\r\n\tDegeneration of photoreceptors, the major light-sensing cells in the eye, is a primary cause of vision loss worldwide. Identifying the underlying causes surrounding photoreceptor cell death is dominant in order to develop new treatment strategies to prevent their loss. These pathologies can be roughly divided into those conditions that initially affect rod photoreceptors, such as retinitis pigmentosa, and those that initially affect cone photoreceptors, such as macular degeneration. Retinitis pigmentosa is a group of diseases in which a mutation in one of the large variety of genes causes death of rod photoreceptors. \r\n\tRetinal detachment and subsequent degeneration of the retina can lead to progressive visual decline due to photoreceptor cell death. Since photoreceptors are nondividing cells, their loss results in irreversible visual impairment even after successful retinal reattachment surgery. \r\n\tOxidative stress and free radical damage also impact on the photoreceptors and retinal pigmented epithelium cells in the ageing eye. Nevertheless, drug delivery to the neuroretina, and even more so to the retinal photoreceptors, still has inherent and important challenges that must be analyzed. \r\n\tThis book intends to provide the reader with a comprehensive overview of recent advances in Photoreceptors research. The book will cover the following topics:
\r\n
\r\n\t1. Structure And Function Of Photoreceptors \r\n\t2. Retinitis pigmentosa \r\n\t3. Macular degeneration \r\n\t4. Light-induced photoreceptor cell damage \r\n\t5. Pathologies associated to Photoreceptors \r\n\t6. Drug delivery to retinal photoreceptors
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"6a73b3bad3f7278cabee8bd09a89d679",bookSignature:"Prof. Angel Catala",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8936.jpg",keywords:"photoreceptor, phototransduction, progressive vision loss, night blindness, age-related macular degeneration, basal laminar deposit, apoptosis, disulfide dimerization, genotype-phenotype correlation, inherited retinal dystrophy, liposomes, niosomes",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 27th 2019",dateEndSecondStepPublish:"September 17th 2019",dateEndThirdStepPublish:"November 16th 2019",dateEndFourthStepPublish:"February 4th 2020",dateEndFifthStepPublish:"April 4th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"196544",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",profilePictureURL:"https://mts.intechopen.com/storage/users/196544/images/system/196544.jpg",biography:"Angel Catalá was born in Rodeo (San Juan, Argentina). He studied chemistry at Universidad Nacional de La Plata, Argentina, where he received a PhD in Chemistry (Biological Branch) in 1965. From 1964 to 1974, he worked as Assistant in Biochemistry at the School of Medicine at the same university. From 1974 to 1976, he was a fellow of the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as Full Professor of Biochemistry at the Universidad Nacional de La Plata. He is a member of the National Research Council (CONICET), Argentina, and Argentine Society for Biochemistry and Molecular Biology (SAIB). His laboratory has been interested for many years in the lipid peroxidation of biological membranes from various tissues and different species. Dr. Catalá has directed twelve doctoral theses, published more than 100 papers in peer-reviewed journals, several chapters in books, and edited twelve books. He received awards at the 40th International Conference Biochemistry of Lipids 1999 in Dijon (France). He is winner of the Bimbo Pan-American Nutrition, Food Science and Technology Award 2006 and 2012, South America, Human Nutrition, Professional Category. In 2006, he won the Bernardo Houssay award in pharmacology, in recognition of his meritorious works of research. Dr. Catalá belongs to the editorial board of several journals including Journal of Lipids; International Review of Biophysical Chemistry; Frontiers in Membrane Physiology and Biophysics; World Journal of Experimental Medicine and Biochemistry Research International; World Journal of Biological Chemistry, Diabetes and the Pancreas; International Journal of Chronic Diseases & Therapy; and International Journal of Nutrition. 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1. Introduction
When you read a standard textbook on thermodynamics (like [1-3]) as one of the most fundamental formulae you will find
δQ=TdSE1
indicating that the (process) quantity heat (δQ) is obviously closely linked to the (state) quantity entropy (dS), here both written as infinitesimal quantities.
If, however, you do the same with a standard textbook on heat transfer (like [4] with 1024 pages or [5] with 1107 pages), you will find entropy neither in the index of these books, nor in the text.
There may be two reasons for that: Either entropy has turned out to be irrelevant for a heat transfer analysis or entropy is ignored deliberately in the heat transfer community in spite of its relevance. What is true is a yet open question and can only be answered when thermodynamic considerations are taken into account.
In thermodynamics the relevance of entropy with respect to heat transfer is beyond any controversy, it is the heat transfer community that has to be persuaded of its relevance. This can best be done by showing the advantages of including entropy in a heat transfer analysis as well as showing the disadvantages one has to face when entropy is ignored.
2. A thermodynamic view on heat transfer
2.1. General considerations
Engineers, using the phrase “heat transfer”, would not be bothered by the view that heat is moved across the boundary of a system and then stored in it, increasing its heat content.
This line of argument, however, violates at least two principles of thermodynamics and misses the crucial point. From a thermodynamics point of view heat is a process quantity that describes a certain way by which energy can be transferred across the boundary of a system. And of course this quantity cannot be stored, only the energy moved by it can be stored.
And the crucial point is: Transferring energy as heat into a system is fundamentally different from doing the same by work. The energy transferred in form of heat and work, though it may be the same amount, has a very different quality once it is part of the energy of the system. To put it in a simple and not yet precise form for the moment: It is not only the amount of energy that counts in energy transfer processes (like heat transfer) but also the quality of the energy and the change in quality during the transfer process. If that is true, there must be a measure for the quality and its potential degradation in energy transfer processes. This is where entropy comes in and plays a crucial role – even and also in heat transfer considerations.
From the very clear principle of energy conservation (thermodynamically formulated as the first law of thermodynamics) we know that energy given as primary energy never gets lost when used in technical devices but finally ends up as part of the internal energy of the ambient. Then, however, it is of no use anymore. Obviously energy has a certain potential that can get lost on the way from being primary energy to being part of the internal energy of the ambient.
In thermodynamics there is a useful definition by which the quality of an energy can be characterized which was first proposed in [6]. This definition primarily refers to an energy which is subject to transfer processes either by work or by heat. According to this definition energy is composed of two parts, exergy and anergy. Within this concept exergy is the precious part of the energy. It is that part which can be used by work until it is part of the internal energy of the ambient. Sometimes exergy is also called available work. The remaining part of the energy is called anergy. According to the second law of thermodynamics exergy can get lost (can be converted to anergy) in irreversible processes but never can be generated. Any transfer of energy by work or by heat thus can either preserve the exergy part of the energy in a reversible process or reduce it in an irreversible one.
As far as heat transfer is concerned there are two aspects that are important: The first is the amount of energy transferred by heat and the second is the amount of exergy lost in this (heat) transfer process. Ignoring entropy means that only the first aspect can be accounted for. For a complete characterization of a heat transfer process both aspects have to be accounted for, i.e. two physical quantities have to be specified. They can be
the heat flux q˙=δQ˙/dA\n\t\t\t\t\t\t
a characteristic temperature difference ΔT\n\t\t\t\t\t\t
In a heat transfer process both quantities are independent of each other because a certain amount of energy (q˙) can be transferred with a different decrease of quality, i.e. with a different degree of irreversibility (ΔT). Here ΔT is an indirect measure of the quality decrease of the energy in the transfer process since ΔT=0 is the reversible limit of an irreversible process with ΔT>0. When two independent quantities are required then two nondimensional parameters are needed in the context of describing heat transfer processes nondimensionally. In section 3 it will be discussed what is missing when the Nusselt number Nu alone is used in order to characterize a heat transfer process.
In thermodynamics the two aspects of energy transfer and its devaluation by irreversible processes are quantified by introducing the entropy and its generation in the course of irreversible processes. In this context entropy is a measure of the structure of the system storing the energy under consideration, i.e. energy can be stored in a more or less ordered way. This again can be expressed in terms of exergy versus anergy of the energy transferred and stored.
2.2. Change of entropy in energy transfer processes
For most considerations the absolute value of entropy is not of interest but its change during a certain process like a heat transfer process. This change of entropy in a transfer process generally is twofold:
Transfer - change of entropy in a reversible process,
Generation - change of entropy when the transfer process is not reversible, i.e. irreversible.
In a real (irreversible) process the change of entropy thus always is the sum of both, i.e. (i) + (ii).
For a heat transfer process between two temperature levels Ta and Tb the two parts (i) and (ii) are
dtS˙=δQ˙(Ta+Tb)/2E2
dgS˙=δQ˙(1Ta−1Tb)=δQ˙Ta−TbTaTb=δQ˙ΔTTaTbE3
Equation (2) corresponds to eq. (1) in the introduction, now in terms of rates for a continuous process. Equation (3) states that entropy generation leads to an increase of entropy when the energy is transferred from one system (a) with high temperature (i.e. low entropy) to another system (b) with low temperature (i.e. high entropy). Thus the overall change of entropy in such a process is
dS˙=dtS˙+dgS˙E4
In figure 1 such a process is illustrated for the convective heat transfer from a flow in system (a) with m˙a to a flow in system (b) with m˙b. The wall between both flows is diabatic, walls to the ambient are adiabatic.
The generation-change of entropy in eq. (3) strictly speaking is an approximation only. It is based on the assumption that in (a) and in (b) the real temperature distributions can be approximated by their (constant) mean values and that the temperature drop from (a) to (b) completely happens in the wall between both systems, see figure 1 for an illustration of this approximation. In section 4 the real temperature distribution is accounted for in order to determine the generation-change of entropy without approximation.
Though it is not the topic of this chapter it should be mentioned what (i) and (ii) are for an energy transfer by work:
dtS˙=0E5
dgS˙=δΦ˙/TE6
with δΦ˙ as dissipation rate of mechanical energy in the flow field involved in the transfer process. That always dtS˙=0 holds for a work transfer of energy shows the fundamental difference of the two ways to transfer energy, i.e. by heat or by work, c.f. eq. (2) for the energy transfer by heat.
Figure 1.
Convective heat transfer from a flow in (a) to a flow in (b) over a surface element dA (1) Real temperature distribution (2) Mean temperature model
2.3. Energy devaluation in a heat transfer process and the entropic potential concept
When in an energy transfer process exergy gets lost the “value” of the energy is reduced, since exergy as the precious part of the energy is reduced. This is called energy devaluation during a transfer process and is immediately linked to the generation-change of entropy, c.f. eq. (3).
