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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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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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Uncertainty analysis in a large-scale water quality integrated catchment modelling study.

Antonio M Moreno-Rodenas1, Franz Tscheikner-Gratl2, Jeroen G Langeveld3

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Uncertainty analysis in urban water quality models is crucial. This study quantifies uncertainty sources in dissolved oxygen prediction for a large river catchment, identifying combined sewer overflows and rainfall as key factors.

Keywords:
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Area of Science:

  • Environmental Science
  • Hydrology
  • Water Quality Modeling

Background:

  • Integrated catchment models are essential for urban water quality simulation but often yield uncertain results.
  • Uncertainty analysis is underutilized in practice, with limited understanding of individual model element contributions.
  • Computational and organizational constraints often restrict these analyses to smaller systems.

Purpose of the Study:

  • To present an uncertainty propagation and decomposition scheme for integrated water quality modeling.
  • To evaluate dissolved oxygen dynamics in a large-scale urbanized river catchment in the Netherlands.
  • To identify dominant uncertainty sources in urban river water quality predictions.

Main Methods:

  • Utilized an uncertainty propagation and decomposition scheme for integrated water quality modeling.
  • Applied forward propagation of measured and elicited uncertainty input-parametric distributions.
  • Contrasted model predictions with monitoring data series for calibration and validation.

Main Results:

  • Initial uncertainty in water quality-quantity parameters led to significant dissolved oxygen prediction uncertainty, highlighting the need for calibration.
  • Post-calibration, combined sewer overflow pollution loads and rainfall variability emerged as dominant uncertainty sources.
  • Model insights aid in directing future monitoring and modeling efforts for urban river systems.

Conclusions:

  • Formal calibration is essential to adapt water quality models to local river dynamics.
  • Combined sewer overflows and rainfall variability are critical uncertainty drivers in urban river dissolved oxygen modeling.
  • Existing national guidelines for mitigation selection may need adaptation to account for model uncertainties.