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.
Highlights
With the development of society and economy, noise pollution has become a new challenge for sustainable development in urban areas
In this paper we proposed the following solutions: firstly, in order to achieve certain precision of the noise measurement by smartphones, we designed a set of methods to calibrate the smartphones and the calibration process can be done by the software
We proposed the region-based noise-mapping method, which is based on the distribution of noise in different region types tagged by volunteers, to interpolate and combine them to reduce the influence of spatial heterogeneity and improve the precision of noise mapping
Summary
With the development of society and economy, noise pollution has become a new challenge for sustainable development in urban areas. Environmental noise causes at least 1000 cases of premature death in Europe each year; over 900,000 cases of hypertension are caused by environmental noise each year”. Estimates have shown that approximately 20 million adults complain about environmental noise, more than 8 million people suffer sleep disturbances from environmental noise, and 43,000 cases of hospital admissions are caused by noise pollution each year in Europe [1]. 15% of the U.S population aged 20–69 (26 million people) may have suffered from noise-induced permanent threshold shift caused by excessive exposure to workplace or leisure noise [2]. This problem is pressing in developing countries [3]
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