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  1. Abstract. Endmember mixing analysis (EMMA) is often used by hydrogeochemiststo interpret the sources of stream solutes, but variations in streamconcentrations and discharges remain difficult to explain. We discoveredthat machine learning can be used to highlight patterns in stream chemistrythat reveal information about sources of solutes and subsurface groundwaterflowpaths. The investigation has implications, in turn, for the balance ofCO2 in the atmosphere. For example, CO2-driven weathering ofsilicate minerals removes carbon from the atmosphere over ∼106-year timescales. Weathering of another common mineral, pyrite, releases sulfuricacid that in turn causes dissolution of carbonates. In that process,however, CO2 is released instead of sequestered from the atmosphere. Thus, understanding long-term global CO2 sequestration by weatheringrequires quantification of CO2- versus H2SO4-drivenreactions. Most researchers estimate such weathering fluxes from streamchemistry, but interpreting the reactant minerals and acids dissolved in streams has been fraught with difficulty. We apply a machine-learningtechnique to EMMA in three watersheds to determine the extent of mineraldissolution by each acid, without pre-defining the endmembers. The resultsshow that the watersheds continuously or intermittently sequester CO2, but the extent of CO2 drawdown is diminished in areas heavily affectedby acid rain. Prior to applying the new algorithm, CO2 drawdown wasoverestimated. The new technique, which elucidates the importance ofdifferent subsurface flowpaths and long-timescale changes in the watersheds,should have utility as a new EMMA for investigating water resourcesworldwide. 
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