Noise vulnerability of stone mining and crushing in Dwarka river basin of Eastern India

Abstract

The present paper has intended to explore the noise level and vulnerability to noise produced in the stone mining and crushing area and the surroundings in the heavily stressed stone mining and crushing area of Middle catchment of Dwarka river basin of Eastern India. Field-based noise recording has been done at different times in every recorded days. Fuzzy logic-based weighting and integration of eight parameters have been done in four selected cluster for noise susceptibility mapping. For exploring noise annoyance odd and risk ratio have been computed for different communities. From the recorded noise level in stone crushing clusters, it is found that the noise level is > 85 dBA from 6 a.m. to 4 p.m. when the stone crushers are running on and it is above the ambient noise threshold defined by Central Pollution Control Board in 2000. Maximum noise level is recorded as 112.41 dBA which may cause deafness. Noise intensity gradually decreases from crushing centre towards outside and it prevails up to 500–650 m away from the crushing unit. Noise vulnerable areas constructed based on eight noise level addressing and noise effect indicating parameters using fuzzy logic revealed that about 10.46–27.98% areas fall under very high to high noise vulnerable zones. Therefore, the labourers and the peoples who are living at close proximity of crusher units are highly prone to noise pollution along with stone dust pollution. The effect of noise is highly age and sex sensitive due to their differences in physical strength. These findings could effectively be used for saving the exposed communities from noise vulnerability.

Full text

Vol.:(0123456789)
Environment, Development and Sustainability
https://doi.org/10.1007/s10668-021-01233-2
1 3
Noise vulnerability ofstone mining andcrushing inDwarka
river basin ofEastern India
SwadesPal1· IndrajitMandal1
Received: 12 July 2018 / Accepted: 8 January 2021
© The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021
Abstract
The present paper has intended to explore the noise level and vulnerability to noise pro-
duced in the stone mining and crushing area and the surroundings in the heavily stressed
stone mining and crushing area of Middle catchment of Dwarka river basin of Eastern
India. Field-based noise recording has been done at different times in every recorded
days. Fuzzy logic-based weighting and integration of eight parameters have been done
in four selected cluster for noise susceptibility mapping. For exploring noise annoyance
odd and risk ratio have been computed for different communities. From the recorded noise
level in stone crushing clusters, it is found that the noise level is > 85dBA from 6a.m. to
4p.m. when the stone crushers are running on and it is above the ambient noise threshold
defined by Central Pollution Control Board in 2000. Maximum noise level is recorded as
112.41dBA which may cause deafness. Noise intensity gradually decreases from crushing
centre towards outside and it prevails up to 500–650m away from the crushing unit. Noise
vulnerable areas constructed based on eight noise level addressing and noise effect indicat-
ing parameters using fuzzy logic revealed that about 10.46–27.98% areas fall under very
high to high noise vulnerable zones. Therefore, the labourers and the peoples who are liv-
ing at close proximity of crusher units are highly prone to noise pollution along with stone
dust pollution. The effect of noise is highly age and sex sensitive due to their differences in
physical strength. These findings could effectively be used for saving the exposed commu-
nities from noise vulnerability.
Keywords Stone mining and crushing· Noise level· Noise vulnerability· Fuzzy logic and
noise annoyance
Supplementary Information The online version contains supplementary material available at https ://
doi.org/10.1007/s1066 8-021-01233 -2.
* Swades Pal
swadespal2017@gmail.com
Indrajit Mandal
indrajitgeofarakka@gmail.com
1 Department ofGeography, University ofGour Banga, Malda, India
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S.Pal, I.Mandal
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1 Introduction
World Health Organization (WHO) has defined noise is the undesirable or superfluous
sound that can leave detrimental effects on human psychology, physiology and environ-
mental health. Central Pollution Control Board (CPCB) (2000) has defined the noise levels
in industrial, commercial and residential areas during day and night times as mentioned
in Table1. For instance, in residential areas, noise level should not exceed 55dBA and
45dBA in day and night times, respectively. In recent times, noise pollution is an emerging
issue which causes lots of serial problems to the human beings as well as animal species
and environment all over the world (Rosenburg 2016). Noise pollution in stone mining and
crushing industry is one of the major environmental problems (Farzana et al. 2014; Pal
and Mandal 2017; Hebbal and Kadadevaru 2017). Drilling, blasting, serial crushing are
the major activities which lead to the high level of noise often more than 85dBA (Pal and
Mandal 2019a, b). Sayara (2016) highlighted the different environmental concerns by the
stone quarrying industry such as land disturbance, noise pollution, emission of dust, and
ground vibration. Blasting is the most useful technique of the world for rock fragmentation
in quarry and mining activities which produce high level of noise (> 120dBA) (Armaghani
etal. 2015). These blasting operations as per human perception create massive noise in the
middle catchment area covering parts of the administrative unit like Pakuria block of Pakur
district, Shikaripara, Kathikund and Gopikandar block of Dumka district of Jharkhand
state and Rampurhat I, Md.Bazar and Nalhati I block of Birbhum district of West Bengal
which are the principle areas of interest of the present work. Some of the crushing units are
located mainly within the residential areas, and therefore, the people are the direct suffer-
ers. Ground water and geo-environment of the quarry area have been getting deteriorated
due to the blasting operation of the stone mining centre (Hajihassani etal.2014). Since last
century, man-induce noise pollution has been increasing with very rapid pace (Sati 2015).
