Loading [MathJax]/jax/element/mml/optable/GeneralPunctuation.js
Research article Special Issues

Occurrence and aquatic toxicity of contaminants of emerging concern (CECs) in tributaries of an urbanized section of the Delaware River Watershed

  • Received: 13 May 2020 Accepted: 06 July 2020 Published: 09 July 2020
  • The presence of contaminants of emerging concern (CECs) in environmental matrices is an ongoing issue. This research project was carried out to increase our understanding of the loading, distribution and potential risk of CECs by sampling large and small tributaries in a specific area of the Delaware River watershed (in northeast USA) that is highly urbanized and significantly impacted by wastewater treatment plant effluents. Fifteen target compounds were selected for analysis based on their high frequency of detection in a previous multiyear study conducted on the Delaware River mainstem. Ten sampling sites were chosen on tributaries receiving numerous municipal and industrial discharges. Sampling locations were above and below potential source discharges. Sampling was designed to assess seasonal differences in CECs loadings. The measured environmental concentrations of the target compounds present a detailed picture of urban and industrial impacts on subwatershed receiving waters. An index of concern ranking system was applied to the sample locations by comparing measured environmental concentrations, existing target compound water quality criteria or predicted no effects levels and developing a concern summary variable. Triclocarban and diphenhydramine demonstrated to be compounds of high relative risk (RR) to the aquatic life of the Pennsylvania tributaries to the Delaware River.

    Citation: Djordje Vilimanovic, Gangadhar Andaluri, Robert Hannah, Rominder Suri, A. Ronald MacGillivray. Occurrence and aquatic toxicity of contaminants of emerging concern (CECs) in tributaries of an urbanized section of the Delaware River Watershed[J]. AIMS Environmental Science, 2020, 7(4): 302-319. doi: 10.3934/environsci.2020019

    Related Papers:

    [1] Qinghua Zhou, Li Wan, Hongbo Fu, Qunjiao Zhang . Exponential stability of stochastic Hopfield neural network with mixed multiple delays. AIMS Mathematics, 2021, 6(4): 4142-4155. doi: 10.3934/math.2021245
    [2] Huahai Qiu, Li Wan, Zhigang Zhou, Qunjiao Zhang, Qinghua Zhou . Global exponential periodicity of nonlinear neural networks with multiple time-varying delays. AIMS Mathematics, 2023, 8(5): 12472-12485. doi: 10.3934/math.2023626
    [3] Li Wan, Qinghua Zhou, Hongbo Fu, Qunjiao Zhang . Exponential stability of Hopfield neural networks of neutral type with multiple time-varying delays. AIMS Mathematics, 2021, 6(8): 8030-8043. doi: 10.3934/math.2021466
    [4] Zhigang Zhou, Li Wan, Qunjiao Zhang, Hongbo Fu, Huizhen Li, Qinghua Zhou . Exponential stability of periodic solution for stochastic neural networks involving multiple time-varying delays. AIMS Mathematics, 2024, 9(6): 14932-14948. doi: 10.3934/math.2024723
    [5] Qinghua Zhou, Li Wan, Hongshan Wang, Hongbo Fu, Qunjiao Zhang . Exponential stability of Cohen-Grossberg neural networks with multiple time-varying delays and distributed delays. AIMS Mathematics, 2023, 8(8): 19161-19171. doi: 10.3934/math.2023978
    [6] Nina Huo, Bing Li, Yongkun Li . Global exponential stability and existence of almost periodic solutions in distribution for Clifford-valued stochastic high-order Hopfield neural networks with time-varying delays. AIMS Mathematics, 2022, 7(3): 3653-3679. doi: 10.3934/math.2022202
    [7] Patarawadee Prasertsang, Thongchai Botmart . Improvement of finite-time stability for delayed neural networks via a new Lyapunov-Krasovskii functional. AIMS Mathematics, 2021, 6(1): 998-1023. doi: 10.3934/math.2021060
    [8] Bing Li, Yuan Ning, Yongkun Li . Besicovitch almost periodic solutions to Clifford-valued high-order Hopfield fuzzy neural networks with a D operator. AIMS Mathematics, 2025, 10(5): 12104-12134. doi: 10.3934/math.2025549
    [9] Xiaofang Meng, Yongkun Li . Pseudo almost periodic solutions for quaternion-valued high-order Hopfield neural networks with time-varying delays and leakage delays on time scales. AIMS Mathematics, 2021, 6(9): 10070-10091. doi: 10.3934/math.2021585
    [10] Xin Liu . Stability of random attractors for non-autonomous stochastic p-Laplacian lattice equations with random viscosity. AIMS Mathematics, 2025, 10(3): 7396-7413. doi: 10.3934/math.2025339
  • The presence of contaminants of emerging concern (CECs) in environmental matrices is an ongoing issue. This research project was carried out to increase our understanding of the loading, distribution and potential risk of CECs by sampling large and small tributaries in a specific area of the Delaware River watershed (in northeast USA) that is highly urbanized and significantly impacted by wastewater treatment plant effluents. Fifteen target compounds were selected for analysis based on their high frequency of detection in a previous multiyear study conducted on the Delaware River mainstem. Ten sampling sites were chosen on tributaries receiving numerous municipal and industrial discharges. Sampling locations were above and below potential source discharges. Sampling was designed to assess seasonal differences in CECs loadings. The measured environmental concentrations of the target compounds present a detailed picture of urban and industrial impacts on subwatershed receiving waters. An index of concern ranking system was applied to the sample locations by comparing measured environmental concentrations, existing target compound water quality criteria or predicted no effects levels and developing a concern summary variable. Triclocarban and diphenhydramine demonstrated to be compounds of high relative risk (RR) to the aquatic life of the Pennsylvania tributaries to the Delaware River.


    Dynamic process is a mightful formalistic apparatus for association with a large spectrum analysis of multistage decision making problems. Such problems appear and are congruent in essentially all human activities. Unfavourably, for explicit reasons, the analysis of fuzzy dynamic process is difficult. Fuzzy dynamic process are characteristic of all dynamic process where the variables associated are state and decision variables. Fuzzy dynamic iterative process is established as a process getting preprocessed inputs and having outputs that are furthermore defuzzified for realistic applications.

    In the light of epistemic access, the term fuzzy sets appear as descriptions or perceptions of nonexistent underlying crisp values. As an example, it is noted that the temperature was high form but the numerical value is uncharted. This leads the way, to a number of classical problems which usually provide themselves to fuzzification fashions like Zadeh's generalization theorem [19].

    In functional analysis, the field Banach fixed point theory originate as an imperative apparatus over the last some decades in non-linear sciences and engineering via behavioral science, economics, etc see ([4,6,7,8,11,12,14,16,20,21,23,24,25,26,29,31]). To be unequivocal, while codifying an experiment mathematically, many number of researchers to interrogate the solvability of a functional equation in terms of differential equations, integral equations, or fractional differential equations. Such as the existence and uniqueness of a solution are often achieved by finding fixed point of a particular contraction mapping, (see more [1,3,9,10,13,15,18,30]). The three major structure in Banach fixed point theory are metric structure, topological structure, and discrete structure. These idea was extend by either generalized metric spaces into by modifying the structure of the contraction operators. However, Nadler [22] display the concept of Hausdorff metric discoursed the Banach fixed point theory for multi-valued mapping rather than single-valued mappings.

    On the other hand, Alghamdi et al. [2] improved the idea of partial metric space to b-metric-like space. They produced interesting theorems of fixed point in the newly defined frame. Their concept was expedited by various researchers in many ways (see more [17,27,28]).

    This article regards fuzzy dynamic process as fuzzy dynamic process on b -metric-like space, specifically the mapping of set-valued (extended) fuzzy intervals endowed with the b-metric-like. From that point of view, a natural topic is convergence theorems via fuzzy dynamic process in the class of b -metric-like space. Our view of convergence theorems in b -metric-like space, then, disposes of fuzzy dynamic process entirely. Instead, we just adopt the standard setting of fuzzy dynamic process in b -metric-like space which defines convergence theorems in generalized F-contraction via expectations of fuzzy Suzuki Hardy Rogers type contraction operators. Subsequently, corollaries are originated from the main result. To explain the example in the main section, a table and diagram has been created that best illustrates the Fuzzy dynamic process to the readers. At the end, gives an application of our results in solving Hukuhara differentiability through the fuzzy initial valued problem and fuzzy functions. The pivotal role of Hukuhara differentiability in Fuzzy dynamic process is stated. At last, a summary of the article is described in the conclusion section.

    Formally, an fuzzy set is defined as [32]:

    A fuzzy set on G is a mapping that assigns every value of G to some element in [0,1]. The family of all such mappings is expressed as F(G). For a fuzzy set A on G and </p><p>μ</p><p>∈G, the value A(</p><p>μ</p><p>) is known as the membership grade of </p><p>μ</p><p> in A. The αlevel set of A expressed as [A]α is given by

    {[A]α={μ:A(μ)α},α(0,1];[A]0=¯{μ:A(μ)>0}.

    For a nonempty set G and an ms G, a mapping T:GF(G) is a fuzzy mapping and is a fuzzy subset of G×G having the membership function T(g)(g). T(g)(g) describes the membership grade of g in T(g), while [T(g)]α states the αlevel set of T(g), for more details see [5].

    Definition 2.1. [5] A point gG is called a fuzzy fixed point of a fuzzy mapping T:GF(G) if there is α(0,1] such that g[T(g)]α.

    In the recent past, Wardowski [31] provided the term known as F-contraction and implemented on Banach fixed point theory. Which is the efficient generalization of Banach fixed point theory. Formally, an F-contraction is defined as follows [31]:

    Definition 2.2. Let F is the set of mappig F:R+R satisfying (Fi)(Fiii):

    (Fi) μ1<μ2 implies F(μ1)<F(μ2)forallμ1,μ2(0,+);

    (Fii) For every sequence {μσ} in R+ such that

    limσ+μσ=0ifandonlyiflimσ+F(μσ)=;

    (Fiii) There exist k(0,1) such that limμ0()μkF(μ)=0.

    A mapping T:GG is called an F-contraction on a metric space (G,d), if there is τR+/{0} such that

    d(Tμ1,Tμ2)>0τ+F(Tμ1,Tμ2)F(d(μ1,μ2))foreachμ1,μ2G.

    After, we recall the following some basic idea of dynamic system:

    Let ξ:GC(G) be a mapping. A set

    ˇD(ξ,μ0)={(μa)a0:μaξμa1  forallaN}.

    is called dynamic process ˇD(ξ,μ0) of μ with starting point μ0. Where μ0G be arbitrary and fixed. In the light of ˇD(ξ,μ0), (μa)aN{0} onward has the form (μa) (see more [18]).

    Further, the literature contains many generalizations of the idea of fixed point theory in metric spaces and its topological behavior. In particularly, Alghamdi et al. [2] designed the fashion of b-metric-like space as follows:

    Definition 2.3. [2] Let G be a b -metric-like space with Gϕ and s1. A function d:G×GR+{0} such that for every μ1,μ2,μ3G, the following conditions (bi), (bii) and (biii) hold true:

    (bi) the condition: d(μ1,μ2)=0 implies μ1=μ2;

    (bii) the condition is hold true: d(μ1,μ2)=d(μ2,μ1);

    (biii) the condition is satisfied: d(μ1,μ3)s[d(μ1,μ2)+d(μ2,μ3)].

    The pair (G,d) is known as a b -metric-like space.

    Example 2.4. Define (G,d) with s=2 by\newline

    d(0,0)=0,d(1,1)=d(2,2)=d(0,2)=2,d(0,1)=4,d(1,2)=1,

    with

    d(μ1,μ2)=d(μ2,μ1),

    for all μ1,μ2G={0,1,2}. Then, (G,d) is a b -metric-like space. Clearly, it is neither a b-metric nor a metric-like space, see more detail in [2].

    Remark 2.5. Owing to above definition (2.3), every partial metric is a b -metric-like space but converse may not hold true in general, see more [2]

    Nadler [22], design the idea of Hausdorff metric and extended the Banach contraction theorem for multi-valued operators instead of single-valued operators. Hereinafter, we investigate the concept of Hausdorff b-metric-like as follows. Let (G,μ) be a b-metric-like space. For μ1G and L1G, let db(μ1,L2)=inf{d(μ1,μ2):μ2L2}. Define ^Hb:CB(G)×CB(G)[0,+) by

    ^Hb(L1,L2)=max{supμ1L1db(μ1,L2),supμ2L2db(μ1,L1)},

    for each L1,L2CB(G). Where CB(G) denote the family of all non-empty closed and bounded-subsets of G and CL(G) the family of all non-empty closed-subsets of G.

    Definition 2.6. [5] Let L1,L2V(G), α(0,1]. Then dα(L1,L2)=infgL1α,gL2αd(g,g),

    Hα(L1,L2)=ˆHbl(L1α,L2α),

    where ˆHbl is the Hausdorff distance.

    Lemma 2.7. Let L1 and L2 be nonempty proximal subsets of a b -MLS (G,d). If gL1, then

    d(g,L2)H(L1,L2).

    Lemma 2.8. Let (G,d) be a b -metric-like space. For all L1,L2CB(G) and for any gL1 such that d(g,L2)=d(g,g), where gL2. Then, ˆHbl(L1,L2)d(g,g).

    In the following, the concept of fuzzy dynamic process as a generalization of dynamic process, and some elementary facts about these concepts are discussed.

    In this section, first we deal with some new aspects of the fuzzy dynamic process as follows:

    Definition 3.1. Let T:GF(G) be a fuzzy mapping. If there is α(0,1], and let μ0G be arbitrary and fixed such that

    ˇD([Tμ]α,μ0)={(μj)jN{0}:μj[Tμj1]α,jN}.

