Abstract

Noise pollution is considered to be one of the most prevalent environmental challenges affecting human health. Noise pollution is increasing in cities needing techniques to monitor and predict the noise. The monitoring of traffic noise levels in different parts of the cities at different times has become very difficult due to logistic constraints. It is thus required to measure the noise levels at certain strategic locations, such as, near the noise sources (e.g., roads), and then to utilize it to predict the noise levels at surrounding locations. The challenge of monitoring the noise near several road crossings in a city can be reduced using a smartphone-based noise monitoring technique. However, the prediction of noise levels and showcase it as maps require terrain data, noise data, and noise prediction models. The requirement of terrain data can be met using open-source terrain data, from which various terrain parameters can be extracted and integrated with a standard prediction model on the web platform to predict the noise map for an area. Smartphone-based noise monitoring and its subsequent mapping can be a very popular and effective option, which uses a crowdsourcing approach. The entire methodology is tried to be applied over Lucknow city in India. Noise levels are monitored at three different slots, daily, over 14 road crossings using the smartphone-based app. Further, collected noise levels were calibrated against standard noise meter to ascertain accurate noise levels for these locations. Thereafter, three categories of noise environments are chosen from it and mapped using open-source satellite images and standard noise models, over the web on the GIS platform. The predicted noise levels on the maps were verified with the recorded noise data from similar locations using standard noise meter. For these three crossings at different times the predictions are found to be accurate within ±4.5 dB.
COLLABORATIVE NOISE MAPPING USING SMARTPHONE
Rakesh Dubey 1,*, Shruti Bharadwaj 1, MD Iltaf Zafar 1, Vinamra Bhushan Sharma 1, Susham Biswas 1
1Dept. of Chemical Engineering and Engineering Sciences, Rajiv Gandhi Institute of Petroleum Technology Jais, Amethi,
Uttar Pradesh, INDIA- (pgi19001, pgi17001, mzafar, pgi19002, susham)@rgipt.ac.in
KEYWORDS: GIS, GPS, Noise Mapping, Noise Modeling, Smart Phones, Road Traffic Noise
ABSTRACT:
Noise pollution is considered to be one of the most prevalent environmental challenges affecting human health. Noise pollution is
increasing in cities needing techniques to monitor and predict the noise. The monitoring of traffic noise levels in different parts of the
cities at different times has become very difficult due to logistic constraints. It is thus required to measure the noise levels at certain
strategic locations, such as, near the noise sources (e.g., roads), and then to utilize it to predict the noise levels at surrounding
locations. The challenge of monitoring the noise near several road crossings in a city can be reduced using a smartphone-based noise
monitoring technique. However, the prediction of noise levels and showcase it as maps require terrain data, noise data, and noise
prediction models. The requirement of terrain data can be met using open-source terrain data, from which various terrain parameters
can be extracted and integrated with a standard prediction model on the web platform to predict the noise map for an area.
Smartphone-based noise monitoring and its subsequent mapping can be a very popular and effective option, which uses a
crowdsourcing approach. The entire methodology is tried to be applied over Lucknow city in India. Noise levels are monitored at
three different slots, daily, over 14 road crossings using the smartphone-based app. Further, collected noise levels were calibrated
against standard noise meter to ascertain accurate noise levels for these locations. Thereafter, three categories of noise environments
are chosen from it and mapped using open-source satellite images and standard noise models, over the web on the GIS platform. The
predicted noise levels on the maps were verified with the recorded noise data from similar locations using standard noise meter. For
these three crossings at different times the predictions are found to be accurate within ±4.5 dB.
1. INTRODUCTION
Noise is a general problem that carries important health
concerns. We are moving towards modernization with the
advancement in technology. Better-suited technologies make
our life easy and convenient (Tandel and Sonaviya 2018).
However, newer technologies or progress are also associated
with health hazards. The health issues are more prominent in
developing countries as compared with developed countries.
India is trying to diminish the effect of noise to a manageable
level by monitoring, analyzing, and warning the public of its ill
effect. The problem of noise is prominent in cities. This is more
because of the traffic noise. Other than traffic noise, the
activities in market places, buzzing of loudspeakers, and
ancillary activities add noise hazards in cities. Further in India,
land use pattern is mixed, thus silence zone (hospital, school,
residential area) are next to the busy road or market places.
