Main Article Content
Abstract
According to a 2020 report from the World Health Organization (WHO), household air pollution has led to over 3 million deaths globally, with recent statistics showing a worsening situation in Africa. Integrating Internet of Things (IoT) and Artificial Intelligence (AI) technologies can help address this global challenge. IoT enables real-time data collection for monitoring pollution levels, while AI algorithms predict pollution before it reaches hazardous levels. However, existing solutions are not tailored to the African context, where wood fuel is a primary pollutant, and they predominantly focus on monitoring rather than prediction. This study presents the design and implementation of an IoT-based solution for monitoring and predicting indoor air pollution in rural African households. The system collects data in real time and transmits it to the cloud for storage, processing, and analysis, with alerts to users when pollution is detected. An AI model was successfully trained and tested to predict indoor air pollution based on the collected data. The results indicate that this approach significantly improves the accuracy and timeliness of pollution alerts, potentially reducing health risks associated with indoor air pollution. The successful implementation and testing of the system demonstrate its potential for broader applications in various indoor environments.
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Article Details
References
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References
Al Ahasan, Md. A., Roy, S., Saim, A. H. M., Akter, R., & Hossain, Md. Z. (2018). Arduino-Based Real Time Air Quality and Pollution Monitoring System. International Journal of Innovative Research in Computer Science & Technology, 6(4), 81–86. https://doi.org/10.21276/ijircst.2018.6.4.8
Alabdullah, A. J., Farhat, B. I., & Chtourou, S. (2019). Air Quality Arduino Based Monitoring System. 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019, 1–5. https://doi.org/10.1109/CAIS.2019.8769529
Ana, G. R., Alli, A. S., Uhiara, D. C., & Shendell, D. G. (2019). Indoor air quality and reported health symptoms among hair dressers in salons in Ibadan, Nigeria. Journal of Chemical Health and Safety, 26(1), 23–30. https://doi.org/10.1016/j.jchas.2018.09.004
Dey, D., & Chattopadhyay, A. (2016). Solid Fuel Use in Kitchen and Child Health in India. Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics, 58(4), 365. https://doi.org/10.21648/arthavij/2016/v58/i4/153076
Firdhous, M. F. M., Sudantha, B. H., & Karunaratne, P. M. (2017). IoT enabled proactive indoor air quality monitoring system for sustainable health management. Proceedings of the 2017 2nd International Conference on Computing and Communications Technologies, ICCCT 2017, 216–221. https://doi.org/10.1109/ICCCT2.2017.7972281
Gola, M., Settimo, G., & Capolongo, S. (2019). Indoor air in healing environments: Monitoring chemical pollution in inpatient rooms. Facilities, 37(9–10), 600–623. https://doi.org/10.1108/F-01-2018-0008
Husain, A. M., Rini, T. H., Haque, M. I., & Alam, M. R. (2016). Air Quality Monitoring: The Use of Arduino and Android. Journal of Modern Science and Technology, 4(1), 86–96.
Janarthanan, A., Paramarthalingam, A., Arivunambi, A., & Vincent, P. M. D. R. (2022). Real-time indoor air quality monitoring using the Internet of Things. Proceedings of the 2022 3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies: Computational Intelligence for Smart Systems, ICICICT 2022, 99–104. https://doi.org/10.1109/ICICICT54557.2022.9917990
Jo, J., Jo, B., Kim, J., Kim, S., & Han, W. (2020). Development of an IoT-Based indoor air quality monitoring platform. Journal of Sensors, 2020, 13–15. https://doi.org/10.1155/2020/8749764
Lapshina, P. D., Kurilova, S. P., & Belitsky, A. A. (2019). Development of an Arduino-based CO2 Monitoring Device. Proceedings of the 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2019, 595–597. https://doi.org/10.1109/EIConRus.2019.8656915
Mad Saad, S., Andrew, A. M., Shakaff, A. Y. M., Mohd Saad, A. R., Kamarudin, A. M. Y., Zakaria, A., Saad, S. M., Andrew, A. M., Shakaff, A. Y. M., Dzahir, M. A. M., Hussein, M., Mohamad, M., & Ahmad, Z. A. (2017). Pollutant recognition based on supervised machine learning for Indoor Air Quality monitoring systems. Applied Sciences (Switzerland), 7(8), 11665–11684. https://doi.org/10.3390/s150511665
Marques, G., & Pitarma, R. (2016). An indoor monitoring system for ambient assisted living based on internet of things architecture. International Journal of Environmental Research and Public Health, 13(11). https://doi.org/10.3390/ijerph13111152
Marques, G., Roque Ferreira, C., & Pitarma, R. (2018). A system based on the internet of things for real-time particle monitoring in buildings. International Journal of Environmental Research and Public Health, 15(4). https://doi.org/10.3390/ijerph15040821
Maulana Azad (2017). International Conference on Recent Innovations in Signal Processing and Embedded Systems (RISE-2017) : 27th-29th October 2017 : venue: Department of Electronics and Communication Engineering, MANIT, Bhopal, India - 462003. 27–29.
Rakib, M., Haq, S., Hossain, M. I., & Rahman, T. (2022). IoT Based Air Pollution Monitoring & Prediction System. 2022 International Conference on Innovations in Science, Engineering and Technology, ICISET 2022, 184–189. https://doi.org/10.1109/ICISET54810.2022.9775871
Rocha, Á., Correia, A. M., Adeli, H., Reis, L. P., & Teixeira, M. M. (2016). New advances in information systems and technologies. Advances in Intelligent Systems and Computing, 445, 13–21. https://doi.org/10.1007/978-3-319-31307-8
Sá, J. P., Alvim-Ferraz, M. C. M., Martins, F. G., & Sousa, S. I. V. (2022). Application of the low-cost sensing technology for indoor air quality monitoring: A review. In Environmental Technology and Innovation (Vol. 28). Elsevier B.V. https://doi.org/10.1016/j.eti.2022.102551
Saad, S. M., Andrew, A. M., Shakaff, A. Y. M., Dzahir, M. A. M., Hussein, M., Mohamad, M., & Ahmad, Z. A. (2017). Pollutant recognition based on supervised machine learning for Indoor Air Quality monitoring systems. Applied Sciences (Switzerland), 7(8). https://doi.org/10.3390/app7080823
Sahoo, L., Praharaj, B. B., & Sahoo, M. K. (2021). Air Quality Prediction Using Artificial Neural Network. Advances in Intelligent Systems and Computing, 1248, 31–37. https://doi.org/10.1007/978-981-15-7394-1_3
Saini, J., Dutta, M., & Marques, G. (2020a). Indoor air quality monitoring systems based on internet of things: A systematic review. International Journal of Environmental Research and Public Health, 17(14), 1–22. https://doi.org/10.3390/ijerph17144942
Saini, J., Dutta, M., & Marques, G. (2020b). Indoor air quality prediction systems for smart environments: A systematic review. Journal of Ambient Intelligence and Smart Environments, 12(5), 433–453. https://doi.org/10.3233/AIS-200574
Taştan, M., & Gökozan, H. (2019). Real-time monitoring of indoor air quality with internet of things-based e-nose. Applied Sciences (Switzerland), 9(16). https://doi.org/10.3390/app9163435
Wei, W., Ramalho, O., Malingre, L., Sivanantham, S., Little, J. C., & Mandin, C. (2019). Machine learning and statistical models for predicting indoor air quality. Indoor Air, 29(5), 704–726. https://doi.org/10.1111/ina.12580