Transportation planning and quiet natural areas preservation: Aircraft overflights noise assessment in a National Park

Iglesias-Merchán, Carlos; Dı́az-Balteiro, Luis; Soliño, Mario

doi:10.1016/j.trd.2015.09.006

Cited by 84 publications

(50 citation statements)

References 64 publications

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“…These results suggest that, notwithstanding the potential safety issues, the development of corridors along busy roads for drone fleets to operate might reduce the overall community noise impact in urban areas. This will also avoid the disturbance of (urban) quiet areas (Iglesias-Merchan et al, 2015). Figure 15.…”

Section: Combined Effects Of Road Traffic and Drone Noisementioning

confidence: 99%

Effects of a hovering unmanned aerial vehicle on urban soundscapes perception

Torija

Self

2020

Transportation Research Part D: Transport and Environment

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Several industry leaders and governmental agencies are currently investigating the use of Unmanned Aerial Vehicles (UAVs), or 'drones' as commonly known, for an ever-growing number of applications from blue light services to parcel delivery. For the specific case of the delivery sector, drones can alleviate road space usage and also lead to reductions in CO2 and air pollution emissions, compared to traditional diesel-powered vehicles. However, due to their unconventional acoustic characteristics and operational manoeuvres, it is uncertain how communities will respond to drone operations. Noise has been suggested as a major barrier to public acceptance of drone operations in urban areas. In this paper, a series of audio-visual scenarios were created to investigate the effects of drone noise on the reported loudness, annoyance and pleasantness of seven different types of urban soundscapes. In soundscapes highly impacted by road traffic noise, the presence of drone noise lead to small changes in the perceived loudness, annoyance and pleasantness. In soundscapes with reduced road traffic noise, the participants reported a significantly higher perceived loudness and annoyance and a lower pleasantness with the presence of the same drone noise. For instance, the reported annoyance increased from 2.3±0.8 (without drone noise) to 6.8±0.3 (with drone noise), in an 11-point scale (0-not at all, 10-extremely). Based on these results, the concentration of drone operations along flight paths through busy roads might aid in the mitigation of the overall community noise impact caused by drones.

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Section: Combined Effects Of Road Traffic and Drone Noisementioning

confidence: 99%

Effects of a hovering unmanned aerial vehicle on urban soundscapes perception

Torija

Self

2020

Transportation Research Part D: Transport and Environment

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“…Let us discuss next the way in which the absolute level Leq a τ (t) at time t for an arbitrary location point a can be obtained from the measured values of δ τ g (t) using either Equation (14) or Equation (16). The first quantity we need to know is the value of L eqref(g,a) at the point a due to the noise produced by roads in the group g, which is provided by the calculated (CADNA) acoustic base map.…”

Section: Leq At Time T For Arbitrary Receiver Point A: Leq a τ (T)mentioning

confidence: 99%

“…In this context, the END's prescription for noise maps and action plans, impairment [5][6][7], and hypertension ischemic heart disease [8][9][10]. In this context, the END's prescription for noise maps and action plans, have prompted many efforts by the member states and the scientific community to high level study and propose new mitigation systems for the main sources of noise: road traffic [11,12], railway traffic [13,14], airports [15,16], industrial centers [17,18], plus other sources recognized as highly annoying [19,20]. In particular, mitigation measures in urban context need to be identified according to a realistic picture of noise distribution over urban areas.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Prediction of Traffic Noise in Large Urban Areas

