text:
Simulation parameters for QoS.
\r\n\tThe overall objective of the book is to propose a methodological/ technological state of play and an operational assessment on the complex issues regarding the management and optimization of the multiple components of a transportation system: users, infrastructures, technologies and services.\r\n
\r\n\tThe book welcomes topics such as smart mobility, smart transportation systems, smart vehicle, smart infrastructures, smart people: citizens and users.",isbn:"978-1-83880-823-5",printIsbn:"978-1-83880-802-0",pdfIsbn:"978-1-83880-824-2",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"ef80dab7f0350ea7cb28f40eedea2b35",bookSignature:"Prof. Stefano De Luca, Dr. Roberta Di Pace and Dr. Chiara Fiori",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9872.jpg",keywords:"Transportation, Intelligent Information Systems, Smart Vehicles, Vehicle Management, Driving Assistance Technologies, Smart Infrastructures, Smart Transportation Systems, Sustainable Transportation Systems, Vehicle Routing, Travel Demand Modeling, Life Cycle Assessment, Environmental Impacts Modeling",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 6th 2020",dateEndSecondStepPublish:"May 27th 2020",dateEndThirdStepPublish:"July 26th 2020",dateEndFourthStepPublish:"October 14th 2020",dateEndFifthStepPublish:"December 13th 2020",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Scientific coordinator of the Transportation Planning and Modelling laboratory, a consultant for the Italian Ministry of Transportation, the Transport commission of Campania Region, of Salerno and Avellino Transportation Departments and member of the IEEE Intelligent Transportation Systems Society.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"271061",title:"Prof.",name:"Stefano",middleName:null,surname:"De Luca",slug:"stefano-de-luca",fullName:"Stefano De Luca",profilePictureURL:"https://mts.intechopen.com/storage/users/271061/images/system/271061.jpeg",biography:"Stefano de Luca, got a Ph.D. in transportation engineering at the University of Rome 'La Sapienza" and is an associate professor at the Department of Civil Engineering of the University of Salerno (Italy). He is a professor of Transportation Planning (BSc, Civil Eng. and Environmental Eng.) and Transportation Systems Theory (MSc, Civil Eng.). Currently, he is vice-coordinator of the Ph.D. course on 'Risk and sustainability", scientific coordinator of the Transportation Planning and Modelling laboratory. He is a consultant for the Italian Ministry of Transportation, the Transport commission of Campania Region, of Salerno and Avellino Transportation Departments. His main research interest includes transportation planning techniques, travel demand modeling, users' behavior modeling, signal settings design, traffic assignment models, air transportation. 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She is an aggregate professor of Technique and Transport Economics (BSc, Civil Eng. and Environmental Eng) and Transportation Systems Design (MSc, Civil Eng). Since 2010 she is a member of the Transportation Planning and Modelling Laboratory. Her main research fields include the development of analytical tools for advanced traveler information systems, the traffic flow modeling, the network signal setting design, the advanced traffic management systems. 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She was Visiting Scientist at the European Commission, Joint Research Center, Directorate for Energy, Transport and Climate Change, Ispra, Italy, from 2017 to 2018. Moreover, from 2015 to 2016 she was Visiting Scientist at the Center for Sustainable Mobility of the Virginia Tech Transportation Institute, USA, and, in 2013, Visiting Scholar at the Center for Automotive Research of the Ohio State University, USA. 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The mobile ad hoc networks (MANETs) have evolved significantly in recent decades, where the differential of this type of network is the independence of a centralized infrastructure to organize flow, a router or switch, for example, so if any device present in this network is disconnected or damaged, it can automatically adjust to a new topology, and the routing tables are updated [1].
From the freedom provided by the MANETs, several other devices have started to be connected to the Internet in addition to cell phones, tablets, and notebooks, so that together with wireless sensor networks (WSN), they can play an essential role in the development of applications aimed at Internet of things (IoT) [2].
Among these devices, stand out cars, other types of cars and the highway itself in which they are, i.e., using a MANET network to connect them is the first step toward the creation of autonomous vehicles, vehicular ad hoc networks (VANET), for further study, see [3, 4, 5].
They represent a major advance in mobile network technology in general, since several routing protocols have been developed or adapted for this type of network [6]. In addition, maintaining the quality of service (QoS) even at the high speed that the vehicles reach is one of the main challenges to be overcome in this area.
More recently, because of the popularization and cost reduction of unmanned aerial vehicles (UAVs), flying ad hoc networks (FANETs) [7, 8, 9, 10, 11] have emerged, which are networks composed only of aerial devices that can communicate between UAV-to-UAV and other ground-based UAV-to-ground devices. According to [12] UAVs can be divided into UAVs of high-altitude and long-range and medium-range, small drones and mini drones, the first two being military. In this chapter only the small drones and mini drones will be addressed.
The FANETs can operate either independently and by transmitting the flow received from land-based devices to a remote server or can also support other types of networks, for example, via satellite or cellular, if they are overloaded or unavailable as shown in Figure 1.
Therefore, such technology can play an essential role in the next generation of cellular networks, offering future support to 5G networks [13, 14], which it will reach very high speed and minimum latency, but due to the higher frequency in which they operate, the range is limited in relation to 4G. Thus, FANETs present themselves as a low-cost, scalable solution for maintenance and expansion of the Internet infrastructure worldwide, and it is essential to research, simulate, and validate their utility in different applications, taking into account the limitations of both UAVs and network itself.
In this chapter the main challenges for the implementation of FANETs in Section 2 will be addressed; in Section 3 the applications that can use the FANETs will be discussed and classified according to their characteristics, and Section 4 will address several available routing protocols and their advantages and limitations. Finally, Section 5 will demonstrate simulations of FANET behavior in different scenarios and routing protocols with data transmission and video.
Despite several advances in recent years, FANET networks still have restrictions that may be critical to their operation depending on the application. The main one is energy consumption [15, 16, 17] because it limits the flight time of the drones, the speed of connection, and the range of the signal transmitted by them, so the challenges that need to be overcome to make FANET a reality involve primarily the search for solutions to these limitations, and in addition other factors can affect the performance of the network as the mobility and storage capacity of the drones. Possible solutions to these challenges will be discussed below.
Directional antennas: Most router antennas are omnidirectional, that is to say that the signal transmitted by them is sent equally in all directions, but when using these same antennas in drones, the results may not be very efficient as regards the quality of the antenna and energy consumption. Therefore, new antennas have been developed with beamforming technology [18, 19]; this change allows the transmitted signal to be directed to a specific area close to the UAV as shown in Figure 2. In this way the signal quality at the specific location is significantly better, and the energy consumption of the UAV is also reduced. However, it is still relatively a new technology and still needs to be better evaluated and implemented.
Mobility: One of the biggest differentials of UAVs is the high mobility and speed variation they have, which allows them to access hard-to-reach places and travel long distances in a short time, depending on the UAV model.