    Every membership value of ˇD([Tμ]α,μ0) is called a fuzzy dynamic process of T starting point μ0. The fuzzy dynamic process (μj)jN{0} onward is written as (μj).

    Example 3.2. Let G=C([0,1]) be a Banach space with norm for \mu \in G . Let T:G\rightarrow F(G) be a fuzzy mapping. If there is \alpha \in (0, 1] such that for every \mu \in G , \left[ T\mu \right] _{\alpha } is a set of the function

    \begin{equation*} \delta \longmapsto k\int_{0}^{\delta }\mu \left( r\right) dr,{\rm{ }}k\in \left[ 0,1\right] , \end{equation*}

    that is,

    \begin{equation*} \check{D}\left( \left[ T\mu \right] _{\alpha }\left( \delta \right) ,\mu _{0}\right) = \{k\int_{0}^{\delta }\mu \left( r\right) dr:k\in \left[ 0,1 \right] \},{\rm{ }}\mu \in G, \end{equation*}

    and let \mu _{0}\left(\delta \right) = \delta, \delta \in \left[ 0, 1 \right] . Then the iterative sequence

    \begin{equation*} \mu _{j} = \left \{ \begin{array}{l} (\frac{1}{j!\left( j+1\right) !}\delta^{j+1}),{\rm{ }}j\geq 0; \\ 0{\rm{ elsewehere}}. \end{array} \right. \end{equation*}

    is a fuzzy dynamic process of mapping T with starting point \mu _{0}. The mapping T:G\rightarrow F\left(\mathbb{R}\right) is said to be \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) fuzzy dynamic lower semi-continuous at \mu \in G, if for every fuzzy dynamic process (\mu _{j})\in D(T, \mu _{0}) and for every subsequence (\mu _{j\left(i\right) }) of (\mu _{j}) convergent to \mu

    \begin{equation*} \left[ T\mu \right] _{\alpha }\leq \lim \inf\limits_{i\rightarrow +\infty }\left[ T\mu _{j\left( i\right) }\right] _{\alpha }. \end{equation*}

    In this case, T is fuzzy dynamic lower semi-continuous \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) . If T is fuzzy dynamic lower semi-continuous \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) at each \mu \in G , then T is known as lower semi-continuous. For every sequence (\mu _{j})\subset G and \mu \in G such that (\mu _{j})\rightarrow \mu , we have \left[ T\mu \right] _{\alpha }\leq \lim \inf_{i\rightarrow +\infty }\left[ T\mu \left(j\right) \right] _{\alpha }.

    Example 3.3. Let G = {\mathbb{R}}^{+}\cup \left \{ 0\right \} . Define T:G\rightarrow F\left(G\right) by

    \begin{equation*} T\left( \mu \right) \left( \mu ^{\prime }\right) = \left \{ \begin{array}{l} 1,\;{\rm{ if }}\;0\leq \mu ^{\prime }\leq \frac{\mu }{4}; \\ \frac{1}{2},\;{\rm{ if }}\;\frac{\mu }{4} < \mu ^{\prime }\leq \frac{\mu }{3}; \\ \frac{1}{4},\;{\rm{ if }}\;\frac{\mu }{3} < \mu ^{\prime }\leq \frac{\mu }{2}; \\ 0,\;{\rm{ if }}\;\frac{\mu }{2} < \mu ^{\prime }\leq 1. \end{array} \right. \end{equation*}

    all \mu \in G, there is \alpha \left(\mu \right) = 1 such that \left[ T\mu \right] _{\alpha \left(\mu \right) } = \left[ 0, \frac{\mu }{2} \right]. Apply the following iterative procedure to generate a sequence \{ \mu _{n}\} of fuzzy sets is given by (see Table 1 and Figure 1)

    \begin{equation*} \mu _{i} = \left \{ \begin{array}{l} \mu _{0}h^{i-1},\;{\rm{ if }}\;i\geq 2; \\ 0,\;{\rm{ elsewhere.}} \end{array} \right. \end{equation*}
    Table 1.  Fuzzy dynamic process.
    i\geq 2 \mu _{i}=\mu _{0}g^{i-1} \in \left[ T\mu \right] _{\alpha \left(\mu \right) }=\left[ 0, \frac{\mu }{2}\right]
    \mu _{i=2} 1 - \left[ T\mu _{1}\right] _{\alpha \left(\mu _{1}\right) }=[0, 1]
    \mu _{i=3} \frac{1}{2} - \left[ T\mu _{2}\right] _{\alpha \left(\mu _{2}\right) }=[0, \frac{1}{2}]
    \mu _{i=4} \frac{1}{4} - \left[ T\mu _{3}\right] _{\alpha \left(\mu _{3}\right) }=[0, \frac{1}{4}]
    \mu _{i=5} \frac{1}{8} - \left[ T\mu _{3}\right] _{\alpha \left(\mu _{4}\right) }=[0, \frac{1}{8}]
    \mu _{i=6} \frac{1}{16} - \left[ T\mu _{3}\right] _{\alpha \left(\mu _{5}\right) }=[0, \frac{1}{16}]
    \mu _{i=7} \frac{1}{32} - \left[ T\mu _{3}\right] _{\alpha \left(\mu _{6}\right) }=[0, \frac{1}{32}]
    \mu _{i=8} \frac{1}{64} - \left[ T\mu _{3}\right] _{\alpha \left(\mu _{7}\right) }=[0, \frac{1}{64}]
    \mu _{i=9} \frac{1}{128} - \left[ T\mu _{3}\right] _{\alpha \left(\mu _{8}\right) }=[0, \frac{1 }{128}]
    \mu _{i=10} \frac{1}{256} - \left[ T\mu _{4}\right] _{\alpha \left(\mu _{9}\right) }=[0, \frac{1 }{256}]

     | Show Table
    DownLoad: CSV
    Figure 1.  Fuzzy dynamic process: \check{D}\left(\left[ T \mu \right] _{ \alpha }, \mu _{0}\right) .

    Where \mu _{0} = 2 is intial point and h = \frac{1}{2} .

    We obtain,

    \begin{equation*} \check{D}\left( \left[ T\mu \right] _{\alpha },\mu _{0}\right) = \{ \frac{1}{2 },\frac{1}{4},\frac{1}{8},\frac{1}{16},\frac{1}{32},\frac{1}{64},\frac{1}{128 },\frac{1}{256}\} \end{equation*}

    is a fuzzy dynamic process of T starting at point \mu _{0} = 2.

    Further, in the following we develop fuzzy fixed point theorems with respect to fuzzy dynamic process \check{D} \left(\left[ T \mu \right] _{ \alpha }, \mu _{0}\right) as follows.

    Now, we start with the following main definition:

    Definition 4.1. Let \left(G, d\right) be a b -metric-like space with s\geq 1 . A mapping T:G\rightarrow F\left(G\right) is called a F -fuzzy Suzuki-Hardy-Rogers (abbr., F-FSHR) type contraction with respect to \check{ D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) and \alpha :G\rightarrow (0, 1] such that \left[ T\mu _{i}\right] _{\alpha \left(i\right) } are nonempty closed subsets of G if for some \mathcal{F}\in \nabla _{\digamma } and \tau :(0, +\infty)\rightarrow (0, +\infty) such that

    \begin{equation*} \frac{1}{2s}d_{b}(\mu _{i-1},\left[ T\mu _{i-1}\right] _{\alpha \left( i-1\right) })\leqq d\left( \mu _{i-1},\mu _{i}\right) , \end{equation*}

    we have

    \begin{equation} \tau (U(\mu _{i-1},\mu _{i}))+\mathcal{F}[\hat{H_{b}}(\left[ T\mu _{i} \right] _{\alpha \left( i\right) },\left[ T\mu _{i+1}\right] _{\alpha \left( i+1\right) })]\leqq \mathcal{F}(U(\mu _{i-1},\mu _{i})), \end{equation} (4.1)

    where

    \begin{eqnarray*} U(\mu _{i-1},\mu _{i}) & = &e_{1}\left[ d\left( \mu _{i-1},\mu _{i}\right) \right] +e_{2}[d_{b}(\mu _{i-1},\left[ T\mu _{i-1}\right] _{\alpha \left( i-1\right) })]+e_{3}[d_{b}(\mu _{i},\left[ T\mu _{i}\right] _{\alpha \left( i\right) })] \\ &&+\frac{e_{4}}{2s}[d_{b}(\mu _{i-1},\left[ T\mu _{i}\right] _{\alpha \left( i\right) })]+\frac{e_{5}}{2s}[d_{b}(\mu _{i},\left[ T\mu _{i-1}\right] _{\alpha \left( i-1\right) })], \end{eqnarray*}

    for all \mu _{i}\in \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) , \hat{H}_{b}(\left[ T\mu _{i}\right] _{\alpha \left(i\right) }, \left[ T\mu _{i+1}\right] _{\alpha \left(i+1\right) }) > 0 , where e_{1}, e_{2}, e_{3}, e_{4}, e_{5}\in \left[ 0, 1 \right] such that e_{1}+e_{2}+e_{3}+e_{4}+e_{5} = 1 and 1-e_{3}-e_{5} > 0 .

    Remark 4.2. To continue with our results, the behavior of self distance in b -metric-like space is defined by

    \begin{equation*} d\left( \mu _{1},\mu _{1}\right) \leq 2d\left( \mu _{1},\mu _{2}\right). \end{equation*}

    Additionally, we assume that \mu _{i}\in \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) satisfying fuzzy dynamic process for below condition:

    \begin{equation} d_{b}(\mu _{i},\left[ T\mu _{i}\right]) _{\alpha \left( i\right) } > 0,{\rm{ }} d_{b}(\mu _{i-1},\left[ T\mu _{i-1}\right] _{\alpha \left( i-1\right) }) > 0, \end{equation} (4.2)

    for all i\in \mathbb{N} . If for the investigated process that does not satisfy (4.2), there is some i_{0}\in \mathbb{N} such that

    \begin{equation*} d_{b}(\mu _{i_{0}},\left[ T\mu _{i_{0}}\right] _{\alpha \left( i_{0}\right) }) > 0, \end{equation*}

    and

    \begin{equation*} d_{b}(\mu _{i_{0-1}},\left[ T\mu _{i_{0-1}}\right] _{\alpha \left( i_{0-1}\right) }) = 0, \end{equation*}

    then we get \mu _{i_{0-1}} = \mu _{i_{0}}\in \left[ T\mu _{i_{0-1}}\right] _{\alpha \left(i_{0-1}\right) } which implies the existence of fuzzy fixed point. In the light of this consideration, fuzzy dynamic process satisfying (4.2) does not depreciate a generality of our analysis.

    Now, we proceed to our main result:

    Theorem 4.3. Let (G, d, s) be a complete b -metric-like space. Let T:G\rightarrow \mu _{\alpha }(G) be an F-FSHR type contraction with respect to \mu _{i} . Assume that the following holds:

    \left(i\right) There is a fuzzy dynamic iterative process \mu _{i}\in \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) such that for each l\geq 0 \lim \inf_{k\rightarrow l^{+}}\tau \left(k\right) > 0;

    \left(ii\right) A mapping G\ni \mu _{i}\longmapsto d_{b}(\mu _{i}, \left[ T\mu _{i}\right] _{\alpha \left(i\right) }) is fuzzy dynamic lower semi-continuous \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) ;

    \left(iii\right) If, in addition, \mathcal{F} is super-additive, i.e., for \mu_{1}, \mu_{2}, \xi_{1}, \xi_{2}\in R^{+} we have

    \begin{equation*} \mathcal{F}(\xi_{1}\mu_{1}+\xi_{2}\mu_{2})\leq \xi_{2}F(\mu_{1})+\xi_{2}F(\mu_{2}). \end{equation*}

    Then T has a fuzzy fixed point.

