constellation Mission |
InSAR | interferometric synthetic aperture radar |
RF | random forest |
DEM | digital elevation model |
Fluorine has the highest chemical reactivity among all known elements and occurs mainly as free fluoride ions in natural waters, although some fluoride complexes also exist under specific conditions [1]. In groundwater, the natural concentration of fluoride depends on the geological, chemical and physical characteristics of the aquifer, the porosity and acidity of the soil and rocks, the temperature and the action of other chemical elements [2]. Fluoride ion in drinking water is known for both beneficial and detrimental effects on health. Fluoride in small amounts is an essential component for normal mineralization of bones and formation of dental enamel [3]. However, excessive intake of fluoride can cause dental and skeleton fluorosis [4, 5]. Due to its strong electronegativity, fluoride is attracted by positively charged calcium in teeth and bones [6]. Fluorosis is a considerable health problem worldwide, which is afflicting millions of people in many areas of the world, for example, East Africa [7, 8, 9] and India [10, 11, 12]. According to World Health Organization (WHO) Guidelines for Drinking Water Quality [13], the limit value for fluoride is 1.5 mg/L. The value of 1.5 mg/L is a guiding value, which may be changed based on climatic conditions like temperature, humidity, volume of water intake, fluoride from other sources, etc. for different regions of the world [14]. The source of water supplies in Yemen is mostly from groundwater accumulated during previous and current times [15]. Fluorosis continues to be an endemic problem in Yemen. More areas are being affected by fluorosis in different parts of the country. Recently, a report from General Authority of Rural Water Projects (GARWP) indicates markedly increasing in fluoride content in groundwater (between 2000 and 2006) in districts of some governorates such as Sana’a, Ibb, Dhamar, Taiz, Al-Dhalei and Raimah. The highest fluoride concentration in drinking water was reported in some districts of Sana’a governorate, especially Sanhan [16]. Most Yemenis dwelling in rural areas use deep well water for drinking and household works, and a large number of these wells are contaminated with fluoride in a concentration of 2.5–32 mg [14]. The present study aims to identify the intensity and the spatial extent of the existing groundwater contamination by fluoride in the study area and tries to identify sources pollution responsible for the current pollution of the affected areas through an analytical study in the southern part of the upper Wadi Rasyan of Taiz governorate in Yemen.
\nTo achieve the objectives mentioned above, there has been:
Identifying and understanding of the characteristics of the study area (topographic and hydrological analysis): location, topography and hydrological characteristics using arc Map GIS.
Inventing sources of pollution and production of their maps: inventory of number, type and intensity of human activities and the village’s distribution that is likely to contaminate the groundwater in the study area, view inventory results on the map using arc Map GIS and using this map in the interpretation of the results of the spatial assessment of groundwater quality of the study area.
Inventing of wells in the study area and displaying them on the map using arc Map GIS.
Determining sampling points based on type of wells (dug well and bore well), type of aquifers (alluvial and volcanic), the different depths (from 9 to 500 m) and their location according to the hydrology system and the pollution sources in order to appropriate selection of sampling point and production of the map of sampling points, by using the arc Map GIS.
Taking, transporting and analyzing samples.
Data processing and interpreting by using arc Map GIS and Minitab 18 program software.
Viewing the results of the analysis on the maps in order to know the spatial distribution of fluoride concentration in groundwater of the study area. The spatial distribution of fluoride in groundwater samples in the study area is represented as a thematic layer using IDW tool in the arc Map GIS software program that was used to the prediction of an unknown value for fluoride of the rest of the study area that was not covered by analysis and thus gave the spatial distribution of the fluoride that used in assessing the suitability of groundwater for drinking in the study area as a whole.
Using the results of the groundwater assessment quality to propose alternative strategies to deal with groundwater.
Preparing the final reports (article).
The sampling was collected in polyethylene bottles of 1000-ml capacity after rinsed with distilled water and the water of the well, through months in August, September and October, 2014. The fluoride concentration of groundwater samples was determined using DR 2800 spectrophotometer.
