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Review

A Review of the interrelations of terrestrial carbon sequestration and urban forests

Running title: Terrestrial carbon sequestration and urban forests
  • Received: 25 September 2020 Accepted: 10 November 2020 Published: 13 November 2020
  • Rapid urbanization poses major challenges to the mankind with huge impact on the society and the economy. This paper aims to review relevant literature, mainly recent studies, focused on significant aspects of the interrelations of urban forests and terrestrial carbon sequestration, discussing their implications with reference to urban forests and their roles in climate mitigation, given that the real challenge lies in understanding the integration of carbon sequestration having huge pollution mitigating potentials with other mitigation options. Findings suggest that despite indications of studies that urban forests play significant mitigation roles; they have not been accorded adequate importance vis-à-vis management of ecological disturbances. Findings also suggest that urban forests significantly contribute to terrestrial carbon sequestration and these contributions are in addition to their multiple social-economic-cultural-aesthetic benefits. This paper underscoring the significant contributions of urban forests in maintaining ecological equilibrium, and managing balance between emissions and sequestration to ensure sustainability, offers usefulness to the future researchers, academics, urban-planners and policymakers.

    Citation: Kumari Anjali, YSC Khuman, Jaswant Sokhi. A Review of the interrelations of terrestrial carbon sequestration and urban forests[J]. AIMS Environmental Science, 2020, 7(6): 464-485. doi: 10.3934/environsci.2020030

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  • Rapid urbanization poses major challenges to the mankind with huge impact on the society and the economy. This paper aims to review relevant literature, mainly recent studies, focused on significant aspects of the interrelations of urban forests and terrestrial carbon sequestration, discussing their implications with reference to urban forests and their roles in climate mitigation, given that the real challenge lies in understanding the integration of carbon sequestration having huge pollution mitigating potentials with other mitigation options. Findings suggest that despite indications of studies that urban forests play significant mitigation roles; they have not been accorded adequate importance vis-à-vis management of ecological disturbances. Findings also suggest that urban forests significantly contribute to terrestrial carbon sequestration and these contributions are in addition to their multiple social-economic-cultural-aesthetic benefits. This paper underscoring the significant contributions of urban forests in maintaining ecological equilibrium, and managing balance between emissions and sequestration to ensure sustainability, offers usefulness to the future researchers, academics, urban-planners and policymakers.


    We consider characteristic equations, i.e., equations for eigenvalues and eigenfunctions of the class of integral operators on the Hilbert space L2[l,l] of the form

    KM[w](x)=llGM(x,ξ)w(ξ)dξ,x[l,l], wL2[l,l], (1.1)

    where GM is the Green function [1,2] for the boundary value problem consisting of the fourth-order linear differential equation

    EIu(4)(x)+ku(x)=w(x),x[l,l] (1.2)

    and a well-posed two-point boundary condition

    M(u(l)u(l)u(l)u(l)u(l)u(l)u(l)u(l))T=0. (1.3)

    Here, Mgl(4,8,C) is called a boundary matrix, where gl(4,8,C) is the set of 4×8 matrices with complex entries. For example, the two-point boundary condition u(l)=u(l)=u(l)=u(l)=0 can be expressed by (1.3) with

    M=(10000000010000000000100000000100).

    The differential equation (1.2) is the classical Euler–Bernoulli beam equation [3] which governs the vertical downward deflection u(x) of a linear-shaped beam with finite length 2l resting horizontally on an elastic foundation with spring constant density k. The constants E and I are the Young's modulus and the mass moment of inertia of the beam respectively, and w(x) is the downward load density applied vertically on the beam. The beam deflection problem has been one of the central topics in mechanical engineering with diverse and important applications [3,4,5,6,7,8,9,10,11,12].

    Throughout this paper, we assume that l, E, I, k in (1.2) are positive constants and put α=4k/(EI)>0. When the boundary value problem consisting of (1.2) and (1.3) is well-posed or, equivalently, when (1.2) and (1.3) has a unique solution, we call the boundary matrix M well-posed. The set of well-posed boundary matrices is denoted by wp(4,8,C). It was shown in [2] that, up to a natural equivalence relation, wp(4,8,C) is in one-to-one correspondence with the 16-dimensional algebra gl(4,C) of 4×4 matrices with complex entries.

    For Mwp(4,8,C), we denote by SpecKM the spectrum or, the set of eigenvalues, of the integral operator KM in (1.1). Since KM[w] is the unique solution of the boundary value problem (1.2) and (1.3) for every Mwp(4,8,C), analyzing the behavior of the integral operators KM is important in understanding the beam deflection problem. In general, spectral analysis for integral operators arising from various differential equations is crucial in many applications such as inverse problem [13] and nonlinear problem [5,6]. In contrast to this importance, there are few explicit spectral analyses for the integral operators KM which arise from a most fundamental and basic differential equation (1.2) in the history of mechanical engineering.

    Choi [14] analyzed SpecKQ of a special integral operator KQ in detail, where

    Q=(0α22α100002α3α201000000000α22α100002α3α201), (1.4)

    which is in wp(4,8,C) [2]. The Green function GQ(x,ξ) corresponding to Q is the restriction in [l,l]×[l,l] of the Green function for the boundary value problem consisting of the infinite version EIu(4)(x)+ku(x)=w(x), x(,) of (1.2) and the boundary condition limx±u(x)=0.

    For two positive sequences an, bn, we denote anbn if there exists N>0 such that man/bnM for every n>N for some constants 0<mM<.

    Proposition 1.1 ([14]). For every l>0, the spectrum SpecKQ of the operator KQ is of the form {μn/k|n=1,2,3,}{νn/k|n=1,2,3,}(0,1/k), where 1>μ1>ν1>μ2>ν2>0. Each of μn and νn for n=1,2,3, is determined only by the intrinsic length L=2lα of the beam. μnνnn4, and

    11+{h1(2πn+π2)}4<νn<11+{h1(2πn)}4<μn<11+{h1(2πnπ2)}4,n=1,2,3,,11+{h1(2πnπ2)}4μnνn11+{h1(2πn+π2)}4n5e2πn,11+1L4(2π(n1)π2)4μn11+1L4(2π(n1)+π2)4νnn6.

    Here, h:[0,)[0,) is the strictly increasing real-analytic function defined in Supplementary D, with the properties h(0)=0 and h1(an)an/L for any positive sequence an such that an. See [14] for numerical computations of μn, νn with arbitrary precision.

    Recently, Choi [2] derived explicit characteristic equations for the integral operator KM in (1.1) for arbitrary well-posed Mwp(4,8,C), which are stated in more detail in Section 2. Although these characteristic equations are expressed in terms of the explicit 4×4 matrices G(M), Xλ, Yλ, they still involve determinants of full 4×4 matrices, which makes it hard to analyze the structure of SpecKM for general well-posed boundary matrix M.

    In this paper, we utilize some of the symmetries in the 4×4 matrices Xλ, Yλ to block-diagonalize them with explicit 2×2 blocks X±λ, Y±λ, which enables us to obtain new and simpler forms of characteristic equations for the integral operator KM for arbitrary well-posed boundary matrix Mwp(4,8,C). In particular, the entries of the 2×2 blocks X±λ and Y±λ are represented explicitly with the concrete holomorphic functions δ±(z,κ) and p±(z) introduced in Section 3.

    Our results significantly reduce the complexity of dealing with determinants of 4×4 matrices and facilitate to represent SpecKM for arbitrary Mwp(4,8,C) essentially as the zero set of one explicit holomorphic function composed with the concrete functions δ±(z,κ). For example, Corollary 1 in Section 3 states that 0,1/kλSpecKQ if and only if λ is a zero of the holomorphic function δ+(αl,χ(λ))δ(αl,χ(λ)), where χ is a 4th root transformation introduced in Section 2. In particular, the holomorphic functions δ±(z,κ) unify the real-analytic functions which were analyzed in detail in [14,15] to obtain concrete results on SpecKQ such as Proposition 1.1. The fact that δ±(z,κ) encapsulate condensed information on SpecKQ, and hence on SpecKM in general, is demonstrated in Supplementary D by showing that the seemingly complex-looking conditions φ±(κ)=p(κ), which were derived in [14] with the help of computer algebra systems, can be directly and elegantly recovered from δ±(z,κ).

    Our results open up practical ways to direct and concrete spectral analysis for the whole 16-dimensional class of the integral operators KM arising from arbitrary well-posed boundary value problem of finite beam deflection on elastic foundation.

    After introducing basic notations, definitions, and previous results relevant to our analysis in Section 2, we state our main results Theorems 1, 2 and 3 in Section 3, which are proved in Sections 4, 5 and 6 respectively. Some remarks and future directions are given in Section 7. In Supplementary D, the conditions φ±(κ)=p(κ) on SpecKQ in [14] are derived from our holomorphic functions δ±(z,κ).

    We denote i=1. Denote by Z, R, and C, the set of integers, the set of real numbers, and the set of complex numbers respectively. The set of m×n matrices with entries in C is denoted by gl(m,n,C). When m=n, we also denote gl(m,n,C)=gl(n,C). We write A=(ai,j)1im,1jn when the (i,j)th entry of Agl(m,n,C) is ai,j. When m=n, we also write A=(ai,j)1i,jn. For Agl(m,n,C), we denote the (i,j)th entry of A by Ai,j. The complex conjugate, the transpose, and the conjugate transpose of Agl(m,n,C) are denoted by ¯A, AT, and A respectively. For Agl(n,C), adjA is the classical adjoint of A, so that, if A is invertible then A1=adjA/detA.

    Regardless of size, the identity matrix and the zero matrix are denoted by I and O respectively. The zero column vector with any size is denoted by 0. The diagonal matrix with diagonal entries c1,c2,,cn is denoted by diag(c1,c2,,cn).

    Definition 2.1. Denote ω=eiπ4=12+i12 and ωn=in1ω for nZ. Denote Ω=diag(ω1,ω2,ω3,ω4) and W0=(ωi1j)1i,j4.

    ω1=ω, ω2, ω3, ω4 are the primitive 4th roots of 1 and satisfy

    ¯ω=ω4=ω2=iω,ω3=ω,¯ωn=ω1n, nZ,ω+¯ω=2,ω¯ω=i2,ω2=i,ω¯ω=1. (2.1)

    Definition 2.2. Denote ϵ1=ϵ4=1, ϵ2=ϵ3=1, and ϵn+4=ϵn for nZ. Denote E=diag(ϵ1,ϵ2,ϵ3,ϵ4)=diag(1,1,1,1).