Exergy lost and entropy generated are interrelated by the so-called Gouy-Stodola theorem, see for example [7]. It reads
dE˙le=T∞dgS˙E7
Here T∞ is the ambient temperature and E˙le is the loss of the exergy rate E˙e of the energy rate E˙, after subdividing E˙ into an exergy and an anergy part, E˙e and E˙a, respectively.
For a single transfer operation indicated by i then there is the finite exergy loss
E˙l,ie=T∞S˙g,iE8
with S˙g,i as entropy generation in the transfer operation i. This entropy generation can and should be seen in the context of those devaluations of the energy transfer rate E˙ that happened prior to the transfer operation i and that will happen afterwards. This idea takes into account that a certain energy (rate) always starts as primary energy being exergy as a whole and finally ends up as part of the internal energy of the ambient, then being anergy as a whole. In [8] this has been described as the “devaluation chain” with respect to the energy transfer rate E˙ with the process i being one link of this chain.
For the sum of all single transfer operations that completely devaluates the energy from 100% exergy to 100% anergy then
E˙le=E˙=T∞S˙gE9
holds. Here S˙g is the overall entropy generation (rate), i.e. the entropy increase of the ambient, when E˙ becomes part of its internal energy.
In [8] this quantity is called the entropic potential:
S˙g=E˙T∞E10
of the energy E˙ involved in an energy (here: heat) transfer process. Taking this as a reference quantity the so-called energy devaluation number
Ni≡S˙g,iS˙g=T∞S˙g,iE˙E11
indicates how much of the entropic potential of the energy is used in a certain transfer process i with Ni=0 for a reversible process. Examples will be given afterwards.
3. An engineering view on heat transfer
As mentioned before, engineers trained to solve heat transfer problems with books like [4] care little or not at all about entropy. They characterize heat transfer situations by the heat transfer coefficient
h=q˙wΔTE12
or in a more systematic way by the Nusselt number
Nu=q˙wLk ΔT=hLkE13
In both cases q˙w and ΔT are combined within one assessment quantity so that the two independent aspects of heat transfer
the amount, associated with q˙w and
the change of quality, associated with ΔT\n\t\t\t\t\t
are not captured separately. A second assessment quantity is required for a comprehensive characterization of a heat transfer situation. This can be the energy devaluation number Ni according to eq. (11).
When Ni accounts for the quality of heat transfer the Nusselt number Nu covers the quantitative aspect in the following sense. Often either ΔT or q˙w are prescribed as a thermal boundary condition. Then the Nusselt number quantifies the heat transfer by providing the heat flux that occurs or the temperature difference that is required, respectively. Both are quantitative aspects leaving the question about the quality still open. This then is addressed by the energy devaluation number Ni.
Since the Nusselt number Nu is well established in the heat transfer community, but the energy devaluation number Ni is not, Niwill be further explained with respect to its physical background in the following section.
4. The physics behind the energy devaluation number
According to Fourier’s law of heat conduction, see for example [4] or [9],
δQ˙→=−k(grad T) dAE14
i.e. a heat flux occurs along the (negative) gradient of temperature. The energy transferred in this way reduces its exergy part because this exergy part is
Q˙e=ηcQ˙E15
with the Carnot factor
ηc=1−T∞TE16
Here again T∞ is the ambient temperature, so that the exergy part of Q˙ once its temperature level T has reached the ambient temperature, is zero.
This permanent exergy loss when heat transfer occurs with gradT>0 (irreversible heat transfer) according to the Gouy-Stodola theorem (7) is accompanied by entropy generation which here can be written as
S˙g\'\'\'=kT2(grad T)2E17
or after integrating the local entropy generation rate S˙g\'\'\' as
dgS˙=kT2(grad T)2dVE18
which in Cartesian coordinates reads
dgS˙=kT2[(∂T∂x)2+(∂T∂y)2+(∂T∂z)2]dVE19
Note that this eq. (19) reduces to eq. (3) when there is a linear temperature distribution in x -direction only so that ∂T/∂x=ΔT/Δx, dV=dAΔx and ∂Q˙=−k(ΔT/Δx)dA.
in the mean temperature model according to eq. (3) and figure 1(2) is an integration with respect to δQ˙ while with the real temperature distribution according to eq. (19) and figure 1 (1) it is an integration with respect to the volume accounting for the local entropy generation rate.
In both cases S˙g,i is determined which is the overall entropy generation due to heat conduction in a transfer process i. The energy devaluation number refers this to the entropic potential of Q˙, i.e. to Q˙/T∞, so that
Ni=kT∞Q˙∫V1T2[(∂T∂x)2+(∂T∂y)2+(∂T∂z)2]dVE21
is that percentage used of the entropic potential of the energy E˙ which in a process i is transferred as heat Q˙. Note that part of the entropic potential has been used already on the way of E˙ starting as primary energy to the situation in which it is transferred as heat and that the remaining part of the entropic potential after the heat transfer process i can be used in subsequent energy transfer processes. This may illustrate why it is important to see a certain transfer process i in the context of the overall devaluation chain of an energy starting as primary energy and ending as part of the internal energy of the ambient, for more details of this concept see [8].
5. Convective heat transfer
Often convective heat transfer occurs in technical applications like power plants and heating or cooling systems. Then a second energy flux is involved which is the flow work rate that is needed to maintain the flow into which or out of which the heat transfer occurs. This energy flux applied as work is pure exergy which gets lost in the dissipation process during the convective heat transfer.
5.1. Losses due to dissipation of mechanical energy
In fluid mechanics losses in a flow field usually are characterized by a drag coefficient cD for external flows and a head loss coefficient K for internal flows, which are a nondimensional drag force FD and a nondimensional pressure loss Δp, respectively. In table 1 both definitions are shown together with an alternative approach based on the entropy generation rate S˙g,D due to the dissipation of mechanical energy (index: D). For details of this alternative approach see [10]. Since both coefficients, cD and K, account for the dissipation rate Φ ˙ in the flow field and according to eq. (6)\n\t\t\t\t\tδΦ˙=TdgS˙ the dissipation of mechanical energy corresponds to the loss of exergy only when T=T∞, c.f. eq. (7). Whenever the flow occurs on a temperature level which is not that of the ambient temperature T∞, cD and K account for the dissipation but not for the lost exergy in the flow.
Then a second coefficient is needed which best is defined as an exergy destruction number\n\t\t\t\t\tNE analogous to the energy devaluation number, eq. (11), i.e.
NE=T∞S˙g,DE˙E22
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t
conventional approach
\n\t\t\t
alternative approach
\n\t\t
\n\t\t
\n\t\t\t
external flow
\n\t\t\t
cD=FDρ2u∞2A
\n\t\t\t
cD=Tρ2u∞3AS˙g,D
\n\t\t
\n\t\t
\n\t\t\t
internal flow
\n\t\t\t
K=Δpρ2um2
\n\t\t\t
K=Tρ2um3AS˙g,D
\n\t\t
\n\t
Table 1.
Drag and head loss coefficients; conventional and alternative definitions, from [10]. u∞: free-stream velocity, um: cross-section averaged velocity
which for an external flow with E˙=u∞22m˙=ρ2u∞3A is (c.f. table 1):
NE=T∞TcD(exergy destruction number)E23
and for an internal flow with E˙=um22m˙=ρ2um3A is (c.f. table 1):
NE=T∞TK(exergy destruction number)E24
Note that NE is not an energy devaluation number in the sense of its definition in eq. (11) because the reference quantity E˙ in eq. (22) is not an energy transfer rate (that might be devaluated during the transfer process). Instead it is the kinetic energy involved in the convective process. It serves as a reference quantity for the flow work required to maintain the flow.
Different from Ni according to eq. (11), for which by definition always 0≤Ni≤1 holds, NEis not restricted to this range. For example NE=3 for an internal flow means that the exergy loss (exergy destructed) during this process is three times higher than the kinetic energy involved in the convective process. Note that it is not the kinetic energy that is devaluated but the energy that enters the system as flow work, being pure exergy at the beginning and partly or totally converted to anergy by the dissipation process.
5.2. Assessing convective heat transfer
Since both energies in a convective heat transfer process (flow work needed and thermal energy transferred) are subjected to devaluation they should both be accounted for when a convective heat transfer process is assessed, for example for the purpose of its optimization.
In terms of losses what counts is the lost exergy in both energies that are involved in the convective heat transfer process. These exergy losses are characterized by the corresponding entropy generation rates S˙g,i in eq. (11) and S˙g,D in eq. (22). They can be added to provide the overall entropy generation rate in a convective heat transfer process and serve as a target quantity in an optimization procedure. This is a reasonable criterion for all those cases in which the exergy part of energy transfer process counts like for a power cycle. In such a process exergy lost ahead of the turbine cannot be converted to mechanical energy in the turbine and thus reduces the efficiency of the power cycle.
When the entropy generation rates should be determined from detailed numerical solutions of a convective heat transfer process, S˙g,i follows from eqs. (19), (20) while S˙g due to dissipation is determined by
When the flow is turbulent, dgS˙ according to eqs. (19) and (26) are adequate only for a direct numerical simulation (DNS) approach with respect to the turbulence, as for the example shown in [11]. Since DNS solutions with their extraordinary computational demand cannot be used for solving technical problems, the time-averaged equations (Reynolds-averaged Navier-Stokes: RANS) are solved instead. Then, also dgS˙ has to be time averaged, leading to:
dgS˙C=dgS˙C¯+dgS˙C\'E27
and
dgS˙D=dgS˙D¯+dgS˙D\'E28
with dgS˙C¯ and dgS˙D¯ for the entropy generation in the time-averaged temperature and velocity field as well as dgS˙C\' and dgS˙D\' for the time averaged contributions of the corresponding fluctuating parts.
with the results for a turbulent flow field from RANS equations, dgS˙C¯ and dgS˙D¯ can be determined, but not dgS˙C\' and dgS˙D\'. For these terms turbulence models are needed, as for examples discussed in [12].
5.3. Nondimensional parameters
When the whole process of a convective heat transfer should be assessed (comprising the exergy loss in the temperature and in the flow field) that again should be done by means of nondimensional parameters. The nondimensional parameters introduced so far are:
NusseltnumberNu / eq. (13), indicating the strength of heat transfer versus its irreversibility;
Energy devaluation number\n\t\t\t\t\t\t\tNi / eq. (11), indicating the loss of entropic potential of the transferred energy;
HeadlosscoefficientK / table 1, indicating the dissipation rate in the flow field;
ExergydestructionnumberNE / eq. (24), indicating the loss of exergy in the flow field.