Farzana etal. (2014) studied the effects of noise on health among stone crusher workers. In
Denmark, 200–500 deaths happened in a year due to excessive noise pollution (Sørensen
2016). So many studies have addressed the effects of the noise pollution in several cities
in the world (Bhosale etal. 2010; Hunashal and Patil 2012; Balashanmugam etal. 2015).
Deafness, high blood pressure, heart failure are some of the crudest consequences of noise.
Worldwide, 360 million peoples are prone to hearing loss due to noise. Out of them, 27%
and 22% are in South and East Asia, respectively. Among different forms of noises, noise
at occupational place is the most crucial reason behind hearing loss (WHO 2012). Consid-
ering the concern of noise pollution, European Union announced the regulation 2002/49/
EC for the investigation of the environmental noise through common mapping activities
(Asdrubali and D’Alessandro 2018; Murphy and King 2014). Hunashal and Patil (2012)
also developed the noise pollution indices for highlighting the impact of noise pollution on
Table 1 Noise-level standards
as per CPCB (2000) in different
residential and working areas
The noise pollution (Regulation and Control) Rules, 2000
Category of the area Sound limits in dBA
Day time Night time
Industrial area 75 70
Commercial area 65 55
Residential area 55 45
Silence area 50 40
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Noise vulnerability ofstone mining andcrushing inDwarka river…
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urban environmental and public health in the city of Kolhapur of Maharashtra. The used
machines for mining and crushing activities produce high level of noise and it ranges from
90 to 115dBA. Constant exposure to the high-level noise damages human hearing func-
tions, even it causes diseases like cardiovascular and nervous system (De paiva et. al. 2015;
Paneto etal. 2017; Alves etal. 2018), sleeplessness etc. Apart from the noise produced
from direct mining and crushing activities, some other ancillary activities like movement
of heavy vehicles (mainly truck) for carrying out stone, loading activities are also responsi-
ble for noise pollution.
Analysis of noise at the bare region is quite meaningless. High noise for a long dura-
tion is more vulnerable than the noise at very short duration. At the same time, how many
people at the residential place and working place experience noise effect is also another
dimension of analysing noise vulnerability. Therefore, noise level is not the sole indicator
of what possible effects that may exert on people. Considering this view, multi-paramet-
ric approach of vulnerability is tried to assess. This sort of approach is pertinently used
by Das etal. (2019) in a high traffic dense town of West Bengal, India. Multi-parametric
approaches of susceptibility assessment is frequently applied in different other fields like
landslide susceptibility (Chen etal. 2017; Basu and Pal 2018; Chawla etal. 2018; Kadavi
etal. 2018; Pandey et al. 2018), soil erosion susceptibility (Pal 2016; Pal and Debanshi
2018), vulnerability of wetland loss (Jiang etal. 2017; Mondal and Pal 2017; Das and Pal
2018; Saha and Pal 2019), pollution susceptibility (Nikolaou etal. 2008; Son etal. 2012;
Lautenschlager etal. 2013; Jindal etal. 2018) using relevant proxy variables.
Along with multi-parametric approach of vulnerability study, it is also relevant to men-
tion that noise effect strongly varies from man to man based on their physiological and
mental strength(Mandal and Pal 2020a, b). A person with same age and sex group may
react differently at a same noise intensity (Okokon etal. 2015; Beutel et al. 2016; Das
etal. 2019). For instance, a person prone to heart diseases may react more sensitively than
others (Sørensen etal. 2011; de Kluizenaar etal. 2013). But it is very difficult to quantify
properly. To solve this problem, most of the cases, scholars have adopted sample study
approaches. Few scholars and institutions have tried to propose acceptability scale of noise
based on human perception in general. The US Department of Housing and Urban Devel-
opment (USDHUD 1977) has proposed a noise acceptability scale in this regard. A few
other statistical and mathematical approaches like odds ratio, relative risk ratio are also
commonly used to capture the sensitivity at same intensity of noise.
Noise mapping is done for monitoring the noise level as far the literatures are concerned
but very few of them addressed multi-parametric noise vulnerability state of a region in
general and stone mining and crushing area in particular. The present study in this regard
has intended to monitor the noise level in different times in some selected stone mining
and crushing clusters of the middle catchment of Dwarka river basin of Eastern India and
also applied multi-parametric model for identifying the most vulnerable sites of noise
vulnerability. Measuring noise annoyance among different exposed communities has also
attempted to explore.