\nThe Fisher test was used when comparing dichotomous data separately and Pearson’s correlation coefficient for continuous variables. On the other hand, after verifying the hypotheses of normality and homogeneity, we used the nonparametric Kruskal-Wallis H to test whether three or more samples were drawn from the same population, or from populations with identical characteristics (distribution with the same median). An analysis of variance was used to study the difference in means between the different samples greater than or equal to three and in the multivariate analysis between our samples two by two we chose the Bonferroni test. In the study, Al-Hawban, Al-Burayhi and Hedran and Al-Dhabab sub-basins were all different samples. After, we performed logistic regression analysis. Fluoride was included as a dichotomous variable (lower or greater than 1.5 mg/L). Other variables with p-values < 0.2 in the univariate analysis were entered into the multivariate logistic regression model, which where taken into account in the multivariate logistic analysis. We studied the cause-effect association between the fluoride (lower or greater than 1.5 g/ml) and the included variables using odds ratio (OR). In our first model (crude model), we were satisfied only on the univariate analysis between each variable (independent factors) and the dichotomous concentration of fluoride (dependent variable). In a second model, we performed a simultaneous analysis between the independent variables and the dichotomous dependent variable of fluoride. In order to assess the accuracy of the estimates, we have indicated the 95% confidence interval (IC to 95%) of the average data. A p-value of less than 0.05 at 95% confidence level was considered as statistically significant.
\nThe study area represents the southern part of the Upper Wadi Rasyan catchment area, Taiz governorate, Yemen. The study area is estimated at 472 square kilometers which is densely populated and includes Taiz city which represented the third largest and important cities in Yemen (Figure 1).
\nLocation of the study area [17].
The results of the topographic analysis (morphology, elevation and slopes degree) of the study area are illustrated in Figures 2–4.
\nTopography of the study area.
The elevation in the study area.
The slope degree in the study area.
From the topographic analysis of the study area, we find that the group of mountains and plateaus that surrounding the study area made it semi-closed. Therefore, the north-west corner of the study area formed the outlet for the runoff network as shown in Figure 5. According to the digital elevation model of the study area that was obtained from the NASA site and topographic analysis, the elevations in the study area concentrated in the south and southwest, where the height of the mountain of Sabir was up to 3000 m above sea level, whereas Jabal Habashi has a maximum height of 2400 m above sea level. The lowest elevation is located in the north-west and is 872 m above sea level, and in the east, elevations are between 1200 and 1600 m above sea level (Figure 3). Figure 4 shows the map of the degrees of slope in the study area. The degree of slope determines the flow intensity of the floods and, therefore, the extent of water (or water and pollutants) infiltration into the ground.
\nHydrology system in the study area.
The results of the hydrological analysis [the hydrological limits, direction of surface runoff and then the flow direction of pollutant at the surface (hydrology system) and the hydrological level (main valleys and its tributaries)] of the study area are illustrated in Figures 5 and 6. From the hydrology map of the study area, we found that the watershed drainage (the hydrology system) in the study area is dendritic and the direction of water surface is into the north-west corner of the study area. Based on hydrological characteristics, the study area has been divided into three main sub-catchment area or sub-basins as shown below:
Al-Hawban sub-basin has 146 km2.
Al-Dhabab sub-basin has 112 km2.
Al-Burayhi and Hedran sub-basin (Central sub-basin) has 214 km2.
Main valleys and its tributaries in the study area.
The geology of Yemen is a part of the Arab Shield which consists of a Proterozoic crystalline subsoil covered by a Paleogene sedimentary sequence (Mesozoic sediments). In the west of the country, the sedimentary sequence is covered by Yemen volcanic (Cenozoic volcanic) [18]. The study area occupies the southwestern corner of the Arabian Shield, and the geological complexity of the region is mainly due to its position at the junction of the Red Sea and the Gulf of Aden rift systems. Geological map and geological cross sections for the study area were derived from the geological map of the upper Wadi Rasyan, scale 1: 100,000; that prepared by Dar Yemen Consulting Company [19]. As shown in Figure 7, the geomorphology of the study area is dominated by the tertiary volcano that covers most of the region.
\nGeomorphology of study area (the source of basic map [19]).