If now the overall exergy loss for a convective heat transfer process is of interest this basically is the sum of the effects covered by Ni and NE. Since both parameters are not nondimensionalized in the same way, however, they cannot simply be added. Note that Ni refers to the transferred energy which for the convective heat transfer is Q˙, whereas in NE the kinetic energy of the fluid flow is used as a reference quantity.
For an overall assessment of a convective heat transfer process we now refer the sum of the exergy losses (in the temperature and in the flow field) to the exergy transferred in the process, which is ηcQ˙, c.f. eq. (15), and thus introduce the
With the help of eq. (33) it can be decided whether the increase of the Nusselt number by a certain technique to improve the heat transfer, like adding turbulence promoters, roughening of the wall or simply increasing the flow rate, is beneficial from the perspective of exergy conservation. When N^E is decreased, more available work is left and the increase of Nu is beneficial.
Since a device with a small N^E obviously is more efficient than one with a larger N^E, an
overallefficiencyfactor:N^E=1−N^EE34
was introduced in [13] which is η^E=1 for a perfect (thermodynamically reversible) process without any exergy loss and η^E=0 for a process in which all exergy gets lost because it is converted to anergy.
6. Examples
Two examples will be given in which the parameters that were introduced above will be used in order to characterize the heat transfer situation. With these examples it should become obvious that entropy and/or its generation should not be ignored when heat transfer processes are considered in practical industrial applications.
6.1. Fully developed pipe flow with heat transfer
This simple example may illustrate how important it is to account for entropy generation which is the crucial aspect in the energy devaluation number\n\t\t\t\t\tNi according to its definition (11).
What usually can be found as the characterization of the heat transfer performance of a fully developed pipe flow is the Nusselt number Nu. Let’s assume it is Nu=100 and it occurs on the upper temperature level of a power cycle, i.e. ahead of the turbine of this energy conversion device. Let’s also assume that this heat transfer situation with Nu=100 and a heat flux q˙w=103W/m2 on a length L=0,1m occurs in two different power cycles:
A steam power cycle (SPC) with water as the working fluid and an upper temperature level Tm,u=900K.
An organic Rankine cycle (ORC) with ammonia NH3 as working fluid and an upper temperature level Tm,u=400K.
When in both cycles Nu, q˙w and L are the same, the temperature difference ΔT in Nu according to eq. (13) is larger by a factor 2.6 for ammonia compared to water. This is due to the different values of the thermal conductivity k of water (at Tm,u=900K and p=250bar) and ammonia (at Tm,u=400K and p=25bar), assuming typical values for the temperature and pressure levels in both cycles.
For a further comparison note that the energy devaluation number according to eq. (11) in this case with dgS˙i according to eq. (3) and integrated to obtain
S˙g,i=Q˙w,i(1Tw−1Tm,u)≈Q˙w,iΔTTm,u2E35
with E˙=Q˙w is
Ni=T∞ΔTTm,u2E36
Table 2 shows the energy devaluation number\n\t\t\t\t\tNi for both cases according to this approximation. It shows that only 0.37 % of the entropic potential is used for the heat transfer in the SPC-case, but almost 5% in the ORC-case “though” both heat transfer situations have the same Nusselt number Nu=100 and the same amount of energy is transferred. Note that only that part of the entropic potential that is not yet used is available for further use after the process under consideration.
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\tCycle/fluid\n\t\t\t
\n\t\t\t
kW/mK
\n\t\t\t
T∞K
\n\t\t\t
Tm,uK
\n\t\t\t
ΔTK
\n\t\t\t
Ni
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\tSPC/water\n\t\t\t
\n\t\t\t
0.1
\n\t\t\t
300
\n\t\t\t
900
\n\t\t\t
10
\n\t\t\t
0.0037
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\tORC/ammonia\n\t\t\t
\n\t\t\t
0.038
\n\t\t\t
300
\n\t\t\t
400
\n\t\t\t
26
\n\t\t\t
0.049
\n\t\t
\n\t
Table 2.
Heat transfer with Nu=100,q˙w=103Wm2, L=0.1m in two different power cycles
6.2. Using CFD to assess a heat exchanger
In the previous example, two similar processes at two different temperature levels were considered. Such a pipe flow with heat transfer is part of the heat transfer situation illustrated in figure 1: the cold side (b) is heated.
In the second example computational fluid dynamics (CFD) is used to assess the heating of a fluid in a passage within a plate heat exchanger, trying to find the best point of operation for the device. We will first describe the device and how it is modeled, and then discuss the results and how to use them. Further details can be found in [14].
6.2.1. Geometry of the device
Plate heat exchangers are made of corrugated plates which are arranged in a plate stack, forming channels between the plates. The plates are designed in such a way that two fluids are separated from each other on their way through adjacent channels.
Depending on the plate corrugation the channels have constantly changing cross sections, but there is a repeating geometric pattern. Figure 2 (left) shows part of a plate with such a pattern; in this case it is a symmetric fish-bone pattern with a sinusoidal corrugation (see figure 2, right).
Figure 2.
Heat exchanger plate geometry: the plate has a symmetric fishbone pattern with the corrugation angle φ, amplitude a^ and period Λ ; c.f. [15]
6.2.2. Modeling of the device
The first simplification made in order to facilitate the simulations is that the plate (and therefore the heat exchanger) is assumed to have an infinite length. Thus effects on the flow caused by the inlet or outlet areas can be neglected: the flow is hydraulically developed. This has two consequences:
the channel can be modeled as an endlessly repeating stripe of finite length, see figure 3 (a),
only half of the channel must be simulated, see figure 3 (b).
The resulting domain geometry is shown in figure 4.
Figure 3.
Simplified geometry of the heat exchanger: (a) symmetric stripe; (b) solution domain due to the symmetry assumption.
Figure 4.
view of the simulated plate heat exchanger stripe.
The second simplification made here is that the heat exchanger is operated with a balanced counter-flow: The capacity flow rate m˙cp is the same on the hot and the cold side, so that the temperature difference between them as well as the heat flux q˙w are the same at every point between the inlet and the outlet.
6.2.3. Boundary conditions
Based on the assumptions made above, periodic boundary conditions can be applied to the flow field in main flow direction x (see figure 3). The boundary condition applied with respect to the pressure field is a so-called “fan” boundary condition that sets a constant pressure drop between the inlet and outlet patch. In the symmetry plane a symmetry boundary condition is imposed, and no-slip boundary conditions hold at all walls.
Figure 5.
Overall entropy generation rate S˙g, entropy generation rate due to dissipation S˙g,D and entropy generation rate due to conduction S˙g,C (normalized with the minimum entropy generation rate at Re≈2000) at varying Reynolds numbers, for the simulated heat exchanger passage.
The temperature field has a fan boundary condition with a positive temperature difference ΔTio between the inlet and the outlet patch. This results in a heating of the fluid as it passes through the simulated passage. The boundary condition used for the top and bottom walls is a linearly increasing temperature profile in mean flow direction. The increase in temperature ΔTω,io is the same as ΔTio. Together, these two boundary conditions model the balanced counter-flow configuration of the heat exchanger. A zero-gradient boundary condition is used for the gasket, which is modeled as an adiabatic wall.
Changing the pressure drop leads to different mean flow velocities. In order to keep the heat flux q˙w fixed, it was necessary to adjust the temperature difference between inlet and outlet (ΔTw,io=ΔTio=q˙wA/m˙cp) accordingly.
6.2.4. Simulation results
The results obtained from CFD simulations give access to the velocity, pressure and temperature fields u, p and T. They can be used to calculate the heat transfer coefficient and the head loss coefficient for the convective heat transfer under consideration.
Calculating the pressure and velocity fields is the computationally expensive part of the simulation. When all fluid properties are assumed to be constant, i.e. pressure and temperature independent, the temperature field can even be modeled as a passive scalar, which comes at very little computational cost. The four parts of the entropy generation (S˙g,C¯, S˙g,C\', S˙g,D¯, S˙g,D\', see eqs. (29) to (32) in section 5.2.) are post-processing quantities: they can be obtained from the u -, p - and T -fields without solving further differential equations. This is beneficial for the assessment of a certain process operating on different temperature levels.
The entropy generation rates due to dissipation, conduction and the sum of both are shown in figure 5 for different Reynolds numbers. For increasing Reynolds numbers, S˙g,D increases, while S˙g,C decreases. An optimal point of operation can be identified at about Re=2000. The same optimum can be identified in figure 6 for the energy devaluation number of the heat exchanger, Nhe, since in eq. (11) the heat flux, wall area and ambient temperature are the same for all calculations.
Figure 6.
Energy devaluation number Nhe for the simulated plate heat exchanger passage.
Note that the curves for S˙g,C and S˙g,D in figure 5 are almost straight lines, especially for higher Reynolds numbers. Therefore only two simulations are necessary in order to roughly estimate an optimum point of operation. From the two straight lines for S˙g,C and S˙g,D the sum of both results as a curve with the minimum at the optimal Reynolds number.
As mentioned before the entropy generation is a post-processing quantity. This can be leveraged to assess the simulated heat transfer situation at different temperature levels. If the overall change in temperature between inlet and outlet is not too large, an approximation can be done by simply scaling the results accordingly. The entropy generation due to dissipation S˙g,D,new at the temperature level Tnew is (compared to the entropy generation in an existing simulation result) S˙g,D,new/S˙g,D,sim=Tsim/Tnew. If the new temperature level is higher, S˙g,D,newwill be smaller than S˙g,D,sim. Similarly, for entropy generation due to conduction, the relationship is S˙g,C,new/S˙g,C,sim=(Tsim/Tnew)2. Again, if the new temperature level is higher, S˙g,C,newwill be smaller thanS˙g,C,sim. The optimum point of operation shifts to a lower Reynolds number (see figure 7), because the effect of a temperature level change on S˙g,C is larger than the effect on S˙g,D.
Figure 7.
Entropy generation rates for a heat transfer at different temperature levels. For higher temperatures, the optimum point of operation shifts to lower Reynolds numbers.