2 Study area
Dwarka River basin (total basin area 3882.71 km2, length of the main river channel is
156.54 km) is a sub basin of Mayurakshi River situated in Chotanagpur plateau fringe
(40% of the total basin area) and Rarh tract of West Bengal (region composed mainly with
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S.Pal, I.Mandal
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laterite soil). Geologically, the upper and upper middle catchments are characterized with
Dharwanian sedimentary rocks followed by Hercynian orogeny (360–300 million years
ago) (Jha and Kapat 2009). This deposition was taken place mainly in Cambrian-Silurian
period (500 million years ago), and it is highly rich for stone mining and associated crush-
ing activities. The lower part of the middle catchment of this basin is composed with lat-
eritic soil and clays impregnated with caliche nodules. Elevation of this basin ranges from
2 to 497m. There are 239 stone mining and 982 stone crushing centres as per the tally of
2017, and these are found mainly over the upper middle catchment of the basin (Fig.1).
These mining and crushing centres have been generating massive noise due to mining and
crushing operations. Four major mining and crushing clusters have been taken into consid-
eration for the present study. Climatically, the study region falls under sub-tropical mon-
soon climate characterized by strong seasonal hot and cold temperature spell and dry and
wet rainfall spell. Average annual rainfall of this region is1500–1600mm, out of which
80% happens during monsoon season.
3 Materials andmethods
3.1 Method fornoise recording andspatial noise mapping
For measuring the noise, we have selected four crushing and mining clusters and recorded
the noise level up to 1000m apart from the major noise source point. For recording the
noise level, we have used the Digital Sound Meter (LT SL 4010). This instrument meas-
ured the data ranges from 30 to 130dBA with an accuracy level of ± 1.5dBA and resolu-
tion of 0.1dBA.
Diurnal noise for the month of January 2017, 2018 is measured in12 crushing and
12 mining centres selected from the clusters and average of them is presented in graph
for individual cluster separately. Noise level is recorded from early morning to midnight
(6a.m. to 12 mid night). For spatial noise mapping 240 noise monitoring (60 sites from
each cluster) sites over the selected clusters have been selected and recorded noise during
working period (from 6a.m. to 4 p.m.) when the stone mining and crushing centres run.
Noise recording has been done for 5days and average noise level (log average) is taken for
preparing noise map with the help of digital elevation model (DEM) technique in Erdas
Imagine (Version 9.2). Mehdi etal. (2011) used inverse distance weighting technique for
Karachi city in Pakistan and Tsai etal.(2009) used the same technique for Taiwan city for
noise mapping. To show the change of noise level from the crushing and mining centre
towards the outside area, noise has also been recorded at every 50m distance from source
to 1000m away. Here, it can be mentioned that Sheng and Hu (2003) used this method for
analysing distance decay pattern of noise from source point.
In mathematics, the logarithmic mean expresses as a function of two nonnegative num-
bers which equals to their difference divided by the logarithm of their quotient. Diurnal
average and site-specific average noise is computed based on logarithmic mean. Logarith-
mic mean is computed here because the doubling of noise level does not mean simple dou-
bling of noise intensity rather it is more than that (Eq.1).
(1)
l
.m.=10 log
i=n
∑
i=1
(10)li
10 ∗
ti
tt
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Noise vulnerability ofstone mining andcrushing inDwarka river…
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Fig. 1 Location of the study area: a Dwarka river basin within India, b basin extension within Jharkhand
and West Bengal, c selected clusters over the Dwarka river basin
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S.Pal, I.Mandal
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where l.m = logarit hmic mean, n = number of noise samples, li = the noise level of any ith
sample, ti = time duration of ith sample, tt = total time period of the event.
3.2 Assessing noise vulnerability
3.2.1 Selection andpreparation ofdata layers fornoise vulnerability assessment
Spatial noise mapping is not just sufficient for showing the actual noise vulnerability as
some other issues are concerned with this. Crusher density, density of vehicles, worker
density at work place, population density in the crushing area are very vital factors for
assessing vulnerability of noise. Considering all these facts, noise vulnerable zone has been
prepared using eight selected parameters for all the selected clusters, i.e. (1) average noise
level (2) crusher density (3) daily vehicle frequency (4) duration of noise level (5) density
of workers (6) density of women worker (7) population density (8) mine density. Among
the aforesaid parameters, first four and last parameters are noise-producing variables and
parameters 5, 6, 7 are related to noise exposition. Average noise-level map has been pre-
pared based on recorded noise level at 60 sites following the principles of DEM prepara-
tion. For this latitude, longitude and noise level have been recorded at each site, and these
have been plotted in Erdas Imagine. Crusher density, daily vehicle frequency, mining den-
sity data layers have been prepared following grid method. Each cluster is subdivided into
49 equal grids and respective values have been computed at each grid and representing the
values at the centroid, and DEM for the mentioned parameters has been prepared. Popula-
tion density map has been constructed based on the village-level data within the cluster.
Fig. 2 Spatial data layers for cluster 2 for constructing the noise vulnerability model. a Average noise level,
b crusher density, c daily vehicle frequency, d duration of noise level, e density of worker, f density of
women worker, g population density, h mine density
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Rest other data layers have been developed based on the mine and crusher-specific data.
