Future landfill emissions and the effect of final cover installation--a case study

Municipal solid waste (MSW) landfills are potential long-term sources of emissions. Hence, they need to be managed after closure until they do not pose a threat to humans or the environment. The case study on the Breitenau MSW landfill was performed to evaluate future emission levels for this site and to illustrate the effect of final cover installation with respect to long-term environmental risks. The methodology was based on a comprehensive assessment of the state of the landfill and included analysis of monitoring data, investigations of landfilled waste, and an evaluation of containment systems. A model to estimate future emission levels was established and site-specific predictions of leachate emissions were presented based on scenario analysis. The results are used to evaluate the future pollution potential of the landfill and to compare different aftercare concepts in view of long-term emissions. As some leachable substances became available for water flow during cover construction due to a change in the water flow pattern of the waste, a substantial increase in leachate concentrations could be observed at the site (e.g. concentrations of chloride increased from 200 to 800 mg/l and of ammonia-nitrogen from 140 to about 500 mg/l). A period of intensive flushing before the final cover installation could have reduced the amount of leachable substances within the landfill body and rapidly decreased the leachate concentrations to 11 mg Cl/l and 79 mg NH₄-N/l within 50 years. Contrarily, the minimization of water infiltration is associated with leachate concentrations in a high range for centuries (above 400 mg Cl/l and 200 mg NH₄-N/l) with low concomitant annual emission loads (below 12 kg/year of Cl or 9 kg/year of NH₄-N, respectively). However, an expected gradual decrease of barrier efficiency over time would be associated with higher emission loads of 50 kg of chloride and 30 kg of ammonia-nitrogen at the maximum, but a faster decrease of leachate concentration levels.     

Developing Effective Decision Support for the Application of “Gentle” Remediation Options: The GREENLAND Project

Gentle remediation options (GRO) are risk management strategies/technologies that result in a net gain (or at least no gross reduction) in soil function as well as risk management. They encompass a number of technologies, including the use of plant (phyto‐), fungi (myco‐), and/or bacteria‐based methods, with or without chemical soil additives or amendments, for reducing contaminant transfer to local receptors by in situ stabilization, or extraction, transformation, or degradation of contaminants. Despite offering strong benefits in terms of risk management, deployment costs, and sustainability for a range of site problems, the application of GRO as practical on‐site remedial solutions is still in its relative infancy, particularly for metal(loid)‐contaminated sites. A key barrier to wider adoption of GRO relates to general uncertainties and lack of stakeholder confidence in (and indeed knowledge of) the feasibility or reliability of GRO as practical risk management solutions. The GREENLAND project has therefore developed a simple and transparent decision support framework for promoting the appropriate use of gentle remediation options and encouraging participation of stakeholders, supplemented by a set of specific design aids for use when GRO appear to be a viable option. The framework is presented as a three phased model or Decision Support Tool (DST), in the form of a Microsoft Excel‐based workbook, designed to inform decision‐making and options appraisal during the selection of remedial approaches for contaminated sites. The DST acts as a simple decision support and stakeholder engagement tool for the application of GRO, providing a context for GRO application (particularly where soft end‐use of remediated land is envisaged), quick reference tables (including an economic cost calculator), and supporting information and technical guidance drawing on practical examples of effective GRO application at trace metal(loid) contaminated sites across Europe. This article introduces the decision support framework. ©2015 Wiley Periodicals, Inc.     

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.