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APPLICATION OF ORDINANCE N° 310-2009-MDJM, AND
NOISE POLLUTION FROM THE VEHICLE FLEET IN THE
DISTRICT OF JESÚS MARÍA - 2020
Maria Veliz Garagatti
Federico Villarreal National University, (Peru).
E-mail: mveliz@unfv.edu.pe
ORCID: https://orcid.org/0000-0002-8133-1711
Vicenta Tafur Anzualdo
Graduate University School - EUPG - Federico Villarreal National University, (Peru).
E-mail: itafur@unfv.edu.pe
ORCID: https://orcid.org/0000-0002-1888-7848
Susana Irene Davila Fernandez
Ricardo Palma University, (Peru).
E-mail: susana.davila@urp.edu.pe
ORCID: https://orcid.org/0000-0002-6949-1317
Doris Esenarro Vargas
Federico Villarreal National University, (Peru).
Ricardo Palma University, (Peru).
E-mail: desenarro@unfv.edu.pe
ORCID: https://orcid.org/0000-0002-3942-7832
Recepción: 01/09/2021 Aceptación: 25/10/2021 Publicación: 14/02/2022
Citación sugerida:
Garagatti, M. V., Anzualdo, V. T., Davila, S. I., y Vargas, D. E. (2022). Application of ordinance n°
310-2009-mdjm, and noise pollution from the vehicle eet in the district of Jesús María - 2020. 3C
Tecnología. Glosas de innovación aplicadas a la pyme, Edición Especial, (febrero 2022), 47-63. https://doi.
org/10.17993/3ctecno.2022.specialissue9.47-63
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ABSTRACT
The present research work analyzes the noise pollution of the automobile eet in the
District of Jesús María located in Lima - Perú. The general objective was “To determine
the incidence of the application of Ordinance N° 310- 2009-MDJM. in the noise pollution
of the vehicle eet in the district of Jesús María, 2020” the type of research is applied;
descriptive and explanatory level and non-experimental design. The population object
of study was constituted by the inhabitants of the district and the oating population,
considering a non-probabilistic sample of 210 people. The instruments used were:
measurements of sound pressure in xed stations, counting of public and private vehicles
in the time of measurement of good pressure, and a questionnaire that was validated by
expert judges, the same one that constituted 32 items of the scale of Likert. The results
found in the sound pressure measurements exceeded the maximum sustainability of the
analyzed Ordinance, showing noise pollution. The statistical test used was the Spearman
correlation coecient, determining that there are dierences in noise pollution at the
stations and not at the timetables; that public and private vehicles increase sound pressure
measurements under dierent conditions; concluding that: “The application of Ordinance
N° 310- 2009-MDJM signicantly aects noise pollution in the vehicle eet in the district
of Jesús María, 2020”.
KEYWORDS
Noise Pollution, Vehicle eet, Ordinance, Public vehicles, Private vehicles.
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1. INTRODUCTION
When we think of environmental pollution, we think of it as strange elements that alter
the conditions of natural development in space. Generally, it is a consequence of the
participation of human beings trying to improve their living conditions; however, if the
limits that allow maintaining natural balances are exceeded, these actions considered
as benecial become detrimental to the development of living beings, and that is when
pollution in all its aspects appears (Alvarado et al., 2020).
One of them is noise pollution, recognized as a polluting agent in the Environmental
Congress organized by the United Nations in Stockholm in 1972 and identied for
generating unwanted sounds because they lack harmony and are called noise, or is
sharper than living beings can withstand without being damaged; Moreover, it is punctual
pollution, which does not generate waste and has the particularity of being easy to adapt in
individuals, in such a way that it decreases the sensitivity of those who are in contact with
it, that is why scholars of this pollution, among others, consider the inuence in addition
to physical parameters, other subjective parameters to describe it, so it is inconvenient to
clearly explain to the citizen that there is a problem called noise, which is aecting their
physical, mental health, and their social quality (Alomoto, 2018).
Several human actions generate this contamination; one of them is the vehicle eet in urban
public spaces, especially where vehicles are old and technical revisions are not a priority,
there is no adequate organization of vehicular trac, and urban downtown areas begin to
concentrate their activities both public and private in small spaces which generate a high
concentration of citizens who inhabit these spaces and live with the oating population that
is developing their work activities, education, visits to health centers among others and need
means of transportation to mobilize.