For instance, worker density map has been prepared based on the workers employed at
each crusher and mine unit. Figure2 displays the spatial data layers for cluster 2. In case of
other clusters, spatial data layers have been prepared following same method as prepared in
case of cluster 2. Selection of parameters are based on the field experience and the works
of the predecessors in this field. Jakoyljevic etal. (2009) and Mehdi etal. (2011) also stud-
ied the noise vulnerability by using the same kind of parameters for their respective study
regions. Individual figure in all eight cases show that noise intensity, vehicle density, popu-
lation density, duration of crusher operation, density of workers are high in the middle part
of the selected clusters area where mining and crusher density are considerably high. Noise
level in this area ranges from 85 to 109dBA, duration of crusher machine operation is
6–9h in the highly dense crusher areas. Considering all these, the present study area has
been selected as a representative case.
3.2.2 Vulnerability zoning using fuzzy logic
Fuzzy logic or fuzzy inference system, also known as fuzzy expert system, is a mathe-
matical system which is formed by multivalue logic base mathematical tool. Zadeh (1965)
introduced the theory of fuzzy set, since then it has been broadly applied in complex model
development in different fields. Since this approach defines different degrees of vulnerabil-
ity, it has a wide applicability in geo-environmental vulnerability assessment. Fuzzy logic
system simulates the whole system to depict conjecture out of inputs spatial data layers of
the model. The Mamdani method (Yanar 2003) is the greatest used fuzzy logic system.
This Mamdani method attempts higher performance to appraise people’s know-how and
factual involvement in classification as well as accountability (Mamdani 1977). This sys-
tem mainly constitutes of four stages (Sami etal. 2014) namely—(1) fuzzification (2) rule
evaluation (3) inference of fuzzy products (iv) de-fuzzification.
This mathematical system flourishes through the fuzzy inference tool in the ArcGIS
Software (v.10.3). The entire model has been accomplished in ArcGIS environment by
applying two tangible tools—(1) membership fuzzy tool (2) fuzzy overlay tool. Firstly, all
the data are needed to be converted into fuzzy sets and then classified into four catego-
ries highlighting the high vulnerability to low according to the expert’s judgement. Con-
sequently, amid a few subsisting membership function, we have used large membership
operation with the help of Eq.2. Large membership function has been used to indicate the
fact that large values of the input raster have high membership in the fuzzy set.
where µ(x) = fuzzy membership function, fi = spread (default is 5 for large membership in
ArcGIS environment) fj = midpoint of the range of values of the input raster data layers,
x = degree of a particular element of a fuzzy set. According to this approach, membership
value of the different elements indicate varying degree of support and confidence ranging
from 0 to 1 (Ercanoglu and Gokceoglu 2002). Here the fuzzy set is formulated in Eq.3.
where a = fuzzy set, x = membership value of the elements, fx = fuzzy membership func-
tion. In this study, fuzzy ‘if–then’ method is formulated to make a zonation of noise
(2)
𝜇
(x)=
1
1+(x
f
j
)−fi
(3)
a={x(fx)}
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S.Pal, I.Mandal
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vulnerability. The noise vulnerability maps have been categorized into different zones
(very high, high, moderate and low) to explore the spatial differences of that vulnerability.
The fuzzy rules have been defined considering the following logical structure—If average
noise level is high or crusher density is high or daily vehicle frequency is high or duration
of noise level is high or population density is high or density of worker and female workers
is high then noise vulnerability will be high.
3.2.3 Method forvalidating thenoise vulnerability model
For validating the prepared noise vulnerability models, receiver operating characteristics
(ROC) curve has been prepared for all the clusters. This receiver operating characteristics
(ROC) curve is developed to represent the true positive rate (TPR) in respect of false posi-
tive rate (FPR) at assorted threshold context. The area under curve is defined as the rate of
goodness of fit. It helps to compute succession and prediction rate. Among the total train-
ing data set, 82% is used for calculating the succession rate and remaining 18% dataset is
used for calculating the prediction rate. Rasyid etal. (2016) categorized this area under
curve into five classes such as (1) excellent (0.90–1.00), (2) good (0.80–0.90), (3) satisfac-
tory (0.70–0.80), (4) poor (0.60–0.70) and (5) fail (0.50–0.60). SPSS software (v.17.0) has
been used for preparing the ROC curve as well as the success and prediction rates.
