The impact of geochemical and life-cycle variables on carbon dioxide removal by enhanced rock weathering: Development and application of the Stella ERW model

Abstract

The carbon dioxide removal (CDR) potential of enhanced rock weathering (ERW) depends on dynamic interactions between several biogeochemical and life-cycle variables. This paper reports results from a systems model developed to account for key variable interactions and provide a computational tool for optimizing ERW applications. We discuss the model development, comparisons with laboratory and field test data, and results from a series of sensitivity analyses for several hypothetical ERW applications. The simulations were performed using a model developed in Stella Architect, an object-oriented systems dynamics modeling code. The model tracks the amount of carbon dioxide consumed by the weathering of a user-specified mass of rock powder in a soil environment. The primary model variables are: rock powder particle size distribution, soil pH, soil temperature, soil pore water saturation index, and a biological weathering factor. The rock bulk composition is also a key variable as it defines the enhanced weathering potential of the rock powder. Life cycle variables such as carbon emissions from mining, rock crushing, and transport are also quantified. The model predictions are consistent with measured data. It predicts CDR values ranging from 1 to 12 t CO2 ha−1 (depending on conditions) for single applications of 20–40 t of rock ha−1 over a 10-year interval. Applying the model to a hypothetical large-scale ERW campaign in which basalt rock powder is applied to 25% and 75% of Brazilian cropland results in CDR values of 0.15 and 0.5 Gt CO2 over ten years, respectively (for a single application). Model results show that combinations of suboptimal rock type (i.e., low concentrations of divalent cations and slow weathering rates for field conditions) and suboptimal life-cycle variables (i.e., high vehicle emission factors and long rock transport distances) can result in net positive CO2 emissions for some hypothetical ERW scenarios. It is, therefore, emphasized that the quantitative co-optimization of geochemical, biological, and life-cycle variables is essential in the planning stages of ERW applications.