Research article Special Issues

GIS based spatial noise impact analysis (SNIA) of the broadening of national highway in Sikkim Himalayas: a case study

  • Mountainous areas create a complex and challenging environment to conduct noise impact analysis of development projects. This paper presents a noise impact analysis methodology using Geographic Information Systems (GIS) and Traffic Noise Model (FHWA TNM 2.5) to portray spatial distribution of noise due to the broadening of the national highway in the mountainous terrain of East Sikkim. Two noise level indices viz., Hourly Equivalent Sound Level (Leq(H)) and Day and Night Average Sound Level (Ldn) were calculated for the year 2004 as pre-project scenario, 2014 as project implementation scenario and 2039 as post-project scenario. The overall trend shows that the proportion of area under adverse noise level decreases from pre-project scenario to project implementation scenario. Over the time the adverse noise impact in the post-project scenario reaches very close to pre-project scenario in case of both the noise indices. Overlay analysis of noise based landuse maps over actual landuse map show that non-compliance of noise based landuse will show similar trend. This trend is mainly attributed to traffic composition and highway broadening induced-traffic volume. The study shows that TNM and spatial interpolation of noise data using Empirical Bayesian Kriging (EBK) are reliable tools to perform noise impact analysis in mountainous areas. Multiple regression analysis show that, radial distance and elevation difference of noise receivers from the nearest point in the highway are significant predictors of Leq(H) and Ldn at lower percentage of heavy trucks in traffic composition.

    Citation: Polash Banerjee, Mrinal K. Ghose, Ratika Pradhan. GIS based spatial noise impact analysis (SNIA) of the broadening of national highway in Sikkim Himalayas: a case study[J]. AIMS Environmental Science, 2016, 3(4): 714-738. doi: 10.3934/environsci.2016.4.714

    Related Papers:

    [1] Mahtab Mehrabbeik, Fatemeh Parastesh, Janarthanan Ramadoss, Karthikeyan Rajagopal, Hamidreza Namazi, Sajad Jafari . Synchronization and chimera states in the network of electrochemically coupled memristive Rulkov neuron maps. Mathematical Biosciences and Engineering, 2021, 18(6): 9394-9409. doi: 10.3934/mbe.2021462
    [2] Stefano Cosenza, Paolo Crucitti, Luigi Fortuna, Mattia Frasca, Manuela La Rosa, Cecilia Stagni, Lisa Usai . From Net Topology to Synchronization in HR Neuron Grids. Mathematical Biosciences and Engineering, 2005, 2(1): 53-77. doi: 10.3934/mbe.2005.2.53
    [3] Massimo Fioranelli, O. Eze Aru, Maria Grazia Roccia, Aroonkumar Beesham, Dana Flavin . A model for analyzing evolutions of neurons by using EEG waves. Mathematical Biosciences and Engineering, 2022, 19(12): 12936-12949. doi: 10.3934/mbe.2022604
    [4] Karim El Laithy, Martin Bogdan . Synaptic energy drives the information processing mechanisms in spiking neural networks. Mathematical Biosciences and Engineering, 2014, 11(2): 233-256. doi: 10.3934/mbe.2014.11.233
    [5] Manuela Aguiar, Ana Dias, Miriam Manoel . Gradient and Hamiltonian coupled systems on undirected networks. Mathematical Biosciences and Engineering, 2019, 16(5): 4622-4644. doi: 10.3934/mbe.2019232
    [6] Anna Cattani . FitzHugh-Nagumo equations with generalized diffusive coupling. Mathematical Biosciences and Engineering, 2014, 11(2): 203-215. doi: 10.3934/mbe.2014.11.203
    [7] Guowei Wang, Yan Fu . Spatiotemporal patterns and collective dynamics of bi-layer coupled Izhikevich neural networks with multi-area channels. Mathematical Biosciences and Engineering, 2023, 20(2): 3944-3969. doi: 10.3934/mbe.2023184
    [8] Prasina Alexander, Fatemeh Parastesh, Ibrahim Ismael Hamarash, Anitha Karthikeyan, Sajad Jafari, Shaobo He . Effect of the electromagnetic induction on a modified memristive neural map model. Mathematical Biosciences and Engineering, 2023, 20(10): 17849-17865. doi: 10.3934/mbe.2023793
    [9] Stefano Fasani, Sergio Rinaldi . Local stabilization and network synchronization: The case of stationary regimes. Mathematical Biosciences and Engineering, 2010, 7(3): 623-639. doi: 10.3934/mbe.2010.7.623
    [10] Sridevi Sriram, Hayder Natiq, Karthikeyan Rajagopal, Ondrej Krejcar, Hamidreza Namazi . Dynamics of a two-layer neuronal network with asymmetry in coupling. Mathematical Biosciences and Engineering, 2023, 20(2): 2908-2919. doi: 10.3934/mbe.2023137
  • Mountainous areas create a complex and challenging environment to conduct noise impact analysis of development projects. This paper presents a noise impact analysis methodology using Geographic Information Systems (GIS) and Traffic Noise Model (FHWA TNM 2.5) to portray spatial distribution of noise due to the broadening of the national highway in the mountainous terrain of East Sikkim. Two noise level indices viz., Hourly Equivalent Sound Level (Leq(H)) and Day and Night Average Sound Level (Ldn) were calculated for the year 2004 as pre-project scenario, 2014 as project implementation scenario and 2039 as post-project scenario. The overall trend shows that the proportion of area under adverse noise level decreases from pre-project scenario to project implementation scenario. Over the time the adverse noise impact in the post-project scenario reaches very close to pre-project scenario in case of both the noise indices. Overlay analysis of noise based landuse maps over actual landuse map show that non-compliance of noise based landuse will show similar trend. This trend is mainly attributed to traffic composition and highway broadening induced-traffic volume. The study shows that TNM and spatial interpolation of noise data using Empirical Bayesian Kriging (EBK) are reliable tools to perform noise impact analysis in mountainous areas. Multiple regression analysis show that, radial distance and elevation difference of noise receivers from the nearest point in the highway are significant predictors of Leq(H) and Ldn at lower percentage of heavy trucks in traffic composition.


