Mapping Noise Pollution

As noise pollution continues to be a major issue for expanding urban areas, novel spatial technologies have been deployed to better monitor and measure noise pollution.

Mapping Noise Pollution with Smartphones

Novel applications include the use of smart phones, such as the NoiseTube application, that measure the location and strength of given noise pollution. By having these sensors deployed via mobile phones, mapping noise pollution can be at a higher density and, consequently, be more accurate over a wider spatial extent.[1] Although mobile phones are not as sensitive as larger noise evaluation equipment, the fact that they are ubiquitous makes it more likely that a broader and fuller understanding of noise pollution levels can be made, particularly as variation occurs throughout the day in large cities such as Paris.

Using Mobile Sensors to Map Noise Pollution

In another technique applied in Ghent, sound sensors were attached on bicycles and compared to fixed sound sensors on buildings and stationary areas. In this technique, a continuous monitoring across space and time on mounted sensors versus stationary sensors was found to be more accurate. Additionally, even when interpolation techniques, such as inverse distance weighting, were used on stationary sensors, the mobile and continuous mapping techniques were found to be more accurate in producing useful noise pollution maps.[2]

Map of noise pollution in Cartagena, Colombia collected from NoiseTube smartphone data. Source: El Universal, 2016.

Map of noise pollution in Cartagena, Colombia collected from NoiseTube smartphone data. Source: El Universal, 2016.

Modeling the Affect of Noise Pollution on Species

Other techniques have used modeling platforms within common tools such as ArcGIS along public data to measure potential areas where noise pollution could impact species sensitive to certain decibel levels. Information such as land cover, weather, temperature, humidity, and wind can be incorporated in a suite called Sound Mapping Tools, used within ArcGIS, to estimate the acoustic impact and potential for impact. By knowing what species and their thresholds for noise sensitivity are relevant, this model then allows scientists to measure areas where given species are likely to be adversely affected by noise levels. This was tested in national parks where snowmobiles are present and areas where drilling occurs, where these areas have noise that potentially impacts species such as mule deer.[3]

Map showing the predicted acoustic impact of drilling four new wells in and near a BLM's Area of Critical Environmental Concern (ACEC) affecting mule deer in Shales Ridge Management Area in Colorado. Source: Keyel et. al, 2017.

Map showing the predicted acoustic impact of drilling four new wells in and near a BLM’s Area of Critical Environmental Concern (ACEC) affecting mule deer in Shales Ridge Management Area in Colorado. Source: Keyel et. al, 2017.

IoT solutions for Noise Monitoring

Noise pollution can also be a problem at smaller scales, such as within a building, as well as wider areas. Similar to what was discussed earlier, new technologies use mobile applications that apply Internet of Thing (IoT) technology that connects sensors and devices. Cheap microchips can be deployed with WIFI connectivity. Data can then be transmitted between devices where the data are also collected in a cloud-based repository. Outputs of noise pollution can be displayed on the internet via normal web browsers, providing an easy way to see places where given noise levels are occurring without deploying more expensive and often fixed noise sensors.[4] In fact, other engineering approaches have focused more on using cloud-centric applications in applying IoT solutions, connecting hardware monitoring devices with software, for observing noise as well as other pollution for monitoring urban-based pollutants. The intent is to make noise and other pollution to be monitored like traffic, where urban officials can then react to real time information.[5]

Continuous Data monitoring and Simulation for Estimation of Noise

Using ISO 9612:2009 for acoustics measuring, ubiquitous noise monitoring has also been used to now simulate patterns of noise to estimate when in the future given areas or regions could experience different levels of noise. Similar to how traffic patterns in the future are now used to estimate given journey times in the future, the use of simulation techniques offers the potential to anticipate where noise levels will occur and how likely they will affect a given region.[6] The combination of continuous monitoring with low resolution sensors (e.g., smart phones) and simulation offers urban areas a low cost way to monitor and predict patterns of noise that aid in development and planning efforts.

We see that technologies have evolved more in the direction of using low cost and easily available methods, such as the use of mobile devices. Furthermore, it was shown interpolation techniques were not sufficient enough to provide the levels of high-resolution accuracy needed for urban planners. Increasingly, it has become clear that IoT integration, use of simulations, and mobile technologies will be applied together to create more effective and high spatial resolution monitoring of noise at different temporal scales.

References

[1] For more on the use of mobile phones for monitoring noise pollution, see:  Nicolas, M., Matthias, S. & Bartek, O. (2010) Participatory noise pollution monitoring using mobile phones. Information Polity. [Online] (1,2), 51–71.

[2] For more on this method applied in Ghent, see:  Can, A., Dekoninck, L. & Botteldooren, D. (2014) Measurement network for urban noise assessment: Comparison of mobile measurements and spatial interpolation approaches. Applied Acoustics. [Online] 83, 32–39. Available from: doi:10.1016/j.apacoust.2014.03.012.

[3] and [4] For more on the Sound Mapping Tools and for more on this noise sensor approach using affordable technologies, see:  Keyel, A.C., Reed, S.E., Nuessly, K., Cinto-Meija, E., et al. (2017) Evaluating anthropogenic noise impacts on animals in natural areas. [Online] Available from: doi:10.1101/171728

[5] For more on the use of IoT solutions for noise monitoring, see:  Jin, J., Gubbi, J., Marusic, S. & Palaniswami, M. (2014) An Information Framework for Creating a Smart City Through Internet of Things. IEEE Internet of Things Journal. [Online] 1 (2), 112–121. Available from: doi:10.1109/JIOT.2013.2296516.


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[6] For more on using continuous data monitoring and simulation for estimation of noise, see:  Hu, M., Che, W., Zhang, Q., Luo, Q., et al. (2015) A Multi-Stage Method for Connecting Participatory Sensing and Noise Simulations. Sensors. [Online] 15 (2), 2265–2282. Available from: doi:10.3390/s150202265.

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