Landfill Emission Potential: Modeling, Uncertainty, and Geophysical Insights

Abstract

The long-term environmental management of closed landfills presents significant challenges due to persistent uncertainty regarding residual contamination and pollutant release processes. While conventional aftercare practices, such as those mandated by the European Landfill Directive, focus on emission monitoring and maintenance of engineered barriers, they often overlook the complex subsurface dynamics of pollutant mobility within landfill waste bodies. Accurately quantifying the internal releasable pollutant content, referred to as the emission potential, is essential for developing realistic and scientifically-grounded aftercare strategies.
In this dissertation, I present an integrated framework to estimate and predict landfill emission potentials by combining stochastic modeling, Bayesian uncertainty quantification, data assimilation, and hydrogeophysical measurements. The research introduces a stochastic Lagrangian-based travel time modeling approach to simulate the heterogeneous water flow and solute transport within landfill bodies. This method, unlike traditional grid-based models, captures preferential flow phenomena and accommodates the spatial variability inherent in landfill waste structures.
The model calibration is performed using Bayesian inference, employing long-term observational data of leachate production and quality from the Braambergen landfill in the Netherlands. This probabilistic calibration explicitly quantifies uncertainties in model parameters and outputs, providing more credible risk assessments and long-term predictions of leachate emissions.
Recognizing the risk of error accumulation in history-matching methods, I further implement data assimilation techniques, notably the Weakly Coupled Particle Filter (WCPF) and a hybrid Particle Filter–Markov Chain Monte Carlo (PF-MCMC) method. These approaches enable sequential updating of model parameters and system states as new data become available, improving the predictive performance and reducing uncertainty over time. The PF-MCMC method, in particular, can estimate parameters and hidden processes, which is very helpful for understanding the dynamics in the landfill.
To further enhance the accuracy of emission potential estimations, the framework integrates hydrogeophysical data obtained through Electrical Resistivity Tomography (ERT). Using a Bayesian evidential learning approach, resistivity measurements are directly mapped into probabilistic water storage estimates within landfill waste bodies. This additional constraint strengthens the characterization of subsurface hydrological conditions, distinguishing between leachable and isolated water fractions.
The dissertation is structured across six chapters, beginning with an overview of the landfill aftercare problem, followed by the development of the stochastic modeling framework, the application of particle filtering and PF-MCMC, the incorporation of ERT data through Bayesian evidential learning, and concluding with a synthesis of findings and recommendations for future research.
Overall, this work advances the scientific understanding of landfill emission dynamics by offering a unified methodological framework that integrates stochastic modeling, data assimilation, and hydrogeophysical surveying. The contributions herein support the development of more robust, data-driven, and cost-effective strategies for landfill aftercare, ensuring long-term environmental protection and sustainability.