    The synchronization of complex networks has been one of the most critical issues in recent years [1]. Complex networks, composed of interacting dynamical systems, can be used to represent and analyze real networks [2,3]. The synchronization phenomenon is termed when two or more dynamical systems evolve in common behavior [4]. Numerous natural events can be related to synchronization [1]. Therefore, many studies have focused on the synchronization of coupled oscillators in different fields [5,6]. The synchronizability of a network is strongly associated with the network topology and the coupling between the nodes [4,7]. There are some studies that have considered the synchronization in time-varying networks [8,9]. Belykh et al. [10] showed that adding a few random connections to a network with blinking links enhances synchrony by reducing the required couplings strength for synchronization. Frasca et al. [11] investigated the synchronization of Rossler systems with switching coupling and found that switching between two coupling strengths that cannot separately synchronize the system can lead to synchronization.

    In the nervous system, the communications and interactions among neurons create complex networks [12,13]. Therefore, many of their activities lead to the formation and representation of collective behaviors such as synchronization [14]. Previous studies have revealed the significance of the synchronous behavior of neurons in information processing and cognitive tasks [15]. Subsequently, the study of synchronization in neuronal networks has attracted the attention of many scientists [16,17,18]. Belykh et al. [19] investigated the bursting neurons coupled nonlinearly through chemical synapses and found that synchronization depends on the receiving signals by each neuron. In contrast, synchronization of the electrically coupled neurons relies on the network's topology [20]. Previous studies have considered different network topologies such as small-world, scale-free, modular, hierarchical, etc. [21,22,23]. Furthermore, with the development of the multilayer networks that provide multiple interactions, the synchronization of the neurons was also investigated in this type of topology [8,9]. Some of the studies have considered the factors that can enhance the synchronization in neuronal networks. It has been proved that the time delay [24], the activity-dependent coupling [25], considering the neuron–astrocyte interaction [26], proper adjustment of autapse [27], etc., can help enhance the synchronous behavior among neurons.

    The neuronal functions rely on the communications between them [28,29]. These communications which occur in the cellular regions are called synapses. It is known that the transmissions among neurons can be through electrical or chemical synapses. The electrical synapses occur through channels (gap junctions) when the cytoplasms of two neurons are connected [30]. While in the chemical synapses, the neurotransmitters are released from one cell and received by another [31]. These two types of transmissions exist in different brain regions and are essential for their normal function. Furthermore, Pereda [28] highlighted that the interaction between these two synapses in the healthy brain is also crucial. There is evidence that the electrical synapse transits to chemical synapses in particular brain regions [32,33,34]. In other words, the formation and elimination of electrical and chemical synapses are sequential [35,36]. Szabo et al. [37] represented this serial synaptic evolution in motoneurons from the snail Helisoma. Furthermore, there is a hypothesis that the former electrical synapses are necessary for developing the chemical synapses. Todd et al. [38] represented this fact in the leech neurons.

    Motivated by the above description about the transition of synapses, this paper studies the network of coupled Hindmarsh-Rose neurons coupled with transient synapses. It is assumed that the electrical synapses are initially present, and they are replaced with chemical synapses over time. This sequence of transformations happens with a specific period. The synchronization of the neurons is studied under different conditions by varying the coupling strengths, the period of transitions, and the duration time of the presence of the synapses. The time series of the neurons is also under consideration. Therefore, in the next section, the model, the network, and the coupling are described. The results are presented in the third section, and the conclusions are given in the last section.

