- 25 March, 2023
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| Designation: |
Senior Scientist
|
| Email Id: | a_middey[at]neeri[dot]res[dot]in |
| Qualification: |
M.Sc. (Atmospheric Sc.), Ph.D. (Atmospheric Sc.) |
| Specialization: |
Atmospheric Science – Meteorology
|
| Address: |
APC Division, NEERI, Nagpur
|
| Biodata: |
-
|
| Sr. No. | Project Name |
|---|---|
| 1 |
Environmental Assessment Study Before and After the Proposed Urban Conservation and Tourism Development Project of Area around the World Heritage Site, Taj Mahal, Agra
The research goal was to generate a design brief through a participatory process between the researchers and Taj Gunj residents, traders (and their associations), government agencies (U.P. Tourism, Agra Development Authority and ASI). The research methodology included holding workshops with the stakeholders to understand their aspirations and create a dialogue towards a sustainable plan. This research aims to understand the social, cultural and physical workings of Taj Ganj and to frame recommendations towards planning for an improved visitor experience that benefits local community and protects the living cultural heritage of Taj Ganj |
| 2 |
National Clean Air Mission
Assessment and Management of Air Pollution and Health problems in sources, pathways and receptors. Assessment of Indoor air pollution and Green house gases. |
| 3 |
Study on Development and Application of GAINS-City Model for Indian Cities, Technology Information Forecasting & Assessment Council TIFAC
Basic data collection and model framework design. Development of city emission model based on GAINS model. Update of emission factors based on a comprehensive review of local measurements. Estimation of air pollutants and GHG emissions under different scenarios for the city. Evaluate the air quality benefit for different scenarios. Training of students who will lead subsequent analysis in individual cities with present focus on one city. Final report summarizing methods, results and future work. |
| 4 |
Macro Level EIA Study for Cluster of Iron OreMines in the State of Goa
To carry out environmental impact assessment study with a view of having macro level impacts of mining in Goa on flora, fauna, the local inhabitant, air, water, noise pollution and overall biological environment. |
| 5 |
Performance Evaluation of Air Pollution Control Systems (APC) of Uttam Galva Metallics Ltd., Wardha
Main Objective of the study is to carry out air quality monitoring and source apportionment study in Uttam Galva region. Study the possibility of reducing air pollution in Uttam Galva air Quality management approach. |
| 6 |
Performance Evaluation of Air Pollution Control Systems (APC) of Sunflag Steel Works, Bhandara
Probing study on use of Pickling sludge at Sinter plant along with associated emission characterisation and environmental aspects |
| 7 |
National Ambient Air Quality Monitoring
To study ambient air quality status of six Indian cities for pollutant as per MOU(Chennai, Delhi, Hyderabad, Kolkata,Mumbai). To study tendency of the air pollutants levels in six cities. To generate ambient air pollutants levels data base for supply to regulatory authorities etc and for in house R&D. |
| 8 |
Performance Evaluation of Air Pollution Control System (APC) of Raymond UCO Denim Pvt. Ltd., Yavatmal
To conduct the performance evaluation of all Air Pollution Control system of Raymond UCO Denim Pvt. Ltd. |
| 9 |
Air Quality Monitoring and Source apportionment studies for Ten cities of Maharashtra
The main objective of Ambient Air Quality Monitoring is to generate baseline data of ambient concentration of critical air pollutants and source apportionment study for utants and source apportionment study for PM10 in different parts of the cities. |
| Sr. No. | Publication Name |
|---|---|
| 1 |
An investigation on the predictability of thunderstorms over Kolkata, India using fuzzy inference system and graph connectivity
The purpose of this study was to develop a computing system (CS) with fuzzy membership and graph connectivity approach to estimate the predictability of thunderstorms during the pre-monsoon season (April–May) over Kolkata (22°32′N, 88°20′E), India. The stability indices are taken to form the inputs of the CS. Ten important stability indices are selected to prepare the input of the fuzzy set. The data analysis during the period from 1997 to 2006 led to identify the ranges of the stability indices through membership function for preparing the fuzzy inputs. The possibility of thunderstorms with the given ranges of the stability indices is validated with the bipartite graph connectivity method. The bipartite graphs are prepared with two sets of vertices, one set for three membership functions (strong, moderate and weak) with the stability indices and the other set includes the three membership functions for the probability of thunderstorms (high, medium and low). The percentages of degree of vertex (ΔG) are computed from a sample set of bipartite graph on thunderstorm days and are assigned as the measure of the likelihood of thunderstorms. The results obtained from graph connectivity analysis are found to be in conformity with the output of fuzzy interface system (FIS). The result reveals that the skill of graph connectivity is better and supports the FIS in estimating the predictability of thunderstorms over Kolkata during the pre-monsoon season. The result further reveals from the minimum degree of vertex connectivity that among the ten selected stability indices, only four indices: lifted index, bulk Richardson number, Boyden index and convective available potential energy, are most relevant for estimating the predictability of thunderstorms over Kolkata, India. |
