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What To Do About Environmental Noise?
in Chittenden County, Vermont (Kaliski et al., 2007). Their work found that 30% of residents were exposed to road traf- fic noise levels above 45-dB(A) Leq despite Chittenden be- ing considered a rural county in US terms. More recently, Seong et al. (2011) undertook road noise mapping for Ful- ton County, Georgia, including noise mapping of down- town Atlanta. Their estimates of population exposure from the construction of noise maps found that 48% of the resi- dent population was exposed to noise levels of 55 dB(A) or higher during daytime, with 32% exposed to 50 dB(A) or higher during nighttime. Recent work by King et al. (2014) utilized the noise-mapping process to create a noise map of the city of Hartford, CT, while somewhat related work uti- lized smartphone noise apps to create a strategic noise map of West Hartford, CT (Murphy and King, 2016a). Larger cit- ies such as San Francisco have also mapped the city’s traf- fic noise levels (Hammer et al., 2014). All of the foregoing demonstrates the possibility of utilizing the EU approach for undertaking strategic noise mapping in US cities.
In the United States, the TNM is the FHWA-accepted calcu- lation method for predicting noise from active highways. It is packaged in the form of an approved computer program, and it is only this program that is validated for use in the United States by the FHWA. Other computer applications can be used to predict noise levels near highways but only if the FHWA has determined that it is consistent with the methodology of the TNM. The TNM was developed pri- marily to assess the impact of highways and was not meant to be applied to the mapping of complex city environments involving a grid of many receiver points with more reflec- tions and diffraction than would typically be experienced near highways. Because of this, it has not been widely used for noise mapping. In fact, King et al. (2014) assessed the ap- plicability of TNM to mapping studies and found that, in its current form, it is unsuitable for the development of urban noise maps. If the United States is to develop a noise-map- ping program in the future, this is a clear obstacle that will need to be addressed as a priority.
The Role of Technology
It is important also to consider the role that smart technolo- gy will have in the future monitoring of noise pollution, par-
ticularly in cities. The precise role is certainly up for debate, but there seems little doubt that there is likely to be more technology-based passive monitoring of all forms of envi- ronmental pollution in the future. With respect to environ- mental noise, there are two crucial areas of importance. The first relates to the imminent development of electric cars. Given that road traffic noise is the most important source of environmental noise, there is a significant opportunity to re- duce pollution and associated health problems through the wider substitution and use of these vehicles over those that are chemically powered. Electric cars are much quieter and so long as the addition of artificial sounds to such vehicles is restricted, they have considerable potential to aid with re- ductions in annoyance and sleep disturbance. The second relates to the development of low-cost noise measurement devices that are likely to play a greater role in the future. In particular, the development of noise apps for smartphones is likely to become more important as technology improves in the future (see the article by Ben Faber in this issue of Acous- tics Today for examples).
The technology utilized in mobile/cell phones is Micro- ElectroMechanical Systems (MEMS) microphones that can be constructed relatively cheaply and at a low cost. Indeed, recent research testing smartphones and apps in the labo- ratory has demonstrated that when a significant number of samples are taken, some of the apps currently on the market are already remarkably reliable if not yet perfect (Murphy and King, 2016b). As the reliability of these microphones improves in the future, they will undoubtedly provide a much better scope for measurement-based noise mapping, something that has already been undertaken in the United States (Murphy and King, 2016a). In addition, low-cost vali- dation of noise modeling results as well as more accurate and reliable once-off measurements may be possible.
More broadly, it is also conceivable that the public could contribute much more significantly than at present in pro- viding noise measurement data through mobile/cell phones in a form of citizen science initiative that could aid noise mapping. This would certainly serve to create a sense of em- powerment for citizens with regard to their role in monitor- ing the quality of their environment.
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