IJEP 44(3): 282-288 : Vol. 44 Issue. 3 (March 2024)
1. Koneru Lakshmaiah Education Foundation (Deemed to be University), Department of Atmospheric Sciences, Vijayawada – 522 302, Andhra Pradesh, India
2. Koneru Lakshmaiah Education Foundation (Deemed to be University), Department of Civil Engineering, Vijayawada – 522 302, Andhra Pradesh, India
Abstract
The population’s increasing energy consumption as a result of urbanization has led to a declining situation for fossil fuels. Therefore, it is imperative to transition to using energy from traditional natural resources as soon as possible. Urban waste to energy is one of the finest renewable energy sources to use in place of currently used fossil fuels, which are rapidly running out. It has also been shown to be the best method for energy generation because it does not cause pollution or global warming. Some trustworthy ways to produce this energy include incinerators, waste-to-energy plants, bio-methanation or composting facilities and anaerobic digestion. To address the city’s energy needs, it has been suggested that an integrated solid waste treatment plant be built in the capital of Amaravati. Utilizing renewable energy sources and geospatial technologies facilitates quick decision-making for resource assessment, site selection, best routing, logistics planning, potential for renewable energy, statistical analysis of fuel use and petrol emissions, etc. The proposed implementation would also serve as a data archive for upcoming successes.
Keywords
Urban waste, Energy, Eenewable resource, Solid waste management
References
- Hoornweg, D., P. Bhada-Tata and C. Kennedy. 2013. Environment: Waste production must peak this century. Nature. 502: 615-617.
- Scarlat, N., et al. 2015. Evaluation of energy potential of municipal solid waste from African urban areas. Renew. Sustain. Energy Reviews. 50: 1269-1286.
- Agarwal, S. and S. Kumar. 2020. Urban flood modelling using SWMM for historical and future extre-me rainfall events under climate change scenario. Indian J. Ecol., 47(11): 48-53.
- Agarwal, S. and S. Kumar. 2019. Applicability of SWMM for semi-urban catchment flood modelling using extreme rainfall events. Int. J. Recent Tech. Eng., 8(2): 245-251.
- Richa, K., V.V. Tyagi and A. Pathak. 2010. Waste-to-energy: A way from renewable energy sources to sustainable development. Renew. Sustain. Energy Reviews. 14(9): 3164-3170.
- Jantiku, J., et al. 2014. A geospatial approach for solid waste dumpsites for sustainable development in Minna, Niger state, Nigeria. IOSR J. Env. Sci. Toxicol. Food Tech., 8(10): 16-28.
- Ehisienmhen, N.O., T.O. Alaga and S. O. Olowojoba. 2016. The geospatial analysis of solid waste collection practices. The distribution, disposal and management system in OAU Campus, Ile Ife, Osun state, Nigeria. Int. J. Trend Res. Develop., 3(6): 318-322.
- Moonmoon, H., et al. 2017. Emerging role of geographical information system (GIS), life cycle assessment (LCA) and spatial LCA (GIS-LCA) in sustainable bioenergy planning. Bioresour. Tech., 242: 218-226
- Ramachandra, T. V. and B. V. Shruthi. 2007. Spatial mapping of renewable energy potential. Renew. Sustain. Energy Reviews. 11(7): 1460-1480.
- Chalkias, C. and K. Lasaridi. 2009. A GIS based model for the optimisation of municipal solid waste collection: the case study of Nikea, Athens, Greece. WSEAS Trans. Env. Develop., 10(5): 640- 650.
- Ahamed, S.M.F. and S. Agarwal. 2019. Urban flood modelling and management using SWMM for new RR pet region, Vijayawada, India. International Conference on Advances in civil engineering (ICACE-2019). Proceedings, vol 7(6C2): 317-322.
- Agarwal, S., et al. 2019. Assessment of water supply-demand using water evaluation and planning (WEAP) model for Ur river watershed, Madhya Pradesh, India. J. Institution Eng. (India) Series A. 100: 21-32.
- Agarwal, S., A. S. Suchithra and S.P. Singh. 2021. Analysis and interpretation of rainfall trend using Mann-Kendall’s and Sen’s slope method. Indian J. Ecol., 48(2): 453-457.
- Agarwal, S., S. Kumar and U.K. Singh. 2021. Intensity duration frequency curve generation using historical and future downscaled rainfall data. Indian J. Ecol., 48(1): 275-280.
