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Classifying Matter by Composition03:35

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Matter: Pure Substances and Mixtures
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Gravimetric analysis is a quantitative method where the analyte is isolated and weighed directly or after conversion into a substance of known composition. Gravimetric analysis can be classified as precipitation, electrogravimetry, volatilization, and particulate gravimetry, based on the method used to isolate the analyte.
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Predicting the composition of solid waste at the county scale.

Joshua T Grassel1, Adolfo R Escobedo2, Rajesh Buch3

  • 1North Carolina State University, Operations Research Graduate Program, 915 Partners Way, Raleigh, NC, 27606, USA.

Waste Management (New York, N.Y.)
|December 18, 2024
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Summary
This summary is machine-generated.

This study introduces a new two-phase method for predicting municipal solid waste (MSW) composition and quantity. This approach supports data-driven solid waste management and the circular economy by providing detailed waste stream estimates.

Keywords:
Municipal solid wasteWaste characterizationWaste compositionWaste prediction

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Area of Science:

  • Environmental Science
  • Waste Management Science
  • Data Science

Background:

  • Current solid waste management (SWM) methods often lack detailed waste composition data.
  • Existing models typically categorize municipal solid waste (MSW) into limited categories or aggregate totals.
  • Transitioning to a circular economy requires accurate data on waste composition and quantity.

Purpose of the Study:

  • To develop a novel two-phase strategy for predicting MSW composition and quantity.
  • To facilitate data-driven decision-making in SWM.
  • To support the transition towards a circular economy through comprehensive waste estimation.

Main Methods:

  • A two-phase prediction strategy: first for waste composition, then for total quantity.
  • Utilized publicly available demographic, economic, and spatial data, alongside waste sampling reports.
  • Developed a Least Absolute Shrinkage and Selection Operator (LASSO) regression model for estimating MSW composition across 43 categories.

Main Results:

  • The LASSO model successfully estimates MSW composition distinctly from quantity.
  • The novel approach enables prediction of dozens of waste material streams, exceeding limitations of existing methods.
  • Case studies demonstrated the model's capability for detailed waste estimation at the U.S. county level.

Conclusions:

  • The proposed two-phase strategy offers a significant advancement in waste data estimation.
  • This method provides detailed insights crucial for effective SWM and circular economy initiatives.
  • The model's flexibility and data utilization pave the way for improved waste management practices.