    By Definitions 2.1, 2.2 and (2.1), we have

    eEΩz=diag(eω1z,eω2z,eω3z,eω4z)=diag(eωz,e¯ωz,eωz,e¯ωz)=(diag(eωz,e¯ωz)OOdiag(eωz,e¯ωz)),zC. (2.2)

    Definition 2.3. Denote

    V=12(IIII)=12(1010010110100101),ˆV=(1000001001000001).

    Note that V and ˆV are orthogonal and

    V1=VT,ˆV1=ˆVT=ˆV,detV=1,detˆV=1. (2.3)

    Lemma 2.1. V(ABBA)VT=(A+BOOAB) for A,Bgl(2,C).

    Proof. By Definition 2.3,

    V(ABBA)VT=12(IIII)(ABBA)12(IIII)=12(A+BA+BA+BAB)(IIII)=(A+BOOAB).

    By (2.2) and Lemma 2.1,

    VeEΩzVT=(diag(eωz,e¯ωz)+OOOdiag(eωz,e¯ωz)O)=(diag(eωz,e¯ωz)OOdiag(eωz,e¯ωz))=eEΩz,zC. (2.4)

    By (2.1),

    detdiag(eωz,e¯ωz)=eωze¯ωz=e(ω+¯ω)z=e2z,zC. (2.5)

    Definition 2.4. For λC{0,1/k}, define χ(λ) to be the unique complex number satisfying χ(λ)4=11/(λk) and 0Argχ(λ)<π/2.

    Note that χ is a one-to-one correspondence from C{0,1/k} to the set {κC|0Argκ<π/2}{0,1}.

    Definition 2.5. Let 0λC and xR. For λ1/k, let κ=χ(λ). Denote

    W(x)=(y(x)y(x)y(x)y(x))T,Wλ(x)=(y(i1)λ,j(x))1i,j4,

    where y(x)=(eω1αxeω2αxeω3αxeω4αx)T and yλ,j(x)={1(j1)!xj1,if λ=1/k,eωjκαx,if λ1/k,j=1,2,3,4. Denote Xλ(x)=diag(0,1,1,0)W(x)1Wλ(x)+diag(1,0,0,1)W(x)1Wλ(x). When detXλ(x)0, denote Yλ(x)=Xλ(x)Xλ(x)1I.

    Definition 2.6. Define G:wp(4,8,C)gl(4,C) by

    G(M)={MW(l)+M+W(l)}1M+W(l)Ediag(1,0,0,1),

    where M,M+gl(4,C) are the 4×4 minors of M such that M=(MM+). Define ψ:gl(4,C)gl(4,8,C) by

    ψ(G)=({diag(0,1,1,0)GE}W(l)1{diag(1,0,0,1)+GE}W(l)1).

    The map G in Definition 2.6 is well defined since, for M=(MM+)gl(4,8,C), Mwp(4,8,C) if and only of det{MW(l)+M+W(l)}0 [2,Lemma 3.1]. G(M) is denoted by GM in [2]. Define the equivalence relation on wp(4,8,C) by MN if and only if M=AN for some invertible Agl(4,C).

    Proposition 2.1. (a) ([2,Lemma 6.1]) For M,Nwp(4,8,C), the following (i), (ii), (iii) are equivalent: (i)MN, (ii)G(M)=G(N), (iii)KM=KN.

    (b)([2,Eq 6.4]) For Ggl(4,C), ψ(G)wp(4,8,C) and G(ψ(G))=G.

    Denote by wp(C) the quotient set wp(4,8,C)/ of wp(4,8,C) with respect to the relation . For Mwp(4,8,C), denote by [M] the equivalence class in wp(4,8,C)/ which contains M. Then we have the canonical projection π:wp(4,8,C)wp(C) defined by π(M)=[M]. By Proposition 2.1, the map πψ:gl(4,C)wp(C) is a one-to-one correspondence, and we denote its inverse by Γ:wp(C)gl(4,C). Thus we have the commutative diagram in Figure 1 which holds for any invertible Agl(4,C). Here, the map PA:wp(4,8,C)wp(4,8,C) is defined by PA(M)=AM.

    Figure 1.  The commutative diagram showing the one-to-one correspondence Γ between gl(4,C) and the set wp(C) of all equivalent well-posed boundary matrices. wp(C) is also in one-to-one correspondence with the set of all integral operators KM in (1.1). This commutative diagram holds for any invertible Agl(4,C), where PA(M)=AM. π is the canonical projection which maps a well-posed boundary matrix M to its equivalence class [M] with respect to . The maps G and ψ defined in Definition 2.6 are explicitly computable.

    By Proposition 2.1, the set of integral operators KM in (1.1) is in one-to-one correspondence with the set wp(C) of equivalent well-posed boundary matrices, and hence is also in one-to-one correspondence with gl(4,C). Note that both of the maps G and ψ in Definition 2.6 are explicitly computable, hence Γ and its inverse Γ1 are explicitly computable. For the special boundary matrix Q in (1.4), we have [2,Eq 6.2]

    G(Q)=O. (2.6)

    Proposition 2.2. For Mwp(4,8,C) and λC, the following (a) and (b) hold.

    (a) ([2,Theorem 1 and Corollary 1]) KM[u]=λu for some 0uL2[l,l] if and only if λ0 and u=cTyλ for some 0cgl(4,1,C) such that [G(M){Xλ(l)Xλ(l)}+Xλ(l)]c=0. KQ[u]=λu for some 0uL2[l,l] if and only if λ0 and u=cTyλ for some 0cgl(4,1,C) such that Xλ(l)c=0. In particular, 0λSpecKQ if and only if detXλ(l)=0.

    (b) ([2,Corollary 2]) Let 0λCSpecKQ. Then λSpecKM if and only if det{G(M)Yλ(l)I}=0.

    The following is well defined since the range χ(C{0,1/k}) of χ in Definition 2.4 does not contain 1,1,i,i.

    Definition 3.1. For λC{0,1/k} and xR, denote

    X±λ(x)=1κ44diag(eωz,e¯ωz)(eωκz1κ±eωκz1+κe¯ωκz1iκ±e¯ωκz1+iκeωκz1+iκ±eωκz1iκe¯ωκz1κ±e¯ωκz1+κ),

    where z=αx and κ=χ(λ).

    The following is well defined, since

    (1+κ21κ2)2(2κ1κ2)2=1,κC{1,1},(1κ21+κ2)2+(2κ1+κ2)2=1,κC{i,i}.

    Definition 3.2. Denote by β(κ) any holomorphic branch in C{1,1} satisfying

    coshβ(κ)=1+κ21κ2,sinhβ(κ)=2κ1κ2,

    and denote by γ(κ) any holomorphic branch in C{i,i} satisfying

    cosγ(κ)=1κ21+κ2,sinγ(κ)=2κ1+κ2.

    For zC and κC{1,1,i,i}, define

    δ±(z,κ)=sinh(2κz+β(κ))±sin(2κz+γ(κ)).

    β(κ) and γ(κ) are holomorphic branches of 2arctanhκ and 2arctanκ respectively, which, in turn, are anti-derivatives of 2/(1κ2) and 2/(1+κ2) respectively.

    Definition 3.3. Define F:wp(4,8,C)gl(4,C) by F(M)=VG(M)VT and ϕ:gl(4,C)wp(4,8,C) by ϕ(G)=ψ(VTGV). F(M) is called the fundamental boundary matrix corresponding to the well-posed boundary matrix Mwp(4,8,C).

    Denote by SimVT,SimV:gl(4,C)gl(4,C) the similarity transforms defined by SimVTG=VGVT and SimVG=VTGV respectively, so that F=SimVTG and ϕ=ψSimV by Definition 3.3. By (2.3), Sim1VT=SimV, hence, by Proposition 2.1 (b), F(ϕ(G))=SimVTG(ψ(SimVG))=SimVTSimVG=G for Ggl(4,C). Thus Definition 3.3 gives a new one-to-one correspondence Φ:wp(C)gl(4,C) defined by Φ=SimVTΓ. See Figure 2 for a commutative diagram which expands the one in Figure 1 to incorporate Φ.

    Figure 2.  Commutative diagram showing the one-to-one correspondence Φ between gl(4,C) and the set wp(C) of all equivalent well-posed boundary matrices, which is also in one-to-one correspondence with the set of all integral operators KM in (1.1). This commutative diagram holds for any invertible Agl(4,C), and extends the one for the map Γ in Figure 1 to incorporate Φ. SimVT and SimV are the similarity transforms defined by SimVTG=VGVT and SimVG=VTGV respectively. The maps F and ϕ defined in Definition 3.3 are explicitly computable.

    By Proposition 2.1 and Definition 3.3, the set of integral operators KM in (1.1) is in one-to-one correspondence with the 16-dimensional algebra gl(4,C). Both of Φ and its inverse Φ1 are explicitly computable by using the maps F and ϕ in Definition 3.3.

    Theorem 1. For λC{0,1/k}, the following (a) and (b) hold.

    (a) For Mwp(4,8,C), KM[u]=λu for some 0uL2[l,l] if and only if u=cTyλ for some 0cgl(4,1,C) such that

    {F(M)(X+λ(l)X+λ(l)OOXλ(l)Xλ(l))(X+λ(l)OOXλ(l))}Vc=0.

    KQ[u]=λu for some 0uL2[l,l] if and only if u=cTyλ for some 0cgl(4,1,C) such that (X+λ(l)OOXλ(l))Vc=0.

    (b) Let κ=χ(λ) and z=αx. Then, for xR,

    detX±λ(x)=e2zκ(1κ4)4δ±(z,κ),detXλ(x)=detX+λ(x)detXλ(x)=e22zκ2(1κ4)216δ+(z,κ)δ(z,κ).

    The proof of Theorem 1 will be given at the end of Section 4.

    By Proposition 1.1, 0,1/kSpecKQ for every l>0. Note that κ0 and κ41 when κ=χ(λ) and λC{0,1/k}. Thus, by Proposition 2.2 (a) and Theorem 1, the zero sets of the holomorphic functions δ±(z,κ) in Definition 3.2 completely describe SpecKQ in Proposition 1.1.

    Corollary 1. For every l>0, λC is in SpecKQ if and only if λ0, λ1/k, and δ+(αl,χ(λ))δ(αl,χ(λ))=0.

    Definition 3.4. For zC, denote pn(z)=nr=0ωnrr!zr, n=0,1,2,3, where it is understood that 00=1, and denote

    P+(z)=(¯p0(¯z)¯p2(¯z)p0(z)p2(z)),P(z)=(¯p1(¯z)¯p3(¯z)p1(z)p3(z)).

    For xR, denote

    X+1/k(x)=122diag(eωz,e¯ωz)P+(z)diag(1,α2),X1/k(x)=122diag(eωz,e¯ωz)P(z)diag(α1,α3),

    where z=αx.