    A network of Hindmarsh-Rose neurons with electrical and chemical synapses in a small-world structure is constructed as follows:

    ˙xi=yi+3x2ix3izi+Iext+Nj=1gij(ge(xjxi)+gc(vsxi)1+expexp(λ(xjϵs))),˙yi=15x2iyi,˙zi=r(s(xi+1.6)zi),i=1,,N (1)

    where x,y,z represent the action potential, the fast and slow recovery variables of the neuron. The parameters of the chemical coupling are vs=2, ϵs=0.25,λ=10. gij refers to the network structure such that gij=1 shows the presence of a link between nodes i and j. The coupling strength of the electrical and chemical synapses are ge and gc, respectively. N=100 nodes are connected in a small-world configuration with 20 nearest-neighbor couplings and the probability of 0.1. The parameters of the Hindmarsh-Rose model are set at r=0.006, s=4, Iext=2.8, such that the single neuron exhibits periodic bursting. The coupling between neutrons is through periodic time-varying coupling with electrical and chemical synapses. As discussed before, the synapses are transient and change in the time interval τ. In a part of this time interval (0<t<θτ), the electrical synapses are on, and in the next part (θτ<t<τ), the electrical synapses disappear, and the chemical synapses appear. Therefore, we have:

    0<t<θτ:gc=0,ge0θτ<t<τ:gc0,ge=0 (2)

    The equations of the network are solved numerically by using the fourth-order Runge-Kutta method with a total run time 3000 and time step 0.01. The network is investigated by considering different periods (τ) for the coupling function. The time of presence of the electrical synapses to chemical ones (θ) is also varied. To quantify the synchronization of the neurons, the average synchronization error is computed as follow:

    E=1N1Nj=2(x1xj)2+(y1yj)2+(z1zj)2t, (3)

    At first, we consider that both synapses exist among neurons constantly. The electrical coupling strength is varied, and the synchronization error is computed. Three cases are assumed for the chemical coupling strength as: 1) gc=0.5ge, 2) gc=ge, 3) gc=2ge. The synchronization errors for three cases are illustrated in Figure 1. When the chemical coupling is smaller than the electrical, gc=0.5ge, the neurons become completely synchronous in the region 0.054<ge<0.072. The time series of the synchronous neurons is shown in Figure 2a. As the electrical coupling grows from 0.072, the complete synchronization disappears, and the neurons become burst synchronous. The time series of neurons, in this case, are shown in Figure 2b and represent that although the neurons burst synchronously, there is a small error due to the difference between neurons when they are repolarized. By strengthening the chemical coupling, the synchronization error decays to zero for smaller electrical coupling strength. But the neurons do not oscillate synchronously and reach the resting state. The time series of the neurons in ge=0.2, and for gc=ge, gc=2ge are illustrated in Figure2c and 2d, respectively.

    Figure 1.  The synchronization error of the neurons is coupled with constant electrical and chemical synapses according to the electrical coupling strength. The red, blue, and yellow curves refer respectively to gc=0.5ge, gc=ge, and gc=2ge.
    Figure 2.  The time series of the neuron's action potentials coupled with constant electrical and chemical synapses. (a) The neurons are completely synchronous for ge=0.07 and gc=0.5ge. (b) When gc=0.5ge, the burst synchronization appears for ge>0.072. The resting state is obtained when (c) gc=ge, ge=0.2, and (d) gc=2ge, ge=0.2.

    When the neurons are completely synchronous, all of the variables of the neurons are the same, i.e., x1=x2==xN, y1=y2==yN, z1=z2==zN Therefore, the equation of the synchronous manifold can be derived as

    ˙xi=yi+3x2ix3izi+Iext+gc(vsxi)1+expexp(λ(xiϵs))Nj=1gij,˙yi=15x2iyi,˙zi=r(s(xi+1.6)zi). (4)

    When the chemical coupling strength increases, the coupling term gc(vsxi)1+expexp(λ(xiϵs))Nj=1gij intensifies and overcomes the dynamics of the neurons and leads the neurons to be quiescent.

    Next, we investigate the case of transient synapses. A specific period (τ) is considered, and in a part of the period, the electrical synapses are on (gc=0), and in the next part, the synapses are replaced with the chemical ones (ge=0). Firstly, it is assumed that the duration of both synapses is the same, i.e., θ=0.5, and the effect of the transition period is studied. As mentioned in the previous section, with the parameters selected for the Hindmarsh-Rose model, the single neuron bursts periodically, and the period of bursts is T=132. Thus, the synaptic transition period is considered to be the coefficients of the bursts periods as τ=33,66,132,264,396, and 528. The synchronization errors for different periods are presented in Figure 3. Similar to the previous case, as the chemical coupling strength increases, the error reaches its minimum in more miniature electrical couplings. This can be inferred from comparing the error curves in parts a to c. Furthermore, in all cases, the errors do not reach zero, and there is a minimum error which shows that the neurons are burst synchronized. This minimum error becomes larger by increasing the period of synaptic transitions. Moreover, it leads to a change in the waveform of the oscillations and the frequency of the bursts.

    Figure 3.  The synchronization error of the neurons coupled with transient electrical and chemical synapses with θ=0.5 according to the electrical coupling strength. (a) gc=0.5ge, (b) gc=ge, (c) gc=2ge. Different colors represent the errors for different synaptic transition periods. With the increase of the period, the final synchronization error increases.

    The time series of all neurons for different periods are shown in Figure 4. Although the parameters of the neurons were set in such a way to show square-wave bursting, adding the time-varying coupling term alters the neurons' firing pattern. It can be observed that for lower period values, i.e., for τ=33 (Figure 4a) and τ=66 (Figure 4b), the pattern consists of a single spike followed by a relaxation oscillation with short duration, which can be called pseudo-plateau burst. By increasing the period, the pattern changes to relaxation oscillation (for τ=132,264 in Figure 4c, 4d). Raising the period to τ=396 leads to the pattern to be constructed by multiple rapid spikes and a depolarized silence that is known as circle/fold cycle bursters or depolarization block bursting (Figure 4e) [39]. For τ=528, it seems that each burst is composed of both plateau burst type and pseudo-plateau burst type (Figure4f). As mentioned, the neurons are not fully synchronized, and there is a small error that refers to the depolarized silence duration. Figure 4g shows the enlargement of one burst of neurons relating to part e. The difference between neurons in the depolarized silence duration is obvious in this figure.

