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Derivatives: Problem Solving01:26

Derivatives: Problem Solving

Temperature-Dependent Growth of Brook TroutThe growth of brook trout is closely influenced by water temperature. Experimental data demonstrate how trout weight changes over a 24-day period in response to varying water temperatures. At lower temperatures, such as 15.5 degrees Celsius, brook trout show significant weight gain. However, as the temperature increases, the amount of weight gained steadily decreases. At the highest temperature measured, 24.4 degrees Celsius, trout experience a net...

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

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Published on: September 26, 2017

Temporal scale effect of loading data on instream nitrate-nitrogen load computation.

Abhijit Patil1, Zhiqiang Deng

  • 1Dept. of Civil & Environmental Engineering, Louisiana State University, Baton Rouge, LA, USA.

Water Science and Technology : a Journal of the International Association on Water Pollution Research
|June 9, 2012
PubMed
Summary
This summary is machine-generated.

Varying the temporal scale of loading data significantly impacts nitrate-nitrogen load calculations. Using longer time scales, like monthly averages, reduces variability and simplifies nutrient monitoring compared to daily data.

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Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx
07:14

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Published on: December 20, 2016

Area of Science:

  • Environmental Science
  • Water Quality Management
  • Hydrology

Background:

  • Nitrate-nitrogen pollution is a significant environmental concern, impacting water bodies and ecosystems.
  • Accurate estimation of nutrient loads is crucial for effective water quality management and regulatory compliance.
  • The temporal scale of data used in load computations can influence the reliability and interpretation of results.

Purpose of the Study:

  • To investigate the effect of different temporal data scales on nitrate-nitrogen load computations.
  • To assess the variability in load estimates based on daily, weekly, bi-weekly, and monthly data.
  • To develop a method for simplifying nutrient monitoring and reducing associated costs.

Main Methods:

  • Utilized Hydrologic Simulation Program-FORTRAN (HSPF) model outputs for the Amite River, Louisiana.
  • Simulated daily nitrate-nitrogen concentrations were averaged to create datasets at daily, weekly, bi-weekly, and monthly scales.
  • Developed load duration curves for each temporal scale to analyze load variations.

Main Results:

  • Load duration curves showed higher variability with daily data compared to bi-weekly and monthly data.
  • Nitrate-nitrogen load estimates decreased as the temporal scale of data increased (daily to monthly).
  • A linear inverse correlation was found between the coefficient of variation and the logarithm of the time scale.

Conclusions:

  • The temporal scale of loading data is a critical factor in nitrate-nitrogen load computation accuracy.
  • Longer temporal scales (e.g., monthly) provide more stable and less variable load estimates.
  • Proposed empirical equations can extrapolate near real-time flow and nitrate-nitrogen data, enhancing monitoring efficiency and cost-effectiveness.