Quantitative assessments of municipal waste management systems: Using different indicators to compare and rank programs in New York State

The primary objective of waste management technologies and policies in the United States is to reduce the harmful environmental impacts of waste, particularly those relating to energy consumption and climate change. Performance indicators are frequently used to evaluate the environmental quality of municipal waste systems, as well as to compare and rank programs relative to each other in terms of environmental performance. However, there currently is no consensus on the best indicator for performing these environmental evaluations. The purpose of this study is to examine the common performance indicators used to assess the environmental benefits of municipal waste systems to determine if there is agreement between them regarding which system performs best environmentally. Focus is placed on how indicator selection influences comparisons between municipal waste management programs and subsequent system rankings. The waste systems of ten municipalities in the state of New York, USA, were evaluated using each common performance indicator and Spearman correlations were calculated to see if there was a significant association between system rank orderings. Analyses showed that rank orders of waste systems differ substantially when different indicators are used. Therefore, comparative system assessments based on indicators should be considered carefully, especially those intended to gauge environmental quality. Insight was also gained into specific factors which may lead to one system achieving higher rankings than another. However, despite the insufficiencies of indicators for comparative quality assessments, they do provide important information for waste managers and they can assist in evaluating internal programmatic performance and progress. To enhance these types of assessments, a framework for scoring indicators based on criteria that evaluate their utility and value for system evaluations was developed. This framework was used to construct an improved model for waste system performance assessments.     

Characterization of fine particulate matter produced by combustion of residual fuel oil

Combustion experiments were carried out on four different residual fuel oils in a 732-kW boiler. PM emission samples were separated aerodynamically by a cyclone into fractions that were nominally less than and greater than 2.5 microns in diameter. However, examination of several of the samples by computer-controlled scanning electron microscopy (CCSEM) revealed that part of the PM2.5 fraction consists of carbonaceous cenospheres and vesicular particles that range up to 10 microns in diameter. X-ray absorption fine structure (XAFS) spectroscopy data were obtained at the S, V, Ni, Fe, Cu, Zn, and As K-edges and at the Pb L-edge. Deconvolution of the X-ray absorption near edge structure (XANES) region of the S spectra established that the dominant molecular forms of S present were sulfate (26-84% of total S) and thiophene (13-39% of total S). Sulfate was greater in the PM2.5 samples than in the PM2.5+ samples. Inorganic sulfides and elemental sulfur were present in lower percentages. The Ni XANES spectra from all of the samples agreed fairly well with that of NiSO4, while most of the V spectra closely resembled that of vanadyl sulfate (VO.SO4.xH2O). The other metals investigated (i.e., Fe, Cu, Zn, and Pb) also were present predominantly as sulfates. Arsenic was present as an arsenate (As+5). X-ray diffraction patterns of the PM2.5 fraction exhibit sharp lines due to sulfate compounds (Zn, V, Ni, Ca, etc.) superimposed on broad peaks due to amorphous carbons. All of the samples contain a significant organic component, with the loss on ignition (LOI) ranging from 64 to 87% for the PM2.5 fraction and from 88 to 97% for the PM2.5+ fraction. Based on 13C nuclear magnetic resonance (NMR) analysis, the carbon is predominantly condensed in graphitic structures. Aliphatic structure was detected in only one of seven samples examined.     

Common ectomycorrhizal networks may maintain monodominance in a tropical rain forest

Most tropical rain forests contain diverse arrays of tree species that form arbuscular mycorrhizae. In contrast, the less common monodominant rain forests, in which one tree species comprises more than 50% of the canopy, frequently contain ectomycorrhizal (ECM) associates. In this study, I explored the potential for common ECM networks, created by aggregations of ECM trees, to enhance seedling survivorship near parent trees. I determined the benefit conferred by the common ECM network on seedling growth and survivorship of an ECM monodominant species in Guyana. Seedlings with access to an ECM network had greater growth (73% greater), leaf number (55% more), and survivorship (47% greater) than seedlings without such access, suggesting that the ECM network provides a survivorship advantage. A survey of wild seedlings showed positive distance-dependent distribution and survival with respect to conspecific adults. These experimental and survey results suggest that the negative distance-dependent mechanisms at the seedling stage thought to maintain tropical rain forest diversity are reversed for ECM seedlings, which experience positive feedbacks from the ECM network. These results may in part explain the local monodominance of an ECM tree species within the matrix of high-diversity, tropical rain forest.     

Developing Peak Discharges for Future Flood Risk Studies Using IPCC's CMIP5 Climate Model Results and USGS WREG Program

Extreme climate events, floods, and drought, cause huge impact on daily lives. In order to produce society resilient to extreme events, it is necessary to assess the impact of frequent and high intensity storm events on design parameters. This article describes a methodology to develop future peak “design discharges” throughout the United States that can be used as a guidance to map future floodplains. In order to develop a lower and upper limit for anticipated peak flow discharges, two future growth scenarios — Representative Concentration Pathways (RCPs)‐RCP 2.6 and 8.5 were identified as the weak and strong climate scenario respectively based on the output from the global climate models. The Generalized Least Square technique in United States Geological Survey's Weighted Multiple Regression (WREG) program was used to develop regression equations that relate peak discharges to basin and climate parameters of the contributing watershed. The design discharges reflect the most recent climate model results. Number of frost days, heavy rainfall days, high temperature days, and snow depth were found to be the common extreme climate parameters influencing the regression equations. This methodology can be extended to other flood frequency events if rainfall data is available. The future discharges can be utilized in hydraulics models to estimate floodplains that can assist in resilient infrastructure planning and outline climate change adaptation strategies.     

A weighted fuzzy linear programming model in economic input–output analysis: an application to risk management of energy system disruptions

Climate change exposes economic systems to numerous risks, including reduced agricultural production and electric power supply shortages. The interdependent nature of economic systems causes disruptions in any sector to cascade to other sectors via forward and backward linkages. This work develops an optimization model with which allocation of scarce goods or resources can be optimized; the model uses an overall index of satisfaction of fuzzy economic output goals under conditions of scarcity caused by climatic disruptions. The proposed model includes a vulnerability measure that integrates information elicited from expert judgment. A case study based on a scenario of drought-induced electricity shortage in the Philippine economy is examined. Results show that trade, transportation and service-oriented industries suffer losses in gross domestic product in the Philippine case.     

Risk assessment: A site-specific approach to establishing acceptable soil cleanup levels

In 1980 the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) was passed to instigate the cleanup of uncontrolled hazardous waste sites. CERCLA necessitated the development of a set of criteria for estimating the severity of the contamination at these sites, the potential impact on human health and the environment, and establishing appropriate cleanup levels for the contaminated media. The risk assessment (RA) format was developed to meet these challenges. Though RAs vary dramatically in their scope, emphasis, and regulatory application, there are two primary objectives common to all RAs: (1) to evaluate potential risks to human health and the environment posed by the release of hazardous substances and (2) to evaluate and establish safe cleanup levels based primarily on the number and type of potential receptors, the toxicity and mobility of the contaminants, and the types of exposure pathways present. Achieving these objectives may be a relatively simple task or an extremely complex and difficult one depending on the type of material released and our understanding of its behavior in the environment, the site conditions, and the governing regulations. This article presents an approach for establishing acceptable cleanup levels for subsurface soils and illustrates the application of this approach to three different regulated sites.     

Nickel and sulfur speciation of residual oil fly ashes from two electric utility steam-generating units

Representative duplicate fly ash samples were obtained from the stacks of 400- and 385-MW utility boilers (Unit A and Unit B, respectively) using a modified U.S. Environmental Protection Agency (EPA) Method 17 sampling train assembly as they burned 0.9 and 0.3 wt % S residual (No. 6 fuel) oils, respectively, during routine power plant operations. Residual oil fly ash (ROFA) samples were analyzed for Ni concentrations and speciation using inductively coupled plasma-atomic emission spectroscopy, X-ray absorption fine structure (XAFS) spectroscopy, and X-ray diffraction (XRD). ROFA deionized H2O extraction residues were also analyzed for Ni speciation using XAFS and XRD. Total Ni concentrations in the ROFAs were similar, ranging from 1.3-1.5 wt%; however, stack gas Ni concentrations in the Unit A were 0.990 microg/Nm3 compared with 0.620 microg/Nm3 for Unit B because of the greater residual oil feed rates employed at Unit A to attain higher 400-MW load conditions with a lower heating value oil. Ni speciation analysis results indicated that ROFAs from Unit A contain approximately 3 wt % NiSO4 x xH2O (where x is assumed to be 6 for calculation purposes) and appoximately 4.5 wt% of a Ni-containing spinel compound, similar in composition to (Mg,Ni)(Al,Fe)2O4. ROFAs from Unit B contain on average 2 wt% NiSO4 x 6 H20 and 1.1 wt% NiO. XAFS and XRD analyses did not detect any nickel sulfide compounds, including carcinogenic nickel subsulfide (Ni3S2) (XAFS detection limit is 5% of the total Ni concentration). In addition, XAFS measurements indicated that inorganic sulfate and organic thiophene species accounted for > 97% of the total S in the ROFAs. Unit A ROFAs contained much lower thiophene proportions because cyclone-separated ROFA reinjection is employed on this unit to collect and reburn the larger carbonaceous particles.