Geomorphology of the study area was characterized as modern rock units, which was formed during the Cenozoic (Cenozoic volcanic group), which was formed by a series of eruptions and volcanic eruptions that were affected by Yemen in general and the province of Taiz, especially during the third geological age as a result of movements of the successive continental shelf separation tectonics along the fault line of large expanses of the Gulf of Aden, in the southern Dead Sea to north and the emergence of the Red Sea gorge and the separation of the Arabian plate from the African plate where it was accompanied by the emergence of streaks parallel to the axis of the Red Sea, which represented the levels of weakness and pathways of magmatic systems [20]. A major crack extends from the east to the west of the study area. There are also local cracks stretching group north-west to south-east perpendicular to the main fault (Figure 8).
\nGeology faults in the study area (the source of basic map [19]).
According to [19], the geology of the study area consists of Cretaceous Tawilah Sandstone (Kt), Lower Basalt (Tb1), Low Volcanic Acids (Tr1), Basic Volcanic Medium (Tb2), Second Volcanic Acids (Tr2), Granite (Tgr): granite in the mountain of Sabir contains some older volcanic rocks and finally Quaternary (QW): Wadi sediments are deposited by seasonal floods and wind-deposited soils derived from the alteration of volcanic ash and tufa mainly of (Tr1). Thickness can be up to 70 m. Figures 9 and 10 illustrate geological cross-sections of some study areas.
\nThe geology cross-section of Jabal Habashi to Hedran (the source of basic map [19]).
The geology cross-section of Jabal Sabir to Al-Hawban (the source of basic map [19]).
According to [19], groundwater in the study area is being produced from three aquifers: the Quaternary alluvium, the Tertiary fractured volcanic and the Cretaceous Tawilah Sandstone. Cretaceous Tawilah Sandstone in the study area is located in the lower classes, in the southwest of the study area and extends to the north, as shown in Figure 11.
\nCretaceous Tawilah sandstone in the study area (the source of basic map [19]).
Alluvial aquifers form the highest and shallow aquifers in the region. These sediments exist along the Wadis path (Figure 6). The total thickness rarely exceeds 30–40 m, but they can locally reach considerable thicknesses (up to 70 m). Hydraulic properties vary from site to other. Intergranular groundwater flow is dominant [19].
\nVolcanic aquifers consist of the tertiary volcanic sequence. The thickness of this sequence may exceed 600–700 m. Groundwater flows mainly in this type of aquifers through the cracks/faults. The sandstone aquifer includes Tawilah Sandstone, Southwest of the study area (Al-Dhabab), and this formation is soaked to an expected depth of more than 500 m. In general, sandstone is largely silicified and fractured, so that the dominant groundwater flow in this aquifer is of intergranular type and mixed fracture. The quality of this aquifer is excellent to good [19].
\nGroundwater aquifers in the study area are recharged by many sources of water, as follows:
In Al-Hawban sub-basin, the aquifers are recharged with rainwater (either pure or loaded with wastewater that is disposed of in floodwaters), wastewater disposed of in sewers, industrial wastewater in the eastern part of the sub-basin where there is an industrial food complex.
In Al-Burayhi and Hedran sub-basin, the aquifers are recharge with the floods coming from the Al-Hawban sub-basin that loaded with liquid and solid waste types, domestic wastewater that is transported from the city of Taiz across the sewerage network is deposited in sedimentation ponds in this sub-basin, wastewater used to irrigate crops in this sub-basin, irrigation water, which goes downloaded with high concentrations of salts, sedimentation ponds for industrial wastewater in the western part of the sub-basin, the floods that coming from the Al-Dhabab sub-basin to the south-west of the Al-Burayhi and Hedran basin, these floods reach the Al-Burayhi and Hedran sub-basin, which is mostly pure but soon to be contaminated by the remnants of industrial activities located west of the Al-Burayhi and Hedran sub-basin.
In the Al-Dhabab sub-basin, rainwater is almost the only source of recharge for the aquifers.
According to [19], in general, the groundwater movement in the study area is a function of the hydrological system into the north-west corner of the study area (toward the Al-Burayhi and Hedran sub-basin). In the volcanic aquifers, the direction of the groundwater movement is subject to the direction of the faults.
\nThe results and spatial distribution maps of village and inventory’s contamination source in the study area are shown in Figures 12 and 13.
\nSpatial distribution of pollution sources in the study area.
Village’s distribution in the study area.
The sampling sites are illustrated in Figure 14.
\nSampling sites.