The above mentioned happens in the district of Jesus Maria, a very central area within
the capital of Peru, Lima; so, we wanted to know if the district authorities were looking for
solutions to this problem, considering that they have the Ordinance No. 310-2009- MDJM,
a document which establishes the maximum allowable limits for the generation of noise
nuisance and regulates the policies of prevention and noise control to educate the neighbors
about this pollution (AMBIO, 2018).
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Environmental pollution is an alteration of the natural environment generated by foreign
elements that cause undesirable changes in the spaces where they occur, which in many
cases generate disease in humans; these alterations can be of natural origin or generated
by the activities carried out by man. One of these contaminations is called noise pollution,
produced by waves that move in space which generate sounds perceived by living beings;
in the case of man, he can feel pleasant sounds that can favor his emotions, but there are
also unpleasant sounds that can aect his physical, psychological and social health. Noise
pollution is generated by dierent causes; some of the most important are those produced
by the vehicle eet, that is, by the circulation of numerous vehicles that travel through the
streets and avenues of a city, which is why it is called urban noise pollution (CSU).
The CSU is increasing exponentially in the big cities of the world; recent studies found
that Guangzhou in China has the highest noise pollution; this information corresponds to
the measurement made by the global index of hearing, which was created by the founders
of the digital hearing application Mimi Hearing Technologies GmbH, they analyzed the
results of the hearing tests of 200 000 of its users (Amable et al., 2017).
2. METHOD
2.1. TYPE OF RESEARCH
It is applied; this base is based on the technological ndings of basic research, linking theory,
and reality (Reyes et al., 2021).
Scientic theories are considered concerning the deterioration of the urban environment
due to the excess noise generated by the automobile eet.
2.2. LOCATION OF THE STUDY AREA
The district of Jesús María is one of the 43 districts that are part of the province of Lima
located in the Department of Lima. It is bordered on the north by Breña and Cercado, on
the west by Pueblo Libre, on the south by Magdalena and San Isidro, and on the east by
Lince and Crecado (Bizkaia, 2018).
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Table 1. Number of inhabitants of the district of Jesús María.
TOTAL DENSITY
71 589 hab. 17 897,3 hab/km2
Source: own elaboration.
3. RESULTS
3.1. SOUND PRESSURE MEASUREMENT
Table 2. Sound pressure measurements at seven stations. Factor: LAeqT.
Station
UTM Coordenatess LAeqT 10min (dBA)
Average
(dB)
East South 07:00-09:00 14:00-16:00 19:00-21:00
E01 277716 8664258 71,3 69,9 71,8 71,0
E02 277687 8663750 75,9 75,7 72,0 74,5
E03 277284 8663443 65,7 70,3 69,8 68,6
E04 276831 8662929 66,6 64,6 65,1 65,4
E05 276496 8662269 72,6 73,0 71,1 73,9
E06 276012 8662589 73,5 71,0 68,3 70,9
E07 275538 8662897 70,9 75,1 68,9 71,6
Source: own elaboration.
Table 2 presents the sound pressure measurements taken at the seven work stations, with
the highest gures found at E02 in the morning and afternoon hours with 75.9 and 75.7
dB respectively, and the lowest sound pressure gures at E04, in the afternoon and twilight
hours with 64.6 and 65.1 dB respectively, which are reected in the average measurements.
It is noted that all sound pressure measurements are more signicant than 60dB.
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Table 3. Tests of inter-subject effects.
Dependent variable: Sound pressure
Origin Type III sum of
squares gl Media cuadrática F Sig.
Modelo corregido 162,820a8 20,353 4,910 ,007
Intersección 104742,172 1 104742,172 25270,490 ,000
Estaciones 150,505 6 25,084 6,052 ,004
Horarios 12,315 2 6,158 1,486 ,265
Error 49,738 12 4,145
Total 104954,730 21
Total, corregido 212,558 20
Source: own elaboration.
Table 3 shows the sources of variation, sums of squares, degrees of freedom, root mean
squares, F-statistics, and critical levels associated with the three eects present in a two-
factor model (Alcántara- Malca & Esenarro, 2018).