Thus, regardless of the mode of operation of the drones being both fully autonomous and controlled by a base station, it is necessary that critical information for the mobility of one or more drones is transmitted to the other drones in the network or to the base station as prevention alerts collision, GPS, flight time, environmental and climatic conditions, as well as the transmission of drone drive commands if they are controlled by a base station (Figure 3).
Routing protocols: Routing protocols are the brain of FANETs and control all flow both between UAVs and other devices connected to them, and although there are already several routing protocols available, these protocols sometimes cannot cope with mobility and the speed of the UAVs which causes a high rate of errors in the connection and until the drop of the network in certain cases. In this way, new protocols were developed with the focus on mobile ad hoc networks, with FANETs being one of them, and just like beamforming, these protocols are still being tested but at a later stage. Because the evaluation of routing protocols is one of the main research focuses in FANETs, it will be addressed in more depth in Section 4.
Due to its physical and architectural characteristics, there are several applications for FANETs. Some of them are mentioned in different scenarios.
In some cases of disasters, a human being may encounter obstacles that prevent the analysis of the entire affected area. In this situation, it is possible to use FANETs to evaluate the scenario completely [7].
There are several possibilities for the use of FANETs in agriculture such as complete crop evaluation, plant health analysis, and mapping of possible areas for planting expansion (Figure 4) [7].
In rescue situations where conventional mobile networks are damaged, the FANETs can be used to search for hostages in the affected area. And, because of the size of the UAVs, it is possible to access places in which a human would have difficulty [9].
Sensor networks are mainly used for data collection and can be used together with FANETs in various situations [20]. Due to the ease of the UAVs to access any location without great difficulties, there will be an improvement in the performance of the networks when evaluating the scenarios in which they are applied [9].
With the use of FANETs, it is possible to analyze constructions, verify their progress and their quality, and also evaluate in advance the conditions of the environment to be used for the work in order to prevent possible calamities [7].
In order to reduce their costs and improve the quality of their services, some companies already idealize the use of UAVs for product delivery [7]. The service will be done autonomously through the implementation of an intelligent system in the UAVs [21].
The FANETs are widely used by military personnel primarily for communication between soldiers or between their barracks. It also can be used in civil operations to maintain the security of society [9].
Due to some characteristics such as high mobility, the constant changes in network topology, unpredictable environmental and climatic factors, and energy consumption, the communication of nodes in FANETs becomes a challenge [7]. The nodes represent communication points in the networks, being computers, servers, or, in the case of FANETs, UAVs [22]. Therefore, in order for the network to have the desired performance, it is necessary that the routing protocols are adequate to handle various scenarios and conditions [23]. Here are the three types of protocols used in FANETs, which are proactive, reactive, and hybrid (Figure 5).
Proactive protocols are those that update their routing tables at fixed time intervals. This feature allows the packets to be sent faster through the network because the nodes already know the changes in the routes [23]. The disadvantage of this type of protocol is the need for greater bandwidth to make constant updates [7]. The two main proactive protocols for FANETs are:
The OLSR is a specific protocol of ad hoc networks, and it has the main characteristic to select some nodes of the network to act as relays, called multipoint relay (MPR) [24]. The purpose of this mechanism is to avoid flooding in the network, caused by the excess of packets received by each node [25].
In the DSDV protocol, in addition to the information of the network itself, the routing tables contain a sequence of numbers that changes according to the network topology [3]. These numbers are sent to all the nodes of the network, keeping them always updated, in order to avoid loops occurring between the nodes [23].
Unlike the proactive ones, the reactive protocols only establish communication in the network when requested by the nodes [7]. Due to this feature, routing tables are only updated when there are packets to be sent [23]. Therefore, since it is necessary for the node to search the route before sending the package, the time to complete the entire process until the delivery is made becomes greater [9]. For use in FANETs, the following protocols are proposed:
The AODV protocol first makes the discovery of routes when necessary to send a packet and stores that route, just after the packet is sent to its destination [26]. In addition, because it is a mobile network protocol, if there is a connection interruption, the maintenance of routes starts to update the routing tables and thus maintains the communication between the nodes [24].
The DSR is a widely used protocol in multi-hop wireless networks, and it has the feature of its source node storing the entire route up to the destination node [24]. As the AODV, the DSR protocol also performs route discovery when there is a need for communication between two nodes to send packets [27]. And it performs maintenance of routes if there is a change in the topology of the network and communication is interrupted [24].
The hybrid protocols are the combination of reactive and proactive protocols, using the best resources of each, being used mainly for large networks [24]. They are based on the concept of zones, where proactive protocols are used for intra-zone routing (nodes within the same zone) and reactive routing protocols are used for inter-zone routing [23]. The main protocols of this type are:
In this protocol, each node has a different zone, so the neighboring node zones overlap [24]. In intra-zone routing, proactive protocols are used to maintain the routes; due to this, if the source and destination nodes are in the same zone, packet sending is done immediately [23]. Already in the inter-zone routing, we use reactive protocols to find the routes and maintain them [23].
The TORA protocol is characterized by each node of the network maintaining only the routing information of its neighbors updated [23]. Because it is a highly adaptive protocol, its reactions to network topology changes are reduced in order to minimize the propagation of control messages, so the network does not look for new routes when there is no need [24]. The TORA mainly uses reactive protocols but also uses proactive protocols in some situations [23].
The performance of FANET networks can be analyzed through computer simulations. In order to correctly evaluate these network types, two scenarios were simulated to simulate different transmission types, and each one used three of the aforementioned routing protocols: AODV, OLSR, and DSDV [28].
The network simulator 3 (NS-3) software was used for the construction of simulation scenarios based on C++ language [29]. Each simulation has specific parameters that constitute different situations for this type of network. The details of each scenario were defined according to Tables 1 and 2.
|Simulation 1: UDP flow on a FANET network with UAVs
|Simulation time
|60 s
|Simulation area
|400 × 400 × 400 m2
|UAVs' quantity
|UAVs' speed
|0–15 m/s
|Mobility model
|Transmission range
|40 m
|MAC protocol
|IEEE 802.11a
|Routing protocols
|OLSR, AODV, and DSDV
|Transport protocol
|Internet protocol
|Maximum transmission rate
|100 kbps
|Packet size
|512 bytes
|Simulation 2: Streaming video on a FANET network with UAVs
|Time simulation
|80 s
|Simulation area
|400 × 400 × 100 (L × P × A) m2
|UAVs' quantity
|UAVs' speed
|0–15 m/s
|Mobility model
|Transmission range
|40 m
|MAC protocol
|IEEE 802.11b
|Routing protocols
|OLSR, AODV, and DSDV
|Transport protocol
|Internet protocol
The scenario of Table 1 is to evaluate the performance of packet flow between UAVs and a server; this type of application is very common in a network of sensors for data collection or monitoring. In this scenario, all drones send data to the server at the same time and vice versa.
