Background: Noise mapping being an established practice in Europe is hardly practiced for noise management in India although it is mandatory in Indian mines as per guidelines of the Directorate General of Mines Safety (DGMS). As a pilot study, noise mapping was conducted in an opencast mine with three different models; one based on the baseline operating conditions in two shifts (Situation A), and two other virtual situations where either production targets were enhanced by extending working hours to three shifts (Situation B) or only by increased mechanization and not changing the duration of work (Situation C). Methods: Noise sources were categorized as point, line, area, and moving sources. Considering measured power of the sources, specific meteorological and geographical parameters, noise maps were generated using Predictor LimA software. Results: In all three situations, Lden values were 95 dB(A) and 70–80 dB(A) near drill machine and haul roads, respectively. Noise contours were wider in Situation C due to increase in frequency of dumpers. Lden values near Shovel 1 and Shovel 2 under Situation B increased by 5 dB and 3 dB, respectively due to expansion of working hours. In Situation C, noise levels were >82 dB(A) around shovels. Noise levels on both sides of conveyor belts were in the range of 80–85 dB(A) in Situations A and C whereas it was 85–90 dB(A) in Situation B. Near crusher plants, it ranged from 80 to 90 dB(A) in Situations A and C and between 85 and 95 dB(A) in Situation B. In all situations, noise levels near residential areas exceeded the Central Pollution Control Board (CPCB) limits, i.e., 55 dB(A). Conclusions: For all situations, predicted noise levels exceeded CPCB limits within the mine and nearby residential area. Residential areas near the crusher plants are vulnerable to increased noise propagation. It is recommended to put an acoustic barrier near the crusher plant to attenuate the noise propagation.
Keywords: Industrial noise, ISO 9613, mine environment, noise mapping, noise sources
|How to cite this article:|
Manwar VD, Mandal BB, Pal AK. Environmental propagation of noise in mines and nearby villages: A study through noise mapping. Noise Health 2016;18:185-93
| Introduction|| |
Noise is recognized as a major pollutant of the mining environment. Due to improvement in mechanization and continuous operation in mines, the equipment inventory has undergone a substantial change. The spatial distribution of mining activity also changes faster nowadays due to faster mining with more machines at work. The problems of noise in Indian mines exist since long and practically no trend in improvement in the working environment is visible.
Occupational noise exposure because of deployment and operation of these machines and plants is a major health hazard that affects millions of mine workers as well as the residential areas in and around the mining complexes. Exposure to noise leads to multiple adverse effects on physical and mental state of the mining community as a whole. Some of these effects, for example, tinnitus, and noise induced hearing loss (NIHL), reduced performance, sleeping difficulties, disturbance in conversation, annoyance or stress, etc. are well known.
In India, health surveillance studies conducted by National Institute of Miners’ Health (NIMH) signaled that many workers engaged in mining operations might be suffering from NIHL. This affected population was found to vary from 12.4% to 25.7% in various mines. The Central Government in February 2011 has also declared NIHL as notified disease under the Mines Act, 1952.
Various methods are used for assessing noise level in mines. For example, area noise monitoring is used to measure noise level of a particular area, whereas personal noise dosimetry is used to measure the percentage of noise dose to which a person is exposed during movements in different noisy or quieter areas during a working shift in the plant or within the mines. All the above methods represent either an area or an individual under study and do not represent the overall scenario of the noise level distribution. This could be a reason why it is so difficult to provide an effective noise control measure through either noise control engineering or business process reengineering based on such measurements. Assessment of environmental noise is traditionally based on measurement of noise levels at receiver points, which neglects the sources which emanate the sound energy.
In the west, noise mapping has been more widely used in the field of traffic and city noise modeling. Prediction based on acoustic and terrain modeling currently dominates research in environmental propagation of noise. Probst highlighted the power of noise mapping which lies in the completeness of an acoustic model. The acoustic model acts as the link between the technical parameters and the resulting noise exposure of affected people. Noise mapping as an experiment with existing or simulated parameters opens up many possibilities to evaluate alternatives in planning stage of a project and also during its execution. Similarly, Aletta and Kang used noise maps for depicting the spatial distribution of sound pressure level creating from vehicle traffic using acoustic models.
