Calculating AQI Using Secondary Pollutants for Smart Air Management System
With the onslaught of the industrial revolution, the environment is suffering from severe pollution leading to major imbalances. Air Quality Dispersion Modelling can be done through one of the most efficient model “Eulerian Grid based model”. Various existing methods of prediction work on the basis of models resulting in satisfactory outcomes but with some certain loopholes. This project involves methods of predicting pollutants’ concentration and air quality using machine learning. The data of different sites are collected and the pollutants contributing maximum to the pollution is elucidated using machine learning based methods. Also in this project, a user-friendly, smart application system is developed which can be used to monitor the pollution produced at an individual level. The analysis of the feature stimulating the pollution level (to reach at a dangerous level) can be done with the help of machine learning tools. This paper involves calculating the amount of harmful pollutants released by any individual during their journey. Further solutions can be identified at government level to reduce these pollutants raising the pollution level.
KeywordsAir pollutants AQI Eulerian grid based model Machine learning Smart air pollution system
- 1.Nikzad, N., Verma, N., Ziftci, C., Bales, E., Quick, N., Zappi, P., et al. (2012). CitiSense: Improving geospatial environmental assessment of air quality using a wireless personal exposure monitoring system. In Proceedings of the ACM Conference on Wireless Health, San Diego, CA, USA, October 23–25, 2012.Google Scholar
- 2.Dutta, P., Aoki, P. M., Kumar, N., Mainwaring, A., Myers, C., Willett, W., & Woodruff, A. (2009). Common sense: Participatory urban sensing using a network of handheld air quality monitors. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, Berkeley, CA, USA, November 4–6, 2009.Google Scholar
- 4.Brandt, J., Christensen, J. H., Frohn, L. M., & Zlatev, Z. (2002). Operational air pollution forecast modelling using the THOR system. National Environmental Research Institute, Department of Atmospheric Environment.Google Scholar