Estimation of urban noise with the assimilation of observations crowdsensed by the mobile application Ambiciti

We investigated the estimation of urban noise by merging simulated noise maps and observations collected by the mobile application Ambiciti. Both the simulated noise map and the mobile observations are subject to uncertainties that are taken into account in the merging. Large errors in the mobile observations are due to the microphone inaccuracies, the processing of the mobile operating system, the GPS location errors and the lack of temporal representativeness of the measurements. We will explain how the variances of these errors can be quantified, so that the total observational error variance can be computed. We will also introduce a model for the error covariances in the simulated noise map, which takes into account the streets geometry. Using these error covariances, we computed the so-called best linear unbiased estimator (BLUE), which is a classical estimator in data assimilation techniques. This estimator produces an analysis noise map from the background (simulated) noise map and the mobile observations, so that the variance of the analysis error is minimized. A posteriori statistical tests were carried out in order to check for the consistency of the various error variances. The analysis was also evaluated using cross-validation and accurate observations from a sound level meter.