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reported in (17) show a reverberation time at 500-1000Hz of 1.86s. In the following steps the area of Largo
Saluzzo has been approximated to a large court\DUGZLWKWRWDOO\DEVRUSWLYHFHLOLQJDQGVWUHHWVH[LWVWKHUHE\an equivalent absorption area has been estimated (Atot= 2582,52 m2) considering these areas with anabsorption coefficient equal to 1 and the other surfaces with the properties introduced in (17).
City of Torino decided to start facing problems of this venue by a data-driven approach based on longterm monitoring and in 2016 has been put in place in Largo Saluzzo a video-surveillance system and a
low-cost IoT noise monitoring network. These facilities have been integrated in the last years within the
MONICA (19) and ROCK Projects in the framework of European Union’s Horizon 2020 research andinnovation programme.
2.2
Environmental Noise Monitoring
Considering the high spatial variability of noise in gathering places, a sound level meter monitoring
network based on five low-cost IoT sensors has been deployed in San Salvario district by City of Torino,
thus integrating previous shorter monitoring campaigns with Class 1 Sound Level Meters (SLMs), thusachieving a better noise knowledge across the area.
This network has been temporary integrated with Class 1 IoT SMLs in the framework of MONICA
project since November 2018 (20 ,QSDUWLFXODUclass 1 SLM 00305 (largo Saluzzo) was added to S01 (via
Saluzzo) and S03 (largo Saluzzo) – see Figure 1.
Data collected with a sampling time of 1 second are continuously sent via 4G to IoT Open Data
Platform to be aggregate on LAeq on hour and night basis. Monthly calendar plots as shown in Figure 2 areelaborated for each noise sensor, providing an overview of hourly noise levels distribution.
All collected data shows that the overall noise levels in the most impacted area have high variability,with LAeq night levels between 60 dB and 72 dB and picks on Friday and Saturday nights between 11 PM and3 AM levels between 62 dB and 75 dB LA1 hour (10).

Figure 2: calendar plot of noise levels in Largo Saluzzo, November 2018
The chart shows a certain regularity of the noise levels on weekend nights, from 23 PM to 3 AM, aswell as in case of midweek feast days (e.g. Hallowing before All Saints’ day on the 1st November)reflecting the presence of people in the venue.
For the monitoring period between 25th October-31st December 2018 some extra analysis has been
carried on, in order to compare data collected by the low-cost IoT network and by the Class 1 SLM.
Results of the Spearman correlation indicated that there was a very strong correlation between low-cost
IoT and Class 1 SLM noise LAeq 1hour levels, (rs[605] = .95, p < .001).
While the scatter plot and a comparison between the two distributions confirm a good coherence
between 60 and 80 dB LA1 hour, at lower levels the two sensors show differences (Figure 3): the sensor
responses and dynamic should be investigated, as well as local variations of background noise.

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337

(LAeq, dB). However, the A-weighted statistical level that is exceeded for 90% of the measuring time (LA90,dB) has been found (24) to be more appropriate for the background noise estimation mainly due to babble
noise (§crowd noise) and thus has been used instead of LAeq.
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(1)

Figure 6 shows LA90 as function of the number of people in the square recorded during the period
between 25th October-31st December 2018. The analysis has been performed separately for each clustershown in sec. 3.1.
The equations of the linear regression lines have been added to describe the measured data (red andgreen dots) and compare to the predicted equation (blue dots). The background sound pressure levels below
logN=1.8 (§63 people corresponding to a crowd density of 0.03 people/m2) are around 46 dB (red dots),which indicates that there is a Lombard effect change-point at this level. Therefore, the prediction model,
i.e. Eq. 1 has been applied only from this point on. Eq.1 has been applied using g=3.5, c=0.25/0.45/0.55,
and Ap=0.7m2. A value of g=3.5 indicates that 29% of the people is speaking, which remains the same forthe different clusters. It can be noticed that the main difference between the clusters is due to the Lombard
slope, i.e. the value of c. This value increases for cluster 3 reaching a value of 0.55, which is in the typicalrange of this parameter for social gatherings (0.5-0.7). It can be noticed that for the same number of people
the values of the background noise result different (e.g. for logN=2.5, LA90 is around 57dB for cluster 1,
63dB for cluster 2 and 65dB for cluster 3). This suggest that there might be also other behavioural factorsthat influence the highest values of the background level (e.g. related to the age of the active people withineach cluster). Ap values have been set at 0.7 m2, which was considered adequate for a person in wintercloths.

Figure 6: LA90 as function of the number of people in the square. Red and green dots indicate the338measured background noise levels. The blue dots indicate the predicted values with Eq.1.
A further analysis has been made regarding the acoustic capacity of this square. This parameter has been
introduced by Rindel (17) in order to indicate a design criteria that could be controlled to maintain asufficient quality of the verbal communication. This is achieved when the background noise is lower than
the threshold of 71dB and it limits the maximum number of people allowed to be present simultaneously inthe same environment. It can be useful to calculate the maximum number of people (Nmax) allowed also
within Largo Saluzzo square once that the background noise levels threshold are set. Usually these levels
are given as LAeq, however here we refer to as LA90. The parameters that have been tuned above for eachcluster based on the prediction model are used in Eq. 1 where the unknown factor becomes the number ofpeople (N=Nmax a limit of density of 2 persons/m2 has been set.
Table 1 shows these estimations for the three clusters. It shows that considering the parameters (g, c,
Ap) for cluster 3, the Nmax is lower than for the other clusters. Consequently, the number of people needs tobe either limited or their behavior needs to be addressed towards FOXVWHURU
Table 1: Number of people compared to the background sound pressure level at thresholds indicatedwith LN,A values. The values of g, c and Ap have been obtained in the previous paragraph.
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CONCLUSIONS

After a few years of data collection, a good knowledge of outdoor noise level, including a yearly
spectrum of hourly noise levels, is available in some gathering place in Torino, Italy.
Regularities on overall noise levels in relation with the seven nights of the week allow to cluster data
into three groups, with medium, high and very high crowd noise levels. A certain degree of seasonality canbe enlightened, with maximum levels in spring and autumn.
A deeper investigation of the relationship between number of people and background noise has been
performed, coupling noise and crowd IoT sensors.
The assumption that in social open air gathering places communication interaction are similar to thosein restaurants or reception halls has been applied using Rindel model. This shows a strong correlation of
number of people and LA90 as background noise indicator, well described by group size and Lombard
effect, given the acoustic absorption of the venue and people. As each cluster of nights shows somedifferences on the Lombard slope only, it is supposed that it is due to some influences of contextual andbehavioral factors. However, this will need a more systematic questionnaire-based study, which has notbeen performed in the period considered here.
The same model allows the assessment of the noise level of the crowd is related to given noise
thresholds, and suggests that communication actions increasing awareness of attendees could led to a noise
reduction.
Both these elements could help the implementation of reduction initiatives of noise pollution in social
gathering places, that remains an open challenge.

ACKNOWLEDGEMENTS
The study is conducted in the framework of the EU H2020 “MONICA” project (grant agreement No
732350), with the support of the EU H2020 “ROCK” project (grant agreement No 730280).

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