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Rapid Granulation and Start-up of a Hybrid Anoxic Reactor for Biological Denitrification(Chemical Engineering Technology)
(Bhuvanesh, S., Sreekrishnan, T.R.,,,,Year : 2016)
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Rapid granulation of biomass and reactor start-up has been studied in a novel denitrifying reactor. The effect of wastewater characteristics, reactor operating conditions and reactor geometry on microbial granulation has been studied. It was possible to achieve granulation in just 15 days of reactor start-up. In 15 days the settling velocity of the granules was 1.5 cm s-1, which is almost 10 folds higher than that of seed sludge. The reactor was able to handle a nitrate loading rate of 50 g NO3-N m-3 day-1 in 3 days of reactor start-up with rates reaching up to 460 g NO3-N m-3 day-1 in just 30 days of reactor start-up with a removal efficiency of almost 100%. Based on the experimental observation, a hypothesis for the cause of rapid granulation has been proposed.
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Removal of m-phenylenediamine by adsorption onto activated carbon: Kinetics, equilibrium and process design(Desalination and Water Treatment)
(Patel, M. Rathod, K. H. Mody,57 (9),,4205-4219,Year : 2016)
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The adsorption behavior of m-phenylene diamine (m-PDA) from aqueous solution onto activated carbon was investigated under various experimental conditions, such as contact time, adsorbate concentration, and temperature. Maximum adsorption capacity for m-PDA was found to be 33.17 mg?g?1 at pH 7.0 and temperature 303 K. The adsorption kinetics data were best described by the pseudo-second-order rate equation and the equilibrium was achieved after 120 min. The m-PDA adsorption was governed by film diffusion process. Besides, equilibrium data were very well represented by the Redlich–Peterson model. A model for prediction of the dose of adsorbent required to achieve a range of m-PDA removals for a given number of adsorption–desorption cycles has been developed and validated based on the Langmuir isotherm. Thermodynamic parameters indicated the spontaneous, endothermic, and increased random nature of m-PDA adsorption. The amide, carboxylic acid, and nitro groups of the activated carbon were involved in chemical interaction with the m-PDA molecules. Results suggested that the activated carbon has good potential for remediation of m-PDA contaminated waters.
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Extent of sewage pollution in coastal environment of Mumbai, India: an object based image analysis(Water and Environment Journal)
(Ritesh Vijay,Vikash K. Kushwaha, Neha Pandey, Tapas Nandy and S. R. Wate,29,3,365-374,Year : 2016)
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The coastal water quality of Mumbai is deteriorating by receiving partially treated effluent from wastewater treatment facilities, sewage discharges from ocean outfalls and discharges from point and non?point sources in the creek and coast. A novel approach of object?based image analysis has been used in this research study to assess the extent of sewage pollution in the coastal environment of Mumbai. For this, Indian Remote Sensing P6 Linear Imaging Self Scanning IV image was used for multiresolution segmentation and rule?based image classification as per normalised difference water index and normalised difference turbidity index. Water quality regions as per classification were strongly correlated with observed water quality parameters. Based on classified regions and water quality parameters, extent of sewage pollution in the coast was ranked from high to least polluted. The approach developed in this methodology should be tested in similarly polluted waters to ascertain its adaptability for assessing the spatial extent of sewage pollution.
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Assessment of honking impact on traffic noise in heterogeneous traffic environment of Nagpur, India(Journal of Environmental Health Science and Engineering)
(Ritesh Vijay, A. Sharma, T. Chakrabarti and Rajesh Gupta ,13,,10,Year : 2016)
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Background In context of increasing traffic noise in urban India, the objective of the research study is to assess noise due to heterogeneous traffic conditions and the impact of honking on it. Method Traffic volume, noise levels, honking, road geometry and vehicular speed were measured on national highway, major and minor roads in Nagpur, India. Results Initial study showed lack of correlation between traffic volume and equivalent noise due to some factors, later identified as honking, road geometry and vehicular speed. Further, frequency analysis of traffic noise showed that honking contributed an additional 2 to 5 dB (A) noise, which is quite significant. Vehicular speed was also found to increase traffic noise. Statistical method of analysis of variance (ANOVA) confirms that frequent honking (p?