A summarized statistic descriptive of results of fluoride’s concentration (mg/L) in groundwater of the study area is shown in Table 1, and the minimum, maximum, means and standard deviation of results based on each sub-basin are illustrated in Table 2.
\nVariable | \nMin | \nQ1 | \nMedian | \nQ3 | \nMax | \nMean | \nStDev | \n
---|---|---|---|---|---|---|---|
F- mg/L | \n0.100 | \n1.470 | \n1.890 | \n2.980 | \n6.000 | \n2.353 | \n1.449 | \n
Descriptive statistics of F- mg/L for all samples.
\n | Al-Hawban, sub-basin | \nAl-Burayhi and Hedran, sub-basin | \nAl-Dhabab, sub-basin | \n
---|---|---|---|
No. of wells | \n56 | \n29 | \n8 | \n
Minimum | \n0.98 | \n0.1 | \n0.58 | \n
Maximum | \n5.81 | \n5.45 | \n1.11 | \n
Mean | \n2.32 | \n2.66 | \n0.85 | \n
S.D | \n1.15 | \n1.26 | \n0.189 | \n
Descriptive statistics of fluoride values mg/L in groundwater samples of the study area by sub-basin.
SD, standard deviation.
We used boxplot tool in order to provide a simplified presentation of how the values of fluoride’s concentration are distributed, the boxplot (Figure 15) illustrated, the values’ distributions are dissimilarities in their distribution in three sub-basins.
\nBox plot to provide a simplified presentation of how the values of fluoride’s concentration are distributed in groundwater of three sub-basins.
The correlation analysis between the fluoride concentration and the different physicochemical parameters showed that the fluoride concentration is positively correlated with the Cl, EC, TDS, K, Na, Mg and T. H at the significance level of 0.01 and with the parameters Ca and HCO3 at the significance level of 0.05 (Table 3).
\n\n | \n | C° | \npH | \nEC (μs/cm) | \nTDS (mg/L) | \nT.H (mg/L) | \nT.A (mg/L) | \nCa (mg/L) | \nMg (mg/L) | \nNa (mg/L) | \nK (mg/L) | \nFe (mg/L) | \nCl (mg/L) | \nSO4 (mg/L) | \nHCO3 (mg/L) | \nNO3 (mg/L) | \nF- (mg/L) | \n
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
F- mg/L | \nP C | \n0.086 | \n−0.055 | \n0.491\n**\n\n | \n0.491\n**\n\n | \n0.394\n**\n\n | \n0.014 | \n0.211\n*\n\n | \n0.427\n**\n\n | \n0.453\n**\n\n | \n0.489\n**\n\n | \n0.072 | \n0.536\n**\n\n | \n0.168 | \n0.262\n*\n\n | \n0.014 | \n1 | \n
Sig. (2-tailed) | \n0.414 | \n0.602 | \n0.000 | \n0.000 | \n0.000 | \n0.897 | \n0.043 | \n0.000 | \n0.000 | \n0.000 | \n0.490 | \n0.000 | \n0.107 | \n0.012 | \n0.895 | \n\n | |
No | \n93 | \n93 | \n93 | \n93 | \n92 | \n93 | \n93 | \n93 | \n93 | \n93 | \n93 | \n93 | \n93 | \n92 | \n93 | \n93 | \n
Correlation between fluoride and different physico-chemical parameters.
Correlation is significant at the 0.05 level (two-tailed).
Correlation is significant at the 0.01 level (two-tailed).
PC, Pearson Correlation.
\nFigure 16 illustrated how the values of fluoride’s concentration are distributed according to water type, and Figure 17 show the comparison of the mean of F-mg/L according to water type with 95% confidence interval.
\nThe distribution’s values of fluoride according to water type.
Comparison of the mean of F- mg/L according to water type with 95% confidence interval.
