Noise Assessment during Motor Race Events: New Approach and
Innovative Indicators
AURORA MASCOLO1, DOMENICO ROSSI1, ANTONIO PASCALE3, SIMONA MANCINI2,
MARGARIDA C. COELHO3, CLAUDIO GUARNACCIA1
1Department of Civil Engineering, University of Salerno,
Via Giovanni Paolo II, I-84084 Fisciano (SA),
ITALY
2Department of Information and Electric Engineering and Applied Mathematics,
University of Salerno,
Via Giovanni Paolo II, I-84084 Fisciano (SA),
ITALY
3Department of Mechanical Engineering, Centre for Mechanical Technology and Automation
(TEMA) & Intelligent Systems Associate Laboratory (LASI),
University of Aveiro,
Campus Universitário de Santiago, 3810-193,
PORTUGAL
Abstract: - Motorsport races significantly affect, on a local scale, noise pollution even if they do not represent
the majority of its contribution, which is a prerogative of road transportation, railways, airports, and industries.
Nevertheless, such noise emissions surely affect the well-being of inhabitants in the surrounding area of the
circuit. In fact, during a motor race event, vehicles produce high noise emissions while on tests, qualifying, and
race sessions. Since noise indicators commonly used in national regulations are computed over fixed times, it is
challenging to properly assess the total noise emission and immission at the receivers during such events.
Moreover, in literature, only a few works can be found assessing this specific issue, and consequently, there’s
also a lack of appropriate methods to properly measure the global noise emission of each event. In this
contribution, the authors report the characterization of noise emission during motor race events by using two
new acoustic indicators, namely LEL (Lap Equivalent Level) and REL (Race Equivalent Level) starting from
noise data collected on different points along a racing circuit. Measurements show that the REL tends to
stabilize its value during a race, suggesting that its modelling can be achieved only based on the average LEL
and the number of vehicles participating in a race. These indicators will allow predicting the total noise
emission at a certain receiver of a motor race event by knowing the number and type of cars involved, without
using the duration of the race itself.
Key-Words: - Acoustics, Noise Control, Noise Emission, Motor Race Events, Modelling and Simulation
Received: December 9, 2022. Accepted: January 5, 2023. Published: January 31, 2023.
1 Introduction
Among all the pollutants daily threatening human
health, noise is one of the most relevant. In urban
areas, the primary contribution to noise level comes
from transportation (road, railway, and aircraft), and
the effects on human beings are widely studied and
documented, [1]. For instance, the sleep quality of
people exposed to constant high-level traffic noise
could be compromised, leading to potential short-
term (tiredness, lack of concentration) and long-
term (chronic hypertension) consequences, [2], [3],
[4], [5], [6], [7], [8], [9], [10], [11]. Several studies
can be found in the literature assessing and
modelling road traffic noise emissions and
propagation (see for instance [12], [13], [14], [15]).
On the other hand, sports cars' noise emissions
(during motor race events) have peculiar aspects
that need to be deeper investigated. These events are
characterized by large sound pressure levels
produced by sports cars, which usually do not
embed mufflers and are not required to respect the
noise emission limit of the passenger cars. Due to
the number of homologated tracks present on the
Italian territory (18 racetracks, 17 kart tracks, and 10
mini tracks, a total of 45), it appears clear that the
number of people exposed to these particular noise
emissions is not negligible, not only in terms of
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Aurora Mascolo, Domenico Rossi,
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spectators and workers but also citizens living
nearby, [16]. Since specific scientific papers are not
many in the literature, more and deeper
investigations could be useful. [17], as an example,
in a whole study of the effects of motor race events
in Australia also consider noise pollution but the
approach is generalist and not focused on the noise
emission problem. [19], collected noise data during
the Formula 1 Gran Prix of Canada in 2013; the
recorded noise levels reached a threshold that
suggested wearing appropriate protections. In this
case, common A-weighted continuous equivalent
levels (Leq,A) are used to assess the noise emitted
from the racing cars, applying it to the time of
measurement. Other subsequent works have focused
on noise emissions during a car race, [19], [20],
[21], [22], but still, no new and dedicated indicators
are presented. The lack of specific regulation for
noise emission reflects on the missing indications
for such noise assessment, thus general national
regulations for circuits functioning are applied.
Commonly adopted indicators are computed over
fixed times, without any assessment of the particular
race. Thus, the used indicators are not perfectly
suitable to determine the noise emitted from races,
typically with variable time length.
To fill this gap, in this contribution, the authors
report the new approach presented in, [23], to
deepen the characterization of noise emissions
during motor race events by introducing two new
acoustic indicators, namely LEL (Lap Equivalent
Level) and REL (Race Equivalent Level). These
indicators will allow predicting the total noise
emission at a certain receiver of a motor race event
by knowing the number and type of cars involved.
2 Methodology
The computation of specific indicators, able to
assess the noise levels produced in motorsport
events at a certain receiver point, is needed, due to
the particular nature of the events themselves. The
most used indicator in literature and many
regulations is the equivalent continuous A-weighted
sound pressure level Leq,A. It is an energetic
parameter that provides a very useful description of
fluctuating (time-varying) sound levels. It is
expressed as in equation (1):
 󰇧