The scenario in Table 2 seeks to evaluate the performance of a video transmission between a drone and a server; this type of application is widely used in disaster monitoring and rescue in locations inaccessible by the ground. In this scenario, a drone flies on a certain space while transmitting a video to the server on the ground.
Different types of simulation parameters were considered for each of the analyzed scenarios. In simulation 1, three network performance parameters were considered: packet received rate, delay, and network throughput. In simulation 2, only a few parameters were considered in relation to simulation 1, the received packet rate and the delay. However, the video transmission that occurs in this scenario requires specific quality of experience (QoE) parameters to be analyzed, such as structural similarity (SSIM), signal peaks (PSNR), and video quality metrics (VQM).
This parameter is defined by the ratio between the number of packets that are sent and the number of packets that are actually received at the destination. The final fee is displayed as a percentage based on total packets sent.
This parameter is defined by the amount of time a packet takes to traverse from the source to the destination in a transmission. Several factors directly influence this parameter, such as the routing protocols and the transmission rate of the network.
This parameter is defined by the bandwidth of a transmission between two nodes of a network over time. It is generally expressed in bits/sec.
It is a method used to measure the similarity between two images, determining the quality of the image received in the transmission [30]. In simulation 2, because it is a video, each frame receives a specific value. The SSIM metric was developed to extract structural characteristics of images, configuring the proximity between the pixels as a key factor to quantify the structures and to approach the visual quality of the images.
It is defined by the ratio between the signal power and the noise introduced by the transmission thereof [31]. In the case of video transmission, this noise influences the fidelity of the video representation at the destination. The PSNR is an approximation of human perception of the quality of reconstruction and is generally measured in decibels (dB).
This parameter is defined by the perceptual defect measurements of various video deficiencies, such as blur, rough motion, overall noise, block distortion, and color distortion. These measures are combined into a single metric that provides an overall quality forecast [32].
In order to obtain the data, it is essential to organize and manipulate this data in a correct way, in order to analyze the performance of each parameter individually, considering the relation between them. In both scenarios, the flow monitor capture library [33] was used, which enabled the capture of the discrete-time performance parameters in each stream in the simulation scenario.
It was necessary to treat the data of the video transmission made by Evalvid in simulation 2 to extract the metrics of the network as delay and loss of packages. In order to obtain the PSNR and SSIM metrics, it was necessary to use the MSU Video Quality Measurement Tool (MSU VQMT), which reconstructs the transmitted video and compares it with the original Evalvid library video [34].
Comprising four UAVs and one server, the scenario simulates UDP packet flow between the server and the UAVs, which is initially in the server sense for UAVs (download) and UAVs for the server (upload).
Two graphs were generated for each analyzed parameter, one for upload flow and the other for download flow. Each displayed data represents the average of all the flows captured at that instant of time. After 60 seconds of simulation, all the performance parameters were analyzed.
The received packet rate had similar results in both directions of flow. Figure 6 shows the performance in the upload; the OLSR had the best performance, remaining above 70% in most of the simulation; on the other hand, the DSDV showed the worst performance, remaining below 40% in almost all the simulation.
In the download flow, the protocols had similar results throughout the transmission. The biggest difference between them can be seen in stability, especially the OLSR, which has remained stable close to 70% in most of the transmission. The AODV protocol exhibits poor performance in the initial simulation period, due to the reduced routing table of the protocol, which updates according to the need for transmission (Figure 7).
The resulting delay in the upload flow was noticeable only in the OLSR protocol, due to the common overhead of the proactive protocols [25]. On the other hand, in the download flow, the result was equivalent in all the protocols, concentrating the greater delays at the beginning and the end of the transmission. Despite the high mobility of the drones, the delay had good results in download flow, in which no routing protocol exceeded 60 ms, as can be seen in Figures 8 and 9.
The protocols performed differently depending on the flow direction. In the upload flow, shown in Figure 10, the OLSR had the highest average bandwidth between the protocols tested; on the other hand, the AODV and DSDV stabilized below half of the available maximum bandwidth, damaging the packet delivery, as previously seen in Figure 6. In the download flow, all the protocols have stabilized over time, since there being only one transmitter and several receivers, the packet flow became simpler, improving the overall performance of the protocols (Figure 11).
Comprised of one UAV and one server, simulation 2 performs a video transmission between the server and the sleeping device. With the help of the Evalvid utility for NS-3, it was possible to analyze performance parameters and quality of experience (QoE) in all frames of the video, resulting in a detailed capture of protocol performance in this scenario. One was generated for each performance parameter, and four graphs for each QoE parameter.
The AODV and OLSR protocols had similar results along the transmission, with a higher performance in the first 17 seconds, followed by a significant decay due to the mobility of the FANET. The DSDV had the worst performance, as a result of an even greater decline, with no significant stability (Figure 12).
The delay was well-designed in all protocols, which stabilized throughout the transmission. The main factor that contributed to the result shown in Figure 8 was the implementation of the Aar WiFi Manager function in simulation 2, an algorithm that works on the physical layer of the NS-3, which controls the transmission rate in the network [35]. With values below 3 ms in most of the transmission, the delay cannot be considered one of the reasons for packet loss (Figure 13).
Figure 14 shows the mean SSIM in each protocol, which may have values of maximum 1 and have a minimum of 0.994 and a maximum of 0.998, highlighting a very low variation in transmission quality.
Figures 15, 16, and 17 show the SSIM value in each frame of the video, further emphasizing the similarity in performance between the protocols. Thus, the routing protocols did not interfere directly in this metric; this was due to the direct connection between drone and server during the simulation, without the need to perform much jumps in the network.
The PSNR shows the relationship between signal and video noise where a higher ratio means better quality. According to Figure 18, the DSDV obtained the best result, well above the other protocols, which are almost 7 dB difference, a significant value in this metric because it is a logarithmic scale.
The resulting VQM of Figure 19 composes a correlation between seven different video quality parameters. The results show a low correlation coefficient in the AODV and OLSR protocol tests of slightly more than 0.4 between the original and transmitted videos. With the DSDV protocol, the resultant was even worse, with a value <0.05 correlation. In visual perception, this disparity results in a significant loss in video quality, something that was not shown in the other QoE parameters.
The evolution of the FANETs will allow a new range of application of this network, making several other devices connected to the Internet, such as sensors, cars, etc. Soon, FANETs will be essential for the construction of interim air networks. The applications discussed in this chapter have demonstrated the high flexibility of such networks, such as the use in rescue and monitoring, smart grids, etc. The challenges present in the FANETs are limited to problems in energy efficiency and routing protocols as seen in the simulations.
Due to the high mobility and flexibility of the UAVs, it is difficult to guarantee efficiency in all cases; simulations 1 and 2 have shown that proactive protocols are more efficient in scenarios that communicate with an onshore server but may not be the case in limited broadcasts or mobile server, which may have longer delays due to the frequent updating of the routing tables and the high mobility of the UAVs, which cause frequent loss of connection.