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Modeling of subsurface horizontal flow constructed wetlands using OpenFOAM?. (Modeling Earth Systems and Environment)
(Kadaverugu, R.,255,,,Year : 2016)
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No information is available
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The role of sand, marble chips and Typha latifolia in domestic wastewater treatment a column study on constructed wetlands(Environmental Technology)
(Kadaverugu, R., Shingare, R.P., Raghunathan, K., Juwarkar, A.A., Thawale, P.R. and Singh ,22,,p.1-10,Year : 2016)
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No information is available
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Shaharikaran Evam Vishwastariya Jalvayu Parivartan(Urbanization and Climate Change)
(,,,,Year : 2016)
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No information is available
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Risk Based Analysis for Contamination Event Selection and Optimal Sensor Placement for Intermittent Water Distribution Network Security(Water Resources Management)
(Shweta Rathi, Rajesh Gupta, Swapnil Kamble, Aabha Sargaonkar, 30,,2671-2685,Year : 2016)
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DOI 10.1007/s11269-016-1309-7
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Predictability of landfall location and surge height of tropical cyclones over North Indian Ocean (NIO)(Natural Hazards)
(C. S., Goswami S., Middey A., Das D., Chowdhary S.,75,,1369-1388,Year : 2015)
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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.
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An investigation on the predictability of thunderstorms over Kolkata, India using fuzzy inference system and graph connectivity(Natural Hazards)
(Chaudhari S, Das D, Middey A.,76,,63-81,Year : 2015)
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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.
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Separation of WPCBs by dissolution of brominated epoxy resins using DMSO and NMP: A comparative study (Chemical Engineering Journal)(18th International Conference on Research and Industrial Practices exclusive on Non-ferrous Minerals and Metals (ICNFMM 2014))
(M.N. Katariya,,280,391 398,Year : 2015)
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No information is available
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An investigation on the evolution process of thunderstroms over a metropolis of India using DWR Max_Z products and genetic algorithm. (Meteorology and Atmospheric Physics)
(S. C., Khan F., Pal J, Goswami S., Middey A.,127,,75-93,Year : 2015)
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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.
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Auxin Treatment of Wetland and Non-wetland Plant Species to Enhance Their Phytoremediation Efficiency to Treat Municipal Wastewater.(Journal of Scientific & Industrial Research)
(S. A Tandon, S Parsana,Vol. 74,,702-707,Year : 2015)
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Auxin treatment of wetland and non-wetland plant species for increasing their phytoremediation efficiency to treat municipal wastewater was studied. The mesocosms were set up with gravels and polyethylene balls as the inert support media. The wetland plant species (Alternanthera philoxeroides, Eichhornia crassipes) and non-wetland species (Chrysopogon zizanioides, Festuca arundinaceae) were treated with six concentrations (0.5, 1.0, 2.0, 4.0, 8.0 and 10.0 mg/L) of natural auxins (Indole-3-acetic acid, Indole-3-butyric acid) and a synthetic auxin (1-Naphathaleneacetic acid). The optimum auxin concentration was found to be 2 mg/L of IAA, 1mg/L of IAA and 1mg/L of IBA for Alternanthera philoxeroides, Festuca arundinaceae and Chrysopogon zizanioides, respectively. The removal efficiencies of auxin treated Alternanthera philoxeroides, Festuca arundinaceae and Chrysopogon zizanioides for BOD, Nitrate and Phosphate was 12-15, 30-44 and 29-42 % more than the untreated plants.