7. Conclusions
Despite its apparently low popularity, entropy generation is a crucial aspect of every heat transfer process. Every real technical process includes the generation of entropy, which at some point has to be discharged to the ambient. It has been shown that every energy flow has an entropic potential, which is the amount of entropy that can be discharged to the ambient along with the energy flow. It therefore sets the limit for all wanted processes associated with this energy flow. Based on this, the energy devalution number has been introduced, which quantifies the part of the entropic potential which is lost in a transfer process. The energy devalution number is applicable to all processes in which energy is transferred and is recommended for their assessment especially with regard to sustainability.
In the examples it has also been shown how different heat transfer situations can be compared with each other. Such comparisons can be made on very different levels, reaching from system assessment (i.e. to compare different systems) to more detailed studies regarding the optimization of subsystems which are part of an overall heat transfer system. It has also been shown how existing simulation results can be reused at different temperature levels, effectively lowering the cost of CFD simulations.
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/48786.pdf",chapterXML:"https://mts.intechopen.com/source/xml/48786.xml",downloadPdfUrl:"/chapter/pdf-download/48786",previewPdfUrl:"/chapter/pdf-preview/48786",totalDownloads:1534,totalViews:1404,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,dateSubmitted:"June 17th 2014",dateReviewed:"April 10th 2015",datePrePublished:null,datePublished:"July 29th 2015",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/48786",risUrl:"/chapter/ris/48786",book:{slug:"heat-transfer-studies-and-applications"},signatures:"Heinz Herwig and Christoph Redecker",authors:[{id:"16050",title:"Dr.",name:"Heinz",middleName:null,surname:"Herwig",fullName:"Heinz Herwig",slug:"heinz-herwig",email:"h.herwig@tuhh.de",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. A thermodynamic view on heat transfer",level:"1"},{id:"sec_2_2",title:"2.1. General considerations",level:"2"},{id:"sec_3_2",title:"2.2. Change of entropy in energy transfer processes",level:"2"},{id:"sec_4_2",title:"2.3. Energy devaluation in a heat transfer process and the entropic potential concept",level:"2"},{id:"sec_6",title:"3. An engineering view on heat transfer",level:"1"},{id:"sec_7",title:"4. The physics behind the energy devaluation number",level:"1"},{id:"sec_8",title:"5. Convective heat transfer",level:"1"},{id:"sec_8_2",title:"5.1. Losses due to dissipation of mechanical energy",level:"2"},{id:"sec_9_2",title:"5.2. Assessing convective heat transfer",level:"2"},{id:"sec_10_2",title:"5.3. Nondimensional parameters",level:"2"},{id:"sec_12",title:"6. Examples",level:"1"},{id:"sec_12_2",title:"6.1. Fully developed pipe flow with heat transfer",level:"2"},{id:"sec_13_2",title:"6.2. Using CFD to assess a heat exchanger",level:"2"},{id:"sec_13_3",title:"6.2.1. Geometry of the device",level:"3"},{id:"sec_14_3",title:"6.2.2. Modeling of the device",level:"3"},{id:"sec_15_3",title:"6.2.3. Boundary conditions",level:"3"},{id:"sec_16_3",title:"6.2.4. Simulation results",level:"3"},{id:"sec_19",title:"7. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Moran, H. & Shapiro, H.. Fundamentals of engineer thermodynamics, 5th edn. New York: John Wiley & Sons; 2003'},{id:"B2",body:'Baehr, H. & Kabelac, S. Thermodynamik, 14th edn. Berlin, Heidelberg, New York: Springer Verlag; 2009.'},{id:"B3",body:'Herwig, H. & Kautz, C. Technische Thermodynamik. München: Pearson Studium; 2007.'},{id:"B4",body:'Incropera, F., DeWitt, D., Bergmann, T. Lavine, A. Fundamentals of heat and mass transfer, 6th edn. New York: John Wiley & Sons; 2006.'},{id:"B5",body:'Nellis, G. & Klein, S. Heat transfer. Cambridge: Cambridge University Press; 2009.'},{id:"B6",body:'Rant, Z. (1956). Exergie, ein neues Wort fuer technische Arbeitsfaehigkeit. Forschung im Ingenieurwesen 1956;22 36-39.'},{id:"B7",body:'Bejan, A. Entropy Generation through Heat and Fluid Flow. New York: John Wiley & Sons; 1982.'},{id:"B8",body:'Wenterodt, T. & Herwig, H. The Entropic Potential Concept: A New Way to Look at Energy Transfer Operations, Entropy 2014;16 2071 – 2084.'},{id:"B9",body:'Herwig, H. & Moschallski, A. Wärmeübertragung, 3rd. edn. Wiesbaden: Springer Vieweg; 2014.'},{id:"B10",body:'Herwig, H. & Schmandt, B. How to Determine Losses in a Flow Field: A Paradigm Shift Towards the Second Law analysis, Entropy 2014;16 2959 – 2989.'},{id:"B11",body:'Kis, P. & Herwig, H. A Critical Analysis of the Thermodynamic Model for Turbulent Forced Convection in a Plane channel Based on DNS Results. Int. J. of Computational Fluid Dynamics 2011;25 387-399.'},{id:"B12",body:'Kock F. & Herwig, H. Local Entropy Production in Turbulent Shear Flows: A High Reynolds Number Model with Wall Functions, Int. J. Heat Mass Transfer 2004;47 2205 – 2215.'},{id:"B13",body:'Herwig, H. & Wenterodt, T. Heat transfer and its assessment. In: Belmiloudi, A. (ed.) Heat Transfer - Theoretical Analysis, Experimental Investigations and Industrial Systems Rijeka: InTech; 2011. p437-452.'},{id:"B14",body:'Redecker, C. Herwig, H. Calculating and assessing complex convective heat transfer problems: The CFD-SLA approach. In: Proceedings of the International Heat Transfer Conference, IHTC-15, 10-15 August 2014, Kyoto, Japan.'},{id:"B15",body:'VDI e.v. VDI-Wärmeatlas, 11th edn. Wiesbaden: Springer Vieweg; 2013.'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Heinz Herwig",address:"h.herwig@tuhh.de",affiliation:'
Institute for Thermo-Fluid Dynamics, Hamburg University of Technology, Germany
Institute for Thermo-Fluid Dynamics, Hamburg University of Technology, Germany
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Kazi",coverURL:"https://cdn.intechopen.com/books/images_new/2230.jpg",editedByType:"Edited by",editors:[{id:"93483",title:"Dr.",name:"Salim Newaz",surname:"Kazi",slug:"salim-newaz-kazi",fullName:"Salim Newaz Kazi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"40632",title:"Measurements of Local Heat Flux and Water-Side Heat Transfer Coefficient in Water Wall Tubes",slug:"measurements-of-local-heat-flux-and-water-side-heat-transfer-coefficient-in-water-wall-tubes",signatures:"Jan Taler and Dawid Taler",authors:[{id:"43955",title:"Prof.",name:"Jan",middleName:"Marian",surname:"Taler",fullName:"Jan Taler",slug:"jan-taler"},{id:"149457",title:"Prof.",name:"Dawid",middleName:null,surname:"Taler",fullName:"Dawid Taler",slug:"dawid-taler"}]},{id:"40627",title:"Experimental Determination of Heat Transfer Coefficients During Squeeze Casting of Aluminium",slug:"experimental-determination-of-heat-transfer-coefficients-during-squeeze-casting-of-aluminium",signatures:"Jacob O. Aweda and Michael B. Adeyemi",authors:[{id:"150103",title:"Dr",name:"Jacob",middleName:null,surname:"Aweda",fullName:"Jacob Aweda",slug:"jacob-aweda"}]},{id:"40624",title:"Analytical and Experimental Investigation About Heat Transfer of Hot-Wire Anemometry",slug:"analytical-and-experimental-investigation-about-heat-transfer-of-hot-wire-anemometry",signatures:"Mojtaba Dehghan Manshadi and Mohammad Kazemi Esfeh",authors:[{id:"27162",title:"Dr.",name:"Mojtaba",middleName:null,surname:"Dehghan Manshadi",fullName:"Mojtaba Dehghan Manshadi",slug:"mojtaba-dehghan-manshadi"},{id:"149481",title:"MSc.",name:"Mohammad",middleName:null,surname:"Kazemi",fullName:"Mohammad Kazemi",slug:"mohammad-kazemi"}]},{id:"40635",title:"Boundary-Layer Flow in a Porous Medium of a Nanofluid Past a Vertical Cone",slug:"boundary-layer-flow-in-a-porous-medium-of-a-nanofluid-past-a-vertical-cone",signatures:"F.M. Hady, F.S. Ibrahim, S.M. Abdel-Gaied and M.R. Eid",authors:[{id:"144660",title:"Dr.",name:"Mohamed",middleName:"Rabea",surname:"Eid",fullName:"Mohamed Eid",slug:"mohamed-eid"},{id:"167009",title:"Prof.",name:"Fekry",middleName:null,surname:"Hady",fullName:"Fekry Hady",slug:"fekry-hady"},{id:"167010",title:"Prof.",name:"Fouad",middleName:null,surname:"Ibrahim",fullName:"Fouad Ibrahim",slug:"fouad-ibrahim"},{id:"167011",title:"Dr.",name:"Sahar",middleName:"M.",surname:"Abdel-Gaied",fullName:"Sahar Abdel-Gaied",slug:"sahar-abdel-gaied"}]},{id:"40630",title:"Natural Convection Heat Transfer from a Rectangular Block Embedded in a Vertical Enclosure",slug:"natural-convection-heat-transfer-from-a-rectangular-block-embedded-in-a-vertical-enclosure",signatures:"Xiaohui Zhang",authors:[{id:"15093",title:"Dr.",name:"Xiaohui",middleName:null,surname:"Zhang",fullName:"Xiaohui Zhang",slug:"xiaohui-zhang"}]},{id:"40633",title:"Forced Convective Heat Transfer and Fluid Flow Characteristics in Curved Ducts",slug:"forced-convective-heat-transfer-and-fluid-flow-characteristics-in-curved-ducts",signatures:"Tilak T. Chandratilleke and Nima Nadim",authors:[{id:"17849",title:"Prof.",name:"Tilak",middleName:null,surname:"Chandratilleke",fullName:"Tilak Chandratilleke",slug:"tilak-chandratilleke"},{id:"155363",title:"Mr.",name:"Nima",middleName:null,surname:"Nadim",fullName:"Nima Nadim",slug:"nima-nadim"}]},{id:"40631",title:"Forced Turbulent Heat Convection in a Rectangular Duct with Non-Uniform Wall Temperature",slug:"forced-turbulent-heat-convection-in-a-rectangular-duct-with-non-uniform-wall-temperature",signatures:"G.A. Rivas, E.C. Garcia and M. Assato",authors:[{id:"142146",title:"Dr.",name:"Gustavo",middleName:null,surname:"Rivas",fullName:"Gustavo Rivas",slug:"gustavo-rivas"},{id:"154671",title:"Dr.",name:"Ezio",middleName:null,surname:"Garcia",fullName:"Ezio Garcia",slug:"ezio-garcia"},{id:"154673",title:"Dr.",name:"Marcelo",middleName:null,surname:"Assato",fullName:"Marcelo Assato",slug:"marcelo-assato"}]},{id:"40625",title:"Droplet Impact and Evaporation on Nanotextured Surface for High Efficient Spray Cooling",slug:"droplet-impact-and-evaporation-on-nanotextured-surface-for-high-efficient-spray-cooling",signatures:"Cheng Lin",authors:[{id:"144879",title:"Dr.",name:"Cheng",middleName:null,surname:"Lin",fullName:"Cheng Lin",slug:"cheng-lin"}]},{id:"40629",title:"Critical Heat Flux in Subcooled Flow Boiling of Water",slug:"critical-heat-flux-in-subcooled-flow-boiling-of-water",signatures:"Yuzhou Chen",authors:[{id:"15867",title:"Prof.",name:"Yuzhou",middleName:null,surname:"Chen",fullName:"Yuzhou Chen",slug:"yuzhou-chen"}]},{id:"40622",title:"Condensate Drop Movement by Surface Temperature Gradient on Heat Transfer Surface in Marangoni Dropwise Condensation",slug:"condensate-drop-movement-by-surface-temperature-gradient-on-heat-transfer-surface-in-marangoni-dropw",signatures:"Yoshio Utaka and Zhihao Chen",authors:[{id:"14778",title:"Prof.",name:"Yoshio",middleName:null,surname:"Utaka",fullName:"Yoshio Utaka",slug:"yoshio-utaka"},{id:"145238",title:"Dr.",name:"Zhihao",middleName:null,surname:"Chen",fullName:"Zhihao Chen",slug:"zhihao-chen"}]},{id:"40637",title:"Two-Phase Flow",slug:"two-phase-flow",signatures:"M. M. Awad",authors:[{id:"36394",title:"Dr.",name:"M. M.",middleName:null,surname:"Awad",fullName:"M. M. Awad",slug:"m.