The authors also acknowledge the financial support by PROPESP/UFPA.
Water is the second most important need for life to exist after air. As a result, water quality has been described extensively in the scientific literature. The most popular definition of water quality is "it is the physical, chemical, and biological characteristics of water" [1, 2]. Water quality is a measure of the condition of water relative to the requirements of one or more biotic species and/or to any human need or purpose [3, 4].
Based on its source, water can be divided into ground water and surface water [5]. Both types of water can be exposed to contamination risks from agricultural, industrial, and domestic activities, which may include many types of pollutants such as heavy metals, pesticides, fertilizers, hazardous chemicals, and oils [6].
Water quality can be classified into four types—potable water, palatable water, contaminated (polluted) water, and infected water [7]. The most common scientific definitions of these types of water quality are as follows:
Potable water: It is safe to drink, pleasant to taste, and usable for domestic purposes [1, 7].
Palatable water: It is esthetically pleasing; it considers the presence of chemicals that do not cause a threat to human health [7].
Contaminated (polluted) water: It is that water containing unwanted physical, chemical, biological, or radiological substances, and it is unfit for drinking or domestic use [7].
Infected water: It is contaminated with pathogenic organism [7].
There are three types of water quality parameters physical, chemical, and biological [8, 9]. They are summarized in Table 1.
Turbidity is the cloudiness of water [10]. It is a measure of the ability of light to pass through water. It is caused by suspended material such as clay, silt, organic material, plankton, and other particulate materials in water [2].
Turbidity in drinking water is esthetically unacceptable, which makes the water look unappetizing. The impact of turbidity can be summarized in the following points:
It can increase the cost of water treatment for various uses [11].
The particulates can provide hiding places for harmful microorganisms and thereby shield them from the disinfection process [12].
Suspended materials can clog or damage fish gills, decreasing its resistance to diseases, reducing its growth rates, affecting egg and larval maturing, and affecting the efficiency of fish catching method [13, 14].
Suspended particles provide adsorption media for heavy metals such as mercury, chromium, lead, cadmium, and many hazardous organic pollutants such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and many pesticides [15].
The amount of available food is reduced [15] because higher turbidity raises water temperatures in light of the fact that suspended particles absorb more sun heat. Consequently, the concentration of the dissolved oxygen (DO) can be decreased since warm water carries less dissolved oxygen than cold water.
Turbidity is measured by an instrument called nephelometric turbidimeter, which expresses turbidity in terms of NTU or TU. A TU is equivalent to 1 mg/L of silica in suspension [10].
Turbidity more than 5 NTU can be visible to the average person while turbidity in muddy water, it exceeds 100 NTU [10]. Groundwater normally has very low turbidity because of the natural filtration that occurs as the water penetrates through the soil [9, 16].
Palatability, viscosity, solubility, odors, and chemical reactions are influenced by temperature [10]. Thereby, the sedimentation and chlorination processes and biological oxygen demand (BOD) are temperature dependent [11]. It also affects the biosorption process of the dissolved heavy metals in water [17, 18]. Most people find water at temperatures of 10–15°C most palatable [10, 19].
Materials decayed from organic matter, namely, vegetation and inorganic matter such as soil, stones, and rocks impart color to water, which is objectionable for esthetic reasons, not for health reasons [10, 20].
Color is measured by comparing the water sample with standard color solutions or colored glass disks [10]. One color unit is equivalent to the color produced by a 1 mg/L solution of platinum (potassium chloroplatinate (K2PtCl6)) [10].
The color of a water sample can be reported as follows:
Apparent color is the entire water sample color and consists of both dissolved and suspended components color [10].
True color is measured after filtering the water sample to remove all suspended material [19].
Color is graded on scale of 0 (clear) to 70 color units. Pure water is colorless, which is equivalent to 0 color units [10].
Taste and odor in water can be caused by foreign matter such as organic materials, inorganic compounds, or dissolved gasses [19]. These materials may come from natural, domestic, or agricultural sources [21].
The numerical value of odor or taste is determined quantitatively by measuring a volume of sample A and diluting it with a volume of sample B of an odor-free distilled water so that the odor of the resulting mixture is just detectable at a total mixture volume of 200 ml [19, 22]. The unit of odor or taste is expressed in terms of a threshold number as follows:
where TON is the threshold odor number and TTN is the threshold taste number.
Solids occur in water either in solution or in suspension [22]. These two types of solids can be identified by using a glass fiber filter that the water sample passes through [22]. By definition, the suspended solids are retained on the top of the filter and the dissolved solids pass through the filter with the water [10].
If the filtered portion of the water sample is placed in a small dish and then evaporated, the solids as a residue. This material is usually called total dissolved solids or TDS [10].
Water can be classified by the amount of TDS per liter as follows:
freshwater: <1500 mg/L TDS;
brackish water: 1500–5000 mg/L TDS;
saline water: >5000 mg/L TDS.
The residue of TSS and TDS after heating to dryness for a defined period of time and at a specific temperature is defined as fixed solids. Volatile solids are those solids lost on ignition (heating to 550°C) [10].
These measures are helpful to the operators of the wastewater treatment plant because they roughly approximate the amount of organic matter existing in the total solids of wastewater, activated sludge, and industrial wastes [1, 22]. Figure 1 describes the interrelationship of solids found in water [22]. They are calculated as follows [10]:
Total solids:
where TSA = weight of dried residue + dish in milligrams and TSB = weight of dish in milligrams.
Total dissolved solids:
where TDSA = weight of dried residue + dish in milligrams and TDSB = weight of dish in milligrams.
Total suspended solids:
where TSSA = weight of dish and filter paper + dried residue and TSSB = weight of dish and filter paper in milligram.
Fixed and volatile suspended solids:
where VSSA = weight of residue + dish and filter before ignition, mg and VSSB = weight of residue + dish and filter after ignition, mg.
The electrical conductivity (EC) of water is a measure of the ability of a solution to carry or conduct an electrical current [22]. Since the electrical current is carried by ions in solution, the conductivity increases as the concentration [10] of ions increases. Therefore, it is one of the main parameters used to determine the suitability of water for irrigation and firefighting.
Units of its measurement are as follows:
U.S. units = micromhos/cm
S.I. units = milliSiemens/m (mS/m) or dS/m (deciSiemens/m)
Pure water is not a good conductor of electricity [2, 10]. Typical conductivity of water is as follows:
Ultra-pure water: 5.5 × 10−6 S/m;
Drinking water: 0.005–0.05 S/m;
Seawater: 5 S/m.
