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

State-of-the-Art Solid Waste Management Life-Cycle Modeling Workshop

There are many alternatives for the management of solid waste including recycling, biological treatment, thermal treatment and landfill disposal. In many cases, solid waste management systems include the use of several of these processes. Solid waste life-cycle assessment models are often used to evaluate the environmental consequences of various waste management strategies. The foundation of every life-cycle model is the development and use of process models to estimate the emissions from solid waste unit processes. The objective of this workshop is to describe life-cycle modeling of the solid waste processes and systems. The workshop will begin with an introduction to solid waste life-cycle modeling and available models, which will be followed by sessions on life-cycle process modeling for individual processes (e.g., landfills, biological treatment, and thermal treatment). The first part of each session will be used to explain the state-of-the-art for a given solid waste process model and the remainder of the time will be devoted to input and discussion

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

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

EC85-219 1985 Nebraska Swine Report

This 1985 Nebraska Swine Report was prepared by the staff in Animal Science and cooperating departments for use in the Extension and Teaching programs at the University of Nebraska-Lincoln. Authors from the following areas contributed to this publication: Swine Nutrition, swine diseases, pathology, economics, engineering, swine breeding, meats, agronomy, and diagnostic laboratory. It covers the following areas: breeding, disease control, feeding, nutrition, economics, housing and meats

Hyperdominance in Amazonian Forest Carbon Cycling

While Amazonian forests are extraordinarily diverse, the abundance of trees is skewed strongly towards relatively few ‘hyperdominant’ species. In addition to their diversity, Amazonian trees are a key component of the global carbon cycle, assimilating and storing more carbon than any other ecosystem on Earth. Here we ask, using a unique data set of 530 forest plots, if the functions of storing and producing woody carbon are concentrated in a small number of tree species, whether the most abundant species also dominate carbon cycling, and whether dominant species are characterized by specific functional traits. We find that dominance of forest function is even more concentrated in a few species than is dominance of tree abundance, with only ≈1% of Amazon tree species responsible for 50% of carbon storage and productivity. Although those species that contribute most to biomass and productivity are often abundant, species maximum size is also influential, while the identity and ranking of dominant species varies by function and by region

First M87 Event Horizon Telescope Results. VII. Polarization of the Ring

In 2017 April, the Event Horizon Telescope (EHT) observed the near-horizon region around the supermassive black hole at the core of the M87 galaxy. These 1.3 mm wavelength observations revealed a compact asymmetric ring-like source morphology. This structure originates from synchrotron emission produced by relativistic plasma located in the immediate vicinity of the black hole. Here we present the corresponding linear-polarimetric EHT images of the center of M87. We find that only a part of the ring is significantly polarized. The resolved fractional linear polarization has a maximum located in the southwest part of the ring, where it rises to the level of similar to 15%. The polarization position angles are arranged in a nearly azimuthal pattern. We perform quantitative measurements of relevant polarimetric properties of the compact emission and find evidence for the temporal evolution of the polarized source structure over one week of EHT observations. The details of the polarimetric data reduction and calibration methodology are provided. We carry out the data analysis using multiple independent imaging and modeling techniques, each of which is validated against a suite of synthetic data sets. The gross polarimetric structure and its apparent evolution with time are insensitive to the method used to reconstruct the image. These polarimetric images carry information about the structure of the magnetic fields responsible for the synchrotron emission. Their physical interpretation is discussed in an accompanying publication

Constraints on black-hole charges with the 2017 EHT observations of M87*

Our understanding of strong gravity near supermassive compact objects has recently improved thanks to the measurements made by the Event Horizon Telescope (EHT). We use here the M87* shadow size to infer constraints on the physical charges of a large variety of nonrotating or rotating black holes. For example, we show that the quality of the measurements is already sufficient to rule out that M87* is a highly charged dilaton black hole. Similarly, when considering black holes with two physical and independent charges, we are able to exclude considerable regions of the space of parameters for the doubly-charged dilaton and the Sen black holes