This paper explores the convergence of semi-autonomous systems and sensor fusion for monitoring hazardous atmospheric substances in open and semi-confined environments such as open-pit mines. As the heart of this system, a universal and flexible device leveraging wireless technologies to collect and analyze large volumes of data, designed by the authors, is proposed. The article outlines the key components of the platform, emphasizing its potential for increasing personnel safety levels as stipulated by the international public exposure guidelines. In modern mines, it becomes crucial to monitor elevated concentrations of nitrogen and carbon oxides, as well as other pollutants after blasting and the toxic gas emissions produced by the heavy transportation equipment such as mining trucks and excavators. In this paper, industrial-grade electrochemical sensors are compared with price-affordable but less precise microelectrochemical ones. The performed experiments for lowering computational requirements show promising results. The integration of an ultrasonic anemometer enhances the system’s capabilities, contributing to a comprehensive understanding of atmospheric conditions. In parallel, the research discusses the role of computer vision in autonomous control systems, with a focus on the architecture and processing pipeline of the state-of-the-art system-on-module Kria by AMD. Advocating the potential of adaptive computing for enhanced efficiency in dynamic environments, the paper underlines the importance of the integrated approach in developing a semi-autonomous atmospheric surveillance system and highlights the impact of adaptive computing in dynamic scenarios. The main part of the measuring equipment and methods has been experimented with in real conditions in open-pit mines in Bulgaria. This is an initial phase of ongoing research aimed at serving as a foundation for future improvements and the elaboration of a fully autonomous prototype for hazardous substances evaluation in the atmosphere of open-pit mines.

1. Yi H, Zhang X, Yang H, et al. Controlling toxic and harmful gas in blasting with an inhibitor. PLoS ONE. 2023; 18(12): e0291731. doi: 10.1371/journal.pone.0291731
2. Zhang G, Wang E. Risk identification for coal and gas outburst in underground coal mines: A critical review and future directions. Gas Science and Engineering. 2023; 118: 205106. doi: 10.1016/j.jgsce.2023.205106
3. He S, He X, Mitri H, et al. Advances in mining safety theory, technology, and equipment. Advances in Geo-Energy Research. 2023; 10(2): 71-76. doi: 10.46690/ager.2023.11.01
4. Gniewosz M, Stopkowicz A, Cała M. An Analysis of the Impact of Mining Excavation Velocity on the Development of Gaseous and Gaseous Geodynamic Hazards in Copper Ore Mines. Geosciences. 2024; 14(2): 54. doi: 10.3390/geosciences14020054
5. American Industrial Hygiene Association. The AIHA Handbook for ERPG and WEEL. AIHA Press; 2020.
6. National Institute for Occupational Safety and Health. NIOSH Pocket Guide to Chemical Hazards. National Institute for Occupational Safety and Health; 2007.
7. Gerboles M, Buzica D. European Commission. In: Joint Research Centre. Evaluation of Micro-Sensors to Monitor Ozone in Ambient Air. Institute for Environment and Sustainability; 2009. doi: 10.2788/5978
8. Ziętek B, Banasiewicz A, Zimroz R, et al. A Portable Environmental Data-Monitoring System for Air Hazard Evaluation in Deep Underground Mines. Energies. 2020; 13(23): 6331. doi: 10.3390/en13236331
9. Fisher B, Schnittger S. Autonomous and Remote Operation Technologies in the Mining Industry-Benefits and Costs. BAE Research Report; 2012.
10. Global Mining Guidelines Group. Guideline for the Implementation of Autonomous Systems in Mining. Available online: https://gmggroup.org/wp-content/uploads/2019/06/20181008_Implementation_of_Autonomous_Systems-GMG-AM-v01-r01.pdf (accessed on 19 June 2024).
11. Kumar PP, Paul PS, Ananda M. Development of LoRa Communication System for Effective Transmission of Data from Underground Coal Mines. Processes. 2023; 11(6): 1691. doi: 10.3390/pr11061691
12. The DRONESAFE project. Autonomous drones for safer mines. Available online: https://www.ltu.se/research/subjects/RoboticsAI/Forskning/DRONESAFE-1.204090?l=en (accessed on 19 June 2024).