King and Rice in his paper calculated a noise map of Dublin city centre using in-house noise predictor software. Cai et al. developed traffic noise map during day and night using Geographic Information System (GIS) and Global Positioning System (GPS). Noise map of Ilorin Metropolis was developed by Oyedepo based on various noise descriptors.
In India, Alam prepared noise map of Guwahati city at various locations, i.e., commercial zone, residential zones, and silence zone using GIS and the predicted noise level was compared with the limits stipulated by the Central Pollution Control Board (CPCB) and Bureau of Indian Standard (BIS).
Even though instances of application of noise mapping practices in mines are available from few countries including Australia, the subject is considered new in Indian mines and mineral industries., Noise modeling is more complicated for mines compared to other industries or its application in studying city traffic or noise generated by a railway carriage.
Heavy machines like dozers, dumpers, and loaders, are moving sources of noise in mines. In contrast, certain sources are stationary, e.g., crusher plants, screening plants, belt conveyors, etc. Distribution of noise levels in any mining area depends not only on the stationary or moving sources but also on the complex geographical conditions, which are mostly responsible for reflection, refraction, or absorption of sound waves. Meteorological factors produce additional effects on the propagation pattern. In view of the above, traditional workplace monitoring exercise for assessment of noise situation in a mine is inadequate.
Noise mapping is considered as an improved and efficient method to assess and depict the noise level in a large study area having numerous sources, which are continuously or intermittently emitting noise in the environment. Manvell et al. described a noise map to represent a stationary situation computed either with traffic input data correlated with real measured noise data, or a predicted situation based on assumptions about the evolution in the future. Dynamic noise mapping system with continuous real time inputs will largely depend on the processing facility and speed. The concept of noise mapping is a paradigm shift from traditional methods since it is based on the power of the noise sources and scientific propagation models.
Noise mapping has been made mandatory in the Indian mines in the Recommendations of the Tenth Conference on Safety in Mines. Following the development of advanced and fast computational facilities involving large number of variables, it is now possible to monitor the changing pattern of sound propagation in a mining area. Sound power levels at various noise sources can be measured and these can be utilized to calculate the noise level at various simulated receiver locations using propagation models so as to generate a noise map. A noise map as an outcome is a graphical representation of the present sound pressure levels in real time, as well as the simulated future noise environment in case a change is predicted due to the expansion of mining operations or introduction/removal of noise producing machines or installations. Noise map can be simulated even before the starting of a project. It is depicted by colored contours indicating boundaries between different noise levels in a study area. The main noise indicators for noise mapping are Lday, Levening, Lnight, and Lden (day-evening-night). For evaluating community noise, Lden and Lnight are used in which the time considered for day is 12 h, i.e., from 7 AM to 7 PM, the evening is 4 h, i.e., from 7 PM to 11 PM and the night is 8 h, i.e., from 11 PM to 7 AM. All of these indicators are defined in terms of A-weighted decibels.,
Noise sources in mines are of various types, e.g., point source, line source, area source, and moving sources. Moreover, study of outdoor propagation of noise becomes still more complex in nature due to additional geographical and meteorological factors as pointed out earlier. Since noise mapping in Indian mines has not been standardized, a pilot study using selected International Organization for Standardization (ISO) Standards was carried out in an opencast mine in a hilly area in northern India to develop noise map of mining operations to study its probable effects in the mining area and surrounding residential area. Increase in mine productivity was simulated in the model to study the changing pattern through varying noise maps of the study area.
The objective of the study was to assess the noise level of the opencast mine and to create noise maps by envisaging changes in work plan and its probable impact on the nearby residential areas so that preventive control measures can be taken beforehand to reduce noise level.
This study was conducted in a mechanized mine which is a captive source of raw material for a nearby cement plant. The mining lease area comprises a total area of about 231.25 ha out of which 57.3 ha is mined and the remaining is free. Mining is being carried out by opencast method of mining with deep hole drilling and blasting and excavation by shovel-dumper combination. The blasted material is loaded in 50/60 T dumpers with the help of shovels. Crusher plant consist of two crushers namely Crusher I (Bull Dog Hammer, 400 TPH capacity) and Crusher II (Single Rotor Impactor, 1000 TPH capacity) working simultaneously for grinding the raw material. It receives ore from the mine through dumpers. The output from the crushing plant is transported by a series of belt conveyors with transfer point and hopper up to gantry, which is approximately 1 km away from crushing plant.