    Definition 3.5. For zC, denote

    p+(z)=1+z2,p(z)=1+2z+z2+z332.

    Theorem 2. The following (a) and (b) hold.

    (a) For Mwp(4,8,C), KM[u]=1ku for some 0uL2[l,l] if and only if u=cTy1/k for some 0cgl(4,1,C) such that

    {F(M)(X+1/k(l)X+1/k(l)OOX1/k(l)X1/k(l))(X+1/k(l)OOX1/k(l))}ˆVc=0.

    (b) For xR,

    detX+1/k(x)=ie2z4α2p+(z),detX1/k(x)=ie2z4α4p(z),detX1/k(x)=detX+1/k(x)detX1/k(x)=e22z16α6p+(z)p(z),

    where z=αx. detX±1/k(x)0 and detX1/k(x)0 for x>0.

    The proof of Theorem 2 will be given at the end of Section 5.

    Definition 3.6. For 0λC and xR such that detX±λ(x)0, denote Y±λ(x)=X±λ(x)X±λ(x)1I.

    Theorem 3. The following (a) and (b) hold.

    (a) For Mwp(4,8,C) and 0λCSpecKQ, λSpecKM if and only if

    det{F(M)(Y+λ(l)OOYλ(l))I}=0.

    (b) Let 0λC, xR, and z=αx. Suppose that detX±λ(x)0. If λ1/k, then

    Y±λ(x)=1δ±(z,κ)(e2ωzδ±(iz,κ)δ±(z,κ)2ωe2zs±(zκ)2¯ωe2zs±(zκ)e2¯ωzδ±(iz,κ)δ±(z,κ)),

    where κ=χ(λ) and s±(ζ)=sinh(2ζ)±sin(2ζ) for ζC. Also,

    Y±1/k(x)=1p±(z)(e2ωzp±(iz)p±(z)121ωe2zz21121¯ωe2zz21e2¯ωzp±(iz)p±(z)).

    The proof of Theorem 3 will be given at the end of Section 6.

    Definition 4.1. For z,κC, denote

    X(z,κ)=14eEΩz{diag(0,1,1,0)W0diag(1,κ,κ2,κ3)W0eΩκz+diag(1,0,0,1)W0diag(1,κ,κ2,κ3)W0eΩκz}.

    Proposition 4.1. ([2,Eq 7.9]) For λC{0,1/k} and xR, Xλ(x)=X(z,κ), where z=αx and κ=χ(λ).

    Definition 4.2. Denote D=C{0,1,1,i,i}. For zC and κD, denote

    ˆX(z,κ)=11κ4{diag(0,1,1,0)W0diag(1,κ,κ2,κ3)W0eΩκz+diag(1,0,0,1)W0diag(1,κ,κ2,κ3)W0eΩκz}.

    By Definitions 4.1 and 4.2, we have

    X(z,κ)=1κ44eEΩzˆX(z,κ),zC, κD. (4.1)

    Lemma 4.1. For zC and κD, ˆX(z,κ)=(eϵiωjκz1ωjωiκ)1i,j4.

    Proof. By Definition 2.1 and (2.1), W0=(¯ωij1)1i,j4=(ω1ji)1i,j4, hence

    {W0diag(1,κ,κ2,κ3)W0}i,j=4r=1ω1riκr1ωr1j=4r=1(ωjωiκ)r1=1ω4jω4iκ41ωjωiκ=1κ41ωjωiκ

    for 1i,j4. So by Definition 4.2, we have

    ˆX(z,κ)=diag(0,1,1,0)(11ωjωiκ)1i,j4eΩκz+diag(1,0,0,1)(11ωjωiκ)1i,j4eΩκz=diag(0,1,1,0)(eωjκz1ωjωiκ)1i,j4+diag(1,0,0,1)(eωjκz1ωjωiκ)1i,j4.

    Thus the result follows by Definition 2.2.

    Definition 4.3. For zC and κD, denote

    ˆX±(z,κ)=(eωκz1κ±eωκz1+κe¯ωκz1iκ±e¯ωκz1+iκeωκz1+iκ±eωκz1iκe¯ωκz1κ±e¯ωκz1+κ),X±(z,κ)=1κ44diag(eωz,e¯ωz)ˆX±(z,κ).

    Note from Definitions 3.1 and 4.3 that

    X±λ(x)=X±(z,κ),λC{0,1/k}, xR, (4.2)

    where z=αx and κ=χ(λ).

    Lemma 4.2. For zC and κD, VˆX(z,κ)VT=(ˆX+(z,κ)OOˆX(z,κ)).

    Proof. By (2.1), Definition 2.2 and Lemma 4.1,

    ˆX(z,κ)i+2,j+2=eϵi+2ωj+2κz1ωj+2ωi+2κ=e(ϵi)(ωj)κz1(ωj)(ωi)κ=eϵiωjκz1ωjωiκ=ˆX(z,κ)i,j,ˆX(z,κ)i+2,j=eϵi+2ωjκz1ωjωi+2κ=e(ϵi)(ωj+2)κz1(ωj+2)(ωi)κ=eϵiωj+2κz1ωj+2ωiκ=ˆX(z,κ)i,j+2

    for 1i,j2, which implies that ˆX(z,κ)=(ABBA), where we put A={ˆX(z,κ)i,j}1i,j2,B={ˆX(z,κ)i,j+2}1i,j2gl(2,C). So by Lemma 2.1, we have

    VˆX(z,κ)VT=(A+BOOAB). (4.3)

    By Lemma 4.1, we have

    A±B={ˆX(z,κ)i,j}1i,j2±{ˆX(z,κ)i,j+2}1i,j2=(eϵiωjκz1ωjωiκ±eϵiωj+2κz1ωj+2ωiκ)1i,j2=(eϵ1ω1κz1ω1ω1κ±eϵ1ω3κz1ω3ω1κeϵ1ω2κz1ω2ω1κ±eϵ1ω4κz1ω4ω1κeϵ2ω1κz1ω1ω2κ±eϵ2ω3κz1ω3ω2κeϵ2ω2κz1ω2ω2κ±eϵ2ω4κz1ω4ω2κ),

    hence, by (2.1) and Definitions 2.2, 4.3,

    A±B=(eωκz1κ±eωκz1+κe¯ωκz1iκ±e¯ωκz1+iκeωκz1+iκ±eωκz1iκe¯ωκz1κ±e¯ωκz1+κ)=ˆX±(z,κ).

    Thus the lemma follows by (4.3).

    Lemma 4.3. For zC and κD, VX(z,κ)VT=(X+(z,κ)OOX(z,κ)).

    Proof. By (2.3), (2.4), (4.1) and Lemma 4.2,

    VX(z,κ)VT=V{1κ44eEΩzˆX(z,κ)}VT=1κ44VeEΩzVTVˆX(z,κ)VT=1κ44(diag(eωz,e¯ωz)OOdiag(eωz,e¯ωz))(ˆX+(z,κ)OOˆX(z,κ))=1κ44(diag(eωz,e¯ωz)ˆX+(z,κ)OOdiag(eωz,e¯ωz)ˆX(z,κ)).

    Thus the lemma follows by Definition 4.3.

    By Proposition 4.1, (4.2) and Lemma 4.3, we have

    Xλ(x)=VT(X+λ(x)OOXλ(x))V,λC{0,1/k}, xR. (4.4)

    Lemma 4.4. For zC and κD, detˆX±(z,κ)=4κ1κ4δ±(z,κ).

    See Supplementary A for proof of Lemma 4.4.

    Proof of Theorem 1. Let λC{0,1/k} and Mwp(4,8,C). By Proposition 2.2 (a), KM[u]=λu for some 0uL2[l,l] if and only if u=cTyλ for some 0cgl(4,1,C) such that

    0=V[G(M){Xλ(l)Xλ(l)}Xλ(l)]c, (4.5)

    since V is invertible by (2.3). Thus the first assertion in (a) follows, since (4.5) is equivalent to

    0=[VG(M){VT(X+λ(l)OOXλ(l))VVT(X+λ(l)OOXλ(l))V}VVT(X+λ(l)OOXλ(l))V]c=[F(M)(X+λ(l)X+λ(l)OOXλ(l)Xλ(l))(X+λ(l)OOXλ(l))]Vc

    by (4.4) and Definition 3.3. The second assertion in (a) follows from the first one, since F(Q)=VG(Q)VT=O by (2.6) and Definition 3.3.

    Let κ=χ(λ), xR, and z=αx. By (2.3) and (4.4), we have

    detXλ(x)=det{VT(X+λ(x)OOXλ(x))V}=detVT{detX+λ(x)detXλ(x)}detV=detX+λ(x)detXλ(x). (4.6)

    By (4.2) and Definition 4.3,

    detX±λ(x)=detX±(z,κ)=det{1κ44diag(eωz,e¯ωz)ˆX±(z,κ)}=(1κ44)2detdiag(eωz,e¯ωz)detˆX±(z,κ),

    hence, by (2.5) and Lemma 4.4,

    detX±λ(x)=(1κ4)216e2z4κ1κ4δ±(z,κ)=e2zκ(1κ4)4δ±(z,κ).

    So by (4.6), we have

    detXλ(x)=e2zκ(1κ4)4δ+(z,κ)e2zκ(1κ4)4δ(z,κ)=e22zκ2(1κ4)216δ+(z,κ)δ(z,κ).

    Thus we showed (b), and the proof is complete.

    Definition 5.1. For zC, denote

    P(z)=(¯p0(¯z)¯p1(¯z)¯p2(¯z)¯p3(¯z)p0(z)p1(z)p2(z)p3(z)¯p0(¯z)¯p1(¯z)¯p2(¯z)¯p3(¯z)p0(z)p1(z)p2(z)p3(z)).

    Proposition 5.1. (a) ([2,Eq 7.13]) X1/k(x)=14eEΩzP(z)diag(1,α,α2,α3)1 for xR, where z=αx.

    (b) ([2,Lemma B1]) For zC, VP(z)ˆV=2(P+(z)OOP(z)).

    The result in Proposition 5.1 (b) was for zR in [2] originally, but it can immediately be extended to zC.