From the results of fluoride contain in the 93 samples, we found that the very high fluoride’s concentration (4.501–6 mg/L) was associated with the water type both of Na-Cl (5 samples), Na-HCO3 (1 sample) and Mg-Cl (1 sample), the high fluoride’s concentration (3.01–4.5 mg/L) was associated with the water type both of Na-Cl (8), Na-SO4 (3), Mg-Cl (1), Na-HCO3 (2) and Ca-HCO3 (1), the moderately abundant of fluoride’s concentrations (1.5–3.0 mg/L) was associated with the water type both of Ca-Cl (4), Ca-HCO3 (2), Ca-SO4 (2), Mg-Cl (2), Mg-SO4 (1), Na-Cl (13), Na-HCO3 (16) and Na-SO4 (4), the optimal fluoride’s concentration (0.5–1.5 mg/L) was associated with the water type both of Ca-HCO3 (5), Mg-HCO3 (5), Na-Cl (8), Na-HCO3 (7) and Na-SO4 (1) and one sample with water type of Na-SO4 has fluoride’s concentration lower than 0.5 mg/L as shown in Table 4 and Figure 19. The distribution of water type according to sub-basins of the study area is illustrated in Figure 18, and the spatial distribution of both of water type and fluoride concentrations is illustrated in Figure 19.
\nConcentration of fluoride (mg/L) | \nType of water and number of samples | \n
---|---|
0.5–1.5 | \nCa-HCO3 (5), Mg-HCO3 (5), Na-Cl (8), Na-HCO3 (7) and Na-SO4 (2) | \n
1.5–3.0 | \nCa-Cl (4), Ca-HCO3 (2), Ca-SO4 (2), Mg-Cl (2), Mg-SO4 (1), Na-Cl (13), Na-HCO3 (16) and Na-SO4 (4) | \n
3.01–4.5 | \nNa-Cl (8), Na-SO4 (3), Mg-Cl (1), Na-HCO3 (2) and Ca-HCO3 (1) | \n
4.501–10.0 | \nNa-Cl (5), Na-HCO3 (1) and Mg-Cl (1) | \n
Classification of F- mg/L and their relation to the water type.
The distribution of water type according to sub-basins of the study area.
The spatial distribution both of water type and the F- mg/L in the study area.
There are variations for the fluoride concentration in the same of water type as shown in Table 4. Normal concentration of fluoride in Al-Dhabab sub-basin was associated with Ca-HCO3 and Mg-HCO3 water type; however, the sources of samples (wells No. 32 and 43, and spring No. 44) in Al-Hawban with the Ca-HCO3 water type showed high values of fluoride concentration 3.66, 2.36 and 1.96 mg/L containing fluoride concentration that exceeds the WHO drinking water, respectively. These sources are located in the southwest of Al-Hawban sub-basin, likely the sources of fluoride results of geology formation of the Sabir’s mountain granits, according to [21]. The groundwater with high concentration of fluoride is associated with the granits rocks.
\nFor the Na-Cl water type, we found that 26 samples out of 34 samples have high concentration of fluoride, 12 samples located in the Al-Burayhi and Hedran sub-basin [(wells No. 83, 80, 78, 77, 68, 65, 88, 82, 87, 79, 75 and 92), where 4 samples of them (wells No. 83, 80, 78 and 77) have 4 of highest values of fluoride’s concentration of out of 5 the highest values)] and 14 samples located in the Al-Hawban sub-basin (wells No. 6, 12, 2, 11, 1, 36, 30, 25, 42, 29,24, 28, 55 and 34); however, there are 8 wells with water type of Na-Cl that have fluoride’s concentration equalizer or lower than 1.5 mg/L [(7 wells located in the Al-Hawban sub-basin (wells No. 54, 37, 49, 53, 51, 40 and 5) and one well (No. 72) located in the Al-Burayhi and Hedran sub-basin)]. The wells with the Na-HCO3 water type have 19 groundwater samples out of 26 samples (wells and one spring) with a high fluoride’s concentration [(11 wells and one spring in Al-Hawban (dug wells No. 9, 22, 27, 18, 15, 47, 17, 19, 20, 23 and 52, spring No. 16) and 7 wells in Al-Burayhi and Hedran sub-basin (No. 67, 85, 70, 89, 69, 84 and 86)]. There are 7 samples out of 9 groundwater samples with water type of Na-SO4 have a high values of fluoride’s concentration, 6 wells located in the Al-Hawban [(wells No. 39, 33, 4, 46, 3 and 35) and one located in Al-Burayhi and Hedran sub-basin (well No. 66)]. All samples with water type both of Ca-SO4, Mg-Cl, Ca-Cl and Mg-SO4 have abnormal fluoride’s concentration as shown in Table 5. Abnormal concentration of fluoride was more prevalent with groundwater samples with Mg-Cl water type in Al-Hawban sub-basin (well No. 41 with 4.87 mg/L, No. 26 with 1.58 mg/L and well No. 10 with 3.33 mg/L) and Al-Burayhi and Hedran (source No. 81 with 2.66 mg/L); and with water type both of Ca-SO4 (wells No. 38 and 8 in Al-Hawban sub-basin), Ca-Cl (wells No. 71, 73 and 91 in the Al-Burayhi and Hedran sub-basin and well No. 31 in Al-Hawban sub-basin) and Mg-SO4 (well No. 7 in Al-Hawban sub-basin). Figure 20 illustrated that there are no significant different in the fluoride concentration between the sources of groundwater samples according to the water type.
