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Related Concept Videos

Contaminants and Errors01:16

Contaminants and Errors

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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
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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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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
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Contaminant point source localization error estimates as functions of data quantity and model quality.

Scott K Hansen1, Velimir V Vesselinov1

  • 1Computational Earth Science Group, Earth and Environmental Sciences Division (EES-16), Los Alamos National Laboratory, Los Alamos, NM87545, United States.

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Accurately locating contaminant sources in groundwater requires understanding aquifer properties. This study develops error envelopes for contaminant release localization, showing data quantity can offset model quality for spatial, but not space-time, source identification.

Keywords:
Environmental forensicsError quantificationInverse problemsModel errorSolute transportSource identification

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Geophysics

Background:

  • Contaminant transport in heterogeneous aquifers is complex.
  • Accurate source localization is crucial for effective remediation.

Purpose of the Study:

  • Develop empirically-grounded error envelopes for point contamination release localization.
  • Assess the impact of data quantity and model quality on localization accuracy.

Main Methods:

  • Simulated contaminant breakthrough data using high-performance computing.
  • Employed unsupervised machine optimization for source localization.
  • Analyzed 90% and 95% confidence envelopes based on multiple realizations.

Main Results:

  • Increased data quantity compensated for reduced model quality in spatial localization.
  • Space-time localization showed degraded reliability and less improvement from additional data.
  • A multiple-initial-guess optimization strategy enhanced space-time localization performance.

Conclusions:

  • Spatial contaminant source localization benefits significantly from more data.
  • Space-time localization remains challenging, with limited gains from increased data collection.
  • Model fidelity and data quantity are critical factors influencing contaminant source localization accuracy.