    By (2.3), we have

    ˆVTdiag(1,α1,α2,α3)ˆV=ˆVdiag(1,α1,α2,α3)ˆV=(1000001001000001)(10000α10000α20000α3)(1000001001000001)=(100000α200α100000α3)(1000001001000001)=diag(1,α2,α1,α3)=(diag(1,α2)OOdiag(α1,α3)). (5.1)

    By Proposition 5.1 (a) and (2.3),

    VX1/k(x)ˆV=V{14eEΩzP(z)diag(1,α,α2,α3)1}ˆV=14VeEΩzVTVP(z)ˆVˆVTdiag(1,α1,α2,α3)ˆV,

    hence, by (2.4), (5.1) and Proposition 5.1 (b),

    VX1/k(x)ˆV=14(diag(eωz,e¯ωz)OOdiag(eωz,e¯ωz))2(P+(z)OOP(z))(diag(1,α2)OOdiag(α1,α3)).

    Thus, by (2.3) and Definition 3.4, we have

    X1/k(x)=VT(X+1/k(x)OOX1/k(x))ˆV,xR. (5.2)

    By Definition 3.4 and (2.1), we have

    p0(z)=1,p1(z)=ω+z,p2(z)=ω2+ωz+12z2=i+ωz+12z2,p3(z)=ω3+ω2z+12ωz2+16z3=¯ω+iz+12ωz2+16z3. (5.3)

    Lemma 5.1. For zC, detP+(z)=2ip+(z) and detP(z)=2ip(z).

    Proof. By Definitions 3.4, 3.5, (2.1) and (5.3),

    detP+(z)=¯p0(¯z)p2(z)p0(z)¯p2(¯z)=1(i+ωz+12z2)1(i+¯ωz+12z2)=2i+2iz=2ip+(z),detP(z)=¯p1(¯z)p3(z)+p1(z)¯p3(¯z)=(¯ω+z)(¯ω+iz+12ωz2+16z3)+(ω+z)(ωiz+12¯ωz2+16z3)={i2iz(12+i)z2(ω2+¯ω6)z316z4}+{i2iz+(12i)z2+(¯ω2+ω6)z3+16z4}=2i22iz2iz22i3z3=2ip(z).

    Proof of Theorem 2. Let Mwp(4,8,C). By Proposition 2.2 (a), KM[u]=1ku for some 0uL2[l,l] if and only if u=cTy1/k for some cgl(4,1,C) such that

    0=V[G(M){X1/k(l)X1/k(l)}X1/k(l)]c, (5.4)

    since V is invertible by (2.3). Thus (a) follows, since (5.4) is equivalent to

    0=[VG(M){VT(X+1/k(l)OOX1/k(l))ˆVVT(X+1/k(l)OOX1/k(l))ˆV}VVT(X+λ(l)OOXλ(l))ˆV]c=[F(M)(X+1/k(l)X+1/k(l)OOX1/k(l)X1/k(l))(X+1/k(l)OOX1/k(l))]ˆVc

    by (5.2) and Definition 3.3.

    Let xR and z=αx. By (2.3) and (5.2),

    detX1/k(x)=detVTdet(X+1/k(x)OOX1/k(x))detˆV=detX+1/k(x)detX1/k(x). (5.5)

    By (2.5), Definition 3.4 and Lemma 5.1,

    detX+1/k(x)=(122)2detdiag(eωz,e¯ωz)detP+(z)detdiag(1,α2)=18e2z{2ip+(z)}α2=ie2z4α2p+(z), (5.6)
    detX1/k(x)=(122)2detdiag(eωz,e¯ωz)detP(z)detdiag(α1,α3)=18e2z{2ip(z)}α4=ie2z4α4p(z). (5.7)

    By (5.5), (5.6), (5.7),

    detX1/k(x)=ie2z4α2p+(z){ie2z4α4p(z)}=e22z16α6p+(z)p(z).

    It follows that detX±1/k(x)0 and detX1/k(x)0 for x>0, since p±(z)>0 for z>0 by Definition 3.5. Thus we showed (b), and the proof is complete.

    Denote R=(0110). For a,b,c,dC, we have

    R(abcd)R=(0110)(abcd)(0110)=(dcba). (6.1)

    By Definition 4.3,

    adjˆX±(z,κ)=(e¯ωκz1κ±e¯ωκz1+κ(e¯ωκz1iκ±e¯ωκz1+iκ)(eωκz1+iκ±eωκz1iκ)eωκz1κ±eωκz1+κ) (6.2)

    for zC and κD. Note from Definition 4.2 that ¯κD if and only if κD.

    Lemma 6.1. For zC and κD,

    {ˆX±(z,κ)adjˆX±(z,κ)}2,1=¯{ˆX±(¯z,¯κ)adjˆX±(¯z,¯κ)}1,2,{ˆX±(z,κ)adjˆX±(z,κ)}2,2=¯{ˆX±(¯z,¯κ)adjˆX±(¯z,¯κ)}1,1.

    Proof. Let zC and κD. It can be checked from Definition 4.3 and (6.2) that ˆX±(z,κ)2,1=¯ˆX±(¯z,¯κ)1,2, ˆX±(z,κ)2,2=¯ˆX±(¯z,¯κ)1,1, and {adjˆX±(z,κ)}2,1=¯{adjˆX±(¯z,¯κ)}1,2, {adjˆX±(z,κ)}2,2=¯{adjˆX±(¯z,¯κ)}1,1, which, by (6.1), are equivalent to RˆX±(z,κ)R=¯ˆX±(¯z,¯κ), RadjˆX±(z,κ)R=¯adjˆX±(¯z,¯κ). So we have

    R{ˆX±(z,κ)adjˆX±(z,κ)}R={RˆX±(z,κ)R}{RadjˆX±(z,κ)R}=¯ˆX±(¯z,¯κ)¯adjˆX±(¯z,¯κ)=¯{ˆX±(¯z,¯κ)adjˆX±(¯z,¯κ)},

    since R2=I. Thus the result follows by (6.1).

    Lemma 6.2. For zR and κD,

    ˆX±(z,κ)adjˆX±(z,κ)=4κ1κ4(δ±(iz,κ)2ωs±(zκ)2¯ωs±(zκ)δ±(iz,κ)),

    where s±(ζ)=sinh(2ζ)±sin(2ζ) for ζC.

    See Supplementary B for proof of Lemma 6.2.

    Definition 6.1. For zC and κD such that detX±(z,κ)0, denote Y±(z,κ)=X±(z,κ)X±(z,κ)1I.

    By Definitions 3.6, 6.1 and (4.2),

    Y±λ(x)=Y±(z,κ),λC{0,1/k}, xR, detX±λ(x)0, (6.3)

    where z=αx and κ=χ(λ). Note from (2.1) that, for a,b,c,d,δC, δ0,

    1δdiag(eωz,e¯ωz)(abcd)diag(eωz,e¯ωz)I=1δ(e2ωzae2zbe2zce2¯ωzd)I=1δ(e2ωzaδe2zbe2zce2¯ωzdδ). (6.4)

    Lemma 6.3. For zC and κD such that detX±(z,κ)0,

    Y±(z,κ)=1δ±(z,κ)(e2ωzδ±(iz,κ)δ±(z,κ)2ωe2zs±(zκ)2¯ωe2zs±(zκ)e2¯ωzδ±(iz,κ)δ±(z,κ)),

    where s±(ζ)=sinh(2ζ)±sin(2ζ) for ζC.

    Proof. Let zC, κD, and suppose that detX±(z,κ)0. By Definition 4.3,

    X±(z,κ)1={1κ44diag(eωz,e¯ωz)ˆX±(z,κ)}1=41κ4ˆX±(z,κ)1diag(eωz,e¯ωz),

    hence, by Definition 6.1,

    Y±(z,κ)={1κ44diag(eω(z),e¯ω(z))ˆX±(z,κ)}{41κ4ˆX±(z,κ)1diag(eωz,e¯ωz)}I=diag(eωz,e¯ωz)ˆX±(z,κ)ˆX±(z,κ)1diag(eωz,e¯ωz)I. (6.5)

    By Lemmas 4.4 and 6.2,

    ˆX±(z,κ)ˆX±(z,κ)1=1detˆX±(z,κ)ˆX±(z,κ)adjˆX±(z,κ)=14κ1κ4δ±(z,κ)4κ1κ4(δ±(iz,κ)2ωs±(zκ)2¯ωs±(zκ)δ±(iz,κ)),

    hence, by (6.5),

    Y±(z,κ)=1δ±(z,κ)diag(eωz,e¯ωz)(δ±(iz,κ)2ωs±(zκ)2¯ωs±(zκ)δ±(iz,κ))diag(eωz,e¯ωz)I.

    Thus the lemma follows by (6.4).

    By Definition 3.4, we have

    adjP+(z)=(p2(z)¯p2(¯z)p0(z)¯p0(¯z)),adjP(z)=(p3(z)¯p3(¯z)p1(z)¯p1(¯z)),zC. (6.6)

    Lemma 6.4. For zC, P±(z)adjP±(z)=±2i(p±(iz)121ωz21121¯ωz21p±(iz)).

    See Supplementary C for proof of Lemma 6.4.

    Lemma 6.5. Let xR, z=αx, and suppose that detX±1/k(x)0. Then

    Y±1/k(x)=1p±(z)(e2ωzp±(iz)p±(z)121ωe2zz21121¯ωe2zz21e2¯ωzp±(iz)p±(z)).

    Proof. By Definition 3.4,

    X±1/k(x)1={122diag(eωz,e¯ωz)P±(z)diag(α1±12,α5±12)}1=22diag(α1±12,α5±12)1P±(z)1diag(eωz,e¯ωz).

    So by Definitions 3.4 and 3.6,

    Y±1/k(x)={122diag(eω(z),e¯ω(z))P±(z)diag(α1±12,α5±12)}{22diag(α1±12,α5±12)1P±(z)1diag(eωz,e¯ωz)}I=diag(eωz,e¯ωz)P±(z)P±(z)1diag(eωz,e¯ωz)I. (6.7)

    By Lemmas 5.1 and 6.4,

    P±(z)P±(z)1=1detP±(z)P±(z)adjP±(z)=1±2ip±(z){±2i(p±(iz)121ωz21121¯ωz21p±(iz))},

    hence, by (6.7),

    Y±1/k(x)=1p±(z)diag(eωz,e¯ωz)(p±(iz)121ωz21121¯ωz21p±(iz))diag(eωz,e¯ωz)I.

    Thus the lemma follows by (6.4).

    Let 0λC and xR. Suppose that detXλ(x)0, which is equivalent to detX+λ(x)0 and detXλ(x)0 by (4.4) and (5.2). Let A={VT,if λ1/k,ˆV,if λ=1/k. Then by Definition 2.5 and (2.3),

    VYλ(x)VT=V{Xλ(x)Xλ(x)1I}VT=VXλ(x)AA1Xλ(x)1VTI=VXλ(x)A{VXλ(x)A}1I,

    hence, by (2.3), (4.4) and (5.2),

    VYλ(x)VT=(X+λ(x)OOXλ(x))(X+λ(x)OOXλ(x))1I=(X+λ(x)OOXλ(x))(X+λ(x)1OOXλ(x)1)(IOOI)=(X+λ(x)X+λ(x)1IOOXλ(x)Xλ(x)1I).