The corrected model row refers to all model eects taken together (2-factor, interaction,
and intercept or constant eect). The P-value 0.007 < 0.05 tells us that the model explains
a signicant part of the variation observed in the Dependent Variable sound pressure. A
coecient of determination R2 = 0.766 (162,820/212,558) indicates that the 2 eects
included in the model (season and schedule) are explained with the adjusted coecient
of determination 61 % of the variance Dependent Variable sound pressure (Delgadillo &
Pérez, 2017).
The intersection is the constant of the model. The two rows are personal eects of the two
factors: season and schedule. The signicance levels indicate the mean sound pressure of
the groups dened by the variable stations diers (0.004 < 0.05) while the groups dened by
the irregular schedule have non-signicant mean times (0.265 > 0.05) (Figueroa et al., 2018).
The error row is related to the source of error variation or residual. The root means square
of the error (4.145 is a divisor of each F-ratio), an unbiased estimator of the variance of the
populations studied (assumes all equal). The corrected Sum of Squared Total captures the
total variance of each eect plus the error variance.
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Figure 1. Prole plot results.
Source: own elaboration.
Figure 1 shows the sound pressure averages calculated in each subgroup due to combining
each level of the variable stations with each level of the erratic schedule.
3.2. ENVIRONMENTAL NOISE MAPS
Figure 2. Noise map.
Source: own elaboration.
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The afternoon ambient noise map, Figure 1, has been taken because, making use of the
statistical results of the prole graph, Figure 3, indicating the estimated marginal sound
pressure measurements, which show that in the afternoon hours, the highest sound pressure
measurements are presented. In this map, the two most aected stations, E02 and E07, are
shown, expressed in the intense red color that represent the gures of 75.9 dB and 75.1dB,
respectively; another essential point is E05, where a dark orange color can also be seen that
expresses a vital sound pressure gure, 73. 0dB; E06 with 71.1dB, where the orange color is
lower, followed by E01, E03, and E04, with 69.9, 70.3, 64.6, which show even lower orange
colors up to the green, indicating lower gures. The morning and twilight Ambient Noise
maps (Gray, 2017).
3.3. METEOROLOGICAL CONDITIONS ANALYSIS
The monitoring of the meteorological conditions was obtained from the three sampling
days, February 12, 18, and 25, 2020, from which the averages of the meteorological
conditions have been received by using the measurement hours. In addition, information
was obtained from the SENAMHI Mars Field Meteorological Station.
Table 4. Meteorological conditions in the study area.
PARAMETER 12 FEB 18 FEB 25 FEB AVERAGE
Ambient Temperature (°C) 22,9 25,7 22,3 23,6
Relative Humidity (%) 84 72 83 80
Wind speed (m/s) 2,5 2,9 2,6 2,6
Source: own elaboration.
The gures in Table 4 show temperatures ranging from 22.3 to 25.7 degrees Celsius, with
an average of 23.6 °C; relative humidity measurements were also taken in percentages
ranging from 72 to 84 percent, with an average of 80%; wind speed was also measured,
with an average of 2.5 to 2.9 meters per second, with an average of 2.6 m/s (González,
2019).
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3.4. PROFILE GRAPH RESULTS
Figure 3. Estimated marginal averages of vehicle ows.
Source: own elaboration.
Figure 3 shows the vehicle ow averages calculated in each subgroup due to combining
each level of the variable stations with each level of the erratic schedule.
3.5. PUBLIC AND PRIVATE VEHICLE FLEET CONSIDERING VEHICLES TYPE.
We have considered analyzing the ow of public and private vehicles separately. General
cars correspond to the light and dark red colors for each sampling station, and private cars
conform to the green variants (Hidalgo, 2019).
Table 5. Public and private vehicles per station.
STATION % PUBLIC VEHICLES % PRIVATE VEHICLES
E01 55 45
E02 55 45
E03 40 60
E04 36 64
E05 42 58
E06 38 62
E07 30 70
Source: own elaboration.
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Figure 4. Average percentages of public and private vehicles per station.
Source: own elaboration.
Table 5 and Figure 4 present the averages in the percentage of public and private vehicles
to determine the ratio of presence of each of them in each station, and we found the
following: in stations E01 and E02, there was a more signicant presence of public vehicles
in 10% more than private vehicles, while in E03, E04, and E06 there is a more substantial
presence of personal vehicles in a dierence of between 20% to 28% more than public
vehicles, in E05 there is a dierence of only 16% between private and public vehicles,
which indicates that both types of vehicles are essential in that station and in the case of
E07 the dierence is more signicant for personal vehicles with 40% more than public
vehicles (Mamani, 2017).