    Figure 4.  The time series of the neuron's action potentials coupled with transient electrical and chemical synapses with θ=0.5 for the different synaptic transition periods. (a)τ=33, (b) τ=66, (c) τ=132, (d) τ=264, (e) τ=396, (f) τ=528, (g) enlargement of a burst shown by red box in part (e). By increasing τ, the firing pattern changes, and the frequency of the bursts decreases.

    With the increase in the period of synaptic transitions, the bursts become longer, and the frequency of the bursts decreases. Comparing the period of bursts with the transition period shows that for longer periods, i.e., τ=264,396,528, the period of the bursts is equal to the transition period (τ). For shorter periods as τ=66,132, the period of the bursts is twice the transition period (2τ). For shorter periods, there is no relation between these periods. Furthermore, the spikes refer to the presence of the electrical synapses, while the silence mode refers to the time of the chemical synapses. Figure 5 represents this issue for τ=66,396. In these figures, the parts of the time series related to the presence of the electrical and chemical synapses are shown by red and blue colors, respectively.

    Figure 5.  The time series of the neuron's action potentials coupled with transient electrical and chemical synapses with θ=0.5 and (a)τ=66, (b) τ=396. The parts of the time series related to the presence of the electrical and chemical synapses are shown by red and blue colors, respectively.

    Finally, the influence of the time of existence of the electrical and chemical synapses is studied. To this aim, the parameter θ is varied. For example, considering θ=0.1 means that in 0.1τ, the electrical synapses are on, and in the rest of 0.9τ, the chemical synapses are present. Figure 6 represents the synchronization error for τ=132 and θ=0.1,0.3,0.5,0.7, and 0.9 for three cases of chemical coupling strength. It can be observed that similar to the previous cases, the increase of the chemical coupling leads to decay of error in lower electrical strengths. However, the critical electrical coupling for synchronization doesn't depend on the value of chemical strength for θ=0.9, in which the chemical synapses exist in a short interval (0.1τ). Furthermore, as the time of electrical synapses to chemical synapses increases, the final synchronization error decreases. For θ<0.9, by increasing ge, there is a minimum error, and the neurons become burst synchronous. While for θ=0.9, the zero error is obtained, and complete synchronization can be attained. The time series of the neurons for different θ are shown in Figure 7. It can be observed that the time of the presence of synapses also impacts the waveform of the action potential.

    Figure 6.  The synchronization error of the neurons coupled with transient electrical and chemical synapses for τ=132 according to the time duration of the presence of synapses (θ). (a) gc=0.5ge, (b) gc=ge, (c) gc=2ge. Different colors represent the errors for different time ratios of synapses. As θ increases, the final synchronization error decreases. The complete synchronization is obtained for θ=0.9.
    Figure 7.  The time series of the neuron's action potentials coupled with transient electrical and chemical synapses for τ=132, and different timings for synapses. (a) θ=0.1, (b)θ=0.3, (c) θ=0.5, (d) θ=0.7, (e) θ=0.9. Varying θ effects on the shape of time series.

    Figure 8 presents a complete view of the synchrony error of neurons in the parameter planes by varying the transition period and the timing of synapses for gc=ge. The effects of changing the transitions period (τ) are demonstrated in part (a) for θ=0.5. It is observed that increasing τ leads to burst synchronization occurring in a slightly lower coupling strength ge. However, for higher τ, the final synchronization error by increasing ge is more significant. Part (b) of this figure illustrates the impact of varying the presence time of electrical and chemical synapses (θ) for a fixed transition period τ=132. It is attained that in very low values of θ (θ<0.1), i.e. when the present time of electrical synapses is short, the synchrony error does not decay to zero by increasing ge. For 0.1<θ<0.5, the error becomes zero for intermediate values of coupling strength, and there exists a small error when the coupling becomes stronger. As the present time of electrical synapses increases (θ>0.5), this small error does not appear for strong couplings.

    Figure 8.  Synchronization error of the neurons with transient synapses. (a) By varying ge and τ for θ=0.5: Synchronization is disturbed by increasing the transition period. (b) By varying ge and θ for τ=132: Synchronization is enhanced by increasing the presence time of electrical synapses.

    This paper investigated the effects of the transient electrical synapses on the synchronization of the coupled neurons. Previous studies on the brain have revealed that the electrical synapses transit to the chemical synapses in special brain regions. Motivated by this, it was assumed that the electrical synapses exist among neurons for a defined time interval and then change to chemical synapses in the next time interval, and this sequence repeats periodically. The synchronization error of the neurons was computed by considering different periods for synaptic transitions and different time durations for synapses.