13. Bamford T, Medinac F, Esmaeili K. Continuous Monitoring and Improvement of the Blasting Process in Open Pit Mines Using Unmanned Aerial Vehicle Techniques. Remote Sensing. 2020; 12(17): 2801. doi: 10.3390/rs12172801
14. Ren H, Zhao Y, Xiao W, et al. A review of UAV monitoring in mining areas: Current status and future perspectives. International Journal of Coal Science & Technology. 2019; 6(3): 320–333. doi: 10.1007/s40789-019-00264-5
15. Alvarado M, Gonzalez F, Fletcher A, et al. Towards the Development of a Low Cost Airborne Sensing System to Monitor Dust Particles after Blasting at Open-Pit Mine Sites. Sensors. 2015; 15(8): 19667–19687. doi: 10.3390/s150819667
16. Rossi M, Brunelli D. Autonomous Gas Detection and Mapping with Unmanned Aerial Vehicles. IEEE Transactions on Instrumentation and Measurement. 2016; 65(4): 765–775. doi: 10.1109/tim.2015.2506319
17. Janata J. Principles of Chemical Sensors. Springer US; 2009. doi: 10.1007/b136378
18. Cretescu I, Lutic D, Manea LR. Electrochemical Sensors for Monitoring of Indoor and Outdoor Air Pollution. Electrochemical Sensors Technology. 2017. doi: 10.5772/intechopen.68512
19. Liu H, Zhang L, Li KHH, et al. Microhotplates for Metal Oxide Semiconductor Gas Sensor Applications—Towards the CMOS-MEMS Monolithic Approach. Micromachines. 2018; 9(11): 557. doi: 10.3390/mi9110557
20. Judy JW. Microelectromechanical systems (MEMS): Fabrication, design and applications. Smart Materials and Structures. 2001; 10(6): 1115–1134. doi: 10.1088/0964-1726/10/6/301
21. Ionascu ME, Castell N, Boncalo O, et al. Calibration of CO, NO2, and O3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring. Sensors. 2021; 21(23): 7977. doi: 10.3390/s21237977
22. Poluyan LV, Syutkina EV, Guryev ES. Software Systems for Prediction and Immediate Assessment of Emergency Situations on Municipalities Territories. In: Proceedings of the International Conference on Construction, Architecture and Technosphere Safety (ICCATS 2017); 21-22 September 2017; Chelyabinsk, Russian Federation. doi: 10.1088/1757-899x/262/1/012199
23. Young Company. Ultrasonic Anemometer MODEL 81000. R.M. Young Company; 2017.
24. Dinchev Z, Gorbunov Y. Improvement of Measurements of 3D Air Flows in Free and Semi-Restricted Space. Journal of Mining and Geological Sciences. 2017; 60: 39-42.
25. Dinchev Z, Gorbunov Y, Kostadinova N. Velocity Field Visualization, Measured with 3D Ultrasonic Anemometer. Journal of Mining and Geological Sciences. 2018; 61: 39-44.
26. Ahmed MF, Masood K, Fremont V, et al. Active SLAM: A Review on Last Decade. Sensors. 2023; 23(19): 8097. doi: 10.3390/s23198097
27. Jia G, Li X, Zhang D, et al. Visual-SLAM Classical Framework and Key Techniques: A Review. Sensors. 2022; 22(12): 4582. doi: 10.3390/s22124582
28. Kalapothas S, Flamis G, Kitsos P. Efficient Edge-AI Application Deployment for FPGAs. Information. 2022; 13(6): 279. doi: 10.3390/info13060279
29. Zynq DPU. Product Guide (PG338). Available online: https://docs.xilinx.com/r/3.2-English/pg338-dpu/Introduction (accessed on 19 June 2024).
30. Gorbounov Y, Peychinov P. Vision and control capabilities of autonomous systems with the Kria Adaptive System-on-Modules. In: Proceedings of the 2023 31st National Conference with International Participation (TELECOM); 16-17 November 2023; Sofia, Bulgaria. pp. 1-4. doi: 10.1109/telecom59629.2023.10409757