    Proof. Choose an arbitrary point \mu _{0}\in G . In veiw of fuzzy dynamic iterative process, we have

    \begin{equation*} \check{D}\left( \left[ T\mu \right] _{\alpha },\mu _{0}\right) = \{ \left( \mu _{i}\right) _{i\in {\mathbb{N}}\cup \left \{ 0\right \} }:\mu _{i+1} = \mu _{i}\in \left[ T\mu _{i-1}\right] _{\alpha \left( i-1\right) }{\rm{ for all }}i\in {\mathbb{N}}\}. \end{equation*}

    In case that there is i_{0}\in \mathbb{N} such that \mu _{i_{0}} = \mu _{i_{0+1}} , then our proof of Theorem (4.3) go ahead as follows. If we let \mu _{i}\neq \mu _{i+1} for all i\in \mathbb{N} , then we have

    \begin{equation} \frac{1}{2s}d_{b}(\mu _{i},\left[ T\mu _{i}\right] _{\alpha \left( i\right) })\leq d\left( \mu _{i},\mu _{i+1}\right) ,\;{\rm{ for\; all }}\;i\in {\mathbb{N}}. \end{equation} (4.3)

    From (4.1) and in the light of Lemma (2.8), we have

    \begin{eqnarray} \mathcal{F}(d(\mu _{i+1},\mu _{i+2}) &\leq &\mathcal{F}[\hat{H_{b}}(\left[ T\mu _{i}\right] _{\alpha \left( i\right) },\left[ T\mu _{i+1}\right] _{\alpha \left( i+1\right) })] \\ &\leq &\mathcal{F}\left[ e_{1}d\left( \mu _{i},\mu_{i+1}\right) +e_{2}d_{b}\left( \mu _{i},\left[ T\mu _{i} \right] _{\alpha \left( i\right) }\right) +e_{3}d_{b}\left( \mu _{i+1},\left[ T\mu _{i+1}\right] _{\alpha \left( i+1\right) }\right) \right. \\ &&\left. +\frac{e_{4}}{2s}d_{b}\left( \mu _{i},\left[ T\mu _{i+1}\right] _{\alpha \left( i+1\right) }\right) +\frac{e_{5}}{2s}d_{b}\left( \mu _{i+1}, \left[ T\mu _{i}\right] _{\alpha \left( i\right) }\right) \right] \\ &&-\tau \left[ e_{1}d\left( \mu _{i},\mu_{i+1}\right) +e_{2}d_{b}\left( \mu _{i},\left[ T\mu _{i}\right] _{\alpha \left( i\right) }\right) +e_{3}d_{b}\left( \mu _{i+1},\left[ T\mu _{i+1}\right] _{\alpha \left( i+1\right) }\right) \right. \\ &&\left. +\frac{e_{4}}{2s}d_{b}\left( \mu _{i},\left[ T\mu _{i+1}\right] _{\alpha \left( i+1\right) }\right) +\frac{e_{5}}{2s}d_{b}\left( \mu _{i+1}, \left[ T\mu _{i}\right] _{\alpha \left( i\right) }\right) \right] . \end{eqnarray} (4.4)

    Now, we survey to the following inequality

    \begin{equation} d_{b}(\mu _{i+1},\left[ T\mu _{i+1}\right] _{\alpha \left( i+1\right) }) < d_{b}\left( \mu _{i},\left[ T\mu _{i}\right] _{\alpha \left( i\right) }\right), \end{equation} (4.5)

    for all i\in \mathbb{N} . Suppose, on the contrary, there is i_{0}\in \mathbb{N} such that d(\mu _{i_{0}+1}, \left[ T\mu _{i_{0}+1}\right] _{\alpha \left(i_{0}+1\right) })\geq d(\mu _{i_{0}}, \left[ T\mu _{i_{0}} \right] _{\alpha \left(i_{0}\right) }) . By (4.4) and Lemma (2.8), we have

    \begin{eqnarray} \mathcal{F}\left[ d_{b}(\mu _{i_{0}+1},\left[ T\mu _{i_{0}+1}\right] _{\alpha \left( i_{0}+1\right) })\right] & = &\mathcal{F}\left[ d(\mu _{i_{0}+1},\mu _{i_{0}+2})\right] \\ &\leq &{\rm{ }}\mathcal{F}\left[ \hat{H}_{b}\left( \left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) },\left[ T\left( \mu _{i_{0}+1}\right) \right] _{\alpha \left( i_{0}+1\right) }\right) \right] -\tau \left( U(\mu _{i_{0}},\mu _{i_{0}+1})\right) \\ &\leq &\mathcal{F}\left[ e_{1}\left( d_{b}\left( \mu _{i_{0}},\left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) }\right) \right) +e_{2}\left( d_{b}\left( \mu _{i_{0}},\left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) }\right) \right) \right. \\ &&+e_{3}\left( d_{b}\left( \mu _{i_{0}+1},\left[ T\left( \mu _{i_{0}+1}\right) \right] _{\alpha \left( i_{0}+1\right) }\right) \right) \\ &&+\frac{e_{4}}{2s}\left( d_{b}\left( \mu _{i_{0}},\left[ T\left( \mu _{i_{0}+1}\right) \right] _{\alpha \left( i_{0}+1\right) }\right) \right) \\ &&\left. +\frac{e_{5}}{2s}\left( d_{b}\left( \mu _{i_{0}+1},\left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) }\right) \right) \right] -\tau \left( U(\mu _{i_{0}},\mu _{i_{0}+1})\right) \\ &\leq &\mathcal{F}\left[ e_{1}d_{b}\left( \mu _{i_{0}},\left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) }\right) +e_{2}\left( d_{b}\left( \mu _{i_{0}},\left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) }\right) \right) \right. \\ &&+e_{3}\left( d_{b}\left( \mu _{i_{0}+1},\left[ T\left( \mu _{i_{0}+1}\right) \right] _{\alpha \left( i_{0}+1\right) }\right) \right) \\ &&+\frac{se_{4}}{2s}d_{b}\left( \mu _{i_{0}},\left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) }\right) \\ &&+\frac{se_{4}}{2s}d_{b}\left( \left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) },\left[ T\left( \mu _{i_{0}+1}\right) \right] _{\alpha \left( i_{0}+1\right) }\right) \\ &&\left. +\frac{2se_{5}}{2s}\left( d_{b}(\mu _{i_{0}},\left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) }\right) \right] \\ &&-\tau \left( U(\mu _{i_{0}},\mu _{i_{0}+1})\right). \end{eqnarray} (4.6)

    Owing to the above hypothesis, this, in turn, yields:

    \begin{eqnarray} \mathcal{F}\left[ d_{b}(\mu _{i_{0}+1},\left[ T\mu _{i_{0}+1}\right] _{\alpha \left( i_{0}+1\right) })\right] &\leq &\mathcal{F}\left[ e_{1}d_{b}\left( \mu _{i_{0}},\left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) }\right) +e_{2}\left( d_{b}\left( \mu _{i_{0}},\left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) }\right) \right) \right. \\ &&+e_{3}\left( d_{b}\left( \mu _{i_{0}+1},\left[ T\left( \mu _{i_{0}+1}\right) \right] _{\alpha \left( i_{0}+1\right) }\right) \right) \\ &&+e_{4}\left( d_{b}\left( \left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) },\left[ T\left( \mu _{i_{0}+1}\right) \right] _{\alpha \left( i_{0}+1\right) }\right) \right) \\ &&\left. +e_{5}\left( d_{b}\left( \mu _{i_{0}},\left[ T\left( \mu _{i_{0}}\right) \right] _{\alpha \left( i_{0}\right) }\right) \right) \right] -\tau \left( U(\mu _{i_{0}},\mu _{i_{0}+1})\right) . \end{eqnarray}

    Since \mathcal{F} is super-additive, we can write

    \begin{equation*} \mathcal{F}\left[ d_{b}(\mu _{i_{0}+1},\left[ T\mu _{i_{0}+1}\right] _{\alpha \left( i_{0}+1\right) })\right] \leq \frac{(e_{1}+e_{2}+e_{5})}{ (1-e_{3}-e_{4})}\mathcal{F}\left[ d_{b}(\mu _{i_{0}},\left[ T\mu _{i_{0}}\right] _{\alpha \left( i_{0}\right) })\right] -\frac{\tau \left( U(\mu _{i_{0}-1},\mu _{i_{0}})\right) }{(1-e_{3}-e_{4})}. \end{equation*}

    From this, By given condition e_{1}+e_{2}+e_{3}+e_{4}+e_{5} = 1 , we have

    \begin{equation} \mathcal{F}\left[ d_{b}(\mu _{i_{0}+1},\left[ T\mu _{i_{0}+1}\right] _{\alpha \left( i_{0}+1\right) })\right] \leq \mathcal{F}\left[ d_{b}(\mu _{i_{0}},\left[ T\mu _{i_{0}}\right] _{\alpha \left( i_{0}\right) })\right] -\frac{\tau \left( U(\mu _{i_{0}-1},\mu _{i_{0}})\right) }{(1-e_{3}-e_{4})}, \end{equation} (4.7)

    a contradiction. Hence (4.5) holds true. In the light of above hypothesis, Therefore d_{b}\left(\mu _{i}, \left[ T\mu _{i}\right] _{\alpha \left(i\right) }\right) is a decreasing sequence with respect to real number and it is bounded from below. Suppose that there is \Psi \geq 0 such that

    \begin{equation} \Psi = \lim\limits_{i\rightarrow +\infty }d_{b}\left( \mu _{i},\left[ T\mu _{i}\right] _{\alpha \left( i\right) }\right) = \inf \left \{ d_{b}\left( \mu _{i},\left[ T\mu _{i}\right] _{\alpha \left( i\right) }\right) :i\in {\mathbb{N}}\right \} . \end{equation} (4.8)

    We now to prove that \Psi = 0 . Suppose, based on contrary that \Psi > 0. Then, for every \varepsilon > 0 , there is a natural number j such that

    \begin{equation*} d_{b}\left( \mu _{j},\left[ T\mu _{j}\right] _{\alpha \left( j\right) }\right) < \Psi +\varepsilon . \end{equation*}

    By \left(\mathcal{F}_{i}\right) ,

    \begin{equation} \mathcal{F}\left[d_{b}\left( \mu _{j},\left[ T\mu _{j}\right] _{\alpha \left( j\right) }\right) \right] < \mathcal{F}(\Psi +\varepsilon ). \end{equation} (4.9)

    Also, by applying (4.3), we have

    \begin{equation*} \frac{1}{2s}d_{b}\left( \mu _{j},\left[ T\mu _{j}\right] _{\alpha \left( j\right) }\right) \leq d_{b}\left( \mu _{j},\mu _{j+1}\right) ,\;{\rm{ for\; all }} \;i\in {\mathbb{N}}. \end{equation*}

    Since F-FSHR type contraction with respect to \check{D}(T, \mu _{0}) , we have

    \begin{eqnarray*} \mathcal{F}\left[ d_{b}(\mu _{j+1},\left[ T\mu _{j+1}\right] _{\alpha \left( j+1\right) })\right] & = &\mathcal{F}\left[ d(\mu _{j+1},\mu _{j+2})\right] \\ &\leq &{\rm{ }}\mathcal{F}\left[ \hat{H}_{b}\left( \left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) },\left[ T\left( \mu _{j+1}\right) \right] _{\alpha \left( j+1\right) }\right) \right] -\tau \left( U(\mu _{j},\mu _{j+1})\right) \\ &\leq &\mathcal{F}\left[ e_{1}\left( d_{b}\left( \mu _{j},\left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) }\right) \right) +e_{2}\left( d_{b}\left( \mu _{j},\left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) }\right) \right) \right. \\ &&+e_{3}\left( d_{b}\left( \mu _{j+1},\left[ T\left( \mu _{j+1}\right) \right] _{\alpha \left( j+1\right) }\right) \right) \\ &&+\frac{e_{4}}{2s}\left( d_{b}\left( \mu _{j},\left[ T\left( \mu _{j+1}\right) \right] _{\alpha \left( j+1\right) }\right) \right) \\ &&\left. +\frac{e_{5}}{2s}\left( d_{b}\left( \mu _{j+1},\left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) }\right) \right) \right] -\tau \left( U(\mu _{j},\mu _{j+1})\right). \end{eqnarray*}

    Due to the above hypothesis, this, in turn, yields:

    \begin{eqnarray*} \mathcal{F}\left[ d_{b}(\mu _{j+1},\left[ T\mu _{j+1}\right] _{\alpha \left( j+1\right) })\right] &\leq &\mathcal{F}\left[ e_{1}d_{b}\left( \mu _{j},\left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) }\right) +e_{2}\left( d_{b}\left( \mu _{j},\left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) }\right) \right) \right. \\ &&+e_{3}\left( d_{b}\left( \mu _{j+1},\left[ T\left( \mu _{j+1}\right) \right] _{\alpha \left( j+1\right) }\right) \right) \\ &&+\frac{se_{4}}{2s}d_{b}\left( \mu _{j},\left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) }\right) \\ &&+\frac{se_{4}}{2s}d_{b}\left( \left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) },\left[ T\left( \mu _{j+1}\right) \right] _{\alpha \left( j+1\right) }\right) \\ &&\left. +\frac{2se_{5}}{2s}\left( d_{b}(\mu _{j},\left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) }\right) \right] \\ &&-\tau \left( U(\mu _{j},\mu _{j+1})\right) \\ &\leq &\mathcal{F}\left[ e_{1}d_{b}\left( \mu _{j},\left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) }\right) +e_{2}\left( d_{b}\left( \mu _{j},\left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) }\right) \right) \right. \\ &&+e_{3}\left( d_{b}\left( \mu _{j+1},\left[ T\left( \mu _{j+1}\right) \right] _{\alpha \left( j+1\right) }\right) \right) \\ &&+e_{4}\left( d_{b}\left( \mu _{j},\left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) }\right) \right) \\ &&\left. +e_{5}\left( d_{b}\left( \mu _{j},\left[ T\left( \mu _{j}\right) \right] _{\alpha \left( j\right) }\right) \right) \right] -\tau \left( U(\mu _{j},\mu _{j+1})\right) . \end{eqnarray*}

    This implies

    \begin{equation*} \mathcal{F}\left[ d_{b}(\mu _{j+1},\left[ T\mu _{j+1}\right] _{\alpha \left( j+1\right) })\right] \leq \mathcal{F}\left[ d_{b}(\mu _{j},\left[ T\mu _{j} \right] _{\alpha \left( j\right) })\right] -\frac{\tau \left( U(\mu _{j},\mu _{j+1})\right) }{1-e_{3}}. \end{equation*}

    Since

    \begin{equation*} \frac{1}{2s}d_{b}\left( \mu _{j+1},\left[ T\mu _{j+1}\right] _{\alpha \left( j+1\right) }\right) \leq d_{b}\left( \mu _{j+1},\mu _{j+2}\right) ,\;{\rm{ for\; all }}\;i\in {\mathbb{N}}. \end{equation*}

    By appealing to above observation, we obtain

    \begin{equation} \mathcal{F}\left[ d_{b}(\mu _{j+2},\left[ T\mu _{j+2}\right] _{\alpha \left( j+2\right) })\right] \leq \mathcal{F}\left[ d_{b}(\mu _{j+1},\left[ T\mu _{j+1} \right] _{\alpha \left( j\right) })\right] -\frac{\tau \left( U(\mu _{j+1},\mu _{j+2})\right) }{1-e_{3}}. \end{equation} (4.10)

