Pankaj Kumar Roy

Removal of arsenic from drinking water using dual treatment process

This paper focuses on determining an efficient and simple method to remove arsenic from groundwater. Arsenic is a naturally occurring element widely distributed in the earth’s crust. Arsenic is very toxic when found in large quantities in drinking water. This report documents the selected treatment method and laboratory experimentation of arsenic removal from drinking water in small water delivery systems and domestic water systems. The objective is to expand upon research of new and existing arsenic removal technologies or promote a new, alternative process. Several treatment technologies have been considered to perform this function, but cost and reliability concerns prompted the decision to analyze small-scale, community-based filtration units, specifically. Based upon initial test data, the use of dual treatment method comprising of oxidation-coagulation-filtration and adsorption by activated alumina has proven to be more economic having more capacity and superior reliability as compared to other arsenic removal processes using various other media.

An Introduction and Current Trends of Damodar and Rupnarayan River Network

Rivers are the important primary resource of landed community for their primary sustenance. Losses of navigability, gradient fall within a short distance, deltaic formations in lower reaches, anthropogenic actions or manipulations such as construction of embankments and guard walls, silt depositions or encroachment of river beds, monsoon induced changes, etc., can cause a river to die. As per the current status of West Bengal (Eastern part of India) and Jharkhand rivers, the effect of the creation of reservoirs, industrial extractions, and climate change can be observed easily. Damodar and Rupnarayan river systems are two major river networks of eastern India which are one of the major sources of water for irrigation, agriculture, and industrial purposes of the people living in the river banks. The present note tries to give an overview of the current trends, geomorphological characteristics, and economical resources of the two rivers which can give an idea of the impact of vulnerabilities on the natural water resources of the two catchments.

Quality of packaged drinking water in Kolkata City, India and risk to public health

AbstractIn recent years, packaged drinking water (PDW) has become one of the major sources of drinking water and is very popular among consumers. The study was conducted to assess the bacteriological and physicochemical quality of PDW sold in Kolkata city, India, and its effects on public health. The quality of Indian PDW has been compared with that of Kolkata Municipal Corporation (KMC) supply water. Further, the quality of PDW and KMC supply water has been compared with an average quality of bottled water of some branded companies of advanced countries with respect to relevant Indian and international standards (World Health Organization, IBWA, US FDA and EPA). The samples of 27 types of bottled PDW, 10 types of bubble top can PDW of different Indian brands and 18 samples of KMC supply water have been collected from different locations of the city. Physicochemical and bacteriological parameters of collected samples have been tested at School of Water Resources Engineering, Jadavpur University, as per AP...

Accumulation of Carbon Stock Through Plantation in Urban Area

Emission of carbon dioxide (CO2) in urban area is higher compared to the rural area due to the presence of different emission sources within a small area. Carbon is sequestered by the plant photosynthesis and stored as biomass in different parts of the tree. Carbon sequestration rate (CSR) has been measured for young species (6 years age) of Albizzia lebbek in Indian Botanic Garden in Howrah district and Artocarpus integrifolia at Banobitan within Kolkata in the lower Gangetic plain of West Bengal in India by Automated Vaisala Made Instrument, GMP343 and aboveground biomass carbon has been analyzed by CHN analyzer. The specific objective of this article is to measure carbon sequestration rate and accumulation of biomass carbon stock of two young species of A. lebbek and A. integrifolia. The carbon sequestration rates (mean) as CO2 from the ambient air as obtained by A. lebbek and A. integrifolia were 14.86 and 4.22 g/h, respectively. The annual carbon sequestration rates from ambient air were estimated at 11.97 t C/ ha by A. lebbek and 3.33 t C/ha by A. integrifolia. The percentages of carbon content (except root) in the aboveground biomass of A. lebbek and A. integrifolia were 47.12 and 43.33, respectively. The total accumulated aboveground biomass carbon stocks in 6 years as estimated for A. lebbek and A. integrifolia were 6.26 and 7.28 t C/ha, respectively, in these forest stands. Therefore, urban plantation based on better carbon sequestrated species will help to accumulate more biomass carbon stock as well as to offset the increasing CO2 level in ambient air.

