Applus+ designs a real-time environmental monitoring network for a highly complex underground mine

Applus+ designed, implemented, and operated an automatic real-time environmental monitoring network integrated into ambiensQ-suite, a proprietary tool enabling early warnings, georeferenced analysis, and environmental management indicators to support timely decision-making and regulatory compliance.

The challenge was to deploy a comprehensive system capable of measuring and correlating critical variables such as air quality, blasting-related vibrations, domestic and non-domestic discharges, groundwater, and environmental noise. Without continuous and traceable information, the risk of reactive responses to incidents increased, as did the possibility of deviations from permissible limits and tensions with communities and authorities. In addition, the operation required robust technical evidence to identify root causes, anticipate scenarios, and support operational decisions with transparency and rigor.

Applus+ designed and implemented an environmental monitoring network consisting of four air quality stations with meteorological variables, six environmental vibration stations for blasting control, fifteen groundwater monitoring stations in piezometers and wells, three non-domestic wastewater quality monitoring stations, three flow measurement stations for domestic and non-domestic discharges, and one environmental noise monitoring station. All stations transmit data in real time to ambiensQ-suite, which integrates georeferenced visualization, indicators, early warnings, and historical reports, enabling regulatory limit control and immediate response to events.

The automatic network and its integration with ambiensQ-suite transformed environmental management by delivering continuous, reliable, and actionable information, enabling immediate operational decisions to mitigate risks and prevent exceedances. The correlation of air, water, vibration, and noise data strengthened causal analysis and enabled preventive event management. The high resolution of the data made it possible to characterize hourly and shift-based patterns, while historical comparisons supported the anticipation of seasonal variations. The project’s success was driven by automation, technological integration, and a preventive approach, reinforcing compliance and operational sustainability.