󰇨
(1)
where T stands for the time interval of interest,
usually fixed by regulation or by measurement
duration, pA is the A-weighted sound pressure in
Pascal and p0 is the reference sound pressure equal
to 2 ∙ 10-5 Pa. The Leq,A gives a single value in dB(A)
that takes into account the total sound energy over
the entire time period of interest. Starting from this
indicator, the research was mainly focused on the
definition of two new acoustic indicators: the Lap
Equivalent Level (LEL) and the Race Equivalent
Level (REL). They modify the measurement times
of the equivalent continuous sound level, best
applying to the contest of a motor race events,
where the time span of the emitted sound depends
on the whole race which, in turn, depends on how
many times the cars are turning the whole circuit.
Indeed, the LEL can be defined as the equivalent
continuous sound level immitted at a certain
receiver by a single vehicle in one lap of the track.
The mathematical formulation of LEL is reported in
equation (2):
󰇧



󰇨
󰇧
󰇛󰇜
 

󰇨
where, tlap is the time, expressed in seconds, that a
vehicle needs to perform a single lap of the track.
Similarly, the REL can be defined in equation (3)
as the equivalent continuous sound level immitted at
a certain receiver during a race by all the vehicles
running on the track:
󰇧



󰇨
󰇧
󰇛󰇜
 

󰇨
where trace is the entire duration of the race
expressed in seconds.
Although both are defined as equivalent sound
levels, they are characterised by being calculated
over a variable time interval (lap and race time
respectively) rather than a fixed one. This approach
makes it possible to assess the noise emitted by an
individual car during a track lap (in the case of LEL)
and the group of cars during a race (in the case of
REL). Therefore, LEL and REL are indicators that
aim to assess the noise immitted at the receivers
rather than to assess exposure. For the latter,
however, fixed reference times must be considered.
In this paper, the authors apply the LEL and REL
approach both to a single-vehicle track test and
different race sessions, to describe the potentialities
of the method and to demonstrate its validity and
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Aurora Mascolo, Domenico Rossi,
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robustness. A more detailed description of the
applications is reported in [23].
3 Case Study Description
The circuit selected as a case study to test the above-
presented indicators is the “Circuito del Sele”
located in the city of Battipaglia, Italy. Battipaglia is
situated on the northern edge of the Sele plain, in
the district of Salerno and its territory is mainly
hillside. The circuit hosts all kinds of motor events,
such as car races, super motard races, scooter races,
kart races, and safe driving courses. The track has a
total length of 1.69 km and the pit straight is
approximately 400 m long. It is one of the largest
circuits in southern Italy. The route is almost flat
and only in some sections, it presents a slope that
reaches about 1-2%. It should be emphasized that
the road pavement is different from the ones used in
civil applications since it must fulfill the
requirements of the sports federations' regulations.
In this case, it is made of 6 cm-thick circuit-draining
asphalt, which is highly porous and draining.
Figure 1 and Figure 2 show the geographical
position within the Campania region, as well as the
track layout, with the points selected for the
measurements (A, B, C). The technical details of the
circuit are given in Table 1.
A Class 1 sound level meter (FUSION) was
placed at point A (see Figure 2), about 7.5 m away
from the central axis of the pit straight, during a
single vehicle test session. The sound level meter
was opportunely calibrated with a reference signal
of 94 dB at 1000 Hz. Sound pressure level Lp was
measured every 100ms, while a Formula X category
vehicle was running on the circuit. The vehicle was
a single-seater car, equipped with a 1.6 L engine,