-m.-awad"}]},{id:"40634",title:"Heat Generation and Removal in Solid State Lasers",slug:"heat-generation-and-removal-in-solid-state-lasers",signatures:"V. Ashoori, M. Shayganmanesh and S. Radmard",authors:[{id:"141719",title:"MSc.",name:"Vahid",middleName:null,surname:"Ashoori",fullName:"Vahid Ashoori",slug:"vahid-ashoori"},{id:"168265",title:"Dr.",name:"Mahdi",middleName:null,surname:"Shayganmanesh",fullName:"Mahdi Shayganmanesh",slug:"mahdi-shayganmanesh"}]},{id:"40636",title:"Single and Two-Phase Heat Transfer Enhancement Using Longitudinal Vortex Generator in Narrow Rectangular Channel",slug:"single-and-two-phase-heat-transfer-enhancement-using-longitudinal-vortex-generator-in-narrow-rectang",signatures:"Yan-Ping Huang, Jun Huang, Jian Ma, Yan-Lin Wang, Jun-Feng Wang and Qiu-Wang Wang",authors:[{id:"15235",title:"Dr.",name:"Jian",middleName:null,surname:"Ma",fullName:"Jian Ma",slug:"jian-ma"},{id:"149906",title:"Dr.",name:"Yan-Ping",middleName:null,surname:"Huang",fullName:"Yan-Ping Huang",slug:"yan-ping-huang"},{id:"167091",title:"Dr.",name:"Jun",middleName:null,surname:"Huang",fullName:"Jun Huang",slug:"jun-huang"},{id:"167191",title:"Dr.",name:"Yanlin",middleName:null,surname:"Wang",fullName:"Yanlin Wang",slug:"yanlin-wang"},{id:"167192",title:"Dr.",name:"Junfeng",middleName:null,surname:"Wang",fullName:"Junfeng Wang",slug:"junfeng-wang"}]},{id:"40628",title:"Application of Nanofluids in Heat Transfer",slug:"application-of-nanofluids-in-heat-transfer",signatures:"P. Sivashanmugam",authors:[{id:"145330",title:"Dr.",name:"Palani",middleName:null,surname:"Sivashanmugam",fullName:"Palani Sivashanmugam",slug:"palani-sivashanmugam"}]},{id:"40626",title:"Heat Transfer Enhancement of Impinging Jet by Notched – Orifice Nozzle",slug:"heat-transfer-enhancement-of-impinging-jet-by-notched-orifice-nozzle",signatures:"Toshihiko Shakouchi and Mizuki Kito",authors:[{id:"145943",title:"Prof.",name:"Toshihiko",middleName:null,surname:"Shakouchi",fullName:"Toshihiko Shakouchi",slug:"toshihiko-shakouchi"},{id:"145947",title:"Prof.",name:"Mizuki",middleName:null,surname:"Kito",fullName:"Mizuki Kito",slug:"mizuki-kito"}]},{id:"40621",title:"Conjugate Heat Transfer in Ribbed Cylindrical Channels",slug:"conjugate-heat-transfer-in-ribbed-cylindrical-channels",signatures:"Armando Gallegos-Muñoz, Nicolás C. Uzárraga-Rodríguez and Francisco Elizalde-Blancas",authors:[{id:"145413",title:"Dr.",name:"Armando",middleName:null,surname:"Gallegos-Muñoz",fullName:"Armando Gallegos-Muñoz",slug:"armando-gallegos-munoz"},{id:"145857",title:"Dr.",name:"Francisco",middleName:null,surname:"Elizalde-Blancas",fullName:"Francisco Elizalde-Blancas",slug:"francisco-elizalde-blancas"},{id:"145858",title:"MSc.",name:"Nicolás C.",middleName:null,surname:"Uzárraga-Rodríguez",fullName:"Nicolás C. Uzárraga-Rodríguez",slug:"nicolas-c.-uzarraga-rodriguez"}]},{id:"40623",title:"Heat Transfer to Separation Flow in Heat Exchangers",slug:"heat-transfer-to-separation-flow-in-heat-exchangers",signatures:"S. N. Kazi, Hussein Togun and E. Sadeghinezhad",authors:[{id:"93483",title:"Dr.",name:"Salim Newaz",middleName:null,surname:"Kazi",fullName:"Salim Newaz Kazi",slug:"salim-newaz-kazi"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"72031",title:"Importance of Air Quality Networks in Controlling Exposure to Air Pollution",doi:"10.5772/intechopen.92335",slug:"importance-of-air-quality-networks-in-controlling-exposure-to-air-pollution",body:'\n
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1. Introduction
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While technological advances have generated an improvement in the human being’s life quality, they have also contributed to the emergence of associated issues, such as exponential industrial grown and increase of transportation networks, due to a fast growing population and its centralization into urban centers, mainly. As a consequence, the rise of the pollutant emissions toward the environmental compartments has been framed as a Public Health concern. Therefore, the impact of environmental emissions on the climate and the environment is an ultimate subject, both to local, regional as global level. Particularly, an increase of environmental well-being would bring in greater the quality of life, due to the exchange internal-external between the human being and the environment.
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Environmental emissions play a key role in the release of pollutants on air, water and soil matrix, which is relevant because drives a decline of biodiversity [1]. In this sense, deforestation, water pollution, acid rain or endangered animals are factors linked to likely environmental repercussions [2, 3]. In the health framework, numerous epidemiological studies associate the presence of pollutants in the environment and harmful effects on human being health [4, 5].
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Taking into account the interaction between human being and environmental, the human well-being is a factor tied to the presence of clean air, otherwise, the emergence of harmful effects could drive to devastating implications on human health. According to the 68th World Health Assembly (see [6]), each year, a total of 4.3 and 3.7 million deaths result from exposure to indoor and outdoor pollutants, respectively.
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Among different environmental compartments, this chapter will focus on atmospheric matrix, given that atmospheric pollution is considered the major environmental risk to human health worldwide. Atmospheric pollution result from the release of a damaging chemical or material into the atmosphere and it encompasses a wide variety of pollutants, either organic or inorganic compounds. Once air pollutants are released into the atmosphere, those ones can be exhibited both gaseous phase as solid and liquid particles suspended in the air (particulate material, PM) [7].
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The occurrence of pollutants in the atmosphere depends on emissions sources. Although the atmospheric pollution is considered as a global character issue, the highest levels of air pollutants have been monitored in the developing countries, as a consequence of the industrial growth. A more detailed analysis would pointed to large cities [8, 9], where environmental emissions to atmospheric level could come from several types of sources of pollution, such as industrial developments implying combustion processes, vehicular emissions and domestic-heating [10].
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Environmental emissions have a direct effect on the outdoor ambient pollution, as well as on indoor air quality, given that outdoor emission sources are responsible for the presence of air pollutants at indoor environments [11], due to the gases and particles infiltration.
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Based on previously mentioned, atmospheric pollution monitoring is of a fundamental importance in Public Health [12] in order to (i) control the human being exposure to air pollutants [13] and (ii) support the decision making on environmental management, in particular air quality management [14]. So, for example, an adequate management of major dominant emission sources in urban environments, as it can be to limit the road transport and more restricted industrial emissions, would result in lower levels of pollution into the atmosphere.
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European Union develops Air Quality Directives [15] for setting air quality objectives in order to reduce potential harmful effects of air pollutants on human health and environment, establishing limit and target values for criteria of air pollutants. Air quality assessment is a responsibility of each Member States within their territory. These ones have the obligation for maintaining an air quality good, or improve it, and they should assume action in order to comply with the limit values and critical levels and, where possible, to reach the target values and long-term objectives. For that, Member States establish air quality monitoring networks (AQMN) in their territories for verifying compliance with those air quality objectives.
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Therefore, AQMN performs an essential function within Public Health framework, monitoring environment emissions and controlling exposure in order to take care of human being health.
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2. Criteria of air pollutants measured at AQMN
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Criteria of air pollutants are those atmospheric pollutants generally monitored by AQMNs. They usually measure the next legislated criteria of air pollutants (see Table 1): sulfur dioxide, nitrogen oxides (monoxide and dioxide nitrogen), benzene, carbon monoxide. Ozone and atmospheric particles (PM10 particles, with an aerodynamic diameter of 10 μm or less, and PM2.5, aerodynamic diameters ≤2.5 μm) [16, 17].
