Mapping Urban Environmental Noise Using Smartphones

Abstract

Noise mapping is an effective method of visualizing and accessing noise pollution. In this paper, a noise-mapping method based on smartphones to effectively and easily measure environmental noise is proposed. By using this method, a noise map of an entire area can be created using limited measurement data. To achieve the measurement with certain precision, a set of methods was designed to calibrate the smartphones. Measuring noise with mobile phones is different from the traditional static observations. The users may be moving at any time. Therefore, a method of attaching an additional microphone with a windscreen is proposed to reduce the wind effect. However, covering an entire area is impossible. Therefore, an interpolation method is needed to achieve full coverage of the area. To reduce the influence of spatial heterogeneity and improve the precision of noise mapping, a region-based noise-mapping method is proposed in this paper, which is based on the distribution of noise in different region types tagged by volunteers, to interpolate and combine them to create a noise map. To validate the effect of the method, a comparison of the interpolation results was made to analyse our method and the ordinary Kriging method. The result shows that our method is more accurate in reflecting the local distribution of noise and has better interpolation precision. We believe that the proposed noise-mapping method is a feasible and low-cost noise-mapping solution.

Revisiting Acoustics Education Using Mobile Devices to Learn Urban Acoustic Environments: Recent Issues on Current Devices and Applications

“…They used a certain Nokia model with their in-house NoiseTube sensing system and checked the measurement accuracy in detail. Similar studies using smartphones for noise mapping have been reported (for example, see [22][23][24][25]). The accuracy of the results when using a mobile phone for large-scale participatory noise sensing was also studied by Aumond et al [26].…”

High-Definition Medicine

“…The majority of environmental sensing systems are in very early stages and the impact on health outcomes is yet to be determined. Examples of early stage environmental monitoring applications include air quality and/or pollen monitoring for the prevention of hay fever or asthma exacerbations or (Honkoop et al, 2017), water quality sensors that can detect and reduce exposure to pollutants (Xu et al, 2014), environmental noise mapping to reduce exposure to noise pollution (Zuo et al, 2016), and food chain monitoring for unhealthy conditions during processing, storage, and transportation of food (Nukala et al, 2016). When coupled with smart home or other means to reduce environmental exposures, the potential impact on health of environmental monitoring is dramatic.…”

Mobile crowdsensing accuracy for noise mapping in smart cities

“…The ordinary Kriging method for the spatial interpolation was used [31]. It is based on the regression against observed z values of surrounding data points, weighted according to the spatial covariance values s. The Kriging method is one of the most widely used geostatistical interpolation methods for the noise data estimation [32,33]. Figure 4 shows a map of noise pollution obtained from the interpolated noise values, where values lower than 65 dB are considered low, values between 65 dB and 75 dB are slightly elevated, and values greater than 75 dB show high noise levels.…”