\nWater type | \nNormal concentration N (%) | \nAbnormal concentration N (%) | \nP-value | \n
---|---|---|---|
Mg-HCO3 | \n5 (100%) | \nNIL | \n1 | \n
Ca-HCO3 | \n5 (62.5%) | \n3 (37.5%) | \n0.3 | \n
Mg-SO4 | \nNil | \n1 (100%) | \n0.10 | \n
Na-HCO3 | \n7 (26.9%) | \n19 (73.1%) | \n0.01 | \n
Na-SO4 | \n2 (22.22%) | \n7 (77.78%) | \n0.02 | \n
Na-Cl | \n8 (23.53%) | \n26 (76.47%) | \n0.01 | \n
Ca-SO4 | \nNil | \n2 (100.0%) | \n0.05 | \n
Mg-Cl | \nNil | \n4 (100%) | \n0.02 | \n
Ca-Cl | \nNil | \n4 (100%) | \n0.02 | \n
Water type, normal and abnormal of F- mg/L and number of wells.
The difference of means for F- mg/L with 95% Cl.
The distribution type of groundwater according to pH showed that, from Mg-So4 type through a Na-HCO3, Na-So4 and Na-Cl type groundwater to Mg-Cl and ultimately Ca-Cl when the pH range between 7.3 and 8 was more lower than pH ≠ [7.3–8] (Figure 21) and the comparison of the groups “pH [7.3–8]” and “pH ≠ [7.3–8]” showed a significant difference in fluoride concentration (p < 0.05).
\nDistribution of water type according to pH.
Results of the multivariate analysis are shown in Table 6. In the two logistic regression model, after adjusting for pH, water type and sub-basin (ORa = 0.366; CI: 1.76–0.76), the water type (ORa = 1.71; CI: 1.261–2.32) remained dependently associated with abnormal fluoride concentration (Table 6).
\n\n | ORcrude | \nCI for 95% | \np-value | \nORa | \nCI for 95% | \np-value | \n
---|---|---|---|---|---|---|
pH [7.3–8] | \n0.417 | \n0.164–1.062 | \n0.067 | \n0.373 | \n0.139–0.99 | \n0.050 | \n
Water type | \n1.755 | \n1.295–2.378 | \n0.000 | \n1.711 | \n1.261–2.322 | \n0.001 | \n
Sub-basin | \n0.389 | \n0.194–0.778 | \n0.008 | \n0.366 | \n0.176–0.760 | \n0.007 | \n
Logistic regression univariate and multivariate model for fluoride variation.
ORa: odds ratio adjusted. pH ≠ [7.3–8]: reference. Mg-HCO3 water type: reference. Al-Hawban sub-basin: reference.
The results statistic analysis show that the different of the fluoride concentration between the different sources of water samples (dug well, bore well and spring) in the study area is not significantly different. Fluoride concentration decreases not significantly according to well’s type F (2) =2.19, p = 0.121.
\nFluoride concentration is positively and not significantly related to depth of the groundwater (r = 0.046, p > 0.05).
