Long-term emissions of Municipal Solid Waste (MSW) landfills are a burden for future generations because of the required long-term aftercare. To shorten aftercare, treatment methods have to be developed that reduce long-term emissions. A treatment method that reduces emissions at a lysimeter scale is re-circulation of leachate. However, its effectiveness at the field scale still needs to be demonstrated. Field scale design can be improved by theoretical understanding of the processes that control the effectiveness of leachate recirculation treatment. In this study, the simplest and most fundamental sets of processes are distilled that describe the emission data measured during aerobic and anaerobic leachate recirculation in lysimeters. A toolbox is used to select essential processes with objective performance criteria produced by Bayesian statistical analysis. The controlling processes indicate that treatment efficiency is mostly affected by how homogeneously important reactants are spread through the MSW during treatment. A more homogeneous spread of i.e. oxygen or methanogens increases the total amount of carbon degraded. Biodegradable carbon removal is highest under aerobic conditions, however, the hydrolysis rate constant is lower which indicates that hydrolysis is not enhanced intrinsically in aerobic conditions. Controlling processes also indicate that nitrogen removal via sequential nitrification and denitrification is plausible under aerobic conditions as long as sufficient biodegradable carbon is present in the MSW. Major removal pathways for carbon and nitrogen are indicated which are important for monitoring treatment effectiveness at a field scale. Optimization strategies for field scale application of treatments are discussed.

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Our ever-growing amount of solid waste puts a burden on future generations and the environment due to emissions of contaminants such as CO2, CH4, Cl- and heavy-metals for hundreds of years. It is therefore essential that landfill after-care methods are developed that reduce the emission potential of landfills to acceptable levels within the time-span of one generation. Several treatment methods such as aeration and leachate recirculation have shown promising results in reducing concentrations of problematic compounds in leachate and landfill gas emissions. However for application as full-scale technologies, long term evidence of sustainable reduction in emission potential has yet to be provided in practice. It is not possible to measure emission potential directly. Predictions of future emissions from landfills require emission modeling where emission potential is a crucial parameter. The aim of the research presented in this thesis is to present a conceptual modeling approach which increases the confidence in such long term predictions by reducing the parameter and model uncertainty in a systematic way. As such the approach allows us to quantify the emission potential. Chapter 2 and 3 of this thesis present an approach to develop and select biochemical and physical process networks in a generic conceptual model that allows us to optimally describe measured emissions from lysimeter experiments under anaerobic and aerobic conditions. These networks give a detailed description of the mass balances of contaminants and bacteria in the solid, liquid and gas phase. As a consequence, main emission pathways and rate-limiting processes are identified. Our results give strong indications that only a relatively small amount of the solid waste material present contributes to the measured emissions. The toolbox developed for this thesis, integrates information from different databases with approaches to obtain and couple thermodynamic/kinetic parameters and processes in order to efficiently evaluate a wide variety of networks via Bayesian inference using quantitative criteria. In chapter 4, the optimal biochemical and physical process networks calibrated at the lysimeter and column scale, are applied to predict the emissions at landfill scale. This is achieved by coupling the process networks to a water balance model that calculates the leachate production using a stochastic residence time distribution of water within the waste-body. The parameters of the stochastic residence time model are obtained by optimization using daily leachate production, rainfall and evaporation measurements. After calibration, the decrease in mass of different contaminants present in the waste body, gives a quantitative estimate of the full scale emission potential as a function of time. Results are shown for measured time series of leachate quantity and leachate quality (e.g. Cl–, Na+ and NH4+), but can easily be extended to other parameters. In chapter 5, the effectiveness of different aeration strategies is investigated based on modeled distributions of oxygen throughout a waste-body. The model is based on Darcy’s law for two-phase flow with parameters measured in laboratory experiments. Modeled gas extraction rates are in reasonable agreement with extraction rates measured at landfills. The results present optimal well configurations and aeration strategies for effective treatment. The thesis concludes with a list of the most important research steps for reducing the uncertainty in the approaches for quantification of full scale emission potential in the near future. @en

Reliable prediction of the long-term behavior of environmental systems such as Municipal Solid Waste (MSW) landfills is challenging. While many driving forces influence this behavior, characterization of them is limited by measurement techniques. Therefore, a model structure for reliable prediction needs to optimally combine all measured information with suitable mechanistic information from literature. How to get such an optimal model structure? This study presents a toolbox to find and build the model structure that describes an environmental system as close as possible. The toolbox combines environmental frameworks to include all suitable mechanistic information; it fully couples kinetic and equilibrium reactions and contains multiple resources to obtain biogeochemical parameters. Several possible optimal model structures are quickly built and evaluated with objective statistical performance criteria obtained via Bayesian inference. By applying the novel methodology, we select the best model structure for anaerobic digestion of MSW in full scale landfills.

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