3.3 Methods foranalysing noise sensitivity andexposure tonoise
It is not certain whether the impact of noise at a specific intensity to the exposure group
is stressful or not. For calculating sensitivity of noise level in the stone mine and crush-
ing units, the noise acceptability scaling of the United States Department of Housing
and Urban Development (USDHUD 1977) is followed. It devised 4 point (0–4) quali-
tative rating scale where 0 means “not at all”, 1 means “slightly”, 2 means “moder-
ately”, 3 means “highly” and 4 means “extremely” unacceptable. “Not at all” means
the effect of noise is very natural. Noise level according to USDHUD (1977) within
62dBA is acceptable and people can endure this noise intensity but beyond this peo-
ple may face different noise-related problems. Total 764 respondents from worker and
local resident communities have been interviewed and based on their responses; fre-
quency is calculated under each acceptability category. Stratified random sampling is
chosen for the selection of respondents for prioritizing different communities. Total 12
villages, three from each cluster, has been taken into account for this purpose. Total
target population is subdivided into two strata (1) mining and crusher centric worker
group and (2) residents who are not engaged into mining and crushing activities but
staying at the proximity area of mining and crushing units. Out of total mining and
crusher worker population, 382 persons (30% to total workers) has been taken as sample
and from other category 382 persons (10% to total population) has been chosen as sam-
ple. As the worker group are the direct sufferers, sample density is decided to be taken
more from this group. Hypothesis testing of the responses under different levels of noise
unacceptability has been done to judge the randomness of responses as well as selection
of sample. For this, simply it has been hypothesized that a same noise level stimulates
people uniformly. Chi-square (χ2) test has been performed in this purpose to all the
selected times both among the workers and villagers individually. The computed values
have then been tested at 1% level of significance. As deafness and hearing impairment
are not surveyed and no such concrete record was found, people’s perception is studied
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Noise vulnerability ofstone mining andcrushing inDwarka river…
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to know their attitude towards noise qualitatively. Survey conducted among male and
female workers and peoples living at very proximate area and 500m away from the
crushing unit to know how many of them from different groups experience headache.
A good number of female workers are engaged in this sector. It has been tried to know
whether female folk is more susceptible to noise effect than male.
For justifying this fact, odds ratio (OR) and relative risk ratio (RRR) have been com-
puted using four selected parameters (Table5) for this study. The number of respond-
ents is not uniform for all the parameters but in case of individual parameter number of
respondents from noise exposed and unexposed group is same. Exposed and unexposed
groups have been defined based on the field experience and previous literatures. Total
respondents are 764 among these respondents 382 respondents are selected from each
group (exposed and unexposed). For calculating odds ratio (OR) and relative risk ratio
(RRR) of each parameter, 2-by-2 contingency table is generated for all the parameters.
The odds ratio (OR) represents the odds of an outcome among exposed folks relative to
the odds of the same outcome in unexposed folks (Eq.4).
The notation of the equation is given in Table2. Odds ratio may vary from 0 to
infinity. Higher odds value means chance of vulnerability among the exposed group is
greater than unexposed group. For example if odds value is 3.25, it does mean exposed
respondents are 3.25 times more affected than unexposed group. Di Lorenzo et al.
(2014), Hancock and Kent (2016), Kalra (2016) also used the odds ratio for the analysis
of the public health status. The relative risk ratio (RRR) is defined as the ratio of risk in
an exposed group of people which is relative to the risk in an unexposed group of peo-
ple. The relative risk ratio is given in Eq.5.
When the relative risk ratio (RRR) > 1 means the relative risk increases to the expo-
sure group but the relative risk ratio < 1 means the exposure group has a protective
effect. The confidence interval, ci, is calculated using Eq.(6)
where Zα/2 is the critical value of the normal distribution at α/2.
(4)
[
(a∕b)∕(c∕d)=ad∕bc
]
(5)
[
a∕(a+b)]∕
[
c∕(c+d)
]
(6)
ci
=exp

log (OR)±Z𝛼∕2∗

1∕a+1∕b+1∕c+1∕d

Table 2 General structure of 2 × 2 contingency table for calculating odds and relative risk ratio
Respondent group Extremely affected Not or least affected
Exposed group a (affected from exposed group) b (unaffected from exposed group)
Unexposed group c (affected from unexposed group) d (unaffected from unexposed group)
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S.Pal, I.Mandal
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4 Results
4.1 Diurnal andspatial noise‑level status
Figure 3a–d presents the diurnal noise levels of the selected clusters. Noise level in
the densely crushing zone exceeds 85dBA for long period of time (6a.m. to 4p.m.),
and it causes the physical and mental stress of the exposed folks. High-level noise
duration is almost uniform in all the clusters. Crushing areas are more prone to noise,
and it decreases away from the main crushing centres. Spatial extent of high noise is
characterized by the areal coverage of crusher units and density of the same. In case of
clusters 2 and 4, this extent of high noise (> 85dBA) is 30–45% to total area (Fig.4).
This extent is quite low in cluster 1. Spatial extent of high noise level in a cluster is
characterized by the agglomerated location of mines and crushers. Total spatial extent
of each cluster is 19.36km2. The noise level at the proximate areas of crushing unit
most of the cases exceeds the threshold noise level (55dBA during day time) set by the
Central Pollution Control Board (CPCB, 2000). Noise level in this area is even more
than the threshold limits set for commercial (65dBA) and industrial (75dBA) regions.
Figure5 portrays the noise level change from crusher unit towards outside distances.
In all the cases, the noise level decreases with distance with varying rate. Nature of
slope of the noise level is mainly determined by the relative position of the crusher
unit. Stressable distance of noise from crusher unit varies from 500 to 650m in all the
clusters.
