The vulnerability of the European air traffic network to spatial hazards

Wilkinson, Sean; Dunn, Sarah; Ma, Shugen

doi:10.1007/s11069-011-9885-6

Cited by 114 publications

(101 citation statements)

References 17 publications

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“…In the short term and most obviously, aerosol particles can degrade visibility and damage aviation and transport (Wilkinson et al, 2012;Prata and Tupper, 2009). The World Health Organization (WHO), which has reported country estimates of air pollution exposure and its health impact (WHO, 2016), has suggested that 6.5 million deaths (11.6 % of all global deaths) may be associated with indoor or outdoor air pollution, and 92 % of the world's population lives in places where air quality levels do not meet the WHO Ambient Air quality guideline of an annual mean PM 2.5 (particulate matter with a diameter of less than 2.5 µm) concentration of less than 10 µg m −3 .…”

Section: Introductionmentioning

confidence: 99%

JRAero: the Japanese Reanalysis for Aerosol v1.0

et al. 2017

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Abstract. A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a twodimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard the Terra and Aqua satellites every 6 h and has a TL159 horizontal resolution (approximately 1.1 • × 1.1 • ). This paper describes the aerosol transport model, the data assimilation system, the observation data, and the setup of the reanalysis and examines its quality with AOD observations.Comparisons with MODIS AODs that were used for the assimilation showed that the reanalysis showed much better agreement than the free run (without assimilation) of the aerosol model and improved under-and overestimation in the free run, thus confirming the accuracy of the data assimilation system. The reanalysis had a root mean square error (RMSE) of 0.05, a correlation coefficient (R) of 0.96, a mean fractional error (MFE) of 23.7 %, a mean fractional bias (MFB) of 2.8 %, and an index of agreement (IOA) of 0.98. The better agreement of the first guess, compared to the free run, indicates that aerosol fields obtained by the reanalysis can improve short-term forecasts.AOD fields from the reanalysis also agreed well with monthly averaged global AODs obtained by the Aerosol Robotic Network (AERONET) (RMSE = 0.08, R = 0.90, MFE = 28.1 %, MFB = 0.6 %, and IOA = 0.93). Site-by-site comparison showed that the reanalysis was considerably better than the free run; RMSE was less than 0.10 at 86.4 % of the 181 AERONET sites, R was greater than 0.90 at 40.7 % of the sites, and IOA was greater than 0.90 at 43.4 % of the sites. However, the reanalysis tended to have a negative bias at urban sites (in particular, megacities in industrializing countries) and a positive bias at mountain sites, possibly because of insufficient anthropogenic emissions data, the coarse model resolution, and the difference in representativeness between satellite and ground-based observations.

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Section: Introductionmentioning

confidence: 99%

JRAero: the Japanese Reanalysis for Aerosol v1.0

et al. 2017

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“…While the design of infrastructure networks has been of interest to geographers since the 1960s [12], [13], spatial network design has emerged only more recently as an area of scientific research [14]. Many applications of spatial network analysis focus on traffic or transportation networks [15], [14], [16], [17], [18] or physical infrastructures that deliver information, such as the internet or mobile telephony [14], [19]. Others find that the constraints of geographic space can dramatically change the implications found in abstract network models [20], [21].…”

Section: Introductionmentioning

confidence: 99%

“…The first is how the cost of adding edges or otherwise connecting nodes in space influences network structure and design choices [14], [23]. The second strand uses known or theoretical spatial properties of networks to understand their performance in the case of attacks, failures or other contingency events [6], [18], [24].…”

Section: Introductionmentioning

confidence: 99%

When are Decentralized Infrastructure Networks Preferable to Centralized Ones?

Hines

Blumsack

Schlaepfer³

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Many infrastructure networks, such as power, water, and natural gas systems, have similar properties governing flows. However, these systems have distinctly different sizes and topological structures. This paper seeks to understand how these different features can emerge from relatively simple design principles. Specifically, we work to understand the conditions under which it is optimal to build small decentralized network infrastructures, such as a microgrid, rather than centralized ones, such as a large high-voltage power system. While our method is simple it is useful in explaining why sometimes, but not always, it is economical to build large, interconnected networks and in other cases it is preferable to use smaller, distributed systems. The results indicate that there is not a single set of infrastructure cost conditions that cause a transition from centralized networks being optimal, to decentralized architectures. Instead, as capital costs increase network sizes decrease gradually, according to a power-law. And, as the value of reliability increases, network sizes increase abruptly-there is a threshold at which large, highly interconnected networks are preferable to decentralized ones.

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“…The World Airline Network is a coreperiphery network [2] with long-range connections that make possible its relatively small diameter compared to its size. The existence of central hubs that form the interconnected core, together with the long-range connections that separate the hubs to span across the globe, gives the WAN its unique topology [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Model for the growth of the world airline network

Verma

Araújo

Nagler

et al. 2016

Int. J. Mod. Phys. C

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We propose a probabilistic growth model for transport networks which employs a balance between popularity of nodes and the physical distance between nodes. By comparing the degree of each node in the model network and the World Airline Network (WAN), we observe that the difference between the two is minimized for α ≈ 2. Interestingly, this is the value obtained for the node-node correlation function in the WAN. This suggests that our model explains quite well the growth of airline networks.

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The vulnerability of the European air traffic network to spatial hazards

Cited by 114 publications

References 17 publications

JRAero: the Japanese Reanalysis for Aerosol v1.0

JRAero: the Japanese Reanalysis for Aerosol v1.0

When are Decentralized Infrastructure Networks Preferable to Centralized Ones?

Model for the growth of the world airline network

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