From March 17 to May 11, France was in lockdown due to COVID-19 [1, 2]. At these times, leaving your house was limited to essential displacements (buying groceries, work if working from home was impossible, short close-to-home workout, etc). Several locations remained closed after May 11 (like movie theaters), but “non-essential movements” were allowed. As expected, these restrictions had an incredible impact on the motion of people and vehicles, thus, the urban noise. I live next to a 4 lanes avenue, about 8 km from an airport and 3 km from a hospital, so transportation noise is something that is part of my routine. A few days into the start of moving restrictions I had an idea to somehow measure the effect of the lockdown on the noise pollution I’m confronted.
For that, from March 31 to June 30 (92 days), I went to my balcony at 18h30 and recorded 5 minutes of ambiance sound using my smartphone (with Smart Recorder app, at the sampling frequency 44.1 kHz and with automatic gain control disabled). All analysis, from data treatment to plotting, is performed in Python. An example of what I recorded is presented next (note that the audios are downsampled and compressed for publication):
My setup is far from ideal, first because the microphone was not calibrated (the full scale is considered) and there is no certainty that the signal is not processed before generating the audio file. However, since I’m focused in comparing two conditions and have no scientific aspirations, it was a fun homemade “experiment”. If you are looking for a deeper and rigorous analysis, a study performed in Paris by Bruitparif is available here (in french).
A big limitation of my recordings was the climate conditions: for the 92 recordings, 52 of them (56%) had at least one weak wind gust. The wind not only impacts the microphone directly (specially because I don’t have any device to filter that sound) but also creates noise unrelated to human activity, thus, making different days not directly comparable. Not much could be made, since the sampling period is small, so fully impacted by the gusts of wind. In the case of strong wind, where the sound was even outside of the measuring range, the sound levels are shown but ignored.
Moving to the comparison, let’s first focus on directly comparing two signals at equal time distance from the end of lockdown:
As presented on the audios and the plot of the sound signals for 14 days before and after the end of lockdown (April 27 and May 25), the recordings are marked by a periodic emergence of cars, with the interval being probably related to the traffic light cycle. The main difference between the in-lockdown and post-lockdown recordings is not the amplitude (ignoring the peaks at around 260 seconds, from a really loud motorcycle) but the presence of sound. After May 11, the noise is more continuous. Similar behavior is noted for 28 days of “distance”:
To evaluate the evolution of the sound level, the full scale decibel (dBFS), A-weighted, is calculated for the sound power of the complete period, taking as reference the before last recording (June 29). For the signals presented previously, there is a 0.6 dB delta from +14 to -14 days and 0.2 dB for \(\pm\)28 days.
The evolution of the sound levels is noisy (the irony), mostly due to the small sampling period of only 5 minutes. In general, there is less noise in weekends and holidays. Also, it can be seem that after the end of the lockdown there is a tendency of increase of the level represented by the hashed regression lines calculated by ignoring the windy days. The range of variation is of about 5 dB, similar to what was obtained in the Bruitparif report at some of the considered locations, but the curve is far from being as clear as their result. Sadly, I have no reference for before the restrictions, but what seems to be happening is a return to the pre-lockdown levels.
Data from the Cerema, a french public institution responsible for studying the environment, urbanization and transport, show the direct impact of the lockdown on the ground traffic (here I plot the traffic index of the urban area where I live). The return of vehicles into the urban landscape after May 11, noted in my recordings, is well marked (even if not considering the exact same locations). In terms of aeronautical activity, commercial flight data for Charles de Gaulle (CDG) and El Prat (BCN) airports, in Paris and Barcelona, respectively, show only a small but gradual recovery (flight information is from RadarBox). At the end of my recordings, the number of flights were about 1/3 of what was observed before the beginning of restrictions in France and about twice the values at the beginning of my experiment.
Even if the employed procedure does not represent a significant way of measuring the sound emission and to quantitatively evaluate the sound (non-calibrated, limited microphone), it remains a representative dataset of how I am exposed. For more precise results, a longer recording period would be necessary (at least 30 minutes). Also, it is important to note that modifications of the urban road network (addition of cycle paths, for example) were performed after the end of the lockdown, making the direct comparison between the recordings less meaningful. Besides the limitations, an increase of the noise seems evident, following the return of by-passers, workers, cars and planes to the urban landscape.
Recording my sound environment for almost 100 days during and after a lockdown allowed me to take some conclusions. First, is that I live in a quite windy place. Second, the lockdown was, in an acoustic point of view, a beneficial period with about 5 dB less of noise. Finally, I can conclude that life, at least in terms of the number of cars and the sound that I perceive in my balcony, is coming back to normal.
Hoping that my small experiment was interesting to you, I will be glad to read your comments/corrections/suggestions!