The electrical conductivity can be used to estimate the TDS value of water as follows [10, 22]:
TDS can be used to estimate the ionic strength of water in the applications of groundwater recharging by treated wastewater [22]. The normal method of measurement is electrometric method [10].
pH is one of the most important parameters of water quality. It is defined as the negative logarithm of the hydrogen ion concentration [9, 12]. It is a dimensionless number indicating the strength of an acidic or a basic solution [23]. Actually, pH of water is a measure of how acidic/basic water is [19, 20]. Acidic water contains extra hydrogen ions (H+) and basic water contains extra hydroxyl (OH−) ions [2].
As shown in Figure 2, pH ranges from 0 to 14, with 7 being neutral. pH of less than 7 indicates acidity, whereas a pH of greater than 7 indicates a base solution [2, 24]. Pure water is neutral, with a pH close to 7.0 at 25°C. Normal rainfall has a pH of approximately 5.6 (slightly acidic) owing to atmospheric carbon dioxide gas [10]. Safe ranges of pH for drinking water are from 6.5 to 8.5 for domestic use and living organisms need [24].
A change of 1 unit on a pH scale represents a 10-fold change in the pH [10], so that water with pH of 7 is 10 times more acidic than water with a pH of 8, and water with a pH of 5 is 100 times more acidic than water with a pH of 7. There are two methods available for the determination of pH: electrometric and colorimetric methods [10].
Excessively high and low pHs can be detrimental for the use of water. A high pH makes the taste bitter and decreases the effectiveness of the chlorine disinfection, thereby causing the need for additional chlorine [21]. The amount of oxygen in water increases as pH rises. Low-pH water will corrode or dissolve metals and other substances [10].
Pollution can modify the pH of water, which can damage animals and plants that live in the water [10].
The effects of pH on animals and plants can be summarized as follows:
Most aquatic animals and plants have adapted to life in water with a specific pH and may suffer from even a slight change [15].
Even moderately acidic water (low pH) can decrease the number of hatched fish eggs, irritate fish and aquatic insect gills, and damage membranes [14].
Water with very low or high pH is fatal. A pH below 4 or above 10 will kill most fish, and very few animals can endure water with a pH below 3 or above 11 [15].
Amphibians are extremely endangered by low pH because their skin is very sensitive to contaminants [15]. Some scientists believe that the current decrease in amphibian population throughout the globe may be due to low pH levels induced by acid rain.
The effects of pH on other chemicals in water can be summarized as follows:
Heavy metals such as cadmium, lead, and chromium dissolve more easily in highly acidic water (lower pH). This is important because many heavy metals become much more toxic when dissolved in water [21].
A change in the pH can change the forms of some chemicals in the water. Therefore, it may affect aquatic plants and animals [21]. For instance, ammonia is relatively harmless to fish in neutral or acidic water. However, as the water becomes more alkaline (the pH increases), ammonia becomes progressively more poisonous to these same organisms.
Acidity is the measure of acids in a solution. The acidity of water is its quantitative capacity to neutralize a strong base to a selected pH level [10]. Acidity in water is usually due to carbon dioxide, mineral acids, and hydrolyzed salts such as ferric and aluminum sulfates [10]. Acids can influence many processes such as corrosion, chemical reactions and biological activities [10].
Carbon dioxide from the atmosphere or from the respiration of aquatic organisms causes acidity when dissolved in water by forming carbonic acid (H2CO3). The level of acidity is determined by titration with standard sodium hydroxide (0.02 N) using phenolphthalein as an indicator [10, 20].
The alkalinity of water is its acid-neutralizing capacity comprised of the total of all titratable bases [10]. The measurement of alkalinity of water is necessary to determine the amount of lime and soda needed for water softening (e.g., for corrosion control in conditioning the boiler feed water) [22]. Alkalinity of water is mainly caused by the presence of hydroxide ions (OH−), bicarbonate ions (HCO3−), and carbonate ions (CO32−), or a mixture of two of these ions in water. As stated in the following equation, the possibility of OH− and HCO3− ions together are not possible because they react together to produce CO32− ions:
Alkalinity is determined by titration with a standard acid solution (H2SO4 of 0.02 N) using selective indicators (methyl orange or phenolphthalein).
The high levels of either acidity or alkalinity in water may be an indication of industrial or chemical pollution. Alkalinity or acidity can also occur from natural sources such as volcanoes. The acidity and alkalinity in natural waters provide a buffering action that protects fish and other aquatic organisms from sudden changes in pH. For instance, if an acidic chemical has somehow contaminated a lake that had natural alkalinity, a neutralization reaction occurs between the acid and alkaline substances; the pH of the lake water remains unchanged. For the protection of aquatic life, the buffering capacity should be at least 20 mg/L as calcium carbonate.
Chloride occurs naturally in groundwater, streams, and lakes, but the presence of relatively high chloride concentration in freshwater (about 250 mg/L or more) may indicate wastewater pollution [7]. Chlorides may enter surface water from several sources including chloride-containing rock, agricultural runoff, and wastewater.
Chloride ions Cl− in drinking water do not cause any harmful effects on public health, but high concentrations can cause an unpleasant salty taste for most people. Chlorides are not usually harmful to people; however, the sodium part of table salt has been connected to kidney and heart diseases [25]. Small amounts of chlorides are essential for ordinary cell functions in animal and plant life.
Sodium chloride may impart a salty taste at 250 mg/L; however, magnesium or calcium chloride are generally not detected by taste until reaching levels of 1000 mg/L [10]. Standards for public drinking water require chloride levels that do not exceed 250 mg/L. There are many methods to measure the chloride concentration in water, but the normal one is the titration method by silver nitrate [10].
Chlorine (Cl2) does not occur naturally in water but is added to water and wastewater for disinfection [10]. While chlorine itself is a toxic gas, in dilute aqueous solution, it is not harmful to human health. In drinking water, a residual of about 0.2 mg/L is optimal. The residual concentration which is maintained in the water distribution system ensures good sanitary quality of water [11].
Chlorine can react with organics in water forming toxic compounds called trihalomethanes or THMs, which are carcinogens such as chloroform CHCl3 [11, 22]. Chlorine residual is normally measured by a color comparator test kit or spectrophotometer [10].
Sulfate ions (SO42−) occur in natural water and in wastewater. The high concentration of sulfate in natural water is usually caused by leaching of natural deposits of sodium sulfate (Glauber's salt) or magnesium sulfate (Epson salt) [11, 26]. If high concentrations are consumed in drinking water, there may be objectionable tastes or unwanted laxative effects [26], but there is no significant danger to public health.
There are four forms of nitrogen in water and wastewater: organic nitrogen, ammonia nitrogen, nitrite nitrogen, and nitrate nitrogen [10]. If water is contaminated with sewage, most of the nitrogen is in the forms of organic and ammonia, which are transformed by microbes to form nitrites and nitrates [22]. Nitrogen in the nitrate form is a basic nutrient to the growth of plants and can be a growth-limiting nutrient factor [10].
