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Air Quality Index Prediction Using Various Machine Learning Algorithms

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6G Enabled Fog Computing in IoT

Abstract

One of the most critical factors for human survival is air. The quality of air inhaled by humans affects their health and lives significantly. The continuously rising air pollution is a significant concern as it threatens human health and is an environmental issue in many Indian cities. A proper AQI prediction system will help tackle the problem of air pollution more efficiently and mitigate the health risks it causes. Government agencies use the Air Quality Index, a number to indicate the pollution level of the air to the public. It qualitatively illustrates the current state of the air. Aggregate values of PM2.5, PM10, CO2, NO2, and SO2 have been taken to forecast the AQI for Pune city using the dataset collected by Pune Smart City Development Corporation Limited and IISc in 2019. This study aims to find the machine learning method which forecasts the most accurate AQI and its analysis.

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References

  1. WHO air pollution report. Available at: https://www.who.int/health-topics/air-pollution. Accessed 20 Sept 2022.

  2. Air quality data statistics. Available at: https://www.airnow.gov/. Accessed 1 Oct 2022.

  3. Central pollution control board. Available at: https://cpcb.nic.in. Accessed 5 Oct 2022.

  4. Sharma, M., Jain, S., Mittal, S., & Sheikh, T. H. (2021). Forecasting and prediction of air pollutants concentrate using machine learning techniques: The case of India. In IOP conference series: Materials science and engineering (Vol. 1022, No. 1, p. 012123). IOP Publishing.

    Google Scholar 

  5. Mannan, M., & Al-Ghamdi, S. G. (2021). Indoor air quality in buildings: A comprehensive review on the factors influencing air pollution in a residential and commercial structure. International Journal of Environmental Research and Public Health, 18(6), 3276.

    Article  Google Scholar 

  6. Dutta, S., Ghosh, S., & Dinda, S. (2021). Urban air-quality assessment and inferring the association between different factors: A comparative study among Delhi, Kolkata and Chennai megacity of India. Aerosol Science and Engineering, 5(1), 93–111.

    Article  Google Scholar 

  7. Amuthadevi, C., Vijayan, D. S., & Ramachandran, V. (2021). Development of air quality monitoring (AQM) models using different machine learning approaches. Journal of Ambient Intelligence and Humanized Computing, 1–13.

    Google Scholar 

  8. Al-Qaness, M. A., Fan, H., Ewees, A. A., Yousri, D., & Abd Elaziz, M. (2021). Improved ANFIS model for forecasting Wuhan City air quality and analysis COVID-19 lockdown impacts on air quality. Environmental Research, 194, 110607.

    Article  Google Scholar 

  9. Kumar, R. S., Arulanandham, A., & Arumugam, S. (2021, October). Air quality index analysis of Bengaluru city air pollutants using expectation maximization clustering. In 2021 international conference on advancements in electrical, electronics, communication, computing and automation (ICAECA) (pp. 1–4). IEEE.

    Google Scholar 

  10. Fernando, R. M., Ilmini, W. M. K. S., & Vidanagama, D. U. (2022). Prediction of air quality index in Colombo.

    Google Scholar 

  11. Iskandaryan, D., Ramos, F., & Trilles, S. (2021). Features exploration from datasets vision in the air quality prediction domain. Atmosphere, 12(3), 312.

    Article  Google Scholar 

  12. Alemdar, K. D., Kaya, Ö., Canale, A., Çodur, M. Y., & Campisi, T. (2021). Evaluation of air quality index by spatial analysis depending on vehicle traffic during the COVID-19 outbreak in Turkey. Energies, 14(18), 5729.

    Article  Google Scholar 

  13. Gladson, L. A., Cromar, K. R., Ghazipura, M., Knowland, K. E., Keller, C. A., & Duncan, B. (2022). Communicating respiratory health risk among children using a global air quality index. Environment International, 159, 107023.

    Article  Google Scholar 

  14. Sun, X., Li, S., Chen, X., & Wang, K. (2021, March). Air quality index prediction based on improved PSO-BP. In IOP conference series: Earth and environmental science (Vol. 692, No. 3, p. 032069). IOP Publishing.

    Google Scholar 

  15. Phruksahiran, N. (2021). Improvement of air quality index prediction using geographically weighted predictor methodology. Urban Climate, 38, 100890.

    Article  Google Scholar 

  16. Singh, M., Singh, B. B., Singh, R., Upendra, B., Kaur, R., Gill, S. S., & Biswas, M. S. (2021). Quantifying COVID-19-enforced global changes in atmospheric pollutants using cloud computing-based remote sensing. Remote Sensing Applications: Society and Environment, 22, 100489.

    Article  Google Scholar 

  17. Peneti, S., et al. (2021). BDN-GWMNN: Internet of things (IoT) enabled secure smart city applications. Wireless Personal Communications, 119(3), 2469–2485.

    Article  Google Scholar 

  18. Kochetkov, D., Vuković, D., Sadekov, N., & Levkiv, H. (2019). Smart cities and 5G networks: An emerging technological area? Journal of the Geographical Institute “Jovan Cvijić” SASA, 69(3), 289–295.

    Article  Google Scholar 

  19. Li, T., et al. (2021). DRLR: A deep-reinforcement-learning-based recruitment scheme for massive data collections in 6G-based IoT networks. IEEE Internet of Things Journal, 9(16), 14595–14609.

    Article  Google Scholar 

  20. Kumari, A., Gupta, R., & Tanwar, S. (2021). Amalgamation of blockchain and IoT for smart cities underlying 6G communication: A comprehensive review. Computer Communications, 172, 102–118.

    Article  Google Scholar 

  21. Guzel, M., & Ozdemir, S. (2019). A new CEP-based air quality prediction framework for fog based IoT. 2019 International Symposium on Networks, Computers and Communications (ISNCC). IEEE.

    Google Scholar 

  22. Pune smart city dataset. Available at: https://www.kaggle.com/datasets/akshman/pune-smartcity-test-dataset. Accessed 1 Oct 2022.

  23. Soni, K. M., Gupta, A., & Jain, T. (2021). Supervised machine learning approaches for breast cancer classification and a high-performance recurrent neural network. In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 1–7. https://doi.org/10.1109/ICIRCA51532.2021.9544630aset easy and summariz

  24. Jain, T., Verma, V. K., Agarwal, M., Yadav, A. & Jain, A. (2020). Supervised machine learning approach for the prediction of breast cancer. In 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–6. https://doi.org/10.1109/ICSCAN49426.2020.9262403

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Bajpai, M., Jain, T., Bhardwaj, A., Kumar, H., Sharma, R. (2023). Air Quality Index Prediction Using Various Machine Learning Algorithms. In: Kumar, M., Gill, S.S., Samriya, J.K., Uhlig, S. (eds) 6G Enabled Fog Computing in IoT. Springer, Cham. https://doi.org/10.1007/978-3-031-30101-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-30101-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30100-1

  • Online ISBN: 978-3-031-30101-8

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