Enhanced weathering (EW) is a prominent technology in the developing portfolio of carbon dioxide removal (CDR) strategies. Briefly, EW involves spreading crushed rock over large areas of land to alter the geochemistry of the soil and draw down carbon dioxide. This technology is deployable at scale through integration with agricultural operations and could also provide agroeconomic and ecosystem co-benefits. However, like most open-system or “nature-based” strategies, verification of CDR has proven quite challenging, requiring measurement of imperfect proxies amidst a complex network of highly variable Earth system processes. Given these challenges, it remains unclear whether EW is effective and what the associated monitoring and verification costs will be.
The goal of this research is to use state-of-the-art reactive transport models and data science techniques to rigorously address the scientific and spatial uncertainties that are currently inhibiting EW from scaling. Reactive transport models are hydrological and geochemical computational tools with significant potential to elucidate fundamental constraints on EW and test the accuracy of verification proxies. Additionally, as EW deployments take place over vast areas of land, there is significant spatial uncertainty that remains unquantified. This presentation includes results from reactive transport simulations of EW analyzed using a Bayesian approach, alongside a geostatistical framework for quantifying spatial uncertainty in current monitoring and verification approaches. These results and framework will inform a broader, model-driven, data-intensive verification framework for EW, aiming to bring much-needed rigor and transparency to EW and the rapidly expanding carbon removals market.
