Development and Field Evaluation of a Solar-Powered LoRa-Based Air Quality Monitoring and Early-Warning System for a Sand-Mining Area

DOI: https://doi.org/10.33650/jeecom.v8i2.17080
Authors

(1) * Alamsyah -   (Department of Electrical Engineering, Tadulako University)  
        Indonesia
(2)  Aidynal Mustari   (Department of Electrical Engineering, Tadulako University)  
        Indonesia
(3)  Moh. Ikro Fajar Rahman   (Department of Electrical Engineering, Tadulako University)  
        Indonesia
(4)  Mohammed Ikhlayel   (Department of Information Technology and Communications, Al-Quds Open University, Ramallah 51000, Palestine)  
        Palestinian Territory, Occupied
(*) Corresponding Author

Abstract


Air pollution generated by sand-mining activities can degrade environmental quality and increase public-health risks, particularly in locations lacking continuous air-quality monitoring infrastructure. This study presents the development and field evaluation of a solar-powered LoRa-based air-quality monitoring and early-warning system for the Watusampu sand-mining area in Palu City, Indonesia. The main contribution of the proposed system is the integration of autonomous solar power, long-range wireless communication, multi-parameter sensing, real-time visualization, local backup storage, and ISPU-based warning functions into a single deployable platform. The transmitter node combines ZH03B, MiCS-5524, and DHT22 sensors with an ESP32 and RFM95W LoRa module to measure PM₁, PM₂.₅, PM₁₀, carbon monoxide, temperature, and relative humidity. At the receiver node, the acquired data are processed, stored on a microSD card, uploaded to a web database, and displayed through an LCD and real-time dashboard. Air-quality status is classified using Indonesia’s Air Pollutant Standard Index (ISPU). Sensor performance was evaluated by comparison with a reference air-quality detector, while LoRa communication was tested under Line-of-Sight (LoS) and Non-Line-of-Sight (Non-LoS) conditions. The ZH03B sensor produced average errors of 6.61%, 10.58%, and 8.67% for PM₁, PM₂.₅, and PM₁₀, respectively, while the average errors for CO, temperature, and relative humidity were 18.48%, 0.87%, and 0.61%. LoRa communication achieved 0% packet loss up to 1.2 km under LoS conditions and up to 20 m under Non-LoS conditions. These findings indicate that the proposed platform is feasible for autonomous, real-time air-quality monitoring and early warning in sand-mining areas with limited infrastructure.


Keywords

air quality monitoring; LoRa; sensor; solar-powered system; web server







References


Z. Deng, D. Weng, J. Chen, R. Liu, Z. Wang, and J. Bao, “AirVis: visual analytics of air pollution propagation,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 800 - 810, Jan. 2020.

J, Kumaran and S. Mohan, “A review on air pollution prediction using artificial intelligence,” IEEE Access, vol. 14, pp. 26024- 26040, Feb. 2026.

J. Kalajdjieski, K. Trivodaliev, G. Mirceva, S. Kalajdziski, and S. Gievska, “A complete air pollution monitoring and prediction framework,” IEEE Access, vol. 11, pp. 88730 - 88744, Mar. 2023.

S. Jiyal, J. Sheetlani, and R. K. Saini, “Internet of things: a survey on air pollution monitoring, challenges and applications,” in Proc. IEEE International Conference on Computing, Communication and Security (ICCCS), Mar. 2023.

S. Jiyal and R. K. Saini, “Prediction and monitoring of air pollution using internet of things (IoT),” in Proc. IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC), Jan. 2021.

J. M. García, E. R. Sorroche, S. B. Díaz, G. G. Contreras, and A. Muñoz, “Reducing pollution health impact with air quality prediction assisted by mobility data,” IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 12, pp. 9210- 9220, Dec. 2025,.

V. K. Rani and A. L. Vallikanna, “Air pollution monitoring system using internet of vehicles and pollution sensors,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 10, pp. 3962-3971, Jan. 2019.

G. Yue, K.Gu, and J. Qiao, “Effective and efficient photo-based pm2.5 concentration estimation,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 10, pp. 3962-3971, Jan. 2019.

W. Loungon, T. Kreetachat, S. Imman, N. Suriyachai, S. Wongcharee, and P. Thanarat, “Enhancing air quality with an innovative IoT-integrated hybrid air pollution control system,” IEEE Geoinformatics for Spatial-Infrastructure Development in Earth and Allied Sciences (GIS-IDEAS), May 2025.

B. Sudantha, M. Manchanayaka, C. Yang, C. Premachandra, M. Firdhous, and K. Sumathipala, “IoT empowered open sensor network for environmental air pollution monitoring system in smart cities,” in Proc. IEEE International Conference on Information Technology Research (ICITR), Jan. 2024.

J. Pellegrino, H. Aziza, M. Guerin, P. Taranto, W. Rahajandraibe, and B. Ravelo, “Development of a multi-sensor mobile device for urban air quality monitoring at the street corner: the smile project,” IEEE Access, vol. 13, pp. 14857-14871, Jan. 2025.

