TARGETED BIOMINING AND MACHINE LEARNING APPROACHES IN CRITICAL MINERALS REVEALED BY BIOGEOCHEMICAL SURVEY OF A COAL MINE DRAINAGE REMEDIATION SYSTEM

Abandoned coal mine drainage (AMD) remediation systems in Pennsylvania can concentrate critical minerals and materials (CMM) at levels comparable to mining-grade ores. Remediation systems have varying engineering features and are open to the environment, resulting in diverse microbial colonization and seasonal climate influences that may impact CMM speciation. The location of CMMs, the types of bacterial communities tolerant of these pollutant conditions, and the influence of localized climate on CMM rich remediation systems are not well characterized. Through a one-year spatiotemporal survey of biogeochemistry at a remediation system, we have initiated the process to address these questions. Rare Earth Elements (REE) ranged between 180-1,200 ppm and greater than 1,500 bacterial ASVs were classified via 16S rrn sequencing. Analyses indicate biogeochemical differences are heavily influenced by engineering features. REE precipitants correlate strongly with the elements Al, Cu, Zn, Be, and U. Unearthing these trends has refined our line of inquiry to explore biological mining opportunities. We created a Machine Learning Model for predicting AMD REE content, with 89% accuracy, using the data from this study and several others. Further training data is required to create a more reputable model. Recently, global research efforts have prioritized modeling work or the use of the few historical surveys to design experiments. Through our data, we challenge this approach, emphasizing the importance of expanding fundamental survey efforts prior to advanced product design and experimentation.