Figure 5. Types of public and private vehicles by station.
Source: own elaboration.
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Figure 5 shows that E01 and E02 have more public vehicles, with both vans and buses in
fairly close numbers; the dierences do not reach 10% and are therefore not relevant. In
the case of E03, E04, E06, private vehicles are predominant and have similar results in the
arrangement of the dierent types of means of transport. At the same time, E05 presents
gures of private cars with vehicles, vans, and motorcycles with representative statistics and
the inuence of public vehicles that were also present and with signicant gures. In E07,
there is a high presence of private cars with a relatively representative number of vehicles
(46%) (MDJM, 2019).
Figure 6. Sensitivity to noise.
Source: own elaboration.
The sensitivity to noise indicated in Figure 6 has been expressed with 31.43% as moderate,
24.29% as too sensitive, 22.38% as slightly sensitive; the last two indicators do not reach
15% each, so they are not considered relevant (Municipalidad de Jesus María, 2021).
4. DISCUSSIONS
The sound pressure meter measurements at the seven stations are signicant since P-value
0.007 < 0.05 indicates that the null hypothesis is rejected, and the alternative view is accepted.
It is concluded that the sound pressure is not the same for all stations. To know where the
dierence is, we proceeded to a Tukey test, where it can be observed that stations E01 and
E06 do not dier in any case since P-value is more signicant than 0.05, on the other hand,
in stations: E02 dier signicantly with E03 and E04; E03 dier signicantly with E02; E04
dier signicantly with E02, E05, and E07; and E05 and E07 dier signicantly E04 (Ocas,
2018).
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The aforementioned results are expressed in Figure 2, where the results of the Prole graph
are shown, in which we can nd that E02 is the one that presents the highest sound pressure
of all the sampled stations and can be seen in average numbers in Table 4 which indicates
the gure of 74. 5 dB; which compared to E04, reviewing the same tables and gure,
shows that it is the station with the lowest gures, with 65.4 dB of sound pressure and in
the case of E05 and E07, intermediate gures of good pressure are presented with 73.9 dB
and 71.6 dB on average, respectively. The proper pressure measurements can be seen with
greater representativeness by observing them in the noise map, Figure 3, which oers us an
essential geographic appreciation in the space of the analysis of the measurement nuclei
and the dispersion that noise produces spatially, generating more deterioration in the area
where it is made. We must note that the barriers of the environments that can reduce the
strength of the noise have not been considered, although the measurement stations of this
work have vast spaces in which the barriers are distant. It is important to note that all the
sound pressure gures obtained in this work are above 60 dB, considered the maximum
present in any urban public space, as indicated by the Ordinances of dierent districts of
Lima and other districts cities in Peru. Similar results are found in the works developed;
who suggests that in the town of Chimbote, in José Gálvez Avenue, the area with the most
trac, Sound Pressure gures were obtained above 60 dB, considered as maximum in the
Municipal Ordinance of his district; also in Iquitos in the center of the city was measured
up to 84. 24 dB in the town of Cajamarca in its two downtown avenues Av. Hoyos Rubio
and Jr. Manuel Seoane exceeded gures of 100dB (Figueroa et al., 2018).
If we analyze works carried out in Lima, in the campus of the Catholic University of
Peru was measured above 70dB. Therefore, it is considered that the sound pressure
measurements obtained in this research work, as in those carried out in all the results
presented as background, the excellent pressure gures exceeded 60 dB for urban public
spaces in daytime hours (EFE, 2018).
The measurements of vehicle ow in the seven stations are signicant since P-value 0.012
< 0.05 also suggests we reject the null hypothesis and accept the alternative view. And we
conclude that the vehicle ow is not the same for all stations. To locate the dierence, we
proceeded to a Tukey test, where it can be observed that stations E01, E02, E03, and E05
do not dier in any case since P-value is more signicant than 0.05, on the other hand in
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stations: E04 diers signicantly with E06 and E07; in E06 diers signicantly E04; in E07
diers signicantly with E04 (Ordenanza N°1965, 2016).
Let’s look at the prole graph, where the estimated marginal averages of vehicle ow
are presented, Figure 4. We nd that: the similarities between E01, E02, E03, and E05
is expressed by the vehicle count in 10 minutes which are in the range of 500 - 550 units,
while E06 and E07 had a range between 650 - 700 vehicles in 10 minutes count, the highest
gures in our work; nally, E04 presented the range between 300-350 units in 10 minutes.