A. B. Satriobudi, Iskandar, and A. Mustafa, “IoT prototype air quality monitoring using lora communication system on frequency 433 Mhz,” in Proc. International Conference on Telecommunication Systems, Services, and Applications (TSSA), Mar. 2023.

M. A. Izzulhaq, R. T. Widodo, and H. Oktavianto, “LoRa-Based IoT recommendations for surabaya city drainage channel using multi-node multi-hop communication,” Jurnal Rekayasa Elektrika, vol. 21, no. 2, pp. 52-63, Jun. 2025.

V. Rujivorakul and S. Vorapatratorn, “Online low-cost air quality monitoring system using lora-based communication,” in Proc. IEEE International Joint Conference on Computer Science and Software Engineering (JCSSE), Aug. 2024.

G. Vicente and G. Marques, “Air quality monitoring through LoRa technologies: a literature review,” in Proc. IEEE International Conference on Decision Aid Sciences and Application (DASA), Jan. 2021.

H. Yu and I. Zahidi, “Environmental hazards posed by mine dust, and monitoring method of mine dust pollution using remote sensing technologies: an overview,” Science of The Total Environment, vol. 864, Mar. 2023.

Z. Wang, W. Zhou, I. M. Jiskani, H. Luo, Z. Ao, and E. M. Mvula, “Annual dust pollution characteristics and its prevention and control for environmental protection in surface mines,” Science of The Total Environment, vol. 825, Jun. 2022.

S. Al-Eidi, F. Amsaad, O. Darwish, Y. Tashtoush, A. Alqahtani, and Niveshitha, “comparative analysis study for air quality prediction in smart cities using regression techniques,” IEEE Access, vol. 11, pp. 115140-115149, Oct. 2023.

K. Gu, H. Liu, J. Liu, X. Yu, T. Shi, and J. Qiao, “Air pollution prediction in mass rallies with a new temporally-weighted sample-based multitask learner,” IEEE Transactions on Instrumentation and Measurement, vol. 71, Mar. 2022.

X. Li, M. Matahari, Y. Ma, L. Zhang, Y. Zhang, and R. Yang, “Using sensor network for tracing and locating air pollution sources,” IEEE Sensors Journal, vol. 21, no. 10, pp. 12162 - 12170, Mar. 2021.

W. Hernandez, A. Mendez, R. Zalakeviciute, and A. M. Diaz-Marquez, “Analysis of the information obtained from pm2.5 concentration measurements in an urban park,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 9, Jan. 2020.

Z. Chen, T. Zhang, Z. Chen, Y. Xiang, Q. Xuan, and R. P. Dick, “HVAQ: A High-Resolution Vision-Based Air Quality Dataset,” IEEE Transactions on Instrumentation and Measurement, vol. 70, Sep.2021.

L. Samal, A. K. Samal, K. Mahapatra, A. K. Swain, and S. P. Mohanty, “iClean: an intelligent industrial IoT framework for automatic sustainable air quality monitoring,” IEEE Transactions on Sustainable Computing, vol. 11, no. 2, Jan. 2026.

S. Ali, T. Glass, B. Parr, J. Potgieter, and F. Alam, “Low-cost sensor with IoT LoRaWAN connectivity and machine learning-based calibration for air pollution monitoring,” IEEE Transactions on Instrumentation and Measurement, vol. 70, Oct. 2020.

T. Manglani, A. Srivastava, A. Kumar, and R. Sharma, “IoT based air and sound pollution monitoring system for smart environment,” in Proc. IEEE International Conference on Electronics and Renewable Systems (ICEARS), Apr. 2022.

Hanamanth B, “Air quality monitoring system using IoT,” World Journal of Advanced Research and Reviews, vol. 17, no. 2, pp. 971-987, Apr. 2023.

P. Ferrer-Cid, J. M. Barcelo-Ordinas, and J. Garcia-Vidal, “Graph signal reconstruction techniques for IoT air pollution monitoring platforms,” IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25350-25362, 2022.

M. A. Sulimat and N. Katiran, “IoT based air quality monitoring system using LoRaWAN,” Evolution in Electrical and Electronic Engineering, vol. 5, no. 2, pp. 116-125, 2024.

H. M. Joshi, V. G. Joshi, and H. J. Lad, “Distributed embedded system for air quality monitoring based on long range (lora) technology,” Current World Environment, vol. 19, no. 1, pp. 196-206, 2024.

W. A. Jabbar, T. Subramaniam, A. E. Ong, M. I. Shu'Ib, W. Wu, and M. A. Oliveira, “LoRaWAN-based IoT system implementation for long-range outdoor air quality monitoring,” Internet of Things, vol. 19, pp. 1-25, Aug. 2022.


Dimensions, PlumX, and Google Scholar Metrics

10.33650/jeecom.v8i2.17080


Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 Alamsyah -

 
This work is licensed under a Creative Commons Attribution License (CC BY-SA 4.0)

Journal of Electrical Engineering and Computer (JEECOM)
Published by LP3M Nurul Jadid University, Indonesia, Probolinggo, East Java, Indonesia.