    Continuing these fashion, we obtain

    \begin{eqnarray} \mathcal{F}\left[ d_{b}(\mu _{j+i},\left[ T\mu _{j+i}\right] _{\alpha \left( j+i\right) })\right] &\leq &\mathcal{F}\left[ d_{b}\left( \mu _{j+\left( i-1\right) },\left[ T\mu _{j+\left( i-1\right) }\right] _{\alpha \left( j+\left( i-1\right) \right) }\right) \right] -\frac{\tau \left( U(\mu _{j+(i-1)},\mu _{j+i})\right) }{1-e_{3}} \\ &\leq &\mathcal{F}\left[ d_{b}\left( \mu _{j+\left( i-2\right) },\left[ T\mu _{j+\left( i-2\right) }\right] _{\alpha \left( j+\left( i-2\right) \right) }\right) \right] -\left \{ \begin{array}{l} \frac{\tau \left( U(\mu _{j+(i-2)},\mu _{j+(i-1)})\right) }{1-e_{3}} \\ +\frac{\tau \left( U(\mu _{j+(i-1)},\mu _{j+i})\right) }{1-e_{3}} \end{array} \right. \\ &&\vdots \\ &\leq &\mathcal{F}\left[ d_{b}\left( \mu _{j_{0}},\left[ T\mu _{j_{0}}\right] _{\alpha \left( j_{0}\right) }\right) \right] -\frac{\left( n-j_{0}\right) \tau \left( U(\mu _{j_{0-1}},\mu _{j_{0}})\right) }{1-e_{3}} \\ & < &\mathcal{F}(\Psi +\varepsilon )-\frac{\left( n-j_{0}\right) \tau \left( U(\mu _{j_{0-1}},\mu _{j_{0}})\right) }{1-e_{3}}. \end{eqnarray} (4.11)

    Upon setting i\rightarrow +\infty , we have \

    \begin{equation*} \lim\limits_{i\rightarrow +\infty }\mathcal{F}\left[ d_{b}\left( \mu _{j+i},\left[ T\mu _{j+i}\right] _{\alpha \left( j+i\right) }\right) \right] = -\infty . \end{equation*}

    Also, in veiw of \left(\mathcal{F}_{ii}\right), we get

    \begin{equation*} \lim\limits_{i\rightarrow +\infty }\left[ d_{b}(\mu _{j+i},\left[ T\mu _{j+i}\right] _{\alpha \left( j+i\right) })\right] = 0. \end{equation*}

    So, there is i_{1}\in {\mathbb{N}} such that d_{b}(\mu _{j+i}, \left[ T\mu _{j+i}\right] _{\alpha \left(j+i\right) }) < \Psi for all i > i_{1} , which is a contradiction with repect to \Psi . Therefore, we have

    \begin{equation} \lim\limits_{i\rightarrow +\infty }\left[ d_{b}\left( \mu _{i},\left[ T\mu _{i}\right] _{\alpha \left( i\right) }\right) \right] = 0. \end{equation} (4.12)

    Now, we show that

    \begin{equation} \lim\limits_{i,m\rightarrow +\infty }d(\mu _{i},\mu _{m}) = 0. \end{equation} (4.13)

    Let us assume on the contrary that, for every \varepsilon > 0 there are sequences \gamma (i) and \delta (i) in {\mathbb{N}} such that

    \begin{equation} d(\mu _{\gamma _{(i)}},\mu _{\delta _{(i)}})\geq \varepsilon {\rm{, }} d_{b}\left( \mu _{\delta _{(i)-1}},\left[ T\mu _{\gamma _{(i)-1}}\right] _{\alpha \left( \gamma _{(i)-1}\right) }\right) < \varepsilon {\rm{, }}\gamma (i) > \delta (i) > i, \end{equation} (4.14)

    for all i\in {\mathbb{N}} . So, we have

    \begin{eqnarray} d(\mu _{\gamma _{(i)}},\mu _{\delta _{(i)}}) &\leq &sd_{b}\left( \mu _{\gamma _{(i)-1}},\left[ T\mu _{\gamma _{(i)-1}}\right] _{\alpha \left( \gamma _{(i)-1}\right) }\right) +sd_{b}\left( \left[ T\mu _{\gamma _{(i)-1}}\right] _{\alpha \left( \gamma _{(i)-1}\right) },\mu _{\delta _{(i)}}\right) \\ & < &sd_{b}\left( \mu _{\gamma _{(i)}},\left[ T\mu _{\gamma _{(i)-2}}\right] _{\alpha \left( \gamma _{(i)-2}\right) }\right) +s\varepsilon . \end{eqnarray} (4.15)

    By (4.12), \exists i_{2}\in {\mathbb{N}} such that

    \begin{equation} d_{b}\left( \mu _{\gamma _{(i)-1}},\left[ T\mu _{\gamma _{(i)-1}}\right] _{\alpha \left( \gamma _{(i)-1}\right) }\right) < \varepsilon ,{\rm{ }} d_{b}\left( \mu _{\gamma _{(i)}},\left[ T\mu _{\gamma _{(i)}}\right] _{\alpha \left( \gamma _{(i)}\right) }\right) < \varepsilon ,{\rm{ }}d_{b}\left( \mu _{\delta _{(i)}},\left[ T\mu _{\delta _{(i)}}\right] _{\alpha \left( \delta _{\left( i\right) }\right) }\right) < \varepsilon, \end{equation} (4.16)

    for all i > i_{2} , which together with (4.15) yields

    \begin{equation*} d(\mu _{\gamma _{(i)}},\mu _{\delta _{(i)}}) < 2s\varepsilon )\;{\rm{ for \;all }} \;i > i_{2}. \end{equation*}

    In view of (\mathcal{F}_{i}), we can write

    \begin{equation} \mathcal{F}\left( d(\mu _{\gamma _{(i)}},\mu _{\delta _{(i)}})\right) < \mathcal{F}(2s\varepsilon )\;{\rm{ for\; all }}\;i > i_{2}. \end{equation} (4.17)

    From (4.14) and (4.16), we write

    \begin{equation} \frac{1}{2s}d_{b}\left( \mu _{\gamma _{(i)}},\left[ T\mu _{\gamma _{(i)}}\right] _{\alpha \left( \gamma _{(i)}\right) }\right) < \frac{\varepsilon }{2s} < d(\mu _{\gamma _{(i)}},\mu _{\delta _{(i)}})\;{\rm{ for\; all }}\;i > i_{2}. \end{equation} (4.18)

    Applying the triangle inequality, we find that

    \begin{eqnarray} \epsilon \leq d(\mu _{\gamma _{(i)}},\mu _{\gamma _{(i)}}) &\leq &sd\left( \mu _{\gamma _{(i)}},\mu _{\gamma _{(i)+1}}\right) \\ &+&s^{2}d\left( \mu _{\gamma _{(i)+1}},\mu _{\delta _{(i)+1}}\right) +s^{2}d\left( \mu _{\delta _{(i)+1}},\mu _{\delta _{(i)}}\right) . \end{eqnarray} (4.19)

    Next, if we setting to the limit i\rightarrow +\infty in (4.19) and make use of (4.12), then,

    \begin{equation*} \frac{\epsilon }{s^{2}}\leq \lim\limits_{i\rightarrow +\infty }\inf d\left( \mu _{\gamma _{(i)+1}},\mu _{\delta _{(i)+1}}\right) . \end{equation*}

    Also, there is i_{3}\in {\mathbb{N}} such that

    \begin{equation*} d\left( \mu _{\gamma _{(i)+1}},\mu _{\delta _{(i)+1}}\right) > 0, \end{equation*}

    for all i > i_{3} , that is, d\left(\mu _{\gamma _{(i)+1}}, \mu _{\delta _{(i)+1}}\right) > 0 > 0 for i > i_{3}. Further, from (4.1) and Lemma (2.8), we can write

    \begin{eqnarray} &&\mathcal{F}\left[ d\left( \mu _{\gamma _{(i)+1}},\mu _{\delta _{(i)+1}}\right) \right] \leq \mathcal{F}\left( \hat{H}_{b}\left( \left[ T\mu _{\gamma _{(i)}}\right] _{\alpha \left( \gamma _{(i)}\right) },\left[ T\mu _{\delta _{(i)}}\right] _{\alpha \left( \delta _{(i)}\right) }\right) \right) -\tau (U(\mu _{\gamma \left( i\right) },\mu _{\delta \left( i\right) })) \\ &\leq &\mathcal{F}\left[ e_{1}(d(\mu _{\gamma \left( i\right) },\mu _{\delta \left( i\right) }))+e_{2}\left( d_{b}\left(\mu _{\gamma \left( i\right) }, \left[ T\mu _{\gamma _{(i)}}\right] _{\alpha \left( \gamma _{(i)}\right) }\right) \right) \right. \\ &&+e_{3}\left( d_{b}\left( \mu _{\delta \left( i\right) },\left[ T\mu _{\delta _{(i)}}\right] _{\alpha \left( \delta _{(i)}\right) }\right) \right) +e_{4}\left( d_{b}\left( \mu _{\gamma \left( i\right) },\left[ T\mu _{\delta _{(i)}}\right] _{\alpha \left( \delta _{(i)}\right) }\right) \right) \\ &&\left. +e_{5}\left( d_{b}\left( \mu _{\delta \left( i\right) },\left[ T\mu _{\gamma _{(i)}}\right] _{\alpha \left( \gamma _{(i)}\right) }\right) \right) \right] -\tau (U(\mu _{\gamma \left( i\right) },\mu _{\delta \left( i\right) })) \\ &\leq &\mathcal{F}\left[ e_{1}(d(\mu _{\gamma \left( i\right) },\mu _{\delta \left( i\right) }))+e_{2}\left( d_{b}\left(\mu _{\gamma \left( i\right) }, \left[ T\mu _{\gamma _{(i)}}\right] _{\alpha \left( \gamma _{(i)}\right) }\right) \right) \right. \\ &&+e_{3}\left( d_{b}\left( \mu _{\delta \left( i\right) },\left[ T\mu _{\delta _{(i)}}\right] _{\alpha \left( \delta _{(i)}\right) }\right) \right) +se_{4}\left( d\left( \mu _{\gamma \left( i\right) },\mu _{\delta \left( i\right) }\right) \right) \\ &&+se_{4}\left( d_{b}\left( \mu _{\delta \left( i\right) },\left[ T\mu _{\delta _{(i)}}\right] _{\alpha \left( \delta _{(i)}\right) }\right) \right) +se_{5}\left( d\left( \mu _{\delta \left( i\right) },\mu _{\gamma \left( i\right) }\right) \right) \\ &&\left. +se_{5}\left( d_{b}\left( \mu _{\gamma \left( i\right) },\left[ T\mu _{\gamma _{(i)}}\right] _{\alpha \left( \gamma _{(i)}\right) }\right) \right) \right] -\tau \left( U(\mu _{\gamma \left( i\right) },\mu _{\delta \left( i\right) })\right), \end{eqnarray} (4.20)

    for all i > \max \left \{ i_{1}, i_{2}\right \}. In view of (4.16)–(4.18), inequaility (4.20) yields

    \begin{eqnarray} \mathcal{F}\left[ d\left( \mu _{\gamma _{(i)+1}},\mu _{\delta _{(i)+1}}\right) \right] &\leq &\mathcal{F}\left( \hat{H}_{b}\left( \left[ T\mu _{\gamma _{(i)}}\right] _{\alpha \left( \gamma _{(i)}\right) }, \left[ T\mu _{\delta _{(i)}}\right] _{\alpha \left( \delta _{(i)}\right) }\right) \right) \\ &\leq &\mathcal{F}\left[ e_{1}(2s\varepsilon ))+e_{2}\left( d_{b}\left( d(\mu _{\gamma \left( i\right) },\left[ T\mu _{\gamma _{(i)}}\right] _{\alpha \left( \gamma _{(i)}\right) }\right) \right) \right. \\ &&+e_{3}\left( d_{b}\left( \mu _{\delta \left( i\right) },\left[ T\mu _{\delta _{(i)}}\right] _{\alpha \left( \delta _{(i)}\right) }\right) \right) \\ &&+\frac{e_{4}}{2}(s\varepsilon +s\varepsilon )+\frac{e_{5}}{2}(s\varepsilon +\varepsilon ))] \\ &&-\tau \left( U(\mu _{\gamma \left( i\right) },\mu _{\delta \left( i\right) }))\right), \end{eqnarray} (4.21)

    for all i > \max \left \{ i_{1}, i_{2}\right \} . Taking the limit i\rightarrow +\infty in (4.21), we get

    \begin{equation*} \lim\limits_{i\rightarrow +\infty }\mathcal{F}\left[ d\left( \mu _{\gamma _{(i)+1}},\mu _{\delta _{(i)+1}}\right) \right] = -\infty , \end{equation*}

    which by vertue of (\mathcal{F}_{ii}), implies that \lim_{i\rightarrow +\infty }d\left(\mu _{\gamma _{(i)+1}}, \mu _{\delta _{(i)+1}}\right) = 0 . In the light of (4.19), we can write \lim_{i\rightarrow +\infty }d(\mu _{\gamma _{(i)}}, \mu _{\delta _{(i)}}) = 0, which contradicts. Hence (4.13) holds true. Hence \{ \mu _{i}\} is a Cauchy sequence in G. Since G is a complete b -metric-like space, there is a point c\in G such that

    \begin{equation} d(c,c) = \lim\limits_{i\rightarrow +\infty }d(\mu _{i},c) = \lim\limits_{i,j\rightarrow +\infty }d(\mu _{i},\mu _{j}) = 0. \end{equation} (4.22)