Traffic noises are associated with health hazards such as
cardiovascular diseases, hearing disorders, high blood pressure,
annoyances, etc. Hearing a loud noise in city ambiance during
day and night impacts the physiological changes like hearing
impairment. It also can have some psychological hazards
associated with it. In a recent study in Spain (Münzel et al.
2018), a report states that noise pollution has become so
dominant that it has crossed the environmental risk factor of air
pollution. Also, its effect is often overlooked despite being
linked to an increased risk of early death. Incidentally, there are
other factors, e.g., air pollution, hereditary disease, social
lifestyle diseases (overconsumption of alcohol or smoking)
which can also make damages to health, showing similar
symptoms.
______________________
* Corresponding author
So, it becomes challenging to find the real causative factors for
the above health concerns. The main strategies that have to be
made to combat such issues require accurate monitoring of
traffic noise, measurement of traffic noise for large city areas,
determination of noise hazards for different parts of cities, and
estimation of health issues taking the inputs from people of the
city. The government needs to aware of the public frequently
with proper campaigns.
In that context of proper management of the traffic noise, it is
required to comprehensively monitor and /or map the noise
levels of different parts of the cities. It is not feasible to measure
the noise levels for large areas of any city all the time. To do it,
a large number of expensive noise meters or sound pressure
level meters will be required. In the absence of such big
hardware resources, it becomes necessary to measure the noise
levels at certain strategic locations, e.g., near noise corridors of
roads and then model or map it to its nearby areas. Further, the
collection of accurate noise data near noise sources also
becomes a challenge due to the requirement of a noise meter. It
is required to have a low cost, efficient, and convenient
mapping solution. Thus, smartphones giving noise maps are
thought of. However, the smartphone-based technique will
have a problem of accuracy for the collected noise data, which
is required to be improved. Smartphone-based technique if
acceptable will have the advantages of collecting the data from
large areas taking the input from the dense population in cities.
The efficient crowdsourcing technique over the internet
provides a big opportunity to utilize the monitored data to map
the noise levels for cities. Noise mapping requires noise data,
terrain data, and noise model. Once the above data are managed
it can be mapped over the ArcGIS platform.
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2. LITERATURE REVIEW
2.1 GIS-based Noise Mapping
This GIS-based mapping has boomed in the recent past with its
application in merely all the fields. It has also has come up in
broadening of access to spatial data. In the current scenario, in
respect of noise pollution that can have a serious health concern,
GIS-based noise mapping has become a very useful application.
It offers numerically simulated data with software specially
developed for environmental noise prediction applications. It
uses the principles of acoustics from different sources (Bocher
et al. 2019). Here, the GIS-based calculation is done in the
background with proper visualization and analyzation
embedded in the spatial domain. With the above background,
the authors tried to carry out the study in Lucknow city. It was
required to ascertain when, where, or how an efficient mapping
can be carried out. The monitoring of noise data, the use of
spatial data, and modeling to map the noise over arc GIS
platforms are required to be ascertained. The projected noise
maps once prepared are required to be compared spatially for
relative impacts. Further, this map was also required to be
linked with people’s concerns for health due to noise pollution.
In this way, people in a different place, a time slot can
experience and participate in computing out the noise pollution
at their end.
A coherent study was carried out in the city of Bengal where the
time-space and noise distribution for traffic over the roads and
the nearby building were determined using the SPL (Sound
Pressure Level, Meter) and was mapped on web-platform using
the ArcGIS (Banerjee et al. 2008). It showed that the noise level
in all the studied locations exceeds the limit prescribed by
CPCB (Central Pollution Control Board, India). In the same
way on the same experimental setup the study was carried out in
the city of Amman in Al-Shaheed street, where the data was
recorded using the SPL and mapped using the ArcGIS and the
recorded data was computed over the map. Lequivalent values
determined there varied from 60 dB (A) to 77 dB(A). 25.5 %
reduction in light vehicles has resulted in 2.5 % of less noise
levels in some cases (Mofeed, Imam, and Jamrah 2013). On the
other hand, in the city of Nigeria, similar data were recorded
and showcased in the GIS platform.