A high concentration of nitrate in surface water can stimulate the rapid growth of the algae which degrades the water quality [22]. Nitrates can enter the groundwater from chemical fertilizers used in the agricultural areas [22]. Excessive nitrate concentration (more than 10 mg/L) in drinking water causes an immediate and severe health threat to infants [19]. The nitrate ions react with blood hemoglobin, thereby reducing the blood's ability to hold oxygen which leads to a disease called blue baby or methemoglobinemia [10, 19].
A moderate amount of fluoride ions (F−) in drinking water contributes to good dental health [10, 19]. About 1.0 mg/L is effective in preventing tooth decay, particularly in children [10].
Excessive amounts of fluoride cause discolored teeth, a condition known as dental fluorosis [11, 19, 26]. The maximum allowable levels of fluoride in public water supplies depend on local climate [26]. In the warmer regions of the country, the maximum allowable concentration of fluoride for potable water is 1.4 mg/L; in colder climates, up to 2.4 mg/L is allowed.
There are four methods to determine ion fluoride in water; the selection of the used method depends on the type of water sample [10].
Although iron (Fe) and manganese (Mn) do not cause health problems, they impart a noticeable bitter taste to drinking water even at very low concentration [10, 11].
These metals usually occur in groundwater in solution as ferrous (Fe2+) and manganous (Mn2+) ions. When these ions are exposed to air, they form the insoluble ferric (Fe3+) and manganic (Mn3+) forms making the water turbid and unacceptable to most people [10].
These ions can also cause black or brown stains on laundry and plumbing fixtures [7]. They are measured by many instrumental methods such as atomic absorption spectrometry, flame atomic absorption spectrometry, cold vapor atomic absorption spectrometry, electrothermal atomic absorption spectrometry, and inductively coupled plasma (ICP) [10].
Copper (Cu) and zinc (Zn) are nontoxic if found in small concentrations [10]. Actually, they are both essential and beneficial for human health and growth of plants and animals [25]. They can cause undesirable tastes in drinking water. At high concentrations, zinc imparts a milky appearance to the water [10]. They are measured by the same methods used for iron and manganese measurements [10].
Hardness is a term used to express the properties of highly mineralized waters [10]. The dissolved minerals in water cause problems such as scale deposits in hot water pipes and difficulty in producing lather with soap [11].
Calcium (Ca2+) and magnesium (Mg2+) ions cause the greatest portion of hardness in naturally occurring waters [9]. They enter water mainly from contact with soil and rock, particularly limestone deposits [10, 27].
These ions are present as bicarbonates, sulfates, and sometimes as chlorides and nitrates [10, 26]. Generally, groundwater is harder than surface water. There are two types of hardness:
Temporary hardness which is due to carbonates and bicarbonates can be removed by boiling, and
Permanent hardness which is remaining after boiling is caused mainly by sulfates and chlorides [10, 21, 22]
Water with more than 300 mg/L of hardness is generally considered to be hard, and more than 150 mg/L of hardness is noticed by most people, and water with less than 75 mg/L is considered to be soft.
From health viewpoint, hardness up to 500 mg/L is safe, but more than that may cause a laxative effect [10]. Hardness is normally determined by titration with ethylene diamine tetra acidic acid or (EDTA) and Eriochrome Black and Blue indicators. It is usually expressed in terms of mg/L of CaCO3 [10, 19].
An accepted water classification according to its hardness is as in Table 2 [19].
|Types of water quality parameters
|Physical parameters
|Chemical parameters
|Biological parameters
|Taste and odor
|Chlorine residual
|Electrical conductivity (EC)
|Iron and manganese
|Copper and zinc
|Dissolved oxygen
|Biochemical oxygen demand (BOD)
|Chemical oxygen demand (COD)
|Toxic inorganic substances
|Toxic organic substances
|Radioactive substances
|Water classification
|Total hardness concentration as mg/L as CaCO3
|Soft water
|<50 mg/L as CaCO3
|Moderately hard
|50–150 mg/L as CaCO3
|Hard water
|150–300 mg/L as CaCO3
|Very hard
|>300 mg/L as CaCO3
Dissolved oxygen (DO) is considered to be one of the most important parameters of water quality in streams, rivers, and lakes. It is a key test of water pollution [10]. The higher the concentration of dissolved oxygen, the better the water quality.
Oxygen is slightly soluble in water and very sensitive to temperature. For example, the saturation concentration at 20°C is about 9 mg/L and at 0°C is 14.6 mg/L [22].
The actual amount of dissolved oxygen varies depending on pressure, temperature, and salinity of the water. Dissolved oxygen has no direct effect on public health, but drinking water with very little or no oxygen tastes unpalatable to some people.
There are three main methods used for measuring dissolved oxygen concentrations: the colorimetric method—quick and inexpensive, the Winkler titration method—traditional method, and the electrometric method [10].
Bacteria and other microorganisms use organic substances for food. As they metabolize organic material, they consume oxygen [10, 22]. The organics are broken down into simpler compounds, such as CO2 and H2O, and the microbes use the energy released for growth and reproduction [22].
When this process occurs in water, the oxygen consumed is the DO in the water. If oxygen is not continuously replaced by natural or artificial means in the water, the DO concentration will reduce as the microbes decompose the organic materials. This need for oxygen is called the biochemical oxygen demand (BOD). The more organic material there is in the water, the higher the BOD used by the microbes will be. BOD is used as a measure of the power of sewage; strong sewage has a high BOD and weak sewage has low BOD [22].
The complete decomposition of organic material by microorganisms takes time, usually 20 d or more under ordinary circumstances [22]. The quantity of oxygen used in a specified volume of water to fully decompose or stabilize all biodegradable organic substances is called the ultimate BOD or BODL.
BOD is a function of time. At time = 0, no oxygen will have been consumed and the BOD = 0. As each day goes by, oxygen is used by the microbes and the BOD increases. Ultimately, the BODL is reached and the organic materials are completely decomposed.
A graph of the BOD versus time is illustrated as in Figure 3. This is called the BOD curve, which can be expressed mathematically by the following equation:
where BODt = BOD at any time t, mg/L; BODL = ultimate BOD, mg/L; k = a constant representing the rate of the BOD reaction; t = time, d.
The value of the constant rate k depends on the temperature, the type of organic materials, and the type of microbes exerting the BOD [22].
The chemical oxygen demand (COD) is a parameter that measures all organics: the biodegradable and the non-biodegradable substances [22]. It is a chemical test using strong oxidizing chemicals (potassium dichromate), sulfuric acid, and heat, and the result can be available in just 2 h [10]. COD values are always higher than BOD values for the same sample [22].
