Estimating the benthic additionality loss of coastal ocean alkalinity enhancement deployments
编号:1394
访问权限:仅限参会人
更新:2026-09-01 00:15:59 浏览:0次
口头报告
摘要
Ocean alkalinity enhancement (OAE) promotes the oceanic uptake of atmospheric CO2 by deliberately altering the marine carbonate system to lower the pCO2 of seawater. However, perturbing the carbonate system also alters natural sediment alkalinity cycling. Consequently, some of the added alkalinity may replace naturally generated alkalinity, reducing the additionality of OAE. The feedbacks between OAE and sediment alkalinity cycling are poorly constrained but are critical for evaluating the effectiveness of OAE and conducting monitoring, reporting, and verification (MRV) activities. To address this, we used a 1D early diagenetic coastal sediment model to evaluate how changes in the alkalinity of the overlying water affect benthic alkalinity fluxes across a range of sediment types. We fit response functions to the modelled changes in benthic alkalinity fluxes that describe their sensitivity to changes in overlying seawater alkalinity. Combined with a residence time model that predicts the alkalinity perturbation generated by an OAE deployment based on its magnitude and deployment location, these response functions provide rapid estimates of benthic additionality losses across diverse OAE scenarios without requiring computationally intensive simulations. Additionality losses were greatest when OAE produced large and persistent increases in seawater alkalinity over sediments that naturally sustained high rates of carbonate mineral dissolution. Such conditions occurred where substantial alkalinity additions coincided with low-volume or long-residence-time systems and well oxygenated sediments that experienced high particulate inorganic and organic carbon fluxes. This work serves as a practical screening tool, enabling practitioners to rapidly estimate benthic additionality losses and associated uncertainties and determine if they can be neglected, or if site-specific sediment modelling is required.
稿件作者
Max Rintoul
University of Tasmania
发表评论