Noise recordings were done for three slots in a day morning,
noon, and evening. On the GIS platform, different IDW
interpolations were made for the map and the different range
was set according to the WHO standard for the annoyances with
the help of spatial interpolations. The lowest daily average of
noise ranged between 67.2 dB(A) and 76.7 dB(A) across all the
land uses(Akintuyi, Raji, and Wunude 2014). Among the
related articles only one of the two studies has shown the
exposure of the noise effect using the audiometric test. Other
than this some have used the questionnaire survey to ascertain
the noise ambiance in an area. However, these surveys gave a
very average idea of noise for the area, as are limited to the
specific zone due to it’s the inability to consider the terrain
parameters (Biswas and Lohani 2008), and other circumstances
that change over time and place. Generally, these study of noise
pollution or their mapping approaches indicated
Different sorts of models were used in the studies. Also,
the questionnaires used were quite different from each
other as they didn’t have followed the same set of
questionnaires to draw any uniformity.
The sample sizes were different so the comparison of the
data from any referenced base data required extra
regression using a meta-analysis of these articles. Around
76% of the researchers have used the statistical approach.
Out of the reviewed article, 37% of the data have used the
questionnaire survey and were distinct from each other and
mapped roughly.
Noise pollutions in cities/towns of India were different and
show varied health hazards.
2.2 Web-based Noise Mapping
The primary goal of the web-based GIS platform is to provide
an easy and convenient way to derive the map on the ArcGIS
and can be easily be seen over the web by the users. Here, it is
assumed that users are having access to the internet. Different
types of data or parameters are uploaded on the web which is
spatially referenced and mapped over the internet. Further, it
offered the opportunity to share files that can easily be
monitored over the web. ArcGIS merely comprised of four key
parts, firstly the geographically based model for modeling;
secondly, it comprised of various components for storing and
managing geographic information; thirdly it offered the in
inbuilt applications for creating, editing, mapping, analyzing
and disseminating geographic information; and at last
the collection of web services that provided the content and
capabilities to networked software clients (Maguire 2016).
Parts of the ArcGIS software system can be developed on
mobile devices, laptops, and desktop computers and servers.
ArcGIS made it easy to analyze field data, both on the desktop
and the cloud. We can symbolize and classify our data, perform
spatial analysis. We can also present our results online as live
web maps, embed our maps in web pages, and use them to
create story maps. Esri has provided the platform from which all
the members can easily access and use the geographical
information within a collaborative environment. One of the key
ideas of the Esri Geospatial Cloud is the Web GIS pattern, that,
all members of an organization can easily access and use for
geographic information within a collaborative environment. It
provided a convenient interface that a non-technical person who
doesn’t have much of the knowledge can also leverage on the
GIS platform. It also made them usable and more accessible. It
provided a platform for integration, promoting cross-
organizational collaboration, and enabling decision-making
(Esri 2018).
2.3 Collaborative mapping through crowdsourcing using
smartphones
In the crowd-based noise mapping technique, the data is been
inculcated over the web from different sources working over the
region with the same platform of the same database and they all
are connected with the same interface. In this, the data is being
stimulated in a particular range where the correctly collected
data goes in the sink zone and rest goes in the trash which is of
no use. Later this collected data is being monitored and mapped
over the web using the ArcGIS and later on it can be seen using
the smartphone-based android app for GIS mapping. Also, in
this approach, the general public can participate in collaborative
mapping and compare the results over the web. The app-based
smartphone with inbuilt GPS when used can help to measure
the extent of spatial dispersion of any parameters. Thus, this
contribution from each individual is been shared in the form of
geo-localized measurement and then can be displayed with
personally performed annotation to produce any map for
different users. Contribution and data collation at different
stages will make the data more precise close to the true value.
Collection at each level by each individual is also being
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254
monitored/recorded and is possible to be taken into account by
the app for obtaining and retaining the personal interest or
customized maps (Bocher; et al. 2015).
3. RESEARCH GAP
Noise mapping required the detailed noise data of a large area.
Researchers primarily used limited noise data, which are
collected using costly Noise Meter. Further, it becomes a
challenge to collect accurate noise data at different times in a
day, over weeks, and different seasons. Furthermore, the
mapping also required terrain data and the noise propagation
model. The smartphone equipped with a noise monitoring
App provided an opportunity to collect the noise data. Web
mapping with free geospatial data offered terrain information.