The mining work is carried out in two shifts beginning from 5:00 AM to 1:00 PM and again from 2:00 PM to 10:00 PM. In between, there is a general shift from 1:00 PM to 2:00 PM. Daily input requirement of plant is about 15,000 T. Following are the major machineries available in the mine:
- Drilling Machine (two nos)
- Dumper (ten nos: 60 T and 50 T)
- Shovel (five nos), and
- Dozer (two nos)
On an average six dumpers are deployed in a shift of 8 h for a production target of 7500 T per shift.
Crusher I and Crusher II are both surrounded by green vegetation and having wide haul road on one side for movement of the dumpers. The nearest house in the residential area is about 80 m away towards east of the crusher plant [Figure 1].
| Methodology|| |
Input features and data
Sound sources contributing to the overall noise in and around the mine were identified [Table 1]. Crushers were treated as stationary area source whereas dumper traveling on haul roads were taken as moving noise sources. Shovels and drilling machines were considered as point sources and belt conveyors were represented as line source in the model. Occasional variations in the noise levels due to movements of water sprinkler, vehicles used for transportation of supervisory officials, etc. in the beginning and end of a shift were not taken into consideration.
Noise measurements were carried out according to ISO 9613-2:1996 (Acoustics − description, measurement and assessment of environmental noise) and ISO 6395:2008 (Earth Moving Machinery − Determination of sound power level − Dynamic test conditions) and other related standards.,,,, Selection of microphone positions and number of measurements to be taken for assessment of sound power level of any source are amply described in the standards. All these measurements were taken with the help of Casella make Type 1 Sound Level Meter using A-weighting scale. At the same locations, geographic coordinates were collected with the help of Trimble make GPS. In addition, meteorological parameters like temperature, humidity, wind direction, and speed were collected from mine office records.
Surface plan of the mine leasehold area along with nearest residential areas were taken from the mines in DWG file format for detailed and close verification of the features through AUTOCAD. Usually a local coordinate system is used as reference frame in survey maps. Local coordinates were rectified and adjusted to universal coordinate system for integration with other geo-referenced data such as latitude and longitude of a noise source in the mine. Corrected DWG file of surface plan was imported to ARCMAP 10.2. Noise sources were projected on the surface plan with the help of their corresponding GPS data. Thus point locations and shapes of crusher plant, dumper haul roads, locations of shovels and drills were digitized through ARCMAP and stored as shape files.
Noise prediction software uses sound power levels of the noise sources for calculation of propagation of sound waves once they are emanated from the source and further influenced by both geographical and meteorological factors in the environment. So in the next step, sound power level data were generated using Acoustic Determinator (also from Bruel and Kjaer) with sound pressure level readings as inputs, which were earlier taken at different sources. The attenuation during propagation was calculated on the basis of sound power of the source. Conversion of sound pressure levels to corresponding sound power levels at different one-third octave band frequencies is implemented using reverse engineering techniques [Equations 1–7].
Step 1: Pressure to power conversion
Acoustic Determinator calculates the sound power level (Lwr) using the following formulas.
where (*) with an omni-directional microphone the ΔLM=0, (**) is representing spectral values in dB/km (in ISO 8297), Lwr is the sound power level in [dB], MidLp the average sound pressure level of the measure points in [dB], ΔLS the geometric correction in [dB], ΔLα the air absorption correction in [dB], ΔLF the proximity correction in [dB], ΔLM the microphone correction in [dB], Lp the sound pressure level at measure point in [dB], N the number of measure points, Sm the area measure surface in [m2], hm the measure height in [m], l the length measure line in [m], S0 the reference field (1 m2) in [m2], and α the sound absorption coefficient.
Step 2: Propagation and attenuation
For determining the sound pressure level at any receiver point, propagation equations are used as follows:
where Lw is the octave band sound power level, in decibels, produced by point sound source relative to a reference sound power of one picowatt (1 pW). Dc is the directivity correction, in decibels, that describes the extent by which the equivalent continuous sound pressure level from the point sound source deviates in a specified direction from the level of an omni-directional point sound source producing sound power level Lw; Dc equals the directivity index D1 of the point sound source plus an index DΩ that accounts for sound propagation into solid angles less than 4π steradians; for an omni-directional point sound source radiating into free space, Dc = 0 dB; A is the octave band attenuation, in decibels, that occurs during propagation from the point sound source to the receiver.