with 16 valves. Further noise data were collected
during a competition event consisting of several
race sessions, at two different locations, referred to
as points B (at the border of the track) and C (out of
the track), as shown in Figure 2, using two Class 1
sound level meters (respectively Panasonic
Soundbook MK2 and FUSION). These two points
are placed at a turn situated about 15 and 30 m from
the roadway respectively. Figures 3 and 4 offer a
different point of view of the Sound Level Meters
positions in points A and B. It is also important to
emphasize that the equipment was properly
calibrated, and their clocks were synchronized
before the measuring campaigns.
Fig. 1: Map of the circuit location.
Fig. 2: Track layout with indications about
measurement point’s position.
Fig. 3: Sound Level Meter position at point A
during the single vehicle test.
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Fig. 4: Sound Level Meter position at point B during
the race sessions.
Table 1. Technical details of the circuit as a kart
track.
Technical features
Kart track
Mini-car track
Length [m]
1345
1800
Max. width [m]
11
13
Min. width [m]
8
9
Pit straight length [m]
-
~400
Direction of travel
Clockwise
Clockwise
Slope
2-3%
2-3%
Roadway width [m]
10
10
4 Results and Discussion
The recorded noise signal during the single-vehicle
test session of the Formula X car is reported in
Figure 5, where values of Lp[dB(A)] are plotted
against time, with a temporal resolution of 100 ms.
The vehicle drove around the circuit for a total of
17 laps. The graph clearly shows a peculiar trend,
with peaks (>100dB(A)) periodically repeating
through time. Such peaks occurred when the car
passed in the proximity of the receiver. Based on the
Lp values measured, the LEL values were
determined using equation 2. Since the lap race is
approximately constant through the progress of the
race itself, it makes sense to compare the distance
between peaks and the lap time. Thus, the lap times
were estimated by calculating the temporal distance
between the peaks recorded in the noise signal
during the test. De facto, comparing the lap time
estimations with the information retrieved from the
telemetry system, we found a very sharp correlation:
the time distance between each peak corresponds to
a lap time. The maximum absolute difference
between lap time estimations and lap time values
from telemetry is 0.4 seconds which is no bigger
than 0.49%. All the results of this measurement
campaign, in terms of lap time estimation and
measurement, absolute errors, and LEL values are
reported in Table 2, with the total average and
standard deviation of each column. Table 3 shows
recorded values of REL during the race sessions,
respectively for point B and point C. Each row
refers to a category of the competition event.
From a theoretical point of view, fixed all the
possible boundary conditions, given two receivers
(as in the case reported in Table 3), it is expected to
have a higher noise emission at the receiver that is
nearer to the carriageway. However, in the
presented case, REL values are generally higher at
receiver C than at receiver B even though it was
positioned at 30 meters from the carriageway while
receiver B was at 15 meters (see Figure 4). This can
be due to the fact that REL values depend on many
factors such as source-receiver distance, type of
vehicle, number of vehicles, speed of vehicles,
drivers’ behavior, and engine revolution per minute
(engaged gear).
Table 2. Single-vehicle test noise output.
Lap
Lap Time
estimation
[s]
Lap time
from
telemetry
[s]
Time-
lap
absolute
error [s]
LEL
[dB(A)]
1
82.0
82.4
0.4
86.2
2
76.5
76.3
0.2
87.1
3
73.2
73.2
0.0
86.1
4
71.9
71.6
0.3
85.8
5
74.1
74.4
0.3
84.6
6
71.3
71.4
0.1
84.7
7
70.4
70.3
0.1
84.7
8
69.9
70.0
0.0
84.0
9
68.9
68.9
0.0
85.2
10
69.7
69.6
0.1
85.1
11
69.3
69.3
0.0
85.0
12
67.7
67.8
0.1
84.7
13
69.6
69.5
0.1
84.5
14
69.3
69.2
0.1
84.6
15
68.2
68.5
0.3
84.3
16
69.0
69.0
0.0
84.6
17
69.5
69.5
0.0
83.9
Average
71.2
71.2
0.1
85.0
St. Dev.
3.6
3.6
0.1
0.9