The integration of noise data with terrain data using the noise
model can generate noise maps. A smartphone-based technique
where users are encouraged to participate in noise monitoring to
determine the noise map for their surrounding areas will enable
a crowdsourcing technique for mapping.
4. OBJECTIVES
The authors here have tried to find out the techniques with
which they can measure the extent of noise pollution in city
areas using a low cost, and convenient means. Authors will try
to find the technique for
1. Use of smartphones with noise measuring applications to
capture the noise levels.
2. Use of open-source map to satisfy the need for geospatial
data.
3. Use of crowdsourced noise data and open-source map to
generate the noise map for an area using noise model.
5. METHODOLOGY
The authors have chosen Lucknow city for noise mapping. The
city offered different types of traffic environments. It was
planned to characterize the traffic noises in different categories
and then utilize the data for mapping the noise for surrounding
areas. The noise data were collected for 14 locations at different
slots (morning afternoon and evening) using a smartphone-
based noise measuring application. Further, the mapping is
planned to be done using measured noise data. Once the noise
data were captured, it was combined with terrain data. The
terrain parameters required for noise mapping were extracted
from open source terrain data. The extraction of terrain
parameters and mapping is conducted using previously
developed algorithms by the authors. Due to the lower
precision of noise measurement, noise application in the
smartphone was also calibrated with a standard noise meter or
sound pressure level meter. The calibration values were then
applied over the noise data captured through the noise app, to
gather accurately measured noise data for an area. The
smartphone-based technique for monitoring was planned to
work as a user-friendly and convenient means of data collection.
The app which has been calibrated with the standard noise
meter was used to generate the noise map for three categories of
locations (varying in noise character). This was done after
measuring the noise levels at 14 locations of Lucknow city with
10 minutes sampling in three slots daily, through the mobile
app.
5.1 Calibration of the Mobile app with SPL
The literature reviews suggested, that the authors can easily
predict using iPhone and flagship phones of Samsung like
note8, note9, A30, A50, etc giving better results as compared to
other phones with good microphone quality. The app is being
made to collect noise data. However, the recorded noise levels
may not be accurate from the mobile app, requiring calibration
with the standard sound pressure level meter (noise meter). A
different technique for calibration can be a mathematical
approach and a conventional approach with varying sounds. In
the present study, the tonal sounds and the white noise from a
good source were played continuously. The uncalibrated
smartphone was placed next to the calibrated or the class 1 SPL
at a distance of 1m away from the source. Most of the
smartphones have dynamic operating noise of high amplitude
above 80 dB(A) and were not taken into account. Continuously
varying the tonal sounds were played and recorded using the
smartphone and SPL meter. This had provided the variation or
changes over the octave bands. The author could predict the
variational changes from the data that was made in the app. In
the current research, a noise App called NOISECAPTURE was
calibrated with the standard sound pressure level meter CESVA
SC310.
5.2 Computation
Various terrain parameters such as the distance of a receiver
point from source, path loss in diffraction, etc are determined
and used for computation of noise reduction or attenuations at
different locations around the noise sources (i.e., roads). These
are used finally to predict the noise levels and display them as a
map. Different computations which are been used in calculating
the attenuations are as follows:
(1)
(2)
(3)
where D.A.= distance attenuation
L = logarithmic summation
B.A.= barrier attenuation
D = direct transmission path
N = Fresnel number
Also, in this, the authors have computed for many points around
the road crossings and building/barriers, and then the map is
generated in the ArcGIS platform. Here in this, the barrier
attenuation value lies between 0 to 5, with higher values for
larger path loss. The height of the building is being determined
using the previous knowledge of the same in the area. The
background noise for the area is assumed to be equal to be 30
dB(A). Thus, during noise mapping, the authors have changed
the values of distant points, if the determined value after
attenuation was found to be less than 30 dB(A).