Fig. 3 Average (logarithmic) noise level of a cluster 1, b cluster 2, c cluster 3 and d cluster 4 from 6.00a.m.
to 12 mid night
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4.2 Noise vulnerability model andits accuracy assessment
Figure6a–d displays the noise vulnerability model of the selected clusters. Each model
is classified into four categories, i.e. very high, high, moderate and low vulnerable
zones. Very high-noise vulnerable zone covers 6.13%, 12.31%, 10.32% and 9.27% areas,
respectively, in clusters 1 to 4 (Table3). Very high and high-noise vulnerable areas have
been found at very proximate areas of stone crushing unit (Fig.6). Density and parallel
Fig. 4 Spatial pattern of noise level of the selected clusters. a Cluster 1, b cluster 2, c cluster 3 and d cluster
4
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S.Pal, I.Mandal
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Fig. 5 Average noise-level change from crushing centre to 1000m apart. Average of ten cross sections of
noise level in each clusters is taken into account for showing distance decay of noise. a indicates average
noise-level change at cluster 1, b at cluster 2, c at cluster 3 and d at cluster 4
Fig. 6 Noise vulnerability model using fuzzy inference system for a cluster 1, b cluster 2, c cluster 3 and d
cluster, e proportion of area under different vulnerable zones in case of different models, f receiver operat-
ing characteristics (ROC) curve of the noise vulnerability models
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running of all the crusher units yield excessive noise to the environment and make the
area vulnerable to noise pollution.
ROC curves help to estimate succession and prediction rate of the models (Fig.6f). The
rate of succession is computed based on the training database. The area under curve (AUC)
is 0.807 for cluster 1, 0.801 for cluster 2, 0.906 for cluster 3 and 0.914 for cluster 4. These
values indicate good success rate of the model.
4.3 Noise exposure andannoyance
Table4 displays that the 100% respondents opined that during the off period (mainly even-
ing and night time) of crushing/mining centres, noise level is generally acceptable. But
during working period, 73.55% workers at the crusher unit and 78.53% local residents who
live within 500m periphery from the crusher centres have responded that the noise level is
normally to clearly unacceptable. It does indicate that majority of the workers and villagers
are the victims of the noise exposure. Interestingly, the proportion is slightly less in case of
working population. But it does not indicate that they are less impacted. In fact, the work-
ers are quite habituated with this condition. At 3 degree of freedom and 1% level of sig-
nificance, the tabulated χ2 (Eq.7) value is 11.34 which is far less than computed values as
shown in Table4. Based on this, it can be stated that same noise intensity stimulates people
Table 3 Areal coverage of
different noise vulnerable zones
(in percent)
Name of the cluster
(total area 77.44 km2)
Low Moderate High Very High
Cluster 1 (19.36 km2) 78.76 10.76 04.33 06.13
Cluster 2 (19.36 km2) 44.21 27.79 15.67 12.31
Cluster 3 (19.36 km2) 54.06 19.86 15.74 10.32
Cluster 4 (19.36 km2) 56.30 20.68 13.73 09.27
Table 4 Exposure of workers and local villagers to different noise levels classified by the US Department of
Housing and Urban Development (USDHUD 1977)
NB: In day time crusher units are in operation from 6 am to 4pm; in evening and night time these are in
rest
Acceptability of
noise
extent of
noise annoy-
ance
Noise level
(dBA)
Percentage of respondents
(workers), n = 382
Percentage of respondents
(villagers), n = 382
Day time
(6 a.m. to
4p.m.)
Evening or
night time
Day time
(6 a.m. to
4p.m.)
Evening or
night time
Clearly accept-
able
Slightly < 49 12.04% 48.95% 9.88% 38.98%
Normally
acceptable
Moderately 49 to < 62 14.39% 51.05% 11.57% 61.01%
Normally unac-
ceptable
Very High 62 to < 76 28.01% – 25.05% –
Clearly unac-
ceptable
Extremely > 76 45.54% – 53.48% –
Chi-square (χ2) 31.21 27.01 77.92 30.26
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S.Pal, I.Mandal
1 3
differently and it is statistically proved. Earlier, it has been mentioned that noise level up
to 62dBA is normally acceptable as defined by USDHUD (1977). In this state, annoyance
level is not so high. But > 45% people have used to experience extremely high annoyance
level with a noise level of > 76dBA. Noise level above 65 dBA starts to effect on human
mind and physiology as per CPCB. So, noise level > 76 are really high to annoy people and
create stress on human health.
where Oij = observed frequency of sample, Eij = expected frequency of sample.
Table5 shows the Odds ratio (OR) and relative risk ratio (RRR) of four defined com-
munities among which some are exposed and some are the least unexposed to the noise.
Female workers are highly sensitive (OR value = 8.96 for cluster 1; 7.32 in cluster 2) than
the male workers in all the selected clusters (OR ranges from 1.65 to 2.11). From the OR
and RRR, it is clear that exposed aged people is 2.49 times more affected by the noise than
exposed young group. In case of parameter 3, it is revealed that those people who are liv-
ing in the mining/crushing area are 4.95 times annoyed than those are living in 500m away
from that area. From these analyses, it can be stated that noise annoyance is age, sex and
proximity to crushing/mining centre sensitive.