    Thus, by (2.3) and Definition 3.6, we have

    Yλ(x)=VT(Y+λ(x)OOYλ(x))V,0λC, xR, detX±λ(x)0. (6.8)

    Proof of Theorem 3. Let Mwp(4,8,C) and 0λCSpecKQ. By Proposition 2.2 (b), λSpecKM if and only if

    det[V{G(M)Yλ(l)I}VT]=0, (6.9)

    since V is invertible by (2.3). Thus (a) follows, since (6.9) is equivalent to

    0=det{VG(M)VT(Y+λ(l)OOYλ(l))VVTVVT}=det{F(M)(Y+λ(l)OOYλ(l))I}

    by (6.8) and Definition 3.3.

    Let 0λC, xR, and z=αx. Suppose that detX±λ(x)0. (b) follows from (6.3) and Lemma 6.3 when λ1/k, and from Lemma 6.5 when λ=1/k. Thus the proof is complete.

    The boundary conditions usually considered in practice are only a few in number, including clamped, free, or hinged conditions at each end of the beam. An important aspect of our results is that we have obtained explicit and manageable characteristic equations for the whole 16-dimensional class of integral operators KM arising from arbitrary well-posed boundary value problem of the Euler–Bernoulli beam equation.

    In our characteristic equations in Theorems 1, 2, and 3, the explicit matrices X±λ and Y±λ are not affected by specific boundary conditions. The effect of the boundary condition M is encoded separately in the fundamental boundary matrix F(M). The set of equivalent well-posed boundary matrices wp(C), and hence the set of integral operators KM in (1.1), is in one-to-one correspondence with the 16-dimensional algebra gl(4,C) via the map Φ. Φ and its inverse Φ1 are explicitly computable using the maps F and ϕ in Definition 3.3. See Figure 2 in Section 3 for a commutative diagram showing the details.

    The 2×2 matrices X±λ and Y±λ themselves are pre-calculated in terms of the explicit functions δ±(z,κ) and p±(z). Thus our characteristic equations have simple and manageable expressions with the functions δ±(z,κ) and p±(z), which are amenable to concrete analysis similar to that in [14].

    By inverting the 2×2 matrices Y±λ(l) in Theorem 3, we would have alternate forms of the characteristic equations in Theorem 1 (a) and Theorem 2 (a) with matrix entries also explicitly expressed by δ±(z,κ) and p±(z). However, these forms are suppressed in this paper due to the nontrivial problem of identifying the zeros of detY±λ(l) or det{X±λ(l)X±λ(l)}, which will be dealt in future works.

    Although our results are for boundary matrices with complex entries in general, boundary conditions of practical importance are those represented by boundary matrices with real entries. See [2] for the characterization of these real boundary conditions M in terms of G(M) by using the R-algebra ¯π(4)gl(4,C).

    An immediate application of our results would be spectral analysis for a few typical boundary conditions encountered frequently in practice. Specifically, concrete spectral analysis for the following combinations of clamped, free, and hinged boundary conditions at each end of the beam are now possible, which will be performed in future works.

    clamped-clamped or bi-clamped.

    free-free or bi-free.

    hinged-hinged or bi-hinged.

    clamped-free or cantilevered.

    hinged-free.

    clamped-hinged.

    In fact, it turns out that the fundamental boundary matrices F(M) corresponding to the first three symmetric boundary conditions M above also have the following block-diagonal form with 2×2 blocks.

    F(M)=(F(M)+OOF(M)).

    In these cases, our characteristic equations in Theorems 1, 2, and 3 are completely separable into 2×2 blocks, resulting in further simplified forms which involve determinants of 2×2 matrices only.

    The author thanks the anonymous reviewers for their careful and constructive comments which helped to improve the manuscript.

    The author declares no conflict of interest in this paper.

    By Definition 4.3 and (2.1),

    detˆX±(z,κ)=ˆX±(z,κ)1,1ˆX±(z,κ)2,2ˆX±(z,κ)2,1ˆX±(z,κ)1,2=(eωκz1κ±eωκz1+κ)(e¯ωκz1κ±e¯ωκz1+κ)(eωκz1+iκ±eωκz1iκ)(e¯ωκz1iκ±e¯ωκz1+iκ)=e2κz(1κ)2+e2κz(1+κ)2±ei2κz1κ2±ei2κz1κ2e2κz1+κ2e2κz1+κ2ei2κz(1iκ)2ei2κz(1+iκ)2={1(1κ)211+κ2}e2κz+{1(1+κ)211+κ2}e2κz±{11κ21(1iκ)2}ei2κz±{11κ21(1+iκ)2}ei2κz=2κ(1κ)2(1+κ2)e2κz2κ(1+κ)2(1+κ2)e2κz2iκ(1κ2)(1iκ)2ei2κz±2iκ(1κ2)(1+iκ)2ei2κz=2κ(1κ2)2(1+κ2){(1+κ)2e2κz(1κ)2e2κz}2iκ(1κ2)(1+κ2)2{(1+iκ)2ei2κz(1iκ)2ei2κz}=2κ(1κ4)(1κ2){2(1+κ2)sinh(2κz)+4κcosh(2κz)}2iκ(1κ4)(1+κ2){2i(1κ2)sin(2κz)+4iκcos(2κz)}=4κ1κ4{1+κ21κ2sinh(2κz)+2κ1κ2cosh(2κz)}±4κ1κ4{1κ21+κ2sin(2κz)+2κ1+κ2cos(2κz)}.

    Thus, by Definition 3.2,

    detˆX±(z,κ)=4κ1κ4{sinh(2κz)coshβ(κ)+cosh(2κz)sinhβ(κ)}±4κ1κ4{sin(2κz)cosγ(κ)+cos(2κz)sinγ(κ)}=4κ1κ4{sinh(2κz+β(κ))±sin(2κz+γ(κ))}=4κ1κ4δ±(z,κ).

    Let zC and κD. By Definition 4.3, (2.1) and (6.2),

    {ˆX±(z,κ)adjˆX±(z,κ)}1,1=ˆX±(z,κ)1,1{adjˆX±(z,κ)}1,1+ˆX±(z,κ)1,2{adjˆX±(z,κ)}2,1=(eωκ(z)1κ±eωκ(z)1+κ)(e¯ωκz1κ±e¯ωκz1+κ)(e¯ωκ(z)1iκ±e¯ωκ(z)1+iκ)(eωκz1+iκ±eωκz1iκ)=ei2κz(1+κ)2+ei2κz(1κ)2±e2κz1κ2±e2κz1κ2ei2κz1+κ2ei2κz1+κ2e2κz(1iκ)2e2κz(1+iκ)2={1(1+κ)211+κ2}ei2κz+{1(1κ)211+κ2}ei2κz{1(1iκ)211κ2}e2κz{1(1+iκ)211κ2}e2κz=2κ(1+κ)2(1+κ2)ei2κz+2κ(1κ)2(1+κ2)ei2κz2iκ(1iκ)2(1κ2)e2κz±2iκ(1+iκ)2(1κ2)e2κz=2κ(1κ2)2(1+κ2){(1κ)2ei2κz(1+κ)2ei2κz}2iκ(1+κ2)2(1κ2){(1+iκ)2e2κz(1iκ)2e2κz}=2κ(1κ4)(1κ2){2i(1+κ2)sin(2κz)4κcos(2κz)}2iκ(1κ4)(1+κ2){2(1κ2)sinh(2κz)+4iκcosh(2κz)}=4κ1κ4{1+κ21κ2sinh(i2κz)2κ1κ2cosh(i2κz)}4κ1κ4{1κ21+κ2sin(i2κz)2κ1+κ2cos(i2κz)},

    hence, by Definition 3.2,

    {ˆX±(z,κ)adjˆX±(z,κ)}1,1=4κ1κ4{sinh(i2κz)coshβ(κ)cosh(i2κz)sinhβ(κ)}4κ1κ4{sin(i2κz)cosγ(κ)cos(i2κz)sinγ(κ)}=4κ1κ4{sinh(i2κz+β(κ))±sin(i2κz+γ(κ))}=4κ1κ4δ±(iz,κ). (B.1)

    By Definition 4.3, (2.1) and (6.2),

    {ˆX±(z,κ)adjˆX±(z,κ)}1,2=ˆX±(z,κ)1,1{adjˆX±(z,κ)}1,2+ˆX±(z,κ)1,2{adjˆX±(z,κ)}2,2=(eωκ(z)1κ±eωκ(z)1+κ)(e¯ωκz1iκ±e¯ωκz1+iκ)+(e¯ωκ(z)1iκ±e¯ωκ(z)1+iκ)(eωκz1κ±eωκz1+κ)=e2κz(1+κ)(1+iκ)e2κz(1κ)(1iκ)ei2κz(1+κ)(1iκ)ei2κz(1κ)(1+iκ)+e2κz(1κ)(1iκ)+e2κz(1+κ)(1+iκ)±ei2κz(1κ)(1+iκ)±ei2κz(1+κ)(1iκ)={1(1κ)(1iκ)1(1+κ)(1+iκ)}(e2κze2κz)±{1(1κ)(1+iκ)1(1+κ)(1iκ)}(ei2κzei2κz)=(1+κ)(1+iκ)(1κ)(1iκ)1κ42sinh(2κz)±(1+κ)(1iκ)(1κ)(1+iκ)1κ42isin(2κz)=2(1+i)κ1κ42sinh(2κz)±2(1i)κ1κ42isinh(2κz)=2ω4κ1κ4{sinh(2κz)±sin(2κz)}=4κ1κ42ωs±(zκ). (B.2)

    By Lemma 6.1, (B.1), (B.2) and Definition 3.2,

    {ˆX±(z,κ)adjˆX±(z,κ)}2,1=¯{4¯κ1¯κ42ωs±(¯zκ)}=4κ1κ42¯ωs±(zκ), (B.3)
    {ˆX±(z,κ)adjˆX±(z,κ)}2,2=¯{4¯κ1¯κ4δ±(i¯z,¯κ)}=4κ1κ4δ±(iz,κ). (B.4)

    Thus the lemma follows from (B.1), (B.2), (B.3), (B.4).

