Assessing Real-Time Health Impacts of outdoor Air Pollution through IoT Integration
Received: 30 January 2024 | Revised: 19 February 2024 and 5 March 2024 | Accepted: 13 March 2024 | Online: 24 March 2024
Corresponding author: Nagalinagam Rajeswaran
Abstract
Air pollution constitutes a significant global challenge in both public health and the environment, particularly for countries undergoing industrialization and transitioning from low- to middle-income economies. This study aims to investigate the feasibility and effectiveness of a real-time air quality prediction system based on data collected from Internet of Things (IoT) sensors to help people and public institutions track and manage atmospheric pollution. The primary objective of this study was to investigate whether an IoT-based approach can provide accurate and continuous real-time air quality forecasting. The standard dataset provided by the Indian government was analyzed using regression, traditional Long-Short-Term Memory (LTSM), and bidirectional LSTM (BLSTM) models to evaluate their performance on multivariate air quality features. The results show that the proposed BLSTM model outperformed the other models in minimizing RMSE errors and avoiding overfitting.
Keywords:
Internet of Things, air pollution, LSTM, health controllersDownloads
References
N. Harkat, A. Rahmane, and I. Bendjemila, "The Impact of Industrial Air Pollution on the Urban Environment of Setif: Modeling and Mapping of Total Suspended Particles," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9431–9439, Dec. 2022.
H. Ritchie and M. Roser, "Indoor Air Pollution," Our World in Data, Mar. 2024, [Online]. Available: https://ourworldindata.org/indoor-air-pollution.
E. Fazakas, I. A. Neamtiu, and E. S. Gurzau, "Health effects of air pollutant mixtures (volatile organic compounds, particulate matter, sulfur and nitrogen oxides) – a review of the literature," Reviews on Environmental Health, Mar. 2023.
C. Li et al., "The role of mental health professionals in the climate crisis: an urgent call to action," International Review of Psychiatry, vol. 34, no. 5, pp. 563–570, Jul. 2022.
H. Wu, Y. Li, Y. Hao, S. Ren, and P. Zhang, "Environmental decentralization, local government competition, and regional green development: Evidence from China," Science of The Total Environment, vol. 708, Mar. 2020, Art. no. 135085.
F. Ekici, G. Orhan, Ö. Gümüş, and A. B. Bahce, "A policy on the externality problem and solution suggestions in air transportation: The environment and sustainability," Energy, vol. 258, Nov. 2022, Art. no. 124827.
M. O. Raimi, Z. Adio, O. O. Emmanuel, Timothy Kayode Samson, B. S. Ajayi, and T. J. Ogunleye, "Impact of Sawmill Industry on Ambient Air Quality: A Case Study of Ilorin Metropolis, Kwara State, Nigeria," Energy and Earth Science, vol. 3, no. 1, 2020.
D. L. Mendoza et al., "Air Quality and Behavioral Impacts of Anti-Idling Campaigns in School Drop-Off Zones," Atmosphere, vol. 13, no. 5, May 2022, Art. no. 706.
X. Zhang et al., "Linking urbanization and air quality together: A review and a perspective on the future sustainable urban development," Journal of Cleaner Production, vol. 346, Apr. 2022, Art. no. 130988.
P. Kokate, S. Sadistap, and A. Middey, "Atmospheric CO2 Level Measurement and Discomfort Index Calculation with the use of Low-Cost Drones," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11728–11734, Oct. 2023.
G. Marques and R. Pitarma, "mHealth: Indoor Environmental Quality Measuring System for Enhanced Health and Well-Being Based on Internet of Things," Journal of Sensor and Actuator Networks, vol. 8, no. 3, Sep. 2019, Art. no. 43.
Q. Guo et al., "Applications of artificial intelligence in the field of air pollution: A bibliometric analysis," Frontiers in Public Health, vol. 10, Sep. 2022.
R. Kumar, P. Kumar, and Y. Kumar, "Time Series Data Prediction using IoT and Machine Learning Technique," Procedia Computer Science, vol. 167, pp. 373–381, Jan. 2020.
N. Zaini, L. W. Ean, A. N. Ahmed, and M. A. Malek, "A systematic literature review of deep learning neural network for time series air quality forecasting," Environmental Science and Pollution Research, vol. 29, no. 4, pp. 4958–4990, Jan. 2022.
A. Gilik, A. S. Ogrenci, and A. Ozmen, "Air quality prediction using CNN+LSTM-based hybrid deep learning architecture," Environmental Science and Pollution Research, vol. 29, no. 8, pp. 11920–11938, Feb. 2022.
J. Srishtishree, S. Mohana Kumar, and C. Shetty, "Air Quality Monitoring with IoT and Prediction Model using Data Analytics," in Innovations in Computer Science and Engineering: Proceedings of 7th ICICSE, H. S. Saini, R. Sayal, R. Buyya, and G. Aliseri, Eds. Singapore: Springer, 2020, pp. 535–544.
P. Nath, P. Saha, A. I. Middya, and S. Roy, "Long-term time-series pollution forecast using statistical and deep learning methods," Neural Computing and Applications, vol. 33, no. 19, pp. 12551–12570, Oct. 2021.
D. A. Pamplona and C. J. P. Alves, "Civil Aircraft Emissions Study and Pollutant Forecasting at a Brazilian Airport," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5217–5220, Feb. 2020.
H. Liu, G. Yan, Z. Duan, and C. Chen, "Intelligent modeling strategies for forecasting air quality time series: A review," Applied Soft Computing, vol. 102, Apr. 2021, Art. no. 106957.
M. Mondal, "Air Quality Index." [Online]. Available: https://kaggle.com/code/mahadevmondal/air-quality-index.
Q. B. Jamali et al., "Analysis of CO2, CO, NO, NO2, and PM Particulates of a Diesel Engine Exhaust," Engineering, Technology & Applied Science Research, vol. 9, no. 6, pp. 4912–4916, Dec. 2019.
Z. Zhang and M. Tao, "Deep Learning for Wireless Coded Caching With Unknown and Time-Variant Content Popularity," IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 1152–1163, Oct. 2021.
M. S. Balamurugan and R. Manojkumar, "Study of short term rain forecasting using machine learning based approach," Wireless Networks, vol. 27, no. 8, pp. 5429–5434, Nov. 2021.
C. Zhang et al., "Satellite UV-Vis spectroscopy: implications for air quality trends and their driving forces in China during 2005–2017," Light: Science & Applications, vol. 8, no. 1, Nov. 2019, Art. no. 100.
Downloads
How to Cite
License
Copyright (c) 2024 Pradeep Mullangi, K. M. V. Madan Kumar, Gera Vijaya Nirmala, Ramesh Chandra Aditya Komperla, Nagalinagam Rajeswaran, Amar Y. Jaffar, Abdullah Alwabli, Saeed Faisal Malky
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.