Design and implementation PM2.5 station for model school with IoT on Google Data Studio

Main Article Content

Somchat Sonasang
Phatcharanat Saeng-on
Noraset Thaitea
Manaphan Phoyan

Abstract

This article presents the design and construction of an IoT measuring station with an IoT system, to be displayed on Google Data Studio, designed and implemented with an IoT for measuring and alarming PM2.5 dust content.By the ESP8266 board, a PM2.5 dust sensor, a temperature and CO gas. The results of the percentage difference of PM2.5 dust measurement results were 39.06 26.56 23.43 14.06 13.95 and 12.72 micrograms per cubic meter. The measurement results recorded in Google sheet for collecting the PM2.5 measurement results were 4 -11 micrograms per cubic meter. The temperature and relative humidity measurement results were 33, 40, 40, 36, 35 and 34 degrees, and the humidity was related to the same trend, and the CO measurement results were 120 132 123 142 162 140 ppm and the mean was 136.5 ppm. The test results and compared trends in the same direction. The research result is an extension of the installation area to determine the plan. The ppolicy for reducing PM2.5 dust during winter and drought.

Article Details

How to Cite
Sonasang, S., Saeng-on, P. ., Thaitea, N. ., & Phoyan , M. . . (2022). Design and implementation PM2.5 station for model school with IoT on Google Data Studio. Journal of Engineering Technology Access (JETA) (Online), 2(02), 1–15. https://doi.org/10.14456/jeta.2022.6
Section
Research Articles
Author Biography

Manaphan Phoyan , Nakhon Phanom University

Manaphan  Phoyan was born in Nakhon Phanom, Thailand, in 1976. He received the Higher Diploma in Technology degree in Electrical and Telecommunication from Pathumwan Technical College, Bangkok, Thailand, and Master of Engineering degree in Research For Education Development form Nakhon Phanom University, Thailand. Present, Present, Lecturer with the Department of Electronic Technology, Nakhon Phanom University, Nakhon Phanom, Thailand, His current research interests include electrical installation, telecommunication, electronic circuit.  

References

B. Zou, Q. Pu, M. Bilal, Q. Weng, L. Zhai and J. E. Nichol, "High-Resolution Satellite Mapping of Fine

Particulates Based on Geographically Weighted Regression," in IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 4, pp. 495-499, April 2016, doi: 10.1109/LGRS.2016.2520480.

Y. Gao et al., "Mosaic: A low-cost mobile sensing system for urban air quality monitoring," IEEE

INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, 2016, pp. 1-9, doi: 10.1109/INFOCOM.2016.7524478.

S. Kumar and A. Jasuja, "Air quality monitoring system based on IoT using Raspberry Pi," 2017

International Conference on Computing, Communication and Automation (ICCCA), 2017, pp. 1341-1346, doi: 10.1109/CCAA.2017.8230005.

Y. Hu, G. Dai, J. Fan, Y. Wu and H. Zhang, "BlueAer: A fine-grained urban PM2.5 3D monitoring

system using mobile sensing," IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, 2016, pp. 1-9, doi: 10.1109/INFOCOM.2016.7524479.

P. Das, S. Ghosh, S. Chatterjee and S. De, "Energy Harvesting-enabled 5G Advanced Air Pollution

Monitoring Device," 2020 IEEE 3rd 5G World Forum (5GWF), 2020, pp. 218-223, doi: 10.1109/5GWF49715.2020.9221330.

A. Kumar, M. Kumari and H. Gupta, "Design and Analysis of IoT based Air Quality Monitoring

System," 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020, pp. 242-245, doi: 10.1109/PARC49193.2020.236600.

S. K. Jha et al., "Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors," in IEEE

Sensors Journal, vol. 21, no. 22, pp. 25941-25949, 15 Nov.15, 2021, doi: 10.1109/JSEN.2021.3118454.

J. Yun and J. Woo, "IoT-Enabled Particulate Matter Monitoring and Forecasting Method Based on

Cluster Analysis," in IEEE Internet of Things Journal, vol. 8, no. 9, pp. 7380-7393, 1 May1, 2021, doi: 10.1109/JIOT.2020.3038862.

M. G. A. Mapili, K. A. D. Rodriguez and J. T. Sese, "Smart Air Filtration System Using IoT and Kalman

Filter Algorithm for Indoor Air Quality and Plant Monitoring," 2021 IEEE 11th International Conference on System Engineering and Technology (ICSET), 2021, pp. 309-314, doi: 10.1109/ICSET53708.2021.9612560.