    Now, we show futher the following inequatlity

    \begin{equation} \frac{1}{2s}d_{b}\left( \mu _{i},\left[ T\mu _{i}\right] _{\alpha \left( i\right) }\right) < d(\mu _{i},c){\rm{ or }}\frac{1}{2s}d_{b}\left( \mu _{i+1}, \left[ T\mu _{i+1}\right] _{\alpha \left( i+1\right) }\right) < d\left( \mu _{i+1},c\right) . \end{equation} (4.23)

    Assume on the contrary that \exists i_{0}\in {\mathbb{N}} such that

    \begin{equation} \frac{1}{2s}d_{b}(\mu _{i_{0}},\left[ T\mu _{i_{0}}\right] _{\alpha \left( i_{0}\right) })\geq d(\mu _{i_{0}},c),{\rm{ }}\frac{1}{2s}d_{b}\left( \mu _{i_{0+1}},\left[ T\mu _{i_{0}+1}\right] _{\alpha \left( i_{0}+1\right) }\right) \geq d\left( \mu _{i_{0}+1},c\right) . \end{equation} (4.24)

    Then from (4.5) and (4.24), we have

    \begin{eqnarray*} d_{b}\left( \mu _{i_{0}},\left[ T\mu _{i_{0}}\right] _{\alpha \left( i_{0}\right) }\right) &\leq &sd(\mu _{i_{0}},c)+sd_{b}\left( c,\left[ T\mu _{i_{0}}\right] _{\alpha \left( i_{0}\right) }\right) \\ &\leq &\frac{1}{2s}sd_{b}\left( \mu _{i_{0}},\left[ T\mu _{i_{0}}\right] _{\alpha \left( i_{0}\right) }\right) +\frac{1}{2s}sd_{b}\left( \mu _{i_{0+1}}, \left[ T\mu _{i_{0+1}}\right] _{\alpha \left( i_{0+1}\right) }\right) \\ &\leq &\frac{1}{2}d_{b}\left( \mu _{i_{0}},\left[ T\mu _{i_{0}}\right] _{\alpha \left( i_{0}\right) }\right) +\frac{1}{2}d_{b}\left( \mu _{i_{0}},\left[ T\mu _{i_{0}}\right] _{\alpha \left( i_{0}\right) }\right) \\ &\leq &d_{b}\left( \mu _{i_{0}},\left[ T\mu _{i_{0}}\right] _{\alpha \left( i_{0}\right) }\right) , \end{eqnarray*}

    a contradiction. Thus (4.23) holds true. So, we can write

    \begin{eqnarray} \mathcal{F}\left( d_{b}\left( \mu _{i+1},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) \right) &\leq &\mathcal{F}\left[ \hat{H} _{b}(\left[ T(\mu _{i})\right] _{\alpha \left( i\right) },\left[ T\left( c\right) \right] _{\alpha \left( c\right) })\right] -\tau \left( U(\mu _{i},c)\right) \\ &\leq &\mathcal{F}\left[ e_{1}(d(\mu _{i},c))+e_{2}d_{b}\left( \mu _{i},\left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) }\right) \right. \\ &&+e_{3}d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) +\frac{e_{4}}{2s}d_{b}\left( \mu _{i},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) \\ &&\left. +\frac{e_{5}}{2s}d_{b}\left( c,\left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) }\right) \right] -\tau \left( U(\mu _{i},c)\right), \end{eqnarray} (4.25)

    or

    \begin{eqnarray} \mathcal{F}\left( d\left( \mu _{i+2},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) \right) &\leq &\mathcal{F}\left[ \hat{H} _{b}(\left[ T(\mu _{i+1})\right] _{\alpha \left( i+1\right) },\left[ T\left( c\right) \right] _{\alpha \left( c\right) })\right] -\tau \left( U(\mu _{i},c)\right) \\ &\leq &\mathcal{F}\left[ e_{1}(d(\mu _{i+1},c))+e_{2}d_{b}\left( \mu _{i+1}, \left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+1\right) }\right) \right. \\ &&+e_{3}d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) +\frac{e_{4}}{2s}\left( \mu _{i+1},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) \\ &&\left. +\frac{e_{5}}{2s}d_{b}\left( c,\left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+1\right) }\right) \right] -\tau \left( U(\mu _{i+1},c)\right) . \end{eqnarray} (4.26)

    Now, let us now examine the following cases:

    Case 1. Assume that (4.25) holds true. From (4.25), we have

    \begin{eqnarray} \mathcal{F}\left( d_{b}\left( \mu _{i+1},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) \right) &\leq &\mathcal{F}\left[ \hat{H} _{b}(\left[ T(\mu _{i})\right] _{\alpha \left( i\right) },\left[ T\left( c\right) \right] _{\alpha \left( c\right) })\right] -\tau \left( U(\mu _{i},c)\right) \\ &\leq &\mathcal{F}\left[ e_{1}(d(\mu _{i},c))+e_{2}d_{b}\left( \mu _{i},\left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) }\right) \right. \\ &&+e_{3}d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) +\frac{e_{4}}{2}d_{b}\left( \mu _{i},c\right) \\ &&+\frac{e_{4}}{2}d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) +\frac{e_{5}}{2}d\left( c,\mu _{i}\right) \\ &&\left. +\frac{e_{5}}{2}d_{b}\left( \mu _{i},\left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) }\right) \right] -\tau \left( U(\mu _{i},c)\right) . \end{eqnarray} (4.27)

    By (4.12) and (4.22), there is i_{4}\in {\mathbb{N}} such that for some \varepsilon _{1} > 0

    \begin{equation} d(c,\mu _{i}) < \varepsilon _{1}{\rm{, }}d_{b}(\mu _{i},\left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) }) < \varepsilon _{1},\;{\rm{ for }}\; i > i_{4}. \end{equation} (4.28)

    From (4.27) and (4.28), we have

    \begin{eqnarray} \mathcal{F}\left( d_{b}\left( \mu _{i+1},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) \right) &\leq &\mathcal{F}\left[ \hat{H} _{b}(\left[ T(\mu _{i})\right] _{\alpha \left( i\right) },\left[ T\left( c\right) \right] _{\alpha \left( c\right) })\right] -\tau \left( U(\mu _{i},c)\right), \\ &\leq &\mathcal{F}\left[ e_{1}(d(\mu _{i},c))+e_{2}d_{b}\left( \mu _{i},\left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) }\right) \right. \\ &&+e_{3}d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) +\frac{e_{4}}{2}\left( \varepsilon _{1}\right) \\ &&+\frac{e_{4}}{2}d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) +e_{5}\left( \varepsilon _{1}\right) \\ &&-\tau \left( U(\mu _{i},c)\right), \end{eqnarray} (4.29)

    for all i > i_{4}. Taking the limit as i\rightarrow +\infty in (4.29), we find that \lim_{i\rightarrow +\infty }\mathcal{F}\left(d_{b}\left(\mu _{i+1}, \left[ T\left(c\right) \right] _{\alpha \left(c\right) }\right) \right) = -\infty . By means of (\mathcal{F}_{ii}), we have

    \begin{equation*} \lim\limits_{i\rightarrow +\infty }d_{b}(\mu _{i+1},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }) = 0. \end{equation*}

    On the other hand, we see that

    \begin{equation*} d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) \leq d\left( c,\mu _{i+1}\right) +d_{b}\left( \mu _{i+1},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) . \end{equation*}

    Further, in the light of above hypothesis with respect to G\ni c\longmapsto d_{b}(c, \left[ T\left(\mu _{i}\right) \right] _{\alpha \left(i\right) }) is \check{D}(T, \mu _{0}) -fuzzy dynamic lower semi-continuous, we have

    \begin{equation*} d_{b}(c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) })\leq \lim\limits_{n\rightarrow +\infty }\inf d_{b}(c,\left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) })+0 = 0. \end{equation*}

    Also, the closedness of \left[ T\left(c\right) \right] _{\alpha \left(c\right) } implies that c\in \left[ T\left(c\right) \right] _{\alpha \left(c\right) } which means that c is a fuzzy fixed point of T.

    Case 2. Assume that (4.26) holds true. From (4.26), we can write

    \begin{eqnarray} \mathcal{F}\left( d_{b}\left( \mu _{i+2},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) \right) &\leq &\mathcal{F}\left[ \hat{H} _{b}(\left[ T(\mu _{i+1})\right] _{\alpha \left( i+1\right) },\left[ T\left( c\right) \right] _{\alpha \left( c\right) })\right] -\tau \left( U(\mu _{i+1},c)\right) \\ &\leq &\mathcal{F}\left[ e_{1}(d(\mu _{i+1},c))+e_{2}d_{b}\left( \mu _{i+1}, \left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+1\right) }\right) \right. \\ &&+e_{3}d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) +\frac{e_{4}}{2}d\left( \mu _{i+1},c\right) \\ &&+\frac{e_{4}}{2}d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) +\frac{e_{5}}{2}d\left( c,\mu _{i+1}\right) \\ &&\left. +\frac{e_{5}}{2}d_{b}\left( \mu _{i+1},\left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+!\right) }\right) \right] -\tau \left( U(\mu _{i+1},c)\right) . \end{eqnarray} (4.30)

    From (4.12) and (4.22), there is i_{5}\in {\mathbb{N}} such that for some \varepsilon _{2} > 0

    \begin{equation} d(c,\mu _{i+1}) < \varepsilon _{2}{\rm{, }}d_{b}(\mu _{i+1},\left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+1\right) }) < \varepsilon _{2},{\rm{ for }}i > i_{5}. \end{equation} (4.31)

    Now, from (4.30) and (4.31), we have

    \begin{eqnarray} \mathcal{F}\left( d_{b}\left( \mu _{i+2},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) \right) &\leq &\mathcal{F}\left[ \hat{H} _{b}(\left[ T(\mu _{i+1})\right] _{\alpha \left( i+1\right) },\left[ T\left( c\right) \right] _{\alpha \left( c\right) })\right] -\tau \left( U(\mu _{i+1},c)\right) \\ &\leq &\mathcal{F}\left[ e_{1}(d(\mu _{i+1},c))+e_{2}d_{b}\left( \mu _{i+1}, \left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+1\right) }\right) \right. \\ &&+e_{3}d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) +\frac{e_{4}}{2}\left( \varepsilon _{1}\right) \\ &&+\frac{e_{4}}{2}d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) +e_{5}\left( \varepsilon _{1}\right) \\ &&-\tau \left( U(\mu _{i+1},c)\right) . \end{eqnarray} (4.32)

    for all i > i_{5}. Taking the limit as i\rightarrow +\infty in (4.32), we see that \lim_{i\rightarrow +\infty }\mathcal{F}\left(d_{b}\left(\mu _{i+2}, \left[ T\left(c\right) \right] _{\alpha \left(c\right) }\right) \right) = -\infty . By means of (\mathcal{F}_{ii}), we have

    \begin{equation*} \lim\limits_{i\rightarrow +\infty }d_{b}(\mu _{i+2},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }) = 0. \end{equation*}

    Consequently,

    \begin{equation*} d_{b}\left( c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) \leq d\left( c,\mu _{i+2}\right) +d_{b}\left( \mu _{i+2},\left[ T\left( c\right) \right] _{\alpha \left( c\right) }\right) . \end{equation*}

    Further, in view of above fashion with respect to G\ni c\longmapsto d_{b}(c, \left[ T\left(\mu _{i}\right) \right] _{\alpha \left(i\right) }) is \check{D}(T, \mu _{0}) -fuzzy dynamic lower semi-continuous, we have

    \begin{equation*} d_{b}(c,\left[ T\left( c\right) \right] _{\alpha \left( c\right) })\leq \lim\limits_{i\rightarrow +\infty }\inf d_{b}(c,\left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+1\right) })+0 = 0. \end{equation*}

    Also, the closedness of \left[ T\left(c\right) \right] _{\alpha \left(c\right) }, which implies that c\in \left[ T\left(c\right) \right] _{\alpha \left(c\right) } . Hence, c is a fuzzy fixed point of T.

    Corollary 4.4. Let \left(G, d\right) be a b -metric-like space with s\geq 1 . Assume that T:G\rightarrow \mu (G) is a F-fuzzy Suzuki-Kannan (abbr., F-FSK) type contraction with respect to fuzzy dynamic system \check{D} \left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) and \alpha :G\rightarrow \lbrack 0, 1] such that \left[ T\left(\mu _{i}\right) \right] _{\alpha \left(i\right) } are nonempty closed subsets of G. Assume that for some \mathcal{F}\in \nabla _{\digamma } and \tau :(0, +\infty)\rightarrow (0, +\infty) such that

    \begin{equation*} \frac{1}{2s}d_{b}\left( \mu _{i-1},\left[ T\left( \mu _{i-1}\right) \right] _{\alpha \left( i-1\right) }\right) \leq d\left( \mu _{i-1},\mu _{i}\right) , \end{equation*}

    we have

    \begin{equation*} \tau (U(\mu _{i-1},\mu _{i}))+\mathcal{F}\left[ \hat{H}_{b}\left( \left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) },\left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+1\right) }\right) \right] \leq \mathcal{F}(U(\mu _{i-1},\mu _{i})), \end{equation*}

    where

    \begin{equation*} U(\mu _{i-1},\mu _{i}) = e_{2}d_{b}\left( \mu _{i-1},\left[ T\left( \mu _{i-1}\right) \right] _{\alpha \left( i-1\right) }\right) +e_{3}d_{b}\left( \mu _{i},\left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) }\right), \end{equation*}

    for all \mu _{i}\in \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) , \hat{H}_{b}\left(\left[ T\left(\mu _{i}\right) \right] _{\alpha \left(i\right) }, \left[ T\left(\mu _{i+1}\right) \right] _{\alpha \left(i+1\right) }\right) > 0 , where e_{2}, e_{3}\in \left[ 0, 1\right] such that e_{1}+e_{2} = 1 . Assume that \left(i\right) \left(iii\right) are satisfied. Then T has a fuzzy fixed point.