5 Discussion
Different effects of stone mining and crushing on environment and human health is well-
discussed in public and private levels. Noise effect of stone crushing on people is not
untold but less explored. Noise mapping and monitoring is not any new practice. The
present study has monitored 240 sites for recording noise data and noise maps have been
prepared based on these dataset. Apart from noise mapping at different period of times,
noise vulnerability mapping has also been done successfully using multi-parametric spatial
dataset. One group of parameter has been used to describe the noise producing variable
and another group depicted the noise exposure variable. So, the selection of parameters
satisfies the cause-effect relationship in noise vulnerability. This type of approach may help
the decision makers to justify the location of stone crushing within the human locality. It is
also the fact that as the resource is available in some specified geographical locations; other
factors are less decisive for choosing location site of the mining and crushing industries. If
population is already there, when mining starts, most of the cases peoples are compelled
to shift from the proposed mining area but sometimes they do not move far away from
the proposed site in hope of new livelihood. Sometimes people from outside also comes
nearer to the industries and settles there for getting livelihood security. Total 209 to 3160
(approx.) people live in the selected clusters and population density is 48 persons/km2 to
163 persons/km2 in the very high-noise vulnerable zone. Stone mining and crushing indus-
tries has been generating pollution in different forms, and this effect may be transmitted
among the people who are living at the adjacent part. Therefore, dwelling place of the
workers and other people should be quite away from crusher site for getting rid off from
high noise. But socio-economic constraints often hinder them to shift their settlement away
from the crusher unit. In some cases, crusher owners also install their crushing unit very
close to the village for getting labour facility.
(7)
𝜒
2=∑(Oij −Eij)
2
E
ij
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Noise vulnerability ofstone mining andcrushing inDwarka river…
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Table 5 Calculated odds ratio (OR) and relative risk ratio (RRR) among different communities in cluster 1
Parameters Relative risk 95% CI P value Odds ratio 95% CI P value
Exposed ad unexposed male workers experience headache problem 1.61 0.83–3.11 P = 0.1528 1.70 0.82–3.53 P = 0.1511
Exposed female workers experience headache problem 5.69 3.27–9.88 P < 0.0001 8.96 4.73–16.96 P < 0.0001
People living in mining or crushing area and 500m away 2.92 2.02–4.23 P < 0.0001 4.95 2.96–8.27 P < 0.0001
Age group (old: > 60 yrs. young: < 15–49years) 1.91 1.35–2.70 P = 0.0002 2.49 1.55–4.01 P = 0.0002
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S.Pal, I.Mandal
1 3
As per the CPCB (2000) the standards threshold noise limit in industrial area is 75dBA
in day time but in the current study area the outdoor and indoor noise level both are much
higher (> 85dBA) than acceptable limit. This noise level > 85dBA for long hours is really
injurious for health and psychology (Evans and Hygge 2005; Niemann etal. 2004; Stans-
feld and Matheson 2003; Meister and Donatelle 2000; Murphy and King 2014). Uninter-
rupted operation of crusher machines emits noise all through a day and it hammers on
their ear pot. It can create deep annoyance, headache and hearing impairment (Niemann
etal., 2004). Forcible annoyance is an etiological chain which may exist between the three
steps, e.g. health—annoyance—disease (Niemann etal. 2004). Most of the workers (> 73%
respondents) are annoyed with high noise level but a group of worker responded that they
are habituated with this noise. But, research revealed that whether they are habituated or
not that is different issue but constant high noise can bring failure of some organs like
ear and heart (Miller 1974; Griffiths et al. 1978; Berglund and Lindvall 1995; Passchier-
Vermeer and Passchier 2000; Singh and Davar 2004; Nelson etal. 2005; Afeni and Osasan
2009; Shepherd etal. 2011; Banerjee 2012; Ismail et al. 2013; Murphy and King 2014;
Sayara 2016). Although how many people affected by deafness or related diseases was not
surveyed in this present study but it is the fact that such a high intensity noise for long
duration may bring hearing impairment and other health injury. Nearby health centres reg-
istered that a significant proportion of patient they are having problems like respiratory
diseases and hearing impairment. No detail deafness record is found from any concerned
organization or no such survey has been conducted regarding this. The present work also
found that the noise effect is significantly age and sex sensitive. Stronger physical strength
of male population and relatively weak endurance of the aged people than the younger one
are the major reason behind sex and age sensitive effects of noise. Usually, aged people
have relatively poor body functioning and often they suffer from different diseases. High
noise creates additional stress on them (Murphy and King 2014). As the work is involved
mainly with mapping and monitoring of noise, very less attention has been paid but exten-
sion of this work assessing the noise on human health in fact will make the work more
interesting and purposeful. Many research works have revealed noise-induced hearing loss
in the high noise-generating occupational sectors like mining, blasting in mining, crushing,
steel industries, saw mills, paper mills and automobile sectors (Onder etal. 2012; Ismail
etal. 2013; Sayara 2016; Pouryaghoub etal. 2017). In case of large coal industries, about
76% are exposed to harmful noise and this causes about 25% of severe hearing trouble
and about 80% of hearing impairment among the workers at the age of retirement (Cen-
tre for Disease Control and Prevention, 2015). Nelson etal (2005) has reported that the
occupational noise induced hearing loss (7–21%) over the world and this rate is high in the
developing and under developed regions. Long exposure time, high noise-producing poor
machine quality, lack of any protective safety measures are the major reasons behind this.