Estimation of Soil Carbon Stock and Soil Respiration Rate of Recreational and Natural Forests in India

Soil contains good amount of carbon stock. The amount of carbon stock depends on soil texture, climatic parameters, vegetation, land-use pattern, and soil moisture. The study has been conducted at four sites in the recreational and natural forests in India. The main objective of this study is to estimate the soil carbon stock and soil respiration rate of recreational and natural forests in plain land in eastern India. At Banobitan – a recreational forest, soil was slightly alkaline; moisture content ranged between 7.26% and 9.74%, and soil texture was sandy loam. Total carbon and soil organic carbon (SOC) ranged from 24.2 to 36.5 and 2.8–8.3 g/kg, respectively. At Indian Botanic Garden – a recreational forest, soil was slightly acidic in nature; moisture content varied between 16.2% and 21.7%, and soil texture was clayey loam. Total carbon and soil organic carbon in the soil varied between 58 and 80.1 and 8.3 and 12.6 g/kg, respectively. At Chandra – a natural forest, soil was slightly acidic in nature; moisture content ranged between 3.2% and 11.4%, and soil texture was sandy loam. Total carbon and soil organic carbon ranged from 15 to 23.2 and 1.4–1.5 g/kg, respectively. At Chilapata forest – a natural forest, soil was slightly acidic in nature; moisture content varied between 22.1% and 26.0% and soil texture was loamy. Total carbon and soil organic carbon in the soil varied between 45.7 and 62.5 and 7.4 and 12.8 g/kg, respectively. Estimated mean soil total carbon and mean soil organic carbon stock at Banobitan, Indian Botanic Garden, Chandra, and Chilapata forests were 43.70 and 7.99, 96.32 and 14.57, 27.31 and 2.07, and 75.52 and 13.73 Mg C/ha, respectively. Estimated annual soil respiration rates of Banobitan, Indian Botanic Garden, Chandra, and Chilapata were 2.07, 3.34, 0.61, and 4.18 t C/ha/year, respectively.

Impact of Climate Change on the Availability of Virtual Water Estimated with the Help of Distributed Neurogenetic Models

Impact of climate change on virtual water of a tropical multireservoir system was estimated with the help of models developed by neural network and genetic algorithm. Virtual water or embedded water or embodied water, or hidden water refers to the water used in the production of goods or services. For instance, it takes 1,300 m3 of water on an average to produce 1 t of wheat. The precise volume can be more or less depending on climatic conditions and agricultural practice. The virtual water has major impacts on productive use of water and global trade policy especially in water-scarce regions. The impact of climate change on virtual water could open a path for the efficient use of virtual water in the face of climatic uncertainties, which may directly impact availability of raw water. The present study tried to estimate the future virtual water with the help of neurogenetic models, which estimates stream flow as function of various hydrological, meterological variables, and basin characteristics. The models prepared were distributed in nature and also consider temporal variability. In total, two models were prepared with rainfall, time of concentration, and catchment loss as input and stream flow as output. One model was prepared by classifying the dataset, based on the magnitude of the variable, and the other model was prepared with normal dataset. First, the better performing model was identified and then output from RCM-PRECIS model was applied to the chosen model to estimate the impact of climate change on stream flow. The estimation results were used to calculate the amount of virtual water, and the result was compared with the present-day virtual water to analyze the change in virtual water availability due to climate change. According to the results, model prepared with normal dataset was identified as a better model, and from the estimations it could be concluded that virtual water availability would increase in case of both A2 and B2 scenario of climate change where the change would be more pronounced in case of the latter.

Application of Parity Classified Neurogenetic Models to Analyze the Impact of Climatic Uncertainty on Water Footprint

Water footprint of an individual, community, or business is defined as the total volume of freshwater that is used to produce the goods and services consumed by the individual or community, or produced by the business. Neurogenetic models were widely used in the prediction of hydrologic variables, and outcome of such applications were found to be satisfactory. The irregular rainfall and temperature pattern, and degradation of watersheds were causing worldwide reduction of water availability (UNFCC). As water footprint is directly related to water availability and also shows the demand from industrial consumers, the present study tried to estimate the impact of climate change on water footprint between two river basins of East India with the help of neurogenetic models. The climate change scenarios were generated with the help of PRECIS climate models, and future runoff was estimated by a neurogenetic model trained with orthopareto dataset. The output from the neurogenetic model, named as PARITYCGD, was compared with a neurogenetic model trained with normal dataset (NGHYD) and conceptual hydrologic models. According to the results, the neurogenetic model trained with orthopareto dataset was selected as the better model among the five models, which shows that neural models trained with orthopareto dataset learn a problem better than a neurogenetic model trained with normal dataset. From the prediction of stream flow, water footprint of the sampling regions were calculated and according to the estimations, water footprint would be reduced in both A2 and B2 climate change scenarios where reductions would be more pronounced in A2 than in B2. Although, due to data dependency of neurogenetic models, the PARITYCGD model may not work for other basins but for the present study, it was found to have better accuracy than the conceptual hydrologic model.

