Updated framework and signal-to-noise analysis of soil mass balance approaches for quantifying enhanced weathering on managed lands

Enhanced weathering is a promising approach for removing carbon dioxide from the atmosphere at scale while improving agricultural yields. However, accurately quantifying carbon dioxide removal in the field is critical for this approach to scale, particularly given that nearly all of the current deployment activity caters to the voluntary carbon market. Here, we present an updated framework and a signal-to-noise analysis for using soil-based mass balance approaches to quantify rock powder dissolution from field-scale data of soil composition. With additional assumptions, the quantification of rock powder dissolution can be used to estimate carbon dioxide removal potential of EW deployments. The framework we present explicitly accounts for the enrichment of immobile elements in topsoil due to feedstock mass loss and demonstrates that omission of this process systematically overestimates feedstock dissolution. We suggest that the framework should only be used when average post-weathering sample compositions fall within the parameter space representing physically meaningful results (i.e., set out by the mixing relationships between soil, feedstock, and a hypothetical weathered feedstock residue endmember). Building from this, we provide support for the idea that feedstock dissolution should be quantified using the sample population mean rather than individual samples. Given the potential for signal-to-noise issues with this framework, it is critical that it is utilized only when signals are statistically robust. To illustrate this, we present a signal-to-noise analysis based on a new dataset of soil cation heterogeneity from high-density spatial sampling of 5 fields (0.6-19.2 samples ha-1, 7.1-39.6 pooled cores ha-1). The analysis is based on simulated geolocated sample pairs and suggests that detecting rock powder dissolution via soil mass balance should be feasible when application rates, dissolution fractions, and sampling frequencies are above certain threshold values. When planning deployments, signal emergence can be optimized through careful selection of feedstock composition, strategic feedstock application, and improved sampling protocols.