| 2 |
Predictability of landfall location and surge height of tropical cyclones over North Indian Ocean (NIO)
Thunderstorms are well-known severe weather phenomena of the Gangetic West Bengal (GWB) region of India. The objective of the present study is to identify the ranges of Max_Z parameters of Doppler Weather Radar (DWR) associated with precipitating clouds that eventually grow into thunderstorms and to obtain a model to assess the predictability of thunderstorm and non-thunderstorm events with maximum possible accuracy during the pre-monsoon season (April–May) over the metropolis Kolkata (22.6°N; 88.4°E) enclosed within GWB (20–26°N, 85–91°E), India. The DWR imageries are analyzed to identify the stages of thunderstorm development. The survival of the fittest principle of genetic algorithm (GA) is implemented to find a suitable combination of the DWR Max_Z parameters; the reflectivity, distance of the first detected echo from Kolkata where the DWR is installed and the echo top height for the genesis of thunderstorms. The problem is posed as an optimization problem and the values of the parameters are converted into binary strings. The result reveals that the echoes with reflectivity between 44 and 48 dBZ at a distance of 250–300 km from Kolkata with echo top height between 13 and 15 km have the maximum possibility to grow into a thunderstorm. The artificial neural network (ANN) model is developed with the values of the Max_Z parameters optimized by GA as the inputs. The target of the ANN model is to forecast the type of the echo cells leading either to thunderstorm or non-thunderstorm events. The result further reveals that the ANN model with three hidden layers and one node in each layer is the most suitable model for estimating the likelihood of thunderstorm/non-thunderstorm events with mean absolute error (MAE) of 0.71/2.81. The result of the study is validated with the observation of India Meteorological Department. |
| 3 |
An investigation on the evolution process of thunderstroms over a metropolis of India using DWR Max_Z products and genetic algorithm.
Thunderstorms are well-known severe weather phenomena of the Gangetic West Bengal (GWB) region of India. The objective of the present study is to identify the ranges of Max_Z parameters of Doppler Weather Radar (DWR) associated with precipitating clouds that eventually grow into thunderstorms and to obtain a model to assess the predictability of thunderstorm and non-thunderstorm events with maximum possible accuracy during the pre-monsoon season (April–May) over the metropolis Kolkata (22.6°N; 88.4°E) enclosed within GWB (20–26°N, 85–91°E), India. The DWR imageries are analyzed to identify the stages of thunderstorm development. The survival of the fittest principle of genetic algorithm (GA) is implemented to find a suitable combination of the DWR Max_Z parameters; the reflectivity, distance of the first detected echo from Kolkata where the DWR is installed and the echo top height for the genesis of thunderstorms. The problem is posed as an optimization problem and the values of the parameters are converted into binary strings. The result reveals that the echoes with reflectivity between 44 and 48 dBZ at a distance of 250–300 km from Kolkata with echo top height between 13 and 15 km have the maximum possibility to grow into a thunderstorm. The artificial neural network (ANN) model is developed with the values of the Max_Z parameters optimized by GA as the inputs. The target of the ANN model is to forecast the type of the echo cells leading either to thunderstorm or non-thunderstorm events. The result further reveals that the ANN model with three hidden layers and one node in each layer is the most suitable model for estimating the likelihood of thunderstorm/non-thunderstorm events with mean absolute error (MAE) of 0.71/2.81. The result of the study is validated with the observation of India Meteorological Department. |
| 4 |
Disposition of Lightning Activity Due to Pollution Load during Dissimilar Seasons as observed from Satellite and Ground-Based Data