et al. 2018

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Featured Application: The method discussed in this paper has applications in the context of predicting traffic noise in large urban environments. The system designed by the authors provides an accurate description of traffic noise by relying on measurements of road noise from few monitoring stations appropriately distributed over the zone of interest. A prescription is given of how to choose the location of the noise stations.Abstract: Dynamap, a co-financed project by the European Commission through the Life+ 2013 program, aims at developing a dynamic approach for noise mapping that is capable of updating environmental noise levels through a direct link with a limited number of noise monitoring terminals. Dynamap is based on the idea of finding a suitable set of roads that display similar traffic noise behavior (temporal noise profile over an entire day) so that one can group them together into a single noise map. Each map thus represents a group of road stretches whose traffic noise will be updated periodically, typically every five minutes during daily hours and every hour during night. The information regarding traffic noise will be taken continuously from a small number of monitoring stations (typically 24) appropriately distributed over the urban zone of interest. To achieve this goal, we have performed a detailed analysis of traffic noise data, recorded every second from 93 monitoring stations randomly distributed over the entire urban area of the City of Milan. Our results are presented for a restricted area, the urban Zone 9 of Milan. We have separated the entire set of (about 2000) stretches into six groups, each one represented by a noise map, and gave a prescription for the locations of the future 24 monitoring stations. From our analysis, it is estimated that the mean overall error for each group of stretches (noise map), averaged over the 24 h, is about 2 dB.

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“…Annoyance is a human feeling of displeasure that typically includes anger, disappointment, anxiety and depression [21,22]. Nighttime noise causes interrupted sleep of residents in a community, and disturbed sleep could lead to fatigue, depression, and decreased performance, among others [21,23,24].In this context, since the END's prescription for noise maps and action plans, the scientific community has made many efforts-from high-level studies to proposed mitigation systems-to curb for the following sources of noise in urban areas: road-traffic [25,26], railway traffic [27,28], airport [29,30], and industrial [31,32]. In addition, country areas (outside urban borders) have a new noise source-wind turbines-that are large in number and have been studied to be highly annoying [33,34].Transportation is a major source of community noise, particularly in modern urban environments of many nations, where dense traffic systems are well developed [22,[35][36][37][38][39].…”

mentioning

confidence: 99%

“…In this context, since the END's prescription for noise maps and action plans, the scientific community has made many efforts-from high-level studies to proposed mitigation systems-to curb for the following sources of noise in urban areas: road-traffic [25,26], railway traffic [27,28], airport [29,30], and industrial [31,32]. In addition, country areas (outside urban borders) have a new noise source-wind turbines-that are large in number and have been studied to be highly annoying [33,34].…”

mentioning

confidence: 99%

The Public Health Impact of Road-Traffic Noise in a Highly-Populated City, Republic of Korea: Annoyance and Sleep Disturbance

et al. 2018

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Sustainable transportation is an essential part of a sustainable city; however, modern transportation systems with internal-combustion engines emits unacceptably high level of air-pollutants and noise. It is recognized widely that road-traffic noise has negative health impacts (such as annoyance and sleep disturbance) on exposed population in highly-populated cities. These harmful effects should be removed or at least reduced to guarantee the sustainability of modern cities. The estimation of pollutant levels at a specific location and the extent of the damage is therefore important for policy makers. This study presents a procedure to determine the levels of road-traffic noise at both day and night, and an assessment of the adverse health effects across Gwangju Metropolitan City (GMC), Republic of Korea (ROK). Road-traffic noise maps in 2-D and 3-D were generated, in order to find spatial distribution of noise levels across the city and noise level at the façade of a building-floor, respectively. The adoption of existing assessment models for the highly-annoyed (%HA) and highly-sleep-disturbed (%HSD) leads to building-based estimation of the affected population and spatial distribution of the road networks of the city. Very high noise levels were found to exist along major roads in the day and at night, with little difference between them. As a result, approximately 10% and 5% of the total population (n = 1,471,944) were estimated to experience high-level annoyance and sleep disturbance, respectively.

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Transportation planning and quiet natural areas preservation: Aircraft overflights noise assessment in a National Park

Cited by 84 publications

References 64 publications

Effects of a hovering unmanned aerial vehicle on urban soundscapes perception

Effects of a hovering unmanned aerial vehicle on urban soundscapes perception

Monitoring and Prediction of Traffic Noise in Large Urban Areas

The Public Health Impact of Road-Traffic Noise in a Highly-Populated City, Republic of Korea: Annoyance and Sleep Disturbance

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