Sensors and IoT Systems for Environmental Monitoring in Ghanaian Mining Sites

Ghana's mining industry faces significant environmental challenges such as air and water pollution, which can impact public health and ecosystem balance. A multidisciplinary approach including sensor design, data acquisition, and machine learning algorithms was employed. Field tests were conducted at two major mining sites. Real-time air quality data indicated a significant reduction (20-30%) in particulate matter levels compared to historical records, highlighting the effectiveness of the deployed sensors. The IoT-based monitoring system demonstrated high accuracy and reliability in environmental surveillance, contributing to improved public health outcomes. Further deployment should be considered across other mining sites in Ghana for comprehensive coverage and continuous improvement based on feedback loops. Environmental Monitoring, Mining Sites, IoT Systems, Sensor Technology, Air Quality The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.