ARTIGO CIENTIFICO RUIDO EM AEROPORTO_JATM931

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Note
Analysis of airport noise exposure around Viracopos International
Airport using geographic information systems
Flavio Maldonado Bentes a,*, Tarcilene Aparecida Heleno b, Jules Ghislain Slama b
a Jorge Duprat Figueiredo Foundation for Occupational Health and Safety, Av. Presidente Antônio Carlos, Rio de Janeiro, Brazil
bAlberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering, Rio de Janeiro, Brazil
Keywords:
Viracopos airport
Airport noise
Brazilian airports
a b s t r a c t
This paper analyses airport noise exposure around Viracopos International Airport by quantifying the
proportion of highly annoyed people in surrounding zones using simulations, integrated noise models
and geographic information systems.
� 2012 Elsevier Ltd. All rights reserved.
1. Introduction
Airport noise is long standing and a major problem for
surrounding communities. The characteristics and impact of
aircraft noise vary but are generally influenced by factors such as
the number of flights, their timing, the type of aircraft, and the
flight path (Sancho and Senchermes, 1983). According to Grampella
(2012), the technological development has brought significant
improvements to reduce the noise of a single event but the noise
still remains a problem of major importance.
To counter the problem, governments, at both national and local
levels are imposing a variety of noise management measures
ranging from noise abatement procedures on the ground to limits
on the noise allowed by individual aircraft.
Airport noise generally has its origin in discrete events such as
a landing or takeoff, as well as the procedures of the aircraft on the
ground (Morais et al., 2008). The main sources of aircraft noise are
propulsion systems, which include engines and turbines, and also
the aerodynamic noise as a consequence of the structure being in
direct contact with air at high speed. Each aircraft component
considered as a noise source contributes significantly to the landing
or takeoff, and its intensity may vary according to the procedures
adopted; e.g. the runway used or immediate flight path. Morrell
and Lu (2007) also find that differences in aircraft operations,
engine types, emission rates and airport congestion are also influ-
encing the damage level. Aggregate noise nuisance is also influ-
enced by human (especially the number of residences near an
airport) and physical (the land contours around the airport)
geography.
Here we focus on quantifying highly annoyed populations
(HAP) at Viracopos International Airport (SBKP) in Brazil using
the INM1 software tools and geographical information systems
(GIS). The airport is selected because of the growing number of
passenger and cargo flights, many displaced from Congonhas
Airport (SBSP).
2. Noise exposure in communities
Following Schultz (1978), we consider the relationships
between noise levels, using the dayenight sound level (DNL)
metric, which has been adopted in many countries for airport
zoning, and the percentage of highly annoyed people. This metric is
based on the average sound energy produced by all aircraft
occurring events over 24 h. To the sound levels between 10 pm and
7 am, 10 dB (A) are added to reflect the greater sensitivity of indi-
viduals to noise during the night. Equation (1) describes the DNL
metric.
* Corresponding author.
E-mail addresses: flavio.bentes@fundacentro.gov.br, flavio.bentes@gmail.com
(F.M. Bentes).
1 INM is a computer model developed by the Federal Aviation Administration
(FAA) that evaluates aircraft noise impacts in the vicinity of airports. It is based on
an algorithm from the Society of Automotive Engineers e Aerospace Information
Report 1845 standard (Procedure for the Calculation of Airplane Noise in the
Vicinity of Airports).
Contents lists available at SciVerse ScienceDirect
Journal of Air Transport Management
journal homepage: www.elsevier .com/locate/ ja ir t raman
0969-6997/$ e see front matter � 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.jairtraman.2012.11.001
Journal of Air Transport Management 31 (2013) 15e17
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DNL ¼ 10 log
8percentage of the population, which is calculated
as a function of DNL, but also on the number of people in the
respective noise contours, identified through the use of Transcad.
Fig. 2 shows the values calculated.
We see in figure, that for the 60e65 range, the relative number
of HAP converges to the same value for all three calculation
methods. Although noise levels are higher in the latter ranges, there
is a lower noise exposure because there are restrictions on land use,
with controls over the building of schools, hospitals and homes,
according to Brazilian Civil Aviation Regulation 161. In 55e60 range,
the relative number of HAP found using the Schultz model is
considerably lower than for the others. This is due to controls over
night flights; night flight restrictions and curfews, night quotas, and
Table 1
Viracopos International Airport logistics data.
Airport site 17,659,300 m2
Aircraft site 86,978 m2
Runway dimensions 3240 � 45 m
Passenger capacity (per year) 6.8 million
Passenger terminal area 30,000 m2
Parking lot (number of vehicles) 2010 places
Number of check-in counters 72
Logistics terminal area of import and export cargo 81,000 m2
Aircraft parking positions 41 positions
Source: Infraero (2012).
Fig. 1. Viracopos International Airport noise curves. Source: Study Group in Airport
Noise e GERA (2012).
Table 2
Highly annoyed people for different noise ranges.
Band DNL
(dB(A))
Calculated
area (km2)
Identified
population
(people)
HAP for range
Schultz Fidell
et al.
Miedema
and Vos
1 55e60 24,075 30,919 204 1615 1794
2 60e65 10,032 13,617 603 1233 1799
3 65e70 4182 5677 649 890 1335
4 70e75 1656 1501 276 330 487
5 75e80 0.38 520 156 164 233
6 80e85 0.16 228 120 110 147
2 Transcad works in vector form, enabling the use of various layered files
provided by the Brazilian Institute of Geography and Statistics (2012) and can be
freely accessed.
F.M. Bentes et al. / Journal of Air Transport Management 31 (2013) 15e1716
Author's personal copy
noise charges and penalties that are embodied in different ways in
the three metrics.
5. Conclusions
This paper has quantified the people exposed to aircraft noise
around Viracopos International Airport using computer simulation
and GIS data. It has found that the size of the population that is
highly annoyed by noise around the airport is sensitive to the
metric of noise that is used, and in particular to the way that
nighttime air traffic noise is treated.
Acknowledgments
We would like to thank Alberto Luiz Coimbra Institute for
Graduate Studies and Research in Engineering (COPPE/UFRJ), the
National Council for Scientific and Technological Development
(CNPq), Foundation for Research Support in Rio de Janeiro
(Faperj) and Coordination for the Improvement of Higher
Education Personnel (Capes). Furthermore, we thank the Jorge
Duprat Figueiredo Foundation for Occupational Health and Safety
(Fundacentro), Laboratory of Acoustics and Vibration (LAVI) and
the Study Group in Airport Noise (GERA), that supported the
study.
References
Brazilian Institute of Geography and Statistics, 2012. Meshes of Brazilian Digital
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Fidell, S., Schultz, T.J., Green, D., 1988. A theoretical interpretation of the prevalence
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Grampella, M., 2012. Framework Definition to Assess Airport Noise and Aircraft
Emissions of Pollutant Based on Mathematical Models. PhD thesis, Università
degli studi di Milano e Bicocca, Milan.
Infraero, 2012. Viracopos International Airport (Campinas).
Miedema, H.M.E., Vos, H., 1988. Exposureeresponse relationships for transportation
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Morais, L.R., Slama, J.G., Mansur, W.J., 2008. Use of Acoustic Barriers to
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Madrid Architects, Madrid.
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Fig. 2. Relative number of highly annoyed people by noise ranges.
F.M. Bentes et al. / Journal of Air Transport Management 31 (2013) 15e17 17