6. RESULTS
In this basically, the calibration of smartphones with standard
sound pressure level meter was shown in Table 1 and Table 2
respectively where the tonal sound (having single frequency)
was allowed to monitor or measure the noise of different
frequencies simultaneously with noise capture and the standard
sound pressure level meter or noise meter keeping it at a
distance of 1 meter away from the sound source. From the
observation, authors found that generally, the
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255
NOISECAPTURE measures the different tonal sounds at a
variation of around 1 to 5 dB(A) less than standard noise meter
value. These calibrated values were also added on noises of
different frequencies to determine the accurate noise data as
measured through the smartphone-based NOISECAPTURE app
Further, the authors also conducted separate measurements at a
few road crossings. Where noise levels were measured using
NOISE CAPTURE and standard noise meter. Typical
parameters such as N50 and maximum and minimum noise
levels were measured through these instruments and were used
to ascertain ranges of calibration as given in the following
tables.
In Table 1, basically calibration of Polytechnic chauraha data
collected using Sound Pressure level meter and Mobile based
app is been shown. Here in this the 4 coordinates having
different values where both the SPL and Mobile based app are
used together to monitor the noise. The difference between the
SPL and mobile app is been calculated and then the average is
found which show the range of variation of value
Polytechnic Chauraha
N50
(SPL)
M50
(app)
Diff.=
N50-
M50
Range
X
Y
80.9958°
26.8731°
67.9
68.9
-1
±4.225
80.9936°
26.8728°
78.6
68.9
9.7
81.0005°
26.8732°
74.0
68.9
5.1
80.9999°
26.8738°
71.0
68.9
2.1
Table 1. Table showing the calibration using the L50 values
from the mobile app and SPL.
In Table 2, the maximum value and the minimum value both at
the SPL and the mobile app is measured and then the difference
in both the instrument is calculated. With the calculated value
authors will find the average of difference that gives the
marginal average change in the maximum-minimum values. So
from the below calculations, the authors have shown the
calibration.
Location
Source
Max.
Value
Min.
Value
Polytechnic
SPL
83.6
64.7
App
75.7
63.6
Difference
7.9
1.1
Average
±4.5
Table 2. Table showing the calibration using the difference in
the maximum and minimum value of SPL and mobile App
Out of 14 locations, authors have chosen the three different
noise ambiance that are polytechnic chauraha, Hazrat ganj
chauraha, and haniman chauraha. Polytechnic chauraha which
comes in high noise zone area comprises a mixed traffic noise
pattern. This crossing is merely surrounded by government
offices, malls, college, and few shops and the main source of
noise are due to the traffic through heavy vehicles and dense
traffic that remains throughout the day. Hazratganj which
merely comprises of the market area and the main source is
surrounding noise and traffic noise. haniman chauraha is ear to
residential area and hospitals with a low level of noise
throughout the day. Noise ambiance is analyzed for three
different slots for three chosen locations. Figure 1 shows the
noise map for the Polytechnic morning slot.
Figure 1. Noise map of Polytechnic Chauraha morning slot with
value variation is between 30.00 to 75.08. Barrier and distance
attenuation is also calculated and mapped.
For the same location, the traffic noise variation is analyzed for
the afternoon slot which has been shown in Figure 2 with the
same parameters
Figure 2. Noise map of Polytechnic Chauraha afternoon slot
with value variation is between 30.00 to 75.99. Barrier and
distance attenuation is also calculated and mapped
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For the evening slot, the noise map for Polytechnic Chauraha
has shown in Figure 3.
Figure 3. The noise map of Polytechnic Chauraha evening slot
with value variation is between 30.00 to 76.57. Barrier and
distance attenuation is also calculated and mapped
Secondly, for Hazratganj Chauraha where the crossing is
surrounded by large buildings and complexes. The main source
is the vehicles, environmental noise, and market area noise, that
remains throughout the day. This comes in a medium noise zone
area. Figure 4 shows the noise map for the morning slot.
Figure 4. The Noise map of Hazratganj Chauraha of morning
slot with value variation is between 30.00 to 69.39. Barrier and
distance attenuation is also calculated and mapped.
The afternoon slot for Hazratganj Chauraha noise map has
shown in Figure 5.
Figure 5. The noise map of Hazratganj Chauraha afternoon slot
with value variation is between 30.00 to 69.68. Barrier and
distance attenuation is also calculated and mapped.
For the evening slot, the noise map has shown in Figure 6. The
noise values are less as compared to the morning and afternoon
slots.
