A New Model for Discarded Municipal Waste Composition in the US
Jon Powell
Yale University,
Jon is a researcher and environmental engineer with wide-ranging interests across the spectrum of waste materials management. His research career began with operating and optimizing full-scale landfills, with a focus on in-situ aeration of the waste and the mechanisms underlying gas production and emissions changes. In subsequent years, his research portfolio expanded to include assessing the beneficial use of non-hazardous waste materials (e.g., industrial and construction wastes), waste management policy interventions, municipal landfill mining, and life-cycle assessment. In recent years, he has worked to bring informatics to the waste sector by using contemporary analytical tools to create large-scale data sets across multiple dimensions of waste management to inform current management outcomes (e.g., the efficiency of landfill gas capture), highlight emergent issues (e.g., the scale of disposal sites reaching the end of the aftercare period), and pinpoint areas where intervention strategies - such as diversion - should take place based on spatially- and temporally-resolved analytical models. Jon holds a Bachelor's degree and Master's degree in Environmental Engineering Sciences from the University of Florida, is a registered professional engineer in Florida, Connecticut, and Puerto Rico, and is completing his doctorate in Chemical & Environmental Engineering at Yale University.
Abstract
The US EPA has published national-level estimates of MSW management approximately annually since the 1990s. These data are heavily relied upon by researchers, state environmental agencies, local planners, and researchers... [ view full abstract ]
The US EPA has published national-level estimates of MSW management approximately annually since the 1990s. These data are heavily relied upon by researchers, state environmental agencies, local planners, and researchers because of the authoritative nature of the source, the frequency of published estimates, and the availability of the information. The method used by the US EPA to estimate MSW generation, disposal quantities and waste composition uses a materials flow analysis (MFA) approach. Data from industry associations, relevant businesses, Department of Commerce, and the US Census Bureau are principally used to create US EPA’s annual MSW management estimates (United States Environmental Protection Agency, 2015b). In addition, waste characterization studies and other sources are used to supplement the main data sets that the US EPA uses. An acknowledged limitation with US EPA’s MSW management report is the lack of measured data used to develop waste management estimates. Additionally, national-level estimates of waste disposal and composition are helpful for understanding big-picture waste management trends, but the lack of granularity limits the utility of such data.
Here we demonstrate a new model of waste composition discarded into MSW landfills, built using three large data sets reflecting facility-level measurements of total waste disposal, categorical waste component (e.g., municipal, construction, and industrial waste) disposal, and individual waste component disposal. Taken together, we portray the most granular, complete, and geospatially-resolved picture of disposal into MSW landfills to date. We find that a substantial number of the 1100+ landfills reporting data to the US EPA's Greenhouse Gas Reporting Program (which will be used in the US Greenhouse Gas Sources and Sinks estimates) are over-reporting MSW disposal, resulting in an over-estimate in greenhouse gas emissions. Our model will be useful in tracking the efficacy of diversion programs - such as the many food waste disposal reduction efforts taking place across the US - by reflecting changes in waste disposal on a national, regional, state, and local level on an annual basis.
Authors
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Jon Powell
(Yale University,)
Topic Areas
• Open source data, big data, data mining and industrial ecology , • Management and technology for sustainable and resilient energy, water, food, materials, , • Decision support methods and tools
Session
ThS-15 » Special session: "Waste Informatics and Data Quality in Industrial Ecology" (11:30 - Thursday, 29th June, Room F)
