To support regulatory requirements, Minnow designed a multi-layered monitoring and analysis approach that combined field data collection with advanced predictive modeling and statistical interpretation. The work spanned both broad-scale regional monitoring and focused site-specific programs, ensuring a comprehensive understanding of mining impacts and overall ecosystem health.
The team conducted regular assessments of key constituents of concern (e.g., selenium, nitrate, sulphate, cadmium, nickel) in water, sediment, and/or biological tissues, tracking how these constituents moved through the environment, where possible. Alongside these efforts, Minnow also closely monitored the benthic invertebrate communities, which serve as key indicators of aquatic ecosystem health. These organisms, which spend their lives in and around aquatic substrates, are valued ecosystem components that provide insight into habitat function and food availability for fish and other wildlife.
To address the complex relationships among benthic invertebrate communities, habitat conditions, and mine-related inputs, the team decided to develop its own predictive benthic invertebrate community model based on an advanced machine learning artificial intelligence (AI) platform. The model integrated vast datasets, including GIS-based habitat data, mine-related water and habitat stressor data, and reference site comparisons, to generate predictions about what a healthy benthic invertebrate community should look like without mining on the landscape.
By comparing these predictions to real-world field data, Minnow could begin to better understand whether observed changes were due to water quality stressors, habitat modifications, or natural seasonal variability. The shift to machine learning provided greater predictive accuracy and a more nuanced understanding of environmental changes. To support this effort, Trinity Consultants invested in high-performance computational infrastructure that allowed Minnow to process the large datasets more efficiently and maximize the value of the information collected.