Figure 6. The Noise map of Hazratganj Chauraha evening slot
with value variation is between 30.00 to 67.78. Barrier and
distance attenuation is also calculated and mapped.
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The haniman chauraha which is near to the residential area and
surrounded by house and hospital and clinics. Here the
background noise, environmental noise, and few vehicles over
the period are the main source of the noise. Figure 7 shows the
noise ambiance for Haniman Chauraha in the morning slot.
Figure 7. The noise map of Haniman Chauraha of the morning
slot with value variation is between 30.00 to 56.63. Barrier and
distance attenuation is also calculated and mapped.
The afternoon slot for Haniman Chauraha noise map has shown
in Figure 8.
Figure 8. The noise map of Haniman Chauraha of the afternoon
slot with value variation is between 30.00 to 59.79. Barrier and
distance attenuation is also calculated and mapped.
The evening slot for Haniman Chauraha noise map has shown
in Figure 9.
Figure 9. The noise map of Haniman Chauraha of evening slot
with value variation is between 30.00 to 60.07. Barrier and
distance attenuation is also calculated and mapped
The three locations Polytechnic Chauraha, Hazratganj
Chauraha, and Haniman Chauraha, were also considered based
on the noise value that has been measured and also based on
recurring survey. By inculcating the prediction model over the
noise sources the values over the whole region have been
calculated and that provide the variational change in the noise
value throughout the day over different slots and the
characterized changes can help in determining the level of
noise. In the below Table 3, noise ambiance of 14 location has
been shown which is the detailed noise survey of the locations
that are Hazratganj, Parivartan Chowk, Command Hospital,
Loreto Chauraha, Alambagh, Avadh Chauraha, Engineering
College, Munshi puliya, Polytechnic Chauraha, IGP Chauraha,
Amity Tiraha, Haniman chauraha, Patrakar Puram, and
Husadiya Chauraha. The data for the above-mentioned locations
have been collected using the NOISE CAPTURE mobile app.
Different nomenclature has been used in the table to rectify the
problem of knowing the parameters. Here in this M, A, E stands
for Morning, Afternoon, and Evening slots respectively. On the
other hand L, M, H stands for Low, Medium, and High level of
noise that has been monitored. The Leq (Lequivalent) value is
measured fro different slots for all the locations and later the
average value for the location is calculated that has been shown
below. To know the level of exposure throughout the day as
sudden a variation in the value may merely affect more over the
health aspect of various individuals. From the below values, one
can easily predict out that certain locations are having the Leq
(Lequivalent) values higher than the permissible exposure limit of
the CPCB (central pollution control board, India). People
concerning the above crossings complained about impacts
various losses like physical, and psychological.
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From the above result authors could know that after calibration
of a smartphone with standard sound pressure level meter using
the tonal sound, the authors had to do a top-up of 0-4.5 dB(A) in
different frequency-specific noises to determine the accurate
value of noise as measured through NOISECAPTURE. Also,
from the map showing a different color pattern, the authors
could determine that Polytechnic Chauraha comes in high-risk
noise zone area, on the other hand, for medium noise zone area
i.e. Hazratganj Chauraha is there and for Haniman Chauraha
which comes in low-risk zone area which is showing very less
variation in noise level. The mapped noise levels were also
compared in the above 3 locations with the values measured
through standard noise meter (sound pressure level meter).
Generally, for several test sites, the variations were found to be
within ± 3 to 4 dB(A). Road traffic noises can have hazardous
health implications. People concerning the above crossings
complained about annoyance, hypertension, tinnitus,
cardiovascular disease, sleep disturbance, etc. This type of
problem in human beings impacts various losses like physical,
and psychological.
