Carbon Capture and Storage (CCS) provides a mechanism by which CO2 can be removed from the atmosphere and stored in reservoirs. Regulations and stakeholder assurance require monitoring to show storage is robust. The marine environment is heterogeneous and dynamic, and baselines are extremely variable. Hence, distinguishing anomalous CO2 from natural variability is challenging. Monitoring schemes must be designed to identify releases early and with certainty, whilst being cost effective. A key question is how to deploy the smallest number of sensors to ensure effective monitoring?

We approached this problem through a 3D hydrodynamic model (FVCOM) coupled to a carbonate system. The unstructured grid resolution ranges from 0.5 km to 3 m and simulates seabed release scenarios ranging from 3 t d−1 to 300 t d−1 using the Goldeneye complex as an exemplar test bed. This configuration allows us to characterise and analyse the fate of CO2 in the water column, with the spatial and temporal CO2 patterns shown to be affected by both tides and seasonal mixing/stratification.

A weighted greedy set algorithm is used to identify the positions within the model domain which yield the greatest combined coverage for the smallest number of sampling stations, further limited by selecting only a feasible number of sample sites. The weighted greedy set algorithm incorporates the effect of the variable grid spacing as well as the proximity of the sample locations to the Goldeneye complex. The weighted greedy set can identify releases sooner, with a stronger signal than a regular sampling approach.

碳捕集与封存 (CCS) 提供了一种机制，通过该机制可以将二氧化碳从大气中去除并储存在水库中。法规和利益相关者保证需要监控以证明存储是可靠的。海洋环境是异质和动态的，基线变化很大。因此，将异常 CO2 与自然变异区分开是具有挑战性的。监测计划的设计必须能够及早确定地识别排放，同时具有成本效益。一个关键问题是如何部署最少数量的传感器来确保有效监控？

我们通过与碳酸盐系统耦合的 3D 流体动力学模型 (FVCOM) 解决了这个问题。非结构化网格分辨率范围为 0.5 km 至 3 m，并使用 Goldeneye 复合体作为示例测试床模拟 3 td-1 至 300 td-1 范围内的海底释放场景。这种配置使我们能够表征和分析水体中 CO2 的归宿，同时空间和时间 CO2 模式显示受潮汐和季节性混合/分层的影响。

加权贪婪集算法用于识别模型域内的位置，这些位置为最少数量的采样站产生最大的组合覆盖，通过仅选择可行数量的采样点进一步限制。加权贪婪集算法结合了可变网格间距以及样本位置与 Goldeneye 复合体的接近度的影响。加权贪婪集可以更快地识别发布，比常规采样方法具有更强的信号。