A wide variety of inorganic toxic substances may be found in water in very small or trace amounts. Even in trace amounts, they can be a danger to public health [11]. Some toxic substances occur from natural sources but many others occur due to industrial activities and/or improper management of hazardous waste [22]. They can be divided into two groups:
Metallic compounds: This group includes some heavy metals that are toxic, namely, cadmium (Cd), chromium (Cr), lead (Pb), mercury (Hg), silver (Ag), arsenic (As), barium (Ba), thallium (Tl), and selenium (Se) [22, 28]. They have a wide range of dangerous effects that differ from one metal to another. They may be acute fatal poisons such as (As) and (Cr6+) or may produce chronic diseases such as (Cd, Hg, Pb, and Tl) [21, 29, 30, 31, 32]. The heavy metals concentration can be determined by atomic absorption photometers, spectrophotometer, or inductively coupled plasma (ICP) for very low concentration [10].
Nonmetallic compounds: This group includes nitrates (NO3−) and cyanides (CN−), nitrate has been discussed with the nitrogen in the previous section. Regarding cyanide, as Mackenzie stated [11] it causes oxygen deprivation by binding the hemoglobin sites and prevents the red blood cell from carrying the oxygen [11]. This causes a blue skin color syndrome, which is called cyanosis [33]. It also causes chronic effects on the central nervous system and thyroid [33]. Cyanide is normally measured by colorimetric, titrimetric, or electrometric methods [10].
There are more than 100 compounds in water that have been listed in the literature as toxic organic compounds [11, 22]. They will not be found naturally in water; they are usually man-made pollutants. These compounds include insecticides, pesticides, solvents, detergents, and disinfectants [11, 21, 22]. They are measured by highly sophisticated instrumental methods, namely, gas chromatographic (GC), high-performance liquid chromatographic (HPLC), and mass spectrophotometric [10].
Potential sources of radioactive substances in water include wastes from nuclear power plants, industries, or medical research using radioactive chemicals and mining of uranium ores or other radioactive materials [11, 21]. When radioactive substances decay, they release beta, alpha, and gamma radiation [34]. Exposure of humans and other living things to radiation can cause genetic and somatic damage to the living tissues [34, 35].
Radon gas is of a great health concern because it occurs naturally in groundwater and is a highly volatile gas, which can be inhaled during the showering process [35]. For drinking water, there are established standards commonly used for alpha particles, beta particles, photons emitters, radium-226 and -228, and uranium [34, 35].
The unit of radioactivity used in water quality applications is the picocurie per liter (pCi/L); 1 pCi is equivalent to about two atoms disintegrating per minute. There are many sophisticated instrumental methods to measure it [35].
One of the most helpful indicators of water quality may be the presence or lack of living organisms [10, 15]. Biologists can survey fish and insect life of natural waters and assess the water quality on the basis of a computed species diversity index (SDI) [15, 19, 36, 37]; hence, a water body with a large number of well-balanced species is regarded as a healthy system [17]. Some organisms can be used as an indication for the existence of pollutants based on their known tolerance for a specified pollutant [17].
Microorganisms exist everywhere in nature [38]. Human bodies maintain a normal population of microbes in the intestinal tract; a big portion of which is made up of coliform bacteria [38]. Although there are millions of microbes per milliliter in wastewater, most of them are harmless [37]. It is only harmful when wastewater contains wastes from people infected with diseases that the presence of harmful microorganisms in wastewater is likely to occur [38].
Bacteria are considered to be single-celled plants because of their cell structure and the way they ingest food [10, 37]. Bacteria occur in three basic cell shapes: rod-shaped or bacillus, sphere-shaped or coccus, and spiral-shaped or spirellus [19]. In less than 30 min, a single bacterial cell can mature and divide into two new cells [39].
Under favorable conditions of food supply, temperature, and pH, bacteria can reproduce so rapidly that a bacterial culture may contain 20 million cells per milliliter after just 1 day [22, 37]. This rapid growth of visible colonies of bacteria on a suitable nutrient medium makes it possible to detect and count the number of bacteria in water [39].
There are several distinctions among the various species of bacteria. One distinction depends on how they metabolize their food [38]. Bacteria that require oxygen for their metabolism are called aerobic bacteria, while those live only in an oxygen-free environment are called anaerobic bacteria. Some species called facultative bacteria can live in either the absence or the presence of oxygen [37, 38, 39].
At low temperatures, bacteria grow and reproduce slowly. As the temperature increases, the rate of growth and reproduction doubles in every additional 10°C (up to the optimum temperature for the species) [38]. The majority of the species of bacteria having an optimal temperature of about 35°C [39].
A lot of dangerous waterborne diseases are caused by bacteria, namely, typhoid and paratyphoid fever, leptospirosis, tularemia, shigellosis, and cholera [19]. Sometimes, the absence of good sanitary practices results in gastroenteritis outbreaks of one or more of those diseases [19].
Algae are microscopic plants, which contain photosynthetic pigments, such as chlorophyll [37, 39]. They are autotrophic organisms and support themselves by converting inorganic materials into organic matter by using energy from the sun, during this process they take in carbon dioxide and give off oxygen [38, 39]. They are also important for wastewater treatment in stabilization ponds [22]. Algae are primarily nuisance organisms in the water supply because of the taste and odor problems they create [2, 16]. Certain species of algae cause serious environmental and public health problems; for example, blue-green algae can kill cattle and other domestic animals if the animals drink water containing those species [37, 39].
Viruses are the smallest biological structures known to contain all genetic information necessary for their own reproduction [19]. They can only be seen by a powerful electronic microscope [39]. Viruses are parasites that need a host to live [39]. They can pass through filters that do not permit the passage of bacteria [37]. Waterborne viral pathogens are known to cause infectious hepatitis and poliomyelitis [19, 25, 37]. Most of the waterborne viruses can be deactivated by the disinfection process conducted in the water treatment plant [19].
Protozoa are single-celled microscopic animal [19], consume solid organic particles, bacteria, and algae for food, and they are in turn ingested as food by higher level multicellular animals [37]. Aquatic protozoa are floating freely in water and sometimes called zooplankton [37]. They form cysts that are difficult to inactivate by disinfection [19].
A very important biological indicator of water and pollution is the group of bacteria called coliforms [20]. Pathogenic coliforms always exist in the intestinal system of humans, and millions are excreted with body wastes [37]. Consequently, water that has been recently contaminated with sewage will always contain coliforms [19].
A particular species of coliforms found in domestic sewage is Escherichia coli or E. coli [22]. Even if the water is only slightly polluted, they are very likely to be found. There are roughly 3 million of E. coli bacteria in 100 mL volume of untreated sewage [10]. Coliform bacteria are aggressive organisms and survive in the water longer than most pathogens. There are normally two methods to test the coliform bacteria—the membrane filter method and multiple-tube fermentation method [10, 37]. Since the test of coliform bacteria is very important for public health, the first method will be described in details in the coming section.
A measured volume of sample is filtered through a special membrane filter by applying a partial vacuum [10, 39].
The filter, a flat paper-like disk, has uniform microscopic pores small enough to retain the bacteria on its surface while allowing the water to pass through. The filter paper is then placed in a sterile container called a petri dish, which contains a special culture medium that the bacteria use as a food source [39].