    Let zC. By Definition 3.4 and (6.6), we have

    P+(z)adjP+(z)=(¯p0(¯z)¯p2(¯z)p0(z)p2(z))(p2(z)¯p2(¯z)p0(z)¯p0(¯z))=(¯p0(¯z)p2(z)p0(z)¯p2(¯z)¯p0(¯z)¯p2(¯z)+¯p0(¯z)¯p2(¯z)p0(z)p2(z)p0(z)p2(z)p0(z)¯p2(¯z)+¯p0(¯z)p2(z)), (C.1)
    P(z)adjP(z)=(¯p1(¯z)¯p3(¯z)p1(z)p3(z))(p3(z)¯p3(¯z)p1(z)¯p1(¯z))=(¯p1(¯z)p3(z)+p1(z)¯p3(¯z)¯p1(¯z)¯p3(¯z)+¯p1(¯z)¯p3(¯z)p1(z)p3(z)p1(z)p3(z)p1(z)¯p3(¯z)¯p1(¯z)p3(z)). (C.2)

    So, by (2.1), (5.3) and Definition 3.5,

    {P+(z)adjP+(z)}1,1=¯p0(¯z)p2(z)p0(z)¯p2(¯z)=1(i+ωz+12z2)1¯(iω¯z+12¯z2)=2i+2z=2i{1+(iz)2}=2ip+(iz), (C.3)
    {P+(z)adjP+(z)}2,1=p0(z)p2(z)p0(z)p2(z)=1(i+ωz+12z2)1(iωz+12z2)=2ωz, (C.4)
    {P(z)adjP(z)}1,1=¯p1(¯z)p3(z)+p1(z)¯p3(¯z)=¯(ω¯z)(¯ω+iz+12ωz2+16z3)+(ω+z)¯(¯ωi¯z+12ω¯z216¯z3)={i2z+(12+i)z2+(ω2¯ω6)z3+16z4}+{i2z+(12+i)z2+(¯ω2ω6)z316z4}=2(i2z+iz2+132z3)=2i{1+2(iz)+(iz)2+132(iz)3}=2ip(iz), (C.5)
    {P(z)adjP(z)}2,1=p1(z)p3(z)p1(z)p3(z)=(ωz)(¯ω+iz+12ωz2+16z3)(ω+z)(¯ωiz+12ωz216z3)=(1i2z2ω3z316z4)+(1+i2z2ω3z3+16z4)=2ω3z3. (C.6)

    Note from (C.1) and (C.2) that

    {P±(z)adjP±(z)}1,2=¯{P±(¯z)adjP±(¯z)}2,1,{P±(z)adjP±(z)}2,2=¯{P±(¯z)adjP±(¯z)}1,1.

    So by (C.3), (C.4), (C.5), (C.6),

    {P+(z)adjP+(z)}1,2=¯{P+(¯z)adjP+(¯z)}2,1=¯(2ω¯z)=2¯ωz, (C.7)
    {P+(z)adjP+(z)}2,2=¯{P+(¯z)adjP+(¯z)}1,1=¯{2ip+(i¯z)}=2ip+(iz), (C.8)
    {P(z)adjP(z)}1,2=¯{P(¯z)adjP(¯z)}2,1=¯(2ω3¯z3)=2¯ω3z3, (C.9)
    {P(z)adjP(z)}2,2=¯{P(¯z)adjP(¯z)}1,1=¯{2ip(i¯z)}=2ip(iz). (C.10)

    Thus, by (C.3), (C.4), (C.5), (C.6), (C.7), (C.8), (C.9), (C.10), we have

    P+(z)adjP+(z)=(2ip+(iz)2¯ωz2ωz2ip+(iz))=2i(p+(iz)ωz¯ωzp+(iz)),P(z)adjP(z)=(2ip(iz)2¯ω3z32ω3z32ip(iz))=2i(p(iz)ω3z3¯ω3z3p(iz)),

    and the proof is complete.

    We start with some exotic definitions in [14]. For κ0, let

    p(κ)=12κ+κ21+2κ+κ2,φ±(κ)=eLκ1±sinh(κ)cosh(κ). (D.1)

    Here, L=2lα is the intrinsic length of the beam and

    h(κ)=Lκˆh(κ), (D.2)

    where ˆh:[0,)R is defined by

    ˆh(κ)={arctan{22κ(κ21)κ44κ2+1},if 0κ<312,π2,if κ=312,π+arctan{22κ(κ21)κ44κ2+1},if 312κ3+12,3π2,if κ=3+12,2π+arctan{22κ(κ21)κ44κ2+1},if κ>3+12. (D.3)

    The branch of arctan here is taken such that arctan0=0. ˆh is a strictly decreasing real-analytic function with ˆh(0)=0 and limκˆh(κ)=2π, hence h:[0,)R is a strictly increasing real-analytic function with h(0)=0 and limκh(κ)=.

    Proposition D.1. ([14,Eqs 8 and 25]) λC is an eigenvalue of KQ=Kl,α,k if and only if λ=1k11+κ4 for κ>0 such that φ+(κ)=p(κ) or φ(κ)=p(κ).

    Now we demonstrate how the seemingly ad hoc and complex conditions φ±(κ)=p(κ) in Proposition D.1, which were practically unobtainable without help of computer algebra systems as indicated in [14], can be derived so naturally and elegantly from our holomorphic functions δ±(z,κ).

    By Definition 3.2,

    eiγ(κ)=cosγ(κ)+isinγ(κ)=1κ21+κ2+i2κ1+κ2=(1+iκ)21+κ2=1+iκ1iκ,κD, (D.4)

    where D=C{0,1,1,i,i} by Definition 4.2.

    Lemma D.1. For κ0, p(κ)=ei{γ(ωκ)γ(¯ωκ)} and eiˆh(κ)=ei{γ(ωκ)+γ(¯ωκ)}.

    Proof. By (2.1), (D.1), (D.4),

    ei{γ(ωκ)γ(¯ωκ)}=eiγ(ωκ)eiγ(¯ωκ)=1+iωκ1iωκ1i¯ωκ1+i¯ωκ=1¯ωκ1+¯ωκ1ωκ1+ωκ=12κ+κ21+2κ+κ2=p(κ).

    By (2.1) and (D.4),

    ei{γ(ωκ)+γ(¯ωκ)}=eiγ(ωκ)eiγ(¯ωκ)=1+iωκ1iωκ1+i¯ωκ1i¯ωκ=1¯ωκ1+¯ωκ1+ωκ1ωκ=1+i2κκ21i2κκ2=(1+i2κκ2)2(1i2κκ2)(1+i2κκ2)=(14κ2+κ4)+i22κ(1κ2)(1κ2)2+2κ2.

    So we have

    cos{γ(ωκ)+γ(¯ωκ)}=14κ2+κ41+κ4,sin{γ(ωκ)+γ(¯ωκ)}=22κ(1κ2)1+κ4,

    hence

    tan{γ(ωκ)+γ(¯ωκ)}=22κ(1κ2)κ44κ2+1.

    Thus, by (D.3),

    tan{ˆh(κ)}=tanˆh(κ)=22κ(1κ2)κ44κ2+1=tan{γ(ωκ)+γ(¯ωκ)}.

    It follows that eiˆh(κ)=ei{γ(ωκ)+γ(¯ωκ)}, and the proof is complete.

    By (D.2) and Lemma D.1,

    eih(κ)=ei{Lκˆh(κ)}=eiLκeiˆh(κ)=eiLκei{γ(ωκ)+γ(¯ωκ)}=ei{Lκ+γ(ωκ)+γ(¯ωκ)}.

    So we have cosh(κ)=cos{Lκ+γ(ωκ)+γ(¯ωκ)}, sinh(κ)=sin{Lκ+γ(ωκ)+γ(¯ωκ)}, hence, by (D.1),

    φ±(κ)=eLκ1±sin{Lκ+γ(ωκ)+γ(¯ωκ)}cos{Lκ+γ(ωκ)+γ(¯ωκ)}. (D.5)

    By Definition 3.2,

    eβ(κ)=coshβ(κ)+sinhβ(κ)=1+κ21κ2+2κ1κ2=(1+κ)21κ2=1+κ1κ,κD. (D.6)

    Comparing (D.4) and (D.6), we have eiγ(κ)=eβ(iκ) for κD, hence

    eβ(ωκ)=eβ(i(iωκ))=eiγ(¯ωκ),κD, (D.7)

    since iω=¯ω by (2.1).

    Now let λ=1k11+κ4 for κ>0, and let z=lα so that

    2κz=Lκ. (D.8)

    By Definitions 2.1 and 2.4,

    χ(λ)=411(1k11+κ4)k=4κ4=ωκ,

    hence δ±(lα,χ(λ))=δ±(z,ωκ). So by Corollary 1, λSpecKQ if and only if δ+(z,ωκ)=0 or δ(z,ωκ)=0. By Definition 1, 2ω=1+i, hence, by Definition 3.2 and (D.7),

    2δ±(z,ωκ)={e2ωκzeβ(ωκ)e2ωκzeβ(ωκ)}i{ei2ωκzeiγ(ωκ)ei2ωκzeiγ(ωκ)}={eκzeiκzeiγ(¯ωκ)eκzeiκzeiγ(¯ωκ)}i{eκzeiκzeiγ(ωκ)eκzeiκzeiγ(ωκ)}=eκz{eiκzeiγ(¯ωκ)±ieiκzeiγ(ωκ)}eκz{eiκzeiγ(¯ωκ)±ieiκzeiγ(ωκ)}.

    So δ±(z,ωκ)=0 if and only if

    e2κz=eiκzeiγ(¯ωκ)±ieiκzeiγ(ωκ)eiκzeiγ(¯ωκ)±ieiκzeiγ(ωκ)=eiκzeiγ(¯ωκ)±ieiκzeiγ(ωκ)eiκzeiγ(¯ωκ)±ieiκzeiγ(ωκ)eiκzeiγ(¯ωκ)ieiκzeiγ(ωκ)eiκzeiγ(¯ωκ)ieiκzeiγ(ωκ)=2ie2iκzei{γ(ωκ)+γ(¯ωκ)}±ie2iκzei{γ(ωκ)+γ(¯ωκ)}e2iκzei2γ(ωκ)+e2iκzei2γ(¯ω)=2i{e2iκzei{γ(ωκ)+γ(¯ωκ)}e2iκzei{γ(ωκ)+γ(¯ωκ)}}ei{γ(ωκ)γ(¯ωκ)}{e2iκzei{γ(ωκ)+γ(¯ωκ)}+e2iκzei{γ(ωκ)+γ(¯ωκ)}}=ei{γ(ωκ)γ(¯ωκ)}1±sin{2κz+γ(ωκ)+γ(¯ωκ)}cos{2κz+γ(ωκ)+γ(¯ωκ)},

    which is equivalent to p(κ)=φ±(κ) by Lemma D.1, (D.5) and (D.8). Thus we conclude that λSpecKQ if and only if p(κ)=φ+(κ) or p(κ)=φ(κ), which is exactly the condition in Proposition D.1.