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Table 3. REL measured in points B and C during
races and the time of each race.
Race
category
RELB
[dB(A)]
RELC
[dB(A)]
trace
[s]
Number
of cars
Assominicar1
84.4
86.3
963
12
Assominicar2
88.2
89.5
1014
8
N 1400
83.3
79.3
401
6
N 1600
85.0
81.4
401
8
Gr. 5700
86.3
87.0
408
6
GT CUP
87.6
89.8
232
6
GT CUP’’
85.4
86.8
357
4
Single-seater
89.0
90.9
352
4
N.B.: GT CUP’ and GT CUP’’ are related respectively to the
GT CUP race before and after the red flag.
Source-receiver distance is the easiest
contribution to be explained: the greater the distance
the lower the recorded emission levels. Hence, the
choice of the receiver position is crucial for a
correct evaluation of LEL and REL.
It also has to be taken into account that various
types of vehicles, having diverse kinds of engines,
mufflers, and insulation, correspond to different
noise emission levels. In the case analyzed in this
work, there are vehicles obtained from historic twin-
cylinder-engine cars, with free exhaust pipes and
series-derived cars, characterized by more efficient
acoustic insulation of the engine.
Furthermore, as widely reported in the literature
(see for instance, [24]), the high speed of vehicles
correlates with high sound levels. Since REL is an
equivalent sound level calculated over a race time
interval, its value is dependent on speed as well. For
the same reason, since each race can be
characterized by a specific number of participating
vehicles, it must be considered for the REL
estimation.
Another crucial contribution is given by the
drivers’ behavior. For example, in the hypothesis
that a driver maintains a slightly lower speed (as in
the measuring point B of the studied circuit) by
using the lowest gear, the engine revolution per
minute increases. This leads to a higher sound
power level of the engine source and, consequently,
to higher REL (and vice versa).
Finally, a strategic position also makes a
difference. As an example, REL and LEL values,
recorded at receivers positioned on a straight part of
the track, will be particularly high: in fact, vehicles
pass at maximum (full) speed on rectilinear parts,
generating high sound levels. On the opposite,
vehicles’ speed could be particularly low along
chicanes and turns, and as a consequence of that,
LEL and REL values could be lower at these
receiver points.
All these parameters are interdependent: Table 3
focuses on the comparison between REL measured
in points B and C during different races showing
that the source-receiver distance is not always the
predominant factor influencing noise levels, as
would be expected. This is visible for “N1400” and
“N1600” races.
Figure 6 shows Lp recorded during the N 1600
race at points B and C, and the “running REL”
curve, i.e. the integral shown in equation (3)
calculated as a function of time, increasing the
upper limit during the event. It must be stressed that
running REL values are plotted at every tenth of a
second (always considering the start of the race as
the initial time for REL computations).
Peaks in Lp curve, all around 95 dB(A), have to
be related to the cars’ passing in front of the two
receivers. It is interesting to notice how REL value
tends to stabilize after about two laps by the race
starts (comparable values have been found for other
races, data not shown). It can be empirically
suggested, then, that running REL value, after
stabilization, can be estimated through vehicle types
and numbers, and position of the receivers, without
using race time. The periodic pattern of the sound
pressure level, due to the repetition of the laps, leads
to a little influence of the overall trace on the running
REL estimation (see also literature, [22], [25]).
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Fig. 5: Noise signal during a single vehicle track test.
Fig. 6: Noise signal and running REL values for the N 1600 race at points B and C.
With such a consideration, the following
equation is proposed to estimate REL value for a
determined race as in, [23]:

