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.