AI & Soc DOI 10.1007/s00146-016-0650-y

OPEN FORUM

Assessing environmental impacts of aviation on connected cities using environmental vulnerability studies and fluid dynamics: an Indian case study G. Ramchandran1,2 • J. Nagawkar1,3 • K. Ramaswamy1,4 • S. Ghosh1,5 • A. Goenka1,6 • A. Verma1

Received: 26 October 2015 / Accepted: 22 January 2016 Ó Springer-Verlag London 2016

Abstract As the annual air passenger traffic in India is increasing steeply (13.52 million in 2012 compared to 11.02 million in 2010), an environmental impact assessment on important cities connected by air is becoming increasingly indispensable. This study proposes an innovative screening method that uses a modified Environmental Vulnerability Index (EVI). This modified EVI calculator includes aviation-related parameters and can be used to assess the environmental vulnerabilities of political states and cities, in addition to countries as is being already done. This study also suggests the need to include aspects of human comfort in the screening process through the use of state-of-the-art computational fluid dynamical software and large eddy simulations which can be used to estimate forces experienced by aircraft of different sizes during inflight turbulence for various weather conditions. A comparative analysis is presented on how changing the size of the aircraft operating in a particular route between the cities of Chennai and Bengaluru has better implications on both passenger comfort and the environment. It is observed that if commercial airlines incorporated fewer mediumsized aircraft, there would be a significant reduction in the environmental vulnerability of the two connected cities. & S. Ghosh [email protected] 1

VIT University, Vellore, India

2

Purdue University, West Lafayette, IN, USA

3

Chalmers University of Technology, Gothenburg, Sweden

4

University of Illinois Urbana-Champaign, Champaign, IL, USA

5

University of Leeds, Leeds, UK

6

Imperial College London, London, UK

Keywords Environmental impact assessment (EIA)  Environmental Vulnerability Index (EVI)  Computational fluid dynamics (CFD)  Passenger comfort

1 Introduction Chennai and Bengaluru are two important cities in southern India. While Bengaluru is a software hub, Chennai is an important centre for trade and manufacture. As such, Chennai and Bengaluru need to be well connected. Figure 1a, b shows a steep increase in air travel over the past decade in Chennai and Bengaluru. The aviation sector has undergone massive infrastructural development in both cities. The yearly growth in the number of flights operating in this sector could take a heavy toll on the overall environmental health of the two cities. This calls for an EIA to be conducted to analyse the effect that this increase in the number of passenger flights between the two connected cities has on their environment. This paper uses a modified version of the EVI as a screening tool. It is modified to include factors related to the aviation industry, which play a key role in enhancing the environmental vulnerability of the two cities. In India, domestic flights are becoming increasingly affordable to the burgeoning middle class. Although relatively affluent, the Indian upwardly mobile are not necessarily always well informed. Women, children, elderly people and the nouveau riche business community with modest levels of education have no idea about the effects of frequent flying on vulnerable people—pregnant women, people with physical disabilities, diabetic and heart patients. Unlike the affluent Western world, Indian passengers are never briefed about the possibilities of a rough

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AI & Soc Fig. 1 a Passenger traffic trends in Bengaluru (2005–2012). Source Association of Private Airport Operators [Internet]. c2012–2013. Statistics: Bengaluru International Airport Limited; [cited 2012 December 8]. Available from: http://www. apaoindia.com/?page_id=840. b Passenger traffic trends in Chennai (1993–2010). Source Directorate General of Civil Aviation. Yearly Statistics; [cited 2013 December 8]. Available from: http://www. dgca.nic.in/reports/stat-ind.htm. c Passenger traffic trends in Chennai and Bengaluru for the same timescale (2005–2009)

flight—this is mainly true for many of the private airlines who wish to cash in on these well-to-do passengers. It must be borne in mind that India is a monsoon-driven country—

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the south-west monsoon lasts well for over 3 months and is characterized by turbulent weather and concomitant uncomfortable flights. In addition, Chennai bears the brunt

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of a second monsoon season, the north-east monsoon, which again lasts for approximately 8–10 weeks. These clearly defined and well-marked rough periods require special considerations vis-a`-vis flight management and citizens’ well-being. This has not been done systematically—pilots have access to India Meteorological Department weather charts usually from the Met station at the airport itself. However, technology has moved on—with the advent of state-of-the-art weather forecasting models like the weather research and forecasting model (WRF) and large eddy simulations (LES) models, it is possible to predict rough weather on selected routes 5 days in advance (Michalakes et al. 2001). The LES models provide a detailed velocity distribution pattern right from the ground up to the tropopause—they are particularly well suited to resolve in-cloud turbulence. Monsoon activity is associated with aircraft having to endure large patches of waterborne towering cumuli and cumulonimbus clouds with updraught velocities of the order of 2.5 ms-1(Fig. 3b). Flying times through these turbulent patches can be picked up by sophisticated fluid dynamical simulations to inform the aviation industry and the population at large. This falls within the remit of an EIA (especially through a rigorous screening study) of aviation corridors along some of India’s busiest routes. One way of catering to passenger comfort is to change the type of aircraft used over a sector—this move, however, can play a part in increasing or decreasing the impact on the environment of the two cities. A comparative analysis is put forth in order to better understand the impact on the environment of the two cities by changing the type of aircraft (with respect to size) commonly being flown along this route. In this study, we have applied CFD and LES to in-cloud turbulence in order to quantify passenger comfort. This quantified value is then incorporated into the modified EVI as a means of introducing passenger comfort into the EIA screening process. To our knowledge, this direct application of CFD to a screening analysis of an aviation corridor has not been undertaken before. A modified, downscaled EVI calculator has been used as a screening tool incorporating aviation-related parameters to determine the EVI of the two connected Indian cities, Chennai and Bengaluru. In addition, the importance of incorporating passenger comfort into this EIA study is discussed and a methodology is proposed to allow for the introduction of this parameter. This is done by changing the size of the aircraft used on the given route with regard to in-flight turbulence experienced during regular and abnormal weather conditions. This is done through the use of CFD and LES of cumulus clouds present in the path of the flight. The overarching research question that the paper wishes to address is how can fluid mechanics support an

environment impact analysis of the burgeoning air travel between the two most important South Indian cities of Chennai and Bengaluru? Secondly, how does the application of contemporary modelling tools enhance a deeper assessment of the most vulnerable environmental indices?

2 A downscaled and modified EVI of Chennai and Bengaluru In order to identify and quantify the impact of growth in the aviation sector on the two cities in question, we have modified an existing version of the EVI which was formulated under the aegis of the South Pacific Applied Geoscience Commission (SOPAC), the United Nations Environment Program (UNEP), collaborating countries, institutions and experts. We depict how the EVI may be used as a screening tool, as has been done in this case study, for the particular scenario of the effect of aviation on the environmental well-being of the two cities. This screening study may be used as a prelude to a more extensive EIA (Kværner et al. 2006). The EVI was initially designed in order to quantify the vulnerability of a country to environmental factors. In their technical report, Kaly et al. (2004) demonstrate the way in which the EVI of a country may be indexed as a consequence of deducing the impact of 50 indicators identified on the environment. Based on the data available from around the world, these indicators have been suitably defined and a scale of 1–7 has been proposed for each indicator. These scales depict the degree of vulnerability of a particular country to that indicator, with 1 being highly resilient and 7 being most vulnerable. There is a need to apply the EVI to quantify the impact on smaller regions within the country, such as states or cities. Every state possesses a different environmental vulnerability—this is due to subtle or vast differences in environmental qualities between them as well as variations in policies adopted by each form of state government. Quantifying the EVI of an entire country suppresses these individual regional impacts. For these reasons, a downscaled version of the EVI has been implemented for this study on the cities of Chennai and Bengaluru. The downscaled EVI calculator is used to quantify the contributions of the aviation industry to the environmental vulnerability of two cities. Out of the 50 indicators considered, aviation plays a major role on 7. Table 1 lists definitions of these indicators and shows corresponding figures for the cities of Chennai and Bengaluru—based on these, scores are ascribed suitably in Table 2. In order to rank these indicators according to the impact that aviation has on them, we identify 6 aviation-related

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AI & Soc Table 1 Indicators for EVI study on Chennai and Bengaluru Indicator

Definition

Comments

Population growth

Annual human population growth rate over the last 5 years

Chennai population increased from 6,560,242 in 2001 to 8,696,010 in 2011; growth of 3.3 % per year (Srivathsan 2011) Bengaluru population increased from 5,101,000 to 8,425,970 in 2011 (Census of India 2011); growth of 6.52 % per year

Waste production

Environmental openness

Average annual net amount of generated and imported toxic, hazardous and municipal wastes per km2 land area over the last 5 years

4500 tonnes of solid waste generated per day (Corporation of Chennai 2014); 1381 tonnes waste generated every year per km2 of land (Chennai metropolitan area being 1189 km2)

Total USD freight imports per year over the past 5 years by any means per km2 land area

357,191 freight in tonnes for 2011 (Airports Authority of India 2012); 1502 freight per km2; 15.02 USD 1000’s km-2 for Chennai (assuming a minimum cost of 10 USD per freight shipment tonne)

2500 tonnes of solid waste generated per day (Van Beukering et al. 1999); 1232 tonnes of waste generated every year per km2 of land (Bengaluru land area is 741 km2)

224,949 freight in tonnes for 2011 (Airports Authority of India 2012); 304 freight per km2; 3.04 USD 1000’s km-2 for Bengaluru (assuming a minimum cost of 10 USD per freight shipment tonne) Tourists

Habitat fragmentation

Average annual number of international tourist-days per km2 of land over the last five years

Average of 18 international tourists km-2 (area of Tamil Nadu being 130,058 km2) (TNN 2010)

Total length of all roads in the city (km)/land area (km2)

Total length of roads in Chennai is 2780 km; land area of Chennai is 1189 km2 (Chennai Metropolitan Development Authority)

2 international tourists km-2 (area of Karnataka being 191,791 km2) (Department of Tourism, Government of India 2014)

Total length of roads in Bengaluru is 2679 km; land area of Bengaluru is 741 km2(Government of Karnataka 2013) Number of vehicles per km2 of land area (most recent data)

Vehicles

3.64 million vehicles in Chennai (land area of Chennai is 1189 km2)(Selvaraj 2012) 4,171,062 vehicles as of October 2012 (land area of Bengaluru is 741 km2)(Bangalore City Traffic Police 2014)

Number of humans per km2 of land area

Human population density

4,681,087 people per 1189 km2 land (Population Census 2011) 9,621,551 people per 741 km2 land area (Karnataka Population Census 2011)

Table 2 EVI scores for Chennai and Bengaluru based on air traffic rise Tourists

Population growth

Waste production

Environmental openness

Habitat fragmentation

Vehicles

Human population density

Bengaluru city EVI

1

7

7

5

7

7

7

Chennai city EVI

4

7

6

5

7

7

7

Data years considered

2007, 2009

2001–2011

2012

2011

2007

2012

2011

parameters which may have a substantial effect on one or more of the indicators. According to the number of aviation parameters affecting a particular EVI indicator (indicated by an ‘‘X’’), a suitable multiplication factor is coupled with the EVI values obtained for that indicator. For instance, from Table 3, since 4 out of 6 aviation parameters affect the EVI indicator ‘‘Tourists’’, we multiply the EVI values obtained for ‘‘Tourists’’ with the factor 4/6.

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As the populations of Chennai and Bengaluru grew, 41 % of the populace ascended to the middle higher income group within a span of 20 years (see ticket price affordability vis-a`vis population growth in Table 3 of the revised paper) (Beinhocker et al. 2007), and this ascendency caused a larger disposable income—some of this is used for air travel. In fact, this increasing affluence has already seen a tremendous surge in the number of tourists flying on weekend getaways,

AI & Soc Table 3 Ranking scheme for EVI indicators with respect to aviation Aviation parameters Fuel consumption

Aircraft noise

Airport operations

Ticket price affordability

Aircraft emissions

Number of flights for a single route

X

Multiplication factor

EVI indicators Population growth

X

X

X

X

X

Human population density Tourists

X X

X X

X X

X

X X

Environmental openness

X

X

X

X

X

5/6 2/6

Waste production

X

X

Habitat fragmentation

X

X

Vehicles

X

flying out from Chennai to Bengaluru to hill stations and coastal resorts (The Hindu: A million discoveries now 2008) (see Tourists—row 3 of Table 3). The city of Chennai connects Puducherry along a scenic beachway called the East Coast Road (ECR) which has a plethora of hotels and resorts. This has caused a surge in built-up areas, replacing lush and verdant crop lands (see Habitat fragmentation—row 6 of Table 3). Many of the fishing villages have been totally lost from the ECR. Likewise, for the city of Bengaluru, when the new airport was constructed, 4000 acres of land was reclaimed causing relocation of the most vulnerable population (Habitat fragmentation—row 6 of Table 3). A greater number of flying tourists result in greater consumption of pre-packaged food and drinks which are served in aircraft. These more often use plastic and aluminium foil. Recent reports indicate increasing amounts of plastic and foil generated by the aviation industry (The Hindu: Choking on Plastic in Chennai 2013). This waste management mechanism is not optimal in all developing countries including India (Waste production—row 5, Table 3). Coming to the issue of environmental openness, we first give a formal definition. It is defined as ‘‘Average annual USD freight imports over the past 5 years by any means per km2 land area’’ (source:https://en.wikipedia.org/wiki/ Environmental_Vulnerability_Index). Airborne freight imports over Chennai and Bengaluru have significantly increased between years 2000 and 2010 (Air Cargo Logistics in India 2012) When there is a larger demand for more frequent air travels (number of flights for a single route), there is a deterioration of the air quality indirectly (aviation parameter—Aircraft emissions—column 5 of Table 3). Firstly, more aviation fuel has to be transported over to airports. This transportation is effected by means of diesel-operated vehicles (Vehicles—row 7 of Table 3). India’s metropolises are already choking with exhausts from road transportation (aviation parameter—Aircraft operation; column 3 of Table 3).

6/6 5/6 4/6

2/6 1/6

Bengaluru International Airport is situated much further away from the city centre than the Chennai International Airport. The distances to the city centre are 35.7 and 20.6 km, respectively. The residential areas around Meenambakkam in Chennai are densely clustered and aircraft noise in indeed immense as of now. With air traffic growth projected in Fig. 1 of the paper, there are no plans of moving Chennai airport further afield—a spanking new international airport terminal has just been built by the Airports Authority of India under a private–public partnership scheme very recently. This new terminal complex is virtually next door to the existing complex. As the middle-income group in Chennai expands, air travel will also expand in both cities. But Chennai city will experience the menace of aircraft noise more than Bengaluru. Currently, an Aerodrome Advisory Circular is in place. This has been issued by the Government of India’s Office of Civil Aviation, and there are clear guidelines on ground handling services including transportation and loading on to and unloading from the aircraft. Aircraft services comprise of fuel and oil handling, surface transport, catering services, etc. Both Chennai and Bengaluru airports are designed to accommodate large- as well as medium-sized aircraft requiring appropriate spacing requirements as well as a strict compliance to the safety issues. For instance, Indian airports require that while positioning equipment, care is taken to ensure adequate clearance of vehicles. It is also stipulated that the ground handling personnel shall strictly follow the refuelling procedure as strictly stipulated by Rule 25A of the Aircraft Rules (Department General of Civil Aviation (DGCA) 2012). The new EVI values obtained are tabulated, and the total EVI values are calculated for Chennai and Bengaluru and then compared. Table 4 depicts the same. From this screening study, we can address the issue of reducing the environmental vulnerability of the two cities in a very direct manner simply by reducing the impact of the aviation factors on the EVI indicators. One way this can

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AI & Soc Table 4 Final modified EVI values for Chennai and Bengaluru Indicators

Chennai EVI

Bengaluru EVI

Multiplication factor

Population growth

7

7

1

7

7

7

Human population density

7

7

5/6

5.83

5.83

5.83

Tourists

4

1

4/6

2.67

0.67

4.67

Environmental openness

5

5

5/6

4.17

4.17

5.83

Waste production

6

7

2/6

2

2.33

2.33

Habitat fragmentation

7

7

2/6

2.33

2.33

2.33

Vehicles Total

7

7

1/6

1.17 25.17

1.17 23.50

1.17 29.17

be done is by operating a larger aircraft on that route, thereby reducing the total number of aircraft being operated by an airline every day for that particular route. Presently, the aircraft operated include Boeing 737-800, Boeing 737-900, Aerospatiale/Alenia ATR 72, Bombardier Q200 and Airbus A320 with average flying times of 45–75 min spanning cruising altitudes from 7500 to 12,000 m. We considered, as an example, a situation in which low-cost carriers such as Jetlite and JetKonnect (operated by Jet Airways) replace the lighter ATR 72 flights operating on the Chennai–Bengaluru route with the larger Boeing 737. According to the literature, ‘‘large aircraft have lower environmental per passenger km costs than small aircraft’’ (Peeters et al. 2005). CO2 emissions from aviation fuel are estimated to be 3.15 grams per gram of fuel (European CORINAIR Manual 2013). From this, for 162 seats in a Boeing 737, we calculate the fuel consumed to be 36.6 grams per passenger km, whereas it is found to be 63 grams per passenger km for 74 seats in an ATR 72 (Dubois 2009). Considering the total number of seats in each aircraft, we find that an airline operating 5 ATR 72’s from Chennai to Bengaluru per day will have the same fuel consumption as that of four Boeing 737s. This also implies a reduction in aircraft noise (by virtue of operating fewer aircraft on a given route on any given day), operating costs and ticket prices, which ultimately results in a lower impact on the EVI indicators considered. Hence, we recommend that the impact of replacing smaller aircraft by larger ones (on relatively short routes, such as the Chennai–Bengaluru corridor) must be considered in a full EIA study.

3 Application of fluid mechanical calculations to address human comfort during domestic flights This section deals with incorporating passenger comfort into the screening process discussed in the previous section. Here, the methodology and use of CFD and LES in

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Scaled Chennai EVI

Scaled Bengaluru EVI

Idealized rescaled EVI scores

order to estimate in-cloud turbulence for different weather scenarios are discussed. With the cheaper computational resources and real-time data transmission available today, the use of CFD simulations can help predict the amount of turbulence a pilot might face while flying over cloudy routes, especially during monsoons. By performing simulations such as these, one may calculate the degree of vertical velocity perturbations in the cumuli and cumulonimbus clouds over a particular range of the flight path. With this, we can estimate the incremental aircraft vertical acceleration in units of ‘‘g’’ (ms-2) and find whether these values fall within the moderate or safe turbulence limits for aircraft. Human comfort being one of the key factors in this screening study, we perform CFD calculations to better understand the discomfort an airline passenger might face on a bad weather day. The ANSYS CFX software is used to model flows in simulated environments (ANSYS Inc. 2012). This application is used extensively in aerospace and related industries to model fluid flow in different situations. It uses iterative numeric codes to solve fluid mechanical as well as heat and momentum transfer equations (such as Navier–Stokes equations) with specific boundary conditions input by the user. For this study, a 1:33 scale model of an Airbus A320 (Fig. 2a, b), which is one of the most widely used variants on this corridor, was hybrid-meshed using the ANSYS Workbench meshing software (meshing is the process of breaking down a volume into differentially small volumes). Navier–Stokes solutions are calculated for each node within the subdomain, and the results are extrapolated. Solid domain characteristics corresponding to aluminium (typically used aircraft material) were plugged in (Baker 2005). In order for two flows to be similar, they must have the same Reynolds and Euler numbers; to ensure this, the scaled dynamic viscosity is kept 33 times the unscaled dynamic viscosity. In contrast to actual flying conditions (of a velocity of 250 ms-1 and pressure equal to 0.8 atm at an altitude of 9000 m), the

AI & Soc Fig. 2 a Pressure distribution over an Airbus A320 aircraft (top c view). b Pressure distribution over an Airbus A320 aircraft (bottom view). c Pressure distribution over an ATR 72 aircraft (top view). Comparing with a, we note the preponderance of negative pressure contours in the ATR simulation over the A320 simulations. With a larger spatial extent of negative pressure contours, the onrush of air from higher pressure will have a higher buffeting effect on the lighter aircraft, exacerbating passenger discomfort. d Pressure distribution over an ATR 72 aircraft (bottom view)

model airspeed was calculated as 289 ms-1 corresponding to a pressure of 1 atm. A k-x shear stress transport (SST) model (Menter 1994) coupled with a high turbulence (10 % intensity) profile was used to observe airflow around the model. Air tends to move from regions of high pressure (below the aircraft) to regions of lower pressure (above the aircraft). In the event of an updraught, air rushes towards lowpressure areas, reducing the pressure gradient and creating instability during flight. Figure 2a, b shows top and bottom views of pressure distributions for an A320 simulation— one notices regions of high pressure covering the bottom of the wing and the belly of the aircraft. This pressure gradient across the wing keeps the aircraft in flight. Owing to the presence of unequal pressure distributions below the aircraft, in-cloud turbulence (particularly during approach and landing) acts in tandem with the uneven pressure distributions to sometimes require a sudden increase in engine power (and hence, fuel consumption) during the flight. By utilizing the smaller ATR 72 aircraft over the Chennai– Bengaluru corridor, we cause an adverse response on the EVI indicators because of a higher fuel consumption, higher emissions and most importantly, by having five airplanes in the air as compared to the situation where we only require four Boeing 737s. Thus, we strongly recommend the importance of incorporating in-cloud turbulence and passenger comfort to an EIA, especially since the environmental vulnerability of two major cities is in question. Comparing Fig. 2a and c in tandem with b and d, we find a much larger spatial extension of negative pressure contours in the later, i.e. the ATR simulation. When there are negative pressure regions, there is a concomitant onrush of air from positive pressure regions buffeting the aircraft. The two sets of simulations clearly indicate that a given succession of up- and downdraughts in the ambient air makes the lighter ATR aircraft more vulnerable to the action of air turbulence. Passengers in the lighter ATR therefore are more vulnerable to in-cloud atmospheric turbulence adding to higher discomfiture scores in an EVI calculation. We have addressed the effects of turbulence on aircraft vertical displacement due to these clumpy, water-laden cumulus clouds frequently encountered on this route.

The UK Met office large eddy model (LEM) was used (Gary et al. 2001). This high-resolution model can simulate a wide range of fluid dynamical problems. It uses periodic

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boundary conditions and a Boussinesq-type approximation set ahead in time and includes parameterizations for subgrid turbulence, cloud microphysics and radiation. The prognostic variables in the LEM are the three components of the 3D velocity vector (u, v, w), potential temperature perturbations and a number of other scalar variables— usually mixing ratios and number concentrations of water species. Figure 3a shows the typical variation of the vertical wind velocity perturbations within a cumulus cloud modelled for Chennai city in November 2011, which corresponds to the north-east monsoon period where the cloud covers are generally at their maximum. Additionally, weather data obtained for days wherein in-flight turbulence effects could be drastic (such as those spanning the landfall of cyclone Neelam, as shown in Fig. 3b, were modelled using the LEM. It was found that the updraught range was much larger, spanning over a distance of 2 km as compared to 20 m on a normal day. The velocity perturbations were also higher in magnitude (2.5 ms-1 as opposed to

1.4 ms-1). As the airplane passes through such clouds, it experiences turbulence. Larger vertical velocity perturbations could contribute to a sudden loss in aircraft lift. An aircraft on this route typically spends 20 min (in ascent and descent) in a 45-min flight in the region where these clouds exist. This often causes passenger discomfort leading to nausea, vomiting and minor accidents within the cabin (Hinninghofen and Enck 2006). Cumulus clouds can accumulate up to a height of 6.2 km over this region (Sarkar and Kumar 2007). This implies that while all aircraft types could encounter such clouds across their respective flight paths, and more so during landing—where there is a preponderance of low-level clouds over this tropical belt—different aircraft types could have different responses to turbulence. A sudden pressure change causes the aircraft to lose lift within a short span of time. The degree of downward acceleration exemplifies the effect of turbulence on passenger comfort within the cabin. Pratt and Walker (1954) derived the gust velocity relationship between an aircraft and a cumulus cloud. It is important to note that the degree of vertical acceleration has a direct dependence on the airplane’s lift curve slope, gross weight and wing area: characteristics that differ from aircraft to aircraft. The incremental aircraft vertical acceleration was calculated for both weather conditions depicted in Fig. 3, considering data available for two different variants of aircraft—a Boeing 737-800 and an ATR 72. The equation used is given by: Daz ¼ ðmq0 SVe K = 2W Þ  Ude

ð1Þ

where K ¼ 0:88 lg = ð5:3 þ lgÞ
lg ¼ 2W = mqcg S

Fig. 3 a 3D plot of vertical velocity perturbations in cumulus clouds in November 2011. b 3D plot of vertical velocity perturbations in cumulus clouds October 31, 2012 (Cyclone Neelam)

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ð2Þ ð3Þ

The definitions of each term used in these equations are given in Table 5. The maximum wind velocities obtained from the LES were plugged into Eq. (1), and the values of maximum incremental aircraft vertical accelerations were found to be 0.4986 gms-2 (Boeing 737) and 0.608 gms-2 (ATR 72) for a maximum updraught of 1.4 ms-1 (Fig. 3a). These values are just within the moderate turbulence limit (Dreyling 1973) for the Boeing 737, indicating that the passengers experience a comfortable flight. In the ATR 72, however, they experience moderate turbulence. For the purpose of drawing a comparison with respect to which aircraft is better suited to fly during extremely turbulent conditions, weather conditions prevailing at the time of Cyclone Neelam were chosen, for which the calculated vertical aircraft acceleration were 0.89 g (Boeing 737) and 1.086 g (ATR 72). This indicated that the airplanes face moderate and severe in-flight turbulence, respectively. Most

AI & Soc Table 5 Parameters used for the calculation of vertical aircraft acceleration Symbol

Daz

Definition

For Boeing 737

Incremental aircraft vertical acceleration (in g units ms-2)

Regular

Stormy

0.4986

0.89

Sources

For ATR 72

Sources

Regular

Stormy

–

0.608

1.086

–

M

Wing lift curve slope

6.58

Rustenburg (1996)

5.966

Nit¸ a˘ (2008)

q0 S

Air density at sea level (in kg m-3) Wing area (m2)

1.225 124.6

– Matthews (2002)

1.225 62.2

– ATR Aircraft (http://www. atraircraft.com/products/ atr-72-500.html)

Ve

Equivalent airspeed (ms-1)

60.02

Being Inc. (internet)

53.35

Air Northwest (http://web pages.charter.net/anw/ ANW/performance.html)

W

Aircraft weight (kg)

65,310

Matthews (2002)

22,350

ATR Aircraft (http://www. atraircraft.com/products/ atr-72-500.html)

Ude

Effective gust vertical velocity (ms-1)

1.4

Gary et al. (2001)

1.4

K

Gust alleviation factor

0.772

–

0.8

–

lg C

Airplane mass ratio Aircraft wing chord (m)

37.9 4.17

– Matthews (2002)

53.53 2.2345

– Nit¸ a˘ (2008)

G

Acceleration due to gravity (ms-2)

9.801

–

9.801

–

1.007

–

1.007

–

q

-3

Density at flight level (kgm )

2.5

2.5

Gary et al. (2001)

Table 6 Types of aircraft used by airlines operating on the Chennai–Bengaluru route Airline

Aircraft type

Frequency

SpiceJet

Bombardier Q200

6–7 Non-stop flights per day

Jet Airways

Boeing 737-800, Boeing 737-900, Aerospatiale/Alenia ATR 72

7–8 Non-stop flights per day

Indigo

Airbus A320

1 Non-stop flight per day

Air India

Airbus A320

1 Non-stop flight per day

Source Seatguru [Internet]. c2001–2014. Find Seat Maps; [cited 2013 November 4]. Available from: http://www.seatguru.com/findseatmap/ findseatmap.php

airlines generally cancel flights during cyclones and storms. However, this study was done in order to show that the level of discomfort faced by passengers in an ATR 72 would be well above even severe turbulence limits (1 g). A Boeing 737 would be able to better negotiate a higher gradient of turbulence than an ATR 72, and this is evident from the study. Only a large eddy simulation is capable of capturing the velocity gradients in a cumulus cloud effectively. The various aircraft used by the domestic airlines in the country operating on the Chennai–Bengaluru route were identified. From Tables 6 and 7, it may be concluded that all the ATR 72s and Bombardier Q200s used by the airlines SpiceJet and Jet Airways may be replaced with aircraft comparable to the Airbus A320 and the Boeing 737 for direct flights flying from Chennai to Bengaluru or vice versa. In order to assess the environmental impact benefits of using a heavier aircraft in place of a lighter one on this particular route, we categorize the factors that determine

Table 7 Aircraft categorized based on size (weight) Aircraft type—I (medium weight: 60,000–70,000 kg)

Aircraft type—II (light weight: 15,000–25,000 kg)

Boeing 737-800

Bombardier Q200

Boeing 737-900

Aerospatiale/Alenia ATR 72

Airbus A320

the selection of the aircraft type and assign weights against the modified EVI values obtained earlier from Table 4. Table 8 depicts the categorization of these factors. We then multiply the final values obtained from Table 8 (i.e. 9 and 6) with the scaled EVI values for Chennai and Bengaluru (25.17 and 23.50, respectively) in Table 4 to find the difference in the environmental vulnerability of the two cities due to the use of different aircraft types. We see that the EVI of Chennai is 226.53 when lighter aircraft are in use, whereas it is only 151.02 when a medium-weight

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AI & Soc Table 8 Environmental implications on Chennai and Bengaluru based on the change in aircraft type

Aircraft Factor

Description

Weight assigned

Type I – Light weight

Type II – Medium weight

High: >50 Low: <50

High: 2 Low: 1

2

1

Severe: 2 Moderate: 1

2

1

Slow: 60 to 80 Fast: <=60

Slow: 2 Fast: 1

2

1

In terms of frequency of flights; higher frequency implies more congestion.

High: 2 Low: 1

2

1

High: >70 Low: <70

High: 2 Low: 1

1

2

9

6

CO2 emissions (gms per passenger km) (1)

Incremental aircraft vertical acceleration (“g” units; ms-2)

(Assuming extreme weather conditions) Moderate: 0.5g to 1g Severe: >1g

Travel time (minutes) [36]

Airport congestion

Aircraft noise (db(A))

Total (To be multiplied with the respective scaled EVI values) a

Here, the icon for emissions is meant to resemble the symbol ‘‘CO2’’ integrated into an aircraft window. Iconography such as these lends a sense of visual appeal to the user

b

It was found that even for a jumbo jet such as a Boeing 747 (not used on short routes), the vertical acceleration values were between 0.5 and 0.8 g (not shown here) during the Cyclone Neelam (extreme weather). Hence, we consider only ‘‘Moderate’’ and ‘‘Severe’’ criteria and ‘‘Normal’’ levels (\0.5 g) are omitted

aircraft is in operation for this route. Similarly, for Bengaluru it is 211.50 when lighter aircraft are operated, while the EVI reduces to 141 when medium-weight aircraft are introduced. Hence, this quantitative approach verifies the need for choosing to operate the right aircraft type, also incorporating the social benefit of passenger comfort for passengers frequenting this route (Table 9).

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Table 9 Environmental Vulnerability Indices after incorporating factors from Table 8 Light weight (factor: 9/10)

Medium weight (factor: 6/10)

Chennai

22.65

15.102

Bengaluru

21.15

14.10

AI & Soc

4 Conclusion Available EVI calculators today have a tremendous developed country bias and are only developed till date for vulnerable island nations. They yield a score based on empirical formulations alone. This study reveals for the first time that indicators should be country specific and situation specific. It is not enough to give a vulnerability index for a large geographical area—this may well point to the region’s overall vulnerability but fails to assign specific scores on specific themes. We suggest more robust mathematical models be used for identifying indices for the expansion of the aviation industry within the developing world where levels of health care and education are not up to the mark. This opens up a new way of prescribing vulnerability indices for specific, need-based environmental situations. We have shown how the incremental aircraft vertical accelerations help in predicting overall comfort and vulnerability should not be limited to the physical environment alone. Capturing velocity gradients within monsoon clouds is tricky—when a pilot negotiates these velocity gradients, he has to ensure the minimum levels of in-flight discomfort—this is not done easily. However, advanced computational fluid dynamical techniques are increasingly affordable and must be used suitably for impact assessment. This has been robustly demonstrated in this study concerned with the aviation industry linking Chennai and Bengaluru. However, the mathematical framework and the application methodology are transferable over any part of the world. Most importantly, this study showcases the designing of an entirely novel EVI calculator applicable to the aviation industry. Again, a modified version of this new calculator can be used anywhere—it is as easy to use as any other EVI calculator because of its telling visual appeal. Societies are vulnerable now with an expanded scale of action of man’s interaction with the environment, particularly man’s interaction in their working lives as they commute from place to place by road, land and air.

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