    Corollary 4.5. Let \left(G, d\right) be a b -metric-like space with s\geq 1 . Assume that T:G\rightarrow \mu (G) is a F-fuzzy Suzuki-Chatterjea (abbr., F-FSC) type contraction with respect to fuzzy dynamic system \check{D} \left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) and \alpha :G\rightarrow \lbrack 0, 1] such that \left[ T\left(\mu _{i}\right) \right] _{\alpha \left(i\right) } are nonempty closed subsets of G. Assume that for some \mathcal{F}\in \nabla _{\digamma } and \tau :(0, +\infty)\rightarrow (0, +\infty) such that

    \begin{equation*} \frac{1}{2s}d_{b}\left( \mu _{i-1},\left[ T\left( \mu _{i-1}\right) \right] _{\alpha \left( i-1\right) }\right) \leq d\left( \mu _{i-1},\mu _{i}\right) , \end{equation*}

    we have

    \begin{equation*} \tau (U(\mu _{i-1},\mu _{i}))+\mathcal{F}\left[ \hat{H}_{b}\left( \left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) },\left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+1\right) }\right) \right] \leq \mathcal{F}(U(\mu _{i-1},\mu _{i})), \end{equation*}

    where

    \begin{equation*} U(\mu _{i-1},\mu _{i}) = e_{4}d_{b}\left( \mu _{i-1},\left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) }\right) +e_{5}d_{b}\left( \mu _{i}, \left[ T\left( \mu _{i-1}\right) \right] _{\alpha \left( i-1\right) }\right), \end{equation*}

    for all \mu _{i}\in \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) , \hat{H}_{b}\left(\left[ T\left(\mu _{i}\right) \right] _{\alpha \left(i\right) }, \left[ T\left(\mu _{i+1}\right) \right] _{\alpha \left(i+1\right) }\right) > 0 , where e_{4}, e_{5}\in \lbrack 0, \frac{1}{2}) . Assume that \left(i\right) \left(iii\right) are satisfied. Then T has a fuzzy fixed point.

    Corollary 4.6. Let \left(G, d\right) be a b -metric-like space with s\geq 1 . Assume that T:G\rightarrow \mu (G) is a F-fuzzy Suzuki-Banach (abbr., F-FSB) type contraction with respect to fuzzy dynamic system \check{D} \left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) and \alpha :G\rightarrow \lbrack 0, 1] such that \left[ T\left(\mu _{i}\right) \right] _{\alpha \left(i\right) } are nonempty closed subsets of G. Assume that for some \mathcal{F}\in \nabla _{\digamma } and \tau :(0, +\infty)\rightarrow (0, +\infty) such that

    \begin{equation*} \frac{1}{2s}d_{b}\left( \mu _{i-1},\left[ T\left( \mu _{i-1}\right) \right] _{\alpha \left( i-1\right) }\right) \leq d\left( \mu _{i-1},\mu _{i}\right) , \end{equation*}

    we have

    \begin{equation*} \tau (d(\mu _{i-1},\mu _{i}))+\mathcal{F}\left[ \hat{H}_{b}\left( \left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) },\left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+1\right) }\right) \right] \leq \mathcal{F}(e_{1}d(\mu _{i-1},\mu _{i})), \end{equation*}

    for all \mu _{i}\in \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) , \hat{H}_{b}\left(\left[ T\left(\mu _{i}\right) \right] _{\alpha \left(i\right) }, \left[ T\left(\mu _{i+1}\right) \right] _{\alpha \left(i+1\right) }\right) > 0 , where e_{1}\in \lbrack 0, 1) . Assume that \left(i\right) and \left(ii\right) are satisfied. Then T has a fuzzy fixed point.

    Corollary 4.7. Let \left(G, d\right) be a b -metric-like space with s\geq 1 . Assume that T:G\rightarrow \mu (G) is a F-fuzzy Banach (abbr., F-FB) type contraction with respect to fuzzy dynamic system \check{D} \left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) and \alpha :G\rightarrow \lbrack 0, 1] such that \left[ T\left(\mu _{i}\right) \right] _{\alpha \left(i\right) } are nonempty closed subsets of G. Assume that for some \mathcal{F}\in \nabla _{\digamma } and \tau :(0, +\infty)\rightarrow (0, +\infty) such that

    \begin{equation*} \tau (d(\mu _{i-1},\mu _{i}))+\mathcal{F}\left[ \hat{H}_{b}\left( \left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) },\left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+1\right) }\right) \right] \leq \mathcal{F}(e_{1}d(\mu _{i-1},\mu _{i})) \end{equation*}

    for all \mu _{i}\in \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right) , \hat{H}_{b}\left(\left[ T\left(\mu _{i}\right) \right] _{\alpha \left(i\right) }, \left[ T\left(\mu _{i+1}\right) \right] _{\alpha \left(i+1\right) }\right) > 0 , where e_{1}\in \lbrack 0, 1) . Assume that \left(i\right) and \left(ii\right) are satisfied. Then T has a fuzzy fixed point.

    Example 4.8. Let G = {\mathbb{R}}^{+}\cup \left \{ 0\right \} and d:G\times G\rightarrow {\mathbb{R}}^{+}\cup \left \{ 0\right \} be a function defined by

    \begin{equation*} d\left( \mu _{1},\mu _{2}\right) = \left( \max \left \{ \mu _{1},\mu _{2}\right \} \right) ^{2}. \end{equation*}

    Clearly, \left(d, G\right) is a complete b -metric-like space with s = \frac{4}{3} . Define a fuzzy mapping T:G\rightarrow F(G) by

    \begin{equation*} T\left( \mu \right) \left( \mu ^{\prime }\right) = \left \{ \begin{array}{l} 1,\;{\rm{ if }}\;0\leq \mu ^{\prime }\leq \frac{\mu }{4}; \\ \frac{1}{2},\;{\rm{ if }}\;\frac{\mu }{4} < \mu ^{\prime }\leq \frac{\mu }{3}; \\ \frac{1}{4},\;{\rm{ if }}\;\frac{\mu }{3} < \mu ^{\prime }\leq \frac{\mu }{2}; \\ 0,\;{\rm{ if }}\;\frac{\mu }{2} < \mu ^{\prime }\leq 1. \end{array} \right. \end{equation*}

    Define \mathcal{F}:{\mathbb{R}}^{+}\rightarrow {\mathbb{R}} and \tau :{ \mathbb{R}}^{+}\rightarrow {\mathbb{R}}^{+} by \mathcal{F}(\mu) = \ln (\mu) and

    \begin{equation*} \tau (h) = \left \{ \begin{array}{l} \ln (1),\quad {\rm{ for }}\;\mu = 0,1; \\ \frac{1}{100},\quad {\rm{ for }}\;\mu \in (1,+\infty ). \end{array} \right. \end{equation*}

    For all \mu \in \check{D}\left(\left[ T\mu \right] _{\alpha }, \mu _{0}\right), there is \alpha \left(\mu \right) = 1 such that \left[ T\mu \right] _{\alpha \left(\mu \right) } = \left[ 0, \frac{\mu }{2}\right]. Then we have

    \begin{equation*} \frac{1}{2s}d_{b}\left( \mu _{i},\left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) }\right) \leq d\left( \mu _{i},\mu _{i+1}\right) , \end{equation*}

    setting e_{2} = e_{3} = e_{4} = e_{4} = 0 and e_{1} = 1 , we obtain

    \begin{equation*} \tau (d(\mu _{i},\mu _{i+1}))+\mathcal{F}\left[ \hat{H}_{b}\left( \left[ T\left( \mu _{i}\right) \right] _{\alpha \left( i\right) },\left[ T\left( \mu _{i+1}\right) \right] _{\alpha \left( i+1\right) }\right) \right] \leq \mathcal{F}(\alpha d(\mu _{i},\mu _{i+1})). \end{equation*}

    Hence all the required possible hypothesis of Corollary 4.6 are satisfied, Thus T has a fuzzy fixed point.

    Fuzzy differential equations and fuzzy integral equations have always been of key importance in dynamical programming and engineering problems. Therefore, various authors used different techniques for solving an fuzzy differential equations and fuzzy integral equations. Among those, Hukuhara differentiability for fuzzy valued function is the most celebrated problem. This section renders solution of a fuzzy differential equations. For this we explore Hukuhara differentiability for fuzzy functions and fuzzy initial valued problem in the setting of b -metric-like space.

    Definition 5.1. A function g: {\mathbb{R}} \rightarrow \left[ 0, 1\right] is called a fuzzy real number if

    \left(i\right) g is normal, i.e., there is \mu _{0}\in {\mathbb{R}} in such a way that g\left(\mu _{0}\right) = 1 ;

    \left(ii\right) ga is fuzzy convex, i.e., g\left(\beta \left(\mu _{1}\right) +\left(1-\beta \right) \mu _{2}\right) \geq \min \left \{ g\left(\mu _{1}), g\left(\mu _{2}\right) \right) \right \} , 0\leq \beta \leq 1 , for all \mu _{1}, \mu _{2}\in {\mathbb{R}} ;

    \left(iii\right) g is upper semi-continuous;

    \left(iiii\right) \left[ g\right] ^{0} = cl\left \{ \mu \in R:g\left(\mu \right) > 0\right \} is compact.

    Note that, for \alpha \in (0, 1],

    \begin{equation*} \left[ g\right] ^{\alpha } = cl\left \{ \mu \in R:g\left( \mu \right) > \alpha \right \} = \left[ g_{s_{1}}^{\alpha },g_{s_{2}}^{\alpha }\right], \end{equation*}

    expresses \alpha -cut of the fuzzy set g. For g\in P^{1} , where P^{1} represents the family of fuzzy real numbers, one can write \left[ g\right] ^{\alpha }\in C_{c}\left({\mathbb{R}}\right) for all \alpha \in \left[ 0, 1\right] , where C_{c}\left(R\right) denotes the set of all compact and convex subsets of \mathbb{R} . The supremum on P^{1} endowed with the b -metric-like is defined by

    \begin{equation*} d^{\ast }(g_{1},g_{2}) = \sup\limits_{\alpha \in \left[ 0,1\right] }\left[ \left \vert g_{1,s_{1}}^{\alpha }-g_{2,s_{1}}^{\alpha }\right \vert +\left \vert g_{1,s_{2}}^{\alpha }-g_{2,s_{2}}^{\alpha }\right \vert \right] ^{2}, \end{equation*}

    for all g_{1}, g_{2}\in P^{1}, g_{1, s_{1}}^{\alpha }-g_{2, s_{1}}^{\alpha } = diam\left(\left[ g\right] \right). Consider the continuous fuzzy function defined on \left[ 0, \Gamma \right] , for \Gamma > 0 as C\left(\left[ 0, \Gamma \right], P^{1}\right) endowed with the complete b -metric-like with respect to b -metric-like as:

    \begin{equation*} d(g_{1},g_{2}) = \sup\limits_{\mu \in \left[ 0,1\right] }\left[ d^{\ast }(g_{1},g_{2}) \right], \end{equation*}

    for all g_{1}, g_{2}\in C^{1}\left(\left[ 0, \Gamma \right], P^{1}\right). Consider the fuzzy initial valued problem:

    \begin{equation} \left \{ \begin{array}{l} g^{\prime }\left( \mu \right) = f\left( \mu ,g\left( \mu \right) \right) , {\rm{ }}\mu \in I = \left[ 0,\Gamma \right] ; \\ g\left( 0\right) = 0, \end{array} \right. \end{equation} (5.1)

    where g^{\prime } is the Hukuhara differentiability and f is the fuzzy function, i.e., f:I\times P^{1}\rightarrow P^{1} is continuous. Denote the set of all continuous fuzzy functions f:I\rightarrow P^{1} which have continuous derivatives by C^{1}\left(I, P^{1}\right). A family \mu \in C^{1}\left(I, P^{1}\right) is a solution of fuzzy initial valued problem (5.1) if and only if

    \begin{equation} g\left( \mu \right) = g_{0}\Theta _{E}\left( -1\right) _{0}^{\mu }f\left( r,g\left( r\right) \right) dr,{\rm{ }}\mu \in I = \left[ 0,\Gamma \right] , \end{equation} (5.2)

    where (5.2) is called a fuzzy Volterra integral equation.

    Theorem 5.2. Let f:I\times P^{1}\rightarrow P^{1} be a continuous function such that

    \begin{equation*} g < g^{\prime }\;implies\;f\left( \mu ,g\left( \mu \right) \right) < f\left( \mu ,g^{\prime }\left( \mu \right) \right), \end{equation*}

    for g, g^{\prime }\in P^{1} , In addition, assume that \tau :(0, +\infty)\rightarrow (0, +\infty) such that

    \begin{equation*} \left[ \left \vert f\left( \mu ,g\left( \mu \right) \right) -f\left( \mu ,g^{\prime }\left( \mu \right) \right) \right \vert \right] ^{2}\leq \tau e^{-\tau }\max\limits_{\mu \in I}\left( d^{\ast }(g_{1}\left( \mu \right) ,g_{2}\left( \mu \right) )e^{-\tau \mu }\right) , \end{equation*}

    where g < g^{\prime } for all \mu \in I and g, g^{\prime }\in P^{1}. Then the FIVP (5.1) has a fuzzy solution with respect to C^{1}\left(I, P^{1}\right).

