Second-generation stoichiometric mathematical model to predict methane emissions from oil sands tailings

TL;DR

This paper presents a new stoichiometric model for predicting methane emissions from oil sands tailings, incorporating long-term biodegradation kinetics and microbial dynamics, validated with laboratory and field data.

Contribution

The study introduces a second-generation, comprehensive stoichiometric model that improves methane emission predictions by considering detailed microbial and chemical processes.

Findings

Model accurately predicts methane production in laboratory settings.

Model outperforms previous first-approximation models.

Validated with field measurements of methane emissions.

Abstract

Microbial metabolism of fugitive hydrocarbons produces greenhouse gas (GHG) emissions from oil sands tailings ponds (OSTP) and end pit lakes (EPL) that retain semisolid wastes from surface mining of oil sands ores. Predicting GHG production, particularly methane (CH4), would help oil sands operators mitigate tailings emissions and would assist regulators evaluating the trajectory of reclamation scenarios. Using empirical datasets from laboratory incubation of OSTP sediments with pertinent hydrocarbons, we developed a stoichiometric model for CH4 generation by indigenous microbes. This model improved on previous first-approximation models by considering long-term biodegradation kinetics for 18 relevant hydrocarbons from three different oil sands operations, lag times, nutrient limitations, and microbial growth and death rates. Laboratory measurements were used to estimate model parameter values and to validate the new model. Goodness of fit analysis showed that the stoichiometric model predicted CH4 production well; normalized mean square error analysis revealed that it surpassed previous models. Comparison of model predictions with field measurements of CH4 emissions further validated the new model. Importantly, the model also identified parameters that are currently lacking but are needed to enable future robust modeling of CH4 production from OSTP and EPL in situ.