    [1] Marzluff JM (2008) Urban ecology: an international perspective on the interaction between humans and nature, Springer.
    [2] Kant N, Anjali K (2020) Climate Strategy Proactivity (CSP): A Stakeholders-Centric Concept, In: Filho WL, Azul AM, Brandli L, et al. (Eds.), Partnerships for the Goals, Encyclopedia of the UN Sustainable Development Goals (Living Reference), Switzerland AG, Springer Nature, 1-16.
    [3] Gruber N, Friedlingstein P, Field C, et al. (2004) The vulnerability of the carbon cycle in the 21st century: An assessment of carbon-climate-human interactions, In: Gruber N, Friedlingstein P, Field CB, et al. (Eds.), The Global Carbon Cycle: Integrating Humans, Climate, and the Natural World, Washington, D.C.; London: Island Press, 45-76.
    [4] Lal M, Singh R (2000) Carbon Sequestration Potential of Indian Forests. Environ Monit Assess 60: 315-327.
    [5] Ravindranath N, Ostwald M (2008) Methods for estimating above ground biomass, In: Ravindranath N, Ostwald M (Eds.), Carbon inventory methods: handbook for greenhouse gas inventory, carbon mitigation and round wood production projects, Netherlands, Springer Science + Business Media B.V.
    [6] IPCC (2000) A special report of the IPCC: Land use, Land-use change and Forestry, UK, Cambridge University Press.
    [7] Cox HM (2012) A Sustainability Initiative to Quantify Carbon Sequestration by Campus Trees. J Geog 111: 173-183.
    [8] United Nations (2016) Urbanization and Development: World Cities Report 2016. Nairobi, United Nations Human Settlements Programme, 2016.
    [9] Nowak DJ (2016) Urban Forests, Assessing the Sustainability of Agricultural and Urban Forests in the United States, 37-52.
    [10] Fang J, Yu G, Liu L, et al. (2018) Climate change, human impacts, and carbon sequestration in China. Proc Natl Acad Sci 115: 4015-4020.
    [11] Yeshaneh E, Wagner W, Exner-Kittridge M, et al. (2013) Identifying land use/cover dynamics in the Koga catchment, Ethiopia, from multi-scale data, and implications for environmental change. ISPRS Int J Geo-Inf 302-323.
    [12] Schwela D (2000) Air Pollution and health in urban areas. Rev Environ Health 15: 13-42.
    [13] Mcpherson EG, Rowntree RA (1993) Energy conservation potential of urban tree. J Aboriculture 321-331.
    [14] McPherson EG (1998) Atmospheric carbon dioxide reduction by Sacramento's urban forest. J Aboriculture 215-223.
    [15] Nowak DJ, Crane DE (2002) Carbon storage and sequestration by urban trees in the USA. Environ Pollut 116: 381-389.
    [16] Yang J, McBride J, Zhou J, et al. (2005) The urban forest in Beijing and its role in air pollution reduction. Urban For Urban Green 3: 65-78.
    [17] Nowak DJ, Crane DE, Stevens JC (2006) Air pollution removal by urban trees and shrubs in the United States. Urban For Urban Green 4: 115-123.
    [18] Zhao X, Lynch JG, Chen Q (2010) Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. J Consum Res 37: 197-206.
    [19] Nowak DJ, Hirabayashi S, Bodine A, et al. (2014) Tree and Forest Effects on Air Quality and Human Health in the United States. Environ Pollut 119-129.
    [20] Skofronick-Jackson G, Petersen WA, Berg W, et al. (2016) The Global Precipitation Measurement (GPM) Mission for Science and Society. Bull Am Meteorol Soc 98: 1679-1695.
    [21] Livesley SJ, McPherson EG, Calfapietra C (2016) The Urban Forest and Ecosystem Services: Impacts on Urban Water, Heat, and Pollution Cycles at the Tree, Street, and City Scale. J Environ Qual 45: 119-124.
    [22] McCarthy MP, Best M, Betts R (2010) Climate change in cities due to global warming and urban effects. Geophys Res Lett 37.
    [23] Borelli S, Conigliaro M, Pineda F (2018) Urban forests in the global context. Unasylva 250 69: 3-10.
    [24] Konijnendijk CC, Randrup TB (2004) Urban Forestry, LANDSCAPE AND PLANNING, 471-478.
    [25] BATJES NH, SOMBROEK WG (1997) Possibilities for carbon sequestration in tropical and subtropical soils. Glob Chang Biol 3: 161-173.
    [26] Canadell JG, Kirschbaum MUF, Kurz WA, et al. (2007) Factoring out natural and indirect human effects on terrestrial carbon sources and sinks. Environ Sci Policy 10: 370-384.
    [27] Lal R (2008) Carbon sequestration. Philos Trans R Soc Biol Sci 363: 815-830.
    [28] IPCC (2014) Climate change 2014: Synthesis report summary for policymakers. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Geneva, Switzerland.
    [29] Salunkhe O, Khare PK, Kumari R, et al. (2018) A systematic review on the aboveground biomass and carbon stocks of Indian forest ecosystems. Ecol Process 7-17.
    [30] Afzal M, Akhtar AM (2013) Factors affecting carbon sequestration in trees. J Agric Res 51: 61-69.
    [31] Sahu C, Sahu KS (2015) Air Pollution Tolerance Index (APTI), Anticipated Performance Index (API), Carbon Sequestration and Dust Collection Potential of Indian Tree Species: A Review. Emerg Res Manag & Technology 4-11.
    [32] Stern NS (2007) The Economics of Climate Change: The Stern Review, Cambridge, UK, Cambridge University Press.
    [33] Stephson NL, Das AJ, Condit R, et al. (2014) Rate of tree carbon accumulation increases continuously with tree size. Nature 507: 90-93.
    [34] WMO (2017) Statement on the state of the global climate change.
    [35] Raupach MR, Canadell JG (2010) Carbon and the Anthropocene. Curr Opin Environ Sustain 210-218.
    [36] Akbari H (2002) Shade trees reduce building energy use and CO2 emissions from power plants. Environ Pollut 116: 119-126.
    [37] McHale MR, McPherson EG, Burke IC (2007) The potential of urban tree plantings to be cost effective in carbon credit markets. Urban For Urban Green 49-60.
    [38] Hartmann F, Perego P, Young A (2013) Carbon Accounting: Challenges for Research in Management Control and Performance Measurement. Abacus 49: 539-563.
    [39] Cairns RD, Lasserre P (2006) Implementing carbon credits for forests based on green accounting. Ecol Econ 56: 610-621.
    [40] Gazioğlu C, Okutan V (2016) Underwater Noise Pollution at the Strait of Istanbul (Bosphorus). Int J Environ Geoinformatics 3: 26-39.
    [41] Sasaki N, Kim S (2009) Biomass carbon sinks in Japanese forests: 1966-2012. Forestry 82: 105-115.
    [42] Suryawanshi M, Patel A, Kale T, et al. (2014) Carbon sequestration potential of tree species in the environment of North Maharashtra University Campus, Jalgaon (MS) India. Biosci Discov 5: 175-179.
    [43] Jha KK (2015) Carbon storage and sequestration rate assessment and allometric model development in young teak plantations of tropical moist deciduous forest, India. J For Res 26: 589-604.
    [44] Das M, Mukherjee A (2015) Carbon sequestration potential with height and girth of selected trees in the Golapbag Campus, Burdwan, West Bengal(India). Indian J Sci Res 10: 53-57.
    [45] Guarna B (2012) An Analysis of Carbon Sequestration on Clarkson University 's Campus. B.S. ES & P '12. 2011-2012 Capstone.
    [46] IPCC (2007) Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.
    [47] Paustian K, Lehmann J, Ogle S, et al. (2016) Climate-Smart Soils. Nature 532: 49-57.
    [48] Sedjo R, Sohngen B, Jagger P (1998) Carbon Sinks in the Post-Kyoto World. RRF Clim Issue 895-908.
    [49] Khurana P (2012) A study on carbon sequestration in natural forests of India. J Appl Nat Sci 4: 132-136.
    [50] Melkania N (2009) Carbon Sequestration in Indian Natural and Planted Forests. Indian For 380-387.
    [51] Brown S (1996) Tropical forests and the global carbon cycle: estimating state and change in biomass density BT-Forest Ecosystems, Forest Management and the Global Carbon Cycle, In: Apps MJ, Price DT (Eds.), Berlin, Heidelberg, Springer Berlin Heidelberg, 135-144.
    [52] Kiran GS, Kinnary S (2011) Carbon Sequatration by urban trees on roadside of Vadodara city. Int J Eng Sci Technol 3: 3066-3070.
    [53] Chavan BL, Rasal GB (2010) Sequestered standing carbon stock in selective tree species grown in University campus at Aurangabad. Int J Eng Sci Technol 12: 3003-3007.
    [54] Yunusa A, Linatoc A (2018) Inventory of Vegetation and Assessment of Carbon Storage Capacity towards a Low Carbon Campus: a Case Study of Universiti Tun Husein. Traektoria Nauk = Path Sci 4: 3001-3006.
    [55] Ramachandran A, Jayakumar S, Haroon R, et al. (2007) Carbon sequestration: estimation of carbon stock in natural Forestsusing Geospatial technology in the Eastern Ghats of TN, India. Curr Sci 323-331.
    [56] Singh R, Pal D, Tripathi N (2018) Land use/Land cover change detection analysis using remote sensing and GIS of Dhanbad District. India. Eurasian J For Sci 6: 1-12.
    [57] Lal R (2004) Soil carbon sequestration impacts on global climate change and food security. Science (80-) 304: 1623-1627.
    [58] Kumar BM (2006) Carbon sequestration potential of tropical garden, In: Kumar BM, Nair P (Eds.), Tropical Home garden: a time tested example of sustainable agroforstry, Netherlands, Springer, 185-204.
    [59] Sheikh M, Kumar M (2010) Carbon sequestration potential of trees on two aspects in sub tropical forest. Int J Conserv Sci 1143-1148.
    [60] Marak T, Khare N (2017) Carbon Sequestration Potential of Selected Tree Species in the Campus of SHUATS. Int J Sci Res Dev 5: 63-66.
    [61] Keenan TF, Williams CA (2018) The Terrestrial Carbon Sink. Annu Rev Environ Resour 43: 219-243.
