Sensors and IoT Systems for Environmental Monitoring in Congolese Mining Sites

Environmental monitoring in mining sites is crucial for safety and compliance with environmental regulations. Congolese mining sites face unique challenges due to their remote locations and harsh operating conditions. Sensors were designed using microcontroller technology and embedded with wireless communication modules. An IoT platform was developed to collect and analyse data from the sensors in real-time. Data analysis included statistical modelling and machine learning techniques. The system achieved a mean accuracy of 98% in temperature monitoring, which is crucial for maintaining optimal working conditions and preventing health risks among miners. Dust levels were successfully monitored with an 85% detection rate, indicating the system's effectiveness in identifying hazardous environments. The IoT-based environmental monitoring system demonstrated robust performance in Congolese mining sites, providing reliable data that supports safer operations and regulatory compliance. Further research should focus on integrating additional sensors for air quality monitoring and expanding the system to include predictive analytics for early detection of potential hazards. Environmental Monitoring, IoT Systems, Mining Sites, Congolese Republic, Sensors The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.