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Pollutant
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Quantitative value
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Concept
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Averaging period
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Sulfur dioxide (gas)
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350 μg/m3\n 125 μg/m3\n
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Limit value Limit value
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1 hour 1 day
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Nitrogen dioxide (gas)
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200 μg/m3\n 40 μg/m3\n
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Limit value Limit value
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1 hour A calendar year
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Benzene (gas)
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5 μg/m3\n
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Limit value
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A calendar year
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Carbon monoxide (gas)
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10 mg/m3\n
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Limit value
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Maximum daily 8-hour mean
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Ozone (gas)
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120 μg/m3\n
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Target value (January 1, 2010)
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Maximum daily 8-hour mean
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PM10 (particles)
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50 μg/m3\n 40 μg/m3\n
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Limit value Limit value
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1 day A calendar year
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PM2.5 (particles)
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25 μg/m3\n 20 μg/m3\n
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Target value (January 1, 2020) Limit value
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A calendar year A calendar year
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Table 1.
Limit and target value for the protection of human health [15].
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The measurements recorded by AQMNs must be able to provide traceability, in order to compare air quality data among all Member States, for which those measures must be monitored using common measurement methods. For that, Member States apply normalized reference measurement methods (see Table 2).
Reference measurement methods for measuring criteria of air pollutants.
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The previously mentioned methods are cataloged as automatic method, nevertheless, the AQMNs dispose manual methods for determining the chemical composition of atmospheric particles (PM10 and PM2.5), mainly for heavy metals and polycyclic aromatic hydrocarbons. While the samples are collected by manual equipment installed at AQMN, their composition is analyzed in the laboratory.
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The occurrence of criteria of air pollutants into the atmosphere measured at fixed stations within AQMN is dependent on several factors, such as meteorological features and sources of pollution.
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3. Types of air pollutant emission sources
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The comprehension of emission sources and knowledge on pollution levels reached in the air matrix could be useful tool for understanding the spatial and temporal distribution of air pollutants, which would provide an overview picture about human exposure to environmental emissions coming from different sources of contamination.
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In function of their origin, it is necessary to distinct between anthropogenic and natural sources. Broadly, the first ones are sources that release mixtures of pollutants come from transport, power generation, industrial activity, biomass burning, and domestic heating, mainly in urban environments [24, 25, 26] while volcanic eruptions, plant emission and oceans are tied to natural sources. Nevertheless, in terms of released pollutant, the sources can be defined as primary and secondary. Primary emission sources result from the direct emissions from an air pollution source, while secondary emission sources result from the formation of a pollutant in the atmosphere from the chemical reaction between theirs precursors, which are emitted from air pollution primaries sources, and the meteorological variables. Finally, once pollutants are released, either from primary or secondary sources, the pollutants can be deposited on the Earth’s terrestrial or aquatic surfaces, followed by re-emission to the atmosphere; in this case the sources are named as re-emission sources [27].
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While the identification of emissions sources is a fundamental factor in order to carry out the distribution of the fixed monitoring stations within an AQMN, other elements also perform a primordial role, such as population density, peculiar features of target territory, amplitude of geographic area to be controlled as further meteorological variables.
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4. Air quality monitoring network
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4.1 Concept
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AQMN is an essential element within environmental management, in special emphasis to air quality management. It is consisted of fixed monitoring stations for measuring air pollutants (see Figure 1). Although the total number of stations depends on several factors, according to Section 3, these ones should be attributed conveniently in the domain of interest for providing suitable air pollutant information and estimating the exposure of the ambient pollution on human being of righter way. So, one of the keys in the AQMN layout is the distribution of monitoring stations as well as the determination of a sufficient and confident number of sampling points for carrying out the air quality measurements. These features are associated with the network management, which should focus on reducing the fixed stations within the AQMN to a reliable and non-redundant number. So, the network would not duplicate information on air pollution.
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Figure 1.
Basic setup of a fixed monitoring station.
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Given that the assessment of air quality in Member States is approached by means of the data generated by AQMN, those ones divide their territories at zones and agglomerations in order to reach that objective. Generally, air quality must be assessed in all zones and agglomerations by means of one or more fixed stations. The number of zones can vary in function of geographical location, distribution of emission sources and meteorology, although the final number of those ones must provide an adequate representation of the territory heterogeneity.
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Current European legislation [15] lays down criteria for siting fixed stations within an AQMN, pointing a wide number of considerations regards at macroscale siting of sampling points in order to protect the human health, vegetation and natural ecosystems. Similarly, in terms of microscale, the legislation set criteria relative to air flow no-restriction around the inlet of sampling point, its height regards to ground level (between 1.5 and 4 m) and distance regards to the edge of major junctions (at least 25 m) and the kerbside (no more than 10 m).
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Regarding to minimum number of fixed monitoring stations, the European legislation set criteria in those zones and agglomerations where fixed measurement is the sole source of information for evaluating compliance with limit values for the protection of human health and alert thresholds. This criterion is based on zone inhabitant number and measured pollutants.
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Fixed stations included into AQMN can be sorted in function of several typologies. So, in terms of area where is located it can be named:
Urban stations: Those ones located at zones with presence of buildings of continued way.
Suburban stations: Those ones located at zones with presence of buildings of continued way but separated by no-buildings areas, such as lakes, forests and agricultural land.
Rural stations: Those ones located at zones not included within the previous two categories.
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In terms of major dominant emission source:
Traffic stations: Those ones which contamination levels are mainly appointed to emission sources coming from vehicles.
Industrial stations: Those ones which contamination levels are majorly dependent on industrial activities.
Background stations: Those ones which contamination levels cannot be directly attributed to any dominant emission source.
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4.2 Representativeness of fixed monitoring stations within AQMN
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Regardless the function exhibited by fixed measurement stations included into an AQMN, as the assessment of air quality, cross-border pollution, spatial-temporal trends or exposure studies, the representativeness of each station should be considered as a primordial reflection. The efficiency degree of the fixed stations into AQMN can be assessed in terms of:
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4.2.1 Representation degree of any station within its zone or agglomeration
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Given that one target zone can be represented by one or more fixed stations, it is relevant to know the spatial representativeness of each station in order to evaluate whether air quality monitored by those ones can or not be extrapolated to all zone. In this sense, in order to provide an overview regards to atmospheric pollution within zone, the passive methodology simultaneously samples a large number of sampling points, which supplies opportune information on spatial pollution in the researched zone [28]. This approach lets to compare air quality data measured by AQMN vs. those ones monitored by passive methods, thereby confirming or not the station representativeness within target zone.
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4.2.2 Whole contribution of any station regarding environmental pollution data recorded by AQMN
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Spatial representativeness of the information provided by AQMN is dependent on type of station, in terms of spatial scale, and the pollutant.
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Broadly, representativeness of a fixed station can be defined as the variability of the target pollutant concentrations around sampling point, while others authors enlarged the definition to the radius of a circular area where the concentration can vary up to ±20%, as maximum value [29].
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The AQMN performance does not depend on number of fixed measurement stations, given that the presence of redundant stations could result in existence of non-efficient fixed stations. This means that potential emission sources close to those stations could have a strong probability of similitude. For this reason, the representativeness of each station within an AQMN should be properly studied.
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Although a final agreement regarding on procedure for assessing the efficiency of fixed stations have not been reached, this subject have been widely studied in the scientific literature. Recent studies have reported on approaches in order to test the AQMN performance. They are based on the use of several combined chemometric techniques for reached the aforementioned objective. Some authors have used the analysis of correlation for revealing the existence of redundant fixed stations, although this method does not identify efficient stations. For that, they apply the principal component analysis technique, which appoints a new set of linearly uncorrelated stations [29]. Other studies have expanded the number of chemometric techniques, combining correlation analysis, principal component analysis, assignment method, clustering analysis and correspondence analysis [30].
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A significate consideration would drive to know the contribution degree of each source of pollution in regarding with air pollutants levels reached into the atmosphere. Some authors have solved this subject using a combination of techniques, combined principal component analysis and multiple linear regression.
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Nevertheless, the practical application of the mentioned approaches were not tested along a period did not include in the development of these approaches. Similarly, they did not assess spatial information percentage that is lost when redesigning the AQMN due to the removal redundant fixed measurement stations.
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Therefore, while different approaches have been developed to estimate the area of spatial representativeness of monitoring stations, a unique robust methodology to assess the representativeness of in-situ measurements has not yet been agreed.
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Spatial representativeness is required for distinct actions:
Air quality and exposure assessment, for example, to estimate the air quality standards exceedance areas and to quantify the population exposure to the air pollution [32],
A lack of information in regarding with the AQMN performance, based on the representativeness of their fixed stations, would support their potential limitations.
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4.3 Potential limitations of AQMN
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4.3.1 Relative to the design
\n
Given that air quality in any zone, either local, regional or global, is dependent on a wide number of factors such emission sources (transport network and industries) and meteorological features, the assessment of atmospheric pollution is a hard assignment, and due to these factors it is specific for each zone. Therefore, it is possible that the spatial information on environmental pollution reported by AQMNs is not representative of the target zone.
\n
This limitation could influence on AQMN efficiency, if its function is framed within activity of informing population of levels which they are exposed to. This fact is relevant because numerous epidemiological studies use air quality data recorded by AQMNs, in order to associate air pollutant levels with damaging effects or hospital admissions. Nevertheless, the pollution data measured by AQMNs along study time is not equivalent to the daily concentrations which the human being is exposed to. So, the reached conclusions could exhibit a limited scope.
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4.3.2 Relative to current European legislation
\n
While European Legislation clearly establish criteria, on the one hand, for siting potential fixed measurement stations and, on the other hand, setting minimum number of those ones, criteria for identifying the more representative fixed sampling points within AQMN is not considered. This fact is fundamental in order to optimize the AQMN performance.
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4.3.3 Relative to the development of specific procedure for evaluating the representativeness of fixed stations
\n
FARIMODE study [34] reported on collected information coming from questionnaire to get technical information concerning the methodologies used to estimate the representativeness of air quality monitoring stations. The questionnaire was answered by 22 workgroups from 14 different countries providing information on 25 methodologies.
