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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
Bioremediation00:46

Bioremediation

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Related Experiment Video

Updated: Jul 15, 2026

Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure
06:52

Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure

Published on: July 19, 2018

Landfill modelling in LCA - a contribution based on empirical data.

Gudrun Obersteiner1, Erwin Binner, Peter Mostbauer

  • 1Institute of Waste Management, Department Water Atmosphere Environment, BOKU University of Natural Resources and Applied Life Science, Muthgasse 107, 1190 Vienna, Austria. gudrun.obersteiner@boku.ac.at

Waste Management (New York, N.Y.)
|April 17, 2007
PubMed
Summary

Life-cycle assessment (LCA) of landfills requires careful consideration of long-term emissions and reliable Life Cycle Inventory (LCI) data. This study discusses time frames and LCI approaches for accurate landfill modeling in Central Europe.

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Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

Published on: October 21, 2016

Area of Science:

  • Environmental Science
  • Waste Management Engineering
  • Life Cycle Assessment

Background:

  • Landfills vary widely in Europe, from uncontrolled dumps to engineered facilities managing leachate and gas.
  • Emissions and environmental impacts (leachate, air pollutants, operational energy) depend on waste type, design, and location.
  • Newer landfill types (MBT, MSWI residues) lack mid-term behavior data for Life Cycle Assessment (LCA).

Purpose of the Study:

  • To address methodological challenges in modeling landfills within LCA.
  • To investigate the impact of time frames on LCA results for landfilling.
  • To evaluate the influence of Life Cycle Inventory (LCI) data quality and approach on LCA reliability.

Main Methods:

  • Discussion of different time horizons for landfill emissions in LCA.
  • Comparison of multi-input inventory tools versus empirical data for LCI.
  • Provision of high-quality empirical LCI data for Central European landfills.

Main Results:

  • Landfill emissions can persist for thousands of years, significantly affecting LCA results based on the chosen time frame.
  • The choice between multi-input inventory tools and empirical data influences LCA reliability.
  • Data gaps in empirical results can limit the inclusion of impact categories.

Conclusions:

  • Accurate LCA of landfills necessitates careful selection of time frames and LCI data sources.
  • High-quality, empirical LCI data is crucial for reliable environmental impact assessments of landfills.
  • Further research on mid-term behavior of novel landfill types is needed for comprehensive LCA.