    [62] Chavan BL, Rasal GB (2009) Carbon Storage in Selective Tree Species in University campus at Aurangabad, Maharashtra, India, International Conference and Exhibition on RAEP, Agra, india., 119-130.
    [63] Yirdaw M (2018) Carbon Stock Sequestered by Selected Tree Species Plantation in Wondo Genet College, Ethiopia. J Earth Sci Clim Chang 9: 1-5.
    [64] Kongsager R, Napier J, Mertz O (2013) The carbon sequestration potential of tree crop plantations. Mitig Adapt Strateg Glob Chang 18: 1197-1213.
    [65] Verchot L, Noordwijk M, Kandii S, et al. (2007) Climate change: Linking adaptation and mitigation through agroforestry. Mitig Adapt Strateg Glob Chang 12: 901-918.
    [66] Soto-Pinto L, Anzueto M, Mendoza J, et al. (2010) Carbon sequestration through agroforestry in indigineous community of chiapas, Mexico. Agrofor Syst 78: 1-39.
    [67] Nowak DJ (1993) Atmospheric carbon reduction by urban trees. J EnvironManagement 37: 207-217.
    [68] Montagnini F, Nair PKR (2004) Carbon sequestration: An un- derexploited environmental benefit of agroforestry systems. Agro- for Syst 61: 281-295.
    [69] Brack CL (2002) Pollution mitigation and carbon sequestration by an urban forest. Environ Pollut 116: 195-200.
    [70] Churkina G (2012) Carbon cycle in urban ecosystem, In: Lal R, Augustin B (Eds.), Carbon Sequestration in urban ecosystems, Netherlands, Springer.
    [71] Joshi RK, Dhyani S (2019) Biomass, carbon density and diversity of tree species in tropical dry deciduous forests in Central India. Acta Ecol Sin 39: 289-299.
    [72] Flora G, Athista GM, Derisha L, et al. (2018) Estimation of Carbon Storage in the Tree Growth of St. Mary's College (Autonomous) Campus, Thoothukudi, Tamilnadu, India. Int J Emerg Technol Innov Res 5: 260-266.
    [73] Ketterings QM, Coe R, van Noordwijk M, et al. (2001) Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. For Ecol Manage 146: 199-209.
    [74] ANAMIKA A, PRADEEP C (2016) Urban Vegetation and Air Pollution Mitigation: Some Issues from India. Chinese J Urban Environ Stud 04: 1-10.
    [75] Salazar S, Sanchez L, Galiendo P, et al. (2010) AG tree biomass equation and nutrient pool for a para climax chestnut stand and for a climax oak stand. Sci Res Essays 1294-1301.
    [76] Parresol B (1999) Assessing tree and stand biomass: A review with examples and critical comparisions. For Sci 45: 573-593.
    [77] Pragasan L (2014) Carbon Stock Assessment in the Vegetation of the Chitteri Reserve Forest of the Eastern Ghats in India Based on Non-Destructive Method Using Tree Inventory Data. J Earth Sci Clim Chang 11-21.
    [78] Brown S, Lugo AE (1982) The Storage and Production of Organic Matter in Tropical Forests and Their Role in the Global Carbon Cycle. Biotropica 14: 161-187.
    [79] Haripriya GS (2000) Estimates of biomass in Indian forests. Biomass and Bioenergy 19: 245-258.
    [80] Haghparast H, Delbari AS, Kulkarni D (2013) Carbon sequestration in Pune University Campus with special reference to Geographical information system. J Ann Biol Res 2013, 4 4: 169-175.
    [81] Vashum KT, Jayakumar S (2012) Methods to Estimate Above-Ground Biomass and Carbon Stock in Natural Forests: A Review. J Ecosyst Ecogr 2-4.
    [82] Nowak DJ (2012) Contrasting natural regeneration and tree planting in 14 North American cities. Urban For Urban Green 11: 374-382.
    [83] Nowak DJ, Dwyer JF (2007) Understanding the benefits and costs of urban forest ecosystems, In: Kuser J (Ed.), Urban and community forestry in the Northeast, New York: Springer, 25-46.
    [84] Patel J, Vasava A, Patel N (2017) Occurence of the Forest Owlet Heteroglaux blewitti in Navsari and Valsad Districts of Gujrat, India. Indian Birds 13: 78-79.
    [85] Yang J, Yu Q, Gong P (2008) Quantifying air pollution removal by green roofs in Chicago. Atmos Environ 42: 7266-7273.
    [86] Chiesura A (2004) The role of urban parks for the sustainable city. Landsc Urban Plan 68: 129-138.
    [87] Vailshery LS, Jaganmohan M, Nagendra H (2013) Effect of street trees on microclimate and air pollution. Urban For Urban Green 12: 408-415.
    [88] Dhadse S, Gajghate DG, Chaudhari PR, et al. (2011) Interaction of urban vegetation cover to sequester air pollution from ambient air environment, In: Moldovennu AM (Ed.), Air pollution: new developments, Rijeka, Croatia, InTech.
    [89] Dhyani SK, Ram A, Dev I (2016) Potential of agroforestry systems in carbon sequestration in India. Indian J Agric Sci 86: 1103-1112.
    [90] Chaudhry P (2016) Urban greening: a review of some Indian studies. Brazilian J Biol Sci 3: 425-432.
    [91] Jim CY, Chen WY (2006) Recreation amenity use and contingent valuation of urban green spaces in Guangzhou, China. Landsc Urban Plan 52: 117-133.
    [92] Stoner T, Rapp C (2008) Open Spaces Sacred Places.
    [93] Tripathi M, Joshi H (2015) Carbon flow in Delhi urban forest ecosystems. Ann Biol Res 6: 13-17.
    [94] Nowak DJ, Crane D, Stevens J, et al. (2008) A ground-based method of assessing urban forest structure and ecosystem services. Arboric Urban For 34: 347-358.
    [95] Peper PJ, McPherson EG (2003) Evaluation of four methods for estimating leaf area of isolated trees. Urban For Urban Green 2: 19-29.
    [96] Dobbs C, Martinez-Harms M, Kendal D (2017) Ecosystem services, In: Ferrini F, Bosch CK van den, A. Fini (Eds.), Routledge handbook of urban forestry, Abingdon, UK, Routledge.
    [97] Ramaiah M, Avtar R (2019) Urban Green Spaces and Their Need in Cities of Rapidly Urbanizing India: A Review. Urban Sci 3.
    [98] Arnold A V, Edward D (2014) A model for Low Carbon Campus in Kerala. Int J Sci Eng Res 5: 95-99.
    [99] Jim CY (2018) Protecting heritage trees in urban and peri-urban environments. Unasylva 250 69: 1.
    [100] Arya A, Shalini Negi S, Kathota JC, et al. (2017) Carbon Sequestration Analysis of dominant tree species using Geo-informatics Technology in Gujarat State (INDIA). Int J Environ Geoinformatics 4: 79-93.
    [101] Narayana J, Savinaya MS, Manjunath S, et al. (2017) Distribution and diversity of flora and fauna in and around Kuvempu University campus, Bhadra Wildlife Sanctuary range, Karnataka. Int J Plant Anim Env Sci 7: 89-99.
    [102] Khera N, Mehta V, Sabata BC (2009) Interrelationship of birds and habitat features in urban green spaces in Delhi, India. Urban For Urban Green 8: 187-196.
    [103] Nath A jyothi, Das A kumar (2011) Carbon storage and sequestration in bamboo-based smallholder homegardens of Barak Valley, Assam. Curr Sci 100: 229-233.
    [104] Roy S, Byrne J, Pickering C (2012) A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban For Urban Green 11: 351-363.
    [105] Song XP, Tan PY, Edwards P, et al. (2017) The economic benefits and costs of trees in urban forest stewardship: A systematic review. Urban For Urban Greening2.
    [106] Nowak DJ (2010) Urban Biodiversity and Climate Change, In: Muller N, Warner P, Kelcey J (Eds.), Urban Biodiversity and Design, Hoboken, NJ, Wiley-Blackwell Publishing, 101-117.
    [107] IGNOU (2020) IGNOU Profile 2020, Indira Gandhi National Open University, New Delhi, India.
    [108] Silver WL, Ostertag R, Lugo AE (2000) The potential for carbon sequestration through reforestation of abandoned tropical agricultural and pasture lands. Restor Ecol 394-407.
    [109] Nero BF, Conche DC, Anning A, et al. (2017) Urban Green Spaces Enhance Climate Change Mitigation in Cities of the Global South: the case of kumasi Ghana. Procedia Eng 68-83.
    [110] Abdollahi KK, Ning ZH, Appeaning A (2000) Global climate change & the urban forest, GCRCC/Franklin Press, Baton Rouge, LA.
    [111] Wilby R, Perry G (2006) Climate change, biodiversity and the Urban Environment: A critical review based on London, UK. Prog Phys Geogr 30: 73-98.
    [112] Gill S., Handley J., Ennos A., et al. (2007) Adapting Cities for Climate Change: The Role of the Green Infrastructure. Built Environ 33: 115-133.
    [113] Uddin S, Al Ghadban A, Dousari AA, et al. (2010) A remote sensing classification for Land cover changes and micro climate in Kuwait. Int J Sustain Dev Plan 5: 367-377.
    [114] Chaudhry P, Tewari VP (2011) Urban forestry in India: development and research scenario. Interdiscip Environ Rev 12: 80.
    [115] Bolund P, Hunhammar S (1999) Ecosystem services in urban areas. Ecol Econ 29: 293-301.
    [116] Ferrini F, Fini A (2011) Sustainable management techniques for trees in the urban areas. J Biodivers Ecol Sci 1: 1-20.
    [117] Tripathi M (2016) Carbon Management with Urban Trees. Indian J Fundam Appl Life Sci 6: 40-46.
    [118] Chaudhry P, Dollo M, Bagra K, et al. (2011) Traditional biodiversity conservation and natural resource management system of some tribes of Arunachal Pradesh, India. Interdiscip Environ Rev 12: 338.
    [119] Ugle P, Rao S, Ramachandra TV (2010) Carbon sequestration potential of Urban trees, Wetlands, Biodiversity and Climate Change Conference at IISc, Bengaluru, Bengaluru, India, 1-12.
    [120] Ren Z, Zhal C, Shen G, et al. (2015) Effects of forest type and urbanization on carbon storage of urban forest in changchun, NE. China, Springer.
    [121] Potadar VR, Patil SS (2017) Potential of carbon sequestration and storage by trees in and around B. A. M. University campus of Aurangabad city in Maharashtra, India. Int J Sci Dev Res 2: 28-33.
    [122] Velasco E, Roth M, Norford L, et al. (2016) Does urban vegetation enhance carbon sequestration? Landsc Urban Plan 148: 99-107.
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