Mass-Balance MRV for Carbon Dioxide Removal by Enhanced Rock Weathering: Methods, Simulation, and Inference

TL;DR

This paper reviews mass-balance methods for quantifying carbon removal in enhanced rock weathering, introduces a software tool for analysis, and demonstrates Bayesian inference for uncertainty quantification using simulated data.

Contribution

It provides a comprehensive review of mass-balance approaches, offers best practices for experimental design, and introduces a Bayesian modeling framework with a software package for ERW carbon quantification.

Findings

Bayesian inference accurately recovers carbon removal estimates from simulated data.

The software package facilitates uncertainty analysis and experimental planning.

Mass-balance methods are effective for quantifying inorganic carbon in ERW.

Abstract

Carbon dioxide will likely need to be removed from the atmosphere to avoid significant future warming and climate change. Technologies are being developed to remove large quantities of carbon from the atmosphere. Enhanced rock weathering (ERW), where fine-grained silicate minerals are spread on soil, is a promising carbon removal method that can also support crop yields and maintain overall soil health. Quantifying the amount of carbon removed by ERW is crucial for understanding the potential of ERW globally and for building trust in commercial operations. However, reliable and scalable quantification in complex media like soil is challenging and there is not yet a consensus on the best method of doing so. Here we discuss mass-balance methods, where stocks of base cations in soil are monitored over time to infer the amount of inorganic carbon brought into solution by weathering reactions. First, we review the fundamental concepts of mass-balance methods and explain different ways of approaching the mass-balance problem. Then we discuss experimental planning and data collection, suggesting some best practices. Next, we present a software package designed to facilitate a range of tasks in ERW like uncertainty analysis, planning field trials, and validating statistical methods. Finally, we briefly review ways of estimating carbon removal using mass balance before discussing some advantages of Bayesian inference in this context and presenting an example Bayesian model. The model is fit to simulated data and recovers the correct answer with a clear representation of uncertainty.