Ramachandra T.V.,Sustainable Transportation and Urban Planning |
Hegde G.,Center for Sustainable Technologies Astra |
Krishnadas G.,Energy and Wetlands Research Group
International Journal of Renewable Energy Research | Year: 2014
Wind is one of the viable renewable energy resources with a promising potential of feasible alternative to fast depleting fossil fuels. Wind mills for grinding grains and pumping water have been used in rural areas since centuries. It can be advantageously harnessed in a decentralized manner for various applications in remote localities and rural areas. Water pumping through decentralized energy promotes multiple cropping, which helps in the provision of local employment and also the development of a region. Wind resource assessment is the primary step towards understanding the local wind dynamics and evaluating available potential of a region. Climatic average datasets of meteorological variables containing wind speed data for the period between 1961 and 1990 (compiled from various sources) were used for the potential assessment of wind speed in Uttara Kannada district, Karnataka State, India. These were validated with the ground data of meteorological observatories at Karwar, Honnavar and Shirali which were obtained from the Indian Meteorological Department, Government of India, Pune. Analyses showed seasonal variations of wind speed in the region. Wind speed varies from 1.9 ms-1 (6.84 km/hr) to 3.93 ms-1 (14.15 km/hr) throughout the year with minimum in October and maximum in June and July (Monsoon). The district experiences mean annual wind speed of 2.5 ms-1 to 3.0 ms-1 in all taluks, fostering prospects for Wind Energy Conversion System (WECS) installation. Decentralized electricity generation from WECS and hybridizing wind energy systems with other locally available resources (solar, bioenergy etc.) would assure the supply of reliable energy to meet the energy demand of the respective regions.
Kumar U.,Energy and Wetlands Research Group |
Kumar U.,Indian Institute of Science |
Kumar U.,International Institute of Information Technology Bangalore |
Mukhopadhyay C.,Indian Institute of Science |
And 3 more authors.
Boletin Geologico y Minero | Year: 2014
Many regional environmental problems are the consequence of anthropogenic activities involving land cover changes. Temporal land cover data with social aspects are critical in tracing relationships of cause and effect on variables of interest with the effects of context on behaviour, or with the process of human environmental interaction and are also useful for the governance of urbanising cities. Many cities are now rapidly becoming urbanised and undergoing redevelopment for economic purposes with new roads, infrastructure improvements, etc. raising the necessity to understand the dynamics of the urban growth process for the planning of natural resources. Cellular automata (CA), an artificial intelligence technique based on pixels, states, neighbourhoods and transition rules is useful in modelling the urban growth process due to its ability to ft such complex spatial nature, using simple and effective rules. This study develops the calibration of a CA model by taking into account spatial and temporal dynamics of urban growth. The effectiveness of this technique is demonstrated by capturing the growth pattern of Bangalore, a city in India, with historical remote sensing and population data. © 2014 Instituto Geologico y Minero de Espana. All Rights reserved.
Aithal B.H.,Energy and Wetlands Research Group |
Aithal B.H.,Center for Sustainable Technologies Astra |
Vinay S.,Energy and Wetlands Research Group |
Venugopal Rao K.,Indian National Remote Sensing Centre |
And 3 more authors.
11th IEEE India Conference: Emerging Trends and Innovation in Technology, INDICON 2014 | Year: 2015
The potential of Markov chain and cellular automata model with help of agents that play a vital role in a cities urbanisation through fuzziness in the data and hierarchal weights (for principal agents) have been used to understand and predict the urban growth for the Pune city, India. The model utilizes temporal land use changes with probable growth agents such as roads drainage networks, railway connectivity, slope, bus network, industrial establishments, educational network etc., to simulate the growth of Pune for 2025 using two scenarios of development - implementation of City Development Plan (CDP) and without CDP. In the study, multi temporal land use datasets, derived from remotely-sensed images of 1992, 2000, 2010 and 2013, were used for simulation and validation. Prediction reveals that future urban expansion would be in northwest and southeast regions with intensification near the central business district. This approach provides insights to urban growth dynamics required for city planning and management. © 2014 IEEE.
Ramachandra T.V.,Energy and Wetlands Research Group |
Ramachandra T.V.,Center for Sustainable Technologies Astra |
Ramachandra T.V.,Indian Institute of Science |
Aithal B.H.,Energy and Wetlands Research Group |
And 2 more authors.
Renewable and Sustainable Energy Reviews | Year: 2015
Concentration of greenhouse gases (GHG) in the atmosphere has been increasing rapidly during the last century due to ever increasing anthropogenic activities resulting in significant increases in the temperature of the Earth causing global warming. Major sources of GHG are forests (due to human induced land cover changes leading to deforestation), power generation (burning of fossil fuels), transportation (burning fossil fuel), agriculture (livestock, farming, rice cultivation and burning of crop residues), water bodies (wetlands), industry and urban activities (building, construction, transport, solid and liquid waste). Aggregation of GHG (CO2 and non-CO2 gases), in terms of Carbon dioxide equivalent (CO2e), indicate the GHG footprint. GHG footprint is thus a measure of the impact of human activities on the environment in terms of the amount of greenhouse gases produced. This study focuses on accounting of the amount of three important greenhouses gases namely carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) and thereby developing GHG footprint of the major cities in India. National GHG inventories have been used for quantification of sector-wise greenhouse gas emissions. Country specific emission factors are used where all the emission factors are available. Default emission factors from IPCC guidelines are used when there are no country specific emission factors. Emission of each greenhouse gas is estimated by multiplying fuel consumption by the corresponding emission factor. The current study estimates GHG footprint or GHG emissions (in terms of CO2 equivalent) for Indian major cities and explores the linkages with the population and GDP. GHG footprint (Aggregation of Carbon dioxide equivalent emissions of GHG's) of Delhi, Greater Mumbai, Kolkata, Chennai, Greater Bangalore, Hyderabad and Ahmedabad are found to be 38,633.2 Gg, 22,783.08 Gg, 14,812.10 Gg, 22,090.55 Gg, 19,796.5 Gg, 13,734.59 Gg and 91,24.45 Gg CO2 eq., respectively. The major contributors sectors are transportation sector (contributing 32%, 17.4%, 13.3%, 19.5%, 43.5%, 56.86% and 25%), domestic sector (contributing 30.26%, 37.2%, 42.78%, 39%, 21.6%, 17.05% and 27.9%) and industrial sector (contributing 7.9%, 7.9%, 17.66%, 20.25%, 12.31%, 11.38% and 22.41%) of the total emissions in Delhi, Greater Mumbai, Kolkata, Chennai, Greater Bangalore, Hyderabad and Ahmedabad, respectively. Chennai emits 4.79 t of CO2 equivalent emissions per capita, the highest among all the cities followed by Kolkata which emits 3.29 t of CO2 equivalent emissions per capita. Also Chennai emits the highest CO2 equivalent emissions per GDP (2.55 t CO2 eq./Lakh Rs.) followed by Greater Bangalore which emits 2.18 t CO2 eq./Lakh Rs. © 2015 Elsevier Ltd. All rights reserved.