The precise role of air pollution on the climate and local weather has been an issue for quite a long time. Among the diverse issues, the effects of air pollution on lightning are of recent interest. Exploration over several years (2004 to 2011) has been made over Gangetic West Bengal of India using lightning flash data from TRMM-LIS (Tropical Rainfall Measuring Mission-Lightning Imaging Sensor), atmospheric pollutants, and rainfall data during pre-monsoon (April and May) and monsoon (June, July, August and September) seasons. Near-surface pollutants such as PM10 and SO2 have a good positive association with aerosol optical depth (AOD) for both the pre-monsoon and monsoon months. High atmospheric aerosol loading correlates well with pre-monsoon and monsoon lightning flashes. However, rainfall has a dissimilar effect on lightning flashes. Flash count is positively associated with pre-monsoon rainfall (r = 0.64), but the reverse relation (r = −0.4) is observed for monsoon rainfall. Apart from meteorological factors, wet deposition of atmospheric pollutant may be considered a crucial factor for decreased lightning flash count in monsoon. The variation in the monthly average tropospheric column amount of NO2, from the Tropospheric Emission Monitoring Internet Service (TEMIS), is synchronic with average lightning flash rate. It has a good linear association with flash count for both pre-monsoon and monsoon seasons. The effect of lightning on tropospheric NO2 production is evident from the monthly average variation in NO2 on lightning and non-lightning days |
| 5 |
Prediction of remotely sensed cloud related parameters over an inland urban city of India
Artificial neural network (ANN) is a mathematical model useful for forecasting on the any type of available data. This tool is not only useful in environment but also covers wide ranges of applicability. Utilizing this model, a study was carried out in an inland area of Nagpur for forecasting satellite-derived cloud parameters. Nine ANN architects are developed based on five pollutant parameter (aerosol optical depth, RSPM, SPM, SO2, NOx), meteorological and some cloud parameter. The models are used to simulate concentration of pollutants as well as the forecast and validation of cloud top temperature, cloud ice water path and cloud liquid water path during different seasons (winter, pre-monsoon and post-monsoon). Models based on back-propagation neural network were tested using the collected data of study area. The ANN models were trained using gradient descent algorithms to check the robustness and adaptability of the models. ANN models based on both satellite and ground-based data variables demonstrate the best performance and are skilled at resolving patterns of pollutant dispersion to the atmosphere during 2006–2013 for Nagpur city |
| 6 |
Prediction and Examination of Seasonal Variation of Ozone with Meteorological parameter Through Artificial Neural Network
The present study focused on seasonal relations and predictions of the ozone (O3) coupled with NO2 and meteorology. Monitoring of ozone concentration throughout year shows an increasing trend during summer and a decreasing trend in the winter season. A comparison between three types of ANN; multilayer perceptron trained (MLP) with back-propagation, radial basis functions (RBF) and generalized regression neural network (GRNN) for short prediction of ozone are conclusively demonstrated. The model results are validated with observations from next monsoon. Based on the model's performance, the MLP back propagation model gives the best correlation between observed and predicted ozone concentrations than other models. Performance assessment parameters considered in the study also indicates that MLP is the best-fit model for prediction of ozone concentration throughout the year |
| 7 |
Future trend assessment of Regional Climate Variability screening past 20 years meteorological status
The tasks of providing multi - decadal climate projections and seasonal climate predictions are of significant societal interest and pose major scientific challenges. The present study describes the global climate system context in which to interpret Nagpur and surrounding region environmental change to support planning and implementation of various strategies in the face of climate change. Here the classification and analysis of various climatic and meteorological parameters has been undertaken that have been proposed as relevant for understanding variations in climatic conditions of the Nagpur region (21.15 ?N, 79.09 ?E). The statistical and numerical analysis of past two decades data has been done. Two patterns of season stand out in our analysis i.e. the winter (December, January, February) and pre - monsoon (March, April, May). Some thermodynamic parameters (CAPE, CIN and sensible heat flux), rainfall, and surface evaporation along with planetary boundary layer have been studied in this work. The results obtained from the statistical analysis of past decades data are being utilized for predicting the future scenario using various trend projection techniques. These experiments, however, are only preliminary, and form the first stage of a wider study into how the climate variability occurs due to such meteorological parameters and in the future under various scenarios of future climate change |