7. CONCLUSION
The authors have reviewed the work of various researchers
working in noise mapping area. The challenge of noise data
monitoring for large areas of cities is tried to be handled with a
smartphone-based noise capturing. Authors have tried to predict
the noise map using the noise model on a web platform. The
approach involved two aspects: easy and effective measurement
of traffic noise and creating a noise map of the whole area using
limited measurement data. As the authors are moving toward
modernization the need for a smart, convenient, and low-cost
technique for collection and mapping of noise data has been
fulfilled. A smartphone-based noise app has been utilized to
limit the need for costly SPL or Noise meter. Also,
crowdsourcing has been proposed on the web platforms to
accumulate noise data along with terrain data. Extraction of
terrain parameters and use of them with noise modeling
algorithms will cater to the need for the generation of noise
maps over the web environment. The ESRI’s ArcGIS offers the
opportunity to predict the noise values for different locations in
the city environment, and showcase the results as colorful maps
in the web platform. The collaborative mapping, and linking
that with the health parameters of users will give an idea about
hazardous scenarios present at various city crossings. The
detailed time, season, and location-specific mapping will inform
the users about the imminent danger. If a person needs to work
near any red zone of the map for a long duration, then he will be
getting greater exposure to noise with high dB values. Thus,
they will have a higher risk of health concerns as compared to
the person residing or working near any green zone.
8. FUTURE SCOPE
The users will be able to ascertain the likely impacts easily, at a
low cost using the mobile phone. Thus, it can be applied to any
desired location. People can get detailed information about
noise ambiance at a place and relate it to likely noise hazards,
unlike conventional practices. The study can also focus on
finding the choices of people of different areas for the hospital,
school, residential places vis a vis the noise impact to those
places. This will give the best or preferred locations for noise-
sensitive infrastructures. The cue from the above study will
guide determining the best locations for setting up a new
hospital, school, and residential buildings. As the entire
prediction could be made geospatially in a GIS environment, the
additional cue of distance from house to the hospital, house to
school, etc., the availability of conveyance, the nature of
transport, traffic condition, etc. could also be weighted to decide
the best or preferred location for noise-sensitive infrastructure.
ACKNOWLEDGEMENT
Authors would like to acknowledge the support from google
especially the google map for providing the authors the access
to their resources so that the authors can make the noise map of
different places without violating any norms and policies. Also,
the authors like to thank the noise capture app using which the
authors have collected the data and executed the work. Special
thanks to ESRI for helping the authors in mapping with their
software.
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Location
Type
of
noise
Leq
(M)
Leq
(A)
Leq
(E)
1
Hazratganj
M
69.4
67.8
69.7
2
Parivartan
Chowk
M
69.8
67.4
68.4
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Command
Hospital
H
75.4
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73.5
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Loreto
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H
71.2
74.5
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Alambagh
L
60.1
65.6
72.4
6
Avadh
Chauraha
M
69.7
65.4
72.3
7
Engineering
College
H
72.7
68.4
75.1
8
Munshi
Puliya
H
73.2
70.8
75.4
9
Polytechnic
Chauraha
H
75.1
76.0
76.6
10
IGP Chauraha
H
72.3
69.4
74.9
11
Amity Tiraha
L
62.8
60.2
64.6
12
Haniman
Chauraha
L
56.7
59.8
60.1
13
Patrakar
Puram
M
64.4
69.1
73.4
14
Husadiya
Chauraha
L
56.8
62.6
64.8
Table 3. The Leq values at a different location and the
different slot has been shown. Nomenclature H, M, L are
high, medium, low-level noise, and here M, A, E stands
for the morning, afternoon, and evening.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B4-2020, 2020
XXIV ISPRS Congress (2020 edition)
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-253-2020 | © Authors 2020. CC BY 4.0 License.
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XXIV ISPRS Congress (2020 edition)
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https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-253-2020 | © Authors 2020. CC BY 4.0 License.
260
... It must be specified that other similar events could have been organized, without having given rise to the creation of a specific tag, and without having informed the developers of the application. For example, this is the case for several recently published research works [53][54][55][56][57]. ...
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GIS-Based Assessment and Mapping of Noise Pollution in Bariga Area of Lagos State, Nigeria GIS-Based Assessment and Mapping of Noise Pollution in Bariga Area Of Lagos State
  • Akin Akintuyi
  • S Adekunle
  • Emmanuel Raji
  • Wunude
Akintuyi, Akin, Adekunle S Raji, and Emmanuel Wunude. 2014. "GIS-Based Assessment and Mapping of Noise Pollution in Bariga Area of Lagos State, Nigeria GIS-Based Assessment and Mapping of Noise Pollution in Bariga Area Of Lagos State, Nigeria Akintuyi, A. O *;
ArcGIS Secure Mobile Implementation Patterns
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Esri, An. 2018. "ArcGIS Secure Mobile Implementation Patterns." (November).