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Related Concept Videos

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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)...
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Precipitation Gravimetry01:03

Precipitation Gravimetry

Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...

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Related Experiment Video

Updated: May 24, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Modelling nutrient loads in data-scarce large catchments using spatially regularized ensemble calibration.

Matteo Masi1, Maryam Barati Moghaddam2, Fabio Castelli1

  • 1Department of Civil and Environmental Engineering, University of Florence, Via di S. Marta 3, 50139, Firenze, Italy.

The Science of the Total Environment
|May 22, 2026
PubMed
Summary

This study introduces a new model to track nutrient pollution in rivers, helping water managers identify pollution sources and improve water quality, even with limited data. It effectively pinpoints pollution hotspots in large river systems.

Keywords:
Iterative ensemble smootherNutrient dynamicsReactive transportSurface water qualityTikhonov regularizationUncertainty quantification

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Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
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Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

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Last Updated: May 24, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

Area of Science:

  • Environmental Science
  • Hydrology
  • Water Quality Management

Background:

  • Nutrient pollution is a significant threat to freshwater ecosystems, necessitating advanced modeling for effective management.
  • Catchment-scale water quality modeling faces challenges due to sparse data and high parameter uncertainty in complex systems.

Purpose of the Study:

  • To develop and validate an integrated modeling framework for simulating nutrient dynamics and water quality in large river catchments.
  • To estimate distributed pollutant loads and quantify associated uncertainties in data-scarce environments.

Main Methods:

  • Integrated MOBIDIC-ADR hydrological model with a novel BIO-ALGAE reactive component.
  • Spatially regularized ensemble calibration using PEST++ iterative ensemble smoother.
  • Application to the Arno River catchment (7990 km²), simulating 8 water quality constituents over 10 years (2011-2020).

Main Results:

  • Successfully reproduced observed water quality patterns across multiple constituents using sparse monitoring data.
  • Identified pollution hotspots, linking urban areas to elevated biochemical oxygen demand and ammonium loads.
  • Phosphorus distribution indicated multiple source contributions; model provided spatially explicit load estimates with uncertainty bounds.

Conclusions:

  • The integrated framework provides a practical decision-support tool for targeted water quality management in data-limited regions.
  • The model effectively captures key reactive processes and aids in understanding nutrient dynamics across large catchments.
  • This approach enhances water management strategies by providing spatially explicit pollutant load information with uncertainty quantification.