- 25 June, 2022
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| Designation: |
Chief Scientist & Head
|
| Email Id: | gl_bodhe[at]neeri[dot]res[dot]in |
| Qualification: | PhD (Elec Eng.), M.Tech(Elec.Engg), BE (Electronics) |
| Specialization: |
Electronic Engg. & Technology
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| Address: |
QMSD Division, NEERI, Nagpur
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| Biodata: |
| Sr. No. | Project Name |
|---|---|
| 1 |
Development of real time wireless embedded multi-sensor system for monitoring and analyzing water quality parameters
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| 2 |
Procurement and maintenance of analytical instruments of Central Analytical Facility,
Management of major analytical instruments as a central facility with 90% up time |
| 3 |
Drone enabled auto sampler design for water sampling in remote and in-accessible areas on river
This work is related to environmental sampling applications which can reduce the sampling expenditure and assist in representative sampling at a difficult to approach water bodies. This arrangement is typically viable in hazardous locations inaccessible to human beings. |
| Sr. No. | Publication Name |
|---|---|
| 1 |
Evaluation of practical framework for Industrial noise mapping: A case study
|
| 2 |
Realizing Modeling and Mapping Tools to Study the Upsurge of Traffic Noise Pollution as a Result of Open-cast Mining and Transportation Activities
Introduction: In open-cast mines, noise pollution has become a serious concern due to the extreme use of heavy earth moving machinery (HEMM). Materials and Methods: This study is focused to measure and assess the effects of the existing noise levels of major operational mines in the Keonjhar, Sundergadh, and Mayurbhanj districts of Odisha, India. The transportation noise levels were also considered in this study, which was predicted using the modified Federal Highway Administration (FHWA) model. Result and Discussion: It was observed that noise induced by HEMM such as rock breakers, jackhammers, dumpers, and excavators, blasting noise in the mining terrain, as well as associated transportation noise became a major source of annoyance to the habitants living in proximity to the mines. The noise produced by mechanized mining operations was observed between 74.3 and 115.2 dB(A), and its impact on residential areas was observed between 49.4 and 58.9 dB(A). In addition, the noise contour maps of sound level dispersion were demonstrated with the utilization of advanced noise prediction software tools for better understanding. Conclusion: Finally, the predicted values at residential zone and traffic noise are correlated with observed values, and the coefficient of determination, R2, was calculated to be 0.6891 and 0.5967, respectively. |
| 3 |
Assessment of heterogeneous road traffic noise in Nagpur
"he objective of the study is to assess the noise scenario and evaluate prediction model for heterogeneous traffic conditions. In the past few years, road traffic of Nagpur has increased significantly due to the rapid increase in the number of vehicles. Noise levels are monitored at six different squares, characterized as interrupted traffic flow due to traffic signals, high population density and heavy traffic where the major sources of noise are engines, exhausts, tires interacting with the road, horns, sound of gear boxes, breaks, etc. The A-weighted time-average sound levels (LAeq;T) are measured at the different time of day during peak and off-peak traffic hours. To assess the traffic noise more precisely, the noise descriptors such as L10, L50, L90, LAeq;T, TNI (Traffic Noise Index), NPL (Noise Pollution Level) and NC (Noise Climate) are used. In the present study, the Federal Highway Administration (FHWA) noise prediction model is used for prediction of noise levels and it is observed that one-hour duration measured LAeq;T ranged from 71 to 76 dB(A) and 71.6 to 76.3 dB(A) during peak and off peak hours respectively. Due to the heavy traffic the peak hour Sound Exposure Levels (LAE) at all locations are exceeding permissible limit of 70 dB(A) prescribed by the World Health Organization (W.H.O). Off-peak traffic hour noise levels are within permissible limit except at two locations, Jagnade and HB town square. Significant correlation was obtained when best fit lines generated between measured and predicted values gives R2 of 0.455 for all time intervals. Chi-Square test (?2) was also computed to investigate the noise levels at different squares. The results show that the inhabitants of Nagpur city are exposed to high transportation noise during daytime." |
| 4 |
Future Prospects of Plasma Treatment technology for disinfection" book chapter no. 10 in 'Emerging Technologies of 21st century' (ISBN: 978-93-83305-33-9)
|
| 5 |
Appraisal of Noise Level Dissemination Surrounding Mining and Industrial Areas of Keonjhar, Odisha:
Noise mapping is a well-established practice among the European nations, and it has been follow for almost two decades. Recently, as per guidelines of the Directorate General of Mines Safety (DGMS), India, noise mapping has been made mandatory in the mining expanses. This study is an effort to map the noise levels in nearby areas of mines in the northern Keonjhar district. The motive of this study is to quantify the existing A-weighted time-average sound level (LAeq, T) in the study area to probe its effects on the human dwellings and noise sensitive areas with the probability of future development of the mines, roads, and industrial and commercial zone. The LAeq, T was measured at 39 identified locations, including industrial, commercial, residential, and sensitive zones, 15 open cast mines, 3 major highways, and 3 haulage roads. With the utilisation of Predictor LimA Software and other GIS tools, the worked out data is mapped and noise contours are developed for the visualisation and identification of the extent and distribution of sound levels across the study area. This investigation discloses that the present noise level at 60% of the locations in silence and residential zone exposed to significantly high noise levels surpasses the prescribed limit of Central Pollution Control Board (CPCB), India. The observed day and night time LAeq, T level of both zones ranged between 43.2–62.2 dB(A) and 30.5–53.4 dB(A), respectively, whereas, the average Ldn values vary between 32.7 and 51.2 dB(A). The extensive mobility of heavy vehicles adjoining the sensitive areas and a nearby plethora of open cast mines is the leading cause of exceeded noise levels. The study divulges that the delicate establishments like schools and hospitals are susceptible to high noise levels throughout the day and night. A correlation between observed and software predicted values gives R2 of 0.605 for Ld, 0.217 for Ln, and 0.524 for Ldn. Finally, the mitigation measure is proposed and demonstrated using a contour map showing a significant reduction in the noise levels by 0–5.3 dB(A). |
| 6 |
An adaptive neuro-fuzzy interface system model for traffic classification and noise prediction
In present study, two adaptive neuro-fuzzy models have been developed for traffic classification and noise prediction, respectively. The traffic classification model (ANFIS-TC) classifies extracted sound features of different categories of vehicles based on their acoustic signatures. The model also compute total number of vehicles passes through a particular sampling point. The results have been used for the estimation of the equivalent traffic flow (QE). The noise prediction model (ANFIS-TNP) has three inputs, namely equivalent traffic flow (QE), equivalent vehicle speed (SE) and honking. The equivalent traffic flow (QE) is the output of ANFIS-TC model, while equivalent vehicle speed (SE) and honking are computed from observed averaged speed of different categories of vehicles and number of recorded horns blow per minute. The model assumes that the distance between sound level meter and road centerline is fixed for particular sampling point. The performance of both the models has been validated by field observations. The results show that traffic classification is 100% accurate, while correlation coefficients between observed and predicted traffic noise range from 0.75 to 0.96. Both the models are validated with random samples of data, and it is observed that both the models are generalized and could be employed for traffic classification and traffic noise prediction in small urban heterogeneous traffic environment for noise pollution assessment and control. |
| 7 |
“Online Instrument Controlling and Information Management System
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| 8 |
Microcontroller based temperature controller/programmer for thermoluminescence (TL) dosimetry
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| 9 |
An adaptive neuro-fuzzy interface system model for traffic classification and noise predictions
In present study, two adaptive neuro-fuzzy models have been developed for traffic classification and noise prediction, respectively. The traffic classification model (ANFIS-TC) classifies extracted sound features of different categories of vehicles based on their acoustic signatures. The model also compute total number of vehicles passes through a particular sampling point. The results have been used for the estimation of the equivalent traffic flow (QE). The noise prediction model (ANFIS-TNP) has three inputs, namely equivalent traffic flow (QE), equivalent vehicle speed (SE) and honking. The equivalent traffic flow (QE) is the output of ANFIS-TC model, while equivalent vehicle speed (SE) and honking are computed from observed averaged speed of different categories of vehicles and number of recorded horns blow per minute. The model assumes that the distance between sound level meter and road centerline is fixed for particular sampling point. The performance of both the models has been validated by field observations. The results show that traffic classification is 100% accurate, while correlation coefficients between observed and predicted traffic noise range from 0.75 to 0.96. Both the models are validated with random samples of data, and it is observed that both the models are generalized and could be employed for traffic classification and traffic noise prediction in small urban heterogeneous traffic environment for noise pollution assessment and control. |