\nThe comparison of the abnormal and normal fluoride concentration according to TDS (total dissolved solids) in the three sub-basins showed a significant differences between the three sub-basins (p < 0.0001) and positive relationship between the fluoride concentration and TDS (r = 0.5; p < 0.0001). A multiple comparison of median concentration among these sub-basins in fluoride shows that Al-Hawban and Al-Burayhi and Hedran sub-basins reach higher fluoride content which is more than 1.5 mg/L and similar but significantly higher than the Al-Dhabab sub-basin; Al-Hawban-Al-Burayhi and Hedran: (p > 0.072); Al-Hawban-Al-Dhabab: (p < 0.003); Al-Dhabab-Al-Burayhi and Hedran: (p < 0.0001). Moreover, this increase in TDS has been found only at two sub-basins when the fluoride is abnormal content (Figure 22). The results of the study samples of electrical conductivity and TDS according to study sub-basins are given in Figure 23.
\nBox plot for the maxi, min and average of the fluoride content in groundwater according to TDS of three sub-basins.
Box plot for the max, min and average of electrical conductivity and total dissolved solids according to study sub-basins.
In order to enable sustainable development of groundwater resources, it is necessary to delineate the safe and unsafe zones with reference of fluoride content (1.5 mg/L); hence spatial distribution of fluoride’s concentration was mapped in the three sub-basins of the study area (Figure 24).
\nSpatial distribution of fluoride concentration in the study area.
Based on the Kruskal-Wallis test for the various sub-basins, the level of significance was (p < 0.05). This result illustrated that there were a significant differences between three sun-basins with regard to the concentration of fluoride in groundwater. The differences of means for fluoride concentration in groundwater for the three sub-basins show the mean’s fluoride in Al-Burayhi and Hedran (F-) > Al-Hawban (F-) > Al-Dhabab (F-). A multiple comparison of mean concentration of fluoride among these sub-basins shows that there is no a significant difference between the Al-Hawban and Al-Burayhi and Hedran sub-basins P-value 0.277, there is a significant difference between the Al-Hawban and Al-Dhabab sub-basins P-value 0.017 and there is a significant difference between the Al-Burayhi and Hedran and Al-Dhabab sub-basins P-value 0.001.
\nFluoride concentration variation is widely in the study area from 0.1 mg/L (in well No.93 in Al-Burayhi and Hedran sub-basin) to 6 mg/L [in Well No. 83, of the same sub-basin (Al-Burayhi and Hedran sub-basin)]. We observed that the concentration of fluoride in the Al-Dhabab sub-basin is the optimal concentration according to the WHO drinking water guidelines value of 1.5 mg/L.
\nWaters with high fluoride concentrations occur in large and extensive geographical belts associated with (a) sediments of marine origin in mountainous areas, (b) volcanic rocks and (c) granitic and gneissic rocks [21], and the high concentration of fluoride widely accepted that most of the F are derived mainly from acidic volcanic rocks such as pumice, obsidians, pyroclastic deposits, ignimbrites and rhyolite, and the main minerals for F are fluorite, fluorapatite, micas and hornblende [22]. Because the geology of study mainly constituent from the acid and basic volcanic and grants rocks, the level of fluoride concentration in the Al-Dhabab sub-basin can be explained by the nature on aquiver in this study area (Cretaceous Tawilah Sandstone), while the groundwater in the other sub-basin is produced either from Tertiary fractured volcanic (that have F- bearing mineral and the groundwater in this aquiver have long-time contact with aquiver, which adjudge the important factors leading to the high fluoride concentration result of interaction between the groundwater and the aquiver) or from the Quaternary alluvium aquiver, where the Wadi sediments deposited are derived from the alteration of volcanic ash and tufa mainly of (Tr1); this quiver depends on their recharge mainly on the wastewater of the urban and industrial activates; this aquiver exposed to over exploration of their groundwater and finally the dry and semi-dry condition plays an important role in the degradation of groundwater in this aquiver.
\nIt is clearly observed that the Al-Burayhi and Hedran and Al-Hawban sub-basins have the highest concentration of fluoride ion in the chemistry of water. Highest concentrations were found to be 6 mg/L from Al-Burayhi and Hedran sub-basin, 5.81 mg/L from Al-Hawban unlike the Al-Dhabab sub-basin which remains unaffected by the contamination fluoride of groundwater. According to the report of [23], the dental fluorosis is the widely fluoride disease observed in the affected areas, and there is a positive relationship between fluoride in water and the occurrence of dental fluorosis in Taiz region.
