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Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

50
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
50
Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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...
33
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

72
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
73
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
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...
45
  1. Home
  3. Research Domains
  5. Engineering
  7. Environmental Engineering
  9. Air Pollution Modelling And Control
  11. Current Limitations And Opportunities For Improvements Of Agent-based Transport Models For Noise Exposure Assessment.
  1. Home
  3. Research Domains
  5. Engineering
  7. Environmental Engineering
  9. Air Pollution Modelling And Control
  11. Current Limitations And Opportunities For Improvements Of Agent-based Transport Models For Noise Exposure Assessment.

Related Experiment Video

Author Spotlight: Deciphering the Long-Term Effects of Low-Level Blast Exposures in Mice
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Author Spotlight: Deciphering the Long-Term Effects of Low-Level Blast Exposures in Mice

Published on: May 24, 2024

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Current limitations and opportunities for improvements of agent-based transport models for noise exposure assessment.

Leonardo Galassi Luquezi1, Valentin Le Bescond1, Pierre Aumond1

  • 1Univ Gustave Eiffel, CEREMA, UMRAE, F-44344 Bouguenais, France; Institut de Recherche des Sciences et Techniques de la Ville (IRSTV), Ecole Centrale de Nantes, F-44321, Nantes Cedex 3, France.

Journal of Environmental Management
|August 20, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Agent-based models coupled with environmental models offer a novel way to assess individual noise exposure from transport. This approach enhances traditional methods by considering spatial, temporal, and activity patterns for better environmental noise impact analysis.

Keywords:
Activity-based transport modelsAgent-based transport modelsEnvironmental assessmentNoise exposure assessment

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Area of Science:

  • Environmental Science
  • Transportation Engineering
  • Computational Social Science

Background:

  • Agent-based models (ABMs) are increasingly used for transport system simulation.
  • Standard noise exposure assessments often lack individual-level detail.
  • Integrating transport ABMs with environmental models can improve noise impact analysis.

Purpose of the Study:

  • To investigate the utility of coupled agent-based transport and environmental models for assessing individual noise exposure.
  • To propose and evaluate a method for this assessment across spatial, temporal, individual, and activity dimensions.
  • To demonstrate the approach using a case study in the Lyon Metropolitan Area.

Main Methods:

  • Coupling of agent-based transport models (e.g., MATSim) with environmental noise models (e.g., NoiseModelling, EQASim).
Noise modeling
Urban environment
  • Development of a four-dimensional evaluation framework: spatial, temporal, individual, and activity patterns.
  • Application of the framework to a real-world case study using open-source tools.

    • Main Results:

    • The study identified key challenges in conceptualizing exposure contexts, activity spaces, and acoustic resolution.
    • It highlighted issues related to data disaggregation, individual variability in noise perception, and social-exposure correlations.
    • The exemplification model provided insights into the practical application and limitations of the coupled approach.

    Conclusions:

    • Coupled agent-based models offer a promising, detailed approach to individual transport noise exposure assessment.
    • Further research is needed on momentary noise exposure, social epidemiology, and refining model components.
    • The findings provide a foundation for advancing exposure assessment methodologies in urban environments.