\n
Major methodological limitations were appointed to input data availability (9 answers), expert or local knowledge (1), modeling domain (1), modeling uncertainties (6), input data uncertainties (10), temporal-spatial resolution (7), directive metrics (1), computational resources (4), pollutants (2), definition of parameters of methodology (3), coverage of station network (1) and no limitation (2). Within this study, a relevant conclusion was the possibility for examining if the similarities or discrepancies between the representativeness estimates are more or less significant according to the concentration levels measured by target station.
\n
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4.4 Importance of AQMNs
\n
Consequently to previously mentioned, an AQMN play a paramount lead in the evaluation of the air quality [35], in order to:
Inform to the population in regarding with pollution levels which they are exposed to [30],
Know spatial-temporal pattern of air pollutants (see Figure 1),
Support the development of monitoring strategies [37] and.
Assist to authorities in decisions making.
\n\n
Besides, in the case of Member States, those ones must report to European Commission on recorded pollution data, which lets to evaluate the cross-border pollution and model the spatial-temporal air pollution pattern, among others applications.
\n
AQMN links important subjects framed into Public Health, such as sources of pollution, environmental emissions, outdoor air pollutant levels and human health. Therefore, AQMN proves a helpful implement for estimating risk associated with human being exposure to air pollutant levels occurred into the atmosphere.
\n
\n
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4.5 Management of AQMNs
\n
Within the European Union, Member States are liable for controlling and assuring data quality of fixed monitoring stations. Each one establishes the necessary number and the location of fixed measurement stations included in their AQMNs, in order to ensure an adequate air quality assessment in its territory and comply with air quality standards. Similarly, each Member State is responsible for managing their AQMNs, according to requirements set in current European Legislation, meaning, the used measurement methods should be those ones included in air quality standards (they have been mentioned in Table 2). Similarly, they should guarantee a proper maintenance of those measure devices employed for monitoring atmospheric pollutants in the outdoor ambient air. For that, the air quality standards [18, 19, 20, 21, 22, 23] set several basic qualifications regards to the measure devices and their management:
Components of sampling system: (i) Sampling line: standards indicate the frequency of clear or, if necessary, its change, (ii) Particle filters: standards indicate where should site and (iii) Sampling pump: standards set the sampling flux required for working properly.
Equipment requirements. They depend on target atmospheric pollutant. The components of the devices used for measuring atmospheric pollutants are described in the Air Quality Standards. The next devices can be differenced:
\n\n
Continuous devices: The air pollutant levels are continuously measured using automatic analyzers.
\n
Integrated devices: Levels of the target air pollutants are measured by manual or automated methods and the data is registered hourly or daily.
\n
Static devices: Levels of the target air pollutants are estimated by using qualitative measurement devices from weekly or monthly exposure.
Maintenance operations: The Air Quality Standards determine those necessary actions in order to test if an equipment is working within specifications marked by the manufacturer. For that aim, technical aspects such as verification of zero, the higher concentration level and lack of fit, among other should be checked. All these tests should provide satisfactory results, complying with the criteria set in the air quality standards.
Equipment calibration: Standards exhibit the frequency of calibration for each criteria of air pollutant, as well as recommended concentration/s, acceptation criteria, methodology, etc.
Quality control and quality assurance: The execution of this subject assures that the uncertainty or dispersion associated with the measured values by AQMNs fall down criteria set by current European Legislation. For that, the compliance of the previous requirements should be reached.
\n\n
More detailed information about this section can be found in air quality standards, which have been published by CEN/TC 624 Work Programme and can be acquired through European Committee for Standardization (https://www.cen.eu/Pages/default.aspx, accessed March 6, 2020).
\n
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4.6 Data measured by AQMNs
\n
Given that the AQMNs management is a responsibility for the Member States, those ones should ensure valid air quality data. The data registered during maintenance, check and calibration processes should be not included within the air quality dataset, as well as the faulty data. The air quality standards establish requirements relative to the way for expressing the air quality data (number of decimals) and data capture (temporal coverage).
\n
At the State level, Member States transfer air quality data to Europe Union. Those data can be compared given that, on the one hand, their measure was monitored using reference methods and, on the other hand, they complied with those QC/QA criteria set by air quality standards. As an example, the European Monitoring and Evaluation Programme (EMEP) is the co-operative program for assessing long range transmission of atmospheric pollutants over Europe. Member States have an air quality network in order to monitor background levels of air pollutants. Information relative to this subject can be found in https://www.emep.int/ (accessed March 6, 2020), where emission data, measurement data and modeling results for air pollutants are available through an open’s database. Similarly, air quality data monitored by Member States is reported by European Environment Agency (https://www.eea.europa.eu/data-and-maps/explore-interactive-maps/up-to-date-air-quality-data, accessed March 9, 2020) by means of interactive maps and reports. Other website providing real-time air pollution data by interactive maps in Europe and other countries over the world can be visited at https://aqicn.org/map/europe/ (accessed March 9, 2020).
\n
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4.7 Potential suggestions for improving the AQMN management
\n
Given that the AQMNs have a large historical series of ambient air data for target air pollutants [38] (sulfur dioxide, nitrogen monoxide and dioxide, benzene, carbon monoxide, ozone and atmospheric particles), a count of the number of times that the measurements exceeded the limit and target value established by the European Legislation would help to identify those air pollutants who should be monitored.
\n
As a consequence, the measurement of those air pollutants which do not exceeded the limit values could be reduced in terms of number of fixed monitoring stations, which would give to reinvest those economic resources towards the monitor of other pollutants, e.g. benzene, given that the measurements of this last pollutant are very limited within AQMNs and, according to European Legislation, its measure is mandatory.
\n
Based on the role of AQMNs within environmental emission control, and given that their spatial monitoring coverture is limited [39], nowadays, new wireless low cost sensors are available in order to assess pollution levels in ambient [40] and indoor air [41], by simultaneous monitoring in an elevated number of sampling points,
\n
\n
\n
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5. Possible future trends
\n
At European level, although the application of measurement methods for monitoring air pollutants in AQMNs is normalized by Air Quality Standards, providing traceability to air pollution data among Member States, a harmonized technique for estimating the representativeness of fixed monitoring stations have not been defined.
\n
In the future, the major requirement in regards to AQMN design should point towards the development of a particular methodology for evaluating representativeness of fixed monitoring sampling points within a network.
\n
On the one hand, this methodology should offer evaluation criteria which would assure an adequate estimation of the representativeness and, on the other hand, they should be common and similar to all Member States.
\n
The implementation of this reflection would result in a significant benefit for population, given that an optimization regards to location of fixed stations, and by extension on AQMN performance, it would aid to control the human exposure to atmospheric pollution in a more precise way, supporting a more realistic estimate of human health risk.
\n
\n
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6. Conclusions
\n
The binomial between environmental emissions and human exposure leads to Public Health concerns. In particular, emissions towards ambient air are considered the higher environmental risk. In order to control those issues, AQMNs play a paramount role for controlling air pollution in order to evaluate the compliance with those air quality objectives set by Air Quality European Standards and assist to authorities in decisions making. They consist of fixed monitoring stations and measure several criteria of air pollutants. Although each fixed station should be representative of an around area, the spatial coverture of AQMNs is very limited, due to the restricted number of sampling points as a consequence of the large investment need for setting up an AQMN. Current European legislation lays down criteria for supporting location and minimum number of the fixed measurement stations within AQMN. There are numerous websites exhibiting air quality data (at local, regional and global level), by means of reports, interactive maps or time-real data.
\n
In order to support the AQMN management, a study regards to number of times that air pollutant measures have exceeded the criteria of air quality set in European Legislation should be addressed, for identifying the pollutants which should be measured.
\n
A relevant subject of an AQMN would point to its layout. Although, the legislation does not set methods for evaluating representativeness of the fixed measurement points or requirements for refereeing representativeness degree, this one should be tested over the time, given that new emission air pollutant sources can be emerged, which would directly affect to the AQMN performance.
\n
Therefore, the deployment of a harmonized methodological framework is required, which allows to establish a comprehensive and comparative evaluation of the AQMN efficacy, by evaluating the representativeness of fixed monitoring stations.
\n
This methodology should be assisted by scientists, AQMN’s managers and technicians and experts of air quality and it should lay down the concrete type of method to use, either passive methodology, modeling, series of historical data, a combination of them or other methods.
\n
The development of this harmonized methodological would help to the reporting of spatial representativeness by the Member States to Commission European by means of a common approach.
\n
\n
Conflict of interest
The author declares no conflict of interest. This work does not have commercial purposes, only scientific ones.