Intensity of noise and exposure time is two vital issues for noise-induced hearing problem.
Previous studies on this over the world have documented that out of total cases of hearing
impairment, in 60% cases, peoples are victimized with a noise level of > 90dBA and noise
exposure with > 8 h in a day (Omubo-Pepple and Tamunobereton-ari 2011; Nirmalya and
Gandhari 2011). Studies have also reported that this problem is very prominent to those
workers who are working in this environment for constantly in 5years. In the present study
area, noise level is > 90dBA in very proximate zone of the mining and crushing units and
noise exposure to the workers are 8–10h in a day. This will lead to hearing impairment
among the workers and among the people who are residing very nearer to the noise pro-
ducing units.
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Noise vulnerability ofstone mining andcrushing inDwarka river…
1 3
National institution of occupational safety and health (NIOSH), The National insti-
tute of miners’ health (NIMH) of the Government of India believe traditional five-stage
hierarchy of noise control (Fig.7). The first stage is Elimination which is a process that
eradicates the noise source but it is always not possible at all workplaces because some
works cannot be possible without manufacturing noise. Advance arrangement and intro-
ducing an appropriate purchasing or hire strategy are indispensable to reduce the level
of noise at work. But in the developing countries, most of the cases, owners are reluctant
to spend more regarding this as it will not turn any extra profit of their own. In the pre-
sent study area, most of the noise-producing machines is open and workers are engaged
at very vicinity to the machine and therefore experiencing direct noise constantly. At
substitution stage (second stage), replacement of fuel engines by electric engines, press-
ing, use of gluing instead of forging and hammering, fibre bearings instead of metal
bearing, rubber or plastic chutes and containers in lieu of metal chutes and containers
are some alternative solutions which may reduce noise up to a certain limit. At third
stage (engineering control), it is essential to make a noise absorbing barrier between
high noise-producing work station to other sector of allied works, use of enclosure of
noisy machinery with sound-absorbing stuff, use of acoustical silencers in intake and
exhaust systems, maintaining optimum speed of machines or its particular apparatus,
repairing and replacing loose-fitting rotating parts, worn bearings and gears, customary
maintenance and servicing of the machineries can effectively be done. It can consider-
ably reduce the noise level from at workplace. At fourth stage (administrative control),
administrator can strategically plan how to provide relief to the people from exposure
Fig.7 Traditional five-stage hierarchy of noise control as per The National Institute of Occupational Safety
and Health (NIOSH)
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S.Pal, I.Mandal
1 3
to high noise for longer duration. Shifting duties for 2–3h a day can reduce the effect
on them. Rest time of the workers or other ancillary works should be quite apart from
the noisy area. Providing scientific guidelines to use equipment which can protect them
from noise is also a good step. At fifth stage, personal preventive equipments like ear
plug, ear muff, and acoustic foam could be provided to every workers from owner side.
Suitable protector type should be devised clinically based on the noise level and sensi-
tivity of the hearing organ of the individual workers.
6 Conclusion
This paper has explored the noise state and noise vulnerable area in the stone mining and
crushing dominated area of upper middle catchment of the Dwarka river and people’s
views about this. The stone crushing and mining centres used to produce 80–105dBA and
72–115dBA noise, respectively, in the study area. Diurnal noise monitoring has revealed
that high level of noise beyond threshold limit as defined by CPCB (2000) persists from
6a.m. to 4p.m. and its effect spreads up to 500m to 650m away from the crushing unit.
Multi-parametric analysis of noise vulnerability has revealed that 6.13 to 12.31% area to
the total fall under very high-noise vulnerable zone. Noise effect is highly age and sex sen-
sitive as has been revealed from the opinion poll of the victims. As such stone mining and
crushing industries have strong future demand, it is not wise to stop such work. Moreover,
employment opportunity issue is also very crucial. Considering the demand of such mining
and crushing industries in the constructive developing world, it cannot be stopped. At the
same time, crucial negative impacts on health of the exposed communities especially the
workers should not be overlooked. Therefore, scientific strategies should be taken for curb-
ing the effects of it on people. Not only noise-related problem, workers also strongly suffer
from respiratory diseases due to constant inhaling of stone dust. Hierarchic control method
for noise regulation and minimization as devised by National institution of occupational
safety and health (NIOSH), The National institute of miners’ health (NIMH) of the Gov-
ernment of India, can be adopted. Government’s effort for policy implementation all over
the country and responsible attitude of the mine and crusher owners can save the marginal
workers from health impacts.
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