󰇛󰇜
(4)
by having the number of vehicles (N) and an
average Lap Equivalent Level (
) at a certain
receiver. Another assumption is that vehicles
participating in the race belong to the same category
and have approximately the same noise emission
level. LEL values are not available for the vehicles
competing in the races and this prevents the
estimation of REL through the above-mentioned
equation. Nevertheless, during the GT CUP race, a
red flag occurred, and the race restarted with two
fewer vehicles: it was then possible to compute LEL
by inverting equation (4) and knowing car numbers
before and after the red flag, by the mean of the
REL values at the two receivers, as in equation (5).
󰇫
󰆒󰇛󰇜

󰆒󰇛󰇜
In particular, considering that the REL
measured at point B and point C before the race was
interrupted through the red flag is respectively 87.6
dB(A) and 89.8 dB(A) and that the number of sports
cars initially present on the circuit are 6, 
values
can be obtained at the same points of 79.9 dB(A)
and 82.0 dB(A).
This leads to the estimation of REL’’ values as in
the equations (6).

󰆒󰆒 󰇛󰇜

󰆒󰆒 󰇛󰇜
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In the analyzed case, taking into account the
evaluated values of LELB and LELC, and that the
number of the participating cars decreased to 4 after
the red flag, REL’’ values at point B and point C
were respectively 85.9 dB(A) and 88.0 dB(A).
It can be observed (see Table 3) as the values of
REL in points B and C are overestimated by 0.5
dB(A) and 1.2 dB(A), which corresponds to 0.57%
and 1.32%.
The graphical slopes of sound pressure level at
the receivers B and C, and of the running REL
values in the two points, measured during the GT
CUP race, are reported in Figure 7 and Figure 8,
respectively before and after the red flag.
Fig. 7: Noise signal and running REL values for the GT CUP race (before the red flag) at points B and C.
Fig. 8: Noise signal and running REL values for the GT CUP race (after the red flag) at points B and C.
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5 Conclusions
In this work, the novel approach presented in, [23],
aimed at assessing noise levels produced during
motorsport events based on two innovative
indicators, LEL and REL, was reported and
validated in test and race sessions.
The methodology allows assessing noise levels
produced during an event at any sensible receiver
point and can be applied at any circuit layout, for
any vehicle category, and for any time the race
would last. Moreover, it must be highlighted that the
novelty of this work is in the nature of the
indicators. Both LEL and REL are defined as
equivalent continuous A-weighted sound pressure
levels that evaluate the acoustic energy of a single
car during a lap and a bunch of vehicles during a
race at a given receiver, respectively. However, the
reference times (i.e., the lap time for LEL and the
race time for REL) are not fixed. This leads to the
advantage of comparing events characterized by a
different duration and can overcome the lack of
specific regulation for the assessment of noise
emitted from a circuit during a specific race.
Future works will be aimed at generating the
noise emission curves (sound power level Lw
against speed) for different sports vehicles in order
to feed the model and simulate several operating
conditions of the tracks. From the telemetry
information, the Lw can be assessed at any position
assumed by the vehicle on track, and therefore Lp
values at different receiver points can be easily
obtained through a sound propagation model. The
latter can be used to assess LEL and REL values.
This procedure can be used to draw noise maps,
both for existing and new infrastructures, in the area
surrounding the circuit, with the advantage of
separating the contribution of track operations from
the other sources to the equivalent sound levels. In
addition, these innovative indicators can provide
new insights for the development of regulations for
environmental assessment and protection, being
more oriented at the real noise immitted by the
circuits in the surroundings and being thus more
effective for the estimation of the annoyance and the
risk for human health.
Acknowledgement:
This work has been presented at the ENCEMA2022
conference and an extended version of this paper
can be found in, [23].
Authors deeply thank the Automobile Club Salerno
for supporting this work within the agreement with
the Department of Civil Engineering, University of
Salerno. The authors are also grateful to the owners
of the “Circuito del Sele” in Battipaglia, in
particular to Nicola Rinaldi, for the availability and
support during the measurements collection. Finally,
the authors thank all the students participating in the
measurements, in particular Paola Barra, for the
efforts pursued during her master's thesis work.
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Aurora Mascolo, Domenico Rossi,
Antonio Pascale, Simona Mancini,
Margarida C. Coelho, Claudio Guarnaccia
E-ISSN: 2224-3496
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Conceptualization: C. Guarnaccia, A. Pascale
Data curation: A. Pascale, A. Mascolo, D. Rossi, S.
Mancini
Methodology: A. Pascale, C. Guarnaccia
Software: A. Mascolo, D. Rossi, C. Guarnaccia
Supervision: C. Guarnaccia, M. C. Coelho
Visualization: A. Mascolo, D. Rossi, S. Mancini
Writing - original draft: A. Mascolo, D. Rossi
Writing - review & editing: A. Mascolo, D. Rossi,
A. Pascale, S. Mancini, M. C. Coelho, C.
Guarnaccia
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
A. Pascale and M. C. Coelho acknowledge the
support of the following projects:
UIDB/00481/2020, UIDP/00481/2020 FCT, and
CENTRO-01-0145-FEDER-022083. A. Pascale
acknowledges the support of FCT for the
Scholarship 2020.05106.BD.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2023.19.7
Aurora Mascolo, Domenico Rossi,
Antonio Pascale, Simona Mancini,
Margarida C. Coelho, Claudio Guarnaccia
E-ISSN: 2224-3496
88
Volume 19, 2023
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
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