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Effects of unregulated anthropogenic activities on mixing ratios of volatile organic air pollutants(Air & Waste Management Association)
(Rao P. S, Majumdar, Dipanjali ,65,Issue 9,,Year : 2015)
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During the months of October to November, many important festivals are celebrated in India. Celebration of these festivals are marked by extensive use of fireworks or pyrotechnics, bonfire, incense burning, open air community cooking, and temporary eateries using crude fuel such as coal, wood, kerosene, cow dung, burning of raw/semiwood, and coconut shells. The present study deals with the influence of these unregulated anthropogenic activities on ambient mixing level of volatile organic compounds (VOCs), especially some carbonyl compounds. The study was undertaken in the metropolitan city of Kolkata, India, with very high population density, which is even higher during festival period. The average total carbonyl level at different sites in Kolkata varied from 134.8 to 516.5 μg m(-3) in pre-festival season, whereas in post-festival season the same varied from 252.2 to 589.3 μg m(-3). Formaldehyde to acetaldehyde ratio altered from 0.62 in pre-festival season to 1.78 in post-festival season. Diurnal variation also altered, indicating variation in source composition of carbonyls. The total ozone forming potential calculated for all 14 carbonyls in pre-festival season increased by 35% in post-festival season. The effect of anthropogenic activities typical to the event of Diwali night characterized by intense execution of pyrotechnics resulted in significantly high level of carbonyl VOCs. Principal component analysis study for the event of Diwali shows clear contribution of the event on certain carbonyl VOCs. The results indicate elevated primary emissions of these pollutants and also their effect on formation of secondary pollutants. The study emphasizes the need of generating awareness among the communities in society as well as need for regulations to minimize the emissions and related hazards to the extent possible
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Exceedance analysis of PM10 concentration in central Indian city: predicting time between two exceedances(Aerosol & Air Quality Research)
(A. B. Chelani
,15(5),,2158-2167,Year : 2015)
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In this study the gap between the two exceedances is analyzed using time series analysis. The time series of PM_(10) (particulate matter of size less than 10 micron) observed during 2005-2013 in two cities; Nagpur and Chandrapur in central India is considered. Higher PM_(10) concentration is observed in Chandrapur as compared to Nagpur. Exponential relationship is observed between the average time between the two exceedances and annual average PM10 concentration. This information along with the PM_(10) concentration prediction model is utilized to predict the average number of observations between the two exceedances for the following year. ?k?-nearest neighbor approach is used for forecasting PM_(10) concentration which enabled estimating the average number of observations between two exceedances using exponential relationship. The approach can be used for estimating the average number of observations between the two exceedances over a year, which can further be utilized to make appropriate decision to control and manage high particulate matter pollution in an area.
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Nearest neighbour based forecast model for PM10 forecasting: Individual and combination forecasting, (Aerosol & Air Quality Research)
(A. B. Chelani
,15(3),,1130-1136,Year : 2015)
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Air quality forecasting using nearest neighbour technique provides an alternative to statistical and neural network models, which needs the information on predictor variables and understanding of underlying patterns in the data. k-nearest neighbour method of forecasting that does not assume any linear or nonlinear form of the data is used in this study to obtain the next step forecast of PM10 concentrations. Various function approximation techniques such as mean, median, linear combination and kernel regression of nearest neighbours are evaluated. It is observed that kernel regression of nearest neighbours outperforms the other individual models including bench mark persistence model for obtaining the next step forecasts. As the data may involve both linear and nonlinear patterns and any individual model cannot capture both types of patterns, combination forecasting is suggested as an alternative. The forecast error showed the outperformance of combination forecasting over individual forecast, which is quite obvious as it assigns more weightage to the model with minimum error. The study is useful when the data on predictor variables that influence the air pollutant concentrations is not available. The assumption on the underlying distribution of the data is also not required for the approach
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Study of temporal variations in aerosol optical depth over central India(I. J. Environmental Protection)
(A. B. Chelani
,5(1),,25-31,Year : 2015)
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Monthly aerosol optical depth (AOD) data over central India during 2001-2010 obtained from Moderate Resolution Imaging Spectroradiometer are analyzed for trend and periodicity. For this purpose, spectral analysis and linear trend analysis are performed. High AOD during monsoon followed by summer months are observed. Spatial analysis did not show any significant spatial variations in AOD levels. Spectral analysis suggested two dominant periods; 12 months and 6 months consistent with the annual and seasonal patterns. Trend analysis showed an insignificant trend at all the locations. Decadal change in AOD is the highest in Nagpur, which is an urban agglomeration station. Less developed and nonurban areas, however show decreasing or insignificant trend in AOD levels. Correlation with change in population over the last decade at different locations showed significant positive relationship with percentage change in AOD levels suggesting the effect of urban agglomeration on AOD in central India
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Impact assessment of tourists on noise environment in heritage site(Sustainable Tourism)
( Kori. Chandan, Mardikar. Trupti
,,,,Year : 2015)
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No information is available
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Research Journal of Chemistry and Environment(Research Journal of Chemistry and Environment)
( Bodkhe, S.Y, Vol. 19,No.9,p. 11-15,Year : 2015)
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Research Journal of Chemistry and Environment" ( Research Journal of Chemistry and Environment ) ( Bodkhe, S.Y (2015) “Biogas Generation from Low Strength Domestic Wastewater by Using Partially Phased Anaerobic Process” , Vol. 19, No.9, pp. 11-15 , 2015 )
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Prediction of remotely sensed cloud related parameters over an inland urban city of India(Annals of GIS)
(Navneet Kumar, Anirban Middey and Padma Rao,22,Issue 1,71-84,Year : 2015)
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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
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