    At first, the network with constant electrical and chemical synapses was investigated. It was observed that when the chemical coupling was weak, the neurons were completely synchronous in a specific range of electrical coupling strength, further which the burst synchronization emerged. As the chemical coupling became more robust, the oscillations were suppressed, and the neurons stayed in a resting state. When the synapses were transient, the neurons were oscillating in all cases, and the steady resting state was not observed. The results showed that complete synchronization was only obtained when the time duration of electrical synapses was high. In other cases, the burst synchronization was observed. Increasing the period of the synaptic transitions led to the increase of the absolute synchronization error. In all cases, increasing the chemical coupling caused the decay of the synchronization error in lower electrical strengths. Moreover, both the period and the time duration of the electrical and chemical synapses influenced the shape of the time series of the action potentials.

    This work is supported by the Natural Science Basic Research Program of Shaanxi (2021JM-533, 2021JQ-880, 2020JM-646), the Innovation Capability Support Program of Shaanxi (2018GHJD-21), the Science and Technology Program of Xi'an (2019218414GXRC020CG021-GXYD20.3), the Support Plan for Sanqin Scholars Innovation Team in Shaanxi Province of China, the Scientific Research Program Funded by Shaanxi Provincial Education Department (21JK0960), the Youth Innovation Team of Shaanxi Universities, the Scientific Research Foundation of Xijing University (XJ21B01), and the Scientific Research Foundation of Xijing University (XJ200202). This work is partially funded by Centre for Nonlinear Systems, Chennai Institute of Technology, India vide funding number CIT/CNS/2021/RD/064.

    The authors declare that they have no conflict of interest.