\nIn order to understand the vertical distribution of the fluoride ion concentration from the water of the study area, the type of the sample water (dug well, bore well and spring) evaluated separately. There was no significant difference between the three well types, dug well sample, springs and bore wells. It can be concluded that shallow aquifers do not reflect higher fluoride contamination than deeper aquifers. It is observed that most of the water samples showed enhanced concentrations with generally increasing trends to the low elevated area (Al-Burayhi and Hedran sub-basin), while the high elevation shows low concentration of fluoride (Al-Dhabab sub-basin). All the water samples collected from the uphill zones of Al-Dhabab sub-basin were exhibited low fluoride concentration.
\nCompared with Na-HCO3 type groundwater, Ca-HCO3 type groundwater is known to generally contain lower fluoride [24]. Its hydrochemistry is characterized by increased Ca2+ ion concentration with increasing total dissolved solid due to the gradual dissolution of carbonate minerals or Ca2+ bearing plagioclase in aquifer materials [25, 26]. The Na-HCO3 type groundwater is generally enriched in fluoride and sodium ions, due to the dissolution of silicates as well as the removal of Ca2+ by calcite precipitation and cation exchange [27, 28]. The solubility limits for fluorite and calcite provide a natural control on water composition in a view that calcium, fluoride and carbonate activities are interdependent [29, 30]. In addition to the effect of those areas by different liquid waste by runoff and sewage disposal, the heavy pumping of well water is also contributed because of the scarcity of water which leads to the increase of the concentration of salt in the water. TDS levels ranged widely from 291 to 6188 mg/L with most station levels above 400 mg/L and many of the samples studied were higher than the permissible limit of 1500 mg/L according to WHO (2003). This wide variation in TDS values indicates that the area hydrochemistry is influenced by diverse processes such as water-rock interaction and anthropogenic pollution. Fluoride concentrations frequently are proportional to the degree of water-rock interaction because fluoride primarily originates from the geology [9, 31, 32, 33, 34]. Due to the high rainfall, rugged topography, factories, lack of total coverage per sewerage network, population density and faults in the study areas could also explain this high fluoride content by runoff and infiltration of chemical fertilizers in agricultural areas, septic and sewage treatment system discharges with fluoridated water supplies and liquid waste from industrial sources. The topography of the study areas varies from level plain to steep slopes. Study area ranges in elevation between 900 and 3000 m above sea level. Taiz plain receives about 500 mm/year of rainfall and significant recharge form runoff of surrounding mountains [35]. In addition to this groundwater fluoride pollution that can affect human health, there have been indications that uptake of fluoride from other sources like food, dust and beverages may be many times higher than that of water [36]. About the fluorosis in selected villages of Taiz Governorate, the percentage of children with fluorosis was very high. Not only because of drinking water, various food habits (like drinking black tea and Chewing Qat) indicated a high contribution of fluoride to food. In AL-Hawban sub-basin, some of children, especially from Jabal Sabir area, used to chew Qat daily, and the Qat are cultivated in the man-made terraces of Jabal Sabir alkali granite, where it expected to be the main source of F- reach minerals like fluorite [23].
\nOn the other hand, the use of fluoridated water for cooking increases the fluoride content significantly especially in dry foods like maize flour which absorbs much water during cooking. It has been reported that fluoride availability may be influenced by simultaneous intake of food and fluoride containing compounds in a positive or negative manner depending on the food type, mode of administration and type of fluoride compound [37].
\nMuch of the fluoride entering the body is from water and the high concentration of fluoride in water’s sources is therefore a major concern. The fluoride is found in the atmosphere, soil and water. It enters the soil through weathering of rocks, precipitation or waste runoff. Understanding of the fluoride occurrence is important in the management of the fluoride related epidemiological problems. Al-Hawban and Al-Burayhi and Hedran are the worst sub-basins affected by fluoride contamination in drinking water. 71% of samples (66 samples out of 93 samples) in the study area have F- concentration (mg/L) above the permissible limit and alternate water sources will be difficult. Therefore, defluoridation of drinking water is the only practicable option to overcome the problem of excessive fluoride in drinking water in these areas. More refined studies however need to be done before any long-term intervention efforts can be planned. In the meantime, there is a critical need to educate young Yemenis about fluorosis and simple intervention measures to avoid long-term health problems. Other studies in the region are urged studying the cause and effect relationship between the abnormal content fluoride and population health.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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