The attenuation term A in equation (8) is given by equation (9)
where Adiv is the attenuation due to geometrical divergence, Aatm is the attenuation due to atmospheric absorption, Agr is the attenuation due to the ground effect, Abar is the attenuation due to a barrier, and Amisc is the attenuation due to miscellaneous other effects.
Sources of other types such as line source or area source are treated as composite sources made of many point sources in various configurations.
Surface plan of the mine in AUTOCAD file (DWG) was imported in Predictor LimA software before implementing the calculation procedure for ascertaining the propagation of noise in the study area. Also all the shape files created in ARCMAP 10.2 were imported as noise sources in the project within Predictor LimA. Sound power level data were then added as attributes to the respective individual sources. The shape files of all the noise sources along with their individual attributes with meteorological data constitute the basic noise model for the mine. Individual attributes contain description of source, type of source, coordinates, emission level (power level data), and working hours.
Predictor Lima Software was programmed to calculate output noise levels at 10 m × 10 m grid intersections using ISO 9613 calculation method. The entire process was repeated for each scenario separately as described below.
Step 3: Modeling for different situations
Situation A: This is the current set-up of the mine having production target of 15,000 TPD (tonnes per day) consisting of two working shifts, i.e., from 6 AM to 2 PM and 2 PM to 10 PM. The current operation comprises of digging blast holes with drill machine followed by blasting with explosives. The blasted ore or overburden is then loaded by shovel on to dumpers for transportation. The blasted material is loaded in 50/60 T dumpers and is carried through haul road up to the Crusher Plants I and II. Both crushers operate simultaneously for downsizing the raw material. The output from the crushing plant is transported by a series of belt conveyors with transfer point and hopper up to gantry, which is approximately 1 km away from crushing plant. Thus, by considering all the aforesaid noise sources of the mines, meteorological parameters, and other factors, a noise model was developed and noise map was generated for the existing scenario [Figure 2].
Situation B: It shows three shifts working, i.e., from 6 AM to 6 AM round the clock which includes night shift. The production target is 22,500 TPD which is calculated as three times the production of a single shift, i.e., 7500 T. The equipment deployed to achieve this is same as in Situation A. The only difference is that the machinery and plant operate in all the three shifts instead of only two shifts as in Situation A. Noise map of situation B was generated using the same model as was used in A but the time attributes were changed to suit the 24 h working in the mine [Figure 3].
Situation C: It considers two shifts working, i.e., from 6 AM to 10 PM (16 h) and does not include working in night shift. The production target is 22,500 TPD which is to be achieved through two shifts working but with increased level of mechanization. Noise model of situation C was generated using more number of equipments, which means more noise sources in the model [Figure 4]. To achieve this, one more shovel and three dumpers were added to the existing fleet in the mine so as to increase the output. The model envisages increased output based on the capacity of the mine but uses increased mechanization as strategy for achieving the target. This might add to the overall noise levels in the mine surroundings as well as exposure of more people (operators, maintenance workers, etc.) to the noisy environment.
| Results and Discussion|| |
Three different scenarios were built within the prediction model: one real and the other two virtual. Accordingly, noise maps of these three situations were generated using Predictor LimA software.
According to CPCB guidelines the noise level limits specified for different zones are shown in [Table 2]. Definitions of span of day and night in CPCB guidelines in India are different from what has been described in the European Noise Directive (END). Incidentally, the calculation methods within Predictor LimA using ISO 9613-1:2003 protocol use various time periods which are by default in accordance with the END [Table 3]. Of course, Predictor V9.10 offers flexibility to allow user-defined calculation periods. Since the predicted noise levels of three different situations were to be compared with CPCB guidelines, the authors used overall permissible Ldn values based on combining the day and night noise levels set by CPCB as described in [Table 2]. Day and night spans were set following the CPCB guidelines. Later on Lden values computed by Predictor LimA were compared with Ldn limiting values calculated from CPCB guidelines. All the computed values are based on a complete 24 h exposure period.
|Table 2 CPCB Ambient Air Quality Standards in Respect of Noise* and Corresponding Ldn Values|
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|Table 3 Definitions of Day, Evening, and Night Periods in CPCB Guidelines and END|
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Different color codes indicate different Lden noise levels in dB(A) in all three noise maps. Noise levels ranging from 50 to 55 dB(A) and 55–60 dB(A) are indicated by different shades of green, 60–65 dB(A), 65–70 dB(A), 70–75 dB(A), and 75–80 dB(A) are indicated by different shades of yellow, 80–85 dB(A), 85–90 dB(A), and 90–95 dB(A) are indicated by different shades of red whereas the noise level ranging from 95 to 100 dB(A) is indicated in dark violet bands.