    [1] Barthwal RR (2012) Environmental impact assessment (2nd Ed). New age international publishers, New Delhi, ISBN: 978-81-224-3227-5.
    [2] Canter LW (1996) Environmental impact assessment (2nd Ed). McGraw-Hill, New York.
    [3] Glasson J, Therivel R, Chadwi A (2005) Introduction to environmental impact assessment. Routledge. ISBN 0-415-33837-9.
    [4] Takyl SA (2012) Review of environment impact assessment- approach, process and challenges, University of North British Colombia.
    [5] Agarwal ML (2005) A spatial quantitative approach for environmental impact assessment of highway projects. PhD thesis, Dept. of Civil Engineering, IIT Khargpur, India.
    [6] Chang KT (2008) Introduction to geographic information systems. McGraw Hill education (India) private. Ltd. New Delhi. ISBN: 987-0-07-065898-1.
    [7] Xu Q, Ju Y, Ge H (2012) Application of GIS techniques in Environmental Impact Assessment. Adv mater res 610-613: 831-835.
    [8] Nega T, Smith C, Bethune J, et al (2012) An analysis of landscape penetration by road infrastructure and traffic noise. Computer environ urban syst 36: 245-256. doi: 10.1016/j.compenvurbsys.2011.09.001
    [9] Bande SU, Nawathe MP, Patil, CR (2013) Road traffic noise assessment in India- a review. Int J Eng Appl Technol 2: 82-87.
    [10] Ouis D (2001) Annoyance from road traffic noise: a review. J Environ Psychol 21: 101-120. doi: 10.1006/jevp.2000.0187
    [11] Stansfeld SA, Matheson MP (2003) Noise Pollution: non-auditory effects on health. Brit Med Bull 68: 243-257. doi: 10.1093/bmb/ldg033
    [12] Seto EYW, Holt A, Rivard T, et al (2007) Spatial distribution of traffic induced noise exposures in a US city: an analytic tool for assessing the health impacts of urban planning decisions. Int J Health Geogr 6: 1-16. doi: 10.1186/1476-072X-6-1
    [13] Passchier-Vermeer W, Passchier WF (2000) Noise exposure and public health. Environ Health Perspect 2000, 108 Suppl 1: 123-131.
    [14] Schulz TJ (1978) Synthesis of social surveys on noise annoyance. J Acoust Soc Am 64: 377-405.
    [15] Arévalo JE, Newhard K (2011) Traffic noise affects forest bird species in a protected tropical forest. Rev Biol Trop 59: 969-980.
    [16] Barber JR, Burdett CL, Reed SE, et al (2011) Anthropogenic noise exposure in protected natural areas: estimating the scale of ecological consequences. Landscape ecol 26: 1281-1295.
    [17] Barber JR, Crooks KR, Fristrup KM (2009) The cost of chronic noise exposure for terrestrial organisms. Trends Ecol Evol 25: 180-189.
    [18] Perisa SJ, Pescador M (2004) Effects of traffic noise on paserine populations in Mediterranean wooded pastures. Appl Acoust 65: 357-366. doi: 10.1016/j.apacoust.2003.10.005
    [19] Cheng H, Harris RA, Yin M (2011) Using micro data to assess traffic noise impact on residential property value: an application of Hedonic model. Transport research board annual meeting, Washington, TRB 90th meeting, 2011, paper # 11-0531
    [20] Pamanikabud P, Tansatcha M (2003) Geographical information system for traffic noise analysis and forecasting with the appearance of barriers. Environ Model Software 18: 959-973.
    [21] Bhattacharya CC, Jain SS, Singh SP, et al (2001) Development of comprehensive highway noise model for Indian conditions. Indian Road Congress, Paper No. 481
    [22] FHWA (1998) FHWA traffic noise model- technical manual. Federal Highway Administration, FHWA-10-96-010. U.S. Department of Transportation Office of Environment and Planning Research and Special Programs Administration, John A. Volpe National Transportation Systems Centre, Acoustics Facility, Cambridge, MA 02142-1093.
    [23] Vij GS, Agarwal ML (2013) A review paper on research and development efforts in assessing the traffic noise on highways. Recent Res Sci Tech 5: 54-58.
    [24] CISMHE (2008) Environmental impact assessment of Rongni Hydroelectric Project. Centre for Inter-disciplinary studies of mountain and hill environment, University of Delhi, Delhi, prepared for Madhya Bharat Power Corporation.
    [25] NHAI (2013) Revised draft of Environmental Impact Assessment Report: consultancy services for preparation of feasibility cum preliminary design for 4/6- laning of Goa –Karnataka border- Kesndapur section of NH-66. SNC- Lavalin infrastructure Pvt. Ltd. Noida for NHAI.
    [26] Alesheikh AA, Omidvari M (2010) Application of GIS in urban traffic noise pollution. Int J occup hyg 2: 79-84.
    [27] Matejicek L, Janour Z (2011) Modelling of traffic related environmental pollution in the GIS. Geographic Information System, Nova Science Publishers, Inc. Pg: 133-145, ISBN: 978-1-61209-925-5.
    [28] Pamanikabud P, Tansatcha M (2001) Utilization of geographic information system for simulation of traffic noise. J Eastern Asia Society of transportation studies 4: 89-104.
    [29] Sadek S, Kaysi I, Bedran M (2000) Geotechnical and environmental considerations in highway layouts: an integrated GIS assessment approach. Int J Appl Earth Obs 2: 190-198.
    [30] Reed SE, Boggs JL, Mann JP (2010) SPreAD-GIS: an ArcGIS toolbox for modelling the propagation of engine noise in a wildland setting, version 2.0. San Francisco (CA): The Wilderness Society.