| 10 |
Development of adaptive and customizable Base Station System in Wireless Sensor Network
This paper presents a development of dynamic base station for Wireless sensor network application, which highly capable to adapt itself for any other application in WSN. The proposed base station system has more memory, higher processing and communication capabilities. This system is able to query sensor data, process them, store them and deliver that data to user's cell phone, instantly. The base station system is built around ARM11 architecture microcontroller with Windows Embedded CE 6.0, so that it could possess good processing power, reliability, user friendly GUI and security. For communication purpose, IEEE802.15.4 based ZigBee RF module and GSM modem are used. The data received by base station is processed and stored in memory. The application of Base station is dynamic and customizable. User can change it easily as per the experimental or research requirements |
| 11 |
Extremely Low Frequency Electromagnetic Field (ELF-EMF) and childhood leukemia near transmission lines: A review
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| 12 |
Adoptive Neuro-Fuzzy Inference System for Traffic Noise Prediction
An adaptive neuro-fuzzy inference system (ANFIS) is implemented to evaluate traffic noise under heterogeneous traffic conditions of Nagpur city, India. The major factors which affect the traffic noise are traffic flow, vehicle speed and honking. These factors are considered as input parameters to ANFIS model for traffic noise estimation. The proposed ANFIS model has implemented for traffic noise estimation at eight locations. The results have been compared and analyzed with observed noise levels and the coefficient of co-relation between observed and predicted noise level was found to be in range of 0.70 to 0.95. The model performance has also been compared with Federal Highway Administration (FHWA), Calculation of road traffic noise (CRTN) and regression noise models and it is observed that the model performs better than conventional statistical noise model. The propose |
| 13 |
Development of a traffic noise prediction model for an urban environment
"The objective of this study is to develop a traffic noise model under diverse traffic conditions in metropolitan cities. The model has been developed to calculate equivalent traffic noise based on four input variables i.e. equivalent traffic flow (Q e ), equivalent vehicle speed (S e ) and distance (d) and honking (h). The traffic data is collected and statistically analyzed in three different cases for 15-min during morning and evening rush hours. Case I represents congested traffic where equivalent vehicle speed is <30 km/h while case II represents free-flowing traffic where equivalent vehicle speed is >30 km/h and case III represents calm traffic where no honking is recorded. The noise model showed better results than earlier developed noise model for Indian traffic conditions. A comparative assessment between present and earlier developed noise model has also been presented in the study. The model is validated with measured noise levels and the correlation coefficients between measured and predicted noise levels were found to be 0.75, 0.83 and 0.86 for case I, II and III respectively. The noise model performs reasonably well under different traffic conditions and could be implemented for traffic noise prediction at other region as well. (PDF) Development of a traffic noise prediction model for an urban environment. Available from: https://www.researchgate.net/publication/260445464_Development_of_a_traffic_noise_prediction_model_for_an_urban_environment [accessed Sep 17 2018]." |
| Sr. No. | Training Program Title |
|---|---|
| 1 |
Advance Analytical Instrumental Techniques
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| 2 |
Advance Analytical Instrumental Technique
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| 3 |
International Symposium on Halogenated Persistent Organic Pollutants
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| 4 |
International Symposium on Halogenated Persistent Organic Pollutant
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| 5 |
Advanced Instrumentation Techniques
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| 6 |
Advanced Instrumentation Technique
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| 7 |
Quality assurance and quality control in laboratory analysis”, Sponsoring Agency: Central Pollution Control Board (CPCB)
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| 8 |
Quality assurance and quality control in laboratory analysis", Sponsorings Agency Central Pollution Control Board (CPCB)
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View Biodata [0.2 MB]
Training Program