In this analysis, we found that not always the highest sound pressure gure is shown in the
station where more vehicles are transiting through a station, as is the case of E02, which
has the highest sound pressure gure (75 dB average); however, it was found that it is not the
most signicant number of cars circulating in that space (500-550. Public vehicles in E02
stop in any room to pick up passengers, creating vehicular congestion and not allowing the
ow of the vehicle eet. In addition, E02 is a Special Protection station due to the location
of the Rebagliati Hospital; therefore, its maximum allowed level is 50 dB, and an excess of
21.6 dB was found (Table 10).
The study of noise pollution in the city of Tarapoto, where she indicates that the most
circulating vehicle is the motorcycle cart, which is a means of public transport and is who
has generated a relatively high sound pressure exceeding by 20 dB to 27 dB more than the
noise quality standards in the commercial area; also Cerna, 2015 who indicates among
other points that, the high noise level is due to the presence of public vehicles in the study
conducted on Av. Universitaria in the city of Lima in 2014 (Delgadillo & Pérez, 2017),
indicates that the highest sound pressure levels are due to motorcycle cabs, who notably
raised the sound pressure gures in the area of San Juan de Lurigancho- Lima; in the case
of E02 in this work, it was primarily observed buses and combis signicantly deteriorated
by the excess of years of service that they present. Therefore, a public vehicle eet that is
inadequate or in poor condition and poor organization in the transit system can generate
signicant noise pollution (Zeballos, 2019).
If we analyze the E07, also of Special Protection due to the location of the Military
Hospital, there were also high sound pressure gures with an average of 71.6 dB, in this
case exceeding 21. In this case, 21.6 dB exceeded the maximum allowed, which is 50 dB,
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due to the large inux of private vehicles, since in this station, between 650-700 cars were
measured in 10 minutes of counting. Also, the poor management of the trac system
generates an inadequate movement of vehicles entering from Brazil Avenue to Faustino
Sanchez Carrion Avenue, making a semicircular movement causing a trac jam, which
produces the high sound pressure level in this station (Rosales, 2017).
In the case of E05, where the second-highest average sound pressure level was measured
(73.9 dB), we found a similar presence of both public (42% average) and private (58%
average) vehicles, which also generates signicant trac congestion in addition to the
increased company of motorcycles by 13%, the highest in the entire sampling area, which
contributed to develop the high average sound pressure level of 73.9 dB. Therefore, we can
indicate that several causes can produce high sound pressure levels generated by the vehicle
eet in our study area, as in E05 of this research work (Zeballos, 2019).
Analyzing the surveys made to the users in the stations worked, the Cronbach’s Alpha
statistic shows the instrument’s reliability presents the tables and gures that support these
results. Regarding noise sensitivity, in Figure 6, 53% of people felt light or moderate noise;
this indicates that, despite the high sound pressure obtained in the sampled stations, the
human body quickly adapts to environmental conditions, which does not mean that the
human body quickly adapts to environmental conditions the adverse eects are eliminated.
Rodriguez found a similar result in 2015 in the urban center of Zaragoza (Spain), where
40.4% of students showed indierence when asked about the issue of noise from the vehicle
eet.
5. CONCLUSIONS
The application of Ordinance No. 310-2009-MDJM has a signicant impact on noise
pollution from the vehicle eet in the district of Jesús María, 2020.
The application of Ordinance No. 310-2009-MDJM has a signicant impact on noise
pollution expressed in the noise map of the vehicle eet in the district of Jesús María, 2020.
The application of Ordinance N° 310-2009-MDJM has had a signicant impact on noise
pollution from private vehicles in the district of Jesús María, 2020.
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The application of Ordinance N° 310-2009-MDJM has had a signicant impact on noise
pollution from public vehicles in the district of Jesús María, 2020.
The Municipality of Jesus Maria maintains the development of the process of surveillance
and monitoring of environmental noise that has begun in late 2019, through the mayoral
decree No. 008-2019-MDJM of April 29, 2019, so that it achieves the goal of maintaining
the maximum permitted levels present in Ordinance No. 310-2009-MDJM, considering
that with this research work has demonstrated the high degree of noise pollution by the
motor vehicle eet that exists in its geographical space.
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