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Related Concept Videos

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

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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...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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In electrical circuits, sources play a crucial role in providing power for the operation of the circuit. These sources can be broadly categorized into two types: independent and dependent.
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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.
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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Related Experiment Video

Updated: Aug 20, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children
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Dynamic harmonization of source-oriented and receptor models for source apportionment.

Xiaole Zhang1, Xiaoxiao Feng2, Jie Tian3

  • 1Institute of Environmental Engineering (IfU), ETH Zürich, Zürich CH-8093, Switzerland; Laboratory for Advanced Analytical Technologies, Empa, Dübendorf CH-8600, Switzerland; Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing, China.

The Science of the Total Environment
|November 20, 2022
PubMed
Summary
This summary is machine-generated.

Harmonizing air pollution models reveals significant reductions in fine particulate matter (PM2.5) emissions in Beijing. This dynamic approach successfully identified emission changes, particularly during the Spring Festival.

Keywords:
Bayesian inferenceEmission inventoryReceptor modelSource apportionmentSource-oriented model

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Air Quality Management

Background:

  • Air pollution from fine particulate matter (PM2.5) causes millions of premature deaths globally each year.
  • Accurate source apportionment of PM2.5 is crucial for effective pollution control but often yields inconsistent results between different methods.
  • Existing source apportionment models, including source-oriented and receptor models, require harmonization for improved accuracy.

Purpose of the Study:

  • To dynamically harmonize results from source-oriented and receptor models for PM2.5 source apportionment.
  • To update primary PM2.5 emission inventories using Bayesian Inference and an adjoint model.
  • To assess PM2.5 emission changes in Beijing during January-February 2021, including impacts of the Spring Festival.

Main Methods:

  • Developed an adjoint model to efficiently construct the source-receptor sensitivity matrix.
  • Employed Bayesian Inference to update primary PM2.5 emission inventories from major sectors.
  • Applied the harmonized method to PM2.5 measurement data from a Beijing campaign (Jan-Feb 2021).

Main Results:

  • Significant reductions in primary PM2.5 emissions were observed in Beijing compared to the 2017 baseline.
  • Local residential combustion and industry emissions declined by approximately 90%, while traffic emissions decreased by about 50%.
  • The method successfully captured temporally dynamic emission changes associated with the Spring Festival.

Conclusions:

  • The proposed dynamic harmonization method effectively integrates source-oriented and receptor models for PM2.5 apportionment.
  • The study highlights substantial emission reductions in Beijing, particularly from residential, industrial, and traffic sources.
  • This approach offers a promising pathway for reconciling and improving PM2.5 source apportionment.