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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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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...
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Spatially resolved environmental fate models: A review.

Parisa Falakdin1, Elisa Terzaghi1, Antonio Di Guardo1

  • 1Department of Science and High Technology, University of Insubria, Via Valleggio 11, 22100, Como, CO, Italy.

Chemosphere
|December 26, 2021
PubMed
Summary

This review compares spatially resolved environmental models, highlighting challenges in their development and data requirements. It offers insights into spatial multimedia fate models for improved environmental risk assessment.

Keywords:
Atmospheric modelsGISMathematical modelsMultimedia chemical fate modelsSpatially explicit modelsSpatio-temporal resolution

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Area of Science:

  • Environmental modeling
  • Spatial analysis
  • Chemical risk assessment

Background:

  • Spatially resolved environmental models are crucial for capturing real-world spatial variability.
  • Developing and evaluating these models is challenging due to setup, computational costs, and data acquisition.

Purpose of the Study:

  • To review and compare various spatially resolved environmental models.
  • To provide a detailed analysis of spatial multimedia fate models.
  • To understand model strengths, limitations, and requirements for improvement.

Main Methods:

  • Comparative review of spatial, temporal, and chemical domains of environmental models.
  • In-depth examination of spatial multimedia fate models.
  • Analysis of challenges in model development and data integration.

Main Results:

  • Different spatial models (e.g., atmospheric transport, multimedia fate) serve distinct purposes.
  • Spatial multimedia fate models are valuable for regulatory risk and life cycle assessments.
  • Key challenges include model setup, computational demands, and high-resolution data needs.

Conclusions:

  • Further improvements and integration of spatial environmental models are necessary.
  • Understanding model domains and limitations is essential for effective application.
  • Addressing data and computational challenges will enhance model utility.