This paper presents the development of a robot-oriented Distributed Acoustic Sensing (DAS) system designed for environmental monitoring and hazard detection on ground robotic platforms. Unlike conventional DAS solutions primarily intended for stationary or quasi-stationary infrastructures, the proposed approach explicitly accounts for robot-induced mechanical vibrations, mobility constraints, and limited onboard resources. A dedicated anti-jitter signal processing pipeline combined with edge-based data processing is introduced to suppress motion-induced strain components while preserving weak external acoustic signals. The system integrates optical fiber deployment along the robot structure using flexible guides and vibration-isolated clamps, ensuring stable mechanical coupling under continuous motion. Experimental validation, including laboratory tests and preliminary outdoor field trials, demonstrates reliable detection of acoustic events in the 10–200 Hz frequency range, with reduced processing latency of 80–100 ms and a detection reliability of up to 95%. Comparative analysis with conventional sensors confirms the advantages of the proposed DAS-based approach in terms of sensitivity, spatial coverage, and robustness. The results demonstrate the feasibility and effectiveness of DAS technology for real-time sensing applications on mobile robotic platforms.

This study focuses on the near-surface soil layers in suburban areas of Astana, Northern Kazakhstan, to address the critical issue of soil pollution caused by anthropogenic activities, particularly coal dust dispersion from open railway freight transportation. Existing geophysical methods for soil conductivity measurement lack precision due to interference from upper soil layers and seasonal moisture variations, limiting reliable pollution source identification. To enhance the precision of measurements, researchers modified the measuring probes. This improvement, combined with geophysical studies and Global Positioning System topographic referencing, allowed for identifying new patterns in pollutant behavior. A strong correlation was established between electromagnetic anomalies and human activities, including transportation, logistics, and urbanization. The study revealed that soil electrical conductivity near railway tracks was three times higher due to coal dust, with peak values reaching 4.8 mS/m in spring. Modified probes improved measurement accuracy by 28–32 % depending on the season, enabling precise detection of subsurface pollution patterns. The findings provide insights into urban pollution dynamics and its long-term effects. Based on experimental data, recommendations were developed such as transition to renewable energy will reduce coal dependency and pollution. In conclusion, the study highlights key issues surrounding soil pollution and provides recommendations to mitigate its effects. This approach supports sustainable land management, regulatory enforcement, and pollution mitigation strategies in urban-suburban interfaces worldwide