The GIS integrated noise prediction software (Predictor V9.10) computes the noise levels in the study area taking into account three different situations. The calculated Ldn values based on day time and night time prescribed by CPCB are given in [Table 2]. The different locations in the study area mentioned are categorized as industrial area and residential area [Table 4].
From the predicted noise levels (Lden value), it was observed that in Situation A, the highest noise level near drill machine was 95 dB(A) [Table 4]. Noise levels around Shovel No. 1 and Shovel No. 2 which were operating simultaneously on different mining benches were 80 dB(A) and 85 dB(A), respectively. Noise level along the haul road ranged 70–80 dB(A) and on conveyor belt it was in the range of 80–85 dB(A). Predicted noise near Crusher Plants I and II was in the range of 80–90 dB(A). In all industrial locations within the study area, the calculated noise levels exceeded the CPCB Ldn limits, i.e., 73.87 dB(A). As we move away from the crusher plant, the corresponding noise levels decreased. Noise level was found to be 69 dB(A) near the residential area close to the lease boundary which is again beyond the CPCB limits, i.e., 53.23 dB(A). Continuous operation of crusher plants as well as movement of dumpers for unloading activities seemingly contributed to the overall increase in noise level at nearby residential areas.
Situation B was designed maintaining same degree of mechanization and pattern of equipment deployment as in situation A but the working hours were increased by adding the night shift. In such case, sources of noise remained the same while their duration of emission increased by another 8 h. After computation, it showed that noise levels around Crusher Plants I and II were in the range of 85–95 dB(A). As we move away from the crusher plant, the corresponding noise levels decreased to 75–80 dB(A). The Lden value near residential area which is lying towards east of the crusher plant was 73 dB(A) again surpassing the limit. The noise level near drill machine was found to be in the range of 95 dB(A) while the noise level on both sides of conveyor belts was 5 dB more than in Situation A or C. Similarly, the noise level near Shovel 1 and Shovel 2 which were operating at different benches were found to be 85 dB(A) and 88 dB(A), respectively while near the haul road the noise level was 70–80 dB(A). For situation B, noise levels exceeded the limits specified by CPCB at all locations.
Noise Map of Scenario C was generated by adding one more shovel and three dumpers to the existing operation (Situation A) carried out in the mine. Working in night shift was not in consideration. After such modification in the model, noise levels around the Crusher Plants I and II reduced to 80–90 dB(A) which is about 5 dB(A) less compared to situation B but the same as in Situation A. Noise levels on both sides of conveyor belt was in the range of 80–85 dB(A). Additional shovel dumper combination had resulted in overall increase in the noise level. Because of increase in the frequency of movement of dumpers through the existing haul road, the noise level raised to 70–80 dB(A). The Lden value near drill machine was found to be 95 dB(A) whereas noise levels near Shovel No. 1, Shovel No. 2, and Shovel No. 3 which were operating simultaneously at different benches were found to be 82 dB(A), 85 dB(A), and 82 dB(A), respectively. The noise level near residential area which is lying towards east of the crusher plant came down to 69 dB(A) but of course the same remained higher than CPCB limits.
In general, noise levels of Situation B were higher when compared to Situation A due to involvement of night shift to increase the mine output. The study area is situated in hilly area and generally activities during night shifts in hilly area are avoided. Highly undulated terrain and many other weather conditions cause safety concerns. When Situation A is compared with Situation C, a small increase in noise levels in Situation C is noticed due to increase in degree of mechanization in later situation.
Situation B and Situation C have same production targets, i.e., 22,500 TPD. The major difference in Situation B is that the night shift is taken into consideration while in Scenario C mechanization has been increased to reach the same target without working in night shift. Between the two situations, average noise indicators of Situation B were higher by 5 dB(A) at various locations [Table 4].