Estimation of Reservoir Discharge with the Help of Clustered Neurogenetic Algorithm

This chapter presents a new approach of reservoir out flow prediction using a clustered neurogenetic algorithm. The algorithm combines the learning ability of artificial neural networks with searching capability of the genetic algorithm. The model is tested on the Panchet reservoir in river Damodar using the historical, hydrological, and water supply dataset. The values of the input parameters are classified into six groups based on the magnitude of the input parameters. The results showed a highly adaptive and flexible investigating ability of the model in prediction of nonlinear relationships among different variables.

Estimation of the Spatial Variation of Water Quality by Neural Models and Surface Algorithms

The present study was a continuation of the scientific investigation described in Chapter 9. The present research tried to estimate spatial variation of water quality, expressed by Weighted Average Water Quality (WAWQ), from the estimated spatial variation of stream flow as explained in Chapter 9. The relationship between WAWQ and stream flow was estimated with the help of neurogenetic models, and the spatial variation was predicted by radial basis surface algorithm. According to the results, upstream of Damodar River was found to have low quality of water than the upstream of river Barakar, downstream of river Damodar, and the entire river networks of Rupnarayan. But in the future, quality of river water will be estimated to degrade with time for both the scenarios of climate change, which was depicted by the surface diagrams of the future, where area of low WAWQ circles were seemed to be increased with time from 2010 to 2100. The change was more or less similar for both A2 and B2 scenario of climate change.

Estimation of the Spatial Variation of Pollution Load by Neural Models and Surface Algorithms

The present study tried to predict spatial variation of water pollutants with the help of two pollution factors: spatial variation of stream flow and spatial variation of water quality and neurogenetic algorithms. The two pollution factors were, respectively, industrial pollution (IP) factor, which identifies the intensity and presence of industrial pollutants from common water quality parameters that got influenced due to the release of industrial effluents in a river and organic pollution (OP) factor, which tries to estimate the intensity of organic pollutants from common quality parameters that get affected due to anthropogenic presence in the adjacent catchments. A neurogenetic model was prepared to estimate industrial pollution (IP) and organic pollution (OP) factors where observed stream flow data from 42 gauged and ungauged sampling points within two river networks in the Eastern India and land use of adjacent catchments of the sampling points were taken as input. The IP and OP factors are prepared to be directly proportional to water pollution, that is, if the factors are more than 0.7, water is polluted and if the same are less than 0.5, water is not polluted at all. The pattern identification capability of neurogenetic models enforces the authors for selection of neurogenetic models for the prediction of the above two factors. After the model was validated with the help of common validation equations, the selected model was applied to predict future IP and OP of the same region due to changed climate scenario generated by PRECIS climate model. The output of PRECIS was fed to PARITYCGD model (9), which estimated the stream flow due to the changed climatic scenario that was again used to predict IP and OP of the sampling points. The estimated values were fed to a surface algorithm to show the spatial variation of the two factors within the river basins. According to the results, area under water pollution from industries were more than the area that was not under pollution during the A2 scenario of climate change, but in B2 the trend reverses and more area without industrial pollution would emerge. But in case of water polluted by organic wastes, more area was predicted to be without pollution than area under pollution in case of A2 scenario and for B2 scenario of climate change the area without pollution will get increased in a fast rate from 2010 to 2100 and in 2071–2100 the increase would be maximum. As A2 scenario was predicted to be economic but without any restrictions on CO2 emission, the future land use was generated as industrially active but still area under pollution was more in A2 than in B2, which was imagined to be environmentally stable and with severe restrictions on CO2 emission.