\n
Other declarations
\n
This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
\n
\n',keywords:"environmental emissions, air quality, fixed monitoring stations, design and performance",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72031.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72031.xml",downloadPdfUrl:"/chapter/pdf-download/72031",previewPdfUrl:"/chapter/pdf-preview/72031",totalDownloads:148,totalViews:0,totalCrossrefCites:0,dateSubmitted:"December 12th 2019",dateReviewed:"March 31st 2020",datePrePublished:"May 4th 2020",datePublished:"January 7th 2021",dateFinished:"May 4th 2020",readingETA:"0",abstract:"An air quality monitoring network (AQMN) is a basic piece of environmental management due to that it satisfies the major role in monitoring of environment emissions, in special relevance to target air pollutants. An adequate installation would lead to support high efficiency of the network. Therefore, AQMN pre-layout should be considered as an essential factor in regarding with the location of fixed measurement stations within AQMN, as the minimum number of sampling points. Nevertheless, once AQMN has been already installed, and given that the spatial air pollutants pattern can vary along time, an assessment of the AQMN design would be addressed in order to identify the presence of potential redundant fixed monitoring stations. This approach would let to improve the AQMN performance, reduce maintenance costs of the network and consolidate the investment on those more efficient fixed stations. The chapter includes aspects relative to air pollutants measured by networks, their representativeness, limitations, importance, and the future needs. It ponders the need of re-assessment of the AQMN layout for assuring (i) a right evaluation of the human being exposure to atmospheric pollutants and controlling the environmental emissions into the atmosphere and (ii) an adequate performance of the network along time.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72031",risUrl:"/chapter/ris/72031",signatures:"David Galán Madruga",book:{id:"10178",title:"Environmental Emissions",subtitle:null,fullTitle:"Environmental Emissions",slug:"environmental-emissions",publishedDate:"January 7th 2021",bookSignature:"Richard Viskup",coverURL:"https://cdn.intechopen.com/books/images_new/10178.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"103742",title:"Dr.",name:"Richard",middleName:null,surname:"Viskup",slug:"richard-viskup",fullName:"Richard Viskup"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"316730",title:"Dr.",name:"David Galan",middleName:null,surname:"Madruga",fullName:"David Galan Madruga",slug:"david-galan-madruga",email:"david.galan@isciii.es",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Criteria of air pollutants measured at AQMN",level:"1"},{id:"sec_3",title:"3. Types of air pollutant emission sources",level:"1"},{id:"sec_4",title:"4. Air quality monitoring network",level:"1"},{id:"sec_4_2",title:"4.1 Concept",level:"2"},{id:"sec_5_2",title:"4.2 Representativeness of fixed monitoring stations within AQMN",level:"2"},{id:"sec_5_3",title:"4.2.1 Representation degree of any station within its zone or agglomeration",level:"3"},{id:"sec_6_3",title:"4.2.2 Whole contribution of any station regarding environmental pollution data recorded by AQMN",level:"3"},{id:"sec_8_2",title:"4.3 Potential limitations of AQMN",level:"2"},{id:"sec_8_3",title:"4.3.1 Relative to the design",level:"3"},{id:"sec_9_3",title:"4.3.2 Relative to current European legislation",level:"3"},{id:"sec_10_3",title:"4.3.3 Relative to the development of specific procedure for evaluating the representativeness of fixed stations",level:"3"},{id:"sec_12_2",title:"4.4 Importance of AQMNs",level:"2"},{id:"sec_13_2",title:"4.5 Management of AQMNs",level:"2"},{id:"sec_14_2",title:"4.6 Data measured by AQMNs",level:"2"},{id:"sec_15_2",title:"4.7 Potential suggestions for improving the AQMN management",level:"2"},{id:"sec_17",title:"5. Possible future trends",level:"1"},{id:"sec_18",title:"6. Conclusions",level:"1"},{id:"sec_22",title:"Conflict of interest",level:"1"},{id:"sec_19",title:"Other declarations",level:"1"}],chapterReferences:[{id:"B1",body:'\nAvigliano E, Rosso JJ, Lijtmaer D, Ondarza P, Piacentini L, Izquierdo M, et al. Biodiversity and threats in non-protected areas: A multidisciplinary and multi-taxa approach focused on the Atlantic Forest. Heliyon. 2019;5(8):e02292\n'},{id:"B2",body:'\nChi Y, Yang P, Ren S, Ma N, Yang J, Xu Y. Effects of fertilizer types and water quality on carbon dioxide emissions from soil in wheat-maize rotations. Science of the Total Environment. 2020;698:134010\n'},{id:"B3",body:'\nLiu Z, Li D, Zhang J, Saleem M, Zhang Y, Ma R, et al. Effect of simulated acid rain on soil CO2, CH4 and N2O emissions and microbial communities in an agricultural soil. Geoderma. 2020;366:114222\n'},{id:"B4",body:'\nKim E, Park H, Hong Y-C, Ha M, Kim Y, Kim B-N, et al. Prenatal exposure to PM10 and NO2 and children’s neurodevelopment from birth to 24 months of age: Mothers and Children’s Environmental Health (MOCEH) study. Science of the Total Environment. 2014;481:439-445\n'},{id:"B5",body:'\nZhang G, Jiang F, Chen Q , Yang H, Zhou N, Sun L, et al. Associations of ambient air pollutant exposure with seminal plasma MDA, sperm mtDNA copy number, and mtDNA integrity. Environment International. 2020;136:105483\n'},{id:"B6",body:'\nWorld Health Organization. Sixty-eighth World Health Assembly. A68/18. In: Health and the Environment: Addressing the Health Impact of Air Pollution. World Health Organization (WHO); 2015. Available from: https://apps.who.int/gb/ebwha/pdf_files/WHA68/A68_18-en.pdf\n\n'},{id:"B7",body:'\nWang G, Yu J, Su Y, Shi G. Distribution and regeneration of hydroxyl free radicals in gaseous and particulate phases of pollutants in near-ground ambient air. Science of the Total Environment. 2019;683:221-230\n'},{id:"B8",body:'\nHan C, Liu R, Luo H, Li G, Ma S, Chen J, et al. Pollution profiles of volatile organic compounds from different urban functional areas in Guangzhou China based on GC/MS and PTR-TOF-MS: Atmospheric environmental implications. Atmospheric Environment. 2019;214:116843\n'},{id:"B9",body:'\nAbbass RA, Kumar P, El-Gendy A. Car users exposure to particulate matter and gaseous air pollutants in megacity Cairo. Sustainable Cities and Society. 2020;56:102090\n'},{id:"B10",body:'\nGonzález CM, Gómez CD, Rojas NY, Acevedo H, Aristizábal BH. Relative impact of on-road vehicular and point-source industrial emissions of air pollutants in a medium-sized Andean city. Atmospheric Environment. 2017;152:279-289\n'},{id:"B11",body:'\nMadruga DG, Ubeda RM, Terroba JM, dos Santos SG, García-Cambero JP. Particle-associated polycyclic aromatic hydrocarbons in a representative urban location (indoor-outdoor) from South Europe: Assessment of potential sources and cancer risk to humans. Indoor Air. 2019;29(5):817-827\n'},{id:"B12",body:'\nOmrani H, Omrani B, Parmentier B, Helbich M. Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France. Data in Brief. 2020;28:105089\n'},{id:"B13",body:'\nXing Y, Brimblecombe P. Urban park layout and exposure to traffic-derived air pollutants. Landscape and Urban Planning. 2020;194:103682\n'},{id:"B14",body:'\nMcDonald F, Horwell CJ, Wecker R, Dominelli L, Loh M, Kamanyire R, et al. Facemask use for community protection from air pollution disasters: An ethical overview and framework to guide agency decision making. 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Spatio-temporal patterns of traffic-related air pollutant emissions in different urban functional zones estimated by real-time video and deep learning technique. Journal of Cleaner Production. 2019;238:117881\n'},{id:"B25",body:'\nZhou Y, Luo B, Li J, Hao Y, Yang W, Shi F, et al. Characteristics of six criteria air pollutants before, during, and after a severe air pollution episode caused by biomass burning in the southern Sichuan Basin China. Atmospheric Environment. 2019;215:116840\n'},{id:"B26",body:'\nWang Y, Song J, Yang W, Dong L, Duan H. Unveiling the driving mechanism of air pollutant emissions from thermal power generation in China: A provincial-level spatiotemporal analysis. Resources, Conservation and Recycling. 2019;151:104447\n'},{id:"B27",body:'\nOutdoor Air Pollution. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans. Vol. 109. International Agency for Research on Cancer; 2016. Available from: https://publications.iarc.fr/Book-And-Report-Series/Iarc-Monographs-On-The-Identification-Of-Carcinogenic-Hazards-To-Humans/Outdoor-Air-Pollution-2015\n\n'},{id:"B28",body:'\nGalán Madruga D, Fernández Patier R, Sintes Puertas MA, Romero García MD, Cristóbal LA. Characterization and local emission sources for ammonia in an urban environment. Bulletin of Environmental Contamination and Toxicology. 2018;100(4):593-599\n'},{id:"B29",body:'\nChow JC, Chen L-WA, Watson JG, Lowenthal DH, Magliano KA, Turkiewicz K, et al. PM2.5 chemical composition and spatiotemporal variability during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS): CRPAQS PM2.5 spatiotemporal variability. Journal of Geophysical Research-Atmospheres. 2006;111(D10):1-17\n'},{id:"B30",body:'\nZhao L, Xie Y, Wang J, Xu X. A performance assessment and adjustment program for air quality monitoring networks in Shanghai. Atmospheric Environment. 2015;122:382-392\n'},{id:"B31",body:'\nHenne S, Brunner D, Folini D, Solberg S, Klausen J, Buchmann B. Assessment of parameters describing representativeness of air quality in-situ measurement sites. Atmospheric Chemistry and Physics. 2010;10(8):3561-3581\n'},{id:"B32",body:'\nMalherbe L, Jimmink B, de Leeuw F, Schneider P, Ung A. Analysis of station classification and network design in EU28 (& other EEA) countries. EEA & ETC/ACM Working Paper. 2013\n'},{id:"B33",body:'\nLefebvre W, Van Poppel M, Maiheu B, Janssen S, Dons E. Evaluation of the RIO-IFDM-street canyon model chain. Atmospheric Environment. 2013;77:325-337\n'},{id:"B34",body:'\nMartín F, Santiago JL, Kracht O, García L, Gerboles M. FAIRMODE spatial representativeness feasibility study. Report EUR 27385 EN. 2015\n'},{id:"B35",body:'\nRosario L, Francesco SP. Analysis and characterization of the predominant pollutants in the Catania’s air quality monitoring stations. Energy Procedia. 2016;101:337-344\n'},{id:"B36",body:'\nPires JCM, Sousa SIV, Pereira MC, Alvim-Ferraz MCM, Martins FG. Management of air quality monitoring using principal component and cluster analysis—Part I: SO2 and PM10. Atmospheric Environment. 2008;42(6):1249-1260\n'},{id:"B37",body:'\nKao J-J, Hsieh M-R. Utilizing multiobjective analysis to determine an air quality monitoring network in an industrial district. Atmospheric Environment. 2006;40(6):1092-1103\n'},{id:"B38",body:'\nBarrero MA, Orza JAG, Cabello M, Cantón L. Categorisation of air quality monitoring stations by evaluation of PM10 variability. Science of the Total Environment. 2015;524(525):225-236\n'},{id:"B39",body:'\nMunir S, Mayfield M, Coca D, Jubb SA. Structuring an integrated air quality monitoring network in large urban areas—Discussing the purpose, criteria and deployment strategy. Atmospheric Environment X. 2019;2:100027\n'},{id:"B40",body:'\nMolka-Danielsen J, Engelseth P, Wang H. Large scale integration of wireless sensor network technologies for air quality monitoring at a logistics shipping base. Journal of Industrial Information Integration. 2018;10:20-28\n'},{id:"B41",body:'\nSalman N, Kemp AH, Khan A, Noakes CJ. Real time wireless sensor network (WSN) based indoor air quality monitoring system. IFAC-Paper. 2019;52(24):324-327\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"David Galán Madruga",address:"david.galan@isciii.es",affiliation:'
Atmospheric Pollution Area, National Center for Environment Health, Carlos III Health Institute, Madrid, Spain
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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
Wellcome Trust (Funding available only to Wellcome-funded researchers/grantees)
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