Then, the petri dish is usually placed in an incubator, which keeps the temperature at 35°C, for 24 h. After incubation, colonies of coliform bacteria each containing millions of organisms will be visible [10]. The coliform concentration is obtained by counting the number of colonies on the filter; each colony counted represents only one coliform in the original sample [10, 39].
Coliform concentrations are expressed in terms of the number of organisms per 100 mL of water as follows:
Water quality requirements differ depending on the proposed used of water [19]. As reported by Tchobanoglous et al. [19], "water unsuitable for one use may be quite satisfactory for another and water may be considered acceptable for a particular use if water of better quality is not available."
Water quality requirements should be agreed with the water quality standards, which are put down by the governmental agency and represent the legislation requirements. In general, there are three types of standards: in-stream, potable water, and wastewater effluent [19], each type has its own criteria by using the same methods of measurement. The World Health Organization (WHO) has established minimum standards for drinking water that all countries are recommended to meet [25].
The physical, chemical, and biological parameters of water quality are reviewed in terms of definition, sources, impacts, effects, and measuring methods. The classification of water according to its quality is also covered with a specific definition for each type.
In our mission to support the dissemination of knowledge, we travel throughout the world to present our publications, support our Authors and Academic Editors at international symposia, conferences, and workshops, as well as to attend business meetings with science, academic and publishing professionals. We are always happy to meet our contributors in our offices to discuss further collaborations. Take a look at where we've been, who we've met and where we're going.",metaTitle:"IntechOpen events",metaDescription:"In our mission to support the dissemination of knowledge, we travel worldwide to present our publications, authors and editors at international symposia, conferences, and workshops, as well as attend business meetings with science, academia and publishing professionals. We are always happy to host our scientists in our office to discuss further collaborations. Take a look at where we've been, who we've met and where we're going.",metaKeywords:null,canonicalURL:"/page/events",contentRaw:'[{"type":"htmlEditorComponent","content":"
16-18 June 2020 Smart University Forum #SUF20, Ankara, Turkey\\n\\n
19 December 2019 IntechOpen and Enago webinar: A Guide to Academic Book Writing and Publishing\\n\\n
27-28 November 2019 14th Munin Conference on Scholarly Publishing, UiT The Arctic University of Norway, Tromsø campus\\n\\n
23 October 2019 Break on Through to the Open Side - Global Online Panel Discussion\\n\\n
21 October 2019 SUFTalks - Istanbul, Sheraton Istanbul Ataköy Hotel, Istanbul, Turkey\\n\\n
21-27 October 2019 OPEN ACCESS WEEK\\n\\n
16-20 October 2019 Frankfurt Book Fair\\n\\n
10 October 2019 IntechOpen - Knowledge Unlatched Lecture & Drinks, The Shard, London, United Kingdom\\n\\n
16-18 September, 2019 Open Science Fair Porto, Portugal\\n\\n
12 September, 2019 ALPSP Annual Conference and Awards, De Vere Beaumont Estate, Old Windsor, United Kingdom. Panel: New Horizons in Open Research: Innovation in open books\\n\\n
27-28 August, 2019 Smart University Foundation, Ankara, Turkey\\n\\n
18-19 July, 2019 Spanish Research Council Meeting, Madrid, Spain\\n\\n
12 July, 2019 Lectures, Kings College London Waterloo Campus, United Kingdom Lecture. 1: Intro to Robotics (focus on robotics in the area of health) Lecture. 2: My career in publishing, my background, scientific open access publication and IntechOpen\\n\\n
24 June, 2019 Atypon Conference, Munich. The Future of Books\\n\\n
20-22 May, 2019 IntechOpen attends series of talks "Pint of Science Festival", Bilbao. Glass and Txakoli, Nanoscience and Nanotechnology, Autonomous Cars\\n\\n
7 March, 2019 Humans in Space Lecture Series, Shard London\\n\\n
13 September, 2018 Peer Review Week, King's College London\\n\\n
1-5 October, 2018 IROS Conference, Madrid. Legal issues, Cybersecurity and Policy in AI robotics, Interview with Prof. George Dekoulis\\n\\n
24-26 April, 2018 STM US 2018\\n\\n
19-23 March, 2018 Immuno Oncology Summit\\n\\n
19 March, 2018 Earth dynamics and the development of plate tectonics scientific meeting\\n\\n
15 March, 2018 5th symposium on molecular pathology in oncology – Hot Topics\\n\\n
13-14 March, 2018 WIRED Health\\n"}]'},components:[{type:"htmlEditorComponent",content:'
16-18 June 2020 Smart University Forum #SUF20, Ankara, Turkey\n\n
19 December 2019 IntechOpen and Enago webinar: A Guide to Academic Book Writing and Publishing\n\n
27-28 November 2019 14th Munin Conference on Scholarly Publishing, UiT The Arctic University of Norway, Tromsø campus\n\n
23 October 2019 Break on Through to the Open Side - Global Online Panel Discussion\n\n
21 October 2019 SUFTalks - Istanbul, Sheraton Istanbul Ataköy Hotel, Istanbul, Turkey\n\n
21-27 October 2019 OPEN ACCESS WEEK\n\n
16-20 October 2019 Frankfurt Book Fair\n\n
10 October 2019 IntechOpen - Knowledge Unlatched Lecture & Drinks, The Shard, London, United Kingdom\n\n
16-18 September, 2019 Open Science Fair Porto, Portugal\n\n
12 September, 2019 ALPSP Annual Conference and Awards, De Vere Beaumont Estate, Old Windsor, United Kingdom. Panel: New Horizons in Open Research: Innovation in open books\n\n
27-28 August, 2019 Smart University Foundation, Ankara, Turkey\n\n
18-19 July, 2019 Spanish Research Council Meeting, Madrid, Spain\n\n
12 July, 2019 Lectures, Kings College London Waterloo Campus, United Kingdom Lecture. 1: Intro to Robotics (focus on robotics in the area of health) Lecture. 2: My career in publishing, my background, scientific open access publication and IntechOpen\n\n
24 June, 2019 Atypon Conference, Munich. The Future of Books\n\n
20-22 May, 2019 IntechOpen attends series of talks "Pint of Science Festival", Bilbao. Glass and Txakoli, Nanoscience and Nanotechnology, Autonomous Cars\n\n
7 March, 2019 Humans in Space Lecture Series, Shard London\n\n
13 September, 2018 Peer Review Week, King's College London\n\n
1-5 October, 2018 IROS Conference, Madrid. Legal issues, Cybersecurity and Policy in AI robotics, Interview with Prof. George Dekoulis\n\n
24-26 April, 2018 STM US 2018\n\n
19-23 March, 2018 Immuno Oncology Summit\n\n
19 March, 2018 Earth dynamics and the development of plate tectonics scientific meeting\n\n
15 March, 2018 5th symposium on molecular pathology in oncology – Hot Topics\n\n
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