The Application of Life‐Cycle Assessment to Solid Waste Management: Applications, Challenges and Modeling Techniques

Researchers have been working on the application of life-cycle assessment (LCA) to solid waste management systems for over two decades. Over this time, the state-of-the-art of LCA has advanced considerably, yet major challenges remain in the use of LCA to evaluate actual solid waste systems. The objective of this presentation is to present some perspective on the major accomplishments to date, applicable modeling techniques and the challenges that remain. This presentation is intended to serve as a catalyst for informal discussion in small groups throughout the conference. There are many potential uses for LCA models with a considerable range in the required complexity. Perhaps the simplest application is their use as an educational tool to teach high school students and university undergraduates about solid waste and the need to consider environmental impacts. More detailed LCA models are required to effectively evaluate the environmental impacts of alternate policies. For example, in the U.S., the EPA adopted a waste hierarchy in the 1990s that is not supported by LCA analyses. LCA may also be used to guide a city or region in the evaluation of alternatives for solid waste management. Over the past decade, the NC State research group has had the opportunity to work with the State of Delaware as well as Wake County, NC, U.S. on solid waste planning. Such real case studies have served to identify challenges with the application of life-cycle models to real systems. Finally, from a research perspective, models may be used for scholarly pursuits that may include methodological research to improve the overall application of LCA to waste management. The use of LCA models in all of the applications described above has helped to identify challenges and limitations. One challenge is the tradeoff between simple models that have a limited number of model inputs and more complex and flexible models that require relatively large amounts of input data. While simple models are user friendly and accessible to less experienced LCA and solid waste practitioners, they may oversimplify to the point where the results are not reliable or they may not be flexible enough to consider variations in processes or input values. Nonetheless, simple models can quickly provide first-order comparisons, and may serve to initiate new users in life-cycle thinking. While more complicated models overcome these limitations, they typically require more time and effort to learn to apply properly. Additional challenges for applying life-cycle models to SWM include data uncertainty, particularly as it applies to the benefits of using recycled materials as a raw material. There is tremendous uncertainty in the available upstream data. Beyond the numerical uncertainty, there are issues related to the location of a process which influences its environmental impact. For example, the emissions associated with plastic or fiber manufacture may be very different in different countries with different emissions regulations. The location of emissions may also significantly alter the risks to human health and the environment. Consider the case of aluminum, which is perhaps the most valuable material to recycle based on energy savings. The energy savings likely occurs at mines and smelters that are distant from the point of use, while additional emissions may be associated with the extra vehicle to collect recyclable materials. Similarly, the benefits of recovered energy from waste may be distant from the solid waste facility at which energy is generated. Another challenge is tradeoffs between different emissions and impacts. While much work has focused on greenhouse gas emissions, there are examples in which a solid waste management alternative that minimizes greenhouse gas emissions does not minimize, for example, eutrophication or toxicity potential. Additional challenges include the complexity of LCA results and the need to convey simple summary information, and the fact that waste composition, the energy grid, and environmental policies are all likely to change over the relevant time horizon. For example, there is a strong policy focus on landfill diversion of waste, but if paper in the waste stream continues to decrease and food waste continues to increase, then SWM strategies will need to adapt to even maintain current diversion rates. While there are not perfect solutions to the challenges identified above, the use of sensitivity analyses including contribution analysis, parametric analyses, and Monte Carlo t echniques can help to assess the key inputs and assumptions and robustness of results. The use of life-cycle optimization models has also helped to develop and evaluate novel SWM strategies that minimize environmental impacts or economic costs, while meeting user defined constraints (e.g., diversion targets, budget or emission limits). The application of modeling-to-generate alternatives (MGA) in these models facilitates exploration of the variability in alternative strategies, if any, that exist to meet the same goals. Multi-stage optimization models have also been developed that that allow multiple factors (e.g., waste generation and composition and fuel and electricity mix and prices) to change with time

Optimization of municipal solid waste management using externality costs

Economic and environmental impacts associated with solid waste management (SWM) systems should be considered to ensure sustainability of such systems. Societal life cycle costing (S-LCC) can be used for this purpose since it includes “budget costs” and “externality costs.” While budget costs represent market goods and services in monetary terms, i.e. economic impacts, externality costs include effects outside the economic system such as environmental impacts (translated in monetary terms).1 Numerous models have been developed to determine the environmental and economic impacts associated with SWM systems (e.g., EASETECH2) by using “what-if” scenario analyses. While these models are an essential foundation that enables a systematic integrated analysis of SWM systems, they do not provide information about the overall optimal solution as done with optimization models such as SWOLF.3 This study represents the first attempt to optimize SWM systems using externality costs in SWOLF. The assessment identifies the waste strategy that minimizes externality costs and other criteria (budget costs and landfilling) for a specific case study. The latter represents a hypothetical U.S. county with annual waste generation of 320,000 Mg. The externality cost includes the damage costs of fossil CO2, CH4, N2O, PM2.5, PM10, NOX, SO2 , VOC, CO, NH3, CO, Hg, Pb, Cd, Cr (VI), Ni, As, and dioxins. Table 1 shows the results of the optimization including: i) optimization criteria, ii) waste flows and iii) eco-efficiency indicator (ratio between externality costs and budget costs). Minimal externality costs are obtained when incinerating most of the waste (88%) and commingled collection of recyclables (12%). The eco-efficiency of this waste strategy corresponds to -0.6, i.e. its environmental benefits (negative externality costs) correspond to approximately half of its budget costs. On the other hand, there is the solution with minimal budget costs (100% of the waste is landfilled) in which the environmental load (positive externality cost) represent one third of the budget costs (positive eco-efficiency indicator). In between these options, there is a strategy with minimal landfilling in which the organic waste is sent to anaerobic digestion, the recyclables to a single stream MRF and the residual to a mixed waste MRF. Most of the externality costs of the three strategies stem from SO2, NOx and GHG as suggested by Woon & Lo.4 The case study shows that waste solutions identified by optimization modelling differ from common SWM systems selected for analysis in state-of-the-art accounting modelling Please click Additional Files below to see the full abstract

Physical and Biological Release of Poly- and Perfluoroalkyl Substances (PFASs) from Municipal Solid Waste in Anaerobic Model Landfill Reactors

A wide variety of consumer products that are treated with poly- and perfluoroalkyl substances (PFASs) and related formulations are disposed in landfills. Landfill leachate has significant concentrations of PFASs and acts as secondary point sources to surface water. Here, we model how PFASs enter leachate using four lab-scale anaerobic bioreactors filled with municipal solid waste (MSW) and operated over 273 days. Duplicate reactors were monitored under live and abiotic conditions to evaluate influences attributable to biological activity. The biologically-active reactors simulated the methanogenic conditions that develop in all landfills, producing ~140 mL CH₄/dry g refuse. The average total PFAS leaching measured in live reactors (16.7 nmol/kg dry-refuse) was greater than the average for abiotic reactors (2.83 nmol/kg dry-refuse), indicating biological processes were primarily responsible for leaching. The low level leaching in the abiotic reactors was primarily due to PFCAs ≤C8 (2.48 nmol/kg dry-refuse). Concentrations of known biodegradation intermediates, including methylperfluorobutane sulfonamide acetic acid and the n:2 and n:3 fluorotelomer carboxylates, increased steadily in concentration after the onset of methanogenesis, with the 5:3 fluorotelomer carboxylate becoming the single most concentrated PFAS observed in live reactors (9.53 nmol/kg dry-refuse)

Evaluation of Externality Costs in Life-Cycle Optimization of Municipal Solid Waste Management Systems

The development of sustainable solid waste management (SWM) systems requires consideration of both economic and environmental impacts. Societal life-cycle costing (S-LCC) provides a quantitative framework to estimate both economic and environmental impacts, by including “budget costs” and “externality costs”. Budget costs include market goods and services (economic impact), whereas externality costs include effects outside the economic system (e.g., environmental impact). This study demonstrates the applicability of S-LCC to SWM life-cycle optimization through a case study based on an average suburban U.S. county of 500 000 people generating 320 000 Mg of waste annually. Estimated externality costs are based on emissions of CO₂, CH₄, N₂O, PM_2.5, PM₁₀, NO_<i>x</i>, SO₂, VOC, CO, NH₃, Hg, Pb, Cd, Cr (VI), Ni, As, and dioxins. The results indicate that incorporating S-LCC into optimized SWM strategy development encourages the use of a mixed waste material recovery facility with residues going to incineration, and separated organics to anaerobic digestion. Results are sensitive to waste composition, energy mix and recycling rates. Most of the externality costs stem from SO₂, NO_<i>x</i>, PM_2.5, CH₄, fossil CO₂, and NH₃ emissions. S-LCC proved to be a valuable tool for policy analysis, but additional data on key externality costs such as organic compounds emissions to water would improve future analyses

Toward Identifying the Next Generation of Superfund and Hazardous Waste Site Contaminants

Reproduced with permission from Environmental Health Perspectives."This commentary evolved from a workshop sponsored by the National Institute of Environmental Health Sciences titled "Superfund Contaminants: The Next Generation" held in Tucson, Arizona, in August 2009. All the authors were workshop participants." doi:10.1289/ehp.1002497Our aim was to initiate a dynamic, adaptable process for identifying contaminants of emerging concern (CECs) that are likely to be found in future hazardous waste sites, and to identify the gaps in primary research that cause uncertainty in determining future hazardous waste site contaminants. Superfund-relevant CECs can be characterized by specific attributes: they are persistent, bioaccumulative, toxic, occur in large quantities, and have localized accumulation with a likelihood of exposure. Although still under development and incompletely applied, methods to quantify these attributes can assist in winnowing down the list of candidates from the universe of potential CECs. Unfortunately, significant research gaps exist in detection and quantification, environmental fate and transport, health and risk assessment, and site exploration and remediation for CECs. Addressing these gaps is prerequisite to a preventive approach to generating and managing hazardous waste sites.Support for the workshop, from which this article evolved, was provided by the National Institute of Environmental Health Sciences Superfund Research Program (P42-ES04940)

Toward Identifying the Next Generation of Superfund and Hazardous Waste Site Contaminants

Reproduced with permission from Environmental Health Perspectives."This commentary evolved from a workshop sponsored by the National Institute of Environmental Health Sciences titled "Superfund Contaminants: The Next Generation" held in Tucson, Arizona, in August 2009. All the authors were workshop participants." doi:10.1289/ehp.1002497Our aim was to initiate a dynamic, adaptable process for identifying contaminants of emerging concern (CECs) that are likely to be found in future hazardous waste sites, and to identify the gaps in primary research that cause uncertainty in determining future hazardous waste site contaminants. Superfund-relevant CECs can be characterized by specific attributes: they are persistent, bioaccumulative, toxic, occur in large quantities, and have localized accumulation with a likelihood of exposure. Although still under development and incompletely applied, methods to quantify these attributes can assist in winnowing down the list of candidates from the universe of potential CECs. Unfortunately, significant research gaps exist in detection and quantification, environmental fate and transport, health and risk assessment, and site exploration and remediation for CECs. Addressing these gaps is prerequisite to a preventive approach to generating and managing hazardous waste sites.Support for the workshop, from which this article evolved, was provided by the National Institute of Environmental Health Sciences Superfund Research Program (P42-ES04940)