    [31] Yilmaz G, Hocanli Y (2007) Mapping noise by using GIS in Sanliurfa. Environ monitor assess 121: 103-108.
    [32] Eason S (2013) Strategic noise mapping with GIS for Universitat Jaume I smart campus: best methodology practice (Master’s thesis). Lisbon: Universidade NOVA.
    [33] Kurakula V (2007) A GIS based approach for 3D noise modelling using 3D city models. Master thesis, International institute for geo-information science and earth observation, Enschede, the Netherlands.
    [34] Murphy E, Henry J, Meshell C (2006). Environmental noise prediction, noise mapping and GIS integration: the case of inner Dublin, Ireland. East- European acoustical association, 8th international symposium, Transport noise and vibration, St. Petersburg, Russia.
    [35] Taghizadeh MR, Zare M, Zare S (2013) Mapping of noise pollution by different interpolation method in recovery section of Ghandi telecommunication Cables Company. J occup health epidemiol 2: 1-11.
    [36] Banerjee P, Ghose MK (2016) A Geographic Information System-based socioeconomic impact assessment of the broadening of national highway in Sikkim Himalaya: a case study. Environ Dev Sustain, 1-22.
    [37] Cui H, Stein A, Myers DE (1995) Extension of spatial information, bayesian kriging and updating of prior variogram parameters. Environmetrics 6: 373-384. doi: 10.1002/env.3170060406
    [38] Krivoruchko K (2012) Empirical Bayesian Kriging- implementated in ArcGIS Geostatistical Analyst. Au Fall 2012 esri.com, 6-10.
    [39] Pilz J, Speck G (2008) Why do we need and how should we implement Bayesian kriging methods. Stoch Env Res Risk A 22: 621-632.
    [40] USDI (2012) Timber mountain off-highway vehicle area noise assessment. US Department of the Interior, Bureau of land management, Medford district office, Oregon- 97504.
    [41] CFC (2009) Traffic noise evaluation report- Mountain Vista subarea plan- Fort Collins, Colorado. City of Fort Collins, CO 80522, FHU Ref. No. 08-164.
    [42] Banerjee P, Ghose MK (2016) Spatial analysis of environmental impacts of highway projects with special emphasis on mountainous areas: An overview. Impact assess proj appraisal 2016: 1-15.
    [43] Chakrabarti A (2009) Tourism in Sikkim: Quest for a Self-Reliant Economy. NEHU J 8: 81-104.
    [44] PC (2008) Sikkim development report by Planning Commission, Govt. of India. New Delhi: Academic foundation ISBN13: 9788171886685.
    [45] Arrawatia ML, Tambe, S (2011) Biodiversity of Sikkim Exploring and Conserving a Global Hotspot. Department of Information and Public Relations, Government of Sikkim, Gangtok. ISBN: 978-81-920437-0-9.
    [46] EIAN (1994) The Environmental Impact Assessment Notification 1994, Govt. of India
    [47] S.O. 382 (E) (2015) Notification number S.O. 382 (E) dated 3rd February 2015 under The Environmental Impact Assessment (Amendment) Notification 2006, Govt. of India
    [48] ASCI (2010) Administrative Staff College of India, 2010. Environmental impact assessment guidance manual for highways, MoEF, New Delhi.
    [49] Bhalla P, Bhattacharya P, Gupta NC (2015) Sound levels assessment in an ecotourism destination: A case study on Binsar Wildlife Sanctuary of Indian Himalayan Region. Int J Scie Res Public 5:1-7.
    [50] Harrison RT, Clark RN, Stankey GH (1980) Predicting impact of noise on recreationists. USDA Forest Service, Equipment Technology & Development Center, San Dimas, CA.
    [51] Lynch E, Joyce D, Fristrup K (2011) An assessment of noise audibility and sound levels in U.S. National Parks. Landscape Ecol 26: 297-309. doi: 10.1007/s10980-010-9545-3
    [52] Hanson CE, Towers, DA, Lance D (2006) Transit Noise and Vibration Impact Assessment. Office of Planning and Environment, Federal Transit Administration Washington, DC 20590. FTA-VA-90-1003-06
    [53] Govind P, Soni D (2012) Traffic noise prediction using FHWA model on national highway - 28 in India. J Environ Res Dev 7: 107-115.
    [54] Shukla AK, Jain SS, Parida M, et al (2009) Performance of FHWA model for predicting traffic noise: A case study of Metropolitan city, Lucknow (India). Transport 24: 234-240. doi: 10.3846/1648-4142.2009.24.234-240
    [55] Lloyd CD (2010) Spatial data Analysis- an introduction for GIS users. Oxford University Press, Oxford OX2 6DP.
    [56] Antunes P, Santos R, Jordao L (2001) The application of Geographic Information Systems to determine Environmental Impact Significance. Environ impact assess rev 21: 511-535. doi: 10.1016/S0195-9255(01)00090-7
    [57] Bowerman BL, O’Connell RT (1990) Linear statistical models: An applied approach (2nd ed.). Belmont, CA: Duxbury.
    [58] Field A (2009) Discovering statistics using SPSS (3rd ed.). London: Sage ISBN 978-1-84787-906-6.
    [59] Menard S (1995) Applied logistic regression analysis. Sage university paper series on quantitative applications in the social sciences, 07-106. Thousand Oaks, CA: Sage.
    [60] Myers R (1990) Classical and modern regression with applications (2nd ed.). Boston, MA: Duxbury.
    [61] Dubey B, Pal AK, Singh G (2013) Assessment of vehicular pollution in dhanbad city using CALINE 4 model. Int J Geol Earth Environ Sci 3: 156-164.
  • This article has been cited by:

    1. Hongliang Miao, Amit Gupta, Coordinated Development of China’s Regional Economy and Ethnic Diversity under the Background of Big Data and the Internet of Things, 2022, 2022, 1875-905X, 1, 10.1155/2022/6424505
    2. Yule Wang, Arwa Abdulkreem AL‐Huqail, Shadi Salimimoghadam, Khidhair Jasim Mohammed, Amin Jan, H. Elhosiny Ali, Mohamed Amine Khadimallah, Hamid Assilzadeh, The metaheuristic optimization of the mechanical properties of sustainable energies using artificial neural networks and genetic algorithm: A case study by eggshell fine waste, 2022, 46, 0363-907X, 21338, 10.1002/er.8255
    3. Khaled Benkouider, Sundarapandian Vaidyanathan, Aceng Sambas, Esteban Tlelo-Cuautle, Ahmed A. Abd El-Latif, Bassem Abd-El-Atty, Ciro Fabian Bermudez-Marquez, Ibrahim Mohammed Sulaiman, Aliyu Muhammed Awwal, Poom Kumam, A New 5-D Multistable Hyperchaotic System With Three Positive Lyapunov Exponents: Bifurcation Analysis, Circuit Design, FPGA Realization and Image Encryption, 2022, 10, 2169-3536, 90111, 10.1109/ACCESS.2022.3197790
    4. Linsen Li, Wen-Tsao Pan, Node Location Method of Ecological Environment Monitoring Network Based on Zigbee, 2022, 2022, 1607-887X, 1, 10.1155/2022/5854569
    5. Hong Huo, Huanning Xu, Arpit Bhardwaj, Construction of Emergency Procurement System and System Improvement Based on Convolutional Neural Network, 2022, 2022, 1687-5273, 1, 10.1155/2022/6139706
    6. Qian Wang, Wen-Tsao Pan, Population Economic Transfer Function Model Based on Genetic Tabu Search Algorithm, 2022, 2022, 1607-887X, 1, 10.1155/2022/1356909
    7. Liping Chen, Amit Gupta, Coordinated Development of Smart City and Regional Industrial Economy under the Background of Internet of Things, 2022, 2022, 1875-905X, 1, 10.1155/2022/6986090
    8. K. Immanuvel Arokia James, R. Prabakaran, A. Karthikeyan, R. R. Prianka, Co-operative beam forming selection with energy balanced operation for wireless sensor network, 2022, 28, 1022-0038, 3653, 10.1007/s11276-022-03067-w
    9. Nimet Korkmaz, İbrahim Ethem Saçu, An alternative perspective on determining the optimum fractional orders of the synaptic coupling functions for the simultaneous neural patterns, 2022, 110, 0924-090X, 3791, 10.1007/s11071-022-07782-z
    10. Fang Li, Tao Li, Wen-Tsao Pan, Tourism Consumer Demand Forecasting under the Background of Big Data, 2022, 2022, 1563-5147, 1, 10.1155/2022/4335718
    11. Kimia Latifi, Ahoo Ebrahimi, Mehdi Ranjbaran, Arman Mirzaei, Zahra Fakhri, Efficient customer relationship management systems for online retailing: The investigation of the influential factors, 2022, 1833-3672, 1, 10.1017/jmo.2022.65
    12. Tingting Cai, Dongmin Yu, Huanan Liu, Fengkai Gao, Computational Analysis of Variational Inequalities Using Mean Extra-Gradient Approach, 2022, 10, 2227-7390, 2318, 10.3390/math10132318
    13. Lan Zheng, Ning Cao, Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model, 2022, 2022, 1687-5273, 1, 10.1155/2022/1835798
    14. Caixia Wu, Zhenrong Zhao, Yuanyuan Liu, Bayan Omar Mohammed, Quantum-dot cellular automata-based design for three-level nanoscale full-subtractor, 2022, 05779073, 10.1016/j.cjph.2022.10.014
    15. Shanshan Yu, Hao Wang, Yajun Wang, Pradeep Tomar, Optimization Design of Street Public Space Layout on Account of Internet of Things and Deep Learning, 2022, 2022, 1687-5273, 1, 10.1155/2022/7274525
    16. Eman A. Atta, Ahmed F. Ali, Ahmed A. Elshamy, Kathiravan Srinivasan, A modified weighted chimp optimization algorithm for training feed-forward neural network, 2023, 18, 1932-6203, e0282514, 10.1371/journal.pone.0282514
    17. Seyed Sajad Ahmadpour, Nima Jafari Navimipour, Mohammad Mosleh, Senay Yalcin, Nano-design of ultra-efficient reversible block based on quantum-dot cellular automata, 2023, 24, 2095-9184, 447, 10.1631/FITEE.2200095
    18. Prasina Alexander, Fatemeh Parastesh, Ibrahim Ismael Hamarash, Anitha Karthikeyan, Sajad Jafari, Shaobo He, Effect of the electromagnetic induction on a modified memristive neural map model, 2023, 20, 1551-0018, 17849, 10.3934/mbe.2023793
    19. Arash Heidari, Nima Jafari Navimipour, Hasan Dag, Mehmet Unal, Deepfake detection using deep learning methods: A systematic and comprehensive review, 2024, 14, 1942-4787, 10.1002/widm.1520
    20. Gabrielle S. Prince, Molly Reynolds, Verdion Martina, HaoSheng Sun, Gene-environmental regulation of the postnatal post-mitotic neuronal maturation, 2024, 40, 01689525, 480, 10.1016/j.tig.2024.03.006
    21. Xiaodi Li, Ying Xu, Energy level transition and mode transition in a neuron, 2024, 112, 0924-090X, 2253, 10.1007/s11071-023-09147-6
    22. Xinqing Duan, Lei Li, Zehui Peng, Mingqiang Wang, Yanxin Liu, Dar‐Jen Hsieh, Kuan‐Chang Chang, Ultralow Power, Cleft Size‐Adjustable and pH‐Sensitive Hyaluronic Acid (HA) Biodevices for Acid‐Sensing Ion Channels Emulation, 2024, 1613-6810, 10.1002/smll.202405207
    23. Jindong Liu, Zhen Wang, Huaigu Tian, Fei Xie, Zeljko Stevic, Complex Dynamic Analysis, Circuit Design and Simplified Predefined Time Synchronization for a Jerk Absolute Memristor Chaotic System, 2023, 2023, 1099-0526, 1, 10.1155/2023/5912191
    24. Xinlin Song, Feifei Yang, A light-temperature neuron and its adaptive regulation, 2024, 99, 0031-8949, 125247, 10.1088/1402-4896/ad8fe4
    25. Mahtab Mehrabbeik, Fatemeh Parastesh, Karthikeyan Rajagopal, Sajad Jafari, Matjaz Perc, Riccardo Meucci, Minimal universal laser network model: Synchronization, extreme events, and multistability, 2024, 13, 2192-8029, 10.1515/nleng-2024-0044
    26. Zhenhua Yu, Kailong Zhu, Ya Wang, Feifei Yang, Dynamics of a neuron with a hybrid memristive ion channel, 2025, 194, 09600779, 116233, 10.1016/j.chaos.2025.116233
    27. Xiaowei Han, Hagiwara Akifumi, Pin Lv, Jiahuan Liu, Xiaowei Huang, Renyuan Liu, Xiaojing Long, Yang Liu, Jiangong Zhang, Guolin Ma, Bing Zhang, Low‐Intensity Focused Ultrasound Ameliorates Cisplatin‐Induced Cognitive Impairment by Attenuating Hippocampal Neuroinflammation and Enhancing Synaptic Plasticity in Rats, 2025, 2834-2860, 10.1002/ird3.70003
  • Reader Comments
  • © 2016 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(9369) PDF downloads(1429) Cited by(11)

Figures and Tables

Figures(11)  /  Tables(9)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog