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Conteúdo (article):

CYCLISTS\' NOISE EXPOSURE IN A BRAZILIAN MEDIUM-SIZED CITY

Thiago da Cunha Ramos; E-mail: thiagramos88@gmail.com Institution: Department of Transportation Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, SP, Brazil.
Antônio Nélson Rodrigues da Silva; E-mail: anelson@sc.usp.br Institution: Department of Transportation Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, SP, Brazil.
Léa Cristina Lucas de Souza; E-mail: leacrist.ufscar@gmail.com Institution: Department of Civil Engineering, Center of Exact Sciences and Technology, Federal University of São Carlos, São Carlos, SP, Brazil.
Luc Dekoninck; E-mail: luc.dekoninck@ugent.be Institution: Department of Information Technology, Ghent University, B-9000 Ghent, Belgium.
Dick Botteldooren; E-mail: Dick.Botteldooren@ugent.be Institution: Department of Information Technology, Ghent University, B-9000 Ghent, Belgium.
Inaian Pignatti Teixeira; E-mail: Inaianteixeira@hotmail.com Institution: Department of Transportation Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, SP, Brazil.





Abstract
This study aimed to assess cyclists’ exposure to noise in a medium-sized Brazilian city. Mobile sensors were used to conduct noise measurements in streets with and without dedicated cycling infrastructures. The method can be summarized in the following procedures: i) characterization of the study area; ii) data collection and validation; iii) calculation of exposure indicators; and iv) comparison and representation of the results on maps. Two strategies were adopted for the analysis, namely, spatial data aggregation and temporal data aggregation. Thus, measurements were initially organized in 1,200 nodes distributed along the paths. The results indicate that bicycle riders in São Carlos may be exposed, in some routes, to a high proportion of high noise-level segments. In the two routes selected for this study, the cyclist was exposed to noise levels above the adopted threshold (> 75dBA) in 33.2% and 18.9% of the nodes. Also, the possibility of simultaneously working with two related indicators has broadened the classification criteria of the route segments regarding noise exposure.

Keywords: noise; noise, transportation; noise measurement.


INTRODUCTION
The city growth process is usually associated with increasing noise levels. Higher population densities and urban sprawl are among the common changes observed in growing urbanized areas in the last decades1-3. Therefore, additional trips were required and the demand for fast and efficient trips increased3. Motorized vehicles massively occupied the cities4-8 with the popularization of transport technologies. Gradually, the movements of people and goods became noisy and harmful9, and noise levels above a healthy threshold for human beings became usual10.
An alternative to the high volumes of motorized vehicles and a tool for the improvement of well-being and health11-13, cycling is currently promoted in various cities. However, the initiative seems to ignore that cyclists and pedestrians are increasingly exposed to air pollution and noise generated by vehicles operating with internal combustion engines14-16 in the same street network.
Nevertheless, noise exposure-related problems are gaining importance in health studies. Associations with other health effects, mainly those derived from long-term exposure to traffic noise, replaced the isolated perspective of hearing loss. Noise produces stress17, which is likely to interfere with sleep18; 19 and affects the neuroendocrinological systems20. This may result in various problems, such as difficulty in reproduction21, cognition22, blood pressure issues23; 24, myocardial infarction25; 26, and even type 2 diabetes27 and premature mortality28.
The effects of noise pollution have been compared to the effects of air pollution29; 30. Various studies have analyzed noise alone or in combination with air pollution, such as those conducted in eleven Dutch cities31, in Gent, Belgium32, Bangalore, India33 or in Montreal, Canada34. New noise measurement strategies were applied in some of these studies, such as the devices for mobile data collection campaigns conducted in Gent35, and communication tools, such as mobile phones used in Cambridge, UK36.
In Brazil, noise studies are usually conducted in large and medium-sized cities, based on noise maps of residential neighborhoods, city centers and commercial areas2; 37-40. Other studies compared the efficiency of different measurement times in fixed points41, focused on the noise resulting from the interaction between tires and pavement42, or analyzed, in nearby neighborhoods, the impact of noise produced at airports and lumber facilities, and by road traffic43-48. On the other hand, studies focusing on the sound landscape, which can be understood as the acoustic environment perceived and experienced by an individual in a particular context49, indicate that an intense noise level is not always a disturbance to the receptor. It depends on the relationship between the individual, the activity and the place, space and time.
In that context, this study aimed to assess cyclists’ exposure to noise in a medium-sized Brazilian city. Mobile sensors were employed to conduct noise measurements in streets with and without dedicated cycling infrastructures. As the routes analyzed can be used by cyclists for commuting trips, the analysis considered the trip as part of the daily working journey.

METHOD
The method used to assess cyclists’ exposure to noise can be summarized as follows: i) characterization of the study area; ii) data collection and validation; iii) calculation of exposure indicators; and iv) comparison of indicators’ values and representation of the results on maps.

Characterization of the Study Area
São Carlos has an estimated population of 244,000 inhabitants50 and is a medium-sized city (according to Brazilian standards) located in the state of São Paulo. The implementation of cycleways and cycle paths in the city started in 2012, but the proposed cycle network is still incomplete. Therefore, this study has considered two routes (hereafter referred as Route 1 and Route 2) that contain parts of the existing cycling paths and parts of the road network that are regularly used by cyclists (Figure 1).

**** Insert Figure 1 ******
Figure 1. Cycling routes (with the identification of routes and segments) used for noise data collection campaigns in the city of São Carlos - SP.

Route 1 includes four separated cycling infrastructures, all located in areas of environmental interest due to the presence of water bodies. This route also contains links of the street network that are potentially attractive for regular bicycle trips. These are mainly located in the central part of the city, which is an area with historical interest. The route contains 12 segments, all named with letters and numbers (from 1A to 12A, in which the numbers indicate sequential positions in the data collection trips and the letters indicate the locations of the segments, as shown in Figure 1).
The definition of Route 2 was primarily based on the actual demand for cycling trips. It is also located in the central part of the city, but in an area without cycleways or cycle paths. It contains two segments, which were named as A and B, as shown in Figure 1.
In summary, the selected routes are located in a mixed land use zone served with good road infrastructure. They are located in a consolidated part of the city, according to the city master plan51.
Other relevant information for this study is that more than half of Route 1 is located in wide streets, close to green areas and rivers. In contrast, Route 2 follows narrower streets, along which the buildings have relatively high façades.

Data Collection and Validation
The data collection campaigns used a mobile device developed by the INTEC-Acoustics research team at Gent University, in Belgium, to record noise data. The device automatically collects noise data at every 1 second, along with the geographic coordinates associated with the noise records obtained during the displacements.
A regular bicycle was adapted to carry the mobile sensor, in such a way that the sensor was positioned in front of the cyclist. With that arrangement, the noise captured by the sensor was essentially the surrounding traffic noise and not the noise generated by the bicycle itself. That was also the reason why the basket containing the sensor was internally protected with layers of soft material to reduce the empty spaces and the impacts of vibration (Figure 2).

**** Insert Figure 2 ******
Figure 2. Noise sensor (left) and its position in the bicycle (right) used for data collection campaigns in the city of São Carlos - SP.

A maximum speed of 18 km/h was also observed during the campaigns in order to reduce the aerodynamic noise.
Campaigns were conducted in morning and evening traffic peak hours (between 7h30 and 8h30 and between 17h30 and 18h30, respectively) in typical weekdays (Tuesdays, Wednesdays, and Thursdays). After the trips, the dataset was carefully examined to confirm whether the geographic coordinates, the time stamps and the noise levels recorded were all within reasonable ranges. This validation process was performed to verify whether the calibration of the sensor was not strongly affected by the natural movement and vibration that occurred during the data collection trips. Another essential aspect observed was the evidence of problems in the GPS data, which could eventually misplace the actual geographic coordinates.
We have conducted fifteen data collection campaigns, but only eight of them were considered valid for the study. In total, 26,914 valid registered records were obtained in Route 1 (22,221 records in five trips) and Route 2 (4,693 records in three trips). The Ethics Committee approved the research at the School of Arts, Sciences, and Humanities, University of São Paulo (under registration number 2.323.525).

Calculation of Indicators
Two indicators were selected for data analysis: the Sound Exposure Level (SEL) and the equivalent continuous sound level (LAeq), which can be described by equations 1 and 2, respectively.
(Equation 1)
where:
SEL - Sound Exposure Level in dBA
l1 - the first sound level measurement, in dBA
ln - the last sound level measurement, in dBA

(Equation 2)
LAeq - the equivalent continuous sound level, in dBA
SEL - Sound Exposure Level in dBA
T - the moment when the exposure ends
T0 - the moment when the exposure begins

In the first part of the analysis, the calculation of the indicator LAeq was simply a spatial combination of reference points that worked as aggregation nodes. The aggregation process was based on the logarithmic mean of the LAeq values registered within a 20 meters’ radius of each node. The process resulted in 1,200 nodes, 926 of which were along the 12 segments of Route 1 and 274 along the two segments of Route 2. These aggregation nodes were sequentially numbered from 1 to 1,200.
In the second part of the analysis, the values of the indicators were calculated by the temporal aggregation of data continuously collected in three distinct time intervals: 5 seconds (SEL5s and LAeq,5s), 3 seconds (SEL3s and LAeq,3s) and 2 seconds (SEL2s and LAeq,2s).

Comparison and Representation of Results on Maps
The analysis of spatially aggregated data made possible to identify the number of aggregation nodes with values above 75 and 85 dBA, which are thresholds of moderate and harmful noise levels, respectively, as suggested by the literature52. This can be performed individually for each trip and the results of all trips of each route combined to find the proportions of moderate and harmful noise per route segment.
In the analysis of temporally aggregated data, we verified the number of times the indicators were above the threshold of harmful noise (i.e., 85 dBA) in each segment. The results of both comparisons were subsequently plotted on a map with the mean percentages of the indicators values on both routes.

RESULTS
The presentation of the results was organized according to the aggregation strategies adopted for analysis, namely, spatial and temporal aggregation.

Analysis of Spatially Aggregated Data
The results obtained with spatial aggregation of data (Table 1) show that the cyclist was exposed to moderate noise levels (> 75 dBA) in 33.2% of the nodes laid out along Route 1, whereas the same condition was observed in only 18.9% of Route 2 nodes. The exposure to harmful noise levels (> 85 dBA) occurred in 1.0 and 1.7% of the nodes of Route 1 and 2, respectively. In other words, the cyclist was exposed to acceptable noise levels (< 75 dBA) in 66.8% and 81.1% of the nodes of Route 1 and 2, respectively.
Table 1
It is not difficult to find in Table 1 a repetition of nodes with moderate and harmful noise levels in different days. This suggests the existence of spots with specific and recurrent problems. In one of the crossings of Segment 1A, for example, relatively high speeds were observed in the ascending direction of the street. This often resulted in sudden braking and acceleration maneuvers of motorized vehicles, which increased the traffic noise levels to which cyclists were exposed. The first two nodes with issues in segment 3C were close to a crossing with traffic lights located right at the beginning of a long street with a speed limit of 60 km/h. In that case, the green light of the traffic signal also resulted in sudden and noisy acceleration maneuvers of motorized vehicles. The other two nodes of segment 3C were close to a roundabout with multiple entry points, which required frequent braking and acceleration maneuvers of the approaching motorized vehicles. The noise problems found in segment 8E were related to the street ascending gradient of approximately 12%, the proximity of a bus stop and a high concentration of motorized vehicles. In the case of segment 10C, the noise was mainly produced by motorized vehicles that sped up due to the considerable distance between the two successive crossings.
In Route 2, six nodes had a large number of records with moderate or harmful noise levels. Only two nodes of Segment B, which is an essential street for motorized traffic in the city of São Carlos, had three records with harmful noise levels. These two nodes with three records and two other with two records were all located in a block where the street is relatively narrow and bounded by a high factory wall. They were also close to a bus stop. The harmful noise levels observed in the two nodes of segment A were mostly caused by braking and acceleration maneuvers of motorized vehicles close to street crossings.
Lastly, it is important to mention that the mean percentages of LAeq for the three time intervals considered remained practically stable in both segments of this route.

Analysis of Temporally Aggregated Data
Besides the spatial distribution of noise, we also attempted to analyze how long the cyclist was exposed to inadequate noise levels. The relationship between temporal indicators (SEL5s, SEL3s, SEL2s - blue lines) and spatial indicators (LAeq,5s, LAeq,3s e LAeq,2s - red lines) can be seen in the data displayed in Figure 3. The example shown in Figure 3 makes clear that the larger the time interval (in that case, 5 seconds), the higher the SEL values. All accumulated exposure levels (SELseconds) show, regardless of the time interval considered, that long time exposures were determinant in the identification of route segments with harmful noise levels. This is not so evident if the analysis is performed only with LAeq seconds.
For a better visualization of the results and comparison of the segments, Figure 4 contains a map in which the mean values of the indicators (µ%) are shown. Among the SEL¬seconds values, only those of SEL2s are shown on the map, because these values are sufficient to highlight the segments with high noise exposure levels. However, in the case of LAeq seconds, we decided to show the mean percentages for all three time intervals considered.
Regarding Route 1, both indicators (SEL and LAeq seconds) highlighted the segments with the highest or lowest noise levels. Segments 4D and 9D were the segments in which the cyclist had the lowest noise exposure, whereas segments 3C and 10C were the segments with the highest noise exposure levels.
Concerning Route 2, the SEL indicator was very effective to highlight segment B as the segment in which the cyclist had the highest noise exposure in all time intervals considered. The local conditions, in which the narrow street was combined with a high wall and a nearby bus stop, may explain the duration of the noise and the 10 percentage points difference between the SEL values found in segments A and B.

**** Insert Figure 3 ******
Figure 3. Distribution of SEL and LAeq values for different time intervals.

**** Insert Figure 4 ******
Figure 4. Nodes with harmful or moderate noise levels and mean percentages of the indicators obtained in both routes analyzed in São Carlos, SP, Brazil.

Segment 3C had the largest number of nodes above the harmful noise level threshold (see Table 1), although the mean values of SEL and LAeq seconds were lower than those found in segment 10C. The latter is the segment in which the cyclist was exposed to the highest noise exposure levels, with mean percentages of 51% for SEL and 29% for LAeq seconds, as shown in Figure 4.
In contrast, Route 1 segments 4D and 9D, which share the same nodes in both directions, were the least hostile segments. Served by a cycleway separated from vehicular traffic and surrounded by greenery and a lovely landscape, those were the segments with the lowest mean LAeq seconds and SEL2s values. The slight variation between the values found in those segments can be explained by the different concentrations of motorized traffic during the data collection campaigns.

DISCUSSION
Traffic noise is not only annoying, but it also affects the overall well-being of individuals. The results of this study indicate that cyclists riding a bicycle in some parts of the city of São Carlos may be exposed to high noise levels. We confirmed that the assessment of noise exposure over time is as essential as the instantaneous exposure, which can be characterized by LAeq, seconds, for the classification of noise exposure. The louder and more prolonged the noise, the more harmful it is to humans. It is important to mention that the problems associated with noise exposure transcend hearing problems53. Studies suggest an association of noise exposure with hypertension and other cardiovascular diseases54,55, in addition to mental health issues, such as low concentration capacity, aggressive behavior, and high stress levels56, among others.
It is important to mention that the adverse effects of noise on health do not occur only after prolonged exposure. Acute effects, such as high systolic and diastolic arterial pressure, heart rate changes, and stress hormones release can be observed even after a single event57. Nevertheless, chronic harm, such as increased risk factors for arterial hypertension, dyslipidemia, changes in the blood viscosity and glucose level 57,58 tend to be more systemic and complex.
Furthermore, it is worth mentioning that the source of this noise also influences the health outcome since, while prolonged exposure to noise is harmful to health, occupational noise risk estimates tend to be higher than those for urban sound pollution59.
In large cities, a significant share of noise pollution is related to how people move around the city, such as an undesirable output of motorized vehicles (e.g., horns, braking, and acceleration). Thus, it seems reasonable to assume that the replacement of motorized trips by active modes trips would have a positive effect on noise emissions. However, right now, we observe that users of those modes are still vulnerable to air and noise pollution.
Rabl and Nazelle60 have estimated the impact of replacing cars with active transport means. Assuming 5 km/day, five days per week and a relative cost to the health of 0.76 Euros per kilometer, the replacement would produce an annual benefit of 1,800 Euros per person as a consequence of lower noise pollution.

Strengths and Limitations of the Study
The main advantage of mobile data collection campaigns is their low cost, given that a single person riding a bicycle can instantaneously record sound pressure levels occurring during the trip. In general, as the equipment and the cyclists are exposed nearly to the same conditions along the way, mobile measurements can be an improvement in the assessment of the noise exposure of bicycle users. Also, the use of two related indicators increased the criteria for segments classification regarding noise. The mean percentages of LAeq seconds served as a simple and clear classification tool. However, the SELseconds indicator was helpful to distinguish segments that only apparently had similar situations.
Mobile campaigns for data collection also have a negative aspect. One cannot separate the noise coming from the adjacent vehicular traffic from the noise produced by other sources, such as the aerodynamic noise and other noises produced by the cyclist or the bicycle itself.

CONCLUSIONS
The use of mobile sensors for noise measurements has been adequate and compatible with the context of dynamic exposure of cyclists, which vary in time and space. The collection of data at every one second has been a practical and feasible procedure for the following data aggregation in nodes or time intervals and further analysis of accumulated noise exposure levels or equivalent continuous sound levels.
Although we have found high noise levels in all segments, the application of the method in two distinct routes facilitated the identification of cyclist exposure in different parts of the urban street network. We also observed that the implementation of cycling infrastructures in streets with wide open spaces did not result in reduced noise exposure for the cyclist, contrary to what was initially expected. This was the condition of a segment of Route 1, which is located in a large avenue with a segregated cycleway along a river valley. In that case, the high-speed limit of motorized traffic resulted in high noise levels.

ACKNOWLEDGMENTS
This research was supported by FAPESP (Grant 2015/50129-5) and CNPq - National Council for Scientific and Technological Development (Grant 308436/2015-6). This study was also financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

REFERENCES
1. Maia MAL. Contribuição ao mapeamento do ruído urbano na cidade de Porto Alegre - RS [MSc thesis]. Porto Alegre: UFRGS; 2003.
2. Ramos JOP. Dinâmica urbana na cidade de São Paulo o desafio do desenho das soluções acústicas [MSc thesis]. São Paulo: USP; 2007.
3. Balzan KL. Avaliação do ruído de tráfego veicular em área central de Chapecó - SC [MSc thesis]. Santa Maria: UFSM; 2011.
4. Ouis D. Annoyance from road traffic noise: a review. J Environ Psy 2001; 21(1):101-120.
5. Piccolo A, Plutino, D Cannistraro, G. Evaluation and analysis of the environmental noise of Messina, Italy. Appl Acoust 2005; 66(4):447-465.
6. Singal, SP. Noise pollution and control strategy. Oxford - RU: Alpha Science International, 2005.
7. Rajakumara HN, Mahalinge Gowda RM. Road traffic noise prediction models: a review. Int J Sust Develop Plan 2008; 3(3):257-271.
8. Souza Filho JJ. Avaliação do ruído urbano na cidade de Campo Grande/MS [MSc thesis]. Campo Grande: UFMS; 2012.
9. Rodrigues F. Análise de ruído em terminais de transportes coletivos urbanos: desenvolvimento de modelos de previsão [MSc thesis]. Uberlândia: UFU; 2006.
10. Bortoli PS. Análise da poluição sonora urbana em zoneamentos distintos da cidade de Curitiba [MSc thesis]. Curitiba: CEFET-PR; 2002.
11. Hamer M, Chida Y. Active commuting and cardiovascular risk: a meta-analytic review. Prev Med 2008; 46(1):9-13.
12. Gordon-Larsen P, Boone-Heinonen J, Sidney S, Sternfeld B, Jacobs DR, Lewis CE. Active commuting and cardiovascular disease risk: the CARDIA study. Arc Int Med 2009; 169(13):1216-1223.
13. Hatzopoulou M, Weichenthal S, Dugum H, Pickett G, Miranda-Moreno L, Kulka R, Andersen R, Goldberg M. The impact of traffic volume, composition, and road geometry on personal air pollution exposures among cyclists in Montreal, Canada. J Exp Sci Environ Epidem 2013; 23(1):46-51.
14. Pucher J, Dijkstra L. Promoting safe walking and cycling to improve public health: lessons from the Netherlands and Germany. Amer Jour Pub Heal 2003; 93(9):1509-1516.
15. Elvik R. The non-linearity of risk and the promotion of environmentally sustainable transport. Acc Anal & Prev 2009; 41(4):849-855.
16. De Hartog JJ, Boogaard H, Nijland H, Hoek G. Do the health benefits of cycling outweigh the risks? Envir Heal Persp 2010; 118(8):1109-1116.
17. Michaud DS, Keith SE, MCMURCHY D. Annoyance and disturbance of daily activities from road traffic noise in Canada. J Acoust Soc Amer 2008; 123(2):784-792.
18. Omlin S, Bauer GF, Brink M. Effects of noise from non-traffic-related ambient sources on sleep: Review of the literature of 1990 - 2010. Noise Health 2011; 13(53):299-309.
19. Laszlo H, Mcrobie E, Stansfeld S, Hansell A. Annoyance and other reaction measures to changes in noise exposure - a review. Sci Total Environ 2012; 435-436: 551-562.
20. Ising H, Kruppa B. Health effects caused by noise: evidence in the literature from the past 25 years. Noise Health 2004; 6(22):5-13.
21. Ristovska G, Laszlo HE, Hansell AL. Reproductive outcomes associated with noise exposure - a systematic review of the literature. Int J Envir R Public Health 2014; 11(8):7931-7952.
22. Basner M, Babisch W, Davis A, Brink M, Clark C, Janssen S, Stanfeld S. Auditory and non-auditory effects of noise on health. Lancet 2014; 383(9925):1325-1332.
23. Babisch W, Wolf K, Petz M, Heinrich J, Cyris J, Peters A. Associations between traffic noise, particulate air pollution, hypertension, and isolated systolic hypertension in adults: the KORA study. Envir Heal Perspect 2014; 122(5):492.
24. Paunović K, Stanfeld S, Clark C, Belojević G. Epidemological studies on noise and blood pressure in children: observations and suggestions. Environ Int 2011; 37(5):1030-1041.
25. Peters A, Von Klot S, Heier M, Trentinaglia I, Hormann A, Wichmann HE, Lowel H. Exposure to traffic and the onset of myocardial infarction. N Engl J Med 2004; 351(17):1721-1730.
26. Van Kempen E, Babisch W. The quantitative relationship between road traffic noise and hypertension: a meta-analysis. J Hypertens 2012; 30(6):1075-1086.
27. Dzhambov AM. Long term noise exposure and the risk for type 2 diabetes: a meta-analysis. Noise Health 2015; 17(74):23-33.
28. Halonen JI, Hansell AL, Gulliver J, Morley D, Blangiardo M, Fecht D, Toledano MB, Beevers SD, Anderson HR, Kelly FJ. Road traffic noise is associated with increased cardiovascular morbidity and mortality and all-cause mortality in London. Eur Heart J 2015; 36(39):2653-2661.
29. Fritschi L, Brown L, Kim R, Schwela D, Kephalopolous S. Burden of disease from environmental noise - quantification of healthy life years lost in Europe. World Health Organization, 2011.
30. Hänninen O, Knol AB, Jantunen M, Lim TA, Conrad A, Rappolder M, Carrer P, Fanetti AC, Kim R, Buekers, J. Environmental burden of disease in Europe: assessing nine risk factors in six countries. Environ Heal Perspect 2014; 122(5):439-446.
31. Boogaard H, Borgman F, Kamminga J, Hoek G. Exposure to ultrafine and fine particles and noise during cycling and driving in 11 Dutch cities. Atmospheric Environ 2009; 43(27):4234-4242.
32. Dekoninck L, Botteldooren D, Panis LI. An instantaneous spatiotemporal model to predict a bicyclist\'s black carbon exposure based on mobile noise measurements. Atmospheric Environ 2013; 79(2013):623-631.
33. Dekoninck L, Botteldooren D, Panis LI, Hankey S, Jain G, Karthik S, Marshall J. Applicability of a noise-based model to estimate in-traffic exposure to black carbon and particle number concentrations in different cultures. Envir Int 2015; 74(2015):89-98.
34. Apparicio P, Carrier M, Gelb J, Séguin AM, Kingham S. Cyclists’ exposure to air pollution and road traffic noise in central city neighbourhoods of Montreal. J Trans Geog 2016; 57(2016):63-69.
35. Can A, Leclerq L, Lelong J. Dynamic estimation of urban traffic noise: influence of traffic and noise source representation. Appl Acoust 2008; 69(10):858-867.
36. Kanjo E. Noisespy: a real-time mobile phone platform for urban noise monitoring and mapping. Mob Net Appl 2010; 15(4):562-574.
37. Guedes ICM. Influência da forma urbana em ambiente sonoro: um estudo no bairro Jardins em Aracajú (SE) [MSc thesis]. Campinas: Unicamp; 2005.
38. Fritsch RC. Avaliação do ruído urbano: o caso da área central de Passo Fundo - RS [MSc thesis]. Passo Fundo: UPF; 2006.
39. Mardones MDM. Mapeamento dos níveis de ruído em Copacabana, Rio de Janeiro, através de simulação computacional [MSc thesis]. Rio de Janeiro: UFRJ; 2009.
40. Suriano MT, Souza LCL, Rodrigues da Silva, NA. Ferramenta de apoio à decisão para o controle da poluição sonora urbana. Ciên Saúde Col 2015; 20(7):2201-2210.
41. Mendonça ABD. Relações entre índices urbanísticos da forma urbana e a acústica ambiental [MSc thesis]. São Carlos: UFSCar; 2013.
42. Cabral MFF, Albuquerque FS, Neto OF, Albuquerque TMA. Estudo dos mecanismos de geração de ruído de tráfego na interface pneu-pavimento. Transportes 2014; 22(1):1-20.
43. Nabinger LB. Medições de ruído aeronáutico dentro da área II do plano específico de zoneamento de ruído do aeroporto Salgado Filho, Porto Alegre/RS [MSc thesis]. Porto Alegre: UFRGS; 2005.
44. Madruga JFS. Impacto sonoro das atividades madeireiras na qualidade de vida da população do bairro da Torre, João Pessoa, PB [MSc thesis]. João Pessoa: UFPB; 2008.
45. Lessa BPV. Avaliação de poluição sonora em assentamento informal - estudo de caso comunidade Santa Marta - RJ [MSc thesis]. Rio de Janeiro: UFRJ; 2012.
46. Fiedler PEK. Poluição sonora nos eixos estruturais de transporte da cidade de Curitiba - PR [MSc thesis]. Curitiba: UFPR; 2013.
47. Jesus ADS. Impacto ambiental sonoro sobre a cidade de Águas Claras, Brasília-DF, com a construção da linha verde [MSc thesis]. Brasília: UnB; 2013.
48. Hecht VCES. Análise da incerteza na medição dos níveis de ruído aeronáuticos [TCC]. Rio de Janeiro: UFRJ; 2014.
49. International Organization for Standardization, ISO 12913-1:2014 Acoustics – Soundscape - Part 1: Definition and conceptual framework, ISO, Genebra, Suíça, 2014
50. Instituto Brasileiro de Geografia e Estatística (IBGE). IBGE - Cidades @. [Acesso em: 07 jan. 2017]. Disponível em: http://www.ibge.gov.br/cidadesat/topwindow.htm?1.
51. São Carlos. Plano Diretor. Lei n.º 13.691 de 25 de novembro de 2005.
52. Bistafa SR. Acústica aplicada ao controle de ruído. 2ª ed. São Paulo - SP: Blucher; 2011.
53. Berglund B, Lindvall T, Schwela D. Guidelines for Community Noise - World Health Organization. [Acesso em: 07 jan. 2017]. Disponível em: http://www.who.int/docstore/peh/noise/Comnoise-3.pdf.
54. Babisch W, Beule B, Schust M, Kersten N, Ising H. Traffic noise and risk of myocardial infarction. Epidemiology 2005; 16(1):33–40.
55. Van Kempen E, Kruize H, Bashuizen HC, Ameling CB, Ataatsen BA, De Hollander AE. The association between noise exposure and blood pressure and ischemic heart disease: A meta-analysis. Environ Health Perspect 2002; 110(3):307–17.
56. Stansfeld S, Matheson M. Noise pollution: non-auditory effects on health. Br Med Bull 2003; 68(1):243–57.
57. Babisch W. Cardiovascular effects of noise. In: NRIAGU JO, ed. Encyclopedia of Environmental Health. Burlington: Elsevier; 2011. p. 532–42
58. Babisch W. Cardiovascular effects of noise. Editorial. Noise Health 2011; 13(52):201–04.
59. Basner M, Babisch W, Davis D, Brink M, Clark C, Janssen S, Stansfeld S Auditory and non-auditory effects of noise on health. Lancet 2014; 383(9925):1325–32.
60. Rabl A, De Nazelle A. Benefits of shift from car to active transport. Transport Policy 2012; 19(1):121–31.