    Proof. Let \tau :(0, +\infty)\rightarrow (0, +\infty) and the family C^{1}\left(I, P^{1}\right) endow with the b -metric-like as:

    \begin{equation*} d_{\tau }(g,g^{\prime }) = \sup\limits_{\mu \in \left[ 0,1\right] }\left[ d^{\ast }(g\left( \mu \right) ,g^{\prime }\left( \mu \right) )e^{-\tau \mu }\right], \end{equation*}

    for all g, g^{\prime }\in C^{1}\left(I, P^{1}\right). Let S:G\rightarrow (0, 1] . Due to (5.2) for g\in G, one can write

    \begin{equation*} Y_{g}\left( \mu \right) = g_{0}\Theta _{E}\left( -1\right) _{0}^{\mu }f\left( r,g\left( r\right) \right) dr,{\rm{ }}\mu \in I. \end{equation*}

    Assume that g < g . Then we have

    \begin{eqnarray*} Y_{g}\left( \mu \right) & = &g_{0}\Theta _{E}\left( -1\right) _{0}^{\mu }f\left( r,g\left( r\right) \right) dr \\ & < &g_{0}\Theta _{E}\left( -1\right) _{0}^{\mu }f\left( r,g^{\prime }\left( r\right) \right) dr \\ & = &Y_{g^{\prime }}\left( \mu \right) . \end{eqnarray*}

    This implies Y_{g}\left(\mu \right) \neq Y_{g^{\prime }}\left(\mu \right) . Assume a fuzzy mapping T:G\rightarrow P^{G} is defined by

    \begin{equation*} \left \{ \begin{array}{c} \eta _{Tg}\left( t\right) = \left \{ \begin{array}{l} Y\left( g\right) ,{\rm{ }}t\left( \mu \right) = Y_{g}\left( \mu \right); \\ 0,\;{\rm{ otherwise}}. \end{array} \right. \\ \eta _{Tg^{\prime }}\left( t\right) = \left \{ \begin{array}{l} Y\left( g^{\prime }\right) ,{\rm{ }}t\left( \mu \right) = Y_{g^{\prime }}\left( \mu \right); \\ 0,\;{\rm{ otherwise.}} \end{array} \right. \end{array} \right. \end{equation*}

    Owing to \alpha \left(g\right) = S\left(g\right) and \alpha \left(g^{\prime }\right) = S\left(g^{\prime }\right), we have

    \begin{equation*} \left[ Tg\right] _{\alpha \left( g\right) } = \left \{ t\in G:Tg\left( \mu \right) \geq S\left( g\right) \right \} = Y_{g}\left( \mu \right), \end{equation*}

    and on the same fashion, we have

    \begin{equation*} \left[ Tg^{\prime }\right] _{\alpha \left( g^{\prime }\right) } = \left \{ t\in G:Tg^{\prime }\left( \mu \right) \geq S\left( g^{\prime }\right) \right \} = Y_{g^{\prime }}\left( \mu \right) . \end{equation*}

    Therefore,

    \begin{eqnarray*} \hat{H}_{b}\left( \left[ Tg\right] _{\alpha \left( g\right) },\left[ Tg^{\prime }\right] _{\alpha \left( g^{\prime }\right) }\right) & = &\max \left \{ \begin{array}{c} \sup\limits_{g\in \left[ Tg\right] _{\alpha \left( g\right) }}\inf\limits_{g^{\prime }\in \left[ Tg^{\prime }\right] _{\alpha \left( g^{\prime }\right) }}d\left( g,g^{\prime }\right) , \\ \sup\limits_{g^{\prime }\in \left[ Tg^{\prime }\right] _{\alpha \left( g^{\prime }\right) }}\inf\limits_{g\in \left[ Tg\right] _{\alpha \left( g\right) }}d\left( g,g^{\prime }\right), \end{array} \right \} \\ &\leq &\max \left \{ \sup\limits_{\mu \in I}\left[ \left \vert Y_{g}\left( \mu \right) \right \vert +\left \vert Y_{g^{\prime }}\left( \mu \right) \right \vert \right] ^{2}\right \} \\ & = &\sup\limits_{\mu \in I}\left[ \left \vert Y_{g}\left( \mu \right) \right \vert +\left \vert Y_{g^{\prime }}\left( \mu \right) \right \vert \right] ^{2} \\ & = &\sup\limits_{\mu \in I}\left[ \left \vert g_{0}\Theta _{E}\left( -1\right) _{0}^{\mu }f\left( r,g\left( r\right) \right) dr\right \vert +\left \vert g_{0}\Theta _{E}\left( -1\right) _{0}^{\mu }f\left( r,g^{\prime }\left( r\right) \right) dr\right \vert \right] ^{2} \\ & = &\sup\limits_{\mu \in I}\left[ g_{0}\Theta _{E}\left( -1\right) _{0}^{\mu }\left( \left \vert f\left( r,g\left( r\right) \right) dr\right \vert +\left \vert f\left( r,g^{\prime }\left( r\right) \right) dr\right \vert \right) \right] ^{2}. \end{eqnarray*}

    Then, in view of above hypothesis we have:,

    \begin{eqnarray*} \hat{H}_{b}\left( \left[ Tg\right] _{\alpha \left( g\right) },\left[ Tg^{\prime }\right] _{\alpha \left( g^{\prime }\right) }\right) &\leq &\sup\limits_{\mu \in I}\left[ \left \vert _{0}^{\mu }f\left( r,g\left( r\right) \right) \right \vert +\left \vert _{0}^{\mu }f\left( r,g^{\prime }\left( r\right) \right) \right \vert dr\right] ^{2} \\ &\leq &\sup\limits_{\mu \in I}\left[ \left \vert _{0}^{\mu }f\left( r,g\left( r\right) \right) \right \vert ^{\frac{1}{2}}+\left \vert _{0}^{\mu }f\left( r,g^{\prime }\left( r\right) \right) \right \vert ^{\frac{1}{2}}dr\right] ^{2} \\ &\leq &\sup\limits_{\mu \in I}\left \{ _{0}^{\mu }\tau e^{-\tau }\left \vert g\left( r\right) -g^{\prime }\left( r\right) \right \vert e^{-\tau r}e^{\tau r}dr\right \} \\ & = &\tau e^{-\tau }\frac{1}{\tau }d_{\tau }(g,g^{\prime })e^{\tau r}. \end{eqnarray*}

    By appealing to the above fashion, we obtain

    \begin{equation*} \hat{H}_{b}\left( \left[ Tg\right] _{\alpha \left( g\right) },\left[ Tg^{\prime }\right] _{\alpha \left( g^{\prime }\right) }\right) e^{-\tau r}\leq e^{-\tau }d_{\tau }(g,g^{\prime }), \end{equation*}

    or equivalently,

    \begin{equation*} \hat{H}_{b}\left( \left[ Tg\right] _{\alpha \left( g\right) },\left[ Tg^{\prime }\right] _{\alpha \left( g^{\prime }\right) }\right) \leq e^{-\tau }d_{\tau }(g,g^{\prime }). \end{equation*}

    Owing to logarithms, we have

    \begin{equation*} \ln \left( \hat{H}_{b}\left( \left[ Tg\right] _{\alpha \left( g\right) },\left[ Tg^{\prime }\right] _{\alpha \left( g^{\prime }\right) }\right) \right) \leq \ln \left( e^{-\tau }d_{\tau }(g,g^{\prime })\right) , \end{equation*}

    Owing to the above speculation, this, in turn, yields:

    \begin{equation*} \tau\left(d_{\tau }\left(g,g^{\prime }\right)\right)+\ln \left( \hat{H}_{b}\left( \left[ Tg\right] _{\alpha \left( g\right) },\left[ Tg^{\prime } \right] _{\alpha \left( g^{\prime }\right) }\right) \right) \leq \ln \left( d_{\tau }(g,g^{\prime })\right) . \end{equation*}

    Due to \mathcal{F} -contraction, with the setting \mathcal{F}\left(\mu \right) = \ln \mu, for all \mu \in C^{1}\left(I, P^{1}\right) , we have

    \begin{equation*} \tau\left(d_{\tau }\left(g,g^{\prime }\right)\right)+\mathcal{F}\left( \hat{H}_{b}\left( \left[ Tg\right] _{\alpha \left( g\right) },\left[ Tg^{\prime } \right] _{\alpha \left( g^{\prime }\right) }\right) \right) \leq \mathcal{F} \left( d_{\tau }(g,g^{\prime })\right) . \end{equation*}

    It follows that there is c\in C^{1}\left(I, P^{1}\right) such that c\in \left[ Tc\right] _{\alpha \left(c\right) }. Hence all the possible hypothesis of Corollary 4.7 are satisfied and consequently fuzzy initial valued problem (5.1) has a fuzzy solution c\in C^{1}\left(I, P^{1}\right) in C^{1}\left(I, P^{1}\right).

    The article regards with new approach of fuzzy dynamic process on b -metric-like space, specifically the mapping of set-valued (extended) fuzzy intervals endowed with the b -metric-like. After we just adopt the standard setting of fuzzy dynamic process in b -metric-like space which defines convergence theorems in generalized \mathcal{F} -contraction via expectations of fuzzy Suzuki-type contraction mappings. Subsequently, corollaries are originated from the main result. To explain the example in the main section, a graphically interpretation has been created that best illustrates the fuzzy dynamic process to the readers. At the end, gives an application of our results in solving Hukuhara differentiability through the fuzzy initial valued problem and fuzzy functions. The pivotal role of Hukuhara differentiability in fuzzy dynamic process is stated. In future, this methodology can be inspected intuitionistic fuzzy and picture fuzzy sets the fuzzy dynamic process for a hybrid pair of mappings can be examined.

    All the authors have equal contribution, read and approved the final manuscript. The authors S. Subhi, N. Mlaiki and W. Shatanawi would like to thank Prince Sultan University for paying the publication fees for this work through TAS LAB.

    The authors declare that they have no conflicts of interests.