Mohalik and Pal developed a noise map of a mining complex using propagation equations but without line, area, and dynamic sources. The present authors added these features and for the first time generated noise maps in India using state-of-the-art technologies. This work also includes simulation of virtual mining situations, which are useful in impact assessment of a project implementation.
In the Environmental Impact Assessment (EIA) report of Eaglefield mining expansion project prepared by Noise Mapping Australia in 2010, virtual noise maps were produced by utilizing available mining data and operating conditions. It was recommended that the dump truck route to the out-of-pit dump should be at or close to the natural surface and the ideal position of overburden dump was also determined based on the analysis. The proposed pit and operational design of Eaglefield mining expansion project was found effective in controlling noise at all locations except one. Similarly, in the present study, apprehended harmful effects on health and well being of the affected community could have been avoided or minimized had this mapping exercise been carried out prior to the implementation of the project as a part of EIA. The current location of the crusher plant, which seems to be the major contributor to the elevated noise level in the vicinity as well as outside the lease boundary could have been relocated at the stage of mine planning itself.
In another case, Boddington Mine in Western Australia successfully responded to the community concerns about noise. The mine after noise mapping has modified day-night scheduling of work. Similarly, our study presents two virtual noise maps in an Indian mine which showed, the noise was affecting the nearby residential community during both day and night time. In case the mine management plans for expansion of the project, Situation C would be a better option provided properly designed barrier near the crusher plants or green belts of different heights in between crusher plant and residential areas are placed. Considering the villages near the mine boundary, it is suggested that night shift workings should be avoided in the daily work schedule even if the expansion program is implemented without relocating the crusher plant.
A comparison of the measured and predicted values at the points for validation of the models provided the deviation of the calculated and measured values in the range of ±2.0 dB(A). In a similar industrial noise modeling and mapping study by Prascevic et al. using an earlier version of LimA, the deviation was found to be ±2.5 dB(A). The present acoustic modeling seems to be more accurate compared to Prascevic et al. Verification of the model was made on the basis of measurements conducted in calibration points both inside and outside the mining lease boundary.
| Conclusions|| |
It is evident from [Table 4] that in all the three cases (Situations A, B, and C), predicted noise levels exceeded CPCB limits irrespective of whether they belonged to the industrial area or residential area. It is also observed that the existing situation of mine might have negative impact on the health of the workers and to the people residing nearby the mines. It is recommended that the workers around drill machine and shovel should wear personal protective equipment (PPE). Also persons visiting those areas should also wear protective equipment because of higher noise levels, which may cause hearing impairment in the long run.
Also noisy activity of the crusher plant will adversely affect the physical and mental wellbeing of the population residing in the nearby villages. It is recommended that if a barrier is placed near the Crusher Plant II, it might be helpful in reducing noise propagation from crusher plant towards residential areas. The barrier should be properly designed and its effects should be studied once it has been placed near the crusher.
Out of the two expansion programs studied here, Situation C is a better option compared to Situation B. However, protective measures are to be in place before implementing either of them.
The present study area comprises of 231.25 ha mining lease area out of which 57.3 ha is mined and the remaining is free. Keeping in mind the current ore reserve and demand for minerals, expansion of mines may take place to augment the production capacity. Expansion of mines will result in increase in productivity but might pose a threat to the wellbeing of the people living nearby and also to the mine workers. The current mine working itself is generating noise levels beyond permissible limits in most parts of the study area. Hence, proper preventive control measures are to be implemented before any further expansion program is implemented.
Source of support: Ministry of Mines, Government of India and National Institute of Miners’ Health, Nagpur (India).
Financial support and sponsorship
This work was supported by the Ministry of Mines, Government of India under Science and Technoloy Project ‘Development of standard framework and guidelines for noise mapping in mines and surrounding community’ [Grant No. 14/39/2012-Met. IV dated 28.01.2013].
Conflict of interest
There are no conflicts of interest.
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Dr. Bibhuti B Mandal
Head of the Department, Department of Occupational Hygiene, National Institute of Miners' Health, JNARDDC Campus, Amravati Road, Wadi, Nagpur 440 023
Source of Support: None, Conflict of Interest: None
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4]