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Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

Accuracy and Precision

Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. Highly accurate measurements...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations

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Published on: September 11, 2016

Quantifying the Accuracy, Uncertainty, and Sensitivity of Soil Geochemical Multisurface Models.

Wietse Wiersma^1,2, Elise Van Eynde³, Rob N J Comans¹

¹Soil Chemistry Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands.

Environmental Science & Technology

|March 5, 2025

Summary

This study quantifies model parameter uncertainty for metal speciation in soils, finding new generic parameters improve accuracy, especially for zinc. Simplified soil property assessments negligibly affect model performance, aiding environmental challenge solutions.

Keywords:

affinity nonideality assemblage model environmental protection generic adsorption parameters heavy metals

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Area of Science:

Environmental Chemistry
Geochemistry
Soil Science

Background:

Geochemical multisurface models are crucial for understanding metal partitioning and speciation.
Previous evaluations of model parameter uncertainty and sensitivity in soils were insufficient.

Purpose of the Study:

To quantify uncertainty and sensitivity of model parameters and input values for metal speciation in diverse soils.
To establish improved generic parameters for the nonideal competitive adsorption-Donnan (NICA-Donnan) model.
To assess the impact of simplified soil property inputs on model accuracy.

Main Methods:

Utilized statistical tools and diverse soil data to evaluate the NICA-Donnan model coupled with the generalized two-layer model.
Quantified uncertainty in model parameters, input values, and speciation predictions for Cadmium (Cd), Copper (Cu), and Zinc (Zn).
Determined sensitivity to model parameters and input values, identifying key influential factors.

Main Results:

Established new generic NICA-Donnan parameters that significantly enhanced model accuracy, particularly for Zn.
Observed uncertainty levels followed the trend Cu < Cd < Zn.
Organic matter (OM) was identified as the primary binding surface, with affinity parameters being most influential.
A simplified scenario using assumptions about OM fractionation and metal-oxide surface area had negligible impact on model accuracy and uncertainty.

Conclusions:

The developed generic parameters offer improved accuracy for metal speciation modeling in soils.
Mechanistic multisurface models can be adopted more broadly for environmental applications with reliable performance measures.
Simplified soil characterization approaches are viable without compromising model accuracy, facilitating wider use.