    [1] Fent K, Weston AA, Caminada D (2006) Ecotoxicology of human pharmaceuticals. Aquat Toxicol 76: 122-159. doi: 10.1016/j.aquatox.2005.09.009
    [2] Barceló D, Petrovic M (2007) Pharmaceuticals and personal care products (PPCPs) in the environment. Anal Bioanal Chem 387: 1141-1142. doi: 10.1007/s00216-006-1012-2
    [3] Kim JW, Jang HS, Kim JG, et al. (2009) Occurrence of Pharmaceutical and Personal Care Products (PPCPs) in Surface Water from Mankyung River, South Korea. J Heal Sci 55: 249-258. doi: 10.1248/jhs.55.249
    [4] Vidal-Dorsch DE, Bay SM, Maruya K, et al. (2012) Contaminants of emerging concern in municipal wastewater effluents and marine receiving water. Environ Toxicol Chem 31: 2674-2682. doi: 10.1002/etc.2004
    [5] Pal A, Gin KYH, Lin AYC, et al. (2010) Impacts of emerging organic contaminants on freshwater resources: Review of recent occurrences, sources, fate and effects. Sci Total Environ 408: 6062-6069. doi: 10.1016/j.scitotenv.2010.09.026
    [6] Blair BD, Crago JP, Hedman CJ, et al. (2013) Pharmaceuticals and personal care products found in the Great Lakes above concentrations of environmental concern. Chemosphere 93: 2116-2123. doi: 10.1016/j.chemosphere.2013.07.057
    [7] Fairbairn DJ, Karpuzcu ME, Arnold WA, et al. (2015) Sediment-water distribution of contaminants of emerging concern in a mixed use watershed. Sci Total Environ 505: 896-904. doi: 10.1016/j.scitotenv.2014.10.046
    [8] Paíga P, Santos LHMLM, Ramos S, et al. (2016) Presence of pharmaceuticals in the Lis river (Portugal): Sources, fate and seasonal variation. Sci Total Environ 573: 164-177. doi: 10.1016/j.scitotenv.2016.08.089
    [9] Andaluri G, Suri RPS, Graham K (2017) Steroid hormones in environmental matrices: extraction method comparison. Environ Monit Assess 189: 626. doi: 10.1007/s10661-017-6345-0
    [10] Andaluri G, Suri RPS, Kumar K (2012) Occurrence of estrogen hormones in biosolids, animal manure and mushroom compost. Environ Monit Assess 184: 1197-1205. doi: 10.1007/s10661-011-2032-8
    [11] Bean TG, Rattner BA, Lazarus RS, et al. (2018) Pharmaceuticals in water, fish and osprey nestlings in Delaware River and Bay. Environ Pollut 232: 533-545. doi: 10.1016/j.envpol.2017.09.083
    [12] Jones OAH, Voulvoulis N, Lester JN (2002) Aquatic environmental assessment of the top 25 English prescription pharmaceuticals. Water Res 36: 5013-5022. doi: 10.1016/S0043-1354(02)00227-0
    [13] Carlsson C, Johansson AK, Alvan G, et al. (2006) Are pharmaceuticals potent environmental pollutants?. Part I: Environmental risk assessments of selected active pharmaceutical ingredients. Sci Total Environ 364: 67-87.
    [14] Sui Q, Huang J, Deng S, et al. (2010) Occurrence and removal of pharmaceuticals, caffeine and DEET in wastewater treatment plants of Beijing, China. Water Res 44: 417-426. doi: 10.1016/j.watres.2009.07.010
    [15] Hansen M, Krogh KA, Björklund E, et al. (2009) Environmental risk assessment of ionophores. TrAC - Trends Anal Chem 28: 534-542. doi: 10.1016/j.trac.2009.02.015
    [16] EEA (2014) Chapter 6: Ecological Risk Assessment. Eur Environ Agency 4-7.
    [17] Higgins CP, Paesani ZJ, Chalew TEA, et al. (2009) Pharmaceuticals and Personal Care Products in the Environment BIOACCUMULATION OF TRICLOCARBAN IN LUMBRICULUS VARIEGATUS. Environ Toxicol 28: 2663-2670. doi: 10.1897/08-485.1
    [18] DRBC (2019) State of the Basin.
    [19] MacGillivray AR (2013) Contaminants of Emerging Concern In the Tidal Delaware River.
    [20] USEPA (2007) Method 1694: Pharmaceuticals and Personal Care Products in Water, Soil, Sediment, and Biosolids by HPLC / MS / MS.
    [21] CENSUS UBO (2010) U.S. Census Bureau, US Census Bureau 2010 Census, 2010. Available from: http://www.census.gov/2010census/.
    [22] Scheurer M, Michel A, Brauch HJ, et al. (2012) Occurrence and fate of the antidiabetic drug metformin and its metabolite guanylurea in the environment and during drinking water treatment. Water Res 46: 4790-4802. doi: 10.1016/j.watres.2012.06.019
    [23] Kosma CI, Lambropoulou DA, Albanis TA (2015) Comprehensive study of the antidiabetic drug metformin and its transformation product guanylurea in Greek wastewaters. Water Res 70: 436-448. doi: 10.1016/j.watres.2014.12.010
    [24] Niemuth NJ, Jordan R, Crago J, et al. (2015) Metformin exposure at environmentally relevant concentrations causes potential endocrine disruption in adult male fish. Environ Toxicol Chem 34: 291-296. doi: 10.1002/etc.2793
    [25] Karpuzcu ME, Fairbairn D, Arnold WA, et al. (2014) Identifying sources of emerging organic contaminants in a mixed use watershed using principal components analysis. Environ Sci Process Impacts 16: 2390-2399. doi: 10.1039/C4EM00324A
    [26] Trautwein C, Kümmerer K (2011) Incomplete aerobic degradation of the antidiabetic drug Metformin and identification of the bacterial dead-end transformation product Guanylurea. Chemosphere 85: 765-773. doi: 10.1016/j.chemosphere.2011.06.057
    [27] Tamura I, Kagota KI, Yasuda Y, et al. (2013) Ecotoxicity and screening level ecotoxicological risk assessment of five antimicrobial agents: Triclosan, triclocarban, resorcinol, phenoxyethanol and p-thymol. J Appl Toxicol 33: 1222-1229.
    [28] WET Center (2016) WET Center Pharmaceutical PNEC list.
    [29] Ferrari B, Mons R, Vollat B, et al. (2004) Environmental Risk Assessment of Six Human Pharmaceuticals: Are the Current Environmental Risk Assessment Procedures Sufficient for the Protection of the Aquatic Environment? Environ Toxicol Chem 23: 1344. doi: 10.1897/03-246
    [30] Ferrari G, Junghans M, Korkaric M, et al. (2019) Antibiotikaresistenzbildung in der Umwelt. Herleitung von UQK für Antibiotika unter Berücksichtigung von Resistenzbildung. Aqua Gas 52-59.
    [31] Isidori M, Parrella A, Pistillo P, et al. (2009) Effects of ranitidine and its photoderivatives in the aquatic environment. Environ Int 35: 821-825. doi: 10.1016/j.envint.2008.12.002
    [32] Isidori M, Nardelli A, Pascarella L, et al. (2007) Toxic and genotoxic impact of fibrates and their photoproducts on non-target organisms. Environ Int 33: 635-641. doi: 10.1016/j.envint.2007.01.006
    [33] US EPA (2014) Ecological Structure Activity Relationships (ECOSAR).
    [34] Deo RP (2014) Pharmaceuticals in the Surface Water of the USA: A Review. Curr Environ Heal reports 1: 113-122. doi: 10.1007/s40572-014-0015-y
    [35] Kim Y, Choi K, Jung J, et al. (2007) Aquatic toxicity of acetaminophen, carbamazepine, cimetidine, diltiazem and six major sulfonamides, and their potential ecological risks in Korea. Environ Int 33: 370-375. doi: 10.1016/j.envint.2006.11.017
    [36] Cunningham VL, Buzby M, Hutchinson T, et al. (2006) Effects of human pharmaceuticals on aquatic life: Next steps. Environ Sci Technol 40: 3456-3462. doi: 10.1021/es063017b
    [37] ECHA (2008) Guidance on information requirements and chmical safety assessment. Chapter R.10: Characterisation of dose [concentration]-response for environment. Eur Chem Agency 1-65.
    [38] Caldwell JC, Evans M V., Krishnan K (2012) Cutting edge PBPK models and analyses: Providing the basis for future modeling efforts and bridges to emerging toxicology paradigms. J Toxicol 2012: 1-10.
    [39] Heidler J, Sapkota A, Halden RU (2006) Partitioning, persistence, and accumulation in digested sludge of the topical antiseptic triclocarban during wastewater treatment. Environ Sci Technol 40: 3634-3639. doi: 10.1021/es052245n
    [40] Brausch JM, Rand GM (2011) A review of personal care products in the aquatic environment: Environmental concentrations and toxicity. Chemosphere 82: 1518-1532. doi: 10.1016/j.chemosphere.2010.11.018
    [41] DeLeo PC, Sedlak RI (2014) Comment on 'on the need and speed of regulating triclosan and triclocarban in the United States'. Environ Sci Technol 48: 11021-11022. doi: 10.1021/es503494j
    [42] U.S. Food and Drug Administration (2016) Focus on Surfactants, FDA issues final rule on safety and effectiveness of antibacterial soaps, 2016. Available from: https://www.fda.gov/news-events/press-announcements/fda-issues-final-rule-safety-and-effectiveness-antibacterial-soaps.
    [43] Halden RU (2020) Triclosan and Triclocarban: Exposures, Toxicity and Testing - Environmental Health Symposium, 2020. Available from: http://environmentalhealthsymposium.com/blog/2020/2/3/triclosan-and-triclocarbon-exposures-toxicity-and-testing.
    [44] Berninger JP, Du B, Connors KA, et al. (2011) Effects of the antihistamine diphenhydramine on selected aquatic organisms. Environ Toxicol Chem 30: 2065-2072. doi: 10.1002/etc.590
    [45] Ramirez AJ, Mottaleb MA, Brooks BW, et al. (2007) Analysis of pharmaceuticals in fish using liquid chromatography-tandem mass spectrometry. Anal Chem 79: 3155-3163. doi: 10.1021/ac062215i
    [46] Andreozzi R, Marotta R, Pinto G, et al. (2002) Carbamazepine in water: Persistence in the environment, ozonation treatment and preliminary assessment on algal toxicity. Water Res 36: 2869-2877. doi: 10.1016/S0043-1354(01)00500-0
    [47] Garber AJ, Duncan TG, Goodman AM, et al. (1997) Efficacy of metformin in type II diabetes: Results of a double-blind, placebo-controlled, dose-response trial. Am J Med 103: 491-497. doi: 10.1016/S0002-9343(97)00254-4
    [48] Ecotox Centre Eawag-EPFL (2017) Proposals for Acute and Chronic Quality Standards | Oekotoxzentrum, 2017. Available from: http://www.ecotoxcentre.ch/expert-service/quality-standards/proposals-for-acute-and-chronic-quality-standards/.
    [49] Isidori M, Lavorgna M, Nardelli A, et al. (2005) Toxic and genotoxic evaluation of six antibiotics on non-target organisms. Sci Total Environ 346: 87-98. doi: 10.1016/j.scitotenv.2004.11.017
    [50] Danner MC, Robertson A, Behrends V, et al. (2019) Antibiotic pollution in surface fresh waters: Occurrence and effects. Sci Total Environ 664: 793-804. doi: 10.1016/j.scitotenv.2019.01.406
    [51] Luo Y, Guo W, Ngo HH, et al. (2014) A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment. Sci Total Environ 473-474: 619-641. doi: 10.1016/j.scitotenv.2013.12.065
    [52] Hirsch R, Ternes T, Haberer K, et al. (1999) Occurrence of antibiotics in the aquatic environment. Sci Total Environ 225: 109-118. doi: 10.1016/S0048-9697(98)00337-4
    [53] US EPA (2004) Overview of the Ecological Risk Assessment Process in the Office of Pesticide Programs - Endangered and Threatened Species Effects Determinations.
    [54] He W, Goodkind D, Kowal P (2016) An Aging World: 2015 International Population Reports. Aging (Albany NY) 165.
    [55] Nakashima M, Canda ER (2005) Positive dying and resiliency in later life: A qualitative study. J Aging Stud 19: 109-125. doi: 10.1016/j.jaging.2004.02.002
    [56] OECD (2019) Pharmaceutical Residues in Freshwater - Hazards and Policy Responses
    [57] Christensen NS, Wood AW, Voisin N, et al. (2004) The effects of climate change on the hydrology and water resources of the Colorado River basin. Clim Change 62: 337-363. doi: 10.1023/B:CLIM.0000013684.13621.1f
    [58] Pennsylvania Environmental Council (2020) Stormwater Resources for Philadelphia & Urban Centers, 2020. Available from: https://pecpa.org/stormwater-philadelphia-urban-centers/.
    [59] Kricun A (2018) Using a Triple Bottom Line Approach To Reduce Combined Sewage Flooding and Provide Community Benefit in Camden City.
    [60] Jeffries KM, Brander SM, Britton MT, et al. (2015) Chronic exposures to low and high concentrations of ibuprofen elicit different gene response patterns in a euryhaline fish. Environ Sci Pollut Res 22: 17397-17413. doi: 10.1007/s11356-015-4227-y
    [61] MedlinePlus (NIH) (2015) Diphenhydramine: MedlinePlus Drug Information, 2015. Available from: https://www.nlm.nih.gov/medlineplus/druginfo/meds/a682539.html.
    [62] National Cancer Institute (2019) NCI Thesaurus, 2019. Available from: https://ncit.nci.nih.gov/ncitbrowser/ConceptReport.jsp?dictionary=NCI_Thesaurus&ns=ncit&code=C873.
    [63] Cerner Multum I (2010) Thiabendazole Uses, Side Effects &amp; Warnings - Drugs.com, 2010. Available from: https://www.drugs.com/mtm/thiabendazole.html.
    [64] Johnson AC, Keller V, Dumont E, et al. (2015) Assessing the concentrations and risks of toxicity from the antibiotics ciprofloxacin, sulfamethoxazole, trimethoprim and erythromycin in European rivers. Sci Total Environ 511: 747-755. doi: 10.1016/j.scitotenv.2014.12.055
    [65] Wright SW, Wrenn KD, Haynes ML (1999) Trimethoprim-sulfamethoxazole resistance among urinary coliform isolates. J Gen Intern Med 14: 606-609. doi: 10.1046/j.1525-1497.1999.10128.x
    [66] Heberer T (2002) Tracking persistent pharmaceutical residues from municipal sewage to drinking water. J Hydrol 266: 175-189. doi: 10.1016/S0022-1694(02)00165-8
    [67] PWD (2011) Schuylkill | Philadelphia Water Department, 2011. Available from: http://www.phillywatersheds.org/your_watershed/schuylkill.
    [68] Sunger N, Teske SS, Nappier S, et al. (2012) Recreational use assessment of water-based activities, using time-lapse construction cameras. J Expo Sci Environ Epidemiol 22: 281-290. doi: 10.1038/jes.2012.4
    [69] Cunningham VL, Binks SP, Olson MJ (2009) Human health risk assessment from the presence of human pharmaceuticals in the aquatic environment. Regul Toxicol Pharmacol 53: 39-45. doi: 10.1016/j.yrtph.2008.10.006
    [70] Kostich MS, Lazorchak JM (2008) Risks to aquatic organisms posed by human pharmaceutical use. Sci Total Environ 389: 329-339. doi: 10.1016/j.scitotenv.2007.09.008
    [71] Kostich MS, Batt AL, Lazorchak JM (2014) Concentrations of prioritized pharmaceuticals in effluents from 50 large wastewater treatment plants in the US and implications for risk estimation. Environ Pollut 184: 354-359. doi: 10.1016/j.envpol.2013.09.013
    [72] Schwab BW, Hayes EP, Fiori JM, et al. (2005) Human pharmaceuticals in US surface waters: A human health risk assessment. Regul Toxicol Pharmacol 42: 296-312. doi: 10.1016/j.yrtph.2005.05.005
    [73] Collier AC (2007) Pharmaceutical contaminants in potable water: Potential concerns for pregnant women and children. Ecohealth 4: 164-171. doi: 10.1007/s10393-007-0105-5
    [74] Bruce GM, Pleus RC, Snyder SA (2010) Toxicological relevance of pharmaceuticals in drinking water. Environ Sci Technol 44: 5619-5626. doi: 10.1021/es1004895
    [75] Kumar A, Xagoraraki I (2010) Human health risk assessment of pharmaceuticals in water: An uncertainty analysis for meprobamate, carbamazepine, and phenytoin. Regul Toxicol Pharmacol 57: 146-156. doi: 10.1016/j.yrtph.2010.02.002
    [76] Kumar A, Xagoraraki I (2010) Pharmaceuticals, personal care products and endocrine-disrupting chemicals in U.S. surface and finished drinking waters: A proposed ranking system. Sci Total Environ 408: 5972-5989.
    [77] Ottmar KJ, Colosi LM, Smith JA (2010) Development and application of a model to estimate wastewater treatment plant prescription pharmaceutical influent loadings and concentrations. Bull Environ Contam Toxicol 84: 507-512. doi: 10.1007/s00128-010-9990-3
  • Environ-07-04-19-s1.pdf
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5740) PDF downloads(386) Cited by(6)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog