IOT (INTERNET OF THINGS) REAL-TIME SMART MONITORING OF ENVIRONMENTAL PARAMETERS USING ARDUINO UNO REV. 3
DOI:
https://doi.org/10.60787/jnamp.vol72no.678Keywords:
Atmospheric Pollution, Internet of things, Sensors, Smart monitoring, NGROK serverAbstract
Atmospheric pollution affects our daily activities and quality of life. Laying a threat to the ecosystem and the quality of life on the planet. It has become imperative to monitor air quality. Due to an increase in industrial activities. People need to know the extent to which their activities in real time affect air quality. This project proposes an air pollution monitoring system. The system was developed using the Arduno One Anu microcontroller, the SQM135 and MQ6 gas sensors, DHT11 for temperature and humidity and other circuit elements. This atmospheric pollution monitoring system has been designed to monitor and analyze the air quality in real time and record data on a remote server, maintaining the data updated via the Internet. The air quality measurements were based on parts per million metrics (ppm) and analyzed using Microsoft Excel. The air quality measures taken from the designed system have been accurate. The result was shown in the designed hardware display interface and can be accessible via the Cloud (NGROK server) on any smart mobile device.
Downloads
References
Wallace M. and Hobbs P.V. (2006). Atmospheric Science An Introductory Survey. Second Edition John Academic Press. Elsevier, Amsterdam.
World Health Organization. Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease. 2016. Available online: https://apps.who.int/iris/bitstream/handle/10665/250141/?sequence=1 (accessed on 28 October 2024).
Dominski, F.H.; Branco, J.H.L.; Buonanno, G., Stabile, L., da Silva, M.G.and Andrade, A. (2021). Effects of air pollution on health: A mapping review of systematic reviews and meta-analyses. Environ. Res. 2021, 2021, 111487.
Pavanaditya, B., Arun, K. and Jayapriya, J. (2023). Meta-analysis of health effects of ambient air pollution exposure in low-and middle-income countries. Environ. Res. 2023, 216, 114604.
Hernandez, W., Mendez, A.; González-Posadas, V., Jiménez-Martín, J.L. and Camejo, I.M. 2021). Robust Inferential Techniques Applied to the Analysis of the Tropospheric Ozone Concentration in an Urban Area. Sensors 21, 277.
Kelly F.J. and Fussell J.C. (2015) Air pollution and public health: emerging hazards and improved understanding of risk. Environ Geochem Health 37(4):631–649. https://doi.org/10.1007/s10653-015-9720-1
Hassan NA, Hashim Z, and Hashim J.H. (2016) Impact of climate change on air quality and public health in urban areas. Asia Pac J Public Health 28(2–suppl):38–48. https://doi.org/10.1177/1010539515592951.
Sunyer J., Jarvis D., Gotschi T., Garcia-Esteban R., Jacquemin B., Aguilera I., Ackerman U., De Marco R., Forsberg B., Gislason T., Heinrich J., Norbäck D., Villani S. and Künzli N (2006) Chronic bronchitis and urban air pollution in an international study. Occup Environ Med 63(12):836–843. tps://doi.org/10.1136/o em.2006.027995
Kravchenko J., Akushevich I., Abernethy A.P., Holman S, Ross W.G. Jr. and Lyerly H.K. (2014). Long-term dynamics of death rates of emphysema, asthma, and pneumonia and improving air quality. Int J Chron Obstruct Pulmon Dis 9:613–627. https://doi.org/10.2147/COPD.S59995
Sarnat JA, Holguin F. (2007). Asthma and air quality. Curr Opin Pulm Med 13(1):63–66. ttps://doi.org/10.1 097/MCP.0b013e3280117d25
P. Mohan P. and Patil K.K.(2017). Weather and crop prediction using Modified Self organizing Map for Mysore region. International Journal of Intelligent Engineering and Systems 11 (2). 192–199.
Gololo, M.G.D.; Nyathi, C.W.; Boateng, L.; Nkadimeng, E.K.; Mckenzie, R.P.; Atif, I.; Kong, J.; Mahboob, M.A.; Cheng, L.and Mellado, B. (2024). Review of IoT Systems for Air Quality Measurements Based on LTE/4G and LoRa Communications. IoT 2024, 5, 711–729. https://doi.org/ 10.3390/iot5040032
Xu Y. and Zhu Y (2016) When remote sensing data meet ubiquitous urban data: fine-grained air quality inference. In: 2016 IEEE international conference on big data (Big Data), pp. 1252–1261. https://doi.org/10.110 9/BigData.2016.7840729. https://ieeexplore.ieee.org/abstract/document/7840729?casa_token=19h3K_aEp1wAAAAA:L0Ac9g2DS3I09GuHaVv7I_wFYc4hq6_uhGdL0Vcmyt_wHxGgaIsgi6xe26J0r0WNN735pdth8Q Accessed 25 July 2024
Smys S.S. (2020). A survey on internet of things (IoT) based smart systems, Journal of ISMAC 2 (4). 181–189. https://doi.org/10.36548/ jismac.2020.4.001.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 The Journals of the Nigerian Association of Mathematical Physics

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

